bluuebunny/crossref_metadata_2025 huggingface dataset:
EDUKASI PENCEGAHAN PENYAKIT YANG DISEBABKAN BAKTERI YANG TERDAPAT DIDALAM AIR DI WILAYAH KERJA PUSKESMAS PENURUNAN KOTA BENGKULU
Authors: Suryani Lilis , Puspitasari Septi , Yurman Yurman , Hayati Inayah , Hepiyansori Hepiyansori , Bahar Mardiyansyah
Published: August 2024

Based on data from the Bengkulu Provincial Health Service in 2023, Diarrhea cases rank 19th out of 56 cases of the most diseases with a total of 2,975 cases in Bengkulu Province. The most common cause of diarrhea cases is the lack of public knowledge about the quality of clean water which is free from microorganisms, especially bacteria. This service aims toprovide education about preventing diseases caused by bacteria found in water in the community in the work area of the Penurunan City Health center, Bengkulu city. The method of this service is health education and discussions which were carried out on April 5 2024 involving 20 people. The implementation of the outreach can be categorized as successful and running smoothly, and the results of the evaluation to the community are that they said that after receiving education/information they knew and understood the quality of water that was suitable for use as drinking water. The conclusion of this service activity is that in general it can increace community knowledge and health education methods are effective in stimulating the community to take action to prevent diseases caused by bacteria found in water, specially Escherichia coli bacteria which can cause diarrhea.
Optimizing Strategies for Enhanced Effectiveness in Blended Learning Models
Authors: Wafik H M Atif , Mahbub Salehin , Das Joy
Published: August 2024

Blended learning models, which combine traditional face-to-face instruction with online learning, have gained significant traction in educational settings due to their potential to offer a more flexible, personalized, and engaging learning experience. This study explores strategies to optimize the effectiveness of blended learning environments, drawing on a comprehensive literature review and data collected through a structured questionnaire administered to educators and students. Key areas of focus include personalized learning pathways, technology integration, professional development, collaborative learning, assessment and feedback, content design, accessibility and inclusivity, and self-regulated learning. The findings indicate a positive perception of adaptive learning technologies and their widespread use, highlighting the importance of personalized learning. Technological tools are effective in enhancing student engagement, though challenges in their implementation persist. Professional development is crucial for the successful integration of blended learning strategies, with a need for ongoing support tailored to educators' specific needs. Collaborative learning is valued but requires more effective implementation strategies. Continuous assessment and timely feedback are essential, yet educators face difficulties in these areas. Moreover, designing engaging and relevant content remains a challenge, despite the prevalent use of multimedia elements. Efforts to ensure accessibility and inclusivity are ongoing but require further attention. Promoting self-regulated learning is recognized as important, but additional support is necessary to foster these skills in students. Future research should focus on longitudinal studies of adaptive learning technologies, effective professional development programs, strategies to overcome technological challenges, methods to enhance collaborative learning, innovative assessment practices, engaging content design, implementation of Universal Design for Learning (UDL) principles, and strategies to promote self-regulated learning. Addressing these areas will contribute to the development of more effective and inclusive blended learning models, ultimately enhancing educational outcomes for diverse learners.
OS 7 “CERTOS” E O CICLO CRÍTICO DAS ACTIVIDADES PRIMÁRIAS LOGÍSTICAS NA SATISFAÇÃO DOS CLIENTES
Authors: Camango Crispim
Published: August 2024

Os 7 “certos” da logística têm estado em evidência nos últimos anos pela sua importância na movimentação do produto certo, no momento certo, na quantidade certa, no lugar certo, na condição certa, ao mínimo custo e ao cliente certo. O presente estudo pretendeu analisar a relação dos 7 “certos” com o ciclo crítico das actividades primárias da logística na satisfação dos clientes.  Foi utilizada uma metodologia bibliográfica, qualitativa e exploratória. Foi feita uma busca na Web of Science, Google Académico e Scielo, em que foram encontradas mais de 80 obras que deram suporte científico a este artigo. Os resultados apontam que a movimentação dos produtos deve ter em conta os 7 “certos” da logística que, por sua vez, para funcionarem de forma eficiente e eficaz, necessitam das habilidades de suporte. Além disso, o ciclo crítico das actividades logísticas primárias deve funcionar à luz dos 7 “certos” para deixar o cliente satisfeito.
Comparative Analysis of GDM2000, WGS84, and MRT68 Coordinate Reference Systems based on Different Converter Modules
Authors: Hussaini Alhaji , Tang Kang Wee Kelvin , Izzati Abdul Hamid Amalina , Abubakar Auwal Graba , Tan Chee Wee
Published: August 2024

The employment of multiple data and coordinate systems in Malaysia has not only resulted in challenges in surveying and mapping purposes but has also caused data compatibility issues with the local positioning system. This study examines the disparities in coordinates converted from the localized Geocentric Datum of Malaysia 2000 (GDM2000) to the global coordinate system, the World Geodetic System 1984 (WGS84), and from GDM2000 to the Malayan Revised Triangulation 1968 (MRT68). Several coordinate converter tools, available in the mCOORD mobile app, Geodetic Datum Transformation System (GDTS), Global Mapper, and Quantum GIS (QGIS), were employed to analyze the variation between converted coordinates. The locally developed coordinate converter tools, mCOORD and GDTS, exhibit similar levels of accuracy and conform to the standards set by the local survey department. In contrast, the reliability of the coordinate conversion tools in Global Mapper and QGIS seems uncertain. It is recommended that each data revision should establish transparency to the latest geodetic reference frame, with publicly accessible transformation parameters.
A Detection Android Cybercrime Model utilizing Machine Learning Technology
Authors: Ghabban Fahad M.
Published: August 2024

The present study developed a Detection Android cybercrime Model (DACM), deploying the design science approach to detect different Android-related cybercrimes. The developed model consists of five stages: problem identification and data collection, data preprocessing and feature extraction, model selection and training, model evaluation and validation, and model deployment and monitoring. Compared to the existing cybercrime detection models on the Android, the developed DACM is comprehensive and covers all the existing detection phases. It provides a robust and effective way to spot cybercrime in the Android ecosystem by following Machine Learning (ML) technology. The model covers all the detection stages that are normally included in similar models, so it provides an integrated and holistic approach to combating cybercrime.
Machine Learning Techniques for Power Quality Enhancement of Power Distribution Systems with FACTS Devices
Authors: Swarupa Malladi Lakshmi , Rayudu Katuri , Kumar Chava Sunil , Gundebommu Sree Lakshmi , Kamalakar P.
Published: August 2024

The power quality problem refers to the issues caused by the sudden rise of nonstandard voltage, current, or frequency. The problems that emerge from poor power quality due to non-linear loads are voltage sag, swell, interruptions, harmonics, and transients in distribution systems. Various compensation devices are used nowadays to improve power quality. The advances in power electronic technologies improve the reliability and functionality of power electronic-based controllers, resulting in increased applications of FACTS devices like DSTATCOM and Dynamic Voltage Restorer (DVR) which are fast, flexible, and efficient solutions to power quality problems. These devices are used to restore the source, load voltage, and current disturbances caused by different loads and faults. These devices were tested in a standard IEEE 14-bus system for Total Harmonic Distortion (THD) minimization while utilizing PI-based Artificial Neural Networks (ANNs) and Linear Regression (LR). The results were analyzed and compared.
Vehicle-to-Home: Implementation and Design of an Intelligent Home Energy Management System that uses Renewable Energy
Authors: Alkhudhayr Hanadi , Subahi Alanoud
Published: August 2024

Using energy storage technology, such as batteries and electric vehicles, is crucial in combating energy shortages. Wind turbines and solar panels are two prominent alternative energy sources. This study examines the impact of Vehicle-to-Home (V2H) technology, specifically during the hours when solar radiation is at its highest. V2H enables electric car batteries to be a primary solution for home energy needs. An accurate scheduling system has been established to improve the organization's energy sustainability. The proposed algorithm effectively controls the allocation, availability, and retention of energy transported by electric vehicles (EVs). The model incorporates constraints to ensure that the family's electricity needs are met regardless of the prevailing weather conditions, whether sunny or cloudy. The Intelligent Home Energy Management System (IHEMS) is being developed to regulate energy use efficiently across different applications and sources. A multi-agent system (MAS) is used to improve operational efficiency and effectively meet the energy needs of devices in the system. An experimental database in Saudi Arabia examines and monitors production costs and energy consumption, considering weather conditions and equipment utilization. The results demonstrate the great potential of V2H technology as a practical storage option that efficiently addresses energy shortages.
Utilizing Ant Colony Optimization for Result Merging in Federated Search
Authors: Garba Adamu , Khalid Shah , Aleryni Aliya , Ullah Irfan , Tairan Nasser Mansoor , Shah Habib , Mumin Diyawu
Published: August 2024

Federated search or distributed information retrieval routes the user's search query to multiple component collections and presents a merged result list in ranked order by comparing the relevance score of each returned result. However, the heterogeneity of the component collections makes it challenging for the central broker to compare these relevance scores while fusing the results into a single ranked list. To address this issue, most existing approaches merge the returned results by converting the document ranks to their ranking scores or downloading the documents and computing their relevance score. However, these approaches are not efficient enough, because the former methods suffer from limited efficacy of result merging due to the negligible number of overlapping documents and the latter are resource intensive. The current paper addresses this problem by proposing a new method that extracts features of both documents and component collections from the available information provided by the collections at query time. Each document and its collection features are exploited together to establish the document relevance score. The ant colony optimization is used for information retrieval to create a merged result list. The experimental results with the TREC 2013 FedWeb dataset demonstrate that the proposed method significantly outperforms the baseline approaches.
NIST CSF-2.0 Compliant GPU Shader Execution
Authors: Lungu Nelson , Al Rababah Ahmad Abdulqadir , Dash Bibhuti Bhusan , Syed Asif Hassan , Barik Lalbihari , Rout Suchismita , Tembo Simon , Lubobya Charles , Patra Sudhansu Shekhar
Published: August 2024

This article introduces a mechanism for ensuring trusted GPU shader execution that adheres to the NIST Cybersecurity Framework (CSF) 2.0 standard. The CSF is a set of best practices for reducing cybersecurity risks. We focus on the CSF’s identification, protection, detection, and response mechanisms for GPU-specific security. To this end, we exploit recent advancements in side-channel analysis and hardware-assisted security for the real-time and introspective monitoring of shader execution. We prototype our solution and measure its performance across different GPU platforms. The evaluation results demonstrate the effectiveness of the proposed mechanism in detecting anomalous shader behaviors that only incur modest overhead at runtime. Integrating the CSF 2.0 principles into the proposed GPU shader pipeline leads to an organizational recipe for securing heterogeneous computing resources.
Development of an Ankle Sensor for Ground Reaction Force Measurement in Intelligent Prosthesis
Authors: Bulbul Ali Ihsan , Mayetin Umut , Kucuk Serdar
Published: August 2024

In this study, a new low-cost, three-degree-of-freedom force sensor is developed to measure Ground Reaction Forces (GRFs) and to be used in the safe control of active transfemoral prosthesis. Initially, the proposed sensor was designed with the Finite Element Method (FEM). Then, the sensor's control board was developed to include an electronic circuit with its microcontroller module, four load cell amplifiers, and an orientation sensor. A test platform was also developed to conduct the sensor tests. To test the accuracy of the results obtained from the developed test platform, the same tests were also carried out with a commercial sensor and similar results were obtained, thus proving that the sensor is suitable for use in prosthetics.
An Enhanced Framework to Mitigate Post-Installation Cyber Attacks on Android Apps
Authors: Koka Vijay , Muppavaram Kireet
Published: August 2024

The widespread use of smartphones worldwide has led to a corresponding rise in the number of mobile applications available for Android devices. These apps offer users convenient ways to perform various daily tasks, but their proliferation has also created an environment in which attackers can steal sensitive information. Insecure options employed by many app developers create vulnerabilities that can be exploited by attackers to gain access to most smartphones. While existing methods can detect malware during app installation, they do not sufficiently address post-installation attacks, such as those resulting from fake apps or Man-in-the-Disk (MitD) attacks. To address this issue, the current study conducted research on post-installation attacks, including data leakage, malware injection, repackaging, reverse engineering, privilege escalation, and UI spoofing. MitD attacks are particularly challenging to counter, so, to mitigate this risk, the Post-Installation App Detection Method is proposed to monitor and regulate sensitive information flow and prevent MitD attacks.
Development of a Machine for Cleaning the Core of Grass Straws
Authors: Nguyen Ngoc-Kien , Nguyen Hai-Nam , Nguyen Van-Tinh
Published: August 2024

Nowadays, plastic waste is one of the most pressing issues, especially in developing countries. Plastic waste comes from everyday products such as plastic food boxes, plastic bottles, disposable products, or plastic straws causing negative effects on the environment. On the other hand, grass straws are eco-friendly and safe and can be easily found in Southeast Asian countries such as Vietnam, Indonesia, and Malaysia. However, the process of grass straw production is implemented manually, resulting in low productivity and unsafe labor conditions. Among the stages of producing grass straws, cleaning the core of the straw is the most important because it determines its quality. Aiming to automate the grass straw production process, this paper introduces the first grass straw core cleaning machine in the world.
Optimizing Quantum Key Distribution Protocols using Decoy State Techniques and Experimental Validation
Authors: Ali Sellami , Djaouida Benlahcene
Published: August 2024

This paper simulated the operation of vacuum state and single decoy state protocols in the BB84 and SARG04 QKD schemes by utilizing the features of the commercial ID-3000 QKD system. Numerical modeling identified an optimal signal-to-decoy state ratio of 0.95:0.05 and an intensity of μ=0.85 for the signal state and ν1=0.05 for the decoy state, ensuring the highest key generation rate and a secure distance of up to 50 km. These protocols were validated experimentally over various transmission distances with standard telecom fiber, using the ID-3000 QKD system in a conventional bi-directional plug-and-play setup. Simulations predicted secure key rates of 1.2 × 10 5 bits/s for SARG04 and 8.5 × 104 bits/s for BB84 at 10 km, with secure distances of 45 km and 35 km, respectively. The experimental results confirmed these predictions, showing a 30% higher key rate and 20% longer secure distance compared to non-decoy methods. The SARG04 protocol surpassed BB84 in key rate and secure distance, highlighting the two-photon component's role in key generation. This study concludes that the decoy-state method significantly enhances key generation rates and secure distances, optimizing QKD protocols for secure quantum communication.
Identification and Improvement of Image Similarity using Autoencoder
Authors: Merugu Suresh , Yadav Rajesh , Pathi Venkatesh , Perianayagam Herbert Raj
Published: August 2024

Identifying the similarity between fine-grained images requires sophisticated techniques. This study presents a deep learning approach to the image similarity problem as an unsupervised learning task. The proposed autoencoder, built on a Deep Neural Network (DNN), autonomously learns image representations by computing cosine similarity distances between extracted features. This paper presents several applications, including training the autoencoder, transforming images, and evaluating the DNN model. In each instance, the generated images exhibit sharpness and closely resemble natural photographs, demonstrating the effectiveness and versatility of the proposed deep learning framework in computer vision tasks. The results suggest that the proposed approach is well-suited for tasks that require accurate image similarity assessments and image generation, highlighting its potential for various applications in image retrieval, data augmentation, and pattern recognition. This study contributes to the advancement of the computer vision field by providing a robust and efficient method for learning image representations and evaluating image similarity in an unsupervised manner.
Application of the TOPSIS Method for Multi-Objective Optimization of a Two-Stage Helical Gearbox
Authors: Tran Huu-Danh , Dinh Van-Thanh , Vu Duc-Binh , Vu Duong , Luu Anh-Tung , Vu Ngoc Pi
Published: August 2024

In order to design a high-efficiency two-stage gearbox to reduce power loss and conserve energy, a Multi-Criterion Decision-Making (MCDM) method is selected for solving the Multi-Objective Optimization Problem (MOOP) in this research. The study's objective is to determine the best primary design factors that will increase gearbox efficiency and decrease gearbox mass. To that end, the first stage's gear ratio and the first and second stages' Coefficients of Wheel Face Width (CWFW) were chosen as the three main design elements. Furthermore, two distinct goals were analyzed: the lowest gearbox mass and the highest gearbox efficiency. Additionally, the MOOP is carried out in two steps: phase 1 solves the Single-Objective Optimization Problem (SOOP) to close the gap between variable levels, and phase 2 solves the MOOP to determine the optimal primary design factors. Furthermore, the TOPSIS approach was selected to address the MOOP. For the first time, an MCDM technique is used to solve the MOOP of a two-stage helical gearbox considering the power losses during idle motion. When designing the gearbox, the optimal values for three crucial design parameters were ascertained according to the study's results.
Nanocellulose-Based Adsorbent for Cu(II) Adsorption
Authors: Ibrahim Haziqatulhanis , Sazali Norazlianie , Kadirgama Kumaran , Wan Salleh Wan Norharyati , Gunawan Triyanda , Widiastuti Nurul , Junaidi Afdhal
Published: August 2024

This study addresses the critical issue of copper removal from wastewater due to environmental and health concerns. Choosing pandan leaves as a source of cellulose was a deliberate decision due to their abundant availability in nature and minimal ecological footprint. Through the utilization of these properties, this study synthesized nanocellulose with enhanced adsorption capabilities by employing chemical pretreatments, sulfuric acid hydrolysis, and acrylamide grafting with the aid of ceric ammonium nitrate (CAN) as an initiator. In order to thoroughly evaluate the synthesized material, X-Ray diffractometer (XRD) and Fourier transform infrared (FTIR) spectroscopy were used. These characterization methods revealed insights into the morphology, functionality, and crystallinity of nanocellulose. The removal of copper(II) ions is investigated by employing an atomic absorption spectrometer (AAS), focusing on three important factors: pH variation, initial concentration, and adsorbent dosage, which are carefully examined. Grafted nanocellulose demonstrates superior performance, achieving over 85% grafting efficiency. Optimal Cu(II) removal conditions are identified at pH 6, with an initial metal ion concentration of 30 ppm and an adsorbent dose of 2.2 g/L. This study not only addresses a critical concern in wastewater treatment, but also explores the potential of pandan leaf-derived nanocellulose as a sustainable solution for heavy metal removal.
Simultaneous Influence of Imperfect Length and Load on the Dynamic Buckling of Plane Trusses under Step Loading
Authors: Van Tran Thi Thuy , Tien Dao Ngoc , Hien Ta Duy
Published: August 2024

The influence of imperfections in element length and loading on the dynamic buckling of plane trusses is investigated in the present study. Finite element formulation and the Euler formula are employed to tackle the problem of large displacements. Equivalently, the Newmark integration method and the Newton–Raphson iteration algorithm are deployed to solve the nonlinear dynamic equilibrium equations. The dynamic applied load considered in this study is a step-imperfect load with an imperfection in the element length. The relationship between the load and maximum displacement is determined, and the simultaneous influence of the imperfect parameters on the dynamic limit load is discussed. The imperfect element length and loading significantly affect the dynamic limit load, demonstrating the need to consider both imperfections when studying the dynamic buckling of truss systems.
Utilization of Multi-Channel Hybrid Deep Neural Networks for Avocado Ripeness Classification
Authors: Nuanmeesri Sumitra
Published: August 2024

Ripeness classification is crucial in ensuring the quality and marketability of avocados. This paper aims to develop the Multi-Channel Hybrid Deep Neural Networks (MCHDNN) model between Visual Geometry Group 16 (VGG16) and EfficientNetB0 architectures, tailored explicitly for avocado ripeness classification in five classes: firm, breaking, ripe, overripe, and rotten. Each feature extracted is concatenated in an early fusion-based to classify the ripeness. The image dataset used for each avocado fruit was captured from six sides: front, back, left, right, bottom, and pedicel to provide a multi-channel input image in of a Convolution Neural Network (CNN). The results showed that the developed fine-tuned MCHDNN had an accuracy of 94.10% in training, 90.13% in validation, and 90.18% in testing. In addition, when considering individual class classification in the confusion matrix of the training set, it was found that the 'ripe' class had the highest accuracy of 94.58%, followed by the 'firm' and 'rotten' classes with 94.50% and 93.75% accuracy, respectively. Moreover, compared with the single-channel model, the fine-tuned MCHDNN model performs 7.70% more accurately than the fine-tuned VGG16 model and 7.77% more accurately than the fine-tuned EfficientNetB0 model.
Controlling Output Power to Enhance the Investment Efficiency of Wind Farms by Maximizing the Capacity of Transmission Transformers and Integrating Energy Storage Systems
Authors: Anh Truong Viet , Linh Nguyen Tung , Sang Dinh Ngoc
Published: August 2024

This study addresses inherent challenges stemming from uncertainty associated with the integration of wind energy into the electricity market. A novel approach is proposed to leverage the capabilities of dynamic transformers to optimize the utilization of uncertain wind power output, thereby enhancing financial investment efficiency for wind power stakeholders. The flexible combination of wind turbines (WTB), transmission transformers (TTS), and Energy Storage Systems (ESS) can actively reserve or provision electricity. Electricity generation control is based on optimal analysis results using linear integer programming algorithms that consider temperature fluctuations, lifespan of transformers, and electricity market prices. Maximizing the dynamic transformer's efficiency as proposed and optimizing revenue and costs from the fluctuating wind power output significantly improves financial performance metrics when investing in wind farm projects. Financial figures highlighted in the paper emphasize notable benefits, particularly for wind farm expansion projects. The potential return on investment ratio is expected to increase up to 5.64 times compared to conventional wind farm investment scenarios, with an improvement to increase from 4.4% to 24.8.
Investigating the Impact of Leadership Quality and Educational Practices on Student Outcomes through Teacher Attitude and Behavior in Pakistani Educational Institutions: An Applied Science Perspective
Authors: Xiang Wei , Ullah Muhammad Rizwan
Published: August 2024

The present study stresses how leadership quality and educational practices affect student outcomes through the way teacher attitudes and behavior are expressed in Pakistani educational institutions. The quantitative data for this study were collected via random sampling from investors in different cities of Pakistan. A simple random sampling technique was adopted recruiting 1000 teachers from different levels of educational institutions in various areas of Pakistan for data analysis. Therefore, the study considered several factors to ensure that a representative sample, which reflects the large population of Pakistan’s teachers, was taken, allowing for more accurate generalizations based on the study findings. The Partial Least Squares (PLS) method was employed to analyze the data gathered for this study. The latter concluded that educational practices and leadership grades play a significant role in improving student learning. It emphasized the relationship between educational institution management, teachers’ mindsets and actions, and student learning results. These relationships can enhance educational outcomes, leadership development, and teacher preparation. Implications include policy considerations, teacher pedagogical training, leadership skills, and pursuing a more student-centered and holistic educational approach.
Semantic IoT Transformation: Elevating Wireless Networking Performance through Innovative Communication Paradigms
Authors: Alzahrani Ibrahim R.
Published: August 2024

This paper addresses the privacy concerns inherent in semantic communication within the Internet of Things (IoT) and proposes a Secure Semantic Communication Framework (SSCF) to ascertain confidentiality and communication accuracy without compromising semantic integrity. The proposed framework uses the Advanced Encryption Standard (AES) for encryption to address privacy breaches in semantic communication. Additionally, it introduces a novel approach employing Deep Q-Networks (DQN) for adversarial training to maintain semantic communication accuracy in both unencrypted and encrypted modes. SSCF combines universality and confidentiality, ensuring secure and efficient semantic communication. Experimental evaluations showed that SSCF, with its adversarial encryption learning scheme, effectively ensures communication accuracy and privacy. Regardless of encryption status, SSCF significantly hinders attackers from restoring original semantic data from intercepted messages. The integration of heuristic algorithms enhances performance and security. The proposed framework is based on a shared database for training network modules. The originality of the proposed approach lies in the introduction of a DQN-based adversarial training technique to balance confidentiality and semantic communication accuracy, address key privacy concerns, and enhance the security and reliability of IoT communication systems.
An Image Processing-based and Deep Learning Model to Classify Brain Cancer
Authors: Al-Shahrani Amal , Al-Amoudi Wusaylah , Bazaraah Raghad , Al-Sharief Atheer , Farouquee Hanan
Published: August 2024

In recent years, the prevalence of cancer has increased significantly around the world. Cancer is considered one of the most dangerous diseases in humans. Cancer screening devices, such as Magnetic Resonance Imaging (MRI), X-ray imaging, ultrasound imaging, and others, play an important role in its early detection. This study aims to facilitate cancer tumor detection on mobile phones with high accuracy in a short period of time using deep learning techniques. A brain tumor dataset was used, consisting of 4,489 images and 14 classified types, and experiments were carried out using ResNet 12, DenseNet, YOLOv8, and MobileNet to evaluate them in terms of accuracy, speed, and model size. ResNet12, DenseNet, YOLOv8, and MobileNet results indicated satisfactory accuracy ranging from 88% to 97.3%. YOLOv8 was the most suitable model, as its fastest inference time of 1.8 ms, preprocessing time of 0.1 ms, highest accuracy of 97.3%, and compact model size make it ideal for real-time mobile applications.
Transfer Learning Artificial Neural Network-based Ensemble Voting of Water Quality Classification for Different Types of Farming
Authors: Nuanmeesri Sumitra , Tharasawatpipat Chaisri , Poomhiran Lap
Published: August 2024

This study aims to develop a model for characterizing water quality in seawater-influenced areas for salt farming, fish farming, and crop farming. The water quality classification model was based on transfer learning trained by the Multi-Layer Perceptron Neural Network (MLPNN) and then classified by conventional Machine Learning (ML) methods, such as Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM). The results of each ML classification were ensemble voted together, comparing the efficiency between hard and soft voting. The collected imbalanced dataset had a difference ratio between the majority and minority classes of 1:0.0138. However, after 900% resampling by applying the k-mean SMOTE technique, the data ratio between the majority and minority classes was 1:0.9778. The results show that the proposed ensemble approach improved accuracy by up to 2.15% in classifying water quality for salt farming, fish farming, and crop farming in seawater-influenced areas.
Investigation of Strength and Long-Term Durability Properties of stabilized Coal Mine Overburden Material for Base and Subbase Layer of Pavement
Authors: Chandra Subhash , Sinha Sanjeev
Published: August 2024

The purpose of this study is to examine the possibility of using Coal Mine Overburden (CMOB) material as a secondary aggregate in low volume roadways' sub-base and/or base layer. Such roads usually experience less traffic, which means that weaker materials like CMOB could be used in various layers of the road after stabilization, either alone or in combination with cement or fly ash. After 7 and 28 days of curing, samples taken from Jharkhand mines were used to assess the strength characteristics of the stabilized samples, namely Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR). The findings demonstrate that the material, which has CBR of 80% or above and UCS of 3 MPa at 6% for Cement-Treated (CT-CMOB) and Cement-Fly Ash-Treated (CFA-CMOB) samples, may be utilized successfully as a secondary aggregate in low-volume road building. The results were then validated through standard acceptance as per IRC provisions and microstructural analysis. Additionally, correlations were established between the 7 and 28-day UCS properties of CT-CMOB and CFA-CMOB samples. This information can be beneficial for pavement engineers to estimate the strength properties associated with the base and subbase layer of pavement using CMOB as a suitable alternative to conventional aggregates.
Currency and Object Detection for Blind People Using Yolo Architecture
Authors: Amina N  , Harikrishnan S R
Published: August 2024

This project is designed to significantly improve the independence and quality of life for visually impaired individuals by developing an advanced system utilizing the YOLO (You Only Look Once) architecture for real-time currency and object detection. The YOLO architecture, known for its high speed and accuracy, employs convolutional neural networks to perform object detection in a single forward pass, making it an ideal choice for applications requiring real-time performance. The system integrates a lightweight, efficient detection model capable of processing live video feeds from a camera to identify and differentiate between various denominations of currency and a wide range of everyday objects. This detection process is crucial for enabling visually impaired individuals to handle financial transactions with confidence, ensuring that they can correctly identify and use different denominations of money. The project also includes extensive training and testing phases to ensure high accuracy and reliability in diverse environments and lighting conditions. By incorporating advanced machine learning techniques and a comprehensive dataset, we aim to achieve a robust detection system that performs well in real-world scenarios. The anticipated outcome is a significant enhancement in the autonomy and safety of visually impaired individuals, enabling them to navigate their daily lives with greater ease and confidence. This innovative solution promises to bridge the gap between technological advancements and accessibility, offering a practical tool that can profoundly impact the lives of those with visual impairments
Trividha Pariksha – Diagnostic Importance And Its Application In Clinical Practices
Authors: Jadhav Tejal A  , Kulkarni Archana A
Published: August 2024

Health is a state of equilibrium of the Dosha, Dhatu, and Mala along with the proper functioning of Indriya, Manas, and Atma. To treat the disease, it is necessary to keep a balance between Doshas and to bring back them to their normal condition. The prime duty of any Physician is to diagnose the ailment of the patient. The diagnosis cannot be done just based on one type of examination. In Ayurveda, different types of examinations have been mentioned which were and still are useful in diagnosing the various diseases in patients. These basic methods which are practiced today, with modern technologies play significant role in diagnosis of disease. The purpose is to study the application of Trividha pariksha in current clinical practices and to study darshan, sparshana, and prashna pariksha from Ayurvedic texts and their application in today's day-to-day practice. Various ayurvedic and modern texts, research papers and information available on the internet were reviewed. Trividha pariksha is the root of all diagnostic methods. Modern diagnostic methods like X-rays, CT scans, MRI, etc are just a medium in the form of modern technology. Comprehensive and applied knowledge of Trivdha pariksha is needed for perfect diagnosis and treatment i.e. good clinical practice. Trividha pariksha in Ayurveda has a key role in current clinical practice for diagnostic as well as prognostic purposes. The importance of early diagnosis is that the earlier we diagnose fast is the recovery, also the early diagnosis helps in the prevention of complications, and the accurate diagnosis helps in planning precise treatment for destroying the root cause and so the recurrence of disease
Dampak Lingkungan Pertemanan Toxic Terhadap Kesehatan Mental Remaja
Authors: Fadhilla Rahmad , Siregar Anggita Pratiwi
Published: August 2024

Pertemanan memiliki peran penting dalam perkembangan remaja karena seringkali mereka menghabiskan lebih banyak waktu dengan teman sebaya daripada keluarga. Interaksi yang positif dapat memberikan dukungan sosial dan pengalaman belajar sosial yang berharga. Namun, pertemanan yang toxic dapat menyebabkan dampak serius terhadap kesehatan mental remaja, seperti stres, kecemasan, dan depresi. Penelitian ini menggunakan pendekatan kualitatif deskriptif untuk memahami fenomena kompleks ini. Data dikumpulkan melalui observasi dan wawancara dengan tiga remaja berusia 13-16 tahun. Hasilnya menunjukkan bahwa lingkungan pertemanan yang toxic menimbulkan stres, kecemasan, depresi, penurunan harga diri, perilaku merugikan diri, dan isolasi sosial. Orang tua, sekolah, dan komunitas memiliki peran krusial dalam mendukung remaja menghadapi tantangan ini. Dukungan holistik dari lingkungan sekitar dapat membantu remaja membangun hubungan yang sehat dan meningkatkan kesejahteraan mereka.
Pengembangan Media Dakon untuk Perkalian dan Pembagian Matematika pada Siswa SD
Authors: Khatulistiwa Putri Shelvisyer Aero , Kartinah Kartinah , Wijayanti Arfilia
Published: August 2024

Materi perkalian dan pembagian merupakan materi yang penting bagi siswa Sekolah Dasar, namun beberapa siswa mengalami permasalahan khususnya dalam melakukan operasi perkalian dan pembagian.. Salah satu upaya untuk meminilakan permasalaha dengan mengembangkan alat permainan dakon untuk melatih penyelesaian materi perkalian dan pembagian. Penelitian ini merupakan penelitian Research and Development (R&D) yang menggunakan model pengembangan ADDIE dengan melibatkan siswa kelas IV SDN Lamper Tengah 01. Analisis data diperoleh dari hasil diskusi, observasi, dan saran – saran para ahli sebagai pertimbangan dalam melakukan penelitian pengembangan media dakon untuk siswa kelas IV dalam penyelesaian materi perkalian dan pembagian.. Hasil dari ahli materi adalah 88% yang dapat dinyatakan sangat valid. Hasil dari ahli media adalah 98% yang dapat dinyatakan sangat valid. Validasi juga dilakukan oleh angket guru wali kelas IV sebagai ahli pembelajaran, dari hasil validasi tersebut diperoleh 96% yang dapat dinyatakan sangat valid. Adapun uji coba lapangan yang dilakukan untuk mengetahui kemenarikan produk yang diperoleh dari nilai angket 22 siswa dengan presentase 90,5% yang dapat dinyatakan sangat menarik. Sehingga dapat disimpulkan bahwa media pembelajaran dakon valid dan layak untuk dikembangkan serta media pembelajaran menggunakan sangat praktis dan efektif dalam proses belajar mengajar matematika perkalian dan pembagian.
Problems and prospects of family hotel business development in Georgia
Authors: Azmaiparashvili Maia , Orjonikidze Nino , Goderdzishvili Ia
Published: August 2024

In many countries, the tourism and hospitality industry is dominated by hotel family businesses. Georgia, as a mountainous country, has great resources for the development of small and family hotels. The beautiful landscape of the mountainous country makes it diverse and motivates the local population to develop the hotel business. Despite the lack of development of tourist infrastructure and other hindering conditions, the interest in the mountains of Georgia is growing more and more. In order to receive tourists, the issue of building small hotels with relatively small costs or converting existing houses into guest houses becomes urgent. Each family hotel tells us its own story with originality, local ethno-cultural values and traditions. The presented work deals with the business development of family hotels, the life cycle, the assessment of the current situation, the investments made and the challenges faced by family hotels. Quantitative and qualitative research methods, empirical research, comparative and statistical analysis methods are used in the work.
Ефективність трансформаційного процесу: сучасний підхід та система оцінки в контексті сталого розвитку
Authors: Гетьман Ольга , Величко Яна
Published: August 2024

Стаття зосереджена на аналізі ефективності трансформації організаційної структури підприємств у контексті сталого розвитку. Створено модель трансформації організаційної структури підприємства, яка об'єднує різні елементи організаційних змін, адаптовані до динамічних умов зовнішнього середовища. Стаття надає важливі рекомендації для практиків у сфері управління змінами, підкреслюючи значення комплексного підходу в оцінці та управлінні трансформаційними процесами у рамках сталого розвитку.
Оптимізація внутрішнього контролю витрат сільськогосподарських підприємств: ключові підходи та практичні рекомендації
Authors: Гайдучок Тетяна , Івашко Тетяна
Published: August 2024

Основна мета цієї статті полягає в розгляді питань оптимізації внутрішнього контролю витрат на сільськогосподарських підприємствах. Обговорюються ключові підходи та пропоную практичні рекомендації для зменшення витрат та підвищення ефективності управління ресурсами. Доведено, що внутрішній контроль спрямований на забезпечення виконання внутрішніх вимог керівництва та гарантує, що персонал виконує свої обов’язки і забезпечує ефективну діяльність кожного структурного підрозділу підприємства. Стаття акцентує увагу на важливості аналізу витрат, раціонального управління запасами, використанні сучасних технологій та підвищенні енергоефективності. Також, дані рекомендації можуть бути корисними для розробки стратегій раціонального використання ресурсів та програм енергозбереження на сільськогосподарських підприємствах. Автори підкреслюють актуальність теми в контексті постійних змін на ринку та зростання витрат, що потребує постійного удосконалення та розробки нових стратегій управління.
Співвідношення цифрового та віртуального активу
Authors: Гулейков Ігор
Published: August 2024

У роботі розглянути питання співвідношення цифрових та віртуальних активів. Визначено, що українське законодавство не розмежовує ці терміни, проте це має негативний вплив для стабілізації правоувторення у цій сфері. Запропоновано розглядати віртуальні активи видом цифрових активів, які є більш широкою правовою категорією. Визначено, що цифрові активи – це нематеріальний актив, що існує в електронному форматі, зберігається та фіксується у цифровій формі та може бути використаний для майнових та немайнових цілей за допомогою технології блокчейну або іншій технології розподіленого реєстру (електронні документи, цифрові медіафайли (фотографії, відео, аудіофайли), програмне забезпечення, веб-сайти та доменні імен, електронні книги, цифрові валюти (криптовалюти) ін.).
Альтернативные методы интубации пациентов
Authors: Батырханова Сауле , Шедірбаева Әйгерім , Қадырбай Төребек , Жайсанбаева Лаура
Published: August 2024

Несмотря на развитие анестезиологии и реаниматологии, трудные дыхательные пути продолжают оставаться серьезной проблемой. Потеря доступа к дыхательным путям у критического пациента создает для последнего потенциально жизнеопасную ситуацию. Развитие технологий обеспечения проходимости верхних дыхательных путей и систем оценки снижает вероятность летального исхода у пациента, но всегда остаются ситуации, в которых сложности с дыхательными путями возникают внезапно и сложно поддаются контролю. В статье проведен обзор альтернативных методов интубации трахеи у пациентов.
Bridging the digital divide in STEM education: Strategies and best practices
Authors: Olatunbosun Bartholomew Joseph  , Nwankwo Charles Uzondu
Published: August 2024

This study meticulously examines interdisciplinary STEM education, analyzing its conceptual framework, historical development, current curriculum models, design strategies, technology integration, institutional challenges and student impact. The primary purpose is to elucidate the multifaceted benefits and challenges of interdisciplinary STEM education and provide recommendations for its effective implementation. Utilizing a comprehensive literature review, the study synthesizes findings from diverse sources to present a cohesive understanding of interdisciplinary STEM education. Historical perspectives reveal a shift from isolated disciplinary teaching to integrated, holistic approaches. The study explores current curriculum models, such as project-based, problem-based and inquiry-based learning, for their efficacy in fostering critical thinking, creativity and problem-solving skills. Effective design strategies, including hands-on activities, cross-disciplinary collaboration, and technology integration, are identified as pivotal for enhancing learning outcomes. Significant institutional challenges are identified, including resistance to change, resource limitations and the need for ongoing professional development for educators. Overcoming these challenges requires strategic planning, investment in infrastructure and continuous support for teachers. The research also highlights the importance of aligning assessment practices with the goals of interdisciplinary STEM education, suggesting performance-based assessments to better evaluate students' critical thinking, problem-solving, and collaboration skills. The impact on students is profound, with evidence indicating increased interest in STEM fields, improved academic performance and the development of essential skills like teamwork and communication. These outcomes are crucial for preparing students for the complexities of the modern workforce and fostering a lifelong interest in STEM disciplines. The study concludes with recommendations for flexible curricula, robust industry partnerships and the integration of emerging technologies. This research underscores the transformative potential of interdisciplinary STEM education and provides actionable insights for educators and policymakers to foster a dynamic and inclusive learning environment.
Keywords: Interdisciplinary STEM Education, Curriculum Models, Technology Integration, Student Impact, Professional Development.