Search Results (10,845)

This article examines the statistical relationships between ENUS, defined as per capita energy use, and Environmental, Social, and Governance variables, with particular emphasis on the Environmental dimension and its connections with national energy systems. The study investigates whether systematic associations exist between ESG indicators and the cross-country and temporal variation in ENUS as per capita energy use, and to what extent machine learning methods can contribute to the description and interpretation of these relationships in comparison with panel econometric models. The analysis is based on a large World Bank dataset covering approximately 161 countries over the period 2004–2023 and follows a three-step methodological strategy. First, fixed-effects, random-effects, and Weighted Least Squares panel models are estimated to explore the statistical associations between a broad set of ESG variables and ENUS as per capita energy use, while controlling for unobserved country-level heterogeneity. Second, clustering techniques are applied to identify groups of countries with similar joint patterns in multidimensional variables related to energy systems, emissions, climate conditions, and natural resource use. Third, several machine learning models are implemented, with particular attention to the performance of the K-Nearest Neighbors algorithm evaluated through normalized measures of predictive accuracy and goodness of fit. Model interpretability is enhanced using dropout loss and additive explanation methods to assess the contribution of ESG variables to the prediction of ENUS as per capita energy use. Overall, the results reveal a rich and multidimensional structure of relationships between ESG indicators and ENUS expressed as per capita energy use. In particular, the evidence indicates a close association between ENUS and key environmental variables such as emissions intensity, energy intensity as a control variable, and the use of natural resources, together with Social and Governance factors related to development, institutional quality, and economic structure. These findings suggest that cross-country differences in ENUS as per capita energy use correspond to distinct environmental, social, and governance profiles within the ESG framework. Full article

In this paper, the effects of Poland’s forest management evolution after 1945 on forest biodiversity at the genetic level were analysed. Forest biodiversity changes across the two politically and economically different eras (socialism, 1945–1989, and democracy, from 1990) are interpreted using three indirect indicators: forest regeneration and expansion, tree genetic resources, and threatened forest species. In the era of socialism, the total area of regeneration and reforestation gradually decreased, with these activities relying almost exclusively on cultivated reproductive material. After 1990, there was a relative stabilisation in the total area, with a noticeable increase in the use of natural processes to diversify the tree gene pool. Work on verifying and protecting the forest tree seed base, as well as on assessing the conservation status of an increasingly wide range of organisms, began in the era of socialism; however, it was intensified only in the era of democracy. In the latter case, the increase in the number of endangered species suggests a potentially negative trend. However, the actual assessment of the changes is not entirely clear due to subsequent changes in threat classification and increased knowledge of the occurrence of individual species. Dilemmas and problems related to the following issues require further discussion and resolution or implementation of further measures: the consequences of past choices regarding planted trees; the use of natural regeneration; the reduction in the forest tree gene pool as a result of artificial selection; incomplete knowledge about threats to the forest gene pool; the continued impact of threats and the possibilities for counteracting them; and securing funding for measures to protect biodiversity at the genetic level. Full article

Probabilistic streamflow models play a pivotal role in quantifying hydrological uncertainty and form the backbone of modern risk management strategies for flood and drought forecasting, water allocation planning, and the design of resilient infrastructure. Unlike deterministic approaches that yield single-point estimates, these models provide a spectrum of possible outcomes, enabling a more realistic assessment of extreme events and supporting informed, sustainable water resource decisions. By explicitly accounting for natural variability and uncertainty, probabilistic models promote transparent, robust, and equitable risk evaluations, helping decision-makers balance economic costs, societal benefits, and environmental protection for long-term sustainability. In this study, we introduce the bounded half-logistic distribution (BHLD), a novel heavy-tailed probability model constructed using the T–Y method for distribution generation, where T denotes a transformer distribution and Y represents a baseline generator. Although the BHLD is conceptually related to the Pareto and log-logistic families, it offers several distinctive advantages for streamflow modeling, including a flexible hazard rate that can be unimodal or monotonically decreasing, a finite lower bound, and closed-form expressions for key risk measures such as Value at Risk (VaR) and Tail Value at Risk (TVaR). The proposed distribution is defined on a lower-bounded domain, allowing it to realistically capture physical constraints inherent in flood processes, while a log-logistic-based tail structure provides the flexibility needed to model extreme hydrological events. Moreover, the BHLD is analytically characterized through a governing differential equation and further examined via its characteristic function and the maximum entropy principle, ensuring stable and efficient parameter estimation. It integrates a half-logistic generator with a log-logistic baseline, yielding a power-law tail decay governed by the parameter $\beta$, which is particularly effective for representing extreme flows. Fundamental properties, including the hazard rate function, moments, and entropy measures, are derived in closed form, and model parameters are estimated using the maximum likelihood method. Applied to four real streamflow data sets, the BHLD demonstrates superior performance over nine competing distributions in goodness-of-fit analyses, with notable improvements in tail representation. The model facilitates accurate computation of hydrological risk metrics such as VaR, TVaR, and tail variance, uncovering pronounced temporal variations in flood risk and establishing the BHLD as a powerful and reliable tool for streamflow modeling under changing environmental conditions. Full article

Green roofs in Southern Europe are interest-growing nature-based solutions, capable of improving urban sustainability by positively impacting the water cycle, biodiversity, pollution, and, in some cases, energy consumption and carbon sequestration. Native plants adapted to Mediterranean climates exhibit drought-resistant traits, making them highly suitable for the challenging microclimate of green roofs. This microclimate features intense solar radiation, strong winds, and higher temperatures, in comparison to ground level, leading to increased atmospheric evaporative demand, driven by the interplay of radiation, wind, temperature, and humidity. Consequently, native plants from ecosystems resembling this microclimate are likely better suited for green roofs than local ground-level species. The current review synthesizes current knowledge on the use of native plants in Southern European green roofs, focusing on water management challenges given the region’s climate and scarce water resources. Out of roughly 12,500 native plant species in the Mediterranean basin, only about 124 have been examined in the past 20 years for green roof applications, with just 16% appearing in multiple scientific studies, highlighting a significant knowledge gap. The data indicate that ca. 85% of these species are perennials, valued for their low maintenance needs, a key consideration for green roof sustainability. Some of the studied species retain adequate aesthetic value when cultivated on green roofs with limited water availability. These species are mainly associated with four habitat types—rocky, coastal, dry, or well-drained environments—with a few linked to humid or adaptable conditions. This study aims to document the selection of drought-adapted native plant species best suited for green roof implementation in Southern Europe, contributing to enhancing sustainable urban design in the region, considering water management best practices and water use efficiency. Full article

Pore structure and fractal dimension analysis of nephrite deposits are essential for assessing potential quality, conducting investigations, and exploiting jade resources. This research explored nephrite (tremolite) jade from Tonggou in the Luanchuan Group, utilizing techniques such as scanning electron microscopy (SEM), X-ray diffraction (XRD), and low-temperature nitrogen adsorption (LT-N₂GA) to illustrate the pore structure of the jade deposit and to examine its developmental features, complexity, and implications for jade quality assessment. The findings revealed that the Tonggou nephrite jade deposit comprises three varieties of micropores. The nitrogen adsorption curve was similar to type IV, featuring hysteresis loops that were mainly classified as H₂ and H₃, suggesting a predominantly mesoporous nature. The fractal dimensions (D_F1 and D_F2), determined using the FHH model, averaged 2.474 and 2.572, respectively. This implies that the pore surface of the Tonggou jade deposit is irregular, the pore structure is intricate, and displays a pronounced heterogeneity. In the Tonggou deposit, the specific surface area (SSA) and pore volume (PV) show moderate positive and negative correlations with antigorite and calcite, respectively. Tremolite exhibits a strong negative correlation with SSA. The fractal dimension reveals weak, moderate, and strong negative correlations with SSA, PV, and average pore size (APS), respectively. As the content of siliceous minerals increases, the fractal dimension gradually increases. Conversely, a rise in carbonate mineral content results in a gradual decrease in the fractal dimension. Full article

Background/Objectives: British trans and gender-expansive individuals face stigma and consistently experience lengthy waits for gender-enhancing interventions. Researchers are using a range of qualitative methodologies to give this marginalised community a voice. In this study the focus is on the lived experiences of British transgender men seeking medical intervention around their gender identity. The aim was to explore how psychological well-being for this group of transmen was both threatened and supported. Methods: Online semi-structured interviews using auto-photography were conducted with eleven transmen aged between 18 and 68 years. Both verbal and visual data were analysed together using interpretive phenomenological analysis. Analysis: Three themes focus on challenges and supportive strategies utilised by participants both before their decision to transition and after. All participants expressed significant mental health difficulties before commencing their transition, typically originating from childhood and continuing until they gained access to gender-affirming medical care. Their transition journeys damaged their well-being through resistance and rejection from families and communities, and difficulties navigating healthcare systems. A series of resources which significantly enhanced well-being were also reported. Unconditional acceptance and belonging found within and beyond the trans community, connecting with nature and ultimately progressing with gender-affirming healthcare were key elements in protecting and promoting well-being. Conclusions: The toll on the well-being of trans and other gender-expansive individuals is considerable and recent changes in UK law have exacerbated the hostile environment faced by TGE individuals. Community-based allyship and access to affirmative professional psychological support at all stages is vital. Full article

With the rising demand for maritime surveys of infrastructure, energy resources, and environmental conditions, autonomous robotic fish have emerged as a promising solution with their biomimetic propulsion, agile motion, efficiency, and capacity for underwater inspection, monitoring, data collection, and exploration tasks in complex aquatic environments. Inspired by fish spines, rigid-link fish robots (RLFRs), a category of robotic fish, are widely utilized in robotics research and applications. Their rigid, actuated joints enable them to reproduce the undulatory locomotion and high maneuverability of biological fishes, while the modular nature of rigid links between joints makes them cost-effective and easy to assemble. This review examines and presents recent approaches and advancements in the field of structural design, as well as Reinforcement learning (RL)-enabled controls with sensors and actuators. Existing designs are classified by joint configuration, with key structural, material, fabrication, and propulsion considerations summarized. The review highlights the use of Q-learning, Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG) algorithms for RLFR controllers, showing their impact on adaptability, motion control, and learning in dynamic hydrodynamic conditions. Technical challenges—including unstructured environments and complex fluid–body interactions—are discussed, along with future directions. This review aims to clarify current progress and identify technological gaps for advancing rigid-link robotic fish. Full article

Under intensifying climate change and anthropogenic pressures, extreme low-flow events increasingly jeopardize water security in the Yellow River water supply region. This study develops the Inter-basin Multi-source Water Joint Allocation and Interconnected Routes Regulation System (IMWA-IRRS) to optimize spatiotemporal allocation of multi-source water and simulate topological relationships in complex water networks. The model integrates system dynamics simulation with multi-objective optimization, validated through multi-criteria calibration using three performance indicators: correlation coefficient (R), Nash-Sutcliffe Efficiency (E_ns), and percent bias (PBIAS). Application results demonstrated exceptional predictive performance in the study area: Monthly runoff simulations at four hydrological stations yielded R > 0.98 and E_ns > 0.98 between simulated and observed data during both calibration and validation periods, with |PBIAS| < 10%; human-impacted runoff simulations at four hydrological stations achieved R > 0.8 between simulated and observed values, accompanied by PBIAS within ±10%; sectoral water consumption across the Yellow River Basin exhibited PBIAS < 5%, while source-specific water supply simulations maintained PBIAS generally within 10%. Comparative analysis revealed the IMWA-IRRS model achieves simulation performance comparable to the WEAP model for natural runoff, human-impacted runoff, water consumption, and water supply dynamics in the Yellow River Basin. The 2035 water allocation scheme for Yellow River water supply region projects total water supply of 59.691 billion m³ with an unmet water demand of 3.462 billion m³ under 75% low-flow conditions and 58.746 billion m³ with 4.407 billion m³ unmet demand under 95% low-flow conditions. Limited coverage of the South-to-North Water Diversion Project’s Middle and Eastern Routes constrains water supply security, necessitating future expansion of their service areas to leverage inter-route complementarity while implementing demand-side management strategies. Collectively, the IMWA-IRRS model provides a robust decision-support tool for refined water resources management in complex inter-basin diversion systems. Full article

Air–ground joint jamming enables three-dimensional, distributed jamming configurations, making it effective against air–ground communication networks with complex, dynamically adjustable links. Once the jamming layout is fixed, dynamic jamming power scheduling becomes essential to conserve energy and prolong jamming duration. However, existing methods suffer from poor applicability in such scenarios, primarily due to their sparse deployment and adversarial nature. To address this limitation, this paper develops a set of mathematical models and a dedicated algorithm for air–ground communication countermeasures. Specifically, we (1) randomly select communication nodes to determine the jammer operation sequence; (2) schedule the number of active jammers by sorting transmission path losses in ascending order; and (3) estimate jamming effects using electromagnetic wave propagation characteristics to adjust jamming power dynamically. This approach formally converts the original dynamic, stochastic jamming resource scheduling problem into a static, deterministic one via cognitive certainty of dynamic parameters and deterministic modeling of stochastic factors—enabling rapid adaptation to unknown, dynamic communication power strategies and resolving the coordination challenge in air–ground joint jamming. Experimental results demonstrate that the proposed Transmission Loss Ordering Algorithm (TLOA) extends the system operating duration by up to 41.6% compared to benchmark methods (e.g., genetic algorithm). Full article

Rigid polyurethane (PUR) foams are widely used across multiple industries due to their excellent thermal insulation and mechanical properties. However, their environmental impact, flammability, and limited thermal stability pose challenges for sustainable development. In this study, selected natural minerals—including talc, montmorillonite, halloysite, and diatomite—were incorporated into water-blown polyurethane foams to improve their performance while enhancing sustainability. The prepared foams were characterized in terms of apparent density, water uptake, compressive strength, dimensional stability, and thermal and fire resistance. The results indicate that the inclusion of mineral additives significantly improves the physical and mechanical properties of polyurethane foams, increasing durability, resistance to high temperatures, and fire safety. By using naturally occurring minerals, the study promotes the development of polyurethane foams with reduced environmental footprint, longer service life, and safer application potential in construction, automotive, and heating systems. These findings highlight the contribution of mineral-reinforced polyurethane foams to sustainable materials engineering and resource-efficient industrial applications. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly integrated into Internet of Things (IoT) ecosystems for applications such as surveillance, disaster response, environmental monitoring, and logistics. These missions demand reliable and secure communication between UAVs and cloud platforms for command, control, and data storage. However, UAV communication channels are highly vulnerable to eavesdropping, spoofing, and man-in-the-middle attacks due to their wireless and often long-range nature. Traditional cryptographic schemes either impose excessive computational overhead on resource-constrained UAVs or lack sufficient robustness for cloud-level security. To address this challenge, we propose a dual-layer encryption architecture that balances lightweight efficiency with strong cryptographic guarantees. Unlike prior dual-layer approaches, the proposed framework introduces a context-aware adaptive lightweight layer for UAV-to-gateway communication and a hybrid post-quantum layer for gateway-to-cloud security, enabling dynamic cipher selection, energy-aware key scheduling, and quantum-resilient key establishment. In the first layer, UAV-to-gateway communication employs a lightweight symmetric encryption scheme optimized for low latency and minimal energy consumption. In the second layer, gateway-to-cloud communication uses post-quantum asymmetric encryption to ensure resilience against emerging quantum threats. The architecture is further reinforced with optional multi-path hardening and blockchain-assisted key lifecycle management to enhance scalability and tamper-proof auditability. Experimental evaluation using a UAV testbed and cloud integration shows that the proposed framework achieves 99.85% confidentiality preservation, reduces computational overhead on UAVs by 42%, and improves end-to-end latency by 35% compared to conventional single-layer encryption schemes. These results confirm that the proposed adaptive and hybridized dual-layer design provides a scalable, secure, and resource-aware solution for UAV-to-cloud communication, offering both present-day practicality and future-proof cryptographic resilience. Full article

This review synthetizes experimental evidence on collagen-related bioactivity and the biomaterial potential of plant species native to the Chihuahuan Desert, aiming to identify natural compounds that could enhance next-generation dermal bioinks for 3D bioprinting. A structured search across major databases included studies characterizing plant extracts or metabolites, with reported effects on collagen synthesis, fibroblast activity, inflammation, oxidative balance, or interactions with polymers commonly used in skin-engineering materials being developed. Evidence was organized thematically to reveal mechanistic patterns despite methodological heterogeneity. Several species, among them Larrea tridentata, Opuntia spp., Aloe spp., Matricaria chamomilla, Simmondsia chinensis, Prosopis glandulosa, and Artemisia ludoviciana, repeatedly demonstrated the presence of bioactive metabolites such as lignans, flavonoids, phenolic acids, terpenoids, and polysaccharides. These compounds support pathways central to extracellular matrix repair, including stimulation of fibroblast migration and collagen I/III expression, modulation of inflammatory cascades, antioxidant protection, and stabilization of ECM structures. Notably, several metabolites also influence viscoelastic and crosslinking behaviors, suggesting that they may enhance the printability, mechanical stability, and cell-supportive properties of collagen-, GelMA-, and hyaluronic acid-based bioinks. The review also reflects on the bioethical and sustainability considerations regarding endemic floral resources, highlighting the importance of responsible sourcing, conservation extraction practices, and alignment with international biodiversity and access to benefit/sharing frameworks. Taken together, these findings point to a promising, yet largely unexplored, opportunity: integrating regionally derived phytochemicals into bioinks to create biologically active, environmentally conscious, and clinically relevant materials capable of improving collagen remodeling and regenerative outcomes in 3D-printed skin. Full article

The Yazoo–Mississippi Delta is an agricultural production zone and flyway for migratory birds. During winter, agricultural field-flooding practices are routinely used to support bird conservation and local recreational hunting opportunities. In response to the 2010 Deepwater Horizon oil spill, federal agencies incentivized flooding in summer and fall to mitigate the risks to migratory bird populations. This funding ceased in 2017, yet the United States Department of Agriculture Natural Resources Conservation Service Environmental Quality Incentives Program Practice 644 and a local non-profit continue to incentivize flooding during fall. Ensuring that contractual water levels are met is challenging to determine. To that end, we developed the Field Inundation Tool/Survey, an integrated remote sensing approach using PlanetScope imagery (Planet Labs, San Francisco, CA, USA) to quantify associated hydrology patterns. We used the Normalized Difference Water Index and an Iso Cluster Unsupervised Classification to estimate field inundation and associated habitat types over a three-year period. The results indicate dynamic field inundation can be estimated via PlanetScope imagery. Derived inundation metrics were comparable with in situ sensor and digital elevation models among some treatment types. We documented future refinements for image quality and soil patterns. Our work can improve conservation incentivization by tracking spatial and temporal patterns in adoption and has applicability to other agroecosystems. Full article

Forest biometrics has emerged as one of the fastest-growing scientific disciplines within environmental sciences. Machine learning (ML), an increasingly essential approach that uses effective algorithms, has proven to be an accurate and cost-efficient solution to forest-related problems. Recently, ML methods have evolved, from traditional machine learning (TML) algorithms to more sophisticated approaches, such as deep learning (DL) and ensemble (ENS) methods. To uncover these developments, a structured review and analysis of 150 peer-reviewed studies was conducted, following a standardized workflow. The analysis reveals clear shifts in methodological adoption. During the most recent five-year period (2021–2025), DL and shallow neural network (SNN) methods dominated the literature, accounting for 37.5% of published studies, followed by ENS and TML methods, contributing 29.2% and 27.1%, respectively, presenting a marked increase in the utilization of artificial neural networks (ANNs) and related algorithms across the domains of forest biometrics. Nevertheless, overall trends indicate that the benefits of TML methods still need further exploration for ground-based received data. Advances in remote sensing and satellite data have brought large-scale remotely sensed data into environmental research, further boosting ML utilization. However, each field could be strengthened by implementing standardized evaluation metrics and broader geographic representation. In this way, robust and widely transferable modeling frameworks for forest ecosystems can be developed. At the same time, further research on algorithms and their applicability to natural resources proves a key component for comprehensive and sustainable forest management. Full article

This study examines the performance implications of CEO duality from a configurational governance perspective, with particular attention given to its relevance within an ESG-oriented framework. While prior research on CEO duality has produced inconsistent findings, much of the literature relies on variable-centered approaches that overlook the systemic and context-dependent nature of governance mechanisms. Drawing on agency theory, stewardship theory, and resource dependence theory, we analyze 59 publicly listed South Korean firms between 2018 and 2022 using fuzzy-set qualitative comparative analysis (fsQCA). Five governance-related conditions—CEO duality, ownership concentration, CEO tenure, institutional ownership, and environmental dynamism—are calibrated into fuzzy sets to identify causal configurations associated with high firm performance, defined as membership in the top 30% of return on assets (ROA). The results reveal six equifinal pathways to high performance, two of which exhibit particularly strong consistency and coverage. These dominant configurations show that CEO duality contributes positively to performance when embedded in either strong internal governance alignment or robust external monitoring under dynamic conditions. By demonstrating that the effectiveness of CEO duality is contingent upon its governance configuration, this study challenges one-size-fits-all prescriptions and contributes to the ESG literature by highlighting the conditional role of leadership structure in sustainable value creation. Full article