Search results for: training needs assessment model

Authors: Magdy Abdel-Aleem Abdel-Ati Mayouf, Islam Al Sayed Hussein Al Sayed

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Development of tourism and hospitality faculties' graduates is a key to the future health of hospitality and tourism sectors. Meanwhile information and communication technologies (ICTs) increasingly become the driving engine for productivity improvement and business opportunities in tourism and hospitality industry. Tourism and hospitality education and training must address these developments to enhance the ability of future managers to adopt a variety of ICT tools and strategies to increase their organization's efficiency and competitiveness. Therefore, this study aims to explore the types and effectiveness of ICT training offered by faculties of tourism and hotels in Egypt, and evaluating the importance of that training from the graduate's point of view. The study targets the graduates who graduated in the present ten years from three different faculties of tourism and hotels. Results argued the types, levels and effectiveness of ICT training offered in these faculties and the extent to which training programs were appreciated by graduates working in different fields, and finally, it recommended particular practices to enhance the training efficiency and raising the perceived benefits of it for workers in tourism and hospitality fields.

Keywords: training, IT, graduated, tourism and hospitality, education

Procedia PDF Downloads 346

Authors: Vera Karla S. Caingles, Glen A. Lorenzo

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Soil, as the main material of landslides, plays a vital role in landslide assessment. An efficient and accurate method of doing an assessment is significantly important to prevent damage of properties and loss of lives. The study has two phases: to establish an empirical correlation of the residual soil thickness with the slope angle and to investigate the geotechnical characteristics of residual soil. Digital Elevation Model (DEM) in Geographic Information System (GIS) was used to establish the slope map and to program sampling points for field investigation. Physical and index property tests were undertaken on the 20 soil samples obtained from the area with Pliocene-Pleistocene geology and different slope angle in Kibawe, Bukidnon. The regression analysis result shows that the best fitting model that can describe the soil thickness-slope angle relationship is an exponential function. The physical property results revealed that soils contain a high percentage of clay and silts ranges from 41% - 99.52%. Based on the index properties test results, the soil exhibits a high degree of plasticity and expansion but not collapsible. It is deemed that this compendium will serve as primary data for slope stability analysis and deterministic landslide assessment.

Keywords: collapsibility, correlation, expansiveness, landslide, plasticity

Procedia PDF Downloads 148

Authors: Caglayan Hizal, Hasan Emre Demirci, Engin Aktas, Alper Sezer

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Offshore wind turbines consist of a long slender tower with a heavy fixed mass on the top of the tower (nacelle), together with a heavy rotating mass (blades and hub). They are always subjected to environmental loads including wind and wave loads in their service life. This study presents a three-stage methodology for reliability-based condition assessment of offshore wind-turbines against the seismic, wave and wind induced effects considering the soil-structure interaction. In this context, failure criterions are considered as serviceability limits of a monopile supporting an Offshore Wind Turbine: (a) allowable horizontal displacement at pile head should not exceed 0.2 m, (b) rotations at pile head should not exceed 0.5°. A Bayesian system identification framework is adapted to the classical reliability analysis procedure. Using this framework, a reliability assessment can be directly implemented to the updated finite element model without performing time-consuming methods. For numerical verification, simulation data of the finite model of a real offshore wind-turbine structure is investigated using the three-stage methodology.

Keywords: Offshore wind turbines, SHM, reliability assessment, soil-structure interaction

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Authors: Chorom Baek, Sungwon Choi

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The purpose of this study is to analyze the supervision system, and the training and continuing education of mental health professionals in USA, UK, Australia (New Zealand), Japan, and so on, and to deduce the implications of Korean mental health service system. In order to accomplish the purpose of this study, following methodologies were adopted: review on the related literatures, statistical data, the related manuals, online materials, and previous studies concerning issues in those countries for the past five years. The training program in Korea was compared with the others’ through this literature analysis. The induced matters were divided with some parts such as training program, continuing education, educational procedure, and curriculum. Based on the analysis, discussion and implications, the conclusion and further suggestion of this study are as follows: First, Korean Clinical Psychology of Association (KCPA) should become more powerful health main training agency for quality control. Second, actual authority of health main training agency should be a grant to training centers. Third, quality control of mental health professionals should be through standardization and systemization of promotion and qualification management. Fourth, education and training about work of supervisors and unification of criteria for supervision should be held. Fifth, the training program for mental health license should be offered by graduate schools. Sixth, legitimated system to protect the right of mental health trainees is needed. Seventh, regularly continuing education after licensed should be compulsory to keep the certification. Eighth, the training program in training centers should meet KCPA requirement. If not, KCPA can cancel the certification of the centers.

Keywords: clinical psychology, Korea, mental health system, training program

Procedia PDF Downloads 217

Authors: Hossam Shalabi, George Hadjisophocleous

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The Canadian Nuclear Power Plants (NPPs) use some portions of NUREG/CR-6850 in carrying out Fire Probabilistic Risk Assessment (PRA). An assessment for the applicability of NUREG/CR-6850 to CANDU reactors was performed and a CANDU Fire PRA was introduced. There are 19 operating CANDU reactors in Canada at five sites (Bruce A, Bruce B, Darlington, Pickering and Point Lepreau). A fire load density survey was done for all Fire Safe Shutdown Analysis (FSSA) fire zones in all CANDU sites in Canada. National Fire Protection Association (NFPA) Standard 557 proposes that a fire load survey must be conducted by either the weighing method or the inventory method or a combination of both. The combination method results in the most accurate values for fire loads. An updated CANDU Fire PRA model is demonstrated in this paper that includes the fuel survey in all Canadian CANDU stations. A qualitative screening step for the CANDU fire PRA is illustrated in this paper to include any fire events that can damage any part of the emergency power supply in addition to FSSA cables.

Keywords: fire safety, CANDU, nuclear, fuel densities, FDS, qualitative analysis, fire probabilistic risk assessment

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Authors: Yaolin Tian, Shanxiong Chen, Fujia Zhao, Xiaoyu Lin, Hailing Xiong

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Ancient books are significant culture inheritors and their background textures convey the potential history information. However, multi-style texture recovery of ancient books has received little attention. Restricted by insufficient ancient textures and complex handling process, the generation of ancient textures confronts with new challenges. For instance, training without sufficient data usually brings about overfitting or mode collapse, so some of the outputs are prone to be fake. Recently, image generation and style transfer based on deep learning are widely applied in computer vision. Breakthroughs within the field make it possible to conduct research upon multi-style texture recovery of ancient books. Under the circumstances, we proposed a network of layout analysis and image fusion system. Firstly, we trained models by using Deep Convolution Generative against Networks (DCGAN) to synthesize multi-style ancient textures; then, we analyzed layouts based on the Position Rearrangement (PR) algorithm that we proposed to adjust the layout structure of foreground content; at last, we realized our goal by fusing rearranged foreground texts and generated background. In experiments, diversified samples such as ancient Yi, Jurchen, Seal were selected as our training sets. Then, the performances of different fine-turning models were gradually improved by adjusting DCGAN model in parameters as well as structures. In order to evaluate the results scientifically, cross entropy loss function and Fréchet Inception Distance (FID) are selected to be our assessment criteria. Eventually, we got model M8 with lowest FID score. Compared with DCGAN model proposed by Radford at el., the FID score of M8 improved by 19.26%, enhancing the quality of the synthetic images profoundly.

Keywords: deep learning, image fusion, image generation, layout analysis

Procedia PDF Downloads 143

Authors: Yonggyu Lee, Byungkyu Jung, Bom Yoon, Jongil Yoon

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MR-based simulation devices have been recently used in various fields such as entertainment, medicine, manufacturing, and education. Different simulation devices are also being developed for military equipment training. This is to address the concerns regarding safety accidents as well as cost issues associated with training with expensive equipment. An important aspect of developing simulation devices to replicate military training is that trainees experience the same effect as training with real devices. In this study, the criteria for performance evaluation are established to compare the training effect of an MR-based simulation device to that of an actual device. K-9 Self-propelled artillery (SPA) operators are selected as training subjects. First, MR-based software is developed to simulate the training ground and training scenarios currently used for training SPA operators in South Korea. Hardware that replicates the interior of SPA is designed, and a simulation device that is linked to the software is developed. Second, criteria are established to evaluate the simulation device based on real-life training scenarios. A total of nine performance evaluation criteria were selected based on the actual SPA operation training scenarios. Evaluation items were selected to evaluate whether the simulation device was designed such that trainees would experience the same effect as training in the field with a real SPA. To eval-uate the level of replication by the simulation device of the actual training environments (driving and passing through trenches, pools, protrusions, vertical obstacles, and slopes) and driving conditions (rapid steering, rapid accelerating, and rapid braking) as per the training scenarios, tests were performed under the actual training conditions and in the simulation device, followed by the comparison of the results. In addition, the level of noise felt by operators during training was also selected as an evaluation criterion. Due to the nature of the simulation device, there may be data latency between HW and SW. If the la-tency in data transmission is significant, the VR image information delivered to trainees as they maneuver HW might not be consistent. This latency in data transmission was also selected as an evaluation criterion to improve the effectiveness of the training. Through this study, the key evaluation metrics were selected to achieve the same training effect as training with real equipment in a training ground during the develop-ment of the simulation device for military equipment training.

Keywords: K-9 self-propelled artillery, mixed reality, simulation device, synchronization

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Authors: Petchpong Mayukhachot

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The purposes of this research were (1) to develop a distance training package on topic of Production of Handbook and Report writing for innovative learning and teaching for Vocational Teachers of Office of The Vocational Education Commission; (2) to study the effects of using the distance training package on topic Production of Handbook and Report writing for innovative learning and teaching for Vocational Teachers of Office of The Vocational Education Commission. and (3) to study the samples’ opinion on the distance training package on topic Production of Handbook and Report writing for innovative learning and teaching for Vocational Teachers of Office of The Vocational Education Commission Research and Development was used in this research. The purposive sampling group of this research was 39 Vocational Teachers of Office of The Vocational Education Commission. Instruments were; (1) the distance training package, (2) achievement tests on understanding of Production of Handbook and Report writing for innovative learning and teaching and learning activities to develop practical skills, and (3) a questionnaire for sample’s opinion on the distance training package. Percent, Mean, Standard Deviation, the E1/E2 efficiency index and t-test were used for data analysis. The findings of the research were as follows: (1) The efficiency of the distance training package was established as 80.90 / 81.90. The distance training package composed of the distance training package document and a manual for the distance training package. The distance training package document consisted of the name of the distance training package, direction for studying the distance training package, content’s structure, concepts, objectives, and activities after studying the distance training package. The manual for the distance training package consisted of the explanation of the distance training package and objectives, direction for using the distance training package, training schedule, documents as a manual of speech, and evaluations. (2) The effects of using the distance training package on topic Production of Handbook and Report writing for innovative learning and teaching for Vocational Teachers of Office of The Vocational Education Commission were the posttest average scores of achievement on understanding of Technology and Occupations teaching for development of critical thinking of the sample group were higher than the pretest average scores. (3) The most appropriate of trainees’ opinion were contents of the distance training package is beneficial to performance. That can be utilized in Teaching or operations. Due to the content of the two units is consistent and activities assigned to the appropriate content.

Keywords: distance training package, handbook writing for innovative learning, teaching report writing for innovative learning, teaching

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Authors: Jamaiah H. Yahaya, Aziz Deraman, Abdul Razak Hamdan, Yusmadi Yah Jusoh

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One of the most essential issues in software products is to maintain it relevancy to the dynamics of the user’s requirements and expectation. Many studies have been carried out in quality aspect of software products to overcome these problems. Previous software quality assessment models and metrics have been introduced with strengths and limitations. In order to enhance the assurance and buoyancy of the software products, certification models have been introduced and developed. From our previous experiences in certification exercises and case studies collaborating with several agencies in Malaysia, the requirements for user based software certification approach is identified and demanded. The emergence of social network applications, the new development approach such as agile method and other varieties of software in the market have led to the domination of users over the software. As software become more accessible to the public through internet applications, users are becoming more critical in the quality of the services provided by the software. There are several categories of users in web-based systems with different interests and perspectives. The classifications and metrics are identified through brain storming approach with includes researchers, users and experts in this area. The new paradigm in software quality assessment is the main focus in our research. This paper discusses the classifications of users in web-based software system assessment and their associated factors and metrics for quality measurement. The quality model is derived based on IEEE structure and FCM model. The developments are beneficial and valuable to overcome the constraints and improve the application of software certification model in future.

Keywords: software certification model, user centric approach, software quality factors, metrics and measurements, web-based system

Procedia PDF Downloads 389

Authors: Kien-Trinh Thi Bui, Cuong Manh Nguyen

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In order to simulate and reproduce the operational characteristics of a dam visually, it is necessary to capture the displacement at different measurement points and analyze the observed movement data promptly to forecast the dam safety. The accuracy of forecasts is further improved by applying machine learning methods to data analysis progress. In this study, the horizontal displacement monitoring data of the Ialy hydroelectric dam was applied to machine learning algorithms: Gaussian processes, multi-layer perceptron neural networks, and the M5-rules algorithm for modelling and forecasting of horizontal displacement of the Ialy hydropower dam (Vietnam), respectively, for analysing. The database which used in this research was built by collecting time series of data from 2006 to 2021 and divided into two parts: training dataset and validating dataset. The final results show all three algorithms have high performance for both training and model validation, but the MLPs is the best model. The usability of them are further investigated by comparison with a benchmark models created by multi-linear regression. The result show the performance which obtained from all the GP model, the MLPs model and the M5-Rules model are much better, therefore these three models should be used to analyze and predict the horizontal displacement of the dam.

Keywords: Gaussian processes, horizontal displacement, hydropower dam, Ialy dam, M5-Rules, multi-layer perception neural networks

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Authors: Mikiyas Adugna

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Effort estimation is among the activities carried out in software development processes. An accurate model of estimation leads to project success. The method of agile effort estimation is a complex task because of the dynamic nature of software development. Researchers are still conducting studies on agile effort estimation to enhance prediction accuracy. Due to these reasons, we investigated and proposed a model on LASSO and Elastic Net regression to enhance estimation accuracy. The proposed model has major components: preprocessing, train-test split, training with default parameters, and cross-validation. During the preprocessing phase, the entire dataset is normalized. After normalization, a train-test split is performed on the dataset, setting training at 80% and testing set to 20%. We chose two different phases for training the two algorithms (Elastic Net and LASSO) regression following the train-test-split. In the first phase, the two algorithms are trained using their default parameters and evaluated on the testing data. In the second phase, the grid search technique (the grid is used to search for tuning and select optimum parameters) and 5-fold cross-validation to get the final trained model. Finally, the final trained model is evaluated using the testing set. The experimental work is applied to the agile story point dataset of 21 software projects collected from six firms. The results show that both Elastic Net and LASSO regression outperformed the compared ones. Compared to the proposed algorithms, LASSO regression achieved better predictive performance and has acquired PRED (8%) and PRED (25%) results of 100.0, MMRE of 0.0491, MMER of 0.0551, MdMRE of 0.0593, MdMER of 0.063, and MSE of 0.0007. The result implies LASSO regression algorithm trained model is the most acceptable, and higher estimation performance exists in the literature.

Keywords: agile software development, effort estimation, elastic net regression, LASSO

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Authors: F. Nur Baran Aksakal, Özge Dinç, H. Tanju Besler, Begüm Mutuş, Özlem Üliç Çatar, Orhan Aydoğdu, Serhat Ünal

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Healthy eating habits are associated not only with the newborn, child, and maternal health but also with longer life expectancy by acting as a protective factor against non-communicable diseases such as obesity, diabetes, cardiovascular diseases, and cancer. The role of nutrition in medical education is to provide information about the relationship between healthy nutrition and malnutrition as well as diet-related non-communicable diseases. Considering the information pollution experienced in the field of nutrition and health in the society, it is seen that more than half of the population receives information from family physicians as the closest counseling unit. However, postgraduate nutrition education programs for physicians and other health professionals who wish to improve their current knowledge of the role of nutrition communication in the prevention and management of chronic diseases are limited worldwide. However, nutrition courses are either not included in the undergraduate medical education curriculum of physicians or they are insufficient. Based on this need, the main aim of the study group was to develop a "Nutrition and Nutrition Communication Training for Physicians" program that would be conducted in cooperation with the Sabri Ülker Foundation and the Federation of Family Physicians Associations (AHEF). This program is the first online nutrition and nutrition communication information platform for physicians in Turkey. This program aims to present the concept of adequate and balanced nutrition to physicians, the importance of nutrition in diseases with scientific data, and to gain communication skills that may be necessary while transferring scientific information to the public. A needs assessment questionnaire was applied to identify pre-program training needs. A study plan was made to allow the participation of all family physicians in the population, and a complete inventory was targeted. In other words, we aimed to reach the whole source without taking a section of the population. Participation in the training is based on volunteerism. The needs assessment study is conducted using 25,102 family physicians for whom email addresses are available. The online questionnaire was sent to all the family physicians with a reminder email one week after the first one, and 1308 responded. Considering the topics determined, a training program was prepared for family physicians under eight online training titles, starting in March 2022, and conducted once every two weeks. The number of audience members present at each session was between 1217 and 1673, and a minimum of 17 and a maximum of 53 questions were received in each session. We strongly believe that to prevent individuals' health problems and to have better control over chronic diseases, the information level of physicians should be increased via these kinds of interventions, and better collaboration between family physicians and dieticians should be established.

Keywords: nutrition communication, nutrition training, communication, nutrition

Procedia PDF Downloads 89

Authors: Mohamed Mohamed, Philip Bonello, Peter Russhard

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Blade Tip Timing (BTT) is a technology concerned with the estimation of both frequency and amplitude of rotating blades. A BTT system comprises two main parts: (a) the arrival time measurement system, and (b) the analysis algorithms. Simulators play an important role in the development of the analysis algorithms since they generate blade tip displacement data from the simulated blade vibration under controlled conditions. This enables an assessment of the performance of the different algorithms with respect to their ability to accurately reproduce the original simulated vibration. Such an assessment is usually not possible with real engine data since there is no practical alternative to BTT for blade vibration measurement. Most simulators used in the literature are based on a simple spring-mass-damper model to determine the vibration. In this work, a more realistic experimentally validated simulator based on the Finite Element (FE) model of a bladed disc (blisk) is first presented. It is then used to generate the necessary data for the assessment of different BTT algorithms. The FE modelling is validated using both a hammer test and two firewire cameras for the mode shapes. A number of autoregressive methods, fitting methods and state-of-the-art inverse methods (i.e. Russhard) are compared. All methods are compared with respect to both synchronous and asynchronous excitations with both single and simultaneous frequencies. The study assesses the applicability of each method for different conditions of vibration, amount of sampling data, and testing facilities, according to its performance and efficiency under these conditions.

Keywords: blade tip timing, blisk, finite element, vibration measurement

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Authors: Ororho Maureen Ekpelezie

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This study investigates language educators' perceptions and experiences regarding AI-driven personalized teacher training modules in Awka South, Anambra State, Nigeria. Utilizing a stratified random sampling technique, 25 schools across various educational levels were selected to ensure a representative sample. A total of 1000 questionnaires were distributed among language teachers in these schools, focusing on assessing their perceptions and experiences related to AI-driven personalized teacher training. With an impressive response rate of 99.1%, the study garnered valuable insights into language teachers' attitudes towards AI-driven personalized teacher training and its effectiveness in enhancing language teaching competence. The quantitative analysis revealed predominantly positive perceptions towards AI-driven personalized training modules, indicating their efficacy in addressing individual learning needs. However, challenges were identified in the long-term retention and transfer of AI-enhanced skills, underscoring the necessity for further refinement of personalized training approaches. Recommendations stemming from these findings emphasize the need for continued refinement of training methodologies and the development of tailored professional development programs to alleviate educators' concerns. Overall, this research enriches discussions on the integration of AI technology in teacher training and professional development, with the aim of bolstering language teaching competence and effectiveness in educational settings.

Keywords: language teacher training, AI-driven personalized learning, professional development, language teaching competence, personalized teacher training

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Authors: U. Ugochukwu, J. Lee, P. Conley

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According to a recent survey by the UT Southwestern Workplace Safety Committee, the three most common on-the-job injuries reported by workers at the medical center are musculoskeletal injuries, slip-and-fall injuries and repetitive motion injuries. Last year alone, of the 650 documented workplace injuries and accidents, 45% were seen in employees in their first-two years of employment. UT Southwestern New Employee On-Boarding program was created and modeled to follows OSHA’s model that consist of: determining if training is needed, identifying training needs, identifying goals and objectives, developing learning activities, conducting the training, evaluating program effectiveness, and improving the program. The hospital’s management best practices were recreated to limit and control workplace injuries and accidents. Regular trainings and workshops on workplace safety and compliance were initiated for new employees. Various computer workstations were evaluated and recommendations were made to reduce musculoskeletal disorders. Post exposure protocols and workers protection programs were remodeled for infectious agents and chemicals used in the hospital, and medical surveillance programs were updated, for every emerging threat, to ensure they are in compliance with the US policy, regulatory and standard setting organizations. If ignorance of specific job hazards and of proper work practices is to blame for this higher injury rate, then training will help to provide a solution. Use of this program in training activities is just one of many ways UT Southwestern complied with the OSHA standards that relate to training while enhancing the safety and health of their employees.

Keywords: ergonomics, hazard, on-boarding, surveillance, workplace

Procedia PDF Downloads 323

Authors: Hanan Algarni

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Background: Impairment of walking and balance skills has negative impact on functional independence and community participation after stroke. Gait recovery is considered a primary goal in rehabilitation by both patients and physiotherapists. Treadmill training coupled with virtual reality technology is a new emerging approach that offers patients with feedback, open and random skills practice while walking and interacting with virtual environmental scenes. Objectives: To synthesize the evidence around the effects of the VR treadmill training on gait speed and balance primarily, functional independence and community participation secondarily in stroke patients. Methods: Systematic review was conducted; search strategy included electronic data bases: MEDLINE, AMED, Cochrane, CINAHL, EMBASE, PEDro, Web of Science, and unpublished literature. Inclusion criteria: Participant: adult >18 years, stroke, ambulatory, without severe visual or cognitive impartments. Intervention: VR treadmill training alone or with physiotherapy. Comparator: any other interventions. Outcomes: gait speed, balance, function, community participation. Characteristics of included studies were extracted for analysis. Risk of bias assessment was performed using Cochrane's ROB tool. Narrative synthesis of findings was undertaken and summary of findings in each outcome was reported using GRADEpro. Results: Four studies were included involving 84 stroke participants with chronic hemiparesis. Interventions intensity ranged (6-12 sessions, 20 minutes-1 hour/session). Three studies investigated the effects on gait speed and balance. 2 studies investigated functional outcomes and one study assessed community participation. ROB assessment showed 50% unclear risk of selection bias and 25% of unclear risk of detection bias across the studies. Heterogeneity was identified in the intervention effects at post training and follow up. Outcome measures, training intensity and durations also varied across the studies, grade of evidence was low for balance, moderate for speed and function outcomes, and high for community participation. However, it is important to note that grading was done on few numbers of studies in each outcome. Conclusions: The summary of findings suggests positive and statistically significant effects (p<0.05) of VR treadmill training compared to other interventions on gait speed, dynamic balance skills, function and participation directly after training. However, the effects were not sustained at follow up in two studies (2 weeks-1 month) and other studies did not perform follow up measurements. More RCTs with larger sample sizes and higher methodological quality are required to examine the long term effects of VR treadmill effects on function independence and community participation after stroke, in order to draw conclusions and produce stronger robust evidence.

Keywords: virtual reality, treadmill, stroke, gait rehabilitation

Procedia PDF Downloads 267

Authors: Zainab Mutlaq Ibrahim, Mohamed Bader-El-Den, Mihaela Cocea

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Students' textual feedback can hold unique patterns and useful information about learning process, it can hold information about advantages and disadvantages of teaching methods, assessment components, facilities, and other aspects of teaching. The results of analysing such a feedback can form a key point for institutions’ decision makers to advance and update their systems accordingly. This paper proposes a data mining framework for analysing end of unit general textual feedback using part of speech feature (PoS) with four machine learning algorithms: support vector machines, decision tree, random forest, and naive bays. The proposed framework has two tasks: first, to use the above algorithms to build an optimal model that automatically classifies the whole data set into two subsets, one subset is tailored to assessment practices (assessment related), and the other one is the non-assessment related data. Second task to use the same algorithms to build an optimal model for whole data set, and the new data subsets to automatically detect their sentiment. The significance of this paper is to compare the performance of the above four algorithms using part of speech feature to the performance of the same algorithms using n-grams feature. The paper follows Knowledge Discovery and Data Mining (KDDM) framework to construct the classification and sentiment analysis models, which is understanding the assessment domain, cleaning and pre-processing the data set, selecting and running the data mining algorithm, interpreting mined patterns, and consolidating the discovered knowledge. The results of this paper experiments show that both models which used both features performed very well regarding first task. But regarding the second task, models that used part of speech feature has underperformed in comparison with models that used unigrams and bigrams.

Keywords: assessment, part of speech, sentiment analysis, student feedback

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Authors: K. Shyamnath, K. Suresh Kutty

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The purpose of the study was to find out the effect of three resistance training methods on performance-related variables of powerlifters. A total of forty male students (N=40) who had participated in Kannur University powerlifting championship were selected as subjects. The age group of the subjects ranged from 18 years old to 25 years old. The selected subjects were equally divided into four groups (n=10) of three experimental groups and a control group. The experimental Group I underwent traditional resistance training (TRTG), Group II underwent combined traditional resistance training and plyometrics (TRTPG), and Group III underwent combined traditional resistance training and resistance training with high rhythm (TRTHRG). Group IV acted as the control group (CG) receiving no training during the experimental period. The duration of the experimental period was sixteen weeks, five days per week. Powerlifting performance was assessed by the 1RM test in the squat, bench press and deadlift. Performance-related variables assessed were chest girth, arm girth, forearm girth, thigh girth, and calf girth. Pre-test and post-test were conducted a day before and two days after the experimental period on all groups. Analysis of covariance (ANCOVA) was applied to analyze the significant difference. The 0.05 level of confidence was fixed as the level of significance to test the F ratio obtained by the analysis of covariance. The result indicates that there is a significant effect of all the selected resistance training methods on the performance and selected performance-related variables of powerlifters. Combined traditional resistance training and plyometrics and combined traditional resistance training and resistance training with high rhythm proved better than the traditional resistance training in improving performance and selected performance-related variables of powerlifters. There was no significant difference between combined traditional resistance training and plyometrics and combined traditional resistance training and resistance training with high rhythm in improving performance and selected performance-related variables of powerlifters.

Keywords: girth, plyometrics, powerlifting, resistance training

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Authors: A. Clementking, C. Jothi Venkateswaran

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Mathematical and computational functionalities such as descriptive mining, optimization, and predictions are espoused to resolve natural resource planning. The water quality prediction and its attributes influence determinations are adopted optimization techniques. The water properties are tainted while merging water resource one with another. This work aimed to predict influencing water resource distribution connectivity in accordance to water quality and sediment using an innovative proposed master-slave neural network back-propagation model. The experiment results are arrived through collecting water quality attributes, computation of water quality index, design and development of neural network model to determine water quality and sediment, master–slave back propagation neural network back-propagation model to determine variations on water quality and sediment attributes between the water resources and the recommendation for connectivity. The homogeneous and parallel biochemical reactions are influences water quality and sediment while distributing water from one location to another. Therefore, an innovative master-slave neural network model [M (9:9:2)::S(9:9:2)] designed and developed to predict the attribute variations. The result of training dataset given as an input to master model and its maximum weights are assigned as an input to the slave model to predict the water quality. The developed master-slave model is predicted physicochemical attributes weight variations for 85 % to 90% of water quality as a target values.The sediment level variations also predicated from 0.01 to 0.05% of each water quality percentage. The model produced the significant variations on physiochemical attribute weights. According to the predicated experimental weight variation on training data set, effective recommendations are made to connect different resources.

Keywords: master-slave back propagation neural network model(MSBPNNM), water quality analysis, multivariate analysis, environmental mining

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Authors: Peiming Li

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This paper presents an innovative blockchain-based approach to enhance the reliability and efficiency of federated learning systems. By integrating a decentralized oracle consensus mechanism into the federated learning framework, we address key challenges of data and model integrity. Our approach utilizes a network of redundant oracles, functioning as independent validators within an epoch-based training system in the federated learning model. In federated learning, data is decentralized, residing on various participants' devices. This scenario often leads to concerns about data integrity and model quality. Our solution employs blockchain technology to establish a transparent and tamper-proof environment, ensuring secure data sharing and aggregation. The decentralized oracles, a concept borrowed from blockchain systems, act as unbiased validators. They assess the contributions of each participant using a Hidden Markov Model (HMM), which is crucial for evaluating the consistency of participant inputs and safeguarding against model poisoning and malicious activities. Our methodology's distinct feature is its epoch-based training. An epoch here refers to a specific training phase where data is updated and assessed for quality and relevance. The redundant oracles work in concert to validate data updates during these epochs, enhancing the system's resilience to security threats and data corruption. The effectiveness of this system was tested using the Mnist dataset, a standard in machine learning for benchmarking. Results demonstrate that our blockchain-oriented federated learning approach significantly boosts system resilience, addressing the common challenges of federated environments. This paper aims to make these advanced concepts accessible, even to those with a limited background in blockchain or federated learning. We provide a foundational understanding of how blockchain technology can revolutionize data integrity in decentralized systems and explain the role of oracles in maintaining model accuracy and reliability.

Keywords: federated learning system, block chain, decentralized oracles, hidden markov model

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Authors: Yi-Hsiang Pan, Chen-Hui Huang, Wei-Ting Hsu

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The purposes of the study were to understand these topics as follows: 1. To explore the effect of merging teaching personal and social responsibility (TPSR) with sports education model on students' game performance and responsibility. 2. To explore the effect of sports education model on students' game performance and responsibility. 3. To compare the difference between "merging TPSR with sports education model" and "sports education model" on students' game performance and responsibility. The participants include three high school physical education teachers and six physical education classes. Every teacher teaches an experimental group and a control group. The participants had 121 students, including 65 students in the experimental group and 56 students in the control group. The research methods had game performance assessment, questionnaire investigation, interview, focus group meeting. The research instruments include personal and social responsibility questionnaire and game performance assessment instrument. Paired t-test test and MANCOVA were used to test the difference between "merging TPSR with sports education model" and "sports education model" on students' learning performance. 1) "Merging TPSR with sports education model" showed significant improvements in students' game performance, and responsibilities with self-direction, helping others, cooperation. 2) "Sports education model" also had significant improvements in students' game performance, and responsibilities with effort, self-direction, helping others. 3.) There was no significant difference in game performance and responsibilities between "merging TPSR with sports education model" and "sports education model". 4)."Merging TPSR with sports education model" significantly improve learning atmosphere and peer relationships, it may be developed in the physical education curriculum. The conclusions were as follows: Both "Merging TPSR with sports education model" and "sports education model" can help improve students' responsibility and game performance. However, "Merging TPSR with sports education model" can reduce the competitive atmosphere in highly intensive games between students. The curricular projects of hybrid TPSR-Sport Education model is a good approach for moral character education.

Keywords: curriculum and teaching model, sports self-efficacy, sport enthusiastic, character education

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Authors: Rui Yin, Haojie Wang, Xun Li

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Recently, mainstream dense retrieval methods have obtained state-of-the-art results on some datasets and tasks. However, they require large amounts of training data, which is not available in most domains. The severe performance degradation of dense retrievers on new data domains has limited the use of dense retrieval methods to only a few domains with large training datasets. In this paper, we propose an unsupervised domain-adaptive approach based on query generation. First, a generative model is used to generate relevant queries for each passage in the target corpus, and then, the generated queries are used for mining negative passages. Finally, the query-passage pairs are labeled with a cross-encoder and used to train a domain-adapted dense retriever. We also explore contrastive learning as a method for training domain-adapted dense retrievers and show that it leads to strong performance in various retrieval settings. Experiments show that our approach is more robust than previous methods in target domains that require less unlabeled data.

Keywords: dense retrieval, query generation, contrastive learning, unsupervised training

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Authors: Avinash Kharel

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Background: - Linear sprint training is one possible approach to developing sprint performance, a crucial skill to focus on in soccer. Numerous methods, including various on-field training options, can be employed to attain this goal. However, the effect of In-season linear sprint training on sprint performance and related kinetics changes are unknown in a professional setting. The study aimed to investigate the effect of in-season linear sprint training on the sprint kinematics of amateur soccer players. Methods: - After familiarization, a 4-week training protocol was completed with sprint performance and Force Velocity (FV) profiles was compared before and after the training. Eighteen amateur soccer male players (Age 22 ± 2 years: Height: 178 ± 7cm; body-mass: 74 ± 8 Kg, 30-m split-time: 4.398 ± s) participated in the study. Sprint kinematics variables, including maximum Sprint Velocity (V0), Theoretical Maximum Force (F0), Maximum Force Output per kilogram of body weight (N/KG), Maximum Velocity (V(0)), Maximum Power Output (P MAX (W)), Ratio of Force to Velocity (FV), and Ratio of Force to Velocity at Peak power were measured. Results: - Results showed significant improvements in Maximum Sprint Velocity (p<0.01, ES=0.89), Theoretical Maximum Force (p<0.05, ES=0.50), Maximum Force Output per kilogram of body weight (p<0.05, ES=0.42), Maximum Power Output (p<0.05, ES=0.52), and Ratio of Force to Velocity at Peak Power (RF PEAK) (p<0.05, ES=0.44) post-training. There were no significant changes in the ratio of Force to Velocity (FV) and Maximum Velocity V (0) post-training (p>0.05). Conclusion: - These findings suggest that In-season linear sprint training can effectively improve certain sprint kinematics variables in amateur soccer players. Coaches and players should consider incorporating linear sprint training into their in-season training programs to improve sprint performance.

Keywords: sprint performance, training intervention, soccer, kinematics

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Authors: Abdelhadi Lotfi, Abdelkader Benyettou

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In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance.

Keywords: classification, probabilistic neural networks, network optimization, pattern recognition

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Authors: Lancer Scott

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Purpose: High quality clinical disaster medicine requires providers working collaboratively to care for multiple patients in chaotic environments; however, many providers lack adequate training. To address this deficit, we created a competency-based, 5-hour Emergency Preparedness Training (EPT) curriculum using didactics, small-group discussion, and kinetic learning. The goal was to evaluate the effect of a short course on improving provider knowledge, confidence and skills in disaster scenarios. Methods: Diverse groups of medical university students, health care professionals, and community members were enrolled between 2011 and 2014. The course consisted of didactic lectures, small group exercises, and two live, multi-patient mass casualty incident (MCI) scenarios. The outcome measures were based on core competencies and performance objectives developed by a curriculum task force and assessed via trained facilitator observation, pre- and post-testing, and a course evaluation. Results: 708 participants completed were trained between November 2011 and August 2014, including 49.9% physicians, 31.9% medical students, 7.2% nurses, and 11% various other healthcare professions. 100% of participants completed the pre-test and 71.9% completed the post-test, with average correct answers increasing from 39% to 60%. Following didactics, trainees met 73% and 96% of performance objectives for the two small group exercises and 68.5% and 61.1% of performance objectives for the two MCI scenarios. Average trainee self-assessment of both overall knowledge and skill with clinical disasters improved from 33/100 to 74/100 (overall knowledge) and 33/100 to 77/100 (overall skill). The course assessment was completed by 34.3% participants, of whom 91.5% highly recommended the course. Conclusion: A relatively short, intensive EPT course can improve the ability of a diverse group of disaster care providers to respond effectively to mass casualty scenarios.

Keywords: clinical disaster medicine, training, hospital preparedness, surge capacity, education, curriculum, research, performance, training, student, physicians, nurses, health care providers, health care

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Authors: Aisulu Tileukulova, Zhexebay Dauren

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Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

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Authors: Debarati Bhaumik, Diptish Dey

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Multilevel models, increasingly deployed in industries such as insurance, food production, and entertainment within functions such as marketing and supply chain management, need to be transparent and ethical. Applications usually result in binary classification within groups or hierarchies based on a set of input features. Using open-source datasets, we demonstrate that popular explainability methods, such as SHAP and LIME, consistently underperform inaccuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution to the outcome). Besides accuracy, the computational intractability of SHAP for binomial classification is a cause of concern. For transparent and ethical applications of these hierarchical statistical models, sound audit frameworks need to be developed. In this paper, we propose an audit framework for technical assessment of multilevel regression models focusing on three aspects: (i) model assumptions & statistical properties, (ii) model transparency using different explainability methods, and (iii) discrimination assessment. To this end, we undertake a quantitative approach and compare intrinsic model methods with SHAP and LIME. The framework comprises a shortlist of KPIs, such as PoCE (Percentage of Correct Explanations) and MDG (Mean Discriminatory Gap) per feature, for each of these three aspects. A traffic light risk assessment method is furthermore coupled to these KPIs. The audit framework will assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying multilevel models to be future-proof and aligned with the European Commission’s proposed Regulation on Artificial Intelligence.

Keywords: audit, multilevel model, model transparency, model explainability, discrimination, ethics

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Authors: Nahlah Abbas, Saleh A. Wasimi, Nadhir Al-Ansari

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The Greater Zab and Lesser Zab are the major tributaries of Tigris River contributing the largest flow volumes into the river. The impacts of climate change on water resources in these basins have not been well addressed. To gain a better understanding of the effects of climate change on water resources of the study area in near future (2049-2069) as well as in distant future (2080-2099), Soil and Water Assessment Tool (SWAT) was applied. The model was first calibrated for the period from 1979 to 2004 to test its suitability in describing the hydrological processes in the basins. The SWAT model showed a good performance in simulating streamflow. The calibrated model was then used to evaluate the impacts of climate change on water resources. Six general circulation models (GCMs) from phase five of the Coupled Model Intercomparison Project (CMIP5) under three Representative Concentration Pathways (RCPs) RCP 2.6, RCP 4.5, and RCP 8.5 for periods of 2049-2069 and 2080-2099 were used to project the climate change impacts on these basins. The results demonstrated a significant decline in water resources availability in the future.

Keywords: Tigris River, climate change, water resources, SWAT

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Authors: Martina Holenko Dlab, Natasa Hoic-Bozic

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This paper presents an original approach to student and group activity level assessment that relies on certainty factors theory. Activity level is used to represent quantity and continuity of student’s contributions in individual and collaborative e‑learning activities (e‑tivities) and is calculated to assist teachers in assessing quantitative aspects of student's achievements. Calculated activity levels are also used to raise awareness and provide recommendations during the learning process. The proposed approach was implemented within the educational recommender system ELARS and validated using data obtained from e‑tivity realized during a blended learning course. The results showed that the proposed approach can be used to estimate activity level in the context of e-tivities realized using Web 2.0 tools as well as to facilitate the assessment of quantitative aspect of students’ participation in e‑tivities.

Keywords: assessment, ELARS, e-learning, recommender systems, student model

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Authors: A. Esra İdemen, Sinan M. Şener, Emrah Acar

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The technological paradigm of the disaster management field, especially in the case of governmental intervention strategies, is generally based on rapid and flexible accommodation solutions. From various technical solution patterns used to address the immediate housing needs of disaster victims, the adaptive re-use of existing buildings can be considered to be both low-cost and practical. However, there is a scarcity of analytical methods to screen, select and adapt buildings to help decision makers in cases of emergency. Following an extensive literature review, this paper aims to highlight key points and problem areas associated with the adaptive re-use of buildings within the disaster management context. In other disciplines such as real estate management, the adaptive re-use potential (ARP) of existing buildings is typically based on the prioritization of a set of technical and non-technical criteria which are then weighted to arrive at an economically viable investment decision. After a disaster, however, the assessment of the ARP of buildings requires consideration of different/additional layers of analysis which stem from general disaster management principles and the peculiarities of different types of disasters, as well as of their victims. In this paper, a discussion of the development of an adaptive re-use potential (ARP) assessment model is presented. It is thought that governmental and non-governmental decision makers who are required to take quick decisions to accommodate displaced masses following disasters are likely to benefit from the implementation of such a model.

Keywords: adaptive re-use of buildings, disaster management, temporary housing, assessment model

Procedia PDF Downloads 320