Search Results (122)

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

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

Background: Torture, as a fundamental violation of human rights, is unequivocally condemned by all international organizations. Sexual torture is one of the most severe forms of torture, encompassing forced nudity, various forms of humiliation, and physical abuse, including rape. Despite testimonial evidence indicating the incidental use of sexual torture by Israeli authorities, there is a lack of epidemiological research providing a comprehensive understanding of this issue. This study aims to analyze the prevalence and characteristics of ill treatment and sexual torture among Palestinian male detainees and the subsequent impacts. Methods: This cross-sectional study analyzed a database of 517 former male detainees. The interview protocol included items related to psychological and physical methods of sexual torture, medical impacts, subjective psychological impacts, clinical medical and psychological measures, and psychosocial and community impacts. Results: The findings indicate that the majority of detainees experienced some form of sexual torture, with humiliation being the most common type. The impact of sexual torture are severe, affecting both clinical and social domains. The impacts of sexual torture persist over time and, in some cases, worsen, particularly regarding physical health outcomes. Socially, the consequences extend to the detainees’ families and communities. Conclusions: The prevalence of such torture tactics calls for urgent responses from both the authorities and civil society. These findings highlight the need for proactive measures to address and mitigate the impacts of sexual torture, including independent investigations, robust monitoring, secure reporting mechanisms, the prosecution of perpetrators and comprehensive reparation for victims. Full article

Dynamic Message Signs (DMSs) play a critical role in conveying real-time traffic information to drivers; however, their effectiveness heavily relies on how messages are structured and displayed, particularly regarding phasing duration and content length. This study examines the influence of these two factors on driver readability, comprehension, and gaze behavior using an advanced virtual reality (VR) driving simulator. Controlled experiments simulated four DMS scenarios, combining two phasing intervals (2.5 and 4 s) with short and long message formats, adhering to Michigan Department of Transportation (MDOT) guidelines. The experiment integrated eye-tracking technology to measure fixation duration and frequency, while statistical methods, including survival analysis and LASSO regression, were employed to identify significant predictors of message readability. Results revealed that shorter messages with shorter phasing intervals led to the highest comprehension rates and reduced cognitive strain. Furthermore, individual characteristics such as gender, driving speed, and highway driving experience significantly affected how drivers engaged with DMS messages. These findings contribute to the development of more effective DMS deployment strategies and provide practical design recommendations to enhance traffic safety and information delivery on high-speed roadways. Full article

Electric scooters and other micromobility innovations are becoming standard fare in urban transportation networks. Yet there are several obstacles that must be overcome, including concerns about users’ satisfaction and safety. This study aimed primarily at developing a user-centered methodological framework that combined different user-centered engineering tools such as voice of customers analysis, needs–metrics mapping, Pugh’s matrix and morphological design, strategic analysis approaches such as SWOT and PESTEL, and, a key innovation, the smart terrain-adaptive power management system (STAPMS), an AI-based feature that dynamically adjusts power output and regenerative braking based on Budapest’s varied topography and road conditions to improve energy efficiency and ride comfort. This innovative framework offers insights into redesign options aimed at enhancing customer satisfaction, product quality, and business growth. The proposed framework was validated on Lime electric scooters, particularly the S2 generation type. Three design concepts were generated and evaluated through a systematic approach to provide an optimal balance between users’ needs, technical performance, and strategic feasibility. The proposed user-centered framework shows significant potential to improve users’ satisfaction, enhanced usability, extended range, and increased market competitiveness, validating its viability for micromobility innovative solutions. The findings also demonstrate the necessity for systematic frameworks that link user experience with engineering design and can be generalized to other micromobility products. Full article

The Syrian conflict has created one of the largest displacement crises of the twenty-first century, disproportionately affecting adolescent girls. Syrian girls have been primarily portrayed as victims of war or “the lost generation”, neglecting the plurality of their experiences. Building on Bandura’s social cognitive theory, Giddens’ structuration theory, Kabeer’s empowerment framework, and Mahmood’s modalities of agency, this study examines how Syrian refugee adolescent girls in Lebanon enact agency within contexts of forced displacement and how structural factors shape these processes. We conducted a secondary analysis of 293 first-person narratives from Syrian girls and mothers collected in 2016 using Cognitive Edge’s SenseMaker^®. Thematic analysis revealed seven structural barriers—restricted access to education, economic insecurity, inadequate infrastructure/living conditions, limited healthcare, gender and social norms, xenophobia, and lack of legal status—as well as key enablers including community services, parental support, and peer networks. Girls expressed agency through seven interconnected processes: awareness/acknowledgement of barriers, emotional navigation, resource identification, decision-making, future planning, reflection, and action execution. These processes were adaptive and recursive, highlighting that agency during displacement is dynamic, relational, and conditioned by structural forces. These findings inform approaches that both reduce structural barriers and enable refugee girls’ agency. Full article

Background: First-trimester prenatal screening is a fundamental component of modern obstetric care, offering early insights into fetal health and development. A key focus of this screening is the detection of chromosomal abnormalities, such as Trisomy 21 (Down syndrome), which can have significant implications for pregnancy management and parental counseling. Over the years, various non-invasive methods have been developed, with ultrasound-based assessments becoming a cornerstone of early evaluation. Among these, the measurement of Nuchal Translucency (NT) has emerged as a critical marker. This sonographic measurement, typically performed between 11- and 13-weeks 6+ days of gestation, quantifies the fluid-filled space at the back of the fetal neck. An increased NT measurement is a well-established indicator of a higher risk for aneuploidies and other congenital conditions, including heart defects. The Fetal Medicine Foundation has established standardized criteria for this measurement to ensure its reliability and widespread adoption in clinical practice. Methods: We utilized two datasets comprising 2425 ultrasound images from Shenzhen People’s Hospital China and the National Hospital of Obstetrics and Gynecology Vietnam. The methodology employs a two-stage Deep Learning framework: first, a DenseNet121 model assesses image quality to filter non-standard planes; second, a novel DenseNet-based segmentation delineates the NT region for automated measurement. Results: The quality assessment module achieved 94% accuracy in distinguishing standard from non-standard planes. For segmentation, the proposed model achieved a Dice coefficient of 0.897 and an overall accuracy of 98.9%, outperforming the standard U-Net architecture. Clinically, 55.47% of automated measurements deviated by less than 1 mm from expert annotations, and the system demonstrated > 90% sensitivity and specificity for identifying high-risk cases (NT ≥ 2.5 mm). Conclusions: The proposed framework successfully integrates quality assurance with automated measurement, offering a robust decision-support tool to reduce variability and improve screening accuracy in prenatal care. Full article

The use of machine learning (ML) and deep learning (DL) in lung cancer detection and classification offers great promise for improving early diagnosis and reducing death rates. Despite major advances in research, there is still a significant gap between successful model development and clinical use. This review identifies the main obstacles preventing ML/DL tools from being adopted in real healthcare settings and suggests practical advice to tackle them. Using PRISMA guidelines, we examined over 100 studies published between 2022 and 2024, focusing on technical accuracy, clinical relevance, and ethical aspects. Most of the reviewed studies rely on computed tomography (CT) imaging, reflecting its dominant role in current lung cancer screening workflows. While many models achieve high performance on public datasets (e.g., >95% sensitivity on LUNA16), they often perform poorly on real clinical data due to issues like domain shift and bias, especially toward underrepresented groups. Promising solutions include federated learning for data privacy, synthetic data to support rare subtypes, and explainable AI to build trust. We also present a checklist to guide the development of clinically applicable tools, emphasizing generalizability, transparency, and workflow integration. The study recommends early collaboration between developers, clinicians, and policymakers to ensure practical adoption. Ultimately, for ML/DL solutions to gain clinical acceptance, they must be designed with healthcare professionals from the beginning. Full article

Wireless Power Transfer (WPT) technologies are rapidly maturing, offering alternatives to traditional wired connections in applications ranging from consumer electronics to industrial automation. This review provides a technical analysis of WPT methodologies published between 2010 and 2025, explicitly distinguishing between non-radiative near-field techniques (specifically Inductive Power Transfer [IPT] and Capacitive Power Transfer [CPT]) and radiative far-field systems (Microwave Power Transfer [MPT] and Laser Power Transfer [LPT]). Unlike previous reviews that categorize primarily by coupling mechanism, this paper proposes a novel multi-parametric classification framework incorporating efficiency, alignment sensitivity, and emerging operational paradigms such as AI-optimized tuning and acoustic transfer. The analysis evaluates the engineering trade-offs between short-range, high-efficiency inductive systems and long-range, lower-efficiency radiative links. Furthermore, the paper identifies critical technical barriers to commercialization, specifically focusing on electromagnetic compatibility (EMC), biological safety (SAR) limits, and end-to-end system efficiency. Finally, the review extends beyond the physics to provide a rigorous economic analysis of the Total Cost of Ownership (TCO) for electric vehicle infrastructure and industrial IoT, highlighting the strategic viability of WPT in future smart grids. Full article

This research presents a detailed assessment of the wind power potential in six Palestinian cities—Bethlehem, Jericho, Jenin, Nablus, Ramallah, and Tulkarm—utilizing daily wind speed data from the years 2015 to 2021. The primary goal of this study is to formulate a robust, data-driven framework for the strategic placement of turbines and the economical production of energy in areas with limited wind resources. A critical aspect of this research is the application of nine numerical methods, including the Maximum Likelihood Method (MLM) and the Energy Pattern Factor Method (EPF), to analyze the wind data. These methods were employed to estimate the shape and scale parameters of the Probability Distribution Function (PDF) that represents the Weibull distribution for various shape factor values. The accuracy of the numerical methods was validated through five statistical tools, including the Root Mean Square Error (RMSE) and Chi-square tests ($X^2$). The Weibull parameters obtained from the numerical techniques indicated shape factors ranging from 1.27 to 1.96 and scale factors between 1.16 and 3.21 m/s. The energy output was calculated based on the swept area of the wind turbine, following Betz’s limit. The estimated annual energy production per square meter in the six cities is as follows: Ramallah—123 kWh/m², Bethlehem—24.42 kWh/m², Jenin—31.12 kWh/m², Nablus—22 kWh/m², Tulkarm—15.5 kWh/m², and Jericho—10.36 kWh/m². A 5 kW small-scale wind turbine was utilized to evaluate the technical feasibility, sustainability, and economic viability of small-scale wind energy applications. The anticipated energy output from the proposed wind turbine is 2054 kWh, with an estimated payback period of approximately 11.6 years. Full article

Background/Objectives: Tuberculosis remains a significant global health problem, particularly in resource-limited environments. Its mortality and spread can be considerably decreased by early and precise detection via chest X-ray imaging. This study introduces a novel approach based on hybrid deep learning for Tuberculosis detection from chest X-ray images. Methods: The introduced approach combines GhostNet, a lightweight convolutional neural network tuned for computational efficiency, and MobileViT, a transformer-based model that can capture both local spatial patterns and global contextual dependencies. Through such integration, the model attains a balanced trade-off between classification accuracy and computational efficiency. The architecture employs feature fusion, where spatial features from GhostNet and contextual representations from MobileViT are globally pooled and concatenated, which allows the model to learn discriminative and robust feature representations. Results: The suggested model was assessed on two publicly available chest X-ray datasets and contrasted against several cutting-edge convolutional neural network architectures. Findings showed that the introduced hybrid model surpasses individual baselines, attaining 99.52% accuracy on dataset 1 and 99.17% on dataset 2, while keeping low computational cost (7.73M parameters, 282.11M Floating Point Operations). Conclusions: These outcomes verify the efficacy of feature-level fusion between a convolutional neural network and transformer branches, allowing robust tuberculosis detection with low inference overhead. The model is ideal for clinical deployment and resource-constrained contexts due to its high accuracy and lightweight design. Full article

Various maximum power point tracking (MPPT) techniques have been proposed to optimize the efficiency of solar photovoltaic (PV) systems. These techniques differ in several aspects such as design simplicity, convergence speed, implementation types (analog or digital), decision optimal point accuracy, effectiveness range, hardware costs, and algorithmic modes. Choosing the most suitable MPPT controller is crucial in PV system design, as it directly impacts the overall cost of PV solar modules. This paper presents a comprehensive exploration of 64 MPPT techniques for PV solar systems, covering optimization, traditional, intelligent, and hybrid methodologies. A comparative analysis of these techniques, considering cost, tracking speed, and system stability, indicates that hybrid approaches exhibit higher efficiency albeit with increased complexity and cost. Amidst the existing PV system review literature, this paper serves as an updated comprehensive reference for researchers involved in MPPT PV solar system design. Full article

Background/Objectives: Paediatricians’ recommendations influence parental decisions to vaccinate their children. On 19 January 2022, the World Health Organization authorized the Pfizer-BioNTech COVID-19 vaccine (BNT162b2) under Emergency Use Listing for children under 12 years as a measure to mitigate disease spread and direct protection for children with underlying conditions. We assessed knowledge, attitudes, and practices (KAP) of Palestinian paediatricians regarding COVID-19 vaccination for children under 12 years and identified factors affecting support for vaccination. Methods: From 1 October to 8 November 2023, we surveyed paediatricians across the West Bank using structured telephone interviews. We collected data on sociodemographic characteristics and KAP regarding COVID-19 vaccination and calculated KAP scores from eight, nine, and nine items, respectively, with total scores categorized as poor/moderate/good. We performed bivariable and multivariable analyses to identify factors associated with paediatricians supporting COVID-19 vaccination for children under 12 years. Results: Of the 367 eligible paediatricians, 323 (88%) responded; the median age was 51 years (range: 28–70); 27% supported COVID-19 vaccination for children. Mean scores for knowledge (range 0–8), attitude (0–9), and practice (0–9) were 3.0 ± 2.1, 3.9 ± 2.4, and 4.0 ± 1.7, respectively. The mean overall KAP score (0–26) was 11 ± 4.8. Safety and efficacy concerns and lack of long-term data were the main reasons for hesitancy. Higher knowledge scores (PR = 1.8, 95% CI: 1.3–2.5, p = 0.001) and positive attitudes (PR = 1.6, 95% CI: 1.1–2.3, p = 0.01) were significantly associated with paediatricians’ support for vaccination. After adjustment for other factors, participants with regular continuing medical education attendance (aPR = 1.4, 95% CI: 1.0–2.6, p = 0.045), trusting WHO recommendations (aPR = 3.1, 95% CI: 1.4–7.8, p = 0.047), having a positive attitude score (aPR = 1.3, 95% CI: 0.4–4.4, p = 0.041), and a good total KAP score (aPR = 1.1, 95% CI: 1.0–1.2, p = 0.044) supported COVID-19 vaccination for children. Conclusions: Support for COVID-19 vaccination among Palestinian paediatricians was low, associated with their knowledge, attitudes, and trust in health authorities. The revised WHO recommendations from 10 November 2023, decreasing the priority of vaccinating healthy children, could influence the opinion of paediatricians. However, the low support for COVID-19 vaccinations could affect the performance of other vaccination programmes and should be carefully addressed through targeted education. Full article