Search Results (16,255)

Accurate trajectory tracking remains a central challenge in differential-drive mobile robots (DDMRs), particularly when operating under real-world conditions. Model Predictive Control (MPC) provides a powerful framework for this task, but its performance degrades when the robot deviates significantly from the nominal path. To address this limitation, robust recovery mechanisms are required to ensure stable and precise tracking. This work presents an experimental validation of an MPC controller applied to a four-wheel DDMR, whose odometry is corrected by a SLAM algorithm running in ROS 2. The MPC is formulated as a quadratic program with state and input constraints on linear (v) and angular ($\omega$) velocities, using a prediction horizon of $N_p=15$ future states, adjusted to the computational resources of the onboard computer. A novel dynamic look-ahead re-entry strategy is proposed, which activates when the robot exits a predefined lateral error band ($\delta =0.05$ m) and interpolates a smooth reconnection trajectory based on a forward look-ahead point, ensuring gradual convergence and avoiding abrupt re-entry actions. Accuracy was evaluated through lateral and heading errors measured via geometric projection onto the nominal path, ensuring fair comparison. From these errors, RMSE, MAE, $P_{95}$, and in-band percentage were computed as quantitative metrics. The framework was tested on real hardware at 50 Hz through 5 nominal experiments and 3 perturbed experiments. Perturbations consisted of externally imposed velocity commands at specific points along the path, while configuration parameters were systematically varied across trials, including the weight R, smoothing distance $L_{\mathrm{smooth}}$, and activation of the re-entry strategy. In nominal conditions, the best configuration (ID 2) achieved a lateral RMSE of 0.05 m, a heading RMSE of 0.06 rad, and maintained 68.8% of the trajectory within the validation band. Under perturbations, the proposed strategy substantially improved robustness. For instance, in experiment ID 6 the robot sustained a lateral RMSE of 0.12 m and preserved 51.4% in-band, outperforming MPC without re-entry, which suffered from larger deviations and slower recoveries. The results confirm that integrating MPC with the proposed re-entry strategy enhances both accuracy and robustness in DDMR trajectory tracking. By combining predictive control with a spatially grounded recovery mechanism, the approach ensures consistent performance in challenging scenarios, underscoring its relevance for reliable mobile robot navigation in uncertain environments. Full article

Microfluidic paper-based analytical devices (µPADs) convert ordinary cellulose into an active analytical platform where capillary gradients shape transport, surface chemistry guides recognition, and embedded electrodes or optical probes translate biochemical events into readable signals. Progress in fabrication—from wax and stencil barriers to laser-defined grooves, inkjet-printed conductive lattices, and 3D-structured multilayers—has expanded reaction capacity while preserving portability. Detection strategies span colorimetric fields that respond within porous fibers, fluorescence and ratiometric architectures tuned for low abundance biomarkers, and electrochemical interfaces resilient to turbidity, salinity, and biological noise. Applications now include diagnosing human body fluids, checking food safety, monitoring the environment, and testing for pesticides and illegal drugs, often in places with limited resources. Researchers are now using learning algorithms to read minute gradients or currents imperceptible to the human eye, effectively enhancing and assisting the measurement process. This perspective article focuses on the newest advancements in the design, fabrication, material selection, testing methods, and applications of µPADs, and it explains how they work, where they can be used, and what their future might hold. Full article

Heading date (HD) is a key adaptive trait determining wheat regional suitability, yield stability, and resilience to environmental stresses. We dissected the genetic architecture of heading date (HD) by phenotyping a doubled haploid (DH) population (178 lines, CASL7AS × ZNL12) across five environments and constructing a high-density genetic map with the wheat 55K SNP array. A total of 38 QTLs associated with HD were identified on 12 chromosomes, among which 10 were consistently detected across multiple environments. Two major stable loci, QHD.ZAFU.2B and QHD.ZAFU.4A, explained substantial phenotypic variation and were considered key regulators of heading time. Candidate gene analysis revealed Ppd-B1 (TraesCSU02G196100) as the causal gene for QHD.ZAFU.2B. Within QHD.ZAFU.4A, a zinc finger RNA-binding protein gene (TraesCS4A02G394400) exhibiting strong flag-leaf expression at the heading stage was identified as the most promising candidate. Notably, most favorable alleles were derived from ZNL12, highlighting its potential for breeding applications aimed at manipulating heading time. These results provide valuable genomic resources and molecular targets for marker-assisted selection aimed at optimizing flowering time and improving wheat adaptation. Full article

Mangrove ecosystems along the Pacific coast of Panama are increasingly exposed to anthropogenic pressures such as urban expansion and deforestation. These habitats provide resources for orchid bees (tribe Euglossini), yet information on their assemblages in mangrove environments remains limited. In this study, we documented the diversity and composition of orchid bee communities in mangrove–forest edges from two coastal areas with contrasting levels of human disturbance: Panama Bay and Chame Bay. Orchid bee sampling was carried out during two independent periods: from April to July 2022 at three sites in Panama Bay, and from December 2022 to January 2023 at one site in Panama Bay and one site in Chame Bay, using McPhail traps baited with eucalyptus oil and distributed across multiple zones within each site. A total of 427 individuals representing 14 species and three genera were recorded. Observed species richness and abundance were lower at the more urbanized mangrove sites, where collections were dominated by a few widespread species, particularly Eulaema nigrita. Multivariate analyses revealed differences in community composition between sites. These patterns suggest associations between anthropogenic context and orchid bee assemblage structure in mangrove edges, although longer-term and multi-method studies are required to evaluate temporal consistency and underlying mechanisms. This study provides baseline information to support future monitoring of orchid bee communities in tropical coastal ecosystems. Full article

With the advancement of digital technology, serious games have become an essential tool for disseminating and educating individuals about cultural heritage. However, systematic empirical research remains limited with respect to how visual elements influence users’ cognitive and emotional engagement through interactive behaviors. Using the “Macau Historic Centre Science Popularization System” as a case study, this mixed-methods study investigates the mechanisms by which visual elements affect user experience and learning outcomes in digital interactive environments. Eye-tracking data, behavioral logs, questionnaires, and semi-structured interviews from 30 participants were collected to examine the impact of visual elements on cognitive resource allocation and emotional engagement. The results indicate that the game intervention significantly enhanced participants’ retention and comprehension of cultural knowledge. Eye-tracking data showed that props, text boxes, historic buildings, and the architectural light and shadow shows (as incentive feedback elements) had the highest total fixation duration (TFD) and fixation count (FC). Active-interaction visual elements showed a stronger association with emotional arousal and were more likely to elicit high-arousal experiences than passive-interaction elements. The FC of architectural light and shadow shows a positive correlation with positive emotions, immersion, and a sense of accomplishment. Interview findings revealed users’ subjective experiences regarding visual design and narrative immersion. This study proposes an integrated analytical framework linking “visual elements–interaction behaviors–cognition–emotion.” By combining eye-tracking and information dynamics analysis, it enables multidimensional measurement of users’ cognitive processes and emotional responses, providing empirical evidence to inform visual design, interaction mechanisms, and incentive strategies in serious games for cultural heritage. Full article

Virtual Field Trips (VFTs) have emerged as an alternative to Traditional Field Trips (TFTs), addressing logistical, financial, and accessibility constraints in geoscience education. This study presents a comparative analysis of the educational impact of a VFT and a TFT implemented with the same 10th-grade class in a Portuguese secondary school. The VFT, focused on volcanism and its socioeconomic impacts, used Google Earth to explore the island of São Miguel in the Azores. The TFT, centred on the rock cycle, was conducted at the Lavadores Beach geological site. Both interventions followed the field-based learning model by Orion and were structured around three phases: preparation, field trip (virtual or traditional), and post-activity synthesis. Data was collected through diagnostic tests, schematization, observation grids, student reports (snapshot), group projects, and written responses to a fieldwork guide recorded on Padlet during the VFT and TFT. The results showed that both VFTs and TFTs enhance conceptual understanding and student engagement, though they foster different skills: VFTs strengthen digital literacy, improve accessibility and inclusion for students with mobility or geographic constraints, allow for content revisitation, foster collaboration among students, integrate multimedia resources, and enable virtual exploration of remote locations that would otherwise be inaccessible. They also offer reduced costs, greater scheduling flexibility, and allow for individualised pacing of student learning. In contrast, TFTs provide richer sensory and practical experiences that are essential for hands-on scientific inquiry and foster stronger connections with the natural environment. The study concludes that a complementary use of both strategies offers the most inclusive and effective approach to teaching geosciences. Full article

The energy transition in Ibero-American countries demands significant diversification, yet the vast potential of offshore renewable energies (ORE) remains largely untapped. Slow adoption is often attributed to the hostile marine environment, high investment costs, and a lack of institutional, regulatory, and industrial readiness. A critical barrier for policymakers is the absence of methodologically robust tools to assess national preparedness. Existing indices typically rely on simplistic weighting schemes or are susceptible to known flaws, such as the rank reversal phenomenon, which undermines their credibility for strategic decision-making. This study addresses this gap by developing a multi-criteria decision-making (MCDM) framework based on a problem-specific synthesis of established optimization principles to construct a comprehensive Offshore Readiness Index (ORI) for 13 Ibero-American countries. The framework moves beyond traditional methods by employing an advanced weight-elicitation model rooted in the Robust Ordinal Regression (ROR) paradigm to analyze 42 sub-criteria across five domains: Regulation, Planning, Resource, Industry, and Grid. Its methodological core is a non-linear objective function that synergistically combines a Shannon entropy term to promote a maximally unbiased weight distribution and to prevent criterion exclusion, with an epistemic regularization penalty that anchors the solution to expert-derived priorities within each domain. The model is guided by high-level hierarchical constraints that reflect overarching policy assumptions, such as the primacy of Regulation and Planning, thereby ensuring strategic alignment. The resulting ORI ranks Spain first, followed by Mexico and Costa Rica. Spain’s leadership is underpinned by its exceptional performance in key domains, supported by specific enablers, such as a dedicated renewable energy roadmap. The optimized block weights validate the model’s structure, with Regulation (0.272) and Electric Grid (0.272) receiving the highest importance. In contrast, lower-ranked countries exhibit systemic deficiencies across multiple domains. This research offers a dual contribution: methodological innovation in readiness assessment and an actionable tool for policy instruments. The primary policy conclusion is clear: robust regulatory frameworks and strategic planning are the pivotal enabling conditions for ORE development, while industrial capacity and infrastructure are consequent steps that must follow, not precede, a solid policy foundation. Full article

This work addresses the increasing complexity of urban mobility by proposing an intelligent optimization and resource-allocation framework for Vehicle-to-Infrastructure (V2I) communications. The model integrates a macroscopic mobility analysis, an Integer Linear Programming (ILP) formulation for optimal Road-Side Unit (RSU) placement, and a Smart Generic Network Controller (SGNC) based on Q-learning for dynamic radio-resource allocation. Simulation results in a realistic georeferenced urban scenario with 380 candidate sites show that the ILP model activates only 2.9% of RSUs while guaranteeing more than 90% vehicular coverage. The reinforcement-learning-based SGNC achieves stable allocation behavior, successfully managing 10 antennas and 120 total resources, and maintaining efficient operation when the system exceeds 70% capacity by reallocating resources dynamically through the $\lambda$-based alert mechanism. Compared with static allocation, the proposed method improves resource efficiency and coverage consistency under varying traffic demand, demonstrating its potential for scalable V2I deployment in next-generation intelligent transportation systems. Full article

Early detection of small-scale fires is crucial for minimizing damage and enabling rapid emergency response. While recent deep learning-based fire detection systems have achieved high accuracy, they still face three key challenges: (1) limited deployability in resource-constrained edge environments due to high computational costs, (2) performance degradation caused by feature interference when jointly learning flame and smoke features in a single backbone, and (3) low sensitivity to small flames and thin smoke in the initial stages. To address these issues, we propose a lightweight dual-stream fire detection architecture based on YOLOv5n, which learns flame and smoke features separately to improve both accuracy and efficiency under strict edge constraints. The proposed method integrates two specialized attention modules: ESCFM++, which enhances spatial and channel discrimination for sharp boundaries and local flame structures (flame), and ESCFM-RS, which captures low-contrast, diffuse smoke patterns through depthwise convolutions and residual scaling (smoke). On the D-Fire dataset, the flame detector achieved 74.5% mAP@50 with only 1.89 M parameters, while the smoke detector achieved 89.2% mAP@50. When deployed on an NVIDIA Jetson Xavier NX(NVIDIA Corporation, Santa Clara, CA, USA)., the system achieved 59.7 FPS (single-stream) and 28.3 FPS (dual-tream) with GPU utilization below 90% and power consumption under 17 W. Under identical on-device conditions, it outperforms YOLOv9t and YOLOv12n by 36–62% in FPS and 0.7–2.0% in detection accuracy. We further validate deployment via outdoor day/night long-range live-stream tests on Jetson using our flame detector , showing reliable capture of small, distant flames that appear as tiny cues on the screen, particularly in challenging daytime scenes. These results demonstrate overall that modality-specific stream specialization and ESCFM attention reduce feature interference while improving detection accuracy and computational efficiency for real-time edge-device fire monitoring. Full article

Lactic acid bacteria (LAB), as the core microorganisms in silage fermentation, play a crucial role in improving silage quality and ensuring feed safety, making the screening, identification, and functional characterization of LAB strains a significant research focus. Researchers initially isolate and purify LAB from various samples, followed by identification through a combination of morphological, physiological, biochemical, and molecular biological methods. Systematic screening has been conducted to identify LAB strains tolerant to extreme environments (e.g., low temperature, high temperature, high salinity) and those possessing functional traits such as antimicrobial activity, antioxidant capacity, production of feruloyl esterase and bacteriocins, as well as cellulose degradation, yielding a series of notable findings. Furthermore, modern technologies, including microbiomics, metabolomics, metagenomics, and transcriptomics, have been employed to analyze the structure and functional potential of microbial communities, as well as metabolic dynamics during the ensiling process. The addition of superior LAB inoculants not only facilitates rapid acidification to reduce nutrient loss, inhibit harmful microorganisms, and improve fermentation quality and palatability but also demonstrates potential functions such as degrading mycotoxins, adsorbing heavy metals, and reducing methane emissions. However, its application efficacy is directly constrained by factors such as strain-crop specific interactions, high dependence on raw material conditions, limited functionality of bacterial strains, and relatively high application costs. In summary, the integration of multi-omics technologies with traditional methods, along with in-depth exploration of novel resources like phyllosphere endophytic LAB, will provide new directions for developing efficient and targeted LAB inoculants for silage. Full article

Space Surveillance and Tracking (SST) plays a crucial role in ensuring space safety. To this end, accurate and numerous observational resources are needed to build and maintain a catalog of space objects. In particular, it is essential to develop optimal observation strategies to maximize both the number and the quality of detections obtained from a sensor network. This represents a key step in the assessment of the network through simulations. This work presents the integrated development of sensor tasking strategies for optical systems and a track-to-track correlation pipeline within S$\mathrm{\Xi }$NSIT, a software environment designed to simulate sensor network configurations and evaluate cataloging performance. For high-altitude low Earth orbit (HLEO) targets, which are fast-moving and widely distributed, tasking strategies emphasize systematic scans of the Earth’s shadow boundary to exploit favorable phase angles and improve observational accuracy, while medium- and geostationary-Earth orbits (MEO–GEO) rely on equatorial-plane scans. The correlation pipeline employs Two-Body Integrals, uncertainty propagation, and a ${\chi }^2$-test with the Squared Mahalanobis Distance to associate tracks and perform initial orbit determination of newly detected objects. Results indicate that the integrated approach significantly enhances detection coverage, leading to greater catalog build-up efficiency and improved SST performance. Consequently, it facilitates the cataloging of numerous uncataloged objects within a reduced timeframe. Full article

This study addresses the persistent inefficiencies in incident management within regional public institutions, where dispersed offices and limited digital infrastructure constrain timely technical support. The research aims to evaluate whether a hybrid web architecture integrating AI-assisted interaction and mobile notifications can significantly improve efficiency in this context. The ITIMS (Intelligent Technical Incident Management System) was designed using a Laravel 10 MVC backend, a responsive Bootstrap 5 interface, and a relational MariaDB/MySQL model optimized with migrations and composite indexes, and incorporated two low-cost integrations: a stateless AI chatbot through the OpenRouter API and asynchronous mobile notifications using the Telegram Bot API managed via Laravel Queues and webhooks. Developed through four Scrum sprints and deployed on an institutional XAMPP environment, the solution was evaluated from January to April 2025 with 100 participants using operational metrics and the QWU usability instrument. Results show a reduction in incident resolution time from 120 to 31 min (74.17%), an 85.48% chatbot interaction success rate, a 94.12% notification open rate, and a 99.34% incident resolution rate, alongside an 88% usability score. These findings indicate that a modular, low-cost, and scalable architecture can effectively strengthen digital transformation efforts in the public sector, especially in regions with resource and connectivity constraints. Full article

STEM education increasingly relies on technology-enhanced environments that utilize data-driven strategies, digital tools, and adaptable learning models. To support the evaluation of contemporary STEM teaching methods, this study proposes a multi-criteria analytical framework based on expert assessment. Semi-structured interviews were conducted with 41 experienced teachers from Bulgarian schools (N = 41), who evaluated six key indicators (m = 6) of STEM integration: Effectiveness, Engagement, Applicability, Flexibility, Validity, and Accessibility. The qualitative data were transformed into numerical values and analyzed using the Target Parameter Ranking method. The degree of expert agreement was assessed through the Morris–Kendall coefficient, yielding a statistically significant moderate agreement (w_k = 0.137; χ² = 28.085, df = 5, p = 3.50 × 10⁻⁵ (p < 0.001)). The results indicate that Engagement (W_j = 0.206), Flexibility (W_j = 0.188), and Effectiveness (W_j = 0.186) are the most highly weighted criteria, reflecting teachers’ prioritization of active participation, learning outcomes, and adaptability in technology-rich STEM environments. In comparison, Applicability and Accessibility show higher variability, highlighting their dependence on contextual factors such as infrastructure and resource availability. The proposed framework provides a structured, data-driven basis for evaluating and refining STEM teaching practices and can be integrated into educational decision-support systems. Full article

The success of large-scale deep learning models in remote sensing tasks has been transformative, enabling significant advances in image classification, object detection, and image–text retrieval. However, their computational and memory demands pose challenges for deployment in resource-constrained environments. Knowledge distillation (KD) alleviates these issues by transferring knowledge from a strong teacher to a student model, which can be compact for efficient deployment or architecturally matched to improve accuracy under the same inference budget. In this paper, we introduce Hierarchical Multi-Segment Knowledge Distillation (HIMS_KD), a multi-stage framework that sequentially distills knowledge from a teacher into multiple assistant models specialized in low-, mid-, and high-level representations, and then aggregates their knowledge into the final student. We integrate feature-level alignment, auxiliary similarity-logit alignment, and supervised loss during distillation. Experiments on benchmark remote sensing datasets (RSITMD and RSICD) show that HIMS_KD improves retrieval performance and enhances zero-shot classification; and when a compact student is used, it reduces deployment cost while retaining strong accuracy. Full article

Background/Objectives: Medication incidents remain a significant concern in hospital settings. Integrated medication systems, regarding organized processes, policies, technologies and professional practices are designed to enhance patient safety; however, their safety performance is still suboptimal. The use of valid and reliable instruments to assess hospital medication system safety can be a valuable resource for health care management. The aim of this study was to describe the development and psychometric validation of the Hospital Medication System Safety Assessment Questionnaire (HMSSA-Q) for assessing the safety of hospital medication systems and its processes in Portugal. Methods: The HMSSA-Q was developed through a literature review and two rounds of expert panel consultation. Following consensus, a pilot methodological study was conducted in 95 Portuguese hospitals. Construct validity was assessed using principal component factor analysis, and reliability was evaluated through internal consistency (Cronbach’s alpha). Results: The instrument is theoretically structured into five predefined domains/subscales: Organizational Environment, Safe Medication Prescribing, Safe Medication in Hospital Pharmacy, Safe Medication Preparation and Administration, and Information and Patient Education. Principal component analyses performed separately for each domain supported their internal structure. The overall scale showed excellent internal consistency (Cronbach’s α = 0.97), with Cronbach’s alpha values for the domains ranging from 0.86 to 0.94. Conclusions: The HMSSA-Q is a valid and reliable instrument for assessing the safety of hospital medication systems and has the potential to serve as an innovative management tool for improving patient safety. Full article