Search Results (47,157)

The HEROn magazine was created as an innovation project by the S-TEAM team for the 2024/2025 SUBMERGED season of the FIRST^® LEGO^® League Challenge category. The primary aim of the HEROn project was to implement game-based learning methods to enhance environmental awareness, particularly concerning the protection of our water resources. This initiative is designed to engage individuals from ages 9 to 99 in a creative and enjoyable manner. At the core of the HEROn project is a well-known game that challenges players to find the differences between two photos. This activity not only provides entertainment but also educates participants about the importance of protecting and preserving the aquatic environment. By discovering subtle differences between images, players become more attuned to environmental issues, which promotes a deeper understanding and appreciation of water conservation. The chapters of the HEROn magazine are thoughtfully organised into themes, each focusing on various aspects of water’s importance, its protection, and sustainable usage. Additionally, a random sample of participants was surveyed to gather opinions and feedback on HEROn magazine as part of the project and this research. This feedback is invaluable for assessing the magazine’s impact and for improving future editions to better serve the goals of raising environmental consciousness. The online HEROn questionnaire consisted of 10 items and employed a 5-point Likert scale for responses. Data were collected over a three-month period (28 January–28 April 2025), with 630 Hungarian respondents participating in the survey. The HEROn magazine was generally well received, with mean scores ranging from 4.2 to 4.6. Age-group differences were examined using nonparametric Kruskal–Wallis tests, with Dunn–Bonferroni post hoc comparisons. These analyses show statistically significant differences between adults (30–89) and the younger cohorts for aggregated awareness, design/engagement, and branding measures, while teenagers (9–15) and young adults (16–29) did not differ significantly from each other. The Find-the-Difference game showed the greatest variability across groups, with young adults giving the lowest mean. Full article

In driver attention zone prediction tasks, accurately identifying and locating the driver’s attention zone is crucial. Traditional models have significant limitations in complex driving scenarios due to their failure to fully utilize multidimensional driving environment information. To address these issues, this paper proposes a multi-attention feature fusion network (MAFF-HRNet) for driver attention region prediction. The proposed network combines high-resolution feature extraction with bimodal RGB–semantic inputs, multi-scale feature fusion, attention-based feature refinement, and temporal modeling. The experimental results on the DR(eye)VE dataset show that MAFF-HRNet improves driver attention region prediction under the current evaluation protocol. These results indicate that semantic scene information, multi-scale spatial representation, and temporal context are beneficial for generating more accurate driver attention heatmaps in complex driving scenes. Full article

Multi-UAV cooperative delivery is a key technology for intelligent low-altitude logistics, with applications in mountainous-area transport, urban last-mile delivery, and emergency resupply. In complex three-dimensional (3D) low-altitude environments, obstacle-constrained airspace, fleet heterogeneity, payload limits, and time windows make the realistic representation of flight costs difficult and substantially restrict the feasible region of cooperative planning. To address these challenges, this paper proposes TeCoR-UAV, a two-stage topology extraction and cooperative route planning framework. The proposed method first precomputes executable flight trajectories in obstacle-constrained airspace and constructs a topological graph that captures realistic flight costs. A bi-objective optimization model is then formulated to minimize operational cost and maximize service quality. Furthermore, a hierarchical genetic solver is designed to improve solution quality and feasibility jointly through global task allocation and single-UAV execution sequence optimization. Experimental results show that the proposed method can better reflect realistic flight costs in complex environments. Compared with existing benchmark methods, TeCoR-UAV achieves better bi-objective trade-offs in most medium- and large-scale scenarios, as well as in topologically constrained scenarios, and improves service quality by an average of 18.5 percentage points, indicating its scenario adaptability and potential for practical application. Full article

This qualitative study incorporates caregiver perspectives to identify their (1) experiences with medical and educational supports for their children with chronic TBI following inpatient rehabilitation and across the recovery trajectory and (2) recommendations to improve service provision for young people with TBI. Nineteen caregivers of children with complicated-mild-to-severe TBI participated in semi-structured virtual interviews. Participants were from a large Midwestern U.S. city. Researchers used reflexive thematic analysis, incorporating an experiential orientation and a deductive approach. Standards for Reporting Qualitative Research guided this process. Children were an average of 5.2 years post-injury, and age at injury ranged from 2.6 to 18.0 years, providing depth of caregiver experiences discussed in interviews. Four primary themes were identified: (1) TBI leads to lasting changes in the child, (2) the healthcare environment is overwhelming, (3) TBI forces a shift in caregiver responsibilities, and (4) school challenges persist over time. Caregivers generated concrete, experience-based recommendations, highlighting the need for increased support, resources, and education in specific areas following pediatric TBI. By centering caregiver voices across recovery, this study underscores their unique expertise in identifying system-level gaps and informing the development of interventions, services, and policies that better support children with TBI and their families over time. Full article

To support the achievement of the carbon peaking and carbon neutrality goals and promote the resource utilization of industrial solid waste, a ternary solid waste composite cementitious material was prepared by blending ground granulated blast-furnace slag (GGBFS), fly ash (FA), and carbide slag (CS) with cement. The optimal mix ratio was determined through single-factor experiments and response surface methodology. The synergistic hydration mechanism was elucidated using microstructural characterization techniques, including XRD, FTIR, TG-DTG, and SEM. The composite material was then applied to a semirigid base course, and its mechanical properties and durability were systematically evaluated. The results indicate that the optimal levels of FA, GGBFS, and CS investigated in the single-factor experiments are 20–40%, 30–50%, and 2–6%, respectively. The optimal mix ratio of the ternary solid waste composite is 21.0% FA, 36.3% GGBFS, and 5.7% CS. The underlying microstructural mechanism is that carbide slag creates a highly alkaline environment, which activates the pozzolanic activity of GGBFS and fly ash, leading to the formation of hydration products dominated by C-(A)-S-H gel. With increasing curing age, the gel structure evolves from a loose and disordered state to a dense and ordered state, ultimately forming a compact microstructure based on a highly polymerized C-(A)-S-H gel matrix. The 7-day unconfined compressive strength of the stabilized gravel using the solid waste-based composite cementitious material reached 5.93 MPa, and the 28-day drying shrinkage coefficient was reduced by 18.3% compared with that of cement-stabilized gravel. After 18 freeze–thaw cycles, the compressive strength increased by 2.4%, with the pore structure characterized by a “macropores decreasing, micropores increasing” refinement pattern. After 18 wetting–drying cycles, the cumulative strength loss was 11.26%, outperforming cement-stabilized gravel. Combined with SEM observations, these performance improvements are attributed to the densely intertwined hydration products, particularly C-S-H gel, which effectively fill the voids between aggregate particles and significantly enhance the volume stability, freeze–thaw resistance, and wetting–drying durability of the stabilized gravel. The application of this cementitious material in a semirigid base course demonstrates excellent mechanical and durability properties, providing a theoretical basis and technical support for the widespread application of industrial solid waste in road engineering. Full article

In this study, we present a novel adaptive algorithm, social learning-enhanced deep reinforcement learning (SLDRL), which integrates social learning mechanisms into deep reinforcement learning (DRL) to improve agent performance in both discrete and continuous state-space environments. The proposed hybrid control architecture enables agents to autonomously decide when and how to exploit socially acquired behaviors, balancing social learning with individual exploration through an entropy-based intrinsic motivation mechanism. The framework incorporates online imitation and enactment mechanisms that allow agents to observe and selectively reuse behavioral sequences acquired from other agents during training. We evaluate SLDRL in a sparse-reward discrete grid-based foraging task and in the dense-reward continuous-state/discrete-action CartPole problem. In both domains, SLDRL agents outperform baseline DRL agents, achieving faster learning and higher cumulative rewards. The results show that socially acquired behaviors are utilized adaptively throughout training, with the balance between imitation and individual learning emerging dynamically according to the structure of the environment and the agent’s experience. Comparisons with a behavioral cloning baseline further indicate that selectively integrating observed behaviors can yield more robust long-term learning than direct imitation of demonstration trajectories. Overall, the results demonstrate that SLDRL can effectively leverage online social learning in diverse environments. Full article

Saline–alkaline aquaculture is a promising strategy to alleviate freshwater shortages; however, such environments severely impair fish growth and physiological homeostasis. Nutritional regulation has been proposed to improve stress tolerance, yet the benefits of single additives are often limited under the multifactorial challenges imposed by saline–alkaline conditions. Therefore, a compound feed additive (CFA) consisting of glutamate, cholesterol, β-glucan, myo-inositol, zinc methionine, and curcumin was developed and evaluated in Nile tilapia (Oreochromis niloticus). To assess the robustness and practical applicability of this nutritional strategy, three independent feeding trials were conducted using different commercial basal diets as validation systems. Within each dietary system, fish were reared under freshwater (FW), saline–alkaline water (SAW), or saline–alkaline water supplemented with CFA (SAW+CFA). Saline–alkaline stress significantly reduced WG and SR, increased FCR, and elevated VSI and HSI, indicating impaired growth performance and metabolic burden. These changes were accompanied by increased serum glucose and ion concentrations (Na⁺, K⁺, Cl⁻), elevated ammonia levels, and reduced crude protein content. Dietary CFA improved growth and feed utilization under saline–alkaline conditions. It enhanced hepatic glycogen content and reduced serum glucose levels. Meanwhile, it downregulated glycolysis-related genes (hk, pfk1, pk) and upregulated genes involved in gluconeogenesis and the pentose phosphate pathway (g6pase, pc, g6pdh), indicating altered glucose metabolism and improved energy homeostasis. Saline–alkaline stress induced oxidative stress, apoptosis, and histological damage in the liver, whereas CFA alleviated these alterations by reducing MDA levels, enhancing antioxidant enzyme activities (CAT, GSH-Px, T-SOD) and suppressing apoptosis-related genes (caspases, p53, c-myc). In addition, CFA alleviated saline–alkaline stress-induced gill structural damage and reduced serum ion concentrations while modulating ion transport-related gene expression, suggesting improved osmoregulatory capacity. It also enhanced ammonia metabolism and transport, as reflected by reduced serum ammonia levels and altered expression of related genes. Furthermore, Saline–alkaline stress impaired intestinal structure and function, whereas CFA improved intestinal villus structure, increased digestive enzyme activities (amylase, trypsin, lipase), and suppressed pro-inflammatory genes (il-1β, il-8). Importantly, similar beneficial response patterns were observed across the three independently analyzed dietary systems. Overall, CFA improved saline–alkaline adaptability of Nile tilapia and was associated with improvements in energy metabolism, oxidative homeostasis, osmoregulation, ammonia detoxification, and intestinal function, providing a practical nutritional strategy for saline–alkaline aquaculture. Full article

Achieving rapid and accurate obstacle avoidance in complex and dynamic environments remains a significant challenge for robots. To enhance the adaptability and flexibility of humanoid arms for obstacle avoidance, a brain-inspired multi-pathway motion decision-making method is proposed to modulate rational planning and habitual actions of humanoid arms. Firstly, a novel framework integrating both a slow and a fast pathway is designed for motion decision-making tasks. Imitating the rational planning function of the prefrontal cortex, the slow pathway employs an improved planning approach based on Real-Time Rapidly exploring Random Tree Star (RT-RRT${}^{*}$) to execute deliberate decisions, along with an improvement in planning via the Smart technique and the high-efficiency neighbor searching method. Meanwhile, mimicking the habitual responses governed by the striatum, the fast pathway utilizes an action model trained by Soft Actor-Critic to make quick and habitual motions. The model in the fast pathway is also used to guide the sampling strategy in the slow pathway. Moreover, to facilitate the integration and smooth transition between the two pathways, an emotional neural network is designed as the modulation module with inspiration from the structure and function of the amygdala. Based on body and obstacle information, the network generates emotional signals to modulate the involvement degree of the two pathways before each decision-making process. Experimental results demonstrate that the proposed multi-pathway framework achieves a higher obstacle-avoidance success rate than existing methods while generating motion characteristics that are consistent with certain aspects of human obstacle-avoidance behavior. Full article

Responding to residents’ differentiated health-promoting needs has become important for improving the adaptability of street renewal in old urban neighborhoods. Based on 1404 valid questionnaires from residents in old urban neighborhoods of Jinan, China, this study develops an analytical framework linking group classification, environmental responses, and renewal strategies from the perspective of health literacy–activity behavior mismatch. Health literacy and activity behavior indices were constructed, and K-means clustering was used to identify mismatch groups. Estimated marginal means, average marginal effects, and multiple-response analysis were then employed to compare group-specific response trajectories and improvement preferences across four street environmental dimensions: slow-mobility space, service function, natural aesthetics, and activity facilities. Further interpretation of the obtained analytical results demonstrates that the investigated resident samples are partitioned into four typical subgroups: behavior-driven, high-literacy/high-behavior, literacy-driven, and low-literacy/low-behavior groups. Slow-mobility space was mainly associated with participation willingness and mismatch adjustment; natural aesthetics was primarily related to environmental cognition and perceived attractiveness; activity facilities were more relevant to mismatch changes among low-literacy/low-behavior residents; and service function mainly provided everyday convenience support. Improvement preferences were generally concentrated on basic environmental conditions, especially traffic safety, natural environment, and public activity spaces. These findings provide empirical evidence for group-based health-promoting street renewal and highlight its relevance to socially inclusive and sustainable urban regeneration. Full article

This study investigates the mechanical properties and uniaxial compression behavior of nano-TiO₂-modified coral concrete (NTCC). Twelve groups of specimens with different nano-TiO₂ contents were prepared and cured in freshwater, seawater, and oxalic acid environments. Cube compressive strength, splitting tensile strength, and uniaxial compression tests were conducted according to relevant standards. The results indicate that nano-TiO₂ significantly enhances the mechanical performance of coral concrete. The compressive and tensile strengths initially increased and then decreased with increasing nano-TiO₂ content, with the maximum strength improvement reaching approximately 22%. Furthermore, increasing the nano-TiO₂ dosage reduced the brittle failure characteristics of NTCC under compression. The curing environment had a significant influence on the performance of NTCC. Specimens cured in seawater exhibited superior early-age strength, whereas those cured in freshwater achieved the highest later-age strength. The stress–strain response of NTCC under uniaxial compression can be divided into three stages: the elastic stage, elastoplastic stage, and descending stage. Based on the experimental results, an empirical constitutive model was proposed for NTCC. The predicted stress–strain curves showed good agreement with the experimental results, demonstrating the applicability of the proposed model for describing the compressive behavior of NTCC. Full article

Replacing Ordinary Portland Cement (OPC) with high volumes of fly ash (FA) offers a practical approach to reducing the environmental impacts associated with cement manufacturing and landfill disposal. However, high FA replacement levels, particularly up to 60%, often lead to lower early-age strength. This study developed a green cement mortar containing 60% FA and waste bottle glass nanoparticles (WBGNPs). The WBGNPs were incorporated at replacement levels of 2%, 4%, 6%, 8%, and 10% by volume of the OPC–FA binder. The findings showed that the addition of 4–6% WBGNPs significantly promoted the formation of dense reaction gels and enhanced compressive strength by 12.5–39.1%. Similar performance trends were observed in both the engineering and microstructural properties. The combined incorporation of FA and WBGNPs also improved drying shrinkage performance by reducing capillary stresses during water evaporation and minimizing crack development within the cement matrix. Additionally, a proposed shrinkage prediction model was validated using experimental data and demonstrated good agreement, with an average prediction error of approximately 8%. Overall, the incorporation of WBGNPs provides an effective method for producing high-volume FA cement mortars with satisfactory engineering properties suitable for concrete applications in tropical environments. This approach further supports sustainability by reducing waste generation, lowering landfill demand, and minimizing environmental pollution. Full article

Against the backdrop of geopolitical restructuring and rapid digital transformation, foreign investment security reviews in host countries have become an increasingly important institutional constraint on cross-border business activities. From a broader organizational perspective, such regulatory mechanisms shape the external environment in which emerging market multinational enterprises (EMNEs) make international investment decisions and conduct cross-border acquisitions. This paper explores the moderating effects of EMNE network and internal agency problems on the duration of reverse cross-border M&A (CBMA) under host country security reviews. Utilizing a Negative Binomial regression model, we empirically analyze 503 reverse CBMAs undertaken by Chinese firms in developed economies from 2003 to 2022. The findings reveal that host country security reviews significantly prolong M&A duration. Notably, business group affiliation weakens this positive relationship, whereas political networking strengthens it. Regarding internal governance factors, Type I agency problems reinforce the delaying effect of foreign investment security reviews, while Type II agency problems weaken it. This study provides practical implications for managers and policymakers seeking to improve firms’ responses to increasingly complex regulatory environments. Full article

To address the problem of three-dimensional (3D) localization in underwater environments, this paper proposes a 3D positioning method based on magnetic induction communication (MI in short). Dual transmitters equipped with 3D coils are used to transmit magnetic field signals, while a single receiver with 3D coils is adopted to receive signals. Three-dimensional position calculation is realized by collecting induced voltage from the receiving 3D coils, which enables any device at a known position in space to provide positioning services for other devices. To eliminate abnormal data and suppress environmental noise interference in underwater received signals and further improve positioning performance, an improved density clustering algorithm named the Density-Based Spatial Clustering Method in Magnetic Positioning is proposed to remove erroneous positioning data. In addition, Kalman filtering is introduced to jointly suppress environmental noise interference. Experimental results demonstrate that the average positioning error of the proposed localization method is 0.67 m and maximum positioning error is 0.83 m; therefore, this paper provides a novel technical solution for underwater positioning in non-line-of-sight environments. Full article

This study evaluates an Adaptive Monte Carlo Localization-Extended Kalman Filter (AMCL-EKF) pose-estimation stack for repeatable 2D LiDAR SLAM in GPS-denied indoor inspection scenarios. AMCL was used as an online map-referenced correction source fused with LiDAR odometry and Inertial Measurement Unit (IMU) data, and the resulting pose estimate was supplied online to three SLAM backends: Cartographer, GMapping, and SLAM Toolbox. Experiments were performed with a wheeled Husarion Panther and a quadruped Boston Dynamics Spot in three indoor environments of different geometric complexity, producing 720 SLAM executions. Trajectory repeatability was assessed using SE(2)-aligned pairwise and centroid-based ATE-style dispersion and translational RPE, while map repeatability was evaluated with occupied-cell IoU. Accordingly, the metrics were used to quantify between-run dispersion rather than absolute accuracy against external ground-truth data. The results show that AMCL-EKF fusion is highly dependent on the environment, platform, and SLAM backend. AMCL improved selected configurations, especially for Spot in structured environments and for Panther map consistency, but degraded others in geometrically repetitive corridors and mixed-structure spaces. The study also shows that the presence of AMCL-assisted odometry correction alone does not determine final trajectory repeatability, because each SLAM backend incorporates the supplied fused pose estimate differently. The findings support confidence-aware AMCL integration and motivate integrated SLAM architectures resistant to over-correction. These results provide guidance for robust autonomous mapping and inspection with heterogeneous mobile robotic platforms in real environments. Full article

Background: Stigma is a persistent barrier to psychosocial wellbeing and recovery of individuals with mental illnesses. Aim: This study aims to examine stigma experienced by individuals diagnosed with mental illnesses. Method: A systematic review and meta-synthesis of peer-reviewed qualitative primary studies followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Five databases: CINAHL Ultimate, APA PsycArticles, APA PsycINFO, MEDLINE Ultimate, and Embase were searched for studies published between 2021 and 2025. A total of 17 studies were included after rigorous screening. Results: Thematic analysis identified three major themes and fifteen sub-themes. These are manifestations of stigma (prevalence of stigma, operation of stigma, stigma related to mental health diagnosis, stigma related to motherhood and impacts of stigma), multiple factors shaping stigma (ignorance and misunderstanding, spiritual and religious beliefs, family and friends, cultural beliefs) and management of stigma (management strategies, internalising stigma, isolation due to stigma, disclosure of mental illness, non-disclosure of mental illness and help-seeking for mental illness). Conclusions: Stigma related to mental illness remains widespread and continues to influence individuals’ experiences, relationships, and help-seeking behaviours. Addressing stigma through improved mental health awareness and supportive environments is essential to promote recovery and wellbeing. Full article