Search Results (18,318)

In the operation mode arrangement of bulk power systems, unreasonable reactive power injection data at nodes tend to result in power flow calculation non-convergence. Owing to the extremely high dimension of the variable space and the heterogeneous impacts of different variables on power flow convergence, it is imperative to accurately identify the key variables inducing non-convergence and provide physical justifications. For this purpose, this paper proposes a data-driven key variable identification and adjustment method: firstly, based on the blocking cut-set theory and the characteristic that the active unbalanced power ΔP of intermediate power flow exhibits opposite signs at the sending and receiving ends of the cut-set, a blocking cut-set identification method leveraging the characteristics of the active unbalanced power of intermediate power flow is developed; secondly, relying on the feature that the reactive unbalanced power ΔQ of intermediate power flow is less than zero, a key variable identification method based on the characteristics of the reactive unbalanced power of intermediate power flow is presented; finally, a key variable adjustment method grounded in the numerical value of ΔQ is proposed. The validity of the proposed approach was validated via simulated computations using both the IEEE 39 bus system and a practical bulk power system. Full article

Multiple Unmanned Aerial Vehicle (UAV) collaborative coverage path planning is widely applied in fields such as regional surveillance. However, optimizing the trade-off between deployment costs and task execution efficiency remains challenging. To balance resource costs and execution efficiency with an uncertain number of UAVs, this paper analyzes the characteristics of irregular mission areas and formulates a bi-level optimization model for multi-UAV collaborative CPP. The model aims to minimize both the number of UAVs and the total path length. First, in the upper level, an improved Best Fit Decreasing algorithm based on binary search is designed. Straight-line scanning paths are generated by determining the minimum span direction of the irregular regions. Task allocation follows a longest-path-first, minimum-residual-range rule to rapidly determine the minimum number of UAVs required for complete coverage. Considering UAV’s turning radius constraints, Dubins curves are employed to plan transition paths between scanning regions, ensuring path feasibility. Second, the lower level transforms the problem into a Multiple Traveling Salesman Problem that considers path continuity, range constraints, and non-overlapping path allocation. This problem is solved using an Improved Biased Random Key Genetic Algorithm. The algorithm employs a variable-length master–slave chromosome encoding structure to adapt to the task allocation of each UAV. By integrating biased crossover operators with 2-opt interval mutation operators, the algorithm accelerates convergence and improves solution quality. Finally, comparative experiments on mission regions of varying scales demonstrate that, compared with single-level optimization and other intelligent algorithms, the proposed method reduces the required number of UAVs and shortens the total path length, while ensuring complete coverage of irregular regions. This method provides an efficient and practical solution for multi-UAV collaborative CPP in complex environments. Full article

Accurate measurement of material mechanics parameters is crucial for evaluating process quality and product reliability and is a major challenge in the development of 3D heterogeneous integration technology. Aiming to perform high-accuracy measurements of the elastoplastic nonlinear constitutive parameters of microelectronic materials using the nanoindentation testing technique, we take advantage of a neural network to construct a forward characterization model to characterize these response characteristic parameters for different materials, design an improved algorithm for obtaining a reverse iterative solution of the forward characterization model, and develop a material mechanics parameter measurement method to solve overdetermined equations using the least-squares method. This method was further improved by addressing the issues of algorithm stability and solution uniqueness, achieving high-precision and fast reverse solutions for elastoplastic constitutive parameters. The relative error of the material parameters is less than 3% (95% confidence interval), the maximum error is less than 8%, and the inversion convergence error of the key indentation response characteristic parameters is less than 0.1%. The difference between the measured material parameters and the theoretical model in the influence on the process stress of TCV (through ceramic via) products is verified through finite element simulation. Full article

This study investigates the vegetation dynamics and phenotypic differentiation of five triticale (×Triticosecale Wittm.) varieties under the region-specific agroecological conditions of Southern Dobruja, Bulgaria, across two growing seasons (2024–2025), with the aim of evaluating how local climatic variability shapes vegetation index patterns. UAV-based multispectral imaging was employed throughout key phenological stages to obtain reflectance indices, including NDVI, SAVI, EVI2, and NIRI, which served as indicators of canopy development and physiological status. NDVI was used as the primary reference index, and a baseline value (NDVIbase), defined as the mean NDVI across all varieties on a given date, was applied to evaluate relative varietal deviations over time. Multiple linear regression analyses were performed to assess the relationship between NDVI and baseline biometric parameters for each variety, revealing that varieties 22/78 and 20/52 exhibited reflectance dynamics most closely aligned with expected developmental trends in 2025. In addition, the relationship between NDVI and meteorological variables was examined for the variety Kolorit, demonstrating that relative humidity exerted a pronounced influence on index variability. The findings highlight the sensitivity of triticale vegetation indices to both varietal characteristics and short-term climatic fluctuations. Overall, the study provides a methodological framework for integrating UAV-based multispectral data with meteorological information, emphasizing the importance of region-specific, time-resolved monitoring for improving precision agriculture practices, optimizing crop management, and supporting informed variety selection. Full article

Addressing the challenge of optimizing the fatigue life of cylindrical roller bearings under high-speed and heavy-duty conditions, a collaborative multi-parameter optimization design method is proposed. First, a novel five-parameter profiling equation is introduced to overcome the limitations of traditional profiling methods based on the elastohydrodynamic lubrication property of the roller–raceway contact pair. Second, a nonlinear constrained optimization model that comprehensively considers key bearing structural parameters and the new profiling characteristics is constructed. In this model, the fatigue life is taken as the direct optimization objective, and geometric constraints, strength conditions, and lubrication performance are contained. Finally, using a NU2218E cylindrical roller bearing as the study case, the synchronous optimization achieved about a 196% enhancement in fatigue life over that of optimizing structural or profiling parameters alone. The proposed multi-parameter collaborative optimization framework and the innovative profiling approach provide new technical approaches and theoretical foundations for the design of high-performance rolling bearings. Full article

Diffraction-based velocity analysis is a key data interpretation technique in geophysical exploration, typically relying on the geometric characteristics, energy distribution, or propagation paths of diffraction waves. The hyperbola-based method is a classical strategy in this category, which extracts depth-dependent velocity (or dielectric properties) by correlating the hyperbolic shape of diffraction events with subsurface parameters for characterizing subsurface structures and material compositions. In this study, we propose a layer-stripping velocity analysis method applicable to ground-penetrating radar (GPR) and lunar-penetrating radar (LPR) data, with two main innovations: (1) replacing traditional local optimization algorithms with an intuitive parallelism check scheme, eliminating the need for complex nonlinear iterations; (2) performing depth-progressive velocity scanning of radargram diffraction signals, where shallow-layer velocity analysis constrains deeper-layer calculations. This strategy avoids misinterpretations of deep geological objects’ burial depth, morphology, and physical properties caused by a single average velocity or independent deep-layer velocity assumptions. The workflow of the proposed method is first demonstrated using a synthetic rock-fragment layered model, then applied to derive the near-surface dielectric constant distribution (down to 27 m) at the Chang’e-4 landing site. The estimated values range from 2.55 to 6, with the depth-dependent profile revealing lunar regolith stratification and interlayer material property variations. Consistent with previously reported results for the Chang’e-4 region, our findings confirm the method’s applicability to LPR data, providing a new technical framework for high-resolution subsurface structure reconstruction. Full article

The solid structure of industrial sinter comprises seven mineral associations (A, B, C, D, Ds, E, N) which have different relative abundances of key minerals, textures and spatial relationships to micro-macropores and hematite nuclei. Among the key characteristics of the mineral associations: (MA), MA-A comprises abundant SFCA-I microplates with hematite; MA-B consists of disseminated fine-grained magnetite in a network of SFCA-III microplates; MA-C is similar to MA-B but contains patches of dendritic SFCA-III with larnite and minor glass; MA-D comprises magnetite surrounded by coarse prisms of SFCA within glass; MA-Ds, a subtype of MA-D, includes SFCA with secondary skeletal hematite; MA-E consists of anhedral to skeletal magnetite or hematite in a matrix of glass; and MA-N comprises unmelted hematite nuclei from iron ore feedstock. SFCA-III and SFCA-I are dominant in MA-B and MA-A, respectively, whilst magnetite is the most common mineral in MA-C, MA-D/Ds and MA-E. Low-temperature sintering samples are largely of MA-A to MA-D (62 area %), which contain higher combined levels of SFCA-SFCA-III and lower levels of magnetite-dominant MA-E (12.6 area %), whereas high-temperature/magnetite sintering examples had high levels of magnetite-dominant MA-E (31.6 area %) and MA-D/Ds (52.1 area %) and low levels of MA-A to MA-C (8.9 area %). It is proposed that the formation of each MA is controlled by the peak sintering temperature attained, the dwell time at higher temperature which adversely allows fractional crystallisation to tie up more Fe in magnetite rather than forming SFCA phases during cooling, and especially a slower rate of cooling which promotes the formation of more SFCA family phases at lower temperatures. However, local variations in chemistry inherited from raw material granulation and assimilation during sintering of Si-rich gangue or ore nuclei are also important. Full article

The rapid advancement of sequencing technologies has led to the generation of complex multi-omics data, which are often high-dimensional, noisy, and imbalanced, posing significant challenges for traditional machine learning methods. The novelty of this work resides in the architecture-level integration of autoencoders with Generative Adversarial Network (GAN) and Conditional Tabular Generative Adversarial Network (CTGAN) models, where the autoencoder is employed for latent feature extraction and noise reduction, while GAN-based models are used for realistic sample generation and class imbalance mitigation in multi-omics cancer datasets. This study proposes a novel framework that combines an autoencoder for dimensionality reduction and a CTGAN for generating synthetic samples to balance underrepresented classes. The process starts with selecting the most discriminative features, then extracting latent representations for each omic type, merging them, and generating new minority samples. Finally, all samples are used to train a neural network to predict specific cancer outcomes, defined here as clinically relevant biomarkers or patient characteristics. In this work, the considered outcome in the bladder cancer is Tumor Mutational Burden (TMB), while the breast cancer outcome is menopausal status, a key factor in treatment planning. Experimental results show that the proposed model achieves high precision, with an average precision of 0.9929 for TMB prediction in bladder cancer and 0.9748 for menopausal status in breast cancer, and reaches perfect precision (1.000) for the positive class in both cases. In addition, the proposed AE–CTGAN framework consistently outperformed an autoencoder combined with a standard GAN across all evaluation metrics, achieving average accuracies of 0.9929 and 0.9748, recall values of 0.9846 and 0.9777, and F1-scores of 0.9922 for bladder and breast cancer datasets, respectively. A comparative fidelity analysis in the latent space further demonstrated the superiority of CTGAN, reducing the average Euclidean distance between real and synthetic samples by approximately 72% for bladder cancer and by up to 84% for breast cancer compared to a standard GAN. These findings confirm that CTGAN generates high-fidelity synthetic samples that preserve the structural characteristics of real multi-omics data, leading to more reliable class balancing and improved predictive performance. Overall, the proposed framework provides an effective and robust solution for handling class imbalance in multi-omics cancer data and enhances the accuracy of clinically relevant outcome prediction. Full article

China boasts abundant cultivated resources of pitahaya, with Guizhou Province being one of its core producing areas. Quality differences in red-fleshed pitahaya among local producing areas have not been fully clarified, and a standardized quantitative evaluation system for these differences remains lacking. This study seeks to identify the key factors influencing regional variations in quality and establish a comprehensive evaluation standard. In this study, 15 samples of red-fleshed pitahaya were collected from four major producing areas in Guizhou and used as research materials. Based on 15 quality characteristic indicators of the fruits, an analysis of quality differences and establishment of an evaluation system were carried out using multivariate statistical analysis. The results showed that 14 of the 15 quality indicators exhibited significant differences among pitahaya samples from different producing areas (p < 0.05), with the a* value being the sole exception. Cluster analysis classified the 15 samples into four groups. Principal component analysis (PCA) extracted four principal components, with a cumulative variance contribution rate of 81.07%, which clearly identified betacyanin, betaxanthin, 1,1-diphenyl-2-picrylhydrazyl (DPPH) free-radical scavenging rate, vitamin C, fruit shape index, and transverse diameter as the core evaluation indicators. This study systematically clarifies the differences in quality characteristics and the internal correlations among quality indicators of red-fleshed pitahaya from different major producing areas in Guizhou. It further provides an important scientific basis for pitahaya variety breeding, cultivation regulation, and market positioning in this region and fills the research gap existing in the field of comprehensive quality evaluation of pitahaya. This is of significant practical importance for promoting the standardized upgrading of local specialty fruit industries, enhancing the market competitiveness of products, and facilitating the high-quality development of the agricultural economy. Full article

Background/Objectives: Subsequent musculoskeletal injuries are frequent among physically active young adults, yet the roles of body composition, training weekly load (TWL), and physical activity intensity in subsequent injury occurrence remain unclear. This study examined the associations of body composition indices and training-related variables with subsequent injuries in university students and explored whether combining key markers from body composition and training exposure improves discrimination compared with single markers. Methods: The analysis included 418 students from two cohorts merged after confirming negligible between-cohort differences. Participants completed questionnaires on injury history and physical activity and underwent standardized anthropometric and body composition assessments. Intrinsic factors included fat mass index (FMI) and skeletal muscle mass index (SMI), while extrinsic factors comprised training weekly load (TWL), total physical activity (TPA), and vigorous activity percentage (VPA%). Subsequent injury (yes/no) served as the primary outcome. Injuries were assessed retrospectively over the preceding 12 months; subsequent injury was defined as ≥1 injury occurring after a previous (index) injury within this recall period. Analyses used univariate and multivariable logistic regression and exploratory Receiver Operating Characteristic (ROC) analyses for individual markers and combined models. Results: SMI was associated with subsequent injury (OR = 1.09, 95% CI: 1.03–1.15). TWL showed a weak, non-significant association (OR = 1.03, p = 0.307). Models combining SMI and TWL, including their interaction, did not meaningfully improve discrimination compared with SMI alone. ROC analyses indicated limited discriminatory ability across models (AUCs < 0.65), suggesting poor accuracy for identifying individuals with subsequent injury based on these markers. Conclusions: The examined body composition, training weekly load (TWL), and physical activity measures alone or combined showed limited discriminatory utility for subsequent injury status in this cross-sectional sample. These findings support the multifactorial nature of injury susceptibility and indicate that simple anthropometric or TWL-based measures are not suitable as standalone screening tools for subsequent injury in active university populations. Full article

The strong heterogeneity of karst aquifers limits the applicability of traditional pumping test parameter inversion methods, and karst conduits are the key factor causing this heterogeneity. To reveal how karst conduits influence the inversion of hydrogeological parameters, this study established a series of s numerical models in FEFLOW, based on the Lianhuashan mining area in Jingmen. These models was used to systematically analyze the effects of conduit characteristics (hydraulic conductivity, diameter, length, burial depth) and pumping test conditions (pumping rate and distance from the well) on the flow field, drawdown behavior, and parameter inversion results. Results indicate that the well-conduit distance R is the most critical factor: inversion errors exceeded 60% when R < 25 m; the larger the deviation between the conduit permeability coefficient (Kp) and the aquifer permeability coefficient, the larger the inversion error; the conduit length (L) and diameter (D) determine the catchment area and the cross-sectional area for flow, respectively, and are positively correlated with the inversion error; the conduit burial depth (Z) and the pumping rate (Q) affect the lag in vertical recharge and the magnitude of the drawdown, respectively, and have a small impact on the inversion error. The findings provide a theoretical basis for improving parameter estimation and well-field design in karst terrains. Full article

Type II collagen is a primary fibrillar component of articular cartilage, and its early degradation is a key biomarker of joint-degenerative disorders such as osteoarthritis, rheumatoid arthritis, gout, etc. Reliable detection at low concentrations remains challenging due to limited assay accessibility, complex analytical procedures, and nonspecific responses in multicomponent biological matrices. This research reports the development of a Molecularly Imprinted Polymer (MIP)–based electrochemical sensor engineered for the selective recognition of type II collagen. A series of monomer formulations were evaluated, and the 1AAM:2VP composition produced a well-defined imprinted layer on screen-printed carbon electrodes, yielding the highest electrochemical sensitivity and linearity. The optimized sensor exhibited strong anodic and cathodic responses proportional to increasing collagen concentrations, with a calibration slope corresponding to an R² value of 0.9394. Minimal signal interference was observed, confirming high molecular selectivity. The limit of detection (LOD) was calculated to be approximately 0.065 µg/mL. These characteristics demonstrate that the proposed MIP sensor provides a low-cost, accessible, and highly selective analytical platform suitable for early-stage cartilage degeneration monitoring. Full article

Bump feeding is a nutritional management strategy in swine production that involves increasing feed allowance and/or dietary nutrient density during the final weeks of gestation, usually from day 90 to farrowing, to support rapid fetal growth and prepare sows for lactation. This strategy is widely applied to improve piglet birth weight, neonatal viability, and subsequent reproductive performance. This review synthesizes current evidence on the effects of increased maternal feed intake during late gestation on sow body condition and feeding-related behavioral responses, and farrowing outcomes. Available studies suggest that increasing feed allowance during late gestation can influence litter characteristics, piglet survival at birth, and sow energy reserves, as reflected by changes in backfat thickness (BFT) and body condition score (BCS). The nutritional composition of bump-feeding diets, including dietary energy and amino acid balance, is critically evaluated in relation to pregnancy maintenance, farrowing duration, and early lactation performance. In addition, the roles of parity and feeding behavior during late gestation are examined, with particular emphasis on their associations with sow activity patterns, restlessness around parturition, and farrowing efficiency. Despite these reported effects, findings across studies remain inconsistent, particularly regarding the balance between improved reproductive outcomes and the risk of excessive fat deposition in sows. This review highlights key knowledge gaps and underscores the need for optimized, parity-specific bump-feeding strategies that integrate nutritional management with feeding behavior to enhance farrowing performance, piglet survival, sow welfare, and economic sustainability in modern pig production. Full article

Our objective was to conduct a scoping review and narrative synthesis of qualitative studies that examined experiences of partners of cancer patients. We searched MEDLINE, Embase, CINAHL, PsycINFO, and Scopus for qualitative studies involving adult (≥18 years) partners (e.g., in a romantic relationship) of patients diagnosed with cancer and published in English. We extracted findings from included studies, along with key study and participant characteristics, and applied a narrative summary approach, a process that allowed us to identify synthesized themes across studies. Our search identified 15,729 records, of which 159 met the inclusion criteria. Included studies primarily collected data through interviews, with participants being predominantly female and middle-aged (55.2 ± 8.3 years). Partners were commonly in relationships with patients diagnosed with breast, genital–urinary, or gastrointestinal cancer. Our synthesis identified four conceptual themes—transformation of relationship dynamics and roles, distress and burden, coping strategies, and unmet needs and support gaps—which reflect the emotional, relational, and practical challenges partners navigate throughout the cancer trajectory. These findings highlight the need to better recognize and support the role of partners within the cancer care landscape as their wellbeing impacts care and experiences of patients with cancer. Full article

This paper deals with the issue concerning the optimization of energy consumption saving in passenger railway traffic. The background is mainly related to the decision to modernize existing trains or purchase new units, which is a key dilemma for rail transport managers. Concerning the methods used for the determination of the proper results, there is a very wide range of possibilities. This issue is complex, encompassing technical, economic, environmental, and social aspects; therefore, artificial intelligence methods were used for analysis. The obtained results have shown that the choice is not clear-cut, as each option offers both benefits and limitations. The investigations are based on real measurement values obtained from a Polish regional railway. In conclusion, it can be found that the final decision should take into account the long-term goals and the specific characteristics of the given rail system. Several factors influencing the energy consumption were taken into account. So, the aim of this paper was achieved, and the main factors were determined, which have influenced energy consumption and its impact, as well as the possibility of energy consumption reduction. Full article