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During the operation of commingled production wells, inter-layer interference is a key factor affecting the recovery rate and flooding extraction efficiency. This study proposes a well deliverability equation that is characterized by a multi-parameter coupled inter-layer interference coefficient, which is applied to quantify the commingled production wells in a multi-layer reservoir. This study used principal component analysis (PCA) to study the combined effects of correlation parameters for inter-layer interference. The results show that the starting pressure gradient and the crude oil viscosity contrast have the most significant impact on inter-layer interference. Changes in these two parameters directly enhance the heterogeneity between different oil layers, thereby intensifying inter-layer interference. Additionally, the positive correlation between permeability and permeability contrast also highlights the contribution of physical property differences in the oil layers to the interference. The sensitivity analysis shows the main influencing factors of inter-layer interference in commingled production wells. providing references for subsequent improvements and optimization of the exploitation schemes and adjustments in reservoir management measures. The results not only enhance the understanding of the mechanisms of inter-layer interference in the exploitation of multi-layer oil reservoirs but also provide scientific evidence and technical support for oilfield development. [ABSTRACT FROM AUTHOR]

The selection of sites for permanent environmental monitoring of natural water bodies should rely on corresponding source apportionment studies. Tools like the water quality index (WQI) assessment may support this objective. This study aims to analyze a decade-long dataset of measurements of 26 chemical components at 26 observation points within the Irtysh River Basin, aiming to identify priority zones for stricter environmental regulations. It was achieved through the WQI tool integrated with geoinformation systems (GISs) and multivariate statistical techniques. The findings highlighted that both upstream sections of tributaries (Oba and Bukhtarma rivers) and the mainstream of the basin are generally in good condition, with slight fluctuations observed during flooding periods. Areas in the basin experiencing significant impacts from mining and domestic wastewater treatment activities were identified. The rivers Glubochanka (GL) and Krasnoyarka (KR) consistently experienced marginal water quality throughout the observation period. Various contaminant sources were found to influence water quality. The impact of domestic wastewater treatment facilities was represented by twofold elevated concentrations of chemical oxygen demand, reaching 22.6 and 27.1 mg/L for the KR and GL rivers, respectively. Natural factors were indicated by consistent slight exceedings of recommended calcium levels at the KR and GL rivers. These exceedances were most pronounced during the cold seasons, with an average value equal to 96 mg/L. Mining operations introduced extremal concentrations of trace elements like copper, reaching 0.046–0.051 mg/L, which is higher than the threshold by 12–13 times; zinc, which peaked at 1.57–2.96 mg/L, exceeding the set limit by almost 50–100 times; and cadmium, peaking at levels surpassing 1000 times the safe limit, reaching 0.8 mg/L. The adverse impact of mining activities was evident in the Tikhaya, Ulba, and Breksa rivers, showing similar trends in trace element concentrations. Seasonal effects were also investigated. Ice cover formation during cold seasons led to oxygen depletion and the exclusion of pollutants into the stream when ice melted, worsening water quality. Conversely, flooding events led to contaminant dilution, partially improving the WQI during flood seasons. Principal component analysis and hierarchical cluster analysis indicated that local natural processes, mining activities, and domestic wastewater discharge were the predominant influences on water quality within the study area. These findings can serve as a basis for enhanced environmental regulation in light of updated ecological legislation in Kazakhstan, advocating for the establishment of a comprehensive monitoring network and the reinforcement of requirements governing contaminating activities. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The soy protein isolate cheese sticks were developed in order to enrich the cheese stick products, and the tastes were compared with that of homemade traditional cheese sticks. Single factor experiments and Box - Behnken four-factor and three-level experiment design were used to explore the effects of skimmed milk powder and soy protein isolate additions, skimmed milk powder to soy protein isolate mass ratios, complex emulsifying salt additions and compound stabilizer additions on the qualities and tastes of soy protein isolate cheese sticks. The formulation of soy protein isolate cheese sticks was optimized, and its taste was analyzed. The results showed that the optimal formula of soy protein isolate cheese sticks was determined as follow: on the basis of 100 g water, total addition of skimmed milk powder and soy protein isolate of 16.0%, compound emulsified salt addition of 2.0%, compound stabilizer addition of 3.0%, and skimmed milk powder to soy protein isolate mass ratio of 3:1. Compared with the traditional cheese sticks, soy protein isolate cheese sticks presented higher protein content, lower carbohydrate content with richer tastes. According to the optimal formula, the prepared soy protein isolate cheese sticks exhibited uniform structure, good color, moderate softness and hardness with rich milk and bean flavors, which provided technical supports for the marketization of the products. [ABSTRACT FROM AUTHOR]

Accurate wind data are crucial for successful search and rescue (SAR) operations on the sea surface in maritime accidents, as survivors or debris tend to drift with the wind. As maritime accidents frequently occur outside the range of wind stations, SAR operations heavily rely on wind forecasts generated by numerical models. However, numerical models encounter delays in generating results due to spin-up issues, and their predictions can sometimes exhibit inherent biases caused by geographical factors. To overcome these limitations, we reviewed the observations for the first 24 h of the 72-hour forecast from the ECMWF and then post-processed the forecast for the remaining 48 h. By effectively reducing the dimensionality of input variables comprising observation and forecast data using principal component analysis, we improved wind predictions with support vector regression. Our model achieved an average RMSE improvement of 16.01% compared to the original forecast from the ECMWF. Furthermore, it achieved an average RMSE improvement of 5.42% for locations without observation data by employing a model trained on data from the nearest wind station and then applying an adaptive weighting scheme to the output of that model. [ABSTRACT FROM AUTHOR]

Ophiocordyceps sinensis, a medicinal fungus utilized in traditional Chinese medicine, exhibits a range of biological activities and pharmacological functions. In this study, we determined the amino acid composition of 94 amino acids in Ophiocordyceps sinensis using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Fresh samples of Ophiocordyceps sinensis were analyzed under three different drying methods: vacuum freeze drying (DG), oven drying (HG), and air drying (YG). This investigation aims to assess the effects of these drying methods on the content and quality of amino acid metabolites in Ophiocordyceps sinensis. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed for sample classification and the identification of differentially accumulated metabolites (DAMs). The results revealed the detection of 79 amino acid metabolites, which included elevated levels of oxidized L-glutamic acid, L-glutamic acid, and glutathione. Differential amino acid metabolites that met the criteria of fold change (|FC|) ≥ 2, p-value (p) ≤ 0.5, and variable importance in projection (VIP) ≥ 1 were analyzed. Significant differences in 48 amino acid metabolites between the groups were primarily related to protein synthesis. According to the KEGG analysis, all three comparison samples exhibited significant enrichment in several pathways. These pathways included the interaction of neuroactive ligands with receptors, the metabolism of cysteine and methionine, and the biosynthesis of plant hormones. The variations in amino acid metabolite levels observed across the three drying methods may be attributed to the degradation of proteins or amino acid metabolites, influenced by several factors, including temperature, enzyme activity, and moisture content. Additionally, Maillard and oxidative reactions involving substances such as amino acids, sugars, and oxygen may also play a significant role. This study demonstrates that various drying methods significantly influence the amino acid metabolite content of Ophiocordyceps sinensis. Therefore, the selection of drying methods should be tailored to meet specific requirements. This research provides important insights into the metabolite composition of Ophiocordyceps sinensis under different drying techniques, thereby contributing to a more comprehensive understanding of its nutritional and therapeutic properties. [ABSTRACT FROM AUTHOR]

To accommodate the rapid development of the distribution network of China, it is essential to research load forecasting methods with higher accuracy and stronger generalization capabilities in order to optimize distribution system control strategies, ensure the efficient and reliable operation of the power system, and provide a stable power supply to users. In this paper, a short-term load forecasting method is proposed for low-voltage distribution substations based on the bidirectional long short-term memory (BiLSTM) model. First, principal component analysis (PCA) and the fuzzy C-means method based on a genetic algorithm (GA-FCM) are used to extract the main influencing factors and classify different types of user electricity consumption behaviors. Then, the BiLSTM forecasting model utilizing the stochastic weight averaging (SWA) algorithm to enhance generalization capability is constructed. Finally, the load data from a low-voltage distribution substation in China over recent years are selected as a case study. Compared with conventional LSTM and BiLSTM prediction models, the annual electricity load curves for various user types forecasted by the PCA-BiLSTM model are more closely aligned with actual data curves. The proposed BiLSTM forecasting model exhibits higher accuracy and can forecast user electricity consumption data that more accurately reflect real-life usage. [ABSTRACT FROM AUTHOR]

Chemical reaction networks are powerful tools for modeling cell signaling and its disruptions in diseases like cancer. Realistic chemical reaction networks involve hundreds of proteins and reactions, resulting in a model depending on a consistently large number of kinetic parameters. Since finely calibrating all the parameters would require an unrealistic amount of data, proper sensitivity analysis is required to identify a subset of parameters for which fine tuning is needed and thus provide a fundamental tool for the qualitative analysis of the network. We present a multidisciplinary approach for computing a set of synthetic sensitivity indices. These indices rank the kinetic parameters, based on the impact that errors in their values would have on the protein concentration profile at equilibrium. Our tests on a chemical reaction network devised for colorectal cells demonstrate the effectiveness of the considered sensitivity indices in different scenarios including in-silico drug dosage and novel therapeutic target discovery. The Matlab code for computing the synthetic sensitivity indices and the data concerning the network for colorectal cells are available at https://github.com/theMIDAgroup/CRN_sensitivity. [ABSTRACT FROM AUTHOR]

The modeling of the seat transmissibility is necessary to advance the understanding of the dynamic interactions between compliant seats and occupants. Within this investigation, an optimized artificial neural network (ANN) model was employed to clarify contributions associated with anthropometric parameters to the seat transmissibility. Anthropometric parameters underwent dimensionality reduction through the principal component analysis, and resultant principal components served as input features for the ANN model. Additionally, the ANN structure’s weights and biases values were adjusted using the genetic algorithm (GA), resulting in a PCA-GA-ANN model for the prediction of seat transmissibilities. The results indicated root mean square error (RMSE) values for predicting vertical in-line and horizontal cross-axis transmissibilities from the developed model were 0.061 and 0.055, respectively, demonstrating superior effectiveness in the prediction error and trends when compared with both the ANN and GA-ANN models. The seat transmissibility predicted from the PCA-GA-ANN model exhibited resonance behaviors similar to that observed in the whole-body vibration test. The sensitivity analysis showed that the subject’s age was the most predominant anthropometric parameter for the prediction, followed by gender and body mass index. The ANN model optimized with principal component analysis (PCA) and GA effectively eliminates the redundant information of anthropometric parameters, enhancing the generalization of the seat transmissibility prediction. [ABSTRACT FROM AUTHOR]

In recent years, the exploration of the therapeutic potential of Salvia has gained considerable attention, leading to a growing number of scientific studies emphasizing its pharmacological properties. Despite this, therapeutic applications of Salvia remain underexploited, requiring further investigation. Iran is a major center for sage diversity in Asia, boasting 60 Salvia species, 17 of which are unique to the area. This study aimed to comprehensively explore and compare the extracts of 102 Salvia samples belonging to 20 distinct Salvia species from Iran, providing a deeper understanding of their specific polyphenol content and, consequently, their antioxidant capabilities and potential therapeutic uses. All samples were analyzed to determine the contents of total phenolics, total flavonoids, total tannin, photosynthetic pigments, and ascorbic acid, along with their antioxidant activity. These data were then combined with the forty distinct chemical fingerprints identified by ultrafast high-pressure liquid chromatography coupled with high-resolution mass spectrometry. Multivariate data analysis was employed to find correlations and differences among the huge number of data obtained and to identify Salvia species with similar phytochemical and/or antioxidant properties. The results show that each Salvia species is characterized by a distinct class of polyphenols recognized for their antidiabetic, anti-inflammatory, cardioprotective and neuroprotective properties. Overall, our findings reveal the potential of some Salvia species for targeted therapeutic applications and provide a rational basis for the development of Salvia-derived nutraceuticals, ultimately improving the prospects for the use of Salvia in medicine. [ABSTRACT FROM AUTHOR]

Stress testing refers to the application of adverse financial or macroeconomic scenarios to a portfolio. For this purpose, financial or macroeconomic risk factors are linked with asset returns, typically via a factor model. We expand the range of risk factors by adapting dimension-reduction techniques from unsupervised learning, namely PCA and autoencoders. This results in aggregated risk factors, encompassing a global factor, factors representing broad geographical regions, and factors specific to cyclical and defensive industries. As the adapted PCA and autoencoders provide an interpretation of the latent factors, this methodology is also valuable in other areas where dimension-reduction and explainability are critical. [ABSTRACT FROM AUTHOR]

Copyright of Food & Fermentation Industries is the property of Food & Fermentation Industries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food & Fermentation Industries is the property of Food & Fermentation Industries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The sleeping stage classification plays an important role in the medical science because it helps the diagnosis of the mental health diseases. The conventional approach for performing the sleeping stage classification is based on the electroencephalograms (EEGs). However, it is worth noting that the EEGs reflect the brain activities. Nevertheless, the brain activities are very complicated even though the subjects are sleeping. Hence, performing the sleeping stage classification via the EEGs may yield the low classification accuracy. On the other hand, the electrooculograms (EOGs) are the voltages between the front eyes and the back eyes which are related to the eye ball movement. As it can directly reflect the various sleeping stages, it can achieve a higher classification accuracy. Therefore, this paper employs the two channel EOGs for performing the sleeping stage classification. The major contribution of this paper is to 1) employ the singular spectrum analysis (SSA) to exploit the latent intrinsic high dimensional dynamics of the one dimensional EOGs for performing the sleeping stage classification, 2) employ the approximate entropy as the features for performing the sleeping stage classification, and 3) assign the same features of different SSA components of different channels of the epochs of the EOGs into the same group and perform the principal component analysis (PCA) on each group of the feature vectors so that the properties of each type of the features are preserved. The results show that our proposed method yields the five sleeping stage classification accuracy at 93.73% and the sensitivity of the stage one non-rapid eye movement (S1) at 78.44%, which achieves the significant improvements compared to the existing methods. Therefore, our proposed method could be used to reduce the workload of the medical officers. [ABSTRACT FROM AUTHOR]

The advent of single-cell RNA sequencing (scRNA-seq) technology enables researchers to gain deep insights into cellular heterogeneity. However, the high dimensionality and noise of scRNA-seq data pose significant challenges to clustering. Therefore, we propose a new single-cell type identification method, called CHLSPCA, to address these challenges. In this model, we innovatively combine correntropy with PCA to address the noise and outliers inherent in scRNA-seq data. Meanwhile, we integrate the hypergraph into the model to extract more valuable information from the local structure of the original data. Subsequently, to capture crucial similarity information not considered by the PCA model, we employ the Gaussian kernel function and the Euclidean metric to mine the similarity information between cells, and incorporate this information into the model as the similarity constraint. Furthermore, the principal components (PCs) of PCA are very dense. A new sparse constraint is introduced into the model to gain sparse PCs. Finally, based on the principal direction matrix learned from CHLSPCA, we conduct extensive downstream analyses on real scRNA-seq datasets. The experimental results show that CHLSPCA performs better than many popular clustering methods and is expected to promote the understanding of cellular heterogeneity in scRNA-seq data analysis and support biomedical research. [ABSTRACT FROM AUTHOR]

In order to realize the accurate prediction of the water content of the tobacco under the re-roasting machine, a tobacco water content prediction model based on principal component analysis and BP neural network was proposed. First, principal component analysis was used to extract the most characteristic factors of water content of re-roasted tobacco, and the feature matrix was obtained. Then, the feature matrix was input into BP neural network to construct a prediction model including the feature matrix and the water content of tobacco under re-roasting. The simulation results showed that the proposed model presented significant prediction ability in the prediction of water content of re-baking tobacco, and the coefficient of determination reached 0.92. By using this method, we could assist in the optimization of the control parameters of tobacco re-baking and the improvement of the quality of re-baking tobacco. [ABSTRACT FROM AUTHOR]

With the advancement of society and the improvement of living standards, the significance of students' physical health has become increasingly prominent. However, currently, the assessment and analysis of students' physical health heavily depend on conventional statistical methods. Even with the application of data mining-related methodologies for analysis and evaluation, the exploitation and utilization of physical health big data remain relatively restricted. In this paper, an improved Canopy-K-means algorithm based on principal component analysis (PCA) is used to construct and analyze a portrait of students' physical fitness and health. The method combines data dimensionality reduction techniques and cluster analysis techniques, and its combined performance is the best compared to other algorithms in the ablation experiments for both male and female student data groups. In this paper, the algorithm was used to process the grouping of physical fitness test data of male and female students, realize the construction and analysis of students' physical fitness and health portrait, and give the exercise prescription for different categories of students. In this paper, the physical health test data of students of Yunnan Agricultural University in 2020–2022 were collected to carry out experiments, and the results found that there are differences in physical health status among students of different genders, grades, and majors in this university, and the physical health status of the students of Classes 2018 and 2019 is generally deficient; on different majors, the students of the Faculty of Agricultural Sciences are slightly superior to the Faculty of Science and Technology, and the students of the Faculty of Science and Technology are slightly superior to the students of the Faculty of Humanities and Social Sciences. Our study offers novel methods and ideas for the assessment and analysis of students' physical health, holding significant implications for schools and related departments in formulating scientific and effective physical education policies and health promotion strategies. [ABSTRACT FROM AUTHOR]

Automatic distribution master stations (ADMSs) are software platforms that can be used to monitor energy supply and consumption of power demand-side resources as well as to guarantee grid security. They play a vital role in maintaining the optimal performance and stability of electrical power systems. However, the stability of ADMSs is usually determined by the performance of the information interaction, which includes various challenges and complexities. Therefore, an efficient multiple-indicator filtering-based information interaction evaluation method for the ADMS is proposed in this paper. The proposed method first utilizes the principal component analysis (PCA) to filter the indicators of the ADMS. Then, the data envelopment method and the analytic network process (ANP) are employed to remove the invalid indicators and to determine the indicator weights. In this regard, the secondary screening of the indicators is completed, the effective indicators are obtained, and the accuracy of the evaluation system is improved. Results of the simulation show that the proposed method works well when evaluating information interaction performance, and it offers satisfactory performance evaluation of ADMSs. [ABSTRACT FROM AUTHOR]

An optimal spatio-temporal hybrid model (STHM) based on wavelet transform (WT) is proposed to improve the sensitivity and accuracy of detecting slowly evolving faults that occur in the early stage and easily submerge with noise in complex industrial production systems. Specifically, a WT is performed to denoise the original data, thus reducing the influence of background noise. Then, a principal component analysis (PCA) and the sliding window algorithm are used to acquire the nearest neighbors in both spatial and time dimensions. Subsequently, the cumulative sum (CUSUM) and the mahalanobis distance (MD) are used to reconstruct the hybrid statistic with spatial and temporal sequences. It helps to enhance the correlation between high-frequency temporal dynamics and space and improves fault detection precision. Moreover, the kernel density estimation (KDE) method is used to estimate the upper threshold of the hybrid statistic so as to optimize the fault detection process. Finally, simulations are conducted by applying the WT-based optimal STHM in the early fault detection of the Tennessee Eastman (TE) process, with the aim of proving that the fault detection method proposed has a high fault detection rate (FDR) and a low false alarm rate (FAR), and it can improve both production safety and product quality. [ABSTRACT FROM AUTHOR]

Advanced footwear technology featuring stack heights higher than 30 mm has been proven to improve running economy in elite and recreational runners. While it is understood that the physiological benefit is highly individual, the individual biomechanical response to different stack heights remains unclear. Thirty-one runners performed running trials with three different shoe conditions of 25 mm, 35 mm, and 45 mm stack height on an outdoor running course wearing a STRYD sensor. The STRYD running variables for each participant were normalized to the 25 mm shoe condition and used to cluster participants into three distinct groups. Each cluster showed unique running patterns, with leg spring stiffness and vertical oscillation contributing most to the variance. No significant differences were found between clusters in terms of body height, body weight, leg length, and running speed. This study indicates that runners change running patterns individually when running with footwear featuring different stack heights. Clustering these patterns can help understand subgroups of runners and potentially support running shoe recommendations. [ABSTRACT FROM AUTHOR]

In the manufacturing process of electrical devices, ensuring the cleanliness of technical surfaces, such as direct bonded copper substrates, is crucial. An in-line monitoring system for quality checking must provide sufficiently resolved lateral data in a short time. UV hyperspectral imaging is a promising in-line method for rapid, contactless, and large-scale detection of contamination; thus, UV hyperspectral imaging (225–400 nm) was utilized to characterize the cleanliness of direct bonded copper in a non-destructive way. In total, 11 levels of cleanliness were prepared, and a total of 44 samples were measured to develop multivariate models for characterizing and predicting the cleanliness levels. The setup included a pushbroom imager, a deuterium lamp, and a conveyor belt for laterally resolved measurements of copper surfaces. A principal component analysis (PCA) model effectively differentiated among the sample types based on the first two principal components with approximately 100.0% explained variance. A partial least squares regression (PLS-R) model to determine the optimal sonication time showed reliable performance, with R2cv = 0.928 and RMSECV = 0.849. This model was able to predict the cleanliness of each pixel in a testing sample set, exemplifying a step in the manufacturing process of direct bonded copper substrates. Combined with multivariate data modeling, the in-line UV prototype system demonstrates a significant potential for further advancement towards its application in real-world, large-scale processes. [ABSTRACT FROM AUTHOR]

Background: Humans perceive and interpret the world through the lens of self-reference processes, typically facilitating enhanced performance for the task at hand. However, this research has predominantly emphasized the automatic facet of self-reference processing, overlooking how it interacts with control processes affecting everyday situations. Methods: We investigated this relationship between automatic and control self-reference processing in neuropsychological patients performing self-face perception tasks and the Birmingham frontal task measuring executive functions. Results: Principal component analysis across tasks revealed two components: one loaded on familiarity/orientation judgments reflecting automatic self-reference processing, and the other linked to the cross task and executive function indicating control processing requirements. Voxel-based morphometry and track-wise lesion-mapping analyses showed that impairments in automatic self-reference were associated with reduced grey matter in the ventromedial prefrontal cortex and right inferior temporal gyrus, and white matter damage in the right inferior fronto-occipital fasciculus. Deficits in executive control were linked to reduced grey matter in the bilateral inferior parietal lobule and left anterior insula, and white matter disconnections in the left superior longitudinal fasciculus and arcuate fasciculus. Conclusions: The causal evidence suggests that automatic and control facets of self-reference processes are subserved by distinct yet integrated ventral prefrontal–temporal and dorsal frontal–parietal networks, respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: This study examined the skull characteristics of Holstein and Simmental cattle to provide reference values for future research. We collected 54 skulls of young male cattle from Turkey to conduct 27 measurements and calculate eight indices. Holstein skulls tended to be longer, though not markedly, but they exhibited notably longer nasal bones and shorter skull lengths. Holsteins also had wider faces. A statistical analysis showed that overall size differences did not separate the breeds, but certain skull features did. Simmental skulls had higher basal index values and wider occipital regions, likely due to their larger skull size and weight, which provides more space for muscle attachment. Holsteins had more oval-shaped eye sockets, while Simmentals had wider, rounder ones. These findings help identify breed-specific traits and offer insights into how each breed's skull structure supports their functions. This knowledge is valuable for breeding and conservation, and future research should look into the genetic and environmental factors influencing these traits. This study aimed to reveal the morphological characteristics of pure Holstein and Simmental skulls and to obtain reference values for morphometric analysis. Moreover, 54 skulls from 12- to 14-month-old male Holstein (n = 25) and Simmental (n = 29) cattle were collected from Turkey's Southeastern Anatolia Region between 2023 and 2024. Linear measurements indicated that Holsteins had longer skulls compared to Simmentals. Holsteins exhibited significantly higher values for the greatest length of nasals and the shortest skull length. The facial breadth was wider in Holsteins and statistically distinctive between the breeds. Holsteins had a more oval orbital bony roof, while Simmentals exhibited a wider orbital structure. The orbital index was higher in Holsteins, distinguishing between the two breeds. It was observed that Simmental cattle had a wider occipital region. This difference is likely due to the larger lateral appearance of the Simmental skull, which has more body weight and provides a larger surface area for muscle attachment. These differences not only aid in breed identification but also offer insights into the functional adaptations of each breed. Future research should explore the genetic and environmental factors contributing to these morphological traits, further enriching our knowledge of cattle morphology and its implications for breeding and conservation efforts. [ABSTRACT FROM AUTHOR]

Tuberculosis, one of the most ancient and formidable infectious diseases, primarily arises from the pathogenic bacterium Mycobacterium tuberculosis (Mtb). Phosphoenolpyruvate carboxykinase (Pck), a potential drug target, is essential for the growth of Mtb that involves in the pathway of gluconeogenesis at the centre of phosphoenolpyruvate-pyruvate-oxaloacetate node. This study aims to understand drug-like properties of the compounds derived from artemisinin, and to investigate their inhibitory roles against Mtb. Molecular docking was performed on a set of 56 artemisinin compounds to identify their binding efficacy to the Mtb protein target. Subsequently, the selected top three complexes were subjected to molecular dynamics simulations. By the trajectory analysis, root mean square deviation and radius of gyration indicated the compactness of the systems without much fluctuation. The principal component analysis revealed that the complexes were less dynamic and energetically more favourable, and Gibbs energy landscape revealed the favourable energetic transitions between conformations. The artemisinin dimer primary alcohol holding good bioavailability scores appears highly stable in complex with Pck (MM/GBSA of −37.67 kcal/mol) that stands as a potential inhibitor of the Mtb target. However, further preclinical experiments and investigations are necessary to evaluate the intrinsic properties and to confirm effectiveness of the drug candidate. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The benefit of real‐world applicable indicators in resolving robust traffic‐related source contributions was investigated using tunnel measurement. PM2.5/CO was introduced as a metric to present evidence in non‐exhaust identification indirectly in terms of PM2.5 accumulation and dilution scenarios, and the rates (hourly variation of the difference between PM2.5 and PM2.5/CO) were 0.59−1.88 and −0.79 to −0.65, corresponding to peak and off‐peak hours, respectively. Real‐world PAHs indicators were examined, among which the exhaust indicators showed stable applicability from BghiP, and the non‐exhaust indication noted by BkF and DahA in peak hours were largely weakened in off‐peak hours, showing noticeable profile mixing between exhaust, brake and tyre wear. Source contributions were resolved by principal component analysis (PCA) with support of linear discriminant analysis (LDA) on inter‐group centroid diagnosis. The rate of vehicular non‐exhaust (brake and tyre wear) contribution lifted 10.3 times in peak hours compared with off‐peak hours, and its emission factor was noticeably enhanced 16 times, from 0.03 mg/(km·veh) to 0.48 mg/(km·veh). Guided by global vehicle electrification, the contribution of non‐exhaust in vehicular emissions were estimated to increase with larger ratio of emission factor between non‐exhaust and exhaust, and the growing market share of electric vehicles, under each mode of regenerative braking. The monetary impact of non‐exhaust caused by electric vehicles was calculated reaching the exhaust when electric vehicles increase to 50% in busy commuting megacities. The results provide applicable indicators for accurate source apportionment and support data for the refined control of non‐exhaust emission under rapid vehicle electrification. Plain Language Summary: Vehicle emitted non‐exhaust has garnered considerable global attention as source of heavy metals, microplastics and emerging pollutants. Real‐world applicable indicators were gained to characterize the benefits of robust and dynamic apportionment between exhaust and non‐exhaust emissions. This study estimated the contributions of non‐exhaust to total vehicle emissions for varying levels of emission factor ratios between non‐exhaust and exhaust and market share of electric vehicle. Key Points: PM2.5/CO can be used as a metric to present evidence of non‐exhaust identification indirectly in typical traffic environmentAccurate indicators were determined and applicability for recognizing exhaust and non‐exhaust was examined under heavy and low trafficFuture contribution of non‐exhaust to total vehicle emissions was predicted by emission factor ratios and electric vehicle percentages [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Monthly precipitation data from 58 synoptic stations throughout Botswana, spanning 1981–2016, were used in this study. The data were examined using multivariate analysis to determine regions exhibiting distinct precipitation variability patterns and regimes. To accomplish this, the T-mode of principal component analysis was applied to the correlation matrix of the data. Based on the maximum loading values of the rotational principal component scores, the T-mode indicated three separate subregions with varying precipitation patterns over time. Four clusters with distinct rainfall patterns were identified when cluster analysis was performed on the principal component scores. An assessment of the homogeneity of the clusters was performed using L-moment's heterogeneity measure (H). Statistical analysis was employed to model annual rainfall data using five commonly used rainfall analysis probability distribution functions: normal, lognormal, gamma, Weibull, and Gumbel. The probability distributions with the greatest fit were determined based on the maximum overall score, which was calculated by adding the individual point scores of three chosen goodness-of-fit tests. Each cluster exhibited distinct probability distribution functions, with the gamma, Gumbel, lognormal, and Weibull distributions providing the most accurate descriptions. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To investigate how methyl jasmonate (MeJA) affects the storage quality of fresh-cut Cistanche deserticola, this study immersed fresh-cut Cistanche deserticola in MeJA solutions of 100, 150, and 200 μmol/L concentrations and measured changes in hardness, weight loss rate, total soluble solids (TSS), total colony count, color, browning degree, total phenol content, polyphenol oxidase (PPO), peroxidase (POD), and malondialdehyde (MDA) content during storage.Results indicated that after 14 days of storage, fresh-cut Cistanche deserticola treated with 200 μmol/L MeJA showed a decrease in MDA, PPO, and POD activities by 21.32%, 23.62%, and 24.08%, respectively, while the total phenol content increased by 33.68%, resulting in higher antioxidant capacity and a significant delay in the degree of browning during storage. MeJA treatment also slowed down color changes and weight loss during storage, reduced the total colony count by 23.88%, and increased the hardness and TSS content by 17.89% and 40.22%, respectively, effectively maintaining the storage quality of fresh-cut Cistanche deserticola. This study contributes theoretical support for developing preservation techniques and extending the shelf life of fresh-cut Cistanche deserticola. [ABSTRACT FROM AUTHOR]

The study aimed to establish a comprehensive method to evaluate the effects of thymol microcapsules/konjac glucomannan/low acyl gellan gum (TKL) edible composite coating on postharvest quality of blueberry. The late-ripening variety Baldwin was used as the test material. Based on the sensory, physiological and biochemical, nutrient, color, and texture perspectives, representative indicators were selected to comprehensively evaluate the effect of TKL preharvest spraying on blueberry quality using principal component analysis (PCA), cluster analysis, and correlation analysis. Results showed that the TKL coating treatment slowed down the decrease in sensory, fruit shape index, titratable acid, hardness, L*, b*, H*, and springing during storage of blueberry, reduced the increases in weight loss ratio, decay rate, malondialdehyde, and C*, and increased the contents of soluble solid, chewiness, resilience, total phenols, total flavonoids, and anthocyanidin. The TKL coating effectively slowed down the deterioration of blueberry quality. PCA analysis obtained 2 principal components with a cumulative variance contribution of 86.90%.The fitting of PCA scores with sensory values was an extremely significant positive correlation (P<0.01). Cluster analysis indicated that the blueberry quality of TKL coating treatment at 21-56 days was in the same category as the control at 7-35 days. Correlation showed that PCA scores were extremely significant and positively correlated with sensory evaluation (P<0.01).Cluster analysis showed that the quality of blueberry by TKL coating at 21-56 days was the same as that of the CK group at 7-35 days. The TKL coating treatment extended the storage period of Baldwin blueberry from 42 days to 63 days. The combination of PCA and cluster analysis could more scientifically, systematically, and accurately evaluate the effect of TKL coating on the postharvest preservation of blueberries, providing a theoretical basis for the application of TKL coating on postharvest fruit preservation. [ABSTRACT FROM AUTHOR]

AbstractPeat is an unrenewable resource. Compost can partially replace peat in the growing seedling substrates due to its high electrical conductivity (EC), bulk density (BD), and pH. This study aimed to assess the potential of substituting peat for seedling substrate with compost made from a mixture of agricultural and agro-industrial wastes. Four compost piles (C1–C4) were constructed as follows: C1: a 1:1:1.5 weight ratio mixture of filter mud, mushroom waste, and date-palm fronds, while C2–C4: a 0.5:1 weight ratio mixture of either bagasse, cutting grassland, or date-palm fronds and cattle dung. After four months of decomposition, the compost’s physical, chemical, and biological properties were estimated in comparison to commercial compost (CC), peatmoss (PM), and their ideal ranges (IR) for seed germination and seedling growth. Composts differed significantly in their physical and chemical properties. Some composts revealed property values within the IR. In comparison to PM, Composts have a lower C/N ratio and organic matter, along with higher BD, EC, and pH. Cattle manure enhanced organic matter and carbon, total nitrogen and potassium, and ammonium levels, and reduced ash levels in C2–C4 compared to filter mud in C1. In winter 2018 and 2019, C1–C4 substrates were compared with CC and PM substrates to determine their suitability for producing crisp lettuce ‘Big Bell’ seedlings under plastic-house conditions. Substrates had significant effects on lettuce seedling traits. Shoots of C2–C4 substrate seedlings grew more rapidly than those of other substrate seedlings due to the increased length and diameter of their stem and leaf area. [ABSTRACT FROM AUTHOR]

ALS (Amyotrophic Lateral Sclerosis) is a fatal neurodegenerative disease of the human motor system. It is a group of progressive diseases that affects the nerve cells in the brain and spinal cord that control the muscle movement of the body hence, detection and classification of ALS at the right time is considered to be one of the vital aspects that can save the life of humans. Therefore, in various studies, different AI techniques are used for the detection of ALS, however, these methods are considered to be ineffectual in terms of identifying the disease due to the employment of ineffective algorithms. Hence, the proposed model utilizes Modified Principal Component Analysis (MPCA) and Modified Random Forest (MRF) for performing dimensionality reduction of all the potential features considered for effective classification of the ALS presence and absence of ALS causing mutation in the corresponding gene. The MPCA is adapted for capturing all the Low-Importance Data transformation. Furthermore, The MPCA is objected to performing three various approaches: Covariance Matrix Correlation, Eigen Vector- Eigenvalue decomposition, and selecting the desired principal components. This is done in aspects of implying the LI (Lower-Importance) Data Transformation. By choosing these potential components without any loss of features ensures better viability of selecting the attributes for ALS-causing gene classification. This is followed by the classification of the proposed model by using Modified RF by updating the clump detector technique. The clump detector is proceeded by clustering approach using K-means, and the data reduced by their dimension are grouped accordingly. These clustered data are analyzed either for ALS causing or devoid of causing ALS. Finally, the model's performance is assessed using different evaluation metrics like accuracy, recall, F1 score, and precision, and the proposed model is further compared with the existing models to assess the efficacy of the proposed model. [ABSTRACT FROM AUTHOR]

Background: Alkaloids, important secondary metabolites produced by plants, play a crucial role in responding to environmental stress. Heuchera micrantha, a well-known plant used in landscaping, has the ability to purify air, and absorb toxic and radioactive substances, showing strong environmental adaptability. However, there is still limited understanding of the accumulation characteristics and metabolic mechanism of alkaloids in H. micrantha. Results: In this study, four distinct varieties of H. micrantha were used to investigate the accumulation and metabolic traits of alkaloids in its leaves. We conducted a combined analysis of the plant's metabolome and transcriptome. Our analysis identified 44 alkaloids metabolites in the leaves of the four H. micrantha varieties, with 26 showing different levels of accumulation among the groups. The HT and JQ varieties exhibited higher accumulation of differential alkaloid metabolites compared to YH and HY. We annotated the differential alkaloid metabolites to 22 metabolic pathways, including several alkaloid metabolism. Transcriptome data revealed 5064 differentially expressed genes involved in these metabolic pathways. Multivariate analysis showed that four key metabolites (N-hydroxytryptamine, L-tyramine, tryptamine, and 2-phenylethylamine) and three candidate genes (Cluster-15488.116815, Cluster-15488.146268, and Cluster-15488.173297) that merit further investigation. Conclusions: This study provided preliminarily insight into the molecular mechanism of the biosynthesis of alkaloids in H. micrantha. However, further analysis is required to elucidate the specific regulatory mechanisms of the candidate gene involved in the synthesis of key alkaloid metabolites. In summary, our findings provide important information about how alkaloid metabolites build up and the metabolic pathways involved in H. micrantha varieties. This gives us a good starting point for future research on the regulation mechanism, and development, and utilization of alkaloids in H. micrantha. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION In this work, water transition points (first transition: monolayer–multilayer water; and second transition: multilayer–free and solvent water) of different parts of jasmine rice including white rice, brown rice and bran were identified through the integration of sorption isotherm and dielectric properties data. Desorption isotherm data were fitted to four established models to select the optimal model for describing the sorption behaviors. Then, dielectric properties such as dielectric constant (ε′) and dielectric loss factor (ε″) were measured across various moisture content levels within the frequency range of 200–20 000 MHz.A type III isotherm was observed for all samples and the Peleg model was the best fit with the experimental data. Monolayer moisture content of the samples, estimated using the GAB model, ranged from 3.25% to 4.17% dry basis. For dielectric properties, frequency and moisture dependencies were evident for all sample types. Moreover, the sorption isotherm models effectively described the relationship between water activity (aw) and dielectric properties as reflected by their goodness of fit, and their strong correlation through principal component analysis and Pearson's correlation results.The first water transition occurs at aw values of 0.11, 0.12, and 0.22, while the second transition appears at aw values of 0.9, 0.9 and 0.75–0.85 for white rice, brown rice and bran, respectively. This knowledge will be useful for food processors, providing insights into the optimization of food processing and storage conditions to extend food products' shelf life. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Hierarchical networks typically get rated to be not assortative on the basis of the degrees of the end vertices of the edges. However, degree centrality is not cognizant of the location of the nodes in the hierarchy. Rather, we propose the use of hierarchy-aware metrics (such as the k-core index of a node: the largest value of k for which the node would be part of a k-core and not part of any k′-core, where k′ > k and the number of k-core levels for which there exists a non-empty k-core for the network) for assortativity calculations. We first determine the assortativity index ( A s s I kcore ) of a network on the basis of the k-core indexes of the vertices. We then conduct principal component analysis (PCA) on a dataset of the A s s I kcore and the number of k-core levels for a suite of 77 real-world networks. We quantify the hierarchical k-core assortativity of a network as the weighted sum of the entries in the two principal components of this dataset. [ABSTRACT FROM AUTHOR]

The main aim of the report is to research socioeconomic and environmental problems in farms applying soil health practices in the EU-TUdi-projectI -- partner countries -- Austria, Bulgaria, Czech Republic, Hungary, Italy, and Spain. It was constructed farm typology related to soil heath using two statistical methods. The principal component analysis (PCA) constructed four socioeconomic which evaluate the socioeconomic and environmental problems. On the second step, it was applied cluster analysis (CA) to separate the farmers into homogeneous groups with similar characteristics. There were identified four farm types regarding soil health. [ABSTRACT FROM AUTHOR]

Although objectively measured characteristics of sleep efficiency and quality were found to be better in women than men, women more frequently than men suffer from poor or insufficient or non-restorative sleep. We explored this apparent paradox by testing the sex-associated differences in electroencephalographic (EEG) indicators of two opponent processes of sleep-wake regulation, the drives for sleep and wake. We tried to provide empirical support for the hypothesis that a stronger women's sleep drive can explain better objective characteristics of sleep quality in women than men, while a stronger women's wake drive can be an explanation of a higher frequency of sleep-related complaints in women than men. To our knowledge, this was the first attempt to examine the associations of sex with scores on the 1st and 2nd principal components of the EEG spectrum that can serve as objective spectral EEG markers of the opponent drives for sleep and wake, respectively. The particular prediction was that, in women compared to men, not only the 1st principal component score but also the 2nd principal component score could be higher (i.e. both drives could be stronger). In a sample of 80 university students (40 females), the EEG signals were recorded during 160 afternoon napping attempts (50 min or longer). The difference between male and female students in sleep latencies did not reach a statistically significant level. In accordance with our prediction, both principal component scores were found to be higher in female than in male students irrespective of sleep stage. It is likely that the influence of the wake drive is entirely overlooked in the polysomnographic studies due to the predominant contribution of the indicators of the sleep drive to the conventional objective characteristics of sleep quality. Therefore, a stronger women's sleep drive can be an explanation of women's better sleep quality in the results of polysomnographic studies. On the other hand, if a stronger women's wake drive can influence the perception of their sleep quality, this can explain their more frequent sleep-related complaints. [ABSTRACT FROM AUTHOR]

The study aimed in exploring the spatial and temporal variations in the rate of non-grain of arable land in food production and marketing balance zones and identifying the types of drivers, to provide a theoretical basis for the protection of food security in food production and marketing balance zones. The spatial autocorrelation model was used to analyze the spatial and temporal evolutionary characteristics of arable land non-grain rate in Shanxi Province from 2005 to 2020, the random forest model was used to analyze the influencing factors of the arable land non-grain rate, and the principal component analysis and K-Means clustering method were used to explore the types of arable land non-grain drive in Shanxi Province. The results of the study show that: temporally, the rate of non-grain arable land decreased from 50.21% in 2005 to 40.05% in 2020 with a “W”- shaped fluctuation, exceeding the Shanxi food security threshold by 11.45 percentage points. The rate of non-grain cultivation of arable land varied significantly across space, with a distribution pattern“ high in the north-west and low in the south-east”. In terms of spatial agglomeration, the global Moran′s index of the non-grain rate of arable land in Shanxi Province declined and then increased from 2005 to 2020, and the effect of spatial agglomeration was strengthened with high-high and low-low agglomerations primarily. Land productivity, annual precipitation, and the distance between each district to the provincial capital center played major roles in changes in the rate of non-grain of arable land, showing a negative correlation. In Shanxi Province, there are four types of arable land non-grain drives：resource endowment, transport convenience, agricultural support, and cash crop growth. Identifying the types of arable land non-grain drive in Shanxi Province and developing targeted control measures are critical for the protection of agricultural development and food security. [ABSTRACT FROM AUTHOR]

Transcriptome data are frequently used to investigate coral bleaching; however, the factors controlling gene expression in natural populations of these species are poorly understood. We studied two corals, Montipora capitata and Pocillopora acuta, that inhabit the sheltered Kāne'ohe Bay, Hawai'i. M. capitata colonies in the bay are outbreeding diploids, whereas P. acuta is a mixture of clonal diploids and triploids. Populations were sampled from six reefs and subjected to either control (no stress), thermal stress, pH stress, or combined pH and thermal stress treatments. RNA‐seq data were generated to test two competing hypotheses: (1) gene expression is largely independent of genotype, reflecting a shared treatment‐driven response (TDE) or, (2) genotype dominates gene expression, regardless of treatment (GDE). Our results strongly support the GDE model, even under severe stress. We suggest that post‐transcriptional processes (e.g., control of translation, protein turnover) modify the signal from the transcriptome, and may underlie the observed differences in coral bleaching sensitivity via the downstream proteome and metabolome. [ABSTRACT FROM AUTHOR]

The scapula in quadrupedal mammals is a flat bone that connects the thoracic limb with the trunk above the shoulder joint. For this purpose, scapulae of 34 dogs and 23 cats were modeled using computer tomography. 9 Landmarks and 50 semilandmarks were used. The cat and dog samples differ in scapula shape, as do male and female cats. Centroid size has no apparent covariance with shape, and we did not find any difference between the two sexes in dogs. The scapula of cats was wider. The scapula of dogs was narrower and longer. Margo cranialis was more oval in cats. Angulus cranialis border was not clear in cats. Angulus caudalis was sharper in dogs. Spina scapulae was closer to caudal in cats. In cats, the fossa supraspinata was wider than the fossa infraspinata. Also, the collum scapulae was narrower in cats. The scapula of male cats was wider than that of female cats. In shape, the fossa supraspinata was wider in male cats. In male cats, the spina scapulae were more caudal. Angulus caudalis was wider in female cats. The most significant gender differences in dogs were in tuberculum supraglenoidale and margo caudalis. Male dogs had larger tuberculum supraglenoidale in shape. Margo caudalis was more caudal in male dogs. Geometric morphometrics was found to be effective in distinguishing the scapula of cats and dogs. In addition, this method can be useful in sex estimation. [ABSTRACT FROM AUTHOR]

This paper deals with the extension of principal component analysis, canonical correlation analysis, the Cramer–Rao inequality, and a few other statistical concepts in the real domain to the corresponding complex domain. Optimizations of Hermitian forms under a linear constraint, a bilinear form under Hermitian-form constraints, and similar maxima/minima problems in the complex domain are discussed. Some vector/matrix differential operators are developed to handle the above types of problems. These operators in the complex domain and the optimization problems in the complex domain are believed to be new and novel. These operators will also be useful in maximum likelihood estimation problems, which will be illustrated in the concluding remarks. Detailed steps are given in the derivations so that the methods are easily accessible to everyone. [ABSTRACT FROM AUTHOR]

Background: Drought is a leading abiotic factor limiting groundnut production globally. Therefore, the screening and development of moisture stress tolerant groundnut genotypes is a key issue towards sustainable agriculture. Methods: An experiment was laid in a split plot design taking two conditions, control (T1 ) and moisture stress (T2 ) as main treatments and 7 genotypes of groundnut as sub treatments. Several morpho-physiological traits like dry matter production, dry matter partitioning, photosynthetic traits, relative water content, were measured after 10 days of stress imposition. Yield and yield attributes were also recorded at harvest along with drought tolerance index and principal component analysis. Result: Results showed significant differences among the genotypes at moisture stress and control conditions. Significant decrease was observed in chlorophyll content, total dry matter production, dry matter partitioning efficiency, relative water content, photosynthetic traits, except specific leaf area among all the genotypes. TCGS-1694 have been identified as moisture stress-tolerant genotypes in principal component analysis with higher efficiency interms of total dry matter production, photosynthetic rate, dry matter partitioning to pods, stress tolerance index, yield and yield attributes while Kadiri-6 has been identified as susceptible genotype. [ABSTRACT FROM AUTHOR]

Monitoring of the corrosion process of alloys in real conditions often results in extensive data, which is characterized by complex interdependence, but by a large degree of mutual deviation. First of all, the large dispersion of the obtained results makes it very difficult to draw accurate conclusions about the real influence of the tested parameters on the corrosion behavior of alloys. On the other hand, in many cases, the high interdependence between the corrosion factors can also greatly burden the analyzed system and thus make it significantly difficult to recognize the main influence. Multivariate analysis, especially the principal component analysis, is becoming increasingly popular in processing of this type of data, due to its ability to recognize and eliminate redundant data. The aim of this study was to examine the possibility of using multivariate analysis methods in the processing of the corrosion test results obtained under real conditions. Based on the obtained results, it can be concluded that used multivariate method in combination with energy dispersive spectrometer analysis can be successfully used to identify the most important corrosion factors (type of corrosion environment, exposure time and technological production processes), as well as their influence on the degradation of the tested TiNi alloys under the given conditions. [ABSTRACT FROM AUTHOR]

This study attempts to develop a composite index by integrating meteorological, hydrological and agricultural droughts over semi-arid Banas River basin, Rajasthan, India. To affect this, the standardized precipitation index (SPI), streamflow drought index (SDI), and vegetation condition index (VCI) have been used at 1-, 3-, 5-, 9- and 12-month time scales using station and remote sensing-based data for the period 2000 to 2020. To identify the occurrence of above-stated droughts and most vulnerable drought period at different time scales (1-, 3-, 5-, 9- and 12-month) regarding SPI, SDI and VCI has been validated with foodgrains produced and occurrence of historical drought years. This validation has been found significant with SPI-3 (r = − 0.81), SDI-3 (r = − 0.78) and VCI-5 (r = − 0.80) time scales. Subsequently, these time scales have been coalesced using weights obtained from principal component analysis (PCA) to develop the composite drought index (CDI). The annual CDI developed this way has been further validated with foodgrains produced and occurrence of historical drought years. The results of CDI demonstrate the maximum area under mild drought (73 percent) followed by moderate (21 percent) and severe (4 percent), whereas minuscule area has been detected under wet conditions (2 percent). Finally, this study suggests that individual drought types (meteorological, hydrological, agricultural) do not appropriately arrest the drought severity, hence, the usage of multiple droughts based composite index can be more realistic for effective drought assessment and monitoring in hydrologic systems. [ABSTRACT FROM AUTHOR]

Flax hemp and ramie fibers have been important raw materials for textile products since ancient times. Due to the difference in use and output there has been a significant gap in the market price of these three different species of bast fibers in recent years and the illegal phenomenon of low-price bast fibers posing as high-price bast fibers often occurs. The composition physical and chemical properties of flax hemp and ramie fibers are very close. At present the standard method for identifying bast fibers in China is FZ/ T 01057. 3 2007 The Method for Identification of Textile Fibers Part 3 Microscopy. However the microscopic morphology of flax hemp and ramie is highly similar and the accuracy of identifying bast fibers according to this method is insufficient. The identification of these three species of easily confused bast fibers has always been a difficult task in the field of textile inspection and testing so it is necessary to improve the identification technology of bast fibers to maintain market stability and strengthen quality supervision. Although the predecessors have found that there are relatively slight infrared spectral differences between different kinds of bast fibers no rapid and accurate classification method for bast fibers based on the differences in infrared spectral data has been reported. In our study the principal component analysis PCA and linear discriminant analysis LDA on attenuated total reflection Flourier transformed infrared spectroscopy ATR-FTIR of different kinds of bast fibers were proposed to establish a discriminant model to identify these three kinds of easily confused bast fibers. In this study 60 groups of flax hemp and ramie fibers were selected as sample sets for degumming cleaning treatment and their ATR-FTIR spectra were collected. After spectral normalization infrared spectra in the range of 800 - 2 000 cm- 1 were compressed to scores of sets of principal components PCs by PCA. The PCA results show that with the increase of the number of PCs the PC scores gradually display a clustering trend according to the species of bast fibers and the cumulative variance contribution rate of the first 12 PCs to the normalized spectral data reaches 99. 5% . With the first 12 PC scores as the independent variable and the species of fiber samples as the dependent variable a discriminant model of the bast fibers including typical discriminant functions and classification functions was established by LDA. The discriminant model verification results show that the typical discriminant functions can make the first 12 PC scores achieve a excellent cluster according to the species of fiber samples and the classification functions can reach a classification accuracy of 100% for all fiber samples in the training set and the test set. In addition the classification accuracy of the PCA-LDA model can still reach 99. 6% by leave-one-out cross-validation. These results prove that the differences between ATR-FTIR spectra of flax hemp and ramie can be discerned and utilized for the rapid and accurate identification of these easily confused textile bast fibers by PCA-LDA statistic analysis. In the follow-up study we will introduce more laboratories and samples to participate in the construction and verification of the PCA-LDA model so as to further improve the applicability of the bast fiber classification and identification model. [ABSTRACT FROM AUTHOR]