Search Results (1,457)

Efficient scheduling of agricultural machinery is critical for optimizing resource utilization and reducing operational costs in modern farming operations. This study proposes an Adaptive Genetic Algorithm integrated with Ant Colony Optimization (AGA-ACO) to solve the multi-task machinery scheduling problem. The problem is formulated as a Vehicle Routing Problem with Time Windows (VRPTW), considering time constraints, machinery heterogeneity, and task dependencies. The AGA-ACO algorithm employs a two-phase optimization strategy: genetic algorithms for global exploration and ant colony optimization for local refinement through pheromone-guided search. Experimental evaluation using real-world agricultural data from Hangzhou demonstrates that AGA-ACO achieves cost reductions of 5.92–10.87% compared to genetic algorithms, 5.47–7.75% compared to ant colony optimization, and 6.23–9.51% compared to particle swarm optimization, while converging with fewer iterations. The algorithm maintains stable convergence and high robustness across different farmland scales, reducing computational time while preserving solution quality. A scheduling management system integrating IoT sensors, MQTT protocols, and GIS technologies validates the practical applicability of the proposed approach. This research provides a replicable framework for agricultural machinery optimization, contributing to the advancement of sustainable and precision agriculture. Full article

This study investigates the effects of typical planting patterns on soil nutrient accumulation and associated environmental impacts in agricultural reclamation areas of the southern Dianchi Lake Basin. Taking the cut flower cultivation area in Dahewei Village, Jinning District, Yunnan Province, as the research site, we compared soil physicochemical properties, nutrient contents, and their correlations with environmental factors under open-field and greenhouse cultivation, and analyzed the characteristics of soil fertility changes and non-point-source pollution risks in greenhouses. We found that greenhouse cultivation is associated with altered soil physicochemical properties, including smaller aggregate sizes, increased soil moisture content (from 30.15% to 32.20%), elevated pH values (from 7.11 to 7.23), and 79% higher electrical conductivity compared to open-field conditions (620.82 vs. 347.60 μS cm⁻¹, p < 0.01). Compared with open-field systems, greenhouse cultivation exhibited greater nutrient accumulation, particularly for total nitrogen (TN) and available potassium (AK) in the 0–10 cm topsoil layer, demonstrating pronounced surface enrichment. Additionally, greenhouse conditions showed weaker correlations between soil nutrients and meteorological factors but stronger inter-nutrient coupling. Enhanced soil moisture and temperature conditions were associated with reduced nutrient leaching but simultaneously increased surface nutrient enrichment and salinization risks. These findings provide quantitative evidence for precision fertilization strategies, optimized irrigation management, and targeted soil health interventions in intensive greenhouse systems. The results have practical applications for preventing surface nutrient accumulation and long-term salinization in protected agriculture. Full article

This study systematically investigates settlement sites that record living patterns of ancient humans, aiming to reveal the interactive mechanisms of human–environment relationships. The core issues of landscape archeology research are the surface spatial structure, human spatial cognition, and social practice activities. This article takes the Han Dynasty settlement site in Sanyangzhuang, Neihuang County, Anyang City, Henan Province, as a typical case. It comprehensively uses ArcGIS 10.8 spatial analysis and remote sensing image interpretation techniques to construct spatial distribution models of elevation, slope, and aspect in the study area, and analyzes the process of the Yellow River’s ancient course changes. A regional historical geographic information system was constructed by integrating multiple data sources, including archeological excavation reports, excavated artifacts, and historical documents. At the same time, the sequences of temperature and dry–wet index changes in the study area during the Qin and Han dynasties were quantitatively reconstructed, and a climate evolution map for this period was created based on ancient climate proxy indicators. Drawing on three dimensions of settlement morphology, architectural spatial organization, and agricultural technology systems, this paper provides a deep analysis of the site’s spatial cognitive logic and the ecological wisdom it embodies. The results show the following: (1) The Sanyangzhuang Han Dynasty settlement site reflects the efficient utilization strategy and environmental adaptation mechanism of ancient settlements for land resources, presenting typical scattered characteristics. Its formation mechanism is closely related to the evolution of social systems in the Western Han Dynasty. (2) In terms of site selection, settlements consider practicality and ceremony, which can not only meet basic living needs, but also divide internal functional zones based on the meaning implied by the orientation of the constellations. (3) The widespread use of iron farming tools has promoted the innovation of cultivation techniques, and the implementation of the substitution method has formed an ecological regulation system to cope with seasonal climate change while ensuring agricultural yield. The above results comprehensively reflect three types of ecological wisdom: “ecological adaptation wisdom of integrating homestead and farmland”, “spatial cognitive wisdom of analogy, heaven, law, and earth”, and “agricultural technology wisdom adapted to the times”. This study not only deepens our understanding of the cultural value of the Han Dynasty settlement site in Sanyangzhuang, but also provides a new theoretical perspective, an important paradigm reference, and a methodological reference for the study of ancient settlement ecological wisdom. Full article

Displacement metrology with nanometer-level precision over macroscopic ranges is a key foundation for modern science and engineering. This review provides a comparative overview of Precision Nanometrology, covering measurement ranges from micrometers to meters and accuracies between 0.1 nm and 100 nm. Three main technologies are discussed: the Laser Interferometer (LI), the Grating Interferometer (GI), and the Time Grating Sensor (TGS). The LI is widely regarded as the traceable benchmark for highest resolution; the GI has been developed into a compact and stable solution based on diffraction gratings; and the TGS has emerged as a new approach that converts spatial displacement into the time domain, offering strong resilience to environmental fluctuations. For each technique, the principles, recent progress, and representative systems from the past two decades are reviewed. Particular attention is given to the trade-offs between resolution, robustness, and scalability, which are decisive for practical deployment. The review concludes with a comparative analysis of performance indicators and a perspective on future directions, highlighting hybrid architectures and application-driven requirements in precision manufacturing and advanced instrumentation. Full article

Reducing social exclusion through technology is a key challenge for sustainable development, particularly within the context of accessible tourism. This study, as part of the “MOUNTAINS WITHOUT BARRIERS” project, addresses this issue by aiming to identify optimal locations for specialized all-terrain wheelchair rental stations in mountainous regions. The primary purpose is to ensure these locations are seamlessly integrated with existing local transport systems, fostering genuine accessibility. A dedicated methodology was developed to analyze the spatial integration of the accessible trail network with the transport system in the Beskid Agglomeration. The analysis, conducted using Geographic Information System (GIS) tools, considers access via both individual transport and public transport, with a clear emphasis on prioritizing the latter to promote sustainable mobility patterns. Applying this approach, the study identified potential station locations that are not only conveniently situated at trailheads but are also highly accessible via public transport. The main finding indicates that strategic placement can significantly minimize the necessity for private car usage. Integrating tourism infrastructure with public transport is crucial for increasing the real-world accessibility of mountain areas for people with disabilities. Furthermore, the results and methodology provide valuable recommendations that can serve as a practical input for Sustainable Urban Mobility Plans (SUMP). Full article

Background: Mangrove plants are a core component of coastal ecosystems, directly influencing biodiversity and shoreline stability. However, in recent years, due to the combined pressures of human activities and climate change, nearly half of the mangrove species in China are endangered and require urgent conservation measures. This study analyzed the population dynamics and stress factors affecting four rare and endangered mangrove species—Lumnitzera racemosa, Ceriops tagal, Barringtonia racemosa, and Heritiera littoralis—on the Leizhou Peninsula, providing scientific evidence for their conservation. Methods: Field surveys and plot investigations were conducted, with population dynamics and structure quantified using static life tables, survival rates, mortality rates, and disappearance curves. Additionally, the MaxEnt species distribution model and GIS technology were applied to predict the potentially suitable distribution areas. Results: The findings revealed that the population of L. racemosa exhibits an atypical pyramid structure, with few seedlings and constraining population growth potential. The C. tagal population follows an irregular pyramid structure, with abundant seedlings but fewer mature individuals, suggesting a rapid decline followed by stability. The B. racemosa population also follows an irregular pyramid structure, with many seedlings and a greater proportion of middle-aged and older individuals, facing the risk of early mortality. The H. littoralis population is also in decline, with few seedlings and a severe risk of local extinction. MaxEnt model predictions indicated that temperature is the primary environmental factor, with Area Under the Curve (AUC) values for all species exceeding 0.8, indicating strong predictive ability. The predicted potential suitable areas showed an expanded distribution range compared to current distribution points, providing valuable references for species introduction and propagation. Conclusions: This study described the population structure of the four mangrove species on the Leizhou Peninsula and assessed their primary stress factors. The results provided a theoretical basis for the conservation and restoration of endangered mangrove species and offer important guidance for developing effective conservation strategies in southern China. Full article

In recent years, remote sensing technologies have become indispensable for the documentation, analysis, and virtual preservation of historical, architectural, and archaeological heritage. Advances in 3D scanning have enabled the precise digital recording of complex structures as large-scale point clouds, facilitating highly detailed virtual reconstructions. This study evaluates the capability of LiDAR-based Terrestrial Laser Scanning (TLS) for documenting historical monument façades within a 3D environment and generating accurate visualisation models from registered, colourised point clouds. The integration of high-resolution RGB imagery, processed through Reality Capture 1.5 software, enables the automatic production of realistic 3D models that combine geometric accuracy with visual fidelity. Simultaneously, Geographic Information Systems (GIS), particularly cloud-based platforms like ArcGIS Pro Online, enhance spatial data management, mapping, and analysis. When combined with TLS, GIS is part of a broader remote sensing framework that improves heritage documentation regarding precision, speed, and interpretability. The digital survey of the Shanasheel house in Al-Basrah, Iraq, demonstrates the effectiveness of this interdisciplinary approach. These architecturally and culturally significant buildings, renowned for their intricately decorated wooden façades, were digitally recorded using CAD-based methods to support preservation and mitigation against urban and environmental threats. This interdisciplinary workflow demonstrates how remote sensing technologies can play a vital role in heritage conservation, enabling risk assessment, monitoring of urban encroachment, and the protection of endangered cultural landmarks for future generations. Full article

Flooding is one of the most frequent and destructive natural disasters worldwide, with intensifying socioeconomic and environmental consequences linked to rapid urbanisation and climate change. This review examines flood risk delineation and assessment in Nigeria within a broader Global South perspective, synthesising evidence from peer-reviewed studies that employ remote sensing, GIS-based techniques, and multi-criteria decision analysis. The analysis reveals persistent challenges that undermine effective flood risk management, including incompatible datasets, limited stakeholder participation, and inadequate integration with formal planning systems. To address these gaps, the study introduces the GIS-Integrated Flood Risk Management (GIFRM) Framework, a conceptual model that integrates high-resolution risk mapping, adaptive infrastructure design, sustainable urban planning, and participatory governance. GIFRM advances resilience discourse beyond hazard mapping, offering a practical bridge between science, policy, and implementation by aligning technical geospatial analysis with actionable planning solutions. Comparative case insights from flood-prone countries such as Bangladesh, India, and Kenya highlight transferable strategies, including community-led data integration, modular infrastructure approaches, and localised zoning reforms. The review concludes by critically examining the operational disconnect between advanced geospatial risk assessment and its application in resource-limited, rapidly urbanising settings. It reframes flood risk assessment as an interdisciplinary planning tool with global relevance, delivering lessons for disaster preparedness, urban sustainability, and climate resilience. In the face of escalating hydrometeorological extremes, this research offers applied strategies for embedding GIS technologies into adaptive policy frameworks, positioning flood risk management as a core driver of sustainable development. Full article

Agri-food supply chains, particularly in the rice sector, face persistent challenges in transparency, quality control, and sustainability due to their complexity and fragmentation. Blockchain technology provides a promising solution by ensuring secure, immutable, and verifiable records of production and supply chain activities, supporting both consumer trust and compliance with the EU Common Agricultural Policy (CAP). This study reports on the TRACE-RICE Mediterranean pilot project, which developed a blockchain-enabled traceability system for rice production in Portugal. A Rice Field Data Recording App, built with ArcGIS Survey123, digitized agronomic and compliance records from Integrated Production systems and linked them to blockchain-verified QR codes on consumer packaging. The pilot conducted during the 2023 harvest demonstrated the potential to enhance data consistency and streamline field recording processes, thereby improving transparency in farming practices. A total of 174 QR code interactions, primarily from Lisbon, revealed consumer engagement patterns valuable for future business analysis. The scaling phase during the 2024 harvest confirmed the system’s adaptability to different varieties and production contexts, positioning blockchain as a replicable model for sustainable and competitive rice supply chains. Full article

Digital technology transfer plays a pivotal role in reshaping innovation landscapes and fueling the growth of the digital economy. To investigate this phenomenon, this study draws on data on digital technology transfers from the China National Intellectual Property Administration (CNIPA). Using tools such as Gephi 0.10.1 and ArcGIS 10.8, we construct an inter-city digital technology transfer network and develop a quantitative model to analyse the mechanisms by which it impacts urban digital innovation across multiple geographic scales. The main findings are as follows: (1) The inter-city digital technology transfer network in China forms a “diamond-shaped” spatial structure centred on Beijing, Shanghai, Guangzhou, and Shenzhen, with several regional hubs sustaining its connectivity and organisation. (2) Despite a decline in the proportion of intra-city transfers, the number of participating cities continues to rise, revealing a spatial pattern of diffusion from core cities toward inland provincial capitals. (3) Benchmark regression results show that both inter- and intra-city transfers significantly enhance urban digital innovation capacity, with inter-city transfers exhibiting a more substantial effect than their intra-city counterparts. This finding holds after a series of robustness tests. (4) Heterogeneity analysis, based on categorising cities into higher-tier (municipalities, sub-provincial cities, and provincial capitals) and lower-tier groups, indicates that the effect of digital technology transfer on innovation is more pronounced in lower-tier cities. Full article

The Qinling Mountains ecosystem serves as a vital ecological barrier and geographic demarcation line in China. Monitoring long-term land cover changes in the Qinling Mountains is essential for ecosystem pattern evaluation, environmental protection, and sustainable development. Focusing on the Qinling Mountains in Shaanxi Province, this study aimed to quantify the land cover changes from 1986 to 2020 using remote sensing and GIS technologies. An optimized Support Vector Machine (SVM) classification method was developed using Landsat satellite images and historical field samples. The method was employed to conduct land cover classification across eight discrete time periods: 1986, 1990, 1995, 2000, 2005, 2010, 2015, and 2020. The average overall accuracy (OA) of the classification results for the eight time periods was 96.42%, with a Kappa coefficient (K) of 0.9230, thus confirming the reliability of the mapping results. We subsequently developed a spatiotemporal Geo-information Tupu that facilitated a detailed analysis of land cover changes in the study area across different periods. The results show the following: (1) Forest was the dominant land cover type, followed by cropland. From 1986 to 2020, the forest, impervious surface, and water body areas showed overall increasing trends, although fluctuations were observed over time, and the increase was estimated at 6677.30 km², 557.57 km², and 135.71 km², respectively. In contrast, the areas of cropland, grassland, and bare soil showed a fluctuating decreasing trend, with a decrease in areal coverage of 2790.57 km², 1528.76 km², and 3042.66 km², respectively. During the study period, the forest area experienced the greatest increase but maintained the lowest dynamic degree. In contrast, bare soil showed the largest decrease and the highest dynamic degree. (2) A total of 30.74% of the area underwent dynamic changes during the study period, with the most active transformation occurring after 2010; these changes were mainly manifested in the outflow of cropland (4997.27 km²), the transfer of forest (8557.43 km²), and the expansion of impervious surfaces (771.33 km²). In conclusion, the overall ecological environment is improving. The results demonstrate a land cover reconstruction process that enables the management department to rationally utilize natural resources in the Qinling Mountains. Full article

Industrial site selection holds strategic importance in the layout of industrial facilities. Scientific decision-making in site selection not only enhances the economic and technical feasibility of a project but also lays the foundation for sustainable development. However, industrial site selection is considered an NP-hard problem. The criteria used to evaluate site suitability, the methods proven effective under different conditions, big data sources introduced, and the key data gaps, methodological limitations, and research priorities to improve decision quality are important for researchers and engineers. Based on the Web of Science (WOS) core collection as the data source, this paper retrieved the literature related to the themes of “industrial site selection” and “facility location decision making”, and selected 149 highly relevant papers. It systematically categorizes three mainstream site selection methods: operations research-based methods; the application of geographic information systems in site selection; and the application of artificial intelligence in site selection. On this basis, this paper provides a systematic review of the overall industrial site selection process and methodologies, aiming to offer references for subsequent site selection analysis research and practical site selection work. An “MCDM–GIS–AI” technology convergence roadmap is also proposed for industrial site selection to identify remaining research gaps and offer a set of “good-practice guidelines” to inform both practical applications and future analytical studies. Full article

Renewable energy sources offer a promising future, backed by mature technologies and a viable pathway toward sustainable energy systems. However, careful planning is necessary to efficiently utilize these resources, especially during site selection. Many rural areas lack access to grid electricity, making off-grid hybrid wind-solar power an attractive solution. In the Tigray region of Ethiopia, no such research has been conducted before. This study aims to identify suitable sites for hybrid wind-solar power for rural electrification using Geographic Information System (GIS), Analytic Hierarchy Process, and Monte Carlo simulation. The criteria fall into three categories: Climate, Topography, and Infrastructure, prioritized through pairwise comparisons by thirteen experts from five organizations engaged in renewable energy research, planning, and operations. Monte Carlo simulation was used for sensitivity analysis to address uncertainties in expert judgments and validate the rankings. The spatial analysis reveals 6470 km² as highly suitable for off-grid solar, 76 km² for off-grid wind with predominant easterly winds, and 177 km² as most favorable for hybrid generation. Areas of good suitability measure 447 km² for wind, 44,128 km² for solar, and 16,695 km² for hybrid systems. Based on this assessment, techno-economic analysis quantified the Levelized Cost of Energy (LCOE) under varying solar–wind shares and battery autonomy days. The analysis shows a minimum LCOE of $0.23/kWh with one-day storage and $0.58/kWh with three-day storage, indicating shorter autonomy is more cost-effective while longer autonomy enhances reliability. Sensitivity analysis shows financial parameters, particularly discount rate and battery capital cost, dominate system economics. Full article

Highway construction remains one of the most hazardous sectors in the infrastructure domain, where persistent accident rates challenge the vision of sustainable and safe development. Traditional hazard identification methods rely on manual inspections that are often slow, error-prone, and unable to cope with complex and dynamic site conditions. To address these limitations, this study develops a cognitive-inspired multimodal learning framework integrated with BIM–GIS-enabled digital twins to advance intelligent hazard identification and digital management for highway construction safety. The framework introduces three key innovations: a biologically grounded attention mechanism that simulates inspector search behavior, an adaptive multimodal fusion strategy that integrates visual, textual, and sensor information, and a closed-loop digital twin platform that synchronizes physical and virtual environments in real time. The system was validated across five highway construction projects over an 18-month period. Results show that the framework achieved a hazard detection accuracy of 91.7% with an average response time of 147 ms. Compared with conventional computer vision methods, accuracy improved by 18.2%, while gains over commercial safety systems reached 24.8%. Field deployment demonstrated a 34% reduction in accidents and a 42% increase in inspection efficiency, delivering a positive return on investment within 8.7 months. By linking predictive safety analytics with BIM–GIS semantics and site telemetry, the framework enhances construction safety, reduces delays and rework, and supports more resource-efficient, low-disruption project delivery, highlighting its potential as a sustainable pathway toward zero-accident highway construction. Full article

This study examined the trend changes as well as the spatial distribution of average precipitation and the abrupt change characteristics of precipitation in Gannan Prefecture, China, using daily precipitation monitoring data from 1980 to 2021 at eight meteorological stations. Analytical methods employed included the climate change trend rate, anomaly analysis, Innovative Trend Analysis (ITA), ITA-change boxes (ITA-CB), ArcGIS technology, and BEAST Ensemble Algorithm. Long-term average precipitation variability was comprehensively analyzed across multiple temporal scales. Results indicated that over the 42 years, interannual precipitation exhibited a significant increasing trend, with an annual rate of 14.363 mm/decade, and abrupt changes were detected in 1984, 2003, and 2018. The distribution of average precipitation varied substantially from year to year. July was the month with the highest average monthly precipitation, and December was the month with the lowest. Summer precipitation contributed the most to annual totals (51.33%), whereas winter precipitation contributed the least (2.01%). Interdecadal precipitation exhibited a pattern of an initial decrease followed by an increase over the study period. Based on the mean and standard deviation of the series’ first half, which was divided by the ITA method, we established a three-category classification for mean precipitation (low, medium, and high). Annual average and seasonal average precipitation showed non-monotonic variations. While the overall trend of annual average precipitation showed a modest augmentation, the increasing tendencies in the middle-value and high-value categories slowed. In spring, the decreasing trend in high-value categories slowed. In summer, decreasing trends in middle-value categories and overall zones slowed, with an enhanced increasing trend observed in autumn and winter overall. At the spatial scale, the average precipitation across Gannan Prefecture exhibited a decreasing trend from south to north. Higher precipitation was recorded at meteorological stations in the southwest (Maqu), west (Luqu), and south (Diebu), primarily influenced by the interaction between the Qinghai–Tibetan Plateau monsoon and westerly circulation, as well as regional topographic effects. The research findings have significant implications for agricultural and pastoral production planning and sustainable economic development in Gannan Prefecture, China. Full article