Search Results (22,613)

A field experiment was conducted in 2019–2020 and 2020–2021 at Rogoza, Fala, and Brežice in Slovenia to examine the biological viability of a mixed intercropping system and the effect of winter catch crops (WCCs) on maize growth parameters. The experiment included Italian ryegrass (IR) in pure stands, fertilized with nitrogen (N) in spring (70 kg N ha⁻¹), mixtures of crimson clover and red clover 50:50 (C), and intercropping between IR and C (IR+C). Neither mixture was fertilized with N in spring. We evaluated different competition indices and biological efficiency. Relative crowding coefficient (RCC) and actual yield loss (AYL) exceeded 1, indicating a benefit of IR+C intercropping. The IR in intercropping was more aggressive, as indicated by positive aggressivity (A) and a competitive ratio (CR) > 1, and it dominated over C in IR+C (that had negative A values and CR < 1). The competitive balance index (Cb) differed from zero, the relative yield total (RYT) was 2.24, the land equivalent coefficient (LEC) exceeded 0.25, the area–time equivalent ratio (ATER) exceeded 1, and land use efficiency (LUE) exceeded 100%. IR+C exhibited the highest total aboveground dry matter yield of maize (29.22 t ha⁻¹), the highest nitrogen content in dry matter grain yield of maize (206.35 kg ha⁻¹), the highest nitrogen and potassium content in maize stover (105.7 and 105.7 kg ha⁻¹, respectively), and the highest nitrogen and potassium content in the total aboveground dry matter of maize (312 and 267.3 kg ha⁻¹, respectively). The C/N ratio in dry matter yield of IR was 45.35, and in IR+C it was 33.43, which means that the mixture had a positive effect on nutrient release in maize. The ryegrass–clover mixture, according to the calculated biological indices, had advantages over pure stands and had a positive effect on maize yield. Full article

To address challenges in weld recognition during vacuum electron beam welding caused by dark environments and metal reflections, this study proposes an improved hybrid algorithm combining YOLOv11-seg with adaptive Canny edge detection. By incorporating the UFO-ViT attention mechanism and optimizing the network architecture with the EIoU loss function, along with adaptive threshold setting for the Canny operator using the Otsu method, the recognition performance under complex conditions is significantly enhanced. Experimental results demonstrate that the optimized model achieves an average precision (mAP) of 77.4%, representing a 9-percentage-point improvement over the baseline YOLOv11-seg. The system operates at 20 frames per second (FPS), meeting real-time requirements, with the generated welding trajectories showing an average length deviation of less than 3 mm from actual welds. This approach provides an effective pre-weld visual guidance solution, which is a critical step towards the automation of electron beam welding. Full article

Bee-derived products such as apitoxin, royal jelly, propolis, bee pollen, and honey are increasingly being used as part of cosmetic products because all of them contain a large number of bioactive compounds with antioxidant, anti-inflammatory, antimicrobial, and regenerative properties, which enable them to be used for therapeutic purposes. The aim of this investigation was to assess the performance of an automated growth-based system in order to make a quantitative examination of the total aerobic viable counts in bee-derived personal care products using NF-TVC vials that contained a nutrient-based medium with dextrose as the carbon source. According to USP general chapter <1223>, pivotal validation criteria such as linearity, equivalence of results, operative range, precision, accuracy, ruggedness, limit of quantification, and limit of detection have demonstrated that the automated system can be used for a reliable total aerobic viable count. Moreover, the actual research demonstrated that polysorbates efficiently block the antimicrobiological potential of bioactive compounds, such as phenols, flavonoids, enzymes, peptides, and fatty acids, which naturally occur in apitoxin, royal jelly, propolis, bee pollen, and honey, allowing for efficient microorganism recovery from the bee-made products tested. Therefore, this AGBS could be applied efficiently within the cosmetic industry to assess the total aerobic viable count in bee-derived products such as capillary treatments, toothpaste, and anti-aging cream, affording several benefits associated with faster product release into the market. Full article

Aluminum foam is attracting attention as a multifunctional, ultra-lightweight material. To apply this aluminum foam to actual industrial materials, aluminum foam plates are required. In addition, it is expected that a multi-layer aluminum foam composed of dissimilar aluminum alloy foam layers can further enhance its functionality. In this study, we attempted to fabricate a three-layer aluminum foam composed of commercially pure aluminum AA1050 and Al-Mg-Si aluminum alloy AA6061 by heating and foaming a total of three pieces of AA1050 precursor and AA6061 precursor arranged alternately, followed by immediate roller joining. It was found that, by traversing a roller immediately after foaming the AA1050 and AA6061 precursors, the aluminum foam could be joined while forming it into a flat plate. In addition, X-ray CT images of the fabricated samples revealed that material flow induced by roller traversing ruptured the surface skin layer. Numerous pores were observed within the sample, indicating pores were maintained during the roller traversing and no significant differences in porosities were identified between AA1050 aluminum foam and AA6061 aluminum foam. Furthermore, from the four-point bending test and the observation of samples after bending test, although quantitative mechanical properties were not obtained due to the as-joined samples were used for the bending test, pores were observed at the fracture surfaces, confirming that roller joining achieved seamless joining. Full article

Water-saving agricultural irrigation systems depend heavily on the hydraulic stability of pressure-compensating (PC) emitters, whose performance is fundamentally shaped by internal flow-path geometry. This study analyzes six commercial PC emitters (E1–E6) operated under pressures of 0.8–2.0 bar to quantify how key geometric descriptors influence hydraulic parameters critical for efficient water use, including actual discharge (q_act), discharge coefficient (k), pressure exponent (x), emission uniformity (EU), and flow variability. All emitters had discharge deviations within ±7% of nominal values. Longer and more tortuous labyrinths enhanced compensation stability, while emitters with wider cross-sections and shorter paths produced higher throughput but weaker regulation efficiency. Linear mixed-effects modeling showed that effective flow area increased k, whereas normalized path length and tortuosity reduced both k and x. Predictive equations derived from geometric indicators closely matched measured values, with deviations below ±0.05 L/h for k and ±0.05 for x. These results establish a geometry-based hydraulic framework that supports emitter selection and design in water-saving agricultural irrigation, aligning with broader Agricultural Water–Land–Plant System Engineering objectives and contributing to more efficient and sustainable water-resource utilization. Full article

An accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safety and reliability of energy storage systems and electric vehicles. However, existing methods face challenges: physics-based models are computationally complex, traditional data-driven methods rely heavily on manual feature engineering, and single models lack the ability to capture both local and global degradation patterns. To address these issues, this paper proposes a novel hybrid LSTM–Transformer model for LIB SOH estimation using actual measurement data. The model integrates Long Short-Term Memory (LSTM) networks to capture local temporal dependencies with the Trans-former architecture to model global degradation trends through self-attention mechanisms. Experimental validation was conducted using eight 18650 Nickel Cobalt Manganese (NCM) LIBs subjected to 750 charge–discharge cycles under room temperature conditions. Sixteen statistical features were extracted from voltage and current data during constant current–constant voltage (CC-CV) phases, with feature selection based on the Pearson correlation coefficient and maximum information coefficient analysis. The proposed LSTM–Transformer model demonstrated superior performance compared to the standalone LSTM and Transformer models, achieving a mean absolute error (MAE) as low as 0.001775, root mean square error (RMSE) of 0.002147, and mean absolute percentage error (MAPE) of 0.196% for individual batteries. Core features including cumulative charge (CC Q), charging time, and voltage slope during the constant current phase showed a strong correlation with the SOH (absolute PCC > 0.8). The hybrid model exhibited excellent generalization across different battery cells with consistent error distributions and nearly overlapping prediction curves with actual SOH trajectories. The symmetrical LSTM–Transformer hybrid architecture provides an accurate, robust, and generalizable solution for LIB SOH assessment, effectively overcoming the limitations of traditional methods while offering potential for real-time battery management system applications. This approach enables health feature learning without manual feature engineering, representing an advancement in data-driven battery health monitoring. Full article

Background/Objectives: Falls represent a major health concern for stroke survivors, necessitating effective risk assessment tools. This study proposes the Instrumented Fall Risk Assessment (IFRA) scale, a novel screening tool derived from Instrumented Timed Up and Go (ITUG) test data, designed to capture mobility measures often missed by traditional scales. Methods: We employed a two-step machine learning approach to develop the IFRA scale: first, identifying predictive mobility features from ITUG data and, second, creating a stratification strategy to classify patients into low-, medium-, or high-fall-risk categories. This study included 142 participants, who were divided into training (including synthetic cases), validation, and testing sets (comprising 22 non-fallers and 10 fallers). IFRA’s performance was compared against traditional clinical scales (e.g., standard TUG and Mini-BESTest) using Fisher’s Exact test. Results: Machine learning analysis identified specific features as key predictors, namely vertical and medio-lateral acceleration, and angular velocity during walking and sit-to-walk transitions. IFRA demonstrated a statistically significant association with fall status (Fisher’s Exact test p = 0.004) and was the only scale to assign more than half of the actual fallers to the high-risk category, outperforming the comparative clinical scales in this dataset. Conclusions: This proof-of-concept study demonstrates IFRA’s potential as an automated, complementary approach for fall risk stratification in post-stroke patients. While IFRA shows promising discriminative capability, particularly for identifying high-risk individuals, these preliminary findings require validation in larger cohorts before clinical implementation. Full article

For enhancing the operations of microgrids, especially in places like Bonavista in Newfoundland and Labrador, accurate short-term wind power forecasting is critically important. This is more so for communities which integrate renewable energy. This paper aims to develop and implement deep learning Long Short-Term Memory (LSTM) models for wind power forecasting for three months ahead based on one year of historical data. With a Mean Absolute Error (MAE) of 0.27 m/s and a Root Mean Squared Error (RMSE) of 0.39 m/s, the model demonstrates high predictive accuracy. Estimated power output was calculated using a standard wind turbine power curve, assuming representative turbine parameters, in order to convert wind speed forecasts into useful power inputs for microgrid operations. The LSTM’s potential and significance in microgrid planning and optimization are highlighted by the results, which show that its yield power estimates closely match actual generation. Full article

Predicting the transverse tensile properties of unidirectional CFRP plies is often based on micromechanical representative volume elements (RVEs) with circular fiber cross-sections, whereas microscopic observations show pronounced ellipticity and size variability in actual fibers. A two-dimensional plane-strain micromechanical framework with elliptical fiber cross-sections is developed as a virtual testing tool to quantify how fiber volume fraction, cross-sectional aspect ratio and statistical fluctuations in the semi-minor axis influence the transverse tensile response. Random RVEs are generated by a hard-core random sequential adsorption procedure under periodic boundary conditions and a minimum edge-to-edge gap constraint, and the fiber arrangements are validated against complete spatial randomness using nearest-neighbor statistics, Ripley’s K function and the radial distribution function. The matrix is described by a damage–plasticity model and fiber–matrix interfaces are represented by cohesive elements, so that high equivalent-stress bands in matrix ligaments and the associated crack paths can be resolved explicitly. Parametric analyses show that increasing fiber volume fraction raises the transverse elastic modulus and peak stress by thinning matrix ligaments and promoting longer, more continuous high-stress bands, while the cross-sectional aspect ratio redistributes high stress among ligaments and adjusts the balance between peak strength and the degree of failure localization. The observed size variability is represented by modeling the semi-minor axis as a normal random variable; a larger variance mainly leads to a reduction in transverse peak stress through stronger stress localization near very thin ligaments, whereas the elastic slope and the strain at peak stress remain almost unchanged. The proposed framework thus provides a statistically validated and computationally efficient micromechanical basis for microstructure-sensitive assessment of the transverse behavior of UD-CFRP plies with non-circular fiber cross-sections. Full article

Effective communication in English is a critical behavioral competency for seafarers in a multilingual maritime environment, directly impacting operational safety. However, a gap exists between current Maritime English (ME) training in China and the actual communication demands of global seafaring. This study aims to identify the specific ME skills including linguistic, behavioral, and sociolinguistic dimensions that are most important for on-board performance and safety management from the perspective of pre-service maritime students at Shanghai Maritime University. A mixed-methods approach was used, combining structured questionnaires (n = 313) with in-depth follow-up interviews (n = 10). The results identified 24 highly needed ME skills, particularly focused on areas governing safety-critical behaviors, such as wireless communication, security protocols, and emergency procedures. In addition, based on learner profiling, the study depicts two different learner characteristics: exam-focused and work-focused students, each with different views on the importance of skills. Work-focused students place greater emphasis on the practicality of their skills. The interview data confirms and enriches these quantitative research results. The research findings emphasize that ME courses must be more closely aligned with real-world communicative scenarios and behaviors, prioritize scenario based teaching and practical operations, and tailor differentiated teaching based on learner psychology and behavioral preference. This study offers references for maritime education institutions with similar learner profiles to optimize ME curricula, prioritize secure communication skills, and strengthen industry-education collaboration, thereby enhancing pre-service maritime students’ safety behavior and professional competitiveness in China. Full article

The surface tension of a liquid droplet can be determined by fitting its actual profiles using the Young–Laplace equation, effectively reducing the measurement of surface tension to an accurate determination of the droplet’s profiles. Spectral confocal sensors are high-precision, interference-resistant, non-contact measurement systems for droplet surface profiling, employing a light source together with a dispersive objective lens and a spectrometer to acquire depth-dependent spectral information. The accuracy and stability of surface tension measurements can be effectively enhanced by spectral confocal sensors measuring the droplet surface profile. Although existing spectral confocal sensors have significantly improved measurement range and accuracy, their angular measurement performance remains limited, and deviations may arise at droplet edges with large inclinations or pronounced surface profile variations. This study presents the optical design of a large-angle spectral confocal sensor. By theoretically analyzing the conditions for generating linear axial dispersion in the dispersive objective lens, a front-end dispersive objective lens was designed by combining positive and negative lenses. Based on a Czerny–Turner (C-T) configuration, the back-end spectrometer was designed under the astigmatism-free condition, taking into account both central and edge wavelength effects. Zemax was employed for simulation optimization and tolerance analysis of each optical module. The results show that the designed system achieves an axial dispersion of 1.5 mm over the 430–700 nm wavelength range, with a maximum allowable object angle of ±40° and a theoretical resolution of 3 μm. The proposed spectral confocal sensor maintains high measurement accuracy over a wide angular range, facilitating precise measurement of droplet surface tension at large inclination angles. Full article

Parental perceptions of healthy eating often diverge from children’s actual diets, but this gap is poorly documented in Central Asia. We examined how parents’ priorities for key food groups relate to nutrient intakes in 390 urban children aged 1–6 years in Central Kazakhstan. In a cross-sectional study, parents completed a 24 h multiple-pass dietary recall and rated the importance of fats and sweets, meat and fish, dairy, vegetables and fruits, and bread and potatoes on 5-point scales. Nutrient intakes were calculated using software, compared with national DRIs, and analyzed using rank-based tests and Spearman correlations. Parents reported near-ceiling priority for restricting fats and sweets and consistently high priority for bread and potatoes, whereas vegetables, fruits, meat/fish ,and dairy were rated moderately important, with dairy under-prioritized in 1–2-year-olds. On the recalled day, median intakes of fat, dietary fiber, vitamin C, and calcium were below national recommendations at all ages, and median intakes of iron, thiamine, and niacin were particularly low at 3–4 years, while sodium intake exceeded recommended levels; the 3–4-year group showed the most pronounced clustering of shortfalls. Prevalence estimates indicated that most children had intakes below recommendations for dietary fiber and calcium and above recommendations for sodium, underscoring population-wide nutritional imbalance. Across all scales, parental priorities showed only weak, non-significant associations with nutrient intakes (|r| < 0.11). These findings indicate a perception–intake gap and support interventions that ensure adequate fats, fiber, vitamin C, calcium, and bioavailable iron in preschool diets. Full article

The causes of agricultural drought are complex, and its actual occurrence process is often characterized by rapid onset in terms of time and small scale in terms of space. Monitoring agricultural drought using satellite remote sensing with low spatial resolution makes it difficult to accurately capture the details of small-scale drought events. High-resolution satellite remote sensing has relatively long revisit cycles, making it difficult to capture the rapid evolution of drought conditions. Furthermore, the occurrence of agricultural drought is linked to multiple factors including precipitation, evapotranspiration, soil properties, and crop physiological characteristics. Consequently, relying on a single variable or indicator is insufficient for multidimensional monitoring of agricultural drought. This study takes Hebi City, Henan Province as the research area. It uses Sentinel-1 satellite data (HV, VV), Sentinel-2 data (NDVI, B2, B11), elevation, slope, aspect, and GPM precipitation data from 2019 to 2024 as independent variables. Three machine learning algorithms—Random Forest (RF), Random Forest-Recursive Feature Elimination (RF-RFE), and eXtreme Gradient Boosting (XGBoost)—were employed to construct a multi-dimensional agricultural drought monitoring model at the field scale. Additionally, the study verified the sensitivity of different environmental variables to agricultural drought monitoring and analyzed the accuracy performance of different machine learning algorithms in agricultural drought monitoring. The research results indicate that under the condition of full-factor input, all three models exhibit the optimal predictive performance. Among them, the XGBoost model performs the best, with the smallest Relative Root Mean Square Error (RRMSE) of 0.45 and the highest Correlation Coefficient (R) of 0.79. The absence of Digital Elevation Model (DEM) data impairs the models’ ability to capture the patterns of key features, which in turn leads to a reduction in predictive accuracy. Meanwhile, there is a significant correlation between model performance and sample size. Ultimately, the constructed XGBoost model takes the lead with an accuracy of 89%, while the accuracies of Random Forest (RF) and Random Forest-Recursive Feature Elimination (RF-RFE) are 88% and 86%, respectively. Based on these three drought monitoring models, this study further monitored a drought event that occurred in Hebi City in 2023, presented the spatiotemporal distribution of agricultural drought in Hebi City, and applied the Mann–Kendall test for time series analysis, aiming to identify the abrupt change process of agricultural drought. Meanwhile, on the basis of the research results, the feasibility of verifying drought occurrence using irrigation signals was discussed, and the potential reasons for the significantly lower drought occurrence probability in the western mountainous areas of the study region were analyzed. Full article

Reducing dependency on chemical pesticides is a core ambition of the European Green Deal, yet adoption of low-input practices remains uneven. This systematic review synthesizes evidence on the behavioural determinants of European farmers’ knowledge, attitudes, and practices (KAP) regarding sustainable pesticide use and evaluates the role of digital tools in facilitating Integrated Pest Management (IPM). Following PRISMA 2020 guidelines, we analysed 65 peer-reviewed articles published between 2011 and 2025, which were identified through Scopus and Web of Science. The synthesis reveals that while pro-environmental attitudes drive the intention to change, actual behaviour is frequently inhibited by loss aversion, ‘clean field’ social norms, and perceived economic risks. Digital tools—specifically Decision Support Systems (DSSs) and precision technologies—demonstrate technical potential to reduce pesticide loads but are constrained by the same behavioural barriers: a lack of trust in models, perceived complexity, and costs. Consequently, we propose a Psycho-Digital Integration Framework which posits that digital innovation acts as a catalyst only when embedded in systemic enablers—specifically green insurance schemes and independent advisory networks. These mechanisms are critical to redistribute perceived agricultural risk and bridge the gap between technical potential and behavioral adoption. Full article

Purpose: With the addition of palbociclib to endocrine therapy, many hormone receptor-positive (HR+) metastatic breast cancer (mBC) patients experience toxicities that can lead to dose reductions and cycle delays. We examined the actual doses of palbociclib received by patients and their treatment responses. These dose adjustments, made at the physician’s discretion, are not always consistent with pharmaceutical company recommendations. The aim of this study was to assess the influence of dose adjustments on dose intensity and treatment response in our patients. Methods: Records of patients with HR+ mBC treated with palbociclib between December 2016 and January 2019 at the Sainte-Catherine Institute were retrospectively reviewed. Dose intensity was defined as the total dose of palbociclib received by each patient during the first six months of treatment. Anticipated dose reductions and extended cycle delays were recorded. Treatment response at six months and survival were assessed using statistical analyses. Results: A total of 131 women were included; the median age was 67 years. Forty-six patients (35%) experienced an anticipated dose reduction or an extended cycle delay during the first six months of treatment. Logistic regression analysis showed that factors correlated with six-month treatment response included anticipated dose reduction or extended cycle delay (OR = 14.6, 95% CI 3.74–97.4, p < 0.001), cycle delay > 4 weeks (OR = 5.94, 95% CI 1.58–21, p = 0.01), initial dosage < 125 mg (OR = 4.09, 95% CI 1.13–13.7, p = 0.034), and six-month dose intensity < 14,250 mg (OR = 26.0, 95% CI 4.91–481, p < 0.001). Conclusions: In this real-world assessment of clinical outcomes in French patients with HR+ mBC treated with palbociclib, a palbociclib dose intensity lower than recommended—particularly due to cycle delays longer than four weeks—was associated with an increased risk of six-month disease progression. Full article