Search Results (2,914)

With the rapid advancement of construction machinery electrification, optimizing the energy efficiency of electric loaders requires representative duty cycles that accurately capture real-world operating characteristics. However, most existing studies rely on simplified test-track cycles, which fail to reflect the complexity of actual operations. To address this gap, this paper takes a commercial concrete mixing plant as a case study and proposes a cross-scenario generalization method for the duty cycle aggregation of electric loaders. The method integrates multi-source signal acquisition, task-segment partitioning, feature extraction, and dimensionality reduction via Principal Component Analysis (PCA), enabling the clustering of typical operating modes and reconstruction of representative duty cycles through segment concatenation. The aggregated duty cycles are validated using Jensen–Shannon divergence, showing similarity levels above 93% compared with field measurements from mixing plants in Yiwu and Kunshan. These results demonstrate the method’s strong temporal adaptability and cross-scenario transferability. The proposed approach provides a solid foundation for energy consumption assessment, powertrain matching, and control strategy optimization of electric loaders while also supporting the development of duty cycle databases and future industry standardization. Full article

Groundwater represents a critical global resource, increasingly threatened by overexploitation and pollution from contaminants such as arsenic (As), fluoride (F), nitrates (NO₃⁻), and heavy metals in arid to semi-arid regions like Mexico. Traditional Water Quality Indices (WQIs), while useful, suffer from subjectivity in assigning weights, which can lead to misinterpretations. This study addresses these limitations by developing a novel, objective Groundwater Quality Index (GWQI) through the seamless integration of Machine Learning (ML) models. Utilizing a database of 775 wells from the Mexican National Water Commission (CONAGUA), Principal Component Analysis (PCA) was applied to achieve significant dimensionality reduction. We successfully reduced the required monitoring parameters from 13 to only three key indicators: total dissolved solids (TDSs), chromium (Cr), and manganese (Mn). This reduction allows for an 87% decrease in the number of indicators, maximizing efficiency and generating potential savings in monitoring resources without compromising water quality prediction accuracy. Six WQI methods and six ML models were evaluated for quality prediction. The Unified Water Quality Index (WQIu) demonstrated the best performance among the WQIs evaluated and exhibited the highest correlation (R² = 0.85) with the traditional WQI based on WHO criteria. Furthermore, the ML Support Vector Machine with polynomial kernel (svmPoly) model achieved the maximum predictive accuracy for WQIu (R² = 0.822). This robust GWQI-ML approach establishes an accurate, objective, and efficient tool for large-scale groundwater quality monitoring across Mexico, facilitating informed decision-making for sustainable water management and enhanced public health protection. Full article

A field experiment was conducted on 16-year-old ‘Wen 185’ walnut trees in Aksu, Southern Xinjiang, to identify optimal water and fertilizer management under subsurface drip irrigation. Four irrigation levels were established: 75% ET_c (W1), 100% ET_c (W2), 125% ET_c (W3), and 150% ET_c (W4). These were combined with three fertilizer levels: N 270, P 240, K 300 kg ha⁻¹ (F1), N 360, P 320, K 400 kg ha⁻¹ (F2), and N 450, P 400, K 500 kg ha⁻¹ (F3). This resulted in a total of 12 treatments. This study assessed the impact of different water and fertilizer treatments on walnut growth dynamics, yield, fruit quality, water and fertilizer use efficiency, and soil nitrate residue. Principal component analysis (PCA) was used to construct comprehensive growth and photosynthesis indices (CGI and CPI). Parameters significantly correlated with yield and quality were then screened via Pearson analysis, and a game theory-based combination weighting method was adopted to determine weights for integrating six categories of indicators: growth, photosynthesis, yield, quality, resource use efficiency, and environmental impact. A coupled TOPSIS-GRA model was developed for comprehensive evaluation. Furthermore, binary quadratic regression was employed to optimize the application ranges of water and fertilizer. The results showed that the W2F2 treatment achieved the highest rank by synergistically enhancing growth, photosynthetic performance, yield, and quality. This treatment also maintained high water use efficiency (WUE) and partial factor productivity of fertilizer (PFP) and effectively reduced nitrate accumulation in deep soil layers. The CGI and CPI, derived from PCA, effectively quantified phenological growth and photosynthetic characteristics. Correlation analysis identified seven core parameters, among which IV-CPI correlated most strongly with yield. In contrast, II-CPI was more closely associated with increased single-fruit weight and reduced tannin content. Within the comprehensive evaluation system that used game theory-based combination weighting, yield received the highest weight (0.215), while IV-CPI was assigned the lowest (0.011). The TOPSIS-GRA coupled model identified the W2F2 treatment as the highest-ranked. Furthermore, regression optimization determined the optimal total seasonal application ranges to be 5869.94–6519.81 m³ ha⁻¹ for irrigation and 975.54–1107.49 kg ha⁻¹ for fertilization. The coupled TOPSIS-GRA model enabled a balanced assessment of the objectives: high yield, superior quality, resource use efficiency, and environmental sustainability. Thus, it provides a theoretical foundation and practical guidance for enhancing the productivity and sustainability of subsurface drip-irrigated walnut orchards in Southern Xinjiang. Full article

This study investigates the impact of audio feedback on cognitive performance during VR puzzle games using EEG analysis. Thirty participants played three different VR puzzle games under two conditions (with and without audio) while their brain activity was recorded. To analyze concentration levels and neural engagement patterns, we employed spectral analysis combined with a preprocessing algorithm and an optimized Deep Neural Network (DNN) model. The proposed processing stage integrates feature normalization, automatic labeling based on Principal Component Analysis (PCA), and Gamma band feature extraction, transforming concentration detection into a supervised classification problem. Experimental validation was conducted under the two gaming conditions in order to evaluate the impact of multisensory stimulation on model performance. The results show that the proposed approach significantly outperforms traditional machine learning classifiers (SVM, LR) and baseline deep learning models (DNN, DGCNN), achieving a 97% accuracy in the audio scenario and 83% without audio. These findings confirm that auditory stimulation reinforces neural coherence and improves the discriminability of EEG patterns, while the proposed method maintains a robust performance under less stimulating conditions. Full article

NADESs represent a modern class of extraction media that align with the principles of green chemistry. They are considered non-toxic and biodegradable, but relatively little is known about their biological activity. This study investigated the antioxidant, antibacterial, and antifungal properties of 40 NADESs. The systems were developed from primary (PRIM) based on choline chloride (ChCl), and specialized (HEVO) plant-derived metabolites, particularly based on thymol and menthol. Their antioxidant activity was evaluated using spectrophotometric tests. The antimicrobial activity was evaluated by the disk diffusion method. The data obtained were analyzed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). NADESs based on PRIM exhibited negligible antioxidant activity and relatively low antimicrobial activity. By contrast, NADESs containing HEVO, particularly thymol-based systems, indicated significant antioxidant activity, with stronger activity observed at higher molar proportions of thymol. In the 1,8-cineole:thymol system, ABTS activity ranged from 167.37 ± 24.17 to 861.25 ± 33.03 mg Trolox equivalents/mL NADES (molar ratios 9:1 and 1:9, respectively). The 1,8-cineole:thymol system (1:9) also showed strong antimicrobial activity, with a maximum inhibition zone of 39.33 ± 2.52 mm against Staphylococcus aureus. In summary, NADESs based on HEVO exhibit significantly stronger biological activity than those containing only PRIM. Full article

This paper presents a simulation-driven framework employing an Active Transfer Learning Gaussian Process (ATGP) model for accurate trajectory prediction and reliability analysis of the UR5 robotic manipulator. The method integrates transfer learning, Gaussian Process Regression, and active sampling to address challenges under limited target data. Preprocessing steps such as outlier removal, feature scaling, and Principal Component Analysis enhance data quality. A physically informed synthetic source domain facilitates effective knowledge transfer. Using DH-parameters as input, the ATGP predicts 3D end-effector trajectories over time. Results show a mean absolute error below 0.01, demonstrating consistency and scalability for real-time, uncertainty-aware robotic applications. This is the first ATGP-based UR5 framework that unites PCA-guided, physics-informed source synthesis with multi-output transfer GPR as well as coordinate- and time-resolved reliability analysis under scarce target-domain data. Full article

Background: The PRO-CTCAE provides patient-reported data on symptomatic AEs. A summary metric—the ACS—reflecting total AE burden can be calculated by averaging AE-level composite scores at a given timepoint for each participant. This study investigated the psychometric properties and interpretability of this PRO-CTCAE ACS in patients with breast, lung, or head/neck cancers. Methods: We conducted a secondary analysis of a PRO-CTCAE validation dataset comprising 940 adults undergoing chemotherapy or radiation therapy (clinicaltrials.gov: NCT02158637). We focused on empirically recommended symptom terms for three cancer sites. Analyses included Spearman’s correlations, coefficient alpha, and eigenvalues from the correlation matrices, confirmatory factor analysis (CFA), and principal component analysis (PCA). Latent profile analysis (LPA) was used to assess ACS interpretability in the lung cohort. Results: Mean composite score inter-correlations were moderate (0.30–0.35), and coefficient alphas were high (0.81–0.91). Eigenvalue ratios and CFA supported retention of a single factor/component, with suitable model fit indices. ACS correlated highly with factor scores and the first principal component from the PCA. Reduced sets of terms produced reliable scores that closely approximated the full set scores and aligned with external criteria. LPA in the lung subgroup identified four latent classes; ACS differentiated high vs. low symptom burden groups but did not distinguish the two groups expressing distinct symptom profiles. Conclusion: The ACS demonstrated structural validity through adequately fitting linear factor models and effectively summarized symptomatic AE burden. However, similar ACS values may mask clinically distinct symptomatic AE profiles, underscoring the value of both summary metrics and profile-based approaches. Full article

Wheat and corn are two major food crops in Xinjiang. However, the spectral similarity between these crop types and the complexity of their spatial distribution has posed significant challenges to accurate crop identification. To this end, the study aimed to improve the accuracy of crop distribution identification in complex environments in three ways. First, by analysing the kNDVI and EVI time series, the optimal identification window was determined to be days 156–176—a period when wheat is in the grain-filling to milk-ripening phase and maize is in the jointing to tillering phase—during which, the strongest spectral differences between the two crops occurs. Second, principal component analysis (PCA) was applied to Sentinel-2 data. The top three principal components were extracted to construct the input dataset, effectively integrating visible and near-infrared band information. This approach suppressed redundancy and noise while replacing traditional RGB datasets. Finally, the Convolutional Block Attention Module (CBAM) was integrated into the U-Net model to enhance feature focusing on key crop areas. An improved Atrous Spatial Pyramid Pooling (ASPP) module based on deep separable convolutions was adopted to reduce the computational load while boosting multi-scale context awareness. The experimental results showed the following: (1) Wheat and corn exhibit obvious phenological differences between the 156th and 176th days of the year, which can be used as the optimal time window for identifying their spatial distributions. (2) The method proposed by this research had the best performance, with its mIoU, mPA, F1-score, and overall accuracy (OA) reaching 83.03%, 91.34%, 90.73%, and 90.91%, respectively. Compared to DeeplabV3+, PSPnet, HRnet, Segformer, and U-Net, the OA improved by 5.97%, 4.55%, 2.03%, 8.99%, and 1.5%, respectively. The recognition accuracy of the PCA dataset improved by approximately 2% compared to the RGB dataset. (3) This strategy still had high accuracy when predicting wheat and corn yields in Qitai County, Xinjiang, and had a certain degree of generalisability. In summary, the improved strategy proposed in this study holds considerable application potential for identifying the spatial distribution of wheat and corn in arid regions. Full article

Introduction: Patient comfort is a central concept in nursing practice, and is particularly important in emergency contexts, where clinical complexity and care overload challenge the provision of humanized care. Katharine Kolcaba’s Theory of Comfort offers a robust theoretical framework for assessing and promoting comfort in multiple domains. The main objective is to psychometrically validate the adapted version of Kolcaba’s Comfort Scale—COLCABA-32—in critically ill patients treated in a Portuguese hospital emergency department. Method: A quantitative, descriptive, cross-sectional study was conducted using a sample of 165 adult patients triaged with urgent clinical priority. Data collection was performed through individual interviews. The COLCABA-32 Scale and the Mini-Mental State Examination (MMSE) were used. Statistical analysis included descriptive statistics, principal component analysis (PCA), internal consistency (Cronbach’s alpha), and correlation with clinical priority according to the Manchester Triage. Results: PCA revealed six factors with eigenvalues greater than 1, explaining 59.01% of the total variance of the scale. The dimensions identified were psycho-emotional comfort and autonomy, physical and symptomatic comfort, relational comfort and information, spiritual comfort, environmental comfort and motivational comfort and hope. The overall Cronbach’s alpha was 0.897, indicating excellent internal consistency. Correlations with clinical priority confirmed partial convergent validity. Conclusions: The COLCABA-32 Scale demonstrated adequate psychometric properties for assessing the comfort of critically ill patients in an emergency setting and is a valid, reliable, and sensitive instrument for the multiple dimensions of comfort, as proposed by Kolcaba. Its application can contribute to more person-centered and evidence-based nursing practices. Full article

Fabric properties significantly influence the accuracy of pattern dimensions derived from 3D scanned garment samples. To enhance the generated pattern accuracy, a novel predictive model was proposed to estimate the pattern dimension change ratio by integrating fabric parameters using an artificial neural network (ANN). Thirty fabrics were tested for making flared skirts. The pattern generation involves 3D scanned garment samples, the Bowyer–Watson algorithm for surface reconstruction, and an energy model for surface development. After the pattern’s dimension change ratio was obtained, principal component analysis (PCA) was applied to reduce dimensionality before correlation analysis. Results indicated that thickness, bending rigidity, drapability, and shear performance were the primary fabric parameters influencing dimensional accuracy. Backpropagation (BP) neural networks were constructed to predict the pattern size change ratio using both full fabric parameters or a PCA-reduced set, including a 9-parameter input layer, four hidden layers, and a 12-parameter output layer. The BP ANN models outperformed the radial basis function (RBF) ANN models, achieving accuracies of 96.67% and 96.02% for the full-factor and dimension-reduced models, respectively. After parameter optimization, the dimension-reduced BP ANN model enhanced pattern accuracy by 5.11%, achieving a final 97.73% accuracy. Results validate utilizing fabric parameters and BP neural networks as a sophisticated pattern optimization method. Full article

Diabetes is a major global health problem, with a rapidly increasing prevalence and long-term health complications in both developed and developing countries. If not diagnosed early, it can lead to cardiovascular diseases, kidney failure, vision loss, and nervous system disorders. This study aimed to classify individuals with diabetes or healthy individuals using e-nose sensor data obtained from breath samples taken from 1000 individuals. Six sensor features and one class feature were used in the analysis. Machine learning methods included Artificial Neural Networks (ANN), Decision Trees (DT), Gradient Boosting (GB), Naive Bayes (NB), and AdaBoost (AB). ANOVA and Information Gain analyses were conducted to determine the effectiveness of the sensor data, and the TGS2610 and TGS2611 sensors were found to be critical for classification. Principal Component Analysis (PCA) reduced data size and saved processing time. Experimental results showed that the ANN model provided the most successful classification, with 100% accuracy. AB and GB achieved 99.8% accuracy, while NB achieved 97.6% accuracy. Dimensionality reduction using PCA optimized training and testing times without loss of accuracy. The study presents a data-driven approach to e-nose-based diabetes detection, demonstrates the comparative performance of the models, and highlights the importance of sensor selection and data size optimization. Full article

Phorid flies serve as the main colonizers of human remains in both indoor and burial environments. Their developmental patterns can be utilized to estimate the minimum postmortem interval (minPMI). Accurate species identification, particularly for immature stages, is essential before utilizing their developmental data to estimate minPMI. This study employed Fourier transform infrared spectroscopy (FTIR) coupled with principal components analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to investigate species identification of eggs (0 h, 8 h, 16 h), larvae (12 h, 60 h, 84 h), and pupae (1 d, 5 d, 10 d) of three necrophagous Phoridae species, Dohrniphora cornuta, Diplonevra funebris, and Megaselia scalaris at 24 °C. The results showed that the FTIR spectra within the fingerprint region (1800–900 cm⁻¹) differed among the three immature phorid flies. These differences were primarily manifested in absorption peak intensities. The PLS-DA analysis successfully distinguished the three species at the same developmental stage. This study demonstrated the feasibility of utilizing FTIR spectroscopy coupled with chemometric methods to both rapidly identify the species of immature small flies and simultaneously estimate their age. Full article

This study evaluated the integration of non-invasive remote sensing and colorimetry to classify the maturity stages of Coffea arabica fruits across four varieties: Caturra Amarillo, Excelencia, Milenio, and Típica. Multispectral signatures were captured using a Parrot Sequoia camera at wavelengths of 550 nm, 660 nm, 735 nm, and 790 nm, while colorimetric parameters L*, a*, and b* were measured with a high-precision colorimeter. We conducted multivariate analyses, including Principal Component Analysis (PCA) and multiple linear regression (MLR), to identify color patterns and develop predictors for fruit maturity. Spectral curve analysis revealed consistent changes related to ripening: a decrease in reflectance in the green band (550 nm), a progressive increase in the red band (660 nm), and relative stability in the RedEdge and near-infrared regions (735–790 nm). Colorimetric analysis confirmed systematic trends, indicating that the a* component (green to red) was the most reliable indicator of ripeness. Additionally, L* (lightness) decreased with maturity, and the b* component (yellowness to blue) showed varying importance depending on the variety. PCA accounted for over 98% of the variability across all varieties, demonstrating that these three parameters effectively characterize maturity. MLR models exhibited strong predictive performance, with adjusted R² values ranging between 0.789 and 0.877. Excelencia achieved the highest predictive accuracy, while Milenio demonstrated the lowest, highlighting varietal differences in pigmentation dynamics. These findings show that combining multispectral imaging, colorimetry, and statistical modeling offers a non-destructive, accessible, and cost-effective method for objectively classifying coffee maturity. Integrating this approach into computer vision or remote sensing systems could enhance harvest planning, reduce variability in specialty coffee lots, and improve competitiveness by ensuring greater consistency in cup quality. Full article

Background: Longfin batfish (Platax teira) is an important economic species in southern China. In recent years, its wild population has significantly declined due to overfishing. Around 2015, breakthroughs in the artificial large-scale seedling technology for P. teira have promoted the growth of its aquaculture scale in regions such as Hainan and Guangdong. Methods: To study the genetic diversity, inbreeding status, and population structure of the current P. teira farming populations in China, we performed whole-genome resequencing technology and high-density SNP markers to analyze the genetics of four main farming populations. A total of 109 individuals from four populations (NA, ZP, XL, and XC) were sequenced, identifying 5,384,029 high-quality SNPs. Results: The results showed that the nucleotide diversity (π) of each population ranged from 0.00155 to 0.00165 and observed heterozygosity (Ho) ranged from 0.253 to 0.282, which indicated low levels of genetic diversity. The results of the ROH analysis show significant inbreeding in the NA population. Genetic differentiation analysis revealed that the genetic differentiation among NA, XC, and ZP populations was relatively low (F_ST = 0.021–0.029). Conclusions: NA, XC, and ZP populations likely share a common origin of their fry stocks. Based on a phylogenetic tree, principal component analysis (PCA), and population structure analysis, the four populations were divided into four genetic groups. This study is the first analysis of the genetic diversity and population structure of P. teira farming populations in China, laying the foundation for the establishment of a base breeding population and the implementation of genetic improvement programs. Full article

Background: The fat-tailed coarse-wooled sheep breeds exhibit excellent reproductive performance, exceptional adaptability to pasture conditions, and high precocity, contributing to enhanced meat, fat, and wool productivity in sheep breeding. Despite the significant role of these sheep breeds in Kazakhstan’s livestock production, their genetics remain poorly studied. This raises concerns about the potential loss of unique, breed-specific traits that could be important for the future development and resilience of Kazakh stan’s sheep farming sector. This study aimed to analyze genome-wide genotyping SNP data of local fat-tailed coarse-wooled sheep breeds (Kazakh fat-tailed coarse-wooled, Edilbay, and Gissar) to reveal their genetic diversity, breed characteristics, and phylogenetic relationships with worldwide domestic sheep breeds and wild sheep. Methods: The OvineSNP50 Genotyping BeadChip was used to obtain genome-wide SNP genotyping data from 160 fat-tailed coarse-wooled sheep from Kazakhstan. Population structure analysis, principal component analysis, phylogenetic and the maximum likelihood tree analysis were performed in comparison with foreign domestic sheep breeds and wild sheep populations. Results: Kazakh breeds exhibited high genetic diversity, with Edilbay showing the greatest allelic richness. PCA and Admixture revealed clear differentiation among the three breeds: Edilbay and Gissar formed homogeneous clusters, while Kazakh fat-tailed coarse-wooled sheep displayed admixture and substructure. Evidence of gene flow from Edilbay into other Kazakh populations supports its role as a genetic source for regional breeds. Phylogenetic analysis placed Kazakhstani sheep close to other Central Asian breeds, while clearly distinct from East Asian and European populations. Wild sheep (Argali and Urial) formed separate clades, with Kerman wild sheep clustering closer to Urial. Conclusions: Our results highlight the value of genotyping data for studying genetic diversity and population structure. Developing genetic resources for Kazakhstan’s native sheep breeds will help preserve their unique diversity and ensure it remains available for future use in breeding and adaptation efforts. Full article