bluuebunny/crossref_metadata_2025 huggingface dataset:
A Study on Domestic Energy Situation at different Altitudes of Himachal Pradesh
Authors: Lndra Mani  , Pradeep Kumar  , J.S. Panwar
Published: May 2024

This paper presents energy availability and consumption pattern at three different altitudes in rural areas of Western Himalayas in Himachal Pradesh, India and explores the possibility of implementing efficient conversion of locally available resources of energy like fuel wood, agricultural residues and cow dung to assure proper energy supply. Energy data was collected from 90 households on different household activities and their source of energy. Among the conventional energy sources, the highest contribution was of electrical energy, which was to the tune 7%  of the total energy consumption. The share of non-commercial energy sources accounted for 91% of the total available domestic energy in all the three altitudes. Out of total domestic energy, fuel wood contributed 79-87 per cent, 70%ofwhich was used for cooking.. Space and water heating consumed major portion of fuel wood in all the three altitudes. Although, the agricultural residues recorded second highest share in non commercial energy availability at all the three altitudes but in sharp contrast, to fuel wood their use was just 5 per cent in lew altitude, and almost negligible in other two altitudes. The efficient energy conversion technology like Modified Stoves (chullas) for cooking could save a highest of99.8 MJ/day/household and use of electric kettle (Biju/i Dekchi) and Back Boiler for water heating could save a highest of 80 MJ/day/household of fuel wood energy in high hills. Proper energy management could help ameliorating energy problem in hilly areas and may also save forest by reducing firewood consumption.
Development and Evaluation of Extreme Value Type-I Unit Hydrograph Model
Authors: R. S. Rana
Published: May 2024

The hydrologic data of 452.25 km2 Chaukhutia watershed of Ramganga reservoir catchment in Uttarakhand was analyzed to develop unit hydrograph model using the extreme value type-I distribution function. The parameters of the model were determined by method of moments. The direct runoff hydrographs were developed by convolving effective rainfall values with one-hour unit hydrograph (I-hUH) for evaluating the prediction performance of the model. The quantitative performance based on statistical measures like coefficient of efficiency and absolute relative error in computed peak flow rates were evaluated in order to test the validity and assess the applicability of the model. The average values of coefficient of efficiency and absolute relative error in computed peak flow rates were found to bl; 0.9113 and 7.84 per cent respectively. Thus the extreme value type-I unit hydrograph model can be used to simulate the direct runoff hydrographs of Chaukhutia watershed.
Bio-Methanation of Anaerobic Fermentation of Undiluted Cattle Dung
Authors: D. K Vani  , A.K. Kurchania
Published: May 2024

Non-availability of sufficient water required for necessary dilution of input slurry for biogas plants is considered to be the major hurdle in popularization of technology. The problem can be solved through biomethanation of undiluted cattle dung. Experimentation with undiluted cattle waste through anaerobic fermentation showed good results. Along with substrate '"A" i.e. undiluted cattle dung, two substrates namely, '"8" (prepared by adding water equal to half of the weight of cattle dung and 9% active inoculum) and '"c" (prepared by mixing cattle dung with equal weight of water and 6% active inoculum) were experimented. Total biogas production from 3 kg substrate in 5.3 litre capacity glass bottle fermenters in 80 days were recorded as 130.7, 113.5 and 85.5 litre for substrate A, Band C, respectively. Biodegradability was found as 50.5, 58 and 59% for substrate A, 8 and C, respectively. Results revealed that the solid-state fermentation for longer hydraulic retention time (80 days) is comparable with the traditional practices of biogas production.
Determination of Aquifer Parameters by Least-Square Method Under Linear Flow Conditions in Fractures-Rocks
Authors: M. L. Jat  , L. K. Jain
Published: May 2024

Sen' (1986) equation was modified for analysis of constant discharge pumping test-data in selected hydrogeological formation (Phyllite). The results obtained were compared with Sen (1986) graphical method. The least root mean square error was obtained by least-squares method. The advatage of the least squares method is the automatic objecting, which is lacking in the type-curve matching technique
Draft Requirement of a Shovel Type Furrow Opener under Coarse Textured Soil Condition
Authors: D. P. Darmora
Published: May 2024

A 55 mm shovel type furrow opener of combined seed drill was selected for optimization of its design parameters. Four boot wedge angles (20,40,60 and 80 degree) and four rake angles (30, 40, 50 and 60 degree) of the furrow opener were investigated at four operating depths (40, 60, 80 and 100 mm). The tests were conducted in a factorial randomized block design with two replications under sandy loam and loamy sand soil conditions in an indoor soil bin for optimizing the design parameters of the furrow opener in order to minimize its draft requirement. Boot wedge as well as rake angle of40° was found as the optimum value under both the soil conditions. Multiple regression equations were also developed to predict the draft requirements precisely under sandy loam and loamy sand soil conditions.
Study on Plot Pattern and Farm Power Utilization for Major Crops in two Agro-climatic Zones of Hill Agriculture
Authors: D. K. Vatsa  , D. C. Saraswat
Published: May 2024

A study was conducted in two major agro-climatic zones of Himachal Pradesh, namely Sub-montaneous Low Hills Sub-tropical (Zone-I) and Mid Hills Sub-humid (zone-II), to know the plot pattern and power utilization for major crops i.e. maize, paddy and wheat in hill farming. Average percentage of plots was 9.1, 14.8,20.5, 25.3 and 30.3 in zone-I and 32.5, 26.4, 19.4, 14.2 and 7.6 in zone-II for the size less than 100, 100-00,200300,
300-400 and more than 400 m2, respectively. The vertical interval was observed to be 40-50 % higher in zone-II as compared to zone-I. The average plot width was 7.7, 10.5, 14.2, 16.8 and 25.3 min zone-I and 4.7, 8.6, 10.6, 13.4 and 20.5 m in zone-II with the plot size as stated above, which was 10-15% higher in zone-I as compared to zone-II. The shape of plots was 69 and 87% irregular and 31 and 13% regular with fragmented holding in zone-I and II, respectively. Overall animate power (481-619 man-h/ha and 104-138 animal-h/ha) was the major source of power for all the crops under different farm groups in the region. Source wise power utilization per unit area f-Jr major crops was 12-22, 10-12 and 5-14% higher in case of man, animal and tractor, respectively in zone-II as compared to zone-I. Overall tractor power utilization was 2.5-17.3 h/ha (65.0-451.9 kWh/ha) and 2.0-20.20 h/ha (54.6-527.4 kWh/ha) for zone-I and zone-II, respectively with maximum in wheat and minimum in paddy crop. Tractor was not utilized by the farmers of zone-II for puddling due to small sized plots.
Development of Abrasive Peeler cum Polisher for Ginger
Authors: N. K. Jain  , D. S. Doharey  , K. C. Sharma
Published: May 2024

World's 50% ginger is produced in India. Presently 2,63,170 MT of ginger is produced from an area of 77,610 ha. Peeling of ginger is a very tedious and time-consuming unit operation. Generally, ginger is peeled with knife or by rubbing against gunny bags or bamboo mats in order to fasten the drying process. Due care is required to avoid the loss of material during peeling. A simple hand operated abrasion type ginger peeler cum polisher was developed for facilitating the peeling and polishing at farm level. The machine has a perforated drum lined with emery strips at spacing of 25 mm. The drum is rotated manually at 45-50 rpm. It was observed that 74-81% peeling could be achieved with 5 kg batch in 8 to 12 min time. Around 2.5 to 3.8% material loss was recorded. Effective output of machine was found as 25-30 kg/h against 30 and 50 kg/day through manual and gunny bag peeling. The machine was also evaluated for polishing the dried rhizomes and it was found that surface of rhizomes gets smoothened due to friction and abrasion. The capacity for polishing was worked out as 50-60 kg/h. The cost of machine has been estimated as Rs 3000.
Physical and Thermal Properties of Untreated and Chemically Treated Rice Husk
Authors: S. K. Jha  , Amar Singh
Published: May 2024

Moisture depe'1dent physical and thermal properties of untreated, acid and alkali treated rice husk were evaluated in the moisture range of 5 to 35% (db). Moisture content had significant effect on the physical and thermal properties of rice husk. A significant difference in each property was found between untreated, acid and alkali treated husk. Bulk density of untreated, acid treated and alkali treated husk varied from 98-106.5, 91.5-102 and 59-64 kglm3 whereas the corresponding values of true density, angle of repose, specific heat, thermal conductivity and thermal diffusivity varied as 1009-1054, 1004-1054,969-996 kglm3; 47-53.8, 47.252.4,46.9- 51.5 degrees; 1.098-2.754,0.976 to 2.548 and 0.875 to 2.33 kJ/(kg.K); 0.024 to 0.099, 0.023 to 0.092 and 0.026 to 0.102 J/(s.m.K), respectively. The bulk density, true density, angle of repose, specific heat, thermal conductivity and thermal diffusivity increased linearly with increase in moisture content whereas the porosity decreased linearly. The values of bulk density, true density, angle of repose and specific heat were higher for untreated rice husk and were followed by acid and a1kal i treated husk. However, the values of thermal conductivity and thermal diffusivity were higher for alkali treated husk and were followed by untreated and acid treated husk.
Load Distribution on the Neck of Malvi Breed of Bullocks at Different Draft Loads
Authors: R. C. Singh  , C. D. Singh  , V. V. Singh  , U. C. Dubey
Published: May 2024

The load distribution on neck of Malvi breed of bullocks was studied with local/improved Nagpuri yoke and Allahabad three-padded collar double animal harness at different draft loads. The miniature button type load cells (capacity 0 - 500 N) were mounted on three planes (left, middle and right) of the contact surface of the yoke/harness with the neck of the animals. The CIAE animal-loading car was used to apply the different draft loads to the animals. The study showed that the Nagpuri yoke was more efficient and comfortable to pull the draft loads by a pair of bullocks as the mean vertical loads on the contact surface of the Nagpuri yoke with the neck of bullocks were significantly less as compared to that of the local yoke. Similarly, the mean vertical loads were evenly distributed on the top pad of Allahabad three-padded collar double animal harness for the draft loads of800, 1000 and 1200 N. The mean vertical loads were very high at the contact points on left and right side pads of Allahabad three-padded collar harness with the neck of bullocks as compared to others points. Hence, design modification is required on left and right side pads of Allahabad three-padded collar harness for even distribution of vertical loads on the neck of bullocks to enhance the higher work output.
Perancangan Sistem Informasi Akuntansi Penjualan CFD (Car Free Day) dan CFN (Car Free Night) pada Kabupaten Bengkalis
Authors: Hamdika Hary , Safitri Indah , Nabila Nabila , Rahmayani Mentari Tri Indah
Published: May 2024

Perkembangan teknologi informasi pada saat ini merupakan suatu hal yang sangat dibutuhkan guna membantu dalam mempermudah dalam mengakses segala hal dengan efisien. Terdapat berbagai macam permasalahan yang bisa dicari solusinya dengan sebuah teknologi. Seperti pendaftaran penjualan car free day dan car free night pada Kabupaten Bengkalis yang masih manual atau tatap muka. Dengan demikian, dibangunlah rancangan sebuah sistem informasi yang bertujuan dalam memberikan kemudahan bagi penjual khususnya dalam melakukan pendaftaran, pemesanan tempat dan sistem pembayarannya. Sehingga dibangunlah sebuah rancangan sistem informasi akuntansi dengan nama Perancangan Sistem Informasi Akuntansi Penjualan CFD (Car Free Day) dan CFN (Car Free Night) pada Kabupaten Bengkalis dengan melalui penerapan metode pengembangan sistem yang terstruktur, seperti SDLS. Diharapkan sistem ini dapat memberikan manfaat dalam pengurangan waktu pemesanan, meningkatkan kepuasan pelanggan, dan meningkatkan efisiensi operasional penjualan secara keseluruhan.
Significance of Mushrooms in Urological Diseases and Disorders
Authors: Konno Sensuke , Penunuri Andrew , Wong Jonathan
Published: May 2024

Background: Since a number of medicinal mushrooms have various pharmacological properties, we have been studying them mainly for urological diseases/disorders over 24 years. Among them, we present herein the interesting study of anticancer effect of a mushroom on urological cancers to illustrate the significance of mushrooms in urology.
Materials and Methods: Anticancer effect of maitake mushroom extract (PDF) on prostate cancer PC-3 cells in vitro was assessed by cell viability assay, and its synergistic potentiation with vitamin C (VC) was also demonstrated. Whether oxidative stress (OXS) may play a significant role in the anticancer mechanism was also explored. In addition, a possible chemosensitizing effect of PDF, capable of enhancing anticancer activity of chemotherapeutic drugs, was examined.
Results: PDF alone led to a significant reduction in PC-3 cell viability, which became even greater when combined with VC, exhibiting <10% cell viability. Such a profound cell viability reduction was primarily attributed to apoptosis induced by OXS. Moreover, anticancer activity of certain drugs against prostate and bladder cancer cells was also improved when combined with PDF, indicating a chemosensitizing effect of PDF.
Conclusion: This study demonstrates anticancer effect of PDF on urological cancer cells, and the PDF/VC combination is highly effective, ultimately leading to apoptosis. A chemosensitizing effect of PDF demonstrated here implies its possible clinical use as an adjuvant agent in current chemotherapy. Therefore, this study represents only anticancer effect of a mushroom but the significance of mushrooms in urological diseases/disorders can be yet understood.

Key words: Mushrooms, Maitake, Anticancer, Medicinal properties, Urological diseases, Chemosensitization.
Technological Tools in Education: Actionbound Experience of Prospective Biology Teachers
Authors: Akpınarlı Sena Seçil , Köseoğlu Pınar
Published: May 2024

This study aims to analyse the experiences and perspectives of prospective biology teachers regarding their excursions using Actionbound to learn about plant species. The participants in the study consist of 30 undergraduate prospective biology teachers. The focus is on the prospective teachers’ enjoyment of the Actionbound experience, the duration to complete the excursion, their willingness to engage in another task-based game, and their views on implementing mobile device-supported biology teaching, specifically related to the design of a biology lesson using Actionbound. The prospective biology teachers predominantly found their experience with Actionbound enjoyable. Their perspectives centred on the App’s usability in education and its technical usability. Results show that biology lesson designed with mobile devices positively impacts knowledge retention, motivation, interest, and enjoyment. The results underscore the importance of designing learning environments for effective biology education and teacher training.
Regularity and normality modulo hereditary m-spaces
Authors: Al-Omari Ahmad , Noiri Takashi
Published: May 2024

abstract: An m-structure is introduced and investigated in [7]. A hereditary class H is defined and investigated in [1]. In this paper, we introduce and investigate generalizations of regularity and normality in a hereditary m-space (X; m;H).
Results for self-Inversive rational functions
Authors: Qasim Idrees
Published: May 2024

In this paper, we find some relations between maximum modulus of a rational function r(z) satisfying r(z) = B(z)r(1/z) and the maximum modulus of its derivative. We also find analogue of Chon’s Theorem for rational functions.
On submanifolds of a Sasakian manifold
Authors: Shivaprasanna G. S. , Rajendra R. , Reddy P. Siva Kota , Somashekhara G.
Published: May 2024

In this paper, we study submanifolds of a Sasakian manifolds provide with a torqued vector field and also accepting a Ricci-Yamabe soliton of both Sasakian manifold and Sasakian spaceform. Further, we obtain some important results which categorize the submanifolds admitting a Ricci-Yamabe soliton of Sasakian spaceform.
Research on Evaluation and Optimization Algorithm of Athletes' Sports Skills Based on Trajectory Analysis and Machine Learning
Authors: Lin Leng
Published: May 2024

Evaluating athletes' sports skills is significant for optimizing their training strategies and improving competitive outcomes. Although various studies are done to assess athletes' skills, they face challenges because of their limitations in capturing the complex, intricate patterns in athletes' movements. Hence, we proposed an innovative approach utilizing trajectory analysis and machine learning algorithms to evaluate and optimize athletes' sports skills. Initially, the trajectory data is collected using advanced motion tracking technology during either training or competitive sessions of the athletes. The collected data was pre-processed, and then feature extraction was performed to extract the most important and relevant features within the data. Further, the Gradient Boosting Decision Tree (GBDT) was trained using the extracted features to predict the athletes' performances. The GBDT is a powerful machine learning algorithm that can handle complex, non-linear interconnections among variables, enabling it to accurately predict the athletes' performances. Finally, we applied the Whale Optimization Algorithm (WOA) to refine the training process of the GBDT, enabling precise training and accurate predictions. The proposed methodology was implemented in the MATLAB software, and it is validated using the real-world athlete data collected during training sessions. The experimental results are validated in terms of parameters such as accuracy, precision, recall, and f-measure. Furthermore, we made a comparative study with the existing methods to validate the effectiveness and robustness of the proposed technique.
Visual Attention Analysis and Optimization Algorithm in Packaging Design
Authors: An Ouyang
Published: May 2024

Visual attention analysis and optimization algorithms in packaging design refer to computational methods aimed at understanding and improving the visual appeal and effectiveness of packaging materials. This study proposes a novel approach for enhancing packaging design through the integration of visual attention analysis and optimization algorithms, employing autoencoder and whale optimization techniques. Visual attention analysis and optimization algorithm in packaging design is to enhance the effectiveness of packaging materials by maximizing their ability to capture and retain consumer attention. This includes identifying key visual elements within packaging designs that are most likely to attract consumers, optimizing the layout and composition of these elements to increase visual impact, and ultimately improving the overall consumer experience and brand perception. Autoencoders are utilized to capture intricate visual features within packaging designs, aiming to create more creative and eye-catching packaging design, while the whale optimization algorithm facilitates the optimization process. Experimental results demonstrate the superior performance of the developed algorithm across various metrics such as accuracy, precision, recall, and f-measure, compared to existing techniques like RNN, CNN, ANN and SVM. The algorithm's scalability and adaptability are further highlighted through consistent performance across increasing data volumes. Overall, this approach holds promise for revolutionizing packaging design by effectively capturing and retaining consumer attention, thereby enhancing brand perception and product satisfaction.
Research on the Correlation Analysis Algorithm between School Teachers' Equipment and Teaching Quality
Authors: Jing Wang
Published: May 2024

In this research, we delve into the correlation between school teachers' equipment and teaching quality, aiming to uncover insights that contribute to educational advancements. The study employs a specialized Correlation Analysis Algorithm to systematically analyse the relationship between the equipment utilized by school teachers and the resulting impact on the quality of teaching. By examining various factors such as technological tools, teaching aids, and instructional devices, we seek to discern patterns and correlations that influence the effectiveness of educational practices. The research is driven by the overarching goal of enhancing teaching quality through informed decisions regarding the selection and integration of educational tools. The abstract provides a glimpse into the methodology and objectives of the study, emphasizing its potential contributions to the optimization of teaching practices in educational institutions.
Numerical Modelling simulation technology of Nonlinear Multi-Field Coupled Ground Foundation
Authors: Tao Zhang
Published: May 2024

Contemporary civil engineering practices demand sophisticated computational tools to navigate the complexities of structural systems and their interactions with the ground. This paper introduces a novel numerical modelling simulation technology tailored for nonlinear multi-field coupled ground foundations. Ground foundations, pivotal interfaces between structures and underlying soil, undergo intricate interactions influenced by various physical phenomena, including soil-structure interaction, soil-structure-fluid interaction, and thermal effects. Traditional analytical methods often falter in capturing the nuanced nonlinear behavior of these coupled systems. Consequently, this paper proposes a comprehensive numerical modelling framework integrating advanced computational techniques to accurately simulate the complex interplay of multiple fields within ground foundations. The methodology incorporates finite element analysis, multi-physics coupling algorithms, and advanced constitutive models to capture the nonlinear behavior of soil, structure, and fluid interactions. Through detailed case studies and validation against experimental data, the efficacy and accuracy of the proposed simulation technology are demonstrated, emphasizing its potential to revolutionize the design and analysis of ground foundation systems in civil engineering applications. This paper represents a foundational contribution towards advancing the state-of-the-art in numerical modelling simulation technologies for nonlinear multi-field coupled ground foundations, fostering innovation and optimization in civil engineering practice.
Evaluation Model of Sports and Health Knowledge Dissemination Effect Based on Social Media Data and Sentiment Analysis
Authors: Jianhua Zhang
Published: May 2024

In the contemporary era, social media platforms serve as prominent channels for the dissemination of sports and health-related information. Understanding the effectiveness of such dissemination efforts is crucial for optimizing public health interventions and promoting well-being. This paper proposes an innovative evaluation model that harnesses social media data and sentiment analysis techniques to assess the dissemination effect of sports and health knowledge. By leveraging sentiment analysis, the proposed model delves deeper into the public's perceptions and attitudes towards sports and health-related content on social media platforms. Through the integration of advanced analytics, including machine learning algorithms, the model provides insights into the reach, engagement, and impact of information dissemination campaigns. The application of this evaluation model facilitates evidence-based decision-making for policymakers, health practitioners, and stakeholders involved in sports and health promotion initiatives. This paper outlines the theoretical framework, methodology, and practical applications of the proposed evaluation model, offering a comprehensive approach to evaluating the effectiveness of knowledge dissemination efforts in the digital age.
Technological Innovation In the Fintech Industry -An Indian Perspective
Authors: Shreya Virani
Published: May 2024

Information technology is the major force behind innovations in the financial sector thanks to fintech. Fintech is looked up as a breakthrough technological innovation disrupting the financial sector using mobile technology. Globally, no financial service is untouched by the new technologies. The mobile technology which has changed the face of the financial sector are Blockchain, artificial intelligence, and data analytics and they have had a strong impact on various areas of financial services, including deposits, lending, remittances, credits (B2B and P2P), underwriting, insurance, cryptocurrencies and so on. India is also seeing a rise in the fintech industry as the mobile penetration is increasing. The development of fintech in India can be ascribed to several important elements that have combined to foster an atmosphere that is favorable to technological innovation. The current research paper seeks to offer an in-depth understanding of the technological advancements made by the Indian Fintech industry. Further, the authors have proposed a conceptual model as an outcome of the various studies that have been reviewed.
“Research on the construction of internet based aerobics competitive ability assessment model”
Authors: Yan Liu
Published: May 2024

A vast field that focuses on restoring motor abilities through exercise is known as sports rehabilitation training, which is also sometimes referred to as recovery athletic exercise. A background in sporting recovery can assist competitors in getting back into peak physical condition, recovering from injuries, preventing additional damage, and better coping with the demands of regular practice. The importance of sports rehabilitation teaching for physical activity is analyzed and discussed at length throughout this research. The definition of sports therapy is presented first, followed by an examination of how it might be used to fitness. Make use of this information as a reference to discover how to include physical therapy in the workout plan you use for sports conditioning. Many tried-and-true algorithms for monitoring patients' vitals over time have emerged as a result of widespread use and advancements in IHM technology, which has led to an increase in the number of such algorithms. The purpose of this research is to investigate the ways in which intelligent health monitoring systems can be applied to the process of developing rehab methods for athletes. It not only provides the structure for a smart wellness terminal, but it also offers the usual ways for monitoring a moving object. Methods such as this include the optical flow strategy, the frame contrast technique, and the background difference method, to name just a few examples. The process of rehabilitation that an athlete went through in X city is presented as an example to show the processes that are included in an ideal study. According to the findings of the study, 92.6 percent of the athletes with injuries were able to make a full recovery with the support of rehabilitative physical exercise. The researchers were able to determine this by keeping close track on the athletes' vital signs in real time using sophisticated health monitoring equipment.
Application of Bayesian models in building information modeling of residential buildings
Authors: Na Li
Published: May 2024

This paper lays forth a process for continuing Bayesian calibration of full-scale building energy simulation (BES) models by making use of data obtained from building information modeling (BIM) and building energy management systems (BEMS). According to a survey taken in China, it is possible that importing data from BIM and BEMS will dramatically cut down on the amount of time and effort required for the continuing calibration of BES models. After that, the continuous calibration approach that had been developed was examined with the use of a case study that was based on the actual calibration of a building. In China, Building Information Modeling (BIM) and data on the amount of electricity used each month over the course of the preceding three years were both incorporated into the case study. According to the findings, a non-continuous technique on the test dataset fared worse than a continuous Bayesian calibration method in terms of prediction accuracy and reduced uncertainty. The normalized mean biased error (NMBE) and the coefficient of variation of the root mean square error (CVRMSE) are both discussed in this research, and comparisons are drawn between the two.
Method for Evaluating the Effectiveness of Physical Education Classes Utilizing offline and Online Mobile Edge Computing
Authors: Tao Sun
Published: May 2024

Convergence is occurring between the two fast-evolving domains of computing at the edge and artificial intelligence. The synergy between these two technologies seems like it may lead to even better results. In physical education (PE), neither students nor instructors are tied to a single place, and neither the curriculum nor the classes repeat themselves. That's why it's hard for conventional colleges to evaluate their PE courses accurately. This article uses an optimization model created for edge computing to improve the conventional way of assessing the efficacy of PE programs utilized by most schools. The goal of physical education programs in traditional schools is to improve students' overall health and well-being. Consequently, the ratio of final exam scores is given less weight in this teacher evaluation model than the proportion of students who fulfill the requirements for a physical fitness assessment. The study aimed to develop measures for evaluating the efficacy of kinesiology programs in two-year institutions. This approach enables continuous self-assessment of learning and provides immediate responses to teacher effectiveness. One method that might be utilized to fine-tune the setup is edge computing. Based on the findings of the experiments conducted, it seems that the PES designed for this research has the potential to gather student feedback and enhance the scholarly character of PE teaching objectives. The PES for use in inclusive universities presented in this study is a prime example of this. The edge-computation optimization strategy created for this architecture has the ability to decrease the network's transference amount of the total system by 20 percent while simultaneously improving efficiency by 15%.
Vulnerabilities in One Time Passwords and Protection Method Using Secret Key and Biometric Hash Code
Authors: Amanapu Yaswanth
Published: May 2024

In the present era, conducting financial transactions online is both simple and widely embraced for its convenience. However, engaging in internet-based financial transactions exposes individuals to potential threats like password attacks, malware, phishing, and other illicit activities. Recognizing these risks, many banks have enhanced security measures by incorporating One-Time Password (OTP) authentication alongside traditional login credentials. Despite these improvements, security issues persist in online transactions. To mitigate such concerns, OTPs can be delivered via SMS to the account owner's mobile number. Even with heightened security, internet banking remains susceptible to various attacks, including online phishing, Man-in-the-Middle attacks, SQL injection, and brute force attacks. To address these challenges, this proposed model introduces a Biometric mobile OTP-based transaction authentication method.
Machine Learning based Data Sensing Device Network with Optimized Group Formation and Group Head Selection
Authors: Nirmala G.
Published: May 2024

Sensor devices for data sensing (referred to as DSDs) are used in use cases such as border control and vehicle tracking. The architecture of the Data Sensing Device Network (DSDN) is established by integrating numerous DSDs across a given region, forming multiple groups. Within each group, a specific DSD is designated to facilitate communication between independent groups. The multi attribute values are captured and these attribute values effect the selection of head DSDs. For each of DSD this value ranges between 0.1 to 1. The DSD which has the highest value of range will be treated as Group Head in LEACH. The attributes are namely distance, battery level for each DSD. From the source DSD to destination DSD the link formation will happen end to end by making use of DSDs and base station, generally the end-to-end link communication has larger hops. This will have a ripple effect on battery level for DSDs and can cause reduced lifetime.
The Energy based LEACH is modified on top of LEACH by computing the battery level for DSDs and picking DSD with highest battery level. The Energy based LEACH will have two DSDs in each group acting like head DSDs. Machine Learning Data Sensing Device Network (ML-DSDN) is proposed which will first create group DSDNs based on k means machine learning algorithm. ML-DSDN will find the head group DSD based on combination of random forest and SVM algorithm with set theory. The comparison is done of ML-DSDN with respect to ELEACH and LEACH method and it is proved that ML-DSDN performs better with respect to delay, link count, energy consumption, alive DSD count, dead DSD count, lifetime ratio, routing overhead.
Investigation into designing VLSI of a flexible architecture for a deep neural network accelerator
Authors: Aayushi Arya
Published: May 2024

Deep learning, a branch of AI, makes use of specialised neural networks. Computational acceleration of high-performance deep neural network algorithms continues to necessitate efficient architecture, high memory bandwidth, parallel processing, and resources, despite decades of study on such algorithms. Excessive space requirements are a problem for DNN implementations caused by resource-intensive components like activation functions (AF) and multiply-and-accumulate (MAC) units. In addition, edge-AI applications necessitate a densely packed, power-hungry, high-throughput DNN accelerator. If we want to build a DNN accelerator that uses little power and occupies little space, we need to optimise the MAC architecture, the AF, and the network complexity so that data flows efficiently. In addition, providing functional configurability while working with restricted chip surface is a difficulty for DNN hardware designs based on ASICs. This dissertation explores the efficient and low-power VLSI architecture of DNN accelerators, addressing the hardware implementation of DNN and targeting applications with limited resources. In order to assess MAC and non-linear AF operations, we investigate and enhance the CORDIC architecture. The poor throughput is a major downside of CORDIC-based architectures, even though they are area and power efficient. Consequently, we suggest a pipelined design for CORDIC-based MAC and AF that focuses on performance. Due to the increased hardware resource consumption that comes with pipeline stages, this study investigates the mutual exclusivity of CORDIC stages and investigates in depth the accuracy variation related to the number of stages needed to achieve high throughput.
Clothing Design Style Recommendation Using Optimized Semantic-Preserved Generative Adversarial Network
Authors: Guannan Yang
Published: May 2024

Recommendations for clothing design styles provide a tailored and efficient buying experience, decreasing consumers' decision fatigue and boosting interest. There are a number of obstacles to implementing clothing design style recommendations, including the requirement for diverse and accurate data to train efficient algorithms, the possibility of biases in recommendation systems, the complexity of comprehending subtle customer preferences, and the ever-changing nature of fashion trends. To overcome this complication, Clothing Design Style Recommendation Using Optimized Semantic-Preserved Generative Adversarial Network (ICMA-SPGAN-SFOA-CD) are proposed. Initially, the images collected from the clothing dataset are given as input. Afterward, the data are fed to pre-processing. In pre-processing, Reverse Image Filter (RIF) is used for redundant of data and noise removal. The pre-processing output is fed to Pyramidal Convolution Shuffle Attention Neural Network (PCSANN) for clothing design style recommendation. The recommended clothing design style was given for classification using Semantic-Preserved Generative Adversarial Network (SPGAN) optimized with Sheep Flock Optimization Algorithm (SFOA)for classifying clothing design style as Basic clothing, Looseness Season for clothing, Collar type, Colour and Advanced clothing . MATLAB is used to implement the proposed ICMA-SPGAN-SFOA-CD method. The performance of the proposed ICMA-SPGAN-SFOA-CD approach attains 25%, 21.5%, and 22.5% high precision,25.3%, 27.5%, and 21.8% high recall and 27.6%, 24.8%, and 23.2% high F1-Scorecompared with existing methods such as RCNN Framework Using L-Softmax Loss (CAR-RCNN-LSL)  ,Clothing Attribute Recognition Based on Clothing fashion style recognition with design issue graph (CFSR-DCNN-DIG), Personalized Smart Clothing Design Based on Multimodal Visual Data Detection (PSCD-DNN-MVDD) models respectively.
Correlation Analysis of Teachers' Educational Technology Quality and Teachers' Professional Competence by Integrating Collaborative Filtering Algorithm
Authors: Wang Qingxia
Published: May 2024

In today's educational landscape, the effective integration of technology into teaching practices is paramount for fostering engaging and impactful learning experiences. Central to this integration is the competence of educators and the quality of educational technology utilized. This study presents a novel approach to correlating teachers' educational technology quality with their professional competence, utilizing a fusion of collaborative filtering algorithm and Weighted Collaborative Mamdani Optimization (WCMO). By leveraging collaborative filtering, the model extracts patterns from historical data to recommend educational technology tools tailored to individual teachers' needs and preferences. The WCMO framework further refines these recommendations by incorporating weighted factors related to teachers' professional competence, ensuring a more nuanced understanding of their instructional requirements. WCMO considers the collaborative input from multiple stakeholders or sources, assigning weighted values to each input based on their relevance or significance. This collaborative approach allows for a more comprehensive analysis of the problem space, ensuring that diverse perspectives and factors are adequately accounted for in the decision-making process. Additionally, by leveraging Mamdani fuzzy logic, WCMO can handle imprecise or uncertain input data effectively, enabling more robust and adaptive optimization outcomes. Through extensive analysis and evaluation using real-world educational datasets, proposed approach demonstrates a significant correlation between teachers' technology quality and professional competence, achieving a correlation coefficient of 0.92. Moreover, qualitative assessments reveal the effectiveness of the WCMO-enhanced recommendations in enhancing teachers' instructional practices and technology integration.
A Thematic Analysis of English and American Literature Works Based on Text Mining and Sentiment Analysis
Authors: Qu Saiying
Published: May 2024

A theme analysis model integrating text mining and sentiment analysis has emerged as a powerful tool for understanding English and American literary works. By employing techniques such as topic modeling, keyword extraction, and sentiment analysis, this model can identify recurring themes, motifs, and emotional tones within texts. Through text mining, it extracts key concepts and topics, while sentiment analysis discerns the underlying emotions conveyed by the authors. By combining these approaches, researchers can uncover deeper insights into the thematic elements and cultural contexts of English and American literature. This paper explores the application of text mining and sentiment analysis techniques to analyze a dataset comprising American literary works. With computational methods such as bi-gram analysis, multimodal feature extraction, and sentiment analysis using the Bi-gram Multimodal Sentimental Analysis (Bi-gramMSA) approach. With the proposed Bi-gramMSA model the multimodal features in the American Literature are examined to investigate the thematic, emotional, and multimodal aspects of the literature. Through our analysis, we uncover significant bi-grams, extract multimodal features, and assess sentiment distribution across the texts. The results highlight the effectiveness of these computational methodologies in uncovering patterns, sentiments, and features within the literary corpus. The proposed Bi-gramMSA model achives a higher score for the different scores in the Chinese Literature.
Construction of Algorithms for English Learners to Read Foreign Literature Based on Machine Translation Technology
Authors: Li Hongjuan
Published: May 2024

The construction of algorithms for English learners to read foreign literature based on machine translation technology involves several key steps. Initially, the algorithm utilizes machine translation to convert the foreign text into English, ensuring accessibility for learners. Next, it employs natural language processing (NLP) techniques to enhance the readability and comprehension of the translated text, such as simplifying complex sentences, clarifying ambiguous phrases, and providing annotations or explanations for cultural or linguistic nuances. Additionally, the algorithm may incorporate adaptive learning mechanisms to personalize the reading experience, adjusting the difficulty level and content based on the learner's proficiency and preferences. This paper explores the development and implementation of algorithms designed to facilitate English learners' comprehension of foreign literature using machine translation technology. Leveraging Probability Path analysis and classification techniques, investigate the effectiveness of various algorithms in improving translation accuracy, fluency, and comprehension for English learners. Through the Probability Path analysis, we delve into the complexities of language structures, translation probabilities, learner interaction, and linguistic adaptation, illuminating the intricate dynamics of machine translation in the context of language learning. Additionally, classification results provide insights into the performance of different algorithms in translating diverse literary genres, showcasing their potential to support English learners in comprehending foreign texts. The findings reveal translation accuracies ranging from 87.8% to 92.0%, with corresponding fluency scores ranging from 4.0 to 4.6. Additionally, algorithms result in comprehension improvements of 23% to 30% for English learners.
Early Warning of Financial Risk Management using AGRNN-COA Approach
Authors: Xuwei Tang
Published: May 2024

Financial risk management was used to protect an entity's financial performance and stability, entails detecting, evaluating, and reducing potential risks, such as credit defaults and market swings. It includes tactics including hedging, diversification, and risk limitation. Financial authorities must immediately responsive for financial risk management system that works with the current economic development. This paper proposes a hybrid approach for early pre-alarm warning of financial risk management system and economic development. The proposed hybrid approach is the combined performance of both the Alternating Graph-Regularized Neural Network (AGRNN) and Crayfish Optimization Algorithm (COA).Commonly it is named as AGRNN-COA technique. The major objective of the proposed approach is to give early pre-alarm warning of financial risk management. AGRNN is designed to give early pre-alarm warning of the financial system of the country. The financial risk management from the AGRNN are optimized by using the COA. By then, the proposed model is implemented in the MATLAB/Simulink working platform and the execution is calculated with the present procedures. The proposed method shows better results in all existing like Financial Risk Management Supervised Algorithm (FRM-SVM), Financial Risk Management Deep Neural Network (FRM-DNN) and Financial Risk Management Heap Based Optimization (FRM-HBO). The accuracy level of proposed FRM-AGRNN-COA approach is 98% that is higher than the other existing methods. The specificity and the F-score of the proposed FRM-AGRNN-COA approach is 99% and 97%. The error rate of the proposed FRM-AGRNN-COA approach is 1.8%, which is very less compared to other existing techniques. From the result, it is conclude that the proposed approach based error is less compared to existing techniques.
Electronic Information System Signal Noise for Noise Reduction using Variational Bayesian based Robust Adaptive Filter
Authors: Yongjie Peng
Published: May 2024

The most significant issues for a thousand users is the unwanted or loud signals in sound files, it is impossible to eliminate or minimize these noise signals without knowledge their types and ranges. To overcome this issue, present an Electronic Information System Signal Noise for Noise Reduction using Variational Bayesian based Robust Adaptive Filter (EIS-SNR-VBRAF) is proposed. Initially, the data is collected from Adaptive Myriad Filter using Time-Varying Noise and Signal-Dependent Parameters dataset. Then, the data is given for Pre-processing section, Variational Bayesian based Robust Adaptive Filter (VBRAF) is used to reduce the signal noise from Gaussian Noise, Traffic noise and Audio signal back ground noise. The proposed technique is implemented and efficacy of EIS-SNR-VBRAF technique is assessed by support of numerous performances such as BER, Signal-to-Noise-Ratio (SNR), Mean Squared Error, Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error, Structural Similarity Index, Cross-Correlation and Computational Complexity is analyzed. The proposed EIS-SNR-VBRAF method attains 21.18%, 23.52% and 23.65% lower RMSE, 21.52%, 21.76% and 23.24% higher PSNR, 21.19%, 21.73% and 20.48% higher Structural Similarity Index are compared with existing methods like early detection of mechanical malfunctions in vehicles utilizing sound signal processing (ED-MMV-SSP),noise reduction in infrasound signals based on mask coefficient binary weighting generalized cross correlation non-negative matrix factorization algorithm (NRID-MCWG-NMFA) and Click-event sound detection in automotive industry using machine/deep learning (CE-SDSI-DL) respectively.
Impact of Trade Protectionism on Economic Growth Based on Bayesian Networks and Fuzzy Logic
Authors: Yan Li
Published: May 2024

A government policy known as trade protectionism limits international trade in order to support home industries. Since the global financial crisis, the country's economy has seen a slowdown in trade growth. The data is taken from the China Census Bureau (CCB) and after the data is fed in to data processing technique our proposed is Adaptive Square Root Cubature Kalman Filter (ASRCKF). The preprocessed data is fed into the Bayesian Networks (BN) decision making activities in different influencing factors to foreign trade protectionism and uses fuzzy logic (FL) analyze the China’s foreign trade Protectionism. Set of data is-processing-decision making activities in which actual data using Bayesian networks and fuzzy logic are combined in the Trade Protectionism. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such as Particle Optimization Algorithm (POA) Radial Basis Function (RBF) and Support vector neural network (SVNN) method our proposed method obtains 98% of accuracy.