Search Results (1,094)

Increasing global water scarcity has intensified the adoption of water reuse as a sustainable strategy, particularly in regions affected by drought and pressure on natural resources. This paper presents a systematic review of the application of Life Cycle Assessment (LCA) in water reuse projects, focusing on research trends, methodological approaches, and opportunities for improvement. A systematic search was conducted in Web of Science, ScienceDirect, and Google Scholar for studies published from 2020 onwards using combinations of the keywords “life cycle assessment”, “LCA”, “water reuse”, “water recycling”, and “wastewater recycling”. Twelve studies were selected from 57 records identified, based on predefined eligibility criteria requiring quantitative LCA of water reuse systems. The results reveal a predominance of European research, reflecting regulatory advances and strong academic engagement in this field. The most frequently assessed impact categories were global warming, eutrophication, human toxicity and ecotoxicity, highlighting the environmental relevance of reuse systems. Energy consumption and water transport were identified as critical hotspots, especially in scenarios involving long distances and fossil-based energy sources. Nevertheless, most studies demonstrate that water reuse is environmentally viable, particularly when renewable energy and optimized logistics are applied. The review also emphasizes the need to better integrate economic and social dimensions and to adapt LCA methodologies to local conditions. Overall, the findings confirm LCA as a robust decision-support tool for sustainable planning and management of water reuse systems. Full article

The continuous degradation of mangrove ecosystems, considered among the most vulnerable worldwide, reveals multiple threats driven by human activities and climate change. In the Peruvian context, particularly in the Tumbes Mangrove ecosystem, these pressures are intensified by the absence of integrated spatial and educational infrastructures capable of supporting conservation efforts while engaging local communities. In response, this research proposes a Sustainable Interpretation Center for Conservation and Environmental Education in Ecologically Sensitive Areas of the Tumbes Mangrove, Peru. The methodology includes climate data analysis, identification of local flora and fauna, and site topography characterization, supported by digital tools such as Google Earth, AutoCAD 2025, Revit 2025, and 3D Sun Path. The results are reflected in an architectural proposal that incorporates sustainable materials compatible with sensitive ecosystems, including eco-friendly structural solutions based on algarrobo timber, together with resilient strategies addressing climatic variability, such as lightweight structures, elevated platforms, and passive environmental solutions that minimize impact on the mangrove. Furthermore, the proposal integrates a photovoltaic energy system consisting of 12 solar panels with a unit capacity of 450 W, providing a total installed capacity of 5.4 kWp, complemented by a 48 V LiFePO₄ battery storage system designed to ensure energy autonomy during periods of low solar availability. In conclusion, the proposal adheres to principles of sustainability and energy efficiency and aligns with the Sustainable Development Goals (SDGs) 7, 8, 12, 14, and 15, reinforcing the use of clean energy, responsible tourism, sustainable resource management, and the conservation of marine and terrestrial ecosystems. Full article

Mesozooplankton play a pivotal role in marine pelagic food webs, mediating energy and matter transfer between primary producers and higher trophic levels. Daya Bay, a semi-enclosed bay located in the northern South China Sea, has undergone significant environmental changes due to anthropogenic activities, such as thermal discharge from nuclear power plants and eutrophication. This study examined the mesozooplankton community structure, feeding preferences, and food web organization through four seasonal cruises (May 2022, February 2023, August 2023, and November 2023), employing stable isotope analysis and a Bayesian Isotopic Mixing Model. Results indicate that mesozooplankton abundance and diversity were lower in regions affected by thermal discharge, suggesting a suppressive effect of elevated temperatures. Seasonal shifts in dominant species were observed: Penilia avirostris and Dolioletta gegenbauri dominated the community in spring, while Noctiluca scintillans blooms occurred in summer and winter. Isotopic analysis revealed distinct trophic strategies: copepods exhibited omnivorous habits, whereas cladocerans and tunicates showed stronger herbivorous tendencies. N. scintillans functioned as a high-trophic omnivore, preying on copepod larvae and competing for food resources. Overall, the mesozooplankton community was characterized by an omnivory-dominated trophic network, which enhanced resilience yet remains sensitive to anthropogenic disturbances. This study clarifies how human-induced environmental changes reshape trophic pathways in subtropical coastal waters, providing a valuable reference for long-term monitoring and ecosystem management in Daya Bay. Full article

To address the pervasive challenges of resource inefficiency and static management in the hospitality sector, this study proposes a novel management framework that synergistically integrates Model Predictive Control (MPC) with Green Human Resource Management (GHRM). Methodologically, the framework establishes a dynamic closed-loop architecture that cyclically links environmental sensing, predictive optimization, plan execution and organizational learning. The MPC component generates data-driven forecasts and optimal control signals for resource allocation. Crucially, these technical outputs are operationally translated into specific, actionable directives for employees through integrated GHRM practices, including real-time task allocation via management systems, incentives-aligned performance metrics, and structured environmental training. This practical integration ensures that predictive optimization is directly coupled with human behavior. Theoretically, this study redefines hospitality operations as adaptive sociotechnical systems, and advances the hospitality energy-saving management framework by formally incorporating human execution feedback, predictive control theory, and dynamic optimization theory. Empirical validation across a sample of 40 hotels confirms the framework’s effectiveness, demonstrating significant reductions in daily average water consumption by 15.5% and electricity usage by 13.6%. These findings provide a robust, data-driven paradigm for achieving sustainable operational transformations in the hospitality industry. Full article

In educational settings, it is crucial to comprehend and manage individuals’ social interaction behaviors through the physical environment. However, analyzing social interaction patterns manually is a time-consuming and energy-intensive process. This study aims to reveal the socio-behavioral implications of spatial features, based on the Affordance Theory, using artificial intelligence (AI). To this end, the study proposes a unified quantitative methodology that leverages diverse AI approaches. Behavioral data are gathered via systematic observation and analyzed using (1) Deep Learning (DL)-based Human Detection and classified by (2) Machine Learning (ML)-based Interaction Score Prediction approach. The behavioral findings were analyzed in relation to spatial data via (3) Spatial Feature Selection. As the study area, the ATU Faculty of Engineering building complex was selected, and behavioral data from 746 participants were collected in the complex’s open spaces. The results indicated that AI-based approaches provide a high degree of precision in analyzing the relationships between social interaction and spatial features within the addressed context. Also, (1) the existence and (2) the rotation of seating units and (3) shading strategies are identified as the spatial features that contribute to higher interaction scores in the educational settings. The study proposes an integrated and transferable methodology based on diverse AI approaches for determining social interaction and its spatial aspects, leading to a comprehensive and reproducible approach. Full article

Photovoltaic (PV) power generation is essential for achieving carbon neutrality and advancing renewable energy development. In Northwest China, the rapid expansion of PV installations requires accurate and timely spatial data to support effective monitoring and planning. Addressing the limitations of existing datasets in spatiotemporal resolution and driver analysis, this study develops a scalable solar facility inventory framework on the Google Earth Engine (GEE) platform. The framework integrates Sentinel-1 SAR, Sentinel-2 multispectral imagery, and interpretable machine learning. Feature redundancy is first assessed using correlation-based metrics, after which a Random Forest classifier is applied to generate a 10 m resolution distribution map of utility-scale photovoltaic power plants as of December 2023. To elucidate model behavior, SHAP (SHapley Additive exPlanations) is used to identify key predictors, and MaxEnt is incorporated to provide a preliminary quantitative assessment of spatial drivers of PV deployment. The RFECV-optimized model, retaining 44 key features, achieves an overall accuracy of 98.4% and a Kappa coefficient of 0.96. The study region contains approximately 2560 km² of PV installations, with pronounced clusters in northern Ningxia, central Shaanxi, and parts of Xinjiang and Gansu. SHAP analysis highlights the Enhanced Photovoltaic Index (EPVI), the Normalized Difference Built-up Index (NDBI), Sentinel-2 Band 8A, and related texture metrics as primary contributors to model predictions. High EPVI, NDBI, and Sentinel-2 Band 8A values contribute positively to PV classification, whereas vegetation-related indices (e.g., NDVI) exhibit predominantly negative contributions; these results indicate that PV mapping relies on the integrated discrimination of multiple spectral and texture features rather than on a single dominant variable. MaxEnt results indicate that grid accessibility and land-use constraints (e.g., nighttime light intensity reflecting human activity) are dominant drivers of PV clustering, often exerting more influence than solar irradiance alone. This framework provides robust technical support for PV monitoring and offers high-resolution spatial distribution data and driver insights to inform sustainable energy management and regional renewable-energy planning. Full article

The Leaf Area Index (LAI) is a fundamental biophysical parameter quantifying forest canopy structure and regulating water–energy exchange. While Abies Mill. forests constitute a vital component of China’s alpine ecosystems, the spatial heterogeneity of their LAI and its sensitivity to environmental filtering remain underexplored. This study employed Random Forest (RF) and Structural Equation Modeling (SEM) to disentangle the direct and interactive effects of climate, soil, topography, and human footprint (HFP) on LAI across 17 distinct Abies forest types. The results revealed that temperature was the dominant positive driver for the overall Abies forests (Total effect = 2.197), whereas Elevation (DEM) exerted the strongest negative regulation (Total effect = −0.335). However, driver dominance varied substantially among forest types: climatic water availability was the primary constraint for Abies georgei var. smithii (Viguié & Gaussen) W.C.Cheng & L.K.Fu forest (Type 55), while DEM determined LAI in Abies fargesii Franch. forest (Type 49). Notably, we found that HFP could exert positive effects on LAI in specific communities (e.g., Abies densa Griff. forest, Type 58), likely due to understory compensation under moderate disturbance. These findings highlight the necessity of type-specific management strategies and provide a theoretical basis for predicting alpine forest dynamics under changing environments. Full article

Obesity has reached epidemic proportions, driven by energy imbalance and limited capacity for adaptive thermogenesis. Brown (BAT) and beige adipose tissues dissipate energy through non-shivering thermogenesis (NST), primarily via uncoupling protein-1 (UCP1), making them attractive targets for increasing energy expenditure (EE). The canonical β-adrenergic pathway robustly activates NST in rodents through β3 adrenoceptors; however, translational success in humans has been limited by low β3 expression, off-target cardiovascular effects, and the emerging dominance of β2-mediated signaling in human BAT. Consequently, attention has shifted to non-adrenergic and UCP1-independent mechanisms that offer greater tissue distribution and improved safety profiles. This review examines a broad spectrum of alternative receptors and pathways—including GPRs, TRP channels, TGR5, GLP-1R, thyroid hormone receptors, estrogen receptors, growth hormone, BMPs, sirtuins, PPARs, and interleukin signaling—as well as futile substrate cycles (Ca²⁺, creatine, and glycerol-3-phosphate) that sustain thermogenesis in beige adipocytes and skeletal muscle. Pharmacological agents (natural compounds, peptides, and small molecules) and non-pharmacological interventions (cold exposure, exercise, diet, and time shift) targeting these pathways are critically evaluated. We highlight the translational gaps between rodent and human studies, the promise of multimodal therapies combining low-dose adrenergic agents with non-adrenergic activators, and emerging strategies such as sarco/endoplasmic reticulum calcium ATPase protein (SERCA) modulators and tissue-specific delivery. Ultimately, integrating adrenergic and non-adrenergic approaches holds the greatest potential for safe, effective, and sustainable obesity management. Full article

Biomedical antennas are essential components in modern healthcare systems, supporting wireless communication, physiological monitoring, diagnostic imaging, and therapeutic energy delivery. Their performance is strongly influenced by proximity to the human body, creating challenges such as impedance detuning, signal absorption, and size constraints that motivate new materials and fabrication approaches. This work reviews recent advances enabling next-generation wearable and implantable antennas, with emphasis on printed electronics, additive manufacturing, flexible hybrid integration, and metamaterial design. Methods discussed include 3D printing and inkjet, aerosol jet, and screen printing for fabricating conductive traces on textiles, elastomers, and biodegradable substrates, as well as multilayer Flexible Hybrid Electronics that co-integrate sensing, power management, and RF components into thin, body-conforming assemblies. Key results highlight how metamaterial and metasurface concepts provide artificial control over dispersion, radiation, and near-field interactions, enabling antenna miniaturization, enhanced gain and focusing, and improved isolation from lossy biological tissue. These approaches reduce SAR, stabilize impedance under deformation, and support more efficient communication and energy transfer. The review concludes that the convergence of novel materials, engineered electromagnetic structures, and AI-assisted optimization is enabling biomedical antennas that are compact, stretchable, personalized, and highly adaptive, supporting future developments in unobtrusive monitoring, wireless implants, point-of-care diagnostics, and continuous clinical interfacing. Full article

This paper presents an Interoperable User-Centred Digital Twin (I-UCDT) framework for sustainable energy system management, addressing the growing complexity of energy generation, storage, demand, and grid interaction across industrial and community-scale systems. The proposed framework provides a unified environment for the visual representation and management of interconnected energy components, supporting informed decision-making among diverse stakeholder groups. The I-UCDT framework adopts a modular plug-and-play architecture based on the Functional Mock-up Interface (FMI) standard, enabling scalable and interoperable integration of heterogeneous energy models from platforms such as Modelica, MATLAB/Simulink, and EnergyPlus. A standardised data layer processes and structures raw model inputs, while an interactive visualisation layer translates complex energy flows into intuitive, user-accessible insights. By applying human–computer interaction principles, the framework reduces cognitive load and enables users with varying technical backgrounds to explore supply–demand balancing, decarbonisation pathways, and optimisation strategies. It supports the full lifecycle of energy system design, planning, and operation, offering flexibility for both industrial and community-scale applications. A case study demonstrates the framework’s potential to enhance transparency, usability, and energy efficiency. Overall, this work advances digital twin research for energy systems by combining technical interoperability with explicitly formalised user-centred design characteristics (C1–C10) to promote flexible and sustainable energy system management. Full article

Ginseng is widely regarded as the “King of Herbs” in traditional Chinese medicine. In recent years, escalating global warming and intensified human activities have led to a continuous rise in environmental temperatures, posing a significant threat to ginseng cultivation in China. Therefore, understanding how high-temperature stress affects the photosynthetic performance of ginseng is essential for developing efficient and sustainable cultivation practices. In this study, four temperature regimes were established to systematically investigate the impact of elevated temperatures on the photosynthetic system of ginseng leaves: 25/16 °C (CK), 30/20 °C, 35/24 °C, and 40/28 °C (day/night). The results demonstrated that high-temperature stress significantly inhibited photosynthesis. Specifically, the activities of key chlorophyll biosynthesis enzymes—porphobilinogen deaminase and delta-aminolevulinate dehydratase—were markedly reduced, resulting in the accumulation of critical intermediates in the chlorophyll pathway, including protoporphyrinIX, Mg-protoporphyrinIX, and protochlorophyll. Chlorophyll synthesis was severely impaired as a result. Consequently, the contents of chlorophyll a, chlorophyll b, and carotenoids declined by 25.38%, 12.52%, and 54.63%, respectively, indicating substantial disruption of the photosynthetic pigment system. Anatomical observations revealed that high-temperatures induced stomatal closure, impairing stomata exchange and further reducing photosynthetic efficiency. Moreover, chloroplast ultrastructure was severely compromised, characterized by excessive accumulation of osmiophilic granules, disorganized and loosely stacked thylakoid membranes, and impaired capacity for light energy capture and conversion. This study provides theoretical insights into the response mechanisms of ginseng leaf photosynthesis under heat stress and establishes a scientific basis for enhancing thermotolerance through breeding programs and improved cultivation management strategies. Full article

Urban rivers are essential resources for human societies; however, their degradation poses serious public health, economic, and environmental risks. Conventional physical remediation methods can partially mitigate pollution by targeting specific contaminants, but they are often limited in scope, lack long-term sustainability, and fail to restore ecological functions. In contrast, biotechnological approaches integrated with ecological engineering offer sustainable and nature-based solutions for river depollution, conservation, and revitalization. Although these strategies are supported by a solid theoretical framework and successful applications in other aquatic systems, their large-scale implementation in urban rivers has only recently begun to gain momentum. This review critically examines strategies for the revitalization of polluted urban rivers, progressing from conventional remediation techniques to advanced biotechnological interventions. It highlights real-world applications, evaluates their advantages and limitations, and discusses policy frameworks and management strategies required to promote the broader adoption of biotechnological solutions for sustainable urban river restoration. The goal is to demonstrate the transformative potential of integrated biotechnological, eco-engineering, and data-driven approaches—particularly microbial, phytoplankton-based, and biofilm systems—to reduce energy demand and carbon emissions in urban river restoration while highlighting the need for scalable designs, adaptive management, and supportive regulatory frameworks to enable their large-scale implementation. Full article

Marine plants are a rich source of bioactive compounds with unique properties. The Mediterranean seagrass Posidonia oceanica is particularly abundant in phenolics and flavonoids, which exhibit antioxidant and anti-inflammatory activities. In this study, a phenolic-rich extract (POS) was obtained from beach-cast P. oceanica leaves using supercritical fluid extraction (SFE), an eco-friendly technique that preserves thermolabile compounds and avoids organic solvents. POS was incorporated into a base cream (POS-enriched cream) to evaluate its bioactive potential in topical applications. The antioxidant capacity of POS and the cream formulation was firstly evaluated using the DPPH radical scavenging assay, confirming strong radical scavenging activity for the POS (IC₅₀ = 2.32 ± 0.33 mg/mL) and significant activity for the POS-enriched cream (IC₅₀ = 16.76 ± 0.58 mg/mL) compared to a base cream as control (IC₅₀ = 37.62 ± 1.27 mg/mL). The antioxidant and photoprotective effects of POS were investigated in human skin fibroblasts (HS-68) exposed to oxidative stress and UV-induced damage, while anti-melanogenic activity was assessed in human epidermal melanocytes (HEM) by measuring tyrosinase activity and melanin content. POS significantly reduced ROS accumulation and modulated key molecular pathways involved in apoptosis (p-JNK), inflammation (NF-κB), energy balance (p-AMPK), and collagen synthesis (Col1A1) in fibroblasts. In melanocytes, both POS pure extract and POS-enriched cream effectively inhibited tyrosinase activity while maintaining unaltered basal melanin levels, indicating a modulatory rather than fully suppressive effect. These findings highlight the potential of P. oceanica SFE extracts as sustainable natural marine-derived products for photoprotection and anti-melanogenesis, thereby bridging the gap between marine waste stream management and applications in skin health and anti-aging strategies. Full article

The integration of artificial intelligence into the Industrial Internet of Things (IIoT), supported by edge computing architectures, marks a new paradigm of intelligent automation. Tiny Machine Learning (TinyML) is emerging as a key technology that enables the deployment of machine learning models on ultra-low-power devices. This study presents a systematic review of 110 peer-reviewed publications (2020–2025) identified from Scopus, Web of Science, and IEEE Xplore following the PRISMA protocol. Bibliometric and thematic analyses were conducted using Biblioshiny and VOSviewer to identify major trends, architectural approaches, and industrial applications of TinyML. The results reveal four principal research clusters: edge intelligence and energy efficiency, federated and explainable learning, human-centric systems, and sustainable resource management. Importantly, the surveyed industrial implementations report measurable gains—typically reducing inference latency to the millisecond range, lowering on-device energy cost to the sub-milliwatt regime, and sustaining high task accuracy, thereby substantiating the practical feasibility of TinyML in real IIoT settings. The analysis indicates a conceptual shift from engineering- and energy-focused studies toward cognitive, ethical, and security-oriented perspectives aligned with the principles of Industry 5.0. TinyML is positioned as a catalyst for the transition from automation to cognitive autonomy and as a technological foundation for building energy-efficient, ethical, and sustainable industrial ecosystems. Full article

Sodium–glucose cotransporter 2 inhibitors (SGLT2is) have revolutionized the management of type 2 diabetes mellitus, heart failure, and chronic kidney disease (CKD), providing cardiorenal and metabolic benefits that extend beyond glycemic control. While their clinical efficacy is well established, the underlying molecular mechanisms remain only partially understood. This review focuses on current knowledge of SGLT2 expression and regulation in health and metabolic diseases, as well as transcriptional and epigenetic consequences of pharmacological SGLT2 inhibition. Human and experimental studies demonstrate that SGLT2 expression is confined to proximal tubular cells and regulated by insulin, the renin–angiotensin–aldosterone system, the sympathetic nervous system, oxidative stress, and transcriptional and epigenetic pathways. SGLT2 expression follows a biphasic pattern in metabolic disorder-associated CKD: upregulation in early phases and reduction in advanced stages. Evidence from animal models and single-cell transcriptomic studies indicates that SGLT2is normalize metabolic and inflammatory gene networks. To our knowledge, a recent single-cell RNA sequencing study provides the only currently available human dataset linking SGLT2i therapy with tubular metabolic rewiring and suppression of the energy-sensitive mechanistic target of rapamycin complex 1. Collectively, these findings support a model in which SGLT2 inhibition mitigates metabolic stress by restoring energy homeostasis across multiple nephron segments. Full article