Search results for: machine resistance training

Authors: Harrison Gideon Maghra

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The study was conducted on the challenges in the use of ICT in Agricultural Education and Training in Colleges of Education in Adamawa State. Three objectives guided the study, and the objectives were translated into three research questions and the research questions translated into two null hypotheses. Frequency and percentage were used to answer research question one, mean and standard deviation were used to answer research questions two and three, and t-test statistic was used to test the null hypotheses at 0.05 level of significance. The study was descriptive research and a questionnaire was used to solicit responses from the respondent. The instrument for data collection was subjected to face and content validity by 1 expert in the Department of Vocational Education, Modibbo Adama University, Yola and 3 experts from the Department of Vocational and Technical Education, Adamawa State University, Mubi. Pearson Product Moment Correlation Coefficient was used to test the reliability of the instrument and a reliability coefficient of 0.76 was obtained through the test re-test test method. Results from the study revealed that ICT facilities are not available in state-owned colleges of education. Agricultural Education lecturers have a positive attitude toward the use of ICT in teaching agricultural education and training. Based on the findings of the study, recommendations were made, among which: Colleges of Education in the state should organize training on the use of ICT for all lecturers, including those in the Agricultural Education program.

Keywords: challenges, ICT, agricultural education, colleges of education

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Authors: Tahani Farah, Hanène Souli, Jean-Marie Fleureau, Guillaume Kermouche, Jean-Jacques Fry, Benjamin Girard, Denis Aelbrecht

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In this work, the bio-clogging of two soils with different granulometries is presented. The durability of the clogging is also studied under cycles of hydraulic head and under cycles of desaturation- restauration. The studied materials present continuous grain size distributions. The first one corresponding to the "material 1", presents grain sizes between 0.4 and 4 mm. The second material called "material 2" is composed of grains with size varying between 1 and 10 mm. The results show that clogging occurs very quickly after the injection of nutrition and an outlet flow near to 0 is observed. The critical hydraulic head is equal to 0.76 for "material 1", and 0.076 for "material 2". The durability tests show a good resistance to unclogging under cycles of hydraulic head and desaturation-restauration for the "material 1". Indeed, the flow after the cycles is very low. In contrast, "material 2", shows a very bad resistance, especially under the hydraulic head cycles. The resistance under the cycles of desaturation-resaturation is better but an important increase of the flow is observed. The difference of behavior is due to the granulometry of the materials. Indeed, the large grain size contributes to the reduction of the efficiency of the bio-clogging treatment in this material.

Keywords: bio-clogging, granulometry, permeability, nutrition

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Authors: Bipasha Sen, Aditya Agarwal

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Multilingual automatic speech recognition (ASR) system is a single entity capable of transcribing multiple languages sharing a common phone space. Performance of such a system is highly dependent on the compatibility of the languages. State of the art speech recognition systems are built using sequential architectures based on recurrent neural networks (RNN) limiting the computational parallelization in training. This poses a significant challenge in terms of time taken to bootstrap and validate the compatibility of multiple languages for building a robust multilingual system. Complex architectural choices based on self-attention networks are made to improve the parallelization thereby reducing the training time. In this work, we propose Reed, a simple system based on 1D convolutions which uses very short context to improve the training time. To improve the performance of our system, we use raw time-domain speech signals directly as input. This enables the convolutional layers to learn feature representations rather than relying on handcrafted features such as MFCC. We report improvement on training and inference times by atleast a factor of 4x and 7.4x respectively with comparable WERs against standard RNN based baseline systems on SpeechOcean's multilingual low resource dataset.

Keywords: convolutional neural networks, language compatibility, low resource languages, multilingual automatic speech recognition

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Authors: Mustafa Gulsen, Mitat Koz

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The aim of this study is to investigate the effect of lumbar stabilisation and proprioceptive neuromuscular facilitation (PNF) training on muscle strength and muscle endurance. The participants were 64 between the ages of 15-69 (53.04 ± 14.59), who were graded protrusion and bulging lumbar herniation according to 'Macnab Classification'. The participants were divided into four groups as each group had 16 participants: lumbar stabilitation training, PNF training, physical therapy and control groups. Sociodemographic features were recorded. Then their muscle strength tests (by isokinetic dynamometer (Cybex 770 Norm Lumex Inc, Ronkonkoma, NY, USA) were recorded. Before and after applications; visual analogue scale (VAS), Oswestry Disability İndex were applied by a physical therapist. The participants in lumbar stabilisation group performed 45 minutes, 5 days in a week for 4 weeks strength training with a physical therapist observation. The participants in PNF group performed 5 days in a week for 4 weeks with pelvic patterns of PNF by a physiotherapist. The participants in physical therapy group underwent Hotpack, Tens and Ultrasound therapy 5 days in a week for 4 weeks. The participants in control group didn’t take any training programme. After 4 weeks, the evaluations were repeated. There were significant increases in muscle strength and muscle endurance in lumbar stabilization training group. Also in pain intensity at rest and during activity in this group and in Oswestry disability index of patients, there were significant improvements (p < 0.05). In PNF training group likewise, there were significant improvements in muscle strength, muscle endurance, pain intensity at rest and with activity and in Oswestry disability index (p < 0.05). But improvements in the Lumbar Stabilization group was better than PNF Group. We found significant differences only in pain intensity at rest and with activity and in Oswestry disability index (p < 0.05). in the patients in Physical Therapy group. We think that appropriate physiotherapy and rehabilitation program which will be prepared for patients, to protect the waist circumference of patients with low muscle strength and low muscle endurance will increase muscle strength and muscle endurance. And it is expected that will reduce pain and will provide advances toward correcting functional disability of the patients.

Keywords: disc herniation, endurance, lumbar stabilitation exercises, PNF, strength

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Authors: Andréanne Gagné, Jo Anni Joncas, Éric Tendon

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Teaching in vocational training requires an excellent mastery of the trade being taught, but also solid professional skills in pedagogy. Teachers are typically recruited on the basis of their trade expertise, and they do not necessarily have training or experience in pedagogy. In order to counter this lack, the Ministry of Education (Québec, Canada) requires them to complete a 120-credit university program to obtain their teaching certificate. They must complete this training in addition to their teaching duties. This training was rarely planned in the teacher’s life course, and each teacher approaches it differently: some are enthusiastic, but many feel reluctant discouragement and even frustration at the idea of committing to a training program lasting an average of 10 years to completion. However, Quebec is experiencing an unprecedented shortage of teachers, and the perseverance of vocational teachers in their careers requires special attention because of the conditions of their specific integration conditions. Our research examines the perceptions that vocational teachers in training have of their academic experience in pre-service teaching. It differs from previous research in that it focuses on the influence of the academic experience on the teaching employment experience. The goal is that by better understanding the university experience of teachers in vocational education, we can identify support strategies to support their school experience and their teaching. To do this, the research is based on the theoretical framework of the sociology of experience, which allows us to study the way in which these “teachers-students” give meaning to their university program in articulation with their jobs according to three logics of action. The logic of integration is based on the process of socialization, where the action is preceded by the internalization of values, norms, and cultural models associated with the training context. The logic of strategy refers to the usefulness of this experience where the individual constructs a form of rationality according to his objectives, resources, social position, and situational constraints. The logic of subjectivation refers to reflexivity activities aimed at solving problems and making choices. These logics served as a framework for the development of an online questionnaire. Three hundred respondents, newly enrolled in an undergraduate teaching program (bachelor's degree in vocational education), expressed themselves about their academic experience. This paper relates qualitative data (open-ended questions) subjected to an interpretive repertory analysis approach to descriptive data (closed-ended questions) that emerged. The results shed light on how the respondents perceive themselves as teachers and students, their perceptions of university training and the support offered, and the place that training occupies in their professional path. Indeed, their professional and academic paths are inextricably linked, and it seems essential to take them into account simultaneously to better meet their needs and foster the development of their expertise in pedagogy. The discussion focuses on the strengths and limitations of university training from the perspective of the logic of action. The results also suggest support strategies that can be implemented to better support the integration and retention of student teachers in professional education.

Keywords: teacher, vocational training, pre-service training, academic experience

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: Srividya Sheshadri, Christopher Coley, Roa. R. Bhavani

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India currently accounts for 60 percent of the open defecation that is practiced globally. While research in the domain of sanitation development makes it apparent that girls and women living in rural India are disproportionately affected, interventions to address this dilemma are lacking. An important but relatively unexplored connection with poor sanitation is that women living in rural India are not only the largest marginalized group without access to adequate sanitation facilities, they also represent a majority of India’s unskilled workers. By training women to build their own toilets, through an approach that has demonstrated success in empowering marginalized communities through technical and vocational education and training (TVET), a collaborative dynamic emerges that can engage entire communities in the movement towards total sanitation. Designed and implemented by Amrita University, this technology-enhanced, community-based approach to skill development, known as Amrita computerized Vocational Education and Training (or Amrita cVET), has begun to show promise in addressing the struggle to end open defecation, and raise sanitation awareness, as well as strengthen personal and community development among women living in rural India. While Amrita cVET project, known as Women Empowerment: Sanitation, is currently in implementation in seven states throughout India, this paper will discuss early stages of the intervention in rural villages within the Indian states of: Karnataka and Goa, where previous sanitation efforts have failed to take hold.

Keywords: community based development, empowerment studies, sanitation in India, computerized vocational training

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Authors: Aya Belal, Ahmed Maher Eltair, Maggie Ahmed Mashaly

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Geopolymer concrete is gaining recognition as a sustainable alternative to conventional Portland Cement concrete due to its environmentally friendly nature, which is a key goal for Smart City initiatives. It has demonstrated its potential as a reliable material for the design of structural elements. However, the production of Geopolymer concrete is hindered by batch-to-batch variations, which presents a significant challenge to the widespread adoption of Geopolymer concrete. To date, Machine learning has had a profound impact on various fields by enabling models to learn from large datasets and predict outputs accurately. This paper proposes an integration between the current drift to Artificial Intelligence and the composition of Geopolymer mixtures to predict their mechanical properties. This study employs Python software to develop machine learning model in specific Decision Trees. The research uses the percentage oxides and the chemical composition of the Alkali Solution along with the curing conditions as the input independent parameters, irrespective of the waste products used in the mixture yielding the compressive strength of the mix as the output parameter. The results showed 90 % agreement of the predicted values to the actual values having the ratio of the Sodium Silicate to the Sodium Hydroxide solution being the dominant parameter in the mixture.

Keywords: decision trees, geopolymer concrete, machine learning, smart cities, sustainability

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Authors: M. Nakisa, A. Maimun, Yasser M. Ahmed, F. Behrouzi, A. Tarmizi

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The practical application of the Computational Fluid Dynamics (CFD), for predicting the flow pattern around Multipurpose Amphibious Vehicle (MAV) hull has made much progress over the last decade. Today, several of the CFD tools play an important role in the land and water going vehicle hull form design. CFD has been used for analysis of MAV hull resistance, sea-keeping, maneuvering and investigating its variation when changing the hull form due to varying its parameters, which represents a very important task in the principal and final design stages. Resistance analysis based on CFD (Computational Fluid Dynamics) simulation has become a decisive factor in the development of new, economically efficient and environmentally friendly hull forms. Three-dimensional finite volume method (FVM) based on Reynolds Averaged Navier-Stokes equations (RANS) has been used to simulate incompressible flow around three types of MAV hull bow models in steady-state condition. Finally, the flow structure and streamlines, friction and pressure resistance and velocity contours of each type of hull bow will be compared and discussed.

Keywords: RANS simulation, multipurpose amphibious vehicle, viscous flow structure, mechatronic

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Authors: Kwaku Damoah

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This paper presents a methodology and software application (App) designed to empower users in accessing, retrieving, and comparatively exploring data within the hierarchical network framework of the Government Finance Statistics (GFS) system. It explores the ease of navigating the GFS system and identifies the gaps filled by the new methodology and App. The GFS, embodies a complex Hierarchical Network Classification (HNC) structure, encapsulating institutional units, revenues, expenses, assets, liabilities, and economic activities. Navigating this structure demands specialized knowledge, experience, and skill, posing a significant challenge for effective analytics and fiscal policy decision-making. Many professionals encounter difficulties deciphering these classifications, hindering confident utilization of the system. This accessibility barrier obstructs a vast number of professionals, students, policymakers, and the public from leveraging the abundant data and information within the GFS. Leveraging R programming language, Data Science Analytics and Machine Learning, an efficient methodology enabling users to access, navigate, and conduct exploratory comparisons was developed. The machine learning Fiscal Analytics App (FLOWZZ) democratizes access to advanced analytics through its user-friendly interface, breaking down expertise barriers.

Keywords: data science, data wrangling, drilldown analytics, government finance statistics, hierarchical network classification, machine learning, web application.

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Authors: Seongjun Kim, Jinwooung Kim, Sung-Ah Kim

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The development of information and communication technology also affects human cognition and thinking, especially in the field of design, new techniques are being tried. In architecture, new design methodologies such as machine learning or data-driven design are being applied. In particular, these methodologies are used in analyzing the factors related to the value of real estate or analyzing the feasibility in the early planning stage of the apartment housing. However, since the value of apartment buildings is often determined by external factors such as location and traffic conditions, rather than the interior elements of buildings, data is rarely used in the design process. Therefore, although the technical conditions are provided, the internal elements of the apartment are difficult to apply the data-driven design in the design process of the apartment. As a result, the designers of apartment housing were forced to rely on designer experience or modular design alternatives rather than data-driven design at the design stage, resulting in a uniform arrangement of space in the apartment house. The purpose of this study is to propose a methodology to support the designers to design the apartment unit plan with high consumer preference by deriving the correlation and importance of the floor plan elements of the apartment preferred by the consumers through the machine learning and reflecting this information from the early design process. The data on the pre-sale competition rate and the elements of the floor plan are collected as data, and the correlation between pre-sale competition rate and independent variables is analyzed through machine learning. This analytical model can be used to review the apartment unit plan produced by the designer and to assist the designer. Therefore, it is possible to make a floor plan of apartment housing with high preference because it is possible to feedback apartment unit plan by using trained model when it is used in floor plan design of apartment housing.

Keywords: apartment unit plan, data-driven design, design methodology, machine learning

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Authors: Aminah Muchdar

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Efforts to increase soybean production have been pursued for years in Indonesia through the process of intensification and extensification. Increased production through intensification of increasing grain yield per hectare, among others includes the improvement of cultivation system such as the use of cultivars that have superior resistance to drought. Increased soybean production has been through the expansion of planting areas utilizing available idle dry land. However, one of the constraints faced in dryland agriculture was the limited water supply due to low intensity of rainfall that leads to low crop production. In order to ensure that soybeans are cultivated on dry land remains capable of high production, it is necessary to physiologically engineer the soybean with open stomata. The study was conducted in the greenhouse of Balai Penelitian Tanaman Serealia (BALITSEREAL) Maros, Sulawesi, Indonesia with a completely randomized block design h factorial pattern. The first factor was the water stress stadia while the second was the amount of sorbitol osmolit concentration application. Results indicated that there was an interaction between the plant height growth and number of leaves between the water clamping time and concentration of the osmolit sorbitol. The vegetative stage especially during flowering and pod formation was inhibited when the water was clamped, but by spraying osmolit sorbitol, soybean growth in terms of its height and number of leaves was enhanced. This study implies that the application of osmolit sorbitol may enhance the drought resistance of soybean growth. Future research suggested that more work should be done on the application of osmolit sorbital to other agriculture crops to increase their drought resistance in the drylands.

Keywords: DROUGHT, engineered physiology, osmolit sorbitol, soybean

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Authors: Dharmendra Jariwala, Robin Christian

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Thermal exposure is a common problem in every manufacturing industry where heat is used in the manufacturing process. In developing countries like India, a lack of awareness regarding the proper work environmental condition is observed among workers. Improper planning of factory building, arrangement of machineries, ventilation system, etc. play a vital role in the rise of temperature within the manufacturing areas. Due to the uncontrolled thermal stress, workers may be subjected to various heat illnesses from mild disorder to heat stroke. Heat stress is responsible for the health risk and reduction in production. Wet Bulb Globe Temperature (WBGT) index and relative humidity are used to evaluate heat stress conditions. WBGT index is a weighted average of natural wet bulb temperature, globe temperature, dry bulb temperature, which are measured with standard instrument QuestTemp 36 area stress monitor. In this study textile processing units have been selected in the industrial estate in the Surat city. Based on the manufacturing process six locations were identified within the plant at which process was undertaken at 120°C to 180°C. These locations were jet dying machine area, stenter machine area, printing machine, looping machine area, washing area which generate process heat. Office area was also selected for comparision purpose as a sixth location. Present Study was conducted in the winter season and summer season for day and night shift. The results shows that average WBGT index was found above Threshold Limiting Value (TLV) during summer season for day and night shift in all three industries except office area. During summer season highest WBGT index of 32.8°C was found during day shift and 31.5°C was found during night shift at printing machine area. Also during winter season highest WBGT index of 30°C and 29.5°C was found at printing machine area during day shift and night shift respectively.

Keywords: relative humidity, textile industry, thermal stress, WBGT

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Authors: Ejaz Ali, Muhammad Younas, Tahir Saeed

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In this age of information technology, organizations are revisiting their approach in great deal. E-administration is the most popular area to proceed with. Organizations in order to excel over their competitors are spending a substantial chunk of its resources on E-Administration as it is the most effective, transparent and efficient way to achieve their short term as well as long term organizational goals. E-administration being a tool of ICT plays a significant role towards effective management of HR practices resulting into optimal performance of an organization. The present research was carried out to analyze the impact of moderating role of e-administration in the relationships training and performance appraisal aligned with perceived organizational performance. The study is based on RBV and AMO theories, advocating that use of latest technology in execution of human resource (HR) functions enables an organization to achieve and sustain competitive advantage which leads to optimal firm performance.

Keywords: e-administration, human resource management, ict, performance appraisal, training

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Authors: Ehsan Ahmadifar, Mohammad Ali Yousefi, Zahra Roohi

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In this study, the effects of different levels of Ergosan (control group (0), 2, 4 and 6 gr Ergosan per Kg diet) as a nutritional supplement were investigated on growth indices and stress in Zebrafish for 3 months. Larvae (4-day-old after hatching) were fed with experimental diet from the beginning of feeding until adult (adolescence) (average weight: 69.3 g, length: 5.1 cm). Different levels of Ergosan had no significant effect on rate survival (P < 0.05). The results showed that diet containing 6 gr Ergosan significantly caused the best FCR in Zebrafish (P < 0.05). By increasing the Ergosan diet, specific growth rate increased. Body weight gain and condition factor had significant differences (P < 0.05) as the highest and the lowest were observed in treatment 3 gr of Ergosan and control, respectively. The results showed that fish fed with experimental diet, had the highest resistance to environmental stresses compared to control, and the test temperature, oxygen, salinity and alkalinity samples containing 6 gr/kg, was significantly more resistance compared to the other treatments (P < 0.05). Overall, to achieve high resistance to environmental stress and increase final biomass using 6 gr/kg Ergosan in diet fish Zebrafish.

Keywords: Ergosan, stress, growth performance, Danio rerio

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Authors: Newton Muhury, Armando A. Apan, Tek N. Marasani, Gebiaw T. Ayele

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The changing climate has degraded freshwater availability in Australia that influencing vegetation growth to a great extent. This study assessed the vegetation responses to groundwater using Terra’s moderate resolution imaging spectroradiometer (MODIS), Normalised Difference Vegetation Index (NDVI), and soil water content (SWC). A hydrological model, SWAT, has been set up in a southeast Australian river catchment for groundwater analysis. The model was calibrated and validated against monthly streamflow from 2001 to 2006 and 2007 to 2010, respectively. The SWAT simulated soil water content for 43 sub-basins and monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) were applied in the machine learning tool, Waikato Environment for Knowledge Analysis (WEKA), using two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The assessment shows that different types of vegetation response and soil water content vary in the dry and wet seasons. The WEKA model generated high positive relationships (r = 0.76, 0.73, and 0.81) between NDVI values of all vegetation in the sub-basins against soil water content (SWC), the groundwater flow (GW), and the combination of these two variables, respectively, during the dry season. However, these responses were reduced by 36.8% (r = 0.48) and 13.6% (r = 0.63) against GW and SWC, respectively, in the wet season. Although the rainfall pattern is highly variable in the study area, the summer rainfall is very effective for the growth of the grass vegetation type. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

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Authors: Malihe Shahidan, Azin Madhi, Meisam Shahbaz

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In recent decades, the role of supply chains in sustaining businesses and establishing their superiority in the market has been under focus. The realization of the goals and strategies of a business enterprise is largely dependent on the cooperation of the chain, including suppliers, distributors, retailers, etc. Supply chains can potentially be disrupted by both internal and external factors. In this paper, resilience strategies have been identified and analyzed in three levels: sourcing, producing, and distributing by considering economic depression as a current risk factor for the machine-made rugs industry. In this study, semi-structured interviews for data gathering and thematic analysis for data analysis are applied. Supply chain data has been gathered from seven rug factories before and after the economic depression through semi-structured interviews. The identified strategies were derived from literature review and validated by collecting data from a group of eighteen industry and university experts, and the results were analyzed using statistical tests. Finally, the outsourcing of new products and products in the new market, the development and completion of the product portfolio, the flexibility in the composition and volume of products, the expansion of the market to price-sensitive, direct sales, and disintermediation have been determined as strategies affecting supply chain resilience of machine-made rugs' industry during an economic depression.

Keywords: distribution, economic depression, machine-made rug, outsourcing, production, sourcing, supply chain, supply chain resilience

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Authors: Youhang Zhou, Weimin Zeng, Qi Xie

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Abstract: In order to solve the wear recognition problem of the machine tool guide surface, a new machine tool guide surface recognition method based on the radar-graph barycentre feature is presented in this paper. Firstly, the gray mean value, skewness, projection variance, flat degrees and kurtosis features of the guide surface image data are defined as primary characteristics. Secondly, data Visualization technology based on radar graph is used. The visual barycentre graphical feature is demonstrated based on the radar plot of multi-dimensional data. Thirdly, a classifier based on the support vector machine technology is used, the radar-graph barycentre feature and wear original feature are put into the classifier separately for classification and comparative analysis of classification and experiment results. The calculation and experimental results show that the method based on the radar-graph barycentre feature can detect the guide surface effectively.

Keywords: guide surface, wear defects, feature extraction, data visualization

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Authors: Sheldon McCall, Miao Yu, Liyun Gong, Shigang Yue, Stefanos Kollias

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Falls are a significant health concern for older adults globally, and prompt identification is critical to providing necessary healthcare support. Our study proposes a new fall detection method using computer vision based on modern deep learning techniques. Our approach involves training a trans- former model on a large 2D pose dataset for general action recognition, followed by transfer learning. Specifically, we freeze the first few layers of the trained transformer model and train only the last two layers for fall detection. Our experimental results demonstrate that our proposed method outperforms both classical machine learning and deep learning approaches in fall/non-fall classification. Overall, our study suggests that our proposed methodology could be a valuable tool for identifying falls.

Keywords: healthcare, fall detection, transformer, transfer learning

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Authors: Minu Bharati, Priyanka Rai, Satarupa Dutta, Dhiraj Saha

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Culex quinquefasciatus Say, is a mosquito of immense public health concern due to its role in transmission of filariasis, which is an endemic disease in 20 states and union territories of India, putting about 600 million people at the risk of infection. The main strategies to control filaria in India include anti-larval measures in urban areas, Indoor Residual Spray (IRS) in rural areas and mass diethylcarbamazine citrate (DEC) administration. Larval destruction measures and IRS are done with the use of insecticides. In this study, Susceptibility/ Resistance to insecticides were assessed in Culex quinquefasciatus mosquitoes collected from eight densely populated areas of Siliguri subdivision, which has a high rate of filarial infection. To unveil the insecticide susceptibility status of Culex quinquefasciatus, bioassays were performed on field-caught mosquitoes against two major groups of insecticides, i.e. Synthetic Pyrethroids (SPs): 0.05% deltamethrin and 0.05% lambda-cyhalothrin and Organophosphates (OPs): 5% malathion and temephos using World Health Organisation (WHO) discriminating doses. The knockdown rates and knockdown times (KDT50) were also noted against deltamethrin, lambda-cyhalothrin and malathion. Also, activities of major detoxifying enzymes, i.e. α-carboxylesterases, β-carboxylesterases and cytochrome P450 (CYP450) monooxygenases were determined to find the involvement of biochemical mechanisms in resistance phenomenon (if any). The results obtained showed that, majority of the mosquito populations were moderately to severely resistant against both the SPs and one OP, i.e. temephos. Whereas, most of the populations showed 100% susceptibility to malathion. The knockdown rates and KDT50 in response to above-mentioned insecticides showed significant variation among different populations. Variability in activities of carboxylesterases and CYP450 monooxygenases were also observed with hints of their involvement in contribution towards insecticide resistance in some of the tested populations. It may be concluded that, Culex quinquefasciatus has started developing resistance against deltamethrin, lambda-cyhalothrin and temephos in Siliguri subdivision. Malathion seems to hold the greatest potentiality for control of these mosquitoes in this area as revealed through this study. Adoption of Integrated mosquito management (IMM) strategy should be the prime objective of the concerned authorities to delimit the insecticide resistance phenomenon and filariasis infections.

Keywords: Culex quinquefasciatus, detoxifying enzymes, insecticide resistance, knockdown rate

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Authors: Kh. Dehkordi, R. Sharifi Gholam, S. Arshadi

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Objective: Oxidative stress is produced under diabetic conditions and possibly causes various forms of tissue damage inpatients with diabetes. Antioxidants defend against the harmful effect of free radicals, which are associated with heart disease, cancer, arthritis, aging and many other diseases1). Antioxidants are very stable molecules capable of neutralizing free radicals by donating an electron to them.The aim of this study was to examine the effect of swimming training, fenugreek seed extract and glibenclamide on plasma glucose and cardiac antioxidants activity in diabetic rats. Design: For this purpose, fifty male wistar rats were divided into five groups, two groups of control rats (diabetic control [DC] and healthy control [HC]), one group of endurance swimming training (EST), one group of fenugreek seed extract highdose (F1, 1.74 g/kg b.w), one group of fenugreek seed extract middle dose (F2, 0.87 g/kg b.w), one group of glibenclamide (G, 0.5 mg/kg b.w). Materials and Methods: Diabetes induced by streptozotocine (STZ), data was analyzed using the one-way ANOVA followed by a Tukey test. Significance level was 0.05. Results: All of the groups' exception of HC showed significant decrease in body weight (P < 0.05), but the diabetic control and swimming training group exhibited a more decrease. All of the groups have shown a significant decrease in plasma glucose than DC group (P < 0.05) but this reduction was more in G group than DC no HC group. S, G and HC groups have shown significant increase in cardiac antioxidant than DC group (P < 0.05) but there wasn't significant difference in other groups (P > 0.05). Conclusion: The present results indicate that regular swimming training lead to decrease in plasma glucose and enhanced cardiac antioxidants in diabetic rats.

Keywords: swimming, glucose, cardiac, antioxidants

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Authors: Fares Mahdi

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Background: Infection control is an important concern for all health care professionals, especially nurses. Nurses have a higher risk for both self-acquiring and transmitting infections to other patients. Aim of this study: to assess nurses' knowledge regarding infection control for wound care. Methodology: a descriptive research design was used in the study. The total number studied sample was 200 nurses, were conducting in Amran Public Hospitals in Amran City- Yemen. The study covered sample nurses in the hospital according to the study population; a standard closed-ended questionnaire was used to collect the data. Results: The results showed less than half (37.5 %) of nurses were from 22 May Hospital, also followed by (62.5%) of them were from Maternal and Child Hospital. Also according to the department name. Most (22.5%) of nurses worked in an intensive care unit, followed by (20%) of them were working in the pediatric world, also about (19%) of them were working in the surgical department. While in finally, only about (8.5%) of them worked from another department. According to course training, The results showed about (21%) of nurses had course training in wound care management. At the same time, others (79%) of them have not had course training in wound care management. According to the total nurse's knowledge of infection control for wound care, that find more than two-thirds (68%) of nurses had fair knowledge according to total all of nurse's knowledge of infection control wound care. Conclusion:The results showed that more than two-thirds (68%) of nurses had fair knowledge according to total all of the nurse's knowledge of infection control for wound care. Recommendations: There should be providing training program about infection control masseurs and it's important for new employees of nurses. Providing continuing refreshment training courses about infection control programs and about evidence-based practice in infection control for all health care teams.

Keywords: assessment, knowledge, infection control, wound care, nurses, amran hospitals

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Authors: Ying Su, Morgan C. Wang

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Long-term time series forecasting is an important research area for automated machine learning (AutoML). Currently, forecasting based on either machine learning or statistical learning is usually built by experts, and it requires significant manual effort, from model construction, feature engineering, and hyper-parameter tuning to the construction of the time series model. Automation is not possible since there are too many human interventions. To overcome these limitations, this article proposed to use recurrent neural networks (RNN) through the memory state of RNN to perform long-term time series prediction. We have shown that this proposed approach is better than the traditional Autoregressive Integrated Moving Average (ARIMA). In addition, we also found it is better than other network systems, including Fully Connected Neural Networks (FNN), Convolutional Neural Networks (CNN), and Nonpooling Convolutional Neural Networks (NPCNN).

Keywords: automated machines learning, autoregressive integrated moving average, neural networks, time series analysis

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Authors: Amey Kulkarni, Paavan Shetty, Amol Patil, B. Rajiv

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Fuel Efficiency plays a very important role in overall performance of an automobile. In this paper study of fuel efficiency of heavy construction, compaction machine is done. The fuel Consumption trials are performed in order to obtain the consumption of fuel in performing certain set of actions by the compactor. Usually, Heavy Construction machines are put to work in locations where refilling the fuel tank is not an easy task and also the fuel is consumed at a greater rate than a passenger automobile. So it becomes important to have a fuel efficient machine for long working hours. The fuel efficiency is the most important point in determining the future scope of the product. A heavy construction compaction machine operates in five major roles. These five roles are traveling, Static working, High-frequency Low amplitude compaction, Low-frequency High amplitude compaction, low idle. Fuel consumption readings for 1950 rpm, 2000 rpm & 2350 rpm of the engine are taken by using differential fuel flow meter and are analyzed. And the optimum RPM setting which fulfills the fuel efficiency, as well as engine performance criteria, is considered. Also, other factors such as rear end gears, Intake and exhaust restriction for an engine, vehicle operating techniques, air drag, Tribological aspects, Tires are considered for increasing the fuel efficiency of the compactor. The fuel efficiency of compactor can be precisely calculated by using Differential Fuel Flow Meter. By testing the compactor at different combinations of Engine RPM and also considering other factors such as rear end gears, Intake and exhaust restriction of an engine, vehicle operating techniques, air drag, Tribological aspects, The optimum solution was obtained which lead to significant improvement in fuel efficiency of the compactor.

Keywords: differential fuel flow meter, engine RPM, fuel efficiency, heavy construction compaction machine

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Authors: Songul Aktas, Turkan Pasinlioglu, Kiymet Yesilcicek Calik

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Introduction: Emphatic approach during labor increases both quality of care and birth satisfaction of mothers. Besides; maternal satisfaction statements and expressions about midwives who assist labor contribute to a positive birth perception and wish to give vaginal delivery again. Aim: The study aimed at investigating the effect of empathy training given to midwives on mothers’ satisfaction with midwives and their birth perception. Material/Method: This experimental study was undertaken between February 2013 and January 2014 at a public hospital in Trabzon Province. The population of the study was composed of mothers who gave vaginal delivery and the sample was composed of 222 mothers determined with power analyzes. Ethical approval and written informed consents were obtained. Mothers who were assisted by midwives during 1st, 2nd and 3rd phases of delivery and first two postpartum hours were included. Empathy training given to midwives included didactic narration, creative drama, psychodrama techniques and lasted 32 hours. The data were collected before the empathy training (BET), right after empathy training (RAET) and 8 weeks later after birth (8WLAB). Mothers were homogenous in terms of socio-demographic, obstetric characteristics. Data were collected with a questionnaire and were analyzed with Chi-square tests. Findings: Rate of mother’s satisfaction with midwives was 36.5% in BET, 81.1% in RAET and 75.7% in 8WLAB. Key mother’s satisfaction with midwives were as follows: 27.6% of mothers told that midwives were “smiling-kind” in BET, 39.6% of them in RAET and 33.7% of them in 8WLAB; 31% of mothers told that midwives were “understanding” in BET, 38.2% of them in RAET and 33.7% of them in 8WLAB; 15.7% of mothers told that midwives were “reassuring” in BET, 44.9% of them in RAET and 39.3% of them in 8WLAB;19.5% of mothers told that midwives were “encouraging and motivating” in BET, 39.8% of them in RAET and 19.8% of mothers told that midwives were “informative” in BET, 45.6% of them in RAET and 35.1% of them in 8WLAB (p<0.05). Key mother’s dissatisfaction with midwives were as follows: 55.3% of mothers told that midwives were “poorly-informed” in BET, 17% of them in RAET and 27.7% of them in 8WLAB; 56.9% of mothers told that midwives were “poorly-listening” in BET, 17.6% of them in RAET and 25.5% of them in 8WLAB; 53.2% of mothers told that midwives were “judgmental-embarrassing” in BET, 17% of them in RAET and 29.8% of them in 8WLAB; 56.2% of mothers told that midwives had “fierce facial expressions” in BET, 15.6% of them in RAET and 28.1% of them in 8WLAB. Rates of mothers’ perception that labor was “easy” were 8.1% in BET, 21.6% in RAET and 13.5% in 8WLAB and rates of mothers’ perception that labor was “very difficult and tiring” were 41.9% in BET, 5.4% in RAET and 13.5% in 8WLAB (p<0.05). Conclusion: The effect of empathy training given to midwives upon statements that described mothers’ satisfaction with midwives and their birth perception was positive. Note: This study was financially funded by TUBİTAK project with number 113S672.

Keywords: empathy training, labor perception, mother’s satisfaction with midwife, vaginal delivery

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Authors: Rajesh Kumar, Puneet Aggarwal

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In context of understanding problems faced by undergraduate students while carrying out laboratory experiments dealing with high voltages, it was found that most of the students are hesitant to work directly on machine. The reason is that error in the circuitry might lead to deterioration of machine and laboratory instruments. So, it has become inevitable to include modern pedagogic techniques for undergraduate students, which would help them to first carry out experiment in virtual system and then to work on live circuit. Further advantages include that students can try out their intuitive ideas and perform in virtual environment, hence leading to new research and innovations. In this paper, virtual environment used is of MATLAB/Simulink for three-phase induction machines. The performance analysis of three-phase induction machine is carried out using virtual environment which includes Direct Current (DC) Test, No-Load Test, and Block Rotor Test along with speed torque characteristics for different rotor resistances and input voltage, respectively. Further, this paper carries out computer aided teaching of basic Voltage Source Inverter (VSI) drive circuitry. Hence, this paper gave undergraduates a clearer view of experiments performed on virtual machine (No-Load test, Block Rotor test and DC test, respectively). After successful implementation of basic tests, VSI circuitry is implemented, and related harmonic distortion (THD) and Fast Fourier Transform (FFT) of current and voltage waveform are studied.

Keywords: block rotor test, DC test, no load test, virtual environment, voltage source inverter

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Authors: Luca Vascelli, Silvia Iacomini, Giada Gueli, Francesca Cavallini, Carlo Cavallini, Federica Berardo

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The study was conducted to evaluate the effect of training on Big 6 + 6 motor skills to promote daily living skills. Precision teaching (PT) suggests that improved speed of the component behaviors can lead to better performance of composite skills. This study assessed the effects of the repeated timed practice of component motor skills on speed and accuracy of composite skills related to daily living skills. An 18 years old adolescent with intellectual disability participated. A pre post probe single-subject design was used. The results suggest that the participant was able to perform the component skills at his individual aims (endurance was assessed). The speed and accuracy of composite skills were increased; stability and retention were also measured for the composite skill after the training.

Keywords: big 6+6, daily living skills, intellectual disability, precision teaching

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Authors: Zhang Lei, Zhao Qingrong, Wang Chen, Zhang Sufang, Zhang Hanguo

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Larch is the main afforestation and timber tree species in Northeast China, and drought is one of the main factors limiting the growth of Larch and other organisms in Northeast China. In order to further explore the mechanism of Larch drought resistance, PEG was used to simulate drought stress. The full-length sequencing of Larch embryogenic callus under PEG simulated drought stress was carried out by combining Illumina-Hiseq and SMRT-seq. A total of 20.3Gb clean reads and 786492 CCS reads were obtained from the second and third generation sequencing. The de-redundant transcript sequences were predicted by lncRNA, 2083 lncRNAs were obtained, and the target genes were predicted, and a total of 2712 target genes were obtained. The de-redundant transcripts were further screened, and 1654 differentially expressed genes (DEGs )were obtained. Among them, different DEGs respond to drought stress in different ways, such as oxidation-reduction process, starch and sucrose metabolism, plant hormone pathway, carbon metabolism, lignin catabolic/biosynthetic process and so on. This study provides basic full-length sequencing data for the study of Larch drought resistance, and excavates a large number of DEGs in response to drought stress, which helps us to further understand the function of Larch drought resistance genes and provides a reference for in-depth analysis of the molecular mechanism of Larch drought resistance.

Keywords: larch, drought stress, full-length transcriptome sequencing, differentially expressed genes

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Authors: RAM PAL SINGH, VIKASH CHAUDHARY, MONIKA VERMA

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In this paper, we proposed the implementation of optimization based Extreme Learning Machine (ELM) for watermarking of B-channel of color image in discrete wavelet transform (DWT) domain. ELM, a regularization algorithm, works based on generalized single-hidden-layer feed-forward neural networks (SLFNs). However, hidden layer parameters, generally called feature mapping in context of ELM need not to be tuned every time. This paper shows the embedding and extraction processes of watermark with the help of ELM and results are compared with already used machine learning models for watermarking.Here, a cover image is divide into suitable numbers of non-overlapping blocks of required size and DWT is applied to each block to be transformed in low frequency sub-band domain. Basically, ELM gives a unified leaning platform with a feature mapping, that is, mapping between hidden layer and output layer of SLFNs, is tried for watermark embedding and extraction purpose in a cover image. Although ELM has widespread application right from binary classification, multiclass classification to regression and function estimation etc. Unlike SVM based algorithm which achieve suboptimal solution with high computational complexity, ELM can provide better generalization performance results with very small complexity. Efficacy of optimization method based ELM algorithm is measured by using quantitative and qualitative parameters on a watermarked image even though image is subjected to different types of geometrical and conventional attacks.

Keywords: BER, DWT, extreme leaning machine (ELM), PSNR

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Authors: Paula Lazarim

Abstract:

As global mobility thrives, researchers emphasize the urgency of global literacy through training qualified counselors to serve internationally in a culturally competent manner. However, the focus thus far has been on how counselors’ preparation to approach international populations fuses with study abroad experiential learning short-term immersions. Looking for better solutions for cultural competency and skills learning related to international counseling, the author of this manuscript examines international counseling's current status, learning scope and goals, and educational opportunities. A guiding framework grounded on relational pedagogy (Reeves & Le Mare, 2017), relational cultural theory (Jordan, 2017), and intercultural education (Nastasi et al., 2020) is applied with four long-term educational modality projects designed to benefit cultural competence, attitude, relational skills development, and learning an intercultural counseling approach. Suggestions that encourage innovative instruction in counselor education and counseling programs at master and doctoral levels, stimulate self-learning, and educate in intercultural relational competence are linked to strategies for engaging in international counseling based on findings of a literature review and training-projects implementation. Ultimately, the author highlights theoretical and practical implications of suitable training to improve counselors' performance and discusses long-term teaching-learning opportunities that positively impact the international counseling community by sending out internationally culturally competent counselors.

Keywords: international counseling, counselor education, counseling, relational pedagogy, intercultural education, counselors’ training

Procedia PDF Downloads 199