Search Results (48,002)

Major social, cultural, and sociopolitical events routinely disrupt daily life, yet their effects on sleep are rarely conceptualized at the population level beyond anecdotal sharing. The purpose of this Opinion piece is to initiate a preliminary discussion of “public sleep” as a novel construct describing systematic, event-related changes in sleep timing, duration, and quality that emerge coherently within communities in response to shared social experiences. Drawing on similarities with the well-established concept of public mood, we posit that sleep can be shaped by social environments in which shared attention, emotional climate, and coordinated schedules exert systematic influence. In support of this claim, we describe preliminary evidence from diverse domains demonstrating population-level sleep disruption following major events, including the transition to Daylight Saving Time, national elections, prolonged crises such as the COVID-19 pandemic and armed conflicts, and highly salient cultural activities such as major sporting events. These reports from disparate fields provide an initial indication that public sleep disruptions can be acute or prolonged, geographically localized or global, and may be shaped by the duration, emotional intensity, and perceived importance of the associated event. We further highlight the potential public health, safety, social, and economic consequences of collective sleep loss, underscoring its relevance beyond individual well-being. Finally, we outline key directions for future research, emphasizing the need for systematic reviews, mechanistic studies, longitudinal designs, and policy-relevant recommendations. Recognizing public sleep as a measurable population phenomenon would provide a foundation for anticipating, monitoring, and mitigating sleep disruption during periods of collective strain, with implications for both individual health and societal resilience. Full article

The study is based on Land Surface Temperature (LST) and Air Temperature data and Nonparametric Data Envelopment Analysis (DEA) technique to evaluate heat efficiency and detect anomalies in the thermal regime in the Eastern Black Sea Region, particularly in Hopa and Artvin, during the period 2000–2024. The regulating role of the Black Sea has resulted in Hopa having the warmest and most stable temperature patterns, with daytime temperatures 1.8 to 3.7 °C higher than Artvin. Previous DEA analysis of daytime temperatures has shown that the 2018–2020 period had the highest daily temperatures, while the 2001–2010 decade was characterized by the highest nighttime temperatures. A future heat map based on Monte Carlo simulation using six climate change scenarios indicates that in the most optimistic case, assuming a temperature increase of +0.8 °C, efficiency scores could increase as high as 0.995. On the other hand, if global warming leads to a sudden temperature increase above +7.2 °C, there is a 21.7% climate efficiency loss. Sensitivity analysis showed that technological innovation and good governance are the main positive factors affecting climate efficiency. Random Forest (RF) and SHapley Additive Explanations (SHAP) analyses were applied to determine the impact of climate factors on DEA scores and also indicated areas requiring risk assessment. The findings highlight the importance of considering location-specific climate adaptation strategies. Based on the observed thermal contrasts between coastal and inland environments, potential adaptation considerations may include urban heat management and agricultural water stress in coastal areas such as Hopa, and cold-climate resilience and energy-efficient infrastructure in inland locations such as Artvin. Full article

Managers of protected areas (PAs) face growing challenges to conserve biodiversity while responding to multiple land uses such as recreation, tourism, and resource extraction. These pressures are intensified by the impacts of climate change on ecosystems. This highlights the need for planning approaches that support decision-making in the short, medium, and long term. This article profiles Wells Gray Provincial Park as a case study to demonstrate how an ecosystem-based planning approach can be incorporated into PAs planning. Wells Gray is situated in a unique ecosystem in the interior of British Columbia (Canada). We present an innovative model that integrates land cover types, ecosystem mapping, and Biogeoclimatic (BGC) zones derived from the Biogeoclimatic Ecosystem Classification (BEC) system using GIS tools to identify ecosystems and their associated services as Critical Decision Factors (CDFs). By explicitly linking ecosystems, land cover, and spatial patterns, this approach supports the systemic inclusion of ecosystems in management decisions. To account for future uncertainty, BGC zones were projected under climate change scenarios to inform interpretations of potential ecosystem impacts. The results indicate that this integrated analysis can initiate strategic thinking and facilitate dialogue to collaboratively plan with stakeholders. This approach can improve ecosystem-based planning processes in PAs across Canada. Full article

The renewable energy transition is a central component of global strategies to mitigate climate change and achieve sustainable development. However, the large-scale integration of renewable energy sources introduces significant challenges related to variability, system complexity, and operational efficiency. In recent years, artificial intelligence (AI) has emerged as a promising enabler for addressing these challenges through advanced data-driven forecasting, optimization, and decision-support capabilities. This study presents a systematic bibliometric and thematic review of peer-reviewed research on AI applications in the renewable energy transition published between 2015 and 2025, and was conducted following the PRISMA framework. Using the Scopus database, a total of 595 journal articles were analyzed through bibliometric performance indicators, network analysis, and thematic synthesis. The results reveal a rapidly growing and highly collaborative research field, characterized by strong international co-authorship and increasing methodological diversity. Early research predominantly focused on prediction and forecasting tasks, while more recent studies emphasize system-level optimization, energy management, and integrative AI applications across renewable technologies. The review further highlights key research trends, conceptual framing, and methodological orientations shaping the field. By consolidating dispersed literature and mapping its evolution, this study provides a structured overview that supports future research, policy development, and practical implementation of AI-enabled solutions for a sustainable energy transition. Full article

In recent decades, the Ucayali region, the main territory of the Asheninka communities, has experienced increasing socio-environmental pressures associated with climate change, educational inequality, and territorial vulnerability in rural and indigenous contexts. In response, this research proposes the design of a sustainable agricultural school for the Asheninka community, conceived as an educational building that integrates biophilic strategies to enhance environmental performance and spatial quality. The methodological approach comprises a literature review, site-specific environmental analysis based on hydrometeorological data, and the development of an architectural proposal focused on sustainable building design. Digital tools such as Revit and SketchUp were employed alongside official climatic data sources to support design decision-making. The proposal includes twelve biophilic agricultural classrooms incorporating passive design strategies, rainwater harvesting systems with a capacity of 22.5 m³ per day per classroom, and photovoltaic-powered public lighting systems. Results indicate that the integration of natural ventilation, green infrastructure, and locally sourced materials contributes to significant improvements in thermal comfort, humidity control, and energy autonomy within the educational facilities. The architectural complex is complemented by green corridors and collective open spaces that reinforce environmental performance at the site scale. This study demonstrates that sustainable educational buildings adapted to local ecosystems and climatic conditions can function as effective infrastructures for environmental mitigation and resilient rural development, contributing to more sustainable forms of urban and rural living. Full article

Mediterranean forests are increasingly exposed to climate-related risks, including large wildfires, prolonged droughts and rural abandonment, making sustainable forest management (SFM) a key element for climate adaptation and territorial resilience. However, despite its recognised importance, the social acceptance of SFM remains insufficiently understood, particularly in relation to how public perceptions are shaped by media narratives and information ecosystems. This study addresses this gap by analysing the relationship between media framing and social acceptance of SFM in a Mediterranean context. A mixed-methods approach was applied in the Valencian region (Spain), combining (i) a systematic analysis of conventional and digital media, (ii) a system mapping exercise to identify dominant narratives and communication dynamics, and (iii) a population survey (n = 1070) focused on perceptions of forests, climate change and forest management. The results reveal a high level of environmental concern and climate awareness, coexisting with limited knowledge of SFM and simplified or distorted perceptions of forest dynamics. Media coverage is predominantly reactive and event-driven, strongly focused on wildfire events, while preventive and adaptive forest management practices remain largely invisible. In this context, support for SFM increases significantly when management practices are clearly explained and contextualised, indicating that resistance is more closely related to communication gaps than to ideological opposition. These findings highlight the critical role of media framing and communication processes in shaping the social acceptance of SFM. The study contributes to the literature by integrating media analysis and social perception within a forest governance perspective, and provides empirical insights to support more effective communication strategies and policy design in Mediterranean regions facing increasing climate pressures. Full article

Sustainability and the Sustainable Development Goals (SDGs) are garnering significant attention due to growing global challenges, including poverty, inequality, environmental degradation, and climate change, with the latter addressed specifically through SDG 13. This study examined the level of self-reported awareness of six science-related SDGs—SDG 3 (Good Health and Well-Being), SDG 6 (Clean Water and Sanitation), SDG 7 (Affordable and Clean Energy), SDG 13 (Climate Action), SDG 14 (Life Below Water), and SDG 15 (Life on Land)—among science teachers in the Arab sector in Israel as a function of background variables: gender, seniority, degree type, academic institution, school type, area of specialization, and the integration of these SDGs into the science curriculum. The study employed a mixed-methods approach: in the quantitative component, 204 science teachers responded to a Likert-scale questionnaire; the qualitative component consisted of semi-structured interviews with 30 middle school science teachers from the Arab sector. The findings indicated a moderate level of self-assessed awareness regarding SDGs. Significant differences in awareness were found according to teaching subject: environmental studies teachers demonstrated the highest awareness, followed by general science, biology, and physics teachers, with chemistry teachers ranking lowest. No significant differences were found for the remaining variables (p > 0.05). Qualitative findings indicated that while teachers perceived SDG-related content as implicitly present in the curriculum, explicit and systematic integration of the SDG framework is largely absent. Overall, the findings suggest that teachers are not adequately exposed to the SDGs. Therefore, it is recommended to incorporate these topics into teacher-training courses and professional development programs and to further integrate them into curricula. This study contributes to the growing body of research on SDG integration in science education, particularly within underexplored minority educational contexts. Full article

This study quantifies the spatiotemporal evolution of non-communicable disease (NCD) mortality risk associated with temperature extremes in the Metropolitan Area of the Valley of Mexico (MAVM) from 2000 to 2019. Using a Bayesian risk assessment framework, we analyzed 747,131 deaths to evaluate the impact of extreme temperature indices (Tn90p, Tn10p, TNn, Tx90p, Tx10p, TXx, DTR) across demographic and geographic dimensions. Results reveal a significant intensification of mortality risk, particularly for circulatory and metabolic diseases after 2005 and 2014. Risk expansion analysis identified 16 cases of robust relative risk (RR) intensification, predominantly among elderly populations. Females and males aged 65+ with metabolic diseases exhibited the highest thermal vulnerability. Our analysis further indicates a systematic shift in mortality risk toward higher nocturnal temperatures and reduced diurnal variability, suggesting a transition from cold-related stress to persistent nighttime heat exposure. Spatial Bayesian modeling shows a progressive homogenization of environmental risk across the metropolitan area, with high-risk thermal profiles expanding from the urban core toward peripheral municipalities, reducing the extent of previously lower-risk zones. Notably, the number of municipalities in the highest risk category for females aged 65+ with metabolic diseases increased by 550%, while for males of the same age, the expansion reached 163%. These findings indicate that vulnerability in megacities is a dynamic process driven by nocturnal warming and thermal instability. They highlight the urgent need to integrate climate-sensitive planning strategies—such as the identification and preservation of climatic refuge zones—into urban development policies, alongside continuous monitoring of temperature-related health risks. Full article

Forage grasses are an important component of livestock systems due to their contribution to animal feed, soil conservation, and carbon sequestration. In the face of climate change, analyzing stomatal characteristics allows us to understand the mechanisms of adaptation and tolerance to environmental stress. Therefore, the objective of this study was to determine the stomatal characteristics and trichome density of ten forage grasses present in a pine-oak dominated ecosystem. Sampling was carried out in October and November 2022 on a 1938 ha area. Mature, healthy leaves were selected, and epidermal impressions were obtained from the adaxial and abaxial surfaces using the cyanoacrylate method. Observations were made with an optical microscope at 400× magnification, quantifying stomatal density, trichome density, number of epidermal cells, and stomatal index per mm². The results indicated that nine species were amphistomatic, while Schizachyrium scoparium exhibited an epistomatic pattern. Muhlenbergia arizonica showed the highest stomatal density, and Setaria parviflora the lowest. It is concluded that there is high stomatal variability among species, highlighting its importance for the management and improvement of pastures. Full article

As a key node in the global supply chain, the resilience of ports is crucial for coping with multiple risks such as increasingly frequent climate change, operational accidents, and geopolitics, and ensuring the smooth flow of trade and sustainable development. This paper takes Ningbo-Zhoushan Port, which leads the world in throughput, as the research object, aiming to construct a comprehensive port resilience assessment model. Through the system dynamics method, the smart port system is deconstructed into three interrelated subsystems: meteorology, production, and economic-politics, and a simulation model including a causal relationship diagram and a system flow diagram is established accordingly. The model is verified to be effective and robust through historical data testing and sensitivity analysis. By setting different scenarios, this paper quantitatively analyzes the impact of single and compound risk shocks such as extreme weather, production accidents, and tariff policies on port throughput, and classifies port resilience into three levels: strong, medium, and weak. The research results show that Ningbo-Zhoushan Port shows strong resilience to the above-mentioned single risks. Even when the risk parameters are increased by 100%, the change rate of port throughput is less than the historical average annual change rate by 5.06%. However, in the extreme scenario of multiple risk couplings, the decline in port throughput is more significant, highlighting the importance of coping with compound risks. Further strategy simulation reveals that accelerating the economic development of the hinterland, increasing investment in port infrastructure, increasing the frequency of equipment maintenance, expanding the proportion of high-quality employees, and strengthening public facility management for accurate risk prediction are all effective ways to enhance port resilience. This research provides a scientific decision-making support tool for port managers, and the proposed resilience enhancement strategies have important theoretical and practical significance for ensuring the long-term stable operation of ports and the sustainable development of the regional economy. Full article

Due to the high energy demand of buildings, especially educational buildings, it is crucial to improve total building energy consumption. The proposed methodology is the integration of a photovoltaic (PV) system with a smart control plan for educational buildings. The main aim is to improve energy consumption in an educational building (Electrical Engineering Department, Assiut University, Egypt) using photovoltaic integration and a smart control plan to regulate energy and boost indoor comfort without requiring a significant change in the building architecture. This study was conducted in two main phases: field measurements for annual energy consumption in Assiut University over a five-year period from 2009 to 2014, and an analysis of energy consumption for the Electrical Engineering Department. Then, integration of PV panels on the roof to generate electricity was considered, with the calculation of the shading factor and tilt angle to ensure a realistic estimation of energy yield and to improve energy efficiency using smart control plans. The findings indicate that the average annual peak consumption reached about 30 GWh in Assiut University during the academic years 2009 to 2014. The maximum energy consumption for a typical occupied day in the educational building is 47 kWh. An improvement in building energy consumption was achieved using PV, producing 33–35 MWh annually with an effective smart control plan and without installing sensor-based systems. The results of this study will help improve energy consumption for educational buildings in hot arid climates without building modifications. This study highlights that unoccupied periods—when human activity is absent in classrooms and other rooms—account for up to 40% of the scheduled energy consumption. Using PV panels will result in a shading factor of 0.562 from the total roof area. Full article

Conventional Heating, Ventilation, and Air Conditioning (HVAC) control systems primarily rely on environmental and physiological parameters, largely ignoring the critical influence of psychological states on thermal comfort. Overlooking this factor often leads to suboptimal occupant satisfaction, energy inefficiency and thus carbon dioxide (CO₂) emissions. To this aim, this study introduces a novel mood-adaptive HVAC control system integrating psychological feedback to decrease CO₂ emissions in office buildings by reducing energy consumption and optimizing comfort. A total of 7000 thermal facial measurement records and high-resolution camera images were collected across seven mood state conditions using video stimuli and the Profile of Mood States (POMS) questionnaire to evaluate mood variations. A dual artificial intelligence system was developed: a Convolutional Neural Network (CNN) for analyzing facial expressions and an Artificial Neural Network (ANN) for processing facial temperatures via thermal imaging. These models collectively predict occupant mood in real-time, and a custom-designed wearable necklace interface transmits this data to dynamically adjust HVAC setpoints. To evaluate system performance, energy consumption was directly measured in real-life operations using an energy analyzer, without relying on simulations. Results indicate that this prototype personalized mood-driven system has the potential to enhance perceived thermal comfort while achieving up to a 20% reduction in carbon emissions compared to conventional systems. This human-centered approach significantly advances intelligent building management and climate change mitigation. Full article

Climate change is intensifying hydroclimatic extremes and agricultural water scarcity, sharpening trade-offs among yield stability, water saving, and farm incomes in major grain regions. Existing studies often optimise cropping patterns or irrigation schedules separately, seldom embedding yield robustness and policy instruments in one decision framework. We propose an integrated Machine-learning–System-dynamics–Non-dominated-sorting-genetic-algorithm-II (ML–SD–NSGA-II) framework linking long-horizon meteorological scenario generation, crop–water–economy feedback and multi-objective optimisation of crop areas and irrigation depths. ML models generate daily climate sequences to drive an SD model of soil moisture, yield formation, basin-scale allocable water, and farm returns; NSGA-II searches Pareto-optimal strategies that maximise profit and irrigation water productivity while minimising yield deviation. Applied to a rice–wheat irrigation system in the middle Yangtze River Basin, knee-point solutions lift irrigation water productivity by about 14%, maintain near-baseline profits, and reduce yield deviation. Scenario tests with block tariffs, quota-based subsidies, and extreme drought show pricing mainly curbs low-value water use in normal years, while under drought, physical scarcity dominates and economic tools offer limited buffering. This reveals the existence of a scarcity-regime threshold beyond which economic instruments become second-order relative to binding biophysical constraints. The framework supports transparent ex ante testing of tariff–subsidy packages for irrigation governance and adaptation. Full article

Accurate estimation of forest soil organic carbon (SOC) is considered critical for understanding terrestrial carbon cycling and supporting climate change mitigation strategies. However, the canopy block, intricate vertical structure of forests, and the constraints of single-source remote sensing data have presented considerable obstacles for estimating forest SOC. This study proposes a forest SOC estimation and uncertainty analysis (ForSOC-UA) framework to enhance forest SOC estimation and quantify its uncertainty in the natural secondary forests of northern China by integrating hyperspectral imagery (ZY-1F), synthetic aperture radar data (Sentinel-1), and environmental covariates (such as topography, vegetation, and soil indices). The performance of traditional machine learning models (RF, SVM, and CNN), geographically weighted regression (GWR), and a geographically weighted random forest (GWRF) model was compared across three different soil depths (0–5 cm, 5–10 cm, and 10–30 cm). The results showed that GWRF consistently outperformed all other models across all soil depth layers, with the highest accuracy achieved using multi-source data (R² = 0.58, RMSE = 27.49 g/kg, rRMSE = 0.31). Analysis of feature importance revealed that soil moisture, terrain characteristics, and Sentinel-1 polarization attributes were the primary predictors, while spectral derivatives in the red and near-infrared bands from ZY-1F also played a significant role for forest SOC estimation. The uncertainty analysis indicated a forest SOC estimation uncertainty of 37.2 g/kg in the 0–5 cm soil layer, with a decreasing trend as depth increased. This pattern is associated with the vertical spatial distribution of the measured forest SOC. This integrated approach effectively captures spatial heterogeneity and nonlinear relationships between feature and forest SOC, while also assessing estimation uncertainty, so providing a robust methodology for predicting forest SOC. The ForSOC-UA framework addresses the uncertainty quantification of SOC estimation at different vertical depths based on machine learning, providing methodological enhancements for the assessment of large-scale forest SOC and the monitoring of carbon sinks within forest ecosystems. Full article