Search Results (1,238)

Saimaluu-Tash I, located in a high-altitude glacial valley in Kyrgyzstan, preserves one of Central Asia’s largest and most culturally significant concentrations of rock engravings. Despite extensive archaeological research, the physical, mechanical, and chromatic properties of the sandstone substrates relevant for conservation assessment remain poorly characterized. This study integrates petrographic microscopy, scanning electron microscopy, colorimetry, and Vickers hardness testing with the digital documentation of twelve engraved blocks to evaluate weathering processes, engraving practices, and long-term preservation. The engravings are carved into arkosic sandstone with carbonate cement, characterized by a weathered surface enriched in clay minerals and covered by a dark surface coating (patina). Weathered surfaces exhibit significantly lower hardness (0.6 ± 0.2 GPa) than unweathered stone (2.8 ± 0.6 GPa), which facilitated the engraving of the petroglyphs by allowing tools to penetrate more deeply into the stone. Colorimetric analyses reveal a strong chromatic contrast between the surface patina and the lighter sandstone exposed by engraving (ΔE ≈ 22.7). This contrast would have enhanced the original visibility of the petroglyphs and highlights potential conservation issues associated with the progressive reformation of this surface layer. Iconographic analysis identifies recurrent themes related to hunting, herding, mobility, animal management, and symbolic spatial practices within a nomadic high-mountain landscape. Overall, the results demonstrate how an integrated material and interpretative approach contributes to understanding rock art production processes. They support preventive and sustainable conservation strategies for vulnerable engraving landscapes shaped by long-term interactions between geological processes and human activity. Full article

As the Kingdom of Saudi Arabia (KSA) accelerates its transition toward smart mobility under Vision 2030, establishing a robust digital infrastructure is paramount for the safe deployment of autonomous vehicles (AVs). High-definition (HD) maps serve as a critical foundation for this infrastructure, yet their deployment is severely bottlenecked by extreme operational costs, massive data processing payloads, and rapid environmental variations across vast highway networks. To address these challenges, this paper proposes a comprehensive, localized national strategy structured around three key tasks. First, it establishes a unified national HD map standard to guarantee seamless interoperability and data sharing among competing AV manufacturers and government transport authorities. Second, it implements an AI-powered baseline workflow using Mobile Mapping Systems (MMS) for high-fidelity static map construction, anchored and validated within designated pilot zones, including the King Abdulaziz University campus and key sectors in the Kingdom. Third, it deploys a decentralized, vision-based crowdsourcing system that leverages active public and commercial vehicle fleets for real-time map maintenance. By integrating a sovereign edge-cloud AI infrastructure that respects local Personal Data Protection Law (PDPL), this framework bridges the gap between high-accuracy baseline mapping and long-term economic sustainability, offering an actionable technical roadmap for scaling a resilient digital transport layer across the Kingdom. Full article

This study proposes a multi-criteria decision-making framework for determining optimal locations for shared micromobility stations in Kars, Türkiye. The approach integrates spatial data with structured expert evaluation and applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank candidate locations. Eight representative locations were evaluated based on five criteria: points of interest (POId), public transport distance, activity level, accessibility, and installation suitability. Spatial indicators were obtained through map-based measurements, while qualitative criteria were assessed using expert-based scoring by 11 experts. The results indicate that locations with high activity density, strong accessibility, and a high concentration of POIs achieve the highest suitability scores. The city center (L2) and Kafkas University (L1) were identified as the most suitable locations, with closeness coefficients of 0.862 and 0.783, respectively. In contrast, the train station (L5) showed the lowest suitability, with a closeness coefficient of 0.326. A sensitivity analysis confirmed that the ranking structure remained unchanged under moderate variations in criteria weights, indicating the robustness of the proposed model. The findings suggest that micromobility systems are primarily driven by intra-urban mobility demand rather than by long-distance transportation nodes. From a sustainability perspective, the proposed framework supports evidence-based planning of shared micromobility infrastructure, which can contribute to reducing dependence on private automobiles, improving urban accessibility, and promoting low-carbon transportation. The findings provide practical guidance for municipalities seeking to develop environmentally sustainable, socially accessible, and resource-efficient urban mobility systems in medium-sized cities. The framework can also support broader sustainable urban development strategies and contribute to the achievement of sustainable mobility objectives. Full article

The management of livestock manure is associated with substantial ammonia (NH₃) emissions and the accumulation of emerging contaminants, including antibiotics, antibiotic resistance genes (ARGs), and microplastics, posing risks to environmental quality and public health. Biochar has emerged as a promising strategy for mitigating gaseous emissions and reducing contaminant mobility during manure storage and composting processes. This review synthesizes recent research on the application of biochar in livestock manure management systems, focusing on NH₃ emissions, antibiotic degradation, ARG reduction, and microplastic removal. Particular attention is given to the effectiveness of biochar in mitigating pollutants during manure storage, housing operations, and composting processes. Across the literature, reported NH₃ mitigation efficiencies vary widely, from negligible effects to reductions exceeding 90–97%, depending on feedstock type, pyrolysis conditions, particle size, and application strategy. Biochar also promotes antibiotic degradation and ARG mitigation, with reductions of up to 98% reported in composting systems. Emerging evidence further suggests that biochar can reduce microplastics by approximately 15–64% in sludge composting. Plant-derived and chemically modified biochars generally outperform manure-derived biochars due to higher surface area, cation exchange capacity, and greater abundance of functional groups. The review highlights that activation treatments, co-composting strategies, and microbial interactions are key factors controlling pollutant mitigation efficiency. Despite promising outcomes, large-scale application remains limited by economic constraints, variability in biochar properties, and the lack of long-term field-scale validation. Future research should prioritize standardized production protocols, field implementation studies, and integrated environmental and economic assessments to support the practical adoption of biochar in sustainable livestock waste management systems. Full article

The rapid expansion of wireless connectivity has led to vast amounts of radio-frequency (RF) energy being continuously radiated into the environment, much of which is dissipated due to severe propagation losses. Recycling this otherwise wasted RF energy is, therefore, a critical enabler for energy-efficient and sustainable wireless systems. RF energy harvesting nodes and passive backscatter communication devices provide promising solutions by enabling battery-less or low-maintenance operation for future green networks. However, both paradigms suffer from fundamental limitations, including restricted communication range, near–far effects, and insufficient harvested energy at extended distances. This review examines how cooperative relays can address these challenges by harvesting ambient RF energy and assisting both information transfer and power delivery. From a communication perspective, we review cooperative backscatter communication and harvest-then-transmit (HTT) protocols, highlighting how multi-hop relaying significantly extends coverage and improves throughput for energy-constrained devices. Particular emphasis is placed on tag-to-tag (T2T) backscatter systems, relay-assisted architectures, decode-and-forward and amplify-and-forward protocols, and optimal multi-access time allocation strategies that mitigate the doubly near–far problem in passive networks. From an energy-transfer perspective, the review is structured around three pillars: wireless power transfer (WPT), multi-hop energy transfer (MET), and integrated charging-and-sensing frameworks. We discuss relay deployment and placement optimisation, UAV-enabled mobile energy relays, waveform and beam-forming design, and the transition from idealised linear harvesting models to practical nonlinear rectification models. Key practical constraints, such as regulatory limits, safety compliance, self-interference, protocol overhead, synchronisation, and imperfect channel knowledge, are systematically reviewed. The paper concludes by identifying the scalability limits of multi-hop cooperative systems, outlining how the joint optimisation of energy relaying and cooperative communication enables RF energy recycling for sustainable, low-carbon wireless networks and highlighting open challenges and future research directions. Full article

Background: This study seeks to explore the link between smart city development and demand-responsive transport, analyze the ways of integrating demand-responsive transport into smart mobility concepts, estimate public perceptions about those phenomena, and review any international efforts aimed at promoting sustainable environmentally friendly transport options for cities in the EU. Methods: A systematic literature review was used as a basis for the current study; it is grounded in the analysis of scientific sources available in Scopus with the application of both PEO and PRISMA models. Survey analysis was also applied in order to examine public perception towards smart cities and DRT. Results: The results show that, even though the topics of smart cities and smart mobility have received significant attention in scholarly research, their links to demand-responsive transport still need more exploration. This paper describes the main features of smart mobility, urban mobility, and demand-responsive transport as well as the implications associated with each concept from an environmental, social, and operational perspective. In addition, the current international activities and examples of sustainable and flexible mobility systems are discussed. Conclusions: This paper presents a thorough examination of the relationships among smart mobility, urban mobility, and demand-responsive transportation. Full article

Phytoremediation is a sustainable approach for the remediation of heavy metal–contaminated soils; however, the management of contaminated biomass generated during this process remains an insufficiently addressed challenge. Such biomass constitutes a secondary waste stream that may release mobile pollutants and pose environmental risks. In this study, an integrated ecotoxicological assessment framework was applied to evaluate phytoremediation-derived biomass and its transformation products obtained via pyrolysis. Two types of woody biomass with different heavy metal contents and their corresponding biochars produced at 700 °C were investigated. A multitrophic battery of bioassays combining direct exposure assays using terrestrial organisms (higher plants, Eisenia fetida, and soil microbial activity) with leachate-based assays using aquatic organisms (Lemna minor, Daphnia magna, and Aliivibrio fischeri) was applied. Untreated biomass exhibited high to extreme toxicity in aquatic systems (toxic units, TU >100) and significant phytotoxic effects. Pyrolysis substantially reduced contaminant mobility and ecotoxicity of leachates, resulting in lower toxicity (TU typically <15) and no significant effects on plant growth, earthworm survival, or soil microbial functional diversity. Residual toxicity was linked to elevated pH and trace amounts of thermally generated organic substances. These results demonstrate that pyrolysis effectively reduces the environmental risk of contaminated biomass and supports the use of multitrophic ecotoxicological testing for safe waste valorization within circular economy strategies. Full article

This study aims to assess whether micro-, small-, and medium-sized enterprises in Ecuador’s Amazon region show evidence of sustained reorientation toward selected non-extractive activities under persistent extractive dependence. Guided by four empirical propositions concerning sectoral reorientation, differential firm viability, temporal discontinuity and limited reallocation, and territorial heterogeneity, the study uses a longitudinal administrative panel of 769,344 firm-year observations for 2006–2021, complemented by descriptive evidence for 2022–2024. The empirical strategy combines fixed-effects models, non-parametric trend and structural break tests, cohort analysis, survival analysis, and transition matrices. The results indicate an emerging but constrained diversification pattern. New-economy firms increased their relative participation after the 2015–2016 commodity downturn and showed higher survival rates and stronger formal employment generation than extractive firms. However, intersectoral mobility remained limited, and the evidence does not support the interpretation of a completed structural transformation. Provincial heterogeneity further shows that extractive expansion continues to influence local entrepreneurial dynamics, especially in mining-frontier territories. The main limitation is that the analysis captures formally registered firms and does not directly measure informal-sector activity, productivity upgrading, or full regional structural transformation. The study contributes to debates on regional development, sustainability, and firm-level transformation by showing that non-extractive reorientation may emerge in peripheral resource-dependent regions without fully displacing extractive dependence. Full article

Spinal cord injury (SCI) is frequently accompanied by abnormal muscle tone and spasticity, which impair voluntary motor control, mobility, and quality of life. Although classically defined as velocity-dependent hyperreflexia, tone abnormalities after SCI encompass a broader spectrum, including sustained muscle activation, co-contraction, clonus, and non–velocity-dependent resistance to movement. These manifestations arise from distributed changes across spinal and supraspinal motor systems. At the segmental level, SCI induces maladaptive plasticity involving motoneurons, interneurons, sensory afferents, and muscle, including dysregulated persistent inward currents, altered inhibitory neurotransmission, afferent hyperexcitability, synaptic reorganization, and structural muscle remodeling. In parallel, supraspinal adaptations—including cortical motor map reorganization, reduced intracortical inhibition, corticospinal–reticulospinal imbalance, loss of monoaminergic modulation, and altered brainstem and cerebellar regulation—further amplify spinal circuit gain and impair inhibitory control of tone. Current pharmacologic treatments largely suppress symptoms without addressing these underlying circuit changes, while invasive neuromodulatory strategies are limited by surgical risk or state-dependent effects. This review synthesizes emerging insights into the multilevel mechanisms regulating abnormal tone after SCI and examines neuromodulatory approaches targeting spinal and supraspinal networks. Particular attention is given to transcutaneous spinal cord stimulation (TcSCS), a non-invasive method capable of modulating segmental reflex circuits and descending control pathways. Advances in transcriptomic and epigenetic profiling may further enable mechanism-based therapies and biomarker-guided strategies for treating spasticity. Full article

Background: Multiple sclerosis (MS) is a condition that requires long-term, multidisciplinary management. The growing digital transformation in healthcare has highlighted the central role of nurses in supporting key aspects such as patient self-management, continuity of (at home) care, and patient empowerment. However, evidence on nurse-led digital interventions in MS remains fragmented. Objective: To map the available literature on nurse-led digital interventions in MS, focusing on the role of nurses, clinical outcomes, and research gaps. Methods: The review was conducted using the methodological framework of the Joanna Briggs Institute (JBI) and the PRISMA-ScR checklist. A systematic search was performed in PubMed, Scopus, Web of Science, and CINAHL. Studies were included if they described digital or telehealth interventions led or coordinated by nurses in patients with MS. Results: A total of 12 studies published between 2015 and 2025 met the inclusion criteria. Four main thematic areas were identified: (1) telenursing and empowerment-based interventions; (2) mobile and web-based patient self-management programs; (3) digital systems for monitoring and integrated care pathways; and (4) digital interventions targeting symptom management and psychosocial outcomes. Across the studies, nurse-led digital interventions were associated with improvements in self-management, treatment adherence, self-efficacy, and health-promoting behaviors. Positive effects were also reported on clinical outcomes such as fatigue, sleep quality, and balance, as well as on psychosocial variables including quality of life, coping strategies, and emotional well-being. Furthermore, the identified systems, in general, contributed to enhanced continuity of care, patient engagement, and organizational efficiency. Conclusions: Nurse-led digital interventions represent a promising approach in the management of patients with multiple sclerosis, supporting both clinical and psychosocial outcomes while enhancing continuity of care. However, the current evidence base remains limited by small sample sizes, heterogeneity of interventions, and short follow-up periods. Future research should prioritize multicenter randomized studies with larger samples and long-term follow-up to strengthen the evidence. Additionally, the integration of digital interventions into routine clinical practice, along with targeted training for nurses, is essential to ensure sustainability, accessibility, and equitable implementation. Further studies should also explore cost-effectiveness and the impact on caregivers and long-term quality of life. Full article

Arboviral diseases are emerging as important public health threats in Saudi Arabia, driven by rapid urbanization, climate variability, the expansion of Aedes aegypti populations, international travel, and large-scale religious mass gatherings. Dengue virus remains the most established arboviral infection in the Kingdom, particularly in the southwestern regions such as Jazan and the western urban centers of Makkah and Jeddah, where ecological and climatic conditions are conducive to sustained vector survival and transmission. This review synthesizes current evidence on the epidemiology, vector ecology, climatic determinants, diagnostics, and prevention strategies of arboviral diseases in Saudi Arabia. Particular attention is paid to the impacts of rising temperatures, changes in rainfall patterns, urban heat island effects, population mobility, and cross-border movement on vector expansion and disease emergence. The review also identifies gaps in surveillance, diagnostics, insecticide resistance monitoring, and integrated vector management programs. Emerging preparedness strategies include climate-informed early warning systems, Geographic Information System-based risk mapping, multiplex molecular diagnostics, genomic surveillance, and community-based vector control. The review emphasizes the importance of implementing a One Health approach that combines data on humans, the environment, entomology, and climate. Currently, sustained endemic transmission of chikungunya and Zika viruses has not been conclusively demonstrated in Saudi Arabia, but increased environmental suitability and connectivity with other areas highlight the need for proactive surveillance and preparedness. Full article

Cities preparing Sustainable Urban Mobility Plans (SUMPs) increasingly require practical tools capable of merging diverse mobility datasets and transforming them into planning-relevant indicators. This article introduces PRIORITY (Platform for the tRansition to sustaInable zerO-caRbon mobilITY), a prototype platform designed to support mobility analysis and decision-making in urban contexts. The platform integrates Floating Car Data, GTFS feeds describing public transport supply, and detailed land-use and zoning information. By relying on these heterogeneous data streams, PRIORITY generates indicators such as travel and stop times, trip distances, trip volumes, energy consumption, pollutant emissions, external costs, and electric-vehicle charging behavior. The platform is organized into two main components: a back end and a front end. The back end, which constitutes the operational core, manages all collected data and ensures their structured storage in a shared database capable of handling large volumes of information on urban form, individual mobility patterns, public transport services, and modeling outcomes. The front end provides an intuitive and versatile interface that dynamically presents the outputs generated by the platform’s analytical and modeling processes. A case application for the Metropolitan City of Rome (Italy) illustrates the operational use of the prototype and shows how PRIORITY can support transparent and reproducible evaluations during the preparation and monitoring of SUMPs. The demonstrated workflow highlights the prototype’s value for public authorities and planners seeking data-informed approaches to urban mobility assessment and decarbonization strategies. Full article

Forest bioenergy holds significant potential for North American decarbonization and energy security, yet persistently high logistics costs, feedstock quality variability, and geographic dispersion of biomass resources continue to constrain commercial viability. This review asks what it will take for forest bioenergy supply chains to achieve economic and operational lift-off, identifying key bottlenecks and the most promising pathways to scale. We systematically review 237 peer-reviewed studies and technical reports with the majority published between 2000 and 2025, covering feedstock types ranging from logging residues and woody biomass to short rotation woody crops, and end-products spanning solid biofuels, heat and power, thermochemical products, and sustainable aviation fuel. The literature consistently identifies delivered cost, feedstock quality control, and the geographic mismatch between biomass supply and conversion facility location as the three primary barriers to sector viability. Depot-based preprocessing, cascading utilization strategies, and participatory landowner contracting emerge as the most effective near-term solutions for improving supply chain economics and mobilizing economically recoverable biomass. At the frontier, AI-enabled optimization, digital twin modeling, and integrated biorefinery configurations show strong potential to manage spatial variability and unlock the scale economies on which commercial viability depends. Translating these advances into practice will require stable, long-term policy signals and coordinated investment across the full supply chain. Full article

Magnitude alone fails to capture the full complexity of pavement roughness; its spatial distribution along a road is equally vital for effective maintenance planning. While traditional assessment has long relied on specialized survey vehicles, the rise of mobile crowdsensing now allows for massive data acquisition via smartphone sensors. This study investigates the spatial structure of pavement roughness using crowdsensed data from the SmartRoadSense platform. Roughness is quantified through the Power of Prediction Error (PPE) indicator derived from smartphone accelerometer signals. The dataset consists of 475 observations sampled at 20 m intervals over approximately 9.5 km of the A3/E45 motorway in southern Italy. A multi-scale spatial–statistical framework is adopted to analyse the roughness signal. The analysis includes the evaluation of scale-dependent statistical descriptors (mean and coefficient of variation), as well as spatial correlation, spectral, and entropy-based measures. The results indicate a short spatial correlation length (approximately 60–100 m) and the absence of a dominant spatial wavelength, suggesting that pavement roughness behaves as a localized multiscale process. A complementary segmentation analysis based on Classification and Regression Trees (CART) is performed to explore the spatial partitioning of the roughness signal. Our analysis indicates that segmentation complexity spikes once the minimum node size drops below roughly 10 observations. This trend points to the existence of localized irregularities that coarser scales simply overlook. Ultimately, these results suggest that mean roughness values alone are insufficient for describing pavement condition and that hybrid spatial–statistical approaches may support more scalable, data-driven, and spatially targeted pavement monitoring strategies for sustainable transportation infrastructure management. Full article

Artificial intelligence systems increasingly mediate high-stakes human activities, yet urban navigation remains highly challenging for blind and visually impaired individuals. Although digital navigation technologies have significantly improved route planning and accessibility, many existing systems still rely on generic interaction paradigms that insufficiently account for cognitive load, contextual uncertainty, and the adaptive needs of vulnerable users. This challenge highlights the importance of Human-Centred AI approaches capable of supporting not only functional accessibility, but also cognitively sustainable and trustworthy interaction. This paper introduces LAZAR, a human-centred adaptive AI framework for accessible urban mobility grounded in a user-centred design methodology and formalised through a structured Software Requirements Specification. Rather than focusing exclusively on route optimisation, LAZAR approaches assistive navigation as an adaptive human–AI interaction problem in which instructional granularity, interaction frequency, and feedback mechanisms are designed to support user autonomy and situational awareness whilst limiting unnecessary cognitive burden. The proposed framework integrates high-fidelity prototyping, accessibility-oriented interaction modelling, and a modular multi-agent architecture intended to support adaptive and personalised guidance. Central to the approach is a cognitive load-aware interaction layer designed to regulate the presentation and timing of navigational assistance according to user needs and contextual conditions. The proposed multi-agent architecture is presented as a modular design framework whose interaction principles and interface logic were partially operationalised in the evaluated prototype. The complete integration of all adaptive coordination mechanisms, together with large-scale real-world validation, remains part of ongoing and future development work. This work contributes a structured methodology for the design of adaptive assistive AI systems that integrates accessibility requirements, human-centred interaction principles, and cognitively informed guidance strategies. A formative usability evaluation involving eleven visually impaired participants provides preliminary empirical evidence regarding usability, accessibility, and perceived usefulness of the proposed interaction model. The framework establishes a foundation for future research on inclusive and adaptive AI-based navigation systems in urban environments. Full article