Search Results (3,386)

Coastal port cities depend on global seafood flows, yet their food security is increasingly exposed to price volatility and supply disruptions. This study examines Busan citizens’ perceptions of seafood-related food security and seafood supply chain stability, and derives actionable municipal policy priorities for a trade-dependent port city. Anchored in the FAO four-dimensional framework—availability, access, utilization, and stability—we developed 20 seafood-related attributes and surveyed adult residents in Busan (n = 297). The measurement structure was assessed through reliability checks and exploratory factor analysis, and Importance–Performance Analysis (IPA) was used to map attribute-level priorities and identify the largest importance–performance gaps. Overall, respondents regard seafood food security as highly important but only moderately satisfactory. Availability and utilization perform relatively well, indicating perceived strengths in basic supply conditions and safe consumption, whereas access and stability show lower performance relative to importance, reflecting concerns about affordability, uneven physical access for vulnerable groups, price volatility, and exposure to external shocks. Notably, several stability-related attributes emerge as “Concentrate Here” priorities, highlighting the need for strengthened risk management, early warning communication, and resilience-oriented logistics planning at the city level. By integrating the FAO framework with attribute-level IPA, this study demonstrates how citizen perception data can translate macro food security debates into locally implementable priorities for building sustainable food systems in coastal cities. Full article

This paper uses a systematic approach to constructing a mathematical description of the technological process of aluminum production, aimed at addressing control challenges and improving energy sustainability through a comprehensive analysis of technological parameters. Using expert assessment and correlation–regression analysis methods, the most significant technological parameters were identified, and quantitative relationships among them were established. Based on available statistical data from the current supply subsystem, a regression model was constructed that describes the influence of subsystem parameters on the voltage drop across the straight section of the bus and confirms the key role of transition resistances in welded joints in energy loss formation. Using the obtained dependencies, a conceptual model of the electrolysis process and its mathematical representation describing interactions among the electrical, thermal, and physicochemical subsystems of the electrolyzer was developed. The developed model is applicable to the analysis and prediction of technological modes, the construction of digital twins, and the development of automated control systems. In future work, the model is planned to be experimentally verified using a laboratory aluminum electrolysis setup in order to refine model parameters and assess applicability under industrial electrolyzer conditions. Full article

Australia’s National Housing Accord aims to deliver 1.2 million new dwellings between mid-2024 and mid-2029, representing 240,000 annual completions—a 37% increase above the 2024 baseline of 175,000. This study employs a comprehensive system dynamics model with 79 equations (10 stocks, 69 auxiliary variables) to analyze whether this target is structurally achievable, given construction industry capacity constraints. The model integrates builder population dynamics, workforce capacity, construction cost inflation, material supply constraints, and financial market conditions across a ten-year simulation horizon (2024.5–2035). Three policy scenarios test the effectiveness of interventions, including capacity expansion (±10–15%), cost inflation management (±15–20%), planning reforms (+5–15% efficiency), and workforce development programs (+1000–4000 annual graduates). Model validation against Australian Bureau of Statistics data from 2015 to 2024 demonstrates strong empirical foundations. Results show that structural capacity constraints—driven by three simultaneous bottlenecks in material supply, workforce availability, and financing—create a supply ceiling of around 180,000–195,000 annual completions. Even under optimistic policy assumptions, the model projects cumulative completions of 880,000–920,000 dwellings over the Accord period, falling 23–27% short of the 1.2 million target. Critical findings include the following: (1) builder insolvencies exceeding entry rates by 15–25% annually under stress conditions, (2) capacity decline trends of 0.6–0.8% per year due to productivity losses, infrastructure bottlenecks, and regulatory burden, (3) system efficiency degradation from 100% to 96% over the projection period, and (4) non-linear capacity utilization, showing saturation above 82% baseline levels. The analysis reveals that demand-side policies cannot overcome supply-side structural limits, suggesting that policymakers must either substantially reduce targets or implement transformative capacity-building interventions beyond current policy contemplation. Full article

Cultural ecosystem services (CESs) represent a critical link between ecosystems and human well-being and constitute a core foundation for regional sustainable development. The balance between CES supply and demand directly affects the coordination efficiency between ecological conservation and socio-economic development, making it a key prerequisite for ecosystem management, conservation planning, and policy formulation. This study focuses on the circum-Taihu region and integrates multi-source data to assess public perceived demand and spatial supply capacity of CESs. Supply–demand matching relationships are examined across three dimensions, namely, scenic beauty, cultural heritage, and recreation, through the construction of a region-specific CES quantitative indicator system. The impacts of multiple environmental factors on CES supply–demand dynamics are further explored to provide scientific support for coordinated ecological, cultural, and economic sustainability at the regional scale. The findings demonstrate the following: (1) the proposed methodology effectively quantifies CES perception and supply capacity in the circum-Taihu region. Scenic beauty exhibits the highest perception levels, whereas cultural heritage and recreation show lower perception. Cultural heritage displays the strongest supply capacity, whereas scenic beauty and recreation exhibit weaker supply. (2) Significant spatial imbalances exist between CES perception levels and supply capacity across the circum-Taihu region. Areas exhibiting mismatches constitute the largest proportion for cultural heritage CESs, followed by scenic beauty, with recreation displaying the smallest amounts of imbalance. (3) Environmental drivers exert differentiated effects on CES supply–demand relationships. Slope, road network density, and elevation have significant positive effects, whereas the normalized difference vegetation index (NDVI), distance to water bodies, and distance to roads exhibit significant negative effects. Distance to roads imposes the strongest inhibitory influence on CES perception, whereas elevation emerges as the most influential driver of public perceived CES levels. Full article

Background: Cold chain distribution in Agri-Biotech supply chains faces serious challenges due to strict time windows, high temperature sensitivity, and conflict between different operational objectives, and conventional static approaches are unable to address these complexities. Methods: In this study, an integrated decision support framework is presented that combines multi-objective fuzzy modeling and an adaptive digital twin to simultaneously manage logistics costs, product quality degradation, and service time compliance under operational uncertainty. Key uncertain parameters are modeled using triangular fuzzy numbers, and the digital twin dynamically updates the decision parameters based on operational information. The proposed framework is evaluated using real industrial data and comprehensive computational experiments. Results: The results show that the proposed approach is able to produce stable and balanced solutions, provides near-optimal performance in benchmark cases, and is highly robust to demand fluctuations and temperature deviations. Digital twin activation significantly improves the convergence behavior and stability of the solutions. Conclusions: The proposed framework provides a reliable and practical tool for adaptive planning of cold chain distribution in Agri-Biotech industries and effectively reduces the gap between advanced optimization models and real-world operational requirements. Full article

The transition toward a Circular Economy (CE) has received significant attention from academia, industry, and policymakers; however, manufacturing practices remain predominantly linear, generating waste and inefficiencies. This study addresses the lack of accessible sustainability assessment methods by introducing the Circularity Calculator (CC), a novel tool for evaluating circular strategies during the early phases of process development. Unlike existing assessment frameworks, which often require extensive data and customization, the CC can be integrated directly to existing processes to combine environmental and economic impact into a streamlined evaluation process for early decision-making. The research involves collaboration with a leading German automotive manufacturer. Site visits and interviews enabled the identification of material flows and primary waste streams, which informed the definition of relevant indicators. The CC generates a dimensionless index, enabling comparison and prioritization of proposed scenarios without relying on supply-chain-wide data, which is often unavailable at early stages. Implications demonstrate the adaptability of the CC across industrial contexts, supporting conceptual planning and operational phases. Its intuitive design facilitates adoption by practitioners without extensive expertise in sustainability. The tool represents an advance in CE assessment, contributing to Sustainable Development Goals (SDGs) 9, 12, and 17 by promoting sustainable industrial practices, resource circularity, and collaborative evaluation frameworks. Full article

As a high-value fruit crop grown worldwide, apples require efficient harvesting solutions to maintain a stable supply. Intelligent harvesting robots represent a promising approach to address labour shortages. This study introduced a Cartesian robot integrated with a continuous-picking end-effector, providing a cost-effective and mechanically simpler alternative to complex articulated arms. The system employed a hand–eye calibration model to enhance positioning accuracy. To overcome the inefficiencies resulting from disordered harvesting sequences and excessive motion trajectories, the harvesting process was treated as a travelling salesman problem (TSP). The conventional fixed-plane return trajectory of Cartesian robots was enhanced using a three-dimensional continuous picking path strategy based on a fixed retraction distance (H). The value of H was determined through mechanical characterization of the apple stem’s brittle fracture, which eliminated redundant horizontal displacements and improved operational efficiency. Furthermore, an improved grey wolf optimizer (IGWO) was proposed for multi-fruit path planning. Simulations demonstrated that the IGWO achieved shorter path lengths compared to conventional algorithms. Laboratory experiments validated that the system successfully achieved vision-based localization and fruit harvesting through optimal path planning, with a fruit picking success rate of 89%. The proposed methodology provides a practical framework for automated continuous harvesting systems. Full article

Flooding is a recurrent problem in many Brazilian cities, resulting in significant losses that affect health, assets, finance, and the environment. The uncertainty regarding extreme rainfall events due to climate change makes this challenge even more severe, compounded by inadequate urban planning and the occupation of risk areas, particularly for the municipality of Catu, in the state of Bahia, which also suffers from recurrent floods. Critical hotspots include the Santa Rita neighborhood and its surroundings, the main supply center, and the city center—the municipality’s commercial hub. The focus of this research is the unprecedented quantification of the socioeconomic impact of these floods on the low-income population and the region’s informal sector (street vendors). This research focused on analyzing and modeling the destructive potential of intense rainfall in the Santa Rita region (Supply Center) of Catu, Bahia, and its effects on the local economy across different recurrence intervals. A hydrological simulation software suite based on computational and geoprocessing technologies—specifically HEC-RAS 6.4, HEC-HMS 4.11, and QGIS— 3.16 was utilized. Two-dimensional (2D) modeling was applied to assess the flood-prone areas. For the socioeconomic impact assessment, a loss procedure based on linear regression was developed, which correlated the different return periods of extreme events with the potential losses. This methodology, which utilizes validated, indirect data, establishes a replicable framework adaptable to other regions facing similar socioeconomic and drainage challenges. The results revealed that the area becomes impassable during flood events, preventing commercial activities and causing significant economic losses, particularly for local market vendors. The total financial damage for the 100-year extreme event is approximately US $30,000, with the loss model achieving an R² of 0.98. The research concludes that urgent measures are necessary to mitigate flood impacts, particularly as climate change reduces the return period of extreme events. The implementation of adequate infrastructure, informed by the presented risk modeling, and public awareness are essential for reducing vulnerability. Full article

Digital transformation in port logistics represents a profound and systemic shift in the way maritime trade and supply chain operations are designed, coordinated, and governed through the pervasive integration of advanced digital technologies and data-driven management practices. It extends beyond the mere digitization of paper-based documents into electronic formats and beyond the digitalization of isolated processes with IT tools. Transformation involves reconfiguring organizational structures, decision-making logics, and value creation models around connectivity, automation, and predictive intelligence. In practice, it includes the adoption of smart port technologies such as the Internet of Things, 5G communication networks, digital twins, blockchain-based trade documentation, and artificial intelligence applied to vessel scheduling and cargo planning. It also encompasses collaborative platforms like port community systems that link shipping companies, terminal operators, freight forwarders, customs, and hinterland transport providers into data-driven ecosystems. The purpose of digital transformation is not only to improve efficiency and reduce operational bottlenecks, but also to enhance resilience against disruptions, ensure sustainability in line with decarbonization goals, and reposition ports as orchestrators of trade networks rather than passive providers of physical infrastructure. Full article

This paper focuses on whether smart-related built environments are associated with improved housing affordability for economically disadvantaged groups. Smart growth is a planning theme that aims to address the unintended negative consequences of urban sprawl through combining diverse dimensions across land-use diversity, housing diversity, accessibility, and compact development. Focusing on Clackamas County, Multnomah County, and Washington County within the Portland metropolitan area, the analysis uses census-tract-level data to assess both contemporaneous associations in 2013 and changes in affordability between 2013 and 2019. Overall, the findings suggest that smart-growth tools exhibit both potential and limitations with respect to housing affordability. Greater housing-type diversity and lower reliance on single-family residential land use are consistently associated with higher shares and subsequent increases in affordable housing units for low-income groups. In contrast, other smart-growth features—such as land-use mix and accessibility—show weaker or uneven relationships. These findings suggest that smart growth can contribute to expanding affordable housing supply primarily through housing-related components, while other dimensions of smart growth appear to play a limited role. The results underscore that housing-focused strategies play an important role in shaping affordability outcomes under smart growth. Full article

In the modern era, demand forecasting enhances the decision-making tasks of industries for controlling production planning and reducing inventory costs. However, the dynamic nature of the fashion and apparel retail industry necessitates precise demand forecasting to optimize supply chain operations and meet customer expectations. Consequently, this research proposes the Formicary Zebra Optimization-Based Distributed Attention-Guided Convolutional Recurrent Neural Network (FZ-DACR) model for improving the demand forecasting. In the proposed approach, the combination of the Formicary Zebra Optimization and Distributed Attention mechanism enabled deep learning architectures to assist in capturing the complex patterns of the retail sales data. Specifically, the neural networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), facilitate extracting the local features and temporal dependencies to analyze the volatile demand patterns. Furthermore, the proposed model integrates visual and textual data to enhance forecasting accuracy. By leveraging the adaptive optimization capabilities of the Formicary Zebra Algorithm, the proposed model effectively extracts features from product images and historical sales data while addressing the complexities of volatile demand patterns. Based on extensive experimental analysis of the proposed model using diverse datasets, the FZ-DACR model achieves superior performance, with minimum error values including MAE of 1.34, MSE of 4.7, RMS of 2.17, and R² of 93.3% using the DRESS dataset. Moreover, the findings highlight the ability of the proposed model in managing the fluctuating trends and supporting inventory and pricing strategies effectively. This innovative approach has significant implications for retailers, enabling more agile supply chains and improved decision making in a highly competitive market. Full article

Smart-city last-mile rail access, referred to in this entry simply as last-mile access, captures how travelers connect to and from rail stations during the first or last leg of a journey. It encompasses both the design of multimodal connections and the experience of accessibility that results from them. On the supply side, last-mile access involves the coordination of walking, cycling, micromobility, and feeder transit with rail services, supported by digital systems that unify planning, ticketing, and payment. On the demand side, it reflects how efficiently and equitably travelers can reach stations within these coordinated networks. Together, these physical and institutional dimensions extend the functional reach of rail, reduce transfer barriers, and reinforce its role as the backbone of sustainable urban mobility. As cities strive to reduce car dependency while promoting inclusivity and accessibility, last-mile access has become a key indicator of how infrastructure, technology, and governance intersect to deliver more equitable transportation systems. Full article

Understanding the long-term dynamics of global tea production is essential for assessing supply stability, climate sensitivity, and producer competitiveness. This study examines annual tea production data for major producing countries—China, India, Kenya, Sri Lanka, Türkiye, Vietnam, and other producer groups—over the period 1961–2023 and provides production forecasts for 2024–2028 using country-specific ARIMA models. Unlike most existing studies focusing on single countries or short-term horizons, this research offers a unified multi-country and long-term comparative framework that integrates time-series forecasting with market concentration indicators. The results reveal pronounced cross-country heterogeneity in production behavior, with China exhibiting strong structural growth, while other producers display more moderate or climate-sensitive patterns. Forecasts suggest a continued increase in global tea production toward 2028, although projections are subject to uncertainty, as reflected by model-based confidence intervals. Overall, the study contributes robust, statistically validated insights to support evidence-based strategies for sustainable tea supply and international market planning. Forecasts suggest a continued increase in global tea production toward 2028, although projections are subject to uncertainty, as reflected by model-based confidence intervals. These forecasts highlight a robust upward trend in global tea supply due to both technological advancements and market expansion. Full article

Grid-integrated energy service (GIES) projects are characterized by strong cross-energy coupling and long investment horizons, resulting in multidimensional and nonlinear risk profiles. To address these challenges, this study develops an indicator-based risk evaluation framework by integrating an entropy–back-propagation (BP) combined weighting method with fuzzy matter-element theory. A 30-indicator system covering economic, environmental, and safety and reliability dimensions is constructed to support systematic risk assessment. The entropy–BP scheme combines data-driven objectivity with nonlinear correction, producing stable and interpretable indicator weights, as confirmed through robustness tests based on indicator removal and data perturbation. A real-world GIES project in East China is used as a case study. The results show clear risk grade differentiation among alternative scenarios and identify key risk drivers related to renewable energy integration, investment structure, and energy supply reliability. The proposed framework provides effective decision support for GIES project planning and risk management. Full article

Efficient irrigation planning in data-scarce regions remains challenging due to limited access to localized meteorological data, reliance on complex computer-based models, and the technical knowledge required to deploy them at the field scale. Hence, the need for accessible, smartphone-based tools that simplify soil water balance calculations using public data to support practical decision-making in resource-limited contexts. This smartphone-based application estimates Net and Gross Irrigation Requirements using a Soil Water Balance (SWB) framework. The app combines region-specific empirical formulations for Effective Rainfall (Pe) calculation. The application utilizes user-supplied crop and irrigation parameters and meteorological data available in the public domain and operates at multiple temporal scales (daily, 10-day, weekly, and monthly), thereby supporting flexible irrigation schedules. The performance of app was evaluated through simulation-based benchmarking against FAO-CROPWAT 8.0 using harmonized inputs across five representatives agro-climatic region: Central India, Southern Vietnam, Northern Thailand, Western Bangladesh, and Central Sri Lanka. Quantitative comparison showed deviations within ±5% for Effective Rainfall, crop evapotranspiration, Net Irrigation, and Gross Irrigation, and low mean bias values (−2.8% to +3.3%) show the absence of systematic over- or under-estimation compared to CROPWAT model. The application also demonstrated responsiveness to climatic variability. Although the validation is limited to few representative locations and assumed minimal runoff conditions, the results suggest that the proposed method is technically consistent and feasible in practice. This study demonstrates smartphone-based application as a decision support for field-level irrigation planning and water resource management, particularly in data-limited agricultural contexts. Full article