bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-objective optimized task scheduling in cognitive internet of vehicles: towards energy-efficiency
Authors: Divyashree M. , Rangaraju H. G. , Revanna C. R.
Published: November 2024

The rise of intelligent and connected vehicles has led to new vehicular applications, but vehicle computing capabilities remain limited. Mobile edge computing (MEC) can mitigate this by offloading computation tasks to the network's edge. However, limited computational capacities in vehicles lead to increased latency and energy consumption. To address this, roadside units (RSUs) with cloud servers, known as edge computing devices (ECDs), can be expanded to provide energy-efficient scheduling for task computation. A new energy-efficient scheduling method called multi-objective optimization energy computation (MOEC) is proposed, based on multi-objective particle swarm optimization (MOPSO) to reduce ECDs' energy usage and execution time. Simulation results using MATLAB show that MOEC can balance the trade-off between energy usage and execution time, leading to more efficient offloading.
Perspective on the applications of terahertz imaging in skin cancer diagnosis
Authors: Owida Hamza Abu , Al-Nabulsi Jamal I. , Al-Ayyad Muhammad , Turab Nidal , Alshdaifat Nawaf
Published: November 2024

Applications of terahertz (THz) imaging technologies have advanced significantly in the disciplines of biology, medical diagnostics, and non- destructive testing in the past several decades. Significant progress has been made in THz biomedical imaging, allowing for the label-free diagnosis of malignant tumors. Terahertz frequencies, which lie between those of the microwave and infrared, are highly sensitive to water concentration and are significantly muted by water. Terahertz radiation does not cause ionization of biological tissues because of its low photon energy. Recently, terahertz spectra, including spectroscopic investigations of cancer, have been reported at an increasing rate due to the growing interest in their biological applications sparked by these unique features. To improve cancer diagnosis with terahertz imaging, an appropriate differentiation technique is required to increased blood supply and localized rise in tissue water content that commonly accompany the presence of malignancy. Terahertz imaging has been found to benefit from structural alterations in afflicted tissues. This study provides an overview of terahertz technology and briefly discusses the use of terahertz imaging techniques in the detection of skin cancer. Research into the promise and perils of terahertz imaging will also be discussed.
Field programmable gate array implementation frameworks of a variable-length pseudorandom pattern generator
Authors: Somanathan Geethu Remadevi , Bhakthavatchalu Ramesh
Published: November 2024

A pseudorandom pattern generator produces sequences similar to true random sequences. A variable length pseudo-random pattern generator (PRPG), which can be used as a pattern generator for various applications like built-in self-test (BIST) or cryptography, is proposed in this paper. Our work is based on a linear feedback shift register circuit platform. The proposed design can generate patterns corresponding to different characteristic polynomials of any given polynomial degree. These characteristic polynomials are integrated into the linear feedback shift register circuit by providing an option to select the feedback paths of any of these polynomials. This paper implements and evaluates the proposed design for primitive and non-primitive characteristic polynomials of degrees 3 to 15. The circuit generates output patterns of different periods based on user inputs. Compared to other pseudorandom pattern generator circuits, the proposed circuit can generate a large set of patterns and consumes less power. Adequate results from the experiments demonstrate the functionalities and performance of the proposed pattern generator from degrees 3 to 15. The proposed circuit generates pseudorandom patterns that can be used not only for built-in self-test but also for cryptography and wireless communication applications.
Narrative review of the literature: application of mechanical self powered sensors for continuous surveillance of heart functions
Authors: Owida Hamza Abu , Al-Nabulsi Jamal I. , Turab Nidal , Al-Ayyad Muhammad , Al Hawamdeh Nour , Alshdaifat Nawaf
Published: November 2024

Cardiovascular disease consistently occupies a prominent position among the leading global causes of mortality. Continuous and real-time monitoring of cardiovascular signs over an extended duration is necessary to identify irregularities and prompt timely intervention. Due to this reason, researchers have invested heavily in developing adaptive sensors that may be worn or implanted and continuously monitor numerous vital physiological characteristics. Mechanical sensors represent a category of devices capable of precisely capturing the temporal variations in pressure within the heart and arteries. Mechanical sensors possess inherent advantages such as exceptional precision and a wide range of adaptability. This article examines four distinct mechanical sensor technologies that rely on capacitive, piezoresistive, piezoelectric, and triboelectric principles. These technologies show great potential as novel approaches for monitoring the cardiovascular system. The subsequent section provides a comprehensive analysis of the biomechanical components of the cardiovascular system, accompanied by an in-depth examination of the methods employed to monitor these intricate systems. These systems measure blood and endocardial pressure, pulse wave, and heart rhythm. Finally, we discuss the potential benefits of continuing health monitoring in vascular disease treatment and the challenges of integrating it into clinical settings.
Digital adaptive control with pulse width modulation of signals
Authors: Siddikov Isamiddin , Alimova Gulchekhra , Rustamova Malika , Usanov Mustafaqul
Published: November 2024

The paper presented research results of a digital control system for a dynamic plant with pulse-width modulation (PWM) of control impacts. As the control PWM signal is taken the pulse duty cycle, is calculated on each current cycle of the sample from the measured values. A control algorithm is proposed based on a hybrid application of the linear-quadratic optimization procedure and the theory of observers of minimal complexity. To ensure execution that the conditions of Astatism are met, the dynamic model of the plant is supplemented with a discrete integrator. The proposed approach makes it possible to reduce hardware costs and increase the robustness of the control system due to the exclusion of operations for digital–analogue transformations of signals. The proposed algorithm for digital control of a dynamic plant with varying duty cycle values of the PWM signal shows that the PWM model turned out to be linear and practically inertia less, which makes it easy to take into account the modulator model, which significantly simplifies the solution of the problem of synthesizing a control system for a dynamic plant. The possibility of receiving a high-quality modulated control signal allows for significant suppression of signal pulsations and high control accuracy.
Adaptive control techniques for improving anti-lock braking system performance in diverse friction scenarios
Authors: Abdullah Mohammed Fadhl , Qasem Gehad Ali Abdulrahman , Ramadhan Mazen Farid , Lim Heng Siong , Lee Chin Poo , Alsakkaf Nasr Alsakkaf
Published: November 2024

Anti-lock braking systems (ABS) enhance vehicle safety by preventing wheel lock-up, but their effectiveness depends on tire-road friction. Traditional braking systems struggle to maintain effective performance due to the risk of wheel lock-up on varying road surfaces, affecting vehicle stability and control. This study presents a novel method to improve ABS efficiency across varying friction conditions. The proposed approach employs a feedback control mechanism to dynamically adjust the braking force of each wheel based on the prevailing friction coefficient. Specifically, we incorporate a P-controller in the input signal and two additional P-controllers as output and input parameters for friction. By manipulating the proportional control values, key parameters such as wheel speed, stopping distance, and slip rate can be effectively managed. Notably, our investigation reveals intriguing interactions between the proportional controls, highlighting the complexity of ABS optimization. The method was evaluated through simulations across various friction conditions, comparing it to conventional ABS in terms of brake performance, stability, and stopping distances. The results indicate that the proposed method significantly enhances ABS performance across varying friction coefficients; however, additional research is warranted to address stopping distance and time issues, particularly in snowy and icy conditions.
Evaluating geometrically-approximated principal component analysis vs. classical eigenfaces: a quantitative study using image quality metrics
Authors: Ennaama Faouzia , Ennaama Sara , Chakri Sana
Published: November 2024

Principal component analysis (PCA) is essential for diminishing the number of dimensions across various fields, preserving data integrity while simplifying complexity. Eigenfaces, a notable application of PCA, illustrates the method's effectiveness in facial recognition. This paper introduces a novel PCA approximation technique based on maximizing distance and compares it with the traditional eigenfaces approach. We employ several image quality metrics including Euclidean distance, mean absolute error (MAE), peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), and structural similarity index measure (SSIM) for a quantitative assessment. Experiments conducted on the Brazilian FEI database reveal significant differences between the approximated and classical eigenfaces. Despite these differences, our approximation method demonstrates superior performance in retrieval and search tasks, offering faster and parallelizable implementation. The results underscore the practical advantages of our approach, particularly in scenarios requiring rapid processing and expansion capabilities.
Measuring anxiety level on phobia using electrodermal activity, electrocardiogram and respiratory signals
Authors: Ain Khusnul , Rahma Osmalina Nur , Purwanti Endah , Varyan Richa , Ittaqilah Sayyidul Istighfar , Arfensia Danny Sanjaya , Sosialita Tiara Dyah , Qulub Fitriyatul , Chai Rifai
Published: November 2024

People with spider phobia experience excessive anxiety reactions when exposed to spiders that will interfere with daily life. Diagnosing and measuring anxiety levels in patients with spider phobia is a complex challenge. Conventional diagnosis requires psychological evaluations and clinical interviews that take time and often result in a high degree of subjectivity. Therefore, there is a need for a more objective and efficient approach to measuring anxiety levels in patients. This study performs anxiety level classification based on electrodermal activity, electrocardiogram (ECG) and respiratory signals using the dataset of Arachnophobia subjects. Each raw data is preprocessed using 24 types of features. Feature performance is processed using the recursive feature elimination method. Data processing was performed in 3 anxiety levels (high, medium, low) and two anxiety levels (high, low) with the support vector machine method and hold-out validation method (7:3). The performance of the model is evaluated by showing the accuracy, precision, recall and F1 score values. The polynomial kernel can perform optimal classification and obtain 100% accuracy in 2 classes and three classes with 100% precision, recall, and F1 score values. This result shows excellent potential in measuring anxiety levels that correlate with mental health issues.
Performance of 5G and Wi-Fi 6 coexistence: spectrum sharing based on optimized duty cycle
Authors: Zaid Asmaa Helmy , Zaki Fayez Wanis , Nafea Hala Bahy-Eldeen
Published: November 2024

Smart mobile device usage is increasing rapidly; hence, cellular operators face the challenge of spectrum resource shortage. To address this issue, researchers have explored several approaches to achieving a highly efficient utilization of wireless communication network resources. One promising solution lies in the fair coexistence of 5G/Wi-Fi 6 in the unlicensed 5 GHz band. This research investigates a duty cycle mechanism to perform fair spectrum sharing between these two wireless technologies, intending to optimize performance metrics such as throughput, capacity, bit error rate (BER), and latency. The results of this study demonstrate a significant improvement in system performance when employing the proposed coexistence method compared to using 5G alone in a single cell. Specifically, a 40% increase in throughput and a 14% improvement in capacity are reported. Moreover, for a single cell using Wi-Fi 6 only, the BER was reduced by 19%, and the latency was less than one millisecond. Additionally, the duty cycle mechanism reported here is used to prioritize call services, with the blocking probability for voice-over internet protocol (VoIP) and video stream calls being improved. Furthermore, the adaptive bandwidth reservation reduced the blocking probability of video calls from 21.8% to 0.9% compared to the fixed method; no VoIP calls were blocked.
Enhancing monitoring systems with interference-handling radio frequency direction finding: BMKG C-Band weather radar in West Kalimantan Indonesia case study
Authors: Kusumawardhani Eka , Putra Leonardus Sandy Ade , Mawaldi Ikbal , Kurniawan Ade , Kurnawan Wawan
Published: November 2024

This study investigates the effectiveness of the interference-handling radio frequency direction finding (RFDF) monitoring system in detecting and localizing interference sources near the BMKG C-Band weather radar in West Kalimantan, Indonesia. We conducted field tests varying interference transmitter power levels (0-30 dBm) and distances (100, 500, and 1,000 m) from the radar. Results indicate the RFDF system's robust performance, consistently detecting interference within 5-20 minutes and accurately localizing sources with minimal deviation from actual positions. The findings confirm the system's superiority over traditional manual methods, offering a reliable solution for interference management in weather radar operations. However, limitations include controlled test conditions and a need for further exploration of the system's efficacy in diverse environmental settings. This research contributes to improving radar reliability and lays the groundwork for future studies to refine RFDF technology for broader meteorological applications.
Q-learning based forecasting early landslide detection in internet of thing wireless sensor network
Authors: Jeyakumar Devasahayam Joseph , Shanmathi Boominathan , Smitha Parappurathu Bahulayan , Chowdary Shalini , Panneerselvam Thamizharasan , Srinath Rajagopalan , Mariselvam Muthuraj , Murali Mohanan
Published: November 2024

The issue of climate modification and human actions terminates in a chain of hazardous developments, comprehensive of landslides. The traditional approaches of observing the environmental attributes that is actually obtaining rainfall data from places can be cruel and suppressing supervising necessitated for careful infliction. Thus, landslide forecasting and early notice is a significant application via wireless sensor networks (WSN) to reduce loss of life and property. Because of the heavy preparation of sensors in landslide prostrate regions, clustering is a resourceful method to minimize unnecessary transmission. In this article we introduce Q-learning based forecasting early landslide detection (Q-LFD) in internet of things (IoT) WSN. The Q-LFD mechanism utilizes a dingo optimization algorithm (DOA) to choose the best cluster head (CH). Furthermore, the Q-learning algorithm forecast the landslide by soil water capacity, soil layer, soil temperature, Seismic vibrations, and rainfall. Experimental results illustrate the Q-LFD mechanism raises the landslide detection accuracy. In addition, it minimizes the false positive, false negative ratio.
Enhancing routing efficiency in highway environments of vehicular ad hoc networks through fuzzy logic-based protocols
Authors: Al Shugran Mahmoud A. , Abu-Al-Aish Ahmad , Jaradat Ghaith M. , Alghamdi Fahad Ali , Alqurni Jehad Saad , Alsmadi Mutasem Khalil , AL Hawamdeh Majd , Alfagham Hayat , Badawi Usama A. , Gharaibeh Mutaz Falah
Published: November 2024

The predictive directional greedy routing (PDGR) protocol is widely utilized in highway settings within vehicular ad hoc networks (VANETs). However, PDGR encounters a notable challenge when packets lack a suitable vehicle directionally, leading to network disconnections. This triggers a shift to carry and forward recovery mode due to outdated neighbor information in the vehicle's neighbor table (VNT). To address this, our study proposes an improved fuzzy logic-based improved PDGR (IPDGR). This novel algorithm dynamically adjusts beaconing intervals based on real-time network dynamics. Through comprehensive evaluation using VANET simulators, IPDGR demonstrates superior performance compared to PDGR and directional greedy routing (DGR) protocols across various metrics including Inconsistency of vehicle's neighbor's table (IVNT), packet delivery ratio (PDR), routing path length (RPL), and number of hole problem occurrence (NHPO).
Utilization meta-analysis to identify the convenience of eBooks (visual and audio) for learning
Authors: Mailool Jefri , Arlinwibowo Janu , Linguistika Yulia
Published: November 2024

This research aims to conclude the influence of eBooks in the learning process throughout the world. The meta-analysis design taken was a group contrast between control and experimental groups with a random effect size model. The criteria used are time “data published 2018–2023,” published in English, type of publication is a quantitative research article, the research design is a difference between control and experimental groups, containing complete data “mean, sample size, and standard deviation,” and recorded in the Scopus database. Data collection was guided by the PRISMA method. The results of the analysis showed that the data were heterogeneous and free from publication bias. The results of the analysis showed that there was a large “positive” effect as indicated by a p-value <0.001<5% “95% confidence interval” and a total effect size=0.86 [0.61; 1.11]. It can be concluded based on the latest findings that eBooks have an equally good effect on all conditions which are influenced by the type of competency developed, the eBook information base, the type of eBook, and class size.
Towards optimal fillet portioning: a computer vision system for determining the fish fillet volume
Authors: Nguyen Chanh-Nghiem , Vo Ngọc-Tan , Nguyen Ngoc-Thanh , Tran Nhut-Thanh , Nguyen Chi-Ngon
Published: November 2024

Portioning large fish fillets for packaging is usually performed manually by skilled workers. Automating this process and obtaining packaged products with attractive shapes and affordable weights will be beneficial for promoting purchase decisions. Towards developing an automated fish fillet portioning system, this study investigated a computer vision approach for determining the fillet volume. A belt conveyor would transport a fish fillet to the image capture booth, where its cross-section areas would be calculated for volume determination. The developed system could be operated with a conveyor speed ranging from 7.5 to 30.6 mm/s. The system performance was evaluated at a conveyor speed of 7.5 mm/s using small catfish fillets from 142.2 to 225.4 cm3. A mean percent error of 9.2% was observed, and the smallest percent error of 3.8% was obtained with a 225.4 cm3 fillet. With minor measurement errors obtained for larger fillets, the proposed computer vision system showed great potential for developing a cost-effective automated system for customized fish fillet partitioning to accelerate purchase decisions and also for quality control and classification of the fish fillets.
Influence of metal particles shape on direct current voltage electric properties of nanofluids
Authors: Fahmi Daniar , Akbar Muhammad Fadlan , Yulistya Negara I Made , Hernanda I Gusti Ngurah Satriyadi , Asfani Dimas Anton , Zaidan Risyad Alauddin , Fadhilah Arkan
Published: November 2024

It is widely recognized that the application of nanoparticles has the potential to improve the dielectric properties of transformer oil. Nevertheless, there is a scarcity of studies that have utilized pure nanofluids, and in practical applications, it is inevitable for transformer oil to become contaminated. Therefore, this study conducted tests to investigate how the shape and size of metal contaminants impact the dielectric performance of Fe3O4 nanofluids. The findings from the levitation voltage test indicate that as the size and diameter of the particle increase, the levitation voltage value measured also increases, and conversely. Moreover, a higher concentration of nanoparticles leads to a higher measured levitation voltage value. On the other hand, the breakdown voltage test results demonstrate that larger and sharper particles result in lower measured breakdown voltage values, and vice versa. The simulation outcomes regarding electric field distribution reveal that larger and sharper particles correspond to higher measured electric field values, while the opposite is true for smaller and less sharp particles.
An integrated smart water management system for efficient water conservation
Authors: Rajanbabu Jeya , Venkatakrishnan Giri Rajanbabu , Rengaraj Ramasubbu , Rajalakshmi Mohandoss , Jayaprakash Neythra
Published: November 2024

Water is a fundamental resource that sustains life, supports ecosystems, and plays a crucial role in various natural processes on earth. Water wastage is a major problem in the world, with a variety of causes including leaks in infrastructure and inefficient usage methods. A typical cause of water wastage is overflow from reservoirs or tanks as a result of poor maintenance or monitoring. This paper proposes a novel water resource management using internet of things (WARM-IoT) system to monitor and regulate the water level remotely by integrating IoT technology with demand side management (DSM) strategies, real-time monitoring of water levels has been achieved. The approach utilizes an ultrasonic sensor and Raspberry Pi for data acquisition and processing, fuzzy logic for decision-making, and an Android app for remote monitoring and control. The WARM-IoT assesses the system's performance, showcasing its efficacy in managing water levels and lowering electricity expenses. By analyzing consumption costs under different activation timings, significant potential for cost savings is observed, with a notable reduction of up to 6% in electricity expenses. Overall, the proposed WARM-IoT offers a comprehensive solution to water wastage and inefficient electricity usage in water management systems.
Predicting academic performance: toward a model based on machine learning and learner’s intelligences
Authors: Rafiq Jamal Eddine , Abdelali Zakrani , Amraouy Mohammed , Nouh Said , Bennane Abdellah
Published: November 2024

With the rapid evolution of online learning environments, the ability to predict students' academic performance has become crucial for personalizing and enhancing the educational experience. In this article, we present a predictive model based on machine learning techniques, designed to be integrated into online learning platforms using the competency-based approach. This model leverages features from four key dimensions: demographic, social, emotional, and cognitive, to accurately predict learners' academic performance. We detail the methodology for collecting and processing learning traces, distinguishing between explicit traces, such as demographic data, and implicit traces, which capture learners' interactions and behaviors during their learning process. The analysis of these data not only improves the accuracy of performance predictions but also provides valuable insights into skill acquisition and learners' personal development. The results of this study demonstrate the potential of this model to transform online education by making it more adaptive and focused on individual learners' needs.
Hybrid long short-term memory and decision tree model for optimizing patient volume predictions in emergency departments
Authors: Abatal Ahmed , Mzili Mourad , Benlalia Zakaria , Khallouki Hajar , Mzili Toufik , Billah Mohammed El Kaim , Abualigah Laith
Published: November 2024

In this study, we address critical operational inefficiencies in emergency departments (EDs) by developing a hybrid predictive model that integrates long short-term memory (LSTM) networks with decision trees (DT). This model significantly enhances the prediction of patient volumes, a key factor in reducing wait times, optimizing resource allocation, and improving overall service quality in hospitals. By accurately forecasting the number of incoming patients, our model facilitates the efficient distribution of both human and material resources, tailored specifically to anticipated demand. Furthermore, this predictive accuracy ensures that EDs can maintain high service standards even during peak times, ultimately leading to better patient outcomes and more effective use of healthcare facilities. This paper demonstrates how advanced data analytics can be leveraged to solve some of the most pressing challenges faced by emergency medical services today.
Enhancing PETRONAS share price forecasts: a hybrid Holt integrated moving average
Authors: Fozi Nurin Qistina Mohamad , Hasan Nurhasniza Idham Abu , Aziz Azlan Abdul , Zahari Siti Meriam , Ganggayah Mogana Darshini
Published: November 2024

Understanding the variations in PETRONAS share price over time is important for improving the forecast accuracy of PETRONAS share prices to provide stakeholders with reliable analyses for future market predictions. Therefore, the main objective of this study is to improve the accuracy of PETRONAS share price by utilizing a hybrid Holt method with the moving average (MA) from the Box-Jenkins model. Holt's method will address linear trends for non-stationary data, while MA will analyze residual aspects of the data. This combination transforms non-stationary data into stationary by removing noise and averaging out fluctuations. The secondary data used in this study consists of daily observation from bursa Malaysia, the official national stock exchange of Malaysia, covering the period from January 3, 2000, to October 2, 2023. The study encompasses both low and high share price scenarios. The models’ performance was compared using various error metrics across different training and testing splits. The findings highlight that the proposed hybrid [Holt–MA] model called Holt integrated moving average (HIMA) improves the accuracy of forecasting model with the smallest errors for both daily low and high share price. The HIMA model demonstrates significant potential, particularly in reducing residuals and improving prediction accuracy.
An improved reptile search algorithm-based machine learning for sentiment analysis
Authors: Sureja Nitesh , Chaudhari Nandini M. , Bhatt Jalpa , Desai Tushar , Parikh Vruti , Panesar Sonia , Sureja Heli , Kharva Jahnavi
Published: November 2024

The rapid growth of mobile technologies has transformed social media, making it crucial for expressing emotions and thoughts. When making significant decisions, businesses and governments can benefit from understanding public opinion. This information makes sentiment analysis vital for understanding public sentiment polarity. This study develops a hyper tuned deep learning model with swarm intelligence and many approaches for sentiment analysis. convolutional neural network (CNN), bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), CNN-LSTM, BERT-LSTM, and BERT-CNN are the six deep learning models of the sentiment analysis using deep learning with reinforced learning based on reptile search algorithm (SA-DLRLRSA) model. The reptile search algorithm, an enhanced swarm intelligence algorithm (SIA), optimizes deep learning model hyper parameters. Word2Vec word embedding is used to convert textual input sequences to representative embedding spaces. Pre-trained Word2Vec embedding is also used to address issue of unbalanced datasets. Experimental results demonstrate that the SA-DLRLRSA model works best with accuracies of 93.1%, 94.7%, 96.8%, 96.3%, 97.2%, and 98.3% utilizing CNN, LSTM, BERT, CNN-LSTM, BERT-CNN, and BERT-LSTM.
A new enterprise architecture-based approach for smart city value co-creation
Authors: Houari Meryeme El , Haloui M'barek El , Ettaki Badia
Published: November 2024

In an era marked by vertiginous technological advancements and urban complexities, digital transformation has emerged to enable cities to offer smart services. This transformation optimizes interaction and collaboration between smart city actors, instead of working in isolated silos. There is a real need for a federative approach that can be aligned with urban vision and goals to support smart city implementation. In this context, enterprise architecture (EA) is emerging as a pivotal force reshaping smart city and supporting its development and transformation. However, the successful implementation of smart city depends also on the collaborative effort to co-create value among city's stakeholders. The present study develops a new approach based on enterprise architecture within the smart city ecosystem. Through methodical delineation our approach seeks to enhance value co-creation, improve smarter service design, and support community engagement.
Analytic hierarchy process geographic information system based model for sustainable construction and demolition waste landfill site selection
Authors: Soussi Mohamed Ayet Allah Bilel , Madsia Nermine El , Zaki Chamseddine , Ramadan Alaaeddine , Saker Louai , Ibrahim Moustafa
Published: November 2024

Properly managing waste generated by buildings and public works is a significant challenge in Tunisia, particularly in the city of Bizerte. The inadequate disposal of such waste can cause substantial harm to human life, property, and the environment. This paper proposes an multi-criteria decision making (MCDM) that utilizes the analytic hierarchy process (AHP) decision support tool to identify suitable landfill sites for construction and demolition waste (CDW) in Bizerte. The AHP method is widely used in MCDM applications. The approach involves classifying different scenarios based on various exclusion and appreciation criteria to determine the optimal locations for future landfills. Furthermore, the paper develops a conceptual approach for identifying better sites for the disposal of CDW, resulting in a comprehensive database capable of storing, accessing, and extracting information at both conceptual and operational levels. The proposed model considers spatial, technical, and environmental criteria in the selection of a suitable landfill site. The proposed methodology offers an effective and practical solution for properly managing CDW waste in Bizerte, Tunisia, and can be applied to other regions facing similar challenges.
Development of an internet of things based smart cold storage with inventory monitoring system
Authors: Angappan Suganya , Nataraj Aarthi , Krishnan Loganathan Navaneetha , Palanisamy Anbarasu
Published: November 2024

Consuming fresh drupes, vegetables can help lower a chance of developing several chronic diseases. Unfortunately, the post-harvest life cycle's storage stage is where fruits and vegetables (FVs) lose the most of all the food that is produced annually. A failure to recognize important ambient environmental conditions when using cold storage seems to be the main causes of this elevated loss rate. The current monitoring systems for cold storage are only able to measure warmth and moistness and ignoring further crucial acceptable surrounding factors of radiance and gas quantity. Serious matter gets handled in order to lower the system’s harm degree. The real time intelligent monitoring and notification system (RT-IMNS) for icy container is described briefly in the paper. It employs a device management platform (IoT)-enabled technique to continuously monitor hotness, comparative moisture, brightness, fume quantity and alert staff when dangerous thresholds are reached.
Harnessing deep learning for medicinal plant research: a comprehensive study
Authors: Ananda Vidya Hullekere , Rao Narasimha Murthy Madiwala Sathyanarayana , Krishnamurthy Thara Dharmapura
Published: November 2024

In today’s world, people are more prone to diseases due to food adulteration and pollution in the environment, and people have found a way of using herbal medicine as an alternative to allopathic medicine, especially since coronavirus disease 2019 (COVID-19). Medicinal plants are the source of herbal medicines that increase the immunity of humans. Medicinal plants are used in many applications, like pharmaceuticals, cosmetics, and drugs. Medicinal plants are of great importance, and hence this work presents a review of the medicinal plants grown in Karnataka State, India. The work also highlights species identification and disease detection of medicinal plants employing machine learning and deep learning approaches. The paper provides information about datasets available for various medicinal plant leaf images. The deep learning models used for species identification and disease detection in medicinal plants have been discussed along with the results.
Challenges and opportunities to location independent human activity recognition utilizing Wi-Fi sensing
Authors: Abuhoureyah Fahd , Wong Yan Chiew , Al-Taweel Malik Hasan , Abdullah Nihad Ibrahim
Published: November 2024

Wireless sensing has emerged as a dynamic field with diverse applications across smart cities, healthcare, the internet of things (IoT), and virtual reality gaming. This burgeoning area capitalizes on the capacity to detect locations, activities, gestures, and vital signs by assessing their impact on ambient wireless signals. This review critically examines the prevailing challenges within wireless sensing and predicts future research trajectories. Even with the potential for nuanced signal processing facilitated by Wi-Fi propagation, its efficacy is impeded by noise interference in confined areas during transmission and reception. Consequently, this work aims to augment signal processing performance accuracy by delving into the most promising techniques and underexplored methods utilizing channel state information (CSI). Furthermore, the work offers a view into the potential of human activity recognition predicated on CSI properties. The study focusses on exploring location-independent sensing technique based on CSI, discussing relevant considerations and contemporary approaches used in Wi-Fi sensing tasks. The optimal practices in analysis are based on model design, data collection, and result interpretation. The discussions analysis investigates in detail the representative applications and outlines the major considerations of developing human activity recognition human activity recognition (HAR) based on Wi-Fi by analyzing the current critical issues of CSI-based behavior recognition methods and pointing out possible future research directions.
Product reviews analysis to extract sentimental insights with class confidence rate using self-organizing map neural network
Authors: Ahsain Sara , Elyusufi Yasyn , Ait Kbir M'hamed
Published: November 2024

Customer data analysis helps companies to understand customer intentions and behaviors better. This study introduces an analysis of product reviews to help managers adopt a more efficient strategy to extract valuable knowledge and help detect segment of customers that need a special attention and products that need improvement or with the most impact. The used dataset is a set of Amazon reviews divided into multiple categories; each review has a target column called ‘overall’ that takes a value between 1 and 5 (customer's satisfaction). Based on the ‘overall’ column, multiple labeling methods have been used and compared to get a binary target variable, positive or negative, that affects a class to a review. This dataset contains more than one million reviews and can give companies great insight into products’ quality and customers’ retention. This work has materialized by using customer segmentation and competitive learning with self-organizing map (SOM) Model and adopting a new approach to explore the generated network/map, it is based on clustering and map nodes labelling using a majority voting process. The results show that the proposed dual approach combining the prior knowledge, related to supervised learning, and the competitive learning abilities enhances the SOM model’s capabilities.
Comparison of Transcatheter Versus Surgical Tricuspid Repair Among Patients With Tricuspid Regurgitation: Two-Year Results
Authors: Shimoda Tomonari M. , Ueyama Hiroki A. , Miyamoto Yoshihisa , Watanabe Atsuyuki , Gotanda Hiroshi , Kolte Dhaval , Latib Azeem , Kaneko Tsuyoshi , Zajarias Alan , Elmariah Sammy , Takayama Hiroo , Tsugawa Yusuke , Kuno Toshiki
Published: November 2024

BACKGROUND:

Evidence is limited as to whether outcomes differ between patients with tricuspid regurgitation (TR) treated with tricuspid transcatheter edge-to-edge repair (T-TEER) versus surgical tricuspid valve repair. We aimed to compare outcomes between these 2 approaches.

METHODS:

We analyzed the data on Medicare fee-for-service beneficiaries aged 65 to 99 years with TR who underwent T-TEER or isolated surgical repair between July 2016 and December 2020. The primary outcome was 2-year all-cause mortality. Other outcomes included in-hospital mortality and permanent pacemaker implantation as well as 2-year heart failure hospitalization and tricuspid valve reintervention. A propensity score matching weight analysis was used to adjust for potential confounders.

RESULTS:

A total of 1143 patients were included (409 T-TEER versus 734 surgery). The proportion of T-TEER cases increased from 2% in the third quarter of 2016 to 67% in the last quarter of 2020 among all isolated TR procedures. After adjustment for potential confounders, we found no evidence that 2-year all-cause mortality differs between patients treated with T-TEER versus surgical repair (adjusted hazard ratio, 0.84 [95% CI, 0.63–1.13]). Patients treated with T-TEER experienced lower in-hospital mortality (2.5% versus 12.5%,
P
<0.001) and permanent pacemaker implantation rates (0.0% versus 12.7%,
P
<0.001) than those treated by surgical repair. At 2 years, we found no differences in heart failure hospitalizations, but tricuspid valve reinterventions were more frequent in the T-TEER group (subdistribution hazard ratio, 8.03 [95% CI, 2.87–22.48]).

CONCLUSIONS:

Among Medicare beneficiaries with TR, the 2-year mortality rate was comparable between T-TEER and surgical repair. T-TEER showed advantages in perioperative outcomes, including lower in-hospital mortality and pacemaker implantation rates, whereas tricuspid valve reinterventions were more frequent in the T-TEER group. Further studies are necessary to refine indications, patient selections, and optimal timing for intervention with either treatment strategy.
Operational Phased Array Radar Network for Natural Hazard Monitoring and Warnings in Urban Environments over the Greater Bay Area, China
Authors: Zhao Kun , Huang Hao , Lu Yinghui , Chen Haonan , Wu Chong , Zhao Guo , Zhu Yiqing , Tan Zhe-Min , Zhang Yu , Fu Peiling , Huang Xuanxuan , Pan Xiang , Liu Qiqing , Yang Zhonglin , Zhou Ang , Fan Xueqi , Hu Dongming , Chen Binghong , Hu Sheng , Lee Wen-Chau , Li Lin
Published: November 2024

The continuous development of modern Doppler weather radars has substantially augmented our capacity to monitor severe convective storms and gain insights into their dynamic and microphysical structures. Although conventional parabolic antenna-based S-band operational radars are valuable, they exhibit limitations such as low scanning speed and reduced information acquisition in the low atmosphere region, particularly at far ranges from the radar, leading to suboptimal observations of fast-evolving storms. To address these limitations, a dense X-band polarimetric phased array radar (PAR) network, consisting of more than 50 radars, has been strategically constructed and deployed in the Greater Bay Area in South China, which is currently the largest PAR network worldwide. The PARs exhibit satisfactory performance in hydrometeor classification, hail identification, and quantitative precipitation estimation, demonstrating reliable polarimetric data quality. The paper also presents compelling evidence demonstrating the effectiveness of the PAR network in detecting the tornado vortex and capturing fine horizontal and vertical structures of convective storms when compared to nearby S-band operational radars. In addition, the assimilation of supplemental PAR data using the ensemble Kalman filter has yielded discernible vortex circulation fields for tornadic storms, enabling effective prediction of tornadogenesis, which is unattainable solely by assimilating S-band operational radar data. As the PAR network is put into operational use, significant advancements are anticipated in understanding and monitoring severe weather systems in South China.
The Relative Importance of Forced and Unforced Temperature Patterns in Driving the Time Variation of Low-Cloud Feedback
Authors: Lin Yuan-Jen , Cesana Grégory V. , Proistosescu Cristian , Zelinka Mark D. , Armour Kyle C.
Published: November 2024

Atmospheric models forced with observed sea surface temperatures (SSTs) suggest a trend toward a more-stabilizing cloud feedback in recent decades, partly due to the surface cooling trend in the eastern Pacific (EP) and the warming trend in the western Pacific (WP). Here, we show model evidence that the low-cloud feedback has contributions from both forced and unforced feedback components and that its time variation arises in large part through changes in the relative importance of the two over time, rather than through variations in forced or unforced feedbacks themselves. Initial-condition large ensembles (LEs) suggest that the SST patterns are dominated by unforced variations for 30-yr windows ending prior to the 1980s. In general, unforced SSTs are representative of an ENSO-like pattern, which corresponds to weak low-level stability in the tropics and less-stabilizing low-cloud feedback. Since the 1980s, the forced signals have become stronger, outweighing the unforced signals for the 30-yr windows ending after the 2010s. Forced SSTs are characterized by relatively uniform warming with an enhancement in the WP, corresponding to a more-stabilizing low-cloud feedback in most cases. The time-evolving SST pattern due to this increasing importance of forced signals is the dominant contributor to the recent stabilizing shift of low-cloud feedback in the LEs. Using single-forcing LEs, we further find that if only greenhouse gases evolve with time, the transition to the domination of forced signals occurs 10–20 years earlier compared to the LEs with full forcings, which can be understood through the compensating effect between aerosols and greenhouse gases.
The Cultural Mandate According to Paul’s Letter to Titus: Brief Exegetical-Theological Meditations
Authors: Jeon Paul S.
Published: November 2024

While discussions of the ‘cultural mandate’ persist, there has been little reflection on the topic from the perspective of the Pastorals. Accordingly, this essay interacts with a recent study on the subject in light of Paul’s Letter to Titus. Specifically, it provides exegetical support for the neo-Calvinist view of the ‘cultural mandate’ from this overlooked source. In the process, this study elucidates one of the letter’s main theological themes, namely the summons to be ‘zealous for good works’ (2:14).
Through the eyes of healthcare professionals: Compassion fatigue and earthquake stress coping strategies of healthcare professionals in the disaster region following the Kahramanmaras centered earthquakes: A case-control study
Authors: Emirza Elif Güzide , Uzun Sevda , Şenses Medine
Published: November 2024

Background:

Healthcare professionals serving in the earthquake zone may experience mental problems due to the traumatic events they witnessed.

Aim:

The aim of this study was to evaluate the effect of working in earthquake-affected areas on compassion fatigue and coping strategies for earthquake stress in healthcare professionals compared to a control group.

Methods:

This descriptive and correlational study was conducted in three provinces in the northern region of Türkiye. The sample of the study consisted of 146 healthcare professionals working in earthquake-affected regions and 143 healthcare professionals not working in earthquake-affected regions. Data were collected using the ‘Descriptive Information Form’, ‘Compassion Fatigue Short Scale (CF-SS)’, and ‘Coping with Earthquake Stress Scale (CESS)’. Descriptive statistics, t-test for independent samples, Pearson correlation analysis and binary logistic regression analysis were used to analyze the data.

Results:

Statistically significant differences were found between healthcare professionals working in earthquake-affected regions and healthcare professionals not working in these regions in terms of CF-SS total scores, ‘occupational burnout’ sub-dimension mean scores and ‘seeking social support’ mean scores among CESS sub-dimension mean scores ( p < .05). However, no statistically significant difference was found between the mean scores of ‘secondary trauma’ sub-dimension of CF-SS and total-CESS, ‘religious coping’, and ‘positive reappraisal’ ( p > .05). A high and positive correlation ( r = .805; r = .847; r = .847; r = .695, p < .001) was found between total CF-SS scores and total religious coping, positive reappraisal and seeking social support scores of the participants. In addition, working in the earthquake zone negatively affects positive reappraisal and positively affects seeking social support as a significant predictor (β = .081, OR = 0.922, p < .05; β = .111, OR = 1.117, p < .05).

Conclusion:

A significant positive relationship was observed between compassion fatigue and coping with earthquake stress. At the same time, it was determined that having worked in the regions affected by earthquakes had a strong positive effect on coping strategies with earthquake stress.
Flying to conquer. The aeronautical pedagogy in narratives for children in Italy (1911–1945)
Authors: Lucenti Maria , Mamone Graziano
Published: November 2024

This paper analyses the representation of flight in children's narratives in Italy from the Libyan war to the end of the Second World War. For this period, it is possible to see an escalation in the centrality of the theme of flight, functional to the construction of the myth of the aviator which reaches its peak during fascism. The regime used the subject of flight extensively for propaganda purposes and to further consolidate those that were the fascist values: strength, the mastery and the conquest of the earth and the sky, and the supremacy of Italian spirit through technological and engineering advancement. The construction of the myth of the aviator made use of a variety of tools aimed at children, through the definition of an “aeronautical pedagogy”. Such construction aimed to stress the Italian superiority against the Arab and African inferiority. The theme of flight sanctioned and reaffirmed this hierarchy.