Search Results (24,787)

Stingless bee colonies are vulnerable to predators of widely varying sizes, and repeated intrusions can cause stress, reduce productivity, and trigger colony absconding. Existing automated surveillance systems detect only a limited range of predators and often struggle with multi-scale object detection in high-resolution images. This study proposes a real-time predator monitoring system that integrates a Multi-Scale Attention module into the YOLOv11-nano architecture (MSYOLO11) to enhance detection performance across both small and large predators. The proposed model combines convolutional features with an attention mechanism to improve global–local feature fusion. Experimental evaluation shows that MSYOLO11 increases overall Recall from 0.830 to 0.853 compared to YOLOv11-nano, with substantial improvements for small-object classes such as ants (+0.096), humans (+0.083), and H. itama (+0.026), while maintaining comparable Precision (0.868 vs 0.842) and mAP50 (0.898 vs 0.896) at a nearly identical computational cost (6.3 GFLOPs). The system operates at 5 FPS on a Jetson Orin Nano, with an end-to-end latency of 181 ms. A Firebase-integrated mobile application delivers instant push notifications, displays detected predators with bounding boxes, and provides real-time data synchronization. The results demonstrate that MSYOLO11 offers a practical and efficient solution for multi-scale predator detection, supporting continuous hive surveillance and timely beekeeper intervention. Full article

Analyzing classroom engagement is essential for developing effective smart learning environments. Conventional methods often face challenges in achieving reliable identification of individual students, accurately recognizing their behavioral states, and providing timely support. This paper presents a multimodal sensing and supportive feedback system built upon an end–edge–cloud collaborative architecture. By integrating RFID-based seat association, fingerprint verification, and computer vision-based activity analysis, the system establishes a reliable link between student identity and observed activities. Key computational tasks, including activity recognition, spatiotemporal context matching, and rule-based assessment, are executed locally on edge nodes. This enables low-latency, privacy-conscious feedback delivered via Bluetooth, effectively avoiding delays associated with cloud processing. Experimental results indicate that the proposed system significantly enhances both activity recognition accuracy and identity–behavior association reliability in typical classroom scenarios while substantially reducing the average feedback latency compared to traditional approaches. Full article

Background/Objectives: The Black Caribbean population of Honduras, also referred to locally as Negro Inglés, constitutes one of the country’s nine recognized indigenous and Afro-descendant peoples. Predominantly settled in the Bay Islands and sections of the Caribbean coast, this community traces its ancestry predominantly to West Africa and has remained culturally and linguistically distinct for more than three centuries. Despite its demographic and historical relevance, no population-specific short tandem repeat (STR) database has been established for this group. Methods: Allele frequencies for 23 autosomal STR loci were characterized in 100 unrelated Black Caribbean individuals from the department of Islas de la Bahía. DNA was extracted from blood on FTA cards and amplified with the PowerPlex Fusion 6C System (Promega Corporation). Statistical parameters were computed using Genepop v4.2, Arlequin v3.5 and GDA v1.0. Results: A total of 241 distinct alleles were detected across all 23 loci (mean 10.48 ± 3.85 alleles/locus). Expected heterozygosity ranged from 0.6541 (D13S317) to 0.9350 (SE33), with a mean of 0.8150 ± 0.0664—values consistent with a population of predominantly West African origin. No locus exhibited a significant departure from Hardy–Weinberg equilibrium after Bonferroni correction (α = 0.0022). The combined power of discrimination exceeded 99.9999% and the combined chance of exclusion surpassed 99.9999%. Conclusions: This first genetic characterization of the Honduran Black Caribbean population delivers an essential, population-specific reference dataset for forensic casework, paternity testing, and population genetics research. The data also deepen the understanding of Afro-descendant genetic diversity in Central America and constitute a critical step towards equitable forensic genetic services for all Honduran ethnic communities. Full article

As modern society grows increasingly complex, access to essential services such as healthcare, legal aid, tailored education, and psychological support remains heavily gated by socio-economic, neurological, and systemic barriers. This paper explores the transformative potential of Large Language Models (LLMs) and Generative Artificial Intelligence not merely as industrial productivity enhancers, but as vital “social scaffolds” capable of fostering a more inclusive society. Crucially, we propose a paradigm shift in the concept of Ethical Computing—moving from a passive defensive framework of non-maleficence (“do no harm”) to an active mandate of beneficence, where AI systems are explicitly developed to serve marginalized and un(der)served populations. Through this expanded ethical lens, we systematically analyze the democratizing impact of AI across four primary axes of inclusivity: socio-economic (providing zero-cost medical triage and legal translation for undocumented populations), neurospicy (acting as a non-judgmental communicative bridge for individuals with Autism Spectrum Disorder), pedagogical (delivering hyper-personalized executive function support for Special Educational Needs), and psychological (serving as an accessible, first-level triage system for mental health crises). By framing LLMs as a modern social safety net, we outline a clear trajectory for future research, advocating for an “ethical-by-design” development paradigm that explicitly prioritizes equity, accessibility, and the active dismantling of historical barriers for the digitally and socially disenfranchised. Full article

This study was designed to evaluate and explore the ecosystem-based adaptation practices for climate resilience with evidence from smallholder farmers’ perceptions of co-benefits and adoption decisions in Mabalane district, Mozambique. Ecosystem-based adaptation practice emerged as a sustainable approach to enhance rainfed smallholder farmers’ climate resilience while delivering multiple social, economic and environmental co-benefits. This study evaluated and explored the perceived co-benefits from adopting ecosystem-based adaptation practices and examined how they shape adoption decisions among the rainfed smallholder farmers. A mixed-method approach was employed, combining a household survey of 360 farm household heads, key informant interviews and focus group discussions. The main findings of the study revealed mixed cropping (83.9%), integrated crop-livestock (57.2%), and mulch tillage (51.1%) as the most adopted practices, as well as smallholder farmers perceiving multiple ecological and socio-economical co-benefits from adopting ecosystem-based adaptation practices. Although the study confirmed statistically significant relationships between ecosystem-based adaptation practices and the perceived co-benefits, none of the perceived co-benefits were significantly associated with an increase in the number of the adopted practices. Therefore, it is concluded that adoption decisions among smallholder farmers are not shaped by perceived ancillary benefits from ecosystem-based adaptation practices alone, but a combination of enabling conditions and resources endowments. Full article

The efficient on-demand generation of ClO₂ is critical for disinfection and food preservation. However, the development of safe and efficient strategies for gaseous ClO₂ production remains challenging. Herein, we report a stable and efficient electrochemical system for ClO₂ production based on rutile TiO₂ nanorod arrays (TiO₂ NAs). Electrochemical optimization suggests that a cathodic potential of −0.10 V (vs. Ag/AgCl) in an electrolyte solution of 1 M NaClO₃ with 5 M H₂SO₄ achieves the highest ClO₂ production efficiency. Mechanistic studies reveal that ClO₂ generation proceeds via an O₂-induced pathway, in which electrochemically generated H₂O₂ from 2-e⁻ O₂ reduction reacts in situ with ClO₃⁻ to form ClO₂, eliminating the need for external H₂O₂ storage and significantly improving operational safety. Furthermore, when decorated with RuO_x nanoparticles, TiO₂ NA cathodes achieve enhanced catalytic performance and excellent stability. In addition, the generated ClO₂ in the electrolyte solution can be delivered via gas pumping. This ClO₂ atmosphere exhibits antibacterial efficiencies exceeding 99% against Escherichia coli and Staphylococcus aureus, and significantly reduced weight loss and preserved fruit hardness in longan samples during 8 days of storage. Overall, this work presents a safe, efficient approach for ClO₂ generation with strong potential for practical disinfection in the food preservation field. Full article

Background: School feeding programs aim to improve child nutrition, and they may influence weight outcomes insofar as program modalities and household responses alter children’s total energy intake. This is especially relevant in countries facing the double burden of malnutrition, where undernutrition and micronutrient deficiencies coexist with rising overweight and obesity. This study estimates the effect of Peru’s former National School Feeding Program on obesity and excess weight among children aged 36 to 59 months under a selection-on-observables identification strategy and assesses whether impacts differ across operational modalities, particularly breakfast-only versus breakfast plus lunch and ready-to-eat rations versus foods delivered for preparation. Methods: We use repeated cross-sectional microdata from the Demographic and Health Survey (ENDES) pooled over 2014 to 2018 and link them to administrative information. The sample includes 18,959 children aged 36 to 59 months. To improve comparability, we estimate propensity score weights targeting the average treatment effect on the treated (ATT) using a machine learning generalized boosted model (GBM), and assess covariate balance using standardized mean differences and Kolmogorov–Smirnov statistics. Identification assumes conditional independence given observed covariates and overlap (common support). Main estimates rely on weighted probit models with fixed effects, progressively adding exposure duration, modality indicators, and controls. Distributional effects are examined using quantile regression on the continuous weight-for-height z-score. Results: Without differentiating modalities, beneficiary status is not associated with a statistically significant change in obesity, while pooled baseline estimates indicate a statistically significant higher probability of excess weight. Modality-specific results show that obesity declines only when Qali Warma is delivered as breakfast plus lunch through products to be prepared (approximately −1.0 percentage point in parsimonious models and −0.4 percentage points after controls). Evidence for excess weight is directionally consistent by modality but less conclusive once controls are included. Conclusions: Qali Warma’s effects on early-childhood weight outcomes depend on implementation modality. Evaluations of school feeding programs should incorporate operational heterogeneity, particularly during program redesign. Full article

The coexistence of emulsified oil, dissolved organics, and heavy metal ions in industrial oily wastewater makes one-step treatment highly challenging. Herein, an organic-inorganic co-modified PVDF composite membrane (MTSP) was fabricated via nonsolvent-induced phase separation, with tea polyphenols, SiO₂, and fibrous MXene synergistically incorporated. The resulting membrane exhibited a superhydrophilic/underwater oleophobic surface, with a water contact angle of 1° and an underwater oil contact angle of ~136°, owing to the optimized surface chemistry and hierarchical pore structure. As a result, the MTSP membrane effectively suppressed oil fouling while enabling rapid water transport. At 0.1 bar, the optimized membrane delivered an oil/water separation efficiency of ~99.5% and a high flux of 2420–2670 L·m⁻²·h⁻¹, while maintaining >99% separation efficiency for various emulsified oils, including kerosene, edible oil, n-hexane, and 1,2-dichloroethane. It also showed excellent recyclability and chemical stability, retaining >98–99% efficiency after five cycles and after 24 h exposure to pH 1 and pH 12 conditions. Notably, for complex simulated wastewater containing emulsified kerosene, phenol, and Fe³⁺, Cu²⁺, Zn²⁺, and Cd²⁺, the membrane maintained ~99% oil/water separation efficiency and simultaneously removed ~79% of phenol and 70–86% of heavy metal ions in a single filtration process. The superior performance is attributed to the synergistic effects of the superhydrophilic/underwater-oleophobic membrane surface, hierarchical transport channels enabling rapid water permeation, and multifunctional sites that adsorb/coordinate dissolved pollutants. This work provides a simple, scalable design strategy for PVDF-based membranes that integrate high-flux separation, antifouling performance, and multi-pollutant remediation for the treatment of complex oily wastewater. Full article

This paper provides a comparative analysis of the economic crises in Argentina and Lebanon to derive policy-relevant lessons for the design of IMF-supported adjustment programs in fragile economies. Using a structured comparative case study approach, the study examines crisis dynamics, policy responses, and socioeconomic outcomes across both countries, with particular attention given to exchange rate collapse, banking sector distress, public debt, inflation, unemployment, and poverty. The findings suggest that programs centered primarily on macroeconomic stabilization and fiscal austerity, without adequate attention to institutional capacity, social protection, and debt restructuring, risk deepening economic contraction and social vulnerability. The Argentine experience shows that IMF-supported adjustment in institutionally fragile environments may fail to restore confidence or deliver sustainable recovery when reform sequencing is weak and complementary domestic policies are absent. For Lebanon, where the crisis is deeper and compounded by governance failures and geopolitical instability, IMF engagement appears necessary but insufficient on its own. The paper concludes that a sustainable recovery requires a hybrid strategy combining external financial support with country-specific reforms, including exchange rate unification, banking sector restructuring, debt resolution, stronger governance, and targeted social protection. Full article

Background/Objectives: Overcrowding in emergency departments (EDs) remains a critical challenge in modern healthcare systems, driven in part by patient uncertainty regarding symptom urgency and a lack of accessible medical guidance. Recent advances in artificial intelligence, particularly large language models (LLMs), present a novel opportunity to support patient navigation and relieve pressure on ED infrastructures. Methods: A total of 238 unique patient questions were identified through a structured web search. Following deduplication and thematic clustering, 15 representative questions were selected. Each question was submitted to the three LLMs—ChatGPT (v3.5), DeepSeek, and Gemini—using a standardized prompt. Responses were assessed by clinical experts (N = 8) who were blinded to the model source. Reviewers selected the best overall response per question, as well as the individual responses of the three LLMs for each respective question. Results: ChatGPT was selected as the best-performing model in 60% of cases, with DeepSeek and Gemini selected in 23% and 17%, respectively. ChatGPT responses also achieved the highest proportion of “excellent” quality ratings and the lowest proportion of “unsatisfactory” outputs. Across all models, clarity was the most positively rated domain (79% agreement), followed by empathy (72%), length/detail appropriateness (71%), and completeness (65%). Over two-thirds of raters expressed willingness to integrate LLM-based tools into clinical practice for patient education and pre-triage counseling. Conclusions: Large language models demonstrate promising capabilities in responding to emergency care-related patient queries. Their ability to deliver medically sound and communicatively effective answers positions them as potential digital adjuncts in the management of low-acuity ED presentations and prehospital triage. Full article

Leaching carbon residue (LCR) is a carbonaceous solid waste generated during the hydrometallurgical recycling of spent lithium-ion batteries. Although its high graphite content offers substantial potential for resource recovery, the residual heavy metals and fluorides present in LCR pose considerable environmental risks. Currently, LCR has not garnered sufficient attention within the industry, and the lack of recycling technologies suitable for large-scale disposal results in resource wastage and environmental pollution. To address these challenges, this study proposes an innovative strategy based on the concept of multi-field synergistic enhancement. The proposed approach involves recovering and regenerating graphite (RG) from LCR via low-temperature molten salt roasting assisted by high-pressure and mechanical activation. A combination of advanced characterization techniques was employed to compare the physicochemical properties of RG and commercial graphite (CG) and to systematically evaluate the technical feasibility of using regenerated graphite as an anode material for lithium-ion batteries. The results demonstrate that, under optimized molten salt roasting and aqueous leaching conditions, the carbon content of RG reaches 99.94 wt%, indicating the efficient removal of non-carbon impurities from the graphite matrix. Compared to CG, RG retains a typical layered structure; however, a lower carbon content (99.94 wt%) and poorer structural order (I_D/I_G = 0.30) are observed. In terms of electrochemical performance, RG delivers a discharge specific capacity of 394.64 mAh/g during the first cycle and exhibits excellent cycling stability, with a capacity retention of 86.50% after 100 cycles. This electrochemical performance is comparable to that of commercial graphite. The proposed multi-field-assisted low-temperature molten salt roasting technique enables the efficient recovery of high-value graphite resources from LCR, establishing a full-lifecycle recycling strategy tailored for lithium-ion battery applications. Full article

This study addresses a critical methodological gap in evaluating building envelope performance in hot, arid climates, the overreliance on annual energy indicators, which fail to capture transient thermal behavior during peak-load periods. In such environments, instantaneous heat gains, their intensity, and temporal distribution are decisive factors for cooling demand, occupant comfort, and grid stability. To overcome this limitation, a dynamic evaluation framework—the Thermal Adaptation Rating (TAC) system—is proposed. TAC integrates three interrelated indices—peak temperature reduction (ΔT_peak), relative peak cooling load reduction (ΔP_peak, %), and peak thermal delay (Δt_delay), representing thermal damping, load intensity mitigation, and temporal redistribution, respectively. A typical residential building in Karbala was modeled in DesignBuilder using the EnergyPlus engine, with inputs documented and calibration performed against real consumption data following ASHRAE standards (MBE and CV(RMSE)) to ensure reliability. The study examined advanced envelope systems, including thermochromic glass (TG), phase-change materials (PCMs), aerogel materials (AMs), and hybrid combinations. Results revealed that while AM achieved the greatest annual energy savings, its impact on instantaneous cooling load was limited. PCM, by contrast, effectively mitigated and delayed peak loads, enhancing thermal comfort (PMV/PPD). Hybrid systems, particularly TG-PCM, delivered the most balanced performance, simultaneously reducing peak cooling load and shifting its occurrence to reshape the cooling demand curve during critical periods. These findings demonstrate that annual indices alone are insufficient for evaluating envelope performance in extreme climates. Peak-condition analysis, expressed in terms of instantaneous cooling load, as operationalized through TAC, provides a more accurate representation of thermal behavior and offers a practical tool to guide envelope design decisions in hot, dry regions. Full article

Natural deep eutectic solvents (NADES) are promising eco-friendly alternatives to traditional solvents for extracting phenolic compounds from plant materials. However, their effectiveness depends on both solvent composition and the botanical matrix. This study examined water, ethanol, and choline chloride–urea (CCU) and choline chloride–glycerol (CCG) systems containing 30% or 60% NADES for the extraction of anthocyanins, total phenolic content, total flavonoid content, and antioxidant capacity (DPPH and ABTS) from cornflower, safflower, and pomegranate flowers. Pomegranate flowers exhibited the highest levels of anthocyanins, total phenolics, and antioxidants, while safflower showed the highest total flavonoid content. Overall, the 60% NADES formulations delivered the best extraction results, whereas ethanol had the lowest overall efficiency. A combined heatmap analyzing all responses identified 60% CCU and 60% CCG as the most effective solvents across all tested materials. Anthocyanin stability in pomegranate flower extracts was further evaluated over 8 weeks at 4 and 20 °C. First-order kinetic analysis revealed that ethanol and 60% CCG maintained the highest anthocyanin stability, whereas 60% CCU exhibited the lowest stability, especially at 20 °C. These findings support the use of NADES as efficient solvents for floral bioactives, while indicating that the highest extraction yield does not necessarily correlate with the best storage stability. Full article

Objective: We aimed to determine the ability of prenatal ultrasound to detect velamentous cord insertion (VCI) in twin pregnancies and identify factors influencing diagnostic sensitivity. Methods: This single-center retrospective study included twins delivered between April 2018 and March 2024. We excluded monochorionic monoamniotic twins, those without chorionicity or umbilical cord insertion data, and fetuses that died in utero. Umbilical cord insertion sites assessed by second-trimester transabdominal ultrasound (16 + 0 to 21 + 6 weeks of gestation) using color Doppler imaging were classified as normal, marginal, or velamentous. The results of postnatal macroscopic examinations served as reference standards. We calculated accuracy, sensitivity, specificity, positive (PPV) and negative (NPV) predictive values. The effects of examiner expertise, chorionicity, placental location, ultrasound device, and maternal body mass index (BMI) on diagnostic sensitivity were analyzed in subgroups. Results: We confirmed VCI in 45 (8.8%) of 514 delivered fetuses. Prenatal ultrasound correctly identified 14 VCI cases. Sensitivity, specificity, PPV, and NPV were 31.1% (14/45), 98.9% (464/469), 73.7% (14/19), and 93.7% (464/495), respectively. The overall accuracy was 93.0% (478/514). Sensitivity was significantly higher when ultrasound specialists conducted examinations compared with non-specialists and when twins were monochorionic diamniotic twins than dichorionic. Anterior placental location and high-performance ultrasound equipment were also associated with increased sensitivity, but were not statistically significant. Maternal BMI did not affect diagnostic sensitivity. Conclusions: Prenatal ultrasonographic detection of VCI in twin pregnancies has high specificity but limited sensitivity. Diagnostic performance was influenced by examiners’ experience and chorionicity. Routine assessment of cord insertion sites and targeted training might improve detection and support the optimized perinatal management of twin pregnancies. Full article

Accurate estimation of lithium-ion battery State of Health (SoH) is critical for emerging applications such as reconfigurable battery systems. Although data-driven machine learning methods are promising, they often rely on costly, time-intensive aging experiments and extensive feature engineering. This work proposes a lightweight SoH-prediction framework validated on both physics-informed synthetic aging data and the NASA battery aging dataset. We evaluated Random Forest (RF) and Feedforward Neural Network (FNN) models that use only a limited number of samples from an early segment of the raw discharge voltage curve as input. Results show that RF consistently outperforms FNN across input sizes in deterministic or noise-free environments, achieving an RMSE of 0.07% SoH using just 5 voltage samples. In inherently stochastic experimental data, however, FNN can achieve an RMSE 50% lower than RF (1.28 vs. 2.87), but requires 37× more mathematical operations per inference. These findings emphasize the predictive value of the early-discharge-voltage region and demonstrate that compact, low-feature-complexity models can deliver accurate SoH estimates. Overall, the approach supports a goal of combining informed synthetic data with limited real measurements to build robust, scalable SoH predictors, reducing dependence on labor-intensive degradation testing and feature-heavy pipelines. Full article