Search Results (8,614)

Understanding how endangered carnivores partition spatiotemporal distribution in human-dominated landscapes is pivotal for mitigating biodiversity loss in climate-sensitive boreal ecosystems. Here, we used kernel density data derived from a 16-month camera-trap survey (140 UVL7 cameras), cold single-season (November–April) occupancy models, and MaxEnt 3.4.4 to identify the effects of biotic interactions, anthropogenic disturbance, and environmental factors on the spatiotemporal distribution of the wolverine (Gulo gulo) in Beijicun National Nature Reserve, Heilongjiang Province, China. We found that wolverines exhibited crepuscular activity patterns using night-time relative abundance index (NRAI) = 50.29% with bimodal peaks (05:00–07:00, 13:00–15:00), with dawn activity predominant during the warm season (05:00–06:00) and a bimodal activity pattern in the cold season (08:00–09:00, 14:00–15:00). Temporal overlap with prey (overlap coefficient Δ = 0.84) and competitors (Δ = 0.70) was high, but overlap with human-dominated temporal patterns was low (Δ = 0.58). Wolverines avoided human settlements and major roads, preferred moving along forest trails and gentle slopes, and avoided high-altitude deciduous forests. Populations were mainly concentrated in southern Hedong and Qianshao Forest Farms, which are characterized by high habitat integrity, high prey densities, and minimal anthropogenic disturbance. These findings suggest that wolverines may influence boreal trophic networks, especially in areas with intact prey communities, competitors, and spatial refugia from human disturbances. We recommend that habitat protection and management within the natural reserve be prioritized and that sustainable management practices for prey species be implemented to ensure the long-term survival of wolverines. Full article

Driven by the growing demand for sustainable materials, spent coffee grounds have emerged as a promising bio-based reinforcement in polymer composites, particularly for additive manufacturing applications. As a readily available byproduct of the coffee industry, spent coffee grounds contain cellulose, hemicellulose, lignin, proteins, and oils, making them attractive fillers for both thermoplastic and thermoset matrices. Incorporating spent coffee grounds into composites supports waste valorization, cost reduction, and environmental sustainability by transforming organic waste into functional materials. This review first examines the issue of spent coffee ground waste, addressing its environmental footprint and disposal challenges. It then explores the composition and properties of spent coffee grounds. The paper provides a comprehensive overview of composites based on spent coffee grounds for 3D printing, covering processing methods, potential applications, and current challenges in additive manufacturing. Special attention is given to the preparation and processing of these composites, including key steps such as drying, grinding, sieving, and surface modification to enhance compatibility with polymer matrices. Various additive manufacturing techniques influence the printability, processability, and mechanical performance of such composites. While spent coffee grounds offer notable sustainability advantages, challenges such as weak interfacial adhesion, moisture sensitivity, and reduced mechanical properties necessitate optimized processing conditions, surface treatments, and tailored material formulations. This review highlights recent advancements and outlines future research directions, emphasizing the need for stronger interactions between spent coffee grounds and polymer matrices, improved recyclability, and scalable additive manufacturing solutions to establish spent coffee grounds as a viable and eco-friendly alternative for 3D printing applications. Full article

Lubricating oils are utilised in equipment and machinery to reduce friction and enhance material utilisation. The utilisation of oil leads to an increase in its thickness and density over time. Current methods for assessing oil life are slow, expensive, and complex, and often only applicable in laboratory settings and unsuitable for real-time or field use. This leads to unexpected equipment failures, unnecessary oil changes, and economic and environmental losses. A comprehensive review of the extant literature revealed no studies and no national or international patents on neural network algorithm-based oil life modelling and classification using green sensors. In order to address this research gap, this study, for the first time in the literature, provides a green conductivity sensor with high-accuracy prediction of oil life by integrating real-time field measurements and artificial neural networks. This design is based on analysing resistance change using a relatively low-cost, three-dimensional, eco-friendly sensor. The sensor is characterised by its simplicity, speed, precision, instantaneous measurement capability, and user-friendliness. The MLP and LVQ algorithms took as input the resistance values measured in two different oil types (diesel, bench oil) after 5–30 h of use. Depending on their degradation levels, they classified the oils as ‘diesel’ or ‘bench oil’ with 99.77% and 100% accuracy. This study encompasses a sensing system with a sensitivity of 50 µS/cm, demonstrating the proposed methodologies’ efficacy. A next-generation decision support system that will perform oil life determination in real time and with excellent efficiency has been introduced into the literature. The components of the sensor structure under scrutiny in this study are conducive to the creation of zero waste, in addition to being environmentally friendly and biocompatible. The developed three-dimensional green sensor simultaneously detects physical (resistance change) and chemical (oxidation-induced polar group formation) degradation by measuring oil conductivity and resistance changes. Measurements were conducted on simulated contaminated samples in a laboratory environment and on real diesel, gasoline, and industrial oil samples. Thanks to its simplicity, rapid applicability, and low cost, the proposed method enables real-time data collection and decision-making in industrial maintenance processes, contributing to the development of predictive maintenance strategies. It also supports environmental sustainability by preventing unnecessary oil changes and reducing waste. Full article

As a vital transition metal species, cobalt ions (Co²⁺) play a critical role in industrial and medical fields. However, uncontrolled release into ecosystems via industrial effluents presents significant environmental risks. To address this, a prism-coupled surface plasmon resonance (SPR) sensor chip was developed which enables simultaneous high sensitivity, wide detection range, and rapid detection of Co²⁺ under ultra-low detection limit conditions. By depositing a 50 nm Au film and AuNPs on a glass substrate, and integrating carboxyl-functionalized carbon quantum dots (CQDs), the chip achieved the detection range of 10⁻²⁰ mol/L to 10⁻⁴ mol/L, and the response time was reduced from 21 min to 11 min under optimal electric field conditions (1.2 V, 0.15 mol/L electrolyte concentration). The sensor exhibits high selectivity, repeatability, and stability. It can be integrated with optofluidic technology to enable high-throughput microfluidic analysis, thereby facilitating further advancements in related research. Full article

The increasing presence of microplastics (MPs) and nanoplastics (NPs) in environmental matrices presents substantial analytical challenges due to their small size and chemical diversity. This study introduces a novel enzymatic biosensor based on the Surface Plasmon Resonance (SPR) platform for the sensitive detection of MPs and NPs, utilizing laccase as the recognition element. Standard plastic particles, including polystyrene (PS, 0.1 µm), polymethyl methacrylate (PMMA, 1.0 µm and 100 µm), and polyethylene (PE, 34–50 µm), were analyzed using SPR angular interrogation along with a fixed-angle scheme. The angular approach revealed a clear relationship between the resonance angle, particle size, and refractive index, while the fixed-angle method, combined with immobilized laccase, facilitated specific detection through enzyme/substrate interactions. The analytical parameters showed detection limits ranging from 7.5 × 10⁻⁴ µg/mL (PE, 34–50 µm) to 253.2 µg/mL (PMMA, 1 µm), with significant differences based on polymer type and enzymatic affinity. Application of the biosensor to real rainwater samples collected from two regions in Mexico (Tula and Molango) confirmed its functionality, although performance varied depending on matrix composition, exhibiting inhibition in samples with high manganese (Mn²⁺), chromium (Cr²⁺), and zinc (Zn²⁺) content. Despite these limitations, the sensor achieved a 113% recovery rate in Tula rainwater, demonstrating its potential for straightforward in situ environmental monitoring. This study highlights the capabilities of laccase-based SPR biosensors in enhancing microplastic detection and underscores the necessity of considering matrix effects for real-world applications. Full article

Stream temperatures are expected to increase with warming air temperatures, yet the extent and aquatic health impacts vary significantly across heterogeneous landscapes. This study was conducted in a 3360-ha multi-land-use watershed in the Pacific Northwest region of the USA to assess and compare the driving factors for stream temperature heating, cooling, and cool-water refugia along a 12-km mainstem stream longitudinal profile. Study objectives were to (1) determine yearlong stream temperature variability along the entire stream longitudinal profile, and (2) assess stream-environment relationships influencing stream temperature dynamics across forest, agriculture, and urban landscapes within the watershed. Stream and riparian air temperatures, solar radiation, shade, and related stream-riparian characteristics were measured over six years at 21 stations to determine changes, along the longitudinal profile, of thermal sensitivity, maximum and minimum stream temperatures, and correlation between solar radiation and temperature increases, and potential causal factors associated with these changes. Solar radiation was a primary heating factor for an exposed agricultural land use reach with 57% effective shade, while southern stream aspects and incoming tributary conditions were primary factors for forested reaches with greater than 84% effective shade. Potential primary cooling factors were streambank height, groundwater inflows, and hyporheic exchange in an urban reach with moderate effective shade (79%) and forest riparian width (16 m). Combining watershed-scale analysis with on-site stream-environmental data collection helps assess primary temperature heating factors, such as solar radiation and shade, and potential cooling factors, such as groundwater and cool tributary inflows, as conditions change along the longitudinal profile. Full article

Lonicera japonica is a widely utilized medicinal and ornamental plant. Its secondary metabolism is highly sensitive to cold stress. Previous studies have demonstrated how L. japonica accumulates anthocyanin in response to cold stress, with calcium ions playing a potential role in the regulation. To further clarify the regulatory function of calcium ions regarding pigment formation under cold stress, transcriptomic analysis was conducted on exogenous calcium ions and calcium chelator EGTA-treated L. japonica under cold stress. The CaCl₂ treatment markedly delayed changes in the pigmentation, and the plant maintained a higher chlorophyll content, whereas EGTA treatment enhanced anthocyanin accumulation and induced earlier and more intense leaf coloration. A total of 17,296 differentially expressed genes were co-expressed during cold stress, and calcium-responsive genes were predominantly enriched in phenylpropanoid biosynthesis, hormone signaling, and stress response pathways. Notably, key transcription factors such as MYBS3 and BRH1 were identified with expression patterns that closely correlated with pigment changes and stress adaptation. These results indicate the deep involvement of molecular mechanisms of calcium signaling in modulating pigment accumulation in response to cold stress, providing a theoretical foundation for improving both the ornamental and medicinal value of L. japonica under adverse environmental conditions. Full article

This review summarizes the recent advances in the application of nanomaterial coatings in optical fiber sensors, with a particular focus on deposition techniques and the research progress over the past five years in humidity sensing, gas detection, and biosensing. Benefiting from the high specific surface area, abundant surface active sites, and quantum confinement effects of nanomaterials, advanced thin-film fabrication techniques—including spin coating, dip coating, self-assembly, physical/chemical vapor deposition, atomic layer deposition (ALD), electrochemical deposition (ECD), electron beam evaporation (E-beam evaporation), pulsed laser deposition (PLD) and electrospinning, and other techniques—have been widely employed in the construction of functional layers for optical fiber sensors, significantly enhancing their sensitivity, response speed, and environmental stability. Studies have demonstrated that nanocoatings can achieve high-sensitivity detection of targets such as humidity, volatile organic compounds (VOCs), and biomarkers by enhancing evanescent field coupling and enabling optical effects such as surface plasmon resonance (SPR), localized surface plasmon resonance (LSPR), and lossy mode resonance (LMR). This paper first analyzes the principles and optimization strategies of nanocoating fabrication techniques, then explores the mechanisms by which nanomaterials enhance sensor performance across various application domains, and finally presents future research directions in material performance optimization, cost control, and the development of novel nanocomposites. These insights provide a theoretical foundation for the functional design and practical implementation of nanomaterial-based optical fiber sensors. Full article

Due to climate change, the risk of spring frosts has increased and may rise further in the near future. This is pushing winegrowers to adopt active spring frost protection methods (ASFPMs) in their vineyard management practices. This study analyzes the potential contribution of the most commonly used ASFPMs to the environmental impacts of grape production in the Loire Valley region, using the Life Cycle Assessment (LCA) approach, while considering local mesoclimatic conditions. The environmental offsets of ASFPMs are modeled by comparing the viticulture stage impact with and without ASFPM technologies. Furthermore, the present paper proposes an original approach to integrate potential yield loss, simulating frost damage. This sensitivity analysis identifies the yield loss threshold at which the different ASFPMs are environmentally compensated under various mesoclimatic conditions. We show that the environmental contribution of instant ASFPMs varies most significantly based on the number of frost hours, but generally remains the highest across most environmental indicators compared to other impacts of viticulture, e.g., ranging from 35 to 92% for the climate change indicator. Wind machines contribute the least to the viticulture stage, regardless of frost hour occurrence. However, even permanent solutions have a significant impact on at least one environmental indicator, regardless of frost hour occurrence. Additionally, the environmental offset analysis outlines that the yield loss thresholds for ASFPM impact compensation are high, even for the most effective solutions in a frost-prone context. Future research should include passive spring frost protection methods and other types of vineyard management in LCA of the viticulture stage. Full article

Background: Borderline Personality Disorder (BPD) is a complex psychiatric condition with multifactorial origins, with a high proportion of patients reporting early trauma. Stressors such as adverse childhood experiences (ACEs) can shape the epigenetic landscape including DNA methylation (DNAm) and act on gene expression. DNAm is increasingly being investigated as a molecular link between environmental exposures such as ACE and psychiatric outcomes. Differential DNAm of the gene PR domain zinc finger protein 8 (PRDM8), a histone methyltransferase, has recently been reported to be sensitive to early life trauma. Its role in BPD, especially in the context of ACE, remains to be elucidated. Methods: This study investigated DNAm patterns of PRDM8 in peripheral blood and saliva obtained from BPD patients undergoing Dialectic Behavioral Therapy (DBT) compared to healthy control (HC) participants. Associations with ACE and BPD symptom severity were assessed, and therapy-related changes in DNAm were examined. Results: At baseline, BPD patients demonstrated significant hypomethylation of PRDM8 in blood relative to the HC group. Following DBT, a nominally significant increase in DNAm was observed, aligning with inversely correlated symptom severity. No significant differences in saliva were detected. ACE was not associated with PRDM8 DNAm. Conclusions: Our findings suggest that PRDM8 DNAm might be associated with BPD and therapeutic intervention but not with ACE. Together with prior research, the results underscore the importance of future investigation of gene–environment interactions and the functional significance of PRDM8 regulation in the pathophysiology of BPD. Full article

Despite the emergence of eco-friendly solvents and scalable methods for polymeric membrane fabrication, studies on the impacts of solvent synthesis and manufacturing scale-up have not been conducted. To this end, a life cycle assessment (LCA) was developed with the goal of determining the global environmental and health impacts of producing polysulfone (PSf) membranes with the solvents PolarClean and γ-valerolactone (GVL) via doctor blade extrusion (DBE) and slot die coating (SDC). Along with PolarClean and GVL, dimethylacetamide (DMAc) and N-methyl-2-pyyrolidone (NMP) were included in the LCA as conventional solvents for comparison. The dope solution viscosity had a major influence on the material inventories; to produce a normalized membrane unit on a surface area basis, a larger quantity of PSf-PolarClean-GVL materials was required due to its high viscosity. The life cycle impact assessment found electricity and PolarClean to be major contributing parameters to multiple impact categories during membrane fabrication. The commercial synthesis route of PolarClean selected in this study required hazardous materials derived from petrochemicals, which increased its impact on membrane fabrication. Due to more materials being required to fabricate membranes via SDC to account for tool fluid priming, the PSf-PolarClean-GVL membrane fabricated via SDC exhibited the highest impacts. The amount of electricity and concentration of PolarClean were the most sensitive parameters according to Spearman’s rank coefficient analysis. A scenario analysis in which the regional energy grid was substituted found that using the Swedish grid, which comprises far more renewable technologies than the global and US energy grids, significantly lowered impacts in most categories. Despite the reported eco-friendly benefits of using PolarClean and GVL as alternatives to conventional organic solvents, the results in this study provide a wider perspective of membrane fabrication process impacts, highlighting that upstream impacts can counterbalance the beneficial properties of alternative materials. Full article

Inside the transmission group of an agricultural tractor, the efficiency of power transfer to moving parts, their lubrication, and protection from wear are guaranteed by UTTO (Universal Tractor Transmission Oil) fluids, which are also used to operate the hydraulic system. These fluids, with mineral or synthetic origin, are characterized by excellent lubricating properties, high toxicity, and low biodegradability, which makes it important to replace them with more eco-sustainable fluids, such as those based on vegetable oils that are highly biodegradable and have low toxicity. It is also important to consider EU policies on the use of such fluids in sensitive environmental applications. To this end, several experimental bio-UTTO formulations were tested at CREA to evaluate—compared to conventional fluids—their suitability for use as lubricants for transmissions and hydraulic systems through endurance tests carried out in a Fluid Test Rig (FTR) specifically developed by CREA to apply controlled and repeatable work cycles to small volumes of oil, which are characterized by high thermal and mechanical stresses. The technical performance and the main physical–chemical parameters of the fluids were continuously monitored during the work cycles. Based on these experiences, this study describes the first application of a methodological approach aimed at testing an experimental biobased UTTO on a tractor used in normal farm activity. The method was based on a former test at the FTR in which the performance of the bio-UTTO was compared to that of the conventional UTTO recommended by the tractor manufacturer. Given the good results of the FTR test, bio-UTTO was introduced in a 20-year-old medium-power tractor, replacing the mineral fluid originally supplied, for the first reliability tests during its normal use on the CREA farm. After almost 600 h of work, the technical performance and the trend of chemical–physical parameters of bio-UTTO did not undergo significant changes. No damage to the tractor materials or oil leaks was observed. The test is still ongoing, but according to the results, in line with the indications provided by the FTR test, the experimental bio-UTTO seems suitable for replacing the conventional fluid in the tractor used in this study. Full article

This study presents a novel policy-integrated optimization framework for utility-scale hybrid power plants (HPP), including wind–solar–battery, addressing a critical gap in hybrid renewable energy system design by simultaneously evaluating technical, operational, and economic performance under dynamic policy environments. Unlike conventional approaches that treat these factors separately, this multi-objective optimization model uniquely combines (1) technical reliability assessment through Loss of Load Probability (LOLP) metrics, (2) operational efficiency analysis via curtailment minimization, and (3) economic viability evaluation using net present value (NPV) optimization—all while accounting for policy incentive structures. Applying this framework to comparative U.S. and India case studies reveals how tailored policy combinations can enhance project viability compared to single-incentive scenarios. The results indicate that HPPs are financially unviable without policy support, but targeted incentives like Investment Tax Credits (ITCs) and Production Tax Credits (PTCs) in the U.S. and Accelerated Depreciation (AD), Generation-Based Incentives (GBIs), and Viability Gap Funding (VGF) can improve their viability. The U.S. scenario sees a 197% increase in NPV and a reduction in LCOE to USD 0.055/kWh, while India achieves a 107% turnaround in NPV and an LCOE of USD 0.039/kWh. Sensitivity and breakeven analyses reveal that interest rates and consistent policy support are critical, especially in emerging markets. Specific policy thresholds are identified for feasibility, providing actionable benchmarks. By bridging the gap between technical optimization and policy analysis, this work provides both a methodological advance for HPP design and practical insights for policymakers seeking to accelerate HPP. While this study centers on incentive-driven feasibility, it also outlines key modeling limitations and future improvements, such as market participation, environmental constraints, and advanced system design that will support future HPP planning. Full article

Autonomous driving represents a transformative advancement with the potential to significantly impact daily mobility, including enabling independent vehicle operation for individuals with visual disabilities. The commercialization of autonomous driving requires guaranteed safety and accuracy, underscoring the need for robust localization and environmental perception algorithms. In cost-sensitive platforms such as delivery robots and electric vehicles, cameras are increasingly favored for their ability to provide rich visual information at low cost. Despite recent progress, existing visual–inertial odometry systems still suffer from degraded accuracy in challenging conditions, which limits their reliability in real-world autonomous navigation scenarios. Estimating 3D positional changes using only 2D image sequences remains a fundamental challenge primarily due to inherent scale ambiguity and the presence of dynamic scene elements. In this paper, we present a visual–inertial odometry framework incorporating a depth estimation model trained without ground-truth depth supervision. Our approach leverages a self-supervised learning pipeline enhanced with knowledge distillation via foundation models, including both self-distillation and geometry-aware distillation. The proposed method improves depth estimation performance and consequently enhances odometry estimation without modifying the network architecture or increasing the number of parameters. The effectiveness of the proposed method is demonstrated through comparative evaluations on both the public KITTI dataset and a custom campus driving dataset, showing performance improvements over existing approaches. Full article

Enzyme-based biosensors have emerged as a transformative technology, leveraging the specificity and catalytic efficiency of enzymes across various domains, including medical diagnostics, environmental monitoring, food safety, and industrial processes. These biosensors integrate biological recognition elements with advanced transduction mechanisms to provide highly sensitive, selective, and portable solutions for real-time analysis. This review explores the key components, detection mechanisms, applications, and future trends in enzyme-based biosensors. Artificial enzymes, such as nanozymes, play a crucial role in enhancing enzyme-based biosensors by mimicking natural enzyme activity while offering improved stability, cost-effectiveness, and scalability. Their integration can significantly boost sensor performance by increasing the catalytic efficiency and durability. Additionally, lab-on-a-chip and microfluidic devices enable the miniaturization of biosensors, allowing for the development of compact, portable devices that require minimal sample volumes for complex diagnostic tests. The functionality of enzyme-based biosensors is built on three essential components: enzymes as biocatalysts, transducers, and immobilization techniques. Enzymes serve as the biological recognition elements, catalyzing specific reactions with target molecules to produce detectable signals. Transducers, including electrochemical, optical, thermal, and mass-sensitive types, convert these biochemical reactions into measurable outputs. Effective immobilization strategies, such as physical adsorption, covalent bonding, and entrapment, enhance the enzyme stability and reusability, enabling consistent performance. In medical diagnostics, they are widely used for glucose monitoring, cholesterol detection, and biomarker identification. Environmental monitoring benefits from these biosensors by detecting pollutants like pesticides, heavy metals, and nerve agents. The food industry employs them for quality control and contamination monitoring. Their advantages include high sensitivity, rapid response times, cost-effectiveness, and adaptability to field applications. Enzyme-based biosensors face challenges such as enzyme instability, interference from biological matrices, and limited operational lifespans. Addressing these issues involves innovations like the use of synthetic enzymes, advanced immobilization techniques, and the integration of nanomaterials, such as graphene and carbon nanotubes. These advancements enhance the enzyme stability, improve sensitivity, and reduce detection limits, making the technology more robust and scalable. Full article