Search Results (732)

Zambia continues to experience increasingly frequent and intense droughts, with the 2023/2024 season among the most severe in recent history. These events have threatened livelihoods, strained water and food systems, and placed immense pressure on already limited national and local resources. Given the limited knowledge in the literature on drought management in Zambia, this study investigated the state of localized district efforts across the country. By using mixed methods with a total of 161 interviews, it assessed the participation of district governments and sector players across key components of drought governance, including early warning, monitoring, vulnerability and impact assessment, mitigation, and response. Although Zambia has made notable progress in establishing national institutional frameworks and climate policies, key findings reveal a pattern of limited proactive engagement, with most participation occurring only in response to extreme events like the 2023/2024 drought. This reactive posture at the district level is further compounded by inadequate resources, limited coordination, a lack of localized drought planning, and systemic bureaucratic constraints that undermine a timely and effective response. Nonetheless, numerous opportunities exist to strengthen drought management by localizing decision-making, integrating indigenous knowledge into existing early warning systems, and leveraging community-based infrastructures to maximize scarce resources and build long-term resilience. The paper concludes with recommendations for enhancing Zambia’s drought preparedness and response capacity through inclusive, risk-based, and proactive strategies; insights that can be adapted to other developing country contexts. Full article

Recent advancements in deep learning have significantly improved fault diagnosis methods. However, challenges such as insufficient feature extraction, limited long-range dependency modeling, and environmental noise continue to hinder their effectiveness. This paper presents a novel mixture of multi-view convolutional (MOM-Conv) layers integrating the Mixture of Experts (MOE) mechanism. This design effectively captures and fuses both local and contextual information, thereby enhancing feature extraction and representation. This proposed approach aims to improve prediction accuracy under varying noise conditions, particularly in rolling ball bearing systems characterized by noisy signals. Additionally, we propose the Mamba-enhanced adaptive self-attention long short-term memory (MASA-LSTM) model, which effectively captures both global and local dependencies in ultra-long time series data. This model addresses the limitations of traditional models in extracting long-range dependencies from such signals. The architecture also integrates a multi-step temporal state fusion mechanism to optimize information flow and incorporates adaptive parameter tuning, thereby improving dynamic adaptability within the LSTM framework. To further mitigate the impact of noise, we transform vibration signals into denoised multi-channel representations, enhancing model stability in noisy environments. Experimental results show that our proposed model outperforms existing state-of-the-art approaches on both the Paderborn and Case Western Reserve University bearing datasets, demonstrating remarkable robustness and effectiveness across various noise levels. Full article

Disaster management requires the development of effective technologies for managing both pre-and post-disaster processes. Therefore, utilizing effective tools and techniques to mitigate the disaster risks or lower the adversarial impacts is essential. Over the last decade, digital twin (DT) applications have found a wider implementation area for varying purposes, but most importantly, they are utilized for simulating disaster impacts. This study aims to develop an open-source digital twin (DT) framework for earthquake disaster management in the Cayirova district of Kocaeli, Türkiye, one of the country’s most seismically active regions. The primary objective is to enhance local resilience by integrating multi-source data into a unified digital environment that supports risk assessment, response planning, and recovery coordination. The digital model developed using QGIS (3.40.9 Bratislava), Autodesk InfraWorks 2025 software for DT modeling integrates various data types, including geospatial, environmental, transportation, utility, and demographic data. As a result, the developed model is expected to be used as a digital database for disaster management, storing both geospatial and building data in a unified structure. The developed model also aims to contribute to sustainable practices in cities, where disaster risks are particularly critical. In this respect, the developed model is expected to create sustainable logistics chains and sustainable targets aiming to reduce the number of people affected by disasters, reducing the direct economic losses caused by disasters. In this framework, the developed model is expected to further assess seismic risk and mitigate risks with DTs. These capabilities enable the project to establish an open-source district-level DT system implemented for the first time in Cayirova, provide an alternative disaster model focused on region-specific earthquakes, and integrate 2D/3D assets into an operational, ready-to-respond digital database. In terms of practical importance, the model provides a digital database (digital backup) that can be used in emergencies, helping decision-makers make faster, data-driven decisions. The significance of this study lies in bridging the gap between urban digitalization and disaster resilience by providing a scalable and transparent tool for local governments. Ultimately, the developed DT contributes to sustainable urban management, enhancing preparedness, adaptive capacity, and post-disaster recovery efficiency. Full article

Rural resilience building has gained increasing scholarly attention, yet existing literature overlooks the temporal dynamics of resilience evolution and lacks an integrative framework to explain cross-level mechanisms. This paper uses a longitudinal case study to explore how rural resilience transitions from a low-equilibrium to a high-equilibrium state and how neo-endogenous practices emerge in a weak institutional context. The study reveals three key findings. First, the village’s resilience evolved through three phases—institutional intervention, community capital activation, and resilience self-reinforcement—driven by co-evolutionary interactions between an enabling government and the rural community. This process is marked by chain effects of multidimensional community capital (e.g., cultural capital enhancing social capital) and overflow effects from resilience amplification (e.g., multi-scalar network). Second, exogenous resources and endogenous community capital are critical in the neo-endogenous model, but their synergy relies on vertical institutional interventions that foster horizontal networks and enhance communities’ resource absorption capacity. Third, the government enables resilience building by creating a support ecosystem that transitions from institutionally bundled resources to a higher-order composite space, facilitated by urban–rural interactions and community restructuring. The study makes three theoretical contributions: (1) it proposes an analytical framework integrating an enabling government, community capital, and ecosystem upgrading, thus advancing beyond the current community capital-centric paradigm; (2) it introduces a three-phase process model that unpacks spatiotemporal interactions across urban-rural interfaces, multi-scalar networks, and state-community relations, addressing the limitations of static factor-based analyses; (3) it reconceptualizes the role of government as an “enabling government” that mediates local and extra-local resource interfaces, challenging the neo-endogenous theories’ neglect of institutional agency. These insights contribute to rural resilience scholarship through a complex adaptive systems lens and offer policy implications for synergistic urban-rural revitalization. Full article

Residual oil (RO) in terrigenous reservoirs formed after waterflooding can exceed 60% of the original oil in place; approximately 70% is trapped at the macro-scale in barriers and lenses, whereas about 30% remains at the micro-scale as film and capillary-held oil. This review aims to synthesize current knowledge of RO formation mechanisms, localization methods and chemical recovery technologies. It analyzes laboratory, numerical and field studies published from 1970 to 2025. The physical and technological factors governing RO distribution are systematized, and the effects of heterogeneities of various types, imperfections in pressure-maintenance (waterflood) systems and contrasts in oil–water properties are demonstrated. Instrumental monitoring techniques—vertical seismic profiling (VSP), well logging (WL), hydrodynamic well testing (WT) and geochemical well testing (GWT)—are discussed alongside indirect analytical approaches such as retrospective production-data analysis and neural-network forecasting. Industrial experience from more than 30,000 selective permeability-reduction operations, which have yielded over 50 Mt of additional oil, is consolidated. The advantages of gel systems of different chemistries are evaluated, and the prospects of employing waste products from agro-industrial, metallurgical and petroleum sectors as reagents are considered. The findings indicate that integrating multi-level neural-network techniques with instrumental monitoring and adaptive selection of chemical formulations is crucial for maximizing RO recovery. Full article

Agriculture is responsible for nearly one-quarter of global greenhouse gas (GHG) emissions, with livestock and poultry systems contributing significantly through methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂). Achieving net-zero agriculture demands tools that not only quantify emissions but also guide management decisions and foster behavioral change. The Cool Farm Tool (CFT)—a science-based calculator for farm-level carbon footprints, water use, and biodiversity—has been widely adopted across Europe and parts of the United States. Yet, despite its proven potential, no Canadian studies have tested or adapted CFT, leaving a major gap in the country’s progress toward climate-smart farming. This paper addresses that gap by presenting the first surveys of poultry and dairy producers in Atlantic Canada as a foundation for tailoring and localizing CFT. Our mixed-methods surveys examined farm practices, feed, manure, energy use, waste management, sustainability perceptions, and openness to digital tools. Results on 23 responses (20 for poultry, 3 for dairy) revealed limited awareness but moderate interest in emission tracking: dairy farmers, already accustomed to digital systems such as robotic milking and herd software, were receptive and confident about adopting CFT. Poultry farmers, by contrast, voiced greater concerns over cost, complexity, and uncertain benefits, signaling higher adoption barriers in this sector. These findings highlight both the opportunity and the challenge: while dairy farms appear ready for rapid uptake, poultry requires stronger incentives, clearer value demonstration, and sector-specific customization. We conclude that adapting CFT with regionally relevant data, AI-driven decision support, and supportive policy frameworks could make it a cornerstone for achieving net-zero agriculture in Atlantic Canada. Full article

Rural tourism is a key engine for sustainable development, elevated to a strategic level under China’s “Rural Revitalization Strategy”, creating a complex multi-level governance (MLG) policy system whose internal mechanisms are not fully understood. This study aims to analyze the thematic structure, spatio-temporal evolution, and transmission mechanisms of China’s rural tourism policy across central, provincial, and city/county levels. We applied BERTopic topic modeling and spatio-temporal analysis to a corpus of 1174 policy documents from 2005 to 2024. The results reveal a “centrally guided Type I governance” model with a clear functional division: the central level acts as a “top-level strategic designer”, the provincial level as a “key regional hub” for adaptation, and the city/county level as the “frontline of policy implementation”. We identified a vertical transmission chain characterized by a 1–2-year lag, alongside spatial differentiation driven by regional resource endowments at the provincial level and functional needs at the city/county level. This study concludes that China’s rural tourism governance framework is an efficient synergistic system that combines strong central guidance with dynamic local adaptation, providing empirical support for MLG theory in a unitary state and offering insights for optimizing policy coordination. Full article

Coastal areas face unique challenges, with climate change impacts such as sea level rise exacerbating coastal hazards that put people, infrastructure, and habitats at risk. This study evaluates three generations of Shoreline Management Plans (SMPs) in the UK (1993–2024). The study considers whether the system of governance is encouraging sustainable solutions. Policy analysis identifies a range of sustainability principles which have become dominant for SMPs. Findings show that shoreline management planning has evolved considerably over the last 30 years. It has transformed governance from a reactive, engineering-focused, administratively based approach to a risk-based, geostrategic, technically informed approach. SMPs have slowed the increase of coastal vulnerability. In the most recent phase, they have increased consideration of adaptation to the impacts of climatic change. However, strategic goals are not always translated into locally implemented action, because of problems with criteria, collaboration, costs, cultural attitudes, competing priorities for coastal landuse, and contentious decisions, especially those which set public interest against individual interests. So, governing the shoreface will need to evolve further to deal with the tensions between ‘working with nature’, ‘working in partnership with people’, and ‘adapting to future climates’. Full article

The social-ecological resilience of rural settlements refers to their ability to resist and mitigate the risks posed by internal and external disturbances, and to utilize the external environment to achieve a new equilibrium state. Amid rapid urbanization, it is of great significance for mountainous settlements to improve their risk resistance and development ability. Taking Dong’an Town in Chengkou County, located in the eastern part of Qinling–Bashan Mountains in southwestern China, as the research object, this study constructs an evaluation index system for rural residential resilience based on social-ecological resilience theory. It explores the resilience level of rural residences in mountainous areas from the dimensions of internal resilience and external environmental resilience and scientifically proposes an optimization path for the spatial layout of rural residences. This study provides a reference for optimizing the rural living environment, promoting spatial equity, and improving people’s livelihood according to local conditions. The results showed that: (1) The overall level of security resilience of rural settlements in Dong’an Town was relatively high, with 221 patches above the security level, accounting for 19.53% of the total area of the town. (2) The rural residents in Dong’an Town can be categorized into three types: core structure optimization, peripheral system upgrading, and relocation and withdrawal. Different types of rural settlements adapt to internal and external resource conditions and select optimal spatial layout paths according to local conditions. Full article

Heavy-duty diesel trucks (HDDTs) are recognized as significant sources of air pollutants and greenhouse gases (GHGs) along freight corridors in port cities. Despite their impact, few studies have provided detailed spatiotemporal insights into their emissions within port-adjacent highway systems. This study presents a high-resolution, hourly emission inventory at the road-segment level for six major expressways in Shanghai, one of China’s leading port cities. The emission estimates are derived using a locally adapted COPERT V model, calibrated with HDDT GPS trajectory data and detailed road network information from OpenStreetMap. The inventory quantifies emissions of CO₂, NO_x, CO, PM, and VOCs, highlighting distinct temporal and spatial variation patterns. Weekday emissions consistently exceed those of weekends, with three prominent traffic-related peaks occurring between 5:00–7:00, 10:00–12:00, and 14:00–16:00. Spatial analysis identifies the G1503 and S20 expressways as major emission corridors, with S20 exhibiting particularly high emission intensity relative to its length. Combined spatiotemporal patterns reveal that weekday emission hotspots are more concentrated, reflecting typical freight activity cycles such as morning dispatch and afternoon return. The findings provide a scientific basis for formulating more precise emission control measures targeting HDDT operations in urban port environments. Full article

This study presents an analysis of the condition of street lighting based on a selected typical installation in one of the 1459 rural communes in Poland. The analysis was carried out on the basis of publicly available statistical data, local government reports, and information contained in national and European strategic documents. During the analysis, numerous irregularities and differences in the quality and energy efficiency of the lighting infrastructure were indicated. It was found that outdated sodium luminaires with high energy consumption, low durability, and limited luminous efficacy are used in many cases, which generates significant operating costs and negatively affects the environment. The authors emphasize that a lack of regular and professional lighting audits leads to the suboptimal use of energy resources, an insufficient level of road safety, and failure to adapt lighting to current technical standards and the needs of road users. A lighting audit is a key tool for diagnosing the technical condition, efficiency, and compliance of installations with relevant regulations and recommendations. It also allows for the identification of potential savings and determining the directions of modernization and implementation of energy-saving technologies, such as LED luminaires and intelligent control systems.The presented analysis demonstrates that energy audits are an effective tool for confirming efficiency improvements and enhancing visual safety conditions through better compliance with photometric standards (luminance, lighting uniformity). Direct accident statistics were not within the scope of this study. Full article

This study adopts a systems perspective to examine the persistent effects of China’s One-Child Policy (OCP) on the subjective well-being of older adults, emphasizing structural persistence, reinforcing feedback, and path-dependent lock-in in complex socio-technical systems. Using nationally representative data from the China Longitudinal Aging Social Survey (CLASS-2014), we exploit the OCP’s formal rollout at the end of 1979—operationalized with a 1980 cutoff—as a quasi-natural experiment. A Fuzzy Regression Discontinuity (FRD) design identifies the Local Average Treatment Effect of being an only-child parent on late-life well-being, mitigating endogeneity from selection and omitted variables. Theoretically, we integrate three lenses—policy durability and lock-in, intergenerational support, and life course dynamics—to construct a cross-level transmission framework: macro-institutional environments shape substitution capacity and constraint sets; meso-level family restructuring reconfigures support network topology and intergenerational resource flows; micro-level life-course processes accumulate policy-induced adaptations through education, savings, occupation, and residence choices, with effects materializing in old age. Empirically, we find that the OCP significantly reduces subjective well-being among the first generation of affected parents decades later (2SLS estimate ≈ −0.23 on a 1–5 scale). The effects are heterogeneous: rural residents experience large negative impacts, urban effects are muted; men are more adversely affected than women; and individuals without spouses exhibit greater declines than those with spouses. Design validity is supported by a discontinuous shift in fertility at the threshold, smooth density and covariate balance around the cutoff, bandwidth insensitivity, “donut” RD robustness, and a placebo test among ethnic minorities exempt from strict enforcement. These results demonstrate how demographic policies generate lasting impacts on elderly well-being through transforming intergenerational support systems. Policy implications include strengthening rural pension and healthcare systems, expanding community-based eldercare services for spouseless elderly, and developing complementary support programs. Full article

The regionalization of input–output tables enables a granular understanding of economic systems, allowing for interregional and interindustry analysis for goods and services in a local economy. This paper details the construction of an intermunicipal IO matrix for the state of Rio Grande do Sul (Brazil), a region marked by both economic diversification and significant territorial disparities. Using the 16-sector state IO matrix (base year 2019) provided by the state-level treasury (SEFAZ-RS) as a starting point, we adapt the Interregional Input–Output Adjustment System (IIOAS), integrating gravity-based trade modelling and RAS balancing, to produce a disaggregated structure for 497 municipalities. The regionalization follows three main steps: (i) generation of an initial matrix assuming proportional municipal shares in sectoral supply and demand; (ii) iterative RAS-based adjustments to align with municipal and state-level constraints; and (iii) incorporation of complementary municipal data—including employment, GDP, household consumption, and exports—to refine final demand and value-added allocations. The results demonstrate the feasibility of deriving spatially intermunicipal IO structures from limited data. The results show that, while industrial and service activities are concentrated around the Porto Alegre metropolitan area, rural subregions remain specialized in low value-added primary sectors. Full article

Mainlobe interference can severely degrade the performance of distributed phased-array radar systems in the presence of strong jamming or low-reflectivity targets. This paper introduces a signal–data dual-domain cooperative antijamming and localization (SDCAL) framework that integrates adaptive complete ensemble empirical mode decomposition with improved blind source separation and wavelet optimization (CEEMDAN-WOBSS) for signal-level denoising and separation. Following source separation, CFAR-based pulse compression is applied for precise range estimation, and multi-node data fusion is then used to achieve three-dimensional target localization. Under low signal-to-noise ratio (SNR) conditions, the adaptive CEEMDAN–WOBSS approach reconstructs the signal covariance matrix to preserve subspace rank, thereby accelerating convergence of the separation matrix. The subsequent pulse compression and CFAR detection steps provide reliable inter-node distance measurements for accurate fusion. The simulation results demonstrate that, compared to conventional blind-source-separation methods, the proposed framework markedly enhances interference suppression, detection probability, and localization accuracy—validating its effectiveness for robust collaborative sensing in challenging jamming scenarios. Full article

Vertical greening (VG) delivers measurable urban ecosystem benefits, yet maintenance is constrained by at-height safety risks, heterogeneous facade geometries, and low labor efficiency. Although unmanned aerial vehicles show promise, most studies optimize isolated modules rather than providing a user-oriented, system-level pathway. This paper proposes a closed-loop, multi-method framework integrating the Decision-Making Trial and Evaluation Laboratory-Analytic Network Process, the Functional Analysis System Technique, and the Theory of Inventive Problem Solving. DEMATEL-ANP models causal interdependencies among requirements and derives prioritized weights,; FAST decomposes functions and localizes conflicts, and TRIZ converts those conflicts into principle-guided structural concepts—establishing a traceable requirements → functions → conflicts → structure pipeline. We illustrate the approach at the prototype level with Rhino–KeyShot visualizations under near-facade constraints, showing how prioritized requirements propagate into candidate UAV architectures. The framework structures the identification and resolution of tightly coupled technical conflicts, supports adaptability in facade-proximal scenarios, and provides a transparent mapping from user needs to structure-level concepts. Claims are restricted to methodological feasibility; comprehensive quantitative field validation remains for future work. The framework offers a reproducible methodological reference for the systematic design and decision-making of intelligent UAV maintenance systems for VG. Full article