Search Results (16,606)

The gradual decline in wine consumption in Czechia poses significant challenges for domestic winemakers. Moreover, the sector faces mounting pressure from climate change—most notably global warming—which is increasingly affecting viticulture and wine production across the region. Using advanced predictive models, we estimated developmental trends and calculated forecasts for yield-generating components of grapevine cultivation. The results confirm stagnation or modest growth in the sector, with its development remaining strongly influenced by structural changes and external economic factors. While consumer demand is shifting toward white (or lighter) wines, climate change in Czechia is enhancing conditions for cultivating grape varieties suited to red wine production. This article examines the imbalance between supply and demand in the Czech wine market and identifies wine tourism as a possible solution for resolving the discrepancy. Full article

Endolysins, phage-derived enzymes capable of lysing bacterial cell walls, hold significant promise as novel antimicrobials against resistant Gram-positive and Gram-negative pathogens. In this study, we undertook an integrative approach combining extensive in silico analyses and experimental validation to characterize the novel endolysin LysPALS22. Initially, sixteen endolysin sequences were selected based on documented lytic activity and enzymatic diversity, and subjected to multiple sequence alignment and phylogenetic analysis, which revealed highly conserved catalytic and binding domains, particularly localized to the N-terminal region, underscoring their functional importance. Building upon these sequence insights, we generated three-dimensional structural models using Swiss-Model, EBI-EMBL, and AlphaFold Colab, where comparative evaluation via Ramachandran plots and ERRAT scores identified the Swiss-Model prediction as the highest quality structure, featuring over 90% residues in favored conformations and superior atomic interaction profiles. Leveraging this validated model, molecular docking studies were conducted in PyRx with AutoDock Vina, performing blind docking of key peptidoglycan-derived ligands such as N-Acetylmuramic Acid-L-Alanine, which exhibited the strongest binding affinity (−7.3 kcal/mol), with stable hydrogen bonding to catalytic residues ASP46 and TYR61, indicating precise substrate recognition. Visualization of docking poses using Discovery Studio further confirmed critical hydrophobic and polar interactions stabilizing ligand binding. Subsequent molecular dynamics simulations validated the stability of the LysPALS22–NAM-LA complex, showing minimal structural fluctuations, persistent hydrogen bonding, and favorable interaction energies throughout the 100 ns trajectory. Parallel to computational analyses, LysPALS22 was heterologously expressed in Escherichia coli (E. coli) and Pichia pastoris (P. pastoris), where SDS-PAGE and bicinchoninic acid assays validated successful protein production; notably, the P. pastoris-expressed enzyme displayed an increased molecular weight (~45 kDa) consistent with glycosylation, and achieved higher volumetric yields (1.56 ± 0.31 mg/mL) compared to E. coli (1.31 ± 0.16 mg/mL), reflecting advantages of yeast expression for large-scale production. Collectively, these findings provide a robust structural and functional foundation for LysPALS22, highlighting its conserved enzymatic features, specific ligand interactions, and successful recombinant expression, thereby setting the stage for future in vivo antimicrobial efficacy studies and rational engineering efforts aimed at combating multidrug-resistant Gram-negative infections. Full article

Understanding spatial variability in crop yields across fields is critical for developing precision agricultural strategies that optimize productivity while reducing negative environmental impacts. This variability often arises from a complex interplay of topographic features, soil characteristics, and hydrological conditions. This study investigates the influence of hydro-topographic factors on corn and soybean yield variability from 2016 to 2023 at the well-managed experimental sites in Beltsville, Maryland. A high-resolution surface digital elevation model (DEM) and subsurface DEM derived from ground-penetrating radar (GPR) were used to quantify topographic factors (elevation, slope, and aspect) and hydrological factors (surface flow accumulation, depth from the surface to the subsurface-restricting layer, and distance from each crop pixel to the nearest subsurface flow pathway). Topographic variables alone explained yield variation, with a relative root mean square error (RRMSE) of 23.7% (r² = 0.38). Adding hydrological variables reduced the error to 15.3% (r² = 0.73), and further combining with remote sensing data improved the explanatory power to an RRMSE of 10.0% (r² = 0.87). Notably, even without subsurface data, incorporating surface-derived flow accumulation reduced the RRMSE to 18.4% (r² = 0.62), which is especially important for large-scale cropland applications where subsurface data are often unavailable. Annual spatial yield variation maps were generated using hydro-topographic variables, enabling the identification of long-term persistent yield regions (LTRs), which served as stable references to reduce spatial anomalies and enhance model robustness. In addition, by combining remote sensing data with interannual meteorological variables, prediction models were evaluated with and without hydro-topographic inputs. The inclusion of hydro-topographic variables improved spatial characterization and enhanced prediction accuracy, reducing error by an average of 4.5% across multiple model combinations. These findings highlight the critical role of hydro-topography in explaining spatial yield variation for corn and soybean and support the development of precise, site-specific management strategies to enhance productivity and resource efficiency. Full article

Soil erosion is a critical environmental issue that leads to land degradation, reduced agricultural productivity, and ecological imbalance. This study aims to assess soil loss under various land surface conditions by developing 11 distinct scenarios using the RUSLE (Revised Universal Soil Loss Equation) model integrated within the Google Earth Engine (GEE) platform. Remote sensing-derived indices including NDVI, EVI, NDWI, SAVI, and BSI were incorporated to represent vegetation cover, moisture, and bare/built-up surfaces. The K, LS, P, and R factors were held constant, allowing the C factor to vary based on each index, simulating real-world landscape differences. Soil loss maps were generated for each scenario, and spatial variability was analyzed using bubble charts, bar graphs, and C-map visualizations. The results show that vegetation-based indices such as NDVI and EVI lead to significantly lower soil loss estimations, while indices associated with built-up or bare surfaces like BSI predict higher erosion risks. These findings highlight the strong relationship between land cover characteristics and erosion intensity. This study demonstrates the utility of integrating satellite-based indices into erosion modeling and provides a scenario-based framework for supporting land management and soil conservation practices. The proposed approach can aid policymakers and land managers in prioritizing conservation efforts and mitigating erosion risk. Moreover, maintaining and enhancing vegetative cover is emphasized as a key strategy for promoting sustainable land use and long-term ecological resilience. Full article

The estimation of accurate and precise Satellite-Derived Bathymetries (SDBs) is important in marine and coastal applications for a better understanding of the ecosystems and science-based decision-making. Despite the advancements in related Machine Learning (ML) studies, quantifying the anticipated bias per pixel in the SDBs remains a significant challenge. This study aims to address this knowledge gap by developing a spatially explicit uncertainty index of a ML-derived SDB, capable of providing a quantifiable anticipation for biases of 0.5, 1, and 2 m. In addition, we explore the usage of this index for model optimization via the exclusion of training points of high or moderate uncertainty via a six-fold iteration loop. The developed methodology is applied across the national coastal extent of Belize in Central America (~7017 km²) and utilizes remote sensing data from the European Space Agency’s twin satellite system Sentinel-2 and Planet’s NICFI PlanetScope. In total, 876 Sentinel-2 images, nine NICFI six-month basemaps and 28 monthly PlanetScope mosaics are processed in this study. The training dataset is based on NASA’s system Ice, Cloud and Elevation Satellite (ICESat-2), while the validation data are in situ measurements collected with scientific equipment (e.g., multibeam sonar) and were provided by the National Oceanography Centre, UK. According to our results, the presented approach is able to provide a pixel-based (i.e., spatially explicit) uncertainty index for a specific prediction bias and integrate it to refine the SDB. It should be noted that the efficiency of the optimization of the SDBs as well as the correlations of the proposed uncertainty index with the absolute prediction error and the true depth are low. Nevertheless, spatially explicit uncertainty information produced by a ML-related SDB provides substantial insight to advance coastal ecosystem monitoring thanks to its capability to showcase the difficulty of the model to provide a prediction. Such spatially explicit uncertainty products can also aid the communication of coastal aquatic products with decision makers and provide potential improvements in SDB modeling. Full article

The emergence of mass personalized production has increased the adaptability and intelligence requirements of welding robots. To address the challenges associated with mass personalized production, this paper proposes a novel knowledge-driven framework for intelligent welding process planning in cloud robotics systems. This framework integrates cloud-edge-end collaborative computing with ontology-based knowledge representation to enable efficient welding process optimization. A hierarchical knowledge-based architecture was developed using the SQLite 3.38.0, Redis 5.0.4, and HBase 2.1.0 tools. The ontology models formally define the welding tasks, resources, processes, and results, thereby enabling semantic interoperability across heterogeneous systems. A hybrid knowledge evolution method that combines cloud-based welding simulation and transfer learning is presented as a means of achieving inexpensive, efficient, and intelligent evolution of welding process knowledge. Experiments demonstrated that, with respect to pure cloud-based solutions, edge-based knowledge bases can reduce the average response time by 86%. The WeldNet-152 model achieved a welding parameter prediction accuracy of 95.1%, while the knowledge evolution method exhibited a simulation-to-reality transfer accuracy of 78%. The proposed method serves as a foundation for significant enhancements in the adaptability of welding robots to Industry 5.0 manufacturing environments. Full article

The global insect farming sector is rapidly expanding, driven by rising demand for sustainable protein sources and its potential to contribute to food security solutions. This study focuses on the quantification of chitin by comparing two gravimetric methods (ADF-ADL and crude fiber estimation) with a purification method considered as a reference method. It also aims to use the near-infrared spectroscopy (NIRS) to rapidly assess the quality of insect meals, in particular the macronutrients (moisture, protein, fat) and chitin content in a large data set of insect samples. Both alternative methods overestimated chitin content compared to the enzymatic purification method, which is the most reliable but more complex and expensive. Given their advantages (fairly simple, no significant investment, higher sample throughput, relatively short time execution), they can serve for rapid screening when precise chitin determination is not required. Calibration models showed good performance, particularly for protein and fat determination, and satisfactory results for chitin prediction. The NIRS models show promises for rapid and reliable prediction of insect products, although the chitin assessment remains to be further validated. Its implementation could streamline chemical quality control in insect-based food and feed production, offering speed and flexibility for industrial applications. Full article

The quality control of fruit purée products such as mango and mangosteen is crucial for maintaining consumer satisfaction and meeting industry standards. Traditional destructive techniques for assessing key quality parameters like the soluble solid content (SSC) and titratable acidity (TA) are labor-intensive and time-consuming; prompting the need for rapid, nondestructive alternatives. This study investigated the use of deep learning (DL) models including Simple-CNN, AlexNet, EfficientNetB0, MobileNetV2, and ResNeXt for predicting SSC and TA in mango and mangosteen purée and compared their performance with the conventional chemometric method partial least squares regression (PLSR). Spectral data were preprocessed and evaluated using 10-fold cross-validation. For mango purée, the Simple-CNN model achieved the highest predictive accuracy for both SSC (coefficient of determination of cross-validation (${\mathrm{R}}_{\mathrm{CV}}^2$) = 0.914, root mean square error of cross-validation (RMSE_CV) = 0.688, the ratio of prediction to deviation of cross-validation (RPD_CV) = 3.367) and TA (${\mathrm{R}}_{\mathrm{CV}}^2$ = 0.762, RMSE_CV = 0.037, RPD_CV = 2.864), demonstrating a statistically significant improvement over PLSR. For the mangosteen purée, AlexNet exhibited the best SSC prediction performance (${\mathrm{R}}_{\mathrm{CV}}^2$ = 0.702, RMSE_CV = 0.471, RPD_CV = 1.666), though the RPD_CV values (<2.0) indicated limited applicability for precise quantification. TA prediction in mangosteen purée showed low variance in the reference values (standard deviation (SD) = 0.048), which may have restricted model performance. These results highlight the potential of DL for improving NIR-based quality evaluation of fruit purée, while also pointing to the need for further refinement to ensure interpretability, robustness, and practical deployment in industrial quality control. Full article

For the first time, this study presents a cyber–physical framework that reconciles the long-standing conflict between transparent queue analytics and adaptive machine learning in flexible manufacturing systems. Deterministic indicators, utilization, expected queue length, waiting time, and idle probability, are fused with topological embeddings of the routing graph and ingested by a graph convolutional network that predicts station congestion with calibrated confidence intervals. Shapley additive explanations decompose every forecast into causal contributions, and these vectors, together with a percentile-based risk metric, steer a mixed-integer genetic optimizer toward schedules that lift throughput without breaching statistical congestion limits. A cloud dashboard streams forecasts, risk bands, and color-coded explanations, allowing supervisors to accept or modify suggestions; each manual correction is logged and injected into nightly retraining, closing a socio-technical feedback loop. Experiments on an 8704-cycle production census demonstrate a 38 percent reduction in average queue length and a 12 percent rise in throughput while preserving full audit traceability, enabling one-minute rescheduling on volatile shop floors. The results confirm that transparency and adaptivity can coexist when analytical priors, explainable learning, and risk-aware search are unified in a single containerized control stack. Full article

The growing demand for functional bakery products necessitates research on the enrichment of wheat bread with pigweed (Amaranthus spp.) and purslane (Portulaca oleracea) flour. Although these plant-based raw materials offer nutritional and environmental benefits, their inclusion in wheat bread formulations poses challenges in the creation of formulations that may compromise the sensory and structural qualities of the final product. The main objective of this work is to systematically determine the optimal amounts of these alternative flour using multimodal bread characterization techniques that include physicochemical, organoleptic, geometric, and optical evaluations, supported by advanced data reduction techniques and regression models. A total of 70 features were analyzed and reduced to 22 for pigweed flour and 15 for purslane flour informative features. Predictive models (R² = 0.85 for pigweed flour, R² = 0.84 for purslane flour) were developed to optimize the inclusion of alternative flour, resulting in appropriate concentrations of 3.69% for pigweed flour and 7.13% for purslane flour. These formulations balance improved nutritional profiles with acceptable sensory and structural properties. The results obtained not only complement the potential of pigweed and purslane as sustainable functional raw materials but also demonstrate the efficacy of an automated, image-based approach to formulating recipes in food manufacturing. Full article

Efficient labor productivity forecasting is a critical challenge in construction engineering, directly influencing scheduling, cost control, and resource allocation. In reinforced concrete projects, accurate prediction of rebar-fixing productivity enables managers to optimize workforce deployment and mitigate delays. This study proposes a machine learning-based framework to forecast rebar-fixing labor productivity under varying site and environmental conditions. Four regression algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN)—were trained, tuned, and validated using grid search with k-fold cross-validation. RF achieved the highest accuracy, with an $R^2$ of 0.901 and RMSE of 19.94 on the training set and an $R^2$ of 0.877 and RMSE of 22.47 on the test set, indicating strong generalization. Model interpretability was provided through SHapley Additive exPlanations (SHAP), revealing that larger quantities of M32 and M25 rebars increased productivity, while higher temperatures reduced it, likely due to lower labor efficiency. Humidity, wind speed, and precipitation showed minimal influence. The integration of accurate predictive modeling with explainable machine learning offers practical insights for project managers, supporting data-driven decisions to enhance reinforcement task efficiency in diverse construction environments. Full article

Background/Objectives: The pulmonary administration of levodopa enables a rapid absorption and onset of action, making it a suitable administration route for managing OFF episodes in Parkinson’s disease. Currently, one dry powder product for inhalation (Inbrija) is available on the market, while another (Levodopa Cyclops) is in development. These two products differ substantially in terms of inhaler design, their use and resistance, and their powder formulations. This study aimed to investigate whether these differences translate into in vitro differences in aerosol characteristics and dissolution kinetics and whether any differences were also reflected in the in vivo performance. Methods: The in vitro aerosol characteristics were determined via Next Generation Impactor experiments, and the dissolution kinetics were determined with a modified paddle apparatus. A randomized crossover comparative bioavailability study with fasted healthy volunteers was conducted with Inbrija 84 mg and Levodopa Cyclops 45 mg, 90 mg, and 135 mg. Results: The results showed similar aerosol characteristics, but Levodopa Cyclops showed substantially faster dissolution behavior than Inbrija. Despite this in vitro difference, the pharmacokinetic profiles of Inbrija 84 mg and Levodopa Cyclops 90 mg were similar, with no differences in C_max, T_max, and AUC, showing bioequivalence between the two products. Conclusions: This suggests that the systemic absorption of levodopa via the lungs is not limited by dissolution but most likely by its permeation rate. This finding underscores the need to critically apply in vitro tests and critically interpret the results for predicting the in vivo performance of inhaled products. Full article

A very important method of reducing carbon emissions is to make sure industrial plants are operated at optimal energy efficiency. The oil and petrochemical industries spend large amounts of energy in the transportation of petroleum and its various products that have high viscosities. A critical component in these plants is abrupt pipe contraction. Large amounts of energy are lost in pipe contractions. In this paper we investigate the energy losses in pipe contraction using the local entropy generation method after solving the detailed flow field around an abrupt pipe contraction. We have applied the method at various Reynolds numbers covering laminar and turbulent flow regimes. Furthermore, we have used an integral entropy analysis and found excellent agreement between the differential and integral entropy methods when the computational grid is well refined. The differential analysis was able to predict the local entropy generation and find where the large losses are located and therefore be able to minimize these losses effectively. Based on the detailed entropy generation field, it is recommended to use rounded contraction in order to reduce the losses. By introducing rounded contractions in laminar flow, the losses have been reduced by 22%. In the case of the turbulent flow regime, the losses were reduced by 96% by introducing a rounding radius to diameter ratio r/D₂ of 10%. The turbulent flow results for the case of pipe entrance, which is a special case of abrupt contraction (D₂/D₁ goes to zero) agree very well with the present results. This work addresses a large range of D₂/D₁ for laminar and turbulent flows. It is recommended that companies involved in designing petrochemical plants and installations take these findings into consideration to reduce carbon emissions. These recommendations also extend to the design of equipment and piping systems for the food industry and micro-device flows. Full article

The search for keV-scale sterile neutrinos in tritium beta decay is made possible through the theoretically allowed small admixture of electron flavor in right-handed, singlet, massive neutrino states. A distinctive feature of keV-scale sterile-neutrino–induced threshold distortions in the tritium beta spectrum is the presence of quantized nuclear-recoil effects, as predicted for atomic tritium bound to two-dimension materials such as graphene. The sensitivities to the sterile neutrino mass and electron-flavor mixing are considered in the context of the PTOLEMY detector simulation with tritiated graphene substrates. The ability to scan the entire tritium energy spectrum with a narrow energy window, low backgrounds, and high-resolution differential energy measurements provides the opportunity to pinpoint the quantized nuclear-recoil effects. providing an additional tool for identifying the kinematics of the production of sterile neutrinos. Background suppression is achieved by transversely accelerating electrons into a high magnetic field, where semi-relativistic electron tagging can be performed with cyclotron resonance emission RF antennas followed by deceleration through the PTOLEMY filter into a high-resolution differential energy detector operating in a zero-magnetic-field region. The PTOLEMY-based approach to keV-scale searches for sterile neutrinos involves a novel precision apparatus utilizing two-dimensional materials to yield high-resolution, sub-eV mass determination for electron-flavor mixing fractions of $|U_{e4}{|}^2\simeq {10}^{-5}$ and smaller. Full article

Crop aeroponic cultivation still faces issues such as insufficient precision in water supply control and scientifically-based irrigation scheduling. To address this challenge, the present study aims to establish a precision irrigation protocol adapted to the characteristics of crop aeroponic cultivation. Using coriander (Coriandrum sativum L.) as the experimental subject, crop water requirements were estimated utilizing both the FAO56 P-M equation and its revised form. The RMSE between the water requirement measured values and the calculated values using the P-M formula is 2.12 mm, the MAE is 2.0 mm, and the MAPE is 14.29%. The RMSE between the water requirement measured values and the calculated values using the revised P-M formula is 0.88 mm, the MAE is 0.82 mm, and the MAPE is 5.78%. The results indicate that the water requirement values calculated using the revised P-M formula are closer to the measured values. For model development, this study used coriander evapotranspiration as a basis. Major environmental variables influencing water requirement were selected as input features, and the daily reference water requirement served as the output. Three modeling approaches were implemented: Random Forest (RF), Bagging, and M5P Model Tree algorithms. The results indicate that, in comparing various input combinations (C1: air temperature, relative humidity, atmospheric pressure, wind speed, radiation, photoperiod; C2: air temperature, relative humidity, wind speed, radiation; C3: air temperature, relative humidity, radiation), the RF model based on C1 input demonstrated superior performance with RMSE = 0.121 mm/d, MAE = 0.134 mm/d, MAPE = 2.123%, and R² = 0.971. It significantly outperforms the RF models with other input combinations, as well as the Bagging and M5P models across all input scenarios, in terms of convergence rate, determination coefficient, and comprehensive performance. Its predictions aligned more closely with observed data, showing enhanced accuracy and adaptability. This optimized prediction model demonstrates particular suitability for forecasting water requirements in aeroponic coriander production and provides theoretical support for efficient, intelligent water-saving management in crop aeroponic cultivation. Full article