Search Results (222)

Peritoneal ultrafiltration failure is a major complication of peritoneal dialysis and a common cause of technique failure often leading to hemodialysis. Chronic exposure to glucose-based dialysate contributes to inflammation, fibrosis, and peritoneal membrane dysfunction. This study evaluated the effects of dapagliflozin on peritoneal membrane function in patients with ultrafiltration failure undergoing continuous ambulatory peritoneal dialysis. In our pre–post observational study, 32 patients with high/high–average peritoneal transport status and ultrafiltration failure received dapagliflozin 10 mg daily for six months. Peritoneal equilibration tests using a 4.25% dextrose solution were performed during early peritoneal dialysis, at ultrafiltration failure, and after treatment. Ultrafiltration volume, dialysate-to-plasma creatinine ratio, dialysate glucose ratio, sodium dip, and clinical/biochemical parameters were assessed. Dapagliflozin treatment was found to be associated with higher ultrafiltration volume (480 mL vs. 90 mL at ultrafiltration failure, p < 0.001), preservation of the intraperitoneal glucose gradient, changes in the dialysate-to-plasma creatinine ratio, and altered sodium dip parameters. Favorable changes were also observed in blood pressure, body mass index, inflammatory markers, hemoglobin, albumin, sodium, bicarbonate, and glycemic indices. No serious adverse events were reported. These findings suggest that dapagliflozin may improve ultrafiltration efficiency and peritoneal membrane function in peritoneal dialysis patients with ultrafiltration failure. Further randomized controlled trials are warranted. Full article

Natural coumarins are a structurally privileged group of bioactive benzopyranone lactones widely spread across the Apiaceae, Rutaceae, and Leguminosae families, and hold significant potential as inhibitors of pathological protein aggregation in Alzheimer’s disease, Parkinson’s disease, and type 2 diabetes mellitus. The fully planar, rigid bicyclic structure of the coumarin nucleus (~3.4–3.5 Å thickness) is geometrically compatible with intercalative π–π stacking with aggregation-nucleating aromatic residues, including Phe19 of Aβ(1–42), providing a mechanistically coherent pharmacophoric basis for anti-aggregation activity according to computational and indirect biophysical evidence. This review critically evaluates the peer-reviewed literature on naturally occurring coumarins and their synthetic derivatives as candidate β-sheet intercalators, with analysis of SAR at C-3 to C-8 positions; multi-target-directed ligand designs with dual activities of inhibiting AChE, BACE-1, GSK-3β, and MAO-B, and as blood–brain barrier-penetrating neuroprotective agents validated in cellular and rodent models. The critical analysis identifies the translational gap between in vitro IC₅₀ values and attainable brain drug concentrations as the primary pharmacological obstacle. It identifies the absence of systematic investigation of coumarin against IAPP, a directly relevant amyloid target in metabolic neurodegeneration, as the most significant unmet research priority in the field. Full article

Precise weather forecasting plays a critical role in sectors such as agriculture, transport, energy management, and climate change adaptation, and machine learning and deep learning algorithms have been widely used for data-driven time series forecasting problems. In this work, we explore the application of machine learning and deep learning models for multi-weather variable forecasting in a dataset recorded over a period of ten years (2015–2025) for five weather stations in Palestine. The dataset comprises measurements for temperature, relative humidity, wind speed, precipitation, atmospheric pressure, and sunshine hours. To avoid the issue of temporal leakage, a chronological training, validation, and test set splitting approach was used in the evaluation experiments. The models used in this study include ARIMA, SARIMA, Random Forest, XGBoost, CNN, LSTM, GRU, ConvLSTM, CNN-GRU, and CNN-LSTM with station embeddings. Our experimental results indicate that the XGBoost model achieved the highest performance in predicting temperature and relative humidity (R² = 0.953 and R² = 0.670, respectively), while deep learning methods exhibited high accuracy across several weather features. The CNN-LSTM model was successfully able to learn temporal–spatial patterns via station embeddings, while recurrent neural networks performed impressively in forecasting sunshine hours and atmospheric pressure. Full article

Background/Objectives: Obstructive sleep apnea (OSA) is a common and serious sleep-related disorder that causes repeated interruptions in breathing during sleep. Traditional diagnostic methods, such as polysomnography, are accurate but costly, time-consuming, and unsuitable for large-scale screening. This study proposes and evaluates a lightweight diagnostic framework based on an Extreme Learning Machine (ELM) optimized by a set of basic and advanced metaheuristic optimizers. The model aims to evaluate whether metaheuristic optimization can improve ELM-based classification performance using structured demographic, clinical, and sleep-related predictors. Methods: Two real datasets were employed to train and evaluate the proposed framework: (i) a clinical OSA dataset with 274 subjects and 31 demographic/anthropometric and sleep-related predictors, and (ii) a public strongly imbalanced Sleep-Disordered Breathing (SDB) dataset with 500 subjects and 10 structured predictors. Metaheuristic algorithms are used to optimize ELM weights and biases, addressing the instability of random initialization and improving model generalization. The optimized models are evaluated against eight baseline classifiers, including logistic regression (LR), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), XGBoost (XGB), and a standard ELM classifier. Results: Results show that metaheuristic optimization moderately improves ELM on the OSA dataset, increasing ROC-AUC from 0.6527 to about 0.73 and accuracy from 0.6573 to about 0.69–0.70, while on the highly imbalanced SDB dataset, it yields modest ROC-AUC gains (from 0.5132 to about 0.544–0.548) with small decreases in accuracy and F1-score. We additionally assess class-imbalance handling on the SDB dataset and analyze feature importance with permutation importance and SHAP, which shows the models rely heavily on diagnosis-derived predictors. Conclusions: The proposed framework provides a lightweight ELM-based decision-support approach with low inference cost after offline optimization. The results suggest potential value for screening-oriented OSA/SDB classification, but further validation with larger cohorts and a screening-only feature set is needed before clinical implementation. Full article

Herein, p-NbSe₂ thin films were deposited onto n-Si substrates to fabricate an n-Si/p-NbSe₂ (SNS) heterojunction for visible light communication (VLC) applications. Structural analysis revealed that the NbSe₂ films possess a trigonal phase and are composed of slightly elongated and irregularly shaped grains with an average size of 0.131 μm. Electrical characterization showed that the SNS heterojunction exhibits pronounced rectifying behavior, with a bias-dependent asymmetry factor reaching 6.6 × ${10}^3$. The photodetection performance of the device was evaluated under illumination from white, blue, red, tungsten, and infrared LEDs. The device exhibited excellent photodetection characteristics across the visible region, achieving a maximum responsivity of 3.79/3.68 AW⁻¹, external quantum efficiency of 1160/809%, noise equivalent power of 4.43 × 10⁻¹⁴ /4.57 × 10⁻¹⁴ WHz^−1/2, and specific detectivity of 3.91 × 10¹²/3.79 × 10¹² Jones under blue/white light illumination, confirming its practical relevance for VLC systems. In addition, frequency-dependent photocurrent measurements under modulated blue and white LED illumination revealed −3 dB bandwidths of approximately 775 Hz and 716 Hz, respectively, supporting the potential of the n-Si/p-NbSe₂ photodiode for low-frequency VLC-related visible-light detection. Compared with previously reported photodiodes used in VLC and IR technologies, the present device demonstrated superior responsivity and EQE%, together with competitive NEP and detectivity. The enhanced performance is attributed to efficient photocarrier generation and collection across the Si/NbSe2 heterojunction. These results confirm that the fabricated SNS photodiode is a promising candidate for high-sensitivity and efficient visible light communication applications. Full article

The early and accurate detection of lung cancer (LC) is one of the primary challenges in the clinical diagnostics process, which plays a vital role in the treatment of the disease. Although various deep learning (DL) techniques have been presented, the existing DL methods are mainly focused on single-modal images, either computed tomography (CT) or histopathological images, which are associated with poor generalization, diversity, and applicability. To mitigate the existing issues, the present work aims to develop a modality-independent ensemble DL framework that is independently evaluated on CT and histopathological image datasets for LC classification. In this work, the proposed framework was developed using the Beta Normalization Aggregation (BNA) technique, where the performance of three state-of-the-art pre-trained convolutional neural network (CNN) architectures was compared on two distinct imaging modalities images. Based on the comparative analysis of the performance metrics, Xception, DenseNet121, and MobileNetV2, are chosen to develop the Ensemble model. Predictions generated by the selected CNN models are aggregated using the proposed BNA strategy to improve classification robustness, which improves the confidence of the prediction results and discriminative capabilities. The experiments using public data sets have confirmed the excellent performance of the model. On the CT dataset, the proposed BNA Ensemble achieved a testing accuracy of 97.45%, with a precision of 97.88%, recall of 97.45%, F1-score of 97.45%, and an AUC of 0.9986. On the histopathological dataset, the framework achieved an accuracy of 99.80%, with precision, recall, and F1-score all reaching 99.80%, and an AUC of 1.0000. These results demonstrate the effectiveness, robustness, and generalizability of the proposed BNA framework. The analysis of the results using t-SNE plots, confusion matrices, ROC curves, and confidence distributions provided additional insights into feature separability, classification performance, and prediction confidence of the proposed framework. Full article

This study investigates the key determinants of bank stability and profitability in commercial and Islamic banks listed on the Amman Stock Exchange (ASE) in Jordan, with a focus on credit risk and capital adequacy during the period 2018–2024. Using panel data from 15 banks, the study applies fixed effects regression models with clustered standard errors. Liquidity is proxied by the loan-to-deposit ratio (LDR), credit risk by the loans loss provisions-to-total loans ratio, and capital strength by the equity-to-assets ratio, alongside a COVID-19 dummy and an interaction term between liquidity and credit risk. Financial performance and stability are measured using return on assets (ROA), return on equity (ROE), and the logarithmic Z-score. The findings indicate that credit risk has a significant negative effect on both bank performance and financial stability, whereas capital adequacy exerts a positive and significant effect. The COVID-19 pandemic negatively affected financial performance and stability, while liquidity (LDR) shows no significant direct effect. The interaction between liquidity and credit risk was statistically insignificant across all estimated models, suggesting that credit risk remains the dominant determinant regardless of liquidity conditions. The study highlights the importance of effective credit risk management and strong capital buffers in enhancing bank resilience. It contributes to the literature by providing recent evidence from the Jordanian banking sector and by incorporating multiple performance measures, a pandemic shock variable, and risk interaction effects to better understand bank stability within a unified empirical framework for an emerging banking market. Full article

Digital evidence management systems are designed to ensure that the digital evidence is genuine and effectively handle its complexity. In this work, blockchain technology is applied to handle the digital evidence by introducing several layers of security to ensure its protection, data integrity, and confidentiality, as well as trace the evidence throughout all its phases. To store the evidence files and their metadata, the proposed system uses a decentralized storage architecture that utilizes the InterPlanetary File System (IPFS) and Google Drive. Moreover, the proposed system ensures the chain of custody of the digital evidence through the use of Hyperledger Fabric technology. In addition, smart contracts (chaincode) are used in this work to validate the digital evidence, enforce strong access controls, and protect evidence metadata integrity. To ensure reliable transaction sequencing and consistency across the distributed ledger, an ordering service is used. At last, we combine two hash algorithms, symmetric encryption, file fragmentation, and metadata logging to protect the digital evidence from unauthorized access. The proposed framework is integrated with modern forensic tools, including Autopsy. The procedure of acquiring and analyzing digital evidence is made straightforward by the application of a set of forensic procedures. Moreover, the system’s modular design allows users to perform preprocessing operations, administer the decentralized storage, administer the evidence retrieval, test system performance, and enhance the system scalability. Moreover, we implemented secure coding practices and applied large language models to mitigate identified vulnerabilities, including weak system input validation, concurrent access to the system, and an insecure logging system. The experimental results indicate that the proposed framework preserves the digital evidence’s integrity, ensures chain of custody, and records all transactions. Results also indicate that the digital evidence is protected from unauthorized access and change attempts. Finally, by following local relevant regulations and established standards, the digital evidence should be admissible in court. Full article

Background: With the increasing use of cigarettes and electronic nicotine delivery systems (ENDS) in young adults, growing concern exists regarding lung health among university students. While the adverse respiratory effects of smoking are well established in older populations, early functional changes among young adults remain less well studied. Identifying such changes in this vulnerable population is crucial due to the risk of detrimental long-term health effects and the role of implementing early preventive measures. This study aims to compare the effects of nicotine use, sex, and body mass index (BMI) on the spirometric lung function parameters, including FEV₁, FVC, and FEV₁/FVC ratio, of smokers (including cigarette, ENDS, shisha, and midwakh users) and non-smokers in a university population. Methods: This cross-sectional study was conducted at Ajman University, United Arab Emirates. A convenience sample of 652 smokers and non-smokers students was initially recruited voluntarily, of whom 630 participants met the spirometry acceptability criteria and were included in the final analysis. Lung function was assessed using spirometry performed according to the guidelines of the American Thoracic Society and European Respiratory Society. Forced expiratory volume in one second (FEV₁), forced vital capacity (FVC), and the FEV₁/FVC ratio were recorded and expressed as percentages of predicted values based on Global Lung Function Initiative reference equations. Lung function parameters were compared according to smoking status, sex, and BMI. Results: A total of 630 students were included (60.5% males; the majority aged 20–22 years). The prevalence of smoking was 28.9% and was significantly higher among males than females (38.6% vs. 14.1%; OR = 3.86, p < 0.001). Smokers demonstrated a significantly lower FEV₁/FVC ratio compared with non-smokers (0.84 ± 0.07 vs. 0.86 ± 0.07, p < 0.001), despite slightly higher predicted FEV₁ and FVC values. Males exhibited higher predicted lung volumes than females, whereas females had higher FEV₁/FVC ratios (p < 0.001). Lung function varied significantly across BMI categories (p < 0.001), with increasing BMI associated with higher predicted lung volumes but lower FEV₁/FVC ratios. Stratified analysis showed that male smokers had the lowest FEV₁/FVC ratios, while female non-smokers had the highest. Conclusions: Smoking, sex, and BMI significantly influenced lung function in this cohort. Smokers demonstrated reduced FEV₁/FVC ratios, indicating early airflow limitation. Males were more likely to smoke and had higher lung volumes, while females showed higher FEV₁/FVC ratios. Increasing BMI was associated with higher lung volumes but lower FEV₁/FVC ratios. These findings suggest that early pulmonary changes may occur in young adults, highlighting the importance of early screening, careful interpretation of spirometry, and the implementation of targeted public health interventions to reduce nicotine use, promote smoking cessation, and support lung health among university students. Full article

This paper examines whether the interaction between credit risk and liquidity conditions helps explain bank stability in a fragile and institutionally constrained banking environment. Using an annual panel of 13 Palestinian banks over 2011–2024 and measuring stability by the (log) Z-score, we estimate static panel models (pooled OLS, fixed effects, and random effects), a simultaneous two-stage least squares (2SLS) system to probe the direction of causality between credit risk and liquidity, and a dynamic panel GMM specification to address persistence and endogeneity. The static models show that credit risk is negatively associated with stability and that the interaction term is economically meaningful but not robust across static specifications. In the dynamic GMM model, credit risk remains significantly destabilizing, liquidity holdings are stabilizing, and the interaction term is positive and significant—consistent with liquidity buffers mitigating the adverse stability implications of higher credit risk. The 2SLS system suggests no strong contemporaneous reciprocal causality between credit risk and liquidity once controls are included, while regulatory and conflict-period dummies are associated with shifts in the risk profiles. The results highlight the importance of integrated risk management and liquidity buffers for banking stability in high-uncertainty contexts. Full article

Background: Repeated brain computed tomography (CT) scans in children may result in substantial cumulative radiation exposure, particularly in young children, who are more sensitive to ionizing radiation. The purpose of the study was to assess the dose levels of radiation in patients who receive repeated brain CT during childhood and adherence rates to pediatric imaging protocols. Methods: A retrospective cross-sectional study was conducted among 177 patients aged ≤5 years who underwent two or more brain CT examinations with a total of 514 CT examinations. The information was gathered through the hospital Picture Archiving and Communication System (PACS), which included patient demographics, scan parameters, and scanner-reported dose indicators such as volume-averaged computed tomography dose index (CTDIvol) and dose-length product (DLP). The effective dose (ED) was calculated and compared with estimated doses based on a nominal pediatric CT protocol. Results: The findings indicated a great variation in scan parameters, with CTDIvol values of 8.9 to 51.7 mGy and DLP values of 177 to 1310 mGy.cm. The number of repeated scans showed a great increase in the cumulative ED (p < 0.001). The median doses in patients below the age of one year were greater than those in older children. There was also a closer relation of scanner-reported doses to adult protocols, which suggests a lack of an optimized pediatric setting. Conclusions: Children under 5 who undergo repeated brain CT scans may face excessive radiation exposure. The matter is aggravated by the fact that scans are performed repeatedly without optimization of the dose, which leads to significant cumulative ED. Full article

In contemporary debates on the United Nations Sustainable Development Goals, there is growing recognition that artificial intelligence (AI) may contribute meaningfully to SDG 2 (Zero Hunger), particularly by enhancing the efficiency of food aid distribution and resource allocation. However, such optimism must be critically situated within the broader institutional and ethical contexts in which AI operates. This study argues that the effectiveness of AI in conflict-affected settings is contingent not only on technical capacity but also on governance structures, ethical safeguards, and institutional trust, dimensions closely aligned with SDG 16 (Peace, Justice, and Strong Institutions). Using the Gaza Strip as a case study, this article demonstrates that AI-driven food assistance mechanisms may inadvertently reinforce structural vulnerabilities. Specifically, algorithmic targeting of aid risks deepening dependency, exacerbating digital exclusion, and weakening already fragile governance systems. The absence of robust data accountability frameworks further complicates these dynamics, raising concerns regarding transparency, fairness, and long-term sustainability. The findings caution against privileging technical efficiency at the expense of socio-political stability. Rather, they highlight that the sustainability of AI interventions in humanitarian contexts fundamentally depends on the credibility and legitimacy of institutions. Accordingly, this study proposes a conceptual model for AI in hunger relief and digital humanitarianism that integrates technical innovation with institutional accountability and social trust. This study presents a narrative review informed by structural searching that examines the influence of AI on food security interventions in fragile contexts. This analysis applies a combined ethical governance and sustainability lens to assess current applications and risks. This research advances a broader analytical framework that moves beyond purely technical interpretations of AI, emphasizing its role as a socio-political tool, through identifying five key pillars for sustainable AI governance: data sovereignty, algorithmic accountability, inclusive system design, community-led governance, and market integrity. Full article

This study investigates the antioxidant and anticorrosive properties of Mentha pulegium L. essential oil (MP EO) as a sustainable and eco-friendly alternative to synthetic oxidation inhibitors. The antioxidant activity of MP EO was evaluated using the ferric reducing antioxidant power (FRAP) assay, which demonstrated a strong electron-donating capacity and effective reduction of ferric ions, indicating promising antioxidant potential. The anticorrosive performance was assessed on mild steel in 0.5 M H₂SO₄ using potentiodynamic polarization and electrochemical impedance spectroscopy (EIS). The results showed inhibition efficiencies of up to 75.8% at a concentration of 2 g/L. Molecular docking simulations revealed favorable binding interactions between the key oil components (pulegone and menthone) and the ROS-generating enzyme model (PDB ID: 2CDU), providing complementary mechanistic insight into their potential role in oxidative stress modulation. Additionally, quantum chemical calculations highlighted electronic properties favoring adsorption on metallic surfaces. Surface morphology analysis using SEM/EDX confirmed the formation of a protective film on steel in the presence of MP EO. These combined findings position Mentha pulegium essential oil as a potent, biodegradable candidate for both antioxidant applications and corrosion prevention in acidic environments. Full article

Artificial intelligence (AI) has become a significant paradigm of methodology and epistemology in the social sciences. Machine learning (ML), natural language processing (NLP), and generative models enable researchers to work with big, multimodal datasets, identify complex patterns, and recreate events in the social world in ways that previously were not feasible. At the same time, these innovations also lead to ethical challenges related to algorithmic bias, black boxes, data extractivism, and reinforced structural inequalities in welfare, government services, education, and criminal justice. The article critically questions the social sciences in the light of AI on three dimensions that are inextricably linked, namely: (1) the opportunities that AI provides to social-scientific inquiry; (2) the biases and constraints generated through data, models, and institutional application; and (3) ethical pathways that are necessary for the responsible governance of AI-facilitated research and decision support. The article is based on a scoping, critical thematic review of the recent literature, and its conceptualization of AI as a socio-technical infrastructure is that it produces knowledge and, at the same time, offers power. It explains the impact AI practices have on restructuring disciplines like sociology, psychology, political science, and policy analysis, and how it blindly predicts how data practices, design choices, and governance arrangements can either preserve or destroy existing hierarchies. The paper suggests an analytical framework synthesizing AI practices, social research practices, and governance structures in ethical frameworks. It argues that the emancipatory promise of AI in the social sciences is dependent on the attainment of something beyond principle-based claims of so-called ethical AI by operational governance mechanisms that make systems visible, debatable, and responsible in their respective situations. Full article

Avocado (Persea americana Mill.) is one of the most widely consumed fruit tree crops worldwide, with cultivation expanding rapidly beyond its Mesoamerican and northwest South America center of origin. In emerging secondary diversity centers such as East Africa, farmers have long propagated seedling naturalized populations that may hold valuable reservoirs of genetic diversity, yet these resources remain underexplored. To help fill this gap, this study developed the first genomic resources for avocados in Tanzania, where avocado has a long history of introduction and diversification dating to the first Arab incursions and Catholic missionary missions. Low-coverage whole-genome resequencing (lcWGS) data were obtained from 95 trees sampled in Tanzania across the low- to mid-altitude Morogoro region (n = 25) and the Southern Highlands—i.e., the Iringa (n = 20), Mbeya (n = 30) and Ruvuma (n = 20) regions. In order to guide racial assignation, sequences were merged with NCBI-available lcWGS data from 205 avocado trees, including 42 commercial varieties, with reported ancestry. Population stratification as inferred via maximum likelihood phylogenetic inference, genetic principal component analysis, and ADMIXTURE unsupervised clustering suggested that the sampled Tanzanian avocado trees were genetically closer to the West Indian race and more distant from the northwest South American Caribbean and Andean groups. Additionally, while the trees from the low- to mid-altitude region of Morogoro were almost exclusively West Indian type, some trees from the Southern Highlands aligned more closely with West Indian × Guatemalan and West Indian × Mexican hybrids. These trends were equally supported by a subset of 10,460 high-coverage (10×) SNP markers. Together these findings clarify the dynamics of avocado diversification in a secondary center in East Africa, spanning recent introductions from a single Mesoamerican race, adaptation to a wide range of locally geographic conditions, and farmer-driven selection matching local tribal preferences. Characterizing these locally adapted resources is key for identifying underrepresented yet promising provenances, developing resilient and sustainable horticultural production systems, and safeguarding the species’ global genetic heritage. Full article