Search Results (5,498)

As an important component of Chinese folk art, rural paper-cutting embodies rich regional cultural connotations and distinctive aesthetic expressions. In this study, a Chinese rural paper-cutting image dataset covering multiple regions and artistic styles was constructed, and a convolutional neural network (CNN)-based framework was proposed for regional and stylistic identification of paper-cutting works. Five representative mainstream CNN models were evaluated for both tasks. For regional classification, all models achieved high accuracy, with EfficientNet-B1 attaining the highest accuracy of 91.46%. The style classification task was more challenging due to subtle visual differences, with MobileNetV3-Small achieving the highest accuracy of 73.20%. In addition, t-distributed stochastic neighbor embedding (t-SNE) visualizations further confirmed that the models were able to effectively distinguish different regional and stylistic categories in high-dimensional space. To enhance model interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to visualize the optimal models. The results show that the CNNs consistently focus on core structural features of paper-cutting works, suggesting that CNNs can capture visually and culturally meaningful features. Overall, this study demonstrates the feasibility of applying CNNs to the analysis of traditional folk art and provides a practical technical pathway for digital management, intelligent classification, and educational dissemination of rural paper-cutting art. Full article

Shared e-scooter systems are frequently characterized as zero-emission mobility solutions; however, lifecycle greenhouse gas (GHG) emissions depend on manufacturing, electricity generation, and operational logistics. While conventional life cycle assessment (LCA) studies quantify environmental impacts using static average parameters, they rarely integrate lifecycle emissions into real-time fleet decision-making. This study proposes a formally defined carbon-aware operational framework that integrates ride-level telemetry, time-varying electricity grid carbon intensity, amortized production emissions, and dynamically allocated logistics impacts into a unified optimization architecture. Lifecycle emissions are computed at ride-level granularity and incorporated into charging and rebalancing decisions through a constrained optimization framework. A multi-objective extension is introduced to account for environmental–economic trade-offs. An illustrative simulation of 1000 rides was conducted to evaluate the operational performance of the framework. Under the assumed baseline scenario, the illustrative carbon-aware simulation indicated a potential reduction of up to 24.5% relative to conventional scheduling. Sensitivity analysis across variations in grid carbon intensity, scooter lifetime, energy consumption, and logistics emissions demonstrated reduction outcomes ranging between 18% and 29%, indicating robustness to parameter uncertainty. The study does not present large-scale empirical validation but provides a mathematically formalized decision-support architecture that operationalizes lifecycle assessment within shared micro-mobility fleet management. The results suggest that integrating carbon metrics into operational control may substantially enhance the environmental performance of shared e-scooter systems. Future research should validate the framework using real-world fleet data and incorporate a comprehensive economic assessment. The proposed framework provides a scalable methodological basis for integrating environmental metrics into real-time micro-mobility management and urban sustainability planning. Full article

Short-term traffic load prediction is a fundamental component of intelligent transportation systems (ITSs), supporting real-time monitoring, congestion mitigation, and adaptive traffic management in smart cities. Owing to the dynamic and nonlinear nature of urban traffic, identifying prediction models that align with real-world traffic dynamics remains a key challenge. This study presents a comparative evaluation of data-driven traffic load prediction models using high-resolution one-minute traffic data collected from a major urban roundabout in Jeddah, Saudi Arabia. The evaluated models include regression-based machine learning approaches and recurrent deep learning architectures, which are assessed under consistent preprocessing and evaluation conditions. Model performance is evaluated using standard error metrics and complemented by temporal and residual analyses to examine prediction behavior under different traffic regimes. The optimized GRU model achieved the best predictive accuracy with an RMSE of 149.12 veh/h, followed closely by the optimized LSTM model (RMSE = 150.85 veh/h). The results indicate that while conventional machine learning models can effectively capture overall traffic trends under relatively stable conditions, recurrent deep learning models demonstrate stronger capability in modeling nonlinear temporal dependencies and rapid traffic fluctuations when properly configured. In addition, a variability-based regime analysis was conducted to evaluate model robustness under different traffic demand dynamics, revealing that model performance advantages are context-dependent rather than universal. The findings highlight the importance of systematic comparative evaluation and data-driven model selection for developing reliable traffic prediction components in real-time ITS applications and sustainable urban mobility planning. Full article

Background/Objectives: Carbapenem-resistant Acinetobacter spp. represent an emerging concern in human medicine; however, their epidemiology and genetic backgrounds in companion animals in Japan remain unclear. This study aimed to determine the prevalence of carbapenem resistance among Acinetobacter spp. isolated from diseased dogs and cats and elucidate the underlying genetic mechanisms. Methods: In this surveillance study conducted as part of the Japanese Veterinary Antimicrobial Resistance Monitoring (JVARM) program, 139 isolates were collected from diseased companion animals across Japan (84 from dogs and 55 from cats) during 2020, 2021 and 2023. Antimicrobial susceptibility testing was performed for seven antimicrobials and carbapenem-resistant isolates (meropenem MIC ≥ 8 μg/mL) underwent whole-genome sequencing to identify resistance genes, genomic contexts, and associated mobile genetic elements. Results: Resistance rates to all tested antimicrobials were below 20%. Meropenem resistance was detected in three isolates: one from a dog and two from cats. These resistant strains were identified as A. radioresistens, A. proteolyticus, and A. johnsonii, all harboring carbapenemase genes. The A. radioresistens isolate carried chromosomal bla_OXA-23, the A. proteolyticus isolate carried bla_OXA-58, and the A. johnsonii isolate possessed a plasmid containing bla_NDM-1 and bla_OXA-58. This represents the first report of bla_NDM-1-harboring Acinetobacter isolate from companion animals in Japan. Conclusions: Carbapenem-resistant Acinetobacter spp. remain rare in companion animals in Japan; however, insertion sequence mobility may promote resistance gene dissemination. As carbapenems are not approved for veterinary use in Japan, strict antimicrobial stewardship and appropriate hygiene management are essential. Full article

Background: Accurate evaluation of the Near Point of Convergence (NPC) is essential for diagnosing and managing convergence insufficiency (CI). Conventional assessment relies on the patient’s verbal feedback and the examiner’s visual observation, making it subjective and examiner-dependent. The AI-based MobileS platform, previously validated for both diagnosis and home-based therapy of CI, enables smartphone-based measurement and visualisation of NPC through eye tracking, without the need for verbal responses or additional equipment. This study, the third stage of our research programme, examined how ophthalmologists interpret NPC data when presented as videos versus AI-derived graphs. Methods: Twenty-two ophthalmologists completed an online questionnaire with 20 NPC test cases from the validated MobileS database, presented as both silent videos and AI-derived graphs. Accuracy was analysed using mixed-effects logistic regression, and continuous error was assessed using clustered bootstrap. Results: Graph-based interpretation showed higher odds of accurate NPC identification than video-based interpretation at the primary ±5 mm threshold (OR = 19.7, 95% CI: 13.50–28.74; p < 0.0001). Absolute error was lower for graphs than videos (Graphs − Videos: −22.73 mm; 95% CI: −26.88 to −18.59; p < 0.0001). “Uncertain” responses occurred in 28.2% of video-based assessments and 0% of graph-based assessments. Off-target errors decreased from 50.2% (videos) to 3.6% (graphs). Conclusions: AI-derived graphs of eye-movement data were associated with improved NPC estimation, suggesting a potential role in supporting clinical and tele-ophthalmology workflows. Full article

Background: While mobile health (mHealth) interventions show promise for type 2 diabetes management, mechanisms linking user experience to clinical outcomes remain poorly understood. We hypothesized that perceived usability may mediate associations between patient characteristics and short-term clinical changes, with implications for health equity in digital interventions. Methods: Secondary analysis of the intervention arm from a randomized controlled trial in urban Mexican primary care (ClinicalTrials.gov NCT05924516). Participants used a diabetes self-management mobile application for 90 days. We assessed usability with the validated Computer System Usability Questionnaire (CSUQ; 16 items, 7-point scale) and measured clinical changes in body mass index (BMI), systolic blood pressure (SBP), and HbA1c. Bayesian mediation analysis (literature-informed priors) examined interface quality as a mediator of age-related clinical effects. Item-level analysis identified educational disparities in specific usability dimensions using independent t-tests adjusted for multiple comparisons. Results: Mean overall usability was 5.20/7 (SD = 0.89, 74th percentile). Interface quality mediated 39% of the age–SBP association. Participants experiencing high usability (≥6) versus low usability showed BMI reduction −0.78 vs. −0.21 kg/m² (Cohen’s d = 0.56) and SBP reduction −7.3 vs. −1.2 mmHg (Cohen’s d = 0.51). No mediation effect was observed for HbA1c change. Users with ≤primary education (41% of sample) scored 1.9 points lower on error messages (3.2 vs. 5.1, p < 0.01) and 1.4 points lower on help documentation (3.6 vs. 5.0, p < 0.03). These disparities persisted after controlling for age and baseline severity. Conclusions: Perceived usability was associated with a potential mechanistic pathway linking user experience to clinical outcomes. Higher usability scores were associated with clinically meaningful improvements in cardiometabolic parameters. Educational disparities in understanding error messages and helping documentation represent modifiable design barriers. Implementing contextual error explanations with visual examples and plain-language help content may enhance both clinical effectiveness and equity in digital diabetes interventions. Full article

Using perfluorooctanoic acid (PFOA) as a representative highly mobile solute to isolate hydrological controls, we investigated how slope influences the partitioning of vertical and lateral transport pathways. While vertical percolation has been widely examined using conventional column leaching tests, lateral transport driven by topographic gradients remain insufficiently quantified under controlled conditions. Here, laboratory-scale inclined leaching experiments were conducted to resolve the distribution of solute transport among vertical leachate, lateral runoff, and solid-phase retention under systematically varied slope angles (0°, 4°, 9°, and 20°), flow regimes, and leaching volumes. Results show that solute migration shifted from vertical-dominated transport under flat conditions (91% at 0°) to lateral-dominated export at moderate slopes, with lateral pathways accounting for up to 75% of the recovered mass at 9°. This pathway shift was well described by an exponential partitioning model, f₁(α) = f_max (1 − e^−kα), where f_max = 0.80 and k = 0.34°⁻¹ (R² = 0.97), indicating a critical crossover threshold at approximately 4° slope. Flow regime interacted with slope angle to modulate lateral transport efficiency: slower flow enhanced lateral export at moderate slopes, whereas faster flow promoted peak lateral transport under steeper conditions. In contrast, solid-phase retention remained consistently low (5–9%) across all treatments, indicating that the observed redistribution patterns were primarily governed by hydrological pathway partitioning rather than sorption processes. These results demonstrate that even modest topographic gradients can fundamentally alter solute transport pathways in sloped soils. The slope-dependent pathway partitioning framework developed here provides a process-based basis for incorporating lateral transport into hillslope hydrological models and for improving assessments of contaminant redistribution in both managed and natural landscapes. Full article

Mobile Wireless Sensor Networks (MWSNs) enhance traditional wireless sensor networks by allowing sensor nodes to move, resulting in continuously changing network topologies. Although this mobility enables advanced applications such as disaster response, intelligent transportation systems, and mission-critical monitoring, it poses major challenges for secure and scalable key management in large-scale deployments. Most existing key management and key pre-distribution schemes are tailored to static or lightly mobile networks and therefore suffer from limited scalability, excessive memory consumption, inefficient key utilization, and increased vulnerability to node capture when applied to highly mobile environments. This paper proposes a mobility-aware, zone-based key management scheme that integrates an enhanced composite key distribution mechanism with dynamic key refinement. The network is partitioned into logical zones, each maintaining an independent key pool to confine security breaches and improve scalability. To adapt to mobility-induced topology changes, sensor nodes continuously refine their key rings by preserving only the cryptographic keys associated with persistent neighbor relationships. This selective retention strategy significantly reduces storage overhead while strengthening resilience against key compromise and unauthorized access. Comprehensive analytical modeling and performance evaluations demonstrate that the proposed scheme achieves higher secure connectivity, stronger resistance to node capture attacks, and improved scalability compared to existing approaches, particularly in dense and highly mobile MWSN scenarios. Full article

Autonomous mobile robots are transforming industries from e-commerce logistics to field exploration, but their effectiveness depends on onboard energy storage. This study addresses the challenge of selecting optimal battery technologies for autonomous mobile robots, balancing performance, energy efficiency, thermal stability, and cost across diverse applications. We focus on lithium-ion, lithium-polymer, and nickel-metal hydride batteries, the most common power solutions, each with distinct advantages and disadvantages in energy density, form factor, thermal stability, and cost. A dynamic modeling and simulation framework in MapleSim evaluated these chemistries under defined and representative operating conditions, tracking state of charge and temperature during charging and discharging. A Particle Swarm Optimization algorithm evaluated 37 battery configurations by thermal stability, energy efficiency, and cost across five use cases. Key results indicate that for logistics and warehousing, lithium nickel manganese cobalt oxide with graphite is optimal; for healthcare, lithium nickel manganese cobalt oxide with lithium titanate oxide excels; for manufacturing, lithium nickel cobalt aluminum oxide with graphite leads; for agricultural robots, lithium manganese oxide with graphite is best; and for exploration and mining, lithium iron phosphate with graphite is most reliable. These results provide a structured basis for battery selection, showing how simulation-driven, multi-criteria decision-making enhances energy management and operational reliability. Full article

This systematic review maps the landscape of microlearning research within software engineering education, critically examining how this pedagogical approach is being applied to develop the multifaceted competencies required of modern software professionals. Following PRISMA-ScR guidelines, the review synthesized 21 empirical studies from 2015 to 2026, analyzing their pedagogical approaches, technological integrations, curriculum coverage, and evidence of effectiveness. The findings reveal a field marked by creative experimentation yet significant fragmentation: while microlearning effectively engages students and conveys discrete programming and project management knowledge through gamified, mobile, and project-based formats, its application remains narrowly concentrated on introductory coding, leaving advanced competencies such as software architecture, requirements engineering, and testing strategies virtually unexplored. The review further exposes critical gaps in the evidence base, including the absence of longitudinal and transfer studies, the conflation of platform engagement with learning, and methodologically fragile claims of effectiveness. Enthusiasm for microcredentials and AI-personalized learning considerably outstrips empirical support, with implemented systems relying on rule-based logic rather than adaptive intelligence and credentialing frameworks lacking validation of employer recognition or employment outcomes. This review concludes that while microlearning holds genuine potential for just-in-time skill development in a rapidly evolving discipline, its role in software engineering education must be strategic and supplemental rather than comprehensive. The field must urgently move from promotional advocacy toward rigorous, comparative, and longitudinal research that assesses higher-order competencies and authentic professional capability, lest its promise remain unfulfilled. Full article

The concentration of heavy metals in the environment has been steadily increasing, raising concerns about their adverse effects on ecosystems and human health. Fine-grained particulate matter is of particular concern due to its enhanced mobility, bioavailability, and potential for inhalation exposure. Facilities involved in the mechanical processing of electronic waste (e-waste) represent a significant potential source of metal-containing fine particles. In this study, crushed e-waste components containing precious metals were separated into particle-size fractions ranging from 3.0 to 0.15 mm using a vibratory sieving system. The elemental composition of the individual fractions was determined by energy-dispersive X-ray fluorescence spectrometry (ED-XRF), while the spatial distribution of selected metals in fine fractions was further investigated using scanning electron microscopy combined with energy-dispersive X-ray spectroscopy (SEM–EDS). The results demonstrate that e-waste contains a wide range of heavy non-ferrous metals whose distribution is strongly dependent on particle size. A pronounced enrichment of metals was observed in the finest fractions, particularly below 0.25 mm. Compared to the coarse fraction (>3 mm), the zinc concentration increased by approximately one order of magnitude, while chromium, nickel, and cadmium exhibited increases of up to approximately 20-fold. Lead showed particularly high enrichment, reaching approximately 2 wt.% in the finest fraction (<0.15 mm), corresponding to nearly fiftyfold enrichment relative to the coarse fraction. Tin concentrations also increased markedly, in some cases by up to two orders of magnitude. Trace amounts of arsenic and selenium were detected in the finest fractions, whereas mercury was not detected. The combined ED-XRF and SEM–EDS results confirm that fine-grained e-waste fractions are the dominant carriers of hazardous metals and respirable particles generated during mechanical processing. These findings highlight the dual character of fine fractions as both a critical environmental and occupational risk and a potentially valuable secondary resource. The study emphasizes the importance of controlled handling, effective dust management, and targeted processing strategies to minimize human exposure while enabling efficient recovery of valuable metals from e-waste. Full article

Clot-in-transit (CIT) is a free-floating thrombus in the right heart and can enter pulmonary circulation at any moment. Possible treatments include anticoagulation, systemic thrombolysis, surgical embolectomy, and endovascular catheter-based therapies. The optimal treatment is still undetermined, heavily relying on clinical judgment and multidisciplinary team discussion. We report a case of a 70-year-old woman presenting with tachydyspnoea following recent abdominal surgery, who was diagnosed with massive bilateral pulmonary embolism (PE) complicated by a clot-in-transit. Point-of-care ultrasonography revealed a large mobile thrombus in the right atrium with severe right ventricular dysfunction. Due to haemodynamic instability and a contraindication for systemic thrombolysis, mechanical thrombectomy was performed. A large thrombotic burden was aspirated from the right heart and pulmonary arteries, resulting in haemodynamic stabilization and recovery of right ventricular function. The patient remained stable throughout hospitalization and was discharged on oral anticoagulation therapy with complete recovery on follow-up. This case highlights several points. Firstly, CIT is a rare finding but should be considered in patients with massive pulmonary embolism and shock. Furthermore, POCUS is essential for diagnosing CIT. Finally, mechanical thrombectomy is a valuable therapeutic option in high-risk PE patients with contraindications to systemic thrombolysis and haemodynamic instability. Further studies are needed to establish adequate guidelines for the optimal management of CIT patients. Full article

Identifying the plant species comprising a pasture, among other aspects, is crucial for assessing its nutritional value for grazing animals and facilitating its effective management. Traditionally, it requires labor-intensive visual inspection. Artificial Intelligence (AI) offers a solution for automatic classification, yet robust datasets for training such models in natural, uncontrolled environments are scarce. This data descriptor presents a dataset of 741 images collected in pasture lands in the Centre of Portugal using standard cameras at a height of 50 cm. A semi-automated annotation pipeline was employed, utilizing a Faster R-CNN model followed by manual verification and refinement. The dataset contains 1744 annotations across four categories: ‘Shrubs’, ‘Grasses’, ‘Legumes’, and ‘Others’. It includes diverse morphological variations and captures real-world challenges such as occlusion and lighting variability. This dataset serves as a benchmark for training object detection models in agricultural settings, facilitating the development of automated monitoring systems for precision agriculture. Such a mechanism could be incorporated into a mobile application, mounted on a drone, or embedded in an animal-worn device, enabling automated sampling and identification of the plant composition within a pasture. Full article

Soils play a fundamental role in plant nutrition as primary sources of potassium (K) and magnesium (Mg), whose availability depends on soil properties and environmental conditions. The composition of major cations in the soil solution is governed by interacting factors, including soil texture, acidity, mineralogical composition, and seasonal variability during the growing cycle. This study examines the availability, mobility, and seasonal dynamics of K and Mg in the soil solution of seven naturally managed soils across four distinct periods of a complete growing season beginning in spring. An integrated field and laboratory approach was applied to assess the influence of clay mineralogy on K and Mg behavior and overall soil fertility. Seasonal soil samples were analyzed for mineral composition, total elemental chemistry, exchangeable cation pools, and soil solution chemistry. Total elemental concentrations were determined by inductively coupled plasma mass spectrometry (ICP-MS), and clay mineral assemblages were identified by X-ray diffraction (XRD), focusing on 2:1 clay minerals, mixed-layer phases, and hydroxy-interlayered minerals (HIMs). The soils were dominated by 2:1 and mixed-layer assemblages, including illite/smectite (Ill/Sm), mica/illite–vermiculite (M/Vm), and chlorite/smectite (Chl/Sm), as well as transitional HIMs such as hydroxy-interlayered smectite (HIS) and hydroxy-interlayered vermiculite (HIV). Exchangeable Mg (0.28–1.30 cmolc kg⁻¹) and K (0.12–0.97 cmolc kg⁻¹) occurred in relatively high amounts, with maximum base saturation values of 13.14% (Mg) and 4.55% (K). Soil solution concentrations ranged from 1.60 to 3.00 ppm for K⁺ and 0.90–1.70 ppm for Mg²⁺, indicating substantial mobility and enrichment from the solid phase. These findings demonstrate that 2:1 clay minerals and mixed-layer phases act as key reservoirs regulating K and Mg exchangeability and release under natural acidic conditions, thereby sustaining soil fertility and nutrient availability for plant uptake. Full article

This article introduces Memetic/Metaphorical Digital Twins (MDTs) as a novel extension of Digital Twin typologies by twinning conceptual schemes, complementing Industrial, Human, and Cognitive Digital Twins. MDTs embed cultural, organizational, and semiotic knowledge into digital frameworks, enabling the recombination and evolution of knowledge structures across disciplines. Drawing on Schlaile’s economic perspectives and Mavromatidis’s architectural lens of entropy and constructal thermodynamics, this study demonstrates how MDTs can address systemic challenges in communication, knowledge transfer, and design. A Digital Community Platform, under development for supporting decentralized Personal Knowledge Management Systems (PKMS), provides the operational foundation, integrating iterative KM cycles to support knowledge co-creation. Its logic and logistics substitute the traditional document paradigm with a memetic approach by utilizing memes as replicable, adaptive knowledge units, thereby mimicking biological evolution and ecosystem resilience in digital platform environments. It aims to offer distributed, decentralized, bottom-up, affordable, knowledge-worker-centric applications prioritizing personalization, mobility, generativity, and entropy reduction; its mission is to serve a knowledge-co-creating community characterized by highly diverse individual Abilities, Contexts, Means, and Ends (ACME) facing increasingly volatile, uncertain, complex, and ambiguous futures (VUCA). A Boundary Object Taxonomy to Omnify Memetic Storytelling (BOTTOMS) is proposed to further structure atomic units of meaning—such as memes, mythemes, narratemes, and reputemes—into a unified framework for authorship and dissemination. The article situates MDTs within a design science research paradigm, outlines current implementation progress, and identifies future developments, including AI-supported curation, personalized metrics, and expanded boundary objects. Together, these contributions position MDTs as a universal framework for adaptive, transdisciplinary knowledge co-creation. Full article