Search Results (2,555)

Conventional chemical analysis relies on costly laboratory instrumentation, while current e-nose systems are expensive for widespread deployment. New opportunities for low-cost, accessible e-nose applications are emerging for diverse fields due to the rapid evolution of inexpensive sensor technologies. We developed a framework that enables rapid integration of newly available low-cost gas sensors into functional e-nose systems, continuously evaluating them as they become commercially available. By characterizing their performance in multi-sensor arrays that mimic biological olfaction, the framework demonstrates effective odor discrimination in a low-cost e-nose system through coordinated behavior of a heterogeneous sensor array. Our testing approach includes sensor sensitivity, selectivity, and stability, which are to be combined with appropriate pattern recognition and AI algorithms in the future for effective chemical discrimination. This work provides a pathway for continuously updating e-nose technology with the latest available sensors in a cost-effective manner, thereby making advanced chemical sensing accessible for resource-limited settings and enabling large-scale deployment in real-world applications with future potential applications such as food quality monitoring, environmental sensing, smart agriculture, etc. Full article

Toxoplasmosis remains one of the most common parasitic infections affecting humans, with significant implications for pregnancy and fetal health. Maternal primary infection during gestation can result in transplacental transmission of Toxoplasma gondii, leading to a wide spectrum of congenital disease. The risk of vertical transmission increases with gestational age, whereas disease severity is inversely related—early infections causing severe neurological and ocular damage, and late infections often resulting in subclinical forms. Advances in serological testing, including IgG avidity assays and molecular diagnostics such as PCR on amniotic fluid, have improved early detection and management. Prenatal treatment with spiramycin or pyrimethamine–sulfadiazine–folinic acid combinations has been associated with reduced transmission and less severe fetal disease in several studies, although the magnitude of benefit remains debated. Long-term follow-up is essential, as late-onset manifestations, particularly chorioretinitis and neurodevelopmental impairment, are common. This narrative review was based on a comprehensive literature search of major medical databases and summarizes current knowledge on the epidemiology, pathophysiology, diagnosis, treatment, and outcomes of toxoplasmosis in pregnancy. Particular emphasis is placed on high-prevalence countries, where greater parasite genetic diversity, distinct epidemiological patterns, and a higher burden of congenital disease pose unique clinical and public health challenges. Despite progress in understanding parasite biology, pathogenesis, and treatment efficacy, congenital toxoplasmosis continues to be underdiagnosed and underreported, especially in low-resource settings. Ongoing challenges include optimizing screening strategies, ensuring access to standardized therapies, and strengthening surveillance systems. Full article

Sleep-related respiratory disturbances are difficult to monitor continuously outside specialized laboratories because conventional polysomnography is resource-intensive and intrusive. This study presents a contactless edge-AI engineering prototype for detecting controlled voluntary respiratory-motion suppression and motion artifacts using a 60 GHz frequency-modulated continuous-wave radar. The system integrates a 60 GHz radar front end, lightweight local preprocessing, an INT8 one-dimensional convolutional neural network deployed on the Analog Devices MAX78000 CNN accelerator (Analog Devices Thailand, Chon Buri, Thailand), and an event-driven Raspberry Pi Zero 2W gateway for alert transmission. Evaluation was performed using a controlled healthy-volunteer dataset consisting of normal breathing, voluntary breath-holding-induced respiratory suppression, and deliberate motion artifact. The final valid test set contained 270 technically valid 30 s windows balanced across the three classes. The INT8 model achieved an overall accuracy of 92.6% (95% confidence interval: 88.8–95.2%), with a macro-averaged precision, recall, and F1-score of 92.6%, 92.6%, and 92.5%, respectively. Active CNN inference on the MAX78000 consumed 0.152 ± 0.011 mJ and was completed in 5.20 ± 0.11 ms, corresponding to approximately 280-fold lower active inference energy than Python 3.14.6/TensorFlow Lite 2.21.0-based execution on the Raspberry Pi Zero 2W. These results demonstrate the feasibility of privacy-aware, low-power respiratory-pattern classification at the edge. However, the study should be interpreted strictly as an engineering proof-of-concept based on controlled voluntary breathing and movement tasks in healthy volunteers. It is not a clinically validated apnea or obstructive sleep apnea detection system and did not include polysomnography, oxygen saturation measurement, airflow sensing, sleep staging, or diagnosed patient cohorts. Full article

Traumatic fractures are common in small animal emergency care, yet subtle fracture lines may be difficult to identify accurately using routine three-dimensional reconstruction workflows, particularly when access to specialized software is limited. This study describes the use of the open-source platform Three-Dimensional Slicer for computed tomography-based reconstruction and three-dimensional printing in a small dog with cranial trauma, with emphasis on documenting a practical and reproducible workflow through voxel resampling. Imaging data were imported into the software, bone structures were segmented using a rapid workflow, voxel spacing was resampled for smoother surface visualization by volume resampling, and the reconstructed model was processed for physical printing. Digital models of different resolutions were generated within minutes, and a life-size skull model was successfully fabricated using fused deposition modeling in less than three hours at a material cost of under one United States dollar. The enhanced model provided an intuitive representation of fracture morphology and spatial relationships compared with routine reconstruction alone. These findings demonstrate that open-source software combined with low-cost printing can provide a rapid, affordable, and user-friendly approach for practical skeletal reconstruction in small animals, with practical value for fracture assessment, preoperative planning, and broader use in resource-limited veterinary settings. Full article

Global container liner shipping networks (GCLSNs) underpin world trade, yet their organization is increasingly reshaped by geopolitical fragmentation. Existing studies often model GCLSNs as single-layer networks, overlooking how carriers’ geopolitical affiliations structure both connectivity and disruption risk. This study constructs a weighted carrier–geopolitical multiplex network in which layers are defined by carriers’ geopolitical affiliations and coupled through shared port calls. Structural analysis reveals pronounced asymmetry in layer size, cohesion, and inter-layer dependence, with overlap concentrated in a limited set of shared hubs. Using the Red Sea crisis as an empirical stress-test scenario, we develop a load–capacity propagation model, incorporating intra-layer load redistribution, rerouting to substitute shared hubs, and inter-layer resource squeeze at same-port layer copies. Results show that direct losses concentrate in corridor-exposed layers, while indirect losses propagate selectively through bridge hubs, especially Singapore, Shanghai, Shenzhen, and Port Klang. Sensitivity analysis indicates nonlinear amplification when low tolerance, strong inter-layer squeeze, and elevated rerouting pressure coincide. These findings show that multiplexity does not imply resilience by itself; cross-layer connectivity buffers disruption only when spare capacity is distributed but amplifies vulnerability when it converges on a narrow set of shared hubs. The paper contributes a carrier–geopolitical perspective to shipping network analysis and a dynamic framework for studying disruption propagation in complex logistics systems. Full article

The rapidly urbanizing peripheries of the Global South face significant demographic pressures, leading to governance deficits that often neglect the long-term structural safety of new buildings. While regulatory frameworks predominantly emphasize initial construction quality, they frequently overlook the critical “post-occupancy” phase, during which distinct structural risks accumulate. This study introduces a reproducible, open-data risk identification framework designed to trace theoretical “windows of vulnerability” in Çekmeköy, a peripheral district of Istanbul. By triangulating temporal, spatial, and demographic municipal administrative records from 2018 to 2024, we illustrated how low-cost data can serve as proxies for prioritizing structural risk assessments. The findings demonstrate that a 103% population increase between 2008 and 2023, coupled with a 21% reduction in the average household size, has generated urgent housing demand that outpaces supply. We hypothesize that these conditions create high-probability zones for “informality creep,” where demographic pressures induce informal practices, such as unauthorized structural modifications within ostensibly formal high-rise settings. The primary contribution is a transferable algorithmic tool, the Weighted Post-Occupancy Vulnerability Index (POVI). Rather than serving as a deterministic building-level diagnostic, this framework operates much like an epidemiological screening process; it acts as a macroscopic prioritization heuristic that allows resource-constrained municipalities to proactively direct their inspection efforts. By mathematically quantifying the conditions under which post-occupancy risks develop, this framework provides an essential resource for enhancing urban resilience during reactive urbanism planning. Full article

In resource-constrained UAV swarm systems, multimodal sensory data are often affected by complex environmental factors, resulting in modality missing, signal degradation, and asynchrony, which significantly reduce the reliability of multimodal learning and incremental model updates. To address this issue, we propose a Compensatory Collaboration Modality-Balanced Sample Selection framework (CC-MBS), which improves robustness through modality quality modeling and cross-UAV collaborative compensation. Specifically, a modality confidence vector is introduced to quantify modality reliability from missing rate, degradation, and asynchrony. A lightweight collaboration mechanism is designed to exchange low-dimensional confidence information instead of high-dimensional features or model parameters. Based on the compensated confidence, a modality-aware sample selection strategy is further developed to prioritize high-value samples under limited memory. Experimental results in simulated UAV-swarm-inspired benchmark settings show that CC-MBS outperforms representation-based methods such as ShaSpec and its parameter aggregation variants (AVG, PFM, POW) in both modality compensation accuracy and communication–computation efficiency under missing conditions. In addition, it achieves stronger robustness than MBS and training-dynamics-based methods such as EL2N and GraNd in sample selection. These results demonstrate that CC-MBS effectively improves robustness and data efficiency for multimodal incremental learning under incomplete modalities. Full article

Conventional fire monitoring systems frequently exhibit high false alarm rates, delayed response times, and a lack of closed-loop control capabilities, which severely constrain their deployment in complex real-world environments. To address these issues, this paper proposes an embedded fire detection, tracking, and extinguishing system based on multimodal information fusion and a lightweight neural model. The system follows a “Perception–Decision–Execution–Feedback” closed-loop paradigm and is implemented on a heterogeneous cooperative computing architecture comprising OpenMV4 H7 Plus and STM32F103C8T6 microcontrollers. The perception layer implements a decision-level RGB-infrared fusion mechanism that incorporates a pruned, INT8-quantized lightweight FOMO model, enabling real-time fire detection with an inference latency of 210 ms and a model size of merely 1.8 MB under resource-constrained embedded conditions. The decision layer employs a Bayesian inference-based multimodal fusion framework that effectively suppresses spurious fire interference. The vision-only false detection rate is 15.3%. After infrared fusion verification, the system-level false alarm rate is reduced to 2.0% on the interference test set. In the execution layer, a sixth-degree polynomial jet trajectory model was established and combined with an improved PID–PI dual-loop controller to enable dynamic optimization of spray angle and flow rate in real time. Experimental results demonstrate that the proposed system achieves an average fire recognition accuracy of 95.6% with a false alarm rate as low as 1.4%. Furthermore, it realizes an extinguishing accuracy better than ±5 cm within an effective operating range of 10–60 cm and completes the entire perception-to-extinguishing cycle within 8.5 s under illumination conditions ranging from 50 to 100,000 lux. These results demonstrate the excellent real-time capability, robustness, and energy efficiency of the proposed system, providing a practical and scalable solution for autonomous embedded fire-fighting applications in household, industrial, and warehouse environments. Full article

Group Relative Policy Optimization (GRPO) streamlines the alignment of Large Language Models (LLMs) and Vision–Language Models (VLMs) by eliminating the Critic model. However, its efficiency heavily depends on the group size, G. While a larger G improves reward estimation and stabilizes the Advantage, $A_i$, it drastically increases VRAM usage and reduces throughput. Standard heuristics like a fixed G of 64 create significant bottlenecks in resource-constrained settings. This paper introduces an Efficiency-Aware optimization framework utilizing Multi-fidelity Bayesian Optimization and Hyperband (BOHB) to dynamically identify the optimal group size, $G^{*}$. The method uses a multi-objective function that balances reward accuracy, $A_i$ variance, and hardware utilization, applying z-score normalization. By employing Successive Halving to quickly evaluate candidates at low fidelity, the framework reduces search costs by up to 74% compared with random search. Tested across text-only LLMs (Qwen2.5-7B/1.5B) and multimodal VLMs (Qwen2.5-VL-3B), the framework demonstrates that the discovered $G^{*}$ saves up to 72.5% in VRAM compared with the baseline of 64, while maintaining reward accuracy within 5.8%. Sensitivity analyses on hyperparameters like $\lambda$, $\alpha$, and $\beta$ confirm the framework’s robustness. Rather than treating group size as a mere engineering heuristic, this study establishes a principled methodological advance by formalizing the trade-off between statistical estimation stability and hardware constraints into a unified optimization framework for resource-efficient RLHF. Full article

Plastic waste presents a significant environmental and public health concern in Malawi, where rapid urban growth, limited waste collection services, and informal disposal practices contribute to persistent plastic waste hotspots. In Lilongwe City, the waste collection rate has been reported ranges from 10% to 30%. This means that out of the 500 to 600 tons of municipal solid waste produced each day, only about 50 to 150 tons are collected daily. These hotspots occur in settings such as drains, markets, settlement edges, riverbanks, and lakeshore environments. They intensify health-relevant exposure pathways by encouraging stagnant water, increasing flood risk, facilitating open burning, and supporting the formation of plastisphere biofilms that can contain pathogenic and antimicrobial resistant organisms. This research synthesises evidence on the main sources of plastic waste in Malawi, the mechanisms of leakage across different environments, and the associated health implications. It uses a scoping approach aligned with PRISMA-ScR guidance and is informed by the UK Research and Innovation (UKRI) funded Sustainable Plastic Attitudes to benefit Communities and their Environments (SPACES project), which highlights the influence of behavioural, governance, and environmental factors on plastic pollution. A two phase, risk-based decision framework to support targeted management of plastic waste hotspots is described. Phase 1 focuses on rapid harm reduction through the identification and ranking of hotspots according to risk severity, spatial extent, and feasibility, guiding timely interventions such as drain clearance, waste capture, and temporary stabilisation. Phase 2 addresses longer term prevention by tackling upstream drivers through policy measures, improved services, reuse and reduction schemes, and community engagement. The framework has been developed using evidence from Malawi; however, its methodology could be applied to other low- and middle-income countries that experience similar constraints and exposure pathways. The framework offers a transparent and practical tool for decision makers seeking to allocate limited resources effectively while reducing environmental and health risks associated with plastic waste. Full article

Multilingual knowledge editing (MKE) remains challenging because language-specific edits interfere with one another, even when locate-then-edit methods succeed in monolingual settings. We study whether vector merging—combining independently computed per-language updates into a single edit—can mitigate this interference. We evaluate six merging variants with two backbone large language models, two base knowledge editing methods, and 12 languages on the MzsRE benchmark under a large-scale batch-editing setting, and we examine how the weight scaling factor and the rank compression ratio affect editing performance. Summation with shared covariance proves the most reliable strategy overall, whereas naive summation without shared covariance performs poorly. Task Singular Vectors for Merging (TSVM) helps only in specific settings, so its ability to reduce multilingual interference is limited. Performance is also sensitive to both weight scale and rank ratio, with larger-than-default scaling and relatively low rank often yielding the best results. When the results are analyzed by language-resource level, the choice of merging method matters most for the relatively low-resource languages, such as Thai and Vietnamese. These findings clarify the practical strengths and limits of current vector merging methods for MKE and provide guidance for future multilingual knowledge editing research. Full article

Background: Hemophilia care has undergone a major therapeutic transformation with the introduction of extended half-life products, non-replacement therapies, and gene therapy. However, the benefits of these advances are not equally distributed worldwide, and their impact on long-term musculoskeletal outcomes remains uncertain. Objective: To analyze global disparities in hemophilia care and research production in the context of recent therapeutic advances, with particular attention to musculoskeletal management, physiotherapy, and scalable strategies for resource-limited settings. Methods: A narrative review with a structured literature search was conducted. Two conceptual blocks were explored: global disparities and access to care in hemophilia, and recent therapeutic advances, including non-replacement therapies, extended half-life products, and gene therapy. Retrieved records were screened using Rayyan, and a structured workflow diagram was used to summarize the literature identification and selection process. A descriptive analysis was also performed to identify representative authors, institutions, and geographic patterns in hemophilia research. Results: The evidence shows substantial global disparities in diagnosis, access to treatment, healthcare infrastructure, and research production. Scientific output remains concentrated in high-income countries, while low- and middle-income regions are underrepresented. Advanced therapies consistently reduce bleeding rates and treatment burden, but concerns remain regarding access, affordability, durability, breakthrough bleeding, and long-term structural joint outcomes. Musculoskeletal complications, including subclinical bleeding and hemophilic arthropathy, remain clinically relevant despite improved hematologic control. Conclusions: The current paradigm shift in hemophilia care is not uniformly experienced worldwide. Addressing global disparities requires not only expanding access to advanced therapies, but also strengthening research capacity, implementing multidisciplinary care models, and integrating scalable interventions such as physiotherapy, patient education, and simplified diagnostic tools. Accessible musculoskeletal assessment strategies may help improve early detection, functional outcomes, and equity of care in resource-limited settings. Full article

This paper examines whether incremental prompt engineering can enable reliable large language model (LLM)-based pre-annotation of corpus texts in a low-resource language setting. Using Lithuanian as a case study, we systematically evaluate multiple LLM prompt designs and assess their suitability for generating emotional manipulation annotations for corpus development. We find that performance varies with task complexity, and systematic prompt refinement measurably reduces output instability. Cross-model evaluation of the best-performing prompting strategy shows consistent and similar trends over several modern LLMs. Our results demonstrate that while structured prompts substantially improve output consistency and LLM-assisted annotation can roughly approximate human-produced labels for well-defined categories, the quality of results produced by contemporary LLMs is unsatisfactory for automatic pre-annotation of emotional manipulation techniques in a low-resource language. Full article

Face super-resolution (FSR) aims to reconstruct high-quality high-resolution face images from low-resolution inputs. Although CNN–Transformer hybrid models have shown promising performance by jointly modeling local textures and global dependencies, their large parameter sizes and high computational costs hinder practical deployment in resource-constrained scenarios such as mobile devices and embedded systems. Meanwhile, existing lightweight SR models usually reduce complexity by simplifying network depth, channel dimensions, or convolutional operations, which may weaken feature representation capability and lead to insufficient recovery of fine facial structures. To address these issues, this paper proposes HCTIUNet, a lightweight CNN–Transformer hybrid network based on an inverted U-shaped architecture. Specifically, the proposed network integrates lightweight CNN branches for local facial texture extraction and Transformer branches for global dependency modeling, while introducing a multi-scale feature interaction strategy and a global feature refinement module to enhance facial structural details. Experimental results on the FFHQ, CelebA, and Helen datasets demonstrate that HCTIUNet achieves competitive performance under the ×8 face super-resolution setting, obtaining PSNR/SSIM/LPIPS values of 27.55 dB/0.765/0.225, 27.63 dB/0.761/0.212, and 27.53 dB/0.777/0.213, respectively. Moreover, HCTIUNet contains 10.5 M parameters, requires 9.9 G FLOPs, and achieves an inference time of 0.021 s. These results indicate that the proposed method achieves a favorable trade-off between reconstruction accuracy, perceptual quality, and computational efficiency, making it suitable for efficient face super-resolution applications. Full article

This study aims to empirically analyze the patterns, intensity, and perceived barriers to physical activity among Polish university students during the COVID-19 pandemic. The research utilized a diagnostic survey method, employing a questionnaire. The online survey was conducted from December 2020 to May 2022 via the Webankieta.pl platform. The minimum sample size, calculated using the standard formula for estimating a proportion in a large population, was set at 1100 participants and was exceeded, with 1260 students providing valid responses. The results show that over half (55.8%, mainly women) of the respondents did not participate in regular physical activity during the pandemic. Participants cited lack of desire, fatigue, and low motivation—not pandemic restrictions—as primary reasons. Conversely, 44.2% of respondents, mostly men, reported engaging in regular physical activity. Most engaged in moderate-intensity activities two to five times a week, with vigorous activities performed slightly less often. Women were more likely to do both types, while men favored strength training. The most common activities included walking (61.6%), simple gymnastic exercises (43.1%), strength training with equipment (35.0%), cycling (34.5%), and calisthenics (30.2%). The majority (81.3%) exercised at home or nearby (33.4%). Reported barriers, especially among those who exercised regularly, were pandemic-related, such as limited or closed access to gyms, fitness centers, and pools (59.1%), along with time constraints (44.7%) and low motivation or determination (32.0%). The findings emphasize the importance of targeted interventions to boost physical activity among university students, particularly women and those with fewer financial resources. Universities should consider implementing programs that promote accessible, regular activity and initiatives to enhance motivation and foster long-term, health-promoting habits. Full article