Search Results (4,533)

Artificial intelligence (AI) is reshaping tourism businesses by improving decision making, service personalization, operational efficiency, and data-driven management. Beyond these organizational benefits, AI may also strengthen firms’ capacity to cope with market volatility, demand shocks, cost pressures, and other sources of financial fragility. This study provides a systematic literature review and bibliometric analysis of 146 Web of Science articles on AI in tourism published between 2019 and 2023. Following a structured screening process, it identifies the intellectual structure, thematic evolution, and main performance-related drivers associated with AI adoption. The findings show a rapidly expanding field centered on business performance, information technology, big data, robotics, and AI-enabled service innovation. The literature suggests that AI contributes to resilience by enhancing forecasting, resource allocation, customer management, and organizational adaptability under uncertainty. However, explicitly financial perspectives—such as financial vulnerability, resilience, liquidity, solvency, and risk management—remain underdeveloped. This study contributes by reframing AI in tourism as a potential resilience-building capability rather than only a tool for service innovation. Its main limitations are the reliance on Web of Science and a fixed 2019–2023 bibliometric corpus, which future research should extend. Full article

Photovoltaic desert control (PVDC), an innovative model integrating clean energy development and desertification control, faces complex coordination challenges among local governments, local communities, and photovoltaic enterprises. This study constructs a tripartite evolutionary game model to identify the conditions that drive PVDC toward coordinated governance. The model defines a three-dimensional strategy space: government regulatory intensity (Strong vs. Lax), community willingness to cooperate (Active Cooperation vs. Passive Resistance), and enterprise ecological integration (Active Ecological Integration vs. Passive Land Occupation). Replicator dynamic equations are derived to characterize nonlinear interactions, and the stability conditions of eight pure-strategy equilibrium points are identified through Jacobian matrix eigenvalue analysis. Numerical simulations are conducted using a baseline parameter set that satisfies the Evolutionary Stable Strategy conditions for the ideal equilibrium E8, namely Strong Regulation, Active Cooperation, and Active Ecological Integration. The results show that the system can converge to E8 when higher-level rewards cover government regulation, subsidy, and community-support costs; when community cooperation benefits exceed livelihood opportunity costs and compensation incentives from resistance; and when enterprises’ effective ecological integration costs are lower than the combined benefits of subsidies, avoided fines, and long-term returns. Sensitivity analysis further indicates that government subsidies, fines, community support, cooperation income, and enterprise long-term benefits are key drivers of system evolution, while excessive regulation costs, high opportunity costs, and high ecological integration costs may hinder coordination. Qualitative evidence from four PVDC-related cases in Xinjiang provides practical illustrations broadly consistent with the model mechanisms. This study offers a dynamic analytical framework for designing incentive-compatible governance mechanisms in PVDC and similar multi-stakeholder ecological restoration projects. Full article

Agricultural intelligent manipulators are essential for autonomous operations in smart agriculture. However, their industrial deployment faces critical bottlenecks, including perception failures, crop damage, and poor cost–benefit ratios in unstructured environments. Following the PRISMA guidelines, this study reviewed 22 key representative studies and 78 related studies (2015–2026). This review analyzes mechanisms for low-damage and high-precision operations across hardware (rigid–flexible structures), perception (multi-modal fusion), and decision-making (intelligent control). We compare operational efficiency and damage rates in harvesting, transplanting, and sorting, finding that rigid–flexible actuators with vision-guided force control are key to overcoming current limitations. To evaluate these technologies, we established a benchmarking framework across fruit/vegetable harvesting, seedling grafting, and precision plant protection to assess four technological trajectories. We also address engineering challenges: machinery–agronomy misalignment, high sensor costs, and limited edge computing. Notably, we introduce an economic payback period analysis to evaluate commercial feasibility. Ultimately, future research should prioritize lightweight variable-stiffness hardware, synchronous visuo-tactile perception, and digital twins to seamlessly integrate machinery and agronomy. Full article

Distributed vibration sensing based on optical vector network analysis (OVNA) is a promising technique for measuring dynamic perturbations in optical fibers, but its practical use is limited by the high computational cost of short-time Fourier transform (STFT) and cross-correlation stages. In this work, we present a GPU-accelerated signal processing pipeline, together with an optimization strategy based on dataflow reduction, mixed-precision arithmetic, and hardware-aware tuning. The proposed implementation reduces the processing time for 200 sweeps from 64.7 s on a single-core CPU to 0.199 s on a modern GPU, while preserving the final shift results, with zero mismatches over 199,199 measurement points. Benchmarking across three GPU generations further shows that STFT benefits more from large on-chip cache resources, whereas cross-correlation scales more closely with memory bandwidth. These results suggest that modern GPUs can significantly reduce the computational burden of OVNA, as well as other distributed sensing methods with a similar processing flow, enabling kHz-rate aggregate throughput from batched processing, supporting real-time-oriented operation on modern GPUs. Full article

A comprehensive and systematic study was conducted to address a series of key technical challenges encountered in the grinding process at a copper mine. These issues included the complex mechanical properties of the feed ore, which led to low grinding efficiency, difficulty in achieving the required grinding fineness for flotation, uneven particle size distribution in the grinding products, and severe occurrences of overgrinding and undergrinding. Based on the semi-theoretical ball diameter formula, the optimal initial ball size distribution for the ball mill was precisely calculated as Φ70:Φ50:Φ40:Φ30 = 15:25:35:25. Through laboratory-scale grinding tests and Discrete Element Method (DEM) simulations, a systematic analysis of multiple indicators under three different ball loading schemes was performed, including the motion state of particles inside the mill, the collision behavior of the grinding media, and the energy distribution. This analysis confirmed the rationality and effectiveness of the literature scheme. Industrial trial results showed the following: the yield of the +0.20 mm fraction decreased by 4.15 percentage points, and the yield of the −0.010 mm fraction and its proportion relative to the −0.074 mm fraction decreased by 10.17 and 19.10 percentage points, respectively. Conversely, the yields of the intermediate separated fraction (−0.20 + 0.010 mm), the easily separated fraction (−0.074 + 0.018 mm) and the −0.074 mm qualified fraction increased by 14.32, 14.13, and 7.29 percentage points, respectively. The grinding technical efficiency improved by 19.55 percentage points. Furthermore, the specific steel ball consumption decreased by 46 g/t, a reduction of 5.07%. The copper concentrate recovery increased by 0.65 percentage points, resulting in an annual increase of 40.51 tons of copper metal, additional revenue of CNY 3.2483 million, and steel ball cost savings of CNY 603,500. Collectively, this optimization generated a total economic benefit of CNY 3.8518 million. By optimizing the ball size distribution, the particle size composition of the grinding products was significantly improved, the flotation indicators were enhanced, and the grinding media consumption cost was reduced, achieving quality improvement and efficiency increase in the mineral processing. This study provides a valuable reference for solving similar grinding problems. Full article

Digital transformation in higher education has increasingly shifted from a technology-centered agenda toward a multidimensional institutional process involving governance, quality assurance, process redesign, and data-driven decision-making. This study proposes and operationalizes an analytical framework for examining digital transformation in universities through an interactive Human–Machine Interface developed in Python. The framework is structured around three complementary methodological cores: ontology-based modeling, statistical reliability analysis, and multiobjective optimization. The ontology module organizes the semantic structure of digital transformation dimensions, revealing their relational hierarchy and structural relevance. The statistical module evaluates internal consistency and distributional behavior through Cronbach’s alpha, corrected item–total correlation, and density-based inspection. The optimization module formulates intervention selection as a constrained multiobjective problem, allowing the identification of efficient portfolios under cost, readiness gain, equity, and feasibility criteria. The analytical environment also incorporates interactive dashboards, VOSviewer-style relational exploration, and exportable high-resolution figures. Results show that digital transformation readiness is heterogeneous across groups, that governance-oriented dimensions occupy a central semantic role, and that institutional intervention planning benefits from Pareto-efficient decision support rather than single-criterion ranking. The study contributes a coherent bridge between conceptual models of digital transformation and an operational analytical environment capable of supporting institutional diagnosis, evidence-based prioritization, and strategic planning in regulated higher education settings. Full article

Emergency medical response is complex. The need to make time-based decisions that can impact people’s health requires careful examination. Network analysis, among other methods, can support that time-based decision making. This study explores network analysis through a multi-modal transportation network model to represent both fixed-wing air and ground Emergency Medical Services (EMS) resources. Methods: The study utilized open and EMS industry data to build a geospatial multi-modal network to model potential patient transfer across Alberta (Canada). Results: Within the study’s service area, ground transportation alone is more effective within 101 Km, at which threshold the addition of aerial transport begins to be more time effective, saving 9.7 min over ground transportation only. Between this distance and 417 Km, results show a mixed-use area where a combination of ground only and aerial travel is recommended based on the event pickup location, aircraft availability, and ambulance station location relative to high-speed roads. Beyond 417 Km, aerial transportation is consistently more efficient. There is a high correlation (R² = 0.82) between trip length and time difference between using ground only mode and combined air and ground. Lastly, the data showed air travel is 6.6 times more expensive than ground travel, with no modeled transfers identifying air as more time-effective than ground travel. Conclusions: Fixed-wing aircraft travel can have a positive impact on patient transfers; however, fluctuations in flight routes and times may require response agencies to implement time buffers to account for these variabilities. No cost savings were seen using fixed-wing aircraft, and the benefit of their use would be realized with efficient patient transfer times, as well as leaving ground ambulances in localized areas. Full article

Lentils constitute a strategically important crop within sustainable agricultural systems, particularly in the context of rising global demand for plant-based protein sources. In Switzerland, approximately 95% of lentil seeds are imported, underscoring the untapped potential for domestic production. This study systematically evaluated the performance of multiple lentil genotypes, alongside optimal seeding densities and growing seasons, through a series of field experiments conducted over five years. In addition, a sensory evaluation was performed on 12 selected genotypes to assess consumer-relevant quality traits. The findings revealed substantial variability in yield among genotypes, ranging from 0.9 to 1.6 t/ha; however, interannual variation exerted a more pronounced influence, with yields fluctuating between 0.1 and 2.0 t/ha. Notably, autumn-sown lentils achieved yields of up to 2.7 t/ha in three out of four growing seasons, even among genotypes lacking full winter-hardiness, indicating significant production potential under appropriate management conditions. Optimal plant densities were identified within the range of 180–240 plants/m². From an economic standpoint, higher seeding densities appear justifiable, as the increased seed costs are offset by corresponding gains in yield. Since intercropping of lentils with oats did not negatively affect grain yield nor the thousand kernel weight, the benefits of this cropping system are highlighted. Sensory analysis demonstrated statistically significant differences in attributes such as mealiness and juiciness, leading to the classification of genotypes into three distinct sensory clusters. Despite these differences, overall sensory variation was relatively limited, suggesting that genotype selection may be guided primarily by agronomic performance, climatic adaptability, and winter-hardiness, as well as by market preferences for seed colour and size. Collectively, these results highlight the potential of autumn sowing as a viable strategy to enhance lentil production and reduce the risk of crop failure in Swiss agricultural systems. Full article

By partitioning agricultural fields into units with similar yield-limiting factors, Management Zone (MZ) delineation provides the spatial basis for variable-rate application of inputs such as nitrogen, seed, and irrigation. To evaluate the operational implementation of MZ methodologies, this paper analyzes 137 peer-reviewed papers published between 2000 and 2025, extracting data on agronomic contexts, sensing inputs, computational workflows, and validation strategies. Our analysis reveals a clear methodological shift: while early studies relied heavily on data such as soil properties, recent literature is dominated by multisource data fusion that combines static soil proxies (e.g., apparent electrical conductivity) with dynamic remote sensing vegetation indices. Methodologically, the literature relies heavily on similarity-based clustering, specifically fuzzy c-means and k-means, often applied to raw spatial grids or Principal Component Analysis (PCA) transformations. Although machine learning and optimization-based approaches have increased in recent years, rigorous agronomic and economic validation remains limited, while internal cluster validity indices (e.g., FPI, NCE) and inferential statistical tests (e.g., ANOVA) are widely used to assess delineated zones, only 13 of the reviewed papers explicitly evaluated the economic or environmental net returns of the delineated zones. To transition MZ delineation from a classification problem to an operational decision-support tool, the current literature suggests a need to shift validation efforts away from internal clustering metrics toward multi-year yield stability assessments and direct economic cost–benefit analyses. Full article

Utility disruptions may stem from insufficient power generation, inferior infrastructure, or secondary weather perils (e.g., tornadoes, floods, snowstorms) that take energy infrastructure offline. The latter present a unique risk that not all existing power options can mitigate. Regardless of their origin, power disruptions have the potential to cripple food supply chains and undermine food system sustainability. To prepare for managing future disruptions, food and beverage manufacturers may couple electrical microgrid and thermal district heating infrastructure with small modular reactors (SMRs) or smaller microreactor systems to form low-carbon power islands. Although SMR technology is a somewhat new source of energy and has not yet achieved commercial viability, it provides the potential to make food and beverage manufacturing more resilient and sustainable when it becomes broadly available. To assess the potential cost–benefit of activating such technology as a sustainability-oriented resilience investment, we conducted a technoeconomic downtime threshold analysis. The case assumes that the technology is the full-time power source and the SMR yields stronger returns as facility downtime or downtime costs rise. The analysis found the breakeven point to range from 12.3 h down to 613.2 h down annually for a 5 MW system, depending on facility scale and assumed downtime costs. At a representative downtime opportunity cost of $10,000/h, SMR adoption requires approximately 61.3 h (5 MW) of annual outages to break even, highlighting scale effects on feasibility. Incorporating a 20% thermal energy credit reduces required outage thresholds by roughly 20%, lowering the breakeven level to 49.1 h. These results highlight the potential role of SMR-enabled power islanding in supporting sustainable food manufacturing through improved energy resilience, low-carbon power, and thermal energy recovery. Full article

Background: Clubfoot represents a prevalent congenital deformity of the foot and ankle complex that may significantly compromise a child’s walking ability. Contemporary treatment protocols encompass serial manipulations and casting procedures designed to achieve gradual correction of the deformity. Various casting materials have been employed in this therapeutic approach, with plaster of Paris and fiberglass constituting the two predominant options. This study aimed to evaluate the comparative effectiveness of these casting materials and determine whether material selection influences the rate of correction and the clinical indications, specifically regarding the number of casts required before percutaneous Achilles tenotomy. Methods: We conducted a retrospective analysis of prospectively collected data on paediatric patients treated at our tertiary-level institution with both plaster of Paris (POP) and semirigid fiberglass (SRF) by a single orthopaedic surgeon between 2010 and 2020. Treatment was initiated within the first 30 days of life (median age 12 days, range 0–28 days). To reduce confounding bias related to baseline aetiology (e.g., rigid syndromic feet), the primary comparative analysis was restricted to the idiopathic clubfoot subgroup. The Pirani score was used to assess deformity severity at each clinical visit. Results: A cohort of 84 patients (137 feet) was enrolled and treated, comprising patients with a Pirani score ≥ 4.5, excluding non-idiopathic cases. The mean number of casts required was 5.8 ± 1.0 for POP and 5.7 ± 1.2 for SRF, with no statistically significant difference (p = 0.91). Conclusions: Both plaster of Paris and semirigid fiberglass are highly effective casting materials for the initial phase of Ponseti treatment. Both achieve comparable correction sufficient to proceed with Achilles tenotomy. Accordingly, material selection should be guided by clinician proficiency, institutional cost-effectiveness, and patient comfort. Further investigation is needed to evaluate long-term outcomes and the relative benefits of each material in clubfoot management. Full article

With the 2030 deadline for the United Nations Sustainable Development Goals (SDGs) approaching, there is a growing global urgency to identify innovative, scalable, and inclusive AI-based or AI-enabled solutions capable of accelerating progress across sectors. Yet the benefits of AI remain unevenly distributed, particularly in low-resource settings where limited infrastructure, cost barriers, and unequal access to skills constrain adoption. This paper explores how Tiny Machine Learning (TinyML)—a low-power, low-cost edge AI paradigm—offers a concrete technological pathway aligned with the principles of Frugal AI, providing accessible, energy-efficient, and context-adapted tools for sustainable development. We evaluate how participatory citizen science, when combined with TinyML, enables communities to co-create AI applications that address locally defined challenges in environmental monitoring, agriculture, and public health. Drawing on early outcomes from workshops, collaborative projects, and innovation competitions, the paper examines how TinyML-enabled participatory approaches cultivate technical skills, stimulate grassroots entrepreneurship, and generate prototypes suited to low-resource environments. Using a qualitative multiple-case study of 50 participatory TinyML initiatives across 22 countries, we analyse how frugal edge-AI practices support skills formation, prototype development, and early entrepreneurial engagement. The analysis identifies the pedagogical, technical, and institutional frameworks that support successful participatory AI initiatives, emphasizing open educational resources, cross-sector partnerships, and community-driven problem formulation. We introduce the Frugal Edge AI Lean Canvas to help innovators identify novelty, ethical implications, and measurable impact. TinyML-based participatory innovation offers a promising route for accelerating SDG progress by expanding who can create, deploy, and benefit from AI. Full article

Administrative fragmentation, whereby political boundaries are used as analytical extents, can disrupt ecological flows and weaken ecological network planning by creating a mismatch between governance units and ecological processes. However, the pathways through which such fragmentation alters network structure and function remain insufficiently quantified. This study quantifies these effects and identifies the landscape conditions that shape the effectiveness of cross-boundary integration. Using a multi-scale buffer experiment (1–32 km) across 30 representative counties in China, we constructed ecological networks based on Morphological Spatial Pattern Analysis and on the minimum cumulative resistance model. Results show that relaxing administrative boundaries reduced structural distortions and lowered total ecological flow cost, indicating that fragmentation increases connectivity costs. Mechanistically, reducing redundant internal links and forced detours improved network efficiency mainly by shortening corridors and lowering flow costs, whereas mean corridor resistance changed little. This suggests that functional degradation is driven primarily by topological disruption rather than by declines in corridor quality. The benefits of cross-boundary integration were greater in counties with regular shapes, high grassland cover, humid climates, and rugged terrain, but weaker under strong human pressure and warmer temperatures. Improvements leveled off beyond 32 km, suggesting a 32 km buffer (study-specific) for integration and supporting context-specific strategies for ecological network planning. Full article

This study presents a comprehensive assessment of high-temperature performance, economic viability, and environmental sustainability of alkali-activated slag paste (AASB) incorporating municipal solid waste incineration bottom ash (MSWI-BA). The research systematically evaluates the effects of MSWI-BA content (0–12%), alkali content (2–6% Na₂O equivalent), water glass modulus (Ms = 0.75–1.75), and activator type on key performance metrics, both resource recovery and carbon reduction goals. Results show that the optimized formulation (6% MSWI-BA, 4% Na₂O, Ms = 1.5) achieves superior high-temperature resilience, retaining 76% of its initial compressive strength after 800 °C exposure—a stark contrast to OPC, which undergoes near-complete strength loss. Economic analysis reveals that while MSWI-BA offers an 88% reduction in raw precursor cost, the optimized AASB incurs a modest 3.7% total material cost premium over OPC, which is offset by its long-term sustainability benefits. Furthermore, a life-cycle assessment demonstrates that AASB has a 66.95% lower carbon footprint than OPC. Full article

Electric vehicles introduce new considerations for pedestrian safety because their lower operating noise at low speeds may reduce pedestrian detectability in urban traffic environments. This study proposes a simulation-based integrated active–passive pedestrian protection framework for electric vehicles by linking automatic emergency braking, active hood deployment, and post-crash head injury assessment. A total of 688 valid pedestrian–vehicle crash records from the National Highway Traffic Safety Administration database were analyzed, and 5 representative pedestrian crash scenarios were constructed through clustering-informed scenario screening and a benchmark pedestrian AEB scenario. The scenarios were reconstructed in a PreScan–Simulink co-simulation environment to evaluate a time-to-collision-based AEB strategy, while the active hood system was assessed using multi-body dynamics simulation and finite element head impact analysis. The AEB results showed that three scenarios were avoided before pedestrian contact, whereas two remained unavoidable, with residual impact speeds of approximately 31.5 km/h and 46 km/h. The hood reached a stable deployed posture within approximately 0.1 s under the modeled conditions. The HIC₁₅ results at eight selected impact points showed that speed reduction and hood deployment generally reduced head injury metrics, but full compliance with the reference HIC₁₅ threshold of 1000 was not achieved at all points. These findings suggest that the proposed strategy can improve simulated pedestrian head protection performance under selected electric vehicle crash scenarios, while further structural optimization, experimental validation, and cost–benefit assessments are still required. Full article