bluuebunny/crossref_metadata_2025 huggingface dataset:
Partisipasi Masyarakat dalam Pembentukan Peraturan Daerah di Kabupaten Minahasa Selatan
Authors: Siar Lendy , L. Lambonan Marthin , Pondaag Audi Helri
Published: June 2024

This research is useful for finding a model for institutionalizing community participation in the formation of regional regulations in South Minahasa Regency (KMS) so that they comply with the principles of forming good laws and regulations but can be accepted and implemented by the community. Data collection began with extracting legislative literature regarding its formation, both national, provincial and local government. Interviews involved informants from members of the Regional People's Representative Council (DPRD) and local government officials who were selected using purposive sampling. The formation process relies on proposals from the regional government and the DPRD's initiative. In this way, for the community, there is room for participation but it is not institutionalized, so it is passive at every stage. Such conditions are not in line with the principles of forming laws and regulations in the Republic of Indonesia.
Examining Malaysian Chinese Lexical Variations in Newspaper Discourse: A Standard Chinese in China Perspective
Authors: Jin Ma , Chong Peng Hwa , Lee Ai Chat
Published: June 2024

This study aims to examine Malaysian Chinese (MC) lexical variations in newspaper discourse from a Standard Chinese in China (SCC) perspective. Through qualitative content analysis, the research explores the reasons behind the formation of MC lexical variations, providing insights into the dynamic evolution of the Chinese language within diverse cultural and geographical contexts. Data for this research were collected from “Sin Chew Daily” (Online) during October to December 2023 in Malaysia. Microsoft Excel software facilitated data processing, aiding the researcher in analysis and interpretation. The theoretical framework guiding this study is grounded in the theory of language contact, seeking to understand how linguistic interactions and influences contribute to the divergence in MC lexical variations. The findings illustrate that the primary drivers for the emergence of MC lexical variations are the influences of Malay, English, Hokkien, and Cantonese. The study also acknowledges potential limitations and challenges in the analysis process, such as data availability and subjectivity in word classification. Through this comprehensive analysis, the study aims to enrich academic discussions on language dynamics in multicultural societies and provide insights for language planning and policymaking in diverse linguistic contexts.
GST FRAUD: UNVEILING THE MODUS OPERANDI OF INPUT TAX CREDIT FRAUD SCHEMES
Authors: Thakkar Himanshu , Joshi Vedanshi , Bhatt Rishita
Published: June 2024

Goods and Services Tax (GST) is an effective way of raising revenue for the Government, but it also faces the problem of GST Input Tax Credit (ITC) fraud. Input tax credit is a crucial component of the indirect tax system, but it can be misused by fraudsters to evade taxes and cause a huge loss of government tax revenue. This paper studies the GST fraud modus operandi based on newspapers articles. It reveals that fraudsters have devised a unique scheme of fake billing and input tax credit fraud, involving the creation of shell companies. GST fraud poses a challenge for the GST department in India, as fraudsters exploit the loopholes in the GST system. To curb GST fraud and minimize its impact, it is important to understand the GST fraud modus operandi and its types. This paper presents two modus operandi that illustrate how GST frauds are committed by fraudsters.
IMPACT OF OPEN EDUCATIONAL RESOURCES ON HIGHER EDUCATION IN OPEN KNOWLEDGE ERA: A STUDY AMONG THE STUDENTS OF INDIAN INSTITUTE OF TECHNOLOGY JODHPUR
Authors: Patel Kamleshkumar , Parekh Yogesh
Published: June 2024

The concept of "Open Educational Resources (OER)" is gaining eminence in the field of education on a global scale. The exponential growth of technology in the contemporary era has resulted in an unprecedented need for instant accessibility of information and services. Consequently, to investigate the effects of open educational resources on students in higher education institutions, a survey was conducted by the authors. The findings from the survey indicate that there exists a relatively low to moderate level of awareness regarding Open Educational Resources (OER) among students enrolled in higher education institutions in India. Besides, it was observed that the utilisation of OERs in higher education has a positive influence, suggesting the necessity to enhance awareness among students. By doing so, students can effectively utilise these resources to improve their academic performance.
The Importance of Camel Milk and Its Health Benefits
Authors: Hasen Iman Jaber
Published: June 2024

Camel milk's distinctive nutritional qualities and possible health advantages are making it famous around the globe. All around the semi-arid and dry world, people have been considering it as a potential medicinal and nutritional food source. Cow and camel milk are chemically similar; both contain a lot of fat, mostly medium-chain fatty acids, very little lactose, and plenty of iron and vitamin C.  It is widely believed that camel milk and its derivatives have antidiabetic, anti-cancer, and antihypertensive effects, among numerous other beneficial health effects. Some of the potential scientific explanations for camel milk's health benefits have been uncovered by various in vitro and in vivo investigations. These include the presence of bioactive proteins (such as lactoferrin, immunoglobulins, and lactoperoxidases) and the production of bioactive peptides during digestion.  Scientists and health nuts alike have taken an interest in camel milk for its unusual nutritional qualities and possible medicinal uses. Scientific investigations into camel milk's many medicinal, nutritional, and economic uses have yielded promising results. The purpose of this research is to analyse camel milk and investigate its significance while also discussing its many components, benefits, and advantages in terms of health.
Language Change on Social Media
Authors: Luhulima Celine Christina , Linuwih Endar Rachmawaty , Hutagaol Duncan Reinhart , Nofiyanti Nofiyanti
Published: June 2024

Language change is the permanent alteration of a language's features and usage over time, affecting various aspects such as sound, vocabulary, meaning, and syntax. This research explores language change in the context of social media, specifically focusing on YouTube podcasts. The main goal is to understand how certain parts of speech, like pronouns, verbs, and adjectives, transform into specialized terms unique to the social media environment. The study aims to answer the question: "How do words evolve into special terms in the context of social media use, particularly in YouTube podcasts?”. To comprehensively understand this linguistic phenomenon, a descriptive qualitative approach will analyze data from the dynamic realm of social media, focusing on YouTube, particularly YouTube podcasts. The sample set includes content like "Volix Media - EPISODE PENUH SENSOR SAKING BANYAK "RUMPI"NYA | VINIAR : Talk Show." Insights from relevant website articles will also contribute to the analysis. Forty one words from YouTube podcasts were selected and translated into English. The findings suggest language changes evolve with time and societal shifts. Older times featured distinctive words or abbreviations limited to specific communities, whereas today, global trends and social developments contribute to new language variations and the emergence of new terms through language mixing
CANDIDEMIA, UMA BREVE REVISÃO BIBLIOGRÁFICA
Authors: Da Silva Jefferson Vinicius , Ribeiro Dias Fátima , Correa Amaral Andre
Published: June 2024

Candidemia é considerada uma das complicações das infecções causadas por fungos do gênero Candida em pacientes hospitalizados, resultando em altas taxas de mortalidade e morbidade. Trata-se de um processo infeccioso sistêmico originado por espécies do gênero Candida, sendo reconhecido como a quarta causa mais prevalente de infecções sanguíneas. O objetivo deste estudo consiste em realizar uma revisão concisa da literatura abordando temas como   candidemia, resistência, fatores de virulência, diagnóstico e tratamento. Para isso, foi realizada revisão de literatura com análise de dados secundários sobre candidemia em pacientes hospitalizados. Foram utilizados diversos repositórios, como BVS Brasil, Scielo, Pubmed, LILACS, Elsevier e Medline, com descritores específicos. Foram incluídos 29 estudos em português, inglês e espanhol no período de 2018 a 2023, abrangendo artigos de estudo de caso, estudo clínico e revisão sistemática, enquanto trabalhos fora desse período e critérios foram excluídos. A revisão de literatura destaca a crescente preocupação com a candidemia devido à sua alta morbidade e mortalidade, especialmente em pacientes hospitalizados. Diante da diversidade de espécies de Candida e seus fatores de virulência, como a Candida auris, abordagens multidisciplinares com avaliação clínica, culturas laboratoriais e técnicas moleculares são cruciais para diagnóstico e tratamento eficazes, requerendo monitoramento epidemiológico para orientar estratégias de controle e prevenção.
Kadınların Jinekolojik Kanser Farkındalığı ve Sağlık Okuryazarlığına İlişkin Faktörler
Authors: Akcan Kübra , Çapuk Hüseyin , Fidan Hurşit
Published: June 2024

Giriş: Jinekolojik kanserler dünya çapında önde gelen mortalite ve morbidite nedenleri arasındadır. Farkındalık, jinekolojik kanserlerden korunmada önemli olan bir faktördür ve sağlık okuryazarlığı farkındalık oluşturmada etkin bir konuma sahiptir. Amaç: Bu çalışma jinekolojik kanser farkındalığı ile sağlık okuryazarlığa ilişkin faktörleri belirlemek amacıyla yapıldı. Yöntem: Bu çalışma, kesitsel araştırma dizaynında uygulandı. Çalışma Şırnak Kadın Doğum ve Çocuk Hastalıkları Hastanesinin kadın doğum polikliniğine Mart - Haziran 2023 tarihleri arasında başvuran 360 kadın ile yürütüldü. Veriler Tanıtıcı Bilgi Formu, Jinekolojik Kanserler Farkındalık Ölçeği ve Sağlık Okuryazarlığı Ölçeği ile toplandı. Verilerin analizinde, Robust lineer regresyon ve basit doğrusal regresyon analizi kullanıldı.
Bulgular: Katılımcıların jinekolojik kanserler farkındalık ölçeği toplam puan ortalaması 147,49 ± 21,55, sağlık okuryazarlığı ölçeği toplam puan ortalamasının 96,88 ± 18,92 olarak hesaplandı. Eğitim düzeyi, jinekolojik kanser semptomlarını bilmek ve smear testi yaptırmanın jinekolojik kanser farkındalığını etkilediği belirlenmiştir. Ayrıca eğitim düzeyi ve jinekolojik kanser farkındalığı düzeyinin sağlık okuryazarlığını etkilediği sonucuna ulaşıldı (p < 0,05).
Sonuç: Bu çalışmada, jinekolojik kanser farkındalığı arttıkça sağlık okuryazarlığı düzeyinin de arttığı belirlenmiştir.
Optimisation of water use in Kyrgyzstan agriculture: Analysis of modern and traditional irrigation methods to minimise losses and increase efficiency
Authors: Batykova Ainura , Musabayeva Kasiet , Sultanbaeva Victoria , Osmonov Oskon , Shabikova Gulmira
Published: June 2024

The study conducted on analysing the characteristics of wastewater composition before and after re-treatment is relevant in the context of environmental protection. The study aims to investigate the characteristics of wastewater before and after re-treatment using a pressure sorption filter. The results of the analysis of quantitative indicators of wastewater revealed that before treatment it was characterised by a wide range of indicators exceeding the normative levels. The greatest excess was observed in the content of suspended solids in sample No. 10, where their concentration reached 1,000 mg/l, which exceeded the normative values by 1.43 times. A pressure sorption filter was installed to improve the quality of treatment. The analysis showed that after its implementation, the wastewater indicators improved significantly: the content of suspended solids decreased by 6.67 times, which indicated the effectiveness of this treatment method. After treatment, the content of pollutants decreased to the levels corresponding to the norms. Economic calculation showed low costs for the installation and operation of the pressure sorption filter, which amounted to 22,570 KGS. It should be concluded that in the end, the results showed that the use of a drip irrigation system was cost-effective to implement because the investment amount of 2,765.2 thousand KGS paid off with additional profit in the period of 1.3 years. The results of the study can be used in practice by hydrologists, hydraulic engineers, resource scientists, environmental engineers, ecologists, ecologists-hydrobiologists, specialists in water management and environmental monitoring, as well as decision-makers in the field of ecology and environmental management to develop and implement measures for the implementation of environmental programmes and projects aimed at improving the environmental situation in the region
Regional epidemiology of brucellosis infection in modern conditions of animal husbandry technology in Kazakhstan (by the degree of spread and incidence)
Authors: Abutalip Aspen , Bizhanov Alim , Matikhan Nurali , Karabassova Aiken , Orynbayeva Bibizada
Published: June 2024

The research relevance is determined by the prevalence and impact of brucellosis in the developing livestock landscape of the Republic of Kazakhstan, which requires immediate assessment to understand the current state due to dynamic changes in animal breeding technologies. The study aims to implement various diagnostic programmes and compare the data obtained with information provided by other organisations. To achieve this goal, the “gold standard” research methods were used, a set of classical serological methods, which included the Rose-Bengal test, agglutination reaction, complement binding reaction, enzyme-linked immunosorbent assay, as well as the analysis method and graph analytical method. The results showed that the most infected with brucellosis were epizootic units in Aktobe, Kostanay, Mangistau, Atyrau, Akmola and West Kazakhstan regions, with infection rates exceeding 23.4%. At the same time, Almaty, Turkestan, and Karaganda regions were the least vulnerable, with infection rates not exceeding 3.1%. The largest number of animals with brucellosis was registered in West Kazakhstan Region and the smallest – in Zhambyl Region. This is due to the peculiarities of keeping and geographical locations of the regions of Kazakhstan. Thus, we can classify the situation in Almaty, Pavlodar, Karaganda and Zhambyl regions as class A, as having a low degree of infection. Kostanay, Akmola, Kyzylorda and Atyrau regions are considered to be zones with a medium degree of infection, belonging to class B. In turn, all other regions are characterised by a high degree of infection and are assigned to class C. The practical significance of the study is due to the contribution of valuable information to the scientific understanding of the epidemiology of brucellosis in the Republic of Kazakhstan. The findings provide a basis for the development of targeted intervention strategies and policy recommendations to mitigate the impact of brucellosis on livestock
Optimization of the PM-EDM Process Parameters for Ti-35Nb-7Zr-5Ta Bio Alloy
Authors: Hayyawi Ahmed Rabeea , Al-Ethari Haydar , Haleem Ali Hubi
Published: June 2024

Powder-Mixed Electrical Discharge Machining (PM-EDM) is one of the latest advancements in EDM process capability augmentation. This procedure involves effectively mixing a suitable material in fine powder form with the dielectric fluid. The dielectric fluid's breakdown properties are enhanced by the additional powder. The objective of the present research is to machine the Ti-35Nb-7Zr-5Ta alloy prepared by powder metallurgy and study the influence of process parameters, such as peak current, pulse-on time, pulse-off time, powder type (Ag, Si, Ag+Si), and powder concentration. The metal removal rate and SR represent the response parameters. The Taguchi approach was followed to design the experiments. The five-factor three-level design was chosen to use the Taguchi L27 orthogonal array. It was found that the addition of Ag, Si, or Ag+Si powders to the dielectric fluid enhanced the metal removal rate and the surface finish for this alloy. The addition of Ag powder to the dielectric fluid gave a higher Material Removal Rate (MRR) and a lower SR compared to Si or Ag+Si powders. Powder concentration and pulse current are the most effective parameters on MRR and SR followed by powder type, pulse-on, and pulse-off. The maximum Grey Relational Grade (GRG) exists at (I=5 A, Ton=9 µs, Toff=37 µs, PT=Ag, PC=20 g/L). These are the optimal conditions for PM-EDM of the Ti-35Nb-7Zr-5Ta alloy that give maximum MRR with minimum SR.
Ransomware Early Detection Techniques
Authors: Alhashmi Asma A. , Darem Abdulbasit A. , Alshammari Ahmed B. , Darem Laith A. , Sheatah Huda K. , Effghi Rachid
Published: June 2024

Ransomware has become a significant threat to individuals and organizations worldwide, causing substantial financial losses and disruptions. Early detection of ransomware is crucial to mitigate its impact. The significance of early detection lies in the capture of ransomware in the act of encrypting sample files, thus thwarting its progression. A timely response to ransomware is crucial to prevent the encryption of additional files, a scenario not adequately addressed by current antivirus programs. This study evaluates the performance of six machine-learning algorithms for ransomware detection, comparing the accuracy, precision, recall, and F1-score of Logistic Regression, Decision Tree, Naive Bayes, Random Forest, AdaBoost, and XGBoost. Additionally, their computational performance is evaluated, including build time, training time, classification speed, computational time, and Kappa statistic. This analysis provides insight into the practical feasibility of the algorithms for real-world deployment. The findings suggest that Random Forst, Decision Tree, and XGBoost are promising algorithms for ransomware detection due to their high accuracy of 99.37%, 99.42%, and 99.48%, respectively. These algorithms are also relatively efficient in terms of classification speed, which makes them suitable for real-time detection scenarios, as they can effectively identify ransomware samples even in the presence of noise and data variations.
An Effective Heuristic Optimizer with Deep Learning-assisted Diabetic Retinopathy Diagnosis on Retinal Fundus Images
Authors: Nithyeswari Cinnappan , Karthikeyan Ganesan
Published: June 2024

Diabetic Retinopathy (DR), a common diabetes complication affecting retinal blood vessels, may result in vision damage if not addressed promptly. Early and accurate detection is crucial for effective management, and Deep Learning (DL) techniques offer promising tools for the automated screening of Retinal Fundus Images (RFIs). This approach enhances objectivity, reduces inter-observer variability, and has the potential to extend the DR diagnoses to regions with limited access to specialized medical professionals. This manuscript presents the design of the Beluga Whale Optimizer (BWO) with Deep Learning (DL)-assisted DR Diagnosis on RFIs (BWODL-DRDRFI) technique in the Internet of Things (IoT) platform. The proposed technique automatically examines the RFIs for identifying and classifying DR. During the IoT-based data-gathering procedure the patient utilizes a head-mounted camera for capturing the RFI and sends it to a cloud server. Median Filtering (MF)-based image preprocessing is performed to eradicate noise. Next, the BWODL-DRDRFI technique exploits the ShuffleNet-v2 approach to derive feature vectors. For DR recognition, the BWODL-DRDRFI technique applies a deep Stacked AutoEncoder (SAE) model. Finally, the BWO model optimally adjusts the hyperparameter values of the DSAE model for greater classification performance. The simulation output of the BWODL-DRDRFI approach can be examined on a standard image dataset and the outputs are computed on discrete measures. The simulation result highlighted the enhanced performance of the BWODL-DRDRFI approach in the DR diagnosis process.
G-GANS for Adaptive Learning in Dynamic Network Slices
Authors: Alanazi Meshari Huwaytim
Published: June 2024

This paper introduces a novel approach to improve security in dynamic network slices for 5G networks using Graph-based Generative Adversarial Networks (G-GAN). Given the rapidly evolving and adaptable nature of 5G network slices, traditional security mechanisms often fall short in providing real-time, efficient, and scalable defense mechanisms. To address this gap, this study proposes the use of G-GAN, which combines the strengths of Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs) for adaptive learning and anomaly detection in dynamic network environments. The proposed approach utilizes GAN to generate realistic network traffic patterns, both normal and adversarial, whereas GNNs analyze these patterns within the context of the network's graph-based topology. This combination facilitates the early detection of anomalies and potential security threats, adapting to the ever-changing configurations of network slices. The current study presents a comprehensive methodology for implementing G-GAN, including system architecture, data processing, and model training. The experimental analysis demonstrates the efficacy of G-GAN in accurately identifying security threats and adapting to new scenarios, revealing that G-GAN outperformed established models with an accuracy of 97.12%, precision of 96.20%, recall of 97.24%, and F1-Score of 96.72%. This study not only contributes to the field of network security in the context of 5G, but also opens avenues for future exploration in the application of hybrid AI models for real-time security across various domains.
A New Approach for Wireless Sensor Networks based on Tree-based Routing using Hybrid Fuzzy C-Means with Genetic Algorithm
Authors: Sikarwar Neetu , Tomar Ranjeet Singh
Published: June 2024

The rapid development of wireless technology has led to the availability of a wide range of networked devices that support numerous applications. Small wireless devices that are powered by batteries create a Wireless Sensor Network (WSN), which collaborates to communicate data through wireless channels to a Base Station (BS). However, a WSN system faces a number of difficulties, with energy efficiency being the most critical one. In order to provide energy efficiency and increase network lifespan, it is crucial to lessen the energy required for data transmission. This research suggests an energy-efficient optimal cluster-based routing strategy to extend the lifespan of a network. Energy conservation is of paramount importance in WSNs featuring mobile nodes. Numerous routing techniques have been proposed to reduce packet loss and boost energy efficiency in such networks. These protocols are not particularly energy-efficient though, because they cannot build the right clusters. In this paper, the tree-based Hybrid Fuzzy C-Means Genetic Algorithm (HFCM-GA) is presented in an attempt to reduce energy loss and increase the packet delivery ratio. Using node mobility and the node energy attribute, this protocol proposes a centralized cluster creation mechanism that produces optimal clusters. Node mobility, node energy, and node distance are additional criteria that a detached node considers while choosing its ideal cluster head. Simulation outcomes demonstrate that the recommended HFCM-GA is superior to the conventional routing protocols regarding the residual energy and coverage ratio.
A Comparison of RSM-DA and PSO-TOPSIS in optimizing the Finishing Turning of 9XC Steel under MQL Conditions
Authors: Nguyen Thuy Duong , Nguyen Khanh Huyen , Ha Long Nguyen
Published: June 2024

This study compares the effectiveness of RSM-DA and PSO-TOPSIS in optimizing the finishing turning of 9XC steel under Minimum Quantity Lubrication. Experiments using the Box-Behnken Design were conducted. Depth of Cut (ap), Feed Per Tooth (fz), and Cutting Speed (Vc) served as the input parameters while the Material Removal Rate (MRR) and Surface Roughness (Ra) as the output responses. Fifteen experiments based on the Box-Behnken orthogonal array design were carried out. The RSM-DA method yielded optimized values of 3.35999 cm3/min for MRR and 0.25367 μm for Ra when Vc, ap, and fz were set at 180 m/min, 0.2999 mm, and 0.06 mm/rev respectively. The Pareto solutions were obtained by PSO and TOPSIS identified the optimum set. The optimized MRR and Ra roughness values were found to be 4.320 cm3/min and 0.474 μm, respectively when Vc = 180 m/min, ap = 0.3 mm, and fz = 0.10 mm/rev. The research results showed the suitability, strengths, and weaknesses of PSO-TOPSIS and RSM-DA for multi-objective optimization of the turning process of 9XC alloy under Minimum Quantity Lubrication conditions.
Comparison of Modular Stator 1-phase per Module with Switched Reluctance Motor
Authors: Syed Othman Syed Muhammad Naufal , Sulaiman Erwan , Mostaman Nur Afiqah , Ismail Roshada , Jenal Mahyuzie
Published: June 2024

Conventional motors have the advantage of robustness, high torque output capability, and power performance compared to modular motors. However, traditional motor structure inhibits fault tolerance. For that reason, this paper proposes the structure of a modular stator. It focuses on the performance of modular stator outer rotor flux switching permanent magnet motor (MSOR-FSPM) and Segmental Stator Hybrid Excitation Switched Reluctance Motor (SS-HESRM) by simulation using 2D-FEA in no-load and load conditions. Based on the results, the maximum flux linkage of MSOR-FSPM is 0.02 Wb and 0.05 Wb for SS-HESRM. The average torque output for MSOR-FSPM at maximum armature density is 108.43 Nm and 45.26 Nm for SS-HESRM. Therefore, the torque density for MSOR-FSPM and SS-HESRM is 3.78 Nm/kg and 10.63 Nm/kg, respectively. As for the conclusion, a modular stator motor is capable of inherent fault tolerance compared to a conventional motor structure. Moreover, a modular stator motor produces a higher torque and power density because of the low iron core and optimum flux linkage.
Vertical Accuracy of Google Earth Data
Authors: El-Ashmawy Khalid L. A.
Published: June 2024

Digital Elevation Models (DEMs) are an important data source used in many engineering and Geographic Information System (GIS) applications. This paper illustrates a strategy for creating a DEM by utilizing elevation data from Google Earth and evaluating the vertical positional accuracy of the generated DEM adopting a well-defined methodology. To ensure the accuracy of the elevation data obtained from Google Earth, a thorough evaluation was done in three diverse small districts of the northern shoreline in Egypt. The evaluation process involved determining the ground coordinates of reference points utilizing two surveying techniques: total station and Real-Time Kinematic (RTK) Global Positioning System (GPS) surveys. These coordinates were compared with the ones predicated by the DEM generated by putting into service Google Earth's elevation data. Furthermore, the vertical accuracy was assessed using Shuttle Radar Topographic Mission (SRTM) data of Google Earth collected at two different periods in 2015 and 2023. The vertical accuracy of the Google Earth data is detailed utilizing Mean Error (ME), Maximum Absolute Error (MAE), and Root Mean Square Error (RMSE). According to the results, Google Earth's elevation data accuracy remains consistent from 2015 to 2023, and refining SRTM data does not improve the vertical accuracy. The vertical accuracy of the total station survey surpasses the one of the RTK GPS survey, and the elevation accuracy of the RTK GPS survey decreases with increasing height difference. In addition, the vertical accuracy of DEMs was found to be sufficient for some engineering applications but not accurate enough for precise engineering studies. The accuracy achieved in small height difference terrain can be utilized to produce large-scale cadastral maps, city plans, or land use maps. Finally, the elevation data offered by Google Earth can be utilized for preliminary studies at a low cost. However, to ensure the accuracy of these data, it is recommended that users compare them with reference data before implementation.
Design of Symmetrical Voltage Multiplier High Gain Interleaved DC to DC Converter for Photovoltaic Applications
Authors: Sreelatha Edara , Pandian Alagappan , Sarala Pullacheri , Reddy Chilakala Rami
Published: June 2024

High voltage gain interleaved DC to DC boost converters are employed in Photovoltaic (PV) energy conversion for their structural advantage. The proposed converter builds upon the existing two-phase interleaved DC to DC boost converter, which is commonly used in utility grid integration circuits to minimize ripple current from the PV. The aim is to enhance the output voltage of the currently installed PV array in order to cater to high-power applications or grid integration. The key requirements include achieving high-efficiency power conversion and fully utilizing the potential of the PV system. The methods being proposed to increase the PV output voltage suffer from drawbacks such as low efficiency, complexity, and cost. In contrast, the suggested DC-DC converter boasts a remarkable efficiency of 96% and is capable of converting voltage from 25 V to 400 V for a power output of 400 W. The designed converter has been simulated in MATLAB software and the performance is compared to existing converters related to voltage stress, voltage gain against given duty cycle, and efficiency.
Experimental and Numerical Study of the Performance Improvement of the Solar Dryer Equipped with PVT
Authors: Fterich Mohamed , Souissi Ahmed Saadeddine , Toutti Ezzeddinne , Bentaher Hatem
Published: June 2024

This research addresses the improvement of the performance of a solar dryer equipped with a PVT unit by integrating a heat exchanger into the drying system. The results indicated that introducing a heat exchanger into the drying process had a positive impact on enhancing and raising the drying temperature by harnessing the amount of free energy dissipated after the drying operation. The absorbed energy ranged from 30 J/s to 275 J/s from the hot air emitted throughout the drying process during the day, depending on the drying temperature. This paper also discusses the influence of the drying room design on the thermal balance within the room. Consequently, four different designs for the drying room were developed and studied with the COMSOL software. The findings revealed that the design-4, which optimally places two air inlets (one at the bottom and one at the top) on one side, whereas the opposing side has a centralized air outlet, utilizing a fan to ensure effective air circulation, is the best solution in terms of thermal balance and distribution of the drying air inside the drying chamber.
Enhancing Data Security through Machine Learning-based Key Generation and Encryption
Authors: Saini Abhishek , Sehrawat Ruchi
Published: June 2024

In an era marked by growing concerns about data security and privacy, the need for robust encryption techniques has become a matter of paramount importance. The primary goal of this study is to protect sensitive information during transmission while ensuring efficient and reliable decryption at the receiver's side. To generate robust and unique cryptographic keys, the proposed approach trains an autoencoder neural network based on hashing and optionally generated prime numbers in the MNIST dataset. The key serves as the foundation for secure communication. An additional security layer to the cryptographic algorithm passing through the first ciphertext, was employed utilizing the XORed and Blum-Blum-Shub (BBS) generators to make the system resistant to various types of attacks. This approach offers a robust and innovative solution for secure data transmission, combining the strengths of autoencoder-based key generation and cryptographic encryption. Its effectiveness is demonstrated through testing and simulations.
A Novel PIFA Design for SAR Reduction in 5G Networks to Analyze the RF Shield Ιmpact
Authors: Penta Ashok Kumar , Kumar Ch. R. Phani
Published: June 2024

Fifth Generation (5G) Technology, representing the latest advancement in wireless communication networks, has brought attention to the rising concerns regarding Specific Absorption Rate (SAR) due to temperature fluctuations. The negative impacts of SAR, particularly in the context of mobile users' head exposure, have prompted the exploration of effective mitigation strategies. This article introduces a novel approach, employing a Planar Inverted F-Antenna (PIFA) operating at 26 GHz, with the integration of RF shields, specifically a flexible ferrite sheet and a foam absorber, aimed at reducing SAR in the human head. Dosimetry investigations, conducted at frequencies exceeding 26 GHz, reveal that SAR values without shielding materials (1.59 W/kg) approach the safety limit of SAR. The incorporation of ferrite and foam absorber leads to SAR reductions of 1.53 and 1.48 W/kg, respectively. Notably, the proposed antenna demonstrates significant SAR Reduction Factor (SRF) values, particularly at 5G network frequencies (26 GHz). Comparative analysis highlights the superior performance of the foam absorber across various parameters. The prototype of the proposed antenna has been fabricated and subjected to testing, affirming its potential for alleviating SAR in the context of 5G technology.
A Cloud Forensics Framework to Identify, Gather, and Analyze Cloud Computing Incidents
Authors: Al-mugern Rafef , Othman Siti Hajar , Al-Dhaqm Arafat , Ali Abdulalem
Published: June 2024

The focus of cloud forensics is cyber-crime cases, no matter the object, the subject, or the environment involved. Each cloud computing environment has a variety of features that make it unique. Challenges associated with cloud forensics can be found at every stage of the digital forensics process. We need to begin by understanding the cloud forensics landscape (the cloud) in order to provide a holistic solution to overcome these challenges. While designing the cloud forensics framework, the elements that make up the cloud should be taken into consideration, which also impact the forensics process within the cloud. An extensive survey of the current state of research in cloud forensics is presented in this paper. Also, a conceptual cloud forensics framework that facilitates the identification, gathering, and analysis of cloud computing events is proposed, utilizing the design science approach. The proposed conceptual cloud forensics framework consists of six stages: identifying incidents, gathering evidence, preserving evidence, analyzing incidents, documenting incidents, and investigating post-incident events. Each stage has several activities and tasks to assist investigators dealing with cloud computing events. Unlike traditional approaches to cloud forensic investigations, the conceptual framework developed in this study is highly applicable.
Enhancing Rendering Performance in Complex Visualizations by using Optimization Techniques and Algorithms in Browser Environments
Authors: Brekalo Sanja , Pap Klaudio , Trstenjak Bruno
Published: June 2024

This research is based on the hypothesis that optimization techniques can significantly improve the performance of complex visualizations in web browsers. The aim of the former was to determine to which extent the optimization can be achieved. Optimizations were coded to improve visualization, reduce the need for visual rendering, and decrease script execution time as well as the needed resources. To test the hypothesis, various optimization methods and algorithms were implemented on the initial visualization script and were tested. The main goal of this implementation was to assess how optimization methods, including quadtrees, spatial hashing, binning, LOD adjustments, and the use of the map data structure, affect the performance of web visualization. The obtained results confirmed the hypothesis and the original animation was significantly improved. The implementation of optimizations had a positive effect on the performance of visualizations. The conducted tests gave concrete evidence confirming the validity of the initial hypothesis. This led to certain conclusions regarding which methods provide the best results when optimizing complex visualizations. Key recommendations for code optimization, which can be used in the development of complex visualizations in web browsers, were derived.
Congestion Management using the Circulatory System Based Optimization Algorithm
Authors: Tang Gia Tue , Bui Nguyen Duc Huy , Long Duong Thanh
Published: June 2024

Congestion management is one of the most important issues in power system operation, especially in competitive electricity markets. The main aim of Congestion Management (CM) is to eliminate congestion in transmission lines. The most common technique to deal with the CM problem is re-dispatching the generator. However, finding an optimal solution for the CM problem constitutes a challenge for many researchers. Recently, a new biologically inspired metaheuristic algorithm, called Circulatory System Based Optimization (CSBO), was developed and proven to be effective in handling optimization issues. The CSBO algorithm was applied to solve the CM problem for the IEEE-30 bus system in two different cases. The former was compared with the Crayfish Optimization Algorithm (COA), Artificial Rabbits Optimization (ARO), Improved Grey Wolf Optimizer (I-GWO), and other existing methods. The simulation results revealed that the cost obtained from the proposed CSBO algorithm was lower than 14.5%, 11.31%, 9.97%, and 4% compared to PSO, FPA, FFA, and ALO. In addition, the stability of the proposed algorithm was higher than that of the other methods after 30 trials.
Multi-Class Imbalanced Data Classification: A Systematic Mapping Study
Authors: Wang Yujiang , Rosli Marshima Mohd , Musa Norzilah , Li Feng
Published: June 2024

Multi-class data classification is distinguished as a significant and challenging research topic in contemporary machine learning, particularly when concerning imbalanced data sets. Hence, a thorough investigation of multi-class imbalanced data classification is becoming increasingly pertinent. In this paper, an overview of multi-class imbalanced data classification was generated via conducting a systematic mapping study, which endeavors to analyze the state of contemporary multi-class imbalanced data classification, with the primary goal of ascertaining the corpus of research undertaken in machine learning. To achieve this aim, 7,164 papers were assessed and the 147 prominent ones were selected from five digital libraries, which were further categorized according to techniques, issues, and types of datasets. After a thorough review of these papers, a taxonomy of multi-class imbalanced data classification techniques is proposed. Based on the results, researchers widely employ algorithmic-level, ensemble, and oversampling strategies to address the issue of multi-class imbalance in medical datasets, primarily to mitigate the impact of challenging data factors. This research highlights an urgent need for more studies on multi-class imbalanced data classification.
Navigating Business Model Innovation in Chinese Manufacturing: Insights and Implications
Authors: Zhang Yao , Mohi Ud Din Qaiser , Yuan Yuan
Published: June 2024

Despite an increasing number of manufacturing companies innovating their business models in the digital economy, how innovative business models are formed has remained an under-researched area, especially in the manufacturing industry. This study addresses the particular research gap by analyzing the business model innovation process and identifies and explores five conditions that influence the business model innovation process: creative ideas, value proposition optimization, transaction structure reconstruction, profit model exploration, and dynamic potential accumulation. Then, based on the data of 238 respondents in Chinese manufacturing companies, the fuzzy-set approach is employed by conducting Qualitative Comparative Analysis (fsQCA) to explore the configurations of the innovative business model formation process. The results show that high levels of business model innovation can be achieved through different configurations: (1) creative ideas, value proposition optimization, and transaction structure reconstruction combined with dynamic potential accumulation, (2) creative ideas, value proposition optimization, and profit model exploration combined with dynamic potential accumulation, (3) value proposition optimization, and profit model exploration combined with transaction structure reconstruction, and (4) transaction structure reconstruction combined with dynamic potential accumulation. This study contributes to the theoretical literature on business model innovation and provides practical information for manufacturing companies looking to innovate their business models.
Enhancing Disaster Response and Public Safety with Advanced Social Media Analytics and Natural Language Processing
Authors: Alharbi Khalil , Haq Mohd Anul
Published: June 2024

This study investigates the effectiveness of the DistilBERT model in classifying tweets related to disasters. This study achieved significant predictive accuracy through a comprehensive analysis of the dataset and iterative refinement of the model, including adjustments to hyperparameters. The benchmark model developed highlights the benefits of DistilBERT, with its reduced size and improved processing speed contributing to greater computational efficiency while maintaining over 95% of BERT's capabilities. The results indicate an impressive average training accuracy of 92.42% and a validation accuracy of 82.11%, demonstrating the practical advantages of DistilBERT in emergency management and disaster response. These findings underscore the potential of advanced transformer models to analyze social media data, contributing to better public safety and emergency preparedness.
Enhancing 5G Core Network Performance through Optimal Network Fragmentation and Resource Allocation
Authors: Maganti Madhava Rao , Rao Kurra Rajashekar
Published: June 2024

The rise of 5G technology has brought with it a surge in diverse services with demanding and varying requirements. Network fragmentation has emerged as a critical technique to address this challenge, enabling the creation of virtual network segments on a shared infrastructure, allowing for efficient resource utilization and improved performance. This paper investigates the potential of network fragmentation, combined with optimized resource allocation, to enhance the performance of 5G core networks. A novel framework that integrates these two techniques is proposed. The former takes into account factors, such as network traffic patterns, service requirements, and resource availability. The framework aims to optimize network performance metrics, namely throughput, latency, and resource utilization. The experimental results demonstrate the effectiveness of the proposed framework, showcasing a significant improvement in overall network performance, paving thus the way for efficient and robust 5G service delivery.
Effect on Fatigue Behavior of Connecting Rod in Gasoline Engine
Authors: Ketmuang Yodnapha , Wongthong Bundit
Published: June 2024

This article reports the failure analysis of a connecting rod that is broken into 3 pieces and is used in the gasoline engine of a sedan. The connecting rod is made of JIS-S50C medium alloyed steel. Fractography was performed to characterize the failure mode on the fracture surface of this connecting rod through the examination of the macroscopic and microscopic morphologies of the fracture surface, chemical composition, metallographic analysis, mechanical properties of the material, and numerical simulation. The fracture surface of this connecting rod is caused by fatigue, which was the dominant mechanism of failure. This type of crack is indicative of shear failure in the ductile fracture mode, whereas no abnormalities were found in the composite elements of the connecting rod. The microstructure is composed of perlite-ferrite. The results of the numerical simulation and the calculated crushing stress (sc) were compared and were found to be in accordance and within the acceptable values.
Fingerprint Sequencing: An Authentication Mechanism that Integrates Fingerprints and a Knowledge-based Methodology to Promote Security and Usability
Authors: Algarni Mohammad Η.
Published: June 2024

Biometric authentication stands at the forefront of modern security measures, offering a highly sophisticated and reliable method for identity verification. Biometrics aims to identify an individual’s identity by comparing specific characteristics against a stored template. Unlike traditional passwords or PINs, which can be forgotten, shared, or stolen, biometric authentication relies on unique biological or behavioral traits that are inherent to each individual. The current article introduces the innovative concept of multi-fingerprint sequence authentication process to verify users. In contrast to the traditional, single fingerprint methods, this multifactor technique combines the use of multiple fingerprints along with a sequence pattern for enhanced usability and security. Furthermore, this study presents a comprehensive evaluation of an innovative authentication system utilizing a multiple fingerprint sequence pattern as an alternative to biometric usernames and textual passwords, named BioPass. By leveraging an established framework, the research focuses on assessing the proposed system's usability and security aspects, as well as its potential benefits.
A New Design and Implementation of a Three-Phase Four-Wire Shunt Active Power Filter for Mitigating Harmonic Problems caused by Compact Fluorescent Lamps
Authors: Hajjej Mohamed , Sbita Lassaad
Published: June 2024

The massive embedding of nonlinear loads in industrial, commercial, and residential applications has created severe power quality problems in modern power distribution systems. Compact Fluorescent Lamps (CFLs), which have been designed to replace Incandescent Lamps (ILs), due to their lower energy consumption and longer lifetime, are among the most used non-linear loads. These electric devices, equipped with ballasts and power electronic converters, inject harmonic currents, reactive powers, and create unbalance in the electrical system. Active filters are widely implemented to overcome these issues and improve power quality. In this sense, a Shunt Active Power Filter (SAPF) is developed in this paper to eliminate the under-wanted harmonics caused by multiple CFLs and ameliorate the global power factor in 3-phase 4-wire systems. The suggested SAPF is connected in parallel with the loads and it consists of three main blocks, the reference current calculation block, the Voltage Source Inverter (VSI), and the VSI control block. The reference currents are calculated following the Synchronous Reference Frame (SRF) theory. Meanwhile, Pulse Width Modulation (PWM) based control is adopted for controlling the switching signals. In order to investigate the efficiency and applicability of the developed 3-phase 4-wire SAPF, different simulations and experimental tests are carried out. The measurements are performed by employing a power analyzer and are analyzed with the Power Pad III software. The obtained results disclosed that the proposed SAPF reduced the Total Harmonic Distortion (THD) of the CFL current from 89.6% to 1.62% and improved the power factor.