bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptation to zinc restriction in
Streptococcus agalactiae
: role of the ribosomal protein and zinc-importers regulated by AdcR
Authors: Melet M. , Blanchet S. , Barbarin P. , Maunders E. A. , Neville S. L. , Rong V. , Mereghetti L. , McDevitt C. A. , Hiron A.
Published: October 2024

Zinc (Zn) is an essential cofactor for numerous bacterial proteins and altering Zn availability is an important component of host innate immunity. During infection, adaptation to both Zn deprivation and excess is critical for pathogenic bacteria development. To understand the adaptive responses to Zn availability of
Streptococcus agalactiae
, a pathogen causing invasive infections of neonates, global transcriptional profiling was conducted. Results highlight that in response to Zn limitation, genes belonging to the AdcR regulon, the master regulator of Zn homeostasis in streptococci, were overexpressed. Through a combination of
in silico
analysis and experimental validation, new AdcR-regulated targets were identified. Among them, we identified a duplicated ribosomal protein, RpsNb, and an ABC transporter, and examined the role of these genes in bacterial growth under Zn-restricted conditions. Our results indicated that, during Zn restriction, both the RpsNb protein and a potential secondary Zn transporter are important for
S. agalactiae
adaptation to Zn deficiency.

IMPORTANCE

Streptococcus agalactiae
is a bacterial human pathobiont causing invasive diseases in neonates. Upon infection,
S. agalactiae
is presented with Zn limitation and excess but the genetic systems that allow bacterial adaptation to these conditions remain largely undefined. A comprehensive analysis of
S. agalactiae
global transcriptional response to Zn availability shows that this pathogen manages Zn limitation mainly through upregulation of the AdcR regulon. We demonstrate that several AdcR-regulated genes are important for bacterial growth during Zn deficiency, including human biological fluids. Taken together, these findings reveal new mechanisms of
S. agalactiae
adaptation under conditions of metal deprivation.
Radiofrequency treatments for lumbar facet joint syndrome: a systematic review and network meta-analysis
Authors: Park Soyoon , Park Ji-Hoon , Sokpeou Ni , Jang Jae Ni , Kim Young Uk , Choi Young-Soon , Park Sukhee
Published: October 2024

Background

Lumbar facet joint syndrome (FJS) is a common cause of chronic low back pain (LBP). Radiofrequency treatments are commonly used to treat chronic LBP-related FJS that is refractory to conservative treatment, although evidence supporting this treatment is controversial.

Objective

We explored the therapeutic effects of radiofrequency on FJS using a network meta-analysis (NMA).

Evidence review

A comprehensive systematic search of multiple databases was conducted to identify randomized controlled trials (RCTs) that compared radiofrequency with other treatments (sham procedures, facet joint corticosteroid injection, and conservative treatment) for FJS. We searched PubMed, Embase, Web of Science, the Cochrane Database, and handsearching. The primary outcomes were pain score and Oswestry Disability Index (ODI). Statistical analysis included conventional pairwise meta-analysis and NMA using the frequentist method.

Findings

The treatments were ranked using surface under the cumulative ranking curve (SUCRA) values. The search yielded 25 RCTs (1969 patients) and a mixed quality regarding the risk of bias, with most studies exhibiting a low risk of bias for most domains. Endoscopic neurotomy consistently ranked highest in terms of pain reduction and ODI score improvement at 1, 3, 6, and 12 months. At 1 and 6 months, endoscopic neurotomy had the highest SUCRA value for pain reduction (0.833 and 0.860, respectively), followed by medial branch thermal radiofrequency.

Conclusions

This NMA demonstrates that endoscopic neurotomy is the most effective treatment for lumbar FJS, providing superior and sustained pain relief and functional improvement compared with other treatments. Further, high-quality RCTs are needed to confirm these findings and address the existing limitations.

PROSPERO registration number

CRD42024524657.
Evaluating compliance with track and trace and other regulations in Pakistan’s cigarette market
Authors: Dauchy Estelle , Khan Amina , Ansaari Saeed , Ross Hana
Published: October 2024

Background

To control the illicit cigarette market, the government of Pakistan adopted a Tracking and Tracing System (TTS) that was fully operational by July 2022, despite many roadblocks. By this date, major tobacco companies had either registered their brands with the tax authority and/or installed TTS.

Methods

This paper is the first to evaluate the degree of compliance with the TTS by evaluating the extent and nature of illicit trade in tobacco products. We use randomised sampling to collect cigarette packs from waste recycling stores located in the ten most populous cities of Pakistan, to evaluate illicit trade penetration.

Results and policy implications

Almost a third of the packs collected did not bear a tax stamp, mostly due to the lack of compliance by local companies, confirming a recent review of the TTS implementation by the Federal Bureau of Revenue (FBR). Even the largest companies that ostensibly adopted the TTS did not fully comply with the system, signalling poor enforcement. This is a missed opportunity, since a well-functioning TTS combined with proper enforcement is an effective means of controlling illicit tobacco trade, boosting tax revenue and improving public health. However, the enforcement should not be limited to the TTS since 23.6% of packs did not comply with other regulatory requirements.
Laboratory and field evaluation of asphalt concrete modulus and critical strain inputs for M-E flexible pavement design in the state of Illinois
Authors: Maia Renan Santos , Hajj Ramez
Published: October 2024

This study aims to update the current algorithm for modulus prediction of hot-mix asphalt (HMA) in the state of Illinois-USA. Numerically, laboratory and field-obtained modulus and strain levels were connected to advances in asphalt materials and current design criteria. Four mix designs were selected and combined with seven different binders for modulus measurements. The reference experimental results were compared to the current Illinois Department of Transportation (IDOT) modulus algorithm, traditional regression models, a Bayesian Neural Network (BNN) model, and field measurements obtained using the traffic speed deflectometer. Results showed that the current IDOT algorithm under-predicts modulus and BNN (840 data points, R2 = 0.986) outperformed the Witczak and Hirsch models. Field modulus and critical strains were sensitive to temperature and showed considerable variability, but confirmed that HMA in perpetual pavement sections performs under low strain levels. These findings guided considerations for an updated design method: realistic modulus prediction can be achieved using data-driven methods and less conservative strain criterion might be considered.
Ideology: Psychological Similarities and Differences Across the Ideological Spectrum Reexamined
Authors: Kteily Nour S. , Brandt Mark J.
Published: October 2024

A key debate in the psychology of ideology is whether leftists and rightists are psychologically similar or different. A long-standing view holds that left-wing and right-wing people are meaningfully different from one another across a whole host of basic personality and cognitive features. Scholars have recently pushed back, suggesting that left-wing and right-wing people are more psychologically similar than distinct. We review evidence regarding the psychological profiles of left-wing and right-wing people across a wide variety of domains, including their dispositions (values, personality, cognitive rigidity, threat-sensitivity, and authoritarianism), information processing (motivated reasoning and susceptibility to misinformation), and their interpersonal perceptions and behaviors (empathy, prejudice, stereotyping, and violence). Our review paints a nuanced picture: People across the ideological divide are much more similar than scholars sometimes appreciate. And yet, they differ—to varying degrees—in their personality, values, and (perhaps most importantly) in the groups and causes they prioritize, with important implications for downstream attitudes and behavior in the world.
Pushing the Energy-Lifetime Frontier of Li-Ion Batteries: Study of Ni-Rich, Co-Free NMAW Cathode Material
Authors: Hamam Ines , Omessi Roee , Abraham Jeffin James , Dahn J. R.
Published: October 2024

Dopants and coatings have been widely used to improve the performance of Ni-rich positive electrode active materials. Previous studies have aimed to elucidate the mechanism by which Al and W improve lithium metal oxides, providing valuable insight on the design of enhanced electrode materials for Li-ion batteries. In this work, Al and W are compared as individual dopants as well as co-dopants in order to design an optimal Ni-rich, Co-free material. This involved studying the effect of synthesis temperature in the presence of Al and/or W as well as the effect that these metals have on the morphology of the resultant polycrystalline materials. In addition, structural analysis by X-ray diffraction, electrochemical analysis, and characterization of the mechanical strength of the materials were also conducted. The change in performance with the addition of Al and W depends greatly on particle size and chemical composition. Small sized Ni-rich polycrystalline particles (Ni content of 94%) with low contents of Al (3%) and W (1%) showed the greatest enhancement in energy density with long cycle life.
Analysis of Battery-like and Pseudocapacitive Ion Intercalation Kinetics via Distribution of Relaxation Times
Authors: Malik Yoga Trianzar , Braig Michael , Simon Patrice , Zeis Roswitha , Fleischmann Simon
Published: October 2024

Improving the kinetics of electrochemical ion intercalation processes is of interest for realizing high-power electrochemical energy storage. This includes classical battery-like intercalation and pseudocapacitive intercalation processes with a capacitor-like electrochemical signature. Electrochemical methods are needed to probe the kinetics of such complex multistep processes in detail. Here, we present the use of the distribution of relaxation times (DRT) analysis of electrochemical impedance data to identify the kinetic limits of intercalation reactions. We study the lithium intercalation reaction in TiS2 from organic and aqueous electrolytes as a model system. The material can exhibit both battery-like and pseudocapacitive intercalation regimes depending on the potential range, variable diffusion lengths by adjusting its particle size, and a tunable degree of solvent cointercalation by choosing the electrolyte solvent. Using DRT, we can distinguish between the kinetic limitations imposed by solid-state ion diffusion, interfacial ion adsorption and transport, and ion desolvation processes. Thus, DRT analysis can complement existing methods, such as voltammetry or 3D-Bode analysis, to better understand the kinetics of intercalation reactions.
Axial heterogeneity of superficial proximal tubule paracellular transport in mice
Authors: Muto Shigeaki , Moriwaki Kazumasa , Nagata Daisuke , Furuse Mikio
Published: October 2024

Research on isolated perfused S2 segments of proximal tubules in mice, both expressing and lacking claudin-2, indicates that claudin-2 forms leaky monovalent cation-selective paracellular channels within the tight junctions of proximal tubules. This study characterized the paracellular transport properties in isolated and perfused superficial proximal tubule S1–S3 segments in both groups of mice. The findings demonstrate, for the first time, functional heterogeneity in the paracellular pathway along the axis of the superficial proximal tubules.
Regional heterogeneity of cerebral blood flow immediately after the onset of ventricular pacing in anesthetized rats
Authors: Ishii Kei , Komine Hidehiko
Published: October 2024

When lethal tachyarrhythmia occurs, survival is prioritized by counterregulating the cardiovascular system, which is driven by vital brain regions. However, whether limited cerebral blood flow is selectively distributed to vital brain regions is unknown. We demonstrated the preferential maintenance of cerebral blood flow in vital brain regions, depending on the degree of hypotension caused by ventricular pacing, in anesthetized rats. Our data may have clinical implications for syncope and cerebral ischemia management in patients with arrhythmia.
Acute Reperfusion Therapies and Prognosis in Nonagenarian Stroke Patients
Authors: Legris Nicolas , Bicart-See Louise , Ancelet Claire , Venditti-Maillet Laura , Chassin Olivier , Peillet Claire , Sarov-Riviere Mariana , Laine Olga , Vetillard Anne-Laure , Pujol Tom , Brunetti Nicoletta , Dupont Deschamps Caroline , Baudry Elodie , Spelle Laurent , Rouquette Alexandra , Denier Christian
Published: October 2024

Introduction: Thrombolysis and endovascular thrombectomy (EVT) are standard treatments after stroke. We previously reported that these therapies benefit stroke patients over 80 years old. Now, we aimed to study reperfusion therapies specifically in nonagenarians, hypothesizing a poorer prognosis in this group. Methods: Nonagenarian stroke patients were identified from our prospective monocentric cohort, which included consecutive patients ≥80 years old treated with thrombolysis and/or EVT from 2015 to 2019. Baseline characteristics, treatments, and outcomes, as well as complications and mortality were analyzed. Results: Ninety-six nonagenarians were treated with thrombolysis (69.8%) and/or EVT (81.1%). A total of 51% had a pre-stroke modified Rankin score (mRS) ≤2. Cardioembolism was the most common etiology (67.7%). Age was associated with a higher mRS after stroke with a turning point at 90 years old: (90–99 years old: odds ratio [OR] = 0.33, 95% confidence interval [95% CI]: 0.13–0.83, p = 0.02) versus (85–89 years old: OR = 0.72, 95% CI: 0.34–1.50, p = 0.38), with 80- to 84-year-old patients as the reference. In nonagenarians, previous coronary artery disease (OR = 8.02, 95% CI: 1.66–38.68, p = 0.01), initial National Institute of Health Stroke Score (NIHSS) (OR = 1.11, 95% CI: 1.03–1.19, p = 0.01), pre stroke independence (OR = 0.25, 95% CI: 0.08–0.71, p = 0.01), and “drip-and-ship” status (OR = 3.35, 95% CI: 1.22–9.16, p = 0.02) were associated with 3-month mortality. Nonagenarians had more baseline comorbidities (p = 0.003) and lower levels of pre-stroke independence (p = 0.002) than octogenarians (n = 261). Despite no difference in the use of acute treatments, timelines, and rates of successful reperfusion, a good functional status at 3 months was less common in nonagenarians than octogenarians (14.3% vs. 34.0%, p < 0.001) with a higher mortality (60.2% vs. 16.4%, p < 0.001). A total of 9.5% of nonagenarians experienced a symptomatic intracranial hemorrhage. Conclusions: Age is a crucial factor affecting prognosis after stroke with a turning point at 90 years old. However, age alone should not be a limiting factor for treatment decision. Despite higher mortality and poorer functional prognosis overall, some nonagenarians may benefit from reperfusion therapies.
DNA Methylation and Non-Coding RNAs in Metabolic Disorders: Epigenetic Role of Nutrients, Dietary Patterns, and Weight Loss Interventions for Precision Nutrition
Authors: Nicoletti Carolina F. , Assmann Taís S. , Souza Leticia L. , Martinez José Alfredo
Published: October 2024

Background: Dysregulation of epigenetic processes and abnormal epigenetic profiles are associated with various metabolic disorders. Nutrition, as an environmental factor, can induce epigenetic changes through both direct exposure and transgenerational inheritance, continuously altering gene expression and shaping the phenotype. Nutrients consumed through food or supplementation, such as vitamin B12, folate, vitamin B6, and choline, play a pivotal role in DNA methylation, a critical process for gene regulation. Additionally, there is mounting evidence that the expression of non-coding RNAs (ncRNAs) can be modulated by the intake of specific nutrients and natural compounds, thereby influencing processes involved in the onset and progression of metabolic diseases. Summary: Evidence suggests that dietary patterns, weight loss interventions, nutrients and nutritional bioactive compounds can modulate the expression of various microRNA (miRNAs) and DNA methylation levels, contributing to the development of metabolic disorders such as obesity and type 2 diabetes. Furthermore, several studies have proposed that DNA methylation and miRNA expression could serve as biomarkers for the effects of weight loss programs. Key Message: Despite ongoing debate regarding the effects of nutrient supplementation on DNA methylation levels and the expression of ncRNAs, certain DNA methylation marks and ncRNA expressions might predict the risk of metabolic disorders and act as biomarkers for forecasting the success of therapies within the framework of precision medicine and nutrition. The role of DNA methylation and miRNA expression as potential mediators of the effects of weight loss underscores their potential as biomarkers for the outcomes of weight loss programs. This highlights the influence of dietary patterns and weight loss interventions on the regulation of miRNA expression and DNA methylation levels, suggesting an interaction between these epigenetic factors and the body’s response to weight loss.
Intelligent active and reactive power control using multi-layer neural network based MPPT controller for grid tied solar PV system under fault conditions
Authors: Fatima Mehtab , Siddiqui Anwar Shahzad , Sinha Sanjay Kumar
Published: October 2024

The integration of renewable energy sources, particularly grid-tied solar photovoltaic (PV) systems, into the modern power grid has become increasingly prevalent. However, ensuring the reliable and efficient operation of grid-tied PV systems under various grid conditions, including fault scenarios, poses a significant challenge. In the event of grid faults or disturbances, traditional control methods often fall short in maintaining stable and reliable operation. This paper introduces a multi-layer neural network (MLNN) based MPPT controller that adapts intelligently to grid fault conditions, mitigating the impact on the grid-tied PV system's performance and providing low voltage ride through (LVRT). The research employs a detailed simulation framework on MATLAB to validate the effectiveness of the proposed controller under fault conditions. The LVRT capability of the designed system was analyzed and validated according to Indian grid code. The proposed controller leverages its capacity to learn and make real-time decisions to optimize the active and reactive power outputs of the PV system as per the grid code. Simulation results demonstrate that the proposed controller not only improves the fault tolerance of grid-tied PV systems but also enhances their performance, ensuring a stable and continuous power supply in the face of grid disturbances.
Optimizing 2D-to-3D image conversion for precise flat surface detection using laser triangulation and HSV masking
Authors: Rahayu Purwanti Bernadeta Siti , Akhinov Ihsan Auditia , Mulyono Raden Sugeng , Nurtanto Muhammad , Hamid Mustofa Abi
Published: October 2024

This study tackles a critical challenge in converting two-dimensional (2D) images into three-dimensional (3D) representations, focusing on the precise detection of flat surfaces. The research utilizes a triangulation method involving laser and camera systems, emphasizing the optimization of laser shooting angles and camera positioning to accurately determine z-coordinates. The methodology employs hue, saturation, and value (HSV) color masking, which has proven superior to traditional red, green, blue (RGB) methods for isolating red line objects. Key findings indicate that the optimal laser angle, β1=70.65°, significantly minimizes root mean square (RMS) error, thereby enhancing the accuracy of 3D imaging. Additionally, the use of three laser lines at different angles enables a more comprehensive detection of z-coordinates by creating multiple reference points across the surface. This arrangement improves the robustness and precision of the 3D reconstruction process, as the intersecting laser lines generate detailed coordinate data that is critical for accurately mapping surface irregularities. These results not only support existing theories in digital feature extraction but also offer a robust framework for practical applications in manufacturing and quality control, particularly in surface defect detection. The study’s innovative approach advances the field of computer vision, providing new insights and methodologies for optimizing image conversion techniques.
Characterization of A2G UAV communication channels under rician fading conditions
Authors: Guno Yomi , Adiono Trio , Suryana Joko , Triputra Fadjar Rahino , Hidayat Asyaraf , Octaviany Siti Vivi
Published: October 2024

The variation in the k-factor value significantly influences the performance of unmanned aerial vehicle (UAV) air-to-ground point-to-point line of sight (A2G PTP LOS) communications over a Rician channel at 1,800 MHz using quadrature phase shift keying (QPSK) modulation and orthogonal frequency division multiplexing (OFDM) techniques. The research emphasizes the impact of the k-factor, which quantifies the dominance of the line-of-sight component over multipath scattering. The variation in the k-factor significantly influences UAV A2G PTP LOS communication performance for the empirical model (EM), as it involves precise measurements of the received power level in dBm from UAV to ground control station (GCS) across varying distances and altitudes. We introduce a method to compute the k-factor by assessing the ratio of the line-of-sight signal power to the multipath signal power, thereby enhancing channel modeling accuracy. Empirical analysis shows a strong correlation between bit error rate (BER) and signal-to-noise ratio (SNR) with differing k-factor values; a higher k-factor of 16.3 markedly improves performance, virtually eliminating errors at a 10 dB SNR, while a lower k-factor of 2.39 still shows significant errors at a 30 dB SNR. These results highlight the necessity of optimizing the k-factor in UAV A2G PTP LOS systems to ensure stable and reliable communication under diverse operational conditions.
Transformer oil degradation detection system based on color scale analysis
Authors: Hakim Muhammad Fahmi , Prasojo Rahman Azis , Duanaputri Rohmanita , Wijaya Bustani Hadi , Fidya Amaral Hanifiyah Darna , Emzain Zakki Fuadi
Published: October 2024

The rise in power transformer load results in degradation of the condition of the transformer oil and ultimately a deficiency in the distribution of electrical energy. This degradation can be slowed down by reconditioning transformer oil based on oil color detection. This research aims to design, test and validate a transformer oil color testing system based on color sensor and microcontroller. To obtain an accurate system, tests were carried out on selecting the types of sensors, the color of the chamber walls, and the shapes of transformer oil sample vessel used. The oil color scale of the samples was determined visually according to the ASTMD1500, 2009 standard as a benchmark. The test results showed that the TCS3200 color sensor was able to detect the color of all transformer oil samples. White chamber wall and test tube as oil sample containers were chosen to increase system accuracy. Overall, the system is able to detect the color of transformer oil, convert to the ASTMD1500, 2009 standard transformer oil color scale, determine the condition of the transformer oil and conclude the level of transformer oil degradation according to CIGRE-761, 2019. Validation results showed the system had an accuracy level of 92.65%.
Single search investigation of various searches in recent swarm-based metaheuristics
Authors: Kusuma Purba Daru , Dinimaharawati Ashri
Published: October 2024

Swarm intelligence has become a popular framework for developing new metaheuristics or stochastic optimization methods in recent years. Many swarm-based metaheuristics are developed by employing multiple searches whether it is conducted through swarm split, serial searches, stochastic choose. Unfortunately, many existing studies that introduced new metaheuristic focused on assessing the performance of the proposed method as a single package. On the other hand, the contribution of each search constructing the metaheuristic is still unknown as the consequence of the missing of single or individual search assessment. Based on this problem, this work is aimed to investigate the performance of five directed searches that are commonly found in recent swarm-based metaheuristics individually. These five searches include: motion toward the highest quality member, motion relative to a randomly chosen member, motion relative to a random solution along the space, motion toward a randomly chosen higher quality member, and motion toward the middle among higher quality members. In this assessment, these five searches are challenged to find the optimal solution of 23 classic functions. The result shows that the first, fourth, and five searches perform better than the second and third searches.
Enhancing malware detection through self-union feature selection using gray wolf optimizer
Authors: Abualhaj Mosleh M. , Shambour Qusai Y. , Abu-Shareha Ahmad Adel , Al-Khatib Sumaya N. , Amer Amal
Published: October 2024

This research explores the impact of malware on the digital world and presents an innovative system to detect and classify malware instances. The suggested system combines a random forest (RF) classifier and gray wolf optimizer (GWO) to identify and detect malware effectively. Therefore, the suggested system is called RFGWO-Mal. The RFGWO-Mal system employs the GWO for feature selection in binary and multiclass classification scenarios. Then, the RFGWO-Mal system uses a novel self-union feature selection approach, combining features from different subsets of binary and multiclass classification extracted using the GWO optimizer. The RF classifier is then applied for classifying malware and benign data. The comprehensive Obfuscated-MalMem2022 dataset was utilized to evaluate the suggested RFGWO-Mal system, which has been implanted using Python. The suggested RFGWO-Mal system achieves significantly improved results using the novel self-union feature selection approach. Specifically, the RFGWO-Mal system achieves an outstanding accuracy of 99.95% in binary classification and maintains a high accuracy of 86.57% with multiclass classification. The findings underscore the achievement of a self-union feature selection approach in enhancing the performance of malware detection systems, providing a valuable contribution to cybersecurity.
An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting
Authors: Arora Nidhi , Srivastava Shilpa , Tripathi Aprna , Gupta Varuna
Published: October 2024

Non-alcoholic fatty liver disease (NAFLD) is a chronic medical ailment characterized by accumulation of excessive fat in the liver of non-alcoholic patients. In absence of any early visible indications, application of machine learning based predictive techniques for early prediction of NAFLD are quite beneficial. The objective of this paper is to present a complete framework for guided development of varied predictive machine learning models and predict NAFLD disease with high accuracy. The framework employs step–by-step data quality enhancement to medical data such as cleaning, normalization, data upscaling using SMOTE (for handling class imbalances) and correlation analysis-based feature selection to predict NAFLD with high accuracy using only clinically recorded identifiers. Comprehensive comparative analysis of prediction results of seven machine learning predictive models is done using unprocessed as well as quality enhanced data. As per the observed results, XGBoost, random forest and neural network machine learning models reported significantly higher accuracies with improved ‘AUC’ and ‘ROC’ values using preprocessed data in contrast to unprocessed data. The prediction results are also assessed on various quality metrics such as ‘accuracy’, ‘f1-score’, ‘precision’, and ‘recall’ significantly support the need for presented methodologies for qualitative NAFLD prediction modelling.
Predicting student performance using Moodle data and machine learning with feature importance
Authors: Rogers Jamal Kay , Mercado Tamara Cher , Cheng Remelyn
Published: October 2024

Despite the growing technological advancement in education, poor academic performance of students remains challenging for educational institutions worldwide. The study aimed to predict students’ academic performance through modular object-oriented dynamic learning environment (Moodle) data and tree-based machine learning algorithms with feature importance. While previous studies aimed at increasing model performance, this study trained a model with multiple data sets and generic features for improved generalizability. Through a comparative analysis of random forest (RF), XGBoost, and C5.0 decision tree (DT) algorithms, the trained RF model emerged as the best model, achieving a good ROC-AUC score of 0.77 and 0.73 in training and testing sets, respectively. The feature importance aspect of the study identified the submission actions as the most crucial predictor of student performance while the delete actions as the least. The Moodle data used in the study was limited to 2-degree programs from the University of Southeastern Philippines (USeP). The 22 courses still resulted in a small sample size of 1,007. Future research should broaden its focus to increase generalizability. Overall, the findings highlight the potential of machine learning techniques to inform intervention strategies and enhance student support mechanisms in online education settings, contributing to the intersection of data science and education literature.
Enhancing hyperspectral image object classification through robust feature extraction and spatial-spectral fusion using deep learning
Authors: Kochari Vijaylaxmi , Sannakki Sanjeev S. , Rajpurohit Vijay S. , Huddar Mahesh G.
Published: October 2024

Hyperspectral imaging (HSI) has gained significant attention in recent years due to its broad applications across agriculture, environmental monitoring, urban planning, infrastructure management, and defense and security for object detection and classification. Despite its potential, current methodologies face challenges such as insufficient feature extraction, noise interference, and inadequate spatial-spectral fusion, limiting classification accuracy and robustness. This study reviews advancements in HSI object detection and classification methodologies, emphasizing the role of machine-learning (ML) and deep-learning (DL) techniques. Hence, this work proposes a novel framework to address these challenges, prioritizing robust feature extraction, effective spatial-spectral fusion, and comprehensive noise removal mechanisms. By integrating DL techniques and training with HSI noisy data, this framework aims to enhance classification accuracy and robustness. The findings suggest that the proposed approach significantly improves the reliability and performance of HSI-based object classification systems. This research provides a pathway for future development in the domain, promising to elevate the effectiveness of HSI applications in real-world scenarios.
Integrating ELECTRA and BERT models in transformer-based mental healthcare chatbot
Authors: Zeniarja Junta , Paramita Cinantya , Subhiyakto Egia Rosi , Rakasiwi Sindhu , Shidik Guruh Fajar , Andono Pulung Nurtantio , Savicevic Anamarija Jurcev
Published: October 2024

Over the last decade, the surge in mental health disorders has necessitated innovative support methods, notably artificial intelligent (AI) chatbots. These chatbots provide prompt, tailored conversations, becoming crucial in mental health support. This article delves into the use of sophisticated models like convolutional neural network (CNN), long-short term memory (LSTM), efficiently learning an encoder that classifies token replacements accurately (ELECTRA), and bidirectional encoder representation of transformers (BERT) in developing effective mental health chatbots. Despite their importance for emotional assistance, these chatbots struggle with precise and relevant responses to complex mental health issues. BERT, while strong in contextual understanding, lacks in response generation. Conversely, ELECTRA shows promise in text creation but is not fully exploited in mental health contexts. The article investigates merging ELECTRA and BERT to improve chatbot efficiency in mental health situations. By leveraging an extensive mental health dialogue dataset, this integration substantially enhanced chatbot precision, surpassing 99% accuracy in mental health responses. This development is a significant stride in advancing AI chatbot interactions and their contribution to mental health support.
ADALINE-based synchronous detection for enhanced shunt APF performance
Authors: Mebarek Abdesslam Ryad , Merabet Leila , Rahli Chouaib , Saad Salah
Published: October 2024

Power quality issues caused by current harmonics from nonlinear and unbalanced loads are a growing concern. This paper presents a novel control strategy for four-wire shunt active power filters (SAPF) that surpasses existing conventional methods in mitigating harmonics and power factor correction. The strategy employs an improved synchronous detection method (SDM) enhanced by an adaptive linear neural network (ADALINE) trained using the least mean square (LMS) algorithm. This approach accurately estimates harmonic frequencies, enabling the SAPF to generate precise compensation currents. The effectiveness of the proposed method is validated through MATLAB-Simulink simulations under balanced supply conditions, encompassing diverse load scenarios. These simulation results are compared with those obtained using instantaneous power theory (IPT). They demonstrate the ability of the proposed method to achieve excellent harmonic identification and elimination, to comply with IEEE 519 harmonic limits, to ensure sinusoidal and balanced line currents, and to compensate for reactive power and neutral current. Furthermore, its simple architecture and noise robustness make it a promising solution for enhancing power quality.
Virtual inertia evaluation for frequency instability in renewable energy integration
Authors: Kathad Shilpa Keshubhai , Pandya Dharmesh Jagdishchandra
Published: October 2024

In recent trends, the increasing integration of renewable energy sources (RES) into grids has provides a transition in electricity generation and distribution in terms of frequency instability. Recently, the concept of virtual inertia (VI) has developed as a promising solution to minimize frequency instability in interconnected RES. Therefore, this research introduces VI evaluation technique to decrease the frequency instability. Advanced control algorithms are used to create VI, which simulates the stabilizing effect of traditional rotating mass in conventional power systems. The high penetration of RES based on power converters has suggestively decreased the VI which making them susceptible to frequency instability. This work recommends another use of VI control to further develop recurrence dependability of the connected power framework because of high entrance level of RESs. ∆f values differ between 17.4215 and 20.3621 with significant frequency variations due to conventional control. Equally, VI control exhibits a high level of efficiency in reducing frequency deviations; The ∆f values were consistently smaller between 0.0236 and 0.0369 than the conventional control. These findings signify the potential of VI control to improve frequency stability in power systems with RES.
AI in Moroccan education: evaluating student acceptance using machine learning classification models
Authors: Mohamed Khoual , Zineb Elkaimbillah , Zineb Mcharfi , Bouchra El Asri
Published: October 2024

Personalized learning is becoming a reality in education thanks to the rise of AI. This study investigates the possibilities of AI within the realm of education, focusing on the individualization of the learning experience. The research is based on the responses of 395 students from various faculties in Morocco. The questionnaire aimed to assess the students’ opinions of AI, their level of knowledge, their previous experiences, and their perception of the application of AI within educational settings. Employing classification techniques such as decision trees (DT), multilayer perceptron (MLP), and random forests (RF), our aim was to predict the receptivity of AI in education. The findings highlight significant differences in how Moroccan students perceive AI, identifying key factors such as familiarity with the technology, ethical concerns, and perception of its potential impact on the learning experience. Classification models showed varied performance in anticipating these attitudes. This study highlights the critical importance of understanding students’ perspectives on AI in education. These findings offer crucial insights for education policymakers as well as designers of educational technology solutions in Morocco. The findings can be used as a guide to adapt the incorporation of AI into the education sector with discernment, taking into account students’ perceptions and preferences.
Building knowledge graph for relevant degree recommendations using semantic similarity search and named entity recognition
Authors: Zineb Elkaimbillah , Zineb Mcharfi , Mohamed Khoual , Bouchra El Asri
Published: October 2024

Career guidance is a critical and often daunting process, particularly during the transition from high school to higher education within the Moroccan education system. Faced with a vast array of university programs and career options, students frequently struggle to make informed decisions that align with their aspirations and skills. To address this challenge, our research introduces an innovative system that combines semantic similarity search with knowledge graph (KG) construction to enhance the precision and personalization of academic recommendations. By utilizing Sentence-BERT (SBERT) for semantic similarity, we generate embedding vectors that capture nuanced relationships between student profiles and degree descriptions. Subsequently, named entity recognition (NER) is applied to extract essential information such as skills, fields of study, and career opportunities from these profiles and descriptions. The extracted entities and their interrelationships are then structured into a coherent KG, stored in a Neo4j database, enabling efficient querying and visualization of complex data connections. This approach provides a transparent and explainable framework, ultimately delivering tailored advice that aligns with students’ individual needs and educational goals.
Field-level sugarcane yield estimation utilizing Sentinel-2 time-series and machine learning
Authors: B. U. Rekha , Desai Veena V. , Kuri Suresh , Ajawan Pratijnya S , Jha Sunil Kumar , Patil V. C.
Published: October 2024

This work focused on developing a methodology for using machine learning (ML) approaches to establish a pre-harvest yield prediction model for sugarcane at field level by integrating time-series remote sensing imagery data with ML techniques. Ground truth agro data and thirty-one spectral vegetation indices were extracted from Sentinel-2 imagery and were considered for yield modeling. A two-level feature selection technique was used to determine the most significant variables that best correlated with sugarcane yield to predict yield in advance. Seven ML algorithms, including those based on regularization, decision trees, and ensemble methods like boosting, were used to predict yield. The approach achieved the highest R2 score of 0.73 and the lowest root mean squared error (RMSE) of 13.45 t/ha with random forest (RF) among the seven ML models tested. Furthermore, all feature selection procedures identified normalized difference red edge (NDRE), red edge chlorophyll index (RECI), and ratio vegetation index (RVI) as major yield-driving variables. The experiments during feature selection demonstrated the potential of red edge spectral bands in development of a reliable sugarcane-yield prediction approach. The RF model obtained using the proposed methodology outperforms the two baseline models developed using NDVI and GNDVI indices, with an improved RMSE of 16-18%.
Archimedes assisted LSTM model for blockchain based privacy preserving IoT with smart cities
Authors: Somanathan Pillai Sanjaikanth E Vadakkethil , Vallabhaneni Rohith , Vaddadi Srinivas A , Addula Santosh Reddy , Ananthan Bhuvanesh
Published: October 2024

Presently, the emergence of internet of things (IoT) has significantly improved the processing, analysis, and management of the substantial volume of big data generated by smart cities. Among the various applications of smart cities, notable ones include location-based services, urban design and transportation management. These applications, however, come with several challenges, including privacy concerns, mining complexities, visualization issues and data security. The integration of blockchain (BC) technology into IoT (BIoT) introduces a novel approach to secure smart cities. This work presents an Archimedes assisted long short-term memory (LSTM) model intrusion detection for BC based privacy preserving (PP) IoT with smart cities. After the stage of pre-processing, the LSTM is utilized for automated feature extraction and classification. At last, the Archimedes optimizer (AO) is utilized to optimize the LSTM’s hyper-parameters. In addition, the BC technology is utilized for securing the data transmission.
Implementing zero-knowledge proof authentication on Hyperledger fabric to enhance patient privacy and access control
Authors: Joshi Praveena Bolly , Natesan Arivazhagan
Published: October 2024

In recent years, the healthcare sector has encountered significant challenges in authenticating identities for online medical services. A predominant reliance on centralized identity management systems (IDMs) has presented obstacles to the seamless exchange of patient identities among various healthcare institutions, often resulting in data isolation within individual silos. Of paramount concern are the potential privacy breaches associated with centralized IDMs, which may compromise patient confidentiality. In response to these challenges, we propose a novel approach to securely sharing patient details across multiple hospitals utilizing the zero-knowledge access protocol (MediCrypt-ZKAP) within the Hyperledger Fabric blockchain framework. By adopting MediCrypt-ZKAP, hospitals can effectively verify the identities of requesting entities without disclosing sensitive patient information, thereby ensuring the highest levels of confidentiality and privacy protection. The proposed system represents a proactive step towards addressing the critical need for secure and interoperable patient data exchange within the healthcare sector. Through the integration of MediCrypt-ZKAP into existing blockchain infrastructure, our solution aims to enhance data security and privacy while promoting seamless collaboration among healthcare institutions.
Elevating intelligent voice assistant chatbots with natural language processing, and OpenAI technologies
Authors: Korade Nilesh B. , Salunke Mahendra B. , Bhosle Amol A. , Asalkar Gayatri G. , Lal Bechoo , Kumbharkar Prashant B.
Published: October 2024

Businesses can offer support to customers outside of usual business hours and across time zones by employing chatbots, which can provide round-the-clock support. Chatbots can react to user inquiries quickly, reducing wait times and improving customer satisfaction. It becomes challenging for the chatbot to differentiate between two queries that users pose that carry the same meaning, making it harder for it to understand and react appropriately. The aim of this research is to develop a chatbot capable of understanding the semantic meaning of questions as well as recognizing various speech patterns, accents, and dialects to provide accurate responses. In this research, we have implemented a voice-enabled chatbot system where users can verbally pose questions, and the chatbot provides responses through voice assistance. The architecture incorporates several key components: a question-answer database, OpenAI embedding for semantic representation, and OpenAI text-to-speech (TTS) and speech-to-text (STT) for audio-to-text and text-to-audio conversion, respectively. Specifically, OpenAI embedding is utilized to encode questions and responses into vector representations, enabling efficient similarity calculations. Additionally, extreme gradient boosting (XGBoost) is trained on OpenAI embeddings to identify similarities between user queries and questions within the dataset. This framework allows for seamless interaction between users and the chatbot, leveraging state-of-the-art technologies in natural language processing (NLP) and speech recognition. The outcome demonstrates that the XGBoost model delivers excellent outcomes when it is trained on OpenAI embedding and tuned with the particle swarm optimizer (PSO). The OpenAI-generated embedding has good potential for capturing sentence similarity and provides excellent information for models trained on it.
Detection and severity classification of ataxia using gait features and a hybrid model
Authors: Pushpalatha Srikantaswamy , Jayaprakash Vidyarani Hamppayanamaligae , Krishnamurthy Sunitha
Published: October 2024

Ataxia, a neurological disorder characterized by impaired coordination and unsteady movements, presents significant challenges for accurate diagnosis and classification. traditional machine-learning (ML) and deep-learning (DL) models often struggle to achieve high accuracy in predicting and classifying this complex condition. This study addresses these limitations by introducing a novel hybrid model, XGBoost-multi-layer-perceptron (XGB-MLP), specifically designed to enhance the accuracy of ataxia prediction and classification. The objective of this research is to develop a more reliable and precise diagnostic tool that outperforms existing ML and DL approaches. The methodology involved integrating the strengths of XGBoost, known for its powerful gradient boosting, with the multi-layer perceptron (MLP) neural network, creating a robust hybrid model. The proposed XGB-MLP model was rigorously tested against conventional models like random forest (RF), logistic regression (LR), support vector machine (SVM), MLP, and standalone XGBoost. The findings reveal that the XGB-MLP model achieves outstanding accuracy rates of 98.91% for ataxia prediction and 97.91% for classification, significantly surpassing the performance of the traditional models.