bluuebunny/crossref_metadata_2025 huggingface dataset:
Implementation of a radio frequency identification and detection technology based digital class attendance system for university students
Authors: Ohanu Chibuike Peter , Christiana Ugbe Oluchi , Ogbuefi Uche C. , Sutikno Tole
Published: September 2023

Class attendance is a key consideration when assigning final marks for courses taught, student attendance is a critical issue for higher institutions. Some institutions choose to regulate attendance with paper sheets, while others prefer to verify students' attendance with paper sheets and then manually enter this information into a computer. This mode of taking students attendance delays time and reduces the time needed for the lecture to be delivered. A system that eases the burden of time delay in taking attendance, promotes the universities, and supports parents is presented in this paper. The radio frequency identification and detection (RFID) student monitoring system has been developed to track student’s attendance in the classroom using passive RFID technology. The system uses an RFID reader and a contactless smart card to ensure time management during student’s attendance. A reader at a fixed place delivers a signal to a passive RFID chip for detection and identifies a huge number of chips in a short amount of time. The biometric thumb detection module for security was applied to improve the system. The applied enhanced technology sends a message to the parent of any students that fails to attend lectures.
A PSO-TVAC for optimal installation of multiple distributed generations in a radial distribution system
Authors: Rosli Hazwani Mohd , Halim Syahirah Abd , Awalin Lilik Jamilatul , Mustaza Seri Mastura
Published: September 2023

The integration of distributed generations (DGs) into distribution system networks has seen a significant increase owing to the depletion of conventional energy resources and the growing power demand. However, a high penetration of DGs can adversely affect the stability of the distribution networks due to the intermittent nature of their generation capabilities. Hence, it is crucial to design DGs optimally to support grid voltage regulation and improve distribution networks performance. This study utilizes a particle swarm optimization (PSO) with time-varying acceleration coefficients (PSO-TVAC) technique to optimize the location and size of various distributed generation units while minimizing the total active power loss. The initial system power loss was determined using a distribution load flow analysis based on the backward-forward technique. The PSO-TVAC algorithm was then employed to identify the optimal placement and sizing of DGs within the standard IEEE 33-bus radial distribution network. To assess the proposed algorithm's effectiveness, photovoltaic (PV) and wind turbine (WT) were considered as the DGs. In comparison with other algorithms, PSO-TVAC achieved the lowest power loss, measuring 72.79 [kW] and 12.14 [kW] for
 3-PV and 3-WT installations, respectively. Furthermore, the optimal installation of 3-PV and 3-WT improved the distribution system performance by 65.49% and 94.25%, respectively.
Power management on DC microgrid with new DC coupling based on fuzzy logic
Authors: Kusmantoro Adhi , Farikhah Irna
Published: September 2023

In DC microgrids, the utilization of renewable energy results in disruptions. This paper proposes coordinated control of multiple batteries using a fuzzy logic controller (FLC). The goal is to regulate the voltage and power on the DC microgrid. Method used with a modified DC coupling configuration. The proposed new DC coupling topology uses 2 photovoltaic (PV) arrays. Part of the PV array output will be stored in the battery, and part will be provided to the DC bus. Apart from being connected to the DC bus, PV array1 (PV-A1) and PV array2 (PV-A2) are also connected to battery 1 (B1) and battery 2 (B2) via a buck converter. Instead, battery 3 (B3) is connected to the public grid source. The results of the study show that with FLC the voltage deviation and DC bus power are lower when in comparison to the proportional integral (PI) controller. As well, voltage on the DC bus response time with FLC produces a settling time of 0.5 seconds and an overshoot of 0.5%, while the PI controller produces a settling time of 1.15 seconds with an overshoot of 27.6%.
Effects of diesel‐ethanol-waste oil cooking and plastics blends reused in common rail direct injection diesel engine
Authors: Subrahmanyam D.V.S.R.B.M. , Manikandan Manikandan
Published: September 2023

The purpose of this study was to investigate the feasibility of transesterifying waste oils using alcohol and a catalyst to provide a sustainable fuel alternative. To ensure that the resulting biodiesel met American Society for Testing and Materials (ASTM) criteria, we studied its most important parameters of diesel‐ ethanol-waste oil cooking and plastics blends. We were able to separate out three unique fuel mixtures: BF0 (100% diesel), E20 (20% ethanol and 80% diesel), W20 (20% waste cooking oil and 80% diesel), and P20 (20% waste plastics oil and 80% diesel). In this experiment, a single-cylinder, computer-controlled diesel engine with direct injection was used. Fuel consumption increased by 4.3% for W20, 6.7% for E20, and 0.4% for P20, while thermal efficiency decreased by 2.1% for W20, 4.2% for P20, and 2.1% for E20, based on measurements conducted at 23.5° b Top Dead Center (TDC), Compression Ratio (CR19) under full load. At 23.5° b TDC injection time, NOx emissions were observed to be lower. Adding 20% W to conventional fuel at 23.5° b TDC enhanced the engines combustion characteristics.
Economic flexible AC transmission system devices placement/sizing with (N-1) contingency using genetic algorithm
Authors: Shankaregowda Tanuja Koppa , Byalihal Shankaralingappa Channappa
Published: September 2023

The rise in demand for electricity and the high cost of constructing new power networks reckons optimal utilization of electric power overloading and excessive power transfer along transmission lines, high losses, voltage instability, poor power quality, reliability issues. Flexible AC transmission system (FACTS) boosts the static and dynamic performance of power systems. Although efficient power transmission by improving power quality and voltage profile enhancement is controlled using the FACTS devices the placement, types, and sizing are important parameters to be optimized for the power system. This paper develops the economic multiple FACT placements and sizing solution during N-1 contingency conditions. Placement and sizing being the stochastic problem meta-heuristic algorithm genetic algorithm (GA) is used and applied on the standard IEEE 9 bus system. MATLAB-based simulation is developed for economic placement of multiple FACTS and single FACTS devices in different scenarios (without FACTS devices, with single FACTS devices, and with multiple FACTS devices). Both static VAR compensators (SVC) and thyristor-controlled series capacitors (TCSC) are used (either single or multiple) to optimize the transmission loss and total cost. The results show that transmission loss and cost reduction with 70% compensation is working better with about 0.1 MW lesser loss in many cases.
Design and simulation of a photonic crystal sensor for accurate blood plasma diagnostics
Authors: Chattopadhyay Manisha Chakrabarti , Chakraborty Rajib , Kundu Tapanendu
Published: September 2023

Photonic crystals (PC) are materials with periodic refractive index variations, resulting in photonic band gaps that allow light propagation across specific frequencies. PC-based sensors have gained significant attention in the scientific community for their diverse applications in biomedical sensing. This research presents the design of a novel blood plasma PC-based sensor that utilizes a 532 nm laser light source. The sensor incorporates defects within a 2D crystal, enabling precise modification of its characteristics based on the specific type of impurity introduced. The designed sensor, when simulated, demonstrates the ability to effectively detect both regular and infected blood plasma, with the potential for identifying diverse plasma types. Through optimization of device parameters, the sensor achieves optimal sensitivity of 12.5 nm per refractive index unit (RIU), utilizing a 75% defect relative to the total height of the device, which measures 278.46 nm. This sensitivity makes it well-suited for accurate diagnostics in low-volume blood plasma testing. The proposed sensor exhibits versatility and holds promise for potential utilization in a variety of diagnostic applications that necessitate minimal sample volume and high sensitivity requirements.
Design of foreign object debris detecting and monitoring using two sets of sensor system
Authors: Adi Kusworo , Edi Widodo Catur , Puji Widodo Aris , Rohanudin Rifki , Fadila Ihsan
Published: September 2023

Foreign object debris (FOD) is a foreign object that should not be on the runway which might cause the risk of aircraft crashes and put the lives of humans at risk. In this research, we designed a system to detect FOD using two sets of a sensor system for a wider, area of detection. This research was commenced by designed the entire hardware and software and tested the system. The object used as the testing object were rock, bolts, plastic bottles as well as iron pieces with varied distances and position angles. Based on the testing result, both sets of sensor system could detect objects with distance accuracy on the sensor system set of FOD 1 and FOD 2 consecutively were 98% and 94% for rock, 97% and 96% for bolts, 96% and 94% for plastic bottles, 96% and 94% for the iron pieces. The accuracy of angle measurement on the sets of sensor system FOD 1 and FOD 2 consecutively were 96% for rock, 98% for bolts, 97% and 95% for plastic bottles, 97% and 94% for the iron pieces. Monitoring system testing was carried out using black box testing and website response speed test towards the variation of database number.
Evaluation of optimization techniques with support vector machine for identification of dry beans
Authors: Naik Nabin Kumar , Sethy Prabira Kumar , Amat Rajat , Behera Santi Kumari , Biswas Preesat
Published: September 2023

Automated recognition or categorization of fruits or crops through image processing technology is a highly sought-after technique in agriculture.
 This approach has been demonstrated to outperform manual monitoring in humans. It reduces the time and expenses incurred by farmers and ensures that end users receive high-quality products. This study details the process for identifying dry beans. We employed a support vector machine (SVM) model and three optimization techniques: Bayesian optimization, random search optimization, and grid search optimization. This study aimed to determine the optimal optimization technique for identifying dry beans using an SVM model. Various metrics, including the validation accuracy, test accuracy, total validation cost, total test cost, and minimum classification error, were computed and analyzed to compare the performance of each optimization method. In comparison, Bayesian optimization was determined to be the most effective optimization technique for identifying dry beans, with a validation accuracy of 93.06%, test accuracy of 92.29%, and minimum classification error of 0.069384.
Health monitoring of historic buildings using machine learning in real-time internet of things
Authors: Eldeib Ahmed H. , Abdelsalam Ahmed Mohamed , Shehata Ahmed M. , Ali Hesham Sayed Kamel , Fouad Sara
Published: September 2023

In this paper, structural health monitoring (SHM) is used to detect the damage level for the historic building. The damaged level is defined based on the support vector machine (SVM) algorithm to extract the damage feature. Physical checks allow us to detect any damage or structural degeneration. Supervised training machine learning (ML) is used as a tool to examine accelerometer data to ascertain the condition of structures following an occurrence. The three training models, the SVM, the random forest linear classification, and the k-nearest neighbor (KNN) model are tested and compared to classify data. The data obtained from structural health monitoring, teams of responders, and investigators can be used to manage the most vulnerable structures. The accuracy of the SVM algorithm was found up to 94% accurate and precise, at a high level. The internet of things (IoT) architecture is also introduced with SVM learning algorithms for early warning. The proposed system makes use of an SHM system to identify seismic events or accelerations. The IoT system SHM uses real data from the structure, allowing for online damage identification and ongoing monitoring. A dashboard is used to represent the monitoring data and the damage level.
Combining Mindwave, MPU6050, Ultrasonic, and Internet of Things for a reliable, safe, and monitored wheelchair control system
Authors: Artanto Dian , Pranowo Ignatius Deradjad , Wicaksono Martinus Bagus , Siswoyo Agus
Published: September 2023

This paper proposes the development of a wheelchair control system by combining a Mindwave EEG (electroencephalogram) sensor, an MPU6050 inertial sensor, an ultrasonic sensor, and IoT technology. Stroke survivors often face challenges in controlling their wheelchairs due to cognitive and physical impairments, requiring specific solutions to ensure their safety and comfort. The integration of the Mindwave EEG sensor captures the user's cognitive state, such as level of attention and focus, while the MPU6050's inertial sensor monitors physical movement and changes in head orientation. Additionally, the ultrasonic sensor is incorporated to detect obstacles and provide proximity information for safe navigation. This wheelchair control system is also linked with IoT technology to enable monitoring of the user's cognitive, physical, and environmental condition and remote assistance. In this paper, to implement this wheelchair control system, a T-BEAM with an ESP32 controller is used, which is equipped with GPS to provide wheelchair location data and LORA radio communication for remote data transmission, and a TTGO LORA32 to forward the data to Arduino IoT Cloud service over Wi-Fi. The results of the development show that the wheelchair control system is safer and more monitored, and users are easier and more flexible in operating wheelchairs.
ANFIS based method for faults detection in the photovoltaic system
Authors: Lahiouel Younes , Latreche Samia , Khemliche Mabrouk
Published: September 2023

As the case with all electrical and electronic systems, a photovoltaic (PV) system can be exposed to several failures causing it to malfunction; several studies have found that the reliability of the PV systems is highly dependent on the material used for the construction of the PV panels, temperature, humidity and solar radiation. A PV system can have several faults, whether construction type faults, or material and electrical faults caused by climatic conditions. This requires identification, the main objective of which is to provide a tool that can detect and locate these faults in order to guarantee optimal performance of the system, and thus reduce maintenance costs and above all increase productivity by increasing the availability rate facilities in order to have a better performance. The objective of this article is to propose a technique for detecting and locating faults in a PV system, the proposed algorithm is based on adaptive neuro fuzzy inference system (ANFIS). This algorithm is based on the technique of artificial neural networks and fuzzy logic. Nine faults will be examined with simulation results under MATLAB-Simulink in PV system (array, converter DC-DC and battery).
Machine learning-based diagnosis of eye-diseases
Authors: Yousif Thanaa Hasan , Tomah Nahla Ali , Mohsin Marwa Jaleel
Published: September 2023

Over the last several years, artificial intelligence (AI) has been substantially utilized in image processing and classification. Several tools are accessible for visualizing, training, and pre-processing image data. One such tool is orange, which has several pre-processing modules and a particular add-on for image processing methods in addition to excellent data visualization. The tool (version 3.32.0) was used in the suggested study to give a comparative and predictive analysis using several classification models. Three main models have been used to train and predict the three groups image eye diseases. The results were compared based on some criteria, including area-under-a-curve (AUC), the accuracy of classification (CA), F1 score, precision, and recall. These models include K-nearest neighbour (KNN), logistic regression (LR), artificial neural networks (ANN) and stacking model. The stacking model, which is a novel model, is also presented in this work by concatenating the output of the parallel form of ANN and KNN models with the LR model. The best performance belonged to the Stacking model, which offers the best detection and prediction results.
Reduced encoder time complexity using enhanced adaptive multiple transform selection in versatile video coding
Authors: Tummala Anil Chowdary , Nalluri Purnachand
Published: September 2023

When compressing a video, the architecture is provided with the width and height of the frame along with the video as input. The multiple transform selection (MTS) techniques are used to compress video. Traditional discrete cosine transforms (DCT) and other trigonometric (sine and cosine) transforms, such as DCT-8 and DST-7, are included in the pool of transforms. For the best outcomes, two additional transforms-DST-2 and DCT-5-were also utilized to compress any remaining video frames. To improve coding effectiveness, peak signal-to-noise ratio (PSNR), and delay, 2D Transforms are broken into 1D Directional Transforms for block sizes ranging from 4×4 to 64×64. The temporal complexity was greatly decreased by adding DST-2 to the existing video test model (VTM). The MTS’s adaptive multiple transform (AMT) scheme already included a pool of transforms, but DST-2 was introduced as a fourth transform. Encoder time complexity was reduced by 22% on average without compromising PSNR and bitrate.
Polar code-based cryptosystem: comparative study and analysis of efficiency
Authors: Redhu Ritu , Narwal Ekta
Published: September 2023

This review paper aims to examine the use of polar codes in public key cryptosystems, providing a comprehensive overview of the state-of-the-art in this field at present. This study thoroughly searches databases such as IEEE Xplore, Scopus, and the ACM digital library to locate relevant research articles. In this study, we demonstrate the use of polar codes in public key cryptography and provide valuable insights for researchers interested in this field of research. The paper identifies several areas for further research and development, such as improved security and reliability of polar code-based cryptosystems. An analysis of the review highlights the major challenges and open research questions in this area, including the need for efficient key generation algorithms and the trade-offs between security and performance. An analysis of various existing encryption techniques as well as their security proofs is provided in the tables.
Cyclic redundancy check-aided successive cancellation-based polar decoders
Authors: Suhaimi Hamizan , Mohamad Roslina , Ali Darmawaty Mohd , Abdullah Ezmin
Published: September 2023

Research on channel coding for network transmission using polar codes has produced excellent results. By removing error redundancy from the decoding process, cyclic redundancy check (CRC) is frequently used by researchers to increase a system’s performance. In prior research, the application of decoder algorithms for polar codes was examined but not thoroughly compared. For the general capabilities of the previously proposed algorithms to be ascertained, it is crucial to analyze the employment of polar decoders especially successive cancellation (SC)-based polar decoders and the use of CRC in additive white gaussian noise (AWGN). Hence, this paper analyzes the performance of CRC with SC-based polar decoders in AWGN. In the simulation setup, (256,128) polar codes and CRCs with three-bit sizes (6, 8 and 11) were utilized. SC-based polar decoders, such as SC, soft-output cancellation, SC list (SCL) and simplified SC, were applied at the decoder part. The outcomes show that CRC-aided SC-based polar decoders reduced redundancy error. Among all the decoders, the SCL decoder with 11-bits CRC performed well when the normalized signal-to-noise ratio was high. Based on the analysis, removing errors at the highest level is possible using a suitable CRC size for SC-based polar decoders.
Integrating mobile-assisted learning for a dynamic blended approach in higher education
Authors: Vega Nofvia De , Basri Muhammad , Nur Sahril
Published: September 2023

This research investigates the effectiveness of blended learning with mobile-assisted language learning (MALL) in english courses at Borneo University Tarakan. An experimental design was used with 80 english as a foreign language (EFL) student randomly assigned to an experimental group (EG) receiving blended learning with MALL and a control group (CG) receiving traditional instruction. The results demonstrate that blended learning with MALL significantly improves language learning outcomes and increases motivation, engagement, and satisfaction. This study contributes to the literature by highlighting language learning outcomes from a blended approach using MALL and emphasizes the importance of innovative teaching methods and technology integration. The findings have practical implications for educators, suggesting that incorporating MALL in blended learning enhances language learning outcomes and student engagement. Further research can explore the impact of MALL on different language skills and strategies to enhance intrinsic motivation in MALL-blended learning.
 This research provides valuable insights for improving language instruction through blended learning approaches.
A 3.5 GHz microstrip patch antenna design and analysis for wireless applications
Authors: Rana Md. Sohel , Khurshid Md. Naheen , Joy Sanjay Kumar , Sourav Md. Soriful Islam , Hassan Md. Jakaria , Nazmul Md. Jubaer
Published: September 2023

The design, simulation, and analysis of a 3.5 GHz rectangular microstrip patch antenna (RMPA) have been carried out for this research article. The substrate material employed for the design is a lossy form of fr-4, which has a thickness of 0.5 mm, a dielectric permittivity of 4.3, and a loss tangent of 0.0005, respectively. The antenna receives power through a feeding line with an impedance of 50 Ω. The simulation was ultimately finished off with the help of some computer simulation tools. Following completion of the simulation, the findings revealed a directivity gain of 6.05 dBi, a voltage standing wave ratio (VSWR) of 1.0607, and a bandwidth of 144.1 MHz. The return loss was determined to be -30.611 dB. The suggested antenna's primary purpose was to attain a standard value for the VSWR while lowering the return loss. This antenna improves directivity gain and bandwidth and has applications in radars, mobile phones, and wireless local area networks (WLANs). The results of this proposed antenna were superior to those of a variety of studies that had been published in the past.
A design of a smart farm system for cattle monitoring
Authors: El Moutaouakil Khalid , Falih Noureddine
Published: September 2023

The integration of technology in agriculture has led to the adoption of smart farming systems, which are becoming increasingly popular for optimizing resources, reducing labor costs, and improving efficiency. This article presents a design of a smart cattle monitoring farm system, which focuses on monitoring individual animal behavior and health, improving resource management, and optimizing overall farm efficiency. The proposed system integrates various internet of things (IoT) sensors, communication technologies, and cloud computing to provide a real-time monitoring solution for cattle farms. The system uses machine learning (ML) algorithms to analyze data on cattle behavior, health, and performance, which can be accessed through web and mobile applications by farmers to proactively monitor their herd. The adoption of the system promises good results for intelligently transforming traditional cattle farming by reducing manual monitoring time and achieving a good accuracy in early detection of diseases, automated tracking of estrus cycles, and location mapping of cattle across the farm fields.
A novel framework for the diagnosis of Parkinson’s disease using transfer learning with RESNET50 and SVM classifier
Authors: Macharla Anila , Pradeepini Gera
Published: September 2023

Most Parkinson’s patients exhibit voice disorders in the early phases of the condition. Recent study on Parkinson’s disease (PD) has concentrated on identifying speech problems from pronunciation of vowels with people affected by this disease. Proposed algorithm offers analysis of time-frequency images using transfer learning methods with support vector machine (SVM) for classification of PD affected with healthy controls using residual network 50. PD morphology is preserved in 2-dimensional time-frequency graphs
 that were helpful in implementing the proposed approach. The technique employs a hybrid HT/Wigner-Ville distribution to convert one-dimensional PD soundtracks to two-dimensional time-frequency (TF) graphs. After implementation of the proposed approach, classification of healthy and unhealthy controls is done using a pre-trained ResNet50 and the result is further improved through transfer learning. The features are extracted by passing preprocessed 2-D time-frequency diagrams through ResNet50’s FC1000 layer and trained using a binary nonlinear SVM classifier. The training process with 5-fold cross-validation (CV) got accuracy of 95.07% and in testing; it reached 92.13%, attaining better results.
Comparative analysis in the prediction of early-stage diabetes using multiple machine learning techniques
Authors: Flores Leonard , Hernandez Rowell Marquez , Macatangay Lloyd H. , Garcia Shiela Marie G. , Melo Jonnah R.
Published: September 2023

Diabetes is caused by high levels from blood glucose and it is characterized as a chronic disease, and also will disrupt fat and protein absorption.
 The levels rise from blood glucose because it cannot be burned in the cells from the pancreas because of the deficiency of insulin secretion or the insulin produce by the cell are insufficient. By means of early detection, it may decrease the hazards and frequency of diabetes. The application of technology has been an essential part of providing accurate and acceptable results in the prevention and early detection of the illness. This research provided the best machine learning used for predicting the early stage of diabetes. The methods involve the feature selection or dimension reduction using relief-based filter (reliefF), tenfold cross-validation for testing and training data, and different machine learning classifiers such as the random forest (RF), support vector machines (SVM), and neural network (NN) is used. in this research,
 RF recorded the highest precision point at 98.5%, which was able to provide a higher evaluation in terms of accuracy followed by SVM at 96.6% and NN at 96.2%. The results generated from this experiment are essential in contributing a new way that is highly accurate in determining diabetes
 among patients.
Towards secure smart campus: security requirements, attacks and counter measures
Authors: Srhir Ahmed , Mazri Tomader , Benbrahim Mohammed
Published: September 2023

The internet of things (IoT) has the potential to significantly impact growth due to the technological revolution, widespread dissemination of information, and emergence of events. Intelligent housing, urban centers, and educational systems are all forms of intelligence. The IoT gs has garnered significant attention from researchers, who claim that this technology will play a pivotal role in determining the future of the Internet, as per the reports of Cisco Inc., in this context, the concept of a smart campus refers to the establishment of a durable and interconnected setting that enhances learning, efficiency, and the overall living experience. Similar to other intelligent environments, the smart campus is susceptible to a variety of risks and threats this situation presents important security-related challenges that have an influence on the advancement of the campus. This article presents an overview of intelligent campuses by emphasizing the primary applications and technologies utilized. It provides an examination and evaluation of the primary security concerns associated with smart campuses, classified based on their respective types and levels of significance, and determines the security requirements, current threats and attacks, and the state of the art in terms of architectural solutions and the prevention of security vulnerabilities.
Efficient information retrieval model: overcoming challenges in search engines-an overview
Authors: Sharma Simple , Panda Supriya P.
Published: September 2023

Search engines play a vital role in information retrieval (IR) indexing and processing vast and diverse data, which now encompasses the ever-expanding wealth of multimedia content. However, search engine performance relies on the efficiency and effectiveness of their information retrieval systems (IRS). To enhance search engine performance, there is a need to develop more efficient and accurate IRS that retrieves relevant information quickly and accurately. To address this challenge, various approaches, including inverted indexing, query expansion, and relevance feedback, have been proposed for IR. Although these approaches have shown promising results, but their effectiveness and limitations require a comprehensive examination This research aims to investigate the challenges and opportunities in designing an efficient IRS for search engines and identify key areas for improvement and future research. The study involves a comprehensive literature review on information retrieval impacting academia, industry, healthcare, e-commerce, and other domains. Researchers rely on search engines to access relevant scientific papers, professionals use them to gather market intelligence, and consumers utilize them for product research and decision-making. The findings of this study will contribute to the development of more efficient and effective information retrieval systems, leading to improved search engine performance and user satisfaction.
Energy efficient routing protocols in wireless body area networks for health monitoring: a review
Authors: Al-Sofi Soleen Jaladet , Atroshey Salih Mustafa S. , Ali Ismail Amin
Published: September 2023

Technology nowadays is developing at an unprecedented pace in the field of wireless sensor networks (WSN). Especially in the aftermath of the pandemic, wireless body area networks (WBAN), which is a sub-category of WSN, experiences huge prominence, due to its functionality to enable remote diagnosis, treatment, and constant monitoring for patients. This functionality is a great advantage, since it allows elderly people, people with chronical diseases and even emergency cases to be monitored in real-time and, if needed, also enabling timely interventions. Furthermore, it is very cost-efficient since it reduces the occupation of hospital beds, whilst also keeping patients safer. This is possible by deploying small sensors within or around the body, where they can gather physiological data, which then at the end is send to medical terminals, where medical staff have access to them. However, a WBAN, due to its small size, is constricted by several factors. Energy consumption, security, data transmission, as well as heat generation are only a few to be named. To deal with these challenges, whilst enabling a stable communication between the components, routing protocols are required. There are many different routing protocols which are used depending on the application being used. 
Classification of chronic kidney disease based on machine learning techniques
Authors: El Sherbiny Moataz Mohamed , Abdelhalim Eman , Mostafa Hossam El-Din , El-Seddik Mervat Mohamed
Published: September 2023

In recent years, indescribable suffering from various kidney diseases has been experienced by people all over the world. The situation has been significantly worse because of chronic kidney disease (CKD). Only through an early diagnosis of CKD may kidney disease be hinder in its early stages from progressing. However, it is easier to detect the chronic kidney disease with the aid of machine learning (ML) classifier algorithms sooner than any other existing methods. The present work proposes an approach for potentially predicting CKD infection while considering patient health dataset information into consideration, employing nine distinct ML algorithms; random forest (RF), naïve bayes (NB), support vector machine (SVM), decision tree (DT), logistic regression (LR), extreme gradient boosting (XGB), adaptive boosting (ADB), k-nearest neighbors (KNN), and neural network (NN). Machine learning algorithms had been utilized and conducted in four experiments, then they were compared using five performance measures; F1-score, precision, accuracy, recall and run time are used to evaluate the performance. Results had shown that AdaBoost (ADA) outperformed other techniques with achieved accuracy of 99.17%.
Enhancing loan fraud detection process in the banking sector using data mining techniques
Authors: Esmail Fahd Sabry , Alsheref Fahad Kamal , Aboutabl Amal Elsayed
Published: September 2023

Ongoing loan fraud is a source of concern for financial institutions, as it has a direct financial impact and also scares off customers. This pattern, which can be traced to the development of modern technology, the introduction of novel ideas, and the quickening pace of international connections, makes the detection of fraud an expensive endeavour. This article proposes a novel framework for enhancing the fraud detection of loan banking using data mining algorithms. The framework extracts a number of predictive analysis techniques for identifying loan fraud. Several methods employing a wide range of pipeline architectures have been tried in order to select the optimal champion model. Autotuning has also been used to find the best possible setting for the model’s hyperparameters. The results of the evaluation show that autoencoder with gradient boosting outperformed the other classification algorithms with an accuracy of 98.62%. The proposed framework has the potential to significantly improve the fraud detection process of loan banking, which can ultimately lead to better faster fraud detects rates by combining data mining techniques with dimensionality reduction strategies in the feature space.
Physiological Characteristics and Nitrogen and Iron Utilization of Marine Polycultures and Bundles of Hair Algae
Authors: Wang Junxiao
Published: September 2023

In response to the problem of iron limitation in nitrogen-fixing cyanobacteria, this study explores the potential for symbiotic and mutually beneficial community survival of Polychaeta and Trichoderma. The presence of the two elements and their effects on the survival of the cyanobacteria
were first analysed in the context of nitrogen and iron use and cycling in the ocean. The physiological characteristics of the two algae were also analysed in relation to environmental factors such as light, ph and temperature in the marine water column. 730The results of the incubation
experiments showed that the filtrate of Trichoderma reesei had a significant effect on the growth of Polycoccus, with the OD730 rising to 0.0372 within 72 hours of the addition of Trichoderma reesei. The addition of the wild-type iron carrier secretion of G. globulus also promoted
the growth rate and nitrogen fixation efficiency of the algae. The symbiotic relationship between the nitrogen and iron utilisation of the algae was shown to contribute to the growth and nitrogen fixation activities of both species.
Effect of a Self-Made Herbal Decoction Containing Angelica sinensis Combined with an Enhanced Recovery After Surgery Nursing Model on Anxiety, Depression, and Ovarian Function in Patients Undergoing Myomectomy
Authors: Dong Xiaomei , Zheng Yunjiao
Published: September 2023

Angelica sinensis (A. sinensis), a traditional Chinese medicine (TCM), can tonify the blood, promote circulation, and nourish the body. This study investigates the potential of A. sinensis extract in improving ovarian function at the molecular level and its combined
effects with other natural medicinal herbs in patients undergoing myomectomy. The impact of A. sinensis extract was assessed using a rat model of premature ovarian insufficiency. The rats were divided into four groups: control group, model group, low-dose Angelica group, and high-dose
Angelica group. Serum levels of estradiol (E2), luteinizing hormone (LH), and follicle-stimulating hormone (FSH) were determined, and the pathology of ovarian tissue was examined. The results revealed elevated expressions of E2, nuclear factor erythroid 2-related factor
2 (Nrf-2), and heme oxygenase-1 (HO-1) in the treated groups compared to the model group, along with decreased levels of LH, FSH, interleukin (IL)-4, IL-6, and tumor necrosis factor-α. In a clinical trial involving patients after myomectomy, the efficacy of a combination of TCM
decoction containing A. sinensis and enhanced recovery after surgery nursing model was evaluated with anxiety, depression, and ovarian function as indicators. The observation group exhibited reduced levels of anxiety and depression, along with improved ovarian function compared to the
control group. Moreover, it was observed that A. sinensis extract demonstrated the potential in enhancing ovarian function through molecular pathways, possibly involving Nrf-2/HO-1 signaling. These findings provide insights into novel therapeutic approaches for promoting ovarian health
and emphasize the benefits of integrating traditional medicine with modern care strategies.
India-Afghanistan Relations in Changing Regional Geopolitics
Authors: Yousuf Muneeb
Published: September 2023

Afghanistan’s recent political transformations starkly confirm the country’s inevitable connectedness to volatile global geopolitics, currently witnessing the transformation of the role of the entire South Asian region within global geostrategic re-alignments. The US disengagement from Afghanistan put India and China into a new competitive scenario of cultivating their respective relations with Afghanistan. Before this complex backdrop, the article focuses on India’s policy options in Afghanistan, but first of necessity engages briefly with the changing regional geo-politics and the emerging new world order. In light of these new geopolitical realities, it becomes evident that despite much distrust and many misgivings, India’s Afghanistan policies have to be robust, focused on defending its geopolitical interests and concerns against China’s expansionist clout, while seeking to promote a multipolar world order, a strategy that appears like a reincarnation of India’s earlier path of non-alignment.
Microfacies correlation, analysis and palaeoecological reconstruction of Rupelian–Aquitanian succession in the central part of Lorestan Province, Northwestern Iran
Authors: Rajabi Mohammad , Senemari Saeedeh , Tehrani Maryamnaz Bahrammanesh
Published: September 2023

The Oligocene–Miocene Asmari Formation is a sequence of shallow-water carbonate carbonates in the Zagros Basin. This formation is a significant oil reservoir that was formed in the Zagros foreland basin in southwestern Iran. The Razan section, located in the northwest of Zagros in the Lorestan province, was investigated to study microfacies types and evaluate the sedimentary environment. This section was then compared with the MakhmalKuh and Dehloran sections. It comprises limestone units with thin to medium strata, sometimes thick-bedded limestone, and other skeletal and non-skeletal components. Based on the biostratigraphy studies, 24 foraminifer genera and species have been identified. Among the foraminifera, the species of Peneroplis farsensis, Peneroplis evolutus, Austrotrilina howchini, Austrotrilina asmarensis, Dendritinarangi, Quinqueloculina sp., Triloculina trigonula, Pyrgo sp., Polymorphinids, Spiroluculina sp. and Meandropsina anahensis are the most important specimens. Based on identified co-occurrence taxa, the Austrotrilina howchini–Peneroplis evolutus assemblage zone has been recognised in the study area, which indicates early Miocene (Aquitanian). Texture analysis and faunal assemblages led to the recognition of fourteen microfacies. Based on the petrographic analysis, five depositional settings were determined in the studied section: (a) the outer ramp settings dominated by pelagic foraminifera, bryozoan, echinoid and benthic foraminifera such as Nummulites, Rotalia, Dendritina and Meandropesina iranica as a proxy for relatively deep marine conditions; (b) the mid ramp, characterised by sizeable perforate foraminifera such as Amphistegina, Operculina, Heterostegina and planktonic foraminifera; (c) the barrier, marginal marine environment dominated by foraminifera such as Miogypsinoides sp., Peneroplis, Meandropsina, Dendritina and Neoalveolina, as well as peloid and corallinacean algae; (d) the lagoon dominated by benthic foraminifera Austrotrillina, Dendritina, Peneroplis, Miogypsinoides, Borelis, Archaias, miliolids and red algae and (e) the tidal flat, the shallowest part of the inner ramp, dominated by fenestral dolo-mudstone without any fossils. The faunal assemblages represent warm tropical waters under euphotic and mesophotic conditions in a homoclinal ramp.
Legal Underpinnings Drive ESG Amidst Global Flux
Authors: Gossain Rahul
Published: September 2023

The paper studies the complex global dynamics governing Environmental, Social, and Governance (ESG), including the evolving scope and structures of ESG frameworks. The impact of the significant expansion in the scope and spread of legal underpinnings over the last decade studied along with the increase in strategic applications of ESG like risk management. There is specific focus on developments the European Union, and an assessment of the impact of the changing ESG and regulatory scenario on India.

The accelerated alignment of reporting standards, post the release of Task Force on Climate-Related Financial Disclosures (TCFD) guidelines has been complemented by legal and regulatory progress globally. The gradual development of legal underpinnings has put ESG or business sustainability on a stronger footing, by providing binding mandates to transition towards sustainability, albeit in varying in strength and forms across jurisdictions.

The paper studies the development of these legal underpinnings and analyses how it has been critical in ensuring progress on the ESG front. These were critical in ensuring progress continued despite significant headwinds emerging of the Ukraine War and COVID-19, albeit in a slower but sustained manner. This contrasted with the scenario over the last four decades wherein vested interests could easily derail progress on climate change fronts in the absence of the legal underpinnings and binding mandates. This analysis is based on study of recent sustainability mechanisms, including a study of the architecture of the Paris Agreement, the TCFD guidelines, the compliance-based European Union (EU) Sustainability Action Plan in EU, and other international regulatory developments, including India’s BRSR and the recent regulatory proposals by RBI and SEBI etc.

Global financial investors’ strong push for ESG based risk-assessment and investments is analysed along with the introduction of mandated ESG compliances for businesses and trade is pushing companies and countries with older ESG standards to respond, which has important implication for India and Indian businesses.
Book review: Noëlle-Laetitia Perret and Stéphane Péquignot, eds., A Critical Companion to the ‘Mirrors for Princes’
Authors: Boccaccini Enrico
Published: September 2023

Noëlle-Laetitia Perret and Stéphane Péquignot, eds., A Critical Companion to the ‘Mirrors for Princes’. Leiden, Boston: Brill, 2023, 568 pp. ISBN: 9789004518759.
A Bibliometric Analysis of Global Research on Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections
Authors: Verma Shalini , Tripathy Sarvodaya , Kar Sujita Kumar
Published: September 2023

Pediatric autoimmune neuropsychiatric disorders associated with streptococcal infections (PANDAS) are a clinical syndrome characterized by the abrupt onset of major personality, movement, and behavioral changes in a patient with a history of streptococcal infection. We aimed to do a bibliometric analysis of the published research on PANDAS. All the published articles available on the PubMed database from the time of inception till August 2022 were included in the study. All the PubMed IDs (PMID) of the articles were entered in Harvard Catalyst, a free online software, for bibliometric analysis, and data were extracted and verified. A total of 289 relevant published articles were identified. The average number of authors per article was 4.89 and the average number of times an article was cited was 11.19 (excluding self-citation). The H-index of the published articles was 31. The Journal of Child and Adolescent Psychopharmacology published the maximum number of articles ( n = 36). The highest number of average citations (39.50 citations/articles) was for the articles published in the American Journal of Psychiatry. The most common type of articles were journal articles ( n = 259) and of which 27 were original research articles. Most of the original research discusses the effectiveness of various interventions (penicillin and other antibiotic prophylaxis, role of immunoglobulin), neuropsychiatric symptoms, and outcomes in the longitudinal course. Though PANDAS is a well-researched entity, more clinical trials, and large-scale studies will help provide better insight on this subject.
Effective Digital Advertising: The Influence of Customised Ads, Self-esteem and Product Attributes
Authors: Linardi Evelyn Krisnada , Lin Hui-Fei , Yeo Benjamin
Published: September 2023

We investigate consumers’ attitudes and behaviours when receiving customised website advertising, the effect of their psychological beings and content of advertising messages, using a 2 (customisation: customised vs non-customised) × 2 (product attributes: utilitarian vs hedonic) × 2 (self-esteem: high vs low) experiment on 240 participants, aged 16–34 living in Taiwan, to explain their effects on advertising effectiveness and the mediating role of advertising value. High self-esteem consumers have a more favourable attitude and behaviour when receiving a customised ad. For low self-esteem consumers, the customised ad effectively influences purchase intention. In the non-customised advertising condition, a hedonic product attribute fosters greater purchase intention. High self-esteem consumers have a stronger purchase intention when receiving the hedonic product attribute in the non-customised ad condition. Furthermore, customisation influences attitude and purchase intention.