Search Results (2,280)

Tourism destinations increasingly require data-driven tools to interpret competitiveness, capacity use, housing-related pressure, and post-shock change. This study develops a machine-learning-based destination marketing intelligence framework for a non-probability analytical sample of 29 European destinations observed annually between 2015 and 2024. Destinations were retained when sufficiently comparable information was available across the common study window for the six raw indicators required to construct the performance-pressure framework. Tourism demand, accommodation capacity, labor, investment intensity, and housing-cost pressure are transformed into normalized indicators and analyzed using principal component analysis, k-means clustering, classification trees, random forests, and robustness checks. The first three principal components explain 84.2% of total variance. Although silhouette favors three clusters, the four-cluster solution provides stronger Calinski–Harabasz separation and leave-one-destination-out stability. The retained solution identifies four relative destination-state configurations: lower performance with near-average pressure; high rotation, moderate performance, and lower pressure; high performance with lower pressure; and extreme housing pressure. Under leave-one-destination-out validation, random forests achieve 86.6% accuracy and a Cohen’s kappa of 76.9%. The configurations are pressure-sensitive marketing-intelligence categories rather than comprehensive sustainability classifications or permanent country typologies. Full article

The aim of the study is to analyze primary statistics on the use of intellectual property in agriculture in modern Russia, as well as to identify the relationships between human capital, the supply of research labor, and the growth of intellectual capital in agriculture and its intensity relative to agricultural output. To conduct the study, a sample of 34 regions of the Russian Federation for the period from 2017 to 2024 was analyzed. By using count model estimation, it was demonstrated that both economic incentives and human capital have a significant impact on the use of breeding achievements in agriculture. However, this impact of economic incentives is valid only for the extensive expansion of the applied objects of intellectual property. The level of human capital in the region consistently stimulates the growth and intensity of innovation activity to the scale of the agricultural industry. At the same time, the increase in the cost of researcher labor has a negative effect on the number of breeding achievements used or their intensity in agricultural output. The authors hope that this paper will contribute to the literature examining complex, multifactorial influences on regional agricultural production. Full article

Human–robot collaborative assembly line balancing and scheduling constitutes an NP-hard combinatorial optimization problem involving the simultaneous optimization of task assignment, resource allocation, processing mode selection, station-level scheduling, and ergonomic constraints. This study proposes a Q-learning-based hyper-heuristic genetic algorithm (QLHH-GA) to solve the cost-oriented ergonomic mixed-model human–robot collaborative assembly line balancing and scheduling problem. The proposed approach integrates bio-inspired evolutionary mechanisms of population variation and selection with adaptive, Q-learning-guided low-level heuristic selection. The Q-learning layer uses performance feedback to adapt the search strategy to different solution states while maintaining solution feasibility. A mixed-integer linear programming (MILP) model is also developed to minimize the total operating cost, including station opening, labor, robot operation, and energy consumption costs, while enforcing station-level energy expenditure (EE) limits. Computational experiments conducted using benchmark instances of varying sizes and a literature-based industrial case study demonstrate that QLHH-GA produces solutions comparable to those obtained by the MILP model on small-scale instances and maintains strong solution quality on larger instances, for which exact optimization becomes computationally prohibitive. These findings demonstrate the scalability and effectiveness of reinforcement-learning-guided hyper-heuristic search for designing cost-efficient and ergonomically constrained human–robot collaborative assembly lines. Full article

Agricultural drones have been increasingly adopted to improve production efficiency and address labor shortages in agriculture. However, their broader socioeconomic effects remain unclear, particularly regarding whether drone-assisted farming may increase unemployment pressure or attract excessive capital intervention that threatens farmers’ access to farmland and the sustainable development of agricultural production systems. To address these issues, this study evaluates the development scale and economic effects of agricultural drones in China. A comparative analytical framework is constructed to quantify production costs, return on investment (ROI), and labor demand under traditional and drone-assisted farming modes. Corn and citrus are selected as representative grain and fruit crops for empirical analysis. The results indicate the following: (1) Drone-assisted farming reduces production costs. (2) At the baseline prices, the ROI of drone-assisted corn and citrus production increases to 10.53% and 7.65%, respectively, but remains low, suggesting a limited risk of excessive capital intervention. (3) At the national scale, drone-assisted farming generates estimated cost savings for corn and citrus. (4) In terms of labor effects, agricultural drones mainly help alleviate agricultural labor shortages rather than generate large-scale unemployment, while also improving production efficiency and supporting sustainable agricultural development. Full article

Background/Objectives: Although current guidelines (European Society of Gastrointestinal Endoscopy [ESGE] 2024, U.S. Multi-Society Task Force [USMSTF] 2020) recommend cold snare polypectomy (CSP) over cold forceps polypectomy (CFP) for small colorectal polyps, CFP remains widely used. Comparative economic data using real institutional pricing are scarce, limiting evidence for guideline-concordant resource allocation. We evaluated the healthcare economics and procedural outcomes of CSP versus CFP to assess their institutional resource implications. Methods: This prospective cohort study analyzed 153 consecutive patients (181 polyps ≤ 9 mm) at a single tertiary care center (June–October 2025). Technique allocation was based on endoscopist preference. The primary outcome was histopathologically confirmed complete resection; secondary outcomes were one-piece resection rate, procedure time, and cost outcomes based on institutional pricing. Firth penalized logistic regression, propensity score matching, and a neoplastic-restricted sensitivity analysis were performed. Results: Complete resection rates did not differ significantly (CSP 97.4% vs. CFP 94.2%, p = 0.29). CSP achieved higher one-piece resection rates (96.2% vs. 84.5%, p = 0.011) and shorter procedure time (median 3.2 vs. 5.1 min, p < 0.001). For 6–9 mm polyps, piecemeal resection was reduced with CSP (6.8% vs. 28.6%, p = 0.010). Despite higher device costs ($6 vs. $3), direct device-plus-labor cost was lower for CSP ($18.06 vs. $22.24, p < 0.001), with no statistically significant difference in complete resection. High-grade dysplasia was the only variable independently associated with incomplete resection. Findings were consistent in propensity score-matched (n = 61 pairs) and neoplastic-restricted (n = 134) analyses. Conclusions: By reducing direct device-plus-labor costs by 19% while improving one-piece resection, and remaining consistent across multivariable, propensity score-matched, and sensitivity analyses, these findings indicate that, within a cost-minimization framework, CSP is a cost-saving, guideline-concordant technique with no statistically significant difference in complete resection or observed adverse events. Full article

Camera traps are widely used in wildlife surveys but often generate numerous empty images due to false triggers, which are time-consuming to filter manually. Microsoft’s MegaDetector (MD) is commonly applied for empty image filtering; however, setting its confidence threshold is challenging because a high threshold increases the false negative rate (FNR), whereas a low threshold raises the false positive rate (FPR), making it difficult to achieve a satisfactory trade-off between the two. To address this dilemma, we proposed three MD-based methods with different performance and computational requirements. For FNR-sensitive users, we proposed MD_CS, which integrated context image similarity to achieve a very low FNR while controlling the FPR. For FPR-sensitive users, we proposed MD_CL, which used MD to generate supervisory signals for training a classification model, thereby reducing the FPR while controlling the FNR. For users requiring both low FNR and low FPR, we proposed MD_CS_CL, an ensemble method that combined the strengths of MD_CS and MD_CL. Experimental results on multiple datasets showed that these three methods achieved a significantly better balance between FNR and FPR compared to the threshold adjustment method of the MD method. Specifically, MD_CS maintained an FNR of 1.8–5.9% while controlling the FPR at 4.9–8.5%; MD_CL kept the FPR at 0.4–2.6% while controlling the FNR at 3.8–8.4%; MD_CS_CL achieved an optimal balance with an FNR of 1.4–4.0% and an FPR of 0.3–0.9%. On a server equipped with an RTX 4090 GPU, the three methods processed 6.7, 5.4, and 4.0 images per second, respectively, and none required manual annotation. Consequently, the proposed methods reduce labor costs, improve work efficiency, and provide reliable technical support for large-scale wildlife monitoring by achieving a superior FNR–FPR trade-off compared to MD’s traditional threshold regulation. Full article

Understanding river channel evolution is essential for effective management, as lateral erosion shapes riverbeds, floodplains, and riparian habitats. Despite advancements in remote sensing, translating these technologies into operational tools for institutional monitoring remains a challenge. This study introduces a semi-automated framework designed to bridge this gap by quantifying lateral mobility and bank retreat rates in high-energy, gravel-bed rivers. The methodology utilizes a Random Forest classifier applied to Copernicus Sentinel-2 time series (2016–2024) across five rivers in Piedmont (Italy). By detecting pixel-level class shifts between water, vegetation, and sediment, the procedure provides a proxy for lateral mobility. Validated against 120 km of manual delineations, the model achieved high spatial accuracy for both bank retreat rates and eroded bank lengths measurements (Mean Absolute Deviation of 2.64 m/yr and 4.34%, respectively). Integrating discharge data and the hydraulic infrastructure cadaster of the Sesia River, the proposed framework effectively detects reaches prone to significant geomorphic changes, isolates their underlying drivers, simulates 10-year evolutionary trajectories, and demonstrates strong predictive capabilities regarding future channel–infrastructure interactions. This study marks a shift from sporadic, labor-intensive assessments to a dynamic, systematic monitoring framework. Leveraging free satellite data, it provides competent authorities with an objective, cost-effective tool to prioritize interventions, supporting flood risk reduction and river restoration. Full article

Adjusting cropping structure is a key way smallholder agriculture adapts to changing labor availability, production costs, mechanization, and agricultural services. Most studies explain such change through macro-level factors—prices, policies, and resource endowments—and pay less attention to how farmers perceive these changes and adjust their crop choices over time. Taking Xiajin County, Shandong Province, China, as a case, this study combines sown-area data for major crops from 1980 to 2023, provincial cost–benefit records for wheat, maize, and cotton, and retrospective semi-structured interviews with 57 respondents to examine how changing crop choice criteria shaped county-level cropping structure transformation. The county shifted from cotton dominance to wheat–maize dominance, with rising structural concentration. Farmers’ criteria also changed: early decisions reflected drought tolerance, subsistence grain, and cash income, whereas later ones emphasized labor saving, income stability, mechanization compatibility, and outsourcing feasibility. Under labor out-migration, rural aging, rising labor opportunity costs, and service expansion, wheat and maize became more attractive than cotton because their key stages were easier to mechanize and outsource. County-level transformation thus emerged from the accumulation of farmers’ repeated crop choice adjustments under changing constraints, rather than from external change alone. Grain-oriented concentration should be balanced with crop diversity and sustainable farming systems. Full article

Prefabricated construction couples factory production, buffer storage, transportation, on-site installation, and workforce resources. Desynchronization across these stages creates waiting, idle labor, standby, and buffer pressure—non-value-adding losses that local schedule and cost controls leave hidden. This study takes a project-control view of multi-stage prefabricated delivery and develops a full multi-stage synchronization model that makes these losses explicit and controllable during schedule evaluation. From a sustainable-construction perspective, the framework targets operational resource efficiency by reducing non-value-adding waiting, idle labor, buffer burden, and standby, rather than claiming direct carbon or life-cycle effects. The model optimizes three objectives: makespan, total cost, and a resource-efficiency synchronization loss (RESL) that aggregates waiting, crew-idle, buffer, and standby losses; RESL is a schedule-based resource-efficiency proxy, not a carbon or life-cycle measure. A SPEA2-based solver, SI-SAR-SPEA2, adds structured initialization and synchronization-aware light repair. Using project-inspired semi-realistic prefabricated building-delivery instances, the evaluation compares alternative model scopes, algorithm baselines, a pre-fixed fresh-seed extension, ablation variants, and sensitivity settings. The full model exposes cross-stage synchronization losses and substantially reduces RESL relative to a production–transport model, whereas its relative performance against a production–transport–installation model remains marginal, instance-dependent, and weight-dependent. Under the formal benchmark protocol, SI-SAR-SPEA2 improves hypervolume, average rank, and RESL compared with the SPEA2 backbone, but this quality gain requires additional runtime. The results position RESL as an integrated project-control indicator for comparing schedule alternatives in terms of delivery timing, cost exposure, workforce utilization, and synchronization risk. Full article

Background: Guided imagery (GI) is a mind–body intervention that uses sensory visualizations to promote relaxation and healing. While being utilized in various clinical settings, its specific cumulative impact on women’s health across different life stages remains to be comprehensively synthesized. This systematic review aims to evaluate the effectiveness of GI on psychological and physiological outcomes in women’s health, including gynecologic/breast cancer, pregnancy, childbirth, and menopause. Methods: A systematic search was conducted across PubMed, Scopus, Web of Science, and CINAHL databases for studies published between 2014 and 2024. The search terms included keywords related to “guided imagery,” “women’s health,” and “experimental studies”. We included randomized controlled trials and quasi-experimental studies focusing on GI interventions in women. Data were extracted on study characteristics, participant demographics, intervention details, and outcomes. The methodological quality was assessed using the JBI Critical Appraisal Tools. Results: A total of 29 studies (27 RCTs, 2 quasi-experimental) involving 1939 women were included. GI was found to significantly reduce anxiety, stress, depression, pain, and fatigue. It also improved quality of life and the overall labor experience during childbirth. The synthesis revealed that GI has evolved from a simple relaxation tool to a holistic mind–body intervention. Conclusions: Guided imagery is an effective, non-invasive, and low-cost complementary intervention that improves various psychological and physiological health outcomes in women. It should be integrated into clinical practice as part of patient-centered, holistic nursing care. Full article

The transition towards a circular economy of agri-food wastes requires innovative strategies for transforming them into value-added products. This study evaluates the environmental and economic sustainability of a laboratory-scale process that converts 100 g of pomegranate waste, experimentally processed and defined as functional unit (FU), into a cosmetic emulsion. Primary data were collected from laboratory activities carried out in an Italian university, including material and reagent consumption, equipment operating times and energy use. A cradle-to-gate Life Cycle Assessment was performed in SimaPro according to ISO 14040 and 14044 using the Environmental Footprint 3.1 method, while Life Cycle Costing was developed in Microsoft Excel using the same system boundaries. The process valorized the three main pomegranate fractions (arils, mesocarp and exocarp) to obtain a cosmetic emulsion. The exocarp treatment was identified as the main impactful phase. The overall climate change impact reached 328 g CO₂ eq/FU, while fossil resource use amounted to 5.3 MJ/FU. The total production cost was estimated at 189 €/FU, mainly due to labor, reagents, enzymes and equipment costs. Although laboratory-scale operation results in relatively high impacts and costs, the study identifies the main hotspots and provides a baseline for future process optimization and industrial scale-up. Full article

Drought and constrained municipal budgets are increasing demand for establishment practices that conserve water and labor. This study quantified trade-offs between inputs (water, labor, cost) and tree outcomes across irrigation methods and root-ball correction practices during establishment of American sycamore (Platanus occidentalis L.). In December 2022, forty-five 45-gal trees were planted in Gainesville, Florida, in a completely randomized design combining three root treatments (control, shaved, sliced) with three irrigation methods (hand watering, hydrogel bag, conventional slow-release bag). Over 23 months, trunk caliper, height, midday stem water potential, and anchorage (bending stress at 1° inclination) were analyzed using linear mixed-effects models; survival, labor, water, and cost inputs were compared descriptively. Labor differed by two orders of magnitude, but total cost ranked nearly opposite. All trees survived; irrigation method affected no measured response: trees receiving no scheduled irrigation after an initial hydrogel charge were indistinguishable from those hand-watered 48 times (differences within 10–12%). Shaving increased the bending stress required to tilt the trunk 81% over controls, indicating firmer anchorage, without altering growth or water status; slicing had no effect. Under the dormant-season, average-rainfall conditions tested, shaving improved anchorage at negligible cost, and irrigation method had no measurable effect on tree performance. Full article

The modern agricultural sector faces a multidimensional crisis, mainly consisting of an aging workforce, labor shortages, exhausting working conditions, and high rates of work-related accidents. In response, the authors carried out a systematic literature review (SLR), to explore the latest research on smart agricultural technologies, their effects on occupational safety, ergonomics, and worker health, and pinpoint obstacles to sustainable adoption. A thorough search was performed solely in the Scopus database, covering peer-reviewed publications from 2020 to 2026, strictly following the PRISMA 2020 guidelines. Based solely on Scopus, this study provides a focused synthesis, with the results suggesting that hazards such as chemical exposure and musculoskeletal strain are significantly reduced with the use of innovations such as unmanned vehicles, exoskeletons, and collaborative robots. These technologies also show great promise in cutting down resource waste, helping farmers practice sustainable agriculture. However, a recurring gap between research and real-life deployment exists, as adoption is hindered by cost considerations, reliability issues, and ergonomic problems. To achieve a sustainable technological transition in agriculture, it is necessary to simultaneously bridge three critical gaps: technological (ensuring robust field performance), ergonomic (design and testing processes based on real end-users and their needs), and socio-economic (addressing adoption barriers). Full article

Generative artificial intelligence (AI) now allows a single founder, working with a cluster of AI agents as “digital employees,” to run a venture that once required a team. We call this form the one-person company (OPC) and treat it as a human–AI socio-technical system with a “1 + N + AI” architecture, whose viability depends on the density of the surrounding AI ecosystem. Anchored in a systematic review of 2452 studies reported under PRISMA 2020, we build a task-based model in which a founder allocates tasks across her own labor, hired labor, and AI agents; once the local AI ecosystem density crosses a threshold, one person can cover the whole value chain. The model yields three propositions on the level, heterogeneity, and cost channel of OPC entry, which we test on a panel of 35 major Chinese cities (2019–2024). A one-percent increase in a city’s AI enterprise stock raises OPC entry by about 1.06 percent, an estimate robust to a Bartik shift-share instrument; the effect concentrates in initially AI-sparse, ordinary, and central–western cities and strengthens with the tertiary-sector share, as the threshold model predicts. Because OPCs are asset-light and create knowledge-intensive work, AI ecosystem building emerges as a lever for inclusive, sustainable entrepreneurship. Full article

Maintaining durable road infrastructure is crucial for reducing resource consumption, minimizing repair costs, and supporting sustainable urban mobility. However, accurately detecting small and morphologically diverse pavement cracks remains challenging due to variations in lighting, road textures, and crack shapes across different geographic regions. YOLO (You Only Look Once) is one of the most widely adopted deep learning (DL) frameworks for object detection. Traditional inspection methods are labor-intensive and often inconsistent, while existing DL models can be computationally heavy or limited to single crack types, restricting real-time deployment and scalability. To address these challenges, this study presents YOLO26s, a lightweight DL model for multi-class pavement crack detection across diverse environmental and geographic conditions. Using a curated subset of 6972 annotated images from the Road Damage Dataset 2022, YOLO26s identifies four crack types: longitudinal, transverse, pothole, and alligator cracks. Compared to baseline models (YOLOv8s, YOLOv8n, YOLO26n), YOLO26s achieves higher detection accuracy (mAP@0.5 = 89.0%) while reducing computational complexity by 14.3% in parameters and 7.7% in FLOPs, enabling real-time deployment on edge devices. By facilitating early and accurate crack detection, the proposed approach supports proactive maintenance, extends pavement lifespan, and reduces material and energy usage, contributing to more sustainable road network management. These findings highlight the potential of efficient AI-driven inspection systems to enhance environmental and economic sustainability in civil infrastructure. Full article