bluuebunny/crossref_metadata_2025 huggingface dataset:
Prospective validity of a clinical prediction rule for response to non-surgical multidisciplinary management of knee osteoarthritis in tertiary care: a multisite prospective longitudinal study
Authors: Window Peter , Raymer Maree , McPhail Steven M , Vicenzino Bill , Hislop Andrew , Vallini Alex , Elwell Bula , O'Gorman Helen , Phillips Ben , Wake Anneke , Cush Adrian , McCaskill Stuart , Garsden Linda , Dillon Miriam , McLennan Andrew , O'Leary Shaun
Published: March 2024

Objectives

We tested a previously developed clinical prediction tool—a nomogram consisting of four patient measures (lower patient-expected benefit, lower patient-reported knee function, greater knee varus angle and severe medial knee radiological degeneration) that were related to poor response to non-surgical management of knee osteoarthritis. This study sought to prospectively evaluate the predictive validity of this nomogram to identify patients most likely to respond poorly to non-surgical management of knee osteoarthritis.

Design

Multisite prospective longitudinal study.

Setting

Advanced practice physiotherapist-led multidisciplinary service across six tertiary hospitals.

Participants

Participants with knee osteoarthritis deemed appropriate for trial of non-surgical management following an initial assessment from an advanced practice physiotherapist were eligible for inclusion.

Interventions

Baseline clinical nomogram scores were collected before a trial of individualised non-surgical management commenced.

Primary outcome measure

Clinical outcome (Global Rating of Change) was collected 6 months following commencement of non-surgical management and dichotomised to responder (a little better to a very great deal better) or poor responder (almost the same to a very great deal worse). Clinical nomogram accuracy was evaluated from receiver operating characteristics curve analysis and area under the curve, and sensitivity/specificity and positive/negative likelihood ratios were calculated.

Results

A total of 242 participants enrolled. Follow-up scores were obtained from 210 participants (87% response rate). The clinical nomogram demonstrated an area under the curve of 0.70 (p<0.001), with greatest combined sensitivity 0.65 and specificity 0.64. The positive likelihood ratio was 1.81 (95% CI 1.32 to 2.36) and negative likelihood ratio 0.55 (95% CI 0.41 to 0.75).

Conclusions

The knee osteoarthritis clinical nomogram prediction tool may have capacity to identify patients at risk of poor response to non-surgical management. Further work is required to determine the implications for service delivery, feasibility and impact of implementing the nomogram in clinical practice.
Tailored anticoagulant treatment after a first venous thromboembolism: protocol of the Leiden Thrombosis Recurrence Risk Prevention (L-TRRiP) study - cohort-based randomised controlled trial
Authors: Burggraaf-van Delft J Louise I , van Rein Nienke , Bemelmans Remy H H , van den Berg Jan-Willem K , Bruggeman Coty Y , Cloos-van Balen Marissa , Coppens Michiel , Eefting Matthijs , Ende-Verhaar Yvonne , van Es Nick , van Guldener Coen , de Jong Wouter K , Kleijwegt Fleur , Koster Ted , Kroon Cees , Kuipers Saskia , Leentjens Jenneke , Luijten Dieuwke , Mairuhu Albert T A , Meijer Karina , van de Ree Marcel A , Roos Rick , Schrover Ilse , Swart-Heikens Janneke , van der Velden Annette W G , van den Akker-van Marle Elske M , le Cessie Saskia , Geersing Geert-Jan , Middeldorp Saskia , Huisman Menno V , Klok Frederikus A , Cannegieter Suzanne C
Published: March 2024

Introduction

Patients with a first venous thromboembolism (VTE) are at risk of recurrence. Recurrent VTE (rVTE) can be prevented by extended anticoagulant therapy, but this comes at the cost of an increased risk of bleeding. It is still uncertain whether patients with an intermediate recurrence risk or with a high recurrence and high bleeding risk will benefit from extended anticoagulant treatment, and whether a strategy where anticoagulant duration is tailored on the predicted risks of rVTE and bleeding can improve outcomes. The aim of the Leiden Thrombosis Recurrence Risk Prevention (L-TRRiP) study is to evaluate the outcomes of tailored duration of long-term anticoagulant treatment based on individualised assessment of rVTE and major bleeding risks.

Methods and analysis

The L-TRRiP study is a multicentre, open-label, cohort-based, randomised controlled trial, including patients with a first VTE. We classify the risk of rVTE and major bleeding using the L-TRRiP and VTE-BLEED scores, respectively. After 3 months of anticoagulant therapy, patients with a low rVTE risk will discontinue anticoagulant treatment, patients with a high rVTE and low bleeding risk will continue anticoagulant treatment, whereas all other patients will be randomised to continue or discontinue anticoagulant treatment. All patients will be followed up for at least 2 years. Inclusion will continue until the randomised group consists of 608 patients; we estimate to include 1600 patients in total. The primary outcome is the combined incidence of rVTE and major bleeding in the randomised group after 2 years of follow-up. Secondary outcomes include the incidence of rVTE and major bleeding, functional outcomes, quality of life and cost-effectiveness in all patients.

Ethics and dissemination

The protocol was approved by the Medical Research Ethics Committee Leiden-Den Haag-Delft. Results are expected in 2028 and will be disseminated through peer-reviewed journals and during (inter)national conferences.

Trial registration number

NCT06087952.
Longitudinal association of sleep duration with possible sarcopenia: evidence from CHARLS
Authors: Lv Xiaoling , Peng Wenjia , Jia Bingbing , Lin Ping , Yang Zhouxin
Published: March 2024

Objectives

There are limited data on the relationship between sleep duration and possible sarcopenia. Hence, this study aimed to investigate the associations of sleep duration with possible sarcopenia and its defining components based on the China Health and Retirement Longitudinal Study (CHARLS).

Design

A retrospective cohort study.

Setting

This study was conducted on participants aged over 45 years applying the 2011 baseline and 2015 follow-up survey from CHARLS covering 450 villages, 150 counties and 28 provinces.

Participants

Data from 5036 individuals (2568 men and 2468 women) free of possible sarcopenia at baseline were analysed.

Primary and secondary outcome measures

The dose-response relationship between sleep duration and possible sarcopenia.

Results

During 4 years of follow-up, 964 (19.14%) participants developed possible sarcopenia. Compared with participants who slept 6–8 hours per night, those with shorter sleep duration (<6 hours per night) were independently associated with 22% (OR, 1.22; 95% CI, 1.04 to 1.44) increased risk of developing possible sarcopenia and 27% (OR, 1.27; 95% CI, 1.04 to 1.57) increased risk of developing low handgrip strength after controlling for potential confounders. Long sleep duration (>8 hours per night) was not significantly associated with incident possible sarcopenia. The plots of restricted cubic splines exhibited an atypical inverse J-shaped association between sleep duration and possible sarcopenia. Subgroup analysis showed a stronger association between sleep duration and possible sarcopenia in participants aged 45–59 years and composed of male populations.

Conclusions

Short sleep duration was a potential risk factor for possible sarcopenia and low handgrip strength. The improvement of sleep duration should be considered a target in early preventive and administrative strategies against the development of handgrip strength decline and further reduced the occurrence of sarcopenia.
On properties of the distribution of virtual wormholes in a vacuum
Authors: Kirillov A. A. , Savelova E. P.
Published: March 2024

A model of spacetime foam in the form of an arbitrary distribution of spherical Euclidean wormholes is considered. A method for constructing the exact solution of Einstein’s Euclidean equations for the metric corresponding to this model is proposed. In the framework of our model, we obtain the expression for the Euclidean action and its dependence on the parameters of wormholes in the explicit form. It is shown how the solutions obtained makes it possible to determine all possible correlation functions associated with the parameters of virtual wormholes in the vacuum state.
iSwap: A New Memory Page Swap Mechanism for Reducing Ineffective I/O Operations in Cloud Environments
Authors: Wang Zhuohao , Liu Lei , Xiao Limin
Published: March 2024

This article proposes
iSwap
, a new memory page swap mechanism that reduces the ineffective I/O swap operations and improves the QoS for applications with a high priority in cloud environments. iSwap works in the OS kernel. iSwap accurately learns the reuse patterns for memory pages and makes the swap decisions accordingly to avoid ineffective operations. In the cases where memory pressure is high, iSwap compresses pages that belong to the latency-critical (LC) applications (or high-priority applications) and keeps them in main memory, avoiding I/O operations for these LC applications to ensure QoS, and iSwap evicts low-priority applications’ pages out of main memory. iSwap has a low overhead and works well for cloud applications with large memory footprints. We evaluate iSwap on Intel x86 and ARM platforms. The experimental results show that iSwap can significantly reduce ineffective swap operations (8.0%–19.2%) and improve the QoS for LC applications (36.8%–91.3%) in cases where memory pressure is high, compared with the latest LRU-based approach widely used in modern OSes.
The Ammunition Projectile Test and Evaluation Used Polymer Flexible Film Sensors System Design and Application
Authors: Cui Xiangdong , Xu Youliang , Zhang Yanli , Qi Xinglin , Guo Aiqiang
Published: March 2024

Background. The mainly lethal ability of ammunition fragments on creatures is achieved by hitting the effective organs or key parts of the biological body with high-speed projectiles. How to efficiently and accurately obtain the projectile speed and hitting position coordinates when the fragment hits the creature after the ammunition blast is the key to the scientific evaluation of ammunition power. Materials and Methods. For the measurement of fragment velocity and hitting coordinates, a series of flexible film circuit sensors can be generated by printing comb-like circuits on polyethylene terephthalate substrates using silver paste printing technology. These sensors are cheap, flexible, and easy to fold and can be printed into different shapes according to the characteristics of the test target to simulate the biological key organs or lethal parts. At the same time, the software and hardware design of the high-speed data signal reading and processing module can realize the data rapidly recording and processing and quickly give the ammunition fragment parameter test results. Results. The test accuracy of the fragment velocity of the laser light screen target and the flexible circuit sensor is compared through the live-fire test. It is proved that the test accuracy of the flexible sensor based on the polymer substrate can meet the accuracy requirements. The flexible sensor based on the organ simulation can quickly give the accurate hit position of the fragment. Conclusion. The newly polymer substrate printed circuit sensor system is a new type of sensor used to replace the laser screen target, and the copper comb printed circuit in the ammunition power test, which can improve the parameter test accuracy, reduce the test consumption, and improve the test quality.
Funnel-Based Adaptive Neural Fault-Tolerant Control for Nonlinear Systems with Dead-Zone and Actuator Faults: Application to Rigid Robot Manipulator and Inverted Pendulum Systems
Authors: Alruwaily Ymnah , Kharrat Mohamed
Published: March 2024

This study addresses an adaptive neural funnel fault-tolerant control problem for a class of strict-feedback nonlinear systems with actuator faults and input dead zone. To guarantee the boundedness of the tracking error, a modified transformation for funnel error is devised and incorporated into the control design process. To manage unknown nonlinear functions, radial basis function neural networks (RBFNN) are employed in designing an adaptive neural funnel fault-tolerant controller through the backstepping technique. The proposed controller guarantees the output tracking error stays within a predefined funnel, and all signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, simulations of a rigid robot manipulator system and an inverted pendulum system are conducted to validate the practicality and effectiveness of the proposed control method.
Consumption and Informal Trade of Milk in the North of Antioquia (Colombia)
Authors: Zapata-Salas Richard , Guarín José F. , Ríos-Osorio Leonardo A.
Published: March 2024

The excessive and irrational use of antibiotics to control bovine mastitis and the informal trade in milk with antibiotic residues are objects of great interest for public health, due to the problems associated with the consumption of milk with antibiotic residues on human, animal, and environmental health, and antibiotic resistance. The objective of this study was to understand the attitudes of dairy farmers towards the self‐consumption of milk on the farm, the use of milk with antibiotic residues, and the formal and informal milk trade that generates risks for public health. Mixed methods: cross‐sectional and grounded theory. Convergent triangulation design. The study was carried out in 9 dairy municipalities in the North of Antioquia, where 216 dairy farmers participated in the quantitative component; and 17 milk producers and 9 veterinarians took part in the qualitative component. A dairy farmer characterization survey was conducted, as well as a survey on the use and marketing of milk from cows with udder health problems and/or under antibiotic treatment. Semistructured interviews were conducted on the same subject. The variable “Intention to sell milk in the village when the dairy industry does not buy it due to high BTSCC” is associated with the BTSCC variable. The variable “Type of marketing reported” is associated with the CFU variable. 5 categories: self‐consumption of milk, use of milk with antibiotics, informal milk trade, control of the dairy industry, and beliefs about the elimination of antibiotics in milk, were constructed to theorize about udder health and public health. Sociocultural, political, and economic factors affect decision making in dairy farmers regarding the use and marketing of milk from cows with mastitis and antibiotic residues. These attitudes and behaviors have public health implications.
Analysis of Factors Affecting the Severity of Injuries in Electric Two‐Wheeled Vehicle Crashes with or without Violation: A Random Parametric Logit Model considering Heterogeneity of Means and Variances
Authors: Wei Fulu , Han Yuxin , Guo Yanyong , Guo Yongqing
Published: March 2024

Electric two‐wheeled vehicle is one of the main commuting tools in China, but they are also more likely to have violations of the road group. In order to study the effect of the presence of violation on the severity of road traffic crashes among electric two‐wheeler riders, in this study, the effects of rider characteristics, road characteristics, collision characteristics, and environmental characteristics on the severity of injuries of electric two‐wheeled vehicle riders were considered separately analyzed based on the data of 6403 two‐wheeled electric vehicle traffic crashes in a region of Shandong Province from 2015 to 2021, and a random parametric logit model considering the heterogeneity of the mean and the variance (RP‐HMV logit) was established based on the presence or absence of violation behaviors of riders, respectively, in order to explore unobserved heterogeneity. In order to test the validity of the model for modeling the injury severity of pedestrians riding electric two‐wheelers, multinomial logit (MN‐logit model), and random parameter logit model (RP‐logit) were estimated, and the results showed that the RP‐HMV logit model was significantly superior in terms of goodness of fit. The study showed that some of the factors differed somewhat between the two scenarios, such as gender, while the factors that were significant in both scenarios were >60, broken pavement, street lights at night, no street lights at night, mixed motorized and nonmotorized lanes, sidewalks, other angles, no control, severe weather, and visibility <200 m, where the severe weather and visibility <200 m were random parameters obeying normal distributions, there is a significant difference between having street lights and no control at night in both scenarios, and the difference is significant. The results of the study can provide a reference for the development of targeted countermeasures to improve the traffic safety of electric two‐wheeled vehicles in China.
Male Breast Cancer: An Updated Review of Patient Characteristics, Genetics, and Outcome
Authors: Khare Vidhu Shekhar , Huda Farhanul , Misra Subhasis , Amulya Kanmatha Reddy , Raj Nirmal , Karn Summi , Basu Somprakas
Published: March 2024

Male breast cancer (MBC) is a rare entity, underrepresented in population studies and clinical trials, resulting in management of MBC to be informed by current research on female breast cancer (FBC). A literature review was conducted by accessing relevant articles on 2 databases, by searching keywords “male breast cancer”. A total of 29 articles from year 2011 to 2022 were selected for this review. The authors found that male breast cancer generally occurs later in life with higher stage, higher grade, and more estrogen receptor (ER) positive tumours. Most of the studies noted the mean age for MBCs at the time of presentation as >60 years. Risk factors for male breast cancer include family history, obesity, lower physical activity, and syndromes like the Klinefelter syndrome. Positive family history is much higher in MBC compared to FBC (30.9 vs. 18.4%). BRCA 2 cancers constitute a higher proportion compared to FBCs. A lot of genetic mutations have been observed. Some show promise to assess disease-specific survival and proliferative rate like TWIST1 and RUNX3, among others. MBCs usually present with a palpable lump in central region, with a bigger size and chance of nodal involvement and metastasis compared to FBCs. They are mostly infiltrating ductal type and hormone receptor positive, with worse histological grade. Treatment usually follows the same principles as FBCs (systemic therapy, surgical excision, and radiotherapy), with poorer prognosis to same treatment approach, possibly owing to its advanced stage at presentation. This is a rare entity which requires further research to ascertain need for different management approach than FBCs.
Desafíos y responsabilidades de un Contador Público y Auditor frente a la contabilidad ambiental
Authors: Vargas Vera Valentina
Published: March 2024

La contaminación y degradación medioambiental provocada por el creciente desarrollo económico mundial ha sido una problemática que ha tomado relevancia en los últimos años dado que el cambio climático ha podido ser palpable, de esta forma la contabilidad ha podido tomar un rol protagónico en aportar en la creación de un sistema que permita medir los impactos medioambientales que las industrias producen mediante el concepto de contabilidad ambiental. El desarrollo de este trabajo de investigación se logró mediante un enfoque mixto y con un método de investigación documental, además de aplicar una encuesta a estudiantes de CPA de una universidad pública para describir el nivel de conocimiento sobre el tema. Por lo tanto, se realizó la selección de documentos que fueron esenciales para comprender al objeto de estudio y responder a la pregunta de investigación. Como resultado se logró establecer las responsabilidades y desafíos que en la actualidad se enfrenta un Contador Público y Auditor frente a la contabilidad ambiental, generando conocimiento en su rol de informar acerca de la contabilidad ambiental y estableciendo la relación entre la contabilidad ambiental y la profesión contable.
Implementing generative adversarial networks for increasing performance of transmission fault classification
Authors: Goswami Tilottama , Roy Uponika Barman , Kalavala Deepthi , Tripathi Mukesh Kumar
Published: March 2024

An electrical power system is a network that facilitates the sourcing, transfer, and distribution of electrical energy. In the traditional power system, there are eleven types of faults that can occur in the system. This paper focuses on the classification of these faults over a stretch of 100 kilometres. The dataset used is synthetic and generated from a simulated model using MATLAB/Simulink software. Data augmentation is carried out during training to improve the accuracy of the classification. An indirect training approach through generative adversarial network (GAN) is used to classify these overhead transmission line faults. The random forest (RF) classification is used as the base learning model on the original dataset and it achieves accuracy of 84%. However, the base learner RF when used on GAN model generated augmented faulty data, it performs exceptionally well achieving 99% accuracy. One of the recent state-of-art methods is compared with this approach.
DeepCOVID: a deep learning approach for accurate COVID-19 detection in point-of-care lung ultrasound
Authors: Narayanan Uma , Sukumri Renjini Pappadiyil
Published: March 2024

Sickness still continued to spread through several countries when it first appeared in China. The number of COVID-19 cases is rising daily worldwide, posing a severe threat to the government and the populace. As a result of the virus’s rapid spread, doctors are having trouble recognizing positive cases. It is obvious that computer-based diagnosis must be developed to get results at a reasonable cost. The classic convolutional neural network (CNN) is used for this, utilizing the CT dataset, and the upgraded CNN model is used with the lung ultrasound (LUS) dataset. The CT and LUS COVID imaging datasets are compared in the model. The accuracy of both deep learning models is higher. We customized ResNet50, a pre-trained deep learning architecture, for a web application paradigm. First, we suggest a method for normalizing data that addresses its variability because it is collected in hospitals using various CT scanners and ultrasound machines. Second, we identify COVID-19 patients using U-Net segmentation and classification. The CNN architecture is added for deep learning purposes, and Res-Net 50 offers incredible accuracy.
Machine learning for network defense: automated DDoS detection with telegram notification integration
Authors: Tedyyana Agus , Ghazali Osman , Purbo Onno W.
Published: March 2024

As the prevalence and sophistication of distributed denial of service (DDoS) attacks escalate, the imperative for advanced defense mechanisms becomes paramount, especially in rapidly growing digital landscapes like Indonesia. This research presents the development of an innovative intrusion detection system (IDS) that harnesses machine learning (ML) algorithms to automate the detection of DDoS attacks in real time. By monitoring TCP streams, the system utilizes ML-enhanced IDS components to identify malicious traffic patterns indicative of DDoS activities. An automatic alert is dispatched to network administrators via Telegram upon detection, ensuring immediate awareness and facilitating swift countermeasures. Additionally, the system embodies a self-improving architecture by retraining its ML model with newly encountered attack data, thus continuously refining its detection capabilities. The system's efficacy, marked by its adaptive learning and proactive notification system, not only contributes to the fortification of network security but also underscores the potential for ML in cybersecurity within Indonesia’s expanding digital domain. The deployment of this system is anticipated to significantly bolster cybersecurity infrastructure by addressing the urgent need for advanced and responsive defense strategies against the evolving landscape of cyber threats.
Intelligent preliminary diagnosis system for diseases with similar clinical presentation
Authors: Andrade-Arenas Laberiano , Yactayo-Arias Cesar
Published: March 2024

Accurately identifying diseases with similar symptoms, especially in resource-limited medical settings, is a key challenge to diagnostic accuracy. This paper presents a preliminary diagnostic system to address the challenge of diagnosing diseases with similar symptoms. The system has been implemented using the PyQt5 library and employs a unique symptom identification algorithm developed in Python. Furthermore, to carry out the diagnosis, it uses comma-separated values (CSV) and excel files as databases where the diseases and their respective symptoms are stored. The results show that the system has a precision of 95%, a sensitivity of 90%, and a specificity of 93% after evaluating 35 clinical cases covering seven diseases with similar initial symptoms, namely: dengue, zika, chikungunya, COVID-19, influenza, monkey-pox, and the common cold. Furthermore, the positive evaluation of the technical performance of the system by experts supports its practical feasibility and its potential as a valuable tool in medical practice. In conclusion, the system diagnoses diseases by analyzing the symptoms of the information file, highlighting its usefulness in improving the diagnostic accuracy of similar cases and optimizing medical care for the benefit of patients.
MobileNetV2-D and multiple cameras for swiftlet nest classification based on feather intensity
Authors: Indrajaya Denny , Parhusip Hanna Arini , Trihandaru Suryasatriya , Hartanto Djoko
Published: March 2024

MobileNetV2-D is a modified version of MobileNetV2, which is the novelty of this article. The algorithm is used to classify swiftlet nests into seven classes. In 2023, PT Waleta Asia Jaya is required to achieve a 7-fold increase in the export quota of swiftlet nests. To meet the quota, the company made a machine that can recognize swiftlet nest objects, which are classified into seven classes based on feather intensity, namely BRS, BR, BST, BS, BBT, BB, and BB2 for the light feathers to the heavy feathers, respectively. The input image is a combination of four images from four cameras with different positions, which adds to the novelty of MobileNetV2-D for the particular problem here. From the evaluation that has been carried out, the accuracy value of the MobileNetV2-D model was better than the MobileNetV2 model, i.e., the accuracy value of the MobileNetV2-D model was 99.9928% for the training dataset and 94.0723% for the testing dataset. Moreover, the speed of MobileNetV2-D is better than MobileNetV2- architecture.
The synergistic effect of QR decomposition with t-SNE
Authors: Ali Mohsin , Choudhary Jitendra
Published: March 2024

The study utilized non-parametric tests, specifically, the Mann-Whitney U test, to evaluate the performance of a proposed model called QRPCA-t-SNE, along with two other models, MDS and UMAP. The study compared these three models with two datasets on performance metrics such as model accuracy, training accuracy, testing accuracy, mean square error, AUC scores, precision, recall, and F1 scores. Once the model's performance was conducted, the Anderson-Darling test was to check for data normality before applying the hypothesis for model proof. The analysis revealed that Model 1 (QRPCA-t-SNE) significantly outperformed Model 2 (UMAP) and Model 3 (MDS) in terms of accuracy, with p-values of 0.0027 and 0.0003, respectively. This finding suggests that Model 1 (QRPCA-t-SNE) is suitable for high-accuracy and reliability applications, providing valuable insights into predictive analytics with a 95% confidence interval (confidence level α= 0.05).
Web mining and sentiment analysis of COVID-19 discourse in online forum communities
Authors: Mohamad Masurah , Masrom Suraya , Salleh Khairulliza Ahmad , Alfat Lathifah , Nasucha Muhammad , Uddin Nur
Published: March 2024

Recently, various discussions, solutions, data, and methods related to coronavirus disease 2019 (COVID-19) have been posted in online forum communities. Although a vast amount of posting on COVID-19 analytical projects are available in the online forum communities, much of them remain untapped due to limited overview and profiling that focuses on COVID-19 analytic techniques. Thus, it is quite challenging for information diggers and researchers to distinguish the recent trends and challenges of COVID-19 analytic for initiating different and critical studies to fight against the coronavirus. This paper presents the findings of a study that executed a web mining process on COVID-19 data analytical projects from the Stack Overflow and GitHub online community platforms for data scientists. This study provides an insight on what activities can be conducted by novice researchers and others who are interested in data analysis, especially in sentiment analysis. The classification results via Naïve Bayes (NB), support vector machine (SVM) and logistic regression (LR) have returned high accuracy, indicating that the constructed model is efficient in classifying the sentiment data of COVID-19. The findings reported in this paper not only enhance the understanding of COVID-19 related content and analysis but also provides promising framework that can be applied in diverse contexts and domains.
Evaluating the efficacy of univariate LSTM approach for COVID-19 data prediction in Indonesia
Authors: Prasetyo Tegar Arifin , Silitonga Joshua Pratama , Alfredo Matthew , Siahaan Risky Saputra , Saragih Roberd , Handayani Dewi , Chandra Rudy
Published: March 2024

The coronavirus disease 2019 (COVID-19) pandemic, originating in 2020, has emerged as a critical global issue due to its rapid and widespread transmission. Indonesia, among the affected nations, has taken measures to address the situation, including the development of a deep learning model for predicting future COVID-19 infection and spread. This predictive tool serves as a valuable reference for the government and stakeholders, aiding them in making informed decisions and implementing appropriate measures to contain the virus. The deep learning model employs the long short-term memory (LSTM) algorithm, chosen for its ability to recognize temporal patterns in the country’s COVID-19 data. The model creation process involves data collection, preprocessing, model architecture planning, modeling, training, and evaluation. Two LSTM models were developed: a univariate and a multivariate model. Following thorough training and evaluation, the univariate model emerged as the superior choice, boasting evaluation metrics of 16.72 for mean absolute percentage error (MAPE) and 66.36 for root mean squared error (RMSE). This model was then deployed on a publicly accessible website, presenting visualizations of past COVID-19 data and predictions of future cases through line graphs. This user-friendly platform enables the public to access and analyze the data easily.
Predicting child alimony under Islamic shariah law using hybrid fuzzy inference system
Authors: Khadijah Rosili Nur Aqilah , Hassan Rohayanti , Zakaria Noor Hidayah Binti , Che Rose Farid Zamani , Kasim Shahreen , Sutikno Tole
Published: March 2024

This paper proposes a novel approach to predicting child alimony under Islamic Shariah law using a hybrid fuzzy inference system, integrating Mamdani and Takagi-Sugeno-Kang (TSK) fuzzy systems. Machine learning algorithms have become valuable tools for legal decision-making, but judicial process delays can lead to adverse effects. Our model aims to expedite decision-making and minimize legal fees by accurately determining the proper amount of alimony for children after divorce. We collected data from 94 alimony cases and evaluated the model’s performance using accuracy, precision, recall, and F1 score metrics. The hybrid fuzzy system achieved promising results with 88% accuracy, 84% precision, 89% recall, and an 86% F1 score. Notably, the model reduced bias and standardization in decision-making, promoting fairness. However, the study suggests potential areas for improvement and emphasizes trans-parent judgment processes and coordination among judges in assessing alimony costs based on sufficiency and ma’ruf criteria. This research significantly contributes to machine learning applications in the judicial domain. It provides a valuable decision-making tool for judges and lawyers to enhance the judicial process’s efficiency and ensure children’s welfare in divorce cases under Islamic Shariah law. Further research can enhance the model’s effectiveness and reliability, opening avenues for continued exploration in this field.
Analysis of effect of annealing at high temperature on nickel oxide and zinc oxide thin film for solar cell applications
Authors: Rummaja Iskandar Dzulkarnain , Hani Senin Nur Afiqah , Idris Muhammad Idzdihar , Zamani Zarina Baharudin , Ramlee Radi Husin , Bradley Luke
Published: March 2024

The use of thin films in solar cell technology has gained substantial interest because of their potential for cost-effective and efficient energy conversion. nickel oxide (NiO) and zinc oxide (ZnO) have been used as potential materials in solar cells application especially third generation solar cells because of their good characteristics, such as high electrical conductivity, chemical stability, resistance to degradation, and abundance and low cost. However, at high temperatures, both NiO and ZnO can undergo thermal decomposition and exhibit crystal defects and grain boundaries. This work investigates high temperature annealing on the morphology, structural, and optical properties of NiO and ZnO thin films. The deposited material was annealed at 500 ℃, 600 ℃, and 700 ℃ and be characterized via scanning electron microscopy (SEM), XRD, and UV-Vi’s spectroscopy. The results showed that inceasing the annealing temperature can improve both ZnO and NiO thin films in structure and appearance. For ZnO, higher temperatures made the grains bigger and more orderly, and for NiO, the process made the grains more organized, bigger in size, and spread out more evenly. However, annealing at high temperature yields a smaller bandgap energy value for both thin films.
A decentralized HC-ADMM approach for large antenna arrays
Authors: B. R. Jyothi , Naganaik Manjanaik
Published: March 2024

This work addresses the evolving landscape of internet of things (IoT) applications and large antenna array systems, where optimizing spectral efficiency and simplifying design complexities are crucial. Focusing on two key challenges, the study introduces a novel hybrid analog-digital transceiver strategy tailored for frequency-selective channels. By integrating Shannon and Hartley theorems, the approach enhances data transfer rates, thereby optimizing radio frequency (RF) chain utilization in large-scale antennas. To achieve a balance between transceiver performance and hardware complexity, the study employs a decentralized alternating direction method of multipliers (ADMM) framework. The proposed hybrid consensus ADMM algorithm (HC-ADMM) ensures efficient convergence in decentralized optimization scenarios. Comparative analyses with ADMM and existing system transceiver optimization (ESTO) models highlight HC-ADMM's superior performance across key metrics such as spectral efficiency, efficiency per cell, total efficiency, and optimal scheduling of user equipment (UEs). Particularly notable is HC-ADMM's advanced optimization capabilities as the number of transmit antennas increases, positioning it as a promising approach for enhancing overall communication network performance.
Improved DAG in blockchain tangle for IOTA
Authors: Rhee Eugene , Lee Jihoon
Published: March 2024

The internet of things (IoT) enables machine-to-machine communication without human intervention. Consequently, every object connected to the internet can exchange information with each other. Internet of things application (IOTA) has undertaken a project to address the high transaction fees inherent in traditional blockchain systems and enhance the efficiency of microtransactions between machines by combining blockchain and IoT. IOTA employs its unique Tangle technology, which introduces a novel transaction consensus method, addressing the fee issues, limited scalability, and the inability to conduct offline transactions associated with traditional blockchains. This paper provides a detailed overview of the characteristics of the Tangle structure and the concepts applied in IOTA. Additionally, it explores potential approaches for integrating blockchain into IoT.
Effective virtual laboratory to build constructivist thinking in electrical measurement practicum
Authors: Eliza Fivia , Candra Oriza , Putra Yanto Doni Tri , Fadli Radinal , Myori Dwiprima Elvanny , Islami Syaiful , Hidayah Yayuk , Balti Levandra
Published: March 2024

Constructivism serves as a basic theory in education, and plays an important role, because this perspective views learning as a dynamic process in which students actively participate in constructing their knowledge. So, this research aims to produce a virtual laboratory that is valid and effective in developing constructivist thinking. This research and development use the Ploom approach which consists of initial investigation, design, construction/realization, testing, evaluation, revision, and implementation. Thirty-two electrical engineering students from Padang State University participated in this research. The instruments used are validity instruments and constructivism assessment tools. The research results show that the Proteus-based virtual laboratory developed is valid and effective in fostering constructivist thinking in electrical measurement practice. This study contributes to the advancement of virtual laboratories in educational settings, emphasizing the importance of constructivist pedagogy for meaningful and engaging learning experiences in the field of electrical engineering. The results of this research open opportunities for further research regarding the exploration of artificial intelligence to improve constructivism through virtual laboratories.
An internet of things-based pump and aerator control system
Authors: Mawardi Mawardi , Sihombing Panangian Mahadi , Yudisha Nabila
Published: March 2024

Small-scale shrimp farmers in Hamparan Perak District, Deli Serdang Regency, Indonesia, conduct direct water quality supervision and manually use aerators and water pumps. Thus, it is inefficient in meeting the water quality required for shrimp farming and using production costs. This study aims to test the performance of an internet of things (IoT)-based prototype in supervising and controlling the aerator and pump in a shrimp pond. This prototype comprises an ESP32, three sensors: the DS18B20 sensor, MLX90614 sensor, and JSN-SR04T sensor, and two relays to control the aerator and pump automatically. Prototype testing is done directly on shrimp ponds by placing the prototype in an electrical panel connected to a power circuit. Based on the study's results, it is known that the prototype can measure water temperature. The water level and temperature of the aerator motor are pretty accurate. In addition, the prototype can also control the aerator and water pump well and send notifications to users automatically via smartphones.
FPGA-base object tracking: integrating deep learning and sensor fusion with Kalman filter
Authors: Harouna Maloum Abdoul Moumouni , Muchuka Nicasio Maguu , Mutugi Kiruki Cosmas Raymond
Published: March 2024

This research presents an integrated approach for object detection and tracking in autonomous perception systems, combining deep learning techniques for object detection with sensor fusion and field programmable gate array (FPGA-based) hardware implementation of the Kalman filter. This approach is suitable for applications like autonomous vehicles, robotics, and augmented reality. The study explores the seamless integration of pre-trained deep learning models, sensor data from a depth camera, real-sense D435, and FPGA-based Kalman filtering to achieve robust and accurate 3D position and 2D size estimation of tracked objects while maintaining low latency. The object detection and feature extraction are implemented on a central processing unit (CPU), and the Kalman filter sensor fusion with universal asynchronous receiver transmitter (UART) communication is implemented on a Basys 3 FPGA board that performs 8 times faster compared to the software approach. The experimental result provides the hardware resource utilization of about 29% of look-up tables, 6% of lookup table RAMs (LUTRAM), 15% of Flip-flops, 32% of Block-RAM, 38% of DSP blocks operating at 100 MHz, and 230400 baud rates for the UART. The whole FPGA design executes at 2.1 milliseconds, the Kalman filter executes at 240 microseconds, and the UART at 1.86 milliseconds.
Enhancing PAPR reduction efficiency in MIMO-OFDM systems via selective mapping and metaheuristic algorithms
Authors: Amhaimar Lahcen , Nadir Younes , Abdellah Bakhouyi , Mansouri Khalifa , Bayjja Mohamed , Khalidi Abderrahim
Published: March 2024

The relentless evolution of communication systems, driven by the demands of 5G and the impending 6G networks, necessitates heightened data rates and spectral efficiency. orthogonal frequency division multiplexing (OFDM), a form of multicarrier modulation employed in multi-input multi-output (MIMO) systems, stands as a pivotal technology. Yet, OFDM grapples with challenges, notably the peak-to-average power ratio (PAPR) issue. Selective mapping (SLM) has been a favored technique for mitigating PAPR in OFDM, albeit challenged by computational complexities in its pursuit of discovering optimal phase factors. This paper pioneers a transformative approach by integrating metaheuristic algorithms genetic algorithm (GA), particle swarm optimization (PSO), and the innovative fireworks algorithm (FWA) into SLM for PAPR reduction while minimizing computational complexity. Simulation results not only affirm the efficacy of SLM-based techniques but also spotlight the potential of metaheuristic algorithms in addressing PAPR challenges in modern communication systems. The study transcends single-antenna systems, extending to MIMO-OFDM systems based on WiMAX standards, validating the efficacy of these techniques in multi-antenna configurations. Crucially, the FWA, proposed for the first time in this paper, emerges as a robust candidate, striking an enviable balance between computational efficiency and performance, achieving a notable PAPR reduction with a favorable search number.
Exploring open source and proprietary LoRa mesh technologies
Authors: Hammouti Mustapha , Moussaoui Omar , Hassine Mohammed , Betari Abdelkader
Published: March 2024

This paper explores low power wide area network (LPWAN) LoRa and its diverse variants, encompassing open-source and proprietary wireless mesh networks, operating over the physical LoRa or LoRaWAN layer. The primary challenge lies in defining an optimal LoRa mesh solution that balances cost-effectiveness, energy efficiency, low latency, long-range capability, and security. This study also comprehensively examines key LoRa mesh solutions from 2017 to 2024, as proposed by various authors. Furthermore, a detailed analysis is conducted to contrast open-source and commercial solutions, considering their applications, limitations, issues, characteristics, and pros and cons of mesh routing protocols. In the current landscape, the proliferation of open-source and proprietary LoRa mesh solutions has been instrumental in facilitating the connectivity of internet of things (IoT) devices. However, these solutions pose challenges related to energy consumption, latency, and suboptimal transmission throughput. These challenges are influenced by various LoRa characteristics such as spectrum factor, bandwidth, and transmission power, which directly impact the transmission range. Our research aims to perform a comparative analysis of existing LoRa mesh solutions by, systematically studying their advantages and disadvantages. This analysis offers valuable insights for making informed choices among these solutions in diverse domains for IoT applications.
Assessing actual usage and satisfaction factors of Microsoft Teams in online learning
Authors: Wulandari Dewi Arianti , Soeprobowati Tri Retnaningsih , Kusumo Nugraheni Dinar Mutiara
Published: March 2024

This study examines the factors influencing students’ online learning using Microsoft Teams at the PLN Institute of Technology. Using primary data from 301 students, the study found that the use of Microsoft Teams improved student achievement and learning performance during the pandemic. The structural equation model (SEM) method tested the model with low validity, with all variables having an AVE value more than 0.5 and higher than the cross-loading factor valid, the outer model analysis demonstrated good convergent validity. Dependability and trustworthiness were shown by the composite reliability rating, which was over 0.70. Both perceived usefulness (PU) and perceived ease of use (PEOU) were shown to have a strong correlation, and the inner model analysis revealed positive path coefficient values without any weak variables. These results supported by hypothesis testing imply that lecturers can modify their curricula to enhance student performance in the event of a pandemic. There was shown to exist a strong relationship between the perceived ease of use and perceived usefulness.
The Consensual Divorce (ṭalāq) in Palestine
Authors: Polfus Turid Smith
Published: March 2024

Most divorces in Palestine come about through a private unregulated agreement between the spouses referred to as “ṭalāq bit-taradi” (consensual ṭalāq), often called mukhalaʿa or mubāraʾa. Over the last century, women’s economic rights in the unilateral ṭalāq have increased. At the same time, the number of consensual ṭalāq has risen. While the consensual ṭalāq provides a way out of an unwanted marriage for women, it hardly represents a form of ‘gender equality’. Rather, it is a field where kin-patriarchy is asserting itself through women’s dependence on agnates to reach an agreement and to bear the cost of such divorces. Furthermore, the consensual ṭalāq invites abuse and economic extortion from husbands. As the Islamic shariʿa court condemns such behaviour, our study nevertheless finds that the court facilitates the consensual ṭalāq by setting the bar for litigated divorce too high and by letting time pass.
Trafficking and Organs, Tissues, and Cells: An Examination of European and UK Legislation and Gaps
Authors: O’Neill Maria
Published: March 2024

Substantial custodial sentences were handed down from the English Central Criminal Court for organ trafficking. Attempts to exploit vulnerable people for the purposes of organ removal, and for organ, tissue, and cells (otc) trafficking are ongoing in many jurisdictions. It is also clear from the 2019 EU evaluation of the Union legislation on blood, tissues, and cells that there is increasing commercialization in the procurement of human material for medical use, using global supply chains. Full traceability of all human material, for which there is a shortage to meet global medical needs, is required. The medical profession has drafted the Istanbul Declaration on Organ Trafficking and Transplant Tourism 2008, calling for transplant tourism to be prohibited, and full engagement with transnational law enforcement to address developing transnational organized crime in this area. This paper addresses the global, Council of Europe, EU, and UK provisions in this area.