Search results for: machine resistance training

Authors: Mehmet Fahri Sarac, Gokcen Akgun

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Ti–6Al–4V alloys are widely used in biomedical industries because of its attractive mechanical and physicochemical properties. However, they have poor wear resistance. High velocity oxygen fuel (HVOF) coatings were investigated as a way to improve the wear resistance of this alloy. In this paper, different mass ratio of Al₂O₃-TiO₂ powders (60/40, 87/13 and 97/3) was employed to enhance the tribological properties of Ti–6Al–4V. The tribological behavior was investigated by wear tests using ball-on-disc and pin-on-disc tribometer. The microstructures of the contact surfaces were determined by a scanning electron microscopy before and after the test to study the wear mechanism. Uncoated and coated surfaces after wear test are also subjected to micro-hardness tests. The tribological test results showed that the microhardness, friction and wear resistance of coated Ti-6Al-4V alloys increases by increasing TiO₂ content in the powder composite when other experimental conditions were constant. Finally, Al₂O₃-TiO₂ powder composites for the investigated conditions, both coating samples had satisfactory values of friction and wear resistance, and they could be suitable candidates for Ti–6Al–4V material.

Keywords: HVOF (High Velocity Oxygen Fuel), Al₂O₃-TiO₂, Ti-6Al-4V, tribology

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Authors: Noha Seddik, Sherine Youssef, Mohamed Kholeif

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The automated epileptic seizure detection research field has emerged in the recent years; this involves analyzing the Electroencephalogram (EEG) signals instead of the traditional visual inspection performed by expert neurologists. In this study, a Support Vector Machine (SVM) that uses Weighted Permutation Entropy (WPE) as the input feature is proposed for classifying normal and seizure EEG records. WPE is a modified statistical parameter of the permutation entropy (PE) that measures the complexity and irregularity of a time series. It incorporates both the mapped ordinal pattern of the time series and the information contained in the amplitude of its sample points. The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG.

Keywords: electroencephalogram (EEG), epileptic seizure detection, weighted permutation entropy (WPE), support vector machine (SVM)

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Authors: M. O. Adeniyi, R. T. Akinnubi

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The parameterization of turbulent heat fluxes is needed for modeling land-atmosphere interactions in Global Climate Models (GCMs). However, current GCMs still have difficulties with producing reliable turbulent heat fluxes for humid tropical regions, which may be due to inadequate parameterization of the roughness lengths for momentum (z0m) and heat (z0h) transfer. These roughness lengths are usually expressed in term of excess resistance factor (κB^(-1)), and this factor is used to account for different resistances for momentum and heat transfers. In this paper, a more appropriate excess resistance factor (〖 κB〗^(-1)) suitable for low wind speed condition was developed and incorporated into the aerodynamic resistance approach (ARA) in the GCMs. Also, the performance of various standard GCMs κB^(-1) schemes developed for high wind speed conditions were assessed. Based on the in-situ surface heat fluxes and profile measurements of wind speed and temperature from Nigeria Micrometeorological Experimental site (NIMEX), new κB^(-1) was derived through application of the Monin–Obukhov similarity theory and Brutsaert theoretical model for heat transfer. Turbulent flux parameterizations with this new formula provides better estimates of heat fluxes when compared with others estimated using existing GCMs κB^(-1) schemes. The derived κB^(-1) MBE and RMSE in the parameterized QH ranged from -1.15 to – 5.10 Wm-2 and 10.01 to 23.47 Wm-2, while that of QE ranged from - 8.02 to 6.11 Wm-2 and 14.01 to 18.11 Wm-2 respectively. The derived 〖 κB〗^(-1) gave better estimates of QH than QE during daytime. The derived 〖 κB〗^(-1)=6.66〖 Re〗_*^0.02-5.47, where Re_* is the Reynolds number. The derived κB^(-1) scheme which corrects a well documented large overestimation of turbulent heat fluxes is therefore, recommended for most regional models within the tropic where low wind speed is prevalent.

Keywords: humid, tropic, excess resistance factor, overestimation, turbulent heat fluxes

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Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

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This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: visual search, deep learning, convolutional neural network, machine learning

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Authors: Fatima Bouazza, Rachida Hassikou, Lamiae Amallah, Jihane Ennadir, Khadija Khedid

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This study evaluated the in vitro resistance and susceptibility of Enterobacteriaceae (Escherichia coli and Klebsiella oxytoca strains) and Staphylococci strains, isolated from sheep’s milk, against antibiotics and essential oils from Thymus satureioides and Mentha pulegium. Antibiotic resistance tests were done using disc diffusion while essential oils were extracted by steam distillation, and yields were calculated relative to plant dry matter. Gas chromatography-mass Spectrometry (GC-MS) was used to analyze each oil's chemical composition. The AMC, CTX, FOX, NA, CN, CIP, and OFX were very effective against the E. coli strains tested. Half of the strains were resistant to AMC, 60% to TIC, and 80% to TE. The K. oxytoca was resistant against AMC, FOX, and TIC (100%). Antibiotic-resistant testing on Staphylococci strains indicated Staphylococcus capitis and Staphylococcus chromogenes as the most sensitive. Staphylococcus aureus, Staphylococcus xylosus, and Staphylococcus cohnii ureal exhibited less resistance to OX, TE, PT, E, and P. The M. pulegium resulted in a higher yield of essential oil of 3.2% oil compared to T. satureioides with only 1.85% yield. Staphylococcus aureus, Staphylococcus xylosus, and Staphylococcus cohnii ureal had lower OX, TE, PT, E, and P resistance. M. pulegium yielded 3.2% essential oil compared to 1.85% for T. satureioides. The monoterpene oxygenated derivatives, monoterpene hydrocarbons, and phenols are found in essential oil extracts. T. satureioides essential oil had high antibacterial activity even at low concentrations (0.2; 0.55 g/mL). The Minimal Bactericidal Concentration (MBC) values indicate that the essential oils from the plants analyzed had bactericidal effects on all strains tested and are similar to the Minimal Inhibitory Concentration (MIC) values. The high antibacterial properties of these medicinal plants, against bacteria isolated from sheep’s milk, provide an opportunity to use these medicinal plants in the breeding sector as additives and preservatives in the dairy industry.

Keywords: antibiotic resistance, medicinal plants, essential oils, enterobacteriaceae, staphylococci, sheep milk

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Authors: Huseyin A. Yavasoglu, Yusuf Ziya Tengiz, Ali Göksenli

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In this study, an automatic feeding machine for different type and class of bovine animals is designed. Daily nutrition of a bovine consists of grass, corn, straw, silage, oat, wheat and different vitamins and minerals. The amount and mixture amount of each of the nutrition depends on different parameters of the bovine. These parameters are; age, sex, weight and maternity of the bovine, also outside temperature. The problem in a farm is to constitute the correct mixture and amount of nutrition for each animal. Faulty nutrition will cause an insufficient feeding of the animal concluding in an unhealthy bovine. To solve this problem, a new automatic feeding machine is designed. Travelling of the machine is performed by four tires, which is pulled by a tractor. The carrier consists of eight bins, which each of them carries a nutrition type. Capacity of each unit is 250 kg. At the bottom of each chamber is a sensor measuring the weight of the food inside. A funnel is at the bottom of each chamber by which open/close function is controlled by a valve. Each animal will carry a RFID tag including ID on its ear. A receiver on the feeding machine will read this ID and by given previous information by the operator (veterinarian), the system will detect the amount of each nutrition unit which will be given to the selected animal for feeding. In the system, each bin will open its exit gate by the help of the valve under the control of PLC (Programmable Logic Controller). The amount of each nutrition type will be controlled by measuring the open/close time. The exit canals of the bins are collected in a reservoir. To achieve a homogenous nitration, the collected feed will be mixed by a worm gear. Further the mixture will be transported by a help of a funnel to the feeding unit of the animal. The feeding process can be performed in 100 seconds. After feeding of the animal, the tractor pulls the travelling machine to the next animal. By the help of this system animals can be feeded by right amount and mixture of nutrition

Keywords: bovine, feeding, nutrition, transportation, automatic

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Authors: T. N. Girish, B. E. Pradeep, Ravi Parkash

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Insects from tropical wet or dry seasons are likely to cope starvation stress through seasonal phenotypic plasticity in energy metabolites. Accordingly, we analyzed such plastic changes in Zaprionus indianus flies reared under wet or dry season-specific conditions; and also after adult acclimation at 32℃ for 1 to 6 days; and to low (40% RH) or high (70% RH) humidity. Both thermal or humidity acclimation revealed significant accumulation of body lipids for wet season flies but low humidity acclimation did not change the level of body lipids in dry season flies. Developmental and adult acclimation showed sex specific differences i.e., starvation resistance and body lipids were higher in the males of dry season but in females of wet season. We found seasonal and sex specific differences in the relative level for body lipids at death; and in the rates of accumulation or utilization of energy metabolites (body lipids, carbohydrates and proteins). Body lipids constitute the preferred energy source under starvation for flies of both the seasons. However, utilization of carbohydrates (~20% to 30%) and proteins (~20% to 25%) was evident only in dry season flies. Higher starvation resistance after thermal or humidity acclimation is achieved by increased accumulation of lipids. Adult acclimation of wet or dry season flies revealed plastic changes in mean daily fecundity despite reduction in fecundity under starvation. Thus, thermal or humidity induced plastic responses in body lipids support starvation resistance under wet or dry seasons.

Keywords: heat or humidity acclimation, plastic changes in body lipids and starvation resistance, tropical drosophilid, Wet- or Dry seasons, Zaprionus indianus

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Authors: Yu-Chu Yeh, WeiChin Hsu, Chih-Yen Chang

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Substitution, combination, modification, putting to other uses, elimination, and rearrangement (SCAMPER) has been regarded as an effective technique that provides a structured way to help people to produce creative ideas and solutions. Although some neuroscience studies regarding creativity training have been conducted, no study has focused on SCAMPER. This study therefore aimed at examining whether the learning of SCAMPER through video tutorials would result in alternations of neural activation. Thirty college students were randomly assigned to the experimental group or the control group. The experimental group was requested to watch SCAMPER videos, whereas the control group was asked to watch natural-scene videos which were regarded as neutral stimulating materials. Each participant was brain scanned in a Functional magnetic resonance imaging (fMRI) machine while undertaking a creativity test before and after watching the videos. Furthermore, a two-way ANOVA was used to analyze the interaction between groups (the experimental group; the control group) and tasks (C task; M task; X task). The results revealed that the left precuneus significantly activated in the interaction of groups and tasks, as well as in the main effect of group. Furthermore, compared with the control group, the experimental group had greater activation in the default mode network (left precuneus and left inferior parietal cortex) and the motor network (left postcentral gyrus and left supplementary area). The findings suggest that the SCAMPER training may facilitate creativity through the stimulation of the default mode network and the motor network.

Keywords: creativity, default mode network, neural activation, SCAMPER

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Authors: Prashant Verma

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Commercial banks are facing immense pressure, including financial disintermediation, interest rate volatility and digital ways of finance. Retaining an existing customer is 5 to 25 less expensive than acquiring a new one. This paper explores customer churn prediction, based on various statistical & machine learning models and uses under-sampling, to improve the predictive power of these models. The results show that out of the various machine learning models, Random Forest which predicts the churn with 78% accuracy, has been found to be the most powerful model for the scenario. Customer vintage, customer’s age, average balance, occupation code, population code, average withdrawal amount, and an average number of transactions were found to be the variables with high predictive power for the churn prediction model. The model can be deployed by the commercial banks in order to avoid the customer churn so that they may retain the funds, which are kept by savings bank (SB) customers. The article suggests a customized campaign to be initiated by commercial banks to avoid SB customer churn. Hence, by giving better customer satisfaction and experience, the commercial banks can limit the customer churn and maintain their deposits.

Keywords: savings bank, customer churn, customer retention, random forests, machine learning, under-sampling

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Authors: Kemal Polat

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In this study, it has been estimated whether there is skin by using RBG values obtained from the camera and k-nearest neighbor (k-NN) classifier. The dataset used in this study has an unbalanced distribution and a linearly non-separable structure. This problem can also be called a big data problem. The Skin dataset was taken from UCI machine learning repository. As the classifier, we have used the k-NN method to handle this big data problem. For k value of k-NN classifier, we have used as 1. To train and test the k-NN classifier, 50-50% training-testing partition has been used. As the performance metrics, TP rate, FP Rate, Precision, recall, f-measure and AUC values have been used to evaluate the performance of k-NN classifier. These obtained results are as follows: 0.999, 0.001, 0.999, 0.999, 0.999, and 1,00. As can be seen from the obtained results, this proposed method could be used to predict whether the image is skin or not.

Keywords: k-NN classifier, skin or non-skin classification, RGB values, classification

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Authors: Seyed Abolhassan Naeini, Maedeh Akhavan Tavakkoli

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Determining soil engineering properties is essential for geotechnical problems. In addition to high cost, invasive soil survey methods can be time-consuming, so geophysical methods can be an excellent choice to determine soil characteristics. In this study, geoelectric investigation using the Wenner arrangement method has been used to determine the amount of soil corrosion in soil layers in a project site as a case study. This study aims to assess the degree of corrosion of soil layers to a depth of 5 meters and find the variation of soil electrical resistance versus depth. For this purpose, the desired points in the study area were marked and specified, and all withdrawals were made within the specified points. The collected data have been processed by standard and accepted methods, and the results have been presented in the form of calculation tables and curves of electrical resistivity with depth.

Keywords: Wenner array, geoelectric, soil corrosion, electrical soil resistance

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Authors: K. K. Kaidarova, Ye. K. Aibuldinov, Zh. B. Iskakova, G. Zh. Alzhanova, S. Zh. Zayrova

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Currently, the existing road infrastructure of Kazakhstan needs the reconstruction of existing highways and the construction of new roads. The solution to this problem can be achieved by replacing traditional building materials with industrial waste, which in their chemical and mineralogical composition are close to natural raw materials and can partially or completely replace some natural binding materials in road construction. In this regard, the purpose of this study is to develop building materials based on the red sludge of the Pavlodar aluminum plant, blast furnace slag of the Karaganda Metallurgical Plant, lime production waste of the Pavlodar Aluminum Plant as a binder for natural loam. Changes in physical and mechanical properties were studied for uniaxial compression strength, linear expansion coefficient, water resistance, and frost resistance of the samples. Nine mixtures were formed with different percentages of these wastes 1-20:25:4; 2-20:25:6; 3-20:25:8; 4-30:30:4; 5-30:30:6; 6-30:30:8; 7-40:35:4; 8-40:35:6; 9-40:35:8 and the mixture identifier were labeled based on the waste content and composition number. The results of strength measurement during uniaxial compression of the samples showed an almost constant increase in strength and amounted to 0.67–3.56 MPa after three days and 3.33–7.38 MPa after 90 days. This increase in compressive strength is a consequence of the addition of lime and becomes more pronounced over time. The water resistance of the developed materials after 90 days was 7.12 MPa, and the frost resistance for the same period was 7.35 MPa. The maximum values of strength determination were shown by a sample of the composition 9-40:35:8. The study of the mineral composition showed that there was no contamination with heavy metals or dangerous substances. It was determined that road materials made of red sludge, blast furnace slag, lime production waste, and natural loam mixture could be used due to their strength indicators and environmental characteristics.

Keywords: production waste, uniaxial compression, water resistance of materials, frost resistance of samples

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Authors: Soheila Sadeghi

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In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

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Authors: Wan Jen Chang

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Back ground: Taiwan's current adult lifelong learning services have expanded from vocational training to universal lifelong learning. However, both the professional knowledge training of learning guidance and consulting services and the provision of adult online learning consulting service systems still need to be established. Purpose: The purposes of this study are as follows: 1. Analyze the professional training mechanism for cultivating adult lifelong learning consultation and coaching; 2. Explore the feasibility of constructing a system that uses network technology to provide adult learning consultation services. Research design: This study conducts a literature analysis of counseling and coaching policy reports on lifelong learning in European countries and the United States. There are two focus discussions were conducted with 15 lifelong learning scholars, experts and practitioners as research subjects. The following two topics were discussed and suggested: 1. The current situation, needs and professional ability training mechanism of "Adult Lifelong Learning Consulting and Services"; 2. Strategies for establishing an "Adult Lifelong Learning Consulting and Service internet System". Conclusion: 1.Based on adult lifelong learning consulting and service needs, plan a professional knowledge training and certification system.2.Adult lifelong learning consulting and service professional knowledge and skills training should include the use of network technology to provide consulting service skills.3.To establish an adult lifelong learning consultation and service system, the Ministry of Education should promulgate policies and measures at the central level and entrust local governments or private organizations to implement them.4.The adult lifelong learning consulting and service system can combine the national qualifications framework, private sector and NPO to expand learning consulting service partners.

Keywords: adult lifelong learning, profesional knowledge, consulting and service, network system

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Authors: S. N. Al-Bahry, S. K. Al-Musharafi, I. Y. Mahmoud

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Recycled treated sewage effluents (TSE) is used for agriculture, Public park irrigation and industrial purposes. TSE was found to play a major role in the distribution of antibiotic resistant bacteria into the environment. Fecal coliform and enterococci counts were significantly higher during summer compared to winter seasons. Oman has low annual rainfall with annual average temperature varied between 15-45oC. The main source of potable water is from seawater desalination. Resistance of the isolates to 10 antibiotics (Amikacin, Ampicillin, chloramphenicol, gentamycine, minocylin, nalidixicacid, neomycin, streptomycin, Tetracycline, Tobramycin, and Trimethoprim) was tested. Both fecal coliforms and enterococci were multiple resistant to 2-10 antibiotics. However, temperature variation during summer and winter did not affect resistance of the isolates to antibiotics. The significance of this investigation may be indicator to the environmental TSE pollution.

Keywords: antibiotic resistance, bacteria, environment, sewage treated effluent

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Authors: Saurabh Kumar

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In the fiercely competitive landscape of e-commerce, understanding and mitigating customer churn has become paramount for sustainable business growth. This paper presents a thorough investigation into the application of machine learning techniques for churn prediction in e-commerce, aiming to provide actionable insights for businesses seeking to enhance customer retention strategies. We conduct a comparative study of various machine learning algorithms, including traditional statistical methods and ensemble techniques, leveraging a rich dataset sourced from Kaggle. Through rigorous evaluation, we assess the predictive performance, interpretability, and scalability of each method, elucidating their respective strengths and limitations in capturing the intricate dynamics of customer churn. We identified the XGBoost classifier to be the best performing. Our findings not only offer practical guidelines for selecting suitable modeling approaches but also contribute to the broader understanding of customer behavior in the e-commerce domain. Ultimately, this research equips businesses with the knowledge and tools necessary to proactively identify and address churn, thereby fostering long-term customer relationships and sustaining competitive advantage.

Keywords: customer churn, e-commerce, machine learning techniques, predictive performance, sustainable business growth

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Authors: Oscar Kallay

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Objective: This study compares the sustainability, financial implications, and surgical efficiency of two phacoemulsification cassette systems for cataract surgery: a machine with single-use cassettes and another with daily, reusable ones. Methods: The observational study involves retrospective cataract surgery data collection at the Centre Médical de l'Alliance, Braine-L’alleud, Belgium, a tertiary eye care center. Information on cassette weight, quantities, and transport volume was obtained from routine procedures and purchasing records. The costs for each machine were calculated by reviewing the invoices received from the accounting department. Results: We found significant differences across comparisons. The reusable cassette machine, when compared to the single-use machine, used 306.7 kg less plastic (75.3% reduction), required 2,494 cubic meters less storage per 1000 surgeries (67.7% decrease), and cost €54.16 less per 10 procedures (16.9% reduction). The machine with daily reusable cassettes also exhibited a 7-minute priming time advantage for 10 procedures, reducing downtime between cases. Conclusions: Our findings underscore the benefits of adopting reusable cassette systems: reduced plastic consumption, storage volume, and priming time, as well as enhanced efficiency and cost savings. Healthcare professionals and institutions are encouraged to embrace environmentally conscious initiatives. The use of reusable cassette systems for cataract surgeries offers a pathway to sustainable practices.

Keywords: cataract, epidemiolog, surgery treatment, lens and zonules, public health

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Authors: Maria Korman, Carmit Gal, Ella Gabitov, Avi Karni

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The main question addressed in this study was whether the time-of-day wherein training is afforded is a significant factor for motor skill ('how-to', procedural knowledge) acquisition and consolidation into long term memory in the healthy elderly population. Twenty-nine older adults (60-75 years) practiced an explicitly instructed 5-element key-press sequence by repeatedly generating the sequence ‘as fast and accurately as possible’. Contribution of three parameters to acquisition, 24h post-training consolidation, and 1-week retention gains in motor sequence speed was assessed: (a) time of training (morning vs. evening group) (b) sleep quality (actigraphy) and (c) chronotype. All study participants were moderately morning type, according to the Morningness-Eveningness Questionnaire score. All participants had sleep patterns typical of age, with average sleep efficiency of ~ 82%, and approximately 6 hours of sleep. Speed of motor sequence performance in both groups improved to a similar extent during training session. Nevertheless, evening group expressed small but significant overnight consolidation phase gains, while morning group showed only maintenance of performance level attained at the end of training. By 1-week retention test, both groups showed similar performance levels with no significant gains or losses with respect to 24h test. Changes in the tapping patterns at 24h and 1-week post-training were assessed based on normalized Pearson correlation coefficients using the Fisher’s z-transformation in reference to the tapping pattern attained at the end of the training. Significant differences between the groups were found: the evening group showed larger changes in tapping patterns across the consolidation and retention windows. Our results show that morning-oriented older adults effectively acquired, consolidated, and maintained a new sequence of finger movements, following both morning and evening practice sessions. However, time-of-training affected the time-course of skill evolution in terms of performance speed, as well as the re-organization of tapping patterns during the consolidation period. These results are in line with the notion that motor training preceding a sleep interval may be beneficial for the long-term memory in the elderly. Evening training should be considered an appropriate time window for motor skill learning in older adults, even in individuals with morning chronotype.

Keywords: time-of-day, elderly, motor learning, memory consolidation, chronotype

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Authors: Ru-Yin Yang, Chi Wu, Cheng-Yu Tsai, Yin-Tzu Lin, Wen-Te Liu

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Background: Sleep breathing disorder (SDB) is a condition demonstrated by recurrent episodes of the airway obstruction leading to intermittent hypoxia and quality fragmentation during sleep time. However, the procedures for SDB severity examination remain complicated and costly. Objective: The objective of this study is to establish a simplified examination method for SDB by the respiratory impendence pattern sensor combining the signal processing and machine learning model. Methodologies: We records heart rate variability by the electrocardiogram and respiratory pattern by impendence. After the polysomnography (PSG) been done with the diagnosis of SDB by the apnea and hypopnea index (AHI), we calculate the episodes with the absence of flow and arousal index (AI) from device record. Subjects were divided into training and testing groups. Neuron network was used to establish a prediction model to classify the severity of the SDB by the AI, episodes, and body profiles. The performance was evaluated by classification in the testing group compared with PSG. Results: In this study, we enrolled 66 subjects (Male/Female: 37/29; Age:49.9±13.2) with the diagnosis of SDB in a sleep center in Taipei city, Taiwan, from 2015 to 2016. The accuracy from the confusion matrix on the test group by NN is 71.94 %. Conclusion: Based on the models, we established a prediction model for SDB by means of the wearable sensor. With more cases incoming and training, this system may be used to rapidly and automatically screen the risk of SDB in the future.

Keywords: sleep breathing disorder, apnea and hypopnea index, body parameters, neuron network

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Authors: Shun-Tong Chen, Wei-Ping Huang, Hong-Ye Yang, Ming-Chieh Yeh, Chih-Wei Du

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This study presents a novel versatile high-precision integrated micromachining system that combines contact and non-contact micromachining techniques to machine intricate micro-parts precisely. Two broad methods of micro fabrication-1) volume additive (micro co-deposition), and 2) volume subtractive (nanometric flycutting, ultrafine w-EDM (wire Electrical Discharge Machining), and micro honing) - are integrated in the developed micromachining system, and their effectiveness is verified. A multidirectional headstock that supports various machining orientations is designed to evaluate the feasibility of multifunctional micromachining. An exchangeable working-tank that allows for various machining mechanisms is also incorporated into the system. Hence, the micro tool and workpiece need not be unloaded or repositioned until all the planned tasks have been completed. By using the designed servo rotary mechanism, a nanometric flycutting approach with a concentric rotary accuracy of 5-nm is constructed and utilized with the system to machine a diffraction-grating element with a nano-metric scale V-groove array. To improve the wear resistance of the micro tool, the micro co-deposition function is used to provide a micro-abrasive coating by an electrochemical method. The construction of ultrafine w-EDM facilitates the fabrication of micro slots with a width of less than 20-µm on a hardened tool. The hardened tool can thus be employed as a micro honing-tool to hone a micro hole with an internal diameter of 200 µm on SKD-11 molded steel. Experimental results prove that intricate micro-parts can be in-situ manufactured with high-precision by the developed integrated micromachining system.

Keywords: integrated micromachining system, in-situ micromachining, nanometric flycutting, ultrafine w-EDM, micro honing

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Authors: A.Farrag Farrag, A. M. El-Aziz Abdel Aziz, W. Khlifa Khlifa

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Ultrasonic treatment is a promising process nowadays in the engineering field due to its high efficiency and it is a low-cost process. It enhances mechanical properties, corrosion resistance, and homogeneity of the microstructure. In this study, the effect of ultrasonic treatment and several casting conditions on microstructure, hardness and corrosion behavior of AC4C aluminum alloy was examined. Various ultrasonic treatments of the AC4C alloys were carried out to prepare billets for thixocasting process. Treatment temperatures varied from about 630oC and cooled down to under ultrasonic field. Treatment time was about 90s. A 600-watts ultrasonic system with 19.5 kHz and intensity of 170 W/cm2 was used. Billets were reheated to semisolid state and held for 5 minutes at 582 oC and temperatures (soaking) using high-frequency induction system, then thixocasted using a die casting machine. Microstructures of the thixocast parts were studied using optical and SEM microscopes. On the other hand, two samples were conventionally cast and poured at 634 oC and 750 oC. The microstructure showed a globular none dendritic grains for AC4C with the application of UST at 630-582 oC, Less dendritic grains when the sample was conventionally cast without the application of UST and poured at 624 oC and a fully dendritic microstructure When the sample was cast and poured at 750 oC without UST .The ultrasonic treatment during solidification proved that it has a positive influence on the microstructure as it produced the finest and globular grains thus it is expected to increase the mechanical properties of the alloy. Higher values of corrosion resistance and hardness were recorded for the ultrasound-treated sample in comparison to cast one.

Keywords: ultrasonic treatment, aluminum alloys, corrosion behaviour, mechanical behaviour, microstructure

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Authors: Fiorenza Misale

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The painful phenomenon of so-called white deaths and accidents at work, unfortunately, is always current. The key is to act on the culture of security through effective measures of attitudes and behaviors that go far beyond the knowledge and the know-how. It is necessary that there is an ‘introjection’ of safety culture through the conscious involvement of all workers. The legislation on work safety identifies the main tool to promote the culture of safety at work and prevention within the workplace. In law the term education is used to distinguish itself from the information with which they will simply theoretically transmit, and from the training with which they will provide the practical skills. The new decree fact fills several gaps in previous legislation and stresses the importance of training in the workplace, that is, the main activity through which it is possible to achieve the active participation of all workers in the company’s prevention system. This system is built only through the dissemination of risk information, the circulation of information, comparison and dialogue between all actors involved that are the necessary elements for a correct transmission of the culture of worker safety. Training activity should put the focus on work experience in order to bring out all the knowledge needed to identify and assess the risks in the work place, and especially the action to eliminate or control them, integrating, when necessary, the missing knowledge. In addition to traditional training and information systems can be utilized for the purpose of training that are able to affect both one emotionally and aesthetically, team-theatre is one of them. Among the methods of company theater that can be used in work safety we have: Lesson show, theater workshop, improvised theater, forum theater, theater playback. The theater can represent a complementary approach to traditional training and give information on safety measures, demonstrating that there are more engaging outreach tools. Team-theatre allows identification with the characters, a transmission of emotions and moods and it is through the staging of a story that the individual processes new information. It’ also s a means of experiential training that allows you to work with your mind, body, emotions.The aim of one work is the use of corporate theater on the personnel working in the health sector. Through a questionnaire we are able to analyze the knowledge of occupational safety and current risks; in particular in health care which is to be administered before and after the play.

Keywords: theater, training, occupational health, safety

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Authors: Andrej Kocjan

Abstract:

Introduction: Mobility has been shown to play an important role of health and quality of life among older adults. Falls, which are often related to decreased mobility, as well as to neuromuscular deficits, represent the most common injury among older adults. Fall risk has been shown to increase with reduced lower extremity flexibility. The aim of the paper is to assess the importance of flexibility training on joint range of motion and functional performance among elderly population. Methods: We performed literature research on PubMed and evaluated articles published until 2000. The articles found in the search strategy were also added. The population of interest included older adults (≥ 65 years of age). Results: Flexibility training programs still represent an important part of several rehabilitation programs. Static stretching and proprioceptive neuromuscular facilitation are the most frequently used techniques to improve the length of the muscle-tendon complex. Although the effectiveness of type of stretching seems to be related to age and gender, static stretching is a more appropriate technique to enhance shoulder, hip, and ankle range of motion in older adults. Stretching should be performed in multiple sets with holds of more than 60 seconds for a single muscle group. Conclusion: The literature suggests that flexibility training is an effective method to increase joint range of motion in older adults. In the light of increased functional outcome, activities such as strengthening, balance, and aerobic exercises should be incorporated into a training program for older people. Due to relatively little published literature, it is still not possible to prescribe detailed recommendations regarding flexibility training for older adults.

Keywords: elderly, exercise, flexibility, falls

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Authors: Fety Nurlia Muzayanah, Arif Imam Suroso, Mukhamad Najib

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The market share of organic food in Indonesia just reaches 0.5-2 percents from the entire of agricultural products. The aim of this research is to analyze the relation of gender, work, age and final education toward the buying interest of organic food, to identify the factors influencing the resistance of the purchase of organic food, and to identify the market education process. The analysis result of Structural Equation Modeling (SEM) shows the factors causing the resistance of the purchase of organic food are the negative attitude toward organic food, the lack of affordable in range for organic food product and the lack of awareness toward organic food, while the subjective norms have no significant effect toward the buying interest. The market education process which can be done is the education about the use of the health of organic food, the organic certification and the economic value.

Keywords: market education, organic food, consumer behavior, structural equation modeling

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Authors: Mohammed Al Sulaimani, Hamad Al Manhi

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With the development of Remote Sensing technology, the resolution of optical Remote Sensing images has greatly improved, and images have become largely available. Numerous detectors have been developed for detecting different types of objects. In the past few years, Remote Sensing has benefited a lot from deep learning, particularly Deep Convolution Neural Networks (CNNs). Deep learning holds great promise to fulfill the challenging needs of Remote Sensing and solving various problems within different fields and applications. The use of Unmanned Aerial Systems in acquiring Aerial Photos has become highly used and preferred by most organizations to support their activities because of their high resolution and accuracy, which make the identification and detection of very small features much easier than Satellite Images. And this has opened an extreme era of Deep Learning in different applications not only in feature extraction and prediction but also in analysis. This work addresses the capacity of Machine Learning and Deep Learning in detecting and extracting Oil Leaks from Flowlines (Onshore) using High-Resolution Aerial Photos which have been acquired by UAS fixed with RGB Sensor to support early detection of these leaks and prevent the company from the leak’s losses and the most important thing environmental damage. Here, there are two different approaches and different methods of DL have been demonstrated. The first approach focuses on detecting the Oil Leaks from the RAW Aerial Photos (not processed) using a Deep Learning called Single Shoot Detector (SSD). The model draws bounding boxes around the leaks, and the results were extremely good. The second approach focuses on detecting the Oil Leaks from the Ortho-mosaiced Images (Georeferenced Images) by developing three Deep Learning Models using (MaskRCNN, U-Net and PSP-Net Classifier). Then, post-processing is performed to combine the results of these three Deep Learning Models to achieve a better detection result and improved accuracy. Although there is a relatively small amount of datasets available for training purposes, the Trained DL Models have shown good results in extracting the extent of the Oil Leaks and obtaining excellent and accurate detection.

Keywords: GIS, remote sensing, oil leak detection, machine learning, aerial photos, unmanned aerial systems

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Authors: R. Naveen, E. Vanitha, S. Gayathri

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The use of Sandwich composite materials in aerospace and civil infrastructure application has been increasing especially due to their enormously low weight that leads to a reduction in the total weight and fuel consumption, high flexural and transverse shear stiffness, and corrosion resistance. The essential properties of sandwich materials vary according to the application area of the structure. The objectives of this study are to identify the mechanical behaviour and failure mechanisms of sandwich structures made of bamboo, V- board and metal (Aluminium as face sheet and Foam as Core material). The three-point bending test and UTM (Universal testing machine) experimental tests are done for three specimens for each type of sandwich composites. From the experiment results of three sandwich composites, bamboo shows high Young’s modulus of elasticity and low density.

Keywords: bamboo sandwich composite, metal sandwich composite, sandwich composite, v-board sandwich composite

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Authors: Bruce Small, Martin Espig, Alyssa Ryan

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In the agricultural sector, the rapid expansion of herbicide resistant weeds is a major threat to the global sustainability of food and fibre production. Efforts to avoid herbicide resistance have primarily focused on new technologies and farmer education. Yet, despite decades of advice to growers from agricultural scientists and extension professionals of the need for management strategies for herbicide use, herbicide resistance continues to increase. Technological options are running out and current extension efforts to change farmer behaviour are failing to curb the problem. As part of a five-year, government funded, research programme to address herbicide resistance in New Zealand, social science theory and practice are being utilised to investigate the complexities of managing herbicide use and controlling resistance. As an initial step, we are utilising a transdisciplinary, multi-level systems approach to examine the problem definition, knowledge beliefs, attitudes and values of different important actors in the agri-business sector. In this paper, we report early project results from qualitative research examining the similarities and contrasts in the perceptions of scientists, farmer/growers, and rural professionals.

Keywords: behaviour change, herbicide resistant weeds, knowledge beliefs, systems perspective

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Authors: U. K. Tamuli

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Introduction: North East India is graced with natural beauty and hazards. This area is prone to major earthquakes, floods, landslides, accidents, terrorist activities etc. Academy of Trauma (AOT), an NGO of Doctors, conducts training programs, mock drills, field trials amongst the doctors and paramedics in North East India. The present study is to evaluate the efficacy of such training in terms of sensitivity, awareness, and delivery systems of the products. Here the health care delivery system for disaster management is inadequate. Clear guideline of mass casualty management is unavailable. AOT has initiated steps to increase the awareness and handling of mass casualty management to improve the emergency health care delivery system. Method: AOT has conducted training programmes on emergency health management, mass casualty management and hospital preparedness amongst 800 doctors and 1200 paramedics in twenty-two districts of Assam in Northeast India. The training module consists of lectures, hands-on workshop using manikins, mock drills, distribution of manuals, emergency management exercises, periodic exchange of experience and debriefings. AOT evaluates the impact of these trainings by conducting pre and post tests of delegates, trainer’s evaluation, delegate’s satisfaction and confidence level and their suggestions. Results: The module, training, hands-on workshops, mock drills were highly appreciated. There is significant improvement in scores on the post-training tests. The confidence level of the participants has risen to deal with emergency medical situation Conclusion: These kinds of trainings increase the awareness of the medical members to handle mass casualties in different situations. One such training actually sensitises the delegates. Repetition of such training, TOT (Training-of-Trainers) programs, and individual efforts of delegates are extremely important for sustenance and success of health care delivery service during disasters in the developing countries. Further collaboration, assistance, networking, suggestions from established global agencies in this field will be highly appreciated.

Keywords: capacity building, North East India, non-governmental organization, trauma

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Authors: Andrey Yatsun, Andrei Malchikov

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Exoskeletons are used to support and compensate for the load on the human musculoskeletal system. Elastomers are an important component of exoskeletons, providing additional support and compensating for the load. The algorithm of the active elastomer tension system provides the required auxiliary force depending on the angle of rotation and the tilt speed of the operator's torso. Feedback for the drive is provided by a force sensor integrated into the attachment of the exoskeleton vest. The use of direct force measurement ensures the required accuracy in all settings of the man-machine system. Non-adjustable elastic elements make it difficult to move without load, tilt forward and walk. A strategy for the organization of the auxiliary forces management system is proposed based on the allocation of 4 operating modes of the human-machine system.

Keywords: soft exoskeleton, mathematical modeling, pre-tension elastomer, human-machine interaction

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Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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