Search Results (395)

Accurate road-event detection and timely alert message dissemination are essential for the safety of connected and automated vehicles. In many scenarios, alert messages must reach not only nearby vehicles but also remote stakeholders, such as traffic management centers, cloud services, and infrastructure operators. This requirement motivates the adoption of cellular-based communication technologies in addition to short-range vehicle-to-everything (V2X) communications for data dissemination. In this work, we investigate vehicle-to-network-to-everything (V2N2X) communications for the dissemination of alert messages generated after the on-board detection of hazardous road events through machine learning (ML) algorithms. Although V2N2X connectivity is well suited for extending data dissemination beyond the local vehicular environment, its capability to guarantee prompt message delivery under strict latency constraints remains an open challenge, particularly when ML inference is integrated into the end-to-end processing pipeline. To address this issue, we develop and experimentally evaluate a proof-of-concept (PoC) platform that combines real-time road-event detection with relevant message dissemination towards both nearby and remote recipients. The proposed framework leverages 5G connectivity and publish/subscribe messaging protocols. The experimental results showcase that dissemination latency is highly influenced by both the adopted type of 5G deployment (private versus commercial networks) and the load conditions at the message broker. Full article

Smartwatches are increasingly used in safety-critical scenarios, yet their optical heart-rate (HR) measurements often contain noise, artifacts, and missing data, undermining clinical trust. This paper presents Edge Beats, a data-curation layer and end-to-end architecture that enables the low-cost, open source PineTime smartwatch to function as a practical HR sensing node for distributed wearable systems. Heart-rate packets are streamed from PineTime to an ESP32 at the edge layer over Bluetooth Low Energy (BLE), then forwarded via an embedded Message Queuing Telemetry Transport (MQTT) broker to an edge server laptop for processing and visualization. A lightweight multi-stage algorithm cleans and smooths the HR stream using physiological boundary checks, a configurable data imputation technique, and exponential moving average (EMA) smoothing, all designed for real-time operation on resource-constrained hardware. We have evaluated the system over long monitoring sessions and compared the processed PineTime output against a commercial Huawei GT Pro 2 smartwatch. The system suppresses extreme spikes and short-term oscillations, yielding a more stable HR trace with qualitative agreement to the reference trends while keeping values in a physiologically plausible range. Network measurements show low latency (almost 3 ms one-way, 15 ms RTT) and stable throughput, and power measurements (100–450 mW for ESP32 and 3–70 mW for PineTime watch) confirm that continuous HR streaming over BLE and MQTT is feasible within the PineTime’s energy budget. These results imply that data stream processing combined with a modest publish–subscribe architecture improves the stability and usability of HR streams obtained from commodity wearable sensors, making PineTime a candidate as a complementary component for mission-critical health and safety systems. Full article

Foreign-exchange decisions rest on hierarchically organized evidence whose latent structure is inadequately captured by Euclidean representations. Reinforcement-learning agents trained on flat embeddings inherit stability guarantees that do not transfer to the manifold supporting the latent state. We address both limitations through a hybrid architecture in which a schema-constrained structured chain-of-thought is embedded into a Poincaré ball, transported to a qubit register via angle encoding, and processed by an L-layer hardware-efficient variational ansatz on a state-vector backend. The circuit exposes two read-outs to the policy, namely, a scalar Pauli-Z observable and a projected quantum kernel inducing a fidelity-based similarity over magnet-price attractors, the latter identified via kernel-weighted recurrence density and finite-time Lyapunov statistics. The Lipschitz constraint on the action-value function is lifted from the hyperbolic geodesic distance to a joint metric on ${\mathbb{B}}_{\kappa }^n\times \mathbb{P}\left(\mathcal{H}\right)$. A stability theorem yields an explicit bound depending on the read-out operator norm, on the depth–width product of the ansatz, and on the curvature–Hilbert balance. The pipeline is evaluated on nine major FX crosses over a 2015–2025 out-of-sample window, with rolling-origin walk-forward retraining and broker-published transaction costs. The system attains $2.55\%$ pair-averaged non-compounded monthly P&L and $8.83\%$ maximum drawdown, with Sharpe $1.78$, Calmar $3.43$, and Probabilistic Sharpe Ratio exceeding $0.95$ on every cross. The gain remains significant under a deflated-Sharpe-ratio test with $N_{\mathrm{trials}}=42$ correction. Block-wise ablations exhibit strictly monotone degradation: removing the projected kernel costs $-4.15$ p.p. on annualized P&L, the joint Lipschitz penalty $-6.42$ p.p., the attractor module $-7.64$ p.p., and the hyperbolic embedding $-8.40$ p.p. The quantum block thereby instantiates a structurally non-classical, geometry-aware regularizer identifiable through ablation rather than asymptotically advantageous. Full article

Against the backdrop of deep digital economic integration, the synergistic agglomeration of new quality productive forces (NQPFs) and digital transformation (DT) has become a key engine for regional high-quality development. Based on data from 31 Chinese provinces during 2011–2023, this study measured the synergistic level of NQPF and DT. Using a modified gravity model, we convert attribute data into relational data and analyze driving mechanisms via social network analysis and quadratic assignment procedures. The results show that the synergistic agglomeration network presents club convergence rather than homogeneous dispersion, forming a structure comprising “polar-core absorption, hub transmission, hinterland integration, and peripheral marginalization.” Eastern regions act as net beneficiaries; Guangdong, Fujian, and other hubs become net-spillover brokers; central and western regions achieve element equilibrium, yet traditional industrial bases face a widening digital divide. Targeted policy implications are proposed. This study provides references for breaking regional digital barriers and optimizing the spatial layout of high-quality development. Full article

(1) The importance of social structure and dominance hierarchies in cooperative-breeding species is well-documented, yet the inclusion of these processes in conservation translocation planning remains limited. Here, we empirically measured the social networks of three populations of the endangered Floreana Mockingbird, then used model simulations of different translocation scenarios to test the effects of social disruption on the social networks. (2) We used social network analysis and Exponential Random Graph Models (ERGMs) to characterise dominance hierarchies, group structure, and the consequences of selectively removing individuals from family groups. (3) Dominance hierarchies were strongly transitive, with age emerging as the primary determinant of dominance relationships. Simulated removals demonstrated that the loss of individuals occupying different network positions produced variable levels of social disruption. (4) Although age is the principal driver of antagonistic interactions, network properties such as high betweenness centrality and the presence of a broker (individuals that occupy a strategic position) are also critical considerations for translocation design. Incorporating social network structure into management strategies can minimise group disruption and enhance the success of conservation translocations for endangered cooperative breeders. Full article

Accurate tree counting from remote sensing data is essential for forest inventory, biomass estimation, carbon accounting, and ecological monitoring. However, existing approaches predominantly rely on airborne RGB imagery and often struggle in complex forest scenes where neighboring crowns exhibit highly similar textures and colors and where overlapping crown boundaries become ambiguous. To address this limitation, the LiDAR-derived Canopy Height Model (CHM) is introduced as a complementary modality that provides explicit cues on canopy height variation and vertical structure to support RGB-based analysis. Building on this, we propose BCAR-Net, a broker-guided RGB and depth (RGB-D) multimodal framework that couples bidirectional cross-modal interaction, adaptive tri-branch fusion, and auxiliary reconstruction within a two-stage optimization scheme. Specifically, a bidirectional cross-attention U-Net generates an intermediate broker RGB-D representation from paired RGB images and depth maps through symmetric bidirectional cross-attention between the two modalities and direction-aware gating. The original RGB image, depth map, and broker representation are then jointly encoded by three weight-sharing branches and adaptively aggregated by a spatial fusion gate for density-map regression. To regularize the fused latent feature, a multi-scale cross-attention reconstruction decoder provides auxiliary RGB and depth reconstruction supervision by querying multi-scale BCA-UNet encoder features through 2D cross-attention, and a reconstruction-oriented first stage replaces externally generated fused-image supervision, yielding a task-consistent optimization scheme. Experiments on the NEONTreeEvaluation benchmark show that BCAR-Net consistently outperforms single-modality settings and direct RGB-D concatenation multimodal baseline. Additional experiments on a public UAV RGB–LiDAR dataset provide a small-scale supplementary evaluation under a different acquisition setting, where BCAR-Net achieves modest but consistent improvements over RGB-only and depth-only baselines. These results demonstrate that the proposed framework offers an effective but computationally cautious solution for tree counting in complex forest environments. Full article

Java middleware platforms expose powerful management functions through HTTP-accessible interfaces such as Jolokia. This article discusses the analysis of CVE-2023-50780 in Apache ActiveMQ Artemis by framing the vulnerability as a management-plane state-transition problem rather than as a set of isolated exploit recipes. We analyze three remote-code-execution paths that combine Jolokia-accessible MBeans with Log4J2 configuration mutability, Artemis filesystem and deployment semantics, broker or web-server restart behavior, and, in one vector, the Java DiagnosticCommand interface. The study defines a formal attacker model; separates demonstrated preconditions from deployment-dependent assumptions; compares the three vectors across required privileges, network dependencies, writable artifacts, execution triggers, reliability, detection opportunities, and mitigations; and evaluates defensive controls at the level of the exploit stage they interrupt. The paper also clarifies the responsible-disclosure context and reduces operational payload detail in favor of defender-oriented evidence, validation tables, and architectural analysis. The resulting contribution is a reproducible but bounded case study of how legitimate administrative operations can compose into code execution when management interfaces are exposed without sufficient privilege separation, MBean restriction, filesystem hardening, and upgrade controls. Full article

This study investigates the spatial association network structure of Green Transportation Development (GTD) in China to support coordinated regional development. Based on panel data from 30 major Chinese cities over the period 2011–2020, an entropy weighting method is used to evaluate urban GTD levels, while social network analysis (SNA) and the Quadratic Assignment Procedure (QAP) are employed to identify the spatial network topology, clustering patterns, and driving factors of GTD. The results show that GTD exhibits significant intercity spatial associations. The overall network structure is relatively stable and exhibits a loose hierarchical pattern, with network density fluctuating between 0.232 and 0.277. Shanghai, Yinchuan, and Nanjing play prominent roles in the core–periphery structure. Block modelling further classifies the network into four functional groups: “net spillover,” “bilateral spillover,” “net benefit,” and “broker” blocks. In 2020, the network contained 214 association ties, of which 176 were inter-block ties, indicating evident cross-block spillover effects but relatively weak intra-block communication. The QAP regression results further reveal that geographical distance inhibits network formation, whereas differences in economic development and transport-related employment promote intercity GTD associations; differences in technological innovation exert a negative effect. These findings suggest that policymakers should reduce administrative barriers, formulate differentiated GTD policies, strengthen regional linkages, and promote intercity cooperation based on complementary advantages to improve the overall performance of GTD. Full article

The continuous growth of IIoT systems has significantly increased the number of connected devices and message interactions, creating higher requirements for communication mechanisms in terms of scalability and adaptability under dynamic network environments. Although MQTT is widely used for its lightweight communication, its traditional centralized broker architecture limits scalability and fault tolerance in large-scale data transmission, reducing system scalability and fault tolerance. Additionally, static QoS configuration is difficult to adapt to dynamic environmental changes, resulting in high end-to-end latency and limited system throughput. To address these issues, this paper proposes a distributed MQTT communication method based on intelligent QoS routing and hierarchical collaboration (DAIS-MQTT). This method designs a network routing algorithm based on a hierarchical tree structure (LCN), which effectively addresses the scalability limitation of centralized proxies by enabling multi-level proxy collaboration and self-recovery from faults. At the same time, it proposes a QoS routing algorithm based on intelligent decision trees (IQR), which jointly optimizes proxy selection and QoS levels to dynamically adapt to changes in the network environment, thereby solving the problem of insufficient adaptability in static QoS configurations. Experimental results show that compared with the traditional MQTT-based communication method, the DAIS-MQTT method reduces the average message delay by 29.9%, increases system throughput by 28.2%, and maintains a reliable transmission rate of 98.7% in unreliable network environments, making it suitable for high-dynamic and large-scale IIoT communication scenarios. Full article

Background: Family-centered care (FCC) is a foundational principle in pediatric healthcare, yet its implementation in culturally specific contexts remains poorly understood. In Saudi Arabia, Islamic values, collective family structures, and gendered caregiving norms shape how mothers engage with pediatric intensive care in ways that existing Western-derived FCC models do not fully capture. The aim of this study was to explore Saudi mothers’ experiences of family-centered care during their children’s pediatric intensive care unit (PICU) admissions, focusing on perceived barriers, cultural negotiations, and evolving advocacy strategies. Methods: A qualitative descriptive study was conducted with 17 Saudi mothers whose children had been admitted to PICUs across major hospitals in Saudi Arabia within the preceding 12 months. Semi-structured interviews lasting 40–70 min were conducted in Arabic using a pilot-tested, 15-item guide. Data were analyzed through Braun and Clarke’s six-phase reflexive thematic analysis. Trustworthiness was strengthened through member checking, reflexive journaling, negative case analysis, and investigator triangulation. Reporting adheres to the Consolidated Criteria for Reporting Qualitative Research (COREQ). Result: Five interconnected themes emerged: (1) confronting crisis and uncertainty, (2) renegotiating maternal identity, (3) brokering culture within biomedicine, (4) forging trust with care teams, and (5) evolving into advocates. These themes trace a developmental arc from initial disorientation through progressive empowerment, shaped at every stage by culturally grounded resources and constraints. Mothers functioned as cultural brokers performing invisible labor that healthcare systems neither recognized nor supported. Conclusions: Saudi mothers in PICUs engage in sophisticated cultural mediation between family systems and biomedical institutions under conditions of acute stress. Findings underscore the need for structurally embedded cultural responsiveness in PICU policy, including continuous cultural assessment, care-team continuity, and family advocacy support. Full article

Engineering PhDs are expected to act as key knowledge brokers between universities and firms. Drawing on self-determination theory (SDT), we examined the associations among organizational culture shock (OCS), SDT motivations, and knowledge transfer behavior (KTB) of newly hired engineering PhDs in Chinese firms. We also explored whether these associations varied across subgroups defined by gender, career goal at PhD entry, prior industry collaboration experience, and dissertation orientation. Data were collected from 466 engineering PhDs within one year after they entered firms. Structural equation modeling (SEM) analysis revealed that OCS was negatively associated with KTB, including indirect associations through the three types of SDT motivations. Autonomous motivation was positively associated with KTB, whereas controlled motivation and amotivation were negatively associated with it. Multi-group SEM analyses further indicated that the strength of the structural pathways varied across subgroups defined by gender, career goal at PhD entry, industry collaboration experience, and dissertation orientation. These findings suggest that OCS may represent a micro-level barrier to university–industry knowledge transfer. They also indicate that firms and universities may help support knowledge transfer by facilitating PhDs’ adjustment and autonomous motivation. Full article

Due to the high population density and limited land availability in Bangkok, the capital of Thailand, land values have been increasing every year, posing challenges to sustainable housing development. Accurate land valuation is critical not only for investment decisions but also for promoting economic efficiency, social equity, and sustainable urban land use. Inaccurate analysis can lead to losses for real estate developers, project residents, and surrounding communities. However, this process requires extensive knowledge and experience. This research presents an approach for analyzing land values in Bangkok using Deep Learning techniques, which can help real estate developers assess appropriate land values more accurately and precisely. The study collected data on vacant land in Bangkok from an online feasibility study database and analyzed them using Deep Learning techniques. The results showed 30 determinants categorized into five groups. The study conducted 80 parameter adjustments with a ratio of 128:64:32 using a Quadratic Loss Function. The model was validated using k-fold cross-validation to ensure robustness and a Model Simulator operator to test sensitivity analysis. The Deep Learning model resulted in an R-square value of 0.917 and an RMSE of 2620 USD. The results of this research can be used as an effective decision-making tool for real estate developers, landowners, and brokers in determining appropriate buying or selling prices for land to support real estate sustainable development. Full article

This paper analyses seven project deliverables from the Interreg Euro-MED “MAST” project to examine its sustainability protocol as a sociotechnical boundary object facilitating ISO 21401:2018 adoption among Mediterranean tourism SMEs. Using Science and Technology Studies (STS) and boundary object theory, we conducted qualitative content analysis (QCA) to map how the protocol translates global standards into SME roadmaps addressing implementation costs, skill gaps, and legitimacy barriers. Results reveal a tension between managerial scripting (actionable tables and KPIs) and relational openings (peer learning and stakeholder prompts). While enabling SME access to certification, the protocol risks “smart compliance” by prioritising formal verification over substantive transformation. Universities emerge as key boundary brokers, potentially translating technical standards into entrepreneurial competencies and curricula. Limited to pre-implementation project documents, the analysis identifies discursive conditions under which standardised tools could support regenerative governance. Findings suggest university–SME partnerships as promising mechanisms for aligning certification with Mediterranean socio-ecological priorities, warranting empirical testing through SME implementation studies. Full article

Indigenous peoples in Amazonia face major mental health inequities, including high rates of suicidal behaviour among adolescents and young adults in some settings. We conducted a scoping review of the peer-reviewed literature on community health workers (CHWs) and equivalent cadres involved in Indigenous and remote contexts, with a focus on their roles in relation to mental health, psychosocial support, and suicide prevention among Indigenous populations in Amazonia and the Guiana Shield. We reported this review in line with PRISMA-ScR. Searches (September–November 2025) were conducted in PubMed/MEDLINE, Scopus, Web of Science and SciELO, complemented by targeted searches in major publisher platforms and JSTOR. We included English, French, Spanish and Portuguese publications that (i) described CHWs or functionally equivalent cadres in Indigenous/remote contexts and/or (ii) reported CHW-related roles, models, or experiences relevant to mental health, psychosocial support or suicide prevention in Amazonian settings. Global documentation of CHW designations used in Indigenous/remote contexts was compiled; we compiled evidence from Amazonia and the Guiana Shield on CHW roles, programme models, implementation conditions and reported outcomes. Data were charted into a structured template (cadre designation, setting, population, study type, functions, programme features and reported mental health/suicide-related outcomes) and synthesised descriptively and thematically. CHWs commonly function as cultural and linguistic brokers between Indigenous communities and biomedical systems, supporting early detection of distress, psychosocial accompaniment, referral navigation and dialogue with local healing practices. Reported programme models differ markedly: Brazil’s institutionalised Indigenous Health Agents (AIS) offer stability and formal recognition, whereas French Guiana relies more heavily on project-based mediation with innovative practices but greater funding fragility. The available literature remains heterogeneous and uneven across countries, with limited evaluative designs and substantial reliance on descriptive reports. Future work should prioritise stronger implementation and impact evaluation, alongside Indigenous-led governance and sustainable support for CHW cadres. Full article

This study explores the environmental justice issues associated with landfill compensation policies in Bantargebang, Indonesia. Although compensation programs have been implemented for many years, communities living near landfills continue to experience ongoing environmental damage and significant health concerns. Using a qualitative descriptive method, this research explores systemic barriers through in-depth interviews, observations, and water quality analysis. The findings indicate that labeling the program as “Social Assistance” within the Local Government Information System (SIPD) redefines ecological compensation as a fixed form of charity, rather than as a mechanism for genuine environmental restitution. Laboratory data show severe bacteriological contamination, with Total Coliform levels reaching 95%, forcing residents to bear substantial “hidden costs” for clean water, perpetuating a cycle of financial dependence. The growing normalization of health hazards is evident in over 5000 annual cases of acute respiratory infections, and the deadly landslide in March 2026, in which claimed seven lives and injured six others. These incidents underscore the failure of existing remediation approaches to safeguard human dignity and well-being. To address these shortcomings, this study proposes the adoption of an Integrated Compensation Model based on Green Social Work. This model emphasizes structural investment, spatial risk-based indices using quantitative data, and budget coding adjustments within the SIPD. This approach highlights the urgent need to move beyond temporary charitable assistance and instead pursue meaningful environmental justice, while positioning social workers as “Social-Ecological Brokers” who help restore dignity and well-being in communities often treated as “sacrifice zones.” Full article