Search Results (15,008)

Information infrastructure is essential for digital transformation and AI-enabled services, but its operation also involves high electricity consumption and carbon emissions. This study develops a tripartite evolutionary game model involving the government, information-infrastructure operators and the public, and integrates it with system dynamics to examine how regulatory mechanisms influence operators’ net-zero behaviours. The model focuses on operational-stage information infrastructure. Initial parameters are calibrated using the 2023 China Statistical Yearbook on Resources and Environment and expert consultation, with key variables measured by operational revenue, net-zero costs, regulatory costs, incentives, penalties, public scrutiny costs and environmental losses. The results show that operators’ net-zero behaviours may fluctuate under weak or static regulation. Government incentives, penalties and public scrutiny can promote net-zero operations, while dynamic reward–penalty mechanisms are more effective in stabilising behavioural evolution. This study extends evolutionary game theory and system dynamics to the net-zero governance of information infrastructure and provides an adaptive regulatory framework for coordinating government regulation, operator behaviour and public participation. Full article

Cirsium arvense (L.) Scop is a perennial plant of the family Asteraceae that is mainly distributed in the temperate regions of the Northern Hemisphere. Despite being widely recognized as an invasive weed in agriculture, most of the scientific evidence shows its significant phytochemical and pharmacological importance. In the present review article, a comprehensive summary of the available literature on C. arvense’s botanical properties, phytochemical composition, biological activities, standardization potential, and future therapeutic prospects has been carefully provided. This plant has been used traditionally for the treatment of inflammation, infections, bleeding disorders, and liver-related disorders. Phytochemical investigations showed the presence of many bioactive compounds such as flavonoids, phenolic acids, triterpenes, sterols, tannins, glycosides, and volatile compounds. Among the reported biological activities, antioxidants and antimicrobial properties are the most studied activities. In addition, anticancer, antidiabetic, neuroprotective, anti-inflammatory, and antiproliferative activities have also been investigated. The environmental adaptability, rapid growth, and extensive root system of C. arvense highlight its potential for development as a sustainable medicinal and industrial crop. However, there are critical research gaps present in phytochemical standardization, toxicity assessment, pharmacokinetics, and clinical validation, warranting further comprehensive studies. Full article

Rapid urbanization in tropical Asia has led to a critical loss of green cover, exacerbating urban environmental challenges. While green roofs offer a promising Nature-based solution, their implementation in Asian countries is hindered by the prevalence of sloped roofs and high structural conversion costs. This research addresses this gap by developing a novel, lightweight vegetative roof tile designed as a direct structural replacement for conventional roofing materials in Sri Lanka. Existing roofing systems were studied, followed by a laboriousness study to determine the optimum tile dimensions. To meet these requirements, a modular tile measuring 900 mm × 1200 mm with a wave-shaped corrugated profile (a 10 mm rise and a 200 mm pitch) was engineered using SolidWorks 2024 and ABAQUS 2024 to meet Eurocode standards. Field investigations into plant health helped to finalize the depth of the roof tile as 2.5 cm. Following root penetration testing, fiber-reinforced plastic was selected for the tile structure to ensure durability while maintaining a total saturated weight of 52.5 kg/m². Biological testing demonstrated robust greening performance, with Axonopus compressus and Zoysia matrella achieving 100% survival rates and over 80% canopy coverage. This design methodology can be adapted across tropical Asia, contributing significantly to regional green infrastructure development and sustainable building practices. Full article

High-resolution, long-term gridded meteorological datasets from in situ observations are crucial for ecosystem monitoring, soil diagnostics, hydrological modelling, and Earth system model evaluation. This study presents two enhanced real-time adaptations of Thornton’s Daymet V4 interpolation method. Daymet4-r1 uses a traditional calibration strategy with exhaustive parameter search, while Daymet4-r2 applies a global optimization algorithm (find_min_global from the dlib library) to adjust parameters automatically at each time step. Both methods were tested over Tuscany using high-resolution terrain and a dense observation network. Validation with leave-one-out method was carried out for the period 1995–2011 for both versions, while Daymet4-r2 underwent extended evaluation from 1991 to 2024 to assess seasonal dynamics and long-term variability. Results show that Daymet4-r2 outperforms Daymet4-r1 and the original Daymet V4 for all variables (mean absolute error of 1.24 mm, 1.06 °C, 1.29 °C, 6.26%, 0.78 m/s, and 2.04 hPa for precipitation, maximum and minimum temperature, relative humidity, wind speed, and sea level pressure, respectively). The largest improvement was observed in minimum temperature due to an enhanced approach for detecting and modelling thermal inversions. The high performance, flexibility, and ability of Daymet4-r2 to operate without prior calibration highlight its potential for model verification, real-time environmental monitoring, and integration into climate services. Full article

Millions of chicks are hatched daily in commercial hatcheries and due to ease of access and the large availability of chicks produced daily, such chicks have been accepted as a reference and control. Unfortunately, this is not a correct assumption. Chickens evolved to be hatched in nests and to remain in close contact with adult hens, which is important for the transfer of chicken-adapted microbiota from hens to offspring. In the absence of adult hens, chicks from hatcheries are colonised by microbiota of environmental origin. Forgetting this fact has led to many confounding conclusions, including a dogma on the age-dependent development of gut microbiota. In this sense, chicks from hatcheries represent a threat. However, if correctly perceived, the same chicks represent a unique opportunity for host–microbiota studies since there is no alternative animal model in which offspring free of any paternal influence are that readily available. Full article

Background/Objectives: The increasing burden of non-communicable diseases, together with accelerating environmental degradation, highlights the urgent need for sustainable dietary patterns that promote both human and planetary health. The Mediterranean diet (MedDiet), traditionally followed in countries bordering the Mediterranean basin, has gained recognition as a model of sustainable nutrition due to its well-documented health benefits and relatively low environmental impact. However, its broader role within sustainable food systems requires comprehensive and interdisciplinary evaluation. The aim of this review is to provide a state-of-the-art synthesis of the evidence on the MedDiet as a sustainable dietary pattern, integrating its health, environmental, economic, and socio-cultural dimensions. Methods: This state-of-the-art narrative review synthesizes evidence from peer-reviewed literature on the MedDiet and sustainability. Relevant studies were identified through major scientific databases, focusing on publications addressing nutritional, environmental, economic, and socio-cultural dimensions. Both observational and interventional studies, as well as modeling and life cycle assessment analyses, were included. Additional sources from international organizations and policy reports were incorporated to contextualize global trends and challenges. Results: High adherence to the MedDiet is consistently associated with a reduced risk of cardiovascular disease, type 2 diabetes, cancer, and all-cause mortality. From an environmental perspective, the MedDiet is associated with lower greenhouse gas emissions, reduced land and water use, and enhanced biodiversity conservation compared with Western dietary patterns. Economically, it may represent a cost-effective dietary model and support local food systems when grounded in traditional practices, although affordability varies across contexts. Socio-culturally, the MedDiet promotes food heritage, culinary skills, and social cohesion. Nevertheless, globalization, urbanization, and the increasing consumption of ultra-processed foods have contributed to declining adherence, posing significant challenges to its sustainability and scalability. Moreover, the sustainability benefits of the MedDiet seem to be context-dependent rather than intrinsic, raising several challenges and limitations for its adoption. Conclusions: The MedDiet should be viewed not as a definitive solution to global food-system challenges but as a valuable reference model that illustrates how dietary practices can contribute simultaneously to human health, environmental sustainability, and cultural continuity. Modern sustainable dietary strategies should build upon the strengths of the MedDiet while recognizing its limitations, embracing contextual adaptation, and addressing the structural determinants that shape food choices. Full article

The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO₂-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality. Full article

Magnetic anomaly detection is vulnerable to environmental noise and insufficient prior target information, making non-periodic anomaly signals difficult to detect at low-signal-to-noise-ratio (SNR) conditions. This paper proposes a detection method based on a multi-parameter-constrained mirror dual-branch biased monostable stochastic resonance (SR) system. Nonlinear odd-order bias terms are introduced into the conventional biased monostable potential function to build a multi-parameter-controllable SR model. This improves regulation of potential-well width, depth, and wall morphology, enhancing noise-energy utilization and responses to non-periodic features. Considering peak-type, valley-type, and bipolar anomaly morphologies, a mirror dual-branch SR structure is developed to cooperatively detect features with different polarities. To preserve temporal waveforms and time–frequency structures during parameter optimization, a composite metric combining the correlation coefficient and wavelet-domain image structural similarity index is constructed. Multi-fidelity robust Bayesian optimization is used to obtain a unified robust parameter set for the magnetic anomaly signal family. Experiments with simulated colored noise and measured geomagnetic noise show that the proposed method effectively recovers magnetic anomaly features under strong noise. At −19 dB SNR, its detection probability remains above 80%. Compared with orthogonal basis function decomposition, empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise, the method achieves better noise suppression, feature preservation, and detection performance under low-SNR conditions. Full article

Elevated concentrations of fine particulate matter ($P{M}_{2.5}$) are a critical threat to respiratory health worldwide. Therefore, there is an urgent need for precise urban forecasting systems for public health management. Technological advancements in the domains of continuous environmental monitoring and deep learning have enabled large-scale data acquisition, processing, and modeling. Existing predictive models typically depend on auxiliary meteorological inputs, which are frequently inaccessible within standard ground-level monitoring networks. Furthermore, conventional approaches often fail to adequately capture the complex spatio-temporal interactions of pollutants. To address these limitations, this study presents the Spatio-Temporal Endogenous Attention Network (STEA-Net), a forecasting framework designed to operate exclusively without weather variables. Validated on a comprehensive multi-year historical dataset (Jan 2015–Feb 2020) from diverse monitoring stations in India, STEA-Net employs a hybrid adjacency matrix that integrates physical geographical distances with functional clustering to accurately map pollutant transport pathways. Utilizing this structural map, a Graph Attention Network dynamically evaluates the spatial influence of neighboring nodes, while a Bidirectional LSTM processes the underlying temporal sequences. Experimental results demonstrate that STEA-Net substantially surpasses traditional machine learning algorithms and provides competitive performance against advanced deep learning baselines. The proposed model achieves a peak Coefficient of Determination ($R^2$) of 0.9294 (5-seed average: $0.9273\pm 0.0023$) and a peak RMSE of 14.38 µg/m³ (5-seed average: $14.59\pm 0.23$ µg/m³), effectively adapting to the dynamic volatility of urban pollution levels. The model exhibits architectural stability with a Monte Carlo dropout verified deviation of $\pm 2.22$ µg/m³. This research provides a forecasting architecture that retains competitive predictive performance under the strict operational constraint of meteorology-free deployment in resource-constrained urban monitoring environments. Full article

Mineral waters represent unique limnological ecosystems with stable physicochemical conditions and specialised microbial communities adapted to extreme environments. Bulgarian mineral waters remain comparatively underexplored despite their considerable ecological and biotechnological significance. These studies present a systematic narrative review of microbial diversity, ecological functions, and biotechnological potential of microbial communities from Bulgarian mineral springs. A total of 233 scientific sources published between 1990 and 2026 were analysed, of which 33 focused on Bulgarian sites. Data were retrieved from major scientific databases, regional reports and grey literature. Due to strong methodological heterogeneity, a qualitative synthesis was conducted, supported by bibliometric summaries of research focus and environmental context. The available evidence demonstrates that microbial communities in Bulgarian mineral waters include diverse bacteria, archaea, cyanobacteria, and microalgae that adapt to broad thermal and geochemical gradients. These microorganisms actively participate in element cycles, form complex biofilms, and show numerous physiological adaptations to oligotrophic and extreme temperature conditions. Bulgarian systems broadly reflect global microbial patterns but exhibit additional variability linked to contrasting hydrogeological settings. Many taxa produce thermostable enzymes, antimicrobial compounds, and exopolysaccharides with significant biotechnological potential. The review identifies significant research gaps and emphasises the importance of integrated multi-omics approaches for future exploration of Bulgarian mineral water ecosystems. Full article

A virtual training environment offers clear advantages for agricultural robotics. It provides a safe setting in which perception, navigation, and control algorithms can be evaluated without risking damage to either the robot or the crop. It also supports efficient data generation: large volumes of training data can be collected under diverse environmental conditions that would be costly, slow, and often season-dependent in real-world deployments. This broader variability improves model adaptability, reduces the risk of overfitting, and leads to more robust operation. In this paper, we argue that digital twin technology should therefore be understood not merely as a passive mirror of a physical robot, but as an active training environment in which multiple sensor-related subprocesses can be developed, tested, validated, and refined jointly. This paper is based on our experiences with digital twin technology used in the development of a vineyard robot, including a self-driving rover, sensor simulation, procedural map generation, and agriculture-specific movement models. Our contribution is threefold: we reinterpret the digital twin as a training space, propose a layered framework for training agricultural robots in virtual environments, and explain why agriculture is a particularly strong use case, given variable field conditions, expensive real-world experimentation, and persistent labor scarcity. To validate this framework, we present the simulation-based evaluation of an autonomous reinforcement learning agent. The agent has been trained entirely in this virtual environment, which successfully navigated to 155 out of 161 target points in a simulated vineyard demonstration environment. Full article

Over the last two decades, social enterprises in Romania have taken on an increasingly important role in the production and provision of social goods and services for vulnerable groups. Although forms of the social economy have long existed in Romanian society, sustainability remains a constant concern, particularly in the context of dependence on European Union structural funds. This study identifies the multidimensional factors influencing the sustainability of social enterprises in Romania, combining a quantitative analysis of 121 certified social enterprises from the National Register (2016–2022) with qualitative case studies of 15 selected organisations. Revenue diversification was significantly associated with financial sustainability (β = −0.28, p < 0.01), whilst high dependence on EU funding (>50% of revenue) was negatively associated with long-term viability (HR = 2.18, p = 0.002). Participation in networks was associated with markedly higher five-year survival rates (87.2% for network members versus 69.5% for non-members). Six key sustainability strategies were identified: hybrid revenue models, integration into the value chain, community inclusion, adaptive leadership, strategic partnerships, and effective communication of results and impact. Environmental sustainability is addressed with preliminary proxy evidence from the qualitative component; systematic measurement of this dimension represents a priority for future research. The findings confirm the absence of an integrated support framework for the sustainable activities of the social economy and, in some cases, the limited capacity of public institutions to support vulnerable groups. Policy recommendations include phased funding mechanisms, transitional support instruments and the systematic development of regional ecosystems. Full article

This study investigates the design and performance of lime mortars incorporating multi-phase change material (multi-PCM) systems as thermally responsive rendering materials for building-envelope applications under variable conditions. Moving beyond conventional single-PCM lime mortar approaches, this work proposes a controlled multi-PCM design framework in which a fixed total PCM dosage is distributed across selected phase-transition windows. Mortars combining PCMs with different transition temperatures (5–25 °C and 18–25 °C) were produced using two PCM types: silica-supported form-stable systems and polymeric-shell microencapsulated systems supplied as powders or aqueous slurries. All formulations contained 20% PCM and were optimized with polymeric additives, including a polycarboxylate ether-based superplasticiser and a starch-derived adhesion enhancer, to ensure suitable workability and applicability as rendering materials. Microstructural analyses showed that form-stable PCMs generated more heterogeneous pore structures, whereas polymeric-shell microencapsulated systems maintained pore structures similar to PCM-free mortars. Mortars containing metakaolin exhibited enhanced mechanical performance and durability, in some cases outperforming reference mortars, highlighting the importance of matrix refinement in the successful incorporation of multi-PCM systems. Thermal characterization revealed that form-stable systems produced broader phase transitions due to component interactions, while polymeric-shell microencapsulation preserved distinct transitions and enabled a wider, more controllable activation range. Under dynamic thermal conditions (−10 to 50 °C), all multi-PCM mortars demonstrated effective temperature buffering, achieving reductions of up to 1.5 °C during heating and 1.1 °C during cooling. Environmental and economic analyses highlighted that the benefits of PCM incorporation depend on matching PCM transition temperatures to specific climatic and application requirements. These findings position multi-PCM lime mortars as a promising route towards climate-adapted, thermally responsive renders with distributed and tailorable activation profiles. Full article

Soil salinity severely restricts castor (Ricinus communis L.) seed germination, yet the molecular basis of this trait remains poorly understood. Here, we identified and functionally characterized RcnsLTPC, a nonspecific lipid transfer protein gene strongly induced by salt stress, which encodes a plasma membrane-localized nsLTP1 protein. Promoter analyses indicated that RcnsLTPC is responsive to stress-, hormone-, and light-related signals, supporting its potential role in environmental adaptation. Heterologous expression in Saccharomyces cerevisiae and overexpression in Nicotiana tabacum consistently demonstrated that RcnsLTPC acts as a positive regulator of salt tolerance, improving germination, root development, biomass accumulation, antioxidant capacity, and ion homeostasis under NaCl stress. Notably, ZnO-NPs priming further amplified the protective effects of RcnsLTPC, suggesting a synergistic interaction between nanopriming and gene-mediated stress adaptation. Collectively, these findings establish RcnsLTPC as a key regulator of salt tolerance in castor and provide a conceptual basis for combining nanotechnology with genetic enhancement to improve crop performance on saline soils. Full article

The climate crisis, housing precarity, and the loss of everyday architectural heritage are converging challenges in Mediterranean cities. This article investigates the adaptive reuse of early twentieth-century adobe refugee dwellings in Nea Ionia and Kaisariani, neighborhoods of Attica, Greece, as an integrated social, environmental, and cultural strategy. Historical documentation, urban-morphological analysis, field observations, building survey data, material assessment, and design-based microclimatic analysis were combined to evaluate compatible restoration and bioclimatic upgrades as alternatives to demolition and conventional energy retrofit practices, with the main aim of preserving an important part of Greek history and architecture. The study develops a replicable qualitative assessment framework that identifies how existing adobe envelopes, compact layouts, courtyards, thresholds, vegetated pergolas, and low-water evaporative cooling may support low-carbon housing reuse. The results clarify the current preservation conditions and reuse potential of the selected case-study fragments, showing that adobe dwellings can preserve embodied material value, retain thermal mass and hygroscopic regulation, and support social housing when repaired with compatible, low-impact techniques. The article argues that the reuse of adobe refugee dwellings can function as a distributed urban strategy for housing provision, heritage continuity, and microclimatic adaptation. Its main contribution is a transferable analytical framework for assessing overlooked earthen housing stocks in dense Mediterranean contexts. The study argues that adaptive reuse can serve simultaneously as a means of social housing, a mechanism for optimizing the microclimate, and a means of preserving the tangible and intangible heritage of Greek adobe buildings that have been standing for over 100 years. This position extends circular construction debates by prioritizing non-demolition and direct reuse while preserving an important period of history. Full article