Search Results (1,820)

Coastal erosion poses a growing threat to natural systems and critical infrastructures, particularly in touristic coastal areas where beaches represent both ecological assets and economic resources. Beyond shoreline retreat, erosion processes progressively reduce emerged beach surfaces and increase the exposure and vulnerability of coastal roads, railways, and urban settlements, with cascading socio-economic consequences. This study presents an integrated geomorphological and economic assessment of coastal erosion impacts. The Coastal Degradation Calculator (CoDeC) is applied along the Tyrrhenian coast of southern Italy, focusing on the municipality of San Lucido. Shoreline variations are quantified to reconstruct long-term changes in the Surface of the Emerged Beach (SEB) before and after major coastal defense interventions using multi-temporal remote sensing data (1954–2018). Simple, science-based box models are implemented to estimate sediment deficits, restoration needs, and associated economic damages, expressed in both €/m² and €/year. Results highlight a reduction in SEB area exceeding 60%, significant downdrift erosion linked to hard defenses and additional losses caused by coastal urbanization. The methodology proved effective in supporting damage quantification and informed the resolution of a long-standing legal dispute between public authorities. Owing to its transparency and reproducibility, the proposed framework offers a transferable tool for coastal risk assessment and management under increasing climate-driven pressures. Full article

Reliable production forecasting is a critical task for evaluating asset valuation and commercial performance in oil and gas reservoirs. Conventional short-term forecasting methods, such as Arps’ decline curve analysis, rely on simple mathematical curve fitting and often oversimplify reservoir performance. On the other hand, long-term forecasting requires complex multidisciplinary models that integrate geophysics, reservoir engineering, and production engineering, but these approaches are time-consuming and have high turnaround times. To bridge the gap between long and short-term production forecasts, reduced-physics models such as Blasingame type curves have been developed, incorporating transient well behaviour derived from diffusivity equations and Darcy’s law. These models assume homogeneity and uniform reservoir properties, enabling faster results while honouring pressure performance. However, despite their efficiency, they still face limitations in reliability, particularly when extended to long-term forecasts. This paper proposes a hybrid modelling approach that integrates flowing material balance (FMB) concepts into physics-informed neural networks (PiNNs) and machine learning models to improve the accuracy and reliability of production forecasting. The proposed methodology introduces two hybrid strategies: physics-informed models enriched with FMB feature, and PiNNs. The first proposed hybrid model uses a created FMB-derived feature as input to neural networks. The second PiNN model embeds data-driven loss functions with a physics-based envelope to reflect reservoir response into the machine learning model. The primary loss function is mean squared error, ensuring minimization of data misfit between predicted and observed production rates. The study validates both proposed physically informed neural network models through performance metrics such as RMSE, MAE, MAPE, and R². Results application on field data shows that the integration of FMB into neural network models using the PiNN concept guides the neural network models to predict the production rates with higher reliability over the full span of the tested data period, which was the last year of unseen production data. Additionally, the proposed PiNN model is able to predict the well productivity index via hyper-tuning of the PiNN model. Furthermore, the PiNN is not improving the metric performance of conventional neural networks, as it has to satisfy an additional material balance equation. This is due to a lower degree of freedom in the PiNN models. Full article

This paper examines how scam compounds, money mules and crypto-assets operate as interdependent elements of contemporary money-laundering chains. It assesses whether existing anti-money laundering (AML) and crypto-asset regulatory frameworks are capable of disrupting these chains holistically, rather than addressing individual components in isolation, with particular reference to scam-compound activity in Southeast Asia. The study adopts a qualitative comparative case-study methodology grounded in legal and regulatory analysis. Four empirically grounded cases are examined: two Southeast Asian scam-compound enforcement cases (Cambodia and Myanmar) and two European crypto-asset seizure cases (Ireland and Italy). Judicial decisions, enforcement actions and regulatory instruments are analysed through a chain-based analytical framework aligned with Financial Action Task Force (FATF) standards, the EU Markets in Crypto-Assets Regulation (MiCA) and the Anti-Money Laundering Authority (AMLA) framework. The analysis reveals a structural divergence in enforcement strategies: Southeast Asian responses increasingly prioritise network- and infrastructure-level disruption of scam compounds, whereas European approaches remain largely centred on post-offence crypto-asset seizure through traditional proceeds-of-crime mechanisms. Across all jurisdictions, money mules emerge as a critical yet systematically under-regulated intermediary layer enabling the resilience of crypto-laundering operations. The paper advances existing AML typologies by conceptualising scam compounds, money mules and crypto-assets as interconnected components of a single crypto-laundering chain. This chain-based perspective offers a novel analytical and regulatory lens for understanding organised crypto-enabled fraud. The study is based on a qualitative, case-based design and does not aim for statistical generalisation. However, the analytical framework developed is transferable to other jurisdictions experiencing similar scam-compound and crypto-laundering dynamics. The findings suggest that effective AML enforcement requires coordinated intervention across multiple nodes of the laundering chain, including scam compound infrastructure and money mule networks, alongside traditional asset-seizure mechanisms and CASP supervision. By highlighting the structural links between scam compounds, coercive labour and crypto-laundering mechanisms, the paper underscores the broader social harms of crypto-enabled fraud and the need for integrated regulatory responses that address both financial crime and human exploitation. Full article

This paper investigates whether water-related climate stress predicts tail movements in food system assets and whether these spillovers vary across market regimes and investment horizons. Using daily data from January 2012 to January 2026, we examine the relationships among a water-risk proxy, agricultural commodities, agribusiness, and food supply-chain equities, and a fertilizer-related proxy. The analysis combines the cross-quantilogram with quantile spillover analysis in the frequency domain, allowing us to capture directional dependence in the tails of the distribution and short- and long-run connectedness. To account for structural change, we employ data-driven break detection and identify three major regimes: a pre-disruption period, a COVID-related adjustment phase, and a broader food system stress regime from early 2022 onward. The findings indicate that water-related climate stress has its strongest predictive power in the tails, especially for agribusiness and fertilizer-related assets, while the broad agricultural commodity basket is comparatively less sensitive. Lower-tail dependence is predominantly negative and often significant, whereas upper-tail dependence is generally positive, indicating asymmetric transmission under extreme market conditions. The spillover results further show that connectedness in the water–food system is mainly short-run, with agribusiness and fertilizer channels acting as the primary conduits of transmission. From a practical perspective, these findings suggest that investors and risk managers can use water-related market signals as early warning indicators of stress in food system assets, while policymakers can strengthen food system resilience through integrated water management, input market monitoring, and supply chain adaptation measures. The findings suggest that water-related climate stress is not merely an environmental constraint but a systemic source of food system risk with implications for resilience, risk monitoring, and integrated water-agriculture governance. Full article

In the context of political and financial market turmoil, effectively forecasting financial market trends is crucial for investment decisions. Large language models (LLMs) have been applied in extant research to predict market trends, analyze investor sentiments and interpret financial news, all aiming to help investment decision making. However, LLMs face limitations due to training data heterogeneity, restricting multidimensional perspectives and hindering comparative analysis for optimization. This study proposes a “Dual-Agent LLM Debate Mechanism” framework using a Proponent (LLM1: Gemini Pro 3) and an Opponent (LLM2: ChatGPT 5.2) to address single-LLM forecasting gaps: The Proponent generates a baseline forecast (F1) from an Integrated Context, while the Opponent validates and resolves conflicts with the Proponent via up to three rounds of cross-debate to produce a consensus forecast (F2). A controlled experiment was conducted to analyze 75 financial market indicators (FMIs) across five asset categories, revealing that F2 outperforms F1 in accuracy and directional stability, particularly in highly volatile assets like Cryptocurrencies and 10-Year Government Bonds. Paired-sample t-tests confirmed statistical significance, validating the mechanism’s effectiveness. Our study results demonstrate how cross-debate between LLMs enhances forecasting accuracy through structured optimization. Full article

This research article explores whether the climate transition risk (CTR) and climate physical risk (CPR) transmit greater shocks towards the sustainable, gold-backed, energy-related and Sharia-compliant cryptocurrencies during bullish market conditions as compared with the normal and bearish market conditions. We employ the novel quantile vector auto-regression (QVAR)-based connectivity framework. Overall findings suggested that CPR and CTR transmitted greater shocks towards cryptocurrency classes during extremely high and lower quantiles as compared with the median quantile. This U-shaped and non-linear climate risks shock transmission indicates that Sharia-compliant, energy-related and gold-backed cryptocurrencies become more vulnerable during extreme market conditions (higher and lower quantiles) and may not consistently serve as reliable hedging or diversification instruments, particularly during periods of heightened climate uncertainty. Overall findings suggested that both the CPR and CTR transmitted greater shocks towards energy-related, gold-backed, and Sharia-compliant cryptocurrencies as compared with the sustainable cryptocurrencies, across all the quantiles. Therefore, sustainable cryptocurrencies, particularly those with energy-efficient consensus mechanisms such as Stellar, Cardano and Ripple, exhibited resilience to climate risks and can therefore function as stabilizing core holdings in diversified portfolios. Fund managers should incorporate a rebalancing strategy that increases allocation to these climate-resilient, sustainable digital assets during periods of elevated climate risk. Fund managers should integrate CPR and CTR into the quantile-domain forecasting frameworks for predicting digital asset market returns to enhance financial stability. Portfolio managers should undertake dynamic and quantile-contingent climate risk hedging strategies that account for tail-risk exposure rather than relying on average market behavior. Full article

Against the background of deepening “dual carbon” goals and the continuously tightening policies of the national carbon market, the carbon asset risks faced by thermal power enterprises have shifted from short-term compliance cost fluctuations to medium- and long-term systemic risks. Managing these risks effectively is essential for ensuring the financial viability of thermal power operations during the low-carbon transition, thereby supporting the long-term sustainability of the energy sector. This study constructs a risk management framework for carbon assets in thermal power enterprises based on the LSTM model and option portfolios. First, the multi-dimensional characteristics of medium- and long-term carbon asset risks are systematically identified at the policy, market, and enterprise levels. Second, a dual-layer LSTM model with Dropout regularization is employed to simulate medium- and long-term carbon prices. The prediction results indicate a moderate upward trend in future carbon prices, with the fluctuation range gradually narrowing. On this basis, a combined hedging strategy of “core call options + auxiliary put options” is designed, capping the maximum procurement cost at 72.63 CNY/ton and covering over 90% of the risk of carbon price increases. Monte Carlo simulations and rolling window backtesting, conducted using operational data from a thermal power enterprise to validate the framework, verify the effectiveness and robustness of the strategy. The study shows that, through the integration of accurate LSTM predictions and proactive option hedging, thermal power enterprises can transform their carbon asset management from passive compliance to active value creation, thereby enhancing their operational sustainability and resilience during the energy transition. Full article

Children are some of the most vulnerable members of society who must be protected at all costs. Zimbabwe has a long history of disjointed formal and indigenous social protection systems, which have resulted in the exclusion of many children, leading to high levels of child abuse, neglect, exploitation and violence. In policy and practice, there is a strong bias towards the ineffective statist formal system, yet the indigenous social protection system is the mainstay for the protection of most children. The study aimed to explore how asset-based community development can be used as a strategy to integrate the fragmented formal and indigenous social protection systems for sustainable child protection. An explanatory sequential mixed-methods research design was employed, collecting both quantitative and qualitative data from 76 participants. The study findings indicate that asset-based community development by positioning the indigenous social protection system at the centre of the social protection framework provides a blueprint for a community-led and integrated social protection system, which can translate into effective child protection. This system, which utilises a wider network of community and external resources, can counteract the limits of fragmented social protection and sustainably promote child protection among impoverished households in Zimbabwe and similar contexts. The recommendation is that asset-based community development should be promoted as a strategy towards integrated social protection and sustainable child protection. Full article

The transition from isolated distributed ledgers to a unified “Internet of Value” is hindered by the lack of efficient, verifiable, and privacy-preserving cross-chain data retrieval mechanisms. While asset bridging has matured, generalized data indexing remains a critical bottleneck, constrained by the semantic gap between heterogeneous storage layouts and the prohibitive verification tax of cryptographic proofs. In this paper, we present HyperCross, a novel semantic-aware zero-knowledge indexing framework designed to bridge this divide. We first formalize the heterogeneous cross-chain storage optimization problem (HCCSOP) and prove its NP-completeness. To tackle this, HyperCross employs a synergistic tri-layered architecture. At the semantic layer, we introduce a unified data abstraction (UDA) that leverages category-theoretic functors and schema morphisms to ensure mathematically rigorous state mapping for both simple assets and complex smart contract logic. At the indexing layer, a zero-knowledge learning index (ZKLI) shifts prediction intelligence to the client side, integrating zk-SNARKs with silent oblivious transfer to achieve constant-time verification ($O\left(1\right)$) while concealing access patterns. Finally, a multi-level cache (MLC) utilizes predictive prefetching with $\Delta$-bounded staleness to mask network latency. Extensive evaluations demonstrate that HyperCross reduces query latency by $2.4\times$ and storage overhead by $40\%$ compared to state-of-the-art baselines, establishing a scalable foundation for data-intensive inter-chain applications. Full article

This study examines the relationship between institutional embeddedness, community factors, and the outcomes of labor market integration programs in contexts characterized by high social vulnerability and unemployment. The aim is to analyze how the local embeddedness of organizations and the mobilization of community resources influence the effectiveness of interventions designed to enhance employability. A mixed-methods approach was employed, combining qualitative and quantitative techniques. Data were collected from 100 participants in a labor market integration program in a southern Spanish city, using standardized scales that measured the sense of community, perceptions of community assets, employability, and perceived impact of the program. Additionally, the program’s implementation team was interviewed, a documentary analysis was conducted, and direct observations of training and job-placement activities were carried out. The findings highlight that the institutional and community embeddedness of organizations facilitates access, sustained participation, and the contextual adaptation of interventions. Connection with local dynamics is crucial for enhancing the impact of labor market integration programs, allowing for more personalized interventions that are sensitive to sociocultural barriers and focused on improving employability and the overall well-being of individuals at risk of exclusion. Full article

Against the backdrop of accelerating low-carbon transformation in the global energy system and decarbonization in the transportation sector, the widespread adoption of electric vehicles has intensified grid load imbalances and highlighted challenges in integrating intermittent renewable energy generation. Vehicle-to-Grid (V2G) technology has emerged as a key solution to these challenges. This paper systematically traces the global evolution of V2G technology from conceptualization to large-scale deployment, focusing on localized practices in China’s scaled V2G applications. It dissects the logic behind policy evolution, identifies three distinct Chinese V2G models—centralized, distributed, and battery-swapping—and validates the practical outcomes of representative pilot projects. Research reveals three core constraints hindering China’s large-scale V2G adoption: the absence of battery capacity degradation management mechanisms, fragmented standardization systems, and rigid market mechanisms. Based on this, the paper proposes recommendations for scaling V2G in China across three dimensions: power battery second-life utilization, standardization system construction, and market mechanism optimization. Furthermore, aligning with the global demand for large-scale V2G implementation, this paper proactively proposes innovative market models. These include establishing a coordinated trading mechanism between green power and V2G, developing a digitally driven distributed trust and transaction system, and exploring financialization and risk hedging models for battery assets. These concepts provide theoretical foundations and decision-making references for achieving high-quality, large-scale V2G applications worldwide. Full article

Conveyor belts are critical equipment in mining operations, where continuous and reliable material transport is essential for production efficiency. This systematic review aims to analyze computer vision-based techniques applied to conveyor belt condition monitoring. Following PRISMA guidelines, a search was conducted in the Scopus and Web of Science databases, and 80 studies were selected after applying predefined eligibility criteria. These studies were synthesized through quantitative bibliometric methods and structured qualitative thematic categorization. The findings reveal a significant increase in scientific output after 2020, as well as its geographic distribution and potentially the most influential contributions. The main research lines focus on damage detection, deviation detection, and foreign object detection. A clear transition is also observed from traditional image processing methods—such as filtering, segmentation, and geometric analysis—toward deep learning models, including YOLO, CenterNet, and hybrid architectures, with improvements in precision, speed, and stability. Nevertheless, challenges remain related to datasets representativeness, the heterogeneity of evaluation protocols, and variability in operational conditions. Finally, opportunities for advancement are identified through multimodal datasets, adaptive models, and lightweight solutions that facilitate integration into asset management systems and support scalable industrial adoption. Full article

Transportation asset management (TAM) has traditionally centered on technical performance and economic efficiency. In recent years, however, there has been increasing recognition of the environmental and social impacts of maintenance and rehabilitation (M&R) activities. This paper presents a systematic review of how Environmental, Social, and Governance (ESG) metrics are being incorporated into TAM. Using PRISMA 2020, four major databases were searched, identifying 75 studies since 2010. Environmental metrics were the most developed, especially those measuring emissions, energy use, and material consumption. Social metrics appeared less frequently and are typically used descriptively, including indicators of income inequality, user costs, and equity-focused metrics such as the Benefit Distribution Ratio and Social Return on Investment. Governance was the least explored pillar and is generally addressed through fiscal transparency, risk management, or institutional practices rather than explicit measurable indicators. Overall, the review shows growing interest in integrating ESG into TAM, but the adoption of social and governance metrics remains limited. In particular, governance indicators are rarely operationalized as measurable variables within TAM decision-making, highlighting a critical gap in the literature. This study synthesizes ESG-related indicators used in TAM and provides a structured foundation for future research and more comprehensive sustainability-oriented decision frameworks. Full article

Distributed generation (DG) exhibits inherent volatility and intermittency, and its grid-integration expansion presents formidable challenges to microgrid regulation and control. Conventional control strategies often neglect the uncertainties associated with renewable energy generation and the coordinated management of flexible resources. This paper proposes a multi-time-scale optimal control strategy for microgrids that explicitly accounts for uncertainty. The strategy integrates a collaborative scheduling framework for assets, including electric vehicles (EVs) and energy storage systems, alongside a stochastic optimization model for microgrids that comprehensively incorporates uncertainties from wind and solar power generation, EV operations, and load forecasting errors. The improved Archimedean chaotic adaptive whale optimization algorithm is utilized to solve the optimal scheduling model, while the Latin hypercube sampling (LHS) technique is employed to address uncertainty-related problems in the optimization process. Case study results demonstrate that, in comparison with traditional optimal scheduling strategies, the proposed approach more effectively mitigates uncertainties in real-world operations, reduces microgrid operational risks, achieves a significant reduction in scheduling costs, and concurrently fulfills the dual objectives of microgrid economic efficiency and operational security. Full article

Blue space, as an important natural and social composite feature system in cities, not only provides supporting, regulating, and provisioning services, but also plays a key role in human well-being, recreational experience, and urban sustainable development. The blue space cultural ecosystem service (CES) has gradually attracted the attention of academia in recent years, but there is a lack of systematic integration research in related fields. Therefore, it is necessary to conduct a comprehensive analysis of current studies to clarify how, and to what extent, blue spaces influence CESs. This study adopts a PRISMA-based systematic search combined with qualitative synthesis, aiming to review the research status of CES and its developmental trajectory within blue space studies, and to identify future research trends and critical gaps. A total of 52 studies meeting the inclusion criteria were finally selected through database screening. The research innovatively divides the evolution of blue space CES into three stages (2012–2017/2018–2022/2023–2025), revealing a shift in research focus from single value identification to complex policy support. Secondly, through the mapping of six typical blue space types (such as rivers and wetlands) and 10 CES indicators, combined with a Pearson correlation heatmap, it provides quantitative insights into the coupling mechanisms between indicators, such as the significant synergy between spiritual and educational values. Methodologically, it systematically discriminates between the application boundaries of monetary valuation based on the contingent valuation method and non-monetary valuation represented by social media big data and PPGIS, pointing out that technological progress is driving the evaluation toward high dynamics and refinement. Finally, the study points out current bottlenecks such as uneven geographical distribution and insufficient planning transformation, emphasizing that future research should use artificial intelligence to improve data processing accuracy and transform blue space CESs from “invisible welfare” into “explicit policy assets” to guide sustainable urban renewal and healthy space design. Full article