bluuebunny/crossref_metadata_2025 huggingface dataset:
Interventions to prevent violence against women and girls globally: a global systematic review of reviews to update the RESPECT women framework
Authors: Ullman Chelsea , Amin Avni , Bourassa Angela , Chandarana Shikha , Dutra Flávia , Ellsberg Mary
Published: January 2025

Objective

The field of violence against women and girls (VAWG) prevention research has rapidly advanced, with ample evidence now demonstrating that VAWG is preventable. The aim of this systematic review of reviews is to update the evidence that underpins the RESPECT women framework with the newest available evidence from 2013 onward.

Methods

Academic and non-academic databases were searched using terms related to VAWG prevention (January 2013–April 2022). Evaluations had to have a target population of women or girls aged 10 and older. Data were extracted from included reviews, and AMSTAR-2 was used to assess the risk of bias in systematic reviews. The primary outcome of interest was change in any form of VAWG, including physical, sexual and emotional intimate partner violence and sexual violence and harassment from non-partners, including child and adolescent sexual abuse.

Results

From the thousands of articles screened, 178 were included in this study. Six (3%) reviews focused on interventions that aim to strengthen relationship skills, 14 (8%) focused on empowerment of women and girls, 79 (44%) on services for survivors, 5 (3%) on poverty reduction, 16 (9%) on creating safe environments, 36 (20%) on preventing child and adolescent abuse and 22 (12%) on transforming gender attitudes, beliefs and norms. Little new evidence has emerged to meaningfully change the nature and strength of evidence for interventions related to relationship skills strengthening and poverty reduction. However, there is new evidence to reflect the effectiveness of select types of interventions across settings in the other five strategies.

Conclusion

Despite progress in VAWG prevention research, significant gaps in the evidence base persist. Further research is needed to explore intervention areas and marginalised populations in various contexts. Several programmatic approaches exhibiting efficacy in low-income and middle-income countries remain unexplored and unevaluated in high-income countries, and vice versa, warranting further adaptation and evaluation.
Improving the provision of clinical pharmacy services in low- and middle-income countries: a qualitative study in tertiary health facilities in Tanzania
Authors: Mwakawanga Dorkasi L , Mutagonda Ritah F , Mlyuka Hamu J , Mikomangwa Wigilya P , Kilonzi Manase , Kibanga Wema A , Marealle Alphonce Ignace , Mallya Bertha , Katabalo Deogratias , Sanga Sofia , Kalokola Fredrick , Rwegasha John , Magambo Rose , Mmassy John , Kabissi Sungwa , Balati Josephine A , Maduki Peter , Minzi Omary Mashiku , Kamuhabwa Appolinary A R
Published: January 2025

Background

The provision of clinical pharmacy services (CPS) in low- and middle-income countries is still low. The reported challenges in providing CPS include healthcare structures, public policies, resources, workforce, culture and education inequalities. This study aimed to explore the strategies to improve the provision of CPS in tertiary health facilities in Tanzania.

Methods

This study was conducted between August and September 2021. We adopted an exploratory qualitative study to conduct 14 in-depth interviews with hospital administrators and 10 focus group discussions (FGDs) with healthcare providers. A purposeful sampling technique was used to recruit 97 participants, including 45 pharmacists, 31 medical doctors and 21 nurses across five tertiary healthcare institutions. Of those, 14 hospital administrators participated in 14 IDIs and 83 healthcare workers divided into 10 FGDs. Analysis was done using a qualitative thematic approach.

Results

The study identified several strategies that fall under four major themes: (i) strengthen preservice training by reviewing the Bachelor of Pharmacy training curriculum to include clinical pharmacy components; (ii) improve continuing professional training through regular provision of on-job training to pharmacists; (iii) revise the scope of work for pharmacists to include CPS provision in the job description; and (iv) improve operational environment by ensuring availability of guidelines, policies, and adequate number of pharmacists and good inter-professional communication skills.

Conclusions

This study’s findings highlight that improving CPS provision requires strengthening pharmacists’ training, scope of work and operational environment. The latter calls for multifaceted engagement from pharmacists, training institutions, policymakers, regulatory bodies and health systems for sustainable progress.
High tackle headache: implications of referee agreement for tackle height law change
Authors: Leese Ruth , Kolstad Ash , Sant'Anna Ricardo T , McKay Carly D , West Stephen W
Published: January 2025

Objectives

Rugby Union has a relatively high risk of injury. Early evidence suggests a benefit of lowering tackle height to reduce head and neck injuries, although concerns persist among stakeholders regarding implementation challenges. This study aimed to understand whether referees can reach the same conclusion regarding tackle height in a controlled environment (ie, video) and whether priming influenced these decisions.

Methods

Forty-eight active referees completed a questionnaire based on high-tackle decision-making guidelines after watching tackles. Participants were randomly assigned one of two instructional videos containing a high or legal tackle to investigate the impact of priming on law interpretation.

Results

The percent agreement regarding tackle height was 78.1% between participants, 62.7% between participants and an experienced analyst, and 74.0% between participants and a gold-standard referee. Mean intra-rater reliability when determining whether a tackle was high was substantial (percent agreement: 91.2%). For high tackles, 83% of participants agreed on the danger level, 57% on the contact location and 71% on the presence of mitigating factors. No significant effects of priming were observed. Inter-rater agreement among participants and the gold-standard referee was moderate for all items except danger and height, which showed strong agreement.

Conclusion

These results suggest a need for improved referee training to support changes to the legal tackle height.
Abstracts from international emergency medicine journals
Authors: Production EMJ
Published: January 2025

Editor’s note:EMJhas partnered with the journals of multiple international emergency medicine societies to share from each a highlighted research study, as selected by their editors. This edition will feature an abstract from each publication.
Hadron momentum spectra from analytical solutions of relativistic hydrodynamics
Authors: Ali Mahammad Sabir , Biswas Deeptak , Jaiswal Amaresh , Singh Sushant K.
Published: January 2025

We present analytical solution of relativistic hydrodynamics for a system having cylindrical symmetry with boost-invariant longitudinal expansion and Hubble-like transverse expansion. We also consider analytical solution for Hubble-like spherically expanding system. For these two cases, we calculate analytical expression for transverse momentum spectra of hadrons, at constant temperature freeze-out hypersurface using Cooper–Frye prescription. We compare our results for transverse momentum spectra with experimental results from Large Hadron Collider and CERN SPS where one expects cylindrical and spherical geometry of the fireball, respectively. In the case of low-energy collisions with spherical geometry, we calculate rapidity spectra and compare with the results from CERN SPS.
A Bilevel Planning-Based Approach to Rationalizing Water Resource Allocation in River Basins
Authors: Chen Peng , Wu Xianzhong
Published: January 2025

The allocation of water resources in river basins often involves multiple variables and constraints, which may change with time and environment. The bilevel programming model has high flexibility and can be adjusted and optimized according to the actual situation to adapt to different water resource allocation needs. Therefore, a rational allocation method of river basin water resources based on bilevel planning is proposed. The upper layer of the bilevel programming model for rational allocation of water resources in the basin is the economic benefit module, and the price of clean water is the decision variable. The lower layer is the environmental benefit module, with the allocated water volume as the decision variable. By setting a dual objective function, the flexibility of water resource allocation in the watershed can be improved. Due to the inconsistency of the objective function in the bilevel programming model, it is unreasonable for the upper optimal solution to be the control variable of the lower level. To solve this problem, the interactive algorithm is used to optimize the result of the objective function solution, and a more accurate, reliable and flexible rational allocation scheme of water resources in the basin is obtained. The experimental results show that the constructed model takes into account the two main elements, forming a balanced relationship between the two, and is suitable for the rational allocation of water resources in river basins that need economic development and environmental protection. The output results of the model solving algorithm are highly consistent and show efficient convergence ability. The results of rational allocation of water resources in the basin show that the water supply and demand of the departments in each planning period of the experimental area have basically reached a balance, but there is still a small amount of water shortage, of which the ecological environment is the most water-deficient.
Kidney Allograft Torsion After Simultaneous Pancreas Kidney Transplantation: Case Report and Review of Literature
Authors: Obana Ayato , Akabane Miho , Hamilton Matthew , Shah Kejal , Punjala Rithin Sai , Limkemann Ashley , Schenk Austin , Singh Navdeep , Rajab Amer , Bumgardner Ginny , Washburn Kenneth , Alebrahim Musab
Published: January 2025

Kidney allograft torsion (KAT) is a rare but critical complication of kidney transplantation that can lead to graft loss due to acute ischemia. This report presents a case of KAT resulting in graft loss 9 months following intraperitoneal simultaneous pancreas and kidney (SPK) transplant and reviews previous reports to identify potential high‐risk features. A 38‐year‐old female with end‐stage renal disease secondary to Type 1 diabetes mellitus underwent an intraperitoneal enteric drained SPK transplant. Nine months post‐transplantation, she presented with nausea, vomiting, severe abdominal pain, decreased urine output, and diarrhea. An ultrasound showed moderate hydronephrosis and no blood flow to the renal hilum. Exploratory laparotomy revealed a necrotic renal allograft twisted 360° counterclockwise on its vascular pedicles. Despite detorsion, the graft showed no signs of viability, necessitating transplant nephrectomy. This case highlights the rarity and severity of KAT, particularly in intraperitoneal kidney transplants. The patient’s low body mass index (BMI) (23.4 kg/m2), female sex (wider pelvis), and minimal intra‐abdominal adhesions may have contributed to increased graft mobility, predisposing to KAT. Other potential risk factors include elongated vascular pedicle and immunosuppression‐related reduced adhesion formation. The nonspecific presentation of KAT emphasizes the need for high clinical suspicion and prompt ultrasonographic evaluation in cases of graft abnormalities. This report underscores the importance of considering patient‐ and graft‐specific factors in assessing KAT risk and the critical nature of early detection and intervention to prevent graft loss.
METTL3/miR‐192‐5p/SCD1 Axis Regulates Lipid Metabolism to Affect T Cell Differentiation in Asthma
Authors: Chen Zhengrong , Yan Dingwei , Guo Suyu , Song Yiyi , Zhang Xinxing , Gu Wenjing , Dong Heting , Huang Li
Published: January 2025

Background: This study aimed to explore the mechanisms underlying T‐cell differentiation in asthma.

Methods and Results: Flow cytometry was performed to detect Th cells. LC‐MS/MS was performed to assess lipid metabolism. HE staining was performed to assess the pathological changes of the lung tissues. ELISA was performed to detect cytokine levels. The results of quantitative real‐time polymerase chain reaction (qRT‐PCR) and western blot showed that miR‐192‐5p expression was decreased, while SCD1 expression was increased in CD4+T cells isolated from the peripheral blood of children with asthma. The dual luciferase reporter assay determined the direct interaction between miR‐192‐5p and SCD1. MiR‐192‐5p inhibitor reduced ASCL3 and PPARα, increased FASN and SREBP1c mRNA expression and protein levels in mouse spleen CD4+T cells, and elevated Th2 and Th17 cells, but these effects were reversed by the SCD1 inhibitor. Oleic acid (OA) reduced Th1 cells and increased Th2 and Th17 cells in mouse spleen CD4+T cells treated with an SCD1 inhibitor. Additionally, pri‐miR‐192‐5p expression was increased in CD4+T cells isolated from the peripheral blood of asthmatic children, and the deletion of METTL3 upregulated pri‐miR‐192‐5p expression in an m6A‐dependent manner. MiR‐192‐5p mimic and inhibitor both reversed miR‐192‐5p and SCD1 expression affected by overexpression or deletion of METTL3, both in vivo and in vitro. Furthermore, METTL3 overexpression attenuated lung inflammation, elevated Th1 cells, and reduced Th2 and Th17 cells in CD4+T cells isolated from the peripheral blood of asthmatic mice. These effects were reversed by the miR‐192‐5p inhibitor.

Conclusion: These results suggest that METTL3/miR‐192‐5p/SCD1 axis regulates lipid metabolism and affects T cell differentiation, thus affecting asthma progression. This study may provide novel insights into the pathogenesis of asthma and a new treatment strategy.
Eagle strategy-based crow search algorithm for UCP: integration of pumped storage units in smart grid environment
Authors: Rizki Adil , Habachi Rachid , Tahiry Karim , Echchatbi Abdelwahed
Published: January 2025

This paper proposes a hybrid eagle strategy with crow search algorithm (ES-CSA) as local optimizer to solve the unit commitment problem (UCP) in power systems. The algorithm aims to minimize total operational costs while considering pumped storage units as spinning reserves. The proposed methodology combines the exploration capability of ES with CSA's local search efficiency to determine optimal generator scheduling and power dispatch. The approach is validated using standard test cases from the literature, demonstrating improved convergence and cost reduction compared to existing methods. Results confirm the effectiveness of integrating pumped storage units in reducing overall system costs while maintaining reliable operation.
Solving problems of the flexible scheduling machines
Authors: Geci Fis , Bytyçi Eliot
Published: January 2025

Flexible job scheduling problem (JSP) as an optimization problem, tends to find solution for allowing different operations to be processed faster. This problem could be solved by genetic algorithm, as we have proven in another experiment. Now, we have tried to outperform state of the art, by using parallel genetic algorithm. Parallel genetic algorithm has two types and we have chosen the most popular one coarsed grained genetic algorithm, for our specific case. The results have improved time wise and are promising in some of the datasets, while a need exists for improving on other ones. In the future, we will compare both versions of parallel genetic algorithms but also compare the results to another algorithm.
Three-level common emitter-current source inverter equipped with MPPT system for photovoltaic power converter
Authors: Suroso Suroso , Noguchi Toshihiko
Published: January 2025

Common emitter-current source inverter (CE-CSI) has unique features with its common emitter structure of its power switches. Hence, instead of its simpler power supply for gate drive circuits, it also allows higher switching operation because of zero gradient voltage of its power switches. One of interesting applications of current source power inverter is for photovoltaic (PV) power converter. This paper discussed the three-level CE-CSI equipped with current based incremental resistance maximum power point tracking (MPPT) system as a new alternative for PV system power converter. Test results revealed some characteristics of PV power conversion using this inverter. Moreover, in order to investigate the system performance approaching real condition, testing during partial shading condition of PV modules were also conducted. Test results verified the efficacy of the incremental resistance based MPPT algorithm implemented in the CE-CSI circuits for increasing the performance of PV power generation.
Optimized convolutional neural network deep learning for Arabian handwritten text recognition
Authors: Ritonga Mahyudin , L. Bangare Manoj , Manoj Bangare Pushpa , L. Bangare Sunil , Sachin Vanjire Seema , Moholkar Kavita , Kasat Kishori , Rozak Purnama
Published: January 2025

In general, the term handwritten character recognition (HCR) refers to the process of recognizing handwritten characters in any form, whereas handwritten text recognition (HTR) refers to the process of reading scanned document images that include text lines and converting those text lines into editable text. The identification of recurring structures and configurations in data is the primary focus of the field of machine learning known as pattern recognition. Optical character recognition, often known as OCR, is a challenging issue to solve when it comes to the field of pattern recognition. This article presents machine learning enabled framework for accurate identification of Arabian handwriting. This framework has provisions for image processing, image segmentation, feature extraction and classification of handwritten images. Images are enhanced using contrast limited adaptive histogram equalization (CLAHE) algorithm. Image segmentation is performed by k-means algorithm. Classification is performed using convolutional neural network (CNN) VGG 16 and support vector machine (SVM) algorithm. Classification accuracy of CNN VGG 16 is 99.33%.
Machine learning-based and synthetic aperture radar time-series data for rice classification over Sentinel-1 imagery
Authors: Nardkulpat Attawut , Boonpook Wuttichai , Sitthi Asamaporn , Tan Yumin
Published: January 2025

Rice extraction is critical in remote sensing, especially in Suphan Buri province, Thailand, using Sentinel-1 synthetic aperture radar (SAR) time-series data and advanced machine learning algorithms. Given the challenges of varied terrains and diverse crop types, the research employs different polarization modes (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV+VH) to enhance classification accuracy. The study evaluates the performance of three machine learning algorithms: random forest, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). The results demonstrate that combined VV+VH polarization outperforms VV and VH alone, providing better accuracy due to its ability to capture more detailed object features. LightGBM emerged as the most effective among the algorithms, particularly when dealing with large datasets. After hyperparameter tuning (n_estimators: 820, max_depth: 10, and learning_rate: 0.01), LightGBM achieved the highest accuracy. The rice class showed exceptional precision, recall, and F1-score, surpassing other land-use classes (agriculture/forest and urban areas). However, these classes still pose challenges, highlighting the need for future studies to integrate multi-sensor data and explore more sophisticated machine-learning models. This research offers a promising approach to enhancing rice monitoring and management in diverse agricultural landscapes, contributing to more accurate and efficient farming practices.
Wideband and high gain mmWave antenna with phase gradient metasurface
Authors: Soni Devendra , Yadav Dinesh , Tiwari Manish
Published: January 2025

This study presents a phase gradient metasurface (PGM) measuring 30 mm on each side and discusses its development. The use of multiple unit cell sizes is critical to the design of the PGM that was produced. A sand-timer-shaped monopole antenna was designed specifically for wideband millimetre wave (mmWave) applications. An antenna structure is reinforced with a PGM that was specifically designed to increase antenna gain over its bandwidth. This is done to increase the antenna gain overall. The antenna has a bandwidth of 9.18 GHz, which includes mmWave frequencies ranging from 20.78 GHz to 29.96 GHz. For a wideband response, the ground plane must contain flaws. These flaws must exist on the surface of the ground plane. This study presents an in-depth examination of the antenna, PGM design, and operating principles, backed up by experimental verification. The use of PGM results in a 5 dB increase in antenna gain, with an average improvement of 2 dBi across all frequencies.
Effect of gamma radiation on semi-crystalline polyvinyl chloride polymer for low-voltage cable insulator
Authors: Hutagaol Antonio Gogo , Bayquni Muhammad Ilham , Setiawan Jan , Putranto Dwi , Sudjadi Usman , Sungkono Sungkono , Kriswarini Rosika , Masrukan Masrukan , Yunus Muhamad Yasin
Published: January 2025

This study explores the properties of semi-crystalline polyvinyl chloride (PVC) polymer as insulation material for low-voltage (LV) cables under high gamma radiation exposure. Test samples underwent gamma radiation (60Co) at doses of 25, 50, 100, 200, 400, and 800 kGy. The evaluation encompassed surface morphology, electrical conductivity, thermal characteristics, and mechanical properties via tensile tests. Electron microscopy observation indicated surface smoothing and flattening occurred at an irradiation dose of 800 kGy. Gamma radiation with increasing doses results in similar thermogram profiles with slight differences in melting temperature and residue mass. The sample irradiated at a gamma dose of 25 kGy generates an increase in the percentage of crystallinity, indicating the occurrence of crosslinking, while other doses exhibit a decrease of crystallinity with increasing radiation dose. Tensile stress significantly dropped up to 400 kGy but increased at 800 kGy. Elongation at break (EAB) decreased with higher gamma radiation doses. Overall, materials up to 800 kGy remained non-brittle, serving as effective insulators and demonstrating thermal stability within high gamma radiation exposure conditions.
Comparative simulation of phishing attacks on a critical information infrastructure organization: an empirical study
Authors: Sirawongphatsara Patsita , Pornpongtechavanich Phisit , Phanthuna Nattapong , Daengsi Therdpong
Published: January 2025

Nowadays, cybersecurity is crucial. Therefore, cybersecurity awareness should be a concern for businesses, particularly critical infrastructure organizations. The results of this study, using simulated phishing attacks, indicate that in the first attempt, workers of a Thai railway firm received a phony email purporting to inform recipients of a special deal from a reputable retailer of information technology (IT) equipment. The findings showed that 10.9% of the 735 workers fell for the scam. This demonstrates a good level of awareness regarding cyber dangers. The workers who were duped by the initial attack received awareness training. Next, a second attempt was carried out. This time, the strategy was for the workers to change their passwords through an email notification from the fake IT staff. According to the findings, 1.4% of the workers fell victim to both attacks (different email content), and a further 8.0% of the workers who did not fall victim to the first attack were deceived. Furthermore, after the statistical analysis, it was confirmed that there is a difference in the relationship between the workers and the two phishing attack simulations using different content. As a result, this study has demonstrated that different types of content can affect levels of awareness.
Arabic dialect classification using an adaptive deep learning model
Authors: Tibi Nejib , Anouar Ben Messaoud Mohamed
Published: January 2025

In daily life, dialect is the most widely used form of communication. Automatically identifying a dialect is a challenging task, particularly when dealing with similar dialects spoken in the same nation. In this study, we developed an automatic dialect identification of feature extraction based on the deep learning model. First, we extract the cepstral features, the fundamental frequency and glottal instances using our multi-scale product analysis (MPA) of the speech signal. These parameter measurements from the MPA of the speech signal are used as features for the designed Hamilton neural network (HNN) classifier. Our classifier considers both the external and the internal dependencies and allows one to code the dependencies by composing the multi-dimensional features as single entities as well as by determining the correlations between the elements by the recurrent operation. Experimental results show that the proposed dialect identification system achieves significant performance gains compared to current HNN-based approaches. The proposed system is rigorously designed to exploit the strong temporal and spectral relationships of speech, and its components operate independently and in parallel to accelerate processing. In addition, the experimental results indicated the robustness of our deep learning model for the identification of Arabic dialect.
The implementation of the K-nearest neighbor algorithm to detect the KRSRI robot obstacles
Authors: Hamonangan Nasution Tigor , Muhammad Prihandoyo Arza , Seniman Seniman
Published: January 2025

The Indonesian SAR robot contest (KRSRI) is a development of the fire extinguisher robot contest (KRPAI); initially, the robot at KRPAI only put out fires. Still, at KRSRI, the robot was asked to prioritize the SAR function. The robot had to overcome obstacles in this contest to complete it. Based on this, an obstacle detection system for the robot was designed using machine learning with the K-nearest neighbor algorithm and gray level co-occurrence matrix feature extraction. Later, the robot is expected to be able to carry out accurate obstacle detection to prioritize efficiency so that no more time is consumed due to the robot incorrectly detecting an obstacle. The results of the tests that have been carried out show that the detection accuracy based on the test dataset is 80% for rising barriers, 100% for debris obstacles, and 90% for step obstacles, and an error value of 20% for increasing obstacles is obtained, 0% for debris obstacles, and 10% for stair obstacles.
Linear and nonlinear control design for a quadrotor
Authors: Hadid Samira , Boushaki Zamoum Razika , Refis Youcef
Published: January 2025

In the current study, the quadrotor's nonlinear dynamic model is developed using the Newton-Euler approach. Following that, several nonlinear and linear control strategies for tracking the quadrotor's trajectory are applied. First, by employing distinct controllers for each output variable, direct application of the linear proportional integral derivative (PID) controller to the nonlinear system is realized. This system may also be linearized about an operational point to generate linear controllers, according to the linear quadratic regulator (LQR) demonstration. Nevertheless, in practice, the system dynamics may not always be accurately reflected by this linear approximation and may even be relatively wasteful. Nonlinear regulators, including the feedback linearization (FBL) controller, sliding mode controller (SMC), and modified sliding mode controller (MSMC), perform better in such situations. The trajectory tracking capabilities, dynamic performance, and potential disruption impact of both methods are evaluated and compared. The FBL with LQR was the best controller among them all. The SMC and the MSMC were also very good in tracking the trajectory.
Fuzzy logic: a novel approach to compound noun extraction
Authors: Rassam Latifa , Zellou Ahmed
Published: January 2025

Compound noun extraction from textual documents presents a unique challenge due to the inherent complexity and variability in linguistic structures. Traditional approaches often struggle to accurately capture the nuanced semantics of compound nouns, primarily due to their rigid reliance on exact matches. In response, this research underscores the pivotal role of fuzzy logic in addressing the challenges associated with ambiguity and imprecision within compound noun extraction. Leveraging the inherent flexibility of fuzzy logic, we propose a novel approach that surpasses the limitations of traditional methods. Our method embraces the adaptability of fuzzy logic, providing a powerful and context-aware solution for compound noun extraction. Empirical evaluation demonstrates superior performance, with a macro precision of 0.572, recall of 0.607, and F-measure of 0.589, compared to traditional approaches. By incorporating fuzzy logic, our approach excels in handling variations and uncertainties present in natural language, ultimately offering a more accurate and nuanced representation of compound nouns within textual documents. This research not only advances the field of compound noun extraction but also underscores the efficacy of fuzzy logic in overcoming challenges associated with linguistic intricacies in information extraction tasks.
Heart disease detection using machine learning
Authors: Al-Habahbeh Mohammad , Alomari Moath , Khattab Hebatullah , Alazaidah Raed , Alshdaifat Nawaf , Abuowaida Suhaila , Alqatan Saleh , Arabiat Mohammad
Published: January 2025

Heart disease continues to be a major worldwide health issue, requiring accurate prediction models to improve early identification and treatment. This research aims to address two main objectives in light of the increasing prevalence of heart-related disorders. Firstly, it aims to determine the most efficient classifier for identifying heart disease among twenty-nine different classifiers that represent six distinct learning strategies. Furthermore, the research seeks to identify the most effective method for selecting features in heart disease datasets. The results show how well different classifiers and feature selection methods work by using two datasets with different features and judging performance using four important criteria. The evaluation results demonstrate that the RandomCommittee classifier outperforms in diagnosing heart illness, displaying strong skills across various learning strategies. This classifier exhibits favorable results in terms of accuracy, precision, recall, and F1-score metrics, hence confirming its appropriateness for predictive modeling in heart-related datasets. Moreover, the paper examines feature selection methods, specifically aiming to determine the most effective method for enhancing the predicted accuracy of heart disease models. The prediction models' overall performance is enhanced by their capacity to accurately identify and prioritize pertinent variables, thereby facilitating the early detection and management of heart-related problems.
Theoretical and experimental implementation of PID and sliding mode control on an inverted pendulum system
Authors: K. Dawane Mahendra , M. Malwatkar Gajanan
Published: January 2025

This paper explores the theoretical and experimental implementation of proportional-integral-derivative (PID) and sliding mode control (SMC) on an inverted pendulum system, a well-known problem in control engineering that is inherently unstable and highly nonlinear. The primary objective of this study is to evaluate and compare the effectiveness of these two control strategies in achieving system stabilization and robustness against disturbances. The PID controller, widely utilized due to its straightforward design and implementation, is developed based on the linearized model of the inverted pendulum. On the other hand, the SMC technique, known for its robustness to system uncertainties and external disturbances, is employed to tackle the nonlinear nature of the system. The controllers are tested in both simulation and real-time experimental environments to ensure the reliability of the findings. The results from the experiments indicate that while the PID controller performs adequately under nominal conditions, it struggles to maintain stability when faced with parameter variations and external perturbations. In contrast, the SMC exhibits superior performance by consistently stabilizing the pendulum even under adverse conditions, demonstrating its robustness and effectiveness in managing nonlinear systems. This comparative analysis provides valuable insights into the practical applications of PID and SMC, highlighting the trade-offs between simplicity and robustness in control system design.
Transformers for aerial images semantic segmentation of natural disaster-impacted areas in natural disaster assessment
Authors: Wiria Nugraha Deny , Ahmad Ilham Amil , Achmad Andani , Arief Ardiaty
Published: January 2025

Aerial image segmentation of natural disaster-impacted areas and detailed and automatic natural disaster assessment are the main focus of this study. Detecting and recognizing objects on aerial images of areas impacted by natural disasters and assessing natural disaster-impacted areas are still difficult problems. To solve these problems, this study utilizes four of the latest transformer-based semantic segmentation network models, bidirectional encoder representation from image transformers (BEIT), dense prediction transformer (DPT), OneFormer, and SegFormer, and proposes a detailed and automatic natural disaster assessment of the segmented image. The SegFormer model achieved the first-best result, and the OneFormer model achieved the second-best result. The SegFormer model outperformed OneFormer by 1.58% higher for the mean accuracy value and 4.28% for the mean intersection over union (mIoU) value. All receiver operating characteristics (ROC) curves have mean area under curve (AUC) values above 0.9, which means that the SegFormer model performs well in generating semantic segmentation images. The fuzzy c-means (FCM) clustering algorithm performed well and could automatically cluster the natural disaster assessments into four categories. This study has produced semantic segmentation of aerial images of areas impacted by natural disasters and natural disaster assessments, which can be used in natural disaster management systems.
Performance evaluation of feature extraction to improve the classification of PTM in C-glycosylation using XGBoost
Authors: Damayanti Damayanti , Rosyking Lumbanraja Favorisen , Junaidi Akmal , Sutyarso Sutyarso , Nugroho Susanto Gregorius , Hendrastuty Nirwana
Published: January 2025

Protein function is regulated by an important mechanism known as post-translational modification (PTM). Covalent and enzymatic protein modifications are added during protein biosynthesis, and such alterations significantly influence the regulation of gene activity and the functionality of proteins. Glycosylation, one type of PTM, involves adding sugar groups to a protein's structure. Numerous illnesses, such as diabetes, cancer, and the flu, have been linked to glycosylation. Therefore, it is critical to predict the presence of glycosylation, whether it occurs or not. Currently, predicting glycosylation sites is still done manually using biological methods, which require repeated experiments and a significant amount of time. To address these challenges, it is essential to rapidly develop computational data models using machine learning methods. In this study, the extreme gradient boosting (XGBoost) method is implemented, and C-glycosylation data is obtained from the publicly accessible UniProt website. The objective is to enhance the accuracy of C-glycosylation prediction using the XGBoost method. Feature extraction is performed using amino acid index (AAindex), composition, transition, and distribution (CTD), solvent AccessiBiLitiEs (SABLE), hydrophobicity, and pseudo amino acid composition (PseAAC) to improve accuracy. The minimum redundancy maximum relevance (MRMR) method is applied for feature selection. The findings of the study demonstrate that the PTM C-glycosylation prediction achieved 100%.
Medication box management system with automatic dosing integrated with IoT-based Android app and Firebase
Authors: Nurhakim Raihan , Ath Thahirah Al Azhima Silmi , Fahmi Arief Hakim Nurul , Al Qibtiya Mariya , Maulana Luthfi , Rohman Saepul
Published: January 2025

Utilizing Firebase technology and an Android internet of things (IoT) application, the research endeavors to create a smart medicine box in order to enhance the efficacy of automated drug management. Hardware implementation, software implementation, and 3D design planning for automatic dosage adjustment are the methods utilized. The results prove that the application effectively controls the dosage, evacuation schedule, and quantity of the medication based on the user’s input. Boundary value analysis (BVA) black box testing demonstrated that every feature of the application functions as intended. Furthermore, the efficacy of drug production testing indicates that the smart medicine box exhibits a notable level of precision, albeit with a limited number of inaccuracies that could be rectifiable through additional parameter and mechanism optimizations within the drug box. Consequently, the investigation has effectively produced an automated drug management system that has the potential to enhance drug use supervision and safety, particularly for elderly services individuals residing alone.
Wireless sensor network using nRF24L01+ for precision agriculture
Authors: Abidin Zainul , Falah Raisul , Setyawan Raden Arief , Wardana Fitri Candra
Published: January 2025

Precision agriculture is a strategy for varying inputs and cultivation methods to suit varying soil conditions and agricultural crops. In order to optimize precision agriculture, wireless sensor network (WSN) is suitable to be integrated. In this research, network devices that communicate using nRF24L01+ based WSN was proposed. As a prototype, four sensor nodes were employed to measure the parameters of air temperature and humidity, soil moisture, and power supply voltage. While, a sink node serves to store measurement data locally. The data are sent to the sink node with a mesh network topology and saved in a comma-separated values (CSV) file and local database. Experimental results show that each sensor node can measure all parameters and successfully send data to the sink node every 1 minute without losing the data. The mesh topology can route data transfer automatically. Round trip time (RTT) of each sensor node depends on the distance from each node. Average power consumption of all sensor nodes in send mode is between 84 mW and 90 mW. Meanwhile, in sleep mode, the sensor nodes 1 and 2 consumed around 21-22 mW and the sensor nodes 3 and 4 consumed around 30 mW which are lower than the send mode.
Optimization model and development of power estimation in photovoltaics for self-load consumption
Authors: Yosefa Hutajulu Olnes , Dwi Waluyo Bakti , Dodi Suryanto Eka , Haryanto Sinaga Denny , Suwarno Suwarno , Indra Cahyadi Catra , Junaidi Agus
Published: January 2025

The Indonesian government's initiative to meet a renewable electricity supply 2050 of around 100% and its ecological benefits, has an impact on the big challenge where the increasing number of photovoltaic (PV) systems due to high power input and risks to electricity network security conditioned sunny weather for a long time. This research aims to provide predictable power output for network operators, thereby enabling longer planning periods and increasing the protection of the national operational network. Independent consumption by household consumers will impact household energy behavior and affect the security of the electricity network. Consumption itself provides an incentive to increase the cost of energy use which will be much higher than the compensation received, so software is needed which can support PV system prediction patterns. This pattern will close the gap in software development in the process of implementing optimization and configuration based on PV estimates. The consumption planning optimization algorithm itself validates the predetermined scenario optimization process. This research contributes to the efficiency and stability of energy use in households.
Driving training-based optimization technique for estimating synchronous motor excitation current
Authors: Murugesan Karthikeyan , Ramasubbu Rengaraj
Published: January 2025

This paper introduces an innovative application of the driving training-based optimization (DTBO) technique to optimize a multiple linear regression (MLR) model for estimating synchronous motor (SM) excitation current. Inspired by structured learning in driving training, DTBO is utilized to accurately determine regression coefficients with fast convergence. The DTBO-based MLR model is compared with other optimization techniques, such as gravitational search algorithm (GSA), artificial bee colony (ABC), genetic algorithm (GA), symbiotic organisms search (SOS), and various machine learning algorithms. Using a dataset of 557 samples (390 for training, 167 for testing), the DTBO-based model achieves the lowest objective function value, demonstrating superior performance in minimizing estimation errors. Key metrics like maximum error, error percentage, standard deviation, and root mean square error (RMSE) validate the results. The DTBO-based approach not only outperforms other methods but also provides a clear mathematical relationship between excitation current and input features, enabling easier hardware implementation and faster computation. This study establishes the DTBO-based MLR model as a robust and efficient alternative to complex machine learning algorithms for estimating SM excitation current, offering significant contributions to power systems engineering and smart grid applications.
Sliding mode control for speed loop combined with MTPA strategy of IPMSM applied in electric vehicles
Authors: Thi Hoai Thu Anh An , Hung Cuong Tran , Minh Chien Duong
Published: January 2025

The interior permanent magnet synchronous motor (IPMSM) 's outstanding features, such as quick torque mobility capability, broad speed adjustability, robust mechanical structure, and high efficiency, make it particularly suitable for electric vehicle propulsion systems. This paper proposes a speed loop utilising the sliding mode control (SMC) with exponential reaching law and proportional-derivative term-ks, facilitating quicker transient responses and enhancing system stability. Moreover, coupling with the maximum torque per ampere strategy (MTPA) on current to improve motor torque in flux weakening region and to extend the adjustable range of motor speed for electric vehicle propulsion systems is discussed. Furthermore, with the proposed control methods and strategies, the system achieves stability despite environmental noise and uncertainties caused by uncertain parameters. Finally, simulation results conducted on MATLAB/Simulink software verify the correctness of the proposed control methods.
Designing electric braking system for brushless direct current motor as an electric bicycle propulsion
Authors: Supriyono Heru , Supardi Agus , Romadhon Muhammad Rafi Nur , Yulianto Aldi , Utomo Untung Budi
Published: January 2025

One of the problems arising in the conversion of pedal-based bicycles into electric bicycles using brushless direct current (BLDC) motors is how to provide an electric braking system for the BLDC motor. Published research on braking systems for electric bicycles from a practical perspective was still limited. The objective of the article is to develop an electric braking system for BLDC motor as a propulsion of electric bicycle converted from pedal-based bicycle from the empirical point of view. The pedal-based bicycle was converted to the electric bicycle by fully replacing its rear wheel including its chain system with the BLDC motor. The braking system was developed by adding a DC motor as a load for BLDC in the braking mode. Test was conducted by turning on the acceleration handle and then the braking action was applied. The stoppage time was recorded from the start of braking action until the wheel was fully stopped. The test results showed that the addition of a direct current (DC) motor as a load can shorten stoppage time of the electric bicycle dramatically, i.e., needs 1.65 seconds compared to 10.14 seconds and 3.09 seconds for without braking and with braking but without DC motor as a load respectively.
Position control to expand the headlights’ angle of a car by DC motor drive system using PSO algorithm for speed loop
Authors: Thi Hoai Thu Anh An , Cong Duc Pham , Dinh Huan Le
Published: January 2025

Today, along with the robust development of society, cars are almost considered a primary means of transportation. This article focuses on designing headlight controls for older car models that are not equipped with adaptive headlight systems (AHS), which are different from modern cars such as Porsche, BMW, Audi, and Mercedes-Benz vehicles. The design is for a lighting system that operates during nighttime to improve illumination and enhance visibility in curves, with cost-effective and suitable solutions for average vehicles to ensure safety. This system uses a DC motor to control the headlight angle based on the steering wheel rotation. It is combined with the particle swarm optimization (PSO) algorithm to find the best response parameters for the proportional-integral-derivative (PID) controller. Research results on the MATLAB/Simulink and the experimental model show that the model established by this method has good accuracy, the controllers can significantly reduce the excessive deviation of the headlights’ operational precision, and traffic accidents can be minimized, increasing safety for users.
A guide for selection of wireless communication technology for effective and robust early forest fire detection system
Authors: Salaria Anshika , Singh Amandeep
Published: January 2025

The world is facing a major ecosystem crisis due to global warming and pollution. Considering the rate at which the temperatures are rising, one must think about the causes and origins of this extreme environmental shift. Today, countries like India, have cities ranked as some of the most polluted cities in the world. Apart from vehicular traffic and industrial wastes, one of the prime components of the entire problem is forest fires. Burning forests emit tons of harmful gases into the atmosphere. This disaster also leaves drastic aftereffects on the economy and society. Therefore, an efficient system should be designed to monitor the forest fires at the earliest. Highlighting the role of wireless sensor networks in the irregular terrains of forests and considering the technical challenges, it is important to identify, first, the best technology for communication among sensors, in such complex terrains. Second, the identification of an optimization algorithm for the deployment of sensors to achieve maximum coverage This work presents an analysis of state-of-the-art wireless sensor networks to identify a reliable communication technique. Further an optimization algorithm is proposed for maximum coverage with a minimum number of sensors. The algorithm outperforms the other state-of-the-art algorithms in simulation results.