Search Results (4,535)

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions and routes to enable intelligent recommendation, enhance visitor experience, and advance smart tourism, while also informing spatial planning, crowd management, and sustainable destination development. Using Mount Huangshan—a UNESCO World Cultural and Natural Heritage site—as a case study, we integrate GPS trajectories and geo-tagged photographs from 2017–2023. We apply a Density-Field Hotspot Detector (DF-HD), a Space–Time Cube (STC), and spatial gridding to analyze behavior from temporal, spatial, and fully spatiotemporal perspectives. Results show a characteristic “double-peak, double-trough” seasonal pattern in the number of GPS tracks, cumulative track length, and geo-tagged photos. Tourist behavior exhibits pronounced elevation dependence, with clear vertical differentiation. DF-HD efficiently delineates hierarchical hotspot areas and visitor interest zones, providing actionable evidence for demand-responsive crowd diversion. By integrating sequential time slices with geography in a 3D framework, the STC exposes dynamic spatiotemporal associations and evolutionary regularities in visitor flows, supporting real-time crowd diagnosis and optimized spatial resource allocation. Comparative findings further confirm that Huangshan’s seasonal intensity is significantly lower than previously reported, while the high agreement between trajectory density and gridded photos clarifies the multi-tier clustering of route popularity. These insights furnish a scientific basis for designing secondary tour loops, alleviating pressure on core areas, and charting an effective pathway toward internal structural optimization and sustainable development of the Mount Huangshan Scenic Area. Full article

The escalating heatwave risks over the Tibetan Plateau (TP) highlight unresolved gaps in understanding multitype mechanisms and diurnal urban canopy heat island (UCHI) responses. Using Xining’s high-density observational network (2018–2023) and by employing comparative analysis (urban–rural, heatwave versus non-heatwave days) and composite analysis, we found: During the record-breaking July 2022 heatwave across the TP, Xining reached an extreme UCHI peak (z-score: 3.0). Critically asymmetric UCHI responses as daytime heatwaves amplify mean intensity by 0.35 °C via extreme value shifts, whereas nighttime events suppress it by 0.31 °C. Crucially, heatwaves induce negligible daytime UCHI modulation but drive comparable magnitude nighttime UCHI intensification (during daytime events) and reduction (during nighttime events), demonstrating type-dependent and diurnally asymmetric urban thermal sensitivities. Heatwaves driven by distinct synoptic patterns; daytime events are controlled by an anomaly anticyclone (cloudless, dry conditions), while nighttime events occur under plateau-north anticyclones (cloudy, humid conditions). These patterns fundamentally reshape heatwave–UCHI interactions through divergent mechanisms: Daytime/nighttime heatwaves amplify/suppress nocturnal UCHI through enhanced/reduced urban heat storage and accelerated/inhibited rural radiative cooling. Our case study demonstrates that although heatwaves generally amplify nocturnal UCHI, in dry regions, their synoptic drivers significantly modify this nighttime synergy. The nocturnal UCHI during heatwave is not only driven by humidity effects but also modulated by cloud cover-regulated rural radiative cooling and urban thermal storage. These findings establish a mechanistic framework for heatwaves–UCHI interactions and provide actionable insights for heat-resilient planning in high-altitude arid cities. Full article

Dam collapse is a catastrophic event involving an artificial reservoir usually filled with water for hydropower or irrigation purposes. Several cases of dam collapses have overwhelmed entire valleys, reconfiguring their geomorphology, redesigning their landscape, and causing several thousand casualties. These episodes led to more careful regulations and the activation of more effective monitoring and mitigation strategies. A fundamental tool in defining appropriate procedures for alert and risk scenarios is the Dam Emergency Plan (PED), an operational document that establishes the actions and procedures required to manage potential hazards (e.g., geo-hydrological and seismic risk). The aim of this study is to describe a reference methodology for identifying geo-hydrological criticalities based on historical and geomorphological data, applied to civil protection activities. A further objective is to provide a structured inventory of Italian reservoirs, assigning each a potential risk index based on an analytical approach considering several factors (age and construction methodology of the dam, morphological and environmental settings, anthropized environment, and exposed population). The approach identifies that the most significant change in risk over time is not only the dam itself but also the transformation of the territory. This methodology does not incorporate probabilistic forecasting of flood or climate change; instead, it objectively characterizes the exposed territory, offering insights into existing vulnerabilities on which to base effective mitigation strategies. Full article

This work addresses the problem of synurbization, with its causes and effects specified using the example of wild boar (Sus scrofa). It presents basic biological parameters of the species, including those that promote its synurbization—small habitat demands, omnivorism, as well as ecological, behavioral, and demographic flexibility. It also discusses intra-species transformations stemming from wild boar adaptation to the urban space and pinpoints habitat fragmentation, ecological restoration, and phenotypic flexibility as the underlying causes of people–wild boar interactions. These interactions are primarily negative because wild boars attack humans and domestic animals and cause many traffic accidents. An analysis of the literature included in this study shows that, unfortunately, there are currently no fully effective methods that could protect urban areas and their inhabitants from the threats posed by wild boars. In order for sustainable urban development policies to be effectively implemented, there is a need for intensive, holistic research and cooperation between experts in many fields: wildlife, economics, public health, sociology, ethics, psychology, and urban planning. The synurbanization of wild boars is a large and growing social problem, but from an ecological perspective, there is a need to take action and develop methods to mitigate human/wild animal conflicts, not only from a human perspective. A one-sided view and action can be a threat to many animal species. Full article

Agriculture and its allied activities contribute to the primary sector in India and act as the basis for the country’s economy. Available agricultural landholdings are scattered as multiple plots across the country. Land fragmentation has led to problems achieving economies of scale and economies of scope; lower productivity, efficiency, and modernization; loss of biodiversity; and little scope for mechanization and technology. FPOs are small clusters of farmers who collaborate to enhance their bargaining strength through collective procurement, processing, and marketing efforts. To enhance the performance of FPOs at the grassroots level, the engagement of cluster-based business organizations (CBBOs) is vital. Millet FPOs are similar to voluntary farmer groups that are involved in the cultivation and promotion of millets. IIMR-promoted millet FPOs were selected purposively for the present study as they are involved in millet cultivation and farming. A total of 450 millet farmers from 15 FPOs and 3 states were randomly chosen for this action research study. The present research identified 10 key factors and collected farmers’ opinions toward member participation in millet FPOs using interpretive structural modeling. The ISM approach provided a clear understanding of how the selected factors interconnect hierarchically with each other as foundational drivers and dependent outcomes. The results from the MICMAC analysis demonstrated that foundational interventions, such as post-harvest technology availability (V2) and knowledge transfer by KVKs (V5), directly support higher-level objectives. Intermediate factors like economies of scale (V1) and market and credit linkages (V3) transform these services into operational advantages, while the outcome factors of business planning (V8), FPO branding (V7), and bargaining power (V9) emerge as dependent variables. The model demonstrates that V2 catalyzes improvements across the production, market, and institutional domains, cascading through intermediate enablers (V1, V4, V5, V6) to strengthen outcomes (V3, V7, V8, V9, V10). This hierarchy demonstrates that investing in post-harvest technology and complementary extension services is critical for building resilient millet FPOs and enhancing member participation. Full article

Background: Urban disasters expose persistent gaps in the operational picture and timely decision-making for response teams, which require user-centred systems that connect analysis to action. This study proposes and formatively validates an integrated framework that couples digital twins and augmented reality for humanitarian logistics. Methods: A mixed methods design combined a structured literature synthesis with a practitioner survey across architecture, engineering, planning, BIM, and construction to assess perceived value and adoption conditions. Results: Findings indicate that practitioners prioritised digital twins for enhancing situational awareness (71.4%) and augmented reality for providing real-time information overlays (64.3%). A majority judged that integrating these technologies would yield substantial improvements in disaster response (67.9%), despite implementation challenges. Conclusions: The framework links live state estimation and short-horizon simulation to role-specific, in-scene AR cues, with the aim of reducing decision latency and improving coordination. Adoption depends primarily on human and organisational factors, including user accessibility, preparation needs, and clear governance. These results suggest a viable pathway to operationalise the bridge between analysis and field action and outline priorities for pilot evaluation. Full article

Achieving optimal daylighting in buildings necessitates complex and expensive control systems. This research addresses this challenge by proposing a simple and more practical solution: a parametric louver system based on rotating slats controlled by stepper motors, powered by an Integrated Circuit platform (Arduino board), which can translate the digital figures (the rotation angles) to a physical action. The system automatically adjusts the slats in accordance with solar altitudes and reflects them to specific targets over the ceiling. This ensures a uniform and comfortable distribution of daylight throughout a room. This system was developed using Grasshopper as the parametric software, with future control planned via a user-friendly mobile app through a preliminary prototype. This daylighting system prioritises human visual comfort while targeting a significant 53% reduction in electrical lighting energy consumption. The system aims to enhance occupant well-being to significantly increase energy savings, making it a compelling solution for sustainable building design. Full article

Landslides represent a recurrent hazard in tropical mountain environments, where rapid urbanization and extreme rainfall amplify disaster risk. The Sentani region of Papua, Indonesia, is highly vulnerable, as demonstrated by the catastrophic debris flows of March 2019 that caused fatalities and widespread losses. This study developed high-resolution landslide susceptibility maps for Sentani using an ensemble machine learning framework. Three base learners—Random Forest, eXtreme Gradient Boosting (XGBoost), and CatBoost—were combined through a logistic regression meta-learner. Predictor redundancy was controlled using Pearson correlation and Variance Inflation Factor/Tolerance (VIF/TOL). The landslide inventory was constructed from multitemporal satellite imagery, integrating geological, topographic, hydrological, environmental, and seismic factors. Results showed that lithology, Slope Length and Steepness Factor (LS Factor), and earthquake density consistently dominated model predictions. The ensemble achieved the most balanced predictive performance, Area Under the Curve (AUC) > 0.96, and generated susceptibility maps that aligned closely with observed landslide occurrences. SHapley Additive Explanations (SHAP) analyses provided transparent, case-specific insights into the directional influence of key factors. Collectively, the findings highlight both the robustness and interpretability of ensemble learning for landslide susceptibility mapping, offering actionable evidence to support disaster preparedness, land-use planning, and sustainable development in Papua. Full article

Introduction: Despite global improvements in immunization, major gaps persist. By 2024, an estimated 14.3 million infants, predominantly in low- and middle-income countries (LMICs), remained zero-dose (ZD), never having received even the first DTP vaccine. In 2022, 33 million children missed their measles vaccination (22 million missed the first dose, 11 million missed the second dose), highlighting entrenched structural, behavioral, and systemic barriers that continue to exclude marginalized populations. Addressing these inequities requires innovative, context-adapted approaches that strengthen primary health care (PHC) and extend services to the hardest-to-reach populations. Objectives: This study aims to document and synthesize implementation research (IR) projects on immunization programs in LMICs, identifying key enablers and effective strategies that reduce inequities, improve outcomes, and support efforts to reach ZD children. Methods: We conducted a retrospective multiple-case study of 36 IR projects across 13 LMICs, embedded within an evidence review framework and complemented by policy analysis. Data were drawn from systematic document reviews and validation discussions with project leads. A total of 326 strategies were extracted, coded using a structured codebook, and mapped to the WHO–UNICEF PHC Levers for Action. Descriptive analysis synthesized patterns across service delivery and policy outcomes, including coverage gains, improved microplanning, community engagement, and system integration. Results: Of the 326 immunization strategies identified, most (76.1%) aligned with operational PHC levers, particularly monitoring and evaluation (19.3%), workforce development (18.7%), and models of care (12%). Digital technologies (11.7%) were increasingly deployed for real-time tracking and oversight. Core strategic levers comprised 23.9% of strategies, with community engagement (8.9%) and governance frameworks (7.7%) emerging as critical enablers, though sustainable financing (4%) and private-sector engagement (0.9%) were rarely addressed. While the majority of projects focused on routine immunization (n = 32), only a few directly targeted ZD children (n = 3). Interventions yielded improvements in both service delivery and policy outcomes. Improvements in microplanning and data systems (23.5%) reflected the increased uptake of digital dashboards, GIS-enabled tools, and electronic registries. Community engagement (16.2%) emphasized the influence of local leaders and volunteers in building trust, while health system strengthening (15.7%) invested in cold chain, supervision, and workforce capacity. Coverage gains (10.6%) were achieved through delivery innovations, though sustainable financing remained a critical problem (3.4%). Conclusions: Reaching ZD children requires equity-driven strategies that combine digital innovations, community engagement, and resilient system planning. Sustained progress depends on strengthening governance, financing, and research. Embedding IR in immunization programs generates actionable evidence, supports context-specific strategies, and reduces equity gaps, offering practical insights that complement health system research and advance the Immunization Agenda 2030. Full article

Landscapes are vital systems where ecological, cultural, perceptual, and socio-economic values meet, making their quality assessment essential for sustainable development. Landscape Quality (LQ), shaped by the interaction of natural processes and human activities, remains methodologically challenging due to its interdisciplinarity and the need to integrate multiple dimensions. This challenge is particularly perceived in peri-urban areas, predominantly understudied in landscape research. This article addresses this gap in LQ assessment at peri-urban landscapes, through the case of Houch Al Oumaraa, Zahleh, a peri-urban area of patrimonial significance and agricultural landscape value. To evaluate the four spatial dimensions of LQ (structural, ecological, cultural and visual), we adopted a mixed methodology, where a pre-developed set of landscape indicators (LIs) applied within GIS and spatial technics, were supplemented by expert analysis through visual studies. Two questions framed this research: (i) is remote sensing sufficient to assess peri-urban LQ, and (ii) what are the limits of applying pre-developed LIs to diverse landscape contexts? Results show moderate fragmentation (CONTAG 61.6%), low diversity (MSDI 0.27), high density of cultural monuments (PROTAP 4.19) and average visual disharmony (FCDHI 0.49). Findings reveal that spatial dimensions alone are insufficient for assessing LQ of peri-urban landscapes, where socio-economic dimensions must also be integrated. Structural indicators (PLAND, MPA, ED, CONTAG) and MSDI proved transferable, while ECOLBAR was less applicable, cultural indicators (PROTAP, HLE) were limited to tangible heritage, and visual indicators (FCDHI, SDHI) highly context dependent. Establishing a differentiated yet standardized framework would not only enhance methodological precision but also ensure that LQ assessment remain relevant across diverse contexts, providing policymakers with actionable insights to align planning with sustainability goals. Full article

Promoting economic prosperity, social justice, and ecological sustainability requires the rapid decarbonization of our global energy system in favor of renewable sources of energy. Recent news analysis estimates that 15% of counties across the US have banned wind turbines, solar fields, and other green energy developments. We answer two overarching research questions: (1) How do regulations of wind facilities vary at the county level? And (2) what factors appear to explain the variation in local wind regulations? We created a GIS database of energy regulations for all 105 counties in Kansas, a top state for wind potential and a recent hotbed of local actions. We coupled descriptive statistics, mapping, and regression modeling to describe the variation in local policy approaches and identify factors driving the variation. We find counties using at least five different policy approaches to enable or block wind regulations. Factors driving variation include a combination of infrastructure capacity, demographic characteristics that shape local planning capacity, and the apparent reliance on large farming operations for local economic output but not partisan voting patterns or underlying wind capacity. Our findings provide vital insights for policymakers at the federal, state, and local levels, as well as providing a foundation for future scholarship on planning for a just energy future. Full article

This study examines the deployment of a Common Data Environment (CDE) during the extension of a major North American metro line—an infrastructure project marked by complex stakeholder dynamics and fragmented digital practices. Employing a four-phase action research approach (diagnosis, planning, implementation, evaluation), the research identifies inefficiencies in existing document management through contract reviews, field observations, and stakeholder interviews. In response, three standardized processes were introduced to streamline document workflows within the Autodesk Construction Cloud (ACC). These processes enabled partial automation of data handling, reduced reliance on manual inputs, and improved the consistency of information exchanges. While constrained by limited governance and executive engagement, the initiative demonstrates the potential of CDEs to support digital integration and automation in construction. Findings highlight the need for early planning, field-level support, and a strategic framework to ensure sustainable adoption. The results contribute practical insights for leveraging CDEs to enhance automation in large-scale infrastructure projects. Full article

Accurate quantification of campus vegetation carbon stocks is essential for advancing carbon neutrality goals and refining urban carbon management strategies. This study pioneers the integration of drone and backpack LiDAR data to overcome limitations in conventional carbon estimation approaches. The Comparative Shortest-Path (CSP) algorithm was originally developed to segment tree crowns from point cloud data, with its design informed by metabolic ecology theory—specifically, that vascular plants tend to minimize the transport distance to their roots. In this study, we deployed the Comparative Shortest-Path (CSP) algorithm for individual tree recognition across 897 campus trees, achieving 88.52% recall, 72.45% precision, and 79.68% F-score—with 100% accuracy for eight dominant species. Diameter at breast height (DBH) was extracted via least-squares circle fitting, attaining >95% accuracy for key species such as Magnolia grandiflora and Triadica sebifera. Carbon storage was calculated through species-specific allometric models integrated with field inventory data, revealing a total stock of 163,601 kg (mean 182.4 kg/tree). Four dominant species—Cinnamomum camphora, Liriodendron chinense, Salix babylonica, and Metasequoia glyptostroboides—collectively contributed 84.3% of total storage. As the first integrated application of multi-platform LiDAR for campus-scale carbon mapping, this work establishes a replicable framework for precision urban carbon sink assessment, supporting data-driven campus greening strategies and climate action planning. Full article

Cancer is a major challenge to global health, and its incidence rate and mortality are expected to continue to rise in the coming decades. Traditional treatment methods such as surgery, radiotherapy, and chemotherapy have limitations, which has prompted people to explore new treatment strategies. As a promising therapeutic approach, oncolytic viruses can selectively target and lyse tumor cells while avoiding damage to normal tissues. This article systematically reviews the mechanisms by which oncolytic viruses induce various forms of cell death, including apoptosis, autophagy, pyroptosis, necroptosis, and ferroptosis. We explored the direct killing effect of oncolytic viruses and their ability to activate local and systemic antitumor immunity, with a focus on the latest developments in the clinical application of oncolytic viruses, such as the development of novel recombinant viruses. In addition, we also analyzed strategies to enhance the efficacy of oncolytic viruses through gene modification, combination therapy, and targeted delivery systems. A deeper understanding of the multiple mechanisms of action of oncolytic viruses can help us develop more effective and personalized cancer treatment plans. Future research should focus on optimizing oncolytic viruses to overcome tumor drug resistance and improve patient prognosis, making them an important pillar of cancer treatment. Full article

Sustainability is gaining relevance across organizations, yet significant challenges remain in how it is implemented and translated into daily operations. This paper examines how Lean Six Sigma can be used to address operational challenges while also supporting the integration of sustainability objectives in industrial contexts. The study is based on a project conducted in a fish processing plant, aiming to increase production capacity and reduce delays. Using the DMAIC framework, the team addressed key bottlenecks through demand-based workload leveling, earlier production planning, and targeted maintenance to improve equipment performance. These actions led to measurable gains in throughput, resource use, and schedule reliability. In parallel, they contributed to sustainability outcomes, including reduced rework, lower waste, and improved working conditions. The results suggest that Lean Six Sigma, typically focused on performance, can also act as a platform for embedding sustainability into existing routines. The findings offer insight into how performance-driven approaches can support sustainability transitions in process-intensive industries. Full article