A comprehensive study of benzene concentrations and emissions in Houston

NASA Astrophysics Data System (ADS)

Müller, Markus; Eichler, Philipp; Berk Knighton, W.; Estes, Mark; Crawford, James H.; Mikoviny, Tomas; Wisthaler, Armin

2014-05-01

The Houston Metropolitan Area (Greater Houston) has a population of over 6 million people, it ranks among the three fastest growing metropolises in the developed world and population growth scenarios predict it to reach megacity status in the coming two to four decades. Greater Houston is home to the largest petrochemical-manufacturing complex in the world with important consequences for the environment in the region. Direct and fugitive emissions of hydrocarbons adversely affect Houston's air quality which has been subject to intense studies over the past two decades. In 2013, NASA conducted the DISCOVER-AQ field campaign in support of developing a satellite-based capability to assess Houston's air quality in the future. Amongst other measurements, airborne, mobile ground-based and stationary ground-based measurements of benzene were carried out. Benzene is a carcinogenic air toxic with strict exposure regulations in the U.S. and in Europe. We have used the obtained comprehensive dataset to map benzene concentrations in the Houston metropolitan area, locate and identify point sources, compare industrial and traffic emissions and put them in relation to previous measurements and emission inventories. The obtained data will allow a better assessment of health risks associated with benzene exposure in a large metropolitan area that includes both traffic and industrial benzene sources. This work was funded by BMVIT / FFG-ALR in the frame of the Austrian Space Application Programme (ASAP 8, project 833451). PE was funded through the PIMMS ITN (EU-FP7, agreement number 287382). Additional resources were provided through NASA's Earth Venture program (EV-1) and the NASA Postdoctoral Program (NPP). We want to thank Scott Herndon and Aerodyne Research for their support.

[A new machinability test machine and the machinability of composite resins for core built-up].

PubMed

Iwasaki, N

2001-06-01

A new machinability test machine especially for dental materials was contrived. The purpose of this study was to evaluate the effects of grinding conditions on machinability of core built-up resins using this machine, and to confirm the relationship between machinability and other properties of composite resins. The experimental machinability test machine consisted of a dental air-turbine handpiece, a control weight unit, a driving unit of the stage fixing the test specimen, and so on. The machinability was evaluated as the change in volume after grinding using a diamond point. Five kinds of core built-up resins and human teeth were used in this study. The machinabilities of these composite resins increased with an increasing load during grinding, and decreased with repeated grinding. There was no obvious correlation between the machinability and Vickers' hardness; however, a negative correlation was observed between machinability and scratch width.

Relative Performance of Hardwood Sawing Machines

Treesearch

Philip H. Steele; Michael W. Wade; Steven H. Bullard; Philip A. Araman

1991-01-01

Only limited information has been available to hardwood sawmillers on the performance of their sawing machines. This study analyzes a large database of individual machine studies to provide detailed information on 6 machine types. These machine types were band headrig, circular headrig, band linebar resaw, vertical band splitter resaw, single arbor gang resaw and...

A Typology of UK Slot Machine Gamblers: A Longitudinal Observational and Interview Study

ERIC Educational Resources Information Center

Griffiths, Mark D.

2011-01-01

Slot machine gambling is a popular leisure activity worldwide yet there has been very little research into different types of slot machine gamblers. Earlier typologies of slot machine gamblers have only concentrated on adolescents in arcade environments. This study presents a new typology of slot machine players based on over 1000 h of participant…

Relative Kerf and Sawing Variation Values for Some Hardwood Sawing Machines

Treesearch

Philip H. Steele; Michael W. Wade; Steven H. Bullard; Philip A. Araman

1992-01-01

Information on the conversion efficiency of sawing machines is important to those involved in the management, maintenance, and design of sawmills. Little information on the conversion characteristics of hardwood sawing machines has been available. This study, based on 266 studies of 6 machine types, provides an analysis of the machine characteristics of kerf width,...

Man/Machine Interaction Dynamics And Performance (MMIDAP) capability

NASA Technical Reports Server (NTRS)

Frisch, Harold P.

1991-01-01

The creation of an ability to study interaction dynamics between a machine and its human operator can be approached from a myriad of directions. The Man/Machine Interaction Dynamics and Performance (MMIDAP) project seeks to create an ability to study the consequences of machine design alternatives relative to the performance of both machine and operator. The class of machines to which this study is directed includes those that require the intelligent physical exertions of a human operator. While Goddard's Flight Telerobotic's program was expected to be a major user, basic engineering design and biomedical applications reach far beyond telerobotics. Ongoing efforts are outlined of the GSFC and its University and small business collaborators to integrate both human performance and musculoskeletal data bases with analysis capabilities necessary to enable the study of dynamic actions, reactions, and performance of coupled machine/operator systems.

Effect of Machining Parameters on Oxidation Behavior of Mild Steel

NASA Astrophysics Data System (ADS)

Majumdar, P.; Shekhar, S.; Mondal, K.

2015-01-01

This study aims to find out a correlation between machining parameters, resultant microstructure, and isothermal oxidation behavior of lathe-machined mild steel in the temperature range of 660-710 °C. The tool rake angles "α" used were +20°, 0°, and -20°, and cutting speeds used were 41, 232, and 541 mm/s. Under isothermal conditions, non-machined and machined mild steel samples follow parabolic oxidation kinetics with activation energy of 181 and ~400 kJ/mol, respectively. Exaggerated grain growth of the machined surface was observed, whereas, the center part of the machined sample showed minimal grain growth during oxidation at higher temperatures. Grain growth on the surface was attributed to the reduction of strain energy at high temperature oxidation, which was accumulated on the sub-region of the machined surface during machining. It was also observed that characteristic surface oxide controlled the oxidation behavior of the machined samples. This study clearly demonstrates the effect of equivalent strain, roughness, and grain size due to machining, and subsequent grain growth on the oxidation behavior of the mild steel.

Humanizing machines: Anthropomorphization of slot machines increases gambling.

PubMed

Riva, Paolo; Sacchi, Simona; Brambilla, Marco

2015-12-01

Do people gamble more on slot machines if they think that they are playing against humanlike minds rather than mathematical algorithms? Research has shown that people have a strong cognitive tendency to imbue humanlike mental states to nonhuman entities (i.e., anthropomorphism). The present research tested whether anthropomorphizing slot machines would increase gambling. Four studies manipulated slot machine anthropomorphization and found that exposing people to an anthropomorphized description of a slot machine increased gambling behavior and reduced gambling outcomes. Such findings emerged using tasks that focused on gambling behavior (Studies 1 to 3) as well as in experimental paradigms that included gambling outcomes (Studies 2 to 4). We found that gambling outcomes decrease because participants primed with the anthropomorphic slot machine gambled more (Study 4). Furthermore, we found that high-arousal positive emotions (e.g., feeling excited) played a role in the effect of anthropomorphism on gambling behavior (Studies 3 and 4). Our research indicates that the psychological process of gambling-machine anthropomorphism can be advantageous for the gaming industry; however, this may come at great expense for gamblers' (and their families') economic resources and psychological well-being. (c) 2015 APA, all rights reserved).

Performance Analysis of Abrasive Waterjet Machining Process at Low Pressure

NASA Astrophysics Data System (ADS)

Murugan, M.; Gebremariam, MA; Hamedon, Z.; Azhari, A.

2018-03-01

Normally, a commercial waterjet cutting machine can generate water pressure up to 600 MPa. This range of pressure is used to machine a wide variety of materials. Hence, the price of waterjet cutting machine is expensive. Therefore, there is a need to develop a low cost waterjet machine in order to make the technology more accessible for the masses. Due to its low cost, such machines may only be able to generate water pressure at a much reduced rate. The present study attempts to investigate the performance of abrasive water jet machining process at low cutting pressure using self-developed low cost waterjet machine. It aims to study the feasibility of machining various materials at low pressure which later can aid in further development of an effective low cost water jet machine. A total of three different materials were machined at a low pressure of 34 MPa. The materials are mild steel, aluminium alloy 6061 and plastics Delrin®. Furthermore, a traverse rate was varied between 1 to 3 mm/min. The study on cutting performance at low pressure for different materials was conducted in terms of depth penetration, kerf taper ratio and surface roughness. It was found that all samples were able to be machined at low cutting pressure with varied qualities. Also, the depth of penetration decreases with an increase in the traverse rate. Meanwhile, the surface roughness and kerf taper ratio increase with an increase in the traverse rate. It can be concluded that a low cost waterjet machine with a much reduced rate of water pressure can be successfully used for machining certain materials with acceptable qualities.

Effect of the Machining Processes on Low Cycle Fatigue Behavior of a Powder Metallurgy Disk

NASA Technical Reports Server (NTRS)

Telesman, J.; Kantzos, P.; Gabb, T. P.; Ghosn, L. J.

2010-01-01

A study has been performed to investigate the effect of various machining processes on fatigue life of configured low cycle fatigue specimens machined out of a NASA developed LSHR P/M nickel based disk alloy. Two types of configured specimen geometries were employed in the study. To evaluate a broach machining processes a double notch geometry was used with both notches machined using broach tooling. EDM machined notched specimens of the same configuration were tested for comparison purposes. Honing finishing process was evaluated by using a center hole specimen geometry. Comparison testing was again done using EDM machined specimens of the same geometry. The effect of these machining processes on the resulting surface roughness, residual stress distribution and microstructural damage were characterized and used in attempt to explain the low cycle fatigue results.

Studies of machinable ceramics for dental applications. 1. Color analysis.

PubMed

Taira, M; Wakasa, K; Yamaki, M; Tanaka, N; Shintani, H

1989-12-01

Machinable ceramics that can be cut and even lathed have recently been developed in industry. As a first step in evaluating the feasibility of such ceramics in dentistry, eight machinable ceramics were examined for color using the Vita shade guide and a chroma-meter reflectance instrument. We discovered that the studied machinable ceramics varied significantly from the Vita shade guide by the color difference vector, delta E. These machinable ceramics appeared very white and strongly opaque due to their high brightness (L*) values. For intra-oral applications, we expect that L* values of machinable ceramics will be reduced by modification of their microstructures, including their matrix and dispersed phases, while their excellent machinability due to the cleavage of dispersed crystals should be retained.

Machine learning: novel bioinformatics approaches for combating antimicrobial resistance.

PubMed

Macesic, Nenad; Polubriaginof, Fernanda; Tatonetti, Nicholas P

2017-12-01

Antimicrobial resistance (AMR) is a threat to global health and new approaches to combating AMR are needed. Use of machine learning in addressing AMR is in its infancy but has made promising steps. We reviewed the current literature on the use of machine learning for studying bacterial AMR. The advent of large-scale data sets provided by next-generation sequencing and electronic health records make applying machine learning to the study and treatment of AMR possible. To date, it has been used for antimicrobial susceptibility genotype/phenotype prediction, development of AMR clinical decision rules, novel antimicrobial agent discovery and antimicrobial therapy optimization. Application of machine learning to studying AMR is feasible but remains limited. Implementation of machine learning in clinical settings faces barriers to uptake with concerns regarding model interpretability and data quality.Future applications of machine learning to AMR are likely to be laboratory-based, such as antimicrobial susceptibility phenotype prediction.

Revisit of Machine Learning Supported Biological and Biomedical Studies.

PubMed

Yu, Xiang-Tian; Wang, Lu; Zeng, Tao

2018-01-01

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.

Design of a Modular E-Core Flux Concentrating Axial Flux Machine: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Husain, Tausif; Sozer, Yilmaz; Husain, Iqbal

2015-08-24

In this paper a novel E-Core axial flux machine is proposed. The machine has a double-stator, single-rotor configuration with flux-concentrating ferrite magnets and pole windings across each leg of an E-Core stator. E-Core stators with the proposed flux-concentrating rotor arrangement result in better magnet utilization and higher torque density. The machine also has a modular structure facilitating simpler construction. This paper presents a single-phase and a three-phase version of the E-Core machine. Case studies for a 1.1-kW, 400-rpm machine for both the single-phase and three-phase axial flux machines are presented. The results are verified through 3D finite element analysis. facilitatingmore » simpler construction. This paper presents a single-phase and a three-phase version of the E-Core machine. Case studies for a 1.1-kW, 400-rpm machine for both the single-phase and three-phase axial flux machines are presented. The results are verified through 3D finite element analysis.« less

Preliminary Development of Real Time Usage-Phase Monitoring System for CNC Machine Tools with a Case Study on CNC Machine VMC 250

NASA Astrophysics Data System (ADS)

Budi Harja, Herman; Prakosa, Tri; Raharno, Sri; Yuwana Martawirya, Yatna; Nurhadi, Indra; Setyo Nogroho, Alamsyah

2018-03-01

The production characteristic of job-shop industry at which products have wide variety but small amounts causes every machine tool will be shared to conduct production process with dynamic load. Its dynamic condition operation directly affects machine tools component reliability. Hence, determination of maintenance schedule for every component should be calculated based on actual usage of machine tools component. This paper describes study on development of monitoring system to obtaining information about each CNC machine tool component usage in real time approached by component grouping based on its operation phase. A special device has been developed for monitoring machine tool component usage by utilizing usage phase activity data taken from certain electronics components within CNC machine. The components are adaptor, servo driver and spindle driver, as well as some additional components such as microcontroller and relays. The obtained data are utilized for detecting machine utilization phases such as power on state, machine ready state or spindle running state. Experimental result have shown that the developed CNC machine tool monitoring system is capable of obtaining phase information of machine tool usage as well as its duration and displays the information at the user interface application.

Quadrilateral Micro-Hole Array Machining on Invar Thin Film: Wet Etching and Electrochemical Fusion Machining

PubMed Central

Choi, Woong-Kirl; Kim, Seong-Hyun; Choi, Seung-Geon; Lee, Eun-Sang

2018-01-01

Ultra-precision products which contain a micro-hole array have recently shown remarkable demand growth in many fields, especially in the semiconductor and display industries. Photoresist etching and electrochemical machining are widely known as precision methods for machining micro-holes with no residual stress and lower surface roughness on the fabricated products. The Invar shadow masks used for organic light-emitting diodes (OLEDs) contain numerous micro-holes and are currently machined by a photoresist etching method. However, this method has several problems, such as uncontrollable hole machining accuracy, non-etched areas, and overcutting. To solve these problems, a machining method that combines photoresist etching and electrochemical machining can be applied. In this study, negative photoresist with a quadrilateral hole array pattern was dry coated onto 30-µm-thick Invar thin film, and then exposure and development were carried out. After that, photoresist single-side wet etching and a fusion method of wet etching-electrochemical machining were used to machine micro-holes on the Invar. The hole machining geometry, surface quality, and overcutting characteristics of the methods were studied. Wet etching and electrochemical fusion machining can improve the accuracy and surface quality. The overcutting phenomenon can also be controlled by the fusion machining. Experimental results show that the proposed method is promising for the fabrication of Invar film shadow masks. PMID:29351235

The relationship between reinforcement and gaming machine choice.

PubMed

Haw, John

2008-03-01

The present study assessed whether prior reinforcement experiences were related to gaming machine choice and the decision to change gaming machines during a session of gambling. Seventy undergraduate students (48 women, 22 men; mean age = 22.05 years) were presented with two visually identical simulated gaming machines in a practice phase. These simulated machines differed only in the rate of reinforcement. After the practice phase, participants were asked to choose a machine to play in the test phase and were allowed to change machines at will. Two measures of reinforcement were employed; frequency of wins and payback rate. Results indicated that neither measure of reinforcement was related to machine choice, but both were predictors of when participants changed machines. A post-hoc analysis of the 33 participants who changed machines during the test phase found a significant relationship between machine choice and prior reinforcement. For these participants, payback rate was significantly related to machine choice, unlike frequency of wins.

Frequency of Rare Alpha-1 Antitrypsin Variants in Polish Patients with Chronic Respiratory Disorders.

PubMed

Duk, K; Zdral, A; Szumna, B; Roży, A; Chorostowska-Wynimko, J

2016-01-01

The SERPINA1 gene encoding the alpha-1 antitrypsin (A1AT) protein is highly polymorphic. It is known that, apart from the most prevalent PI*S and PI*Z A1AT deficiency variants, other so-called rare variants also predispose individuals to severe chronic respiratory disorders such as emphysema and chronic obstructive pulmonary disease. Our aim was to assess the frequencies of common and rare SERPINA1 mutations in a group of 1033 Polish patients referred for A1AT deficiency diagnostics due to chronic respiratory disorders in the period of January 2014-September 2015. All blood samples were analyzed according to the routine diagnostic protocol, including A1AT serum concentration assessment by nephelometry and immune isoelectric focusing, followed by PCR genotyping and direct sequencing when necessary. A total of 890 out of the 1033 samples (86 %) carried the normal PI*MM genotype, whereas, in 143 samples (14 %), at least one A1AT deficiency variant was detected. In 132 subjects, PI*S (2.1 %) and PI*Z (10.8 %) common deficiency alleles were identified, yielding frequencies of 0.011 and 0.062, respectively. Rare SERPINA1 variants were detected in nine patients: PI*F (c.739C>T) (n = 5) and PI*I (c.187C>T) (n = 4). Samples from the patients with an A1AT serum concentration below 120 mg/dl and presenting a PI*MM-like phenotypic pattern were retrospectively analyzed by direct sequencing for rare SERPINA1 mutations, revealing a PI*M2Obernburg (c.514G>T) mutation in one patient and a non-pathogenic mutation (c.922G>T) in another. We conclude that the deficiency PI*Z A1AT allele is considerably more common in patients with chronic respiratory disorders than in the general Polish population. The prevalence of the PI*F allele seems higher than in other European studies.

Performance study of a data flow architecture

NASA Technical Reports Server (NTRS)

Adams, George

1985-01-01

Teams of scientists studied data flow concepts, static data flow machine architecture, and the VAL language. Each team mapped its application onto the machine and coded it in VAL. The principal findings of the study were: (1) Five of the seven applications used the full power of the target machine. The galactic simulation and multigrid fluid flow teams found that a significantly smaller version of the machine (16 processing elements) would suffice. (2) A number of machine design parameters including processing element (PE) function unit numbers, array memory size and bandwidth, and routing network capability were found to be crucial for optimal machine performance. (3) The study participants readily acquired VAL programming skills. (4) Participants learned that application-based performance evaluation is a sound method of evaluating new computer architectures, even those that are not fully specified. During the course of the study, participants developed models for using computers to solve numerical problems and for evaluating new architectures. These models form the bases for future evaluation studies.

High-efficiency machining methods for aviation materials

NASA Astrophysics Data System (ADS)

Kononov, V. K.

1991-07-01

The papers contained in this volume present results of theoretical and experimental studies aimed at increasing the efficiency of cutting tools during the machining of high-temperature materials and titanium alloys. Specific topics discussed include a study of the performance of disk cutters during the machining of flexible parts of a high-temperature alloy, VZhL14N; a study of the wear resistance of cutters of hard alloys of various types; effect of a deformed electric field on the precision of the electrochemical machining of gas turbine engine components; and efficient machining of parts of composite materials. The discussion also covers the effect of the technological process structure on the residual stress distribution in the blades of gas turbine engines; modeling of the multiparameter assembly of engineering products for a specified priority of geometrical output parameters; and a study of the quality of the surface and surface layer of specimens machined by a high-temperature pulsed plasma.

Impact of the HEALTHY Study on Vending Machine Offerings in Middle Schools.

PubMed

Hartstein, Jill; Cullen, Karen W; Virus, Amy; El Ghormli, Laure; Volpe, Stella L; Staten, Myrlene A; Bridgman, Jessica C; Stadler, Diane D; Gillis, Bonnie; McCormick, Sarah B; Mobley, Connie C

2011-01-01

The purpose of this study is to report the impact of the three-year middle school-based HEALTHY study on intervention school vending machine offerings. There were two goals for the vending machines: serve only dessert/snack foods with 200 kilocalories or less per single serving package, and eliminate 100% fruit juice and beverages with added sugar. Six schools in each of seven cities (Houston, TX, San Antonio, TX, Irvine, CA, Portland, OR, Pittsburg, PA, Philadelphia, PA, and Chapel Hill, NC) were randomized into intervention (n=21 schools) or control (n=21 schools) groups, with three intervention and three control schools per city. All items in vending machine slots were tallied twice in the fall of 2006 for baseline data and twice at the end of the study, in 2009. The percentage of total slots for each food/beverage category was calculated and compared between intervention and control schools at the end of study, using the Pearson chi-square test statistic. At baseline, 15 intervention and 15 control schools had beverage and/or snack vending machines, compared with 11 intervention and 11 control schools at the end of the study. At the end of study, all of the intervention schools with beverage vending machines, but only one out of the nine control schools, met the beverage goal. The snack goal was met by all of the intervention schools and only one of the four control schools with snack vending machines. The HEALTHY study's vending machine beverage and snack goals were successfully achieved in intervention schools, reducing access to less healthy food items outside the school meals program. Although the effect of these changes on student diet, energy balance and growth is unknown, these results suggest that healthier options for snacks can successfully be offered in school vending machines.

The Employment Effects of High-Technology: A Case Study of Machine Vision. Research Report No. 86-19.

ERIC Educational Resources Information Center

Chen, Kan; Stafford, Frank P.

A case study of machine vision was conducted to identify and analyze the employment effects of high technology in general. (Machine vision is the automatic acquisition and analysis of an image to obtain desired information for use in controlling an industrial activity, such as the visual sensor system that gives eyes to a robot.) Machine vision as…

Variability in the skin exposure of machine operators exposed to cutting fluids.

PubMed

Wassenius, O; Järvholm, B; Engström, T; Lillienberg, L; Meding, B

1998-04-01

This study describes a new technique for measuring skin exposure to cutting fluids and evaluates the variability of skin exposure among machine operators performing cyclic (repetitive) work. The technique is based on video recording and subsequent analysis of the video tape by means of computer-synchronized video equipment. The time intervals at which the machine operator's hand was exposed to fluid were registered, and the total wet time of the skin was calculated by assuming different evaporation times for the fluid. The exposure of 12 operators with different work methods was analyzed in 6 different workshops, which included a range of machine types, from highly automated metal cutting machines (ie, actual cutting and chip removal machines) requiring operator supervision to conventional metal cutting machines, where the operator was required to maneuver the machine and manually exchange products. The relative wet time varied between 0% and 100%. A significant association between short cycle time and high relative wet time was noted. However, there was no relationship between the degree of automatization of the metal cutting machines and wet time. The study shows that skin exposure to cutting fluids can vary considerably between machine operators involved in manufacturing processes using different types of metal cutting machines. The machine type was not associated with dermal wetness. The technique appears to give objective information about dermal wetness.

A deviation based assessment methodology for multiple machine health patterns classification and fault detection

NASA Astrophysics Data System (ADS)

Jia, Xiaodong; Jin, Chao; Buzza, Matt; Di, Yuan; Siegel, David; Lee, Jay

2018-01-01

Successful applications of Diffusion Map (DM) in machine failure detection and diagnosis have been reported in several recent studies. DM provides an efficient way to visualize the high-dimensional, complex and nonlinear machine data, and thus suggests more knowledge about the machine under monitoring. In this paper, a DM based methodology named as DM-EVD is proposed for machine degradation assessment, abnormality detection and diagnosis in an online fashion. Several limitations and challenges of using DM for machine health monitoring have been analyzed and addressed. Based on the proposed DM-EVD, a deviation based methodology is then proposed to include more dimension reduction methods. In this work, the incorporation of Laplacian Eigen-map and Principal Component Analysis (PCA) are explored, and the latter algorithm is named as PCA-Dev and is validated in the case study. To show the successful application of the proposed methodology, case studies from diverse fields are presented and investigated in this work. Improved results are reported by benchmarking with other machine learning algorithms.

ChargeOut! : discounted cash flow compared with traditional machine-rate analysis

Treesearch

Ted Bilek

2008-01-01

ChargeOut!, a discounted cash-flow methodology in spreadsheet format for analyzing machine costs, is compared with traditional machine-rate methodologies. Four machine-rate models are compared and a common data set representative of logging skidders’ costs is used to illustrate the differences between ChargeOut! and the machine-rate methods. The study found that the...

MATC Machine Shop '84: Specific Skill Needs Assessment for Machine Shops in the Milwaukee Area.

ERIC Educational Resources Information Center

Roberts, Keith J.

Building on previous research on the future skill needs of workers in southeastern Wisconsin, a study was conducted at Milwaukee Area Technical College (MATC) to gather information on the machine tool industry in the Milwaukee area. Interviews were conducted by MATC Machine Shop and Tool and Die faculty with representatives from 135 machine shops,…

Design and Construction Multi Output Power Transmition with Single Prime Mover on Agricultural Products Machine

NASA Astrophysics Data System (ADS)

Koten, V. K.; Tanamal, C. E.

2017-03-01

Manufacturing agricultural products by the farmers, people or person who involve in medium industry, small industry, and households industry still be done in separately. Although the power on primemover is enough, in operations, primemover was only to move one of several agricultural products machine. This study attempts to design and construct power transmition multi output with single primemover; a single construction that allows primemover move some agricultur products machine in the same or not. This study begins with the determination of production capacity and the power to destroy products, the determination of resources and rotation, normalization of resources and rotation, the determination of the type material used, the size determination of each machine elements, construction machine elements, and assemble machine elements into a construction multi output power transmition with single primemover on agricultural products machine. The results show that with a input normalization 4 PK (2984 Watt), rotation 2000 rpm, the strength of material 60 kg/mm2, and several operating consideration, thus obtained size of machine elements through calculation. Based on the size, the machine elements is made through the use of some machine tools and assembled to form a multi output power transmition with single primemover.

Machinability of Green Powder Metallurgy Components: Part I. Characterization of the Influence of Tool Wear

NASA Astrophysics Data System (ADS)

Robert-Perron, Etienne; Blais, Carl; Pelletier, Sylvain; Thomas, Yannig

2007-06-01

The green machining process is an interesting approach for solving the mediocre machining behavior of high-performance powder metallurgy (PM) steels. This process appears as a promising method for extending tool life and reducing machining costs. Recent improvements in binder/lubricant technologies have led to high green strength systems that enable green machining. So far, tool wear has been considered negligible when characterizing the machinability of green PM specimens. This inaccurate assumption may lead to the selection of suboptimum cutting conditions. The first part of this study involves the optimization of the machining parameters to minimize the effects of tool wear on the machinability in turning of green PM components. The second part of our work compares the sintered mechanical properties of components machined in green state with other machined after sintering.

Compensation strategy for machining optical freeform surfaces by the combined on- and off-machine measurement.

PubMed

Zhang, Xiaodong; Zeng, Zhen; Liu, Xianlei; Fang, Fengzhou

2015-09-21

Freeform surface is promising to be the next generation optics, however it needs high form accuracy for excellent performance. The closed-loop of fabrication-measurement-compensation is necessary for the improvement of the form accuracy. It is difficult to do an off-machine measurement during the freeform machining because the remounting inaccuracy can result in significant form deviations. On the other side, on-machine measurement may hides the systematic errors of the machine because the measuring device is placed in situ on the machine. This study proposes a new compensation strategy based on the combination of on-machine and off-machine measurement. The freeform surface is measured in off-machine mode with nanometric accuracy, and the on-machine probe achieves accurate relative position between the workpiece and machine after remounting. The compensation cutting path is generated according to the calculated relative position and shape errors to avoid employing extra manual adjustment or highly accurate reference-feature fixture. Experimental results verified the effectiveness of the proposed method.

Permutation parity machines for neural cryptography.

PubMed

Reyes, Oscar Mauricio; Zimmermann, Karl-Heinz

2010-06-01

Recently, synchronization was proved for permutation parity machines, multilayer feed-forward neural networks proposed as a binary variant of the tree parity machines. This ability was already used in the case of tree parity machines to introduce a key-exchange protocol. In this paper, a protocol based on permutation parity machines is proposed and its performance against common attacks (simple, geometric, majority and genetic) is studied.

Evaluation of an Integrated Multi-Task Machine Learning System with Humans in the Loop

DTIC Science & Technology

2007-01-01

machine learning components natural language processing, and optimization...was examined with a test explicitly developed to measure the impact of integrated machine learning when used by a human user in a real world setting...study revealed that integrated machine learning does produce a positive impact on overall performance. This paper also discusses how specific machine learning components contributed to human-system

Permutation parity machines for neural cryptography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reyes, Oscar Mauricio; Escuela de Ingenieria Electrica, Electronica y Telecomunicaciones, Universidad Industrial de Santander, Bucaramanga; Zimmermann, Karl-Heinz

2010-06-15

Recently, synchronization was proved for permutation parity machines, multilayer feed-forward neural networks proposed as a binary variant of the tree parity machines. This ability was already used in the case of tree parity machines to introduce a key-exchange protocol. In this paper, a protocol based on permutation parity machines is proposed and its performance against common attacks (simple, geometric, majority and genetic) is studied.

Machine learning in autistic spectrum disorder behavioral research: A review and ways forward.

PubMed

Thabtah, Fadi

2018-02-13

Autistic Spectrum Disorder (ASD) is a mental disorder that retards acquisition of linguistic, communication, cognitive, and social skills and abilities. Despite being diagnosed with ASD, some individuals exhibit outstanding scholastic, non-academic, and artistic capabilities, in such cases posing a challenging task for scientists to provide answers. In the last few years, ASD has been investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning to improve diagnostic timing, precision, and quality. Machine learning is a multidisciplinary research topic that employs intelligent techniques to discover useful concealed patterns, which are utilized in prediction to improve decision making. Machine learning techniques such as support vector machines, decision trees, logistic regressions, and others, have been applied to datasets related to autism in order to construct predictive models. These models claim to enhance the ability of clinicians to provide robust diagnoses and prognoses of ASD. However, studies concerning the use of machine learning in ASD diagnosis and treatment suffer from conceptual, implementation, and data issues such as the way diagnostic codes are used, the type of feature selection employed, the evaluation measures chosen, and class imbalances in data among others. A more serious claim in recent studies is the development of a new method for ASD diagnoses based on machine learning. This article critically analyses these recent investigative studies on autism, not only articulating the aforementioned issues in these studies but also recommending paths forward that enhance machine learning use in ASD with respect to conceptualization, implementation, and data. Future studies concerning machine learning in autism research are greatly benefitted by such proposals.

Investigation of Machine-ability of Inconel 800 in EDM with Coated Electrode

NASA Astrophysics Data System (ADS)

Karunakaran, K.; Chandrasekaran, M.

2017-03-01

The Inconel 800 is a high temperature application alloy which is classified as a nickel based super alloy. It has wide scope in aerospace engineering, gas Turbine etc. The machine-ability studies were found limited on this material. Hence This research focuses on machine-ability studies on EDM of Inconel 800 with Silver Coated Electrolyte Copper Electrode. The purpose of coating on electrode is to reduce tool wear. The factors pulse on Time, Pulse off Time and Peck Current were considered to observe the responses of surface roughness, material removal rate, tool wear rate. Taguchi Full Factorial Design is employed for Design the experiment. Some specific findings were reported and the percentage of contribution of each parameter was furnished

Impact of the HEALTHY study on vending machine offerings in middle schools

USDA-ARS?s Scientific Manuscript database

The purpose of this study is to report the impact of the three-year middle school-based HEALTHY study on intervention school vending machine offerings. There were two goals for the vending machines: serve only dessert/snack foods with 200 kilocalories or less per single serving package, and eliminat...

The study on the nanomachining property and cutting model of single-crystal sapphire by atomic force microscopy.

PubMed

Huang, Jen-Ching; Weng, Yung-Jin

2014-01-01

This study focused on the nanomachining property and cutting model of single-crystal sapphire during nanomachining. The coated diamond probe is used to as a tool, and the atomic force microscopy (AFM) is as an experimental platform for nanomachining. To understand the effect of normal force on single-crystal sapphire machining, this study tested nano-line machining and nano-rectangular pattern machining at different normal force. In nano-line machining test, the experimental results showed that the normal force increased, the groove depth from nano-line machining also increased. And the trend is logarithmic type. In nano-rectangular pattern machining test, it is found when the normal force increases, the groove depth also increased, but rather the accumulation of small chips. This paper combined the blew by air blower, the cleaning by ultrasonic cleaning machine and using contact mode probe to scan the surface topology after nanomaching, and proposed the "criterion of nanomachining cutting model," in order to determine the cutting model of single-crystal sapphire in the nanomachining is ductile regime cutting model or brittle regime cutting model. After analysis, the single-crystal sapphire substrate is processed in small normal force during nano-linear machining; its cutting modes are ductile regime cutting model. In the nano-rectangular pattern machining, due to the impact of machined zones overlap, the cutting mode is converted into a brittle regime cutting model. © 2014 Wiley Periodicals, Inc.

Does machine perfusion decrease ischemia reperfusion injury?

PubMed

Bon, D; Delpech, P-O; Chatauret, N; Hauet, T; Badet, L; Barrou, B

2014-06-01

In 1990's, use of machine perfusion for organ preservation has been abandoned because of improvement of preservation solutions, efficient without perfusion, easy to use and cheaper. Since the last 15 years, a renewed interest for machine perfusion emerged based on studies performed on preclinical model and seems to make consensus in case of expanded criteria donors or deceased after cardiac death donations. We present relevant studies highlighted the efficiency of preservation with hypothermic machine perfusion compared to static cold storage. Machines for organ preservation being in constant evolution, we also summarized recent developments included direct oxygenation of the perfusat. Machine perfusion technology also enables organ reconditioning during the last hours of preservation through a short period of perfusion on hypothermia, subnormothermia or normothermia. We present significant or low advantages for machine perfusion against ischemia reperfusion injuries regarding at least one primary parameter: risk of DFG, organ function or graft survival. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Pre-use anesthesia machine check; certified anesthesia technician based quality improvement audit.

PubMed

Al Suhaibani, Mazen; Al Malki, Assaf; Al Dosary, Saad; Al Barmawi, Hanan; Pogoku, Mahdhav

2014-01-01

Quality assurance of providing a work ready machine in multiple theatre operating rooms in a tertiary modern medical center in Riyadh. The aim of the following study is to keep high quality environment for workers and patients in surgical operating rooms. Technicians based audit by using key performance indicators to assure inspection, passing test of machine worthiness for use daily and in between cases and in case of unexpected failure to provide quick replacement by ready to use another anesthetic machine. The anesthetic machines in all operating rooms are daily and continuously inspected and passed as ready by technicians and verified by anesthesiologist consultant or assistant consultant. The daily records of each machines were collected then inspected for data analysis by quality improvement committee department for descriptive analysis and report the degree of staff compliance to daily inspection as "met" items. Replaced machine during use and overall compliance. Distractive statistic using Microsoft Excel 2003 tables and graphs of sums and percentages of item studied in this audit. Audit obtained highest compliance percentage and low rate of replacement of machine which indicate unexpected machine state of use and quick machine switch. The authors are able to conclude that following regular inspection and running self-check recommended by the manufacturers can contribute to abort any possibility of hazard of anesthesia machine failure during operation. Furthermore in case of unexpected reason to replace the anesthesia machine in quick maneuver contributes to high assured operative utilization of man machine inter-phase in modern surgical operating rooms.

Studying depression using imaging and machine learning methods.

PubMed

Patel, Meenal J; Khalaf, Alexander; Aizenstein, Howard J

2016-01-01

Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

Calculating Synchronous Inductive Reactances of Contactless Machines When Magnetic Circuit is Saturated and of Machines with Superconducting Excitation Windings,

DTIC Science & Technology

The work studies the effect of magnetic circuit saturation on the synchronous inductive reactance of the armature. A practical method is given for...calculating synchronized parameters in saturating synchronized machines with additional clearances and machines with superconducting excitation windings.

Machining of AISI D2 Tool Steel with Multiple Hole Electrodes by EDM Process

NASA Astrophysics Data System (ADS)

Prasad Prathipati, R.; Devuri, Venkateswarlu; Cheepu, Muralimohan; Gudimetla, Kondaiah; Uzwal Kiran, R.

2018-03-01

In recent years, with the increasing of technology the demand for machining processes is increasing for the newly developed materials. The conventional machining processes are not adequate to meet the accuracy of the machining of these materials. The non-conventional machining processes of electrical discharge machining is one of the most efficient machining processes is being widely used to machining of high accuracy products of various industries. The optimum selection of process parameters is very important in machining processes as that of an electrical discharge machining as they determine surface quality and dimensional precision of the obtained parts, even though time consumption rate is higher for machining of large dimension features. In this work, D2 high carbon and chromium tool steel has been machined using electrical discharge machining with the multiple hole electrode technique. The D2 steel has several applications such as forming dies, extrusion dies and thread rolling. But the machining of this tool steel is very hard because of it shard alloyed elements of V, Cr and Mo which enhance its strength and wear properties. However, the machining is possible by using electrical discharge machining process and the present study implemented a new technique to reduce the machining time using a multiple hole copper electrode. In this technique, while machining with multiple holes electrode, fin like projections are obtained, which can be removed easily by chipping. Then the finishing is done by using solid electrode. The machining time is reduced to around 50% while using multiple hole electrode technique for electrical discharge machining.

Volumetric Verification of Multiaxis Machine Tool Using Laser Tracker

PubMed Central

Aguilar, Juan José

2014-01-01

This paper aims to present a method of volumetric verification in machine tools with linear and rotary axes using a laser tracker. Beyond a method for a particular machine, it presents a methodology that can be used in any machine type. Along this paper, the schema and kinematic model of a machine with three axes of movement, two linear and one rotational axes, including the measurement system and the nominal rotation matrix of the rotational axis are presented. Using this, the machine tool volumetric error is obtained and nonlinear optimization techniques are employed to improve the accuracy of the machine tool. The verification provides a mathematical, not physical, compensation, in less time than other methods of verification by means of the indirect measurement of geometric errors of the machine from the linear and rotary axes. This paper presents an extensive study about the appropriateness and drawbacks of the regression function employed depending on the types of movement of the axes of any machine. In the same way, strengths and weaknesses of measurement methods and optimization techniques depending on the space available to place the measurement system are presented. These studies provide the most appropriate strategies to verify each machine tool taking into consideration its configuration and its available work space. PMID:25202744

Variation in access to sugar-sweetened beverages in vending machines across rural, town and urban high schools

PubMed Central

Adachi-Mejia, A.M.; Longacre, M.R.; Skatrud-Mickelson, M.; Li, Z.; Purvis, L.A.; Titus, L.J.; Beach, M.L.; Dalton, M.A.

2013-01-01

SUMMARY Objectives The 2010 Dietary Guidelines for Americans include reducing consumption of sugar-sweetened beverages. Among the many possible routes of access for youth, school vending machines provide ready availability of sugar-sweetened beverages. The purpose of this study was to determine variation in high school student access to sugar-sweetened beverages through vending machines by geographic location – urban, town or rural – and to offer an approach for analysing school vending machine content. Study design Cross-sectional observational study. Methods Between October 2007 and May 2008, trained coders recorded beverage vending machine content and machine-front advertising in 113 machines across 26 schools in New Hampshire and Vermont, USA. Results Compared with town schools, urban schools were significantly less likely to offer sugar-sweetened beverages (P=0.002). Rural schools also offered more sugar-sweetened beverages than urban schools, but this difference was not significant. Advertisements for sugar-sweetened beverages were highly prevalent in town schools. Conclusions High school students have ready access to sugar-sweetened beverages through their school vending machines. Town schools offer the highest risk of exposure; school vending machines located in towns offer up to twice as much access to sugar-sweetened beverages in both content and advertising compared with urban locations. Variation by geographic region suggests that healthier environments are possible and some schools can lead as inspirational role models. PMID:23498924

Cleaning of uranium vs machine coolant formulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cristy, S.S.; Byrd, V.R.; Simandl, R.F.

1984-10-01

This study compares methods for cleaning uranium chips and the residues left on chips from alternate machine coolants based on propylene glycol-water mixtures with either borax, ammonium tetraborate, or triethanolamine tetraborate added as a nuclear poison. Residues left on uranium surfaces machined with perchloroethylene-mineral oil coolant and on surfaces machined with the borax-containing alternate coolant were also compared. In comparing machined surfaces, greater chlorine contamination was found on the surface of the perchloroethylene-mineral oil machined surfaces, but slightly greater oxidation was found on the surfaces machined with the alternate borax-containing coolant. Overall, the differences were small and a change tomore » the alternate coolant does not appear to constitute a significant threat to the integrity of machined uranium parts.« less

Attitude of Employers of Fitting and Machining Apprentices towards Apprentices. [C.A.T. Education Monograph] No. 15.

ERIC Educational Resources Information Center

Richardson, E.; Clayman, Linda

As a result of studies on fitting and machining apprentices attitudes toward employers, a study was conducted to obtain the attitudes of a sample of employers toward apprenticeship. Three hundred questionnaires were distributed to employers of fitting and machine students studying at a number of Sydney (Australia) Technical Colleges. An…

Study of a Variable Mass Atwood's Machine Using a Smartphone

ERIC Educational Resources Information Center

Lopez, Dany; Caprile, Isidora; Corvacho, Fernando; Reyes, Orfa

2018-01-01

The Atwood machine was invented in 1784 by George Atwood and this system has been widely studied both theoretically and experimentally over the years. Nowadays, it is commonplace that many experimental physics courses include both Atwood's machine and variable mass to introduce more complex concepts in physics. To study the dynamics of the masses…

Impact of the HEALTHY Study on Vending Machine Offerings in Middle Schools

ERIC Educational Resources Information Center

Hartstein, Jill; Cullen, Karen W.; Virus, Amy; El Ghormli, Laure; Volpe, Stella L.; Staten, Myrlene A.; Bridgman, Jessica C.; Stadler, Diane D.; Gillis, Bonnie; McCormick, Sarah B.; Mobley, Connie C.

2011-01-01

Purpose/Objectives: The purpose of this study is to report the impact of the three-year middle school-based HEALTHY study on intervention school vending machine offerings. There were two goals for the vending machines: serve only dessert/snack foods with 200 kilocalories or less per single serving package, and eliminate 100% fruit juice and…

Study of a variable mass Atwood's machine using a smartphone

NASA Astrophysics Data System (ADS)

Lopez, Dany; Caprile, Isidora; Corvacho, Fernando; Reyes, Orfa

2018-03-01

The Atwood machine was invented in 1784 by George Atwood and this system has been widely studied both theoretically and experimentally over the years. Nowadays, it is commonplace that many experimental physics courses include both Atwood's machine and variable mass to introduce more complex concepts in physics. To study the dynamics of the masses that compose the variable Atwood's machine, laboratories typically use a smart pulley. Now, the first work that introduced a smartphone as data acquisition equipment to study the acceleration in the Atwood's machine was the one by M. Monteiro et al. Since then, there has been no further information available on the usage of smartphones in variable mass systems. This prompted us to do a study of this kind of system by means of data obtained with a smartphone and to show the practicality of using smartphones in complex experimental situations.

Advanced Design Composite Aircraft (ADCA) Study. Volume I

DTIC Science & Technology

1976-11-01

Aluminum Machined Paits 008 ’— Aluminum Honeycomb 001 - - Steel Machined Parts 0.08 - Titanium 0 66 Fiberglass 1 18 _ Boron Composite 0...Honeycomb 001 ~ Steel Machined Parti 0 09 | Titanium 056 Fi bei glass 037 r i Boron Composite 0 Graphite Composite 6 36 Total 81 2 31 7 42 1...1 Aluminum Machined Parts 006 - 2 1 Aluminum Honeycomb 001 Steel Machined Parts 007 - Trtamum 001 1 Frberglass 029 - Boron Composite 0

Research in Chinese-English Machine Translation. Final Report.

ERIC Educational Resources Information Center

Wang, William S-Y.; And Others

This report documents results of a two-year effort toward the study and investigation of the design of a prototype system for Chinese-English machine translation in the general area of physics. Previous work in Chinese-English machine translation is reviewed. Grammatical considerations in machine translation are discussed and detailed aspects of…

Microbial Contamination of Ice Machines Is Mediated by Activated Charcoal Filtration Systems in a City Hospital.

PubMed

Yorioka, Katsuhiro; Oie, Shigeharu; Hayashi, Koji; Kimoto, Hiroo; Furukawa, Hiroyuki

2016-06-01

Although microbial contamination of ice machines has been reported, no previous study has addressed microbial contamination of ice produced by machines equipped with activated charcoal (AC) filters in hospitals. The aim of this study was to provide clinical data for evaluating AC filters to prevent microbial contamination of ice. We compared microbial contamination in ice samples produced by machines with (n = 20) and without an AC filter (n = 40) in Shunan City Shinnanyo Municipal Hospital. All samples from the ice machine equipped with an AC filter contained 10-116 CFUs/g of glucose nonfermenting gram-negative bacteria such as Pseudomonas aeruginosa and Chryseobacterium meningosepticum. No microorganisms were detected in samples from ice machines without AC filters. After the AC filter was removed from the ice machine that tested positive for Gram-negative bacteria, the ice was resampled (n = 20). Analysis found no contaminants. Ice machines equipped with AC filters pose a serious risk factor for ice contamination. New filter-use guidelines and regulations on bacterial detection limits to prevent contamination of ice in healthcare facilities are necessary.

An analysis of switching and non-switching slot machine player behaviour.

PubMed

Coates, Ewan; Blaszczynski, Alex

2013-12-01

Learning theory predicts that, given the repeated choice to bet between two concurrently available slot machines, gamblers will learn to bet more money on the machine with higher expected return (payback percentage) or higher win probability per spin (volatility). The purpose of this study was to investigate whether this occurs when the two machines vary orthogonally on payback percentage and volatility. The sample comprised 52 first year psychology students (mean age = 20.3 years, 20 females, 32 males) who had played a gaming machine at least once in the previous 12 months. Participants were administered a battery of questionnaires designed to assess level of knowledge on the characteristics and operation of poker machines, frequency of poker machine play in the past 12 months, personality traits of impulsivity and capacity for cognitive reflection, and gambling beliefs. For the experimental task, participants were instructed to play on two PC-simulated electronic gaming machines (EGMs or slot machines) that differed on payback percentage and volatility, with the option of freely switching between EGMs after a practice phase. Results indicated that participants were able to easily discriminate between machines and manifested a preference to play machines offering higher payback or volatility. These findings diverged from previous findings of no preference for play on higher payback/volatility machines, potentially due to of the current study's absence of the option to make multi-line and multi-credit bets. It was concluded that return rate parameters like payback percentage and volatility strongly influenced slot machine preference in the absence of betting options like multi-line bets, though more research is needed to determine the effects of such betting options on player distribution of money between multiple EGMs.

Exploring the influence of constitutive models and associated parameters for the orthogonal machining of Ti6Al4V

NASA Astrophysics Data System (ADS)

Pervaiz, S.; Anwar, S.; Kannan, S.; Almarfadi, A.

2018-04-01

Ti6Al4V is known as difficult-to-cut material due to its inherent properties such as high hot hardness, low thermal conductivity and high chemical reactivity. Though, Ti6Al4V is utilized by industrial sectors such as aeronautics, energy generation, petrochemical and bio-medical etc. For the metal cutting community, competent and cost-effective machining of Ti6Al4V is a challenging task. To optimize cost and machining performance for the machining of Ti6Al4V, finite element based cutting simulation can be a very useful tool. The aim of this paper is to develop a finite element machining model for the simulation of Ti6Al4V machining process. The study incorporates material constitutive models namely Power Law (PL) and Johnson – Cook (JC) material models to mimic the mechanical behaviour of Ti6Al4V. The study investigates cutting temperatures, cutting forces, stresses, and plastic strains with respect to different PL and JC material models with associated parameters. In addition, the numerical study also integrates different cutting tool rake angles in the machining simulations. The simulated results will be beneficial to draw conclusions for improving the overall machining performance of Ti6Al4V.

Contributions a la caracterisation et a l'amelioration de l'usinabilite de pieces d'acier elaborees par metallurgie des poudres

NASA Astrophysics Data System (ADS)

Boilard, Patrick

Even though powder metallurgy (P/M) is a near net shape process, a large number of parts still require one or more machining operations during the course of their elaboration and/or their finishing. The main objectives of the work presented in this thesis are centered on the elaboration of blends with enhanced machinability, as well as helping with the definition and in the characterization of the machinability of P/M parts. Enhancing machinability can be done in various ways, through the use of machinability additives and by decreasing the amount of porosity of the parts. These different ways of enhancing machinability have been investigated thoroughly, by systematically planning and preparing series of samples in order to obtain valid and repeatable results leading to meaningful conclusions relevant to the P/M domain. Results obtained during the course of the work are divided into three main chapters: (1) the effect of machining parameters on machinability, (2) the effect of additives on machinability, and (3) the development and the characterization of high density parts obtained by liquid phase sintering. Regarding the effect of machining parameters on machinability, studies were performed on parameters such as rotating speed, feed, tool position and diameter of the tool. Optimal cutting parameters are found for drilling operations performed on a standard FC-0208 blend, for different machinability criteria. Moreover, study of material removal rates shows the sensitivity of the machinability criteria for different machining parameters and indicates that thrust force is more regular than tool wear and slope of the drillability curve in the characterization of machinability. The chapter discussing the effect of various additives on machinability reveals many interesting results. First, work carried out on MoS2 additions reveals the dissociation of this additive and the creation of metallic sulphides (namely CuxS sulphides) when copper is present. Results also show that it is possible to reduce the amount of MoS2 in the blend so as to lower the dimensional change and the cost (blend Mo8A), while enhancing machinability and keeping hardness values within the same range (70 HRB). Second, adding enstatite (MgO·SiO2) permits the observation of the mechanisms occurring with the use of this additive. It is found that the stability of enstatite limits the diffusion of graphite during sintering, leading to the presence of free graphite in the pores, thus enhancing machinability. Furthermore, a lower amount of graphite in the matrix leads to a lower hardness, which is also beneficial to machinability. It is also found that the presence of copper enhances the diffusion of graphite, through the formation of a liquid phase during sintering. With the objective of improving machinability by reaching higher densities, blends were developed for densification through liquid phase sintering. High density samples are obtained (>7.5 g/cm3) for blends prepared with Fe-C-P constituents, namely with 0.5%P and 2.4%C. By systematically studying the effect of different parameters, the importance of the chemical composition (mainly the carbon content) and the importance of the sintering cycle (particularly the cooling rate) are demonstrated. Moreover, various heat treatments studied illustrate the different microstructures achievable for this system, showing various amounts of cementite, pearlite and free graphite. Although the machinability is limited for samples containing large amounts of cementite, it can be greatly improved with very slow cooling, leading to graphitization of the carbon in presence of phosphorus. Adequate control of the sintering cycle on samples made from FGS1625 powder leads to the obtention of high density (≥7.0 g/cm 3) microstructures containing various amounts of pearlite, ferrite and free graphite. Obtaining ferritic microstructures with free graphite designed for very high machinability (tool wear <1.0%) or fine pearlitic microstructures with excellent mechanical properties (transverse rupture strength >1600 MPa) is therefore possible. These results show that improvement of machinability through higher densities is limited by microstructure. Indeed, for the studied samples, microstructure is dominant in the determination of machinability, far more important than density, judging by the influence of cementite or of the volume fraction of free graphite on machinability for example. (Abstract shortened by UMI.)

Does prediction error drive one-shot declarative learning?

PubMed

Greve, Andrea; Cooper, Elisa; Kaula, Alexander; Anderson, Michael C; Henson, Richard

2017-06-01

The role of prediction error (PE) in driving learning is well-established in fields such as classical and instrumental conditioning, reward learning and procedural memory; however, its role in human one-shot declarative encoding is less clear. According to one recent hypothesis, PE reflects the divergence between two probability distributions: one reflecting the prior probability (from previous experiences) and the other reflecting the sensory evidence (from the current experience). Assuming unimodal probability distributions, PE can be manipulated in three ways: (1) the distance between the mode of the prior and evidence, (2) the precision of the prior, and (3) the precision of the evidence. We tested these three manipulations across five experiments, in terms of peoples' ability to encode a single presentation of a scene-item pairing as a function of previous exposures to that scene and/or item. Memory was probed by presenting the scene together with three choices for the previously paired item, in which the two foil items were from other pairings within the same condition as the target item. In Experiment 1, we manipulated the evidence to be either consistent or inconsistent with prior expectations, predicting PE to be larger, and hence memory better, when the new pairing was inconsistent. In Experiments 2a-c, we manipulated the precision of the priors, predicting better memory for a new pairing when the (inconsistent) priors were more precise. In Experiment 3, we manipulated both visual noise and prior exposure for unfamiliar faces, before pairing them with scenes, predicting better memory when the sensory evidence was more precise. In all experiments, the PE hypotheses were supported. We discuss alternative explanations of individual experiments, and conclude the Predictive Interactive Multiple Memory Signals (PIMMS) framework provides the most parsimonious account of the full pattern of results.

ENLIGHT: European network for Light ion hadron therapy.

PubMed

Dosanjh, Manjit; Amaldi, Ugo; Mayer, Ramona; Poetter, Richard

2018-04-03

The European Network for Light Ion Hadron Therapy (ENLIGHT) was established in 2002 following various European particle therapy network initiatives during the 1980s and 1990s (e.g. EORTC task group, EULIMA/PIMMS accelerator design). ENLIGHT started its work on major topics related to hadron therapy (HT), such as patient selection, clinical trials, technology, radiobiology, imaging and health economics. It was initiated through CERN and ESTRO and dealt with various disciplines such as (medical) physics and engineering, radiation biology and radiation oncology. ENLIGHT was funded until 2005 through the EC FP5 programme. A regular annual meeting structure was started in 2002 and continues until today bringing together the various disciplines and projects and institutions in the field of HT at different European places for regular exchange of information on best practices and research and development. Starting in 2006 ENLIGHT coordination was continued through CERN in collaboration with ESTRO and other partners involved in HT. Major projects within the EC FP7 programme (2008-2014) were launched for R&D and transnational access (ULICE, ENVISION) and education and training networks (Marie Curie ITNs: PARTNER, ENTERVISION). These projects were instrumental for the strengthening of the field of hadron therapy. With the start of 4 European carbon ion and proton centres and the upcoming numerous European proton therapy centres, the future scope of ENLIGHT will focus on strengthening current and developing European particle therapy research, multidisciplinary education and training and general R&D in technology and biology with annual meetings and a continuously strong CERN support. Collaboration with the European Particle Therapy Network (EPTN) and other similar networks will be pursued. Copyright © 2018 CERN. Published by Elsevier B.V. All rights reserved.

Impact of the HEALTHY Study on Vending Machine Offerings in Middle Schools

PubMed Central

Hartstein, Jill; Cullen, Karen W.; Virus, Amy; El Ghormli, Laure; Volpe, Stella L.; Staten, Myrlene A; Bridgman, Jessica C.; Stadler, Diane D.; Gillis, Bonnie; McCormick, Sarah B.; Mobley, Connie C.

2013-01-01

Purpose/Objectives The purpose of this study is to report the impact of the three-year middle school-based HEALTHY study on intervention school vending machine offerings. There were two goals for the vending machines: serve only dessert/snack foods with 200 kilocalories or less per single serving package, and eliminate 100% fruit juice and beverages with added sugar. Methods Six schools in each of seven cities (Houston, TX, San Antonio, TX, Irvine, CA, Portland, OR, Pittsburg, PA, Philadelphia, PA, and Chapel Hill, NC) were randomized into intervention (n=21 schools) or control (n=21 schools) groups, with three intervention and three control schools per city. All items in vending machine slots were tallied twice in the fall of 2006 for baseline data and twice at the end of the study, in 2009. The percentage of total slots for each food/beverage category was calculated and compared between intervention and control schools at the end of study, using the Pearson chi-square test statistic. Results At baseline, 15 intervention and 15 control schools had beverage and/or snack vending machines, compared with 11 intervention and 11 control schools at the end of the study. At the end of study, all of the intervention schools with beverage vending machines, but only one out of the nine control schools, met the beverage goal. The snack goal was met by all of the intervention schools and only one of the four control schools with snack vending machines. Applications to Child Nutrition Professionals The HEALTHY study’s vending machine beverage and snack goals were successfully achieved in intervention schools, reducing access to less healthy food items outside the school meals program. Although the effect of these changes on student diet, energy balance and growth is unknown, these results suggest that healthier options for snacks can successfully be offered in school vending machines. PMID:23687471

Embedded control system for computerized franking machine

NASA Astrophysics Data System (ADS)

Shi, W. M.; Zhang, L. B.; Xu, F.; Zhan, H. W.

2007-12-01

This paper presents a novel control system for franking machine. A methodology for operating a franking machine using the functional controls consisting of connection, configuration and franking electromechanical drive is studied. A set of enabling technologies to synthesize postage management software architectures driven microprocessor-based embedded systems is proposed. The cryptographic algorithm that calculates mail items is analyzed to enhance the postal indicia accountability and security. The study indicated that the franking machine is reliability, performance and flexibility in printing mail items.

Visualization and characterization of individual type III protein secretion machines in live bacteria

PubMed Central

Lara-Tejero, María; Bewersdorf, Jörg; Galán, Jorge E.

2017-01-01

Type III protein secretion machines have evolved to deliver bacterially encoded effector proteins into eukaryotic cells. Although electron microscopy has provided a detailed view of these machines in isolation or fixed samples, little is known about their organization in live bacteria. Here we report the visualization and characterization of the Salmonella type III secretion machine in live bacteria by 2D and 3D single-molecule switching superresolution microscopy. This approach provided access to transient components of this machine, which previously could not be analyzed. We determined the subcellular distribution of individual machines, the stoichiometry of the different components of this machine in situ, and the spatial distribution of the substrates of this machine before secretion. Furthermore, by visualizing this machine in Salmonella mutants we obtained major insights into the machine’s assembly. This study bridges a major resolution gap in the visualization of this nanomachine and may serve as a paradigm for the examination of other bacterially encoded molecular machines. PMID:28533372

Study on Gap Flow Field Simulation in Small Hole Machining of Ultrasonic Assisted EDM

NASA Astrophysics Data System (ADS)

Liu, Yu; Chang, Hao; Zhang, Wenchao; Ma, Fujian; Sha, Zhihua; Zhang, Shengfang

2017-12-01

When machining a small hole with high aspect ratio in EDM, it is hard for the flushing liquid entering the bottom gap and the debris could hardly be removed, which results in the accumulation of debris and affects the machining efficiency and machining accuracy. The assisted ultrasonic vibration can improve the removal of debris in the gap. Based on dynamics simulation software Fluent, a 3D model of debris movement in the gap flow field of EDM small hole machining assisted with side flushing and ultrasonic vibration is established in this paper. When depth to ratio is 3, the laws of different amplitudes and frequencies on debris distribution and removal are quantitatively analysed. The research results show that periodic ultrasonic vibration can promote the movement of debris, which is beneficial to the removal of debris in the machining gap. Compared to traditional small hole machining in EDM, the debris in the machining gap is greatly reduced, which ensures the stability of machining process and improves the machining efficiency.

Machinability of nickel based alloys using electrical discharge machining process

NASA Astrophysics Data System (ADS)

Khan, M. Adam; Gokul, A. K.; Bharani Dharan, M. P.; Jeevakarthikeyan, R. V. S.; Uthayakumar, M.; Thirumalai Kumaran, S.; Duraiselvam, M.

2018-04-01

The high temperature materials such as nickel based alloys and austenitic steel are frequently used for manufacturing critical aero engine turbine components. Literature on conventional and unconventional machining of steel materials is abundant over the past three decades. However the machining studies on superalloy is still a challenging task due to its inherent property and quality. Thus this material is difficult to be cut in conventional processes. Study on unconventional machining process for nickel alloys is focused in this proposed research. Inconel718 and Monel 400 are the two different candidate materials used for electrical discharge machining (EDM) process. Investigation is to prepare a blind hole using copper electrode of 6mm diameter. Electrical parameters are varied to produce plasma spark for diffusion process and machining time is made constant to calculate the experimental results of both the material. Influence of process parameters on tool wear mechanism and material removal are considered from the proposed experimental design. While machining the tool has prone to discharge more materials due to production of high energy plasma spark and eddy current effect. The surface morphology of the machined surface were observed with high resolution FE SEM. Fused electrode found to be a spherical structure over the machined surface as clumps. Surface roughness were also measured with surface profile using profilometer. It is confirmed that there is no deviation and precise roundness of drilling is maintained.

Hazard perception and occupational injuries in the welders and lathe machine operators of Rawalpindi and Islamabad.

PubMed

Shaikh, M A

2001-02-01

To study the prevalence of occupational injuries in the welders and lathe machine operators and their hazard perception. This study was conducted in the welders and lathe machine operators working in the welding and metal working shops in Rawalpindi and Islamabad. A cross-sectional survey was conducted by two trained health interviewers using uniform questionnaire with both close and open-ended questions. Two hundred and eight welders and 104 lathe machine operators were interviewed. Thirty nine (18.7%) welders and 27 (26%) lathe machine operators reported an injury in the past three months, while 63 (30.3%) welders and 76 (73.8%) lathe machine operators reported sustaining an injury in the past twelve months. However, only half of the welders and 31 (29.8%) lathe machine operators believed that their occupation was hazardous for health. For effective public health policy there is a need preventive education and enforcement of safety regulations for the informal occupational sector in Pakistan.

Vending machine assessment methodology. A systematic review.

PubMed

Matthews, Melissa A; Horacek, Tanya M

2015-07-01

The nutritional quality of food and beverage products sold in vending machines has been implicated as a contributing factor to the development of an obesogenic food environment. How comprehensive, reliable, and valid are the current assessment tools for vending machines to support or refute these claims? A systematic review was conducted to summarize, compare, and evaluate the current methodologies and available tools for vending machine assessment. A total of 24 relevant research studies published between 1981 and 2013 met inclusion criteria for this review. The methodological variables reviewed in this study include assessment tool type, study location, machine accessibility, product availability, healthfulness criteria, portion size, price, product promotion, and quality of scientific practice. There were wide variations in the depth of the assessment methodologies and product healthfulness criteria utilized among the reviewed studies. Of the reviewed studies, 39% evaluated machine accessibility, 91% evaluated product availability, 96% established healthfulness criteria, 70% evaluated portion size, 48% evaluated price, 52% evaluated product promotion, and 22% evaluated the quality of scientific practice. Of all reviewed articles, 87% reached conclusions that provided insight into the healthfulness of vended products and/or vending environment. Product healthfulness criteria and complexity for snack and beverage products was also found to be variable between the reviewed studies. These findings make it difficult to compare results between studies. A universal, valid, and reliable vending machine assessment tool that is comprehensive yet user-friendly is recommended. Copyright © 2015 Elsevier Ltd. All rights reserved.

Fun with Physics in the Elementary School.

ERIC Educational Resources Information Center

Ediger, Marlow

Primary grade pupils can become fascinated with simple machines. This paper suggests that teachers have simple machines in the classroom for a unit of study. It proposes some guidelines to create a unit of study for six simple machines that include the fulcrum, inclined plane, pulley, wheel and axle, wedge, and screw. Friction, gravity, force, and…

High speed turning of compacted graphite iron using controlled modulation

NASA Astrophysics Data System (ADS)

Stalbaum, Tyler Paul

Compacted graphite iron (CGI) is a material which emerged as a candidate material to replace cast iron (CI) in the automotive industry for engine block castings. Its thermal and mechanical properties allow the CGI-based engines to operate at higher cylinder pressures and temperatures than CI-based engines, allowing for lower fuel emissions and increased fuel economy. However, these same properties together with the thermomechanical wear mode in the CGI-CBN system result in poor machinability and inhibit CGI from seeing wide spread use in the automotive industry. In industry, machining of CGI is done only at low speeds, less than V = 200 m/min, to avoid encountering rapid wear of the cutting tools during cutting. Studies have suggested intermittent cutting operations such as milling suffer less severe tool wear than continuous cutting. Furthermore, evidence that a hard sulfide layer which forms over the cutting edge in machining CI at high speeds is absent during machining CGI is a major factor in the difference in machinability of these material systems. The present study addresses both of these issues by modification to the conventional machining process to allow intermittent continuous cutting. The application of controlled modulation superimposed onto the cutting process -- modulation-assisted machining (MAM) -- is shown to be quite effective in reducing the wear of cubic boron nitride (CBN) tools when machining CGI at high machining speeds (> 500 m/min). The tool life is at least 20 times greater than found in conventional machining of CGI. This significant reduction in wear is a consequence of reduction in the severity of the tool-work contact conditions with MAM. The propensity for thermochemical wear of CBN is thus reduced. It is found that higher cutting speed (> 700 m/min) leads to lower tool wear with MAM. The MAM configuration employing feed-direction modulation appears feasible for implementation at high speeds and offers a solution to this challenging class of industrial machining applications. This study's approach is by series of high speed turning tests of CGI with CBN tools, comparing conventional machining to MAM for similar parameters otherwise, by tool wear measurements and machinability observations.

Prostate Cancer Probability Prediction By Machine Learning Technique.

PubMed

Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

2017-11-26

The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report.

PubMed

Kim, Dong Wook; Kim, Hwiyoung; Nam, Woong; Kim, Hyung Jun; Cha, In-Ho

2018-04-23

The aim of this study was to build and validate five types of machine learning models that can predict the occurrence of BRONJ associated with dental extraction in patients taking bisphosphonates for the management of osteoporosis. A retrospective review of the medical records was conducted to obtain cases and controls for the study. Total 125 patients consisting of 41 cases and 84 controls were selected for the study. Five machine learning prediction algorithms including multivariable logistic regression model, decision tree, support vector machine, artificial neural network, and random forest were implemented. The outputs of these models were compared with each other and also with conventional methods, such as serum CTX level. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results. The performance of machine learning models was significantly superior to conventional statistical methods and single predictors. The random forest model yielded the best performance (AUC = 0.973), followed by artificial neural network (AUC = 0.915), support vector machine (AUC = 0.882), logistic regression (AUC = 0.844), decision tree (AUC = 0.821), drug holiday alone (AUC = 0.810), and CTX level alone (AUC = 0.630). Machine learning methods showed superior performance in predicting BRONJ associated with dental extraction compared to conventional statistical methods using drug holiday and serum CTX level. Machine learning can thus be applied in a wide range of clinical studies. Copyright © 2017. Published by Elsevier Inc.

Ball Machine Usage in Tennis: Movement Initiation and Swing Timing While Returning Balls from a Ball Machine and from a Real Server

PubMed Central

Carboch, Jan; Süss, Vladimir; Kocib, Tomas

2014-01-01

Practicing with the use of a ball machine could handicap a player compared to playing against an actual opponent. Recent studies have shown some differences in swing timing and movement coordination, when a player faces a ball projection machine as opposed to a human opponent. We focused on the time of movement initiation and on stroke timing during returning tennis serves (simulated by a ball machine or by a real server). Receivers’ movements were measured on a tennis court. In spite of using a serving ball speed from 90 kph to 135 kph, results showed significant differences in movement initiation and backswing duration between serves received from a ball machine and serves received from a real server. Players had shorter movement initiation when they faced a ball machine. Backswing duration was longer for the group using a ball machine. That demonstrates different movement timing of tennis returns when players face a ball machine. Use of ball machines in tennis practice should be limited as it may disrupt stroke timing. Key points Players have shorter initial move time when they are facing the ball machine. Using the ball machine results in different swing timing and movement coordination. The use of the ball machine should be limited. PMID:24790483

Ball machine usage in tennis: movement initiation and swing timing while returning balls from a ball machine and from a real server.

PubMed

Carboch, Jan; Süss, Vladimir; Kocib, Tomas

2014-05-01

Practicing with the use of a ball machine could handicap a player compared to playing against an actual opponent. Recent studies have shown some differences in swing timing and movement coordination, when a player faces a ball projection machine as opposed to a human opponent. We focused on the time of movement initiation and on stroke timing during returning tennis serves (simulated by a ball machine or by a real server). Receivers' movements were measured on a tennis court. In spite of using a serving ball speed from 90 kph to 135 kph, results showed significant differences in movement initiation and backswing duration between serves received from a ball machine and serves received from a real server. Players had shorter movement initiation when they faced a ball machine. Backswing duration was longer for the group using a ball machine. That demonstrates different movement timing of tennis returns when players face a ball machine. Use of ball machines in tennis practice should be limited as it may disrupt stroke timing. Key pointsPlayers have shorter initial move time when they are facing the ball machine.Using the ball machine results in different swing timing and movement coordination.The use of the ball machine should be limited.

Study of an Audio Playback Machine Storage, Distribution, and Repair System. Options for Machine Operation. Study II, Part 1, Phase 2, Final Report.

ERIC Educational Resources Information Center

ManTech Technical Services Corp., Fairfax, VA.

This report presents the results of a management study of audio playback equipment operations conducted by the National Library Service, Library of Congress, its associated network of state and local machine lending agencies (MLA), and other parties that play a role in current operations. The objectives were to document current operations,…

High-speed machining of Space Shuttle External Tank (ET) panels

NASA Technical Reports Server (NTRS)

Miller, J. A.

1983-01-01

Potential production rates and project cost savings achieved by converting the conventional machining process in manufacturing shuttle external tank panels to high speed machining (HSM) techniques were studied. Savings were projected from the comparison of current production rates with HSM rates and with rates attainable on new conventional machines. The HSM estimates were also based on rates attainable by retrofitting existing conventional equipment with high speed spindle motors and rates attainable using new state of the art machines designed and built for HSM.

The role of hemodialysis machines dedication in reducing Hepatitis C transmission in the dialysis setting in Iran: A multicenter prospective interventional study

PubMed Central

Shamshirsaz, Alireza Abdollah; Kamgar, Mohammad; Bekheirnia, Mir Reza; Ayazi, Farzam; Hashemi, Seyed Reza; Bouzari, Navid; Habibzadeh, Mohammad Reza; Pourzahedgilani, Nima; Broumand, Varshasb; Shamshirsaz, Amirhooshang Abdollah; Moradi, Maziyar; Borghei, Mehrdad; Haghighi, Niloofar Nobakht; Broumand, Behrooz

2004-01-01

Background Hepatitis C virus (HCV) infection is a significant problem among patients undergoing maintenance hemodialysis (HD). We conducted a prospective multi-center study to evaluate the effect of dialysis machine separation on the spread of HCV infection. Methods Twelve randomly selected dialysis centers in Tehran, Iran were randomly divided into two groups; those using dedicated machines (D) for HCV infected individuals and those using non-dedicated HD machines (ND). 593 HD cases including 51 HCV positive (RT-PCR) cases and 542 HCV negative patients were enrolled in this study. The prevalence of HCV infection in the D group was 10.1% (range: 4.6%– 13.2%) and it was 7.1% (range: 4.2%–16.8%) in the ND group. During the study conduction 5 new HCV positive cases and 169 new HCV negative cases were added. In the D group, PCR positive patients were dialyzed on dedicated machines. In the ND group all patients shared the same machines. Results In the first follow-up period, the incidence of HCV infection was 1.6% and 4.7% in the D and ND group respectively (p = 0.05). In the second follow-up period, the incidence of HCV infection was 1.3% in the D group and 5.7% in the ND group (p < 0.05). Conclusions In this study the incidence of HCV in HD patients decreased by the use of dedicated HD machines for HCV infected patients. Additional studies may help to clarify the role of machine dedication in conjunction with application of universal precautions in reducing HCV transmission. PMID:15469615

Machining of bone: Analysis of cutting force and surface roughness by turning process.

PubMed

Noordin, M Y; Jiawkok, N; Ndaruhadi, P Y M W; Kurniawan, D

2015-11-01

There are millions of orthopedic surgeries and dental implantation procedures performed every year globally. Most of them involve machining of bones and cartilage. However, theoretical and analytical study on bone machining is lagging behind its practice and implementation. This study views bone machining as a machining process with bovine bone as the workpiece material. Turning process which makes the basis of the actually used drilling process was experimented. The focus is on evaluating the effects of three machining parameters, that is, cutting speed, feed, and depth of cut, to machining responses, that is, cutting forces and surface roughness resulted by the turning process. Response surface methodology was used to quantify the relation between the machining parameters and the machining responses. The turning process was done at various cutting speeds (29-156 m/min), depths of cut (0.03 -0.37 mm), and feeds (0.023-0.11 mm/rev). Empirical models of the resulted cutting force and surface roughness as the functions of cutting speed, depth of cut, and feed were developed. Observation using the developed empirical models found that within the range of machining parameters evaluated, the most influential machining parameter to the cutting force is depth of cut, followed by feed and cutting speed. The lowest cutting force was obtained at the lowest cutting speed, lowest depth of cut, and highest feed setting. For surface roughness, feed is the most significant machining condition, followed by cutting speed, and with depth of cut showed no effect. The finest surface finish was obtained at the lowest cutting speed and feed setting. © IMechE 2015.

The study on force, surface integrity, tool life and chip on laser assisted machining of inconel 718 using Nd:YAG laser source.

PubMed

Venkatesan, K

2017-07-01

Inconel 718, a high-temperature alloy, is a promising material for high-performance aerospace gas turbine engines components. However, the machining of the alloy is difficult owing to immense shear strength, rapid work hardening rate during turning, and less thermal conductivity. Hence, like ceramics and composites, the machining of this alloy is considered as difficult-to-turn materials. Laser assisted turning method has become a promising solution in recent years to lessen cutting stress when materials that are considered difficult-to-turn, such as Inconel 718 is employed. This study investigated the influence of input variables of laser assisted machining on the machinability aspect of the Inconel 718. The comparison of machining characteristics has been carried out to analyze the process benefits with the variation of laser machining variables. The laser assisted machining variables are cutting speeds of 60-150 m/min, feed rates of 0.05-0.125 mm/rev with a laser power between 1200 W and 1300 W. The various output characteristics such as force, roughness, tool life and geometrical characteristic of chip are investigated and compared with conventional machining without application of laser power. From experimental results, at a laser power of 1200 W, laser assisted turning outperforms conventional machining by 2.10 times lessening in cutting force, 46% reduction in surface roughness as well as 66% improvement in tool life when compared that of conventional machining. Compared to conventional machining, with the application of laser, the cutting speed of carbide tool has increased to a cutting condition of 150 m/min, 0.125 mm/rev. Microstructural analysis shows that no damage of the subsurface of the workpiece.

Design of a Modular E-Core Flux Concentrating Axial Flux Machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Husain, Tausif; Sozer, Yilmaz; Husain, Iqbal

2015-09-02

In this paper a novel E-Core axial flux machine is proposed. The machine has a double stator-single rotor configuration with flux concentrating ferrite magnets, and pole windings across each leg of an E-Core stator. E-Core stators with the proposed flux-concentrating rotor arrangement result in better magnet utilization and higher torque density. The machine also has a modular structure facilitating simpler construction. This paper presents a single phase and a three-phase version of the E-Core machine. Case study for a 1.1 kW, 400 rpm machine for both the single phase and three-phase axial flux machine is presented. The results are verifiedmore » through 3D finite element analysis.« less

Discomfort analysis in computerized numeric control machine operations.

PubMed

Muthukumar, Krishnamoorthy; Sankaranarayanasamy, Krishnasamy; Ganguli, Anindya Kumar

2012-06-01

The introduction of computerized numeric control (CNC) technology in manufacturing industries has revolutionized the production process, but there are some health and safety problems associated with these machines. The present study aimed to investigate the extent of postural discomfort in CNC machine operators, and the relationship of this discomfort to the display and control panel height, with a view to validate the anthropometric recommendation for the location of the display and control panel in CNC machines. The postural discomforts associated with CNC machines were studied in 122 male operators using Corlett and Bishop's body part discomfort mapping, subject information, and discomfort level at various time intervals from starting to end of a shift. This information was collected using a questionnaire. Statistical analysis was carried out using ANOVA. Neck discomfort due to the positioning of the machine displays, and shoulder and arm discomfort due to the positioning of controls were identified as common health issues in the operators of these machines. The study revealed that 45.9% of machine operators reported discomfort in the lower back, 41.8% in the neck, 22.1% in the upper-back, 53.3% in the shoulder and arm, and 21.3% of the operators reported discomfort in the leg. Discomfort increased with the progress of the day and was highest at the end of a shift; subject age had no effect on patient tendency to experience discomfort levels.

Discomfort Analysis in Computerized Numeric Control Machine Operations

PubMed Central

Sankaranarayanasamy, Krishnasamy; Ganguli, Anindya Kumar

2012-01-01

Objectives The introduction of computerized numeric control (CNC) technology in manufacturing industries has revolutionized the production process, but there are some health and safety problems associated with these machines. The present study aimed to investigate the extent of postural discomfort in CNC machine operators, and the relationship of this discomfort to the display and control panel height, with a view to validate the anthropometric recommendation for the location of the display and control panel in CNC machines. Methods The postural discomforts associated with CNC machines were studied in 122 male operators using Corlett and Bishop's body part discomfort mapping, subject information, and discomfort level at various time intervals from starting to end of a shift. This information was collected using a questionnaire. Statistical analysis was carried out using ANOVA. Results Neck discomfort due to the positioning of the machine displays, and shoulder and arm discomfort due to the positioning of controls were identified as common health issues in the operators of these machines. The study revealed that 45.9% of machine operators reported discomfort in the lower back, 41.8% in the neck, 22.1% in the upper-back, 53.3% in the shoulder and arm, and 21.3% of the operators reported discomfort in the leg. Conclusion Discomfort increased with the progress of the day and was highest at the end of a shift; subject age had no effect on patient tendency to experience discomfort levels. PMID:22993720

Machine intelligence and robotics: Report of the NASA study group

NASA Technical Reports Server (NTRS)

1980-01-01

Opportunities for the application of machine intelligence and robotics in NASA missions and systems were identified. The benefits of successful adoption of machine intelligence and robotics techniques were estimated and forecasts were prepared to show their growth potential. Program options for research, advanced development, and implementation of machine intelligence and robot technology for use in program planning are presented.

Electrical machines with superconducting windings. Part 3: Homopolar dc machines

NASA Astrophysics Data System (ADS)

Kullman, D.; Henninger, P.

1981-01-01

The losses in rotating liquid metal contacts and the problems in including liquid metals were theoretically and experimentally studied. These machines are shown realiable. For electric ship propulsion, they are a more efficient method of power transmission than mechanical gearboxes. However, weight reduction as compared to mechanical gearboxes can hardly be achieved with machines fully shielded by magnetic iron.

Near-Miss Effects on Response Latencies and Win Estimations of Slot Machine Players

ERIC Educational Resources Information Center

Dixon, Mark R.; Schreiber, James E.

2004-01-01

The present study examined the degree to which slot machine near-miss trials, or trials that displayed 2 of 3 winning symbols on the payoff line, affected response times and win estimations of 12 recreational slot machine players. Participants played a commercial slot machine in a casino-like laboratory for course extra-credit points. Videotaped…

Study of Man-Machine Communications Systems for the Handicapped. Volume III. Final Report.

ERIC Educational Resources Information Center

Kafafian, Haig

The report describes a series of studies conducted to determine the extent to which severly handicapped students who were able to comprehend language and language structure but who were not able to write or type could communicate using various man-machine systems. Included among the systems tested were specialized electric typewriting machines, a…

Mechanical properties of a new mica-based machinable glass ceramic for CAD/CAM restorations.

PubMed

Thompson, J Y; Bayne, S C; Heymann, H O

1996-12-01

Machinable ceramics (Vita Mark II and Dicor MGC) exhibit good short-term clinical performance, but long-term in vivo fracture resistance is still being monitored. The relatively low fracture toughness of currently available machinable ceramics restricts their use to conservative inlays and onlays. A new machinable glass ceramic (MGC-F) has been developed (Corning Inc.) with enhanced fluorescence and machinability. The purpose of this study was to characterize and compare key mechanical properties of MGC-F to Dicor MGC-Light, Dicor MGC-Dark, and Vita Mark II glass ceramics. The mean fracture toughness and indented biaxial flexure strength of MGC-F were each significantly greater (p < or = 0.01) than that of Dicor MGC-Light, Dicor MGC-Dark, and Vita Mark II ceramic materials. The results of this study indicate the potential for better in vivo fracture resistance of MGC-F compared with existing machinable ceramic materials for CAD/CAM restorations.

Engineered Surface Properties of Porous Tungsten from Cryogenic Machining

NASA Astrophysics Data System (ADS)

Schoop, Julius Malte

Porous tungsten is used to manufacture dispenser cathodes due to it refractory properties. Surface porosity is critical to functional performance of dispenser cathodes because it allows for an impregnated ceramic compound to migrate to the emitting surface, lowering its work function. Likewise, surface roughness is important because it is necessary to ensure uniform wetting of the molten impregnate during high temperature service. Current industry practice to achieve surface roughness and surface porosity requirements involves the use of a plastic infiltrant during machining. After machining, the infiltrant is baked and the cathode pellet is impregnated. In this context, cryogenic machining is investigated as a substitutionary process for the current plastic infiltration process. Along with significant reductions in cycle time and resource use, surface quality of cryogenically machined un-infiltrated (as-sintered) porous tungsten has been shown to significantly outperform dry machining. The present study is focused on examining the relationship between machining parameters and cooling condition on the as-machined surface integrity of porous tungsten. The effects of cryogenic pre-cooling, rake angle, cutting speed, depth of cut and feed are all taken into consideration with respect to machining-induced surface morphology. Cermet and Polycrystalline diamond (PCD) cutting tools are used to develop high performance cryogenic machining of porous tungsten. Dry and pre-heated machining were investigated as a means to allow for ductile mode machining, yet severe tool-wear and undesirable smearing limited the feasibility of these approaches. By using modified PCD cutting tools, high speed machining of porous tungsten at cutting speeds up to 400 m/min is achieved for the first time. Beyond a critical speed, brittle fracture and built-up edge are eliminated as the result of a brittle to ductile transition. A model of critical chip thickness ( hc ) effects based on cutting force, temperature and surface roughness data is developed and used to study the deformation mechanisms of porous tungsten under different machining conditions. It is found that when hmax = hc, ductile mode machining of otherwise highly brittle porous tungsten is possible. The value of hc is approximately the same as the average ligament size of the 80% density porous tungsten workpiece.

The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

PubMed

Librenza-Garcia, Diego; Kotzian, Bruno Jaskulski; Yang, Jessica; Mwangi, Benson; Cao, Bo; Pereira Lima, Luiza Nunes; Bermudez, Mariane Bagatin; Boeira, Manuela Vianna; Kapczinski, Flávio; Passos, Ives Cavalcante

2017-09-01

Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Investigations on high speed machining of EN-353 steel alloy under different machining environments

NASA Astrophysics Data System (ADS)

Venkata Vishnu, A.; Jamaleswara Kumar, P.

2018-03-01

The addition of Nano Particles into conventional cutting fluids enhances its cooling capabilities; in the present paper an attempt is made by adding nano sized particles into conventional cutting fluids. Taguchi Robust Design Methodology is employed in order to study the performance characteristics of different turning parameters i.e. cutting speed, feed rate, depth of cut and type of tool under different machining environments i.e. dry machining, machining with lubricant - SAE 40 and machining with mixture of nano sized particles of Boric acid and base fluid SAE 40. A series of turning operations were performed using L27 (3)13 orthogonal array, considering high cutting speeds and the other machining parameters to measure hardness. The results are compared among the different machining environments, and it is concluded that there is considerable improvement in the machining performance using lubricant SAE 40 and mixture of SAE 40 + boric acid compared with dry machining. The ANOVA suggests that the selected parameters and the interactions are significant and cutting speed has most significant effect on hardness.

A single-phase axially-magnetized permanent-magnet oscillating machine for miniature aerospace power sources

NASA Astrophysics Data System (ADS)

Sui, Yi; Zheng, Ping; Cheng, Luming; Wang, Weinan; Liu, Jiaqi

2017-05-01

A single-phase axially-magnetized permanent-magnet (PM) oscillating machine which can be integrated with a free-piston Stirling engine to generate electric power, is investigated for miniature aerospace power sources. Machine structure, operating principle and detent force characteristic are elaborately studied. With the sinusoidal speed characteristic of the mover considered, the proposed machine is designed by 2D finite-element analysis (FEA), and some main structural parameters such as air gap diameter, dimensions of PMs, pole pitches of both stator and mover, and the pole-pitch combinations, etc., are optimized to improve both the power density and force capability. Compared with the three-phase PM linear machines, the proposed single-phase machine features less PM use, simple control and low controller cost. The power density of the proposed machine is higher than that of the three-phase radially-magnetized PM linear machine, but lower than the three-phase axially-magnetized PM linear machine.

Effect of the Machined Surfaces of AISI 4337 Steel to Cutting Conditions on Dry Machining Lathe

NASA Astrophysics Data System (ADS)

Rahim, Robbi; Napid, Suhardi; Hasibuan, Abdurrozzaq; Rahmah Sibuea, Siti; Yusmartato, Y.

2018-04-01

The objective of the research is to obtain a cutting condition which has a good chance of realizing dry machining concept on AISI 4337 steel material by studying surface roughness, microstructure and hardness of machining surface. The data generated from the experiment were then processed and analyzed using the standard Taguchi method L9 (34) orthogonal array. Testing of dry and wet machining used surface test and micro hardness test for each of 27 test specimens. The machining results of the experiments showed that average surface roughness (Raavg) was obtained at optimum cutting conditions when VB 0.1 μm, 0.3 μm and 0.6 μm respectively 1.467 μm, 2.133 μm and 2,800 μm fo r dry machining while which was carried out by wet machining the results obtained were 1,833 μm, 2,667 μm and 3,000 μm. It can be concluded that dry machining provides better surface quality of machinery results than wet machining. Therefore, dry machining is a good choice that may be realized in the manufacturing and automotive industries.

Electronic vending machines for dispensing rapid HIV self-testing kits: a case study.

PubMed

Young, Sean D; Klausner, Jeffrey; Fynn, Risa; Bolan, Robert

2014-02-01

This short report evaluates the feasibility of using electronic vending machines for dispensing oral, fluid, rapid HIV self-testing kits in Los Angeles County. Feasibility criteria that needed to be addressed were defined as: (1) ability to find a manufacturer who would allow dispensing of HIV testing kits and could fit them to the dimensions of a vending machine, (2) ability to identify and address potential initial obstacles, trade-offs in choosing a machine location, and (3) ability to gain community approval for implementing this approach in a community setting. To address these issues, we contracted a vending machine company who could supply a customized, Internet-enabled machine that could dispense HIV kits and partnered with a local health center available to host the machine onsite and provide counseling to participants, if needed. Vending machines appear to be feasible technologies that can be used to distribute HIV testing kits.

Achieving Small Structures in Thin NiTi Sheets for Medical Applications with Water Jet and Micro Machining: A Comparison

NASA Astrophysics Data System (ADS)

Frotscher, M.; Kahleyss, F.; Simon, T.; Biermann, D.; Eggeler, G.

2011-07-01

NiTi shape memory alloys (SMA) are used for a variety of applications including medical implants and tools as well as actuators, making use of their unique properties. However, due to the hardness and strength, in combination with the high elasticity of the material, the machining of components can be challenging. The most common machining techniques used today are laser cutting and electrical discharge machining (EDM). In this study, we report on the machining of small structures into binary NiTi sheets, applying alternative processing methods being well-established for other metallic materials. Our results indicate that water jet machining and micro milling can be used to machine delicate structures, even in very thin NiTi sheets. Further work is required to optimize the cut quality and the machining speed in order to increase the cost-effectiveness and to make both methods more competitive.

Effect of Width of Kerf on Machining Accuracy and Subsurface Layer After WEDM

NASA Astrophysics Data System (ADS)

Mouralova, K.; Kovar, J.; Klakurkova, L.; Prokes, T.

2018-02-01

Wire electrical discharge machining is an unconventional machining technology that applies physical principles to material removal. The material is removed by a series of recurring current discharges between the workpiece and the tool electrode, and a `kerf' is created between the wire and the material being machined. The width of the kerf is directly dependent not only on the diameter of the wire used, but also on the machine parameter settings and, in particular, on the set of mechanical and physical properties of the material being machined. To ensure precise machining, it is important to have the width of the kerf as small as possible. The present study deals with the evaluation of the width of the kerf for four different metallic materials (some of which were subsequently heat treated using several methods) with different machine parameter settings. The kerf is investigated on metallographic cross sections using light and electron microscopy.

Electronic vending machines for dispensing rapid HIV self-testing kits: A case study

PubMed Central

Young, Sean D.; Klausner, Jeffrey; Fynn, Risa; Bolan, Robert

2014-01-01

This short report evaluates the feasibility of using electronic vending machines for dispensing oral, fluid, rapid HIV-self testing kits in Los Angeles County. Feasibility criteria that needed to be addressed were defined as: 1) ability to find a manufacturer who would allow dispensing of HIV testing kits and could fit them to the dimensions of a vending machine, 2) ability to identify and address potential initial obstacles, trade-offs in choosing a machine location, and 3) ability to gain community approval for implementing this approach in a community setting. To address these issues, we contracted a vending machine company who could supply a customized, Internet-enabled machine that could dispense HIV kits and partnered with a local health center available to host the machine onsite and provide counseling to participants, if needed. Vending machines appear to be feasible technologies that can be used to distribute HIV testing kits. PMID:23777528

Safety of stationary grinding machines - impact resistance of work zone enclosures.

PubMed

Mewes, Detlef; Adler, Christian

2017-09-01

Guards on machine tools are intended to protect persons from being injured by parts ejected with high kinetic energy from the work zone of the machine. Stationary grinding machines are a typical example. Generally such machines are provided with abrasive product guards closely enveloping the grinding wheel. However, many machining tasks do not allow the use of abrasive product guards. In such cases, the work zone enclosure has to be dimensioned so that, in case of failure, grinding wheel fragments remain inside the machine's working zone. To obtain data for the dimensioning of work zone enclosures on stationary grinding machines, which must be operated without an abrasive product guard, burst tests were conducted with vitrified grinding wheels. The studies show that, contrary to widely held opinion, narrower grinding wheels can be more critical concerning the impact resistance than wider wheels although their fragment energy is smaller.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vanchurin, Vitaly, E-mail: vvanchur@d.umn.edu

We initiate a formal study of logical inferences in context of the measure problem in cosmology or what we call cosmic logic. We describe a simple computational model of cosmic logic suitable for analysis of, for example, discretized cosmological systems. The construction is based on a particular model of computation, developed by Alan Turing, with cosmic observers (CO), cosmic measures (CM) and cosmic symmetries (CS) described by Turing machines. CO machines always start with a blank tape and CM machines take CO's Turing number (also known as description number or Gödel number) as input and output the corresponding probability. Similarly,more » CS machines take CO's Turing number as input, but output either one if the CO machines are in the same equivalence class or zero otherwise. We argue that CS machines are more fundamental than CM machines and, thus, should be used as building blocks in constructing CM machines. We prove the non-computability of a CS machine which discriminates between two classes of CO machines: mortal that halts in finite time and immortal that runs forever. In context of eternal inflation this result implies that it is impossible to construct CM machines to compute probabilities on the set of all CO machines using cut-off prescriptions. The cut-off measures can still be used if the set is reduced to include only machines which halt after a finite and predetermined number of steps.« less

Cosmic logic: a computational model

NASA Astrophysics Data System (ADS)

Vanchurin, Vitaly

2016-02-01

We initiate a formal study of logical inferences in context of the measure problem in cosmology or what we call cosmic logic. We describe a simple computational model of cosmic logic suitable for analysis of, for example, discretized cosmological systems. The construction is based on a particular model of computation, developed by Alan Turing, with cosmic observers (CO), cosmic measures (CM) and cosmic symmetries (CS) described by Turing machines. CO machines always start with a blank tape and CM machines take CO's Turing number (also known as description number or Gödel number) as input and output the corresponding probability. Similarly, CS machines take CO's Turing number as input, but output either one if the CO machines are in the same equivalence class or zero otherwise. We argue that CS machines are more fundamental than CM machines and, thus, should be used as building blocks in constructing CM machines. We prove the non-computability of a CS machine which discriminates between two classes of CO machines: mortal that halts in finite time and immortal that runs forever. In context of eternal inflation this result implies that it is impossible to construct CM machines to compute probabilities on the set of all CO machines using cut-off prescriptions. The cut-off measures can still be used if the set is reduced to include only machines which halt after a finite and predetermined number of steps.

Conceptual Study of Permanent Magnet Machine Ship Propulsion Systems

DTIC Science & Technology

1977-12-01

cycloconverter subsystem is designed using advanced thyristors and can be either water or air cooled. The machine-cycloconverter, many-phase or parallel...Turnb, Phase, Poles, Air Gap ................................. 3-9 3-5 Machine Characteristics Versus Number of Poles (large machine, 40 000 hp). Poles...cylindrical permanent magnet generator forces the power conditioner to provide for both frequency change and voltage control. The complexity of this dual

Abstracts of AF Materials Laboratory Reports

DTIC Science & Technology

1975-09-01

NO: TITLE: AUTHOR(S): CONTRACT NO; CONTRACTOR: AFML-TR-73-307 200,397 IMPROVED AUTOMATED TAPE LAYING MACHINE M. Poullos, W. J. Murray, D.L...AUTOMATED IMPROVED AUTOMATED TAPE LAYING MACHINE AUTOMATION AUTOMATION OF COATING PROCESSES FOR GAS TURBINE DLADcS AND VANES 203222/111 203072...IMP90VE0 TAPE LAYING MACHINE IMPP)VED AUTOMATED TAPE LAYING MACHINE A STUDY O^ THE STRESS-STRAIN TEHAVIOR OF GRAPHITE

Gram staining with an automatic machine.

PubMed

Felek, S; Arslan, A

1999-01-01

This study was undertaken to develop a new Gram-staining machine controlled by a micro-controller and to investigate the quality of slides that were stained in the machine. The machine was designed and produced by the authors. It uses standard 220 V AC. Staining, washing, and drying periods are controlled by a timer built in the micro-controller. A software was made that contains a certain algorithm and time intervals for the staining mode. One-hundred and forty smears were prepared from Escherichia coli, Staphylococcus aureus, Neisseria sp., blood culture, trypticase soy broth, direct pus and sputum smears for comparison studies. Half of the slides in each group were stained with the machine, the other half by hand and then examined by four different microbiologists. Machine-stained slides had a higher clarity and less debris than the hand-stained slides (p < 0.05). In hand-stained slides, some Gram-positive organisms showed poor Gram-positive staining features (p < 0.05). In conclusion, we suggest that Gram staining with the automatic machine increases the staining quality and helps to decrease the work load in a busy diagnostic laboratory.

Learning Activity Packets for Milling Machines. Unit II--Horizontal Milling Machines.

ERIC Educational Resources Information Center

Oklahoma State Board of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This learning activity packet (LAP) outlines the study activities and performance tasks covered in a related curriculum guide on milling machines. The course of study in this LAP is intended to help students learn to set up and operate a horizontal mill. Tasks addressed in the LAP include mounting style "A" or "B" arbors and adjusting arbor…

Herbaceous Weed Control Trials with a Planting Machine Sprayer and a Crawler-Tractor Sprayer--Fourth Year Pine Response.

Treesearch

James Miller

1990-01-01

Operational trials of herbaceous weed control treatments by machine application were studied at two southern alabama locations for establishing loblolly pine (Pinus taeda). The first study tested the feasibility of a spray attachment for planting machines to apply banded treatments while planting in February and March. Two rates of sulfometuron (Oust...

Alumina additions may improve the damage tolerance of soft machined zirconia-based ceramics.

PubMed

Oilo, Marit; Tvinnereim, Helene M; Gjerdet, Nils Roar

2011-01-01

The aim of this study was to evaluate the damage tolerance of different zirconia-based materials. Bars of one hard machined and one soft machined dental zirconia and an experimental 95% zirconia 5% alumina ceramic were subjected to 100,000 stress cycles (n = 10), indented to provoke cracks on the tensile stress side (n = 10), and left untreated as controls (n = 10). The experimental material demonstrated a higher relative damage tolerance, with a 40% reduction compared to 68% for the hard machined zirconia and 84% for the soft machined zirconia.

Design study and performance analysis of 12S-14P field excitation flux switching motor for hybrid electric vehicle

NASA Astrophysics Data System (ADS)

Husin, Zhafir Aizat; Sulaiman, Erwan; Khan, Faisal; Mazlan, Mohamed Mubin Aizat; Othman, Syed Muhammad Naufal Syed

2015-05-01

This paper presents a new structure of 12slot-14pole field excitation flux switching motor (FEFSM) as an alternative candidate of non-Permanent Magnet (PM) machine for HEV drives. Design study, performance analysis and optimization of field excitation flux switching machine with non-rare-earth magnet for hybrid electric vehicle drive applications is done. The stator of projected machine consists of iron core made of electromagnetic steels, armature coils and field excitation coils as the only field mmf source. The rotor is consisted of only stack of iron and hence, it is reliable and appropriate for high speed operation. The design target is a machine with the maximum torque, power and power density, more than 210Nm, 123kW and 3.5kW/kg, respectively, which competes with interior permanent magnet synchronous machine used in existing hybrid electric vehicle. Some design feasibility studies on FEFSM based on 2D-FEA and deterministic optimization method will be applied to design the proposed machine.

Comparison of two freeze-thaw apparatus.

DOT National Transportation Integrated Search

1982-01-01

The purpose of this study was to compare the results of rapid freezing and thawing tests conducted on machine A with results from machine B, which is intended to replace the aging machine A. Concrete samples were prepared to attain levels of resistan...

Microstructural and hardness changes in aluminum alloy Al-7075: Correlating machining and equal channel angular pressing

NASA Astrophysics Data System (ADS)

Imbrogno, Stano; Segebade, Eric; Fellmeth, Andreas; Gerstenmeyer, Michael; Zanger, Frederik; Schulze, Volker; Umbrello, Domenico

2017-10-01

Recently, the study and understanding of surface integrity of various materials after machining is becoming one of the interpretative keys to quantify a product's quality and life cycle performance. The possibility to provide fundamental details about the mechanical response and the behavior of the affected material layers caused by thermo-mechanical loads resulting from machining operations can help the designer to produce parts with superior quality. The aim of this work is to study the experimental outcomes obtained from orthogonal cutting tests and a Severe Plastic Deformation (SPD) process known as Equal Channel Angular Pressing (ECAP) in order to find possible links regarding induced microstructural and hardness changes between machined surface layer and SPD-bulk material for Al-7075. This scientific investigation aims to establish the basis for an innovative method to study and quantify metallurgical phenomena that occur beneath the machined surface of bulk material.

A control technology evaluation of state-of-the-art, perchloroethylene dry-cleaning machines.

PubMed

Earnest, G Scott

2002-05-01

NIOSH researchers evaluated the ability of fifth-generation dry-cleaning machines to control occupational exposure to perchloroethylene (PERC). Use of these machines is mandated in some countries; however, less than 1 percent of all U.S. shops have them. A study was conducted at a U.S. dry-cleaning shop where two fifth-generation machines were used. Both machines had a refrigerated condenser as a primary control and a carbon adsorber as a secondary control to recover PERC vapors during the dry cycle. These machines were designed to lower the PERC concentration in the cylinder at the end of the dry cycle to below 290 ppm. A single-beam infrared photometer continuously monitors the PERC concentration in the machine cylinder, and a door interlock prevents opening until the concentration is below 290 ppm. Personal breathing zone air samples were measured for the machine operator and presser. The operator had time-weighted average (TWA) PERC exposures that were less than 2 ppm. Highest exposures occurred during loading and unloading the machine and when performing routine machine maintenance. All presser samples were below the limit of detection. Real-time video exposure monitoring showed that the operator had peak exposures near 160 ppm during loading and unloading the machine (below the OSHA maximum of 300 ppm). This exposure (160 ppm) is an order of magnitude lower than exposures with more traditional machines that are widely used in the United States. The evaluated machines were very effective at reducing TWA PERC exposures as well as peak exposures that occur during machine loading and unloading. State-of-the-art dry-cleaning machines equipped with refrigerated condensers, carbon adsorbers, drum monitors, and door interlocks can provide substantially better protection than more traditional machines that are widely used in the United States.

Development of techniques to enhance man/machine communication

NASA Technical Reports Server (NTRS)

Targ, R.; Cole, P.; Puthoff, H.

1974-01-01

A four-state random stimulus generator, considered to function as an ESP teaching machine was used to investigate an approach to facilitating interactions between man and machines. A subject tries to guess in which of four states the machine is. The machine offers the user feedback and reinforcement as to the correctness of his choice. Using this machine, 148 volunteer subjects were screened under various protocols. Several whose learning slope and/or mean score departed significantly from chance expectation were identified. Direct physiological evidence of perception of remote stimuli not presented to any known sense of the percipient using electroencephalographic (EEG) output when a light was flashed in a distant room was also studied.

Global Positioning System (GPS) and Geographic Information System (GIS) analysis of mobile harvesting equipment and sediment delivery to streams during forest harvest operations on steep terrain: Experimental design

Treesearch

Daniel Bowker; Jeff Stringer; Chris Barton; Songlin Fei

2011-01-01

Sediment mobilized by forest harvest machine traffic contributes substantially to the degradation of headwater stream systems. This study monitored forest harvest machine traffic to analyze how it affects sediment delivery to stream channels. Harvest machines were outfitted with global positioning system (GPS) dataloggers, recording machine movements and working status...

Characteristics of the Arcing Plasma Formation Effect in Spark-Assisted Chemical Engraving of Glass, Based on Machine Vision

PubMed Central

Wu, Dung-Sheng

2018-01-01

Spark-assisted chemical engraving (SACE) is a non-traditional machining technology that is used to machine electrically non-conducting materials including glass, ceramics, and quartz. The processing accuracy, machining efficiency, and reproducibility are the key factors in the SACE process. In the present study, a machine vision method is applied to monitor and estimate the status of a SACE-drilled hole in quartz glass. During the machining of quartz glass, the spring-fed tool electrode was pre-pressured on the quartz glass surface to feed the electrode that was in contact with the machining surface of the quartz glass. In situ image acquisition and analysis of the SACE drilling processes were used to analyze the captured image of the state of the spark discharge at the tip and sidewall of the electrode. The results indicated an association between the accumulative size of the SACE-induced spark area and deepness of the hole. The results indicated that the evaluated depths of the SACE-machined holes were a proportional function of the accumulative spark size with a high degree of correlation. The study proposes an innovative computer vision-based method to estimate the deepness and status of SACE-drilled holes in real time. PMID:29565303

Characteristics of the Arcing Plasma Formation Effect in Spark-Assisted Chemical Engraving of Glass, Based on Machine Vision.

PubMed

Ho, Chao-Ching; Wu, Dung-Sheng

2018-03-22

Spark-assisted chemical engraving (SACE) is a non-traditional machining technology that is used to machine electrically non-conducting materials including glass, ceramics, and quartz. The processing accuracy, machining efficiency, and reproducibility are the key factors in the SACE process. In the present study, a machine vision method is applied to monitor and estimate the status of a SACE-drilled hole in quartz glass. During the machining of quartz glass, the spring-fed tool electrode was pre-pressured on the quartz glass surface to feed the electrode that was in contact with the machining surface of the quartz glass. In situ image acquisition and analysis of the SACE drilling processes were used to analyze the captured image of the state of the spark discharge at the tip and sidewall of the electrode. The results indicated an association between the accumulative size of the SACE-induced spark area and deepness of the hole. The results indicated that the evaluated depths of the SACE-machined holes were a proportional function of the accumulative spark size with a high degree of correlation. The study proposes an innovative computer vision-based method to estimate the deepness and status of SACE-drilled holes in real time.

Council-supported condom vending machines: are they acceptable to rural communities?

PubMed

Tomnay, Jane E; Hatch, Beth

2013-11-01

Twenty-four hour access to condoms for young people living in rural Victoria is problematic for many reasons, including the fact that condom vending machines are often located in venues and places they cannot access. We partnered with three rural councils to install condom vending machines in locations that provided improved access to condoms for local young people. Councils regularly checked the machines, refilled the condoms and retrieved the money. They also managed the maintenance of the machine and provided monthly data. In total, 1153 condoms were purchased over 12 months, with 924 (80%) obtained from male toilets and 69% (801 out of 1153) purchased in the second half of the study. Revenue of $2626.10 (AUD) was generated and no negative feedback from residents was received by any council nor was there any negative reporting by local media. Vandalism, tampering or damage occurred at all sites; however, only two significant episodes of damage required a machine to be sent away for repairs. Condom vending machines installed in rural towns in north-east Victoria are accessible to young people after business hours, are cost-effective for councils and have not generated any complaints from residents. The machines have not suffered unrepairable damage and were used more frequently as the study progressed.

Simulation and Community-Based Instruction of Vending Machines with Time Delay.

ERIC Educational Resources Information Center

Browder, Diane M.; And Others

1988-01-01

The study evaluated the use of simulated instruction on vending machine use as an adjunct to community-based instruction with two moderately retarded children. Results showed concurrent acquisition of the vending machine skills across trained and untrained sites. (Author/DB)

Compliance with school nutrition policies in Ontario and Alberta: An assessment of secondary school vending machine data from the COMPASS study.

PubMed

Vine, Michelle M; Harrington, Daniel W; Butler, Alexandra; Patte, Karen; Godin, Katelyn; Leatherdale, Scott T

2017-04-20

We investigated the extent to which a sample of Ontario and Alberta secondary schools are being compliant with their respective provincial nutrition policies, in terms of the food and beverages sold in vending machines. This observational study used objective data on drinks and snacks from vending machines, collected over three years of the COMPASS study (2012/2013-2014/2015 school years). Drink (e.g., sugar-containing carbonated/non-carbonated soft drinks, sports drinks, etc.) and snack (e.g., chips, crackers, etc.) data were coded by number of units available, price, and location of vending machine(s) in the school. Univariate and bivariate analyses were undertaken using R version 3.2.3. In order to assess policy compliancy over time, nutritional information of products in vending machines was compared to nutrition standards set out in P/PM 150 in Ontario, and those set out in the Alberta Nutrition Guidelines for Children and Youth (2012) in Alberta. Results reveal a decline over time in the proportion of schools selling sugar-containing carbonated soft drinks (9% in 2012/2013 vs. 3% in 2014/2015), crackers (26% vs. 17%) and cake products (12% vs. 5%) in vending machines, and inconsistent changes in the proportion selling chips (53%, 67% and 65% over the three school years). Conversely, results highlight increases in the proportion of vending machines selling chocolate bars (7% vs. 13%) and cookies (21% vs. 40%) between the 2012/2013 and 2014/2015 school years. Nutritional standard policies were not adhered to in the majority of schools with respect to vending machines. There is a need for investment in formal monitoring and evaluation of school policies, and the provision of information and tools to support nutrition policy implementation.

Predictive Modeling and Optimization of Vibration-assisted AFM Tip-based Nanomachining

NASA Astrophysics Data System (ADS)

Kong, Xiangcheng

The tip-based vibration-assisted nanomachining process offers a low-cost, low-effort technique in fabricating nanometer scale 2D/3D structures in sub-100 nm regime. To understand its mechanism, as well as provide the guidelines for process planning and optimization, we have systematically studied this nanomachining technique in this work. To understand the mechanism of this nanomachining technique, we firstly analyzed the interaction between the AFM tip and the workpiece surface during the machining process. A 3D voxel-based numerical algorithm has been developed to calculate the material removal rate as well as the contact area between the AFM tip and the workpiece surface. As a critical factor to understand the mechanism of this nanomachining process, the cutting force has been analyzed and modeled. A semi-empirical model has been proposed by correlating the cutting force with the material removal rate, which was validated using experimental data from different machining conditions. With the understanding of its mechanism, we have developed guidelines for process planning of this nanomachining technique. To provide the guideline for parameter selection, the effect of machining parameters on the feature dimensions (depth and width) has been analyzed. Based on ANOVA test results, the feature width is only controlled by the XY vibration amplitude, while the feature depth is affected by several machining parameters such as setpoint force and feed rate. A semi-empirical model was first proposed to predict the machined feature depth under given machining condition. Then, to reduce the computation intensity, linear and nonlinear regression models were also proposed and validated using experimental data. Given the desired feature dimensions, feasible machining parameters could be provided using these predictive feature dimension models. As the tip wear is unavoidable during the machining process, the machining precision will gradually decrease. To maintain the machining quality, the guideline for when to change the tip should be provided. In this study, we have developed several metrics to detect tip wear, such as tip radius and the pull-off force. The effect of machining parameters on the tip wear rate has been studied using these metrics, and the machining distance before a tip must be changed has been modeled using these machining parameters. Finally, the optimization functions have been built for unit production time and unit production cost subject to realistic constraints, and the optimal machining parameters can be found by solving these functions.

Howitzer Ammunition System Procurement (HASP).

DTIC Science & Technology

1991-07-01

machine tools , etc.) * Most critical part of base to reassemble. IPP * Industry to plan round-specific...beyond allowed tolerances. - Conducting tolerance studies and funding machining studies at sul’on "’actors. " Facility development was controlled by the...Manufacturing Balimoy Mfg. of Venice, Inc. Action Manufacturing Co. Lanson Industries Inc. Hercules Aerospace Company CIMA Machine & Tool Co., Inc. Talley Defense Systems Tracor Aerospace Inc. BMY E49030APPBMAC

Comparative adoption of cone beam computed tomography and panoramic radiography machines across Australia.

PubMed

Zhang, A; Critchley, S; Monsour, P A

2016-12-01

The aim of the present study was to assess the current adoption of cone beam computed tomography (CBCT) and panoramic radiography (PR) machines across Australia. Information regarding registered CBCT and PR machines was obtained from radiation regulators across Australia. The number of X-ray machines was correlated with the population size, the number of dentists, and the gross state product (GSP) per capita, to determine the best fitting regression model(s). In 2014, there were 232 CBCT and 1681 PR machines registered in Australia. Based on absolute counts, Queensland had the largest number of CBCT and PR machines whereas the Northern Territory had the smallest number. However, when based on accessibility in terms of the population size and the number of dentists, the Australian Capital Territory had the most CBCT machines and Western Australia had the most PR machines. The number of X-ray machines correlated strongly with both the population size and the number of dentists, but not with the GSP per capita. In 2014, the ratio of PR to CBCT machines was approximately 7:1. Projected increases in either the population size or the number of dentists could positively impact on the adoption of PR and CBCT machines in Australia. © 2016 Australian Dental Association.

Design and implementation of a system for laser assisted milling of advanced materials

NASA Astrophysics Data System (ADS)

Wu, Xuefeng; Feng, Gaocheng; Liu, Xianli

2016-09-01

Laser assisted machining is an effective method to machine advanced materials with the added benefits of longer tool life and increased material removal rates. While extensive studies have investigated the machining properties for laser assisted milling(LAML), few attempts have been made to extend LAML to machining parts with complex geometric features. A methodology for continuous path machining for LAML is developed by integration of a rotary and movable table into an ordinary milling machine with a laser beam system. The machining strategy and processing path are investigated to determine alignment of the machining path with the laser spot. In order to keep the material removal temperatures above the softening temperature of silicon nitride, the transformation is coordinated and the temperature interpolated, establishing a transient thermal model. The temperatures of the laser center and cutting zone are also carefully controlled to achieve optimal machining results and avoid thermal damage. These experiments indicate that the system results in no surface damage as well as good surface roughness, validating the application of this machining strategy and thermal model in the development of a new LAML system for continuous path processing of silicon nitride. The proposed approach can be easily applied in LAML system to achieve continuous processing and improve efficiency in laser assisted machining.

Air Bearings Machined On Ultra Precision, Hydrostatic CNC-Lathe

NASA Astrophysics Data System (ADS)

Knol, Pierre H.; Szepesi, Denis; Deurwaarder, Jan M.

1987-01-01

Micromachining of precision elements requires an adequate machine concept to meet the high demand of surface finish, dimensional and shape accuracy. The Hembrug ultra precision lathes have been exclusively designed with hydrostatic principles for main spindle and guideways. This concept is to be explained with some major advantages of hydrostatics compared with aerostatics at universal micromachining applications. Hembrug has originally developed the conventional Mikroturn ultra precision facing lathes, for diamond turning of computer memory discs. This first generation of machines was followed by the advanced computer numerically controlled types for machining of complex precision workpieces. One of these parts, an aerostatic bearing component has been succesfully machined on the Super-Mikroturn CNC. A case study of airbearing machining confirms the statement that a good result of the micromachining does not depend on machine performance alone, but also on the technology applied.

Time to B. cereus about hot chocolate.

PubMed Central

Nelms, P K; Larson, O; Barnes-Josiah, D

1997-01-01

OBJECTIVE: To determine the cause of illnesses experienced by employees of a Minneapolis manufacturing plant after drinking hot chocolate bought from a vending machine and to explore the prevalence of similar vending machine-related illnesses. METHODS: The authors inspected the vending machines at the manufacturing plant where employees reported illnesses and at other locations in the city where hot chocolate beverages were sold in machines. Tests were performed on dry mix, water, and beverage samples and on machine parts. RESULTS: Laboratory analyses confirmed the presence of B. cereus in dispensed beverages at a concentration capable of causing illness (170,000 count/gm). In citywide testing of vending machines dispensing hot chocolate, 7 of the 39 licensed machines were found to be contaminated, with two contaminated machines having B. cereus levels capable of causing illness. CONCLUSIONS: Hot chocolate sold in vending machines may contain organisms capable of producing toxins that under favorable conditions, can induce illness. Such illnesses are likely to be underreported. Even low concentrations of B. cereus may be dangerous for vulnerable populations such as the aged or immunosuppressed. Periodic testing of vending machines is thus warranted. The relationship between cleaning practices and B. cereus contamination is an issue for further study. PMID:9160059

Time to B. cereus about hot chocolate.

PubMed

Nelms, P K; Larson, O; Barnes-Josiah, D

1997-01-01

To determine the cause of illnesses experienced by employees of a Minneapolis manufacturing plant after drinking hot chocolate bought from a vending machine and to explore the prevalence of similar vending machine-related illnesses. The authors inspected the vending machines at the manufacturing plant where employees reported illnesses and at other locations in the city where hot chocolate beverages were sold in machines. Tests were performed on dry mix, water, and beverage samples and on machine parts. Laboratory analyses confirmed the presence of B. cereus in dispensed beverages at a concentration capable of causing illness (170,000 count/gm). In citywide testing of vending machines dispensing hot chocolate, 7 of the 39 licensed machines were found to be contaminated, with two contaminated machines having B. cereus levels capable of causing illness. Hot chocolate sold in vending machines may contain organisms capable of producing toxins that under favorable conditions, can induce illness. Such illnesses are likely to be underreported. Even low concentrations of B. cereus may be dangerous for vulnerable populations such as the aged or immunosuppressed. Periodic testing of vending machines is thus warranted. The relationship between cleaning practices and B. cereus contamination is an issue for further study.

Actualities and Development of Heavy-Duty CNC Machine Tool Thermal Error Monitoring Technology

NASA Astrophysics Data System (ADS)

Zhou, Zu-De; Gui, Lin; Tan, Yue-Gang; Liu, Ming-Yao; Liu, Yi; Li, Rui-Ya

2017-09-01

Thermal error monitoring technology is the key technological support to solve the thermal error problem of heavy-duty CNC (computer numerical control) machine tools. Currently, there are many review literatures introducing the thermal error research of CNC machine tools, but those mainly focus on the thermal issues in small and medium-sized CNC machine tools and seldom introduce thermal error monitoring technologies. This paper gives an overview of the research on the thermal error of CNC machine tools and emphasizes the study of thermal error of the heavy-duty CNC machine tool in three areas. These areas are the causes of thermal error of heavy-duty CNC machine tool and the issues with the temperature monitoring technology and thermal deformation monitoring technology. A new optical measurement technology called the "fiber Bragg grating (FBG) distributed sensing technology" for heavy-duty CNC machine tools is introduced in detail. This technology forms an intelligent sensing and monitoring system for heavy-duty CNC machine tools. This paper fills in the blank of this kind of review articles to guide the development of this industry field and opens up new areas of research on the heavy-duty CNC machine tool thermal error.

Ex Vivo Machine Perfusion in VCA with a Novel Oxygen Carrier System to Enhance Graft Preservation and Immunologic Outcomes

DTIC Science & Technology

2016-12-01

studies 2. Ex Vivo Study Group (n=4) Machine Perfusion 4 VRAM grafts preserved for 14 hours with MP at 21°C Study group – developments for machine...period. The VRAM grafts were inspected daily and punch biopsies were performed on days 2, 4 and 7. The end- study necropsy was conducted on post ...TNF- α in the MP group, showing the beneficial impact of effective ex -vivo oxygenation in this group (Figure 6). Previous clinical studies in

MEASUREMENT OF INDOOR AIR EMISSIONS FROM DRY-PROCESS PHOTOCOPY MACHINES

EPA Science Inventory

The article provides background information on indoor air emissions from office equipment, with emphasis on dry-process photocopy machines. The test method is described in detail along with results of a study to evaluate the test method using four dry-process photocopy machines. ...

Machinability of an experimental Ti-Ag alloy in terms of tool life in a dental CAD/CAM system.

PubMed

Inagaki, Ryoichi; Kikuchi, Masafumi; Takahashi, Masatoshi; Takada, Yukyo; Sasaki, Keiichi

2015-01-01

Titanium is difficult to machine because of its intrinsic properties. In a previous study, the machinability of titanium was improved by alloying with silver. This study aimed to evaluate the durability of tungsten carbide burs after the fabrication of frameworks using a Ti-20%Ag alloy and titanium with a computer-aided design and computer-aided manufacturing system. There was a significant difference in attrition area ratio between the two metals. Compared with titanium, the ratio of the area of attrition of machining burs was significantly lower for the experimental Ti-20%Ag alloy. The difference in the area of attrition for titanium and Ti-20%Ag became remarkable with increasing number of machining operations. The results show that the same burs can be used for a longer time with Ti-20%Ag than with pure titanium. Therefore, in terms of tool life, the machinability of the Ti-20%Ag alloy is superior to that of titanium.

Experimental investigation into effect of cutting parameters on surface integrity of hardened tool steel

NASA Astrophysics Data System (ADS)

Bashir, K.; Alkali, A. U.; Elmunafi, M. H. S.; Yusof, N. M.

2018-04-01

Recent trend in turning hardened materials have gained popularity because of its immense machinability benefits. However, several machining processes like thermal assisted machining and cryogenic machining have reveal superior machinability benefits over conventional dry turning of hardened materials. Various engineering materials have been studied. However, investigations on AISI O1 tool steel have not been widely reported. In this paper, surface finish and surface integrity dominant when hard turning AISI O1 tool steel is analysed. The study is focused on the performance of wiper coated ceramic tool with respect to surface roughness and surface integrity of hardened tool steel. Hard turned tool steel was machined at varying cutting speed of 100, 155 and 210 m/min and feed rate of 0.05, 0.125 and 0.20mm/rev. The depth of cut of 0.2mm was maintained constant throughout the machining trials. Machining was conducted using dry turning on 200E-axis CNC lathe. The experimental study revealed that the surface finish is relatively superior at higher cutting speed of 210m/min. The surface finish increases when cutting speed increases whereas surface finish is generally better at lower feed rate of 0.05mm/rev. The experimental study conducted have revealed that phenomena such as work piece vibration due to poor or improper mounting on the spindle also contributed to higher surface roughness value of 0.66Ra during turning at 0.2mm/rev. Traces of white layer was observed when viewed with optical microscope which shows evidence of cutting effects on the turned work material at feed rate of 0.2 rev/min

Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T.

PubMed

Citak-Er, Fusun; Firat, Zeynep; Kovanlikaya, Ilhami; Ture, Ugur; Ozturk-Isik, Esin

2018-06-15

The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach. Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification. A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features. In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort. Copyright © 2018. Published by Elsevier Ltd.

Variation in access to sugar-sweetened beverages in vending machines across rural, town and urban high schools.

PubMed

Adachi-Mejia, A M; Longacre, M R; Skatrud-Mickelson, M; Li, Z; Purvis, L A; Titus, L J; Beach, M L; Dalton, M A

2013-05-01

The 2010 Dietary Guidelines for Americans include reducing consumption of sugar-sweetened beverages. Among the many possible routes of access for youth, school vending machines provide ready availability of sugar-sweetened beverages. The purpose of this study was to determine variation in high school student access to sugar-sweetened beverages through vending machines by geographic location - urban, town or rural - and to offer an approach for analysing school vending machine content. Cross-sectional observational study. Between October 2007 and May 2008, trained coders recorded beverage vending machine content and machine-front advertising in 113 machines across 26 schools in New Hampshire and Vermont, USA. Compared with town schools, urban schools were significantly less likely to offer sugar-sweetened beverages (P = 0.002). Rural schools also offered more sugar-sweetened beverages than urban schools, but this difference was not significant. Advertisements for sugar-sweetened beverages were highly prevalent in town schools. High school students have ready access to sugar-sweetened beverages through their school vending machines. Town schools offer the highest risk of exposure; school vending machines located in towns offer up to twice as much access to sugar-sweetened beverages in both content and advertising compared with urban locations. Variation by geographic region suggests that healthier environments are possible and some schools can lead as inspirational role models. Copyright © 2013 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Fault detection in rotating machines with beamforming: Spatial visualization of diagnosis features

NASA Astrophysics Data System (ADS)

Cardenas Cabada, E.; Leclere, Q.; Antoni, J.; Hamzaoui, N.

2017-12-01

Rotating machines diagnosis is conventionally related to vibration analysis. Sensors are usually placed on the machine to gather information about its components. The recorded signals are then processed through a fault detection algorithm allowing the identification of the failing part. This paper proposes an acoustic-based diagnosis method. A microphone array is used to record the acoustic field radiated by the machine. The main advantage over vibration-based diagnosis is that the contact between the sensors and the machine is no longer required. Moreover, the application of acoustic imaging makes possible the identification of the sources of acoustic radiation on the machine surface. The display of information is then spatially continuous while the accelerometers only give it discrete. Beamforming provides the time-varying signals radiated by the machine as a function of space. Any fault detection tool can be applied to the beamforming output. Spectral kurtosis, which highlights the impulsiveness of a signal as function of frequency, is used in this study. The combination of spectral kurtosis with acoustic imaging makes possible the mapping of the impulsiveness as a function of space and frequency. The efficiency of this approach lays on the source separation in the spatial and frequency domains. These mappings make possible the localization of such impulsive sources. The faulty components of the machine have an impulsive behavior and thus will be highlighted on the mappings. The study presents experimental validations of the method on rotating machines.

Assessing Multi-Person and Person-Machine Distributed Decision Making Using an Extended Psychological Distancing Model

DTIC Science & Technology

1990-02-01

human-to- human communication patterns during situation assessment and cooperative problem solving tasks. The research proposed for the second URRP year...Hardware development. In order to create an environment within which to study multi-channeled human-to- human communication , a multi-media observation...that machine-to- human communication can be used to increase cohesion between humans and intelligent machines and to promote human-machine team

Investigation of influence of errors of cutting machines with CNC on displacement trajectory accuracy of their actuating devices

NASA Astrophysics Data System (ADS)

Fedonin, O. N.; Petreshin, D. I.; Ageenko, A. V.

2018-03-01

In the article, the issue of increasing a CNC lathe accuracy by compensating for the static and dynamic errors of the machine is investigated. An algorithm and a diagnostic system for a CNC machine tool are considered, which allows determining the errors of the machine for their compensation. The results of experimental studies on diagnosing and improving the accuracy of a CNC lathe are presented.

Development of a low energy micro sheet forming machine

NASA Astrophysics Data System (ADS)

Razali, A. R.; Ann, C. T.; Shariff, H. M.; Kasim, N. I.; Musa, M. A.; Ahmad, A. F.

2017-10-01

It is expected that with the miniaturization of materials being processed, energy consumption is also being `miniaturized' proportionally. The focus of this study was to design a low energy micro-sheet-forming machine for thin sheet metal application and fabricate a low direct current powered micro-sheet-forming machine. A prototype of low energy system for a micro-sheet-forming machine which includes mechanical and electronic elements was developed. The machine was tested for its performance in terms of natural frequency, punching forces, punching speed and capability, energy consumption (single punch and frequency-time based). Based on the experiments, the machine can do 600 stroke per minute and the process is unaffected by the machine's natural frequency. It was also found that sub-Joule of power was required for a single stroke of punching/blanking process. Up to 100micron thick carbon steel shim was successfully tested and punched. It concludes that low power forming machine is feasible to be developed and be used to replace high powered machineries to form micro-products/parts.

Surface Characteristics of Machined NiTi Shape Memory Alloy: The Effects of Cryogenic Cooling and Preheating Conditions

NASA Astrophysics Data System (ADS)

Kaynak, Y.; Huang, B.; Karaca, H. E.; Jawahir, I. S.

2017-07-01

This experimental study focuses on the phase state and phase transformation response of the surface and subsurface of machined NiTi alloys. X-ray diffraction (XRD) analysis and differential scanning calorimeter techniques were utilized to measure the phase state and the transformation response of machined specimens, respectively. Specimens were machined under dry machining at ambient temperature, preheated conditions, and cryogenic cooling conditions at various cutting speeds. The findings from this research demonstrate that cryogenic machining substantially alters austenite finish temperature of martensitic NiTi alloy. Austenite finish ( A f) temperature shows more than 25 percent increase resulting from cryogenic machining compared with austenite finish temperature of as-received NiTi. Dry and preheated conditions do not substantially alter austenite finish temperature. XRD analysis shows that distinctive transformation from martensite to austenite occurs during machining process in all three conditions. Complete transformation from martensite to austenite is observed in dry cutting at all selected cutting speeds.

Preparation of superhydrophobic copper surface by a novel silk-screen printing aided electrochemical machining method

NASA Astrophysics Data System (ADS)

Yan, X. Y.; Chen, G. X.; Liu, J. W.

2018-03-01

A kind of superhydrophobic copper surface with micro-nanocomposite structure has been successfully fabricated by employing a silk-screen printing aided electrochemical machining method. At first silk-screen printing technology has been used to form a column point array mask, and then the microcolumn array would be fabricated by electrochemical machining (ECM) effect. In this study, the drop contact angles have been studied and scanning electron microscopy (SEM) has been used to study the surface characteristic of the workpiece. The experiment results show that the micro-nanocomposite structure with cylindrical array can be successfully fabricated on the metal surface. And the maximum contact angle is 151° when the fluoroalkylsilane ethanol solution was used to modify the machined surface in this study.

Machinability of Stellite 6 hardfacing

NASA Astrophysics Data System (ADS)

Benghersallah, M.; Boulanouar, L.; Le Coz, G.; Devillez, A.; Dudzinski, D.

2010-06-01

This paper reports some experimental findings concerning the machinability at high cutting speed of nickel-base weld-deposited hardfacings for the manufacture of hot tooling. The forging work involves extreme impacts, forces, stresses and temperatures. Thus, mould dies must be extremely resistant. The aim of the project is to create a rapid prototyping process answering to forging conditions integrating a Stellite 6 hardfacing deposed PTA process. This study talks about the dry machining of the hardfacing, using a two tips machining tool and a high speed milling machine equipped by a power consumption recorder Wattpilote. The aim is to show the machinability of the hardfacing, measuring the power and the tip wear by optical microscope and white light interferometer, using different strategies and cutting conditions.

Machining heavy plastic sections

NASA Technical Reports Server (NTRS)

Stalkup, O. M.

1967-01-01

Machining technique produces consistently satisfactory plane-parallel optical surfaces for pressure windows, made of plexiglass, required to support a photographic study of liquid rocket combustion processes. The surfaces are machined and polished to the required tolerances and show no degradation from stress relaxation over periods as long as 6 months.

An experimental investigation on orthogonal cutting of hybrid CFRP/Ti stacks

NASA Astrophysics Data System (ADS)

Xu, Jinyang; El Mansori, Mohamed

2016-10-01

Hybrid CFRP/Ti stack has been widely used in the modern aerospace industry owing to its superior mechanical/physical properties and excellent structural functions. Several applications require mechanical machining of these hybrid composite stacks in order to achieve dimensional accuracy and assembly performance. However, machining of such composite-to-metal alliance is usually an extremely challenging task in the manufacturing sectors due to the disparate natures of each stacked constituent and their respective poor machinability. Special issues may arise from the high force/heat generation, severe subsurface damage and rapid tool wear. To study the fundamental mechanisms controlling the bi-material machining, this paper presented an experimental study on orthogonal cutting of hybrid CFRP/Ti stack by using superior polycrystalline diamond (PCD) tipped tools. The utilized cutting parameters for hybrid CFRP/Ti machining were rigorously adopted through a compromise selection due to the disparate machinability behaviors of the CFRP laminate and Ti alloy. The key cutting responses in terms of cutting force generation, machined surface quality and tool wear mechanism were precisely addressed. The experimental results highlighted the involved five stages of CFRP/Ti cutting and the predominant crater wear and edge fracture failure governing the PCD cutting process.

Ductile and brittle transition behavior of titanium alloys in ultra-precision machining.

PubMed

Yip, W S; To, S

2018-03-02

Titanium alloys are extensively applied in biomedical industries due to their excellent material properties. However, they are recognized as difficult to cut materials due to their low thermal conductivity, which induces a complexity to their deformation mechanisms and restricts precise productions. This paper presents a new observation about the removal regime of titanium alloys. The experimental results, including the chip formation, thrust force signal and surface profile, showed that there was a critical cutting distance to achieve better surface integrity of machined surface. The machined areas with better surface roughness were located before the clear transition point, defining as the ductile to brittle transition. The machined area at the brittle region displayed the fracture deformation which showed cracks on the surface edge. The relationship between depth of cut and the ductile to brittle transaction behavior of titanium alloys in ultra-precision machining(UPM) was also revealed in this study, it showed that the ductile to brittle transaction behavior of titanium alloys occurred mainly at relatively small depth of cut. The study firstly defines the ductile to brittle transition behavior of titanium alloys in UPM, contributing the information of ductile machining as an optimal machining condition for precise productions of titanium alloys.

Vending machine policies and practices in Delaware.

PubMed

Gemmill, Erin; Cotugna, Nancy

2005-04-01

Overweight has reached alarming proportions among America's youth. Although the cause of the rise in overweight rates in children and adolescents is certainly the result of the interaction of a variety of factors, the presence of vending machines in schools is one issue that has recently come to the forefront. Many states have passed or proposed legislation that limits student access to vending machines in schools or require that vending machines in schools offer healthier choices. The purposes of this study were (a) to assess the food and beverage vending machine offerings in the public school districts in the state of Delaware and (b) to determine whether there are any district vending policies in place other than the current U.S. Department of Agriculture regulations. The results of this study indicate the most commonly sold food and drink items in school vending machines are of minimal nutritional value. School administrators are most frequently in charge of the vending contract, as well as setting and enforcing vending machine policies. Suggestions are offered to assist school nurses, often the only health professional in the school, in becoming advocates for changes in school vending practices and policies that promote the health and well-being of children and adolescents.

[Hygienic assessment of student's nutrition through vending machines (fast food)].

PubMed

Karelin, A O; Pavlova, D V; Babalyan, A V

2015-01-01

The article presents the results of a research work on studying the nutrition of students through vending machines (fast food), taking into account consumer priorities of students of medical University, the features and possible consequences of their use by students. The object of study was assortment of products sold through vending machines on the territory of the First Saint-Petersburg Medical University. Net calories, content of proteins, fats and carbohydrates, glycemic index, glycemic load were determined for each product. Information about the use of vending machines was obtained by questionnaires of students 2 and 4 courses of medical and dental faculties by standardized interview method. As was found, most sold through vending machines products has a high energy value, mainly due to refined carbohydrates, and was characterized by medium and high glycemic load. They have got low protein content. Most of the students (87.3%) take some products from the vending machines, mainly because of lack of time for canteen and buffets visiting. Only 4.2% students like assortment of vending machines. More than 50% students have got gastrointestinal complaints. Statistically significant relationship between time of study at the University and morbidity of gastrointestinal tract, as well as the number of students needing medical diet nutrition was found. The students who need the medical diet use fast food significantly more often (46.6% who need the medical diet and 37.7% who don't need it).

Grinding, Machining Morphological Studies on C/SiC Composites

NASA Astrophysics Data System (ADS)

Xiao, Chun-fang; Han, Bing

2018-05-01

C/SiC composite is a typical material difficult to machine. It is hard and brittle. In machining, the cutting force is large, the material removal rate is low, the edge is prone to collapse, and the tool wear is serious. In this paper, the grinding of C/Si composites material along the direction of fiber distribution is studied respectively. The surface microstructure and mechanical properties of C/SiC composites processed by ultrasonic machining were evaluated. The change of surface quality with the change of processing parameters has also been studied. By comparing the performances of conventional grinding and ultrasonic grinding, the surface roughness and functional characteristics of the material can be improved by optimizing the processing parameters.

A system framework of inter-enterprise machining quality control based on fractal theory

NASA Astrophysics Data System (ADS)

Zhao, Liping; Qin, Yongtao; Yao, Yiyong; Yan, Peng

2014-03-01

In order to meet the quality control requirement of dynamic and complicated product machining processes among enterprises, a system framework of inter-enterprise machining quality control based on fractal was proposed. In this system framework, the fractal-specific characteristic of inter-enterprise machining quality control function was analysed, and the model of inter-enterprise machining quality control was constructed by the nature of fractal structures. Furthermore, the goal-driven strategy of inter-enterprise quality control and the dynamic organisation strategy of inter-enterprise quality improvement were constructed by the characteristic analysis on this model. In addition, the architecture of inter-enterprise machining quality control based on fractal was established by means of Web service. Finally, a case study for application was presented. The result showed that the proposed method was available, and could provide guidance for quality control and support for product reliability in inter-enterprise machining processes.

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

PubMed

Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

2018-05-09

Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

Students' perspectives on promoting healthful food choices from campus vending machines: a qualitative interview study.

PubMed

Ali, Habiba I; Jarrar, Amjad H; Abo-El-Enen, Mostafa; Al Shamsi, Mariam; Al Ashqar, Huda

2015-05-28

Increasing the healthfulness of campus food environments is an important step in promoting healthful food choices among college students. This study explored university students' suggestions on promoting healthful food choices from campus vending machines. It also examined factors influencing students' food choices from vending machines. Peer-led semi-structured individual interviews were conducted with 43 undergraduate students (33 females and 10 males) recruited from students enrolled in an introductory nutrition course in a large national university in the United Arab Emirates. Interviews were audiotaped, transcribed, and coded to generate themes using N-Vivo software. Accessibility, peer influence, and busy schedules were the main factors influencing students' food choices from campus vending machines. Participants expressed the need to improve the nutritional quality of the food items sold in the campus vending machines. Recommendations for students' nutrition educational activities included placing nutrition tips on or beside the vending machines and using active learning methods, such as competitions on nutrition knowledge. The results of this study have useful applications in improving the campus food environment and nutrition education opportunities at the university to assist students in making healthful food choices.

A review of supervised machine learning applied to ageing research.

PubMed

Fabris, Fabio; Magalhães, João Pedro de; Freitas, Alex A

2017-04-01

Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

Machinability of lithium disilicate glass ceramic in in vitro dental diamond bur adjusting process.

PubMed

Song, Xiao-Fei; Ren, Hai-Tao; Yin, Ling

2016-01-01

Esthetic high-strength lithium disilicate glass ceramics (LDGC) are used for monolithic crowns and bridges produced in dental CAD/CAM and oral adjusting processes, which machinability affects the restorative quality. A machinability study has been made in the simulated oral clinical machining of LDGC with a dental handpiece and diamond burs, regarding the diamond tool wear and chip control, machining forces and energy, surface finish and integrity. Machining forces, speeds and energy in in vitro dental adjusting of LDGC were measured by a high-speed data acquisition and force sensor system. Machined LDGC surfaces were assessed using three-dimensional non-contact chromatic confocal optical profilometry and scanning electron microscopy (SEM). Diamond bur morphology and LDGC chip shapes were also examined using SEM. Minimum tool wear but significant LDGC chip accumulations were found. Machining forces and energy significantly depended on machining conditions (p<0.05) and were significantly higher than other glass ceramics (p<0.05). Machining speeds dropped more rapidly with increased removal rates than other glass ceramics (p<0.05). Two material machinability indices associated with the hardness, Young's modulus and fracture toughness were derived based on the normal force-removal rate relations, which ranked LDGC the most difficult to machine among glass ceramics. Surface roughness for machined LDGC was comparable for other glass ceramics. The removal mechanisms of LDGC were dominated by penetration-induced brittle fracture and shear-induced plastic deformation. Unlike most other glass ceramics, distinct intergranular and transgranular fractures of lithium disilicate crystals were found in LDGC. This research provides the fundamental data for dental clinicians on the machinability of LDGC in intraoral adjustments. Copyright © 2015 Elsevier Ltd. All rights reserved.

Drilling Machines: Vocational Machine Shop.

ERIC Educational Resources Information Center

Thomas, John C.

The lessons and supportive information in this field tested instructional block provide a guide for teachers in developing a machine shop course of study in drilling. The document is comprised of operation sheets, information sheets, and transparency masters for 23 lessons. Each lesson plan includes a performance objective, material and tools,…

General Theory of the Double Fed Synchronous Machine. Ph.D. Thesis - Swiss Technological Univ., 1950

NASA Technical Reports Server (NTRS)

El-Magrabi, M. G.

1982-01-01

Motor and generator operation of a double-fed synchronous machine were studied and physically and mathematically treated. Experiments with different connections, voltages, etc. were carried out. It was concluded that a certain degree of asymmetry is necessary for the best utilization of the machine.

Combining Machine Learning and Natural Language Processing to Assess Literary Text Comprehension

ERIC Educational Resources Information Center

Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S.

2017-01-01

This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…

Man-Machine Communication Through a Teletypewriter.

ERIC Educational Resources Information Center

Rubinoff, Morris

A ten-year research study designed a mechanized information system in the information processing field. Special attention was paid to implementation criteria entering into on-line retrieval through man-machine dialog from a remote typewriter or video terminal and four major areas were investigated: search strategies, machine stored indexer aids,…

Machinist (AFSC 42750).

ERIC Educational Resources Information Center

Air Univ., Gunter AFS, Ala. Extension Course Inst.

This four-volume student text is designed for use by Air Force personnel enrolled in a self-study extension course for machinists. Covered in the individual volumes are machine shop fundamentals, metallurgy and advanced machine work, advanced machine work, and tool design and shop management. Each volume in the set contains a series of lessons,…

Development of sacrificial support fixture using deflection analysis

NASA Astrophysics Data System (ADS)

Ramteke, Ashwini M.; Ashtankar, Kishor M.

2018-04-01

Sacrificial support fixtures are the structures used to hold the part during machining while rotating the part about the fourth axis of CNC machining. In Four axis CNC machining part is held in a indexer which is rotated about the fourth axis of rotation. So using traditional fixturing devices to hold the part during machining such as jigs, v blocks and clamping plates needs a several set ups, manufacturing time which increase the cost associated with it. Since the part is rotated about the axis of rotation in four axis CNC machining so using traditional fixturing devices to hold the part while machining we need to reorient the fixture each time for particular orientation of part about the axis of rotation. So our proposed methodology of fixture design eliminates the cost associate with the complicated fixture design for customized parts which in turn reduces the time of manufacturing of the fixtures. But while designing the layout of the fixtures it is found out that the machining the part using four axis CNC machining the accurate machining of the part is directly proportional to the deflection produced in a part. So to machine an accurate part the deflection produced in a part should be minimum. We assume that the deflection produced in a part is a result of the deflection produced in a sacrificial support fixture while machining. So this paper provides the study of the deflection checking in a part machined using sacrificial support fixture by using FEA analysis.

An efficient annealing in Boltzmann machine in Hopfield neural network

NASA Astrophysics Data System (ADS)

Kin, Teoh Yeong; Hasan, Suzanawati Abu; Bulot, Norhisam; Ismail, Mohammad Hafiz

2012-09-01

This paper proposes and implements Boltzmann machine in Hopfield neural network doing logic programming based on the energy minimization system. The temperature scheduling in Boltzmann machine enhancing the performance of doing logic programming in Hopfield neural network. The finest temperature is determined by observing the ratio of global solution and final hamming distance using computer simulations. The study shows that Boltzmann Machine model is more stable and competent in term of representing and solving difficult combinatory problems.

Performance evaluation of the croissant production line with reparable machines

NASA Astrophysics Data System (ADS)

Tsarouhas, Panagiotis H.

2015-03-01

In this study, the analytical probability models for an automated serial production system, bufferless that consists of n-machines in series with common transfer mechanism and control system was developed. Both time to failure and time to repair a failure are assumed to follow exponential distribution. Applying those models, the effect of system parameters on system performance in actual croissant production line was studied. The production line consists of six workstations with different numbers of reparable machines in series. Mathematical models of the croissant production line have been developed using Markov process. The strength of this study is in the classification of the whole system in states, representing failures of different machines. Failure and repair data from the actual production environment have been used to estimate reliability and maintainability for each machine, workstation, and the entire line is based on analytical models. The analysis provides a useful insight into the system's behaviour, helps to find design inherent faults and suggests optimal modifications to upgrade the system and improve its performance.

Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

PubMed

Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

2017-01-01

Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

How much information is in a jet?

NASA Astrophysics Data System (ADS)

Datta, Kaustuv; Larkoski, Andrew

2017-06-01

Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different originating particles. Previous studies of the power of machine learning to jet physics have typically employed image recognition, natural language processing, or other algorithms that have been extensively developed in computer science. While these studies have demonstrated impressive discrimination power, often exceeding that of widely-used observables, they have been formulated in a non-constructive manner and it is not clear what additional information the machines are learning. In this paper, we study machine learning for jet physics constructively, expressing all of the information in a jet onto sets of observables that completely and minimally span N-body phase space. For concreteness, we study the application of machine learning for discrimination of boosted, hadronic decays of Z bosons from jets initiated by QCD processes. Our results demonstrate that the information in a jet that is useful for discrimination power of QCD jets from Z bosons is saturated by only considering observables that are sensitive to 4-body (8 dimensional) phase space.

Task-Oriented, Naturally Elicited Speech (TONE) Database for the Force Requirements Expert System, Hawaii (FRESH)

DTIC Science & Technology

1988-09-01

Group Subgroup Command and control; Computational linguistics; expert system voice recognition; man- machine interface; U.S. Government 19 Abstract...simulates the characteristics of FRESH on a smaller scale. This study assisted NOSC in developing a voice-recognition, man- machine interface that could...scale. This study assisted NOSC in developing a voice-recogni- tion, man- machine interface that could be used with TONE and upgraded at a later date

Cost of unreliability method to estimate loss of revenue based on unreliability data: Case study of Printing Company

NASA Astrophysics Data System (ADS)

alhilman, Judi

2017-12-01

In the production line process of the printing office, the reliability of the printing machine plays a very important role, if the machine fail it can disrupt production target so that the company will suffer huge financial loss. One method to calculate the financial loss cause by machine failure is use the Cost of Unreliability(COUR) method. COUR method works based on down time machine and costs associated with unreliability data. Based on the calculation of COUR method, so the sum of cost due to unreliability printing machine during active repair time and downtime is 1003,747.00.

Next-Generation Machine Learning for Biological Networks.

PubMed

Camacho, Diogo M; Collins, Katherine M; Powers, Rani K; Costello, James C; Collins, James J

2018-06-14

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology. Copyright © 2018 Elsevier Inc. All rights reserved.

Effect of cutting parameters on surface finish and machinability of graphite reinforced Al-8011 matrix composite

NASA Astrophysics Data System (ADS)

Anil, K. C.; Vikas, M. G.; Shanmukha Teja, B.; Sreenivas Rao, K. V.

2017-04-01

Many materials such as alloys, composites find their applications on the basis of machinability, cost and availability. In the present work, graphite (Grp) reinforced Aluminium 8011 is synthesized by convention stir casting process and Surface finish & machinability of prepared composite is examined by using lathe tool dynamometer attached with BANKA Lathe by varying the machining parameters like spindle speed, Depth of cut and Feed rate in 3 levels. Also, Roughness Average (Ra) of machined surfaces is measured by using Surface Roughness Tester (Mitutoyo SJ201). From the studies it is cleared that mechanical properties of a composites increases with addition of Grp and The cutting force were decreased with the reinforcement percentage and thus increases the machinability of composites and also results in increased surface finish.

Automated Solar Module Assembly Line

NASA Technical Reports Server (NTRS)

Bycer, M.

1979-01-01

The gathering of information that led to the design approach of the machine, and a summary of the findings in the areas of study along with a description of each station of the machine are discussed. The machine is a cell stringing and string applique machine which is flexible in design, capable of handling a variety of cells and assembling strings of cells which can then be placed in a matrix up to 4 ft x 2 ft. in series or parallel arrangement. The target machine cycle is to be 5 seconds per cell. This machine is primarily adapted to 100 MM round cells with one or two tabs between cells. It places finished strings of up to twelve cells in a matrix of up to six such strings arranged in series or in parallel.

Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

PubMed Central

Belo, David; Gamboa, Hugo

2017-01-01

The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components. PMID:28831239

AC Loss Analysis of MgB2-Based Fully Superconducting Machines

NASA Astrophysics Data System (ADS)

Feddersen, M.; Haran, K. S.; Berg, F.

2017-12-01

Superconducting electric machines have shown potential for significant increase in power density, making them attractive for size and weight sensitive applications such as offshore wind generation, marine propulsion, and hybrid-electric aircraft propulsion. Superconductors exhibit no loss under dc conditions, though ac current and field produce considerable losses due to hysteresis, eddy currents, and coupling mechanisms. For this reason, many present machines are designed to be partially superconducting, meaning that the dc field components are superconducting while the ac armature coils are conventional conductors. Fully superconducting designs can provide increases in power density with significantly higher armature current; however, a good estimate of ac losses is required to determine the feasibility under the machines intended operating conditions. This paper aims to characterize the expected losses in a fully superconducting machine targeted towards aircraft, based on an actively-shielded, partially superconducting machine from prior work. Various factors are examined such as magnet strength, operating frequency, and machine load to produce a model for the loss in the superconducting components of the machine. This model is then used to optimize the design of the machine for minimal ac loss while maximizing power density. Important observations from the study are discussed.

Feasibility of retrofitting a university library with active workstations to reduce sedentary behavior.

PubMed

Maeda, Hotaka; Quartiroli, Alessandro; Vos, Paul W; Carr, Lucas J; Mahar, Matthew T

2014-05-01

Libraries are an inherently sedentary environment, but are an understudied setting for sedentary behavior interventions. To investigate the feasibility of incorporating portable pedal machines in a university library to reduce sedentary behaviors. The 11-week intervention targeted students at a university library. Thirteen portable pedal machines were placed in the library. Four forms of prompts (e-mail, library website, advertisement monitors, and poster) encouraging pedal machine use were employed during the first 4 weeks. Pedal machine use was measured via automatic timers on each machine and momentary time sampling. Daily library visits were measured using a gate counter. Individualized data were measured by survey. Data were collected in fall 2012 and analyzed in 2013. Mean (SD) cumulative pedal time per day was 95.5 (66.1) minutes. One or more pedal machines were observed being used 15% of the time (N=589). Pedal machines were used at least once by 7% of students (n=527). Controlled for gate count, no linear change of pedal machine use across days was found (b=-0.1 minutes, p=0.75) and the presence of the prompts did not change daily pedal time (p=0.63). Seven of eight items that assessed attitudes toward the intervention supported intervention feasibility (p<0.05). The unique non-individualized approach of retrofitting a library with pedal machines to reduce sedentary behavior seems feasible, but improvement of its effectiveness is needed. This study could inform future studies aimed at reshaping traditionally sedentary settings to improve public health. Copyright © 2014 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

A field study of exposure to whole-body vibration due to agricultural machines in a full-time rice farmer over one year.

PubMed

Tsujimura, Hiroji; Taoda, Kazushi; Kitahara, Teruyo

2015-01-01

The aims of this study were to clarify in detail the levels of whole-body vibration (WBV) exposure from a variety of agricultural machines in a rice farmer over one year, and to evaluate the daily level of exposure compared with European and Japanese threshold limits. The subject was a full-time, male rice farmer. We measured vibration accelerations on the seat pan and at the seat base of four tractors with various implements attached, one rice-planting machine, two combine harvesters, produced by the same manufacturer, and one truck used for transportation of agricultural machines. The position and velocity of the machines were recorded in parallel with WBV measurements. In addition, during the year starting in April 2010, the subject completed a questionnaire regarding his work (date, place, content, hours worked, machines used). We calculated the daily exposure to WBV, A(8), on all the days on which the subject used the agricultural machines. The WBV magnitude in farm fields was relatively high during tasks with high velocity and heavy mechanical load on the machine, and had no dominant axis. The subject worked for 159 days using the agricultural machines during the year, and the proportion of days on which A(8) values exceeded the thresholds was 90% for the Japan occupational exposure limit and 24% for the EU exposure action value. Our findings emphasize the need for rice farmers to have health management strategies suited to the farming seasons and measures to reduce WBV exposure during each farm task.

Power and color Doppler ultrasound settings for inflammatory flow: impact on scoring of disease activity in patients with rheumatoid arthritis.

PubMed

Torp-Pedersen, Søren; Christensen, Robin; Szkudlarek, Marcin; Ellegaard, Karen; D'Agostino, Maria Antonietta; Iagnocco, Annamaria; Naredo, Esperanza; Balint, Peter; Wakefield, Richard J; Torp-Pedersen, Arendse; Terslev, Lene

2015-02-01

To determine how settings for power and color Doppler ultrasound sensitivity vary on different high- and intermediate-range ultrasound machines and to evaluate the impact of these changes on Doppler scoring of inflamed joints. Six different types of ultrasound machines were used. On each machine, the factory setting for superficial musculoskeletal scanning was used unchanged for both color and power Doppler modalities. The settings were then adjusted for increased Doppler sensitivity, and these settings were designated study settings. Eleven patients with rheumatoid arthritis (RA) with wrist involvement were scanned on the 6 machines, each with 4 settings, generating 264 Doppler images for scoring and color quantification. Doppler sensitivity was measured with a quantitative assessment of Doppler activity: color fraction. Higher color fraction indicated higher sensitivity. Power Doppler was more sensitive on half of the machines, whereas color Doppler was more sensitive on the other half, using both factory settings and study settings. There was an average increase in Doppler sensitivity, despite modality, of 78% when study settings were applied. Over the 6 machines, 2 Doppler modalities, and 2 settings, the grades for each of 7 of the patients varied between 0 and 3, while the grades for each of the other 4 patients varied between 0 and 2. The effect of using different machines, Doppler modalities, and settings has a considerable influence on the quantification of inflammation by ultrasound in RA patients, and this must be taken into account in multicenter studies. Copyright © 2015 by the American College of Rheumatology.

Respiratory symptoms and conditions related to occupational exposures in machine shops.

PubMed

Jaakkola, Maritta S; Suuronen, Katri; Luukkonen, Ritva; Järvelä, Merja; Tuomi, Timo; Alanko, Kristiina; Mäkelä, Erja A; Jolanki, Riitta

2009-01-01

Since there are few data on the effects of metalworking in populations representing a variety of metal companies or on dose-response relationships concerning metalworking, this study investigated the relationship between occupational exposures in machine shops and the occurrence of upper and lower respiratory symptoms, asthma, and chronic bronchitis. A cross-sectional study of 726 male machine workers and 84 male office workers from 64 companies was conducted in southern Finland. All of the participants filled out a questionnaire, and aerosol measurements were performed in 57 companies. Exposure to metalworking fluids (MWF) showed a greater risk [odds ratio (OR)>or=2) for upper-airway symptoms, cough, breathlessness, and current asthma than exposures in office work did. Exposure to aerosol levels above the median (>or=0.17 mg/m3 in the general workshop air) was related to an increased risk (OR>or=2) of nasal and throat symptoms, cough, wheezing, breathlessness, chronic bronchitis, and current asthma. Machine workers with a job history of >or=15 years experienced increased throat symptoms, cough, and chronic bronchitis. This large study representing machine shops in southern Finland showed that machine workers experience increased nasal and throat symptoms, cough, wheezing, breathlessness, and asthma even in environments with exposure levels below the current occupational exposure limit for oil mists. The study suggests that improving machine shop environments could benefit the health of this workforce. It also suggests that it is time to consider reducing the current Finnish occupational exposure limit for oil mist or introducing the use of other health-relevant indicators of exposure.

Toward Harnessing User Feedback For Machine Learning

DTIC Science & Technology

2006-10-02

machine learning systems. If this resource-the users themselves-could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users? understanding and trust of the system could improve as well. We conducted a think-aloud study to see how willing users were to provide feedback and to understand what kinds of feedback users could give. Users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. We found that users

Intelligible machine learning with malibu.

PubMed

Langlois, Robert E; Lu, Hui

2008-01-01

malibu is an open-source machine learning work-bench developed in C/C++ for high-performance real-world applications, namely bioinformatics and medical informatics. It leverages third-party machine learning implementations for more robust bug-free software. This workbench handles several well-studied supervised machine learning problems including classification, regression, importance-weighted classification and multiple-instance learning. The malibu interface was designed to create reproducible experiments ideally run in a remote and/or command line environment. The software can be found at: http://proteomics.bioengr. uic.edu/malibu/index.html.

Creating an Electronic Reference and Information Database for Computer-aided ECM Design

NASA Astrophysics Data System (ADS)

Nekhoroshev, M. V.; Pronichev, N. D.; Smirnov, G. V.

2018-01-01

The paper presents a review on electrochemical shaping. An algorithm has been developed to implement a computer shaping model applicable to pulse electrochemical machining. For that purpose, the characteristics of pulse current occurring in electrochemical machining of aviation materials have been studied. Based on integrating the experimental results and comprehensive electrochemical machining process data modeling, a subsystem for computer-aided design of electrochemical machining for gas turbine engine blades has been developed; the subsystem was implemented in the Teamcenter PLM system.

Modeling of heat transfer in compacted machining chips during friction consolidation process

NASA Astrophysics Data System (ADS)

Abbas, Naseer; Deng, Xiaomin; Li, Xiao; Reynolds, Anthony

2018-04-01

The current study aims to provide an understanding of the heat transfer process in compacted aluminum alloy AA6061 machining chips during the friction consolidation process (FCP) through experimental investigations and mathematical modelling and numerical simulation. Compaction and friction consolidation of machining chips is the first stage of the Friction Extrusion Process (FEP), which is a novel method for recycling machining chips to produce useful products such as wires. In this study, compacted machining chips are modelled as a continuum whose material properties vary with density during friction consolidation. Based on density and temperature dependent thermal properties, the temperature field in the chip material and process chamber caused by frictional heating during the friction consolidation process is predicted. The predicted temperature field is found to compare well with temperature measurements at select points where such measurements can be made using thermocouples.

Effects of promotional materials on vending sales of low-fat items in teachers' lounges.

PubMed

Fiske, Amy; Cullen, Karen Weber

2004-01-01

This study examined the impact of an environmental intervention in the form of promotional materials and increased availability of low-fat items on vending machine sales. Ten vending machines were selected and randomly assigned to one of three conditions: control, or one of two experimental conditions. Vending machines in the two intervention conditions received three additional low-fat selections. Low-fat items were promoted at two levels: labels (intervention I), and labels plus signs (intervention II). The number of individual items sold and the total revenue generated was recorded weekly for each machine for 4 weeks. Use of promotional materials resulted in a small, but not significant, increase in the number of low-fat items sold, although machine sales were not significantly impacted by the change in product selection. Results of this study, although not statistically significant, suggest that environmental change may be a realistic means of positively influencing consumer behavior.

A comparative study of electrochemical machining process parameters by using GA and Taguchi method

NASA Astrophysics Data System (ADS)

Soni, S. K.; Thomas, B.

2017-11-01

In electrochemical machining quality of machined surface strongly depend on the selection of optimal parameter settings. This work deals with the application of Taguchi method and genetic algorithm using MATLAB to maximize the metal removal rate and minimize the surface roughness and overcut. In this paper a comparative study is presented for drilling of LM6 AL/B4C composites by comparing the significant impact of numerous machining process parameters such as, electrolyte concentration (g/l),machining voltage (v),frequency (hz) on the response parameters (surface roughness, material removal rate and over cut). Taguchi L27 orthogonal array was chosen in Minitab 17 software, for the investigation of experimental results and also multiobjective optimization done by genetic algorithm is employed by using MATLAB. After obtaining optimized results from Taguchi method and genetic algorithm, a comparative results are presented.

Complementary Machine Intelligence and Human Intelligence in Virtual Teaching Assistant for Tutoring Program Tracing

ERIC Educational Resources Information Center

Chou, Chih-Yueh; Huang, Bau-Hung; Lin, Chi-Jen

2011-01-01

This study proposes a virtual teaching assistant (VTA) to share teacher tutoring tasks in helping students practice program tracing and proposes two mechanisms of complementing machine intelligence and human intelligence to develop the VTA. The first mechanism applies machine intelligence to extend human intelligence (teacher answers) to evaluate…

Developing Preservice Teachers' Understanding of Function Using a Vending Machine Metaphor Applet

ERIC Educational Resources Information Center

McCulloch, Allison; Lovett, Jennifer; Edgington, Cyndi

2017-01-01

The purpose of this study is to examine the use of a Vending Machine applet as a cognitive root for the development of preservice teachers understanding of function. The applet was designed to purposefully problematize common misconceptions associated with the algebraic nature of typical function machines. Findings indicated affordances and…

Study of Man-Machine Communications Systems for Disabled Persons (The Handicapped). Volume IV. Final Report.

ERIC Educational Resources Information Center

Kafafian, Haig

The volume contains experimental instructional materials designed for teacher and handicapped student use with two man-machine communications systems, Cybertype and Cyber-Go-Round, developed as educational aids for the severely handicapped. Cybertype is a writing machine with various possible configurations of portable keyboards with a reduced…

THE ROLE OF REVIEW MATERIAL IN CONTINUOUS PROGRAMMING WITH TEACHING MACHINES.

ERIC Educational Resources Information Center

FERSTER, C.B.

STUDENTS WERE PRESENTED 61 LESSONS BY MEANS OF SEMIAUTOMATIC TEACHING MACHINES. LESSONS WERE ARRANGED SO THAT EACH PARTICIPATING STUDENT STUDIED PART OF THE COURSE MATERIAL WITH A SINGLE REPETITION AND PART WITHOUT REPETITION. DATA WERE OBTAINED FROM TWO TESTS SHOWING TEACHING-MACHINE RESULTS AND ONE FINAL COURSE EXAMINATION. NO SIGNIFICANT…

Simple Machine Junk Cars

ERIC Educational Resources Information Center

Herald, Christine

2010-01-01

During the month of May, the author's eighth-grade physical science students study the six simple machines through hands-on activities, reading assignments, videos, and notes. At the end of the month, they can easily identify the six types of simple machine: inclined plane, wheel and axle, pulley, screw, wedge, and lever. To conclude this unit,…

Alternative Models of Service, Centralized Machine Operations. Phase II Report. Volume II.

ERIC Educational Resources Information Center

Technology Management Corp., Alexandria, VA.

A study was conducted to determine if the centralization of playback machine operations for the national free library program would be feasible, economical, and desirable. An alternative model of playback machine services was constructed and compared with existing network operations considering both cost and service. The alternative model was…

Process Monitoring Evaluation and Implementation for the Wood Abrasive Machining Process

PubMed Central

Saloni, Daniel E.; Lemaster, Richard L.; Jackson, Steven D.

2010-01-01

Wood processing industries have continuously developed and improved technologies and processes to transform wood to obtain better final product quality and thus increase profits. Abrasive machining is one of the most important of these processes and therefore merits special attention and study. The objective of this work was to evaluate and demonstrate a process monitoring system for use in the abrasive machining of wood and wood based products. The system developed increases the life of the belt by detecting (using process monitoring sensors) and removing (by cleaning) the abrasive loading during the machining process. This study focused on abrasive belt machining processes and included substantial background work, which provided a solid base for understanding the behavior of the abrasive, and the different ways that the abrasive machining process can be monitored. In addition, the background research showed that abrasive belts can effectively be cleaned by the appropriate cleaning technique. The process monitoring system developed included acoustic emission sensors which tended to be sensitive to belt wear, as well as platen vibration, but not loading, and optical sensors which were sensitive to abrasive loading. PMID:22163477

Micro Fluidic Channel Machining on Fused Silica Glass Using Powder Blasting

PubMed Central

Jang, Ho-Su; Cho, Myeong-Woo; Park, Dong-Sam

2008-01-01

In this study, micro fluid channels are machined on fused silica glass via powder blasting, a mechanical etching process, and the machining characteristics of the channels are experimentally evaluated. In the process, material removal is performed by the collision of micro abrasives injected by highly compressed air on to the target surface. This approach can be characterized as an integration of brittle mode machining based on micro crack propagation. Fused silica glass, a high purity synthetic amorphous silicon dioxide, is selected as a workpiece material. It has a very low thermal expansion coefficient and excellent optical qualities and exceptional transmittance over a wide spectral range, especially in the ultraviolet range. The powder blasting process parameters affecting the machined results are injection pressure, abrasive particle size and density, stand-off distance, number of nozzle scanning, and shape/size of the required patterns. In this study, the influence of the number of nozzle scanning, abrasive particle size, and pattern size on the formation of micro channels is investigated. Machined shapes and surface roughness are measured using a 3-dimensional vision profiler and the results are discussed. PMID:27879730

Scheduling job shop - A case study

NASA Astrophysics Data System (ADS)

Abas, M.; Abbas, A.; Khan, W. A.

2016-08-01

The scheduling in job shop is important for efficient utilization of machines in the manufacturing industry. There are number of algorithms available for scheduling of jobs which depend on machines tools, indirect consumables and jobs which are to be processed. In this paper a case study is presented for scheduling of jobs when parts are treated on available machines. Through time and motion study setup time and operation time are measured as total processing time for variety of products having different manufacturing processes. Based on due dates different level of priority are assigned to the jobs and the jobs are scheduled on the basis of priority. In view of the measured processing time, the times for processing of some new jobs are estimated and for efficient utilization of the machines available an algorithm is proposed and validated.

Improvement of automatic fish feeder machine design

NASA Astrophysics Data System (ADS)

Chui Wei, How; Salleh, S. M.; Ezree, Abdullah Mohd; Zaman, I.; Hatta, M. H.; Zain, B. A. Md; Mahzan, S.; Rahman, M. N. A.; Mahmud, W. A. W.

2017-10-01

Nation Plan of action for management of fishing is target to achieve an efficient, equitable and transparent management of fishing capacity in marine capture fisheries by 2018. However, several factors influence the fishery production and efficiency of marine system such as automatic fish feeder machine could be taken in consideration. Two latest fish feeder machines have been chosen as the reference for this study. Based on the observation, it has found that the both machine was made with heavy structure, low water and temperature resistance materials. This research’s objective is to develop the automatic feeder machine to increase the efficiency of fish feeding. The experiment has conducted to testing the new design of machine. The new machine with maximum storage of 5 kg and functioning with two DC motors. This machine able to distribute 500 grams of pellets within 90 seconds and longest distance of 4.7 meter. The higher speed could reduce time needed and increase the distance as well. The minimum speed range for both motor is 110 and 120 with same full speed range of 255.

Characteristics for electrochemical machining with nanoscale voltage pulses.

PubMed

Lee, E S; Back, S Y; Lee, J T

2009-06-01

Electrochemical machining has traditionally been used in highly specialized fields, such as those of the aerospace and defense industries. It is now increasingly being applied in other industries, where parts with difficult-to-cut material, complex geometry and tribology, and devices of nanoscale and microscale are required. Electric characteristic plays a principal function role in and chemical characteristic plays an assistant function role in electrochemical machining. Therefore, essential parameters in electrochemical machining can be described current density, machining time, inter-electrode gap size, electrolyte, electrode shape etc. Electrochemical machining provides an economical and effective method for machining high strength, high tension and heat-resistant materials into complex shapes such as turbine blades of titanium and aluminum alloys. The application of nanoscale voltage pulses between a tool electrode and a workpiece in an electrochemical environment allows the three-dimensional machining of conducting materials with sub-micrometer precision. In this study, micro probe are developed by electrochemical etching and micro holes are manufactured using these micro probe as tool electrodes. Micro holes and microgroove can be accurately achieved by using nanoscale voltages pulses.

Predictors of return rate discrimination in slot machine play.

PubMed

Coates, Ewan; Blaszczynski, Alex

2014-09-01

The purpose of this study was to investigate the extent to which accurate estimates of payback percentages and volatility combined with prior learning, enabled players to successfully discriminate between multi-line/multi-credit slot machines that provided differing rates of reinforcement. The aim was to determine if the capacity to discriminate structural characteristics of gaming machines influenced player choices in selecting 'favourite' slot machines. Slot machine gambling history, gambling beliefs and knowledge, impulsivity, illusions of control, and problem solving style were assessed in a sample of 48 first year undergraduate psychology students. Participants were subsequently exposed to a choice paradigm where they could freely select to play either of two concurrently presented PC-simulated slot machines programmed to randomly differ in expected player return rates (payback percentage) and win frequency (volatility). Results suggest that prior learning and cognitions (particularly gambler's fallacy) but not payback, were major contributors to the ability of a player to discriminate volatility between slot machines. Participants displayed a general tendency to discriminate payback, but counter-intuitively placed more bets on the slot machine with lower payback percentage rates.

Effect of Ceramic Surface Treatments After Machine Grinding on the Biaxial Flexural Strength of Different CAD/CAM Dental Ceramics.

PubMed

Bagheri, Hossein; Hooshmand, Tabassom; Aghajani, Farzaneh

2015-09-01

This study aimed to evaluate the effect of different ceramic surface treatments after machining grinding on the biaxial flexural strength (BFS) of machinable dental ceramics with different crystalline phases. Disk-shape specimens (10mm in diameter and 1.3mm in thickness) of machinable ceramic cores (two silica-based and one zirconia-based ceramics) were prepared. Each type of the ceramic surfaces was then randomly treated (n=15) with different treatments as follows: 1) machined finish as control, 2) machined finish and sandblasting with alumina, and 3) machined finish and hydrofluoric acid etching for the leucite and lithium disilicate-based ceramics, and for the zirconia; 1) machined finish and post-sintered as control, 2) machined finish, post-sintered, and sandblasting, and 3) machined finish, post-sintered, and Nd;YAG laser irradiation. The BFS were measured in a universal testing machine. Data based were analyzed by ANOVA and Tukey's multiple comparisons post-hoc test (α=0.05). The mean BFS of machined finish only surfaces for leucite ceramic was significantly higher than that of sandblasted (P=0.001) and acid etched surfaces (P=0.005). A significantly lower BFS was found after sandblasting for lithium disilicate compared with that of other groups (P<0.05). Sandblasting significantly increased the BFS for the zirconia (P<0.05), but the BFS was significantly decreased after laser irradiation (P<0.05). The BFS of the machinable ceramics was affected by the type of ceramic material and surface treatment method. Sandblasting with alumina was detrimental to the strength of only silica-based ceramics. Nd:YAG laser irradiation may lead to substantial strength degradation of zirconia.

Effect of Ceramic Surface Treatments After Machine Grinding on the Biaxial Flexural Strength of Different CAD/CAM Dental Ceramics

PubMed Central

Bagheri, Hossein; Aghajani, Farzaneh

2015-01-01

Objectives: This study aimed to evaluate the effect of different ceramic surface treatments after machining grinding on the biaxial flexural strength (BFS) of machinable dental ceramics with different crystalline phases. Materials and Methods: Disk-shape specimens (10mm in diameter and 1.3mm in thickness) of machinable ceramic cores (two silica-based and one zirconia-based ceramics) were prepared. Each type of the ceramic surfaces was then randomly treated (n=15) with different treatments as follows: 1) machined finish as control, 2) machined finish and sandblasting with alumina, and 3) machined finish and hydrofluoric acid etching for the leucite and lithium disilicate-based ceramics, and for the zirconia; 1) machined finish and post-sintered as control, 2) machined finish, post-sintered, and sandblasting, and 3) machined finish, post-sintered, and Nd;YAG laser irradiation. The BFS were measured in a universal testing machine. Data based were analyzed by ANOVA and Tukey’s multiple comparisons post-hoc test (α=0.05). Results: The mean BFS of machined finish only surfaces for leucite ceramic was significantly higher than that of sandblasted (P=0.001) and acid etched surfaces (P=0.005). A significantly lower BFS was found after sandblasting for lithium disilicate compared with that of other groups (P<0.05). Sandblasting significantly increased the BFS for the zirconia (P<0.05), but the BFS was significantly decreased after laser irradiation (P<0.05). Conclusions: The BFS of the machinable ceramics was affected by the type of ceramic material and surface treatment method. Sandblasting with alumina was detrimental to the strength of only silica-based ceramics. Nd:YAG laser irradiation may lead to substantial strength degradation of zirconia. PMID:27148372

Machine Protection with a 700 MJ Beam

NASA Astrophysics Data System (ADS)

Baer, T.; Schmidt, R.; Wenninger, J.; Wollmann, D.; Zerlauth, M.

After the high luminosity upgrade of the LHC, the stored energy per proton beam will increase by a factor of two as compared to the nominal LHC. Therefore, many damage studies need to be revisited to ensure a safe machine operation with the new beam parameters. Furthermore, new accelerator equipment like crab cavities might cause new failure modes, which are not sufficiently covered by the current machine protection system of the LHC. These failure modes have to be carefully studied and mitigated by new protection systems. Finally the ambitious goals for integrated luminosity delivered to the experiments during the era of HL-LHC require an increase of the machine availability without jeopardizing equipment protection.

Running accuracy analysis of a 3-RRR parallel kinematic machine considering the deformations of the links

NASA Astrophysics Data System (ADS)

Wang, Liping; Jiang, Yao; Li, Tiemin

2014-09-01

Parallel kinematic machines have drawn considerable attention and have been widely used in some special fields. However, high precision is still one of the challenges when they are used for advanced machine tools. One of the main reasons is that the kinematic chains of parallel kinematic machines are composed of elongated links that can easily suffer deformations, especially at high speeds and under heavy loads. A 3-RRR parallel kinematic machine is taken as a study object for investigating its accuracy with the consideration of the deformations of its links during the motion process. Based on the dynamic model constructed by the Newton-Euler method, all the inertia loads and constraint forces of the links are computed and their deformations are derived. Then the kinematic errors of the machine are derived with the consideration of the deformations of the links. Through further derivation, the accuracy of the machine is given in a simple explicit expression, which will be helpful to increase the calculating speed. The accuracy of this machine when following a selected circle path is simulated. The influences of magnitude of the maximum acceleration and external loads on the running accuracy of the machine are investigated. The results show that the external loads will deteriorate the accuracy of the machine tremendously when their direction coincides with the direction of the worst stiffness of the machine. The proposed method provides a solution for predicting the running accuracy of the parallel kinematic machines and can also be used in their design optimization as well as selection of suitable running parameters.

The influence of maintenance quality of hemodialysis machines on hemodialysis efficiency.

PubMed

Azar, Ahmad Taher

2009-01-01

Several studies suggest that there is a correlation between dose of dialysis and machine maintenance. However, in spite of the current practice, there are conflicting reports regarding the relationship between dose of dialysis or patient outcome, and machine maintenance. In order to evaluate the impact of hemodialysis machine maintenance on dialysis adequacy Kt/V and session performance, data were processed on 134 patients on 3-times-per-week dialysis regimens by dividing the patients into four groups and also dividing the hemodialysis machines into four groups according to their year of installation. The equilibrated dialysis dose eq Kt/V, urea reduction ratio (URR) and the overall equipment effectiveness (OEE) were calculated in each group to show the effect hemodialysis machine efficiency on the overall session performance. The average working time per machine per month was 270 hours. The cumulative number of hours according to the year of installation was: 26,122 hours for machines installed in 1998; 21,596 hours for machines installed in 1999, 8362 hours for those installed in 2003 and 2486 hours for those installed in 2005. The mean time between failures (MTBF) was 1.8, 2.1, 4.2 and 6 months between failures for machines installed in 1999, 1998, 2003 and 2005, respectively. Statistical analysis demonstrated that the dialysis dose eq Kt/V and URR were increased as the overall equipment effectiveness (OEE) increases with regular maintenance procedures. Maintenance has become one of the most expedient approaches to guarantee high machine dependability. The efficiency of dialysis machine is relevant in assuring a proper dialysis adequacy.

Sweet and salty. An assessment of the snacks and beverages sold in vending machines on US post-secondary institution campuses.

PubMed

Byrd-Bredbenner, Carol; Johnson, Michelle; Quick, Virginia M; Walsh, Jennifer; Greene, Geoffrey W; Hoerr, Sharon; Colby, Sarah M; Kattelmann, Kendra K; Phillips, Beatrice W; Kidd, Tandalayo; Horacek, Tanya M

2012-06-01

This study assessed the nutritional quality of snacks and beverages sold in vending machines. The contents of snack and beverage vending machines in 78 buildings on 11 US post-secondary education campuses were surveyed. Of the 2607 snack machine slots surveyed, the most common snacks vended were salty snacks (e.g., chips, pretzels) and sweets (i.e., candy and candy bars). The 1650 beverage machine slots assessed contained twice as many sugar-sweetened beverages as non-calorie-containing beverages. Only two institutions sold both milk and 100% juice in vending machines. The portion of snacks and beverages sold averaged more than 200 cal. Neither snacks nor beverages were nutrient dense. The majority of snacks were low in fiber and high in calories and fat and almost half were high in sugar. Most beverages were high in calories and sugar. This study's findings suggest that vending machines provide limited healthful choices. Findings from benchmark assessments of components of the food environment, like the vending options reported here, can provide valuable input to campus administrators, health services, food service, and students who want to establish campus policies to promote healthful eating. Copyright © 2012 Elsevier Ltd. All rights reserved.

Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.

PubMed

Lenhard, Fabian; Sauer, Sebastian; Andersson, Erik; Månsson, Kristoffer Nt; Mataix-Cols, David; Rück, Christian; Serlachius, Eva

2018-03-01

There are no consistent predictors of treatment outcome in paediatric obsessive-compulsive disorder (OCD). One reason for this might be the use of suboptimal statistical methodology. Machine learning is an approach to efficiently analyse complex data. Machine learning has been widely used within other fields, but has rarely been tested in the prediction of paediatric mental health treatment outcomes. To test four different machine learning methods in the prediction of treatment response in a sample of paediatric OCD patients who had received Internet-delivered cognitive behaviour therapy (ICBT). Participants were 61 adolescents (12-17 years) who enrolled in a randomized controlled trial and received ICBT. All clinical baseline variables were used to predict strictly defined treatment response status three months after ICBT. Four machine learning algorithms were implemented. For comparison, we also employed a traditional logistic regression approach. Multivariate logistic regression could not detect any significant predictors. In contrast, all four machine learning algorithms performed well in the prediction of treatment response, with 75 to 83% accuracy. The results suggest that machine learning algorithms can successfully be applied to predict paediatric OCD treatment outcome. Validation studies and studies in other disorders are warranted. Copyright © 2017 John Wiley & Sons, Ltd.

Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram

PubMed Central

Kim, Jongin; Park, Hyeong-jun

2016-01-01

The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two variants of an extreme learning machine with different kernels. Because each single trial consisted of thirty segments, our algorithm decided the label of the single trial by selecting the most frequent output among the outputs of the thirty segments. As a result, we observed that the extreme learning machine and its variants achieved better classification rates than the support vector machine with a radial basis function kernel and linear discrimination analysis. Thus, our results suggested that EEG responses to imagined speech could be successfully classified in a single trial using an extreme learning machine with a radial basis function and linear kernel. This study with classification of imagined speech might contribute to the development of silent speech BCI systems. PMID:28097128

Fundamental investigation on influence of external heat on chip formation during thermal assisted machining

NASA Astrophysics Data System (ADS)

Alkali, A. U.; Ginta, T. L.; Abdulrani, A. M.; Elsiti, N. M.

2018-04-01

Various heat sources have been investigated by numerous researchers to reveal machinability benefits of thermally assisted machining (TAM) process. Fewer engineering materials have been tested. In the same vein, those researches continue to demonstrate effective performance of TAM in terms of bulk material removal rate, improved surface finish, prolong tool life and reduction of cutting forces among others. Experimental investigation on the strain-hardenability and flow stress of material removed with respect to increase in temperature in TAM has not been given attention in previous studies. This study investigated the pattern of chip morphology and segmentation giving close attention to influence of external heat source responsible for strain – hardenability of the material removed during TAM and dry machining at room temperature. Full immersion down cut milling was used throughout the machining conditions. Machining was conducted on AISI 316L using uncoated tungsten carbide end mill insert at varying cutting speeds (V) of 50, 79, and 100 m/min, and feed rates (f) of 0.15, 0.25, and 0.4 mm/tooth while the depth of cut was maintained at 0.2mm throughout the machining trials. The analyses of chip formation, segmentations and stain hardenability were carried out by using LMU light microscope, field emission microscopy and micro indentation. The study observed that build up edge is formed when a stagnation zone develops in front of tool tip which give rise to poor thermal gradient for conduction heat to be transferred within the bulk material during dry machining. This promotes varying strain – hardening of the material removed with evident high chips hardness and thickness, whereas TAM circumvents such impairment by softening the shear zone through local preheat.

Whole-body Vibration Exposure of Drill Operators in Iron Ore Mines and Role of Machine-Related, Individual, and Rock-Related Factors.

PubMed

Chaudhary, Dhanjee Kumar; Bhattacherjee, Ashis; Patra, Aditya Kumar; Chau, Nearkasen

2015-12-01

This study aimed to assess the whole-body vibration (WBV) exposure among large blast hole drill machine operators with regard to the International Organization for Standardization (ISO) recommended threshold values and its association with machine- and rock-related factors and workers' individual characteristics. The study population included 28 drill machine operators who had worked in four opencast iron ore mines in eastern India. The study protocol comprised the following: measurements of WBV exposure [frequency weighted root mean square (RMS) acceleration (m/s(2))], machine-related data (manufacturer of machine, age of machine, seat height, thickness, and rest height) collected from mine management offices, measurements of rock hardness, uniaxial compressive strength and density, and workers' characteristics via face-to-face interviews. More than 90% of the operators were exposed to a higher level WBV than the ISO upper limit and only 3.6% between the lower and upper limits, mainly in the vertical axis. Bivariate correlations revealed that potential predictors of total WBV exposure were: machine manufacturer (r = 0.453, p = 0.015), age of drill (r = 0.533, p = 0.003), and hardness of rock (r = 0.561, p = 0.002). The stepwise multiple regression model revealed that the potential predictors are age of operator (regression coefficient β = -0.052, standard error SE = 0.023), manufacturer (β = 1.093, SE = 0.227), rock hardness (β = 0.045, SE = 0.018), uniaxial compressive strength (β = 0.027, SE = 0.009), and density (β = -1.135, SE = 0.235). Prevention should include using appropriate machines to handle rock hardness, rock uniaxial compressive strength and density, and seat improvement using ergonomic approaches such as including a suspension system.

Whole-body Vibration Exposure of Drill Operators in Iron Ore Mines and Role of Machine-Related, Individual, and Rock-Related Factors

PubMed Central

Chaudhary, Dhanjee Kumar; Bhattacherjee, Ashis; Patra, Aditya Kumar; Chau, Nearkasen

2015-01-01

Background This study aimed to assess the whole-body vibration (WBV) exposure among large blast hole drill machine operators with regard to the International Organization for Standardization (ISO) recommended threshold values and its association with machine- and rock-related factors and workers' individual characteristics. Methods The study population included 28 drill machine operators who had worked in four opencast iron ore mines in eastern India. The study protocol comprised the following: measurements of WBV exposure [frequency weighted root mean square (RMS) acceleration (m/s2)], machine-related data (manufacturer of machine, age of machine, seat height, thickness, and rest height) collected from mine management offices, measurements of rock hardness, uniaxial compressive strength and density, and workers' characteristics via face-to-face interviews. Results More than 90% of the operators were exposed to a higher level WBV than the ISO upper limit and only 3.6% between the lower and upper limits, mainly in the vertical axis. Bivariate correlations revealed that potential predictors of total WBV exposure were: machine manufacturer (r = 0.453, p = 0.015), age of drill (r = 0.533, p = 0.003), and hardness of rock (r = 0.561, p = 0.002). The stepwise multiple regression model revealed that the potential predictors are age of operator (regression coefficient β = −0.052, standard error SE = 0.023), manufacturer (β = 1.093, SE = 0.227), rock hardness (β = 0.045, SE = 0.018), uniaxial compressive strength (β = 0.027, SE = 0.009), and density (β = –1.135, SE = 0.235). Conclusion Prevention should include using appropriate machines to handle rock hardness, rock uniaxial compressive strength and density, and seat improvement using ergonomic approaches such as including a suspension system. PMID:26929838

PMLB: a large benchmark suite for machine learning evaluation and comparison.

PubMed

Olson, Randal S; La Cava, William; Orzechowski, Patryk; Urbanowicz, Ryan J; Moore, Jason H

2017-01-01

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. From this study, we find that existing benchmarks lack the diversity to properly benchmark machine learning algorithms, and there are several gaps in benchmarking problems that still need to be considered. This work represents another important step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.

Research on bearing fault diagnosis of large machinery based on mathematical morphology

NASA Astrophysics Data System (ADS)

Wang, Yu

2018-04-01

To study the automatic diagnosis of large machinery fault based on support vector machine, combining the four common faults of the large machinery, the support vector machine is used to classify and identify the fault. The extracted feature vectors are entered. The feature vector is trained and identified by multi - classification method. The optimal parameters of the support vector machine are searched by trial and error method and cross validation method. Then, the support vector machine is compared with BP neural network. The results show that the support vector machines are short in time and high in classification accuracy. It is more suitable for the research of fault diagnosis in large machinery. Therefore, it can be concluded that the training speed of support vector machines (SVM) is fast and the performance is good.

The Evaluation of Efficiency of the Use of Machine Working Time in the Industrial Company - Case Study

NASA Astrophysics Data System (ADS)

Kardas, Edyta; Brožova, Silvie; Pustějovská, Pavlína; Jursová, Simona

2017-12-01

In the paper the evaluation of efficiency of the use of machines in the selected production company was presented. The OEE method (Overall Equipment Effectiveness) was used for the analysis. The selected company deals with the production of tapered roller bearings. The analysis of effectiveness was done for 17 automatic grinding lines working in the department of grinding rollers. Low level of efficiency of machines was affected by problems with the availability of machines and devices. The causes of machine downtime on these lines was also analyzed. Three basic causes of downtime were identified: no kanban card, diamonding, no operator. Ways to improve the use of these machines were suggested. The analysis takes into account the actual results from the production process and covers the period of one calendar year.

Cogging Torque Minimization in Transverse Flux Machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Husain, Tausif; Hasan, Iftekhar; Sozer, Yilmaz

2017-02-16

This paper presents the design considerations in cogging torque minimization in two types of transverse flux machines. The machines have a double stator-single rotor configuration with flux concentrating ferrite magnets. One of the machines has pole windings across each leg of an E-Core stator. Another machine has quasi-U-shaped stator cores and a ring winding. The flux in the stator back iron is transverse in both machines. Different methods of cogging torque minimization are investigated. Key methods of cogging torque minimization are identified and used as design variables for optimization using a design of experiments (DOE) based on the Taguchi method.more » A three-level DOE is performed to reach an optimum solution with minimum simulations. Finite element analysis is used to study the different effects. Two prototypes are being fabricated for experimental verification.« less

Machinability of experimental Ti-Ag alloys.

PubMed

Kikuchi, Masafumi; Takahashi, Masatoshi; Okuno, Osamu

2008-03-01

This study investigated the machinability of experimental Ti-Ag alloys (5, 10, 20, and 30 mass% Ag) as a new dental titanium alloy candidate for CAD/CAM use. The alloys were slotted with a vertical milling machine and carbide square end mills under two cutting conditions. Machinability was evaluated through cutting force using a three-component force transducer fixed on the table of the milling machine. The horizontal cutting force of the Ti-Ag alloys tended to decrease as the concentration of silver increased. Values of the component of the horizontal cutting force perpendicular to the feed direction for Ti-20% Ag and Ti-30% Ag were more than 20% lower than those for titanium under both cutting conditions. Alloying with silver significantly improved the machinability of titanium in terms of cutting force under the present cutting conditions.

Syringe vending machines for injection drug users: an experiment in Marseille, France.

PubMed Central

Obadia, Y; Feroni, I; Perrin, V; Vlahov, D; Moatti, J P

1999-01-01

OBJECTIVES: This study evaluated the usefulness of vending machines in providing injection drug users with access to sterile syringes in Marseille, France. METHODS: Self-administered questionnaires were offered to 485 injection drug users obtaining syringes from 32 pharmacies, 4 needle exchange programs, and 3 vending machines. RESULTS: Of the 343 respondents (response rate = 70.7%), 21.3% used the vending machines as their primary source of syringes. Primary users of vending machines were more likely than primary users of other sources to be younger than 30 years, to report no history of drug maintenance treatment, and to report no sharing of needles or injection paraphernalia. CONCLUSIONS: Vending machines may be an appropriate strategy for providing access to syringes for younger injection drug users, who have typically avoided needle exchange programs and pharmacies. PMID:10589315

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

PubMed

Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

Improving Machining Accuracy of CNC Machines with Innovative Design Methods

NASA Astrophysics Data System (ADS)

Yemelyanov, N. V.; Yemelyanova, I. V.; Zubenko, V. L.

2018-03-01

The article considers achieving the machining accuracy of CNC machines by applying innovative methods in modelling and design of machining systems, drives and machine processes. The topological method of analysis involves visualizing the system as matrices of block graphs with a varying degree of detail between the upper and lower hierarchy levels. This approach combines the advantages of graph theory and the efficiency of decomposition methods, it also has visual clarity, which is inherent in both topological models and structural matrices, as well as the resiliency of linear algebra as part of the matrix-based research. The focus of the study is on the design of automated machine workstations, systems, machines and units, which can be broken into interrelated parts and presented as algebraic, topological and set-theoretical models. Every model can be transformed into a model of another type, and, as a result, can be interpreted as a system of linear and non-linear equations which solutions determine the system parameters. This paper analyses the dynamic parameters of the 1716PF4 machine at the stages of design and exploitation. Having researched the impact of the system dynamics on the component quality, the authors have developed a range of practical recommendations which have enabled one to reduce considerably the amplitude of relative motion, exclude some resonance zones within the spindle speed range of 0...6000 min-1 and improve machining accuracy.

Modeling of Geometric Error in Linear Guide Way to Improved the vertical three-axis CNC Milling machine’s accuracy

NASA Astrophysics Data System (ADS)

Kwintarini, Widiyanti; Wibowo, Agung; Arthaya, Bagus M.; Yuwana Martawirya, Yatna

2018-03-01

The purpose of this study was to improve the accuracy of three-axis CNC Milling Vertical engines with a general approach by using mathematical modeling methods of machine tool geometric errors. The inaccuracy of CNC machines can be caused by geometric errors that are an important factor during the manufacturing process and during the assembly phase, and are factors for being able to build machines with high-accuracy. To improve the accuracy of the three-axis vertical milling machine, by knowing geometric errors and identifying the error position parameters in the machine tool by arranging the mathematical modeling. The geometric error in the machine tool consists of twenty-one error parameters consisting of nine linear error parameters, nine angle error parameters and three perpendicular error parameters. The mathematical modeling approach of geometric error with the calculated alignment error and angle error in the supporting components of the machine motion is linear guide way and linear motion. The purpose of using this mathematical modeling approach is the identification of geometric errors that can be helpful as reference during the design, assembly and maintenance stages to improve the accuracy of CNC machines. Mathematically modeling geometric errors in CNC machine tools can illustrate the relationship between alignment error, position and angle on a linear guide way of three-axis vertical milling machines.

Space Applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 4: Supplement, Appendix 4.3: Candidate ARAMIS Capabilities

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Potential applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities, and to their related ground support functions, in the years 1985-2000, so that NASA may make informed decisions on which aspects of ARAMIS to develop. The study first identifies the specific tasks which will be required by future space projects. It then defines ARAMIS options which are candidates for those space project tasks, and evaluates the relative merits of these options. Finally, the study identifies promising applications of ARAMIS, and recommends specific areas for further research. The ARAMIS options defined and researched by the study group span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

Effect of focusing flow on stationary spot machining properties in elastic emission machining

PubMed Central

2013-01-01

Ultraprecise optical elements are applied in advanced optical apparatus. Elastic emission machining (EEM) is one of the ultraprecision machining methods used to fabricate shapes with 0.1-nm accuracy. In this study, we proposed and experimentally tested the control of the shape of a stationary spot profile by introducing a focusing-flow state between the nozzle outlet and the workpiece surface in EEM. The simulation results indicate that the focusing-flow nozzle sharpens the distribution of the velocity on the workpiece surface. The results of machining experiments verified those of the simulation. The obtained stationary spot conditions will be useful for surface processing with a high spatial resolution. PMID:23680043

Machine learning in heart failure: ready for prime time.

PubMed

Awan, Saqib Ejaz; Sohel, Ferdous; Sanfilippo, Frank Mario; Bennamoun, Mohammed; Dwivedi, Girish

2018-03-01

The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence. Recent studies have shown that the application of machine learning techniques may have the potential to improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Recently developed deep learning methods are expected to yield even better performance than traditional machine learning techniques in performing complex tasks by learning the intricate patterns hidden in big medical data. The review summarizes the recent developments in the application of machine and deep learning methods in heart failure management.

Critical Speed of The Glass Glue Machine's Creep and Influence Factors Analysis

NASA Astrophysics Data System (ADS)

Yang, Jianxi; Huang, Jian; Wang, Liying; Shi, Jintai

When automatic glass glue machine works, two questions of the machine starting vibrating and stick-slip motion are existing. These problems should be solved. According to these questions, a glue machine's model for studying stick-slip is established. Based on the dynamics system describing of the model, mathematical expression is presented. The creep critical speed expression is constructed referring to existing research achievement and a new conclusion is found. The influencing factors of stiffness, dampness, mass, velocity, difference of static and kinetic coefficient of friction are analyzed through Matlab simulation. Research shows that reasonable choice of influence parameters can improve the creep phenomenon. These all supply the theory evidence for improving the machine's motion stability.

A Study of Multifunctional Document Centers that Are Accessible to People Who Are Visually Impaired

ERIC Educational Resources Information Center

Huffman, Lee A.; Uslan, Mark M.; Burton, Darren M.; Eghtesadi, Caesar

2009-01-01

The capabilities of modern photocopy machines have advanced beyond the simple duplication of documents. In addition to the standard functions of copying, collating, and stapling, such machines can be a part of telecommunication networks and provide printing, scanning, faxing, and e-mailing functions. No longer just copy machines, these devices are…

An Investigation of Data Privacy and Utility Using Machine Learning as a Gauge

ERIC Educational Resources Information Center

Mivule, Kato

2014-01-01

The purpose of this investigation is to study and pursue a user-defined approach in preserving data privacy while maintaining an acceptable level of data utility using machine learning classification techniques as a gauge in the generation of synthetic data sets. This dissertation will deal with data privacy, data utility, machine learning…

Technical and Symbolic Knowledge in CNC Machining: A Study of Technical Workers of Different Backgrounds.

ERIC Educational Resources Information Center

Martin, Laura M. W.; Beach, King

Performances of 45 individuals with varying degrees of formal and informal training in machining and programming were compared on tasks designed to tap intellectual changes that may occur with the introduction of computer numerical control (CNC). Participants--30 machinists, 8 machine operators, and 7 engineers--were asked background questions and…

Parameterizing Phrase Based Statistical Machine Translation Models: An Analytic Study

ERIC Educational Resources Information Center

Cer, Daniel

2011-01-01

The goal of this dissertation is to determine the best way to train a statistical machine translation system. I first develop a state-of-the-art machine translation system called Phrasal and then use it to examine a wide variety of potential learning algorithms and optimization criteria and arrive at two very surprising results. First, despite the…

Using Phun to Study "Perpetual Motion" Machines

ERIC Educational Resources Information Center

Kores, Jaroslav

2012-01-01

The concept of "perpetual motion" has a long history. The Indian astronomer and mathematician Bhaskara II (12th century) was the first person to describe a perpetual motion (PM) machine. An example of a 13th-century PM machine is shown in Fig. 1. Although the law of conservation of energy clearly implies the impossibility of PM construction, over…

Study of Man-Machine Communications Systems for Disabled Persons (The Handicapped). Volume V. Final Report.

ERIC Educational Resources Information Center

Kafafian, Haig

Instructions are given for teaching severely physically and/or neurologically handicapped students to use the 14-key Cybertype man-machine communications system, an electric writing machine with a simplified keyboard to enable persons with limited motor ability or coordination to communicate in written form. Explained are the various possible…

Contrasting State-of-the-Art in the Machine Scoring of Short-Form Constructed Responses

ERIC Educational Resources Information Center

Shermis, Mark D.

2015-01-01

This study compared short-form constructed responses evaluated by both human raters and machine scoring algorithms. The context was a public competition on which both public competitors and commercial vendors vied to develop machine scoring algorithms that would match or exceed the performance of operational human raters in a summative high-stakes…

Learning Activity Packets for Grinding Machines. Unit I--Grinding Machines.

ERIC Educational Resources Information Center

Oklahoma State Board of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This learning activity packet (LAP) is one of three that accompany the curriculum guide on grinding machines. It outlines the study activities and performance tasks for the first unit of this curriculum guide. Its purpose is to aid the student in attaining a working knowledge of this area of training and in achieving a skilled or moderately…

PCD tool wear and its monitoring in machining tungsten

NASA Astrophysics Data System (ADS)

Wang, Lijiang; Zhang, Zhenlie; Sun, Qi; Liu, Pin

The views of Chinese and foreign researchers are quite different as to whether or not polycrystalline diamond (PCD) tools can machine tungsten that is used in the aerospace and electronic industries. A study is presented that shows the possibility of machining tungsten, and a new method is developed for monitoring the tool wear in production.

The compound Atwood machine problem

NASA Astrophysics Data System (ADS)

Lopes Coelho, R.

2017-05-01

The present paper accounts for progress in physics teaching in the sense that a problem, which has been closed to students for being too difficult, is gained for the high school curriculum. This problem is the compound Atwood machine with three bodies. Its introduction into high school classes is based on a recent study on the weighing of an Atwood machine.

Associations between the perceived presence of vending machines and food and beverage logos in schools and adolescents' diet and weight status.

PubMed

Minaker, Leia M; Storey, Kate E; Raine, Kim D; Spence, John C; Forbes, Laura E; Plotnikoff, Ronald C; McCargar, Linda J

2011-08-01

The increasing prevalence of obesity among youth has elicited calls for schools to become more active in promoting healthy weight. The present study examined associations between various aspects of school food environments (specifically the availability of snack- and beverage-vending machines and the presence of snack and beverage logos) and students' weight status, as well as potential influences of indices of diet and food behaviours. A cross-sectional, self-administered web-based survey. A series of multinomial logistic regressions with generalized estimating equations (GEE) were constructed to examine associations between school environment variables (i.e. the reported presence of beverage- and snack-vending machines and logos) and self-reported weight- and diet-related behaviours. Secondary schools in Alberta, Canada. A total of 4936 students from grades 7 to 10. The presence of beverage-vending machines in schools was associated with the weight status of students. The presence of snack-vending machines and logos was associated with students' frequency of consuming vended goods. The presence of snack-vending machines and logos was associated with the frequency of salty snack consumption. The reported presence of snack- and beverage-vending machines and logos in schools is related to some indices of weight status, diet and meal behaviours but not to others. The present study supported the general hypothesis that the presence of vending machines in schools may affect students' weight through increased consumption of vended goods, but notes that the frequency of 'junk' food consumption does not seem to be related to the presence of vending machines, perhaps reflecting the ubiquity of these foods in the daily lives of students.

Effects of Cascaded Voltage Collapse and Protection of Many Induction Machine Loads upon Load Characteristics Viewed from Bulk Transmission System

NASA Astrophysics Data System (ADS)

Kumano, Teruhisa

As known well, two of the fundamental processes which give rise to voltage collapse in power systems are the on load tap changers of transformers and dynamic characteristics of loads such as induction machines. It has been well established that, comparing among these two, the former makes slower collapse while the latter makes faster. However, in realistic situations, the load level of each induction machine is not uniform and it is well expected that only a part of loads collapses first, followed by collapse process of each load which did not go into instability during the preceding collapses. In such situations the over all equivalent collapse behavior viewed from bulk transmission level becomes somewhat different from the simple collapse driven by one aggregated induction machine. This paper studies the process of cascaded voltage collapse among many induction machines by time simulation, where load distribution on a feeder line is modeled by several hundreds of induction machines and static impedance loads. It is shown that in some cases voltage collapse really cascades among induction machines, where the macroscopic load dynamics viewed from upper voltage level makes slower collapse than expected by the aggregated load model. Also shown is the effects of machine protection of induction machines, which also makes slower collapse.

DOE-RCT-0003641 Final Technical Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wagner, Edward; Lesster, Ted

2014-07-30

This program studied novel concepts for an Axial Flux Reluctance Machine to capture energy from marine hydrokinetic sources and compared their attributes to a Radial Flux Reluctance Machine which was designed under a prior Department of Energy program for the same application. Detailed electromagnetic and mechanical analyses were performed to determine the validity of the concept and to provide a direct comparison with the existing conventional Radial Flux Switched Reluctance Machine designed during the Advanced Wave Energy Conversion Project, DE-EE0003641. The alternate design changed the machine topology so that the flux that is switched flows axially rather than radially andmore » the poles themselves are long radially, as opposed to the radial flux machine that has pole pieces that are long axially. It appeared possible to build an axial flux machine that should be considerably more compact than the radial machine. In an “apples to apples” comparison, the same rules with regard to generating magnetic force and the fundamental limitations of flux density hold, so that at the heart of the machine the same torque equations hold. The differences are in the mechanical configuration that limits or enhances the change of permeance with rotor position, in the amount of permeable iron required to channel the flux via the pole pieces to the air-gaps, and in the sizing and complexity of the electrical winding. Accordingly it was anticipated that the magnetic component weight would be similar but that better use of space would result in a shorter machine with accompanying reduction in housing and support structure. For the comparison the pole count was kept the same at 28 though it was also expected that the radial tapering of the slots between pole pieces would permit a higher pole count machine, enabling the generation of greater power at a given speed in some future design. The baseline Radial Flux Machine design was established during the previous DOE program. Its characteristics were tabulated for use in comparing to the Axial Flux Machine. Three basic conceptual designs for the Axial Flux Machine were considered: (1) a machine with a single coil at the inner diameter of the machine, (2) a machine with a single coil at the outside diameter of the machine, and (3) a machine with a coil around each tooth. Slight variations of these basic configurations were considered during the study. Analysis was performed on these configurations to determine the best candidate design to advance to preliminary design, based on size, weight, performance, cost and manufacturability. The configuration selected as the most promising was the multi-pole machine with a coil around each tooth. This configuration provided the least complexity with respect to the mechanical configuration and manufacturing, which would yield the highest reliability and lowest cost machine of the three options. A preliminary design was performed on this selected configuration. For this first ever axial design of the multi rotor configuration the 'apples to apples' comparison was based on using the same length of rotor pole as the axial length of rotor pole in the radial machine and making the mean radius of the rotor in the axial machine the same as the air gap radius in the radial machine. The tooth to slot ratio at the mean radius of the axial machine was the same as the tooth to slot ratio of the radial machine. The comparison between the original radial flux machine and the new axial flux machine indicates that for the same torque, the axial flux machine diameter will be 27% greater, but it will have 30% of the length, and 76% of the weight. Based on these results, it is concluded that an axial flux reluctance machine presents a viable option for large generators to be used for the capture of wave energy. In the analysis of Task 4, below, it is pointed out that our selection of dimensional similarity for the 'apples to apples' comparison did not produce an optimum axial flux design. There is torque capability to spare, implying we could reduce the magnetic structure, but the winding area, constrained by the pole separation at the inner pole radius has a higher resistance than desirable, implying we need more room for copper. The recommendation is to proceed via one cycle of optimization and review to correct this unbalance and then proceed to a detailed design phase to produce manufacturing drawings, followed by the construction of a prototype to test the performance of the machine against predicted results.« less

Exploring the Function Space of Deep-Learning Machines

NASA Astrophysics Data System (ADS)

Li, Bo; Saad, David

2018-06-01

The function space of deep-learning machines is investigated by studying growth in the entropy of functions of a given error with respect to a reference function, realized by a deep-learning machine. Using physics-inspired methods we study both sparsely and densely connected architectures to discover a layerwise convergence of candidate functions, marked by a corresponding reduction in entropy when approaching the reference function, gain insight into the importance of having a large number of layers, and observe phase transitions as the error increases.

DOD Weapon Systems Software Management Study. Appendix E. Bibliography

DTIC Science & Technology

1975-06-01

PROGRAMMING. -U- FakerF.T. IBM Systems Journal. V-11. N-1. P.56-73. 1972. REPORT. International Business Machines Corp. UNCLAS- .* SI’TIED lqpp. 1972. 42...MODULAR PROGRAMS. -U- Myers,G.J. MEMO. International Business Machines Corp., Poughkee- psier N. Y. Pouqhkeepsie Lab. UNCLASSIFIED 81pp. 29JAN73 U7... International Business Machines Corp., Whitp Plains, N. Y. UNCLASSIFIED 20pp. JUN74. 81. DATA SECURTTY AND DATA PROCESSING. VOLUME 2. STUDY SUMMARY. C-320- 1371

Performance prediction: A case study using a multi-ring KSR-1 machine

NASA Technical Reports Server (NTRS)

Sun, Xian-He; Zhu, Jianping

1995-01-01

While computers with tens of thousands of processors have successfully delivered high performance power for solving some of the so-called 'grand-challenge' applications, the notion of scalability is becoming an important metric in the evaluation of parallel machine architectures and algorithms. In this study, the prediction of scalability and its application are carefully investigated. A simple formula is presented to show the relation between scalability, single processor computing power, and degradation of parallelism. A case study is conducted on a multi-ring KSR1 shared virtual memory machine. Experimental and theoretical results show that the influence of topology variation of an architecture is predictable. Therefore, the performance of an algorithm on a sophisticated, heirarchical architecture can be predicted and the best algorithm-machine combination can be selected for a given application.

Swinging Atwood Machine: Experimental and numerical results, and a theoretical study

NASA Astrophysics Data System (ADS)

Pujol, O.; Pérez, J. P.; Ramis, J. P.; Simó, C.; Simon, S.; Weil, J. A.

2010-06-01

A Swinging Atwood Machine ( SAM) is built and some experimental results concerning its dynamic behaviour are presented. Experiments clearly show that pulleys play a role in the motion of the pendulum, since they can rotate and have non-negligible radii and masses. Equations of motion must therefore take into account the moment of inertia of the pulleys, as well as the winding of the rope around them. Their influence is compared to previous studies. A preliminary discussion of the role of dissipation is included. The theoretical behaviour of the system with pulleys is illustrated numerically, and the relevance of different parameters is highlighted. Finally, the integrability of the dynamic system is studied, the main result being that the machine with pulleys is non-integrable. The status of the results on integrability of the pulley-less machine is also recalled.

Fluidics and heat generation of Alcon Infiniti and Legacy, Bausch & Lomb Millennium, and advanced medical optics sovereign phacoemulsification systems.

PubMed

Floyd, Michael S; Valentine, Jeremy R; Olson, Randall J

2006-09-01

To study heat generation, vacuum, and flow characteristics of the Alcon Infiniti and Bausch & Lomb Millennium with results compared with the Alcon Legacy and advanced medical optics (AMO) Sovereign machines previously studied. Experimental study. Heat generation with continuous ultrasound was determined with and without a 200-g weight. Flow and vacuum were determined from 12 to 40-ml/min in 2-ml/min steps. The impact of a STAAR Cruise Control was also tested. Millennium created the most heat/20% of power (5.67 +/- 0.51 degrees C unweighted and 6.80 +/- 0.80 degrees C weighted), followed by Sovereign (4.59 +/- 0.70 degrees C unweighted and 5.65 +/- 0.72 degrees C weighted), Infiniti (2.79 +/- 0.62 degrees C unweighted and 3.96 +/- 0.31 degrees C weighted), and Legacy (1.99 +/- 0.49 degrees C unweighted and 4.27 +/- 0.76 degrees C weighted; P < .0001 for all comparisons between machines except Infiniti vs Legacy, both weighted). Flow studies revealed that Millennium Peristaltic was 17% less than indicated (P < .0001 to all other machines), and all other machines were within 3.5% of indicated. Cruise Control decreased flow by 4.1% (P < .0001 for same machine without it). Millennium Venturi had the greatest vacuum (81% more than the least Sovereign; P < .0001), and Cruise Control increased vacuum in a peristaltic machine 35% more than the Venturi system (P < .0001). Percent power is not consistent in regard to heat generation, however, flow was accurate for all machines except Millennium Peristaltic. Restriction with Cruise Control elevates unoccluded vacuum to levels greater than the Venturi system tested.

Sales of healthy snacks and beverages following the implementation of healthy vending standards in City of Philadelphia vending machines.

PubMed

Pharis, Meagan L; Colby, Lisa; Wagner, Amanda; Mallya, Giridhar

2018-02-01

We examined outcomes following the implementation of employer-wide vending standards, designed to increase healthy snack and beverage options, on the proportion of healthy v. less healthy sales, sales volume and revenue for snack and beverage vending machines. A single-arm evaluation of a policy utilizing monthly sales volume and revenue data provided by the contracted vendor during baseline, machine conversion and post-conversion time periods. Study time periods are full calendar years unless otherwise noted. Property owned or leased by the City of Philadelphia, USA. Approximately 250 vending machines over a 4-year period (2010-2013). At post-conversion, the proportion of sales attributable to healthy items was 40 % for snacks and 46 % for beverages. Healthy snack sales were 323 % higher (38·4 to 162·5 items sold per machine per month) and total snack sales were 17 % lower (486·8 to 402·1 items sold per machine per month). Healthy beverage sales were 33 % higher (68·2 to 90·6 items sold per machine per month) and there was no significant change in total beverage sales (213·2 to 209·6 items sold per machine per month). Revenue was 11 % lower for snacks ($US 468·30 to $US 415·70 per machine per month) and 21 % lower for beverages ($US 344·00 to $US 270·70 per machine per month). Sales of healthy vending items were significantly higher following the implementation of employer-wide vending standards for snack and beverage vending machines. Entities receiving revenue-based commission payments from vending machines should employ strategies to minimize potential revenue losses.

Machinability of IPS Empress 2 framework ceramic.

PubMed

Schmidt, C; Weigl, P

2000-01-01

Using ceramic materials for an automatic production of ceramic dentures by CAD/CAM is a challenge, because many technological, medical, and optical demands must be considered. The IPS Empress 2 framework ceramic meets most of them. This study shows the possibilities for machining this ceramic with economical parameters. The long life-time requirement for ceramic dentures requires a ductile machined surface to avoid the well-known subsurface damages of brittle materials caused by machining. Slow and rapid damage propagation begins at break outs and cracks, and limits life-time significantly. Therefore, ductile machined surfaces are an important demand for machine dental ceramics. The machining tests were performed with various parameters such as tool grain size and feed speed. Denture ceramics were machined by jig grinding on a 5-axis CNC milling machine (Maho HGF 500) with a high-speed spindle up to 120,000 rpm. The results of the wear test indicate low tool wear. With one tool, you can machine eight occlusal surfaces including roughing and finishing. One occlusal surface takes about 60 min machining time. Recommended parameters for roughing are middle diamond grain size (D107), cutting speed v(c) = 4.7 m/s, feed speed v(ft) = 1000 mm/min, depth of cut a(e) = 0.06 mm, width of contact a(p) = 0.8 mm, and for finishing ultra fine diamond grain size (D46), cutting speed v(c) = 4.7 m/s, feed speed v(ft) = 100 mm/min, depth of cut a(e) = 0.02 mm, width of contact a(p) = 0.8 mm. The results of the machining tests give a reference for using IPS Empress(R) 2 framework ceramic in CAD/CAM systems. Copyright 2000 John Wiley & Sons, Inc.

A study of workstation computational performance for real-time flight simulation

NASA Technical Reports Server (NTRS)

Maddalon, Jeffrey M.; Cleveland, Jeff I., II

1995-01-01

With recent advances in microprocessor technology, some have suggested that modern workstations provide enough computational power to properly operate a real-time simulation. This paper presents the results of a computational benchmark, based on actual real-time flight simulation code used at Langley Research Center, which was executed on various workstation-class machines. The benchmark was executed on different machines from several companies including: CONVEX Computer Corporation, Cray Research, Digital Equipment Corporation, Hewlett-Packard, Intel, International Business Machines, Silicon Graphics, and Sun Microsystems. The machines are compared by their execution speed, computational accuracy, and porting effort. The results of this study show that the raw computational power needed for real-time simulation is now offered by workstations.

Electric field prediction for a human body-electric machine system.

PubMed

Ioannides, Maria G; Papadopoulos, Peter J; Dimitropoulou, Eugenia

2004-01-01

A system consisting of an electric machine and a human body is studied and the resulting electric field is predicted. A 3-phase induction machine operating at full load is modeled considering its geometry, windings, and materials. A human model is also constructed approximating its geometry and the electric properties of tissues. Using the finite element technique the electric field distribution in the human body is determined for a distance of 1 and 5 m from the machine and its effects are studied. Particularly, electric field potential variations are determined at specific points inside the human body and for these points the electric field intensity is computed and compared to the limit values for exposure according to international standards.

Space applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 4: Application of ARAMIS capabilities to space project functional elements

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities and their related ground support functions are studied, so that informed decisions can be made on which aspects of ARAMIS to develop. The specific tasks which will be required by future space project tasks are identified and the relative merits of these options are evaluated. The ARAMIS options defined and researched span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

Stroke dynamics and frequency of 3 phacoemulsification machines.

PubMed

Tognetto, Daniele; Cecchini, Paolo; Leon, Pia; Di Nicola, Marta; Ravalico, Giuseppe

2012-02-01

To measure the working frequency and the stroke dynamics of the phaco tip of 3 phacoemulsification machines. University Eye Clinic of Trieste, Italy. Experimental study. A video wet fixture was assembled to measure the working frequency using a micro camera and a micropulsed strobe-light system. A different video wet fixture was created to measure tip displacement as vectorial movement at different phaco powers using a microscopic video apparatus. The working frequency of the Infiniti Ozil machine was 43.0 kHz in longitudinal mode and 31.6 kHz in torsional mode. The frequency of the Whitestar Signature machine was 29.0 kHz in longitudinal mode and 38.0 kHz with the Ellips FX handpiece. The Stellaris machine had a frequency of 28.8 kHz. The longitudinal stroke of the 3 machines at different phaco powers was statistically significantly different. The Stellaris machine had the highest stroke extent (139 μm). The lateral movement of the Infiniti Ozil and Whitestar Signature machines differed significantly. No movement on the y-axis was observed for the Infiniti Ozil machine in torsional mode. The elliptical path of the Ellips FX handpiece had different x and y components at different phaco powers. The 3 phaco machines performed differently in terms of working frequency and stroke dynamics. The knowledge of the peculiar lateral and elliptical path strokes of Infiniti and Whitestar Signature machines may allow the surgeon to fully use these features for lens removal. Copyright © 2012 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Comparison of effects of overload on parameters and performance of samarium-cobalt and strontium-ferrite radially oriented permanent magnet brushless DC motors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Demerdash, N.A.; Nehl, T.W.; Nyamusa, T.A.

1985-08-01

Effects of high momentary overloads on the samarium-cobalt and strontium-ferrite permanent magnets and the magnetic field in electronically commutated brushless dc machines, as well as their impact on the associated machine parameters were studied. The effect of overload on the machine parameters, and subsequently on the machine system performance was also investigated. This was accomplished through the combined use of finite element analysis of the magnetic field in such machines, perturbation of the magnetic energies to determine machine inductances, and dynamic simulation of the performance of brushless dc machines, when energized from voltage source inverters. These effects were investigated throughmore » application of the above methods to two equivalent 15 hp brushless dc motors, one of which was built with samarium-cobalt magnets, while the other was built with strontium- ferrite magnets. For momentary overloads as high as 4.5 p.u. magnet flux reductions of 29% and 42% of the no load flux were obtained in the samarium-cobalt and strontiumferrite machines, respectively. Corresponding reductions in the line to line armature inductances of 52% and 46% of the no load values were reported for the samarium-cobalt and strontium-ferrite cases, respectively. The overload affected the profiles and magnitudes of armature induced back emfs. Subsequently, the effects of overload on machine parameters were found to have significant impact on the performance of the machine systems, where findings indicate that the samarium-cobalt unit is more suited for higher overload duties than the strontium-ferrite machine.« less

Comparison of shear wave velocities on ultrasound elastography between different machines, transducers, and acquisition depths: a phantom study.

PubMed

Shin, Hyun Joo; Kim, Myung-Joon; Kim, Ha Yan; Roh, Yun Ho; Lee, Mi-Jung

2016-10-01

To investigate consistency in shear wave velocities (SWVs) on ultrasound elastography using different machines, transducers and acquisition depths. The SWVs were measured using an elasticity phantom with a Young's modulus of 16.9 kPa, with three recently introduced ultrasound elastography machines (A, B and C from different vendors) and two transducers (low and high frequencies) at four depths (2, 3, 4 and 5 cm). Mean SWVs from 15 measurements and coefficient of variations (CVs) were compared between three machines, two transducers and four acquisition depths. The SWVs using the high frequency transducer were not acquired at 5 cm depth in machine B, and a high frequency transducer was not available in machine C. The mean SWVs in the three machines were different (p ≤ 0.002). The CVs were 0-0.09 in three machines. The mean SWVs between the two transducers were different (p < 0.001) except at 4 and 5 cm depths in machine A. The SWVs were affected by the acquisition depths in all conditions (p < 0.001). There is considerable difference in SWVs on ultrasound elastography depending on different machines, transducers and acquisition depths. Caution is needed when using the cutoff values of SWVs in different conditions. • The shear wave velocities (SWVs) are different between different ultrasound elastography machines • The SWVs are also different between different transducers and acquisition depths • Caution is needed when using the cutoff SWVs measured under different conditions.

Adaptive machine and its thermodynamic costs

NASA Astrophysics Data System (ADS)

Allahverdyan, Armen E.; Wang, Q. A.

2013-03-01

We study the minimal thermodynamically consistent model for an adaptive machine that transfers particles from a higher chemical potential reservoir to a lower one. This model describes essentials of the inhomogeneous catalysis. It is supposed to function with the maximal current under uncertain chemical potentials: if they change, the machine tunes its own structure fitting it to the maximal current under new conditions. This adaptation is possible under two limitations: (i) The degree of freedom that controls the machine's structure has to have a stored energy (described via a negative temperature). The origin of this result is traced back to the Le Chatelier principle. (ii) The machine has to malfunction at a constant environment due to structural fluctuations, whose relative magnitude is controlled solely by the stored energy. We argue that several features of the adaptive machine are similar to those of living organisms (energy storage, aging).

Comparisons between designs for single-sided linear electric motors: Homopolar synchronous and induction

NASA Astrophysics Data System (ADS)

Nondahl, T. A.; Richter, E.

1980-09-01

A design study of two types of single sided (with a passive rail) linear electric machine designs, namely homopolar linear synchronous machines (LSM's) and linear induction machines (LIM's), is described. It is assumed the machines provide tractive effort for several types of light rail vehicles and locomotives. These vehicles are wheel supported and require tractive powers ranging from 200 kW to 3735 kW and top speeds ranging from 112 km/hr to 400 km/hr. All designs are made according to specified magnetic and thermal criteria. The LSM advantages are a higher power factor, much greater restoring forces for track misalignments, and less track heating. The LIM advantages are no need to synchronize the excitation frequency precisely to vehicle speed, simpler machine construction, and a more easily anchored track structure. The relative weights of the two machine types vary with excitation frequency and speed; low frequencies and low speeds favor the LSM.

Catalytic aided electrical discharge machining of polycrystalline diamond - parameter analysis of finishing condition

NASA Astrophysics Data System (ADS)

Haikal Ahmad, M. A.; Zulafif Rahim, M.; Fauzi, M. F. Mohd; Abdullah, Aslam; Omar, Z.; Ding, Songlin; Ismail, A. E.; Rasidi Ibrahim, M.

2018-01-01

Polycrystalline diamond (PCD) is regarded as among the hardest material in the world. Electrical Discharge Machining (EDM) typically used to machine this material because of its non-contact process nature. This investigation was purposely done to compare the EDM performances of PCD when using normal electrode of copper (Cu) and newly proposed graphitization catalyst electrode of copper nickel (CuNi). Two level full factorial design of experiment with 4 center points technique was used to study the influence of main and interaction effects of the machining parameter namely; pulse-on, pulse-off, sparking current, and electrode materials (categorical factor). The paper shows interesting discovery in which the newly proposed electrode presented positive impact to the machining performance. With the same machining parameters of finishing, CuNi delivered more than 100% better in Ra and MRR than ordinary Cu electrode.

Using Phun to Study ``Perpetual Motion'' Machines

NASA Astrophysics Data System (ADS)

Koreš, Jaroslav

2012-05-01

The concept of "perpetual motion" has a long history. The Indian astronomer and mathematician Bhaskara II (12th century) was the first person to describe a perpetual motion (PM) machine. An example of a 13th- century PM machine is shown in Fig. 1. Although the law of conservation of energy clearly implies the impossibility of PM construction, over the centuries numerous proposals for PM have been made, involving ever more elements of modern science in their construction. It is possible to test a variety of PM machines in the classroom using a program called Phun2 or its commercial version Algodoo.3 The programs are designed to simulate physical processes and we can easily simulate mechanical machines using them. They provide an intuitive graphical environment controlled with a mouse; a programming language is not needed. This paper describes simulations of four different (supposed) PM machines.4

Predicting competency in automated machine use in an acquired brain injury population using neuropsychological measures.

PubMed

Crowe, Simon F; Mahony, Kate; Jackson, Martin

2004-08-01

The purpose of the current study was to explore whether performance on standardised neuropsychological measures could predict functional ability with automated machines and services among people with an acquired brain injury (ABI). Participants were 45 individuals who met the criteria for mild, moderate or severe ABI and 15 control participants matched on demographic variables including age- and education. Each participant was required to complete a battery of neuropsychological tests, as well as performing three automated service delivery tasks: a transport automated ticketing machine, an automated teller machine (ATM) and an automated telephone service. The results showed consistently high relationship between the neuropsychological measures, both as single predictors and in combination, and level of competency with the automated machines. Automated machines are part of a relatively new phenomena in service delivery and offer an ecologically valid functional measure of performance that represents a true indication of functional disability.

Machine vision for digital microfluidics

NASA Astrophysics Data System (ADS)

Shin, Yong-Jun; Lee, Jeong-Bong

2010-01-01

Machine vision is widely used in an industrial environment today. It can perform various tasks, such as inspecting and controlling production processes, that may require humanlike intelligence. The importance of imaging technology for biological research or medical diagnosis is greater than ever. For example, fluorescent reporter imaging enables scientists to study the dynamics of gene networks with high spatial and temporal resolution. Such high-throughput imaging is increasingly demanding the use of machine vision for real-time analysis and control. Digital microfluidics is a relatively new technology with expectations of becoming a true lab-on-a-chip platform. Utilizing digital microfluidics, only small amounts of biological samples are required and the experimental procedures can be automatically controlled. There is a strong need for the development of a digital microfluidics system integrated with machine vision for innovative biological research today. In this paper, we show how machine vision can be applied to digital microfluidics by demonstrating two applications: machine vision-based measurement of the kinetics of biomolecular interactions and machine vision-based droplet motion control. It is expected that digital microfluidics-based machine vision system will add intelligence and automation to high-throughput biological imaging in the future.

An Evaluation of Online Machine Translation of Arabic into English News Headlines: Implications on Students' Learning Purposes

ERIC Educational Resources Information Center

Kadhim, Kais A.; Habeeb, Luwaytha S.; Sapar, Ahmad Arifin; Hussin, Zaharah; Abdullah, Muhammad Ridhuan Tony Lim

2013-01-01

Nowadays, online Machine Translation (MT) is used widely with translation software, such as Google and Babylon, being easily available and downloadable. This study aims to test the translation quality of these two machine systems in translating Arabic news headlines into English. 40 Arabic news headlines were selected from three online sources,…

Promoting the Purchase of Low-Calorie Foods from School Vending Machines: A Cluster-Randomized Controlled Study

ERIC Educational Resources Information Center

Kocken, Paul L.; Eeuwijk, Jennifer; van Kesteren, Nicole M.C.; Dusseldorp, Elise; Buijs, Goof; Bassa-Dafesh, Zeina; Snel, Jeltje

2012-01-01

Background: Vending machines account for food sales and revenue in schools. We examined 3 strategies for promoting the sale of lower-calorie food products from vending machines in high schools in the Netherlands. Methods: A school-based randomized controlled trial was conducted in 13 experimental schools and 15 control schools. Three strategies…

Phishtest: Measuring the Impact of Email Headers on the Predictive Accuracy of Machine Learning Techniques

ERIC Educational Resources Information Center

Tout, Hicham

2013-01-01

The majority of documented phishing attacks have been carried by email, yet few studies have measured the impact of email headers on the predictive accuracy of machine learning techniques in detecting email phishing attacks. Research has shown that the inclusion of a limited subset of email headers as features in training machine learning…

Kurzweil Reading Machine: A Partial Evaluation of Its Optical Character Recognition Error Rate.

ERIC Educational Resources Information Center

Goodrich, Gregory L.; And Others

1979-01-01

A study designed to assess the ability of the Kurzweil reading machine (a speech reading device for the visually handicapped) to read three different type styles produced by five different means indicated that the machines tested had different error rates depending upon the means of producing the copy and upon the type style used. (Author/CL)

Advancing Research in Second Language Writing through Computational Tools and Machine Learning Techniques: A Research Agenda

ERIC Educational Resources Information Center

Crossley, Scott A.

2013-01-01

This paper provides an agenda for replication studies focusing on second language (L2) writing and the use of natural language processing (NLP) tools and machine learning algorithms. Specifically, it introduces a range of the available NLP tools and machine learning algorithms and demonstrates how these could be used to replicate seminal studies…

Diverse Effects, Complex Causes: Children Use Information about Machines' Functional Diversity to Infer Internal Complexity

ERIC Educational Resources Information Center

Ahl, Richard E.; Keil, Frank C.

2017-01-01

Four studies explored the abilities of 80 adults and 180 children (4-9 years), from predominantly middle-class families in the Northeastern United States, to use information about machines' observable functional capacities to infer their internal, "hidden" mechanistic complexity. Children as young as 4 and 5 years old used machines'…

Inviting Argument by Analogy: Analogical-Mapping-Based Comparison Activities as a Scaffold for Small-Group Argumentation

ERIC Educational Resources Information Center

Emig, Brandon R.; McDonald, Scott; Zembal-Saul, Carla; Strauss, Susan G.

2014-01-01

This study invited small groups to make several arguments by analogy about simple machines. Groups were first provided training on analogical (structure) mapping and were then invited to use analogical mapping as a scaffold to make arguments. In making these arguments, groups were asked to consider three simple machines: two machines that they had…

High Energy Colliders

NASA Astrophysics Data System (ADS)

Palmer, R. B.; Gallardo, J. C.

INTRODUCTION PHYSICS CONSIDERATIONS GENERAL REQUIRED LUMINOSITY FOR LEPTON COLLIDERS THE EFFECTIVE PHYSICS ENERGIES OF HADRON COLLIDERS HADRON-HADRON MACHINES LUMINOSITY SIZE AND COST CIRCULAR e^{+}e^- MACHINES LUMINOSITY SIZE AND COST e^{+}e^- LINEAR COLLIDERS LUMINOSITY CONVENTIONAL RF SUPERCONDUCTING RF AT HIGHER ENERGIES γ - γ COLLIDERS μ ^{+} μ^- COLLIDERS ADVANTAGES AND DISADVANTAGES DESIGN STUDIES STATUS AND REQUIRED R AND D COMPARISION OF MACHINES CONCLUSIONS DISCUSSION

Using the Atwood machine to study Stokes' law

NASA Astrophysics Data System (ADS)

Greenwood, Margaret Stautberg; Fazio, Frances; Russotto, Marie; Wilkosz, Aaron

1986-10-01

A sphere, on one side of an Atwood machine, is immersed in glycerin and permitted to fall. We timed the descent and measured the terminal speed. We varied the mass m2 on the other side of the Atwood machine and plotted a graph of the terminal speed versus mass m2. Both the slope and the intercept yield the viscosity.

Study of Environmental Data Complexity using Extreme Learning Machine

NASA Astrophysics Data System (ADS)

Leuenberger, Michael; Kanevski, Mikhail

2017-04-01

The main goals of environmental data science using machine learning algorithm deal, in a broad sense, around the calibration, the prediction and the visualization of hidden relationship between input and output variables. In order to optimize the models and to understand the phenomenon under study, the characterization of the complexity (at different levels) should be taken into account. Therefore, the identification of the linear or non-linear behavior between input and output variables adds valuable information for the knowledge of the phenomenon complexity. The present research highlights and investigates the different issues that can occur when identifying the complexity (linear/non-linear) of environmental data using machine learning algorithm. In particular, the main attention is paid to the description of a self-consistent methodology for the use of Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. By applying two ELM models (with linear and non-linear activation functions) and by comparing their efficiency, quantification of the linearity can be evaluated. The considered approach is accompanied by simulated and real high dimensional and multivariate data case studies. In conclusion, the current challenges and future development in complexity quantification using environmental data mining are discussed. References - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

Analysis of surface integrity in machining of AISI 304 stainless steel under various cooling and cutting conditions

NASA Astrophysics Data System (ADS)

Klocke, F.; Döbbeler, B.; Lung, S.; Seelbach, T.; Jawahir, I. S.

2018-05-01

Recent studies have shown that machining under specific cooling and cutting conditions can be used to induce a nanocrystalline surface layer in the workspiece. This layer has beneficial properties, such as improved fatigue strength, wear resistance and tribological behavior. In machining, a promising approach for achieving grain refinement in the surface layer is the application of cryogenic cooling. The aim is to use the last step of the machining operation to induce the desired surface quality to save time-consuming and expensive post machining surface treatments. The material used in this study was AISI 304 stainless steel. This austenitic steel suffers from low yield strength that limits its technological applications. In this paper, liquid nitrogen (LN2) as cryogenic coolant, as well as minimum quantity lubrication (MQL), was applied and investigated. As a reference, conventional flood cooling was examined. Besides the cooling conditions, the feed rate was varied in four steps. A large rounded cutting edge radius and finishing cutting parameters were chosen to increase the mechanical load on the machined surface. The surface integrity was evaluated at both, the microstructural and the topographical levels. After turning experiments, a detailed analysis of the microstructure was carried out including the imaging of the surface layer and hardness measurements at varying depths within the machined layer. Along with microstructural investigations, different topological aspects, e.g., the surface roughness, were analyzed. It was shown that the resulting microstructure strongly depends on the cooling condition. This study also shows that it was possible to increase the micro hardness in the top surface layer significantly.

Implementation of electronic locking devices for adolescents at German tobacco vending machines: intended and unintended changes of supply and demand.

PubMed

Schneider, S; Meyer, C; Yamamoto, S; Solle, D

2009-08-01

Starting from 1 January 2007, electronic locking devices based on proof-of-age (via electronic cash cards or a European driving licence) were installed in approximately 500,000 vending machines across Germany to restrict the purchase of cigarettes to those over the age of 16. To examine changes in the number of tobacco vending machines before and after the introduction of these new measures. The total number of commercial tobacco sources in 2 selected districts (70,000 inhabitants) in Cologne were recorded and mapped. This major German city was the ideal setting for this study as investigators were able to use existing sociogeographical data from the area. A complete inventory was compiled in autumn 2005 and 2007. A total of 780 students aged 12 to 15 were also interviewed in the study areas. The main outcome measures were quantities and locations of commercial tobacco sources. Between 2005 and 2007 the total number of tobacco sources decreased from 315 to 277 within the study area. Although the most obvious reduction was detected in the number of outdoor vending machines (-48%), the number of indoor vending machines also decreased by 8%. Adolescents changed from vending machines to other sources for cigarettes, particularly kiosks or friends (+31% points usage rate, p<0.001; +35% points usage rate, p<0.001, respectively). Although the number of tobacco vending machines decreased, this has not had a significant impact on cigarette acquisition by underage smokers as they were able to circumvent this new security measure in several different ways.

Machine-related injuries in the US mining industry and priorities for safety research.

PubMed

Ruff, Todd; Coleman, Patrick; Martini, Laura

2011-03-01

Researchers at the National Institute for Occupational Safety and Health studied mining accidents that involved a worker entangled in, struck by, or in contact with machinery or equipment in motion. The motivation for this study came from the large number of severe accidents, i.e. accidents resulting in a fatality or permanent disability, that are occurring despite available interventions. Accident descriptions were taken from an accident database maintained by the United States Department of Labor, Mine Safety and Health Administration, and 562 accidents that occurred during 2000-2007 fit the search criteria. Machine-related accidents accounted for 41% of all severe accidents in the mining industry during this period. Machinery most often involved in these accidents included conveyors, rock bolting machines, milling machines and haulage equipment such as trucks and loaders. The most common activities associated with these accidents were operation of the machine and maintenance and repair. The current methods to safeguard workers near machinery include mechanical guarding around moving components, lockout/tagout of machine power during maintenance and backup alarms for mobile equipment. To decrease accidents further, researchers recommend additional efforts in the development of new control technologies, training materials and dissemination of information on best practices.

Inverse Problems in Geodynamics Using Machine Learning Algorithms

NASA Astrophysics Data System (ADS)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R. N.

2018-01-01

During the past few decades numerical studies have been widely employed to explore the style of circulation and mixing in the mantle of Earth and other planets. However, in geodynamical studies there are many properties from mineral physics, geochemistry, and petrology in these numerical models. Machine learning, as a computational statistic-related technique and a subfield of artificial intelligence, has rapidly emerged recently in many fields of sciences and engineering. We focus here on the application of supervised machine learning (SML) algorithms in predictions of mantle flow processes. Specifically, we emphasize on estimating mantle properties by employing machine learning techniques in solving an inverse problem. Using snapshots of numerical convection models as training samples, we enable machine learning models to determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at midmantle depths. Employing support vector machine algorithms, we show that SML techniques can successfully predict the magnitude of mantle density anomalies and can also be used in characterizing mantle flow patterns. The technique can be extended to more complex geodynamic problems in mantle dynamics by employing deep learning algorithms for putting constraints on properties such as viscosity, elastic parameters, and the nature of thermal and chemical anomalies.

One method for life time estimation of a bucket wheel machine for coal moving

NASA Astrophysics Data System (ADS)

Vîlceanu, Fl; Iancu, C.

2016-08-01

Rehabilitation of outdated equipment with lifetime expired, or in the ultimate life period, together with high cost investments for their replacement, makes rational the efforts made to extend their life. Rehabilitation involves checking operational safety based on relevant expertise of metal structures supporting effective resistance and assessing the residual lifetime. The bucket wheel machine for coal constitute basic machine within deposits of coal of power plants. The estimate of remaining life can be done by checking the loading on the most stressed subassembly by Finite Element Analysis on a welding detail. The paper presents step-by-step the method of calculus applied in order to establishing the residual lifetime of a bucket wheel machine for coal moving using non-destructive methods of study (fatigue cracking analysis + FEA). In order to establish the actual state of machine and areas subject to study, was done FEA of this mining equipment, performed on the geometric model of mechanical analyzed structures, with powerful CAD/FEA programs. By applying the method it can be calculated residual lifetime, by extending the results from the most stressed area of the equipment to the entire machine, and thus saving time and money from expensive replacements.

Cage-rotor induction motor inter-turn short circuit fault detection with and without saturation effect by MEC model.

PubMed

Naderi, Peyman

2016-09-01

The inter-turn short fault for the Cage-Rotor-Induction-Machine (CRIM) is studied in this paper and its local saturation is taken into account. However, in order to observe the exact behavior of machine, the Magnetic-Equivalent-Circuit (MEC) and nonlinear B-H curve are proposed to provide an insight into the machine model and saturation effect respectively. The electrical machines are generally operated near to their saturation zone due to some design necessities. Hence, when the machine is exposed to a fault such as short circuit or eccentricities, it is operated within its saturation zone and thus, time and space harmonics are integrated and as a result, current and torque harmonics are generated which the phenomenon cannot be explored when saturation is dismissed. Nonetheless, inter-turn short circuit may lead to local saturation and this occurrence is studied in this paper using MEC model. In order to achieve the mentioned objectives, two and also four-pole machines are modeled as two samples and the machines performances are analyzed in healthy and faulty cases with and without saturation effect. A novel strategy is proposed to precisely detect inter-turn short circuit fault according to the stator׳s lines current signatures and the accuracy of the proposed method is verified by experimental results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A comparison of muscle activation between a Smith machine and free weight bench press.

PubMed

Schick, Evan E; Coburn, Jared W; Brown, Lee E; Judelson, Daniel A; Khamoui, Andy V; Tran, Tai T; Uribe, Brandon P

2010-03-01

The bench press exercise exists in multiple forms including the machine and free weight bench press. It is not clear though how each mode differs in its effect on muscle activation. The purpose of this study was to compare muscle activation of the anterior deltoid, medial deltoid, and pectoralis major during a Smith machine and free weight bench press at lower (70% 1 repetition maximum [1RM]) and higher (90% 1RM) intensities. Normalized electromyography amplitude values were used during the concentric phase of the bench press to compare muscle activity between a free weight and Smith machine bench press. Participants were classified as either experienced or inexperienced bench pressers. Two testing sessions were used, each of which entailed either all free weight or all Smith machine testing. In each testing session, each participant's 1RM was established followed by 2 repetitions at 70% of 1RM and 2 repetitions at 90% of 1RM. Results indicated greater activation of the medial deltoid on the free weight bench press than on the Smith machine bench press. Also, there was greater muscle activation at the 90% 1RM load than at the 70% 1RM load. The results of this study suggest that strength coaches should consider choosing the free weight bench press over the Smith machine bench press because of its potential for greater upper-body muscular development.

Differences in liver stiffness values obtained with new ultrasound elastography machines and Fibroscan: A comparative study.

PubMed

Piscaglia, Fabio; Salvatore, Veronica; Mulazzani, Lorenzo; Cantisani, Vito; Colecchia, Antonio; Di Donato, Roberto; Felicani, Cristina; Ferrarini, Alessia; Gamal, Nesrine; Grasso, Valentina; Marasco, Giovanni; Mazzotta, Elena; Ravaioli, Federico; Ruggieri, Giacomo; Serio, Ilaria; Sitouok Nkamgho, Joules Fabrice; Serra, Carla; Festi, Davide; Schiavone, Cosima; Bolondi, Luigi

2017-07-01

Whether Fibroscan thresholds can be immediately adopted for none, some or all other shear wave elastography techniques has not been tested. The aim of the present study was to test the concordance of the findings obtained from 7 of the most recent ultrasound elastography machines with respect to Fibroscan. Sixteen hepatitis C virus-related patients with fibrosis ≥2 and having reliable results at Fibroscan were investigated in two intercostal spaces using 7 different elastography machines. Coefficients of both precision (an index of data dispersion) and accuracy (an index of bias correction factors expressing different magnitudes of changes in comparison to the reference) were calculated. Median stiffness values differed among the different machines as did coefficients of both precision (range 0.54-0.72) and accuracy (range 0.28-0.87). When the average of the measurements of two intercostal spaces was considered, coefficients of precision significantly increased with all machines (range 0.72-0.90) whereas of accuracy improved more scatteredly and by a smaller degree (range 0.40-0.99). The present results showed only moderate concordance of the majority of elastography machines with the Fibroscan results, preventing the possibility of the immediate universal adoption of Fibroscan thresholds for defining liver fibrosis staging for all new machines. Copyright © 2017 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

A study on the effect of tool electrode thickness on MRR, and TWR in electrical discharge turning process

NASA Astrophysics Data System (ADS)

Gohil, Vikas; Puri, YM

2018-04-01

Turning by electrical discharge machining (EDM) is an emerging area of research. Generally, wire-EDM is used in EDM turning because it is not concerned with electrode tooling cost. In EDM turning wire electrode leaves cusps on the machined surface because of its small diameters and wire breakage which greatly affect the surface finish of the machined part. Moreover, one of the limitations of the process is low machining speed as compared to constituent processes. In this study, conventional EDM was employed for turning purpose in order to generate free-form cylindrical geometries on difficult-to-cut materials. Therefore, a specially designed turning spindle was mounted on a conventional die-sinking EDM machine to rotate the work piece. A conductive preshaped strip of copper as a forming tool is fed (reciprocate) continuously against the rotating work piece; thus, a mirror image of the tool is formed on the circumference of the work piece. In this way, an axisymmetric work piece can be made with small tools. The developed process is termed as the electrical discharge turning (EDT). In the experiments, the effect of machining parameters, such as pulse-on time, peak current, gap voltage and tool thickness on the MRR, and TWR were investigated and practical machining was carried out by turning of SS-304 stainless steel work piece.

Vending Machines of Food and Beverages and Nutritional Profile of their Products at Schools in Madrid, Spain, 2014-2015.

PubMed

Monroy-Parada, Doris Xiomara; Ángeles Moya, María; José Bosqued, María; López, Lázaro; Rodríguez-Artalejo, Fernando; Royo-Bordonada, Miguel Ángel

2016-06-09

Policies restricting access to sugary drinks and unhealthy foods in the school environment are associated with healthier consumption patterns. In 2010, Spain approved a Consensus Document regarding Food at Schools with nutritional criteria to improve the nutritional profile of foods and drinks served at schools. The objective of this study was to describe the frequency of food and drink vending machines at secondary schools in Madrid, the products offered at them and their nutritional profile. Cross-sectional study of a random sample of 330 secondary schools in Madrid in 2014-2015. The characteristics of the schools and the existence of vending machines were recorded through the internet and by telephone interview. The products offered in a representative sample of 6 vending machines were identified by in situ inspection, and its nutritional composition was taken from its labeling. Finally, the nutritional profile of each product was analyzed with the United Kingdom profile model, which classifies products as healthy and less healthy. The prevalence of vending machines was 17.3%. Among the products offered, 80.5% were less healthy food and drinks (high in energy, fat or sugar and poor in nutrients) and 10.5% were healthy products. Vending machines are common at secondary schools in Madrid. Most products are vending machines are still less healthy.

Application of statistical machine translation to public health information: a feasibility study.

PubMed

Kirchhoff, Katrin; Turner, Anne M; Axelrod, Amittai; Saavedra, Francisco

2011-01-01

Accurate, understandable public health information is important for ensuring the health of the nation. The large portion of the US population with Limited English Proficiency is best served by translations of public-health information into other languages. However, a large number of health departments and primary care clinics face significant barriers to fulfilling federal mandates to provide multilingual materials to Limited English Proficiency individuals. This article presents a pilot study on the feasibility of using freely available statistical machine translation technology to translate health promotion materials. The authors gathered health-promotion materials in English from local and national public-health websites. Spanish versions were created by translating the documents using a freely available machine-translation website. Translations were rated for adequacy and fluency, analyzed for errors, manually corrected by a human posteditor, and compared with exclusively manual translations. Machine translation plus postediting took 15-53 min per document, compared to the reported days or even weeks for the standard translation process. A blind comparison of machine-assisted and human translations of six documents revealed overall equivalency between machine-translated and manually translated materials. The analysis of translation errors indicated that the most important errors were word-sense errors. The results indicate that machine translation plus postediting may be an effective method of producing multilingual health materials with equivalent quality but lower cost compared to manual translations.

Application of statistical machine translation to public health information: a feasibility study

PubMed Central

Turner, Anne M; Axelrod, Amittai; Saavedra, Francisco

2011-01-01

Objective Accurate, understandable public health information is important for ensuring the health of the nation. The large portion of the US population with Limited English Proficiency is best served by translations of public-health information into other languages. However, a large number of health departments and primary care clinics face significant barriers to fulfilling federal mandates to provide multilingual materials to Limited English Proficiency individuals. This article presents a pilot study on the feasibility of using freely available statistical machine translation technology to translate health promotion materials. Design The authors gathered health-promotion materials in English from local and national public-health websites. Spanish versions were created by translating the documents using a freely available machine-translation website. Translations were rated for adequacy and fluency, analyzed for errors, manually corrected by a human posteditor, and compared with exclusively manual translations. Results Machine translation plus postediting took 15–53 min per document, compared to the reported days or even weeks for the standard translation process. A blind comparison of machine-assisted and human translations of six documents revealed overall equivalency between machine-translated and manually translated materials. The analysis of translation errors indicated that the most important errors were word-sense errors. Conclusion The results indicate that machine translation plus postediting may be an effective method of producing multilingual health materials with equivalent quality but lower cost compared to manual translations. PMID:21498805

Applications of machine learning in cancer prediction and prognosis.

PubMed

Cruz, Joseph A; Wishart, David S

2007-02-11

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on "older" technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Applications of Machine Learning in Cancer Prediction and Prognosis

PubMed Central

Cruz, Joseph A.; Wishart, David S.

2006-01-01

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression. PMID:19458758

Influence of the direction of selective laser sintering on machinability of parts from 316L steel

NASA Astrophysics Data System (ADS)

Alexeev, V. P.; Balyakin, A. V.; Khaimovich, A. I.

2017-02-01

This work presents the results of research of the impact of layer-by-layer growing of workpieces made of 316L steel on their machinability. The results of determination of residual stresses and measurement of hardness of the workpieces grown have been demonstrated. A series of experimental studies has been performed in order to determine the cutting force which occurs in the process of machining. The microstructure of the workpieces grown has been examined. It has been shown that the workpieces machined using Selective Laser Melting technology have the microstructure which is a totality of ‘microwelded seams’, which have a significant influence on the behavior of deformation processes in case of machining. The studies have shown that in case of lateral milling of the horizontally grown workpiece, the codirectional microwelded borders prevent any significant deformation of the misalignment which increases the cutting force by up to 10% as compared with milling of the vertically grown workpiece.

A review on application of nanofluid MQL in machining

NASA Astrophysics Data System (ADS)

Rifat, Mustafa; Rahman, Md. Habibor; Das, Debashish

2017-12-01

Heat generation is an inevitable phenomenon during machining. To eradicate heat oriented detrimental effects like surface burning, tool wear and so on-different types of cooling system are being used. Traditional flood cooling method is the most widely used technique; however the consumption rate of coolant is very high. Moreover, if it is not deposited or recycled properly, it may also cause environmental hazard. Minimum Quantity Lubrication (MQL), on the other hand, sprays lubricant which decreases the frictional force and heat produced during machining. Nanofluid MQL is the incorporation of especially engineered nanoparticles into the lubricant that increases the heat carrying capacity. In this paper, four manufacturing processes (grinding, turning, milling, and drilling) and the effect of using nanofluid MQL in them are studied and summarized. Parameters that are considered in this study are cutting force, surface roughness, machining temperature, tool wear and environmental aspects. It can be observed that using nanofluids in an optimized manner can be beneficial to the machining processes because of their superior characteristics.

Feasibility study of a brine boiling machine by solar energy

NASA Astrophysics Data System (ADS)

Phayom, W.

2018-06-01

This study presented the technical and operational feasibility of brine boiling machine by using solar energy instead of firewood or husk for salt production. The solar salt brine boiling machine consisted of a boiling chamber with an enhanced thermal efficiency through use of a solar brine heater. The stainless steel solar salt brine boiling chamber had dimensions of 60 cm x 70 cm x 20 cm. The steel brine heater had dimensions of 70 cm x 80 cm x 20 cm. The tilt angle of both the boiling chamber and brine heater was 20 degrees from horizontal. The brine temperature in the reservoir tank was 42°C with a flow rate of 6.64 L/h discharging into the solar boiling machine. It was found that the thermal efficiency and overall efficiency of the solar salt brine boiling machine were 0.63 and 0.38, respectively at a solar irradiance of 787.6 W/m2. The results shows that the potential of using solar energy for salt production system is feasible.

Radar detection with the Neyman-Pearson criterion using supervised-learning-machines trained with the cross-entropy error

NASA Astrophysics Data System (ADS)

Jarabo-Amores, María-Pilar; la Mata-Moya, David de; Gil-Pita, Roberto; Rosa-Zurera, Manuel

2013-12-01

The application of supervised learning machines trained to minimize the Cross-Entropy error to radar detection is explored in this article. The detector is implemented with a learning machine that implements a discriminant function, which output is compared to a threshold selected to fix a desired probability of false alarm. The study is based on the calculation of the function the learning machine approximates to during training, and the application of a sufficient condition for a discriminant function to be used to approximate the optimum Neyman-Pearson (NP) detector. In this article, the function a supervised learning machine approximates to after being trained to minimize the Cross-Entropy error is obtained. This discriminant function can be used to implement the NP detector, which maximizes the probability of detection, maintaining the probability of false alarm below or equal to a predefined value. Some experiments about signal detection using neural networks are also presented to test the validity of the study.

Applying machine learning to identify autistic adults using imitation: An exploratory study.

PubMed

Li, Baihua; Sharma, Arjun; Meng, James; Purushwalkam, Senthil; Gowen, Emma

2017-01-01

Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinematic characteristics in the movement patterns of autistic individuals are poorly studied, preventing advances in understanding the aetiology of motor impairment, or whether a wider range of motor characteristics could be used for diagnosis. The aim of this study was to investigate whether data-driven machine learning based methods could be used to address some fundamental problems with regard to identifying discriminative test conditions and kinematic parameters to classify between ASC and neurotypical controls. Data was based on a previous task where 16 ASC participants and 14 age, IQ matched controls observed then imitated a series of hand movements. 40 kinematic parameters extracted from eight imitation conditions were analysed using machine learning based methods. Two optimal imitation conditions and nine most significant kinematic parameters were identified and compared with some standard attribute evaluators. To our knowledge, this is the first attempt to apply machine learning to kinematic movement parameters measured during imitation of hand movements to investigate the identification of ASC. Although based on a small sample, the work demonstrates the feasibility of applying machine learning methods to analyse high-dimensional data and suggest the potential of machine learning for identifying kinematic biomarkers that could contribute to the diagnostic classification of autism.

Mechanical and Histological Effects of Resorbable Blasting Media Surface Treatment on the Initial Stability of Orthodontic Mini-Implants

PubMed Central

2016-01-01

Introduction. This study aimed to evaluate the effects of resorbable blasting media (RBM) treatment on early stability of orthodontic mini-implants by mechanical, histomorphometric, and histological analyses. Methods. Ninety-six (64 for mechanical study and 32 for histological study and histomorphometric analysis) titanium orthodontic mini-implants (OMIs) with machined (machined group) or RBM-treated (CaP) surface (RBM group) were implanted in the tibiae of 24 rabbits. Maximum initial torque (MIT) was measured during insertion, and maximum removal torque (MRT) and removal angular momentum (RAM) were measured at 2 and 4 weeks after implantation. Bone-to-implant contact (BIC) and bone area (BA) were analyzed at 4 weeks after implantation. Results. RBM group exhibited significantly lower MIT and significantly higher MRT and RAM at 2 weeks than machined group. No significant difference in MRT, RAM, and BIC between the two groups was noted at 4 weeks, although BA was significantly higher in RBM group than in machined group. RBM group showed little bone resorption, whereas machined group showed new bone formation after bone resorption. Conclusions. RBM surface treatment can provide early stability of OMIs around 2 weeks after insertion, whereas stability of machined surface OMIs may decrease in early stages because of bone resorption, although it can subsequently recover by new bone apposition. PMID:26942200

Machine Learning Based Classification of Microsatellite Variation: An Effective Approach for Phylogeographic Characterization of Olive Populations.

PubMed

Torkzaban, Bahareh; Kayvanjoo, Amir Hossein; Ardalan, Arman; Mousavi, Soraya; Mariotti, Roberto; Baldoni, Luciana; Ebrahimie, Esmaeil; Ebrahimi, Mansour; Hosseini-Mazinani, Mehdi

2015-01-01

Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biology. However, these methods have been less frequently used with empirical population genetics data. In this study, we developed a new combined approach of data analysis using microsatellite marker data from our previous studies of olive populations using machine learning algorithms. Herein, 267 olive accessions of various origins including 21 reference cultivars, 132 local ecotypes, and 37 wild olive specimens from the Iranian plateau, together with 77 of the most represented Mediterranean varieties were investigated using a finely selected panel of 11 microsatellite markers. We organized data in two '4-targeted' and '16-targeted' experiments. A strategy of assaying different machine based analyses (i.e. data cleaning, feature selection, and machine learning classification) was devised to identify the most informative loci and the most diagnostic alleles to represent the population and the geography of each olive accession. These analyses revealed microsatellite markers with the highest differentiating capacity and proved efficiency for our method of clustering olive accessions to reflect upon their regions of origin. A distinguished highlight of this study was the discovery of the best combination of markers for better differentiating of populations via machine learning models, which can be exploited to distinguish among other biological populations.

Locking devices on cigarette vending machines: evaluation of a city ordinance.

PubMed Central

Forster, J L; Hourigan, M E; Kelder, S

1992-01-01

OBJECTIVES. Policymakers, researchers, and citizens are beginning to recognize the need to limit minors' access to tobacco by restricting the sale of cigarettes through vending machines. One policy alternative that has been proposed by the tobacco industry is a requirement that vending machines be fitted with electronic locking devices. This study evaluates such a policy as enacted in St. Paul, Minn. METHODS. A random sample of vending machine locations was selected for cigarette purchase attempts conducted before implementation and at 3 and 12 months postimplementation. RESULTS. The rate of noncompliance by merchants was 34% after 3 months and 30% after 1 year. The effect of the law was to reduce the ability of a minor to purchase cigarettes from locations originally selling cigarettes through vending machines from 86% at baseline to 36% at 3 months. The purchase rate at these locations rose to 48% at 1 year. CONCLUSIONS. Our results suggest that cigarette vending machine locking devices may not be as effective as vending machine bans and require additional enforcement to ensure compliance with the law. PMID:1503160

Evaluation of compliance with the self-regulation agreement of the food and drink vending machine sector in primary schools in Madrid, Spain, in 2008.

PubMed

Royo-Bordonada, Miguel A; Martínez-Huedo, María A

2014-01-01

To evaluate compliance with the self-regulation agreement of the food and drink vending machine sector in primary schools in Madrid, Spain. Cross-sectional study of the prevalence of vending machines in 558 primary schools in 2008. Using the directory of all registered primary schools in Madrid, we identified the presence of machines by telephone interviews and evaluated compliance with the agreement by visiting the schools and assessing accessibility, type of publicity, the products offered and knowledge of the agreement. The prevalence of schools with vending machines was 5.8%. None of the schools reported knowledge of the agreement or of its nutritional guidelines, and most machines were accessible to primary school pupils (79.3%) and packed with high-calorie, low-nutrient-dense foods (58.6%). Compliance with the self-regulation agreement of the vending machines sector was low. Stricter regulation should receive priority in the battle against the obesity epidemic. Copyright © 2013 SESPAS. Published by Elsevier Espana. All rights reserved.

Communication Studies of DMP and SMP Machines

NASA Technical Reports Server (NTRS)

Sohn, Andrew; Biswas, Rupak; Chancellor, Marisa K. (Technical Monitor)

1997-01-01

Understanding the interplay between machines and problems is key to obtaining high performance on parallel machines. This paper investigates the interplay between programming paradigms and communication capabilities of parallel machines. In particular, we explicate the communication capabilities of the IBM SP-2 distributed-memory multiprocessor and the SGI PowerCHALLENGEarray symmetric multiprocessor. Two benchmark problems of bitonic sorting and Fast Fourier Transform are selected for experiments. Communication-efficient algorithms are developed to exploit the overlapping capabilities of the machines. Programs are written in Message-Passing Interface for portability and identical codes are used for both machines. Various data sizes and message sizes are used to test the machines' communication capabilities. Experimental results indicate that the communication performance of the multiprocessors are consistent with the size of messages. The SP-2 is sensitive to message size but yields a much higher communication overlapping because of the communication co-processor. The PowerCHALLENGEarray is not highly sensitive to message size and yields a low communication overlapping. Bitonic sorting yields lower performance compared to FFT due to a smaller computation-to-communication ratio.

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

Industrial machine systems risk assessment: a critical review of concepts and methods.

PubMed

Etherton, John R

2007-02-01

Reducing the risk of work-related death and injury to machine operators and maintenance personnel poses a continuing occupational safety challenge. The risk of injury from machinery in U.S. workplaces is high. Between 1992 and 2001, there were, on average, 520 fatalities per year involving machines and, on average, 3.8 cases per 10,000 workers of nonfatal caught-in-running-machine injuries involving lost workdays. A U.S. task group recently developed a technical reference guideline, ANSI B11 TR3, "A Guide to Estimate, Evaluate, & Reduce Risks Associated with Machine Tools," that is intended to bring machine tool risk assessment practice in the United States up to or above the level now required by the international standard, ISO 14121. The ANSI guideline emphasizes identifying tasks and hazards not previously considered, particularly those associated with maintenance; and it further emphasizes teamwork among line workers, engineers, and safety professionals. The value of this critical review of concepts and methods resides in (1) its linking current risk theory to machine system risk assessment and (2) its exploration of how various risk estimation tools translate into risk-informed decisions on industrial machine system design and use. The review was undertaken to set the stage for a field evaluation study on machine risk assessment among users of the ANSI B11 TR3 method.

Finding and defining the natural automata acting in living plants: Toward the synthetic biology for robotics and informatics in vivo.

PubMed

Kawano, Tomonori; Bouteau, François; Mancuso, Stefano

2012-11-01

The automata theory is the mathematical study of abstract machines commonly studied in the theoretical computer science and highly interdisciplinary fields that combine the natural sciences and the theoretical computer science. In the present review article, as the chemical and biological basis for natural computing or informatics, some plants, plant cells or plant-derived molecules involved in signaling are listed and classified as natural sequential machines (namely, the Mealy machines or Moore machines) or finite state automata. By defining the actions (states and transition functions) of these natural automata, the similarity between the computational data processing and plant decision-making processes became obvious. Finally, their putative roles as the parts for plant-based computing or robotic systems are discussed.

Finding and defining the natural automata acting in living plants: Toward the synthetic biology for robotics and informatics in vivo

PubMed Central

Kawano, Tomonori; Bouteau, François; Mancuso, Stefano

2012-01-01

The automata theory is the mathematical study of abstract machines commonly studied in the theoretical computer science and highly interdisciplinary fields that combine the natural sciences and the theoretical computer science. In the present review article, as the chemical and biological basis for natural computing or informatics, some plants, plant cells or plant-derived molecules involved in signaling are listed and classified as natural sequential machines (namely, the Mealy machines or Moore machines) or finite state automata. By defining the actions (states and transition functions) of these natural automata, the similarity between the computational data processing and plant decision-making processes became obvious. Finally, their putative roles as the parts for plant-based computing or robotic systems are discussed. PMID:23336016

A comparative study of machine learning models for ethnicity classification

NASA Astrophysics Data System (ADS)

Trivedi, Advait; Bessie Amali, D. Geraldine

2017-11-01

This paper endeavours to adopt a machine learning approach to solve the problem of ethnicity recognition. Ethnicity identification is an important vision problem with its use cases being extended to various domains. Despite the multitude of complexity involved, ethnicity identification comes naturally to humans. This meta information can be leveraged to make several decisions, be it in target marketing or security. With the recent development of intelligent systems a sub module to efficiently capture ethnicity would be useful in several use cases. Several attempts to identify an ideal learning model to represent a multi-ethnic dataset have been recorded. A comparative study of classifiers such as support vector machines, logistic regression has been documented. Experimental results indicate that the logical classifier provides a much accurate classification than the support vector machine.

Space Applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 1: Executive Summary

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Potential applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities, and to their related ground support functions are explored. The specific tasks which will be required by future space projects are identified. ARAMIS options which are candidates for those space project tasks and the relative merits of these options are defined and evaluated. Promising applications of ARAMIS and specific areas for further research are identified. The ARAMIS options defined and researched by the study group span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

Identifying product order with restricted Boltzmann machines

NASA Astrophysics Data System (ADS)

Rao, Wen-Jia; Li, Zhenyu; Zhu, Qiong; Luo, Mingxing; Wan, Xin

2018-03-01

Unsupervised machine learning via a restricted Boltzmann machine is a useful tool in distinguishing an ordered phase from a disordered phase. Here we study its application on the two-dimensional Ashkin-Teller model, which features a partially ordered product phase. We train the neural network with spin configuration data generated by Monte Carlo simulations and show that distinct features of the product phase can be learned from nonergodic samples resulting from symmetry breaking. Careful analysis of the weight matrices inspires us to define a nontrivial machine-learning motivated quantity of the product form, which resembles the conventional product order parameter.

A Wavelet Support Vector Machine Combination Model for Singapore Tourist Arrival to Malaysia

NASA Astrophysics Data System (ADS)

Rafidah, A.; Shabri, Ani; Nurulhuda, A.; Suhaila, Y.

2017-08-01

In this study, wavelet support vector machine model (WSVM) is proposed and applied for monthly data Singapore tourist time series prediction. The WSVM model is combination between wavelet analysis and support vector machine (SVM). In this study, we have two parts, first part we compare between the kernel function and second part we compare between the developed models with single model, SVM. The result showed that kernel function linear better than RBF while WSVM outperform with single model SVM to forecast monthly Singapore tourist arrival to Malaysia.

Towards Polarised Antiprotons: Machine Developments for Spin-Filtering Studies

NASA Astrophysics Data System (ADS)

Lenisa, Paolo

2016-02-01

We address the commissioning of the experimental equipment and the machine studies required for the first spin-filtering experiment with protons at the COSY ring in Jülich (Germany) at a beam kinetic energy of 49.3 MeV. The implementation of a low-beta insertion made it possible to achieve beam lifetimes of 8000 s in the presence of a dense polarized hydrogen storage cell target. The developed techniques can be directly applied to antiproton machines and allow for the determination of the spin-dependent pbar-p cross sections via spin-filtering.

Chip formation and surface integrity in high-speed machining of hardened steel

NASA Astrophysics Data System (ADS)

Kishawy, Hossam Eldeen A.

Increasing demands for high production rates as well as cost reduction have emphasized the potential for the industrial application of hard turning technology during the past few years. Machining instead of grinding hardened steel components reduces the machining sequence, the machining time, and the specific cutting energy. Hard turning Is characterized by the generation of high temperatures, the formation of saw toothed chips, and the high ratio of thrust to tangential cutting force components. Although a large volume of literature exists on hard turning, the change in machined surface physical properties represents a major challenge. Thus, a better understanding of the cutting mechanism in hard turning is still required. In particular, the chip formation process and the surface integrity of the machined surface are important issues which require further research. In this thesis, a mechanistic model for saw toothed chip formation is presented. This model is based on the concept of crack initiation on the free surface of the workpiece. The model presented explains the mechanism of chip formation. In addition, experimental investigation is conducted in order to study the chip morphology. The effect of process parameters, including edge preparation and tool wear on the chip morphology, is studied using Scanning Electron Microscopy (SEM). The dynamics of chip formation are also investigated. The surface integrity of the machined parts is also investigated. This investigation focusses on residual stresses as well as surface and sub-surface deformation. A three dimensional thermo-elasto-plastic finite element model is developed to predict the machining residual stresses. The effect of flank wear is introduced during the analysis. Although residual stresses have complicated origins and are introduced by many factors, in this model only the thermal and mechanical factors are considered. The finite element analysis demonstrates the significant effect of the heat generated during cutting on the residual stresses. The machined specimens are also examined using x-ray diffraction technique to clarify the effect of different speeds, feeds and depths of cut as well as different edge preparations on the residual stress distribution beneath the machined surface. A reasonable agreement between the predicted and measured residual stress is obtained. The results obtained demonstrate the possibility of eliminating the existence of high tensile residual stresses in the workpiece surface by selecting the proper cutting conditions. The machined surfaces are examined using SEM to study the effect of different process parameters and edge preparations on the quality of the machined surface. The phenomenon of material side flow is investigated to clarify the mechanism of this phenomenon. The effect of process parameters and edge preparations on sub-surface deformation is also investigated.

Release of β-endorphin, adrenocorticotropic hormone and cortisol in response to machine milking of dairy cows.

PubMed

Fazio, E; Medica, P; Cravana, C; Ferlazzo, A

2015-03-01

The present study was undertaken with the objective to obtain insight into the dynamics of the release of β-endorphin, adrenocorticotrophic hormone (ACTH) and cortisol in response to machine milking in dairy cows. A total of 10 healthy multiparous lactating Italian Friesian dairy cows were used in the study. Animals were at the 4(th)-5(th) month of pregnancy and were submitted to machine milking 2 times daily. Blood samples were collected in the morning: In baseline conditions, immediately before milking and after milking; and in the early afternoon: In baseline conditions, before milking and after milking, for 2 consecutive days. Endocrine variables were measured in duplicate, using a commercial radioimmunoassay for circulating β-endorphin and ACTH concentrations and a competitive enzyme-linked immunoassay for cortisol concentration. Data obtained showed a similar biphasic cortisol secretion of lactating dairy cows, with a significant increase of cortisol concentration after morning machine milking, at both the 1(st) and the 2(nd) day (p<0.05), and a decrease after afternoon machine milking at the 2(nd) day (p<0.01). One-way RM ANOVA showed significant effects of the machine milking on the cortisol changes, at both morning (f=22.96; p<0.001) and afternoon (f=15.10; p<0.01) milking, respectively. Two-way RM ANOVA showed a significant interaction between cortisol changes at the 1(st) and the 2(nd) day (f=7.94; p<0.0002), and between the sampling times (f=6.09; p<0.001). Conversely, no significant effects of the machine milking were observed on β-endorphin and ACTH changes, but only a moderate positive correlation (r=0.94; p<0.06) after milking stimuli. A wide range of cortisol concentrations reported in this study showed the complex dynamic patterns of the homeostatic mechanisms involved during machine milking in dairy cows, suggesting that β-endorphin and ACTH were not the main factors that caused the adrenocortical response to milking stimuli.

Investigation of Dynamic Force/Vibration Transmission Characteristics of Four-Square Type Gear Durability Test Machines

NASA Technical Reports Server (NTRS)

Kahraman, Ahmet

2002-01-01

In this study, design requirements for a dynamically viable, four-square type gear test machine are investigated. Variations of four-square type gear test machines have been in use for durability and dynamics testing of both parallel- and cross-axis gear set. The basic layout of these machines is illustrated. The test rig is formed by two gear pairs, of the same reduction ratio, a test gear pair and a reaction gear pair, connected to each other through shafts of certain torsional flexibility to form an efficient, closed-loop system. A desired level of constant torque is input to the circuit through mechanical (a split coupling with a torque arm) or hydraulic (a hydraulic actuator) means. The system is then driven at any desired speed by a small DC motor. The main task in hand is the isolation of the test gear pair from the reaction gear pair under dynamic conditions. Any disturbances originated at the reaction gear mesh might potentially travel to the test gearbox, altering the dynamic loading conditions of the test gear mesh, and hence, influencing the outcome of the durability or dynamics test. Therefore, a proper design of connecting structures becomes a major priority. Also, equally important is the issue of how close the operating speed of the machine is to the resonant frequencies of the gear meshes. This study focuses on a detailed analysis of the current NASA Glenn Research Center gear pitting test machine for evaluation of its resonance and vibration isolation characteristics. A number of these machines as the one illustrated has been used over last 30 years to establish an extensive database regarding the influence of the gear materials, processes surface treatments and lubricants on gear durability. This study is intended to guide an optimum design of next generation test machines for the most desirable dynamic characteristics.

Disease model: a simplified approach for analysis and management of human error: a quality improvement study.

PubMed

Ahmad-Sabry, Mohammad H I

2015-04-01

During 6 weeks, we had 4 incidents of echocardiography machine malfunction. There were 3 in the operating room, which were damaged due to intravenous (IV) fluid spillage over the keyboard of the machine leading to burning of the keyboard electric connection, and 1 in the cardiology department, which was damagaed due to spillage of coffee on it. The malfunction had an economic impact on the hospital (about $ 20,000) in addition to the nonavailability of the ultrasound (US) machine for the cardiac patient after the incident till the end of the case and for consequent cases till the fixation of the machine. We undertook an analysis of the incidents using simplified approach. The first incident happened when changing an empty IV fluid bag for a full one led to spillage of some fluid onto the keyboard. The second incidence was due to the use of needle to depressurize a medication bottle for continuous IV drip, and the third event was due to disconnection of the IV set from the bottle during transfer of the patient from operation room to intensive care unit. The fundamental problem is of course that fluid is harmful to the US machine. In addition, the machines are in a position between the patient bed and anesthesia machine. This means that IV pulls are on each side of the patient bed, which makes the machine vulnerable to fluid spillage. We considered a machine modification, to create a protective cover, but this was hindered by complexity of keyboard of the US machine, technical and financial challenges, and the time it would take to achieve. Second, we considered the creation of a protocol, with putting the machine in a position where no IV pulls are around and transferring the machine out of the room when transferring the patient will endanger the machine by the IV fluid. Third, changing of human behavior; to do this, we announced the protocol in our anesthesia conference to make it known to each and every one. We taught residents, fellows, and staff about the new protocol.Our simplified approach was effective for the prevention of fluid spillage over the US machine.

Building a profile of subjective well-being for social media users.

PubMed

Chen, Lushi; Gong, Tao; Kosinski, Michal; Stillwell, David; Davidson, Robert L

2017-01-01

Subjective well-being includes 'affect' and 'satisfaction with life' (SWL). This study proposes a unified approach to construct a profile of subjective well-being based on social media language in Facebook status updates. We apply sentiment analysis to generate users' affect scores, and train a random forest model to predict SWL using affect scores and other language features of the status updates. Results show that: the computer-selected features resemble the key predictors of SWL as identified in early studies; the machine-predicted SWL is moderately correlated with the self-reported SWL (r = 0.36, p < 0.01), indicating that language-based assessment can constitute valid SWL measures; the machine-assessed affect scores resemble those reported in a previous experimental study; and the machine-predicted subjective well-being profile can also reflect other psychological traits like depression (r = 0.24, p < 0.01). This study provides important insights for psychological prediction using multiple, machine-assessed components and longitudinal or dense psychological assessment using social media language.

Building a profile of subjective well-being for social media users

PubMed Central

Kosinski, Michal; Stillwell, David; Davidson, Robert L.

2017-01-01

Subjective well-being includes ‘affect’ and ‘satisfaction with life’ (SWL). This study proposes a unified approach to construct a profile of subjective well-being based on social media language in Facebook status updates. We apply sentiment analysis to generate users’ affect scores, and train a random forest model to predict SWL using affect scores and other language features of the status updates. Results show that: the computer-selected features resemble the key predictors of SWL as identified in early studies; the machine-predicted SWL is moderately correlated with the self-reported SWL (r = 0.36, p < 0.01), indicating that language-based assessment can constitute valid SWL measures; the machine-assessed affect scores resemble those reported in a previous experimental study; and the machine-predicted subjective well-being profile can also reflect other psychological traits like depression (r = 0.24, p < 0.01). This study provides important insights for psychological prediction using multiple, machine-assessed components and longitudinal or dense psychological assessment using social media language. PMID:29135991

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mou, J.I.; King, C.

The focus of this study is to develop a sensor fused process modeling and control methodology to model, assess, and then enhance the performance of a hexapod machine for precision product realization. Deterministic modeling technique was used to derive models for machine performance assessment and enhancement. Sensor fusion methodology was adopted to identify the parameters of the derived models. Empirical models and computational algorithms were also derived and implemented to model, assess, and then enhance the machine performance. The developed sensor fusion algorithms can be implemented on a PC-based open architecture controller to receive information from various sensors, assess themore » status of the process, determine the proper action, and deliver the command to actuators for task execution. This will enhance a hexapod machine`s capability to produce workpieces within the imposed dimensional tolerances.« less

Design and finite element analysis of micro punch CNC machine modeling for medical devices

NASA Astrophysics Data System (ADS)

Pranoto, Sigiet Haryo; Mahardika, Muslim

2018-03-01

Research on micromanufacturing has been conducted. Miniaturization and weight reduction of various industrial products continue to be developed, machines with high accuracy and good quality of machining results are needed recently. This research includes design and simulation of Micro Punch CNC Machine using Abaqus with pneumatic system. This article concern of modeling simulation of punching miniplate titanium with 0.6 MPa of pressure and 500 µm of thickness. This study explaining von misses stress, safety factor and displacement analysis while the machine had the load of punching. The result gives the reaction forced of punching is 0.5 MPa on punch tip and maximum displacement is 3.237 × 10-1 mm. The safety factor is over than 12, and considered it safe for manufacturing process.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Husain, Tausif; Hasan, Iftekhar; Sozer, Yilmaz

This paper presents the design considerations in cogging torque minimization in two types of transverse flux machines. The machines have a double stator-single rotor configuration with flux concentrating ferrite magnets. One of the machines has pole windings across each leg of an E-Core stator. Another machine has quasi-U-shaped stator cores and a ring winding. The flux in the stator back iron is transverse in both machines. Different methods of cogging torque minimization are investigated. Key methods of cogging torque minimization are identified and used as design variables for optimization using a design of experiments (DOE) based on the Taguchi method.more » A three-level DOE is performed to reach an optimum solution with minimum simulations. Finite element analysis is used to study the different effects. Two prototypes are being fabricated for experimental verification.« less

Thermal Error Test and Intelligent Modeling Research on the Spindle of High Speed CNC Machine Tools

NASA Astrophysics Data System (ADS)

Luo, Zhonghui; Peng, Bin; Xiao, Qijun; Bai, Lu

2018-03-01

Thermal error is the main factor affecting the accuracy of precision machining. Through experiments, this paper studies the thermal error test and intelligent modeling for the spindle of vertical high speed CNC machine tools in respect of current research focuses on thermal error of machine tool. Several testing devices for thermal error are designed, of which 7 temperature sensors are used to measure the temperature of machine tool spindle system and 2 displacement sensors are used to detect the thermal error displacement. A thermal error compensation model, which has a good ability in inversion prediction, is established by applying the principal component analysis technology, optimizing the temperature measuring points, extracting the characteristic values closely associated with the thermal error displacement, and using the artificial neural network technology.

Computational dynamics of soft machines

NASA Astrophysics Data System (ADS)

Hu, Haiyan; Tian, Qiang; Liu, Cheng

2017-06-01

Soft machine refers to a kind of mechanical system made of soft materials to complete sophisticated missions, such as handling a fragile object and crawling along a narrow tunnel corner, under low cost control and actuation. Hence, soft machines have raised great challenges to computational dynamics. In this review article, recent studies of the authors on the dynamic modeling, numerical simulation, and experimental validation of soft machines are summarized in the framework of multibody system dynamics. The dynamic modeling approaches are presented first for the geometric nonlinearities of coupled overall motions and large deformations of a soft component, the physical nonlinearities of a soft component made of hyperelastic or elastoplastic materials, and the frictional contacts/impacts of soft components, respectively. Then the computation approach is outlined for the dynamic simulation of soft machines governed by a set of differential-algebraic equations of very high dimensions, with an emphasis on the efficient computations of the nonlinear elastic force vector of finite elements. The validations of the proposed approaches are given via three case studies, including the locomotion of a soft quadrupedal robot, the spinning deployment of a solar sail of a spacecraft, and the deployment of a mesh reflector of a satellite antenna, as well as the corresponding experimental studies. Finally, some remarks are made for future studies.

A Novel Vaping Machine Dedicated to Fully Controlling the Generation of E-Cigarette Emissions

PubMed Central

Soulet, Sébastien; Pairaud, Charly; Lalo, Hélène

2017-01-01

The accurate study of aerosol composition and nicotine release by electronic cigarettes is a major issue. In order to fully and correctly characterize aerosol, emission generation has to be completely mastered. This study describes an original vaping machine named Universal System for Analysis of Vaping (U-SAV), dedicated to vaping product study, enabling the control and real-time monitoring of applied flow rate and power. Repeatability and stability of the machine are demonstrated on flow rate, power regulation and e-liquid consumption. The emission protocol used to characterize the vaping machine is based on the AFNOR-XP-D90-300-3 standard (15 W power, 1 Ω atomizer resistance, 100 puffs collected per session, 1.1 L/min airflow rate). Each of the parameters has been verified with two standardized liquids by studying mass variations, power regulation and flow rate stability. U-SAV presents the required and necessary stability for the full control of emission generation. The U-SAV is recognised by the French association for standardization (AFNOR), European Committee for Standardization (CEN) and International Standards Organisation (ISO) as a vaping machine. It can be used to highlight the influence of the e-liquid composition, user behaviour and nature of the device, on the e-liquid consumption and aerosol composition. PMID:29036888

A Novel Vaping Machine Dedicated to Fully Controlling the Generation of E-Cigarette Emissions.

PubMed

Soulet, Sébastien; Pairaud, Charly; Lalo, Hélène

2017-10-14

The accurate study of aerosol composition and nicotine release by electronic cigarettes is a major issue. In order to fully and correctly characterize aerosol, emission generation has to be completely mastered. This study describes an original vaping machine named Universal System for Analysis of Vaping (U-SAV), dedicated to vaping product study, enabling the control and real-time monitoring of applied flow rate and power. Repeatability and stability of the machine are demonstrated on flow rate, power regulation and e-liquid consumption. The emission protocol used to characterize the vaping machine is based on the AFNOR-XP-D90-300-3 standard (15 W power, 1 Ω atomizer resistance, 100 puffs collected per session, 1.1 L/min airflow rate). Each of the parameters has been verified with two standardized liquids by studying mass variations, power regulation and flow rate stability. U-SAV presents the required and necessary stability for the full control of emission generation. The U-SAV is recognised by the French association for standardization (AFNOR), European Committee for Standardization (CEN) and International Standards Organisation (ISO) as a vaping machine. It can be used to highlight the influence of the e-liquid composition, user behaviour and nature of the device, on the e-liquid consumption and aerosol composition.

Work stress of women in sewing machine operation.

PubMed

Nag, A; Desai, H; Nag, P K

1992-06-01

The study examined the work stresses of 107 women who were engaged in sewing machine operation in small garment manufacturing units. Of the three types of sewing machines (motor-operated, full and half shuttle foot-operated), 74% of the machines were foot-operated, where throttle action of the lower limb is required to move the shuttle of the machine. The motor-operated machines were faster than the foot-operated machines. The short cycle sewing work involves repetitive action of hand and feet. The women had to maintain a constant seated position on a stool without backrest and the body inclined forward. Long-term sewing work had a cumulative load on the musculo-skeletal structures, including the vertebral column and reflected in the form of high prevalence of discomfort and pain in different body parts. About 68% of the women complained of back pain, among whom 35% reported a persistent low back pain. Common sewing work accident is piercing of the needle through the fingers, particularly the right forefingers. Unsatisfactory man-machine incompatibility, work posture and fatigue, improper coordination of eye, leg and hand are the major problems of the operators. The design mis-match of the work place may be significantly improved by taking women's anthropometric dimensions in modifying the workplace, i.e. the seat surface, seat height, work height, backrest, etc.

Evaluating the Potential Health and Revenue Outcomes of a 100% Healthy Vending Machine Nutrition Policy at a Large Agency in Los Angeles County, 2013-2015.

PubMed

Wickramasekaran, Ranjana N; Robles, Brenda; Dewey, George; Kuo, Tony

Healthy vending machine policies are viewed as a promising strategy for combating the growing obesity epidemic in the United States. Few studies have evaluated the short- and intermediate-term outcomes of healthy vending policies, especially for interventions that require 100% healthy products to be stocked. To evaluate the potential impact of a 100% healthy vending machine nutrition policy. The vendor's quarterly revenue, product sales records, and nutritional information data from 359 unique vending machines were used to conduct a baseline and follow-up policy analysis. County of Los Angeles facilities, 2013-2015. Vending machines in facilities located across Los Angeles County. A healthy vending machine policy executed in 2013 that required 100% of all products sold in contracted machines meet specified nutrition standards. Policy adherence; average number of calories, sugar, and sodium in food products sold; revenue change. Policy adherence increased for snacks and beverages sold by the vending machines by 89% and 98%, respectively. Average snack and beverage revenues decreased by 37% and 34%, respectively, during the sampled period. Although a 100% healthy vending policy represents a promising strategy for encouraging purchases of healthier foods, steps should be taken to counteract potential revenue changes when planning its implementation.

Extracting laboratory test information from biomedical text

PubMed Central

Kang, Yanna Shen; Kayaalp, Mehmet

2013-01-01

Background: No previous study reported the efficacy of current natural language processing (NLP) methods for extracting laboratory test information from narrative documents. This study investigates the pathology informatics question of how accurately such information can be extracted from text with the current tools and techniques, especially machine learning and symbolic NLP methods. The study data came from a text corpus maintained by the U.S. Food and Drug Administration, containing a rich set of information on laboratory tests and test devices. Methods: The authors developed a symbolic information extraction (SIE) system to extract device and test specific information about four types of laboratory test entities: Specimens, analytes, units of measures and detection limits. They compared the performance of SIE and three prominent machine learning based NLP systems, LingPipe, GATE and BANNER, each implementing a distinct supervised machine learning method, hidden Markov models, support vector machines and conditional random fields, respectively. Results: Machine learning systems recognized laboratory test entities with moderately high recall, but low precision rates. Their recall rates were relatively higher when the number of distinct entity values (e.g., the spectrum of specimens) was very limited or when lexical morphology of the entity was distinctive (as in units of measures), yet SIE outperformed them with statistically significant margins on extracting specimen, analyte and detection limit information in both precision and F-measure. Its high recall performance was statistically significant on analyte information extraction. Conclusions: Despite its shortcomings against machine learning methods, a well-tailored symbolic system may better discern relevancy among a pile of information of the same type and may outperform a machine learning system by tapping into lexically non-local contextual information such as the document structure. PMID:24083058

Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study

PubMed Central

Burgansky-Eliash, Zvia; Wollstein, Gadi; Chu, Tianjiao; Ramsey, Joseph D.; Glymour, Clark; Noecker, Robert J.; Ishikawa, Hiroshi; Schuman, Joel S.

2007-01-01

Purpose Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma detection. Methods Forty-seven patients with glaucoma (47 eyes) and 42 healthy subjects (42 eyes) were included in this cross-sectional study. Of the glaucoma patients, 27 had early disease (visual field mean deviation [MD] ≥ −6 dB) and 20 had advanced glaucoma (MD < −6 dB). Machine-learning classifiers were trained to discriminate between glaucomatous and healthy eyes using parameters derived from OCT output. The classifiers were trained with all 38 parameters as well as with only 8 parameters that correlated best with the visual field MD. Five classifiers were tested: linear discriminant analysis, support vector machine, recursive partitioning and regression tree, generalized linear model, and generalized additive model. For the last two classifiers, a backward feature selection was used to find the minimal number of parameters that resulted in the best and most simple prediction. The cross-validated receiver operating characteristic (ROC) curve and accuracies were calculated. Results The largest area under the ROC curve (AROC) for glaucoma detection was achieved with the support vector machine using eight parameters (0.981). The sensitivity at 80% and 95% specificity was 97.9% and 92.5%, respectively. This classifier also performed best when judged by cross-validated accuracy (0.966). The best classification between early glaucoma and advanced glaucoma was obtained with the generalized additive model using only three parameters (AROC = 0.854). Conclusions Automated machine classifiers of OCT data might be useful for enhancing the utility of this technology for detecting glaucomatous abnormality. PMID:16249492

Machine learning approaches to the social determinants of health in the health and retirement study.

PubMed

Seligman, Benjamin; Tuljapurkar, Shripad; Rehkopf, David

2018-04-01

Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study. A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks. All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data ( R 2 between 0.4-0.6, versus <0.3 for all others). Across machine learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10,000, number of children ever born, African-American race, and gender. Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.

Prevalence and associated factors of work related musculoskeletal disorders among commercial milling machine operators in South-Eastern Nigerian markets.

PubMed

Ojukwu, Chidiebele Petronilla; Anyanwu, Godson Emeka; Nwabueze, Augustine Chijindu; Anekwu, Emelie Morris; Chukwu, Sylvester Caesar

2017-01-01

Milling machine operators perform physically demanding tasks that can lead to work related musculoskeletal disorders (WRMSDs), but literature on WRMSDs among milling machine operators is scarce. Knowledge of prevalence and risk factors of WRMSDs can be an appropriate base for planning and implementing ergonomics intervention programs in the workplace. This study aimed to determine the prevalence, pattern and associated factors of WRMSDs among commercial milling machine operators in Enugu, Nigeria. This cross-sectional survey involved 148 commercial milling machine operators (74 hand-operated milling machine operators (HOMMO) and 74 electrically-operated milling machine operators (EOMMO)), within the age range of 18-65 years, who were conveniently selected from four markets in Enugu, Nigeria. A standard Nordic questionnaire was used to assess the prevalence of WRMSDs among the participants. Data were summarized using descriptive statistics. There was a significant difference (p = 0.001) related to prevalence of WRMSDs between HOMMOs (77%) and EOMMOs (50%). All body parts were affected in both groups and shoulders (85.1%) and lower back (46%) had the highest percentage of prevalence. Working in awkward and same postures, working with injury, poor workplace design, repetition of tasks, vibratory working equipments, reduced rest, high job demand and heavy lifting were significantly associated with the prevalence of WRMSDs. WRMSDs are prevalent among commercial milling machine operators with higher occurrence in HOMMOs. Ergonomic interventions, including the re-design of milling machines and appropriate work posture education of machine operators are recommended in the milling industry.

Investigations of Effect of Rotary EDM Electrode on Machining Performance of Al6061 Alloy

NASA Astrophysics Data System (ADS)

Robinson Smart, D. S.; Jenish Smart, Joses; Periasamy, C.; Ratna Kumar, P. S. Samuel

2018-04-01

Electric Discharge Machining is an essential process which is being used for machining desired shape using electrical discharges which creates sparks. There will be electrodes subjected to electric voltage and which are separated by a dielectric liquid. Removing of material will be due to the continuous and rapid current discharges between two electrodes.. The spark is very carefully controlled and localized so that it only affects the surface of the material. Usually in order to prevent the defects which are arising due to the conventional machining, the Electric Discharge Machining (EDM) machining is preferred. Also intricate and complicated shapes can be machined effectively by use of Electric Discharge Machining (EDM). The EDM process usually does not affect the heat treat below the surface. This research work focus on the design and fabrication of rotary EDM tool for machining Al6061alloy and investigation of effect of rotary tool on surface finish, material removal rate and tool wear rate. Also the effect of machining parameters of EDM such as pulse on & off time, current on material Removal Rate (MRR), Surface Roughness (SR) and Electrode wear rate (EWR) have studied. Al6061 alloy can be used for marine and offshore applications by reinforcing some other elements. The investigations have revealed that MRR (material removal rate), surface roughness (Ra) have been improved with the reduction in the tool wear rate (TWR) when the tool is rotating instead of stationary. It was clear that as rotary speed of the tool is increasing the material removal rate is increasing with the reduction of surface finish and tool wear rate.

Experimental Study in Taguchi Method on Surface Quality Predication of HSM

NASA Astrophysics Data System (ADS)

Ji, Yan; Li, Yueen

2018-05-01

Based on the study of ball milling mechanism and machining surface formation mechanism, the formation of high speed ball-end milling surface is a time-varying and cumulative Thermos-mechanical coupling process. The nature of this problem is that the uneven stress field and temperature field affect the machined surface Process, the performance of the processing parameters in the processing interaction in the elastic-plastic materials produced by the elastic recovery and plastic deformation. The surface quality of machining surface is characterized by multivariable nonlinear system. It is still an indispensable and effective method to study the surface quality of high speed ball milling by experiments.

[Card-based age control mechanisms at tobacco vending machines. Effect and consequences].

PubMed

Schneider, S; Meyer, C; Löber, S; Röhrig, S; Solle, D

2010-02-01

Until recently, 700,000 tobacco vending machines provided uncontrolled access to cigarettes for children and adolescents in Germany. On January 1, 2007, a card-based electronic locking device was attached to all tobacco vending machines to prevent the purchase of cigarettes by children and adolescents under 16. Starting in 2009, only persons older than 18 are able to buy cigarettes from tobacco vending machines. The aim of the present investigation (SToP Study: "Sources of Tobacco for Pupils" Study) was to assess changes in the number of tobacco vending machines after the introduction of these new technical devices (supplier's reaction). In addition, the ways smoking adolescents make purchases were assessed (consumer's reaction). We registered and mapped the total number of tobacco points of sale (tobacco POS) before and after the introduction of the card-based electronic locking device in two selected districts of the city of Cologne. Furthermore, pupils from local schools (response rate: 83%) were asked about their tobacco consumption and ways of purchase using a questionnaire. Results indicated that in the area investigated the total number of tobacco POSs decreased from 315 in 2005 to 277 in 2007. The rates of decrease were 48% for outdoor vending machines and 8% for indoor vending machines. Adolescents reported circumventing the card-based electronic locking devices (e.g., by using cards from older friends) and using other tobacco POSs (especially newspaper kiosks) or relying on their social network (mainly friends). The decreasing number of tobacco vending machines has not had a significant impact on cigarette acquisition by adolescent smokers as they tend to circumvent the newly introduced security measures.

Machining of a bioactive nanocomposite orthopedic fixation device.

PubMed

Sparnell, Amie; Aniket; El-Ghannam, Ahmed

2012-08-01

Bioactive ceramics bond to bone and enhance bone formation. However, they have poor mechanical properties which restrict their machinability as well as their application as load bearing implants. The goal of this study was to machine bioactive fixation screws using a silica-calcium phosphate nanocomposite (SCPC50). The effect of compact pressure, holding time, and thermal treatment on the microstructure, machinability, and mechanical properties of SCPC50 cylinders were investigated. Samples prepared by powder metallurgy technique at compact pressure range of 100-300 MPa and treated at 900°C/1 h scored a poor machinability rating of (1/5) due to the significant formation of amorphous silicate phase at the grain boundaries. On the other hand, lowering of compact pressure and sintering temperature to 30 MPa/3 h and 700°C/2 h, respectively, minimized the formation of the amorphous phase and raised the machinability rating to (5/5). The modulus of elasticity and ultimate strength of machinable SCPC50 were 10.8 ± 2.0 GPa and 72.8 ± 22.8 MPa, respectively, which are comparable to the corresponding values for adult human cortical bone. qRT-PCR analyses showed that bone cells attached to SCPC50 significantly upregulated osteocalcin mRNA expression as compared to the cells on Ti-6Al-4V. Moreover, cells attached to SCPC50 produced mineralized bone-like tissue within 8 days. On the other hand, cells attached to Ti-6Al-4V failed to produce bone mineral under the same experimental conditions. Results of the study suggest that machinable SCPC50 has the potential to serve as an attractive new material for orthopedic fixation devices. Copyright © 2012 Wiley Periodicals, Inc.

Machining of Molybdenum by EDM-EP and EDC Processes

NASA Astrophysics Data System (ADS)

Wu, K. L.; Chen, H. J.; Lee, H. M.; Lo, J. S.

2017-12-01

Molybdenum metal (Mo) can be machined with conventional tools and equipment, however, its refractory propertytends to chip when being machined. In this study, the nonconventional processes of electrical discharge machining (EDM) and electro-polishing (EP) have been conducted to investigate the machining of Mo metal and fabrication of Mo grid. Satisfactory surface quality was obtained using appropriate EDM parameters of Ip ≦ 3A and Ton < 80μs at a constant pulse interval of 100μs. The finished Mometal has accomplished by selecting appropriate EP parameters such as electrolyte flow rate of 0.42m/s under EP voltage of 50V and flush time of 20 sec to remove the recast layer and craters on the surface of Mo metal. The surface roughness of machined Mo metal can be improved from Ra of 0.93μm (Rmax = 8.51μm) to 0.23μm (Rmax = 1.48μm). Machined Mo metal surface, when used as grid component in electron gun, needs to be modified by coating materials with high work function, such as silicon carbide (SiC). The main purpose of this study is to explore the electrical discharge coating (EDC) process for coating the SiC layer on EDMed Mo metal. Experimental results proved that the appropriate parameters of Ip = 5A and Ton = 50μs at Toff = 10μs can obtain the deposit with about 60μm thickness. The major phase of deposit on machined Mo surface was SiC ceramic, while the minor phases included MoSi2 and/or SiO2 with the presence of free Si due to improper discharging parameters and the use of silicone oil as the dielectric fluid.

School vending machine purchasing behavior: results from the 2005 YouthStyles survey.

PubMed

Thompson, Olivia M; Yaroch, Amy L; Moser, Richard P; Finney Rutten, Lila J; Agurs-Collins, Tanya

2010-05-01

Competitive foods are often available in school vending machines. Providing youth with access to school vending machines, and thus competitive foods, is of concern, considering the continued high prevalence of childhood obesity: competitive foods tend to be energy dense and nutrient poor and can contribute to increased energy intake in children and adolescents. To evaluate the relationship between school vending machine purchasing behavior and school vending machine access and individual-level dietary characteristics, we used population-level YouthStyles 2005 survey data to compare nutrition-related policy and behavioral characteristics by the number of weekly vending machine purchases made by public school children and adolescents (N = 869). Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were computed using age- and race/ethnicity-adjusted logistic regression models that were weighted on age and sex of child, annual household income, head of household age, and race/ethnicity of the adult in study. Data were collected in 2005 and analyzed in 2008. Compared to participants who did not purchase from a vending machine, participants who purchased >or=3 days/week were more likely to (1) have unrestricted access to a school vending machine (OR = 1.71; 95% CI = 1.13-2.59); (2) consume regular soda and chocolate candy >or=1 time/day (OR = 3.21; 95% CI = 1.87-5.51 and OR = 2.71; 95% CI = 1.34-5.46, respectively); and (3) purchase pizza or fried foods from a school cafeteria >or=1 day/week (OR = 5.05; 95% CI = 3.10-8.22). Future studies are needed to establish the contribution that the school-nutrition environment makes on overall youth dietary intake behavior, paying special attention to health disparities between whites and nonwhites.

Model-based machine learning.

PubMed

Bishop, Christopher M

2013-02-13

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

Feasibility of a portable pedal exercise machine for reducing sedentary time in the workplace.

PubMed

Carr, Lucas J; Walaska, Kristen A; Marcus, Bess H

2012-05-01

Sedentary time is independently associated with an increased risk of metabolic disease. Worksite interventions designed to decrease sedentary time may serve to improve employee health. The purpose of this study is to test the feasibility and use of a pedal exercise machine for reducing workplace sedentary time. Eighteen full-time employees (mean age+SD 40.2+10.7 years; 88% female) working in sedentary occupations were recruited for participation. Demographic and anthropometric data were collected at baseline and 4 weeks. Participants were provided access to a pedal exercise machine for 4 weeks at work. Use of the device was measured objectively by exercise tracking software, which monitors pedal activity and provides the user real-time feedback (eg, speed, time, distance, calories). At 4 weeks, participants completed a feasibility questionnaire. Participants reported sitting 83% of their working days. Participants used the pedal machines an average of 12.2+6.6 out of a possible 20 working days and pedalled an average of 23.4+20.4 min each day used. Feasibility data indicate that participants found the machines feasible for use at work. Participants also reported sedentary time at work decreased due to the machine. Findings from this study suggest that this pedal machine may be a feasible tool for reducing sedentary time while at work. These findings hold public health significance due to the growing number of sedentary jobs in the USA and the potential of the device for use in large-scale worksite health programmes.

Model-based machine learning

PubMed Central

Bishop, Christopher M.

2013-01-01

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis.

PubMed

Allyn, Jérôme; Allou, Nicolas; Augustin, Pascal; Philip, Ivan; Martinet, Olivier; Belghiti, Myriem; Provenchere, Sophie; Montravers, Philippe; Ferdynus, Cyril

2017-01-01

The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755-0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691-0.783) and 0.742 (0.698-0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold. According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction.

In vivo and in vitro performance of a China-made hemodialysis machine: a multi-center prospective controlled study.

PubMed

Wang, Yong; Chen, Xiang-Mei; Cai, Guang-Yan; Li, Wen-Ge; Zhang, Ai-Hua; Hao, Li-Rong; Shi, Ming; Wang, Rong; Jiang, Hong-Li; Luo, Hui-Min; Zhang, Dong; Sun, Xue-Feng

2017-08-02

To evaluate the in vivo and in vitro performance of a China-made dialysis machine (SWS-4000). This was a multi-center prospective controlled study consisting of both long-term in vitro evaluations and cross-over in vivo tests in 132 patients. The China-made SWS-4000 dialysis machine was compared with a German-made dialysis machine (Fresenius 4008) with regard to Kt/V values, URR values, and dialysis-related adverse reactions in patients on maintenance hemodialysis, as well as the ultrafiltration rate, the concentration of electrolytes in the proportioned dialysate, the rate of heparin injection, the flow rate of the blood pump, and the rate of malfunction. The Kt/V and URR values at the 1st and 4th weeks of dialysis as well as the incidence of adverse effects did not differ between the two groups in cross-over in vivo tests (P > 0.05). There were no significant differences between the two groups in the error values of the ultrafiltration rate, the rate of heparin injection or the concentrations of electrolytes in the proportioned dialysate at different time points under different parameter settings. At weeks 2 and 24, with the flow rate of the blood pump set at 300 mL/min, the actual error of the SWS-4000 dialysis machine was significantly higher than that of the Fresenius 4008 dialysis machine (P < 0.05), but there was no significant difference at other time points or under other settings (P > 0.05). The malfunction rate was higher in the SWS-4000 group than in the Fresenius 4008 group (P < 0.05). The in vivo performance of the SWS-4000 dialysis machine is roughly comparable to that of the Fresenius 4008 dialysis machine; however, the malfunction rate of the former is higher than that of the latter in in vitro tests. The stability and long-term accuracy of the SWS-4000 dialysis machine remain to be improved.

The food and beverage vending environment in health care facilities participating in the healthy eating, active communities program.

PubMed

Lawrence, Sally; Boyle, Maria; Craypo, Lisa; Samuels, Sarah

2009-06-01

Little has been done to ensure that the foods sold within health care facilities promote healthy lifestyles. Policies to improve school nutrition environments can serve as models for health care organizations. This study was designed to assess the healthfulness of foods sold in health care facility vending machines as well as how health care organizations are using policies to create healthy food environments. Food and beverage assessments were conducted in 19 California health care facilities that serve children in the Healthy Eating, Active Communities sites. Items sold in vending machines were inventoried at each facility and interviews conducted for information on vending policies. Analyses examined the types of products sold and the healthfulness of these products. Ninety-six vending machines were observed in 15 (79%) of the facilities. Hospitals averaged 9.3 vending machines per facility compared with 3 vending machines per health department and 1.4 per clinic. Sodas comprised the greatest percentage of all beverages offered for sale: 30% in hospital vending machines and 38% in clinic vending machines. Water (20%) was the most prevalent in health departments. Candy comprised the greatest percentage of all foods offered in vending machines: 31% in clinics, 24% in hospitals, and 20% in health department facilities. Across all facilities, 75% of beverages and 81% of foods sold in vending machines did not adhere to the California school nutrition standards (Senate Bill 12). Nine (47%) of the health care facilities had adopted, or were in the process of adopting, policies that set nutrition standards for vending machines. According to the California school nutrition standards, the majority of items found in the vending machines in participating health care facilities were unhealthy. Consumption of sweetened beverages and high-energy-density foods has been linked to increased prevalence of obesity. Some health care facilities are developing policies that set nutrition standards for vending machines. These policies could be effective in increasing access to healthy foods and beverages in institutional settings.

Language Acquisition and Machine Learning.

DTIC Science & Technology

1986-02-01

machine learning and examine its implications for computational models of language acquisition. As a framework for understanding this research, the authors propose four component tasks involved in learning from experience-aggregation, clustering, characterization, and storage. They then consider four common problems studied by machine learning researchers-learning from examples, heuristics learning, conceptual clustering, and learning macro-operators-describing each in terms of our framework. After this, they turn to the problem of grammar

Photoelectron studies of machined brass surfaces

NASA Astrophysics Data System (ADS)

Potts, A. W.; Merrison, J. P.; Tournas, A. D.; Yacoot, A.

UV photoelectron spectroscopy has been used to determine the surface composition of machined brass. The results show a considerable change between the photoelectron surface composition and the bulk composition of the same sample determined by energy-dispersive X-ray fluorescence. On the surface the lead composition is increased by ˜900 G. This is consistent with the important part that lead is believed to play in improving the machinability of this alloy.

A Comparative Study of Teacher Education Institutions and Machine Tool Manufacturers to Determine Course Content for a Machine Tool Maintenance Course in the Woodworking Area.

ERIC Educational Resources Information Center

Polette, Douglas Lee

To determine what type of maintenance training the prospective industrial arts teacher should receive in the woodworking area and how this information should be taught, a research instrument was constructed using information obtained from a review of relevant literature. Specific data on machine tool maintenance was gathered by the use of two…

Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data

PubMed Central

Perryman, Alexander L.; Stratton, Thomas P.; Ekins, Sean; Freundlich, Joel S.

2015-01-01

Purpose Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. Methods Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). Results “Pruning” out the moderately unstable/moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 hour. Conclusions Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources. PMID:26415647

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

PubMed

Perryman, Alexander L; Stratton, Thomas P; Ekins, Sean; Freundlich, Joel S

2016-02-01

Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). "Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h. Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

Virtual Planning, Control, and Machining for a Modular-Based Automated Factory Operation in an Augmented Reality Environment

PubMed Central

Pai, Yun Suen; Yap, Hwa Jen; Md Dawal, Siti Zawiah; Ramesh, S.; Phoon, Sin Ye

2016-01-01

This study presents a modular-based implementation of augmented reality to provide an immersive experience in learning or teaching the planning phase, control system, and machining parameters of a fully automated work cell. The architecture of the system consists of three code modules that can operate independently or combined to create a complete system that is able to guide engineers from the layout planning phase to the prototyping of the final product. The layout planning module determines the best possible arrangement in a layout for the placement of various machines, in this case a conveyor belt for transportation, a robot arm for pick-and-place operations, and a computer numerical control milling machine to generate the final prototype. The robotic arm module simulates the pick-and-place operation offline from the conveyor belt to a computer numerical control (CNC) machine utilising collision detection and inverse kinematics. Finally, the CNC module performs virtual machining based on the Uniform Space Decomposition method and axis aligned bounding box collision detection. The conducted case study revealed that given the situation, a semi-circle shaped arrangement is desirable, whereas the pick-and-place system and the final generated G-code produced the highest deviation of 3.83 mm and 5.8 mm respectively. PMID:27271840

Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine

NASA Astrophysics Data System (ADS)

Imani, Moslem; Kao, Huan-Chin; Lan, Wen-Hau; Kuo, Chung-Yen

2018-02-01

The analysis and the prediction of sea level fluctuations are core requirements of marine meteorology and operational oceanography. Estimates of sea level with hours-to-days warning times are especially important for low-lying regions and coastal zone management. The primary purpose of this study is to examine the applicability and capability of extreme learning machine (ELM) and relevance vector machine (RVM) models for predicting sea level variations and compare their performances with powerful machine learning methods, namely, support vector machine (SVM) and radial basis function (RBF) models. The input dataset from the period of January 2004 to May 2011 used in the study was obtained from the Dongshi tide gauge station in Chiayi, Taiwan. Results showed that the ELM and RVM models outperformed the other methods. The performance of the RVM approach was superior in predicting the daily sea level time series given the minimum root mean square error of 34.73 mm and the maximum determination coefficient of 0.93 (R2) during the testing periods. Furthermore, the obtained results were in close agreement with the original tide-gauge data, which indicates that RVM approach is a promising alternative method for time series prediction and could be successfully used for daily sea level forecasts.

Design and market considerations for axial flux superconducting electric machine design

NASA Astrophysics Data System (ADS)

Ainslie, M. D.; George, A.; Shaw, R.; Dawson, L.; Winfield, A.; Steketee, M.; Stockley, S.

2014-05-01

In this paper, the authors investigate a number of design and market considerations for an axial flux superconducting electric machine design that uses high temperature superconductors. The axial flux machine design is assumed to utilise high temperature superconductors in both wire (stator winding) and bulk (rotor field) forms, to operate over a temperature range of 65-77 K, and to have a power output in the range from 10s of kW up to 1 MW (typical for axial flux machines), with approximately 2-3 T as the peak trapped field in the bulk superconductors. The authors firstly investigate the applicability of this type of machine as a generator in small- and medium-sized wind turbines, including the current and forecasted market and pricing for conventional turbines. Next, a study is also carried out on the machine's applicability as an in-wheel hub motor for electric vehicles. Some recommendations for future applications are made based on the outcome of these two studies. Finally, the cost of YBCO-based superconducting (2G HTS) wire is analysed with respect to competing wire technologies and compared with current conventional material costs and current wire costs for both 1G and 2G HTS are still too great to be economically feasible for such superconducting devices.

Using machine learning algorithms to guide rehabilitation planning for home care clients.

PubMed

Zhu, Mu; Zhang, Zhanyang; Hirdes, John P; Stolee, Paul

2007-12-20

Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms - Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) - to guide rehabilitation planning for home care clients. This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP. The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP. Machine learning algorithms achieved superior predictions than the current protocol. Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.

Virtual Planning, Control, and Machining for a Modular-Based Automated Factory Operation in an Augmented Reality Environment.

PubMed

Pai, Yun Suen; Yap, Hwa Jen; Md Dawal, Siti Zawiah; Ramesh, S; Phoon, Sin Ye

2016-06-07

This study presents a modular-based implementation of augmented reality to provide an immersive experience in learning or teaching the planning phase, control system, and machining parameters of a fully automated work cell. The architecture of the system consists of three code modules that can operate independently or combined to create a complete system that is able to guide engineers from the layout planning phase to the prototyping of the final product. The layout planning module determines the best possible arrangement in a layout for the placement of various machines, in this case a conveyor belt for transportation, a robot arm for pick-and-place operations, and a computer numerical control milling machine to generate the final prototype. The robotic arm module simulates the pick-and-place operation offline from the conveyor belt to a computer numerical control (CNC) machine utilising collision detection and inverse kinematics. Finally, the CNC module performs virtual machining based on the Uniform Space Decomposition method and axis aligned bounding box collision detection. The conducted case study revealed that given the situation, a semi-circle shaped arrangement is desirable, whereas the pick-and-place system and the final generated G-code produced the highest deviation of 3.83 mm and 5.8 mm respectively.

Virtual Planning, Control, and Machining for a Modular-Based Automated Factory Operation in an Augmented Reality Environment

NASA Astrophysics Data System (ADS)

Pai, Yun Suen; Yap, Hwa Jen; Md Dawal, Siti Zawiah; Ramesh, S.; Phoon, Sin Ye

2016-06-01

This study presents a modular-based implementation of augmented reality to provide an immersive experience in learning or teaching the planning phase, control system, and machining parameters of a fully automated work cell. The architecture of the system consists of three code modules that can operate independently or combined to create a complete system that is able to guide engineers from the layout planning phase to the prototyping of the final product. The layout planning module determines the best possible arrangement in a layout for the placement of various machines, in this case a conveyor belt for transportation, a robot arm for pick-and-place operations, and a computer numerical control milling machine to generate the final prototype. The robotic arm module simulates the pick-and-place operation offline from the conveyor belt to a computer numerical control (CNC) machine utilising collision detection and inverse kinematics. Finally, the CNC module performs virtual machining based on the Uniform Space Decomposition method and axis aligned bounding box collision detection. The conducted case study revealed that given the situation, a semi-circle shaped arrangement is desirable, whereas the pick-and-place system and the final generated G-code produced the highest deviation of 3.83 mm and 5.8 mm respectively.

Effect of microstructure on the breakage of tin bronze machining chips during pulverization via jet milling

NASA Astrophysics Data System (ADS)

Afshari, Elham; Ghambari, Mohammad; Farhangi, Hasan

2016-11-01

In this study, jet milling was used to recycle tin bronze machining chips into powder. The main purpose of this study was to assess the effect of the microstructure of tin bronze machining chips on their breakage behavior. An experimental target jet mill was used to pulverize machining chips of three different tin bronze alloys containing 7wt%, 10wt%, and 12wt% of tin. Optical and electron microscopy, as well as sieve analysis, were used to follow the trend of pulverization. Each alloy exhibited a distinct rate of size reduction, particle size distribution, and fracture surface appearance. The results showed that the degree of pulverization substantially increased with increasing tin content. This behavior was attributed to the higher number of machining cracks as well as the increased volume fraction of brittle δ phase in the alloys with higher tin contents. The δ phase was observed to strongly influence the creation of machining cracks as well as the nucleation and propagation of cracks during jet milling. In addition, a direct relationship was observed between the mean δ-phase spacing and the mean size of the jet-milled product; i.e., a decrease in the δ-phase spacing resulted in smaller particles.

Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA.

PubMed

Deng, Li; Wang, Guohua; Yu, Suihuai

2016-01-01

In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm) were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method.

Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA

PubMed Central

Deng, Li; Wang, Guohua; Yu, Suihuai

2016-01-01

In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm) were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method. PMID:26884745

Full-band error control and crack-free surface fabrication techniques for ultra-precision fly cutting of large-aperture KDP crystals

NASA Astrophysics Data System (ADS)

Zhang, F. H.; Wang, S. F.; An, C. H.; Wang, J.; Xu, Q.

2017-06-01

Large-aperture potassium dihydrogen phosphate (KDP) crystals are widely used in the laser path of inertial confinement fusion (ICF) systems. The most common method of manufacturing half-meter KDP crystals is ultra-precision fly cutting. When processing KDP crystals by ultra-precision fly cutting, the dynamic characteristics of the fly cutting machine and fluctuations in the fly cutting environment are translated into surface errors at different spatial frequency bands. These machining errors should be suppressed effectively to guarantee that KDP crystals meet the full-band machining accuracy specified in the evaluation index. In this study, the anisotropic machinability of KDP crystals and the causes of typical surface errors in ultra-precision fly cutting of the material are investigated. The structures of the fly cutting machine and existing processing parameters are optimized to improve the machined surface quality. The findings are theoretically and practically important in the development of high-energy laser systems in China.

Improving machine operation management efficiency via improving the vehicle park structure and using the production operation information database

NASA Astrophysics Data System (ADS)

Koptev, V. Yu

2017-02-01

The work represents the results of studying basic interconnected criteria of separate equipment units of the transport network machines fleet, depending on production and mining factors to improve the transport systems management. Justifying the selection of a control system necessitates employing new methodologies and models, augmented with stability and transport flow criteria, accounting for mining work development dynamics on mining sites. A necessary condition is the accounting of technical and operating parameters related to vehicle operation. Modern open pit mining dispatching systems must include such kinds of the information database. An algorithm forming a machine fleet is presented based on multi-variation task solution in connection with defining reasonable operating features of a machine working as a part of a complex. Proposals cited in the work may apply to mining machines (drilling equipment, excavators) and construction equipment (bulldozers, cranes, pile-drivers), city transport and other types of production activities using machine fleet.

Binary pressure-sensitive paint measurements using miniaturised, colour, machine vision cameras

NASA Astrophysics Data System (ADS)

Quinn, Mark Kenneth

2018-05-01

Recent advances in machine vision technology and capability have led to machine vision cameras becoming applicable for scientific imaging. This study aims to demonstrate the applicability of machine vision colour cameras for the measurement of dual-component pressure-sensitive paint (PSP). The presence of a second luminophore component in the PSP mixture significantly reduces its inherent temperature sensitivity, increasing its applicability at low speeds. All of the devices tested are smaller than the cooled CCD cameras traditionally used and most are of significantly lower cost, thereby increasing the accessibility of such technology and techniques. Comparisons between three machine vision cameras, a three CCD camera, and a commercially available specialist PSP camera are made on a range of parameters, and a detailed PSP calibration is conducted in a static calibration chamber. The findings demonstrate that colour machine vision cameras can be used for quantitative, dual-component, pressure measurements. These results give rise to the possibility of performing on-board dual-component PSP measurements in wind tunnels or on real flight/road vehicles.

Initial planetary base construction techniques and machine implementation

NASA Technical Reports Server (NTRS)

Crockford, William W.

1987-01-01

Conceptual designs of (1) initial planetary base structures, and (2) an unmanned machine to perform the construction of these structures using materials local to the planet are presented. Rock melting is suggested as a possible technique to be used by the machine in fabricating roads, platforms, and interlocking bricks. Identification of problem areas in machine design and materials processing is accomplished. The feasibility of the designs is contingent upon favorable results of an analysis of the engineering behavior of the product materials. The analysis requires knowledge of several parameters for solution of the constitutive equations of the theory of elasticity. An initial collection of these parameters is presented which helps to define research needed to perform a realistic feasibility study. A qualitative approach to estimating power and mass lift requirements for the proposed machine is used which employs specifications of currently available equipment. An initial, unmanned mission scenario is discussed with emphasis on identifying uncompleted tasks and suggesting design considerations for vehicles and primitive structures which use the products of the machine processing.

Mesoplasticity approach to studies of the cutting mechanism in ultra-precision machining

NASA Astrophysics Data System (ADS)

Lee, Rongbin W. B.; Wang, Hao; To, Suet; Cheung, Chi Fai; Chan, Chang Yuen

2014-03-01

There have been various theoretical attempts by researchers worldwide to link up different scales of plasticity studies from the nano-, micro- and macro-scale of observation, based on molecular dynamics, crystal plasticity and continuum mechanics. Very few attempts, however, have been reported in ultra-precision machining studies. A mesoplasticity approach advocated by Lee and Yang is adopted by the authors and is successfully applied to studies of the micro-cutting mechanisms in ultra-precision machining. Traditionally, the shear angle in metal cutting, as well as the cutting force variation, can only be determined from cutting tests. In the pioneering work of the authors, the use of mesoplasticity theory enables prediction of the fluctuation of the shear angle and micro-cutting force, shear band formation, chip morphology in diamond turning and size effect in nano-indentation. These findings are verified by experiments. The mesoplasticity formulation opens up a new direction of studies to enable how the plastic behaviour of materials and their constitutive representations in deformation processing, such as machining can be predicted, assessed and deduced from the basic properties of the materials measurable at the microscale.

Study of the Productivity and Surface Quality of Hybrid EDM

NASA Astrophysics Data System (ADS)

Wankhade, Sandeepkumar Haribhau; Sharma, Sunil Bansilal

2016-01-01

The development of new, advanced engineering materials and the need for precise prototypes and low-volume production have made the electric discharge machining (EDM), an important manufacturing process to meet such demands. It is capable of machining geometrically complex and hard material components, that are precise and difficult-to-machine such as heat treated tool steels, composites, super alloys, ceramics, carbides etc. Conversely the low MRR limits its productivity. Abrasive water jet machine (AJM) tools are quick to setup and offer quick turn-around on the machine and could make parts out of virtually any material. They do not heat the material hence no heat affected zone and can make any intricate shape easily. The main advantages are flexibility, low heat production and ability to machine hard and brittle materials. Main disadvantages comprise the process produces a tapered cut and health hazards due to dry abrasives. To overcome the limitations and exploit the best of each of above processes; an attempt has been made to hybridize the processes of AJM and EDM. The appropriate abrasives routed with compressed air through the hollow electrode to construct the hybrid process i.e., abrasive jet electric discharge machining (AJEDM), the high speed abrasives could impinge on the machined surface to remove the recast layer caused by EDM process. The main process parameters were varied to explore their effects and experimental results show that AJEDM enhances the machining efficiency with better surface finish hence can fit the requirements of modern manufacturing applications.

High-pressure coolant effect on the surface integrity of machining titanium alloy Ti-6Al-4V: a review

NASA Astrophysics Data System (ADS)

Liu, Wentao; Liu, Zhanqiang

2018-03-01

Machinability improvement of titanium alloy Ti-6Al-4V is a challenging work in academic and industrial applications owing to its low thermal conductivity, low elasticity modulus and high chemical affinity at high temperatures. Surface integrity of titanium alloys Ti-6Al-4V is prominent in estimating the quality of machined components. The surface topography (surface defects and surface roughness) and the residual stress induced by machining Ti-6Al-4V occupy pivotal roles for the sustainability of Ti-6Al-4V components. High-pressure coolant (HPC) is a potential choice in meeting the requirements for the manufacture and application of Ti-6Al-4V. This paper reviews the progress towards the improvements of Ti-6Al4V surface integrity under HPC. Various researches of surface integrity characteristics have been reported. In particularly, surface roughness, surface defects, residual stress as well as work hardening are investigated in order to evaluate the machined surface qualities. Several coolant parameters (including coolant type, coolant pressure and the injection position) deserve investigating to provide the guidance for a satisfied machined surface. The review also provides a clear roadmap for applications of HPC in machining Ti-6Al4V. Experimental studies and analysis are reviewed to better understand the surface integrity under HPC machining process. A distinct discussion has been presented regarding the limitations and highlights of the prospective for machining Ti-6Al4V under HPC.

The Necessity of Machine Learning and Epistemology in the Development of Categorization Theories: A Case Study in Prototype-Exemplar Debate

NASA Astrophysics Data System (ADS)

Gagliardi, Francesco

In the present paper we discuss some aspects of the development of categorization theories concerning cognitive psychology and machine learning. We consider the thirty-year debate between prototype-theory and exemplar-theory in the studies of cognitive psychology regarding the categorization processes. We propose this debate is ill-posed, because it neglects some theoretical and empirical results of machine learning about the bias-variance theorem and the existence of some instance-based classifiers which can embed models subsuming both prototype and exemplar theories. Moreover this debate lies on a epistemological error of pursuing a, so called, experimentum crucis. Then we present how an interdisciplinary approach, based on synthetic method for cognitive modelling, can be useful to progress both the fields of cognitive psychology and machine learning.

Optimizing cutting conditions on sustainable machining of aluminum alloy to minimize power consumption

NASA Astrophysics Data System (ADS)

Nur, Rusdi; Suyuti, Muhammad Arsyad; Susanto, Tri Agus

2017-06-01

Aluminum is widely utilized in the industrial sector. There are several advantages of aluminum, i.e. good flexibility and formability, high corrosion resistance and electrical conductivity, and high heat. Despite of these characteristics, however, pure aluminum is rarely used because of its lacks of strength. Thus, most of the aluminum used in the industrial sectors was in the form of alloy form. Sustainable machining can be considered to link with the transformation of input materials and energy/power demand into finished goods. Machining processes are responsible for environmental effects accepting to their power consumption. The cutting conditions have been optimized to minimize the cutting power, which is the power consumed for cutting. This paper presents an experimental study of sustainable machining of Al-11%Si base alloy that was operated without any cooling system to assess the capacity in reducing power consumption. The cutting force was measured and the cutting power was calculated. Both of cutting force and cutting power were analyzed and modeled by using the central composite design (CCD). The result of this study indicated that the cutting speed has an effect on machining performance and that optimum cutting conditions have to be determined, while sustainable machining can be followed in terms of minimizing power consumption and cutting force. The model developed from this study can be used for evaluation process and optimization to determine optimal cutting conditions for the performance of the whole process.

Advanced Propulsion Power Distribution System for Next Generation Electric/Hybrid Vehicle. Phase 1; Preliminary System Studies

NASA Technical Reports Server (NTRS)

Bose, Bimal K.; Kim, Min-Huei

1995-01-01

The report essentially summarizes the work performed in order to satisfy the above project objective. In the beginning, different energy storage devices, such as battery, flywheel and ultra capacitor are reviewed and compared, establishing the superiority of the battery. Then, the possible power sources, such as IC engine, diesel engine, gas turbine and fuel cell are reviewed and compared, and the superiority of IC engine has been established. Different types of machines for drive motor/engine generator, such as induction machine, PM synchronous machine and switched reluctance machine are compared, and the induction machine is established as the superior candidate. Similar discussion was made for power converters and devices. The Insulated Gate Bipolar Transistor (IGBT) appears to be the most superior device although Mercury Cadmium Telluride (MCT) shows future promise. Different types of candidate distribution systems with the possible combinations of power and energy sources have been discussed and the most viable system consisting of battery, IC engine and induction machine has been identified. Then, HFAC system has been compared with the DC system establishing the superiority of the former. The detailed component sizing calculations of HFAC and DC systems reinforce the superiority of the former. A preliminary control strategy has been developed for the candidate HFAC system. Finally, modeling and simulation study have been made to validate the system performance. The study in the report demonstrates the superiority of HFAC distribution system for next generation electric/hybrid vehicle.

Supervised Learning of Two-Layer Perceptron under the Existence of External Noise — Learning Curve of Boolean Functions of Two Variables in Tree-Like Architecture —

NASA Astrophysics Data System (ADS)

Uezu, Tatsuya; Kiyokawa, Shuji

2016-06-01

We investigate the supervised batch learning of Boolean functions expressed by a two-layer perceptron with a tree-like structure. We adopt continuous weights (spherical model) and the Gibbs algorithm. We study the Parity and And machines and two types of noise, input and output noise, together with the noiseless case. We assume that only the teacher suffers from noise. By using the replica method, we derive the saddle point equations for order parameters under the replica symmetric (RS) ansatz. We study the critical value αC of the loading rate α above which the learning phase exists for cases with and without noise. We find that αC is nonzero for the Parity machine, while it is zero for the And machine. We derive the exponents barβ of order parameters expressed as (α - α C)bar{β} when α is near to αC. Furthermore, in the Parity machine, when noise exists, we find a spin glass solution, in which the overlap between the teacher and student vectors is zero but that between student vectors is nonzero. We perform Markov chain Monte Carlo simulations by simulated annealing and also by exchange Monte Carlo simulations in both machines. In the Parity machine, we study the de Almeida-Thouless stability, and by comparing theoretical and numerical results, we find that there exist parameter regions where the RS solution is unstable, and that the spin glass solution is metastable or unstable. We also study asymptotic learning behavior for large α and derive the exponents hat{β } of order parameters expressed as α - hat{β } when α is large in both machines. By simulated annealing simulations, we confirm these results and conclude that learning takes place for the input noise case with any noise amplitude and for the output noise case when the probability that the teacher's output is reversed is less than one-half.

Learning Machine Learning: A Case Study

ERIC Educational Resources Information Center

Lavesson, N.

2010-01-01

This correspondence reports on a case study conducted in the Master's-level Machine Learning (ML) course at Blekinge Institute of Technology, Sweden. The students participated in a self-assessment test and a diagnostic test of prerequisite subjects, and their results on these tests are correlated with their achievement of the course's learning…

Profiles of Major Suppliers to the Automotive Industry : Vol. 7. Machine Tool Suppliers to the Automotive Industry.

DOT National Transportation Integrated Search

1982-08-01

This study summarizes extensive information collected over a two-year period (October 1978 to October 1980) on suppliers of parts and components, materials, and machine tools to the automotive industry in the United States. The objective of the study...

Support vector machines classifiers of physical activities in preschoolers

USDA-ARS?s Scientific Manuscript database

The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a s...

Determination of Machining Parameters of Corn Byproduct Filled Plastics

USDA-ARS?s Scientific Manuscript database

In a collaborative project between the USDA and Northern Illinois University, the use of ethanol corn processing by-products as bio-filler materials in the compression molding of phenolic plastics has been studied. This paper reports on the results of a machinability study in the milling of various ...

Determining Machining Parameters of Corn Byproduct Filled Plastics

USDA-ARS?s Scientific Manuscript database

In a collaborative project between the USDA and Northern Illinois University, the use of corn ethanol processing byproducts (i.e., DDGS) as bio-filler materials in the compression molding of phenolic plastics has been studied. This paper reports on the results of a machinability study in the milling...

Vending Machines: A Narrative Review of Factors Influencing Items Purchased.

PubMed

Hua, Sophia V; Ickovics, Jeannette R

2016-10-01

Vending machines are a ubiquitous part of our food environments. Unfortunately, items found in vending machines tend to be processed foods and beverages high in salt, sugar, and/or fat. The purpose of this review is to describe intervention and case studies designed to promote healthier vending purchases by consumers and identify which manipulations are most effective. All studies analyzed were intervention or case studies that manipulated vending machines and analyzed sales or revenue data. This literature review is limited to studies conducted in the United States within the past 2 decades (ie, 1994 to 2015), regardless of study population or setting. Ten articles met these criteria based on a search conducted using PubMed. Study manipulations included price changes, increase in healthier items, changes to the advertisements wrapped around vending machines, and promotional signs such as a stoplight system to indicate healthfulness of items and to remind consumers to make healthy choices. Overall, seven studies had manipulations that resulted in statistically significant positive changes in purchasing behavior. Two studies used manipulations that did not influence consumer behavior, and one study was equivocal. Although there was no intervention pattern that ensured changes in purchasing, price reductions were most effective overall. Revenue from vending sales did not change substantially regardless of intervention, which will be important to foster initiation and sustainability of healthier vending. Future research should identify price changes that would balance healthier choices and revenue as well as better marketing to promote purchase of healthier items. Copyright © 2016 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Detection of Anomalous Machining Damages in Inconel 718 and TI 6-4 by Eddy Current Techniques

NASA Astrophysics Data System (ADS)

Lo, C. C. H.; Shimon, M.; Nakagawa, N.

2010-02-01

This paper reports on an eddy current (EC) study aimed at detecting anomalous machining damages in Inconel 718 and Ti 6-4 samples, including (i) surface discontinuities such as re-depositing of chips onto the machined surface, and (ii) microstructural damages manifested as a white surface layer and a subsurface layer of distorted grains, typically tens of microns thick. A series of pristine and machine-damaged coupons were studied by EC scans using a differential probe operated at 2 MHz to detect discontinuous surface anomalies, and by swept high frequency EC (SHFEC) measurements from 0.5 MHz to 65.5 MHz using proprietary detection coils to detect surface microstructural damages. In general, the EC c-scan data from machine-damaged surfaces show spatial variations with larger standard deviations than those from the undamaged surfaces. In some cases, the c-scan images exhibit characteristic bipolar indications in good spatial correlation with surface anomalies revealed by optical microscopy and laser profilometry. Results of the SHFEC measurements indicate a reduced near-surface conductivity of the damaged surfaces compared to the undamaged surfaces.

An Adaptive Genetic Association Test Using Double Kernel Machines.

PubMed

Zhan, Xiang; Epstein, Michael P; Ghosh, Debashis

2015-10-01

Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares kernel machine (LSKM) test for testing the effect of the subset of genes. An appealing feature of the kernel machine framework is that it can provide a flexible and unified method for multi-dimensional modeling of the genetic pathway effect allowing for both parametric and nonparametric components. This DKM approach is illustrated with application to simulated data as well as to data from a neuroimaging genetics study.

Testing and Validating Machine Learning Classifiers by Metamorphic Testing☆

PubMed Central

Xie, Xiaoyuan; Ho, Joshua W. K.; Murphy, Christian; Kaiser, Gail; Xu, Baowen; Chen, Tsong Yueh

2011-01-01

Machine Learning algorithms have provided core functionality to many application domains - such as bioinformatics, computational linguistics, etc. However, it is difficult to detect faults in such applications because often there is no “test oracle” to verify the correctness of the computed outputs. To help address the software quality, in this paper we present a technique for testing the implementations of machine learning classification algorithms which support such applications. Our approach is based on the technique “metamorphic testing”, which has been shown to be effective to alleviate the oracle problem. Also presented include a case study on a real-world machine learning application framework, and a discussion of how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also conduct mutation analysis and cross-validation, which reveal that our method has high effectiveness in killing mutants, and that observing expected cross-validation result alone is not sufficiently effective to detect faults in a supervised classification program. The effectiveness of metamorphic testing is further confirmed by the detection of real faults in a popular open-source classification program. PMID:21532969

Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

NASA Astrophysics Data System (ADS)

Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

2017-07-01

Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

Numerical study on the variation of pressure on India Bhabha Atomic Research Center (BARC) and Imperial College plasma focus machines

NASA Astrophysics Data System (ADS)

Singh, Arwinder; Heoh, Saw Sor; Sing, Lee

2017-03-01

In this paper, we use Lee's 5 phase model code to configure both the India Bhabha Atomic Research Center (BARC) Plasma focus machine operating in the pressure (P0) range from 1 Torr to 14 Torr as well as the Imperial College Plasma Focus Machine operating in the pressure (P0) range from 0.5 Torr to 6 Torr to compare the computational neutron yield to the experimental neutron yield as well as to obtain the relationship between axial speed va, radial shock speed vs, piston speed vp and pinch temperature with P0 for these machines.

Learning dominance relations in combinatorial search problems

NASA Technical Reports Server (NTRS)

Yu, Chee-Fen; Wah, Benjamin W.

1988-01-01

Dominance relations commonly are used to prune unnecessary nodes in search graphs, but they are problem-dependent and cannot be derived by a general procedure. The authors identify machine learning of dominance relations and the applicable learning mechanisms. A study of learning dominance relations using learning by experimentation is described. This system has been able to learn dominance relations for the 0/1-knapsack problem, an inventory problem, the reliability-by-replication problem, the two-machine flow shop problem, a number of single-machine scheduling problems, and a two-machine scheduling problem. It is considered that the same methodology can be extended to learn dominance relations in general.

Temperature and oxygenation during organ preservation: friends or foes?

PubMed

Gilbo, Nicholas; Monbaliu, Diethard

2017-06-01

The liberalization of donor selection criteria in organ transplantation, with the increased use of suboptimal grafts, has stimulated interest in ischemia-reperfusion injury prevention and graft reconditioning. Organ preservation technologies are changing considerably, mostly through the reintroduction of dynamic machine preservation. Here, we review the current evidence on the role of temperature and oxygenation during dynamic machine preservation. A large but complex body of evidence exists and comparative studies are few. Oxygenation seems to support an advantageous effect in hypothermic machine preservation and is mandatory in normothermic machine preservation, although in the latter, supraphysiological oxygen tensions should be avoided. High-risk grafts, such as suboptimal organs, may optimally benefit from oxygenated perfusion conditions that support metabolism and activate mechanisms of repair such as subnormothermic machine preservation, controlled oxygenated rewarming, and normothermic machine preservation. For lower risk grafts, oxygenation during hypothermic machine preservation may sufficiently reduce injuries and recharge the cellular energy to secure functional recovery after transplantation. The relationship between temperature and oxygenation in organ preservation is more complex than physiological laws would suggest. Rather than one default perfusion temperature/oxygenation standard, perfusion protocols should be tailored for specific needs of grafts of different quality.

Optimization of processing parameters of UAV integral structural components based on yield response

NASA Astrophysics Data System (ADS)

Chen, Yunsheng

2018-05-01

In order to improve the overall strength of unmanned aerial vehicle (UAV), it is necessary to optimize the processing parameters of UAV structural components, which is affected by initial residual stress in the process of UAV structural components processing. Because machining errors are easy to occur, an optimization model for machining parameters of UAV integral structural components based on yield response is proposed. The finite element method is used to simulate the machining parameters of UAV integral structural components. The prediction model of workpiece surface machining error is established, and the influence of the path of walking knife on residual stress of UAV integral structure is studied, according to the stress of UAV integral component. The yield response of the time-varying stiffness is analyzed, and the yield response and the stress evolution mechanism of the UAV integral structure are analyzed. The simulation results show that this method is used to optimize the machining parameters of UAV integral structural components and improve the precision of UAV milling processing. The machining error is reduced, and the deformation prediction and error compensation of UAV integral structural parts are realized, thus improving the quality of machining.

Stability Assessment of a System Comprising a Single Machine and Inverter with Scalable Ratings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Brian B; Lin, Yashen; Gevorgian, Vahan

Synchronous machines have traditionally acted as the foundation of large-scale electrical infrastructures and their physical properties have formed the cornerstone of system operations. However, with the increased integration of distributed renewable resources and energy-storage technologies, there is a need to systematically acknowledge the dynamics of power-electronics inverters - the primary energy-conversion interface in such systems - in all aspects of modeling, analysis, and control of the bulk power network. In this paper, we assess the properties of coupled machine-inverter systems by studying an elementary system comprised of a synchronous generator, three-phase inverter, and a load. The inverter model is formulatedmore » such that its power rating can be scaled continuously across power levels while preserving its closed-loop response. Accordingly, the properties of the machine-inverter system can be assessed for varying ratios of machine-to-inverter power ratings. After linearizing the model and assessing its eigenvalues, we show that system stability is highly dependent on the inverter current controller and machine exciter, thus uncovering a key concern with mixed machine-inverter systems and motivating the need for next-generation grid-stabilizing inverter controls.« less

The machinability of cast titanium and Ti-6Al-4V.

PubMed

Ohkubo, C; Watanabe, I; Ford, J P; Nakajima, H; Hosoi, T; Okabe, T

2000-02-01

This study investigated the machinability (ease of metal removal) of commercially pure (CP) titanium and Ti-6Al-4V alloy. Both CP Ti and Ti-6Al-4V were cast into magnesia molds. Two types of specimens (with alpha-case and without alpha-case) were made for CP Ti and Ti-6Al-4V. Machinability (n = 5) was evaluated as volume loss (mm3) by cutting/grinding the 3.0 mm surface using fissure burs and silicon carbide (SiC) under two machining conditions: (1) two machining forces (100 or 300 gf) at two rotational speeds (15000 or 30000 rpm) for 1 min, and (2) constant machining force of 100 gf and rotational speed of 15000 rpm for 1, 2, 5, 10, and 30 min. As controls, conventionally cast Co-Cr and Type IV gold alloys were evaluated in the same manner as the titanium. When fissure burs were used, there was a significant difference in the machinability between CP titanium with alpha-case and without alpha-case. On the other hand, there was no appreciable difference in the amount of metal removed for each tested metal when using the SiC points.

Assessing a Novel Method to Reduce Anesthesia Machine Contamination: A Prospective, Observational Trial.

PubMed

Biddle, Chuck J; George-Gay, Beverly; Prasanna, Praveen; Hill, Emily M; Davis, Thomas C; Verhulst, Brad

2018-01-01

Anesthesia machines are known reservoirs of bacterial species, potentially contributing to healthcare associated infections (HAIs). An inexpensive, disposable, nonpermeable, transparent anesthesia machine wrap (AMW) may reduce microbial contamination of the anesthesia machine. This study quantified the density and diversity of bacterial species found on anesthesia machines after terminal cleaning and between cases during actual anesthesia care to assess the impact of the AMW. We hypothesized reduced bioburden with the use of the AMW. In a prospective, experimental research design, the AMW was used in 11 surgical cases (intervention group) and not used in 11 control surgical cases. Cases were consecutively assigned to general surgical operating rooms. Seven frequently touched and difficult to disinfect "hot spots" were cultured on each machine preceding and following each case. The density and diversity of cultured colony forming units (CFUs) between the covered and uncovered machines were compared using Wilcoxon signed-rank test and Student's t -tests. There was a statistically significant reduction in CFU density and diversity when the AMW was employed. The protective effect of the AMW during regular anesthetic care provides a reliable and low-cost method to minimize the transmission of pathogens across patients and potentially reduces HAIs.

Needs of ergonomic design at control units in production industries.

PubMed

Levchuk, I; Schäfer, A; Lang, K-H; Gebhardt, Hj; Klussmann, A

2012-01-01

During the last decades, an increasing use of innovative technologies in manufacturing areas was monitored. A huge amount of physical workload was replaced by the change from conventional machine tools to computer-controlled units. CNC systems spread in current production processes. Because of this, machine operators today mostly have an observational function. This caused increasing of static work (e.g., standing, sitting) and cognitive demands (e.g., process observation). Machine operators have a high responsibility, because mistakes may lead to human injuries as well as to product losses - and in consequence may lead to high monetary losses (for the company) as well. Being usable often means for a CNC machine being efficient. An intuitive usability and an ergonomic organization of CNC workplaces can be an essential basis to reduce the risk of failures in operation as well as physical complaints (e.g. pain or diseases because of bad body posture during work). In contrast to conventional machines, CNC machines are equipped both with hardware and software. An intuitive and clear-sighted operating of CNC systems is a requirement for quick learning of new systems. Within this study, a survey was carried out among trainees learning the operation of CNC machines.

Correlation between use time of machine and decline curve for emerging enterprise information systems

NASA Astrophysics Data System (ADS)

Chang, Yao-Chung; Lai, Chin-Feng; Chuang, Chi-Cheng; Hou, Cheng-Yu

2018-04-01

With the progress of science and technology, more and more machines are adpot to help human life better and more convenient. When the machines have been used for a longer period of time so that the machine components are getting old, the amount of power comsumption will increase and easily cause the machine to overheat. This also causes a waste of invisible resources. If the Internet of Everything (IoE) technologies are able to be applied into the enterprise information systems for monitoring the machines use time, it can not only make energy can be effectively used, but aslo create a safer living environment. To solve the above problem, the correlation predict model is established to collect the data of power consumption converted into power eigenvalues. This study takes the power eigenvalue as the independent variable and use time as the dependent variable in order to establish the decline curve. Ultimately, the scoring and estimation modules are employed to seek the best power eigenvalue as the independent variable. To predict use time, the correlation is discussed between the use time and the decline curve to improve the entire behavioural analysis of the facilitate recognition of the use time of machines.

Theoretical study of cut area of reduction of large surfaces of rotation parts on machines with rotary cutters “Extra”

NASA Astrophysics Data System (ADS)

Bondarenko, J. A.; Fedorenko, M. A.; Pogonin, A. A.

2018-03-01

Large parts can be treated without disassembling machines using “Extra”, having technological and design challenges, which differ from the challenges in the processing of these components on the stationary machine. Extension machines are used to restore large parts up to the condition allowing one to use them in a production environment. To achieve the desired accuracy and surface roughness parameters, the surface after rotary grinding becomes recoverable, which greatly increases complexity. In order to improve production efficiency and productivity of the process, the qualitative rotary processing of the machined surface is applied. The rotary cutting process includes a continuous change of the cutting edge surfaces. The kinematic parameters of a rotary cutting define its main features and patterns, the cutting operation of the rotary cutting instrument.

Power electromagnetic strike machine for engineering-geological surveys

NASA Astrophysics Data System (ADS)

Usanov, K. M.; Volgin, A. V.; Chetverikov, E. A.; Kargin, V. A.; Moiseev, A. P.; Ivanova, Z. I.

2017-10-01

When implementing the processes of dynamic sensing of soils and pulsed nonexplosive seismic exploration, the most common and effective method is the strike one, which is provided by a variety of structure and parameters of pneumatic, hydraulic, electrical machines of strike action. The creation of compact portable strike machines which do not require transportation and use of mechanized means is important. A promising direction in the development of strike machines is the use of pulsed electromagnetic actuator characterized by relatively low energy consumption, relatively high specific performance and efficiency, and providing direct conversion of electrical energy into mechanical work of strike mass with linear movement trajectory. The results of these studies allowed establishing on the basis of linear electromagnetic motors the electromagnetic pulse machines with portable performance for dynamic sensing of soils and land seismic pulse of small depths.

Influence of the Regime of Electropulsing-Assisted Machining on the Plastic Deformation of the Layer Being Cut.

PubMed

Hameed, Saqib; González Rojas, Hernán A; Perat Benavides, José I; Nápoles Alberro, Amelia; Sánchez Egea, Antonio J

2018-05-25

In this article, the influence of electropulsing on the machinability of steel S235 and aluminium 6060 has been studied during conventional and electropulsing-assisted turning processes. The machinability indices such as chip compression ratio ξ , shear plane angle ϕ and specific cutting energy (SCE) are investigated by using different cutting parameters such as cutting speed, cutting feed and depth of cut during electrically-assisted turning process. The results and analysis of this work indicated that the electrically-assisted turning process improves the machinability of steel S235, whereas the machinability of aluminium 6060 gets worse. Finally, due to electropluses (EPs), the chip compression ratio ξ increases with the increase in cutting speed during turning of aluminium 6060 and the SCE decreases during turning of steel S235.

Development of 300 mesh Soy Bean Crusher for Tofu Material Processing

NASA Astrophysics Data System (ADS)

Lee, E. S.; Pratama, P. S.; Supeno, D.; Jeong, S. W.; Byun, J. Y.; Woo, J. H.; Park, C. S.; Choi, W. S.

2018-03-01

A machine such as bean crusher machine is subjected to different loads and vibration. Due to this vibration there will be certain deformations which affect the performance of the machine in adverse manner. This paper proposed a vibration analysis of bean crusher machine using ANSYS. The effect of vibration on the structure was studied in order to ensure the safety using finite element analysis. This research supports the machine designer to create a better product with lower cost and faster development time. To do this, firstly, using Inventor, a CAD model is prepared. Secondly, the analysis is to be carried out using ANSYS 15. The modal analysis and random vibration analysis of the structure was conducted. The analysis shows that the proposed design was successfully shows the minimum deformation when the vibration was applied in normal condition.

Machine Learning Approaches for Clinical Psychology and Psychiatry.

PubMed

Dwyer, Dominic B; Falkai, Peter; Koutsouleris, Nikolaos

2018-05-07

Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide an accessible understanding of why this approach is important for future practice given its potential to augment decisions associated with the diagnosis, prognosis, and treatment of people suffering from mental illness using clinical and biological data. To this end, the limitations of current statistical paradigms in mental health research are critiqued, and an introduction is provided to critical machine learning methods used in clinical studies. A selective literature review is then presented aiming to reinforce the usefulness of machine learning methods and provide evidence of their potential. In the context of promising initial results, the current limitations of machine learning approaches are addressed, and considerations for future clinical translation are outlined.

Computer-aided design studies of the homopolar linear synchronous motor

NASA Astrophysics Data System (ADS)

Dawson, G. E.; Eastham, A. R.; Ong, R.

1984-09-01

The linear induction motor (LIM), as an urban transit drive, can provide good grade-climbing capabilities and propulsion/braking performance that is independent of steel wheel-rail adhesion. In view of its 10-12 mm airgap, the LIM is characterized by a low power factor-efficiency product of order 0.4. A synchronous machine offers high efficiency and controllable power factor. An assessment of the linear homopolar configuration of this machine is presented as an alternative to the LIM. Computer-aided design studies using the finite element technique have been conducted to identify a suitable machine design for urban transit propulsion.

Fundamental Study about the Landscape Estimation and Analysis by CG

NASA Astrophysics Data System (ADS)

Nakashima, Yoshio; Miyagoshi, Takashi; Takamatsu, Mamoru; Sassa, Kazuhiro

In recent years, the color of advertising signboards or vending machines on the streets should be harmonized with the surrounding landscape. In this study, we investigated how the colors (red and white) of the vending machines virtually installed by CG would affect the traditional landscape. 20 subjects estimated landscape samples in Hida-Furukawa by the SD technique. The result of our experiment shows that the vending machines have great influence on the surrounding landscape. On the other hand, we have confirmed that they can harmonize with the circumference landscape by the color to use.

Nano Mechanical Machining Using AFM Probe

NASA Astrophysics Data System (ADS)

Mostofa, Md. Golam

Complex miniaturized components with high form accuracy will play key roles in the future development of many products, as they provide portability, disposability, lower material consumption in production, low power consumption during operation, lower sample requirements for testing, and higher heat transfer due to their very high surface-to-volume ratio. Given the high market demand for such micro and nano featured components, different manufacturing methods have been developed for their fabrication. Some of the common technologies in micro/nano fabrication are photolithography, electron beam lithography, X-ray lithography and other semiconductor processing techniques. Although these methods are capable of fabricating micro/nano structures with a resolution of less than a few nanometers, some of the shortcomings associated with these methods, such as high production costs for customized products, limited material choices, necessitate the development of other fabricating techniques. Micro/nano mechanical machining, such an atomic force microscope (AFM) probe based nano fabrication, has, therefore, been used to overcome some the major restrictions of the traditional processes. This technique removes material from the workpiece by engaging micro/nano size cutting tool (i.e. AFM probe) and is applicable on a wider range of materials compared to the photolithographic process. In spite of the unique benefits of nano mechanical machining, there are also some challenges with this technique, since the scale is reduced, such as size effects, burr formations, chip adhesions, fragility of tools and tool wear. Moreover, AFM based machining does not have any rotational movement, which makes fabrication of 3D features more difficult. Thus, vibration-assisted machining is introduced into AFM probe based nano mechanical machining to overcome the limitations associated with the conventional AFM probe based scratching method. Vibration-assisted machining reduced the cutting forces and burr formations through intermittent cutting. Combining the AFM probe based machining with vibration-assisted machining enhanced nano mechanical machining processes by improving the accuracy, productivity and surface finishes. In this study, several scratching tests are performed with a single crystal diamond AFM probe to investigate the cutting characteristics and model the ploughing cutting forces. Calibration of the probe for lateral force measurements, which is essential, is also extended through the force balance method. Furthermore, vibration-assisted machining system is developed and applied to fabricate different materials to overcome some of the limitations of the AFM probe based single point nano mechanical machining. The novelty of this study includes the application of vibration-assisted AFM probe based nano scale machining to fabricate micro/nano scale features, calibration of an AFM by considering different factors, and the investigation of the nano scale material removal process from a different perspective.

Human factors - Man-machine symbiosis in space

NASA Technical Reports Server (NTRS)

Brown, Jeri W.

1987-01-01

The relation between man and machine in space is studied. Early spaceflight and the goal of establishing a permanent space presence are described. The need to consider the physiological, psychological, and social integration of humans for each space mission is examined. Human factors must also be considered in the design of spacecraft. The effective utilization of man and machine capabilities, and research in anthropometry and biomechanics aimed at determining the limitations of spacecrews are discussed.

Evaluation of machinability and flexural strength of a novel dental machinable glass-ceramic.

PubMed

Qin, Feng; Zheng, Shucan; Luo, Zufeng; Li, Yong; Guo, Ling; Zhao, Yunfeng; Fu, Qiang

2009-10-01

To evaluate the machinability and flexural strength of a novel dental machinable glass-ceramic (named PMC), and to compare the machinability property with that of Vita Mark II and human enamel. The raw batch materials were selected and mixed. Four groups of novel glass-ceramics were formed at different nucleation temperatures, and were assigned to Group 1, Group 2, Group 3 and Group 4. The machinability of the four groups of novel glass-ceramics, Vita Mark II ceramic and freshly extracted human premolars were compared by means of drilling depth measurement. A three-point bending test was used to measure the flexural strength of the novel glass-ceramics. The crystalline phases of the group with the best machinability were identified by X-ray diffraction. In terms of the drilling depth, Group 2 of the novel glass-ceramics proves to have the largest drilling depth. There was no statistical difference among Group 1, Group 4 and the natural teeth. The drilling depth of Vita MK II was statistically less than that of Group 1, Group 4 and the natural teeth. Group 3 had the least drilling depth. In respect of the flexural strength, Group 2 exhibited the maximum flexural strength; Group 1 was statistically weaker than Group 2; there was no statistical difference between Group 3 and Group 4, and they were the weakest materials. XRD of Group 2 ceramic showed that a new type of dental machinable glass-ceramic containing calcium-mica had been developed by the present study and was named PMC. PMC is promising for application as a dental machinable ceramic due to its good machinability and relatively high strength.

Plan for conducting an international machine tool task force

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sutton, G.P.; McClure, E.R.; Schuman, J.F.

1978-08-28

The basic objectives of the Machine Tool Task Force (MTTF) are to characterize and summarize the state of the art of cutting machine tool technology and to identify promising areas of future R and D. These goals will be accomplished with a series of multidisciplinary teams of prominent experts and individuals experienced in the specialized technologies of machine tools or in the management of machine tool operations. Experts will be drawn from all areas of the machine tool community: machine tool users or buyer organizations, builders, and R and D establishments including universities and government laboratories, both domestic and foreign.more » A plan for accomplishing this task is presented. The area of machine tool technology has been divided into about two dozen technology subjects on which teams of one or more experts will work. These teams are, in turn, organized into four principal working groups dealing, respectively, with machine tool accuracy, mechanics, control, and management systems/utilization. Details are presented on specific subjects to be covered, the organization of the Task Force and its four working groups, and the basic approach to determining the state of the art of technology and the future directions of this technology. The planned review procedure, the potential benefits, our management approach, and the schedule, as well as the key participating personnel and their background are discussed. The initial meeting of MTTF members will be held at a plenary session on October 16 and 17, 1978, in Scottsdale, AZ. The MTTF study will culminate in a conference on September 1, 1980, in Chicago, IL, immediately preceeding the 1980 International Machine Tool Show. At this time, our results will be released to the public; a series of reports will be published in late 1980.« less

The upgraded Large Plasma Device, a machine for studying frontier basic plasma physics.

PubMed

Gekelman, W; Pribyl, P; Lucky, Z; Drandell, M; Leneman, D; Maggs, J; Vincena, S; Van Compernolle, B; Tripathi, S K P; Morales, G; Carter, T A; Wang, Y; DeHaas, T

2016-02-01

In 1991 a manuscript describing an instrument for studying magnetized plasmas was published in this journal. The Large Plasma Device (LAPD) was upgraded in 2001 and has become a national user facility for the study of basic plasma physics. The upgrade as well as diagnostics introduced since then has significantly changed the capabilities of the device. All references to the machine still quote the original RSI paper, which at this time is not appropriate. In this work, the properties of the updated LAPD are presented. The strategy of the machine construction, the available diagnostics, the parameters available for experiments, as well as illustrations of several experiments are presented here.

Boosting Learning Algorithm for Stock Price Forecasting

NASA Astrophysics Data System (ADS)

Wang, Chengzhang; Bai, Xiaoming

2018-03-01

To tackle complexity and uncertainty of stock market behavior, more studies have introduced machine learning algorithms to forecast stock price. ANN (artificial neural network) is one of the most successful and promising applications. We propose a boosting-ANN model in this paper to predict the stock close price. On the basis of boosting theory, multiple weak predicting machines, i.e. ANNs, are assembled to build a stronger predictor, i.e. boosting-ANN model. New error criteria of the weak studying machine and rules of weights updating are adopted in this study. We select technical factors from financial markets as forecasting input variables. Final results demonstrate the boosting-ANN model works better than other ones for stock price forecasting.

Vacation model for Markov machine repair problem with two heterogeneous unreliable servers and threshold recovery

NASA Astrophysics Data System (ADS)

Jain, Madhu; Meena, Rakesh Kumar

2018-03-01

Markov model of multi-component machining system comprising two unreliable heterogeneous servers and mixed type of standby support has been studied. The repair job of broken down machines is done on the basis of bi-level threshold policy for the activation of the servers. The server returns back to render repair job when the pre-specified workload of failed machines is build up. The first (second) repairman turns on only when the work load of N1 (N2) failed machines is accumulated in the system. The both servers may go for vacation in case when all the machines are in good condition and there are no pending repair jobs for the repairmen. Runge-Kutta method is implemented to solve the set of governing equations used to formulate the Markov model. Various system metrics including the mean queue length, machine availability, throughput, etc., are derived to determine the performance of the machining system. To provide the computational tractability of the present investigation, a numerical illustration is provided. A cost function is also constructed to determine the optimal repair rate of the server by minimizing the expected cost incurred on the system. The hybrid soft computing method is considered to develop the adaptive neuro-fuzzy inference system (ANFIS). The validation of the numerical results obtained by Runge-Kutta approach is also facilitated by computational results generated by ANFIS.

A comparative study on performance of CBN inserts when turning steel under dry and wet conditions

NASA Astrophysics Data System (ADS)

Abdullah Bagaber, Salem; Razlan Yusoff, Ahmad

2017-10-01

Cutting fluids is the most unsustainable components of machining processes, it is negatively impacting on the environmental and additional energy required. Due to its high strength and corrosion resistance, the machinability of stainless steel has attracted considerable interest. This study aims to evaluate performance of cubic boron nitride (CBN) inserts for the machining parameters includes the power consumption and surface roughness. Due to the high single cutting-edge cost of CBN, the performance of significant is importance for hard finish turning. The present work also deals with a comparative study on power consumption and surface roughness under dry and flood conditions. Turning process of the stainless steel 316 was performed. A response surface methodology based box-behnken design (BBD) was utilized for statistical analysis. The optimum process parameters are determined as the overall performance index. The comparison study has been done between dry and wet stainless-steel cut in terms of minimum value of energy and surface roughness. The result shows the stainless still can be machined under dry condition with 18.57% improvement of power consumption and acceptable quality compare to the wet cutting. The CBN tools under dry cutting stainless steel can be used to reduce the environment impacts in terms of no cutting fluid use and less energy required which is effected in machining productivity and profit.

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2011 CFR

2011-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2012 CFR

2012-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2013 CFR

2013-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2014 CFR

2014-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

15 CFR 700.31 - Metalworking machines.

Code of Federal Regulations, 2010 CFR

2010-01-01

... machines covered by this section include: Bending and forming machines Boring machines Broaching machines... Planers and shapers Polishing, lapping, boring, and finishing machines Punching and shearing machines...

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis

PubMed Central

Allyn, Jérôme; Allou, Nicolas; Augustin, Pascal; Philip, Ivan; Martinet, Olivier; Belghiti, Myriem; Provenchere, Sophie; Montravers, Philippe; Ferdynus, Cyril

2017-01-01

Background The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. Methods and finding We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755–0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691–0.783) and 0.742 (0.698–0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold. Conclusions According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction. PMID:28060903

Tool geometry and damage mechanisms influencing CNC turning efficiency of Ti6Al4V

NASA Astrophysics Data System (ADS)

Suresh, Sangeeth; Hamid, Darulihsan Abdul; Yazid, M. Z. A.; Nasuha, Nurdiyanah; Ain, Siti Nurul

2017-12-01

Ti6Al4V or Grade 5 titanium alloy is widely used in the aerospace, medical, automotive and fabrication industries, due to its distinctive combination of mechanical and physical properties. Ti6Al4V has always been perverse during its machining, strangely due to the same mix of properties mentioned earlier. Ti6Al4V machining has resulted in shorter cutting tool life which has led to objectionable surface integrity and rapid failure of the parts machined. However, the proven functional relevance of this material has prompted extensive research in the optimization of machine parameters and cutting tool characteristics. Cutting tool geometry plays a vital role in ensuring dimensional and geometric accuracy in machined parts. In this study, an experimental investigation is actualized to optimize the nose radius and relief angles of the cutting tools and their interaction to different levels of machining parameters. Low elastic modulus and thermal conductivity of Ti6Al4V contribute to the rapid tool damage. The impact of these properties over the tool tips damage is studied. An experimental design approach is utilized in the CNC turning process of Ti6Al4V to statistically analyze and propose optimum levels of input parameters to lengthen the tool life and enhance surface characteristics of the machined parts. A greater tool nose radius with a straight flank, combined with low feed rates have resulted in a desirable surface integrity. The presence of relief angle has proven to aggravate tool damage and also dimensional instability in the CNC turning of Ti6Al4V.

Examination of redirected continuous miner scrubber discharge configurations for exhaust face ventilation systems

PubMed Central

Organiscak, J.A.; Beck, T.W.

2015-01-01

The U.S. National Institute for Occupational Safety and Health (NIOSH) Office of Mine Safety and Health Research (OMSHR) has recently studied several redirected scrubber discharge configurations in its full-scale continuous miner gallery for both dust and gas control when using an exhaust face ventilation system. Dust and gas measurements around the continuous mining machine in the laboratory showed that the conventional scrubber discharge directed outby the face with a 12.2-m (40-ft) exhaust curtain setback appeared to be one of the better configurations for controlling dust and gas. Redirecting all the air toward the face equally up both sides of the machine increased the dust and gas concentrations around the machine. When all of the air was redirected toward the face on the off-curtain side of the machine, gas accumulations tended to be reduced at the face, at the expense of increased dust levels in the return and on the curtain side of the mining machine. A 6.1-m (20-ft) exhaust curtain setback without the scrubber operating resulted in the lowest dust levels around the continuous mining machine, but this configuration resulted in some of the highest levels of dust in the return and gas on the off-curtain side of the mining face. Two field studies showed some similarities to the laboratory findings, with elevated dust levels at the rear corners of the continuous miner when all of the scrubber exhaust was redirected toward the face either up the off-tubing side or equally up both sides of the mining machine. PMID:26251566

Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms.

PubMed

Roetker, Nicholas S; Page, C David; Yonker, James A; Chang, Vicky; Roan, Carol L; Herd, Pamela; Hauser, Taissa S; Hauser, Robert M; Atwood, Craig S

2013-10-01

We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms. We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors-13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors-18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks. After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors. We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic-environmental-sociobehavioral interactions in depressive symptoms.

Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective.

PubMed

Kim, Yong-Ku; Na, Kyoung-Sae

2018-01-03

Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder. Identifying the distinct and common contributions of these anatomical regions to depression and bipolar disorder have broadened and deepened our understanding of mood disorders. However, the extent to which neuroimaging research findings contribute to clinical practice in the real-world setting is unclear. As traditional or non-machine learning MRI studies have analyzed group-level differences, it is not possible to directly translate findings from research to clinical practice; the knowledge gained pertains to the disorder, but not to individuals. On the other hand, a machine learning approach makes it possible to provide individual-level classifications. For the past two decades, many studies have reported on the classification accuracy of machine learning-based neuroimaging studies from the perspective of diagnosis and treatment response. However, for the application of a machine learning-based brain MRI approach in real world clinical settings, several major issues should be considered. Secondary changes due to illness duration and medication, clinical subtypes and heterogeneity, comorbidities, and cost-effectiveness restrict the generalization of the current machine learning findings. Sophisticated classification of clinical and diagnostic subtypes is needed. Additionally, as the approach is inevitably limited by sample size, multi-site participation and data-sharing are needed in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

Machinability of titanium metal matrix composites (Ti-MMCs)

NASA Astrophysics Data System (ADS)

Aramesh, Maryam

Titanium metal matrix composites (Ti-MMCs), as a new generation of materials, have various potential applications in aerospace and automotive industries. The presence of ceramic particles enhances the physical and mechanical properties of the alloy matrix. However, the hard and abrasive nature of these particles causes various issues in the field of their machinability. Severe tool wear and short tool life are the most important drawbacks of machining this class of materials. There is very limited work in the literature regarding the machinability of this class of materials especially in the area of tool life estimation and tool wear. By far, polycrystalline diamond (PCD) tools appear to be the best choice for machining MMCs from researchers' point of view. However, due to their high cost, economical alternatives are sought. Cubic boron nitride (CBN) inserts, as the second hardest available tools, show superior characteristics such as great wear resistance, high hardness at elevated temperatures, a low coefficient of friction and a high melting point. Yet, so far CBN tools have not been studied during machining of Ti-MMCs. In this study, a comprehensive study has been performed to explore the tool wear mechanisms of CBN inserts during turning of Ti-MMCs. The unique morphology of the worn faces of the tools was investigated for the first time, which led to new insights in the identification of chemical wear mechanisms during machining of Ti-MMCs. Utilizing the full tool life capacity of cutting tools is also very crucial, due to the considerable costs associated with suboptimal replacement of tools. This strongly motivates development of a reliable model for tool life estimation under any cutting conditions. In this study, a novel model based on the survival analysis methodology is developed to estimate the progressive states of tool wear under any cutting conditions during machining of Ti-MMCs. This statistical model takes into account the machining time in addition to the effect of cutting parameters. Thus, promising results were obtained which showed a very good agreement with the experimental results. Moreover, a more advanced model was constructed, by adding the tool wear as another variable to the previous model. Therefore, a new model was proposed for estimating the remaining life of worn inserts under different cutting conditions, using the current tool wear data as an input. The results of this model were validated with the experimental results. The estimated results were well consistent with the results obtained from the experiments.

An Exploratory Study of Problem Gambling on Casino versus Non-Casino Electronic Gaming Machines

ERIC Educational Resources Information Center

Clarke, Dave; Pulford, Justin; Bellringer, Maria; Abbott, Max; Hodgins, David C.

2012-01-01

Electronic gaming machines (EGMs) have been frequently associated with problem gambling. Little research has compared the relative contribution of casino EGMs versus non-casino EGMs on current problem gambling, after controlling for demographic factors and gambling behaviour. Our exploratory study obtained data from questionnaires administered to…

Study of the influence of the cutting temperature on the magnitude of the contact forces in the machining fixtures

NASA Astrophysics Data System (ADS)

Cioată, V. G.; Kiss, I.; Alexa, V.; Raţiu, S. A.; Racov, M.

2018-01-01

In the machining process, the workpieces are installed in machining fixtures in order to establish a strictly determined position with the cutting tool or its trajectory. During the cutting process, the weight of the workpiece, the forces and moments of inertia, cutting forces and moments, clamping forces, the heat released during the cutting process determine the contact forces between the locators and the workpiece. The magnitude of these forces is important because too large value can destroy the surface of the workpiece, and a too small value can cause the workpiece to slip on the locators or even the loss of the contact with the workpiece. Both situations must be avoided. The paper presents a study, realized with CAE software, regarding the influence of the cutting temperature on the magnitude of the contact forces in a machining fixture for the milling a rectangular workpiece.

Exponentially-Biased Ground-State Sampling of Quantum Annealing Machines with Transverse-Field Driving Hamiltonians

NASA Technical Reports Server (NTRS)

Mandra, Salvatore

2017-01-01

We study the performance of the D-Wave 2X quantum annealing machine on systems with well-controlled ground-state degeneracy. While obtaining the ground state of a spin-glass benchmark instance represents a difficult task, the gold standard for any optimization algorithm or machine is to sample all solutions that minimize the Hamiltonian with more or less equal probability. Our results show that while naive transverse-field quantum annealing on the D-Wave 2X device can find the ground-state energy of the problems, it is not well suited in identifying all degenerate ground-state configurations associated to a particular instance. Even worse, some states are exponentially suppressed, in agreement with previous studies on toy model problems [New J. Phys. 11, 073021 (2009)]. These results suggest that more complex driving Hamiltonians are needed in future quantum annealing machines to ensure a fair sampling of the ground-state manifold.

A randomized controlled trial of chair interventions on back and hip pain among sewing machine operators: the los angeles garment study.

PubMed

Wang, Pin-Chieh; Ritz, Beate R; Janowitz, Ira; Harrison, Robert J; Yu, Fei; Chan, Jacqueline; Rempel, David M

2008-03-01

Determine whether an adjustable chair with a curved or a flat seat pan improved monthly back and hip pain scores in sewing machine operators. This 4-month intervention study randomized 293 sewing machine operators with back and hip pain. The participants in the control group received a placebo intervention, and participants in the intervention groups received the placebo intervention and one of the two intervention chairs. Compared with the control group, mean pain improvement for the flat chair intervention was 0.43 points (95% CI = 0.34, 0.51) per month, and mean pain improvement for the curved chair intervention was 0.25 points (95% CI = 0.16, 0.34) per month. A height-adjustable task chair with a swivel function can reduce back and hip pain in sewing machine operators. The findings may be relevant to workers who perform visual- and hand-intensive manufacturing jobs.

Characterizing Slow Slip Applying Machine Learning

NASA Astrophysics Data System (ADS)

Hulbert, C.; Rouet-Leduc, B.; Bolton, D. C.; Ren, C. X.; Marone, C.; Johnson, P. A.

2017-12-01

Over the last two decades it has become apparent from strain and GPS measurements, that slow slip on earthquake faults is a widespread phenomenon. Slow slip is also inferred from small amplitude seismic signals known as tremor and low frequency earthquakes (LFE's) and has been reproduced in laboratory studies, providing useful physical insight into the frictional properties associated with the behavior. From such laboratory studies we ask whether we can obtain quantitative information regarding the physics of friction from only the recorded continuous acoustical data originating from the fault zone. We show that by applying machine learning to the acoustical signal, we can infer upcoming slow slip failure initiation as well as the slip termination, and that we can also infer the magnitudes by a second machine learning procedure based on predicted inter-event times. We speculate that by applying this or other machine learning approaches to continuous seismic data, new information regarding the physics of faulting could be obtained.

The effect of abrading and cutting instruments on machinability of dental ceramics.

PubMed

Sakoda, Satoshi; Nakao, Noriko; Watanabe, Ikuya

2018-03-16

The aim was to investigate the effect of machining instruments on machinability of dental ceramics. Four dental ceramics, including two zirconia ceramics were machined by three types (SiC, diamond vitrified, and diamond sintered) of wheels with a hand-piece engine and two types (diamond and carbide) of burs with a high-speed air turbine. The machining conditions used were abrading speeds of 10,000 and 15,000 r.p.m. with abrading force of 100 gf for the hand-piece engine, and a pressure of 200 kPa and a cutting force of 80 gf for the air-turbine hand-piece. The machinability efficiency was evaluated by volume losses after machining the ceramics. A high-abrading speed had high-abrading efficiency (high-volume loss) compared to low-abrading speed in all abrading instruments used. The diamond vitrified wheels demonstrated higher volume loss for two zirconia ceramics than those of SiC and diamond sintered wheels. When the high-speed air-turbine instruments were used, the diamond points showed higher volume losses compared to the carbide burs for one ceramic and two zirconia ceramics with high-mechanical properties. The results of this study indicated that the machinability of dental ceramics depends on the mechanical and physical properties of dental ceramics and machining instruments. The abrading wheels show autogenous action of abrasive grains, in which ground abrasive grains drop out from the binder during abrasion, then the binder follow to wear out, subsequently new abrasive grains come out onto the instrument surface (autogenous action) and increase the grinding amount (volume loss) of grinding materials.

Method of Individual Forecasting of Technical State of Logging Machines

NASA Astrophysics Data System (ADS)

Kozlov, V. G.; Gulevsky, V. A.; Skrypnikov, A. V.; Logoyda, V. S.; Menzhulova, A. S.

2018-03-01

Development of the model that evaluates the possibility of failure requires the knowledge of changes’ regularities of technical condition parameters of the machines in use. To study the regularities, the need to develop stochastic models that take into account physical essence of the processes of destruction of structural elements of the machines, the technology of their production, degradation and the stochastic properties of the parameters of the technical state and the conditions and modes of operation arose.

Grindability and mechanical property of ceramics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guo, Changsheng; Chand, R.H.

1996-12-31

For cost-effective ceramic machining, material-specific machining methodology is needed. This requires characterizing ceramics from machining view point. In this paper, a preliminary study of the correlation between grindability and mechanical properties is reported. Results indicate that there exists complex correlations between grindability and mechanical properties such as hardness, fracture toughness and elasticity. Some ceramics of similar mechanical properties have different grindabilities, which implies that it is possible to develop ceramics of both superior mechanical properties and good grindability.

Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2017-01-01

This study proposes an adaptive-learning-based method for machine faulty detection and health degradation monitoring. The kernel of the proposed method is an "evolving" model that uses an unsupervised online learning scheme, in which an adaptive hidden Markov model (AHMM) is used for online learning the dynamic health changes of machines in their full life. A statistical index is developed for recognizing the new health states in the machines. Those new health states are then described online by adding of new hidden states in AHMM. Furthermore, the health degradations in machines are quantified online by an AHMM-based health index (HI) that measures the similarity between two density distributions that describe the historic and current health states, respectively. When necessary, the proposed method characterizes the distinct operating modes of the machine and can learn online both abrupt as well as gradual health changes. Our method overcomes some drawbacks of the HIs (e.g., relatively low comprehensibility and applicability) based on fixed monitoring models constructed in the offline phase. Results from its application in a bearing life test reveal that the proposed method is effective in online detection and adaptive assessment of machine health degradation. This study provides a useful guide for developing a condition-based maintenance (CBM) system that uses an online learning method without considerable human intervention.

Machine-z: Rapid Machine-Learned Redshift Indicator for Swift Gamma-Ray Bursts

NASA Technical Reports Server (NTRS)

Ukwatta, T. N.; Wozniak, P. R.; Gehrels, N.

2016-01-01

Studies of high-redshift gamma-ray bursts (GRBs) provide important information about the early Universe such as the rates of stellar collapsars and mergers, the metallicity content, constraints on the re-ionization period, and probes of the Hubble expansion. Rapid selection of high-z candidates from GRB samples reported in real time by dedicated space missions such as Swift is the key to identifying the most distant bursts before the optical afterglow becomes too dim to warrant a good spectrum. Here, we introduce 'machine-z', a redshift prediction algorithm and a 'high-z' classifier for Swift GRBs based on machine learning. Our method relies exclusively on canonical data commonly available within the first few hours after the GRB trigger. Using a sample of 284 bursts with measured redshifts, we trained a randomized ensemble of decision trees (random forest) to perform both regression and classification. Cross-validated performance studies show that the correlation coefficient between machine-z predictions and the true redshift is nearly 0.6. At the same time, our high-z classifier can achieve 80 per cent recall of true high-redshift bursts, while incurring a false positive rate of 20 per cent. With 40 per cent false positive rate the classifier can achieve approximately 100 per cent recall. The most reliable selection of high-redshift GRBs is obtained by combining predictions from both the high-z classifier and the machine-z regressor.

Process Development and Micro-Machining of MARBLE Foam-Cored Rexolite Hemi-Shell Ablator Capsules

DOE PAGES

Randolph, Randall Blaine; Oertel, John A.; Schmidt, Derek William; ...

2016-06-30

For this study, machined CH hemi-shell ablator capsules have been successfully produced by the MST-7 Target Fabrication Team at Los Alamos National Laboratory. Process development and micro-machining techniques have been developed to produce capsules for both the Omega and National Ignition Facility (NIF) campaigns. These capsules are gas filled up to 10 atm and consist of a machined plastic hemi-shell outer layer that accommodates various specially engineered low-density polystyrene foam cores. Machining and assembly of the two-part, step-jointed plastic hemi-shell outer layer required development of new techniques, processes, and tooling while still meeting very aggressive shot schedules for both campaigns.more » Finally, problems encountered and process improvements will be discussed that describe this very unique, complex capsule design approach through the first Omega proof-of-concept version to the larger NIF version.« less

Machine learning modelling for predicting soil liquefaction susceptibility

NASA Astrophysics Data System (ADS)

Samui, P.; Sitharam, T. G.

2011-01-01

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N1)60] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters [(N1)60 and peck ground acceleration (amax/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Numerical simulation of polishing U-tube based on solid-liquid two-phase

NASA Astrophysics Data System (ADS)

Li, Jun-ye; Meng, Wen-qing; Wu, Gui-ling; Hu, Jing-lei; Wang, Bao-zuo

2018-03-01

As the advanced technology to solve the ultra-precision machining of small hole structure parts and complex cavity parts, the abrasive grain flow processing technology has the characteristics of high efficiency, high quality and low cost. So this technology in many areas of precision machining has an important role. Based on the theory of solid-liquid two-phase flow coupling, a solid-liquid two-phase MIXTURE model is used to simulate the abrasive flow polishing process on the inner surface of U-tube, and the temperature, turbulent viscosity and turbulent dissipation rate in the process of abrasive flow machining of U-tube were compared and analyzed under different inlet pressure. In this paper, the influence of different inlet pressure on the surface quality of the workpiece during abrasive flow machining is studied and discussed, which provides a theoretical basis for the research of abrasive flow machining process.

Effects of virtualization on a scientific application - Running a hyperspectral radiative transfer code on virtual machines.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tikotekar, Anand A; Vallee, Geoffroy R; Naughton III, Thomas J

2008-01-01

The topic of system-level virtualization has recently begun to receive interest for high performance computing (HPC). This is in part due to the isolation and encapsulation offered by the virtual machine. These traits enable applications to customize their environments and maintain consistent software configurations in their virtual domains. Additionally, there are mechanisms that can be used for fault tolerance like live virtual machine migration. Given these attractive benefits to virtualization, a fundamental question arises, how does this effect my scientific application? We use this as the premise for our paper and observe a real-world scientific code running on a Xenmore » virtual machine. We studied the effects of running a radiative transfer simulation, Hydrolight, on a virtual machine. We discuss our methodology and report observations regarding the usage of virtualization with this application.« less

CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arendt, Dustin L.; Komurlu, Caner; Blaha, Leslie M.

We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human andmore » machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.« less

Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy.

PubMed

Wang, Zhi-Long; Zhou, Zhi-Guo; Chen, Ying; Li, Xiao-Ting; Sun, Ying-Shi

The aim of this study was to diagnose lymph node metastasis of esophageal cancer by support vector machines model based on computed tomography. A total of 131 esophageal cancer patients with preoperative chemotherapy and radical surgery were included. Various indicators (tumor thickness, tumor length, tumor CT value, total number of lymph nodes, and long axis and short axis sizes of largest lymph node) on CT images before and after neoadjuvant chemotherapy were recorded. A support vector machines model based on these CT indicators was built to predict lymph node metastasis. Support vector machines model diagnosed lymph node metastasis better than preoperative short axis size of largest lymph node on CT. The area under the receiver operating characteristic curves were 0.887 and 0.705, respectively. The support vector machine model of CT images can help diagnose lymph node metastasis in esophageal cancer with preoperative chemotherapy.

Ergonomic risk factor identification for sewing machine operators through supervised occupational therapy fieldwork in Bangladesh: A case study.

PubMed

Habib, Md Monjurul

2015-01-01

Many sewing machine operators are working with high risk factors for musculoskeletal health in the garments industries in Bangladesh. To identify the physical risk factors among sewing machine operators in a Bangladeshi garments factory. Sewing machine operators (327, 83% female), were evaluated. The mean age of the participants was 25.25 years. Six ergonomic risk factors were determined using the Musculoskeletal Disorders risk assessment. Data collection included measurements of sewing machine table and chair heights; this data was combined with information from informal interviews. Significant ergonomic risk factors found included the combination of awkward postures of the neck and back, repetitive hand and arm movements, poor ergonomic workstations and prolonged working hours without adequate breaks; these risk factors resulted in musculoskeletal complaints, sick leave, and switching jobs. One aspect of improving worker health in garment factories includes addressing musculoskeletal risk factors through ergonomic interventions.

Machinability of cast commercial titanium alloys.

PubMed

Watanabe, I; Kiyosue, S; Ohkubo, C; Aoki, T; Okabe, T

2002-01-01

This study investigated the machinability of cast orthopedic titanium (metastable beta) alloys for possible application to dentistry and compared the results with those of cast CP Ti, Ti-6Al-4V, and Ti-6Al-7Nb, which are currently used in dentistry. Machinability was determined as the amount of metal removed with the use of an electric handpiece and a SiC abrasive wheel turning at four different rotational wheel speeds. The ratios of the amount of metal removed and the wheel volume loss (machining ratio) were also evaluated. Based on these two criteria, the two alpha + beta alloys tested generally exhibited better results for most of the wheel speeds compared to all the other metals tested. The machinability of the three beta alloys employed was similar or worse, depending on the speed of the wheel, compared to CP Ti. Copyright 2002 Wiley Periodicals, Inc.

Experiments in balance with a 2D one-legged hopping machine

NASA Astrophysics Data System (ADS)

Raibert, M. H.; Brown, H. B., Jr.

1984-03-01

The ability to balance is important to the mobility obtained by legged creatures found in nature, and may someday lead to versatile legged vehicles. In order to study the role of balance in legged locomotion and to develop appropriate control strategies, a 2D hopping machine was constructed for experimentation. The machine has one leg on which it hops and runs, making balance a prime consideration. Control of the machine's locomotion was decomposed into three separate parts: a vertical height control part, a horizontal velocity part, and an angular attitude control part. Experiments showed that the three part control scheme, while very simple to implement, was powerful enough to permit the machine to hop in place, to run at a desired rate, to translate from place to place, and to leap over obstacles. Results from modeling and computer simulation of a similar one-legged device are described by Raibert (1983).

A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

PubMed

Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

2017-04-01

Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

Do warning signs on electronic gaming machines influence irrational cognitions?

PubMed

Monaghan, Sally; Blaszczynski, Alex; Nower, Lia

2009-08-01

Electronic gaming machines are popular among problem gamblers; in response, governments have introduced "responsible gaming" legislation incorporating the mandatory display of warning signs on or near electronic gaming machines. These signs are designed to correct irrational and erroneous beliefs through the provision of accurate information on probabilities of winning and the concept of randomness. There is minimal empirical data evaluating the effectiveness of such signs. In this study, 93 undergraduate students were randomly allocated to standard and informative messages displayed on an electronic gaming machine during play in a laboratory setting. Results revealed that a majority of participants incorrectly estimated gambling odds and reported irrational gambling-related cognitions prior to play. In addition, there were no significant between-group differences, and few participants recalled the content of messages or modified their gambling-related cognitions. Signs placed on electronic gaming machines may not modify irrational beliefs or alter gambling behaviour.

Machine learning models in breast cancer survival prediction.

PubMed

Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

2016-01-01

Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of accuracy. Therefore, this model is recommended as a useful tool for breast cancer survival prediction as well as medical decision making.

Blue gum gaming machine: an evaluation of responsible gambling features.

PubMed

Blaszczynski, Alexander; Gainsbury, Sally; Karlov, Lisa

2014-09-01

Structural characteristics of gaming machines contribute to persistence in play and excessive losses. The purpose of this study was to evaluate the effectiveness of five proposed responsible gaming features: responsible gaming messages; a bank meter quarantining winnings until termination of play; alarm clock facilitating setting time-reminders; demo mode allowing play without money; and a charity donation feature where residual amounts can be donated rather than played to zero credits. A series of ten modified gaming machines were located in five Australian gambling venues. The sample comprised 300 patrons attending the venue and who played the gaming machines. Participants completed a structured interview eliciting gambling and socio-demographic data and information on their perceptions and experience of play on the index machines. Results showed that one-quarter of participants considered that these features would contribute to preventing recreational gamblers from developing problems. Just under half of the participants rated these effects to be at least moderate or significant. The promising results suggest that further refinements to several of these features could represent a modest but effective approach to minimising excessive gambling on gaming machines.

The impact of sound in modern multiline video slot machine play.

PubMed

Dixon, Mike J; Harrigan, Kevin A; Santesso, Diane L; Graydon, Candice; Fugelsang, Jonathan A; Collins, Karen

2014-12-01

Slot machine wins and losses have distinctive, measurable, physiological effects on players. The contributing factors to these effects remain under-explored. We believe that sound is one of these key contributing factors. Sound plays an important role in reinforcement, and thus on arousal level and stress response of players. It is the use of sound for positive reinforcement in particular that we believe influences the player. In the current study, we investigate the role that sound plays in psychophysical responses to slot machine play. A total of 96 gamblers played a slot machine simulator with and without sound being paired with reinforcement. Skin conductance responses and heart rate, as well as subjective judgments about the gambling experience were examined. The results showed that the sound influenced the arousal of participants both psychophysically and psychologically. The sound also influenced players' preferences, with the majority of players preferring to play slot machines that were accompanied by winning sounds. The sounds also caused players to significantly overestimate the number of times they won while playing the slot machine.

Convective Heat Transfer Coefficients of Automatic Transmission Fluid Jets with Implications for Electric Machine Thermal Management: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bennion, Kevin; Moreno, Gilberto

2015-09-29

Thermal management for electric machines (motors/ generators) is important as the automotive industry continues to transition to more electrically dominant vehicle propulsion systems. Cooling of the electric machine(s) in some electric vehicle traction drive applications is accomplished by impinging automatic transmission fluid (ATF) jets onto the machine's copper windings. In this study, we provide the results of experiments characterizing the thermal performance of ATF jets on surfaces representative of windings, using Ford's Mercon LV ATF. Experiments were carried out at various ATF temperatures and jet velocities to quantify the influence of these parameters on heat transfer coefficients. Fluid temperatures weremore » varied from 50 degrees C to 90 degrees C to encompass potential operating temperatures within an automotive transaxle environment. The jet nozzle velocities were varied from 0.5 to 10 m/s. The experimental ATF heat transfer coefficient results provided in this report are a useful resource for understanding factors that influence the performance of ATF-based cooling systems for electric machines.« less

Comparison of machinability of manganese alloyed austempered ductile iron produced using conventional and two step austempering processes

NASA Astrophysics Data System (ADS)

Hegde, Ananda; Sharma, Sathyashankara

2018-05-01

Austempered Ductile Iron (ADI) is a revolutionary material with high strength and hardness combined with optimum ductility and toughness. The discovery of two step austempering process has lead to the superior combination of all the mechanical properties. However, because of the high strength and hardness of ADI, there is a concern regarding its machinability. In the present study, machinability of ADI produced using conventional and two step heat treatment processes is assessed using tool life and the surface roughness. Speed, feed and depth of cut are considered as the machining parameters in the dry turning operation. The machinability results along with the mechanical properties are compared for ADI produced using both conventional and two step austempering processes. The results have shown that two step austempering process has produced better toughness with good hardness and strength without sacrificing ductility. Addition of 0.64 wt% manganese did not cause any detrimental effect on the machinability of ADI, both in conventional and two step processes. Marginal improvement in tool life and surface roughness were observed in two step process compared to that with conventional process.

A micro-machined source transducer for a parametric array in air.

PubMed

Lee, Haksue; Kang, Daesil; Moon, Wonkyu

2009-04-01

Parametric array applications in air, such as highly directional parametric loudspeaker systems, usually rely on large radiators to generate the high-intensity primary beams required for nonlinear interactions. However, a conventional transducer, as a primary wave projector, requires a great deal of electrical power because its electroacoustic efficiency is very low due to the large characteristic mechanical impedance in air. The feasibility of a micro-machined ultrasonic transducer as an efficient finite-amplitude wave projector was studied. A piezoelectric micro-machined ultrasonic transducer array consisting of lead zirconate titanate uni-morph elements was designed and fabricated for this purpose. Theoretical and experimental evaluations showed that a micro-machined ultrasonic transducer array can be used as an efficient source transducer for a parametric array in air. The beam patterns and propagation curves of the difference frequency wave and the primary wave generated by the micro-machined ultrasonic transducer array were measured. Although the theoretical results were based on ideal parametric array models, the theoretical data explained the experimental results reasonably well. These experiments demonstrated the potential of micro-machined primary wave projector.

The impact of the availability of school vending machines on eating behavior during lunch: the Youth Physical Activity and Nutrition Survey.

PubMed

Park, Sohyun; Sappenfield, William M; Huang, Youjie; Sherry, Bettylou; Bensyl, Diana M

2010-10-01

Childhood obesity is a major public health concern and is associated with substantial morbidities. Access to less-healthy foods might facilitate dietary behaviors that contribute to obesity. However, less-healthy foods are usually available in school vending machines. This cross-sectional study examined the prevalence of students buying snacks or beverages from school vending machines instead of buying school lunch and predictors of this behavior. Analyses were based on the 2003 Florida Youth Physical Activity and Nutrition Survey using a representative sample of 4,322 students in grades six through eight in 73 Florida public middle schools. Analyses included χ2 tests and logistic regression. The outcome measure was buying a snack or beverage from vending machines 2 or more days during the previous 5 days instead of buying lunch. The survey response rate was 72%. Eighteen percent of respondents reported purchasing a snack or beverage from a vending machine 2 or more days during the previous 5 school days instead of buying school lunch. Although healthier options were available, the most commonly purchased vending machine items were chips, pretzels/crackers, candy bars, soda, and sport drinks. More students chose snacks or beverages instead of lunch in schools where beverage vending machines were also available than did students in schools where beverage vending machines were unavailable: 19% and 7%, respectively (P≤0.05). The strongest risk factor for buying snacks or beverages from vending machines instead of buying school lunch was availability of beverage vending machines in schools (adjusted odds ratio=3.5; 95% confidence interval, 2.2 to 5.7). Other statistically significant risk factors were smoking, non-Hispanic black race/ethnicity, Hispanic ethnicity, and older age. Although healthier choices were available, the most common choices were the less-healthy foods. Schools should consider developing policies to reduce the availability of less-healthy choices in vending machines and to reduce access to beverage vending machines. Copyright © 2010 American Dietetic Association. Published by Elsevier Inc. All rights reserved.

Accuracy of tracking forest machines with GPS

Treesearch

M.W. Veal; S.E. Taylor; T.P. McDonald; D.K. McLemore; M.R. Dunn

2001-01-01

This paper describes the results of a study that measured the accuracy of using GPS to track movement of forest machines. Two different commercially available GPS receivers (Trimble ProXR and GeoExplorer II) were used to track\\r\

Physical mechanism of ultrasonic machining

NASA Astrophysics Data System (ADS)

Isaev, A.; Grechishnikov, V.; Kozochkin, M.; Pivkin, P.; Petuhov, Y.; Romanov, V.

2016-04-01

In this paper, the main aspects of ultrasonic machining of constructional materials are considered. Influence of coolant on surface parameters is studied. Results of experiments on ultrasonic lathe cutting with application of tangential vibrations and with use of coolant are considered.

Tunnel Boring Machine Performance Study. Final Report

DOT National Transportation Integrated Search

1984-06-01

Full face tunnel boring machine "TBM" performance during the excavation of 6 tunnels in sedimentary rock is considered in terms of utilization, penetration rates and cutter wear. The construction records are analyzed and the results are used to inves...

A hybrid flowshop scheduling model considering dedicated machines and lot-splitting for the solar cell industry

NASA Astrophysics Data System (ADS)

Wang, Li-Chih; Chen, Yin-Yann; Chen, Tzu-Li; Cheng, Chen-Yang; Chang, Chin-Wei

2014-10-01

This paper studies a solar cell industry scheduling problem, which is similar to traditional hybrid flowshop scheduling (HFS). In a typical HFS problem, the allocation of machine resources for each order should be scheduled in advance. However, the challenge in solar cell manufacturing is the number of machines that can be adjusted dynamically to complete the job. An optimal production scheduling model is developed to explore these issues, considering the practical characteristics, such as hybrid flowshop, parallel machine system, dedicated machines, sequence independent job setup times and sequence dependent job setup times. The objective of this model is to minimise the makespan and to decide the processing sequence of the orders/lots in each stage, lot-splitting decisions for the orders and the number of machines used to satisfy the demands in each stage. From the experimental results, lot-splitting has significant effect on shortening the makespan, and the improvement effect is influenced by the processing time and the setup time of orders. Therefore, the threshold point to improve the makespan can be identified. In addition, the model also indicates that more lot-splitting approaches, that is, the flexibility of allocating orders/lots to machines is larger, will result in a better scheduling performance.

Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

PubMed

Oh, Jooyoung; Cho, Dongrae; Park, Jaesub; Na, Se Hee; Kim, Jongin; Heo, Jaeseok; Shin, Cheung Soo; Kim, Jae-Jin; Park, Jin Young; Lee, Boreom

2018-03-27

Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

Stability Assessment of a System Comprising a Single Machine and Inverter with Scalable Ratings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Brian B; Lin, Yashen; Gevorgian, Vahan

From the inception of power systems, synchronous machines have acted as the foundation of large-scale electrical infrastructures and their physical properties have formed the cornerstone of system operations. However, power electronics interfaces are playing a growing role as they are the primary interface for several types of renewable energy sources and storage technologies. As the role of power electronics in systems continues to grow, it is crucial to investigate the properties of bulk power systems in low inertia settings. In this paper, we assess the properties of coupled machine-inverter systems by studying an elementary system comprised of a synchronous generator,more » three-phase inverter, and a load. Furthermore, the inverter model is formulated such that its power rating can be scaled continuously across power levels while preserving its closed-loop response. Accordingly, the properties of the machine-inverter system can be assessed for varying ratios of machine-to-inverter power ratings and, hence, differing levels of inertia. After linearizing the model and assessing its eigenvalues, we show that system stability is highly dependent on the interaction between the inverter current controller and machine exciter, thus uncovering a key concern with mixed machine-inverter systems and motivating the need for next-generation grid-stabilizing inverter controls.« less

Prediction of antiepileptic drug treatment outcomes using machine learning.

PubMed

Colic, Sinisa; Wither, Robert G; Lang, Min; Zhang, Liang; Eubanks, James H; Bardakjian, Berj L

2017-02-01

Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs. Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome. Previous work have linked the presence of cross-frequency coupling (I CFC ) of the delta (2-5 Hz) rhythm with the fast ripple (400-600 Hz) rhythm in epileptiform discharges. Using the I CFC to label post-treatment outcomes we compared support vector machines (SVMs) and random forest (RF) machine learning classifiers for providing likelihood scores of successful treatment outcomes. (a) There was heterogeneity in AED treatment outcomes, (b) machine learning techniques could be used to rank the efficacy of AEDs by estimating likelihood scores for successful treatment outcome, (c) I CFC features yielded the most effective a priori identification of appropriate AED treatment, and (d) both classifiers performed comparably. Machine learning approaches yielded predictions of successful drug treatment outcomes which in turn could reduce the burdens of drug trials and lead to substantial improvements in patient quality of life.

Effects of machining parameters on tool life and its optimization in turning mild steel with brazed carbide cutting tool

NASA Astrophysics Data System (ADS)

Dasgupta, S.; Mukherjee, S.

2016-09-01

One of the most significant factors in metal cutting is tool life. In this research work, the effects of machining parameters on tool under wet machining environment were studied. Tool life characteristics of brazed carbide cutting tool machined against mild steel and optimization of machining parameters based on Taguchi design of experiments were examined. The experiments were conducted using three factors, spindle speed, feed rate and depth of cut each having three levels. Nine experiments were performed on a high speed semi-automatic precision central lathe. ANOVA was used to determine the level of importance of the machining parameters on tool life. The optimum machining parameter combination was obtained by the analysis of S/N ratio. A mathematical model based on multiple regression analysis was developed to predict the tool life. Taguchi's orthogonal array analysis revealed the optimal combination of parameters at lower levels of spindle speed, feed rate and depth of cut which are 550 rpm, 0.2 mm/rev and 0.5mm respectively. The Main Effects plot reiterated the same. The variation of tool life with different process parameters has been plotted. Feed rate has the most significant effect on tool life followed by spindle speed and depth of cut.

Prediction of antiepileptic drug treatment outcomes using machine learning

NASA Astrophysics Data System (ADS)

Colic, Sinisa; Wither, Robert G.; Lang, Min; Zhang, Liang; Eubanks, James H.; Bardakjian, Berj L.

2017-02-01

Objective. Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs. Approach. Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome. Previous work have linked the presence of cross-frequency coupling (I CFC) of the delta (2-5 Hz) rhythm with the fast ripple (400-600 Hz) rhythm in epileptiform discharges. Using the I CFC to label post-treatment outcomes we compared support vector machines (SVMs) and random forest (RF) machine learning classifiers for providing likelihood scores of successful treatment outcomes. Main results. (a) There was heterogeneity in AED treatment outcomes, (b) machine learning techniques could be used to rank the efficacy of AEDs by estimating likelihood scores for successful treatment outcome, (c) I CFC features yielded the most effective a priori identification of appropriate AED treatment, and (d) both classifiers performed comparably. Significance. Machine learning approaches yielded predictions of successful drug treatment outcomes which in turn could reduce the burdens of drug trials and lead to substantial improvements in patient quality of life.

Developing Parametric Models for the Assembly of Machine Fixtures for Virtual Multiaxial CNC Machining Centers

NASA Astrophysics Data System (ADS)

Balaykin, A. V.; Bezsonov, K. A.; Nekhoroshev, M. V.; Shulepov, A. P.

2018-01-01

This paper dwells upon a variance parameterization method. Variance or dimensional parameterization is based on sketching, with various parametric links superimposed on the sketch objects and user-imposed constraints in the form of an equation system that determines the parametric dependencies. This method is fully integrated in a top-down design methodology to enable the creation of multi-variant and flexible fixture assembly models, as all the modeling operations are hierarchically linked in the built tree. In this research the authors consider a parameterization method of machine tooling used for manufacturing parts using multiaxial CNC machining centers in the real manufacturing process. The developed method allows to significantly reduce tooling design time when making changes of a part’s geometric parameters. The method can also reduce time for designing and engineering preproduction, in particular, for development of control programs for CNC equipment and control and measuring machines, automate the release of design and engineering documentation. Variance parameterization helps to optimize construction of parts as well as machine tooling using integrated CAE systems. In the framework of this study, the authors demonstrate a comprehensive approach to parametric modeling of machine tooling in the CAD package used in the real manufacturing process of aircraft engines.

A Comparative Study of "Google Translate" Translations: An Error Analysis of English-to-Persian and Persian-to-English Translations

ERIC Educational Resources Information Center

Ghasemi, Hadis; Hashemian, Mahmood

2016-01-01

Both lack of time and the need to translate texts for numerous reasons brought about an increase in studying machine translation with a history spanning over 65 years. During the last decades, Google Translate, as a statistical machine translation (SMT), was in the center of attention for supporting 90 languages. Although there are many studies on…

Analysis and design of asymmetrical reluctance machine

NASA Astrophysics Data System (ADS)

Harianto, Cahya A.

Over the past few decades the induction machine has been chosen for many applications due to its structural simplicity and low manufacturing cost. However, modest torque density and control challenges have motivated researchers to find alternative machines. The permanent magnet synchronous machine has been viewed as one of the alternatives because it features higher torque density for a given loss than the induction machine. However, the assembly and permanent magnet material cost, along with safety under fault conditions, have been concerns for this class of machine. An alternative machine type, namely the asymmetrical reluctance machine, is proposed in this work. Since the proposed machine is of the reluctance machine type, it possesses desirable feature, such as near absence of rotor losses, low assembly cost, low no-load rotational losses, modest torque ripple, and rather benign fault conditions. Through theoretical analysis performed herein, it is shown that this machine has a higher torque density for a given loss than typical reluctance machines, although not as high as the permanent magnet machines. Thus, the asymmetrical reluctance machine is a viable and advantageous machine alternative where the use of permanent magnet machines are undesirable.

Study on Electro-polymerization Nano-micro Wiring System Imitating Axonal Growth of Artificial Neurons towards Machine Learning

NASA Astrophysics Data System (ADS)

Dang, Nguyen Tuan; Akai-Kasada, Megumi; Asai, Tetsuya; Saito, Akira; Kuwahara, Yuji; Hokkaido University Collaboration

2015-03-01

Machine learning using the artificial neuron network research is supposed to be the best way to understand how the human brain trains itself to process information. In this study, we have successfully developed the programs using supervised machine learning algorithm. However, these supervised learning processes for the neuron network required the very strong computing configuration. Derivation from the necessity of increasing in computing ability and in reduction of power consumption, accelerator circuits become critical. To develop such accelerator circuits using supervised machine learning algorithm, conducting polymer micro/nanowires growing process was realized and applied as a synaptic weigh controller. In this work, high conductivity Polypyrrole (PPy) and Poly (3, 4 - ethylenedioxythiophene) PEDOT wires were potentiostatically grown crosslinking the designated electrodes, which were prefabricated by lithography, when appropriate square wave AC voltage and appropriate frequency were applied. Micro/nanowire growing process emulated the neurotransmitter release process of synapses inside a biological neuron and wire's resistance variation during the growing process was preferred to as the variation of synaptic weigh in machine learning algorithm. In a cooperation with Graduate School of Information Science and Technology, Hokkaido University.

Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein-Ligand Interactions.

PubMed

Li, Yang; Yang, Jianyi

2017-04-24

The prediction of protein-ligand binding affinity has recently been improved remarkably by machine-learning-based scoring functions. For example, using a set of simple descriptors representing the atomic distance counts, the RF-Score improves the Pearson correlation coefficient to about 0.8 on the core set of the PDBbind 2007 database, which is significantly higher than the performance of any conventional scoring function on the same benchmark. A few studies have been made to discuss the performance of machine-learning-based methods, but the reason for this improvement remains unclear. In this study, by systemically controlling the structural and sequence similarity between the training and test proteins of the PDBbind benchmark, we demonstrate that protein structural and sequence similarity makes a significant impact on machine-learning-based methods. After removal of training proteins that are highly similar to the test proteins identified by structure alignment and sequence alignment, machine-learning-based methods trained on the new training sets do not outperform the conventional scoring functions any more. On the contrary, the performance of conventional functions like X-Score is relatively stable no matter what training data are used to fit the weights of its energy terms.

Machine Learning Approaches in Cardiovascular Imaging.

PubMed

Henglin, Mir; Stein, Gillian; Hushcha, Pavel V; Snoek, Jasper; Wiltschko, Alexander B; Cheng, Susan

2017-10-01

Cardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume of imaging data available for analyses. Machine learning methods can now address data-related problems ranging from simple analytic queries of existing measurement data to the more complex challenges involved in analyzing raw images. To date, machine learning has been used in 2 broad and highly interconnected areas: automation of tasks that might otherwise be performed by a human and generation of clinically important new knowledge. Most cardiovascular imaging studies have focused on task-oriented problems, but more studies involving algorithms aimed at generating new clinical insights are emerging. Continued expansion in the size and dimensionality of cardiovascular imaging databases is driving strong interest in applying powerful deep learning methods, in particular, to analyze these data. Overall, the most effective approaches will require an investment in the resources needed to appropriately prepare such large data sets for analyses. Notwithstanding current technical and logistical challenges, machine learning and especially deep learning methods have much to offer and will substantially impact the future practice and science of cardiovascular imaging. © 2017 American Heart Association, Inc.

[Re-signification of the human in the context of the "ciborgzation": a look at the human being-machine relationship in intensive care].

PubMed

Vargas, Mara Ambrosina de O; Meyer, Dagmar Estermann

2005-06-01

This study discusses the human being-machine relationship in the process called "cyborgzation" of the nurse who works in intensive care, based on post-structuralist Cultural Studies and highlighting Haraway's concept of cyborg. In it, manuals used by nurses in Intensive Care Units have been examined as cultural texts. This cultural analysis tries to decode the various senses of "human" and "machine", with the aim of recognizing processes that turn nurses into cyborgs. The argument is that intensive care nurses fall into a process of "technology embodiment" that turns the body-professional into a hybrid that makes possible to disqualify, at the same time, notions such as machine and body "proper", since it is the hybridization between one and the other that counts there. Like cyborgs, intensive care nurses learn to "be with" the machine, and this connection limits the specificity of their actions. It is suggested that processes of "cyborgzation" such as this are useful for questioning - and to deal with in different ways - the senses of "human" and "humanity" that support a major part of knowledge/action in health.

Multiple performance characteristics optimization for Al 7075 on electric discharge drilling by Taguchi grey relational theory

NASA Astrophysics Data System (ADS)

Khanna, Rajesh; Kumar, Anish; Garg, Mohinder Pal; Singh, Ajit; Sharma, Neeraj

2015-12-01

Electric discharge drill machine (EDDM) is a spark erosion process to produce micro-holes in conductive materials. This process is widely used in aerospace, medical, dental and automobile industries. As for the performance evaluation of the electric discharge drilling machine, it is very necessary to study the process parameters of machine tool. In this research paper, a brass rod 2 mm diameter was selected as a tool electrode. The experiments generate output responses such as tool wear rate (TWR). The best parameters such as pulse on-time, pulse off-time and water pressure were studied for best machining characteristics. This investigation presents the use of Taguchi approach for better TWR in drilling of Al-7075. A plan of experiments, based on L27 Taguchi design method, was selected for drilling of material. Analysis of variance (ANOVA) shows the percentage contribution of the control factor in the machining of Al-7075 in EDDM. The optimal combination levels and the significant drilling parameters on TWR were obtained. The optimization results showed that the combination of maximum pulse on-time and minimum pulse off-time gives maximum MRR.

Reliability Evaluation and Improvement Approach of Chemical Production Man - Machine - Environment System

NASA Astrophysics Data System (ADS)

Miao, Yongchun; Kang, Rongxue; Chen, Xuefeng

2017-12-01

In recent years, with the gradual extension of reliability research, the study of production system reliability has become the hot topic in various industries. Man-machine-environment system is a complex system composed of human factors, machinery equipment and environment. The reliability of individual factor must be analyzed in order to gradually transit to the research of three-factor reliability. Meanwhile, the dynamic relationship among man-machine-environment should be considered to establish an effective blurry evaluation mechanism to truly and effectively analyze the reliability of such systems. In this paper, based on the system engineering, fuzzy theory, reliability theory, human error, environmental impact and machinery equipment failure theory, the reliabilities of human factor, machinery equipment and environment of some chemical production system were studied by the method of fuzzy evaluation. At last, the reliability of man-machine-environment system was calculated to obtain the weighted result, which indicated that the reliability value of this chemical production system was 86.29. Through the given evaluation domain it can be seen that the reliability of man-machine-environment integrated system is in a good status, and the effective measures for further improvement were proposed according to the fuzzy calculation results.

Dental cutting behaviour of mica-based and apatite-based machinable glass-ceramics.

PubMed

Taira, M; Wakasa, K; Yamaki, M; Matsui, A

1990-09-01

Some recently developed industrial ceramics have excellent machinability properties. The objective of this study was to evaluate the dental cutting behaviour of two machinable glass-ceramics, mica-containing Macor-M and apatite- and diopside-containing Bioram-M, and to compare them with the cutting behaviour of a composite resin typodont tooth enamel and bovine enamel. Weight-load cutting tests were conducted, using a diamond point driven by an air-turbine handpiece, While the transverse load applied on the point was varied, the handpiece speed during cutting and the volume of removal upon cutting were measured. In general, an increase in the applied load caused a decrease in cutting speed and an increase in cutting volume. However, the intensity of this trend was found to differ between four workpieces. Cutting Macor-M resulted in the second-most reduced cutting speed and the maximum cutting volume. Cutting Bioram-M gave the least reduced cutting speed and the minimum cutting volume. It was suggested that two machinable glass-ceramics could be employed as typodont teeth. This study may also contribute to the development of new restorative dental ceramic materials, prepared by machining.

An Adaptive Genetic Association Test Using Double Kernel Machines

PubMed Central

Zhan, Xiang; Epstein, Michael P.; Ghosh, Debashis

2014-01-01

Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares kernel machine (LSKM) test for testing the effect of the subset of genes. An appealing feature of the kernel machine framework is that it can provide a flexible and unified method for multi-dimensional modeling of the genetic pathway effect allowing for both parametric and nonparametric components. This DKM approach is illustrated with application to simulated data as well as to data from a neuroimaging genetics study. PMID:26640602

Student Achievement Study, 1970-1974. The IEA Six-Subject Data Bank [machine-readable data file].

ERIC Educational Resources Information Center

International Association for the Evaluation of Educational Achievement, Stockholm (Sweden).

The "Student Achievement Study" machine-readable data files (MRDF) (also referred to as the "IEA Six-Subject Survey") are the result of an international data collection effort during 1970-1974 by 21 designated National Centers, which had agreed to cooperate. The countries involved were: Australia, Belgium, Chile, England-Wales,…

Fluid Resistance Studies with an Atwood Machine

NASA Astrophysics Data System (ADS)

Taylor, Ken; Aragon, Omar; Braun, Russell; Fessahaie, Ellias

2008-03-01

An Atwood machine in which one of its masses moves through water is used to study fluid resistance. In particular, efforts are made to compare the effects of the water resistance for objects of similar geometry but different densities. The presentation describes the apparatus, the computer system used for data acquisition and the various schemes used in the investigation.

Health Promotion and Healthier Products Increase Vending Purchases: A Randomized Factorial Trial.

PubMed

Hua, Sophia V; Kimmel, Lisa; Van Emmenes, Michael; Taherian, Rafi; Remer, Geraldine; Millman, Adam; Ickovics, Jeannette R

2017-07-01

The current food environment has a high prevalence of nutrient-sparse foods and beverages, most starkly seen in vending machine offerings. There are currently few studies that explore different interventions that might lead to healthier vending machine purchases. To examine how healthier product availability, price reductions, and/or promotional signs affect sales and revenue of snack and beverage vending machines. A 2×2×2 factorial randomized controlled trial was conducted. Students, staff, and employees on a university campus. All co-located snack and beverage vending machines (n=56, 28 snack and 28 beverage) were randomized into one of eight conditions: availability of healthier products and/or 25% price reduction for healthier items and/or promotional signs on machines. Aggregate sales and revenue data for the 5-month study period (February to June 2015) were compared with data from the same months 1 year prior. Analyses were conducted July 2015. The change in units sold and revenue between February through June 2014 and 2015. Linear regression models (main effects and interaction effects) and t test analyses were performed. The interaction between healthier product guidelines and promotional signs in snack vending machines documented increased revenue (P<0.05). Beverage machines randomized to meet healthier product guidelines documented increased units sold (P<0.05) with no revenue change. Price reductions alone had no effect, nor were there any effects for the three-way interaction of the factors. Examining top-selling products for all vending machines combined, pre- to postintervention, we found an overall shift to healthier purchasing. When healthier vending snacks are available, promotional signs are also important to ensure consumers purchase those items in greater amounts. Mitigating potential loss in profits is essential for sustainability of a healthier food environment. Copyright © 2017 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

PubMed Central

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

A new optimization tool path planning for 3-axis end milling of free-form surfaces based on efficient machining intervals

NASA Astrophysics Data System (ADS)

Vu, Duy-Duc; Monies, Frédéric; Rubio, Walter

2018-05-01

A large number of studies, based on 3-axis end milling of free-form surfaces, seek to optimize tool path planning. Approaches try to optimize the machining time by reducing the total tool path length while respecting the criterion of the maximum scallop height. Theoretically, the tool path trajectories that remove the most material follow the directions in which the machined width is the largest. The free-form surface is often considered as a single machining area. Therefore, the optimization on the entire surface is limited. Indeed, it is difficult to define tool trajectories with optimal feed directions which generate largest machined widths. Another limiting point of previous approaches for effectively reduce machining time is the inadequate choice of the tool. Researchers use generally a spherical tool on the entire surface. However, the gains proposed by these different methods developed with these tools lead to relatively small time savings. Therefore, this study proposes a new method, using toroidal milling tools, for generating toolpaths in different regions on the machining surface. The surface is divided into several regions based on machining intervals. These intervals ensure that the effective radius of the tool, at each cutter-contact points on the surface, is always greater than the radius of the tool in an optimized feed direction. A parallel plane strategy is then used on the sub-surfaces with an optimal specific feed direction for each sub-surface. This method allows one to mill the entire surface with efficiency greater than with the use of a spherical tool. The proposed method is calculated and modeled using Maple software to find optimal regions and feed directions in each region. This new method is tested on a free-form surface. A comparison is made with a spherical cutter to show the significant gains obtained with a toroidal milling cutter. Comparisons with CAM software and experimental validations are also done. The results show the efficiency of the method.

Modeling of Electrochemical Copying in a Finite-Width Cell

NASA Astrophysics Data System (ADS)

Zhitnikov, V. P.; Sherykhalina, N. M.; Zaripov, A. A.

2017-11-01

The problem of modeling of electrochemical machining is reduced to the solution of the Schwartz problem on a parametrical rectangle with the use of theta-functions. Various conditions (non-equipotentiality of electrodes and inconstancy of current efficiency) at the boundary of a processed surface are considered. Nonstationary, quasistationary, stationary, and limit solutions are studied. Results of machining of surfaces by tool electrodes of various shapes are given. It is shown that machining mode parameters significantly affect the dissolved layer size necessary for obtaining high-precision copying.

Machine learning phases of matter

NASA Astrophysics Data System (ADS)

Carrasquilla, Juan; Stoudenmire, Miles; Melko, Roger

We show how the technology that allows automatic teller machines read hand-written digits in cheques can be used to encode and recognize phases of matter and phase transitions in many-body systems. In particular, we analyze the (quasi-)order-disorder transitions in the classical Ising and XY models. Furthermore, we successfully use machine learning to study classical Z2 gauge theories that have important technological application in the coming wave of quantum information technologies and whose phase transitions have no conventional order parameter.

Adapting human-machine interfaces to user performance.

PubMed

Danziger, Zachary; Fishbach, Alon; Mussa-Ivaldi, Ferdinando A

2008-01-01

The goal of this study was to create and examine machine learning algorithms that adapt in a controlled and cadenced way to foster a harmonious learning environment between the user of a human-machine interface and the controlled device. In this experiment, subjects' high-dimensional finger motions remotely controlled the joint angles of a simulated planar 2-link arm, which was used to hit targets on a computer screen. Subjects were required to move the cursor at the endpoint of the simulated arm.

Parallel machine architecture and compiler design facilities

NASA Technical Reports Server (NTRS)

Kuck, David J.; Yew, Pen-Chung; Padua, David; Sameh, Ahmed; Veidenbaum, Alex

1990-01-01

The objective is to provide an integrated simulation environment for studying and evaluating various issues in designing parallel systems, including machine architectures, parallelizing compiler techniques, and parallel algorithms. The status of Delta project (which objective is to provide a facility to allow rapid prototyping of parallelized compilers that can target toward different machine architectures) is summarized. Included are the surveys of the program manipulation tools developed, the environmental software supporting Delta, and the compiler research projects in which Delta has played a role.

Diamond turning of Si and Ge single crystals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blake, P.; Scattergood, R.O.

Single-point diamond turning studies have been completed on Si and Ge crystals. A new process model was developed for diamond turning which is based on a critical depth of cut for plastic flow-to-brittle fracture transitions. This concept, when combined with the actual machining geometry for single-point turning, predicts that {open_quotes}ductile{close_quotes} machining is a combined action of plasticity and fracture. Interrupted cutting experiments also provide a meant to directly measure the critical depth parameter for given machining conditions.

Antifungal Susceptibility Testing in HIV/AIDS Patients: a Comparison Between Automated Machine and Manual Method.

PubMed

Nelwan, Erni J; Indrasanti, Evi; Sinto, Robert; Nurchaida, Farida; Sosrosumihardjo, Rustadi

2016-01-01

to evaluate the performance of Vitek2 compact machine (Biomerieux Inc. ver 04.02, France) in reference to manual methods for susceptibility test for Candida resistance among HIV/AIDS patients. a comparison study to evaluate Vitek2 compact machine (Biomerieux Inc. ver 04.02, France) in reference to manual methods for susceptibility test for Candida resistance among HIV/AIDS patient was done. Categorical agreement between manual disc diffusion and Vitek2 machine was calculated using predefined criteria. Time to susceptibility result for automated and manual methods were measured. there were 137 Candida isolates comprising eight Candida species with C.albicans and C. glabrata as the first (56.2%) and second (15.3%) most common species, respectively. For fluconazole drug, among the C. albicans, 2.6% was found resistant on manual disc diffusion methods and no resistant was determined by Vitek2 machine; whereas 100% C. krusei was identified as resistant on both methods. Resistant patterns for C. glabrata to fluconazole, voriconazole and amphotericin B were 52.4%, 23.8%, 23.8% vs. 9.5%, 9.5%, 4.8% respectively between manual diffusion disc methods and Vitek2 machine. Time to susceptibility result for automated methods compared to Vitex2 machine was shorter for all Candida species. there is a good categorical agreement between manual disc diffusion and Vitek2 machine, except for C. glabrata for measuring the antifungal resistant. Time to susceptibility result for automated methods is shorter for all Candida species.

Hypothermic machine perfusion permits extended cold ischemia times with improved early graft function.

PubMed

Guy, Alison; McGrogan, Damian; Inston, Nicholas; Ready, Andrew

2015-04-01

The logistics of deceased-donor renal transplants are largely affected by cold ischemia time. However, to attain successful outcomes, other issues must be considered. Extending cold ischemia time to accommodate these issues would be valuable. We investigated the role of hypothermic machine perfusion to extend cold ischaemia time. Deceased-donor kidneys were allocated to a storage method, depending on predicted time to operation. Kidneys to be transplanted from 8:00 AM to 8:00 PM in the transplant room remained in static cold storage. If predicted operating time was out of hours, the kidney was transferred to hypothermic machine perfusion and transplanted at the earliest opportunity on the dedicated transplant list. There were 74 kidneys transplanted from hypothermic machine perfusion and 101 kidneys from static cold storage. Median cold ischemia time was 23.85 hours in the hypothermic machine perfusion group, compared with 13 hours in the static cold storage group (P ≤ .0001). There were 20 kidneys (27%) from hypothermic machine perfusion that had delayed graft function, compared with 47 kidneys (47%) in the static cold storage group (P = .012). There were no other significant differences in graft or postoperative complications. This study demonstrated that improved early graft outcomes can be achieved following longer cold ischemia time by using hypothermic machine perfusion rather than static cold storage. This effect is likely multifactorial including the inherent effects of hypothermic machine perfusion, improved recipient preparation, and possibly better perioperative conditions.

Incidence of MSDs and neck and back pain among logging machine operators in the southern U.S.

PubMed

Lynch, S M; Smidt, M F; Merrill, P D; Sesek, R F

2014-07-01

There are limited data about the incidence and prevalence of musculoskeletal disorders (MSDs) among loggers in the southern U.S. despite the risk factors associated with these occupations. Risk factors are both personal (age, body mass index, etc.) and job-related (awkward postures, repetitive hand and foot movements, vibration, etc.). A survey was conducted to estimate the incidence of self-reported pain and diagnosed MSDs and to study the relationship with known risk factors. Respondents were loggers attending training and continuing education classes. Respondents were asked to identify personal attributes, machine use, awkward postures, repetitive movements, and recent incidence of pain and medical diagnoses. All were male with an average age of 44 (range of 19-67) and an average body mass index of 31.3. Most were machine operators (97%) who have worked in the logging industry for an average of 22.9 years. Most machines identified were manufactured within the past ten years (average machine age 6.7 years). For machine operators, 10.5% (16) reported an MSD diagnosis, 74.3% (113) reported at least mild back pain, and 71.7% (109) reported at least mild neck pain over the past year. Further analysis attempted to identify an association between personal attributes, machine use, posture, and pain. Risk factors related to machine use may be biased since most survey respondents had considerable choice or control in working conditions, as they were firm owners and/or supervisors.

The Design, Synthesis, and Study of Solid-State Molecular Rotors: Structure/Function Relationships for Condensed-Phase Anisotropic Dynamics

NASA Astrophysics Data System (ADS)

Vogelsberg, Cortnie Sue

Amphidynamic crystals are an extremely promising platform for the development of artificial molecular machines and stimuli-responsive materials. In analogy to skeletal muscle, their function will rely upon the collective operation of many densely packed molecular machines (i.e. actin-bound myosin) that are self-assembled in a highly organized anisotropic medium. By choosing lattice-forming elements and moving "parts" with specific functionalities, individual molecular machines may be synthesized and self-assembled in order to carry out desirable functions. In recent years, efforts in the design of amphidynamic materials based on molecular gyroscopes and compasses have shown that a certain amount of free volume is essential to facilitate internal rotation and reorientation within a crystal. In order to further establish structure/function relationships to advance the development of increasingly complex molecular machinery, molecular rotors and a molecular "spinning" top were synthesized and incorporated into a variety of solid-state architectures with different degrees of periodicity, dimensionality, and free volume. Specifically, lamellar molecular crystals, hierarchically ordered periodic mesoporous organosilicas, and metal-organic frameworks were targeted for the development of solid-state molecular machines. Using an array of solid-state nuclear magnetic resonance spectroscopy techniques, the dynamic properties of these novel molecular machine assemblies were determined and correlated with their corresponding structural features. It was found that architecture type has a profound influence on functional dynamics. The study of layered molecular crystals, composed of either molecular rotors or "spinning" tops, probed functional dynamics within dense, highly organized environments. From their study, it was discovered that: 1) crystallographically distinct sites may be utilized to differentiate machine function, 2) halogen bonding interactions are sufficiently strong to direct an assembly of molecular machines, 3) the relative flexibility of the crystal environment proximate to a dynamic component may have a significant effect on its function, and, 4) molecular machines, which possess both solid-state photochemical reactivity and dynamics may show complex reaction kinetics if the correlation time of the dynamic process and the lifetime of the excited state occur on the same time scale and the dynamic moiety inherently participates as a reaction intermediate. The study of periodic mesoporous organosilica with hierarchical order probed molecular dynamics within 2D layers of molecular rotors, organized in only one dimension and with ca. 50% exposed to the mesopore free volume. From their study, it was discovered that: 1) molecular rotors, which comprise the layers of the mesopore walls, form a 2D rotational glass, 2) rotator dynamics within the 2D rotational glass undergo a transition to a 2D rotational fluid, and, 3) a 2D rotational glass transition may be exploited to develop hyper-sensitive thermally activated molecular machines. The study of a metal-organic framework assembled from molecular rotors probed dynamics in a periodic three-dimensional free-volume environment, without the presence of close contacts. From the study of this solid-state material, it was determined that: 1) the intrinsic electronic barrier is one of the few factors, which may affect functional dynamics in a true free-volume environment, and, 2) molecular machines with dynamic barriers <

Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance.

PubMed

Vallmuur, Kirsten; Marucci-Wellman, Helen R; Taylor, Jennifer A; Lehto, Mark; Corns, Helen L; Smith, Gordon S

2016-04-01

Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semiautomatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and PPV and reduced the need for human coding to less than a third of cases in one large occupational injury database. The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of 'big injury narrative data' opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Prophylactic Procurement of University Students in Southern Ethiopia: Stigma and the Value of Condom Machines on Campus

PubMed Central

Wells, Christopher J.; Alano, Abraham

2013-01-01

Introduction Risky sexual behavior among Ethiopian university students, especially females, is a major contributor to young adult morbidity and mortality. Ambaw et al. found that female university students in Ethiopia may fear the humiliation associated with procuring condoms. A study in Thailand suggests condom machines may provide comfortable condom procurement, but the relevance to a high-risk African context is unknown. The objective of this study was to examine if the installation of condom machines in Ethiopia predicts changes in student condom uptake and use, as well as changes in procurement related stigma. Methods Students at a large urban university in Southern Ethiopia completed self reported surveys in 2010 (N  = 2,155 surveys) and again in 2011 (N =  2,000), six months after the installation of condom machines. Mann-Whitney and Chi-square tests were conducted to evaluate significant changes in student sexual behavior, as well as condom procurement and associated stigma over the subsequent one year period. Results After installing condom machines, the average number of trips made to procure condoms on-campus significantly increased 101% for sexually active females and significantly decreased 36% for sexually active males. Additionally, reports of condom use during last sexual intercourse showed a non-significant 4.3% increase for females and a significant 9.0% increase for males. During this time, comfort procuring condoms and ability to convince sexual partners to use condoms were significantly higher for sexually active male students. There was no evidence that the condom machines led to an increase in promiscuity. Conclusions The results suggest that condom machines may be associated with more condom procurement among vulnerable female students in Ethiopia and could be an important component of a comprehensive university health policy. PMID:23565272

A feasibility study of automatic lung nodule detection in chest digital tomosynthesis with machine learning based on support vector machine

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Kim, Ye-seul; Choi, Sunghoon; Lee, Haenghwa; Jo, Byungdu; Choi, Seungyeon; Shin, Jungwook; Kim, Hee-Joung

2017-03-01

The chest digital tomosynthesis(CDT) is recently developed medical device that has several advantage for diagnosing lung disease. For example, CDT provides depth information with relatively low radiation dose compared to computed tomography (CT). However, a major problem with CDT is the image artifacts associated with data incompleteness resulting from limited angle data acquisition in CDT geometry. For this reason, the sensitivity of lung disease was not clear compared to CT. In this study, to improve sensitivity of lung disease detection in CDT, we developed computer aided diagnosis (CAD) systems based on machine learning. For design CAD systems, we used 100 cases of lung nodules cropped images and 100 cases of normal lesion cropped images acquired by lung man phantoms and proto type CDT. We used machine learning techniques based on support vector machine and Gabor filter. The Gabor filter was used for extracting characteristics of lung nodules and we compared performance of feature extraction of Gabor filter with various scale and orientation parameters. We used 3, 4, 5 scales and 4, 6, 8 orientations. After extracting features, support vector machine (SVM) was used for classifying feature of lesions. The linear, polynomial and Gaussian kernels of SVM were compared to decide the best SVM conditions for CDT reconstruction images. The results of CAD system with machine learning showed the capability of automatically lung lesion detection. Furthermore detection performance was the best when Gabor filter with 5 scale and 8 orientation and SVM with Gaussian kernel were used. In conclusion, our suggested CAD system showed improving sensitivity of lung lesion detection in CDT and decide Gabor filter and SVM conditions to achieve higher detection performance of our developed CAD system for CDT.

Clinical data miner: an electronic case report form system with integrated data preprocessing and machine-learning libraries supporting clinical diagnostic model research.

PubMed

Installé, Arnaud Jf; Van den Bosch, Thierry; De Moor, Bart; Timmerman, Dirk

2014-10-20

Using machine-learning techniques, clinical diagnostic model research extracts diagnostic models from patient data. Traditionally, patient data are often collected using electronic Case Report Form (eCRF) systems, while mathematical software is used for analyzing these data using machine-learning techniques. Due to the lack of integration between eCRF systems and mathematical software, extracting diagnostic models is a complex, error-prone process. Moreover, due to the complexity of this process, it is usually only performed once, after a predetermined number of data points have been collected, without insight into the predictive performance of the resulting models. The objective of the study of Clinical Data Miner (CDM) software framework is to offer an eCRF system with integrated data preprocessing and machine-learning libraries, improving efficiency of the clinical diagnostic model research workflow, and to enable optimization of patient inclusion numbers through study performance monitoring. The CDM software framework was developed using a test-driven development (TDD) approach, to ensure high software quality. Architecturally, CDM's design is split over a number of modules, to ensure future extendability. The TDD approach has enabled us to deliver high software quality. CDM's eCRF Web interface is in active use by the studies of the International Endometrial Tumor Analysis consortium, with over 4000 enrolled patients, and more studies planned. Additionally, a derived user interface has been used in six separate interrater agreement studies. CDM's integrated data preprocessing and machine-learning libraries simplify some otherwise manual and error-prone steps in the clinical diagnostic model research workflow. Furthermore, CDM's libraries provide study coordinators with a method to monitor a study's predictive performance as patient inclusions increase. To our knowledge, CDM is the only eCRF system integrating data preprocessing and machine-learning libraries. This integration improves the efficiency of the clinical diagnostic model research workflow. Moreover, by simplifying the generation of learning curves, CDM enables study coordinators to assess more accurately when data collection can be terminated, resulting in better models or lower patient recruitment costs.

Diversity in the association between occupation and lung cancer among black and white men.

PubMed

Swanson, G M; Lin, C S; Burns, P B

1993-01-01

A population-based case comparison study of incident lung cancer and occupational risk factors was conducted in the tricounty Detroit metropolitan area. Nearly 6000 lung cancer cases and a comparison group of 3600 colon cancer cases were interviewed. This report includes 3792 white and black male lung cancer cases and 1966 black and white colon cancer referents. Cigarette smoking, age at diagnosis, and lifetime work history were assessed to determine the relationship between length of employment in specific occupations and industries and lung cancer. Diverse patterns of association between work history and lung cancer were observed for black and white men. Significant associations were seen between lung cancer and increasing length of employment in the following occupations: for white men, concrete and terrazzo finishers, grinding machine operators, heat treating machine operators, miscellaneous machine operators, truck drivers, driver sales, and laborers; for black men, farm workers, automobile mechanics, painting machine operators, furnace operators, and garbage collectors; for both black and white men, farmers, slicing and cutting machine operators, and garbage collectors. Distinct patterns for black and white men also were observed for length of employment by industry. This study clearly demonstrates the need to include black men in studies of occupational cancer etiology and to evaluate black and white men separately. It also indicates the necessity for cigarette smoking history to accurately assess workplace cancer risks. We propose guidelines for incorporating the use of biomarkers into further studies of occupational cancer epidemiology.

Study on the adjustment capability of the excitation system located inside superconducting machine electromagnetic shield

NASA Astrophysics Data System (ADS)

Xia, D.; Xia, Z.

2017-12-01

The ability for the excitation system to adjust quickly plays a very important role in maintaining the normal operation of superconducting machines and power systems. However, the eddy currents in the electromagnetic shield of superconducting machines hinder the exciting magnetic field change and weaken the adjustment capability of the excitation system. To analyze this problem, a finite element calculation model for the transient electromagnetic field with moving parts is established. The effects of three different electromagnetic shields on the exciting magnetic field are analyzed using finite element method. The results show that the electromagnetic shield hinders the field changes significantly, the better its conductivity, the greater the effect on the superconducting machine excitation.

A computer architecture for intelligent machines

NASA Technical Reports Server (NTRS)

Lefebvre, D. R.; Saridis, G. N.

1992-01-01

The theory of intelligent machines proposes a hierarchical organization for the functions of an autonomous robot based on the principle of increasing precision with decreasing intelligence. An analytic formulation of this theory using information-theoretic measures of uncertainty for each level of the intelligent machine has been developed. The authors present a computer architecture that implements the lower two levels of the intelligent machine. The architecture supports an event-driven programming paradigm that is independent of the underlying computer architecture and operating system. Execution-level controllers for motion and vision systems are briefly addressed, as well as the Petri net transducer software used to implement coordination-level functions. A case study illustrates how this computer architecture integrates real-time and higher-level control of manipulator and vision systems.

Operational results for the experimental DOE/NASA Mod-OA wind turbine project

NASA Astrophysics Data System (ADS)

Shaltens, R. K.; Birchenough, A. G.

The Mod-OA wind turbine project which was to gain early experience in the operation of large wind turbines in a utility environment is discussed. The Mod-OA wind turbines were a first generation design, and even though not cost effective, the operating experience and performance characteristics had a significant effect on the design and development of the second and third generation machines. The Mod-OA machines were modified as a result of the operational experience, particularly the blade development and control system strategy. The results of study to investigate the interaction of a Mod-OA wind turbine with an isolated diesel generation system are discussed. The machine configuration, its advantages and disadvantages and the machine performance and availability are discussed.

Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

PubMed

Zhang, Jieru; Ju, Ying; Lu, Huijuan; Xuan, Ping; Zou, Quan

2016-01-01

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

Operational results for the experimental DOE/NASA Mod-OA wind turbine project

NASA Technical Reports Server (NTRS)

Shaltens, R. K.; Birchenough, A. G.

1983-01-01

The Mod-OA wind turbine project which was to gain early experience in the operation of large wind turbines in a utility environment is discussed. The Mod-OA wind turbines were a first generation design, and even though not cost effective, the operating experience and performance characteristics had a significant effect on the design and development of the second and third generation machines. The Mod-OA machines were modified as a result of the operational experience, particularly the blade development and control system strategy. The results of study to investigate the interaction of a Mod-OA wind turbine with an isolated diesel generation system are discussed. The machine configuration, its advantages and disadvantages and the machine performance and availability are discussed.

Machine Learning: A Crucial Tool for Sensor Design

PubMed Central

Zhao, Weixiang; Bhushan, Abhinav; Santamaria, Anthony D.; Simon, Melinda G.; Davis, Cristina E.

2009-01-01

Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies. PMID:20191110

Safety issues in high speed machining

NASA Astrophysics Data System (ADS)

1994-05-01

There are several risks related to High-Speed Milling, but they have not been systematically determined or studied so far. Increased loads by high centrifugal forces may result in dramatic hazards. Flying tools or fragments from a tool with high kinetic energy may damage surrounding people, machines and devices. In the project, mechanical risks were evaluated, theoretic values for kinetic energies of rotating tools were calculated, possible damages of the flying objects were determined and terms to eliminate the risks were considered. The noise levels of the High-Speed Machining center owned by the Helsinki University of Technology (HUT) and the Technical Research Center of Finland (VTT) in practical machining situation were measured and the results were compared to those after basic preventive measures were taken.

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming.

PubMed

Wu, Stephen Gang; Wang, Yuxuan; Jiang, Wu; Oyetunde, Tolutola; Yao, Ruilian; Zhang, Xuehong; Shimizu, Kazuyuki; Tang, Yinjie J; Bao, Forrest Sheng

2016-04-01

13C metabolic flux analysis (13C-MFA) has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux) in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species.

Gambling motivations, money-limiting strategies, and precommitment preferences of problem versus non-problem gamblers.

PubMed

Nower, Lia; Blaszczynski, Alex

2010-09-01

Studies attempting to identify the specific 'addictive' features of electronic gaming machines (EGMs) have yielded largely inconclusive results, suggesting that it is the interaction between a gambler's cognitions and the machine, rather than the machine itself, which fuels excessive play. Research has reported that machine players with gambling problems adopt a number of erroneous cognitive perceptions regarding the probability of winning and the nature of randomness. What is unknown, however, is whether motivations for gambling and attitudes toward pre-session monetary limit-setting vary across levels of gambling severity, and whether proposed precommitment strategies would be useful in minimizing excessive gambling expenditures. The current study explored these concepts in a sample of 127 adults, ages 18 to 81, attending one of four gambling venues in Queensland, Australia. The study found that problem gamblers were more likely than other gamblers to play machines to earn income or escape their problems rather than for fun and enjoyment. Similarly, they were less likely to endorse any type of monetary limit-setting prior to play. They were also reticent to adopt the use of a 'smart card' or other strategy to limit access to money during a session, though they indicated they lost track of money while gambling and were rarely aware of whether they were winning or losing during play. Implications for precommitment policies and further research are discussed.

Study of the Effect of Lubricant Emulsion Percentage and Tool Material on Surface Roughness in Machining of EN-AC 48000 Alloy

NASA Astrophysics Data System (ADS)

Soltani, E.; Shahali, H.; Zarepour, H.

2011-01-01

In this paper, the effect of machining parameters, namely, lubricant emulsion percentage and tool material on surface roughness has been studied in machining process of EN-AC 48000 aluminum alloy. EN-AC 48000 aluminum alloy is an important alloy in industries. Machining of this alloy is of vital importance due to built-up edge and tool wear. A L9 Taguchi standard orthogonal array has been applied as experimental design to investigate the effect of the factors and their interaction. Nine machining tests have been carried out with three random replications resulting in 27 experiments. Three type of cutting tools including coated carbide (CD1810), uncoated carbide (H10), and polycrystalline diamond (CD10) have been used in this research. Emulsion percentage of lubricant is selected at three levels including 3%, 5% and 10%. Statistical analysis has been employed to study the effect of factors and their interactions using ANOVA method. Moreover, the optimal factors level has been achieved through signal to noise ratio (S/N) analysis. Also, a regression model has been provided to predict the surface roughness. Finally, the results of the confirmation tests have been presented to verify the adequacy of the predictive model. In this research, surface quality was improved by 9% using lubricant and statistical optimization method.

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming

PubMed Central

Wu, Stephen Gang; Wang, Yuxuan; Jiang, Wu; Oyetunde, Tolutola; Yao, Ruilian; Zhang, Xuehong; Shimizu, Kazuyuki; Tang, Yinjie J.; Bao, Forrest Sheng

2016-01-01

13C metabolic flux analysis (13C-MFA) has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux) in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species. PMID:27092947

Comparative study of CW, nanosecond- and femtosecond-pulsed laser microcutting of AZ31 magnesium alloy stents.

PubMed

Gökhan Demir, Ali; Previtali, Barbara

2014-06-01

Magnesium alloys constitute an interesting solution for cardiovascular stents due to their biocompatibility and biodegradability in human body. Laser microcutting is the industrially accepted method for stent manufacturing. However, the laser-material interaction should be well investigated to control the quality characteristics of the microcutting process that concern the surface roughness, chemical composition, and microstructure of the final device. Despite the recent developments in industrial laser systems, a universal laser source that can be manipulated flexibly in terms of process parameters is far from reality. Therefore, comparative studies are required to demonstrate processing capabilities. In particular, the laser pulse duration is a key factor determining the processing regime. This work approaches the laser microcutting of AZ31 Mg alloy from the perspective of a comparative study to evaluate the machining capabilities in continuous wave (CW), ns- and fs-pulsed regimes. Three industrial grade machining systems were compared to reach a benchmark in machining quality, productivity, and ease of postprocessing. The results confirmed that moving toward the ultrashort pulse domain the machining quality increases, but the need for postprocessing remains. The real advantage of ultrashort pulsed machining was the ease in postprocessing and maintaining geometrical integrity of the stent mesh after chemical etching. Resultantly, the overall production cycle time was shortest for fs-pulsed laser system, despite the fact that CW laser system provided highest cutting speed.

Assessment of Genetic and Nongenetic Interactions for the Prediction of Depressive Symptomatology: An Analysis of the Wisconsin Longitudinal Study Using Machine Learning Algorithms

PubMed Central

Roetker, Nicholas S.; Yonker, James A.; Chang, Vicky; Roan, Carol L.; Herd, Pamela; Hauser, Taissa S.; Hauser, Robert M.

2013-01-01

Objectives. We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms. Methods. We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors—13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors—18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks. Results. After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors. Conclusions. We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic–environmental–sociobehavioral interactions in depressive symptoms. PMID:23927508

“Hydraulic Cushion” Type Overload Protection Devices Usable in Mechanical Presses. A Patent Study

NASA Astrophysics Data System (ADS)

Cioară, R.

2016-11-01

The possible consequences of machine-tool overload are well-known. In order to prevent such, machine-tools are equipped with various overload protection devices. Mechanical presses, intensively strained machine-tools, are typically equipped with three protection systems: against accidental access to the working area during machine deployment, against torque overload and force overload. Force overload protection systems include either destructible parts and are used in small to medium nominal force mechanical presses, or non-destructible ones used mostly in medium to large nominal force (H-frame) presses. A particular class of force overload protection systems without destructible parts are “hydraulic cushion” type devices. While such systems do not necessarily cause the machine to stop, the slide's stroke does not reach the initial dead centre and consequently cannot exert the designed technological force on the workpiece. By a patent study referencing 19 relevant patents the paper captures both the diversity of the constrictive solutions of “hydraulic cushion” type protection devices and their positioning modalities within the structure of a mechanical press. An important aim of the study is to highlight the reserve of creativity existing in this field, at least from the viewpoint of the hydraulic cushion positioning, as well as to emphasize the essential requirement of a relative motion between the mobile and the fixed parts of the tool, a motion of opposite sense to that of the slide-crank mechanism.

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods.

PubMed

Luo, Gang; Stone, Bryan L; Johnson, Michael D; Tarczy-Hornoch, Peter; Wilcox, Adam B; Mooney, Sean D; Sheng, Xiaoming; Haug, Peter J; Nkoy, Flory L

2017-08-29

To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient's weight kept rising in the past year). This process becomes infeasible with limited budgets. This study's goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data. This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care management allocation and pilot one model with care managers; and (3) perform simulations to estimate the impact of adopting Auto-ML on US patient outcomes. We are currently writing Auto-ML's design document. We intend to finish our study by around the year 2022. Auto-ML will generalize to various clinical prediction/classification problems. With minimal help from data scientists, health care researchers can use Auto-ML to quickly build high-quality models. This will boost wider use of machine learning in health care and improve patient outcomes. ©Gang Luo, Bryan L Stone, Michael D Johnson, Peter Tarczy-Hornoch, Adam B Wilcox, Sean D Mooney, Xiaoming Sheng, Peter J Haug, Flory L Nkoy. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 29.08.2017.

A machine learning approach to the accurate prediction of monitor units for a compact proton machine.

PubMed

Sun, Baozhou; Lam, Dao; Yang, Deshan; Grantham, Kevin; Zhang, Tiezhi; Mutic, Sasa; Zhao, Tianyu

2018-05-01

Clinical treatment planning systems for proton therapy currently do not calculate monitor units (MUs) in passive scatter proton therapy due to the complexity of the beam delivery systems. Physical phantom measurements are commonly employed to determine the field-specific output factors (OFs) but are often subject to limited machine time, measurement uncertainties and intensive labor. In this study, a machine learning-based approach was developed to predict output (cGy/MU) and derive MUs, incorporating the dependencies on gantry angle and field size for a single-room proton therapy system. The goal of this study was to develop a secondary check tool for OF measurements and eventually eliminate patient-specific OF measurements. The OFs of 1754 fields previously measured in a water phantom with calibrated ionization chambers and electrometers for patient-specific fields with various range and modulation width combinations for 23 options were included in this study. The training data sets for machine learning models in three different methods (Random Forest, XGBoost and Cubist) included 1431 (~81%) OFs. Ten-fold cross-validation was used to prevent "overfitting" and to validate each model. The remaining 323 (~19%) OFs were used to test the trained models. The difference between the measured and predicted values from machine learning models was analyzed. Model prediction accuracy was also compared with that of the semi-empirical model developed by Kooy (Phys. Med. Biol. 50, 2005). Additionally, gantry angle dependence of OFs was measured for three groups of options categorized on the selection of the second scatters. Field size dependence of OFs was investigated for the measurements with and without patient-specific apertures. All three machine learning methods showed higher accuracy than the semi-empirical model which shows considerably large discrepancy of up to 7.7% for the treatment fields with full range and full modulation width. The Cubist-based solution outperformed all other models (P < 0.001) with the mean absolute discrepancy of 0.62% and maximum discrepancy of 3.17% between the measured and predicted OFs. The OFs showed a small dependence on gantry angle for small and deep options while they were constant for large options. The OF decreased by 3%-4% as the field radius was reduced to 2.5 cm. Machine learning methods can be used to predict OF for double-scatter proton machines with greater prediction accuracy than the most popular semi-empirical prediction model. By incorporating the gantry angle dependence and field size dependence, the machine learning-based methods can be used for a sanity check of OF measurements and bears the potential to eliminate the time-consuming patient-specific OF measurements. © 2018 American Association of Physicists in Medicine.

Felling and bunching small timber on steep slopes.

Treesearch

Rodger A. Arola; Edwin S. Miyata; John A. Sturos; Helmuth M. Steinhilb

1981-01-01

Discusses the results of a field test of the unique Menzi Muck machine for felling and bunching small trees on steep slopes. Includes the analysis of a detailed time study to determine the productivity, costs, and economic feasibility of this unusual machine.

Robotic edge machining using elastic abrasive tool

NASA Astrophysics Data System (ADS)

Sidorova, A. V.; Semyonov, E. N.; Belomestnykh, A. S.

2018-03-01

The article describes a robotic center designed for automation of finishing operations, and analyzes technological aspects of an elastic abrasive tool applied for edge machining. Based on the experimental studies, practical recommendations on the application of the robotic center for finishing operations were developed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saleh, Z; Tang, X; Song, Y

Purpose: To investigate the long term stability and viability of using EPID-based daily output QA via in-house and vendor driven protocol, to replace conventional QA tools and improve QA efficiency. Methods: Two Varian TrueBeam machines (TB1&TB2) equipped with electronic portal imaging devices (EPID) were employed in this study. Both machines were calibrated per TG-51 and used clinically since Oct 2014. Daily output measurement for 6/15 MV beams were obtained using SunNuclear DailyQA3 device as part of morning QA. In addition, in-house protocol was implemented for EPID output measurement (10×10 cm fields, 100 MU, 100cm SID, output defined over an ROImore » of 2×2 cm around central axis). Moreover, the Varian Machine Performance Check (MPC) was used on both machines to measure machine output. The EPID and DailyQA3 based measurements of the relative machine output were compared and cross-correlated with monthly machine output as measured by an A12 Exradin 0.65cc Ion Chamber (IC) serving as ground truth. The results were correlated using Pearson test. Results: The correlations among DailyQA3, in-house EPID and Varian MPC output measurements, with the IC for 6/15 MV were similar for TB1 (0.83–0.95) and TB2 (0.55–0.67). The machine output for the 6/15MV beams on both machines showed a similar trend, namely an increase over time as indicated by all measurements, requiring a machine recalibration after 6 months. This drift is due to a known issue with pressurized monitor chamber which tends to leak over time. MPC failed occasionally but passed when repeated. Conclusion: The results indicate that the use of EPID for daily output measurements has the potential to become a viable and efficient tool for daily routine LINAC QA, thus eliminating weather (T,P) and human setup variability and increasing efficiency of the QA process.« less

Effectiveness of Direct Safety Regulations on Manufacturers and Users of Industrial Machines: Its Implications on Industrial Safety Policies in Republic of Korea.

PubMed

Choi, Gi Heung

2017-03-01

Despite considerable efforts made in recent years, the industrial accident rate and the fatality rate in the Republic of Korea are much higher than those in most developed countries in Europe and North America. Industrial safety policies and safety regulations are also known to be ineffective and inefficient in some cases. This study focuses on the quantitative evaluation of the effectiveness of direct safety regulations such as safety certification, self-declaration of conformity, and safety inspection of industrial machines in the Republic of Korea. Implications on safety policies to restructure the industrial safety system associated with industrial machines are also explored. Analysis of causes in industrial accidents associated with industrial machines confirms that technical causes need to be resolved to reduce both the frequency and the severity of such industrial accidents. Statistical analysis also confirms that the indirect effects of safety device regulation on users are limited for a variety of reasons. Safety device regulation needs to be shifted to complement safety certification and self-declaration of conformity for more balanced direct regulations on manufacturers and users. An example of cost-benefit analysis on conveyor justifies such a transition. Industrial safety policies and regulations associated with industrial machines must be directed towards eliminating the sources of danger at the stage of danger creation, thereby securing the safe industrial machines. Safety inspection further secures the safety of workers at the stage of danger use. The overall balance between such safety regulations is achieved by proper distribution of industrial machines subject to such regulations and the intensity of each regulation. Rearrangement of industrial machines subject to safety certification and self-declaration of conformity to include more movable industrial machines and other industrial machines with a high level of danger is also suggested.

Machine- z: Rapid machine-learned redshift indicator for Swift gamma-ray bursts

DOE PAGES

Ukwatta, T. N.; Wozniak, P. R.; Gehrels, N.

2016-03-08

Studies of high-redshift gamma-ray bursts (GRBs) provide important information about the early Universe such as the rates of stellar collapsars and mergers, the metallicity content, constraints on the re-ionization period, and probes of the Hubble expansion. Rapid selection of high-z candidates from GRB samples reported in real time by dedicated space missions such as Swift is the key to identifying the most distant bursts before the optical afterglow becomes too dim to warrant a good spectrum. Here, we introduce ‘machine-z’, a redshift prediction algorithm and a ‘high-z’ classifier for Swift GRBs based on machine learning. Our method relies exclusively onmore » canonical data commonly available within the first few hours after the GRB trigger. Using a sample of 284 bursts with measured redshifts, we trained a randomized ensemble of decision trees (random forest) to perform both regression and classification. Cross-validated performance studies show that the correlation coefficient between machine-z predictions and the true redshift is nearly 0.6. At the same time, our high-z classifier can achieve 80 per cent recall of true high-redshift bursts, while incurring a false positive rate of 20 per cent. With 40 per cent false positive rate the classifier can achieve ~100 per cent recall. As a result, the most reliable selection of high-redshift GRBs is obtained by combining predictions from both the high-z classifier and the machine-z regressor.« less

A strategy to apply machine learning to small datasets in materials science

NASA Astrophysics Data System (ADS)

Zhang, Ying; Ling, Chen

2018-12-01

There is growing interest in applying machine learning techniques in the research of materials science. However, although it is recognized that materials datasets are typically smaller and sometimes more diverse compared to other fields, the influence of availability of materials data on training machine learning models has not yet been studied, which prevents the possibility to establish accurate predictive rules using small materials datasets. Here we analyzed the fundamental interplay between the availability of materials data and the predictive capability of machine learning models. Instead of affecting the model precision directly, the effect of data size is mediated by the degree of freedom (DoF) of model, resulting in the phenomenon of association between precision and DoF. The appearance of precision-DoF association signals the issue of underfitting and is characterized by large bias of prediction, which consequently restricts the accurate prediction in unknown domains. We proposed to incorporate the crude estimation of property in the feature space to establish ML models using small sized materials data, which increases the accuracy of prediction without the cost of higher DoF. In three case studies of predicting the band gap of binary semiconductors, lattice thermal conductivity, and elastic properties of zeolites, the integration of crude estimation effectively boosted the predictive capability of machine learning models to state-of-art levels, demonstrating the generality of the proposed strategy to construct accurate machine learning models using small materials dataset.

Feasibility of task-specific brain-machine interface training for upper-extremity paralysis in patients with chronic hemiparetic stroke.

PubMed

Nishimoto, Atsuko; Kawakami, Michiyuki; Fujiwara, Toshiyuki; Hiramoto, Miho; Honaga, Kaoru; Abe, Kaoru; Mizuno, Katsuhiro; Ushiba, Junichi; Liu, Meigen

2018-01-10

Brain-machine interface training was developed for upper-extremity rehabilitation for patients with severe hemiparesis. Its clinical application, however, has been limited because of its lack of feasibility in real-world rehabilitation settings. We developed a new compact task-specific brain-machine interface system that enables task-specific training, including reach-and-grasp tasks, and studied its clinical feasibility and effectiveness for upper-extremity motor paralysis in patients with stroke. Prospective beforeâ€"after study. Twenty-six patients with severe chronic hemiparetic stroke. Participants were trained with the brain-machine interface system to pick up and release pegs during 40-min sessions and 40 min of standard occupational therapy per day for 10 days. Fugl-Meyer upper-extremity motor (FMA) and Motor Activity Log-14 amount of use (MAL-AOU) scores were assessed before and after the intervention. To test its feasibility, 4 occupational therapists who operated the system for the first time assessed it with the Quebec User Evaluation of Satisfaction with assistive Technology (QUEST) 2.0. FMA and MAL-AOU scores improved significantly after brain-machine interface training, with the effect sizes being medium and large, respectively (p<0.01, d=0.55; p<0.01, d=0.88). QUEST effectiveness and safety scores showed feasibility and satisfaction in the clinical setting. Our newly developed compact brain-machine interface system is feasible for use in real-world clinical settings.

Model for Assembly Line Re-Balancing Considering Additional Capacity and Outsourcing to Face Demand Fluctuations

NASA Astrophysics Data System (ADS)

Samadhi, TMAA; Sumihartati, Atin

2016-02-01

The most critical stage in a garment industry is sewing process, because generally, it consists of a number of operations and a large number of sewing machines for each operation. Therefore, it requires a balancing method that can assign task to work station with balance workloads. Many studies on assembly line balancing assume a new assembly line, but in reality, due to demand fluctuation and demand increased a re-balancing is needed. To cope with those fluctuating demand changes, additional capacity can be carried out by investing in spare sewing machine and paying for sewing service through outsourcing. This study develops an assembly line balancing (ALB) model on existing line to cope with fluctuating demand change. Capacity redesign is decided if the fluctuation demand exceeds the available capacity through a combination of making investment on new machines and outsourcing while considering for minimizing the cost of idle capacity in the future. The objective of the model is to minimize the total cost of the line assembly that consists of operating costs, machine cost, adding capacity cost, losses cost due to idle capacity and outsourcing costs. The model develop is based on an integer programming model. The model is tested for a set of data of one year demand with the existing number of sewing machines of 41 units. The result shows that additional maximum capacity up to 76 units of machine required when there is an increase of 60% of the average demand, at the equal cost parameters..

Machine rates for selected forest harvesting machines

Treesearch

R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

2002-01-01

Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...

Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.

PubMed

Wang, Yuanjia; Chen, Tianle; Zeng, Donglin

2016-01-01

Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards regression in standard survival analysis. To account for different at risk populations at observed event times, a time-varying offset is used in estimating risk scores. The resulting optimization is a convex quadratic programming problem that can easily incorporate non-linearity using kernel trick. We demonstrate an interesting link from the profiled empirical risk function of SVHM to the Cox partial likelihood. We then formally show that SVHM is optimal in discriminating covariate-specific hazard function from population average hazard function, and establish the consistency and learning rate of the predicted risk using the estimated risk scores. Simulation studies show improved prediction accuracy of the event times using SVHM compared to existing machine learning methods and standard conventional approaches. Finally, we analyze two real world biomedical study data where we use clinical markers and neuroimaging biomarkers to predict age-at-onset of a disease, and demonstrate superiority of SVHM in distinguishing high risk versus low risk subjects.

Nd:YAG Pulsed Laser Assisted Machining of AMS 5708 Waspaloy Alloy

NASA Astrophysics Data System (ADS)

Sharifi, Zahra; Shoja-Razavi, Reza; Vafaei, Reza; Hashemi, Sayed Hamid

2018-03-01

Due to very high strenght, low thermal conductivity, and high work hardening rate, the machinability of nickel-based superalloys is poor at room temperature. Laser-assisted machining (LAM) can provide a better aspect of machining such alloys. Since the wavelength of Nd:YAG laser is about 1/10th of that of CO2 laser, absorption and heating efficiency of Nd:YAG laser is much higher on metals and especially superalloys. Transmission of Nd:YAG laser through fiber optics to the heating point on the workpiece is a simple task during machining. This makes the LAM process more convenient and practical than the CM process. In this study a model is introduced for LAM of waspaloy, and its machinability is evaluated in terms of ease of material removal. Also, a temperature generation model is introduced for the Nd:YAG laser beam. Furthemore, wear behavior of an uncoated tungsten carbide and the formed chips were compared during the LAM and the CM of waspolay. To study the wear mechanism, the worn cutting tool was studied via scanning electron microscopy (SEM) and energy dispersive x-ray spectroscopy (EDS). The formed chips were also evaluated via SEM and optical microscopy. Based on the results, the optimum LAM conditions were obtained at a cutting speed of 24 m/min and a feed rate of 0.06 mm/rev when a 400 W laser mean power and 80 Hz frequency are applied. Under these conditions, the temperature ahead of the cutting tool edge on the surface of workpiece was estimated to be 524°C. In comparison with CM, a significant improvement in tool wear and a better chip morphology were achieved through LAM, and also specific cutting energy and surface roughness were reduced by 25 and 20%, respectively.

The Modification and Performance of a Large Animal Anesthesia Machine (Tafonius®) in Order to Deliver Xenon to a Horse.

PubMed

Santangelo, Bruna; Robin, Astrid; Simpson, Keith; Potier, Julie; Guichardant, Michel; Portier, Karine

2017-01-01

Xenon, due to its interesting anesthetic properties, could improve the quality of anesthesia protocols in horses despite its high price. This study aimed to modify and test an anesthesia machine capable of delivering xenon to a horse. An equine anesthesia machine (Tafonius, Vetronic Services Ltd., UK) was modified by including a T-connector in the valve block to introduce xenon, so that the xenon was pushed into the machine cylinder by the expired gases. A xenon analyzer was connected to the expiratory limb of the patient circuit. The operation of the machine was modeled and experimentally tested for denitrogenation, wash-in, and maintenance phases. The system was considered to consist of two compartments, one being the horse's lungs, the other being the machine cylinder and circuit. A 15-year-old, 514-kg, healthy gelding horse was anesthetized for 70 min using acepromazine, romifidine, morphine, diazepam, and ketamine. Anesthesia was maintained with xenon and oxygen, co-administered with lidocaine. Ventilation was controlled. Cardiorespiratory variables, expired fraction of xenon (FeXe), blood gases were measured and xenon was detected in plasma. Recovery was unassisted and recorded. FeXe remained around 65%, using a xenon total volume of 250 L. Five additional boli of ketamine were required to maintain anesthesia. PaO 2 was 45 ± 1 mmHg. The recovery was calm. Xenon was detected in blood during the entire administration time. This pilot study describes how to deliver xenon to a horse. Although many technical problems were encountered, their correction could guide future endeavors to study the use of xenon in horses.

What happens after the implementation of electronic locking devices for adolescents at cigarette vending machines? A natural longitudinal experiment from 2005 to 2009 in Germany.

PubMed

Schneider, Sven; Gruber, Johannes; Yamamoto, Shelby; Weidmann, Christian

2011-08-01

As of January 01, 2007, electronic locking devices based on proof of age (electronic cash cards or European driving licenses) were installed on 500,000 cigarette vending machines across Germany to restrict the purchase of cigarettes to those over the age of 16 years. In 2009, the age limit was raised to 18 years. The aim of this study was to compare the number of cigarette vending machines and other commercial sources before and after the enactment of the new law and to examine the association between commercial cigarette sources and area socioeconomic status (SES). We recorded and mapped using Geographical Information System software the total number of commercial cigarette sources in 4 selected districts in the major German city of Cologne. The city was the ideal setting for this study as we were able to use existing sociogeographical data from this area. We compiled a complete inventory of commercial cigarette sources in autumn 2005 and 2009. An interim inventory was also completed in 2007. Between 2005 and 2009, the total number of cigarette sources decreased from 369 to 325 within the study area. Although the most obvious reduction was detected in the number of outdoor vending machines (-44%), the number of indoor vending machines also decreased by 5%. In 2005 as well as in 2009, we found significantly fewer commercial cigarette sources in districts with above-average SES than in districts with below-average SES. Although the number of overall cigarette vending machines decreased, the disparity in distribution of cigarette sources between socially advantaged and disadvantaged areas increased.

Abstract quantum computing machines and quantum computational logics

NASA Astrophysics Data System (ADS)

Chiara, Maria Luisa Dalla; Giuntini, Roberto; Sergioli, Giuseppe; Leporini, Roberto

2016-06-01

Classical and quantum parallelism are deeply different, although it is sometimes claimed that quantum Turing machines are nothing but special examples of classical probabilistic machines. We introduce the concepts of deterministic state machine, classical probabilistic state machine and quantum state machine. On this basis, we discuss the question: To what extent can quantum state machines be simulated by classical probabilistic state machines? Each state machine is devoted to a single task determined by its program. Real computers, however, behave differently, being able to solve different kinds of problems. This capacity can be modeled, in the quantum case, by the mathematical notion of abstract quantum computing machine, whose different programs determine different quantum state machines. The computations of abstract quantum computing machines can be linguistically described by the formulas of a particular form of quantum logic, termed quantum computational logic.

Resource Sharing in Montana: A Study of Interlibrary Loan and Alternatives for a Montana Union Catalog.

ERIC Educational Resources Information Center

Matthews, Joseph R.

This study recommends a variety of actions to create and maintain a Montana union catalog (MONCAT) for more effective usage of in-state resources and library funds. Specifically, it advocates (1) merger of existing COM, machine readable bibliographic records, and OCLC tapes into a single microform catalog; (2) acceptance of only machine readable…