Search Results (21,952)

Unmanned Aerial Vehicles (UAVs) have emerged as a flexible, cost-effective solution for connecting Internet of Things (IoT) devices where traditional infrastructure falls short. However, managing their limited energy alongside the diverse demands of densely deployed devices makes resource allocation a genuinely hard problem. This paper presents a Deep Reinforcement Learning (DRL) framework that jointly optimizes user scheduling, IoT device transmit power, bandwidth, and UAV movement in a 6G-enabled UAV-relay uplink network, using a deterministic large-scale air-to-ground path-loss channel model. The UAV acts as an aerial decode-and-forward relay between IoT devices and a Base Station (BS), with a Deep Q-Network (DQN) making decisions based on queue backlogs, channel conditions, UAV position, and remaining battery. The reward function balances Energy Efficiency (EE), queue stability, fairness, and battery longevity. We benchmark the DQN against six baselines; Round Robin (RR), Random Allocation (RA), the Single-to-Noise Ratio (Max-SNR), Proportional Fair (PF), a Lyapunov heuristic, and a GreedyEE scheme; across a range of device counts, traffic loads, battery budgets, and flight altitudes. Simulations consistently show that the DQN outperforms all baselines, including a RA baseline with equal access to UAV mobility; in EE, throughput, delay, and fairness, confirming that the gain stems from the learned joint control policy rather than from UAV mobility being available. Full article

The ubiquity of surveillance networks in public infrastructure presents a significant, yet underutilized, opportunity to assist vulnerable populations, particularly individuals who are blind or have low vision (BLV). However, transforming these passive video feeds into active guidance systems requires accurate camera calibration, a process that is traditionally labor-intensive and unscalable in large facilities. This paper introduces a novel, automated framework that leverages a mobile quadruped robot (Boston Dynamics Spot) as a dynamic calibration agent. We propose a single-pass approach with two planar calibration algorithms, which mines unstructured robotic trajectories to construct distinct geometric features on the ground plane. By synthesizing “virtual rectangles” from the robot’s odometry, our method first recovers camera focal length through vanishing point estimation by constructing virtual rectangles, and then solves for extrinsic 6-DoF pose using two algorithms: our proposed Virtual Rectangle (ViR) algorithm and a standard planar Perspective-n-Point (PnP) algorithm. Experimental validation using real-world data demonstrates the system’s robustness to sensor noise, maintaining focal length errors around $5\%$, rotational errors around $5^{\circ }$ and relative translation errors of approximately $10\%$, while synthetic simulations indicate focal length errors generally under 10%, rotational errors under $1.5^{\circ }$, and translation errors also around 10% despite heavy image and object disturbances. This work eliminates the need for manual calibration targets for dynamic camera calibration, effectively converting static security infrastructure into a metric sensing network capable of supporting high-fidelity social robotics applications. Full article

Low-impact development (LID) and nonpoint-source pollution reduction facilities are increasingly expected to function as long-term components of urban infrastructure rather than as isolated environmental features. Their sustainability depends not only on technical design but also on institutional review, construction assurance, operation and maintenance, performance verification, information continuity, and adaptive policy learning. This study comparatively examines the content and structure of three major South Korean guidance documents issued in 2013, 2016, and 2020: the national LID Technology-Element Guideline, Seoul’s Guidance for the LID Prior-Consultation System, and the national Manual for the Installation and Management/Operation of Nonpoint-Source Pollution Reduction Facilities. A directed qualitative content analysis was conducted using seven literature-informed lifecycle-governance dimensions covering problem framing, planning and approval, facility selection and sizing, installation and construction control, operation and maintenance, monitoring and performance verification, and adaptive feedback. Documentary provisions were assessed using a four-level ordinal scale ranging from absent to strong coverage. The analysis indicates a functional expansion of governance provisions across the three documents, from LID principles and technology selection to pre-permit review, quantitative runoff-management requirements, installation guidance, operation and maintenance, monitoring, and performance testing. However, the documents collectively show discontinuities between approval and construction verification, construction and long-term operation, maintenance and compliance, monitoring and policy learning, and lifecycle information management. In response, this study proposes a six-stage lifecycle-governance framework linking planning, pre-permit verification, construction commissioning, asset registration, risk-based maintenance and monitoring, and adaptive policy learning. The framework is presented as an analytical and normative synthesis rather than an empirically validated governance model. Because the study is limited to documentary analysis, the findings do not demonstrate actual implementation effectiveness, regulatory compliance, maintenance quality, or environmental performance and should be validated through future empirical and comparative research. Full article

The study investigates the efficacy of various Urban Heat Island (UHI) mitigation measures within the United Nations Educational, Scientific and Cultural Organization UNESCO-listed historical center of Florence. Increasingly frequent and severe heatwaves during the summer season represent significant challenges for urban sustainability and public health in the outdoor urban environment. Using ENVI-met as a simulation tool, the research assesses the current microclimate conditions of a district within a historical center and simulates alternative mitigation scenarios. It quantifies the benefits in terms of microclimate characteristics (Potential Air Temperature-Ta and Mean Radiant Temperature-MRT) and human comfort indexes (Physiological Equivalent Temperature-PET and Universal Thermal Climate Index-UTCI). The proposed interventions include blue and green infrastructures as well as shading systems installed across the district. Current climate conditions are characterized by an average Ta of approximately 32 °C and MRT exceeding 59 °C; consequently, the analyzed area falls into the very strong heat stress category for both the calculated comfort indexes. All evaluated mitigation measures result in improving microclimate conditions as well as enhancing human thermal wellbeing within the outdoor environment. The most effective district-level intervention is the installation of shading fabrics, which reduces average Ta by 0.2 °C and MRT by up to 14 °C compared to the current scenario. Interventions tailored for the main square significantly reduce both UTCI and PET values, shifting the thermal stress from very strong to strong or even moderate. These findings highlight the potential of localized and tailored interventions to successfully integrate climate mitigation strategies within sensitive historic urban districts. Full article

This study evaluates how digital transformation can reshape communication effectiveness in the context of cultural tourism through the restructuring of the technology value chain role. Through the support of technology, cultural values are reinforced and conveyed authentically and engagingly to enhance personal and social experiences, thereby improving communication effectiveness. Through multiple surveys with 393 participants, 380 responses were collected, ultimately yielding 368 valid responses in Da Nang, Vietnam, of which domestic tourists accounted for the majority at 54.6%, with the remainder being international tourists. Data analysis was performed using Microsoft Excel, SPSS, and AMOS, with hypotheses tested. The results show a positive but uneven impact of digital transformation on the quality of digital cultural content (greatest impact), digital engagement (second highest), and digital cultural experience. These results challenge some assumptions that role-playing experiences are the central strategy in creating digital value. It also shows that digital technology effectively supports the quality of media content while enhancing the inherent role of digital media in sharing, interaction, and personal immersion. Furthermore, perceived risks weaken the power of digital transformation on personal digital experiences but do not impact content quality. This result reflects the digital trust paradox, demonstrating both the effectiveness in increasing value for both customers and businesses, and the cautious attitude of tourists towards the potential risks from digital platforms. The distinct role of travel purpose in shaping travel behavior also reflects the contextual nature of the study. This research expands on implications beyond purely technology-accepting models by reflecting how technological infrastructure drives the creation of social value in terms of meaning and cultural tourism experiences. In practical terms, destination managers need to evaluate digital transformation as a strategic tool in creating human-centered content, fostering social–emotional connection and experiences to enhance sustainable destination communication effectiveness. Full article

This study presents a data-driven Digital Twin framework designed for the non-invasive parameter optimization of closed-loop, factory-installed Adaptive Cruise Control (ACC) systems for eco-driving without modifying electronic control unit (ECU) firmware or requiring external infrastructure. High-frequency telematics data (OBD-II and GPS) recorded from a production passenger vehicle along an 11.57 km mixed urban-suburban test corridor were used to calibrate a microscale Digital Twin in PTV Vissim based on Wiedemann 99 car-following logic. The calibrated model achieved satisfactory empirical agreement with baseline vehicle indicators (mean velocity difference of 2.84 km/h, net duration difference of 5 s, and volumetric fuel consumption difference of 0.01 L/100 km) alongside a route-level GEH traffic statistic of 2.67. An iterative, multi-criteria eco-driving search across approximately 100 simulation runs identified an optimal parameter configuration balancing energy conservation and travel time. The optimized Eco configuration reduced mean fuel consumption by 12% (from 4.91 to 4.33 L/100 km) with a 14% increase in trip duration (from 893 to 1019 s), while completely eliminating high-intensity acceleration events (>1.5 m/s²) and decreasing strong braking instances by 0.48 percentage points. The findings confirm that treating closed-source factory ACC logic as a bounded parametric search space provides a viable, non-invasive decision-support alternative to invasive, complex control architectures on standard fleet vehicles. Full article

Coal-resource-exhausted cities face severe spatial conflicts between mining land redevelopment, urban sprawl, agricultural expansion, and ecological restoration, necessitating optimized spatial planning of forest-dominated ecological landscapes. This study constructs an ecology-prioritized identification and multi-scenario optimization framework for forest landscapes in resource-depleted urban fringes, taking Xinqiu District, Fuxin City, China, as a representative case. Coupling the Analytic Hierarchy Process and Entropy Weight Method, a mining-adapted Ecological Protection Importance (EPI) evaluation system was established. Results indicated that geological hazard sensitivity exerted the highest weighting dominance ($w_6=0.2322$) in EPI, establishing an Ecological Priority Control Zone covering 47.86% (6782.04 ha) of the district. Spatial dual-conflict mapping revealed a conflict footprint of 1493.82 ha, heavily dominated by Ecological-Urban/Mining Conflicts (1156.95 ha). Multi-scenario simulation via Fragstats 4.2 demonstrated that Option A (ecology priority-agriculture secondary) achieved the highest landscape score (0.8870), effectively maximizing network cohesion (COHESION = 99.3911) and restoring natural fractal boundary geometry (PAFRAC = 1.4760). The “southern conservation, central remediation, and river corridor connectivity” zonal strategy provides a science-based decision tool for forest network reconstruction in resource-exhausted cities. Full article

Digital transformation has come to the fore as a pivotal force behind corporate green innovation in the digital economy. Although previous research has documented that digital transformation can promote green innovation, the underlying organizational mechanisms remain theoretically fragmented. Moreover, the external conditions that shape the effectiveness of this relationship are not yet fully understood. To address these gaps, we develop an integrated analytical framework anchored in Organizational Information Processing Theory (OIPT) and the Resource-Based View (RBV). This framework explains how digital transformation promotes green innovation through two complementary organizational mechanisms—resource investment and technology application—and incorporates the moderating role of digital infrastructure. Using panel data on Chinese A-share-listed firms covering 2015–2024, this study examines the proposed relationships through a Bidirectional Fixed Effects Model, mediation analysis, and moderation analysis. The findings demonstrate a significant positive association between digital transformation and green innovation. This effect is partially mediated by increased R&D(Resource and Development) investment and deeper digital organizational embedding, which constitute complementary pathways reflecting resource investment and enhanced resource utilization, respectively. Additionally, digital infrastructure positively moderates this relationship by providing a more supportive external digital environment. This study enriches the existing literature by synthesizing previously dispersed perspectives on mechanisms into an OIPT-RBV framework, offering a more comprehensive account of how and under what circumstances digital transformation facilitates green innovation. Our findings also carry practical implications for managers seeking to strengthen innovation capabilities and for policymakers aiming to advance digital infrastructure and firms’ sustainable development. Full article

Background/Objectives: Bereavement care is widely endorsed as a standard of pediatric oncology and palliative care, yet it remains inconsistently implemented across pediatric oncology programs in the United States, and few programs have reported longitudinal delivery metrics. We aim to describe the iterative development and delivery of a multi-component bereavement program within an outpatient pediatric palliative oncology (PPO) program, characterize the population served, and benchmark delivery against national guidelines. Methods: This was a retrospective descriptive analysis of bereavement component delivery for PPO program patients who died between 2017 and 2026, using an iterative quality improvement framework. Program components (calls, condolence cards, packets, books) were tracked prospectively in a registry. Annual completion rates and timeliness were calculated for each family-facing component, and the cohort was characterized by demographic, clinical, and end-of-life variables. Data were reviewed for completeness at extraction; missing values were rare and are noted in the relevant tables. Results: A cohort of 403 deceased patients was included (41% Black, 15% Hispanic/Latino, 50% Medicaid-insured). The program evolved from informal bereavement phone calls (completion 74–81%) during 2017–2019 to a standardized, six-component program by 2020, reaching 93–100% annual delivery for most eligible components during 2022–2025. Early outreach occurred promptly (median 6 days after death for phone calls and condolence cards), and longer-term components were delivered within their intended windows. Conclusions: Comprehensive bereavement was developed over time without a fully formed infrastructure at initiation. This structured program began with one feasible, trackable activity and added components incrementally, achieving highly sustainable delivery that ultimately informed broader institutional practice. Full article

Urban Digital Twins excel at modeling physical infrastructure but remain structurally limited in capturing the qualitative, experiential dimensions of urban life—particularly sense of place, which empirical research links to civic stewardship and long-term sustainability. This study investigates whether generative AI can serve as a participatory elicitation interface for surfacing these missing human data layers. Through a mixed-methods experimental design, 24 residents of Austin, Texas, each selected a personally meaningful public urban space and created visual representations using both hand-drawn sketching and iterative co-creation with the text-to-image model DALL-E. Pre- and post-experiment surveys and semi-structured interviews captured participants’ perceptions of the outputs and self-reported shifts in place awareness. The findings reveal a dialectical tension: DALL-E consistently defaulted to generic visual archetypes, overriding participants’ localized descriptions. However, this algorithmic homogenization paradoxically deepened participants’ sense of place through a process we term ‘validation by contrast’—residents utilized the AI’s inaccurate outputs as a foil to consciously articulate what made their environments authentically meaningful. These findings suggest that for human-centric Digital Twins, the actionable data lies not in the AI-generated image itself, but in the negotiation process through which residents defend and crystallize their authentic spatial identity. Full empirical validation of this pattern, including systematic comparison across representation modalities, is reserved for future work. Full article

Ammonia is increasingly considered a promising hydrogen carrier due to its high hydrogen density and well-established infrastructure. Monitoring ammonia cracking efficiency requires robust, continuous gas analysis across a wide concentration range, with resistance to corrosive gases. Conventional methods such as gas chromatography or mass spectrometry meet these requirements, but are costly and operationally complex. To address this gap, a Thermal Conductivity Analyzer (TCA) was developed based on an OEM module and validated for continuous in situ monitoring of ammonia cracking. The system includes a pump-driven bypass extraction line and a three-stage calibration procedure: zero-point correction, look-up table generation, and span calibration. Measurement stability was assessed using binary H2/N2 mixtures and a quasi-binary surrogate of the ammonia cracking product gas, mixed via mass flow controllers (MFCs). The analyzer was then applied to characterize a monolithic ammonia cracking catalyst from 200– 650${}^{\circ }\mathrm{C}$. With daily zero-point and span calibration, all measured H2 concentrations fell within the mixing uncertainty of the MFCs across 0– 100$\mathrm{vol}.\%$. For the synthetic cracking gas, maximum conversion ratio deviations of +0.317 and −0.224 percentage points were achieved. These results demonstrate that the TCA offers a simple, ammonia-resistant alternative for monitoring NH3 cracking processes, with uncertainties competitive with MFC repeatability. Full article

Urban green spaces play an important role in supporting ecosystem functions, landscape connectivity, and urban resilience; however, their potential contribution may be constrained by fragmentation, inadequate maintenance, and limited integration among spatial planning instruments. This study develops a preliminary green infrastructure (GI) planning framework for the municipality of Noicattaro (Bari, Apulia, Italy) by integrating planning provisions with the observed condition and spatial distribution of existing public green spaces. A GIS-based spatial analysis, complemented by field surveys, assessed 89 public green spaces for compliance with planning requirements and maintenance status. The analysis also considered the spatial structure of the surrounding peri-urban landscape, including the karstic dry valleys (Lame), agricultural land uses, and relevant landscape and hydrogeological planning constraints. The results show that only 20.2% of the surveyed green spaces met both planning and maintenance criteria, whereas 78.4% fell into categories characterised by planning non-compliance, inadequate maintenance, or both. These findings highlight potential gaps between formal planning provisions and the observed condition and management of the municipal green system. The analysis of the Lame further revealed a predominance of agricultural land over natural vegetation, emphasising the importance of considering the wider agricultural and peri-urban landscape when identifying potential ecological connections. Based on the combined spatial and field assessment, a preliminary GI network framework was developed around core ecological areas, buffer zones, and strategically located green spaces, with the aim of identifying potential spatial connections and areas for further investigation. Overall, the study demonstrates the potential of integrating planning analysis, GIS-based assessment, and field observations as a screening and decision-support approach for more context-sensitive GI planning in peri-urban Mediterranean landscapes. The proposed framework should be regarded as a preliminary planning proposal requiring further validation through biodiversity, ecosystem-service, accessibility, and implementation assessments. Full article

Understanding how selected urban route conditions are associated with e-scooter riders’ visual attention and perceived workload can support human-factor evaluation of micromobility environments. This study examines visual attention and perceived workload during real-world e-scooter riding across three selected urban routes in Zagreb with different infrastructure and traffic-environment characteristics. A field-based experimental methodology was used, integrating eye-tracking with post-ride perceived workload assessment through the National Aeronautics and Space Administration Task Load Index (NASA-TLX) questionnaire. Twenty-eight adults, predominantly novice or occasional e-scooter riders, completed the three selected routes. Visual attention and perceived workload were examined across the selected routes; because only one route represented each route condition, the findings are interpreted as route-level evidence rather than general infrastructure-type effects. One-way repeated-measures analyses of variance (ANOVAs) showed that route condition had a significant effect on mean fixation duration and on the weighted NASA-TLX workload score, indicating that riders’ visual attention and perceived workload varied across the examined routes. To further examine these route-related differences, linear mixed-effects models were used as supporting analyses with participant ID as a random intercept. The results showed that R2 was associated with lower weighted workload than R1, while R3 was associated with higher weighted workload than R1. The route-complexity index was retained as an exploratory route-level descriptor rather than a validated continuous predictor. These findings highlight the importance of considering combined route, infrastructure, and traffic-environment characteristics when planning safer micromobility environments and developing appropriate regulations for e-scooter use. They also provide preliminary route-level human-factor evidence that can support future micromobility route evaluation and the development of human-centred guidance for infrastructure planning and e-scooter regulation. Full article

Background: Population aging has led to an increasing proportion of older adults (aged 65 and older) acting as informal caregivers. These caregivers face risks of burden, stress, and depression. While research frequently documents negative psychological outcomes, resilience represents a crucial protective process. This systematic review synthesizes and maps the empirical evidence regarding the association between the socio-ecological resilience process and the well-being of older informal caregivers. Methods: Following Joanna Briggs Institute (JBI) mixed-methods guidelines, a convergent segregated mixed studies systematic review was conducted. A systematic search of Medline (PubMed), CINAHL, PsycINFO, and Scopus identified primary articles (2014–2025) in English and Spanish. Methodological quality was appraised using JBI critical appraisal checklists and the Mixed Methods Appraisal Tool. PROSPERO registration number: CRD420251142190. Results: Thirty-one studies were included. Elevated caregiver resilience was associated with lower reported symptoms of depression and anxiety, higher self-rated health, and positive psychological adaptation. Framed within a social ecological model, personal resources such as spirituality, hope, and self care, alongside relational assets like dyadic relationship quality and mutual coping, demonstrated a positive association with resilience and a reduction in subjective burden. Within the community context, informal peer networks and Online Health Communities functioned as essential supportive resources. On a structural level, both household wealth and the regional availability of long-term care beds acted as moderators for spousal well-being, whereas exceeding 30 weekly caregiving hours acted as a temporal threshold for positive adaptation. Conclusions: These findings suggest that resilience in older caregivers may be best conceptualized not as a static individual trait, but as a multi-level, dynamic socio-ecological process. Rather than relying on individual coping alone, public policies and clinical practice should prioritize systemic, relational, and structural environmental support, including formal respite services and long-term care infrastructure, to preserve the well-being of older spousal caregivers. Full article

Zebra mussels (Dreissena polymorpha) are an invasive species that severely affect irrigation systems by reducing hydraulic performance, increasing maintenance requirements and compromising the sustainability of water infrastructures. Although several chemical treatments have been proposed for their control, the potential application of sulphurous acid has not previously been investigated. This study experimentally evaluated the effectiveness of sulphurous acid generated by a sulphur burner under controlled laboratory conditions. Four 45 L Plexiglas aquaria were used, including three treatment replicates and one untreated control, following a two-week acclimatization period. Two treatment strategies were assessed to distinguish the effects of water acidification and oxygen depletion. Reducing water pH by 1.5 units did not cause mortality after eight days of exposure. In contrast, maintaining dissolved oxygen concentrations below 1 mg L⁻¹ resulted in 100% mortality after five days in all treated replicates. These findings demonstrate that treatment effectiveness is governed by dissolved oxygen depletion rather than by pH reduction alone. Sulphurous acid therefore represents a promising and potentially sustainable alternative for zebra mussel control, contributing to improved irrigation efficiency and more sustainable water resource management. Full article