Search Results (122)

Demographic trends over the last decades and future projections clearly indicate a steady increase in the proportion of older adults (65+) relative to both the working-age (15–64) and child populations (0–15) across Europe. This demographic shift—driven by rising life expectancy and declining fertility—raises pressing challenges for intergenerational equity and questions the sustainability of the implicit formal and informal “social contract” that links generations through the distribution of rights, responsibilities, and resources. In particular, the two fundamental pillars of European post-industrial societies, namely an extensive welfare state and a liberal–democratic institutional framework, appear to be at risk. To address this issue, the notion of “intergenerational fairness”, recently adopted by social policies in both USA and Europe, appears flexible and fundamentally ambiguous. As a substantial variant of neoliberal austerity policies, it is simply used as a justification for further austerity measures, the withdrawal of entitlements to social and economic rights by citizens and the dismantling of welfare states. A second meaning of “intergenerational fairness” is possible starting from the concept of ambivalence used to describe the mix of conflict and solidarity that characterizes intergenerational relations in contemporary post-industrial societies. In this respect, the two concepts of “successful ageing” and “active ageing”, often considered as overlapping, actually involve very different perspectives: successful ageing adopts a substantially reductionist, individualistic, and static approach to the process of ageing, whereas active ageing is a more comprehensive and dynamic strategy that seeks to overcome all these limitations by a life-course perspective. This recognizes that a person’s path to old age is not predetermined but depends primarily on earlier life experiences and their influence: the ageing process affects people of all ages, not just the elderly. And since the subjectivization of ageing in contemporary societies has challenged the conventional notion of “natural life stages”, the new theoretical concept advanced in the article of “ageing styles” becomes central to understanding the ageing process today. Ageing styles are the outcome of the interplay between the objective and subjective dimensions of the life course, represented, respectively, by life chances (social structure) and life choices (agency). A theoretical framework is proposed for analyzing ageing styles that can be used from a life-course perspective to highlight their complex and dynamic nature. An evidence-based European political strategy aimed at promoting active ageing from a perspective of intergenerational fairness, based on the eight principles indicated, can be flexible enough to ensure that everyone can adopt their preferred ageing style without top-down imposition and contribute to the maintenance of the intergenerational social contract. Full article

As digital transformation intensifies, the governance of spatial data infrastructures is becoming increasingly dependent on the capacity to generate and sustain trust—technological, institutional and civic. This challenge is particularly acute in the Mediterranean region, where disparities in how geospatial data are produced, accessed, and validated are created by uneven digital development and fragmented governance structures. In response to this, this paper introduces an integrated framework combining geospatial artificial intelligence (GeoAI) and blockchain technologies to support transparent, verifiable and spatially explicit models of digital trust. Based on case studies from the Horizon 2020 TRUST project, the framework defines trust through territorial indicators across three dimensions: digital infrastructure, institutional transparency, and civic engagement. The system uses interpretable AI models, such as Random Forests, K-means clustering and convolutional neural networks, to classify regions into trust typologies based on multi-source geospatial data. These outputs are then transformed into semantically structured spatial products and anchored to the Ethereum blockchain via smart contracts and decentralized storage (IPFS), thereby ensuring data integrity, auditability and version control. Experimental results from pilot regions in Italy, Greece, Spain and Israel demonstrate the effectiveness of the framework in detecting spatial patterns of trust and producing interoperable, reusable datasets. The findings highlight significant spatial asymmetries in digital trust across the Mediterranean region, suggesting that trust is a measurable territorial condition, not merely a normative ideal. By combining GeoAI with decentralized verification mechanisms, the proposed approach helps to develop accountable, explainable and inclusive spatial data infrastructures, which are essential for democratic digital governance in complex regional environments. Full article

Maximum power point tracking (MPPT) under partial shading is a nonconvex, rapidly varying control problem that challenges multi-agent policies deployed on photovoltaic modules. We present Fuzzy–MAT3D, a fuzzy-augmented multi-agent TD3 (Twin-Delayed Deep Deterministic Policy Gradient) controller trained under centralized training/decentralized execution (CTDE). On the theory side, we prove that differentiable fuzzy partitions of unity endow the actor–critic maps with global Lipschitz regularity, reduce temporal-difference target variance, enlarge the input-to-state stability (ISS) margin, and yield a global $L_{\infty }$$\gamma$-contraction of fixed-policy evaluation (hence, non-expansive with $\kappa =\gamma <1$). We further state a two-time-scale convergence theorem for CTDE-TD3 with fuzzy features; a PL/last-layer-linear corollary implies point convergence and uniqueness of critics. We bound the projected Bellman residual with the correct contraction factor (for $L^{\infty }$ and $L^2\left(\rho \right)$ under measure invariance) and quantified the negative bias induced by $min\{Q_1,Q_2\}$; an N-agent extension is provided. Empirically, a balanced common-random-numbers design across seven scenarios and 20 seeds, analyzed by ANOVA and CRN-paired tests, shows that Fuzzy–MAT3D attains the highest mean MPPT efficiency (92.0% ± 4.0%), outperforming MAT3D and Multi-Agent Deep Deterministic Policy Gradient controller (MADDPG). Overall, fuzzy regularization yields higher efficiency, suppresses steady-state oscillations, and stabilizes learning dynamics, supporting the use of structured, physics-compatible features in multi-agent MPPT controllers. At the level of PV plants, such gains under partial shading translate into higher effective capacity factors and smoother renewable generation without additional hardware. Full article

This study investigates the effectiveness of standard price adjustment formulae for construction contracts in Portugal. Price adjustment is a mechanism that aims to adjust contractual values to market price fluctuations in labor, materials, and equipment. Such formulae are often generalized and applied in contractual practices without being properly tailored to the specific characteristics of each project. To fill this gap, this study, informed by convergent proposals, builds on the argument that the use of different bill of quantities (BOQs)-based price adjustment formulae for the same contract is fairer, more equitable, and with greater capacity to cushion sudden variations in prices. Using a case study, consisting of a public construction contract, the study analyses price differences obtained through the application of the standard formula and compares them, over different periods, with those obtained through the use of two alternative calculation formulae specifically developed according to the project’s characteristics. The performance of all scenarios was evaluated against the evolution of the growth rate of the construction cost index (CCI). The results demonstrate that the standard formula, even when it is readjusted, provides disproportionate results. In contrast, the BOQ-based price adjustment formulae, developed according to different phases of the works, provide results that are very close to those obtained by CCI-based escalation. The behavior of these customized formulae, even during periods of high price indices increases, closely tracks that of inflation. The results of the study call for the enactment of a more comprehensive legal mechanism so that all cost elements of a construction project can be properly reflected in price adjustment methodologies. Full article

Motivated by the policy urgency of China’s dual-carbon goals and the practical obstacle that official input–output (IO) and MRIO tables are sparse and non-consecutive, this study investigates how to generate credible, mechanism-aware provincial–sector forecasts of carbon footprints and embodied transfers for Western China—a region with pronounced structural heterogeneity. We develop a regionalized forecasting pipeline that fuses balance-constrained MRIO completion (RAS–CE) with a Whale-optimized Grey Neural Network (WOA–GNN), bridging the data gap (2007–2017 reconstruction) and delivering 2018–2030 projections at province–sector resolution. The novelty lies in integrating RAS–CE with a meta-heuristic grey learner and layering explainable network analytics—Grey Relational Analysis (GRA) for factor ranking, complex-network measures with QAP regressions for driver identification, and SHAP for post hoc interpretation—so forecasts are not only accurate but also actionable. Empirically, (i) energy mix/intensity and output scale are the dominant amplifiers of footprints, while technology upgrading (process efficiency, electrification) is the most robust mitigator; (ii) a structural sectoral hierarchy persists—S2 (non-metallic minerals) remains clinker/heat-intensive, S3 (general/special equipment) operates as a mid-chain hub, and S6/S7 (electrical machinery/instruments) maintain lower, more controllable intensities as the grid decarbonizes; (iii) by 2030, the embodied carbon network becomes denser and more centralized, with Sichuan–Chongqing–Guizhou–Guangxi forming high-betweenness corridors; and (iv) QAP/SHAP converge on geographic contiguity (D) and economic differentials (E) as the strongest positive drivers (openness Z and technology gaps T secondary; energy-mix differentials F weakly dampening). Policy-wise, the framework points to green-power contracting and trading for hubs, deep retrofits in S2/S3 (low-clinker binders, waste-heat recovery, efficient drives, targeted CCUS), technology diffusion to lagging provinces, and corridor-level governance—demonstrating why the RAS–CE + WOA–GNN coupling is both necessary and impactful for data-constrained regional carbon planning. Full article

Land use and land cover (LULC) changes driven by agricultural and livestock expansion pose significant threats to the Brazilian savanna (Cerrado). This study aimed to analyze, map, and simulate LULC changes in the southern mesoregion of Maranhão State by generating geospatial scenarios projected through 2030. LULC changes between 2015 and 2020 were analyzed using Landsat images classified with the Random Forest machine learning algorithm. A spatial model based on cellular automata was employed to simulate land use and land cover scenarios for the year 2030. When comparing the simulated map with the reference map, an overall accuracy of 70.28% and a Kappa index of 0.608 were observed. Results revealed a decrease in native savanna and grassland areas, with a corresponding increase in agricultural and pasturelands, notably in municipalities such as Balsas, Riachão, Tasso Fragoso, Carolina and Porto Franco. The 2030 simulation predicts continued agricultural expansion and a potential reduction of approximately 19% in native Cerrado vegetation cover, highlighting municipalities of Campestre do Maranhão, Porto Franco, São João do Paraíso, Feira Nova, Estreito, Balsas, Tasso Fragoso and Carolina. These findings underscore the value of integrating remote sensing and spatial modeling techniques within the framework of Geomatics to support environmental monitoring and management of land-use dynamics, including expansion, contraction, diversification, and agricultural intensification. This approach provides critical insights into anthropogenic impacts on sensitive ecosystems, informing sustainable planning in tropical savanna regions. Full article

Against the backdrop of policy-driven transformation in construction industrialization, the EPC general contracting model has emerged as a core pathway for the large-scale development of prefabricated buildings. However, the EPC mode integrates the links of design, procurement, production, and transportation, construction, resulting in a complex coupling correlation among the risk factors of prefabricated construction schedule, which is easy to induce the risk contagion effect and increase the difficulty of risk control of project schedule delay. To address this, this study constructs a hybrid model integrating the “Fuzzy Interpretive Structural Model (FISM)-Coupling Degree Model-Bayesian Network (BN)” to systematically analyze risk contagion mechanisms. Taking an EPC prefabricated building project as an example, FISM is used to reveal the hierarchical structure of risk factors, while the coupling degree model quantifies interaction strengths and maps them into the BN to optimize conditional probability parameters. Through comprehensive hazard analysis, seven key causal risk factors and two critical risk propagation paths are identified. Targeted control measures are designed for the key risk factors, and BN-based simulation is applied to locate critical risk nodes and implement break-chain interventions for the risk paths, resulting in a 23% reduction in the probability of schedule delay. Engineering applications demonstrate that this model can effectively achieve the dynamic identification and blocking of risk paths, providing valuable reference for similar projects and offering informed support for managers in formulating scientific response strategies. Full article

Climate change is reshaping plant distribution and ecological adaptation worldwide. Ficus tikoua Bur., a perennial resource plant native to Southwest and South China, has not been systematically assessed for its future cultivation potential. In this study, we used the Biomod2 ensemble modeling framework, integrating 12 algorithms with 469 occurrence records and 16 environmental variables, to predict the potential distribution and niche dynamics of F. tikoua under current and future climate scenarios (SSP126, SSP370, and SSP585). The ensemble model achieved high predictive accuracy based on multiple algorithms and cross-validation. The minimum temperature of the coldest month (bio6, 43.5%), maximum temperature of the warmest month (bio5, 25.0%), and annual precipitation (bio12, 10.3%) were identified as the dominant factors shaping its distribution. Model projections suggest that suitable habitats will generally expand northwestward, while contracting in the southeast. Core areas, such as the Yunnan–Guizhou Plateau and the Sichuan Basin, are predicted to remain highly stable. In contrast, southeastern marginal regions are likely to experience a decline in suitability due to intensified heat stress. Niche analyses further revealed strong niche conservatism (overlap D = 0.83–0.94), suggesting that the species maintains stable climatic tolerance and adapts primarily through range shifts rather than evolutionary change. This finding suggests limited adaptive flexibility in response to rapid warming. Overall, climate warming may enhance cultivation opportunities for F. tikoua at higher latitudes and elevations, while emphasizing the importance of protecting stable core habitats, planning climate adaptation corridors, and integrating this species into climate-resilient agroforestry strategies. These findings provide practical guidance for biodiversity conservation and land-use planning, offering a scientific basis for regional policy formulation under future climate change. Full article

Accurate cost estimation during the conceptual and feasibility phase of highway projects is essential for informed decision making by public contracting authorities. Existing approaches often rely on pavement cross-section descriptors, general project classifications, or quantity estimates of major work categories that are not reliably available at the early planning stage, while focusing on one or more key asset categories such as roadworks, bridges or tunnels. This study makes a novel contribution to both scientific literature and practice by proposing the first early-stage highway construction cost estimation model that explicitly incorporates roadworks, interchanges, tunnels and bridges, using only readily available or easily derived geometric characteristics. A comprehensive and practical approach was adopted by developing and comparing models across multiple machine learning (ML) methods, including Multilayer Perceptron-Artificial Neural Network (MLP-ANN), Radial Basis Function-Artificial Neural Network (RBF-ANN), Multiple Linear Regression (MLR), Random Forests (RF), Support Vector Regression (SVR), XGBoost Technique, and K-Nearest Neighbors (KNN). Results demonstrate that the MLR model based on six independent variables—mainline length, service road length, number of interchanges, total area of structures, tunnel length, and number of culverts—consistently outperformed more complex alternatives. The full MLR model, including its coefficients and standardized parameters, is provided, enabling direct replication and immediate use by contracting authorities, hence supporting more informed decisions on project funding and procurement. Full article

Risk identification is a critical concern in the construction industry. In recent years, there has been a growing trend of applying artificial intelligence (AI) tools to detect risks from unstructured data sources such as news articles, social media, contracts, and financial reports. The rapid advancement of large language models (LLMs) in text analysis, summarization, and generation offers promising opportunities to improve construction risk identification. This study conducts a comprehensive benchmarking of natural language processing (NLP) and LLM techniques for automating the classification of risk items into a generic risk category. Twelve model configurations are evaluated, ranging from classical NLP pipelines using TF-IDF and Word2Vec to advanced transformer-based models such as BERT and GPT-4 with zero-shot, instruction, and few-shot prompting strategies. The results reveal that LLMs, particularly GPT-4 with few-shot prompts, achieve a competitive performance (F1 = 0.81) approaching that of the best classical model (BERT + SVM; F1 = 0.86), all without the need for training data. Moreover, LLMs exhibit a more balanced performance across imbalanced risk categories, showcasing their adaptability in data-sparse settings. These findings contribute theoretically by positioning LLMs as scalable plug-and-play alternatives to NLP pipelines, offering practical value by highlighting how LLMs can support early-stage project planning and risk assessment in contexts where labeled data and expert resources are limited. Full article

Since the monotonicity of the best approximant is crucial to establish partial ordering methods, in this paper, we, respectively, characterize the best approximants in Banach function spaces and Lorentz spaces ${\mathrm{\Gamma }}_{p,w}$, in which we especially focus on the monotonicity characterizations. We first study monotonicity characterizations of the metric projection operator onto sublattices in general Banach function spaces by the property $H_g$. The sufficient and necessary conditions for monotonicity of the metric projection onto cones and sublattices are then, respectively, established in ${\mathrm{\Gamma }}_{p,w}$. The Lorentz spaces ${\mathrm{\Gamma }}_{p,w}$ are also shown to be reflexive under the condition $R{B}_p$, which is the basis for the existence of the best approximant. As applications, by establishing the partial ordering methods based on the obtained monotonicity characterizations, the solvability and approximation theorems for best proximity points are deduced without imposing any contractive and compact conditions in ${\mathrm{\Gamma }}_{p,w}$. Our results extend and improve many previous results in the field of the approximation and partial ordering theory. Full article

Urban decarbonisation requires governance models that overcome the fragmentation and rigidity of traditional urban planning. This article presents a systemic and digital framework for managing urban decarbonisation portfolios aligned with the EU Mission for Climate-Neutral and Smart Cities. Grounded in systems thinking and portfolio theory, this study develops an analytical taxonomy and an interactive digital tool to support strategic coordination, multistakeholder collaboration, and adaptive decision-making. The framework is empirically validated through the case of Madrid’s Climate City Contract, demonstrating its functionality and transferability. Using a mixed-method approach—combining co-creation workshops, interviews, document analysis, and iterative prototyping—this research maps interdependencies among projects, actors, and levers of change. The digital tool enables real-time visualisation of collaboration patterns, gaps, and synergies, enhancing strategic foresight and coordination capacity. Findings reveal that 75% of initiatives in Madrid’s CCC address climate adaptation, 80.36% are linked to knowledge generation, and key anchor projects serve as integrative hubs within the portfolio. This study concludes that the portfolio approach strengthens systemic innovation and reflexive governance by integrating digital infrastructures with collaborative planning processes. While challenges persist—including data integration, institutional capacity, and political dynamics—this research offers a replicable methodology for embedding mission-oriented strategies into urban governance. The digital portfolio emerges as a complementary governance tool that enhances transparency, organisational learning, and alignment across governance levels. Full article

This article examines Healthy Homes, a program intended to initiate a new approach to housing repairs and maintenance in remote communities in the Northern Territory of Australia. It argues that while the evidence for associations between poor housing and poor health outcomes is clear, greater attention should be paid to the implementation of health-focused housing interventions. Healthy Homes was examined through interviews with public servants, Aboriginal community-controlled organisation staff, and householders, alongside participant observation during maintenance projects and Condition Assessment Tool inspections. Routine housing, inspections, and expenditure datasets were also analysed. Across 5498 houses subject to Healthy Homes and over a twenty-month period, only 1315 Condition Assessment Tool inspections were completed, which is the key mechanism for generating preventive maintenance work. Expenditure on repairs and maintenance was stable between the old maintenance model and under Healthy Homes. Most Healthy Homes remote housing maintenance contracts were awarded to Aboriginal business enterprises. This article finds that Healthy Homes did not effectively shift remote property management to prioritise preventive maintenance. Issues with data collection and monitoring, program implementation, and contractual arrangements impeded more consistent and effective attention paid to the condition of housing health hardware. Future investment into the implementation of health-focused remote housing preventive maintenance programs must attend to the details of program design, including the data collection processes and contractual terms for service providers. Full article

Climate change is reshaping the distribution of forest species globally, yet its effects on the temperate tree genus Carpinus in China remain understudied. This study used an ensemble species distribution modeling framework to predict current and future suitable habitats for 32 Carpinus taxa under three shared socioeconomic pathway (SSP) climate scenarios for the 2090s. Five algorithms were integrated, and models with high predictive performance (AUC > 0.9) were used to generate ensemble forecasts. The ensemble models achieved AUC values no lower than 0.987 and TSS values no lower than 0.904. The results showed a clear trend of northwestward and upslope range shifts, with substantial habitat contractions under high-emission scenarios. Temperature seasonality and annual precipitation were identified as key environmental drivers. Two narrowly distributed species, C. omeiensis and C. londoniana var. lanceolata, are projected to lose all suitable habitats under SSP585, indicating a high extinction risk. These findings emphasize the importance of integrating climate-based risk assessments into conservation strategies and highlight the need to prioritize vulnerable species and high-elevation refugia to safeguard the long-term persistence of Carpinus diversity in China. Full article

The rapid expansion of the Internet of Things (IoT) has made software security and reliability a critical concern. With multi-language programs running on edge computing, embedded systems, and sensors, each connected device represents a potential attack vector, threatening data integrity and privacy. Symbolic execution is a key technique for automated vulnerability detection. However, unknown function interfaces, such as sensor interactions, limit traditional concrete or concolic execution due to uncertain function returns and missing symbolic expressions. Compared with system simulation, the traditional method is to construct an interface abstraction layer for the symbolic execution engine to reduce the cost of simulation. Nevertheless, the disadvantage of this solution is that the manual modeling of these functions is very inefficient and requires professional developers to spend hundreds of hours. In order to improve efficiency, we propose an LLM-based automated approach for modeling unknown functions. By fine-tuning a 20-billion-parameter language model, it automatically generates function models based on annotations and function names. Our method improves symbolic execution efficiency, reducing reliance on manual modeling, which is a limitation of existing frameworks like KLEE. Experimental results primarily focus on comparing the usability, accuracy, and efficiency of LLM-generated models with human-written ones. Our approach was integrated into one verification platform project and applied to the verification of smart contracts with distributed edge computing characteristics. The application of this method directly reduces the manual modeling effort from a month to just a few minutes. This provides a foundational validation of our method’s feasibility, particularly in reducing modeling time while maintaining quality. This work is the first to integrate LLMs into formal verification, offering a scalable and automated verification solution for sensor-driven software, blockchain smart contracts, and WebAssembly systems, expanding the scope of secure IoT development. Full article