Search Results (1,921)

Northern japonica rice holds a significant position in China’s food security. However, the traditional nitrogen fertilizer management model (nitrogen application rate > 225 kg/ha, base fertilizer proportion > 50%) has led to serious sustainability problems: the nitrogen utilization rate is only 25–30%, resulting in a large amount of fertilizer waste and economic losses. At the same time, it causes a decline in rice quality, manifested as a 15–20% increase in chalkiness and an 8–12% decrease in palatability value. It has also brought about environmental problems such as soil acidification and eutrophication of water bodies. As an important japonica rice production area, the Liaohe Plain has significant differences in the response of semi-upright and curved panicle varieties to nitrogen fertilizer. However, the agronomic physiological mechanism for the coordinated improvement of yield and quality of japonica rice with different panicle types is still unclear at present, which limits the sustainable development of rice production in this region. For this purpose, in this study, the typical semi-upright spike variety Shendao 47 and the curved spike variety Shendao 11 from the Liaohe Plain were used as materials, and five nitrogen fertilizer treatments were set up: N1, no nitrogen application; N2–N4, conventional nitrogen application rate of 165–225 kg/ha; and N5, and optimized nitrogen application rate of 195 kg/ha allocated in the proportion of 40% base fertilizer, 15% tillering fertilizer, 25% tillering fertilizer, 15% panicle fertilizer, and 5% grain fertilizer. The synergistic regulatory effect of nitrogen fertilizer management on yield and rice quality was systematically explored, and the key agronomic physiological mechanisms were analyzed. The research results show that: (1) The optimized nitrogen fertilizer treatment (N5) achieved a significant increase in yield while reducing the input of nitrogen fertilizer. The yields of Shendao 47 and Shendao 11 reached 10.71–11.82 t/ha and 9.50–10.62 t/ha, respectively, increasing by more than 35% compared with the treatment without nitrogen. (2) The N5 treatment simultaneously improved the processing quality (the whole polished rice rate increased by 4.11%) and the appearance quality (the chalkiness decreased by 63.8% to 77%). (3) The dry matter accumulation during the tillering stage (≥3.2 t/ha) and the net assimilation rate during the scion development stage (≥12 g/m²/d) were identified as key agronomic physiological indicators for regulating the yield-quality synergy. Optimizing nitrogen fertilizer management ensures an adequate supply of photosynthetic products through the high photosynthetic rate of flag-holding leaves and the extended lifespan of functional leaves. The phased nitrogen application strategy of “40% base fertilizer + 25% tillering fertilizer + 15% panicle fertilizer + 5% grain fertilizer” proposed in this study provides a theoretical and practical basis for the sustainable development of japonica rice production in the Liaohe Plain. This plan has achieved the coordinated realization of multiple goals including resource conservation (reducing nitrogen by 13%), environmental protection (lowering the risk of nitrogen loss), food security guarantee (stable increase in yield), and quality improvement (enhancement of rice quality), effectively promoting the development of the northern japonica rice industry towards a green, efficient and sustainable direction. Develop in the right direction. Full article

The rapid growth of distributed photovoltaic (PV) resources is transforming distribution networks into active systems with highly variable net loads, while the rising frequency and severity of extreme weather events is increasing outage risk and restoration challenges. In this context, utilities require reliability assessment tools that jointly represent operational variability and climate-driven stressors beyond stationary assumptions. This paper presents a weather-aware probabilistic framework to quantify the reliability of active distribution networks with high PV penetration. The approach synthesizes realistic residential demand and PV time series at 15-min resolution, models extreme weather as a low-probability/high-impact escalation of component failure rates and restoration uncertainty, and computes IEEE Std 1366–2022 indices (SAIFI, SAIDI, ENS) through Monte Carlo simulation. The methodology is validated on a modified IEEE 33-bus feeder with parameter values representative of urban/suburban overhead networks. Compared with classical reliability modeling, the proposed framework captures in a unified pipeline the joint effects of load/PV stochasticity, weather-dependent failure escalation, and repair-time dispersion, providing a consistent statistical interpretation supported by kernel density estimation and convergence diagnostics. The results show that (i) extreme weather shifts the distributions of SAIFI, SAIDI and ENS to the right and thickens upper tails (higher exceedance probabilities); (ii) PV penetration yields a non-monotonic response with measurable improvements up to intermediate levels and saturation/partial degradation at very high penetrations; and (iii) compound risk is nonlinear, as the mean ENS surface over $(r_{\mathrm{PV}},P_{\mathrm{ext}})$ exhibits a valley at moderate PV and a ridge for large storm probability. A tornado analysis identifies the base failure rate, storm escalation factor and storm exposure as dominant drivers, in line with resilience literature. Overall, the framework provides an auditable, scenario-based tool to co-design DER hosting and resilience investments. Full article

This study presents the first systematic investigation into the influence of spectral enhancement techniques on the segmentation accuracy of specific soil and vegetation marks associated with palaeochannels. These marks are often subtle and can be seasonally obscured by vegetation dynamics and soil variability. Spectral enhancement methods, such as spectral indices and statistical aggregations, are routinely applied to improve their visual discriminability and interpretability. Despite recent progress in automated detection workflows, no prior research has rigorously quantified the effects of these enhancement techniques on the performance of deep learning–based segmentation models. This gap at the intersection of remote sensing and AI-driven analysis is critical, as addressing it is essential for improving the accuracy, efficiency, and scalability of subsurface feature detection across large and heterogeneous landscapes. In this study, two state-of-the-art deep learning architectures, U-Net and YOLOv8, were trained and tested to assess the influence of these spectral transformations on model performance, using Sentinel-2 imagery acquired across three seasonal windows. Across all experiments, spectral enhancement techniques led to clear improvements in segmentation accuracy compared with raw multispectral inputs. The multi-temporal Median Visualisation (MV) composite provided the most stable performance overall, achieving mean IoU values of 0.22 ± 0.02 in April, 0.07 ± 0.03 in August, and 0.19 ± 0.03 in November for U-Net, outperforming the full 12-band Sentinel-2 stack, which reached only 0.04, 0.02, and 0.03 in the same periods. FCC and VBB also performed competitively, e.g., FCC reached 0.21 ± 0.02 (April) and VBB 0.18 ± 0.03 (April), showing that compact three-band enhancements consistently exceed the segmentation quality obtained from using all spectral bands. Performance varied with environmental conditions, with April yielding the highest accuracy, while August remained challenging across all methods. These results highlight the importance of seasonally informed spectral preprocessing and establish an empirical benchmark for integrating enhancement techniques into AI-based archaeological and geomorphological prospection workflows. Full article

District heating (DH) is a key solution for decarbonising heat supplies, improving energy efficiency, and generating multiple economic, social, and environmental benefits. Identifying, quantifying, and monetising these benefits is crucial to assessing the impact of DH systems, comparing them with alternative heating solutions, and informing investment decisions and policy design. This paper conducts a systematic literature review to identify and classify DH benefits and to analyse the methods used to assess their economic impacts. The identified benefits are classified into four categories: energy system, end users, environment, and society, considering 123 research papers. Across all studies, 26 monetised DH benefits, but only 10 studies explicitly described the methods applied. This work demonstrates the limited but growing use of monetisation approaches for analysing DH benefits. The crucial monetisation approaches are avoided cost, net present value, hedonic pricing, levelised cost of heat, and willingness to pay. However, the absence of a harmonised framework for evaluating and monetising DH benefits limits the comparability and consistency of existing studies. Also, the study shows how emerging technologies like AI, digital twins, IoT, and cyber–physical systems are enhancing DH system performance and associated benefits. The study highlights the need for an integrated and standardised evaluation framework to assist policymakers and investors in financing efficient and sustainable DH projects. Full article

Solar energy systems have increasingly replaced conventional energy systems, driving global efforts to combat climate change and promote sustainability. This study conducts a comprehensive life-cycle cost analysis (LCCA) of photovoltaic (PV) modules, with a focus on the solar market in Eskisehir, Türkiye. Unlike prior research, this work integrates financial analysis with ecological benefits, offering a localized case study. By leveraging primary data from surveys and government sources, the analyses display that investing in PV equipment generates €883.75 in Net Present Value (NPV) savings through the business-as-usual scenario (−€392 under the worst-case and €2350 under the optimistic scenarios) over a 30-year lifespan, demonstrating the financial viability of these systems. Despite high initial costs, PV modules provide ecological and economic advantages that outweigh maintenance expenses, making them a viable solution for reducing fossil fuel dependence. The findings serve as a guideline for decision-makers, consumers, and producers to foster a sustainable solar energy market in Türkiye and similar developing economies by enabling feasible PV investments through appropriate Feed-in tariff mechanisms. Full article

Background/Objectives: This paper presents the results of predicting chronological age from psychophysiological tests using machine learning regressors. Methods: Subjects completed a series of psychological tests measuring various cognitive functions, including reaction time and cognitive conflict, short-term memory, verbal functions, and color and spatial perception. The sample included 99 subjects, 68 percent of whom were men and 32 percent were women. Based on the test results, 43 features were generated. To determine the optimal feature selection method, several approaches were tested alongside the regression models using MAE, $R^2$, and $CV\text{\_}{R}^2$ metrics. SHAP and Permutation Importance (via Random Forest) delivered the best performance with 10 features. Features selected through Permutation Importance were used in subsequent analyses. To predict participants’ age from psychophysiological test results, we evaluated several regression models, including Random Forest, Extra Trees, Gradient Boosting, SVR, Linear Regression, LassoCV, RidgeCV, ElasticNetCV, AdaBoost, and Bagging. Model performance was compared using the determination coefficient ($R^2$) and mean absolute error (MAE). Cross-validated performance ($CV\text{\_}{R}^2$) was estimated via 5-fold cross-validation. To assess metric stability and uncertainty, bootstrapping (1000 resamples) was applied to the test set, yielding distributions of MAE and RMSE from which mean values and 95% confidence intervals were derived. Results: The study identified RidgeCV with winsorization and standardization as the best model for predicting cognitive age, achieving a mean absolute error of 5.7 years and an $R^2$ of 0.60. Feature importance was evaluated using SHAP values and permutation importance. SHAP analysis showed that stroop_time_color and stroop_var_attempt_time were the strongest predictors, followed by several task-timing features with moderate contributions. Permutation importance confirmed this ranking, with these two features causing the largest performance drop when permuted. Partial dependence plots further indicated clear positive relationships between these key features and predicted age. Correlation analysis stratified by sex revealed that most features were significantly associated with age, with stronger effects generally observed in men. Conclusions: Feature selection revealed Stroop timing measures and task-related metrics from math and campimetry tests as the strongest predictors, reflecting core cognitive processes linked to aging. The results underscore the value of careful outlier handling, feature selection, and interpretable regularized models for analyzing psychophysiological data. Future work should include longitudinal studies and integration with biological markers to further improve clinical relevance. Full article

The shift towards renewable energy demands decision-making tools that unite economic performance with environmental stewardship and social equity. The conventional evaluation methods fail to consider these interconnected factors, which results in substandard investment results. The paper establishes a sustainability accounting system that uses the Elimination and Choice Expressing Reality (ELECTRE) method to optimize investment distribution between solar power, wind power, and bioenergy systems. The evaluation framework uses six performance indicators, which include cost efficiency and return on investment, together with CO₂ emissions intensity, job creation, energy output, and financial sustainability indicators, like Net Present Value (NPV) and payback period. The barrier optimization algorithm solved the model in 10 iterations, which took 0.10 s to achieve an optimal objective value of 1.6929. The wind energy source demonstrated superior performance in every evaluation criterion because it achieved the highest concordance scores, lowest discordance levels, best payback period, and strongest NPV. The maximum allocation went to wind at 53.3%, while bioenergy received 31.0%, and solar received 16.7%. The optimized portfolio reached a total sustainability index (SI) of 1.70, which validates the method’s strength. The research shows that using ELECTRE with sustainability accounting creates an exact and open system for renewable energy investment planning. The framework reveals wind as the core alternative yet demonstrates how bioenergy and solar work together to support sustainable development across environmental and economic and social dimensions. Full article

The decarbonisation of energy-intensive separation processes is critical for achieving net-zero goals in the chemical industry. While widely used for separating azeotropic mixtures, pressure swing distillation (PSD) remains highly energy-intensive due to significant thermal demands. This work presents a comprehensive systems-based assessment of electrified distillation designs, with a specific focus on tetrahydrofuran–water separation as a case study. Using Aspen Plus and Aspen Plus Dynamics, key performance indicators, including controllability, thermal and exergy efficiencies, and CO₂ emissions reduction potential, are evaluated. The electrified configurations employed heat pumps as substitutes for conventional steam heating. Disturbance rejection was applied to compare the input–output pairings and select pairings with the best controllability and disturbance rejection indices. Results showed that the conventional PSD (CPSD) exhibited higher Morari Resiliency Index (MRI) and acceptable Condition Number (CN) values, indicating better robustness and disturbance rejection than the heat pump-assisted PSD (HPAPSD). Despite this, HPAPSD achieved a 59% reduction in primary energy demand, a 23% increase in exergy efficiency, and an 82% reduction in CO₂ emissions. This study demonstrates the potential of electrification to transform PSD systems from rigid, energy-intensive operations into flexible and sustainable processes. The findings support a shift towards integrated, systems-driven design strategies in chemical separation, aligning with broader goals in process electrification, circularity, and net-zero manufacturing. Full article

This paper presents an innovative derivation of intensity–duration–frequency (IDF) curves, which play a crucial role in the design of hydraulic infrastructures. IDF curves are herein derived from excess rainfall, that is, the net rainfall obtained by removing abstractions related to hydrological losses from total gross rainfall. When long fine fine-resolution time series of rainfall depth are available at a site, excess rainfall can be obtained by applying a simplified hydrological model of a catchment, including solely the gross-excess rainfall conversion. The application of annual maxima (AM) analysis on excess rainfall intensity data enables the construction of excess rainfall-based intensity–duration–frequency (ERIDF) curves. For assigned rainfall event criticality (return period) and duration, these curves directly provide the associated excess rainfall intensity value. This results in a better preservation of the return period in the rainfall–runoff transformation when used inside the rational formula for estimating peak water discharge, in comparison with the conventional approach adopted by practitioners, based on derivation of IDF curves and on the application of runoff coefficient for gross-excess rainfall conversion inside the rational formula. Full article

Pinus koraiensis, as a keystone tree species, possesses immense economic and ecological value. However, the present cultivation of high-quality seedlings in Pinus koraiensis plantations remains hindered by prohibitively high costs and inadequate technological advancements. Additionally, the species’ prolonged growth cycle and low yield, when compounded by issues such as excessive harvesting, may result in supply constraints. Plant growth regulators (PGRs), a class of naturally occurring or synthetically derived chemical compounds, are capable of modulating plant development and physiology. These regulators exert notable effects by enhancing root proliferation, facilitating lignification, influencing plant architecture, and augmenting yield. Owing to their operational simplicity and relatively low cost, PGR applications hold substantial promise for cultivating Pinus koraiensis seedlings with superior traits. In this study, four-year-old Pinus koraiensis seedlings were employed to evaluate the impacts of three PGRs (paclobutrazol, chlormequat chloride, and diethyl aminoethyl hexanoate), alongside varied application methods (dosage and frequency), on the growth, physiological, and photosynthetic parameters of the seedlings. The findings revealed that treatment with 1.5 g/L paclobutrazol produced the most pronounced effects across a range of indicators. Specifically, this treatment markedly enhanced growth traits (e.g., branch diameter, new shoot length, lateral branch length, aboveground fresh and dry weights, root fresh and dry weights, lateral root dry weight, and number of second-order roots), physiological attributes (e.g., increased superoxide dismutase and peroxidase activities, elevated lignin content, and reduced relative conductivity and malondialdehyde levels), and photosynthetic metrics (e.g., elevated net photosynthetic rate, stomatal conductance, transpiration rate, and maximum net photosynthetic rate), thereby constituting the optimal treatment combination. Full article

Reliable land surface temperature (LST) data are required for monitoring climate variability, hydrological processes, and land–atmosphere interactions. Yet existing satellite-derived LST products, such as those from thermal infrared (TIR) sensors, are limited by gaps due to clouds, while passive microwave (PMW) observations, though less affected by atmospheric interference, suffer from coarse resolution and larger uncertainty. This study presents the first validated clear-sky merged LST product for the USA and combines downscaled PMW data from AMSR-E and AMSR2 with MODIS TIR observations, using a modified U-Net deep learning network. The merged dataset covers 2004–2021 at 5 km resolution, providing a compromise between spatial detail and robustness. The model performs well, with low mean squared errors and $R^2$ values of 0.80 (day) and 0.75 (night). The merged time series captures seasonal trends and shows a marked reduction in cloud-contamination artefacts compared to MODIS and AMSR signals. Spatially, the product is consistent across sensor transitions and reduces artefacts from TIR cloud contamination. Validation against ground stations shows results between those of TIR and PMW, with better accuracy at night and moderate positive biases influenced by land cover and terrain. Although the merged product does not match the fine resolution of TIR data by choice, it enhances spatial coverage over AMSR alone and temporal completeness over MODIS alone, where single-sensor products are limited. Residual temporal and seasonal biases are moderate, with systematic warm and cold deviations linked to land cover, propagation of emissivity errors, and sampling differences. Strong positive biases remain over terrain with complex surface properties as the downscaled AMSR is closer to MODIS temperatures. Results demonstrate the combined benefits of PMW’s broader coverage and cloud tolerance with TIR’s spatial detail. Overall, results demonstrate the potential of sensor fusion for producing spatially consistent LST records suitable for long-term environmental and climate monitoring. Full article

This study evaluated the economic viability and resilience of anaerobic digestion (AD) systems on United States (U.S.) dairy, revealing substantial vulnerabilities to policy and market shocks. While optimal Renewable Natural Gas (RNG) systems demonstrated a 54.0% success probability and positive mean Net Present Value (NPV) ($392,000) under baseline volatility, their viability is catastrophically degraded by federal policy shocks, causing the success probability to plummet to 1.4%. Conversely, Combined Heat and Power (CHP) systems showed a lower baseline success rate (32.6%) and negative mean NPV ($−156,000) but exhibit more gradual vulnerability. These findings were derived from an integrated analytical framework combining deterministic optimization, Monte Carlo simulation, and a novel multidimensional resilience assessment. Deterministic analysis confirmed that revenue diversification is essential for viability, with optimal RNG and CHP configurations achieving breakeven at 655 and 1165 cows, respectively. Our novel Composite Resilience Index (CRI) revealed a counterintuitive finding: despite RNG’s superior baseline profitability, CHP systems achieve a higher overall resilience score (52.3 vs. 47.7) due to better stability and shock resistance. These results highlight the critical importance of incorporating uncertainty and resilience considerations beyond traditional NPV analysis for renewable energy investment decisions. Full article

A multistage coordinated expansion-planning framework for distribution systems is developed to jointly optimize investments in the network, distributed generation (DG), and energy storage systems (ESS). Network reinforcements select from multiple feeder and transformer candidates, while DG installations consider conventional and photovoltaic (PV) options. In this study, a set of candidate buses are considered for the installation of PVs and energy storage systems. Therefore, the expansion plan can determine the optimal installation locations and timing of these candidate assets. The objective minimizes total cost in net-present-value terms, covering investment, maintenance, generation, and operating components. Representative hourly load profiles are incorporated to capture ESS dispatch behavior and PV output variability; operating costs are modeled via piecewise linearization. To preserve connectivity and preclude islanding in the presence of DG and ESS, modified radiality constraints are imposed. The formulation is a mixed-integer linear program solvable efficiently by commercial optimizers, and numerical studies confirm the method’s effectiveness. Full article

Background: Neutrophil extracellular traps (NETs) are granule- and nucleus-derived structures that support innate immunity. While the contribution of NETs to adult infections and autoimmune diseases is well studied, evidence in children is still inconsistent. This review aimed to summarize current findings on NETs in pediatric infections. Methods: This study followed the Cochrane Handbook for Systematic Reviews of Interventions and adhered to the PRISMA guidelines. A search was conducted in major databases (MEDLINE/PubMed and Scopus) from inception until 5 September 2025. The study quality was evaluated using the modified Newcastle–Ottawa Scale. Results: Eleven studies were included in the systematic review. In respiratory disease, the role of NETs was well described and their formation correlated with severity. Patients with febrile urinary tract infections showed elevated urinary NET-associated markers. In COVID-19 infection, NET levels were unchanged in uncomplicated cases but elevated in multisystem inflammatory syndrome in children. Findings in sepsis were inconsistent. Conclusions: This systematic review presents the published evidence on NET formation in the pediatric population, assessing the current knowledge and identifying the gaps to guide research. Future studies should aim to standardize NET detection methods, evaluate their prognostic value in large prospective cohorts, and explore the various NET-associated mechanisms in children. Full article

Background: Colorectal cancer is a multifactorial malignancy implicating a wide variety of risk factors, such as genetic, environmental, nutritional, and lifestyle factors, leading to a certain heterogeneity in the development of the disease. Colorectal cancer is generally classified in terms of a Warburg metabolic phenotype, characterized by an excess of glycolytic axes as compared to oxidative phosphorylation. It is therefore important to better characterize the metabolic heterogeneity of these tumors in relation to their mitochondrial activity. Materials and Methods: Two R-packages (keggmetascore and mitoscore) were developed to explore metabolism, based on KEGG metabolism pathways, and mitochondrial activities, based on mitocarta V3 annotations, for the investigation of diverse transcriptomics data such as bulk or single cell experiments at the single-sample level. Results: Using the two R-packages, we functionally confirmed both regulation of metabolism and mitochondrial activities in LOVO cells after stimulation with metformin. At the single-cell level, in single-cell RNA-sequencing of colorectal tumors, we conjointly observed an activation of metabolism and mitochondrial activities in tumor cells from MSI-high tumors, in contrast to a conjoint repression of metabolism and mitochondrial activity in tumor cells from POLE-mutated tumors. These two types of tumors have distinct responses to immune checkpoint blockade therapy. At the bulk transcriptome level, colorectal tumors present less metabolism/mitochondria activities as compared to normal tissues. Multi-modal integration by co-expression network analysis showed that metabolism/mitochondrial activities are associated with a consensus molecular subtype (CMS) classification of colorectal cancer. Regarding KRAS, BRAF, and TP53 driver gene mutation status, strong repression of metabolism pathways was observed, mainly associated with fewer intra-mitochondrial membrane interactions in tumors harboring a BRAF-V600E mutation. Machine learning using Elastic-net allowed us to build a mixed metabolism/mitochondrial activity score, which was found to be increased in the CMS1-MSI subtype and metastatic samples and to be an independent parameter predictive of BRAF-V600E mutation status in colorectal cancer. Conclusions: These findings underscore the pivotal role of mitochondrial metabolism in colorectal cancer subtyping and highlight its value as a predictive biomarker for personalized therapeutic strategies. Full article