Search Results (705)

The prediction of real estate values is vital for taxation, transactions, mortgages, and urban policy development. Values can be predicted more accurately by statistical or advanced methods together when the size of the data is huge. In metropolitan cities like İstanbul, where size of the real estate data is vast and complex, mass appraisal methods supported by Machine Learning offer a scalable and consistent alternative. This study employs six algorithms: Artificial Neural Network, Extreme Gradient Boosting, K-Nearest Neighbors, Support Vector Regression, Random Forest, and Semi-Log Regression, to estimate the values of real estate on both the Asian and European continent parts of İstanbul. In total, 168,099 residential properties were utilized along with 30 of their features from both sides of the Bosphorus. The results show that RF yielded the best performance in Beşiktaş, while XGBoost performed best in Üsküdar. ANN also produced competitive results, although slightly less accurate than those of XGBoost and RF. In contrast, traditional SVR and SLR models underperformed, especially in terms of R² and RMSE values. With its large-scale dataset, focusing on one of the greatest metropolitan areas, Istanbul, and the usage of multiple ML algorithms, this study stands as a comprehensive and practical contribution to the field of automated real estate valuation. Full article

Building change detection and building damage assessment are two essential tasks in post-disaster analysis. Building change detection focuses on identifying changed building areas between bi-temporal images, while building damage assessment involves segmenting all buildings and classifying their damage severity. These tasks play a critical role in disaster response and urban development monitoring. Although supervised learning has significantly advanced building change detection and damage assessment, its reliance on large labeled datasets remains a major limitation. In contrast, self-supervised learning enables the extraction of meaningful data representations without explicit training labels. To address this challenge, we propose a self-supervised learning approach that unifies denoising autoencoders and contrastive learning, enabling effective data representation for building change detection and damage assessment. The proposed architecture integrates a dual denoising autoencoder with a Vision Transformer backbone and contrastive learning strategy, complemented by a Feature Pyramid Network-ResNet dual decoder and an Edge Guidance Module. This design enhances multi-scale feature extraction and enables edge-aware segmentation for accurate predictions. Extensive experiments were conducted on five public datasets, including xBD, LEVIR, LEVIR+, SYSU, and WHU, to evaluate the performance and generalization capabilities of the model. The results demonstrate that the proposed Denoising AutoEncoder-enhanced Dual-Fusion Network (DAEDFN) approach achieves competitive performance compared with fully supervised methods. On the xBD dataset, the largest dataset for building damage assessment, our proposed method achieves an F1 score of 0.892 for building segmentation, outperforming state-of-the-art methods. For building damage severity classification, the model achieves an F1 score of 0.632. On the building change detection datasets, the proposed method achieves F1 scores of 0.837 (LEVIR), 0.817 (LEVIR+), 0.768 (SYSU), and 0.876 (WHU), demonstrating model generalization across diverse scenarios. Despite these promising results, challenges remain in complex urban environments, small-scale changes, and fine-grained boundary detection. These findings highlight the potential of self-supervised learning in building change detection and damage assessment tasks. Full article

In the digital transformation era, understanding the relationship between digital and real economies is vital for regional development. This study analyses the interaction between these two economies in Henan Province using panel data from 18 cities (2011–2023). It incorporates policy support intensity through fuzzy set theory, applies an integrated weighting method to measure development levels, and uses regression models to assess the digital economy’s impact on the real economy. The coupling coordination degree model, kernel density estimation, and Gini coefficient reveal the coordination status and spatial distribution, while the ecological Lotka–Volterra model identifies the symbiotic patterns. The key findings are as follows: (1) The digital economy does not directly determine the state of the real economy. For example, cities such as Zhoukou and Zhumadian have low digital economy levels but high real economy levels. However, the development of the digital economy promotes the real economy without signs of diminishing returns. (2) The two economies are generally coordinated but differ spatially, with greater coordination in the Central Plains urban agglomeration. (3) The digital and real economies exhibit both collaboration and competition, with reciprocal mutualism as the dominant mode of integration. These insights provide guidance for policymakers and offer a new perspective on the integration of both economies. Full article

E-commerce growth has reshaped consumer behavior and retail services, driving parcel demand and challenging last-mile logistics. Existing research predominantly relies on survey data and global regression models that overlook intra-urban spatial heterogeneity in shopping behaviors. This study bridges this gap by analyzing e-commerce demand’s spatial distribution from a retail service perspective, identifying key drivers, and evaluating implications for omnichannel strategies and logistics. Utilizing waybill big data, spatial analysis, and multiscale geographically weighted regression, we reveal: (1) High-density e-commerce demand areas are predominantly located in central districts, whereas peripheral regions exhibit statistically lower volumes. The spatial distribution pattern of e-commerce demand aligns with the urban development spatial structure. (2) Factors such as population density and education levels significantly influence e-commerce demand. (3) Convenience stores play a dual role as retail service providers and parcel collection points, reinforcing their importance in shaping consumer accessibility and service efficiency, particularly in underserved urban areas. (4) Supermarkets exert a substitution effect on online shopping by offering immediate product availability, highlighting their role in shaping consumer purchasing preferences and retail service strategies. These findings contribute to retail and consumer services research by demonstrating how spatial e-commerce demand patterns reflect consumer shopping preferences, the role of omnichannel retail strategies, and the competitive dynamics between e-commerce and physical retail formats. Full article

This paper presents a multi-strategy enhanced bald eagle search algorithm (EAB-BES) for 3D UAV path planning in urban environments. EAB-BES addresses key limitations of the traditional bald eagle search (BES) algorithm, including slow convergence, susceptibility to local optima, and poor adaptability in complex urban scenarios. The algorithm enhances solution space exploration through elite opposition-based learning, balances global search and local exploitation via an adaptive weight mechanism, and refines local search directions using block-based elite-guided differential mutation. These innovations significantly improve BES’s convergence speed, path accuracy, and adaptability to urban constraints. To validate its effectiveness, six high-density urban environments with varied obstacles were used for comparative experiments against nine advanced algorithms. The results demonstrate that EAB-BES achieves the fastest convergence speed and lowest stable fitness values and generates the shortest, smoothest collision-free 3D paths. Statistical tests and box plot analysis further confirm its superior performance in multiple performance metrics. EAB-BES has greater competitiveness compared with the comparative algorithms and can provide an efficient, reliable and robust solution for UAV autonomous navigation in complex urban environments. Full article

Common ragweed (Ambrosia artemisiifolia L.) has been identified as one of the most harmful invasive weed species in Europe due to its allergenic pollen and competitive growth in diverse habitats. In the first part of this review [Common Ragweed—Ambrosia artemisiifolia L.: A Review with Special Regards to the Latest Results in Biology and Ecology], its biological characteristics and ecological behavior were described in detail. In the current paper, control strategies are summarized, focusing on integrated weed management adapted to the specific habitat where the species causes damage—arable land, semi-natural vegetation, urban areas, or along linear infrastructures. A range of management methods is reviewed, including agrotechnical, mechanical, physical, thermal, biological, and chemical approaches. Particular attention is given to the spread of herbicide resistance and the need for diversified, habitat-specific interventions. Among biological control options, the potential of Ophraella communa LeSage, a leaf beetle native to North America, is highlighted. Furthermore, innovative technologies such as UAV-assisted weed mapping, site-specific herbicide application, and autonomous weeding robots are discussed as environmentally sustainable tools. The role of legal regulations and pollen monitoring networks—particularly those implemented in Hungary—is also emphasized. By combining traditional and advanced methods within a coordinated framework, effective and ecologically sound ragweed control can be achieved. Full article

With the deceleration of China’s economic growth, the crude economic model will progressively diminish in its competitive edge, thereby posing challenges for state-level economic and technological development zones (ETDZs) in terms of transitioning their development model and grappling with low levels of total factor productivity (TFP). This study aims to evaluate the TFP of prominent cities in China, examine the influence of the establishment of state-level ETDZs on urban TFP, and investigate the moderating effect of transportation infrastructure on this relationship. The results show that the aggregate TFP of Chinese urban areas declined from 1999 to 2020, a trend linked to structural economic adjustments and persistent underutilization of capital in several regions. The establishment of state-level ETDZs has been found to exert a notable positive influence on regional TFP. The presence of transportation infrastructure plays a moderating role in facilitating state-level ETDZs, thereby enhancing regional TFP. Among various modes of transportation, highways and railways are particularly prominent in this regard. These conclusions provide a theoretical basis and decision-making reference for further unleashing the policy potential of development zones in China. Full article

This study aimed to analyse the spatiotemporal evolution of phytoplankton community dynamics and its underlying mechanisms in the Liaoning section of the Liao River Basin in 2010, 2015, and 2020. Phytoplankton species diversity increased significantly, with an increase from three phyla and 31 species in 2010 to six phyla and 74 species in 2020. Concurrent increases in α-diversity indicated continuous improvements in habitat heterogeneity. The community structure shifted from a diatom-dominated assemblage to a green algae–diatom co-dominated configuration, contributing to an enhanced water purification capacity. The upstream agricultural zone (Tieling section) had elevated biomass and low diversity, indicating persistent non-point-source pollution stress. The midstream urban–industrial zone (Shenyang–Anshan section) emerged as a phytoplankton diversity hotspot, likely due to expanding niche availability in response to point-source pollution control. The downstream wetland zone (Panjin section) exhibited significant biomass decline and delayed diversity recovery, shaped by the dual pressures of resource competition and habitat filtering. The driving mechanism of community succession shifted from nutrient-dominated factors (NH₃-N, TN) to redox-sensitive factors (DO, pH). These findings support a ‘zoned–graded–staged’ ecological restoration strategy for the Liao River Basin and inform the use of phytoplankton as bioindicators in watershed monitoring networks. Full article

Societies are aiming to have a higher ecological consciousness in wastewater treatment operations and achieve a more sustainable future. With this said, global demands for larger quantities of resources and the consequent waste generated will inevitably lead to the exhaustion of current municipal wastewater treatment works. The utilization of biosolids (particularly microbial proteins) from wastewater treatment operations could generate a sustainable bio-adhesive for the wood industry, reduce carbon footprint, mitigate health concerns related to the use of carcinogenic components, and support a more circular economic option for wastewater treatment. A techno-economic analysis for three 10 MGD wastewater treatment operations producing roughly 11,300 dry pounds of biosolids per day, in conjunction with co-feedstock defatted soy flour protein at varying ratios (i.e., 0%, 15%, and 50% wet weight), was conducted. Aspen Capital Cost Estimator V12 was used to design and estimate installed equipment additions for wastewater treatment plant integration into an urban biorefinery process. Due to the mechanical attributes and market competition, the chosen selling prices of each adhesive per pound were set for analysis as USD 0.75 for Plant Option P1, USD 0.85 for Plant Option P2, and USD 1.00 for Plant Option P3. Over a 20-year life, each plant option demonstrated economic viability with high NPVs of USD 107.9M, USD 178.7M, and USD 502.2M and internal rates of return (IRRs) of 24.0%, 29.0%, and 44.2% respectively. The options examined have low production costs of USD 0.14 and USD 0.19 per pound, minimum selling prices of USD 0.42–USD 0.51 per pound, resulting in between 2- and 4-year payback periods. Sensitivity analysis shows the effects biosolid production fluctuations, raw material market price, and adhesive selling price have on economics. The results proved profitable even with large variations in the feedstock and raw material prices, requiring low market selling prices to reach the hurdle rate of examination. This technology is economically enticing, and the positive environmental impact of waste utilization encourages further development and analysis of the bio-adhesive process. Full article

Automated parcel lockers (APLs) are transforming urban last-mile delivery by reducing failed distributions, decoupling delivery from recipient availability, optimizing carrier routes, reducing carbon foot-print and mitigating traffic congestion. The paper investigates the optimal design of APLs systems under stochastic demand and operational constraints, formulating the problem as a resource allocation optimization with service-level guarantees. We proposed a data-driven discrete-event simulation (DES) model implemented in ARENA to (i) determine optimal locker configurations that ensure customer satisfaction under stochastic parcel arrivals and dwell times, (ii) examine utilization patterns and spatial allocation to enhance system operational efficiency, and (iii) characterize inventory dynamics of undelivered parcels and evaluate system resilience. The results show that the configuration of locker types significantly influences the system’s ability to maintain high customers service levels. While flexibility in locker allocation helps manage excess demand in some configurations, it may also create resource competition among parcel types. The heterogeneity of locker utilization gradients underscores that optimal APLs configurations must balance locker units with their size-dependent functional interdependencies. The Dickey–Fuller GLS test further validates that postponed parcels exhibit stationary inventory dynamics, ensuring scalability for logistics operators. As a theoretical contribution, the paper demonstrates how DES combined with time-series econometrics can address APLs capacity planning in city logistics. For practitioners, the study provides a decision-support framework for locker sizing, emphasizing cost–service trade-offs. Full article

Urban systems are the spatial carriers of social and economic relations at the regional level, and their relational and structural resilience are key to regional coordination and sustainable development, attracting widespread attention from scholars. In order to analyze the internal relationships of urban agglomerations in underdeveloped mountainous regions and optimize their spatial resource allocation and resilience, this study takes the urban agglomeration of Qiandongnan in China as an example and researches their internal relationships, development potential, and influencing factors based on quantitative methods such as social network analysis. The results show that the urban cluster in Qiandongnan presents “large dispersion and small aggregation” distribution characteristics, with the karst landscape as the main influencing factor; the spatial network exhibits a scale-free morphology with an obvious core–periphery structure, demonstrating moderate stability but poor completeness, weak equilibrium, and low overall resilience; only 15.61% of nodes demonstrate high competitiveness; urban units with functional roles serve as critical network nodes; urban units’ development potential is divided into three tiers (with 47.31% being medium-high), although overall levels remain low; and the development potential, overall network, individual network, and network resilience of urban units are all positively correlated, with economic and transportation development conditions being the main influencing factors. Based on the abovementioned findings, this study proposes a “multi-level resilience promotion path for network structure optimization”, which provides a theoretical basis and optimization control methods for the reconstruction and synergistic development of urban agglomerations. It also serves as a reference for the development planning of urban systems in other underdeveloped mountainous regions. Full article

With an economic model characterized by high energy consumption and low efficiency, China is facing serious energy shortages and environmental problems. However, education, as the cornerstone of social progress, has been overlooked in its role in improving energy efficiency. This study aims to enhance our understanding of the impact of educational competitiveness on urban green total factor energy efficiency (GTFEE), helping policymakers to achieve sustainable urban development. This study utilizes panel data from 20 major Chinese cities spanning from 2012 to 2022 and applies a two-way fixed effects model to investigate the relationship and pathways of educational competitiveness (Ec) on GTFEE. Our results show that the Ec index can enhance the major urban GTFEE. Among them, educational resource competitiveness, input competitiveness, efficiency competitiveness, and sustainable competitiveness can all enhance urban GTFEE, but the coefficient of the educational scale is not significant. In addition, Ec can effectively improve GTFEE by promoting green technological innovation, alleviating human resource mismatch, and driving industrial structure upgrading. Furthermore, the impact of Ec on GTFEE shows significant regional heterogeneity, with its effect weakening from the eastern coastal areas to the western inland regions. Full article

Urban dynamics of the 21st century is distinguished by the prevalence and significance of cities and their constituent elements. The objective of this study is to analyze the role of the territorial brand as one of the strategic elements of public governance. The methodology employs a comparative approach between four Brazilian and Portuguese cities, utilizing seven variables pertinent to the concept of a “territorial brand as a cultural product of regional development.” The results underscore the significance of strategic planning in enhancing urban competitiveness, influencing urban public governance, and reflecting on urban, cultural, social, regional, and territorial changes. The study provides insights into the implementation of a territorial brand, particularly in cities with a cultural focus, offering a comprehensive understanding of how this governance strategy can shape urban development and reinforce local cultural identity. Full article

Spice by-products, often discarded as waste, represent an untapped resource for sustainable packaging solutions due to their unique, multifunctional, and bioactive profiles. Unlike typical plant residues, these materials retain diverse phytochemicals—including phenolics, polysaccharides, and other compounds, such as essential oils and vitamins—that exhibit controlled release antimicrobial and antioxidant effects with environmental responsiveness to pH, humidity, and temperature changes. Their distinctive advantage is in preserving volatile bioactives, demonstrating enzyme-inhibiting properties, and maintaining thermal stability during processing. This review encompasses a comprehensive characterization of phytochemicals, an assessment of the re-utilization pathway from waste to active materials, and an investigation of processing methods for transforming by-products into films, coatings, and nanoemulsions through green extraction and packaging film development technologies. It also involves the evaluation of their mechanical strength, barrier performance, controlled release mechanism behavior, and effectiveness of food preservation. Key findings demonstrate that ginger and onion residues significantly enhance antioxidant and antimicrobial properties due to high phenolic acid and sulfur-containing compound concentrations, while cinnamon and garlic waste effectively improve mechanical strength and barrier attributes owing to their dense fiber matrix and bioactive aldehyde content. However, re-using these residues faces challenges, including the long-term storage stability of certain bioactive compounds, mechanical durability during scale-up, natural variability that affects standardization, and cost competitiveness with conventional packaging. Innovative solutions, including encapsulation, nano-reinforcement strategies, intelligent polymeric systems, and agro-biorefinery approaches, show promise for overcoming these barriers. By utilizing these spice by-products, the packaging industry can advance toward a circular bio-economy, depending less on traditional plastics and promoting environmental sustainability in light of growing global population and urbanization trends. Full article

This study investigates the complex dynamics of new-type urbanization in resource-based county-level cities, using Wuan City in Hebei Province, China, as a representative case. As China pursues a high-quality development agenda, cities historically dependent on resource extraction face profound challenges in achieving sustainable and inclusive urban growth. This research employs a multi-method approach—including Theil index analysis, industrial shift-share analysis, a Cobb–Douglas production function model, and a composite urbanization index—to quantitatively diagnose the constraints on Wuan’s development and assess its transformation efforts. Our empirical results reveal a multifaceted situation: while the urban–rural income gap has narrowed, rural income streams remain fragile. The shift-share analysis indicates that although Wuan’s traditional industries have regained competitiveness, the city’s economic structure is still burdened by a persistent negative structural component, hindering diversification. Furthermore, the economy exhibits characteristics of a labor-intensive growth model with inefficient capital deployment. These underlying issues are reflected in a comprehensive urbanization index that, after a period of rapid growth, has recently stagnated, signaling the exhaustion of the city’s traditional development mode. In response, Wuan attempts an “industrial transformation-driven new-type urbanization” path. This study details the three core strategies being implemented: (1) incremental population urbanization through development at the urban fringe and in industrial zones; (2) in situ urbanization of the existing rural population; and (3) the cultivation of specialized “characteristic small towns” to create new, diversified economic nodes. The findings from Wuan offer critical, actionable lessons for other resource-dependent regions. The case demonstrates that successful urban transformation requires not only industrial upgrading but also integrated, spatially aware planning and robust institutional support. We conclude that while Wuan’s model provides a valuable reference, its strategies must be adapted to local contexts, emphasizing the universal importance of institutional innovation, human capital investment, and a people-centered approach to achieving resilient and high-quality urbanization. Full article