Search Results (35,242)

The practical adoption of surface-enhanced Raman scattering (SERS) technology is often hampered by the high cost, complex fabrication, and poor reproducibility of conventional substrates, which typically rely on noble metals or inefficient semiconductors. Herein, we address key challenges in the practical commercialization of surface-enhanced Raman scattering (SERS) technology by reporting a facile, scalable, and environmentally benign strategy for fabricating a hybrid SERS substrate. This approach integrates Au nanoparticles (NPs) with hydrothermally synthesized WO₃ nanowires through a green photoreduction process, which is rapid, organic-solvent-free, and amenable to large-scale production. The design of the Au/WO₃ nanocomposite capitalizes on the synergistic effect between electromagnetic (EM) enhancement from Au NPs and chemical mechanism (CM) enhancement via charge transfer involving the WO₃ semiconductor. This synergy empowers the substrate with exceptional SERS activity, enabling the sensitive detection of Rhodamine 6G (R6G) down to 10⁻¹¹ M and yielding an enhancement factor (EF) of 4.09 × 10⁶. More importantly, this EM-CM synergy proves critical for detecting molecules with weak affinity, such as the nerve agent simulant dimethyl methylphosphonate (DMMP), achieving a significant signal enhancement of 10²–10³ times, which is notably challenging for conventional plasmonic substrates. Beyond sensitivity, the substrate exhibits excellent reproducibility and operational stability, which are paramount for real-world applications. This work presents a nanohybrid strategy that successfully balances scalability, stability, and sensitivity, offering a reliable and cost-effective pathway for advancing SERS technologies toward practical implementation. Full article

Background: Early detection of Alzheimer’s disease (AD) is critical for timely intervention, but clinical assessments and neuroimaging are often costly and resource intensive. Natural language processing (NLP) of clinical records offers a scalable alternative, and integrating geolocation may capture complementary environmental risk signals. Methods: We propose the DMV (Data processing, Model training, Validation) framework that frames early AD detection as a regression task predicting a continuous risk score (“data_value”) from clinical text and structured features. We evaluated embeddings from Llama3-70B, GPT-4o (via text-embedding-ada-002), and GPT-5 (text-embedding-3-large) combined with a Random Forest regressor on a CDC-derived dataset (≈284 k records). Models were trained and assessed using 10-fold cross-validation. Performance metrics included Mean Squared Error (MSE), Mean Absolute Error (MAE), and R²; paired t-tests and Wilcoxon signed-rank tests assessed statistical significance. Results: Including geolocation (latitude and longitude) consistently improved performance across models. For the Random Forest baseline, MSE decreased by 48.6% when geolocation was added. Embedding-based models showed larger gains; GPT-5 with geolocation achieved the best results (MSE = 14.0339, MAE = 2.3715, R² = 0.9783), and the reduction in error from adding geolocation was statistically significant (p < 0.001, paired tests). Conclusions: Combining high-quality text embeddings with patient geolocation yields substantial and statistically significant improvements in AD risk estimation. Incorporating spatial context alongside clinical text may help clinicians account for environmental and regional risk factors and improve early detection in scalable, data-driven workflows. Full article

Gasoline alkali residue raw liquid, a kind of highly toxicity containing organic waste generated during petroleum refining, is characterized by its complex composition, high pollutant levels, and significant emission volume. The effective treatment of this wastewater remains a considerable challenge in environmental engineering. This study systematically investigates the degradation efficiency and mechanism of Fenton oxidation in reducing the chemical oxygen demand (COD) of raw gasoline alkali residue sourced from Kashi. The effects of H₂O₂ concentration and the H₂O₂/Fe²⁺ molar ratio on COD and TOC removal were examined. Results demonstrated that the COD and TOC removal efficiency exhibited an initial decrease followed by an increase with rising concentrations of Fe²⁺ and H₂O₂. Comparative assessment of different combined Fenton processes revealed distinct mechanistic differences among the composite oxidation systems. The integration of pretreatment with UV-Fenton oxidation was identified as the optimal strategy. Under optimal conditions (pH = 3.0, H₂O₂ concentration = 1.0 mol/L, H₂O₂/Fe²⁺ molar ratio = 5:0.10), the COD was reduced from 25,041 mg/L to 543 mg/L, achieving a COD removal rate of 97.8%. This study elucidates the reaction mechanism of the Fenton system in treating alkali residue and provides a theoretical foundation for the advanced treatment of high-concentration organic wastewater. Full article

In response to the sustainability challenges of mining, restrictive policies aimed at improving ecological quality have been enacted in various countries and regions. The purpose of this study is to examine the environmental changes in the Ningdong mining area, located on the Loess Plateau, over the past 25 years, due to many factors, such as coal mining, using the area as a case study. In this study, Landsat satellite images from 2000 to 2024 were used to derive the remote sensing ecological index (RSEI), while the RSEI results were comprehensively analyzed using the Sen+Mann-Kendall method with Geodetector, respectively. Simultaneously, this study utilized land use datasets to calculate the ecological grade (EG) index. The EG index was then analyzed in conjunction with the RSEI. The results show that in the time dimension, the ecological quality of the Ningdong mining area shows a non-monotonic trend of decreasing and then increasing during the 25-year period; The RSEI average reached its lowest value of 0.279 in 2011 and its highest value of 0.511 in 2022. In 2024, the RSEI was 0.428; The coupling matrix between the EG and RSEI indicates that the ecological environment within the mining area has improved. Through ecological factor-driven analysis, we found that the ecological environment quality in the study area is stably controlled by natural topography (slope) and climate (precipitation) factors, while also being disturbed by human activities. This experimental section demonstrates that ecological and environmental evolution is a complex process driven by the nonlinear synergistic interaction of natural and anthropogenic factors. The results of the study are of practical significance and provide scientific guidance for the development of coal mining and ecological environmental protection policies in other mining regions around the world. Full article

The resource utilization of swine manure represents a critical pathway for advancing sustainable agricultural development. This study, based on survey data from 509 swine farmers in Sichuan Province, employs the Ordered Probit (Oprobit) model and the Conditional Mixed Process (CMP) model to analyze the mechanisms and pathways through which cognition about manure treatment, environmental regulation, and their interaction influence farmers’ behaviors towards manure resource utilization. It further delves into the heterogeneous characteristics of influencing factors. The findings reveal the following: (1) Farmers possess a high level of cognition regarding manure treatment, while environmental regulation is moderately implemented. The principal methods of manure resource utilization focus on recycling to fields and organic fertilizer production, with over 95% of farmers adopting at least one method of resource utilization. (2) Both cognition of manure treatment and environmental regulation significantly promote the behavior of manure resource utilization. There are substitutive or complementary effects between moral cognition and constraint regulation, as well as capability cognition and guidance regulation. (3) Among the farming community, the behavior of large-scale farmers is mainly influenced by moral cognition, whereas non-large-scale farmers are more affected by capability cognition and guidance regulation; middle-aged and young farmers are predominantly influenced by capability cognition, incentives, and guidance regulation, whereas the older generation of farmers is driven more by moral cognition and guidance regulation. Based on these insights, this study proposes targeted strategies for enhancing cognition and regulatory alignment across different groups, aiming to elevate the level of manure resource utilization and promote the green transformation of livestock farming. Full article

The National Water Model (NWM)’s streamflow forecasts are widely used by stakeholders to make critical water management decisions. This study evaluates the performance of the NWM v2.1 in simulating streamflow across the Alabama Black Belt Region (ABBR), in the southeastern United States. Using retrospective NWM and USGS observed streamflow data, model performance was assessed across four-time scales—hourly, daily, weekly, and monthly—using three metrics: Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error Ratio (RSR), and Percent Bias (PBIAS). The results demonstrate that the NWM accuracy improves significantly with longer-term forecasts. At the monthly scale, 89% of evaluated stations reached above “Good” classification based on NSE (>0.75), and 85% based on RSR (<0.5). However, consistent negative bias was observed across all time scales, particularly in the underestimating flows. The results highlight the influence of environmental factors, including land use, topography, and soil characteristics, on model performance, as well as potential sources of systematic bias within the model’s processes. Although the NWM does not incorporate regulated protocols, its ability to capture flow variability improves at aggregated scales, suggesting its suitability for long-term planning applications. These findings underscore the need for further model structure refinement and regional calibration to enhance predictive reliability. Full article

Maritime Autonomous Surface Ships (MASSs) raise safety and regulatory challenges that extend beyond technical reliability. This study builds on a published system-theoretic process analysis (STPA) of degraded operations that identified 92 loss scenarios. These scenarios were reformulated into a two-round Delphi survey with 20 experts from academic, industry, seafaring, and regulatory backgrounds. Panelists rated each scenario on severity, likelihood, and detectability. To avoid rank reversal, common in the Risk Priority Number, an adjusted index was applied. Initial concordance was low (Kendall’s W = 0.07), reflecting diverse perspectives. After feedback, Round 2 reached substantial agreement (W = 0.693, χ² = 3265.42, df = 91, p < 0.001) and produced a stable Top 10. High-priority items involved propulsion and machinery, communication links, sensing, integrated control, and human–machine interaction. These risks are further exacerbated by oceanographic conditions, such as strong currents, wave-induced motions, and biofouling, which can impair propulsion efficiency and sensor accuracy. This highlights the importance of environmental resilience in MASS safety. These clusters were translated into five action bundles that addressed fallback procedures, link assurance, sensor fusion, control chain verification, and alarm governance. The findings show that Remote Operator competence and oversight are central to MASS safety. At the same time, MASSs rely on artificial intelligence systems that can fail in degraded states, for example, through reduced explainability in decision making, vulnerabilities in sensor fusion, or adversarial conditions such as fog-obscured cameras. Recognizing these AI-specific challenges highlights the need for both human oversight and resilient algorithmic design. They support explicit inclusion of Remote Operators in the STCW convention, along with watchkeeping and fatigue rules for Remote Operation Centers. This study provides a consensus-based baseline for regulatory debate, while future work should extend these insights through quantitative system modeling. Full article

This article evaluates the effectiveness of Poland’s current mine closure model in promoting the sustainable reuse of post-mining land, particularly in urbanised regions such as the Upper Silesian–Zagłębie Metropolis. A mixed-methods approach is applied, combining archival and cartographic analysis, field surveys, and a comparative policy review. The study examines 81 post-mining areas associated with 20 decommissioned coal mines. Two dominant transformation models are identified: planned redevelopment guided by public–private strategies, and unplanned, market-driven reuse based on opportunistic adaptation. While the system ensures technical and environmental safety via the Mine Restructuring Company (SRK S.A.), it remains weakly integrated with spatial planning policies and often marginalises key stakeholders. This leads to fragmented land reuse, underinvestment, and misalignment with sustainability objectives. A comparative review of models from Germany, the UK, and the Czech Republic highlights the importance of institutional coordination, strategic planning tools, and community involvement. The article concludes with policy recommendations to enhance governance, planning coherence, and social inclusion in post-mining transformation processes in Poland. Full article

Application of low-emission hydrogen production methods in the decarbonization process remains a highly relevant topic, particularly in the context of sustainable hydrogen value chains. This study evaluates hydrogen applications beyond industry, focusing on its role as an energy carrier and applying multi-criteria decision analysis (MCDA) to assess economics, environmental impact, efficiency, and technological readiness. The analysis confirmed that hydrogen use for heating was the most competitive non-industrial application (ranking first in 66%), with favorable efficiency and costs. Power generation placed among the top two alternatives in 75% of cases. Transport end-use was less suitable due to compression requirements, raising emissions to 272–371 g CO₂/kg H₂ and levelizing the cost of hydrogen (LCOH) to 13–17 EUR/kg. When H₂ transport was included, new pipelines and compressed H₂ clearly outperformed other methods for short- and long-distances, adding only 3.2–3.9% to overall LCOH. Sensitivity analysis confirmed that electricity price variations had a stronger influence on LCOH than capital expenditures. Comparing electrolysis technologies yielded that, proton-exchange membrane and solid oxide reduced costs by 12–20% and CO₂ emissions by 15–25% compared to alkaline. The study highlights heating end-use and compressed hydrogen and pipeline transport, proving MCDA to be useful for selecting scalable pathways. Full article

Due to the complexity of the marine environment and the uncertainty of ship movements, altitude control of UAV is particularly important when approaching and landing on the deck of a ship. This paper focuses on unmanned helicopters as its research subject. Conventional altitude control systems may have difficulty in ensuring fast and stable landings under certain extreme conditions. Therefore, designing a new UAV altitude control method that can adapt to complex sea conditions has become a current problem to be solved. Designing a reinforcement learning based rotational speed compensator for UAV as a redundant controller to optimise UAV altitude control performance for the above problem. The compensator is capable of adjusting the UAV’s rotational speed in real time to compensate for altitude deviations due to external environmental disturbances and the UAV’s own dynamic characteristics. By introducing reinforcement learning algorithms, especially the DDPG algorithm, this compensator is able to learn the optimal RPM adjustment strategy in a continuous trial-and-error process, which improves the UAV’s rapidity and stability during the landing process. Full article

In the face of increasingly severe global environmental challenges, the development of low-cost, high-precision, and easily integrable environmental monitoring sensors is of paramount importance. Existing optical refractive index sensors are often limited in application due to their complex structures and high costs, or their bulky size and difficulty in automation. This paper proposes a novel optical microfluidic refractometer, consisting solely of a laser source, an optical microfluidic lens, and a CCD detector. Through an innovative “simple structure + algorithm” design, the sensor achieves high-precision measurement while significantly reducing cost and size and enhancing robustness. With the aid of signal processing algorithms, the device currently enables the detection of refractive index gradients as low as 1.4 × 10⁻⁵ within a refractive index range of 1.33 to 1.48. Full article

While agriculture is experiencing localized crises, its indispensable role as the foundation of humanity’s food supply requires its uninterrupted functioning. This conventional system is therefore in a state of competition with alternative models, particularly agroecology, which offers a different paradigm for food production. Given this situation and the need to gather reliable information on regenerative agriculture (RA), this article provides a literature review on its principles, objectives, and edaphic benefits. Additionally, it presents a case study that offers practical knowledge of the techniques and actions implemented by an agroforestry farm in central Spain. With this goal, this article addresses key aspects of RA, such as the use of cover crops, and the integration of livestock, emphasizing its role in improving soil quality and increasing biodiversity, among other benefits. After reviewing numerous scientific articles, and despite widespread interest in RA, there is no commonly accepted definition, so there is a wide range of ways to define RA. Until a generalized definition is accepted, we advocate making proposals and implementing methods with extreme caution and based on the regional or local context in which it is defined. In this sense, based on the implementation of RA at the Kerbest Foundation farm, we propose regenerative agriculture as a set of agroecological actions and processes that fundamentally provide functional soil quality, food quality, ecosystem services, and, especially, healthy and economically profitable livestock farming. Based on all of the above, we can argue that RA is no longer merely a commitment made by farmers but, rather, an environmentally, economically, and socially sustainable solution grounded in scientific knowledge and technical experience. Full article

The construction sector drives nearly half of global material extraction, energy use, emissions, and waste, yet environmental impact assessment (EIA) remains a static document, fragmented and disconnected from dynamic ecological systems. Here, we propose an upgrade to a five-dimensional (5D) EIA framework that integrates space-time analysis (3D + time = 4D) with real-time monitoring and impact quantification (5D) to account for environmental footprint and prevent irreversible impacts. The methodology included an analysis of over 100 EIA permits and reports, supplemented by interviews, reviews of technologies and process and systems analysis. Central to this approach is the inclusion of 4D building information models (BIM) and nature’s self-cleansing capacity, which is often overlooked in conventional assessments. The proposed Integrated Environmental Decision Support Information System (I-EDSIS) would enable continuous impact tracking, cumulative effect evaluation, and insights into patterns for adaptive mitigation. Drawing on a national-scale case study, we show that building permits correlate with NO_x and PM₁₀ (r = 0.96), while pollutant levels vary by up to 1.5–3 times across months and within a day, revealing potential for time-sensitive adaptive construction and less ecological disruption. This perspective argues for reframing EIA as a proactive tool for sustainability, transparency, active durability, cross-sectoral data integration, and resilience-based development. Full article

This study examines the economic and financial implications of the upcycling process and Design for Disassembly (DfD) applied to an “authorial” building. The objective is to examine the economic benefits deriving from the reuse of construction materials by quantifying the savings obtained through the reduction of disposal costs and CO₂ emissions in comparison with a traditional linear economic model. The methodological approach has been developed with the aid of Building Information Modeling (BIM) in order to provide an accurate estimation of both costs and environmental impacts related to the disassembly and reuse of materials. The financial analysis is based on local market prices to assess the savings associated with the reuse of building components compared to their disposal in landfills. The case study demonstrates the feasibility of this approach under real conditions, underscoring the transformative potential of upcycling in the construction industry, highlighting how this strategy can simultaneously improve economic efficiency and reduce environmental impact. The research offers a significant contribution to the debate on sustainable building practices and may serve as a starting point for future investigations. Full article

Lighting measurements and calculation is an old and widespread process, evolving with the variety of technologies that use light or operate efficiently depending on the natural or artificial light conditions in the ambient environment. The complexity of human activities gives rise to different techniques and approaches to lighting effect analysis, and this paper aims to clarify which type of units, photometric or radiometric, are appropriate, and which light measurement and calculation techniques are optimal for evaluating the environmental microclimate intended for an activity. Quantitative lighting analysis is common and accessible through the measuring devices, calculation formulas, and simulation software available. In contrast, qualitative analysis remains less prevalent, partly due to its complexity and the need to consider human perception as a central component in assessing lighting impact, as emphasized by the human-centric lighting paradigm. Current evaluation frameworks distinguish between the quantitative and qualitative approaches, with actinic calculations addressing biologically relevant aspects of lighting in specific environmental contexts. Full article