Search Results (3,930)

This study investigates the performance, emissions, and physicochemical characteristics of a small-scale gas turbine fueled with Jet A and camelina biodiesel blends (B10, B20, and B30). The blends were characterized by slightly higher density (up to +3%), viscosity (+12–18%), and lower heating value (−7–9%) compared to Jet A. These fuel properties influenced the combustion behavior and overall turbine response. Experimental results showed that exhaust gas temperature decreased by 40–60 °C and specific fuel consumption (SFC) increased by 5–8% at idle, while thrust variation remained below 2% across all operating regimes. Fuel flow was reduced by 4–9% depending on the blend ratio, confirming efficient atomization despite the higher viscosity. Emission measurements indicated a 20–30% reduction in SO₂ and a 10–35% increase in CO at low load, mainly due to the sulfur-free composition and lower combustion temperature of biodiesel. Transient response analysis revealed that biodiesel blends mitigated overshoot and undershoot amplitudes during load changes, improving combustion stability. Overall, the results demonstrate that camelina biodiesel–Jet A blends up to 30% ensure stable turbine operation with quantifiable environmental benefits and minimal performance penalties, confirming their suitability as sustainable aviation fuels (SAFs). Full article

Facing the significant challenges posed by global population growth and urbanization, plant factories, as an efficient closed cultivation system capable of precise environmental control, have become a key direction in the development of modern agriculture. However, high energy consumption, particularly lighting (which accounts for over 50%), remains a major bottleneck limiting their large-scale application. This study systematically explored the effects of dynamic light regulation strategies on lettuce (Lactuca sativa L.) growth, physiological and biochemical indicators (such as chlorophyll, photosynthetic, and fluorescence parameters), nutritional quality, energy utilization efficiency, and post-harvest shelf life. Four different light treatments were designed: a stepwise increasing photosynthetic photon flux density (PPFD) from 160 to 340 μmol·m⁻²·s⁻¹ (T1), a constant light intensity of 250 μmol·m⁻²·s⁻¹ (T2), a three-stage strategy with high light intensity in the middle phase (T3), and a three-stage strategy with sequentially increasing light (T4). The results showed that the T4 treatment exhibited the best overall performance. Compared with the T2 treatment, the T4 treatment increased biomass by 23.4%, significantly improved the net photosynthetic rate by 50.32% at the final measurement, and increased ascorbic acid (AsA) and protein content by 33.36% and 33.19%, respectively. Additionally, this treatment showed the highest energy use efficiency. On the 30th day of treatment, the light energy use efficiency (LUE) and electrical energy use efficiency (EUE) of the T4 treatment were significantly increased, by 23.41% and 23.9%, respectively, compared with the T2 treatment. In summary, dynamic light regulation can synergistically improve crop yield, chlorophyll content, photosynthetic efficiency, nutritional quality, and energy utilization efficiency, providing a theoretical basis and solution for precise light regulation and energy consumption reduction in plant factories. Full article

In protected horticulture, precise regulation of light intensity [i.e., photosynthetic photon flux density (PPFD)], ambient temperature, and ambient CO₂ concentration is crucial for optimizing crop photosynthesis. Tomatoes, a key greenhouse crop, exhibit temporal variations in photosynthetic efficiency across their growth cycle. However, the differences in the dynamic responses of net photosynthetic rate (Pn) of tomatoes to environmental factors during flowering and fruit development stages in winter solar greenhouses, as well as how to utilize these differences respectively to achieve more precise on-demand environmental regulation, still require in-depth exploration. Based on measured data, this study employed decision tree (DT), random forest (RF), and XGBoost (XGB) models to predict net photosynthetic rate (Pn) across two growth periods. The results demonstrated that, in comparison with the early flowering stage, the photosynthetic potential of tomato leaves increased during the fruit development stage, with the Pn peak increasing by 11.5%. The proportion of observed data points in the high Pn range (25–35 μmol m⁻² s⁻¹) at the fruit development stage was 14.2%, which was significantly higher than the 6.7% observed at the early flowering stage. Meanwhile, the sensitivity of tomato leaves to changes in environmental factors also increased during the fruit development stage. On the independent test set, the XGB model exhibited the best predictive performance: the root mean square error (RMSE) for the early flowering stage model was 0.47 μmol m⁻² s⁻¹, with a mean absolute error (MAE) of 0.36 μmol m⁻² s⁻¹; for the fruit development stage, the RMSE was 0.60 μmol m⁻² s⁻¹, and the MAE was 0.41 μmol m⁻² s⁻¹. This study demonstrated the variation patterns of photosynthetic characteristics of tomatoes at different growth stages in response to environment factors. The established XGB model and the generated three-dimensional visualized Pn prediction surfaces provide a quantitative basis and decision-support tools to facilitate precise environmental management strategies for the coordinated dynamic regulation of light, temperature, and CO₂ in solar greenhouses. Full article

Sustainability assessment has emerged as a critical research area given the pressing challenges of balancing economic growth, environmental protection, and social equity. This study aims to develop an objective and reproducible framework to evaluate sustainability efficiency across countries by integrating multiple development dimensions into a unified decision model. Despite substantial prior research, inconsistencies often arise due to data heterogeneity and conflicting criteria. To address this gap, a hybrid multi-criteria decision-making (MCDM) framework was developed by combining the Double Normalization Method based on Removal Effects of Criteria (DNMEREC) for objective weighting and the Double Normalization Measurement of Alternatives and Ranking according to Compromise Solution (DNMARCOS) method for ranking alternatives. This integration ensures balanced consideration of beneficial and non-beneficial criteria while minimizing subjectivity. The model was empirically validated through a comparative assessment of G7 and BRICS countries using twelve sustainability indicators covering economic, environmental, and social dimensions. Results show significant variations in sustainability efficiency, with G7 countries generally demonstrating higher overall performance, while BRICS nations exhibit strong growth potential but face environmental and structural constraints. These findings confirm the robustness of the DNMEREC-DNMARCOS framework and highlight its adaptability to complex, multidimensional datasets. The study contributes a transparent methodological tool for researchers and policymakers seeking evidence-based strategies to enhance global sustainability performance and bridge development gaps. Full article

Background/Objectives: Recently, the importance of physical activity for health promotion has increased the demand for physical activities performed in natural environments. However, environmental characteristics that enhance the efficiency of physical activities and contribute to health promotion have not yet been established. This study aimed to verify the mental and physical health of walking in different environments by measuring EEG and HR responses among middle-aged women living in urban areas during forest trail (GU) and school ground (NF) walking. Methods: In total, 30 middle-aged women participated in a 1.5 km walking, with HR measured during normal, NF, and GU walking. EEGs were recorded before and after walking 5 waves (Delta, Theta, Alpha, Beta, and Gamma). All data were collected under standardized conditions and analyzed using paired t-tests. Results: Alpha, beta, and gamma waves increased after GU walking (p < 0.001) but decreased after NF walking, suggesting that walking in natural environments promotes emotional stability, attentional recovery, and cognitive activation. Mean HR during GU was higher than during NF (p < 0.001), and NF walking corresponded to moderate-intensity exercise, whereas GU walking represented vigorous-intensity activity, likely influenced by its 5% slope and multi-sensory natural stimuli such as forest, sounds, and air quality. Conclusions: This study is not a clinical trial but a health experiment of physical activity, highlighting how walking in natural environments can contribute to improved health. The walking environment elicits distinct mental and physical responses, and forest walking has proven to be more effective in improving health. This result highlights the value of nature-based exercise spaces accessible in urban environments and can help with design and health policies. Full article

A vast number of boreholes in underground mining operations are often plagued by deviation issues, which severely impact both production efficiency and safety. The accurate and rapid acquisition of borehole trajectories is fundamental for subsequent deviation control and correction. However, existing inclinometers are limited by their operational efficiency and estimation accuracy, making them inadequate for large-scale measurement demands. To address this, this paper proposes a novel method for the rapid and accurate reconstruction of underground mine borehole trajectories using a robotic system. We employ a custom-designed robot equipped with an Inertial Measurement Unit (IMU) and a displacement sensor, which travels stably while collecting real-time attitude and depth information. Algorithmically, a complementary filter is used to fuse data from the gyroscope with that from the accelerometer and magnetometer, overcoming both integration drift and environmental disturbances. A cubic spline interpolation algorithm is then utilized to time-register the low-sampling-rate displacement data with the high-frequency attitude data, creating a time-synchronized sequence of ‘attitude–displacement increment’ pairs. Finally, the 3D borehole trajectory is accurately reconstructed by mapping the attitude quaternions to direction vectors and recursively accumulating the displacement increments. Comparative experiments demonstrate that the proposed method significantly improves efficiency. On a complex trajectory, the maximum and mean errors were reduced to 0.38 m and 0.18 m, respectively. This level of accuracy is far superior to that of the conventional static point-by-point measurement mode and effectively suppresses the accumulation of dynamic errors. This work provides a new solution for routine borehole trajectory surveying in mining operations. Full article

Water resource management and agricultural practices in the Mediterranean region, characterised by the excessive use of pesticides, pose significant environmental and human health challenges. As they can be easily and inexpensively produced from various biomass sources, biochars are frequently recommended as a low-cost secondary decontamination strategy to address soil contamination problems. This study investigates the properties and sorption behaviours of biochars produced in a low-cost metallic kiln using local rosemary, giant reed, St. John’s wort, olive, cypress, and palm tree biomass residues to evaluate their potential for environmental remediation, with a special focus on the mobility and retention of contaminants. Analytical and experimental techniques were employed to characterise the biochars’ physicochemical attributes and sorptive capacities. The core analyses included measurement of basic physicochemical properties, including pH, electrical conductivity, functional group identification via Fourier transform infrared (FTIR) spectroscopy, and the molarity of ethanol droplet (MED) test to assess the surface hydrophobicity. Batch sorption experiments were conducted using methylene blue (MB) and two fluorescent tracers—uranine (UR) and sulforhodamine-B (SRB)—as proxies for organic contaminants to assess the adsorption efficiency and molecule–biochar interactions. Furthermore, the adsorption isotherms at 20 °C were fitted to different models to assess the biochars’ specific surface areas. Thermodynamic parameters were also evaluated to understand the nature and strength of the adsorption processes. The results highlight the influence of feedstock type on the resulting biochar’s properties, thus significantly affecting the mechanism of adsorption. Rosemary biochar was found to have the highest specific surface area (SSA) and cation exchange capacity (CEC), allowing it to adsorb a wide range of organic molecules. Giant reed and palm tree biochars showed similar properties. In contrast, wood-derived biochars generally showed very low SSA, moderate CEC, and low hydrophobicity. The contrasting properties of the three dyes—MB (cationic), UR (anionic), and SRB (zwitterionic)—enabled us to highlight the distinct interaction mechanisms between each dye and the surface functional groups of the different biochars. The reactivity and sorption efficiency of a biochar depend strongly on both the nature of the target molecule and the intrinsic properties of the biochar, particularly its pH. The findings of this study demonstrate the importance of matching biochar characteristics to specific contaminant types for optimised environmental applications, providing implications for the use of tailored biochars in pollutant mitigation strategies. Full article

This study addresses the operational and environmental pressures of last-mile delivery in dense cities under limited urban logistics hubs. We propose a resource-efficient framework that repurposes existing convenience stores as robotic delivery hubs and formalize its operation via a two-stage optimization coupling truck and robot routing. In controlled simulations, and in a Seoul street network scenario, the approach reduces total completion time relative to a truck-only benchmark and lowers truck activity (truck-kilometers and curb idling), leading to lower estimated CO₂e under standard emission factors. We also observe a nonlinear relationship between the number of hubs and efficiency, suggesting a coverage “sweet spot”. These results indicate that with minimal new infrastructure, reusing commercial assets can improve operational performance and environmental proxies; social and labor outcomes are not measured here and are left for future field evaluation. Full article

In geophysics, aeromagnetic surveying based on unmanned aerial vehicles (UAV) is a widely employed exploration technique, that can analyze underground structures by conducting data acquisition, processing, and inversion. This method is highly efficient and covers large areas, making it widely applicable in mineral exploration, oil and gas surveys, geological mapping, and engineering and environmental studies. However, during flight, interference from the aircraft’s engine, electronic systems, and metal structures introduces noise into the magnetic data. To ensure accuracy, mathematical models and calibration techniques are employed to eliminate these aircraft-induced magnetic interferences. This enhances measurement precision, ensuring the data faithfully reflect the magnetic characteristics of subsurface geological features. This study focuses on aeromagnetic data processing methods, conducting numerical simulations of magnetic interference for aeromagnetic surveys of UAVs with the Tolles–Lawson (T-L) model. Recognizing the temporal dependencies in aeromagnetic data, we propose a Transformer neural network algorithm for aeromagnetic compensation. The method is applied to both simulated and measured flight data, and its performance is compared with the classical Multilayer Perceptron neural networks (MLP). The results demonstrate that the Transformer neural networks achieve better fitting capability and higher compensation accuracy. Full article

An in-depth and comprehensive evaluation of carbon emission efficiency (CEE) is essential for promoting high-quality development and achieving the “dual-carbon” goals. This study applies a super-efficiency slacks-based measure (Super-SBM) model with carbon emissions treated as an undesirable output to measure provincial CEE and the Malmquist–Luenberger (ML) index across 30 provinces and major comprehensive economic zones in China from 2010 to 2023. Efficiency trends for 2024–2025 are projected using a hybrid Autoregressive Integrated Moving Average (ARIMA)–Long Short-Term Memory (LSTM) approach. Furthermore, CEE patterns are examined at both national and regional levels, and the relationships between CEE and potential drivers are analyzed using Tobit regressions. Combining the regression outcomes with short-term forecasts, this study provides a forward-looking perspective on the evolution of CEE and its associated factors. The results indicate that (1) China’s CEE demonstrates a generally fluctuating upward trajectory, with the southern coastal and eastern coastal regions maintaining the highest efficiency levels, while other regions remain relatively lower. (2) The temporal changes in CEE across economic zones correspond to variations in technical efficiency and technological progress, with the latter contributing more prominently to overall improvement. (3) CEE shows significant associations with multiple factors: population density, economic development, technological advancement, government intervention, and environmental regulation are positively associated with efficiency, whereas urbanization tends to correlate negatively. Based on these findings, policy implications are discussed to promote differentiated pathways for enhancing CEE across China’s regions. Full article

In forest resource plot surveys, tree relative positioning is a crucial task with profound silvicultural and ecological significance. However, traditional methods such as compasses and total stations suffer from low efficiency, high costs, or poor environmental adaptability, while single-sensor technologies (e.g., UWB or IMU) struggle to balance accuracy and stability in complex forest environments. To address these challenges, this study designed a multi-sensor fusion-based tree positioning device. By integrating the high-precision ranging capability of Ultra-Wideband (UWB) with the dynamic motion perception advantages of an Inertial Measurement Unit (IMU), a dynamic weight fusion algorithm was proposed, effectively mitigating UWB static errors and IMU cumulative errors. Experimental results demonstrate that the device achieves system biases of −1.54 cm (X-axis) and 1.27 cm (Y-axis), with root mean square errors (RMSE) of 21.34 cm and 23.93 cm, respectively, across eight test plots. The average linear distance error was 26.23 cm. Furthermore, in single-operator mode, the average measurement time per tree was only 20.89 s, approximately three times faster than traditional tape measurements. This study confirms that the proposed device offers high positioning accuracy and practical utility in complex forest environments, providing efficient and reliable technical support for forest resource surveys. Full article

In arid and water-stressed regions, groundwater desalination plants are critical for ensuring reliable potable water supplies, making improvements in their operational efficiency and cost effectiveness a priority for utilities. In many such facilities, lime and soda ash softening remain common pretreatment practices, which increase chemical consumption and sludge generation, prompting the need for alternative low-chemical strategies. This study evaluates the technical, operational, and economic implications of transitioning a full-scale brackish groundwater desalination plant, from lime–soda ash softening (old plan) to a low-chemical pretreatment strategy based on antiscalant dosing (new plan) upstream of reverse osmosis (RO). Key parameters, including pH, total hardness, calcium and magnesium hardness, silica, iron, alkalinity, and total dissolved solids (TDS), were measured and compared at multiple locations within the treatment plant under both the old and new plans. Removing lime and soda ash caused higher levels of hardness, alkalinity, and silica in the water before RO treatment, increasing the risk of scaling. Operationally, the feed pressure increased from 11.43 ± 0.16 bar (old plan) to a peak of 25.50 ± 0.10 bar in the new plan, accompanied by a decline in water production. Chemical cleaning effectively restored performance, reducing feed pressure to 13.13 ± 0.05 bar, confirming that fouling and scaling were the primary, reversible causes. Despite these challenges, the plant consistently produced water that complied with Saudi Standards for Unbottled Drinking Water (e.g., pH = 7.18 ± 0.09, TDS = 978.27 ± 9.26 mg/L). Economically, the new strategy reduced operating expenditure by approximately 54% (0.295 → 0.135 $/m³), largely due to substantial reductions in chemical and sludge handling costs, although these savings were partially offset by higher energy consumption and more frequent membrane maintenance. Overall, the findings emphasize the importance of systematic performance evaluation during operational transitions, providing guidance for utilities seeking to optimize pretreatment design while maintaining compliance, long-term membrane protection, and environmental sustainability. Full article

Digital finance, representing the deep integration of finance and technology, has become a critical enabler of sustainable industrial transformation. Focusing on resource-based enterprises (RBEs)—key actors in transitioning towards sustainable practices—this study investigates how digital finance development fosters new quality productive forces (NQPFs), a core driver of high-quality, sustainable development. Utilizing panel data from Chinese A-share listed RBEs (2008–2022), we measure NQPF using the entropy method and gauge regional digital finance development with the Peking University Digital Financial Inclusion Index (DFII). Empirical analysis employing two-way fixed effects and panel threshold regression models provides robust evidence that digital finance significantly enhances NQPFs within RBEs. Crucially, mechanism analysis identifies three fundamental pathways underpinning sustainability: (1) mitigating financial constraints; (2) facilitating technological innovation and transformation; (3) strengthening green transition awareness. Furthermore, the impact of digital finance exhibits synergistic enhancement alongside increasing environmental regulation intensity and improved financial resource allocation efficiency. Heterogeneity analysis reveals that the effect is more pronounced in regions with lower marketization, within state-owned enterprises, and among RBEs in recession stages. Collectively, these findings offer significant implications for policymakers and industry practitioners aiming to strategically leverage digital finance to accelerate the sustainable transformation of resource-intensive industries, thereby contributing directly to environmentally sustainable and resilient economic development. Full article

Neurorehabilitation has become increasingly data-enabled, yet the conditions that most strongly modulate recovery, sleep consolidation, circadian alignment, medication ecology, and social–environmental context are rarely captured or acted upon. This opinion paper argues that an exposome perspective, defined as the cumulative pattern of external and internal exposures and their biological imprints across the life course, is not ancillary to rehabilitation but foundational to making therapy learnable, timely, and equitable. We propose a pragmatic model that centers on a minimal exposure dataset collected in minutes and interpreted at the point of care. Two clinical exemplars illustrate feasibility and utility. First, sleep and circadian rhythms: brief actigraphy and standardized reporting can make daily alertness windows visible, allowing teams to align high-intensity sessions to receptive states and to justify environmental adjustments as clinical interventions. Second, anticholinergic burden: a simple, trackable index can be integrated with functional goals to guide deprescribing and optimize cognitive availability for training. Implementation hinges less on new infrastructure than on workflow design: a short intake that surfaces high-yield exposures; embedding targets, e.g., sleep efficiency thresholds or anticholinergic load reductions, into plans of care; enabling secure import of device data; and training staff to interpret rhythm metrics and burden scores. We outline a parallel research agenda comprising pragmatic trials of bundled, exposure-informed care; longitudinal cohorts with time-stamped exposure streams; and causal methods suited to time-varying confounding, all under explicit equity and ethics safeguards. By measuring a few modifiable exposures and linking them to routine decisions, neurorehabilitation can convert context from a source of unexplained variance into actionable levers that improve outcomes and narrow unjust gaps in recovery. Full article

Urban air pollution in Kazakhstan poses persistent risks; this study synthesizes measured concentrations, source evidence, and policy responses to inform mitigation in cold, inversion-prone cities. We compile national monitoring (Kazhydromet), community PM_2.5 sensors, emissions inventories and recent CEMS provisions, and appraise modeling approaches (Gaussian screening, Eulerian CTMs, and data-driven forecasting). Seasonal descriptive comparisons are performed for Astana using 56,944 observations (2023–2024), partitioned into heating and non-heating periods, and published receptor apportionment is integrated. Across major cities, annual PM_2.5 generally exceeds WHO guidelines and winter stagnation drives episodes. In Astana, the heating season means rose relative to non-heating equivalents—PM_2.5 12.3 vs. 10.6 μg m⁻³ (+16%) and SO₂ 21.9 vs. 14.8 μg m⁻³ (+23%)—while NO was unchanged; higher means but lower medians indicate episodic winter peaks. Receptor analyses attribute large shares of PM_2.5 to traffic (spark-ignition engines 30% and diesel 7%) and coal-related contributions including secondary nitrate (15%), consistent with power/heat and vehicle dominance. Evidence supports prioritizing clean heating (coal-to-gas and efficiency), transport emission controls, and dense monitoring to enable accountability within Kazakhstan’s Environmental Code and decarbonization strategy. A tiered modeling workflow can quantify intervention impacts and deweather trends; the near-term focus should be on reducing winter exposures. Full article