Search results for: apprenticeship training

Authors: Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Diego Santibañez Oyarce, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Streptococcus pyogenes is a gram-positive bacteria involved in a wide range of diseases and is a major-human-specific bacterial pathogen. In Chile, this year the 'Ministerio de Salud' declared an alert due to the increase in strains throughout the year. This increase can be attributed to the multitude of factors including antimicrobial resistance (AMR) and Virulence Factors (VF). Understanding these VF and AMR is crucial for developing effective strategies and improving public health responses. Moreover, experimental identification and characterization of these pathogenic mechanisms are labor-intensive and time-consuming. Therefore, new computational methods are required to provide robust techniques for accelerating this identification. Advances in Machine Learning (ML) algorithms represent the opportunity to refine and accelerate the discovery of VF associated with Streptococcus pyogenes. In this work, we evaluate the accuracy of various machine learning models in predicting the virulence factors and antimicrobial resistance of Streptococcus pyogenes, with the objective of providing new methods for identifying the pathogenic mechanisms of this organism.Our comprehensive approach involved the download of 32,798 genbank files of S. pyogenes from NCBI dataset, coupled with the incorporation of data from Virulence Factor Database (VFDB) and Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. These datasets provided labeled examples of both virulent and non-virulent genes, enabling a robust foundation for feature extraction and model training. We employed preprocessing, characterization and feature extraction techniques on primary nucleotide/amino acid sequences and selected the optimal more for model training. The feature set was constructed using sequence-based descriptors (e.g., k-mers and One-hot encoding), and functional annotations based on database prediction. The ML models compared are logistic regression, decision trees, support vector machines, neural networks among others. The results of this work show some differences in accuracy between the algorithms, these differences allow us to identify different aspects that represent unique opportunities for a more precise and efficient characterization and identification of VF and AMR. This comparative analysis underscores the value of integrating machine learning techniques in predicting S. pyogenes virulence and AMR, offering potential pathways for more effective diagnostic and therapeutic strategies. Future work will focus on incorporating additional omics data, such as transcriptomics, and exploring advanced deep learning models to further enhance predictive capabilities.

Keywords: antibiotic resistance, streptococcus pyogenes, virulence factors., machine learning

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Authors: Mahdi Bazarganigilani

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Aortic enlargement, also known as an aortic aneurysm, can occur when the walls of the aorta become weak. This disease can become deadly if overlooked and undiagnosed. In this paper, a computer-aided diagnosis (CAD) system was introduced to accurately diagnose aortic enlargement from chest x-ray images. An enhanced convolutional neural network (CNN) was employed and then trained by transfer learning by using three different main areas from the original images. The areas included the left lung, heart, and right lung. The accuracy of the system was then evaluated on 1001 samples by using 4-fold cross-validation. A promising accuracy of 90% was achieved in terms of the F-measure indicator. The results showed using different areas from the original image in the training phase of CNN could increase the accuracy of predictions. This encouraged the author to evaluate this method on a larger dataset and even on different CAD systems for further enhancement of this methodology.

Keywords: computer-aided diagnosis systems, aortic enlargement, chest X-ray, image processing, convolutional neural networks

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Authors: Mohamed R. Mhereeg

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The paper discusses the design of a .NET Windows Service based agent system called MACS (Multi-Agent Classification System). MACS is a system aims to accurately classify spread-sheet developers competency over a network. It is designed to automatically and autonomously monitor spread-sheet users and gather their development activities based on the utilization of the software Multi-Agent Technology (MAS). This is accomplished in such a way that makes management capable to efficiently allow for precise tailor training activities for future spread-sheet development. The monitoring agents of MACS are intended to be distributed over the WWW in order to satisfy the monitoring and classification of the multiple developer aspect. The Prometheus methodology is used for the design of the agents of MACS. Prometheus has been used to undertake this phase of the system design because it is developed specifically for specifying and designing agent-oriented systems. Additionally, Prometheus specifies also the communication needed between the agents in order to coordinate to achieve their delegated tasks.

Keywords: classification, design, MACS, MAS, prometheus

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Authors: Ali Ulvi Uzer

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Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

Keywords: feature selection, sequential forward selection, support vector machines, soil sample

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Authors: J. L. Andrade-Pineda, J.M. León-Blanco, M. Calle, P. L. González-R

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The enrollment in current Master's degree programs usually pursues gaining the expertise required in real-life workplaces. The experience we present here concerns the learning process of "Project Management Methodology (PMM)", around a cooperative/collaborative mechanism aimed at affording students measurable learning goals and providing the teacher with the ability of focusing on the weaknesses detected. We have designed a mixed summative/formative evaluation, which assures curriculum engage while enriches the comprehension of PMM key concepts. In this experience we converted the students into active actors in the evaluation process itself and we endowed ourselves as teachers with a flexible process in which along with qualifications (score), other attitudinal feedback arises. Despite the high level of self-affirmation on their discussion within the interactive assessment sessions, they ultimately have exhibited a great ability to review and correct the wrong reasoning when that was the case.

Keywords: cooperative-collaborative learning, educational management, formative-summative assessment, leadership training

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Authors: Gabriel P. Fife, Saeyong Lee, David M. O'Sullivan

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Introduction: Although investigations into injury characteristics are represented well in the literature, few have investigated the biomechanical characteristics associated with head impacts in Taekwondo. Therefore, the purpose of this study was to identify the kinematic characteristics of head impacts due to taekwondo kicks among non-elite practitioners. Participants: Male participants (n= 11, 175 + 5.3 cm, 71 + 8.3 kg) with 7.5 + 3.6 years of taekwondo training volunteered for this study. Methods: Participants were asked to perform five repetitions of each technique (i.e., turning kick, spinning hook kick, spinning back kick, front axe kick, and clench axe kick) aimed at the Hybrid III head with their dominant kicking leg. All participants wore a protective foot pad (thickness = 12 mm) that is commonly used in competition and training. To simulate head impact in taekwondo, the target consisted of a Hybrid III 50th Percentile Crash Test Dummy (Hybrid III) head (mass = 5.1 kg) and neck (fitted with taekwondo headgear) secured to an aluminum support frame and positioned to each athlete’s standing height. The Hybrid III head form was instrumented with a 500 g tri-axial accelerometer (PCB Piezotronics) mounted to the head center of gravity to obtain resultant linear accelerations (RLA). Rotational accelerations were collected using three angular rate sensors mounted orthogonally to each other (Diversified Technical Systems ARS-12 K Angular Rate Sensor). The accelerometers were interfaced via a 3-channel, battery-powered integrated circuit piezoelectric sensor signal conditioner (PCB Piezotronics) and connected to a desktop computer for analysis. Acceleration data were captured using LABVIEW Signal Express and processed in accordance with SAE J211-1 channel frequency class 1000. Head injury criteria values (HIC) were calculated using the VSRSoftware. A one-way analysis of variance was used to determine differences between kicks, while the Tukey HSD test was employed for pairwise comparisons. The level of significance was set to an effect size of 0.20. All statistical analyses were done using R 3.1.0. Results: A statistically significant difference was observed in RLA (p = 0.00075); however, these differences were not clinically meaningful (η² = 0.04, 95% CI: -0.94 to 1.03). No differences were identified with ROTA (p = 0.734, η² = 0.0004, 95% CI: -0.98 to 0.98). A statistically significant difference (p < 0.001) between kicks in HIC was observed, with a medium effect (η2= 0.08, 95% CI: -0.98 to 1.07). However, the confidence interval of this difference indicates uncertainty. Tukey HSD test identified differences (p < 0.001) between kicking techniques in RLA and HIC. Conclusion: This study observed head impact levels that were comparable to previous studies of similar objectives and methodology. These data are important as impact measures from this study may be more representative of impact levels experienced by non-elite competitors. Although the clench axe kick elicited a lower RLA, the ROTA of this technique was higher than levels from other techniques (although not large differences in reference to effect sizes). As the axe kick has been reported to cause severe head injury, future studies may consider further study of this kick important.

Keywords: Taekwondo, head injury, biomechanics, kicking

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Authors: Akella Amarendra Babu, Rama Devi Yellasiri, Natukula Sainath

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Where human beings can easily learn and adopt pronunciation variations, machines need training before put into use. Also humans keep minimum vocabulary and their pronunciation variations are stored in front-end of their memory for ready reference, while machines keep the entire pronunciation dictionary for ready reference. Supervised methods are used for preparation of pronunciation dictionaries which take large amounts of manual effort, cost, time and are not suitable for real time use. This paper presents an unsupervised adaptation model for building agile and dynamic pronunciation dictionaries online. These methods mimic human approach in learning the new pronunciations in real time. A new algorithm for measuring sound distances called Dynamic Phone Warping is presented and tested. Performance of the system is measured using an adaptation model and the precision metrics is found to be better than 86 percent.

Keywords: pronunciation variations, dynamic programming, machine learning, natural language processing

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Authors: Fawehinmi Olabisi Alaba, O. R. Adeniyi

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Farming households faces lots of disaster which contribute to endemic poverty. Anticipated increases in extreme weather events will exacerbate this. Primary data was administered to farming household using multi-stage random sampling technique. The result of the analysis shows that majority of the respondents (69.9%) are male, have mean household size, years of formal education and age of 5±1.14, 6±3.41, and 51.06±10.43 respectively. The major (48.9%) type of disaster experienced is flooding. Major coping mechanism adopted is sourcing for support from family and friends. Age, education, experience, access to extension agent, and mitigation control method contribute significantly to vulnerability to disaster. The major adaptation method (62.3%) is construction of drainage. The study revealed that the coping mechanisms employed may become less effective as increasingly fragile livelihood systems struggle to withstand disaster shocks. Thus there is need for training of the farmers on measures to adapt to mitigate the shock from disasters.

Keywords: adaptation, disasters, flooding, vulnerability

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Authors: Dua Hişam, Serhat İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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Authors: Mengjiao Fan, Thomson W. L. Wong

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It is unavoidable that older adults may have to deal with aging-related motor problems. Aging is highly likely to affect motor learning and control as well. For example, older adults may suffer from poor motor function and quality of life due to age-related eye changes. These adverse changes in vision results in impairment of movement automaticity. Reaching is a fundamental component of various complex movements, which is therefore beneficial to explore the changes and adaptation in visuomotor behaviors. The current study aims to explore how aging affects visuomotor behaviors by comparing motor performance and gaze behaviors between two age groups (i.e., young and older adults). Visuomotor behaviors in reaching under providing or blocking online visual feedback (simulated visual deficiency) conditions were investigated in 60 healthy young adults (Mean age=24.49 years, SD=2.12) and 37 older adults (Mean age=70.07 years, SD=2.37) with normal or corrected-to-normal vision. Participants in each group were randomly allocated into two subgroups. Subgroup 1 was provided with online visual feedback of the hand-controlled mouse cursor. However, in subgroup 2, visual feedback was blocked to simulate visual deficiency. The experimental task required participants to complete 20 times of reaching to a target by controlling the mouse cursor on the computer screen. Among all the 20 trials, start position was upright in the center of the screen and target appeared at a randomly selected position by the tailor-made computer program. Primary outcomes of motor performance and gaze behaviours data were recorded by the EyeLink II (SR Research, Canada). The results suggested that aging seems to affect the performance of reaching tasks significantly in both visual feedback conditions. In both age groups, blocking online visual feedback of the cursor in reaching resulted in longer hand movement time (p < .001), longer reaching distance away from the target center (p<.001) and poorer reaching motor accuracy (p < .001). Concerning gaze behaviors, blocking online visual feedback increased the first fixation duration time in young adults (p<.001) but decreased it in older adults (p < .001). Besides, under the condition of providing online visual feedback of the cursor, older adults conducted a longer fixation dwell time on target throughout reaching than the young adults (p < .001) although the effect was not significant under blocking online visual feedback condition (p=.215). Therefore, the results suggested that different levels of visual feedback during movement execution can affect gaze behaviors differently in older and young adults. Differential effects by aging on visuomotor behaviors appear on two visual feedback patterns (i.e., blocking or providing online visual feedback of hand-controlled cursor in reaching). Several specific gaze behaviors among the older adults were found, which imply that blocking of visual feedback may act as a stimulus to seduce extra perceptive load in movement execution and age-related visual degeneration might further deteriorate the situation. It indeed provides us with insight for the future development of potential rehabilitative training method (e.g., well-designed errorless training) in enhancing visuomotor adaptation for our aging population in the context of improving their movement automaticity by facilitating their compensation of visual degeneration.

Keywords: aging effect, movement automaticity, reaching, visuomotor behaviors, visual degeneration

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Authors: Rozafa Shala

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Development of preschool education in Kosovo has passed through several periods. The period after the 1999 war was very intensive period when preschool education started to change. Step-by-step program was one of the programs which were very well extended during the period after the 1999 war until now. The aim of this study is to present the impact of the step-by-step program in the preschool education. This research is based on the hypothesis that: Step-by-step program continues to be present with its elements, in all other programs that the teachers can use. For data collection a questionnaire is constructed which was distributed to 25 teachers of preschool education who work in public preschool institutions. All the teachers have finished the training for step by step program. To support the data from the questionnaire a focus group is also organized with whom the critical issues of the program were discussed. From the results obtained we can conclude that the step-by-step program has a very strong impact in the preschool level. Many specific elements such as: circle time, weather calendar, environment inside the class, portfolios and many other elements are present in most of the preschool classes. The teacher's approach also has many elements of the step-by-step program.

Keywords: preschool education, step-by-step program, impact, teachers

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Authors: Maha Wiss, Wael Khreich

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Animals, objects, foods, plants, and other non-human terms are commonly used as a source of metaphors to describe females in formal and slang language. Comparing women to non-human items not only reflects cultural views that might conceptualize women as subordinates or in a lower position than humans, yet it conveys this degradation to the listeners. Moreover, the dehumanizing representation of females in the language normalizes the derogation and even encourages sexism and aggressiveness against women. Although dehumanization has been a popular research topic for decades, according to our knowledge, no studies have linked women's dehumanizing language to the machine learning field. Therefore, we introduce our research work as one of the first attempts to create a tool for the automated detection of the dehumanizing depiction of females in English texts. We also present the first labeled dataset on the charted topic, which is used for training supervised machine learning algorithms to build an accurate classification model. The importance of this work is that it accomplishes the first step toward mitigating dehumanizing language against females.

Keywords: gender bias, machine learning, NLP, women dehumanization

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Authors: Ewa Rak

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This paper is part of a broader research project on employee development in small enterprises, financed by Polish National Science Centre. The project results will serve as basis for a doctoral dissertation. The paper utilises literature studies and qualitative research conducted in small enterprises operating in the Lower Silesia region of Poland. This paper aims to identify some of the factors that stimulate employee development in small companies operating in Poland. The great variety of business pursuits and applications represented by this sector makes it hard to determine a universal configuration of factors to offer best possible conditions for employee development. Research results suggest that each of the examined companies had one or two of such factors in focus, and serving as the basis for the entire pro-development system. These include: employment security (both for employee and entrepreneur) and extensive knowledge and experience of entrepreneurs, but only if it is combined with a willingness and ability to share it.

Keywords: employee development, factors that stimulate employee development, human resources development, Poland, small enterprises, training

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Authors: Ka'abu Mu'azu

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The study examines the effect of varied intensities of aerobic exercise on Systolic Blood Pressure (SBP) among HIV/AIDS positive patients. Participants of mean age of 20.4 years were randomized into four groups. High Intensity Group (HIG), Moderate Intensity Group (MIG), Low Intensity Group (LIG) and Control Group (COG). SBP was measured at baseline (pre-exercise) and post-exercise (8 weeks). Analysis of variance (ANOVA) indicates a significant training effect on resting values of SBP (F [3, 15] = 8.9, P < 0.05). Sheffe post hoc analysis indicated that both HIG and MIG significantly differ from control (P < 0.05). Dependent t- test indicates difference in HIG (t [7] = 6.5, P < 0.05) and slightly in MIG (t [7] = 5.4, P < 0.05). The study concluded that aerobic exercise is effective in reducing resting values of SBP particularly the activities that are high intensity in nature. The study recommends that high and moderate intensity aerobic exercise should be used for improving health condition of HIV/AIDS patients as regard to decrease in resting value of SBP.

Keywords: systolic blood pressure, aerobic exercise, HIV patients, health sciences

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Authors: Jędrzej Minda

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In aviation, a very narrow spectrum of control inceptors exists, namely centre sticks, side-sticks, pedals, and yokes. However, new types of aircraft are emerging, and with them, a need for new inceptors. A manned multicopter created at AGH University of Science and Technology is an aircraft in which the pilot takes a specific orientation in which classical inceptors may be impractical to use. In this paper, a unique kind of control inceptor is described, which aims to provide a handling quality not unlike standard solutions, and provide a firm grip point for the pilot without the risk of involuntary stick movement. Simulations of the pilot-inceptor model were performed in order to compare the dynamic amplification factors of the design described in this paper with the classical one. A functional prototype is built on which drone pilots carry out a comfort-of-use evaluation. This paper provides a general overview of the project, including a literature review, reasoning behind components selection, and mechanism design finalized by conclusions.

Keywords: mechanisms, mechatronics, embedded control, serious gaming for training rescue missions, rescue robotics

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Authors: Faisal Ali, Mansoor Shuakat, Cui Lirong, Rabia Riasat

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HR is a department that enhances the power of employee performance in regard with their services, and to make the organization strategic objectives. The main concern of HR department is to organize people, focus on policies and their system. The empirical study shows the relationship between HRM (Human Resource Management practices) and their Job Satisfaction. The Hypothesis is testing on a sample of overall 320 employees of 5 different Pharmaceutical departments of different organizations in Pakistan. The important thing as Relationship of Job satisfaction with HR Practices, Impact on Job Satisfaction with HR Practices, Participation of Staff of Different Departments, HR Practices effects the Job satisfaction, Recruitment or Hiring and Selection effects the Job satisfaction, Training and Development, Performance and Appraisals, Compensation affects the Job satisfaction , and Industrial Relationships affects the Job satisfaction. After finishing all data analysis, the conclusion is that lots of Job related activities raise the confidence of Job satisfaction of employees with their salary and other benefits. Implications of HR practices discussed, Limitations, and future research study also offered write the main conclusion for your paper.

Keywords: HRM, HR practices, job satisfaction, TQM

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Authors: Salma Akter, Md. Kamal Uddin

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This study aims to demonstrate how top-down shelter policy and its resultant dwelling environment leads to ‘everyday compromise’ by the grassroots according to subjective (satisfaction) and objective (physical design elements and physical environmental elements) indicators, which are measured across three levels of the settlement; macro (Community), meso (Neighborhood or shelter/built environment) and micro (family). Ashrayan Prakalpa is a resettlement /housing project of Government of Bangladesh for providing shelters and human resources development activities like education, microcredit, and training programme to landless, homeless and rootless people. Despite the integrated nature of the shelter policies (comprises poverty alleviation, employment opportunity, secured tenure, and livelihood training), the ‘quality of life’ issue at the different levels of settlements becomes questionable. As dwellers of shelter units (although formally termed as ‘barracks’ rather shelter or housing) remain on the receiving end of government’s resettlement policies, they often involve with spatial-physical and socio-economic negotiation and assume curious forms of spatial practice, which often upholds contradiction with policy planning. Thus, policy based shelter force dwellers to persistently compromise with their provided built environments both in overtly and covertly. Compromising with prescribed designed space and facilities across living places articulated their negotiation with the quality of allocated space, built form and infrastructures, which in turn exert as less quality of life. The top-down shelter project, Dakshin Chandani Mahal Ashrayan Prakalpa at Dighalia Upazila, the study area located at the Eastern fringe area of Khulna, Bangladesh, is still in progress to resettle internally displaced and homeless people. In terms of methodology, this research is primarily exploratory and adopts a case study method, and an analytical framework is developed through the deductive approach for evaluating the quality of life. Secondary data have been obtained from housing policy analysis and relevant literature review, while key informant interview, focus group discussion, necessary drawings and photographs and participant observation across dwelling, neighborhood, and community level have also been administered as primary data collection methodology. Findings have revealed that various shortages, inadequacies, and negligence of policymakers force to compromise with allocated designed space, physical infrastructure and economic opportunities across dwelling, neighborhood and mostly community level. Thus, the outcome of this study can be beneficial for a global-level understating of the compromising the ‘quality of life’ under top-down shelter policy. Locally, for instance, in the context of Bangladesh, it can help policymakers and concerned authorities to formulate the shelter policies and take initiatives to improve the well-being of marginalized.

Keywords: Ashrayan Prakalpa, compromise, displaced people, quality of life

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Authors: Daniela Tapusi, Adrian Andronic, Naomi Tufan, Ruxandra Erbașu, Ioana Teodorescu

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The proposed theme of this article falls within the policy of reducing carbon emissions imposed by the ‘Green New Deal’ by replacing structural elements made of energy-intensive materials with ecological materials. In this sense, wood has many qualities (high strength/mass and stiffness/mass ratio, low specific gravity, recovery/recycling) that make it competitive with classic building materials. The topological optimization of the linear glulam elements, resulting from different types of analysis (Finite Element Method, simple regression on metamodels), tests on models or by Monte-Carlo simulation, leads to a material reduction of more than 10%. This article proposes a method of obtaining topologically optimized shapes for different types of glued laminated timber beams. The results obtained will constitute the database for AI training.

Keywords: timber, glued laminated timber, artificial-intelligence, environment, carbon emissions

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Authors: Abdulkadir Abu Lawal

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For the dearth of reliable cardinal numerical data, the linked phenomena in productivity indices such as operational costs and company turnovers, etc. could not be investigated. This would not give us insight to the root of productivity problems at unique sites. So, ordinal ranking by professionals who were most directly involved with construction sites was applied for Kendall’s concordance. Responses gathered from independent architects, builders/engineers, and quantity surveyors were herein analyzed. They were responses based on factors that affect sites productivity, and these factors were categorized as head office factors, resource management effectiveness factors, motivational factors, and training/skill development factors. It was found that productivity is low and has to be improved in order to facilitate Nigerian efforts in bridging its infrastructure deficit. The significance of this work is underlined with the Kendall’s coefficient of concordance of 0.78, while remedial measures must be emphasized to stimulate better productivity. Further detailed study can be undertaken by using Fuzzy logic analysis on wider Delphi survey.

Keywords: factors, Kendall's coefficient of concordance, magnitude of agreement, percentage magnitude of dichotomy, ranking variables

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Authors: Maharjan Shree Kumar

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Climate change impacts are observed in all livelihood sectors primarily in agriculture and forestry. Multiple factors have influenced the climate vulnerabilities and adaptations in agricultural at the household level. This study focused on the factors affecting adaptation in agriculture in Madi and Deukhuri valleys of Central and Western Nepal. The systematic random sampling technique was applied to select 154 households in Madi and 150 households in Deukhuri. The main purpose of the study was to analyze the socio-economic factors that either influence or restrain the farmers’ adaptation to climate change at the household level by applying the linear probability model. Based on the analysis, it is revealed that crop diversity, education, training and total land holding (acre) were positively significant for adaptation choices the study sites. Rest of the variables were not significant though indicated positive as expected except age, occupation, ethnicity, family size, and access to credit.

Keywords: adaptation, agriculture, climate, factors, Nepal

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Authors: Manar Aslan, Ayfer Karaaslan, Serap Selçuk

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The research was conducted in order to determine the organizational socialization levels of nurses working in hospitals in the form of a descriptive study. The research population was composed of nurses employed in public and private sector hospitals in the province of Konya with 0-3 years of professional experience in the hospitals (N=1200); and the sample was composed of 495 nurses that accepted to take part in the study voluntarily. Organizational Socialization Scale which was developed by Haueter, Macan and Winter (2003) and whose validity-reliability in Turkish was analyzed by Ataman (2012) was used. Statistical evaluation of data was conducted in SPSS.16 software. The results of the study revealed that the total score taken by nurses at the organizational socialization scale was 262.95; and this was close to the maximum score. Particularly the departmental socialization sub-dimension proved to be higher in comparison to the other two dimensions (organization socialization and task socialization). Statistically meaningful differences were found in the levels of organization socialization in relation to the status of organizational orientation training, level of education and age group.

Keywords: nurses, newcomers, organizational socialization, total score

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Authors: Li Zhang, Yuehong Su

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Lightpipe as a mature solar light tube technique has been employed worldwide. Accurately assessing the performance of lightpipe and evaluate daylighting available has been a challenging topic. Previous research had used regression model and computational simulation methods to estimate the performance of lightpipe. However, due to the nonlinear nature of solar light transferring in lightpipe, the methods mentioned above express inaccurate and time-costing issues. In the present study, a neural network model as an alternative method is investigated to predict the transmittance of lightpipe. Four types of commercial lightpipe with bended angle 0°, 30°, 45° and 60° are discussed under clear, intermediate and overcast sky conditions respectively. The neural network is generated in MATLAB by using the outcomes of an optical software Photopia simulations as targets for networks training and testing. The coefficient of determination (R²) for each model is higher than 0.98, and the mean square error (MSE) is less than 0.0019, which indicate the neural network strong predictive ability and the use of the neural network method could be an efficient technique for determining the performance of lightpipe.

Keywords: neural network, bended lightpipe, transmittance, Photopia

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Authors: Fred Mageto

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A great number of children in mainstream schools across Kenya are currently living with emotional, behavioural difficulties. This study aims to explore teachers’ perceptions of children’s emotional and behavioural difficulties (EBD) and their attributions of the causes of EBD. The relevance of this area of study to current educational practice is illustrated in the fact that primary school teachers in Kenya find classroom behaviour problems one of the major difficulties they face. The information presented in this study was gathered from 182 teachers that responded back to the survey, of whom 27 teachers were later interviewed. In general, teachers’ perceptions of EBD reflect personal experience, training, and attitudes. Teachers appear from this study to use words such as indifferent, frightened, withdrawn, aggressive, disobedient, hyperactive, less ambitious, lacking concentration, and academically weak to describe pupils with emotional and behavioural difficulties (EBD). The implications of this study are envisaged as being extremely important to support teachers addressing children’s EBD and shed light on the contributing factors to EBD for a successful teaching-learning process in Libyan primary schools.

Keywords: teachers, children, learning, emotional and behaviour difficulties

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Authors: Nicholas Milburn, Yu Liang, Dalei Wu

Abstract:

Athlete performance scoring within the climbing do-main presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: classification, climbing, data imbalance, data scarcity, machine learning, time sequence

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Authors: Rachel Nir, Tina Mckee

Abstract:

In the UK, reliance has been placed on the professions to ‘heal themselves’ in improving equality and diversity. This approach has faltered, in part due to the global economic climate, and stimulus is needed to make faster equality progress. Recent empirical evidence has identified specific diversity barriers, namely: the cost of training; the use of high school grades as a primary selection criteria; the significance of prior work experience in recruitment decisions; and recruitment from elite universities. Students from majority groups and affluent backgrounds are advantaged over their counterparts. We as educators are passionate about resisting this. We believe that education can be a key agent of change. As part of this belief, the presenters have recently designed learning and teaching materials for the 2015/16 academic year. These are aimed at undergraduate law students for the purpose of 1) educating them on career barriers; 2) helping them to develop personal strategies to overcome them; and 3) encouraging them to address their own biases, both conscious and implicit, so that they, themselves, may be fairer employers and managers in the future.

Keywords: career barriers, challenging professional bias, education, elitism, personal student strategies

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Authors: Isa Muhammad Zumo, Musa Lawan

Abstract:

This study analyzes the land use/land cover changes in Damaturu metropolis from 1986 to 2005. LandSat TM Images of 1986, 1999, and 2005 were used. Built-up lands, agric lands, water body and other lands were created as themes within ILWIS 3.4 software. The images were displayed in False Colour Composite (FCC) for a better visualization and identification of the themes created. Training sample sets were collected based on the ground truth data during field the checks. Statistical data were then extracted from the classified sample set. Area in hectares for each theme was calculated for each year and the result for each land use/land cover types for each study year was compared. From the result, it was found out that built-up areas have a considerable increase from 37.71 hectares in 1986 to 1062.72 hectares in 2005. It has an annual increase rate of approximately 0.34%. The results also reveal that there is a decrease of 5829.66 hectares of other lands (vacant lands) from 1986 to 2005.

Keywords: land use, changes, analysis, environmental pollution

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Authors: Khader S. Al-Aidmat, Venus W. Samawi

Abstract:

Blurred vision in the misty atmosphere is essential problem which needs to be resolved. To solve this problem, we developed a technique to restore foggy degraded image from its original version using Back-propagation neural network (BP-NN). The suggested technique is based on mapping between foggy scene and its corresponding original scene. Seven different approaches are suggested based on type of features used in image restoration. Features are extracted from spatial and spatial-frequency domain (using DCT). Each of these approaches comes with its own BP-NN architecture depending on type and number of used features. The weight matrix resulted from training each BP-NN represents a fog filter. The performance of these filters are evaluated empirically (using PSNR), and perceptually. By comparing the performance of these filters, the effective features that suits BP-NN technique for restoring foggy images is recognized. This system proved its effectiveness and success in restoring moderate foggy images.

Keywords: artificial neural network, discrete cosine transform, feed forward neural network, foggy image restoration

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Authors: Rana I. Abdulghani, Amera I. Melhum

Abstract:

With the abnormal growth of the usage of computers over networks and under the consideration or agreement of most of the computer security experts who said that the goal of building a secure system is never achieved effectively, all these points led to the design of the intrusion detection systems(IDS). This research adopts a comparison between two techniques for network intrusion detection, The first one used the (Particles Swarm Optimization) that fall within the field (Swarm Intelligence). In this Act, the algorithm Enhanced for the purpose of obtaining the minimum error rate by amending the cluster centers when better fitness function is found through the training stages. Results show that this modification gives more efficient exploration of the original algorithm. The second algorithm used a (Back propagation NN) algorithm. Finally a comparison between the results of two methods used were based on (NSL_KDD) data sets for the construction and evaluation of intrusion detection systems. This research is only interested in clustering the two categories (Normal and Abnormal) for the given connection records. Practices experiments result in intrude detection rate (99.183818%) for EPSO and intrude detection rate (69.446416%) for BP neural network.

Keywords: IDS, SI, BP, NSL_KDD, PSO

Procedia PDF Downloads 382

Authors: Bruno Biagianti, Angela Tseng, Kathy Wannaviroj, Allison Corlett, Megan DuBois, Kyu Lee, Suma Jacob

Abstract:

Background: ASD is characterized by pervasive Sensory Processing Abnormalities (SPA) and social cognitive deficits that persist throughout the course of the illness and have been linked to functional abnormalities in specific neural systems that underlie the perception, processing, and representation of sensory information. SPA and social cognitive deficits are associated with difficulties in interpersonal relationships, poor development of social skills, reduced social interactions and lower academic performance. Importantly, they can hamper the effects of established evidence-based psychological treatments—including PEERS (Program for the Education and Enrichment of Relationship Skills), a parent/caregiver-assisted, 16-weeks social skills intervention—which nonetheless requires a functional brain capable of assimilating and retaining information and skills. As a matter of fact, some adolescents benefit from PEERS more than others, calling for strategies to increase treatment response rates. Objective: We will present interim data on CICADAS (Care Improving Cognition for ADolescents on the Autism Spectrum)—a clinician-assisted, closed-loop technology mobile application for adolescents with ASD. Via ten mobile assessments, CICADAS captures data on sensory processing abnormalities and associated cognitive deficits. These data populate a machine learning algorithm that tailors the delivery of ten neuroplasticity-based social cognitive training (NB-SCT) exercises targeting sensory processing abnormalities. Methods: In collaboration with the Autism Spectrum and Neurodevelopmental Disorders Clinic at the University of Minnesota, we conducted a fully remote, three-arm, randomized crossover trial with adolescents with ASD to document the acceptability of CICADAS and evaluate its potential as a stand-alone treatment or as a treatment enhancer of PEERS. Twenty-four adolescents with ASD (ages 11-18) have been initially randomized to 16 weeks of PEERS + CICADAS (Arm A) vs. 16 weeks of PEERS + computer games vs. 16 weeks of CICADAS alone (Arm C). After 16 weeks, the full battery of assessments has been remotely administered. Results: We have evaluated the acceptability of CICADAS by examining adherence rates, engagement patterns, and exit survey data. We found that: 1) CICADAS is able to serve as a treatment enhancer for PEERS, inducing greater improvements in sensory processing, cognition, symptom reduction, social skills and behaviors, as well as the quality of life compared to computer games; 2) the concurrent delivery of PEERS and CICADAS induces greater improvements in study outcomes compared to CICADAS only. Conclusion: While preliminary, our results indicate that the individualized assessment and treatment approach designed in CICADAS seems effective in inducing adaptive long-term learning about social-emotional events. CICADAS-induced enhancement of processing and cognition facilitates the application of PEERS skills in the environment of adolescents with ASD, thus improving their real-world functioning.

Keywords: ASD, social skills, cognitive training, mobile app

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