Search Results (20,644)

Intercropping, the cultivation of two or more crops in the same field, is known to have numerous environmental and economic benefits. The success of such systems depends on geographical location, climatic conditions, and the choice of crop varieties, especially in organic systems. This study aimed to assess the effect of the sowing method, wheat variety, legume species on wheat grain yield and quality, and macro-elements of soil and plants. A three-year field experiment in intercropping spring wheat and legume species was performed at an organic-certified field of Czech Agrifood Research Center, Prague. Three spring wheat varieties (Alicia, Hystrix, and Toccata), two legume species (pea and faba bean), and two sowing methods (mixed and row-by-row) were used. Although the intercropping of wheat variety and legume species did not improve wheat yield, wheat grain quality and soil and plant nutrition content were enhanced in wheat and legume mixtures compared to monoculture wheat. Notably, the mixed cropping method resulted in significantly higher yields than the row-by-row method. Furthermore, the baking quality of wheat grains from intercropping systems was superior to that of monoculture wheat. The results highlight the potential of tailored intercropping systems to optimize agricultural efficiency and sustainability, especially in the face of changes in climate change. Full article

In synthetic aperture radar (SAR) image sea ice classification, the polarization decomposition techniques are used to enhance classification accuracy. However, traditional methods, such as Freeman–Durden (FD) and H/A/α decomposition, struggle to accurately characterize complex scattering mechanisms, limiting their ability to differentiate between various sea ice types. This paper proposes using the Modified Reflection Symmetry Decomposition (MRSD) method to extract polarization features from Gaofen-3 (GF-3) satellite fully polarimetric SAR data for sea ice classification tests. The study data included three types of sea surface: open water (OW), young ice (YI), and first-year ice (FYI). In this research, backscattering coefficients were combined with FD, H/A/α, and MRSD polarization features to create eight feature combinations for comparative analysis. Three machine learning algorithms, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machines (SVM), were also used for the comparative analysis. The results show that MRSD polarization features significantly improve model performance, particularly distinguishing among sea ice categories. Compared to using only the backscatter coefficient, MRSD polarization features increased model classification accuracy by approximately 4% to 13%, outperforming FD and H/A/α polarization features. The XGBoost model trained with MRSD polarization features achieves excellent classification results, with classification accuracies of 0.9630, 0.9126, and 0.9451 for OW, YI, and FYI. Additionally, the model achieved a Kappa coefficient of 0.9105 and an F1-score of 0.9403. Feature importance and SHapley Additive exPlanations (SHAP) analysis further demonstrate the physical significance of the MRSD polarization features and their role in model decision-making, suggesting that the scattered component power plays a crucial role in the model’s classification decision. Compared to traditional decomposition methods, MRSD provides a more detailed characterization of scattering mechanisms, offering a comprehensive understanding of the physical properties of sea ice. This paper systematically demonstrates the superior effectiveness of MRSD polarization features for sea ice classification, presenting a new scheme for more accurate classification. Full article

Background and Objectives: Extensive research has demonstrated that various approaches to the quadratus lumborum (QL) block offer superior postoperative analgesia compared to the transversus abdominis plane (TAP) block, particularly in reducing opioid consumption. This study aims to compare postoperative analgesia between the blocks in laparoscopic colorectal surgery. Materials and Methods: A retrospective analysis was performed on patients with elective colorectal surgeries who received bilateral US TAP blocks in the supine position or US anterior QL block in the lateral position at the end of the surgery and before extubating, with Ropivacaine 0.25%. Total opioid consumption and time to first intravenous analgesic were noted. Results: Between January 2020 and December 2024, 410 patients underwent elective laparoscopic colorectal oncology surgery under general anesthesia, with peripheral nerve blocks. Of these, we analyzed 116 patients with localized diseases who underwent elective surgeries and who did not require conversion to classical surgery and received either QL or TAP blocks. A total of 62 patients underwent QL block and 54 received TAP block. For the primary outcome, in the QL group, significantly fewer opioids were used than in the TAP group (p < 0.001), and time to first rescue analgesic was prolonged in the QL group at 16 h (IQR 14–18) compared to the TAP group, where the requirement occurred earlier at 8 h (IQR 8–8) postoperatively (p < 0.001). Conclusions: Postoperative bilateral US anterior QL block reduced morphine consumption and improved time to rescue analgesia and LOS compared with midaxillary line bilateral US TAP block. Full article

As global climate change and energy crises intensify, the pursuit of low-carbon integrated energy systems (IESs) has become increasingly important. This paper proposes an improved genetic algorithm (IGA) designed to optimize the multi-objective low-carbon operations of IESs, aiming to minimize both operating costs and carbon emissions. The IGA incorporates circular crossover and polynomial mutation techniques, which not only preserve advantageous traits from the parent population but also enhance genetic diversity, enabling comprehensive exploration of potential solutions. Additionally, the algorithm selects parent populations based on individual fitness and dominance, retaining successful chromosomes and eliminating those that violate constraints. This process ensures that subsequent generations inherit superior genetic traits while minimizing constraint violations, thereby enhancing the feasibility of the solutions. To evaluate the effectiveness of the proposed algorithm, we tested it on three different IES scenarios. The results demonstrate that the IGA successfully reduces equality constraint violations to below 0.3 kW, representing less than 0.2% deviation from the IES’s power demand in each time slot. We compared its performance against a multi-objective genetic algorithm, a multi-objective particle swarm algorithm, and a single-objective genetic algorithm. Compared to conventional genetic algorithms, the IGA achieved maximum 5% improvement in both operational cost reduction and carbon emission minimization objectives compared to the unimproved single-objective genetic algorithm, demonstrating its superior performance in multi-objective optimization for low-carbon IESs. These outcomes underscore the algorithm’s reliability and practical applicability. Full article

Herein, we present in-depth investigations of the biological activities of a CaO/NiO nanocomposite synthesized via a sustainable eco-friendly approach, utilizing Citrus limonium fruit extract as a natural stabilizing and facilitating agent. The efficacy of the nanocomposite is compared with those of individual CaO and NiO nanoparticles. X-ray diffraction analysis confirms the cubic phase of CaO as well as NiO within a unified matrix, demonstrating a refined crystallite size of 48 nm, which is smaller than that of the individual nanoparticles. FTIR study substantiates the occurrence of strong Ca-O-Ni-O bonds, along with CO₃²⁻, C–H, and CH₂ bonds. The CaO, NiO, and CaO/NiO samples exhibit bandgap values of 1.70, 3.46, and 3.44 eV, respectively. Surface morphology analysis reveals that CaO/NiO holds a well-defined heterostructure with porous morphology. An XPS study confirms that Ca and Ni elements exist in the 2+ oxidation state in the CaO/NiO. The nanocomposite exhibits superior antibacterial activity, with inhibition zones of 24.3 mm against Bacillus subtilis and 20.6 mm against Salmonella typhi, and MIC values of 23.4 and 46.8 µg/mL, respectively. It also demonstrates strong antioxidant potential, with IC₅₀ values of 96.8 ± 0.4 µg/mL (DPPH) and 91.8 ± 0.1 µg/mL (superoxide anion). Furthermore, it shows the lowest IC₅₀ for α-amylase (98.6 ± 0.7 µg/mL) and strong α-glucosidase inhibition (81.96 ± 0.5 µg/mL). Consequently, this insightful study reveals how biogenic synthesis helps develop high-performance multifunctional CaO/NiO nanocomposites for biomedical applications. Full article

Genomic selection (GS) is a genetic breeding method that uses genome-wide marker information to improve the accuracy of the prediction of complex traits. The single-step GBLUP (ssGBLUP) model, which integrates pedigree, phenotypic, and genomic data, has improved genomic prediction. However, ssGBLUP assumes that all markers contribute equally to genetic variance, which can limit its predictive accuracy, especially for traits controlled by major genes. To overcome this limitation, we integrate results from genome-wide association studies (GWAS) into an enhanced ssGBLUP framework, termed single-step genome-wide association assisted BLUP (ssGWABLUP). Our approach assigns differential weights to markers on the basis of their GWAS results, thereby increasing the contribution of effective markers while diminishing the influence of ineffective ones during the construction of the genomic relationship matrix. By incorporating pseudo quantitative trait nucleotides (pQTNs) as covariates, we aim to capture the effects of markers closely associated with major causal variants, leading to the development of the ssGWABLUP_pQTNs. Compared with weighted ssGBLUP (WssGBLUP), the ssGWABLUP model demonstrated superior accuracy and dispersion across different genetic architectures. We then compared the performance of our proposed ssGWABLUP_pQTNs model against both ssGBLUP and ssGWABLUP across various genetic scenarios. Our results demonstrate that ssGWABLUP_pQTNs outperforms other models in terms of prediction accuracy, particularly in scenarios with simpler genetic architectures. Additionally, evaluation using pig dataset confirmed the effectiveness of ssGWABLUP_pQTNs, highlighting its potential for practical breeding applications. The incorporation of pQTNs and a weighted genomic relationship matrix presents a promising and potentially scalable approach to further enhance genomic prediction, with potential implications for improving the accuracy of genomic selection in breeding programs. Full article

This study explores the development and optimization of quartz-based filtration media for industrial oil–water separation, focusing on enhancing surface wettability, minimizing fouling, and improving oil rejection efficiency. High-purity quartz particles (SiO₂: 98%, Fe₂O₃: 0.18%, particle size: 0.8–1.8 mm) were evaluated in three configurations: raw, acid-washed, and surface-coated with hydrophilic nanoparticles (Al₂O₃ and P₂O₅). The filtration medium was constructed as a packed-bed of quartz particles rather than a continuous sintered membrane, providing a cost-effective and modular structure for separation processes. Comprehensive material characterization was performed using X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), and Energy Dispersive Spectroscopy (EDS). XRD confirmed the crystalline stability of quartz across all treatments, while SEM and EDS revealed enhanced surface morphology and elemental distribution—especially phosphorus and aluminum—in coated samples. Performance testing with synthetic oily wastewater (initial oil concentration: 183,754.8 mg/L) demonstrated that the coated quartz medium achieved superior separation, reducing residual oil concentration to 29.3 mg/L, compared to 1583.7 mg/L and 1859.8 mg/L for washed and raw quartz, respectively. Contact angle analysis confirmed improved hydrophilicity in coated media, which also exhibited lower fouling propensity. Taguchi optimization (conducted via Minitab 21.3) and regression modeling identified surface coating and operational pressure (optimal at 2.5 bar) as the most significant parameters influencing oil rejection. Post-filtration SEM and XRD confirmed structural integrity and coating durability. Additionally, flux recovery above 90% after backwashing indicated strong regeneration capability. These findings validate surface-modified quartz packed beds as robust, scalable, and economically viable alternatives to conventional membranes in oily wastewater treatment. Future research will explore multilayer coatings, long term performance under aggressive conditions, and AI-based prediction models. Full article

The liver accounts for almost 95% of lipid metabolism in broilers and serves as a crucial metabolic organ. Stress, which occurs when broilers are exposed to a heated environment, inhibits liver metabolism, significantly impacting their growth. This experiment investigated the combination of GBE with TP to improve hepatic lipid metabolism in heat-stressed broiler chickens by inhibiting the AMPK/SREBP-1C/ACC pathway. Three hundred broilers were reared usually until 21 days and randomly divided into six groups, namely CON group, HS group, TP group (300 mg/kg), GBE100 group (GBE100 mg/kg + TP300 mg/kg), GBE300 group (GBE 300 mg/kg + TP 300 mg/kg), GBE600 (600 mg/kg + TP 300 mg/kg) groups, where the CON group was kept at 23 °C, and the HS group and the TP, GBE100, GBE300, and GBE600 groups of each medication group were kept at 35 ± 2 °C for 10 h per day. Liver and serum samples were extracted at 28 and 42 days of age, respectively. The results showed that, at 42 days of age, the GBE600 group exhibited significantly superior performance to the HS group in ADG, ADFI, and F/G (p < 0.01). Serum TG, TC, and LDL-C levels were significantly lower (p < 0.01), while HDL-C levels were significantly higher (p < 0.05). Additionally, the mRNA expression levels of LKB1, AMPK, SREBP-1C, and ACC were markedly reduced (p < 0.01). In contrast, the mRNA expression of HSL and CPT1A was significantly elevated (p < 0.01), indicating that the GBE600 was more effective in mitigating heat stress in broiler chickens at 42 days of age. It showed that the GBE600 was more effective in ameliorating heat stress in broilers at 42 days of age, thus providing an ethical basis for ameliorating the flocculation of hepatic lipid metabolism in heat-stressed broilers. Full article

It is of strategic significance to extract germanium (Ge) in an ecological way for sustainable development. Adsorbents that already adsorb Ge have disadvantages such as poor selectivity and low adsorption capacity. In this study, a novel adsorbent material based on rice husk functionalized with tannic acid was developed for the efficient extraction of Ge from simulated coal fly ash leachate. The adsorption capacity of tannic acid-functionalized rice husk (TA-EPI-ORH) for Ge was 19.9 times higher than that of untreated rice husk, demonstrating significantly improved performance. The results showed that the adsorption process of Ge by TA-EPI-ORH is consistent with pseudo-second-order kinetic and Freundlich isotherm model. TA-EPI-ORH had excellent selective adsorption properties, with adsorption of 1.40 mg L⁻¹ Ge exceeding 95% and solid-liquid partition coefficients of 4380 mL g⁻¹, even in the presence of nine impurity metal ions (average concentration: 479.08 mg L⁻¹). When compared with the two main coexistence ions—aluminum (Al) and calcium (Ca)—both of which have the relatively highest concentrations (Al: 1594.20 mg L⁻¹, Ca: 1740.13 mg L⁻¹), the separation factors for Ge still maintain relatively high level with SF(Ge/Al) = 42.57 and SF(Ge/Ca) = 39.93. Compared to existing studies, TA-EPI-ORH exhibits superior selective adsorption performance even with the presence of more interfering ions. After elution of the adsorbed Ge from TA-EPI-ORH, the extraction rate of Ge with low initial concentration (1.40 mg L⁻¹) reached 85.17%, while the extraction rates of Al and Ca were only 1.02% and 1.18%, respectively. Further research revealed that the catechol groups on the surface of TA-EPI-ORH formed stable complexes with Ge, whereas the complexes with coexisting ions (e.g., Ca and Al) were unstable, thereby ensuring high selectivity for Ge. This green chemistry-based functionalization of rice husk not only enables high-value utilization of agricultural waste but also provides a sustainable and eco-friendly strategy for efficient Ge separation and recovery. Full article

Background: Cancer is a leading cause of death worldwide, and its early detection is crucial for improving patient outcomes. This study aimed to develop and evaluate ensemble learning models, specifically stacking, for the accurate prediction of lung, breast, and cervical cancers using lifestyle and clinical data. Methods: 12 base learners were trained on datasets for lung, breast, and cervical cancer. Stacking ensemble models were then developed using these base learners. The models were evaluated for accuracy, precision, recall, F1-score, AUC-ROC, MCC, and kappa. An explainable AI technique, SHAP, was used to interpret model predictions. Results: The stacking ensemble model outperformed individual base learners across all three cancer types. On average, for three cancer datasets, it achieved 99.28% accuracy, 99.55% precision, 97.56% recall, and 98.49% F1-score. A similar high performance was observed in terms of AUC, Kappa, and MCC. The SHAP analysis revealed the most influential features for each cancer type, e.g., fatigue and alcohol consumption for lung cancer, worst concave points, mean concave points, and worst perimeter for breast cancer and Schiller test for cervical cancer. Conclusions: The stacking-based multi-cancer prediction model demonstrated superior accuracy and interpretability compared with traditional models. Combining diverse base learners with explainable AI offers predictive power and transparency in clinical applications. Key demographic and clinical features driving cancer risk were also identified. Further research should validate the model on more diverse populations and cancer types. Full article

New dual-curing resin cements are constantly launched into the market to improve the bond strength between dentine and indirect restorations when light irradiation is limited by the restoration material. The present study evaluated the microshear bond strength (μSBS) of two dual-cured resin cements, Estecem II Plus (EP) and Variolink Esthetic DC (VAR), when resin composite or dentine substrates were conditioned with their corresponding universal adhesives, Tokuyama Universal Bond II (TUB) and Adhese Universal DC (ADH). The experimental groups (n = 20) were (1) TUB/EP light-cured, (2) TUB/EP self-cured, (3) ADH/VAR light-cured, and (4) ADH/VAR self-cured. A μSBS test was performed after 24 h (T0) or after thermocycling (TC), and failure modes were assessed. Data analysis was performed using three-way ANOVA and Tukey tests (p < 0.05). In composite, TUB/EP self-cured demonstrated the highest μSBS at T0 and TC. After TC, TUB/EP self-cured and ADH/VAR light-cured remained stable (p > 0.05). In dentine, TUB/EP light-cured was statistically superior to TUB/EP self-cured and ADH/VAR self-cured at T0. Thermocycling decreased the μSBS of light-curing groups. TUB/EP achieved optimal μSBS when the manufacturer’s instructions were followed and the adhesive was self-cured, irrespective of the bonding substrate. However, ADH/VAR was more dependent on the type of bonding substrate than on the curing mode of the resin cement. Full article

This paper addresses the challenges of accurately estimating the state of health (SOH) of retired batteries, where factors such as limited historical data, non-linear degradation, and unstable parameters complicate the process. We propose a novel SOH estimation model based on an Integrated Hierarchical Extreme Learning Machine (I-HELM). The model minimizes reliance on historical data and reduces computational complexity by introducing health indicators derived from constant charging time and charging current area. The hierarchical structure of the Extreme Learning Machine (HELM) effectively captures the non-linear relationship between health indicators and battery capacity, improving estimation accuracy and learning efficiency. Additionally, integrating multiple HELM models enhances the stability and robustness of the results, making the approach more reliable across varying operational conditions. The proposed model is validated on experimental datasets collected from two Samsung battery packs, four Samsung single cells, and two Panasonic retired batteries under both constant-current and dynamic conditions. Experimental results demonstrate the superior performance of the model: the maximum error for Samsung battery cells and packs does not exceed 2.2% and 2.6%, respectively, with root mean square errors (RMSEs) below 1%. For Panasonic retired batteries, the maximum error remains under 3%. Full article

A quadratic programming problem with positive definite Hessian subject to box constraints is solved, using an active-set approach. Convex quadratic programming (QP) problems with box constraints appear quite frequently in various real-world applications. The proposed method employs an active-set strategy with Lagrange multipliers, demonstrating rapid convergence. The algorithm, at each iteration, modifies both the minimization parameters in the primal space and the Lagrange multipliers in the dual space. The algorithm is particularly well suited for machine learning, scientific computing, and engineering applications that require solving box constraint QP subproblems efficiently. Key use cases include Support Vector Machines (SVMs), reinforcement learning, portfolio optimization, and trust-region methods in non-linear programming. Extensive numerical experiments demonstrate the method’s superior performance in handling large-scale problems, making it an ideal choice for contemporary optimization tasks. To encourage and facilitate its adoption, the implementation is available in multiple programming languages, ensuring easy integration into existing optimization frameworks. Full article

Fusidic acid (FA), a tetracyclic triterpenoid, has been approved to treat methicillin-resistant Staphylococcus aureus (MRSA) infections. However, there are few reports about FA derivatives with high efficacy superior to FA, manifesting the difficulty of discovering the derivatives based on experience-based drug design. In this study, we employed a stepwise method to discover novel FA derivatives. First, molecular dynamics (MD) simulations were performed to identify the molecular mechanism of FA against elongation factor G (EF-G) and drug resistance. Then, we utilized a scaffold decorator to design novel FA derivatives at the 3- and 21-positions of FA. The ligand-based and structure-based screening models, including Chemprop and RTMScore, were employed to identify promising hits from the generated set. Ten generated FA derivatives with high efficacy in the Chemprop and RTMScore models were synthesized for in vitro testing. Compounds 4 and 10 demonstrated a 2-fold increase in potency against MRSA strains compared to FA. This study highlights the significant impact of AI-based methods on the design of novel FA derivatives with drug efficacy, which provides a new approach for drug discovery. Full article

Douglas fir (Pseudotsuga menziesii [Mirb.] Franco) is a valuable timber species native to western North America that was introduced to Europe in the 19th century. The objective of this study was to select the most valuable and stable Douglas fir provenances in Romania by combining growth and quality traits, using two indices recently used in forest tree species: the multi-trait genotype–ideotype distance index (MGIDI) and the multi-trait stability index (MTSI). The study was conducted across three common garden experiments in Romania, established in 1977, evaluating 61 provenances from the United States, Canada, Germany, France, and Romania. The analyzed traits were diameter at breast height (DBH), total height (TH), and pruned height (PH). Significant genotype–environment interactions were observed, with the Douglas fir showing superior growth performance in one of the testing sites in western Romania (Aleșd). The MGIDI and MTSI identified high-performing provenances from diverse geographic origins, including the Pacific Northwest, Europe, and Canada. Selection differentials ranged from 2.8% to 10.9% for individual traits, highlighting the potential for genetic improvement. The selected provenances represent valuable genetic resources of Douglas fir that are adapted to environmental conditions in the Carpathian region, contributing to the development of climate-adaptive breeding strategies and sustainable forest management. Full article