Search

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA on decoding accuracy (using support vector machines) across a broad range of experimental paradigms. We evaluated several different PCA variations, including group-based and subject-based component decomposition and the application of Varimax rotation or no rotation. We also varied the numbers of PCs that were retained for the decoding analysis. We evaluated the resulting decoding accuracy for seven common event-related potential components (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). We also examined more challenging decoding tasks, including decoding of face identity, facial expression, stimulus location, and stimulus orientation. The datasets also varied in the number and density of electrode sites. Our findings indicated that none of the PCA approaches consistently improved decoding performance related to no PCA, and the application of PCA frequently reduced decoding performance. Researchers should therefore be cautious about using PCA prior to decoding EEG data from similar experimental paradigms, populations, and recording setups.

UNLABELLED: Hip fracture risk assessment is an important but challenging task. Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur. We developed a global FEA-computed fracture risk index to increase the prediction accuracy of hip fracture incidence. PURPOSE: Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur to compute the force (fracture load) and energy necessary to break the proximal femur in a particular loading condition. The fracture loads and energies-to-failure are individually associated with incident hip fracture, and provide different structural information about the proximal femur. METHODS: We used principal component analysis (PCA) to develop a global FEA-computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies-to-failure in four loading conditions of 110 hip fracture subjects and 235 age- and sex-matched control subjects from the AGES-Reykjavik study. Using a logistic regression model, we compared the prediction performance for hip fracture based on the stratified resampling. RESULTS: We referred the first principal component (PC1) of the FE parameters as the global FEA-computed fracture risk index, which was the significant predictor of hip fracture (p-value

BACKGROUND: Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP). METHODS: The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously for 24 hours a day for a week under free-living conditions. ActiGraph and activPAL data were processed to obtain minute-level time-series outputs. Multilevel functional principal component analysis (MFPCA) was applied to minute-level ActiGraph activity counts within activPAL-identified sitting bouts to investigate variation in movement while sitting across subjects and days. The multilevel approach accounted for the nesting of days within subjects. RESULTS: At least 90% of the overall variation of activity counts was explained by two subject-level principal components (PC) and six day-level PCs, hence dramatically reducing the dimensions from the original minute-level scale. The first subject-level PC captured patterns of fluctuation in movement during sitting, whereas the second subject-level PC delineated variation in movement during different lengths of sitting bouts: shorter (< 30 minutes), medium (30 -39 minutes) or longer (> 39 minute). The first subject-level PC scores showed positive association with DBP (standardized β ^ : 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP. CONCLUSION: In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health. TRIAL REGISTRATION: ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684.

It is not currently possible to directly and noninvasively measure in vivo patellofemoral joint contact force during dynamic movement; therefore, indirect methods are required. Simple models may be inaccurate because patellofemoral contact forces vary for the same knee flexion angle, and the patellofemoral joint has substantial out-of-plane motion. More sophisticated models use 3-dimensional kinematics and kinetics coupled to a subject-specific anatomical model to predict contact forces; however, these models are time consuming and expensive. We applied a principal component analysis prediction and regression method to predict patellofemoral joint contact forces derived from a robust musculoskeletal model using exclusively optical motion capture kinematics (external approach), and with both patellofemoral and optical motion capture kinematics (internal approach). We tested this on a heterogeneous population of asymptomatic subjects (n = 8) during ground-level walking (n = 12). We developed equations that successfully capture subject-specific gait characteristics with the internal approach outperforming the external. These approaches were compared with a knee-flexion based model in literature (Brechter model). Both outperformed the Brechter model in interquartile range, limits of agreement, and the coefficient of determination. The equations generated by these approaches are less computationally demanding than a musculoskeletal model and may act as an effective tool in future rapid gait analysis and biofeedback applications. [ABSTRACT FROM AUTHOR]

This study aimed to characterise three Ghanaian local chicken ecotypes, namely, Interior Savannah, Forest, and Coastal Savannah, based on morphological data and single nucleotide polymorphism (SNP) genotypes. Morphological data including body weight, shank length, body girth, back length, thigh length, beak length, comb length, and wattle length were collected from 250 local chickens. DNA isolated from blood of 1,440 local chickens was used for SNP genotyping with the Affymetrix chicken 600k SNP chip. Principal component analysis showed that Forest and Coastal Savannah birds were closely related. Generally, all three ecotypes exhibited high genetic diversity, especially birds from the Interior Savannah zone. Morphological characterisation showed that ecotype (p = 0.016) and sex (p = 0.000) had significant effects on body weight. Birds of the Interior Savannah ecotype were the heaviest (p = 0.004), with mean weights of 1.23 kg for females and 1.40 kg for males. Sex also had a strong significant effect on most of the morphological measurements, but the sex * ecotype interaction effect was not significant. Very few of the feather phenotypes previously reported to be associated with heat resistance-frizzle (2%) and naked neck (1.6%)-were found in the studied populations. It is concluded that the three local ecotypes are genetically diverse but with similar morphological features and the information provided would be useful for future selection decisions.

In order to facilitate the high-quality advancement and digital innovation of the wine industry, a method based on principal component analysis (PCA) and improved quantum particle swarm optimization (IQPSO) to optimize support vector machine (PCA-IQPSO-SVM), was proposed to solve the wine classification problem. First, the feature extraction ability of PCA was used to reduce the input dimension of the model and improve the classification efficiency. At the same time, aiming at the problems that the quantum particle swarm optimization (QPSO) is easy to fall into local optimum and the convergence ability is decreased in the later stage of optimizing SVM, a variety of improvement strategies are used to improve QPSO to find the best parameters of SVM. The experimental results demonstrate that the model of PCA-IQPSO-SVM exhibits superior evaluation indices compared to other models. Moreover, the optimization efficiency of the PCA-IQPSO-SVM model is enhanced by 1.64% to reach an impressive 85.2%, showcasing its remarkable optimization effect. Simultaneously, this study provides a scientific approach for quality classification in the wine industry, thereby facilitating its high-quality development. [ABSTRACT FROM AUTHOR]

Douchi is a highly appreciated product due to its unique flavor, high nutritional value, and health benefits. Douchi from different regions exhibits distinct characteristics. The objective of this study was to investigate the volatile profiles in douchi produced from different geographical origins using gas chromatography-mass spectrometry (GC-MS) and chemometrics. The volatiles were extracted by headspace solid-phase micro-extraction (HS-SPME) and subsequently determined by GC-MS. A comparative analysis was further conducted to identify the variations in volatile compounds among the samples. The correlation between the samples was investigated by employing cluster analysis (CA) and principal component analysis (PCA). An orthogonal partial least squares-discriminant analysis (OPLS-DA) model was established to discriminate samples from different regions and identify key differential compounds. A total of 46 volatile compounds were detected, among which terpenes, alcohols were the main compounds. Furthermore, 14 key differentiating compounds were screened out based on the OPLS-DA modle. The application of GC-MS combined with chemometrics offers a theoretical framework for douchi quality control and classification. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science 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 Shipin Kexue/ Food Science is the property of Food Science 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 Shipin Kexue/ Food Science is the property of Food Science 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.)

Summary The objective of this study was to employ multi‐elemental analysis and chemometric multivariate data analysis for identifying the geographical origin of Camellia sinensis cultivated in Vietnam's northeastern region. Ninety‐two tea leaf samples encompassing four distinct varieties were sourced from Bac Can and Thai Nguyen provinces. These samples underwent analysis through Fourier transform infrared (FTIR) spectroscopy and chemometrics, while elemental quantification was achieved via inductively coupled plasma mass spectrometry (ICP‐MS), utilising mixed standards for 26 elements. The FTIR spectra exhibited characteristic peaks, denoting the functional groups of the potential compounds present. ICP/MS analysis identified the presence of 14 elements in the tea samples, revealing significant variations in content across the four varieties. principal component analysis (PCA) of the FTIR data illustrated that the initial two principal components accounted for 91.26% of the total variance, effectively segregating the four green tea varieties into distinct clusters. Conversely, PCA of the ICP‐MS spectra also distinguished the four varieties, albeit Trung Du and Hybrid F1 exhibited a proximate distribution. This closeness may be incidental and not indicative of correlation, considering the limited variable set. Our findings ascertain that ICP/MS and FTIR, augmented by chemometric software, serve as efficacious tools for ascertaining the geographical origins of tea. [ABSTRACT FROM AUTHOR]

The phosphate solubilizing properties of Lysinibacillus macroides ST-30, Pseudomonas pelleroniana N-26, and Bacillus cereus ST-6 were tested for the chickpea crop of the Tarai region of Uttarakhand. These microbially inoculated plants have shown significant (p > 0.05) improvement in the plant health and crop health parameters, viz., root length, shoot length, fresh weight, dry weight, nodule number, nodule fresh weight, nodule dry weight, chlorophyll content, and nitrate reductase. The highest shoot length (46.10 cm) and chlorophyll content (0.57 mg g-1 fresh weight) were observed in ST-30 at 75 DAS with 20 kg P2O5/ha. Similarly, for plant P content, an increase of 90.12% over control was recorded in the same treatment. Treatments consisting of Lysinibacillus macroides ST-30 along with 20 kg/ha P2O5 were found to be most suitable as phosphatic fertilizer. Conclusively, sustainable agriculture practices in the Tarai as well as the field region may be developed based on a strategy of exploring microbial inoculants from the pristine region of the Western Himalayas. The presence and abundance of bacterial inoculants were confirmed through qRT-PCT. We conclude that the effective plant growth-promoting bacterium Lysinibacillus macroides ST-30 broadens the spectrum of phosphate solubilizers available for field applications and might be used together with 20 Kg/ha P2O5. [ABSTRACT FROM AUTHOR]

In order to explore the influencing factors of spatial and temporal evolution of production-living-ecological space in Beijing Tianjin sandstorm source area, the remote sensing images, natural environment and socio-economic data of Duolun County in Inner Mongolia from 2000 to 2020 were selected, and the spatial auto-correlation model and principal component analysis model were used to analyze the spatial pattern evolution and influencing factors of production-living-ecological space. The results show that: (1) the function of production space decreases slightly, and the degree of spatial agglomeration decreases; (2) The function of living space rose slightly, and its spatial agglomeration degree showed an upward trend; (3) The ecological spatial function showed a slow upward trend, and its spatial agglomeration degree increased; (4) The spatial pattern of production-living-ecological space is characterized by "high in the southwest and low in the northeast"; (5) Precipitation has the greatest impact on the spatial evolution of the production-living-ecological space. The distance from the main residential areas, per capita GDP, the distance from the main roads and the distance from the main waters have strong explanatory power on the spatial evolution of the production-living-ecological space. [ABSTRACT FROM AUTHOR]

An abundance of degrees of freedom (DOF) exist when executing a countermovement jump (CMJ). This research aims to simplify the understanding of this complex system by comparing jump performance and independent functional DOF (fDOF) present in CMJs without (CMJNoArms) and with (CMJArms) an arm swing. Principal component analysis was used on 39 muscle forces and 15 3-dimensional joint contact forces obtained from kinematic and kinetic data, analyzed in FreeBody (a segment-based musculoskeletal model). Jump performance was greater in CMJArms with the increased ground contact time resulting in higher external (p = 0.012), hip (p < 0.001) and ankle (p = 0.009) vertical impulses, and slower hip extension enhancing the proximal-to-distal joint extension strategy. This allowed the hip muscles to generate higher forces and greater time-normalized hip vertical impulse (p = 0.006). Three fDOF were found for the muscle forces and 3-dimensional joint contact forces during CMJNoArms, while four fDOF were present for CMJArms. This suggests that the underlying anatomy provides mechanical constraints during a CMJ, reducing the demand on the control system. The additional fDOF present in CMJArms suggests that the arms are not mechanically coupled with the lower extremity, resulting in additional variation within individual motor strategies. [ABSTRACT FROM AUTHOR]

Korla pear is a characteristic fruit in Xinjiang. Among them, the crisp texture is one of its excellent quality parameters. However, the crispness differences of Korla pears can vary gradually in recent years, due to the origin, variety, and maturity. The current sensory evaluation by experts or trained panelists can be the most accurate to detect the crispness. However, the evaluation process has been limited to the time-consuming and labor-intensity. The accuracy of evaluation can gradually decrease over time. Alternatively, instrument detection can share fast and stable advantages over sensory evaluation. In this study, the instrument detection was performed on the Korla pear flesh crispness using mechanical-acoustic information fusion. 50 pears were selected to test the crispness every 7 days during the 35-day storage periods, resulting in a total of six crispness gradient samples: crisp, relatively crisp, slightly crisp, slightly mealy, relatively mealy, and mealy. Then, the mechanical-acoustic signals during the rupture of pear flesh were synchronously collected at 51 200 Hz sampling rate using a universal material testing machine combined with a microphone. Subsequently, the information on jaggedness analysis interval in mechanical-acoustic signals was fused at the data level. The correlation between mechanical-acoustic signals was utilized to align with the processing mode of the human brain's comprehensive perception of crispness. Later, mechanical-acoustic fusion signals were converted into the different images of the Gramian angular summation field (GASF), Gramian angular difference field (GADF), symmetric dot pattern (SDP), Markov transition field (MTF), and recurrence plot (RP). The deep features of different images were extracted by the ResNet50 network. 8, 8, 9, 10, and 10 principal components were obtained after PCA dimensionality reduction. Furthermore, Pearson’s correlation analysis was made to obtain the absolute mean correlation coefficients between principal components of different image features and sensory crispness scores. The results showed that the MTF image was the most suitable to quantitatively characterize the crispness scores of pear flesh with the highest absolute mean correlation coefficient. Finally, the principal components of MTF images were input into the KNN, ELM, RF, and SVR optimized by PSO. The ResNet50-SVR model achieved the best prediction accuracy and stability. The RP 2, RMSEP, and RPD values were 0.96, 0.24, and 4.88, respectively. Consequently, this finding can provide a strong foundation for instrument detection of the crispness of fruits and vegetables. [ABSTRACT FROM AUTHOR]

Statistical morphological variations at the population level serve as a robust foundation for species delimitation and taxonomic treatment. We employed this approach to investigate species delimitation within the Ptilagrostis dichotoma species complex (Poaceae), which is characterized by open panicles. While three species have been described, the boundaries between these species and the morphological distributions of all taxonomic traits remain unclear. In this study, we examined the geographical variability of 19 morphological traits based on at least six individuals from each of the 28 populations across the entire distributional range of the species complex. Our statistical analyses identified five distinct phenotypic clusters with specific geographical preferences. Three of these clusters corresponded to the previously described species, while the remaining two clusters represented previously undescribed species. Furthermore, our findings highlight several traits, such as basal leaf scabrosity, panicle branch pulvinae, and glume length, which exhibit distinct gaps between phenotypic units and prove highly valuable for species delimitation. Finally, two new species are described and a key is provided to differentiate the five species. This morphological revision establishes a basis for future studies on species boundaries and divergences of the complex based on other evidence. [ABSTRACT FROM AUTHOR]

Successful surgical treatment of drug-resistant epilepsy traditionally relies on the identification of seizure onset zones (SOZs). Connectome-based analyses of electrographic data from stereo electroencephalography (SEEG) may empower improved detection of SOZs. Specifically, connectome-based analyses based on the interictal suppression hypothesis posit that when the patient is not having a seizure, SOZs are inhibited by non-SOZs through high inward connectivity and low outward connectivity. However, it is not clear whether there are other motifs that can better identify potential SOZs. Thus, we sought to use unsupervised machine learning to identify network motifs that elucidate SOZs and investigate if there is another motif that outperforms the ISH. Resting-state SEEG data from 81 patients with drug-resistant epilepsy undergoing a pre-surgical evaluation at Vanderbilt University Medical Center were collected. Directed connectivity matrices were computed using the alpha band (8–13 Hz). Principal component analysis (PCA) was performed on each patient's connectivity matrix. Each patient's components were analysed qualitatively to identify common patterns across patients. A quantitative definition was then used to identify the component that most closely matched the observed pattern in each patient. A motif characteristic of the interictal suppression hypothesis (high-inward and low-outward connectivity) was present in all individuals and found to be the most robust motif for identification of SOZs in 64/81 (79%) patients. This principal component demonstrated significant differences in SOZs compared to non-SOZs. While other motifs for identifying SOZs were present in other patients, they differed for each patient, suggesting that seizure networks are patient specific, but the ISH is present in nearly all networks. We discovered that a potentially suppressive motif based on the interictal suppression hypothesis was present in all patients, and it was the most robust motif for SOZs in 79% of patients. Each patient had additional motifs that further characterized SOZs, but these motifs were not common across all patients. This work has the potential to augment clinical identification of SOZs to improve epilepsy treatment. [ABSTRACT FROM AUTHOR]

Regional economic development objectives are improved economic, political, and social conditions in a certain area. Investment, production, innovation, wealth, and affluence are the four stages of regional economic development that may be identified in each selected area. It becomes more reliant on technological advancements in the industry and less on locally sourced production inputs as the economy region grows. The regional economic growth issue is inequality in the rates of improvement of subnational geographic regions and inequalities in the distribution of wealth. Intellectual algorithms or enhanced and hybrid algorithms based on machine learning, such as Fuzzy C-means clustering (FCM), principal component analysis, and algorithm, can newly achieve more appropriate solutions to practical issues of discrete, non-linear, non-differentiable, and various constraints. A hybrid algorithm combines two or more other algorithms that solve the same problem. Hence, this paper proposes a Principal Component Analysis for the Sustainable Regional Economic Development (PCA-SRED) model to enhance the efficiency in examining regional economic changes and industrial development zones. The data are taken from the Organization for Economic Cooperation and Development (OECD) regional statistics dataset. Using PCA, industries may be categorized based on shared criteria, and the whole spatial distribution law of datasets and common patterns can be uncovered. To create a long-lasting regional economic development plan, it is crucial to categorize, compare, and evaluate the economic growth level of several areas. The research outcomes illustrate that the hybrid algorithms have high accuracy and a fast convergence rate because they can replicate the smart behavior of some clusters in nature while examing the variances in regional economic growth. The experimental outcomes illustrate that the recommended PCA-SRED model enhances the accuracy ratio by 98.2%, industry production ratio by 95.6%, regional economic change prediction ratio by 96.4%, and economic efficiency ratio by 97.8% compared to other popular models. [ABSTRACT FROM AUTHOR]

Modern data are often collected in a matrix form. In this paper, we consider modelling the varying coefficient regression with matrix valued covariate X and scalar index variable U. The proposed model simultaneously makes principal component analysis for both the row and column dimensions of the matrix objects, maintaining the matrix structure while achieving substantial dimension reduction. We develop an iterative estimation method for the involved principal parameters and nonparametric functions. Under regularity conditions, the asymptotic distributions of the estimators are derived. In addition, by incorporating the estimation with the adaptive group Lasso and the group SCAD penalties, variables of X in entire rows or columns are selected. The proximal gradient algorithm is further utilised to solve the regularised optimisation problems. The asymptotic properties of the penalised estimators are also studied. Our model and estimation methods are demonstrated by simulated experiments. Real applications to the primary biliary cirrhosis (PBC) data reveal that the effects of the blood measurements to the survival time vary with levels of age. [ABSTRACT FROM AUTHOR]

To understand the source, distribution and degree of pollution of heavy metals in the soil around a plateau municipal solid waste incineration plant, a municipal solid waste incineration plant in Lhasa was taken as the research object in this study. The soil surrounding the facility was sampled, and the contents of heavy metals, including Cu, Pb, Zn, Cr, Ni, Cd, As, and Hg, in the soil were analyzed. Then, heavy metal pollution and risk assessments were conducted using the geoaccumulation index method, Nemerow index method, and potential ecological risk index method. Additionally, correlation and principal component analysis were used to determine the correlation between heavy metals in the soil and their sources of pollution. The results showed that based on the Nemerow comprehensive pollution index, the soil surrounding the incineration plant generally had a low level of pollution. Through the analysis of individual potential ecological risk indices, it was found that only the heavy metal Cd presented moderate potential risks, while the other seven heavy metals had mild potential risks. The average value of the total potential risk coefficient was 107.31, which was less than 150, indicating that there was a slight potential risk associated with the soil around the incineration plant. There was significant correlation between six heavy metals, namely, Cu, Pb, Zn, Cr, Ni, and Cd, in the soil around the incineration plant. Their pollution sources were similar, and their levels tended to be affected by the incineration plant. However, there was no significant correlation between the heavy metals As and Hg and the other six heavy metals. These two heavy metals had independent sources of pollution but were also influenced by human activities. [ABSTRACT FROM AUTHOR]

Amrasca biguttula biguttula Ishida is the major biotic constraint in okra cultivation that cause considerable economic loss. Screening of okra (Abelmoschus esculentus) genotypes is important to develop leafhopper-resistant high-yielding cultivars for improving crop yield. To categorize leafhopper-resistant okra genotypes, we used a comprehensive collection of morphological and biochemical parameters with multivariate analysis in this study. Plant morphological and biochemical parameters were estimated by collection of leaves from different okra genotypes with three replicates during peak leafhopper infestation period and assay were performed based on specific standard protocols. Completely randomized block design was deployed and significant variations in plant morphometrics, biochemicals, and leafhopper population were recorded from each genotype screened. In the present study the morphological parameter viz., the shortest plant height was observed in resistant genotype AE 23 (17.21 cm) and the tallest plant height was recorded in the susceptible genotype AE 26 (63.22 cm) followed by Pusa Sawani (63.07 cm). The maximum leaf area of 132.24 cm2 was recorded in AE 15 genotype. Biochemical data reveals that, protein content was highest in susceptible genotype AE26 (11.01 mg/g), followed by Pusa Sawani (10.72 mg/g), and the lowest in the resistant genotype AE65 (4.72 mg/g) followed by AE27 (5.54 mg/g). While phenol, OD phenol, surface wax content were maximum in identified moderately resistant genotypes. The Kaiser Criterion or eigenvalue is greater than one for the first four principal components with the cumulative percentage of variance of 78.48 percent. Principal component analysis simplified the understanding of the interactions among leafhopper and biophysical and biochemical characteristics. The genotypes AE65, AE23, AE 27, No.315, and AE4 expressed resistance in terms of higher total phenol, ortho dihydroxy phenols, tissue toughness, and surface wax contents. Our visual observations on the growth and development of okra genotypes under leafhopper pressure were represented by principal component analysis which makes it reliable. Therefore, the identified jassid-resistant okra genotypes could be further utilized as a source of appropriate donor parents in the breeding of leafhopper-resistant okra varieties. [ABSTRACT FROM AUTHOR]

Early childhood is a critical developmental stage where established dietary patterns can impact lifelong health outcomes. This study investigates dietary patterns and their relationships with sociodemographic factors among Malaysian preschoolers. A total of 643 preschoolers of Malay, Indian, Chinese and other ethnicities participating in South East Asian Nutrition Surveys (SEANUTS II) were included. Dietary intake was assessed using a single triple‐pass 24‐h dietary recall method. Dietary patterns were derived using principal component analysis and their association with sociodemographic factors was determined using complex sampling logistic regressions. Five dietary patterns were identified: "healthy eating," "wholegrains and starchy vegetables," "high salt and sugar," "Western food," and "sugary drinks and legumes". Having older siblings was inversely associated with a "healthy eating" pattern (adjusted OR: 0.54, 95% CI: 0.33–0.87). Malay ethnicity was associated with higher odds of adhering to the "high salt and sugar" pattern (adjusted OR: 4.12, 95% CI: 2.20–7.75). Meanwhile, children living in urban areas (adjusted OR: 1.76, 95% CI: 1.03–3.01), those from middle‐income families (adjusted OR: 3.20, 95% CI: 1.38–7.40) and whose fathers were overweight (adjusted OR: 2.79, 95% CI: 1.58–4.94) showed a higher association with "Western food" pattern. Conversely, children whose mothers had overweight were less likely to adhere to the "Western food" pattern (adjusted OR: 0.50, 95% CI: 0.30–0.83). Older age was significantly associated with a greater likelihood of the "sugary drinks and legumes" pattern (adjusted OR: 3.99, 95% CI: 1.75–9.06). There was no significant association between "wholegrains and starchy vegetables" pattern with sociodemographic characteristics (all p > 0.05). These findings suggest that ethnicity, age, residence area, having older siblings, parental weight status and household income level are associated with dietary patterns among multiethnic preschoolers in Malaysia. Thus, these sociodemographic characteristics should be considered when designing targeted dietary strategies and interventions for preschoolers. [ABSTRACT FROM AUTHOR]

This study evaluated the presence of the three pesticides methomyl (MET), carbendazim (CBZ) and chlorpyrifos-ethyl (CPE), as well as the degradation product of CPE (3,5,6-trichloro-2-pyridinol; TCP), in 44 honey samples from all 12 regions of Morocco. With a validated HPLC-UV method occurrence frequencies of 63.6% for MET, 54.5% for CBZ, 95.1% for CPE and 34.1% for TCP were obtained, even at concentrations higher than the maximum residue limits for MET, CPE and TCP. Based on the predominant pesticide, principal component analysis separated sampling regions into three groups. Risk assessment indicated that ingestion of these pesticides, alone or in combination, in honey did not pose a risk to consumers (HQ and HI < 1). [ABSTRACT FROM AUTHOR]

Water quality monitoring is imperative in providing the objective evidence required to make sound decisions about water quality management. This study aimed to examine the water quality status of the Migori River by determining spatio-seasonal variations in water quality parameters, along with associated influencing factors and potential health risks. Therefore, eighteen physico-chemical and bacteriological variables were sampled and analyzed monthly for six months covering the wet and dry seasons from the upstream, midstream, and downstream stations, and a health risk survey was conducted with 90 watershed households. ANOVA and T-test were used to test for the significant spatial and seasonal variations (p < 0.05), respectively; whereas Pearson's correlation was used to identify relationships between parameters. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) were used to find various spatial patterns in the river water quality datasets, while the Canadian Council of Ministries of the Environment Water Quality Index (CCME-WQI) showed the suitability status of the river water quality. The assessed variables showed significant seasonal variability but no significant spatial differences in the river. HCA generated 3 clusters suggesting that water quality deteriorated downstream from the upstream of the watershed. The PCA extracted four PCs explaining 80.5% of the total variance, which suggested that the variations in water quality are attributed to point and nonpoint sources of pollution. While most of the physico-chemical variables were within maximum permissible limits, the bacteriological levels exceeded the prescribed standards. The index ranked the river's water condition between 'poor' to 'marginal'; upstream has better water condition that gradually decreases toward the downstream, and water quality is better in the wet season than the dry season. The study revealed that the water of the Migori River is polluted and potentially hazardous for human usage, and thus suitable pollution control measures are urgently needed to safeguard public health. [ABSTRACT FROM AUTHOR]

This paper presents a novel two-stage progressive search approach with unsupervised feature learning and Q-learning (TSLL) to enhance surrogate-assisted evolutionary optimization for medium-scale expensive problems. The method attempts to address the challenges posed by multi-polar and multi-variable coupling properties in such problems. During the iteration, TSLL splits the optimization process into two distinct stages. First, two unsupervised feature learning techniques: principal component analysis (PCA) and Autoencoder, are utilized to improve the representation of potential optimal samples in the solution space. PCA is used for feature reduction, extracting the most important features. On the other hand, Autoencoder focuses on reconstructing features within the medium-scale solution space. To ensure comprehensive exploration of the entire solution space, TSLL employs two distinct surrogate modeling approaches along with Q-learning in the second stage to dynamically select the mutation strategy for the differential evolution operator. Numerous experiments demonstrate the superiority of TSLL over five state-of-the-art surrogate-assisted approaches and two sophisticated evolutionary algorithms including the winner of CEC 2017 on medium-scale benchmark problems and a wind farm layout problem. [ABSTRACT FROM AUTHOR]

• Identifies factors affecting the workers' bargaining power. • Builds a composite index for the United States using the principal component analysis. • Analyses the relationship between this index and the wage share in the years 1960–2018. • Discusses the link between the workers' bargaining power and inflation. • Estimates the Phillips curve and explains why it has flattened over the last decades. This paper aims to construct a synthetic index of workers' bargaining power and investigate the relationship between it and inflation in the U.S. economy. As a first step, we identify the factors affecting the bargaining power of workers, referring to different groups of variables: labour market indicators; institutional indicators (e.g., collective bargaining coverage, union density); characteristics of the economy (e.g., degree of freedom for capital mobility, share of employment by sector). We then implement Principal Component Analysis (PCA) to assess the adequacy of the indicators and calculate the weights to aggregate the single indicators into a composite index. As a second step, we estimate the impact of our Bargaining Index on inflation by estimating an equation of the determinants of inflation. The composite index thus has a twofold use: it sheds light on the extent to which changes in the labour market in recent decades have weakened workers' bargaining power, and it can be used to test how the evolution of the wage bargaining system affects inflation. [ABSTRACT FROM AUTHOR]

Investigating the geochemical composition of bulk sediments stands as a crucial method for unraveling the complexities of various sedimentary processes. However, the intricacies arising from extensive datasets and alterations in sediment due to diverse factors often impede the clear identification of underlying patterns in geochemical fluctuations. In addressing these, employing multivariate statistical analyses has proven to be an invaluable tool for elucidating intricate patterns within large dataset. In this study, we focus on the utilization of Principal Component Analysis (PCA), a multivariate statistical technique, to uncover the underlying sedimentary processes influencing distinct geochemical dataset. Specifically, our attention is directed towards the examination of geochemical data from the previously published geochemical data of metasediments from Shimla and Chail group (referred to as SCM) and the mudflat sediments of Diu Island (referred to as DMS). Our PCA outcomes reveal that the initial three principal components (PC1, PC2, and PC3) account for 52.51% and 79.30% of the total variance within the SCM and DMS geochemical data, respectively. Notably, the negative loading of SiO2, alongside positive loadings of incompatible elements and those associated with mafic rocks on PC1 within the SCM dataset, indicates sediment origins ranging from felsic to intermediate sources. Additionally, the coexistence of Th, U, Zr, and Sc, exhibiting positive loadings in PC1 and PC2, suggests a significant influence of reworking and recycling from felsic to intermediate sources. In the context of the DMS dataset, PCA analysis highlights the dominant influence of in-situ productivity and mafic sediment sources along the positive axis of PC1. Conversely, the negative axis of PC1 is shaped by intermediate and potentially other sources. Further granularity in interpretation reveals the positive axis of PC2 being attributed to weathering proxies, while the dominance of plagioclase minerals in the clayey fraction controls the positive axis of PC3. Through this investigation, our study underscores the essential role of PCA-assisted geochemical data analysis in unraveling the intricate web of processes contributing to the variance observed within sedimentary systems. By effectively distilling the multifaceted factors driving geochemical variability, this approach emerges as a pivotal asset in enhancing our understanding of sedimentary dynamics. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the statistical association between exposure to air pollution and forced expiratory volume in the first second (FEV1) in both asthmatic and non-asthmatic children and teenagers, in which the response variable FEV1 was repeatedly measured on a monthly basis, characterizing a longitudinal experiment. Due to the nature of the data, an robust linear mixed model (RLMM), combined with a robust principal component analysis (RPCA), is proposed to handle the multicollinearity among the covariates and the impact of extreme observations (high levels of air contaminants) on the estimates. The Huber and Tukey loss functions are considered to obtain robust estimators of the parameters in the linear mixed model (LMM). A finite sample size investigation is conducted under the scenario where the covariates follow linear time series models with and without additive outliers (AO). The impact of the time-correlation and the outliers on the estimates of the fixed effect parameters in the LMM is investigated. In the real data analysis, the robust model strategy evidenced that RPCA exhibits three principal component (PC), mainly related to relative humidity (Hmd), particulate matter with a diameter smaller than 10 μm (PM10) and particulate matter with a diameter smaller than 2.5 μm (PM2.5). [ABSTRACT FROM AUTHOR]

Predictive analytics is crucial in precision medicine for personalized patient care. To aid in precision medicine, this study identifies a subset of genetic and clinical variables that can serve as predictors for classifying diseased tissues/disease types. To achieve this, experiments were performed on diseased tissues obtained from the L1000 dataset to assess differences in the functionality and predictive capabilities of genetic and clinical variables. In this study, the k‐means technique was used for clustering the diseased tissue types, and the multinomial logistic regression (MLR) technique was applied for classifying the diseased tissue types. Dimensionality reduction techniques including principal component analysis and Boruta are used extensively to reduce the dimensionality of genetic and clinical variables. The results showed that landmark genes performed slightly better in clustering diseased tissue types compared to any random set of 978 non‐landmark genes, and the difference is statistically significant. Furthermore, it was evident that both clinical and genetic variables were important in predicting the diseased tissue types. The top three clinical predictors for predicting diseased tissue types were identified as morphology, gender, and age of diagnosis. Additionally, this study explored the possibility of using the latent representations of the clusters of landmark and non‐landmark genes as predictors for an MLR classifier. The classification models built using MLR revealed that landmark genes can serve as a subset of genetic variables and/or as a proxy for clinical variables. This study concludes that combining predictive analytics with dimensionality reduction effectively identifies key predictors in precision medicine, enhancing diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Aromatics constitute the highest proportion among the four asphalt components. The existing literature contains over 20 types of small molecules used to characterise asphalt aromatics, which presents a challenge in constructing asphalt molecular models. In this investigation, quantum chemical calculations were employed to determine the molecular morphology, molecular polarity, and infrared spectrum indicators of asphalt aromatics. Based on principal component analysis and hierarchical clustering, statistical analysis of the aforementioned indicators was conducted to present a recommended scheme for selecting representative molecules of aromatics, thereby reducing the difficulty of constructing asphalt molecular models. The results indicated that N2 was recommended as the representative molecule for aromatics with high molecular weight and long alkane chains, while N3 was recommended as the representative molecule for aromatics with low molecular weight. Based on the variations in substituents of aromatics, it was advisable to select one or more of N7, N10, N14 – N16, and N19 as representative molecules. The investigation guided the selection of aromatic representative molecules in the construction of asphalt molecular models. For major projects with test data, representative molecules could be adjusted according to the test data. [ABSTRACT FROM AUTHOR]

Pumice is an abundant natural resource in New Zealand and its application in horticulture could save significant costs. To investigate the effect of pumice substrates on raspberry growth and fruit quality, two dwarfing selections (sel.8 and sel.110) were grown hydroponically in (1) coconut coir (control); (2) pumice; (3) pumice/coir (50/50 v/v); (4) pumice/flax (50/50 v/v). Results showed that the addition of pumice to coir significantly increased bulk density, which provided better root anchor support for plants, and also increased the water holding capacity (WHC). Pure pumice had a higher bulk density and lower porosity compared to the other tested substrates, which enhanced fruit quality and yield, although the vegetative growth was slightly lower compared to the control. Mixed pumice/flax substrate had the lowest porosity and poorer WHC, resulting in inferior raspberry growth vigour and productivity. Our results furthermore suggested different substrates could affect the one-year-old cane height, crop yield and fruit characteristics. Pumice was more suitable for sel.8, while the pumice/coir mixture promoted a higher yield for sel.110. In conclusion, pumice and pumice-based mix substrates can be successfully used for hydroponic dwarfing raspberry production without compromising yield and fruit quality. [ABSTRACT FROM AUTHOR]

Embedding value investment in portfolio optimization models has always been a challenge. In this paper, we attempt to incorporate it by employing principal component analysis (PCA) to filter out dominant financial ratios from each sector and thereafter, use the portfolio optimization model incorporating second-order stochastic dominance (SSD) criteria to derive an optimal investment. We consider 11 financial ratios corresponding to each sector representing four categories of ratios, namely liquidity, solvency, profitability, and valuation. PCA is then applied over 10 years to extract dominant ratios from each sector in two ways, one from the component solution and the other from each category on the basis of their communalities. The two-step sectoral portfolio optimization (SPO) model is then utilized to build an optimal portfolio. The strategy formed using the formerly extracted ratios is termed PCA-SPO(A) and the latter PCA-SPO(B). The results obtained from the proposed strategies are compared with those from mean-variance, minimum variance, SPO, and nominal SSD models, with and without financial ratios. The empirical performance of proposed strategies is analyzed in two ways, viz., using a rolling window scheme and on different market scenarios for the S &P BSE 500 (India) and S &P 500 (U.S.) indices. We observe that the proposed strategy PCA-SPO(B) outperforms all other models in terms of downside deviation, CVaR, VaR, Sortino, Rachev, and STARR ratios over almost all out-of-sample periods. This highlights the importance of value investment where ratios are carefully selected and embedded quantitatively in the portfolio selection process. [ABSTRACT FROM AUTHOR]

This study analyzed data from the quarterly results of water quality monitoring in the State of Santa Catarina (Southern Brazil) from the Qualiágua Program. The results present water quality collections carried out in 2019-2021 of 23 analytical parameters in 40 municipalities of the State. The Principal Component Analysis (PCA) technique was applied to the analyzed parameters and to verify sources of contamination. The data on land use of the hydrographic basins of each monitoring point were also analyzed. The results showed that electrical conductivity, total and dissolved solids and chloride concentration are responsible for most of the data variability, having more influence at monitoring points close to the ocean. On the other hand, nitrate and turbidity are influenced by soil management due to land use. Total phosphorus, ammoniacal nitrogen and thermotolerant coliforms had a greater relationship with urban areas. The results provide helpful information for decision-making processes related to public policies for water uses in the State. [ABSTRACT FROM AUTHOR]

The flash flood-induced erosion is the primary contributor to soil loss within the Indian Himalayan Region (IHR). This phenomenon is exacerbated by a confluence of factors, including extreme precipitation events, undulating topographical features, and suboptimal soil and water conservation practices. Over the past few decades, several flash flood events have led to the significant degradation of pedosphere strata, which in turn has caused landslides along with fluvial sedimentation in the IHR. Researchers have advocated morphometric, hydrologic, and semi-empirical methods for assessing flash flood-induced soil erosion in hilly watersheds. This study critically examines these methods and their applicability in the Alaknanda River basin of the Indian Himalayan Region. The entire basin is delineated into 12 sub-watersheds, and 13 morphometric parameters are analyzed for each sub-watershed. Thereafter, the ranking of sub-watersheds vulnerability is assigned using the Principal Component Analysis (PCA), compounding method (CM), Geomorphological Instantaneous Unit Hydrograph (GIUH), and Revised Universal Soil Loss Equations (RUSLE) approaches. While the CM method uses all 13 parameters, the PCA approach suggests that the first four principal components are the most important ones, accounting for approximately 89.7% of the total variance observed within the dataset. The GIUH approach highlights the hydrological response of the catchment, incorporating dynamic velocity and instantaneous peak magnifying the flash flood susceptibility, lag time, and the time to peak for each sub-watershed. The RUSLE approach incorporates mathematical equations for estimating annual soil loss utilizing rainfall-runoff erosivity, soil erodibility, topographic, cover management, and supporting practice factors. The variations in vulnerability rankings across various methods indicate that each method captures distinct aspects of the sub-watersheds. The decision-maker can use the weighted average to assign the overall vulnerability to each sub-watershed, aggregating the values from various methods. This study considers an equal weight to the morphometric, hydrological GIUH, and semi-empirical RUSLE techniques to assess the integrated ranking of various sub-watersheds. Vulnerability to flash flood-induced landslides in various sub-watersheds is categorized into three classes. Category I (high-priority) necessitates immediate erosion control measures and slope stabilization. Category II (moderate attention), where rainwater harvesting and sustainable agricultural practices are beneficial. Category III (regular monitoring) suggests periodic community-led soil assessments and afforestation. Sub-watersheds WS11, WS8, WS5, and WS12 are identified under category I, WS7, WS4, WS9, and WS6 under category II, and WS1, WS3, WS2, and WS10 under category III. The occurrence of landslides and flash-flood events and field observations validates the prioritization of sub-watersheds, indicating the need for targeted interventions and regular monitoring activities to mitigate environmental risks and safeguard surrounding ecosystems and communities. [ABSTRACT FROM AUTHOR]

This research is to develop dictated metrics using a multi-proxy approach such as spatial–temporal analysis, statistical evaluation, and hydrogeochemical analysis. We have collected 45 groundwater samples located in the Tamirabarani river basin. To evaluate the aptness of developed metrics for agriculture and domestic needs and eleven years dataset has been analyzed and compared with national and international standards BIS, ICMAR, and WHO Monitoring and all the analysis results revealed that the concentration of calcium (Ca-1679 to 4937 mg/L; and Cl ions 236 to 1126 mg/L) and chloride ions was on the higher side in locations. These higher values may be attributed to the regional point sources as untreated water disposal and off-peak sources as agriculture practices. According to the results of the principal component analysis, the post-monsoon season accounted for an 84.2% variance. The major analyzed cations and anions have been observed in the following order: Na+ > Ca2+ > Mg2+ > K+ and Cl− > HCO3− > SO42− > NO3− respectively. Ca-Mg-HCO3, Mg-Ca–Cl, Na-C1, and infused waters have been discovered in the basin region, indicating that anion and cation dominance is not prevalent. This specifies that groundwater quality in this region is significantly degraded and suffers from extensive salinity due to the urban pollutants mixed with unprotected river sites. [ABSTRACT FROM AUTHOR]

Purpose: The chapter analyses the diversity of the European Union (EU) member states in terms of the transition to a sustainable, green, and just economy. Need for the study: Sustainable development is an important concern that the EU approaches by aiming to transition to a greener economy, fairly and inclusively. Moreover, the actual context, post-pandemic period and ongoing war at the EU border, makes the social and economic development of the EU countries matter even more in the light of this just transition. Methodology: The authors relied on a multidimensional approach, using principal component analysis (PCA) to reduce the dimensionality of the dataset. The cluster analysis allowed us to group the countries in such a way that made it possible to identify the best and worst performers, as well as certain patterns that can be the basis for the formulation of good practices and transferable to the states that need more support in the transition to a green economy. Findings: The results indicated that EU member states can be grouped into five clusters, each needing specific policies to ensure sustainable and inclusive development. Denmark, Finland, and Sweden turned out the best performers, while Estonia, Lithuania, Bulgaria, and Romania proved to have the most unfavourable situation. Practical implications: The findings provide relevant policy implication insights for policy decision-makers in the field of green economy, economic development, and social policies, with directions for future research. [ABSTRACT FROM AUTHOR]

Copyright of Acta Prataculturae Sinica is the property of Acta Prataculturae Sinica 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.)

To properly identify and treat sleep disorders, the stages of sleep must be successfully classified. Both manual and automatic sleep-scoring processes may be affected by such problems, but automatic techniques are more likely to be affected. Electroencephalogram (EEG) is a key factor in diagnosing sleep disorders instead of other biological signs. Historically, this time-consuming, arduous, and subject to subjective mistake task has been carried out manually by professionals through thorough observation. This process can be made significantly easier with automatic sleep "stage scoring" that performs scoring manually, making monitoring sleep disorders at work easier. A novel efficient "sleep stage Scoring" model is proposed using a deep learning approach to meet the requirement. The proposed model comprises four phases: Signal collection, decomposition, Dimensionality reduction and Scoring. Firstly, the raw EEG signals are collected via standard datasets. It is further used for signal decomposition, achieved by Empirical Mode Decomposition (EMD). Further, the dimensionality is reduced by Principal Component Analysis (PCA). Finally, this reduced decomposed signal is fed into the Scoring model as a "Serial Cascaded One-Dimensional Convolutional Neural Network with Bidirectional Long Short-Term Memory (SC1D-BiLSTM) layer". Using this proposed methodology, an average accuracy of 93.17% is achieved. Various performance metrics are utilized to assess the model, and the values are presented in the results section. The datasets used in this study are the Sleep-EDF Database Expanded v1.0.0 and St. Vincent's University Hospital / University College Dublin Sleep Apnea Database from Physionet, and the results are presented in the results section. [ABSTRACT FROM AUTHOR]

The natural and anthropogenic alterations in groundwater are exposing human health to nitrates and fluorides ingestion, especially in arid and semi-arid regions. The study focused on the depth-wise assessment of groundwater aquifers (n = 150) and non-carcinogenic health risks to vulnerable population in semi-arid regions of South, North and Central Punjab, Pakistan. The water quality index delineated the shallow and moderate aquifers unfit for human consumption with high values in North region (88%) as "very poor" to "unsuitable for drinking" as compared to the South and Central regions. The North region (56%) was categorized as high risk zone for nitrate pollution (2.48 times), while fluorides affected aquifers of the South region. The piper diagram elucidated bicarbonates prominence in the North and South, while bicarbonates and chlorides type in the Central region aquifers. The Gibbs plot attributed geogenic exchanges like silicate-weathering and ion-exchange mechanisms as dominant groundwater features. The non-carcinogenic health risks for nitrates were higher in the North region (Children: 1.36E+00 > Adult: 1.34E+00 > Infants: 1.08E+00) while for fluorides, South region (Children: 1.18E+00 > Adults: 1.17E+00) was at risk. The overall health impact was higher in South > North > Central for Children > Adults > Infants, respectively. Nitrates and fluorides made children (age 1–12 years) vulnerable to non-carcinogenic health risks in these regions. It is crucial to prioritize efforts to treat and reduce groundwater pollution to protect human health and the environment. [ABSTRACT FROM AUTHOR]

Elicitors have a substantial impact on the accumulation of phytochemical components in wild plants. The present study aimed to evaluate the impact of salicylic acid (SA) and methyl jasmonate (MJ) on the enrichment of carbazole alkaloids (CAs) in the leaves of Murraya koenigii under controlled environment. Effect of elicitors at 1.0, 1.5 and 2.0 mM and their time of exposition 6 h, 12 h, 24 h, 2 days and 5 days were evaluated. Methanolic extracts of the treated samples were subjected to UPLC-ESI-QTRAP LC-MS/MS analysis for the separation and subsequent quantification of seven identified CAs, viz. murrayamine A, koenigine, koenimbidine, koenimbine, mahanine, 8,8′-biskoenigine and mahanimbine. Accumulation of all the CAs was found to be enhanced with both the elicitors, but it was dependent on the type of elicitor, its concentration, and time of exposition. The data were analyzed through multivariate statistical models, like PCA, HCA and PLS-DA. The analysis revealed that 1.5 mM dose of MJ and SA had a significant influence on CAs accumulation at 24 and 12 h, respectively. Five marker CAs from both MJ and SA-treated samples were identified using VIP scoring. These findings suggest that MJ and SA with their specific concentration and exposition time could be used for the accumulation of particular CA to a substantial limit in this plant. [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.)

Credit risk assessment (CRA) plays an important role in credit decision-making process of financial institutions. Today, developing big data analysis and machine learning methods have marked a new era in credit risk estimation. In recent years, using machine learning methods in credit risk estimation has emerged as an alternative method for financial institutions. The past demographic and financial data of the person whose CRA will be performed is important for creating an automatic artificial intelligence credit score prediction model based on machine learning. It is also necessary to use features correctly to create accurate machine learning models. This article aims to investigate the effects of dimensionality reduction and data splitting steps on the performance of classification algorithms widely used in the literature. In our study, dimensionality reduction was performed using Principal Component Analysis (PCA). Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Naive Bayes (NB) algorithms were chosen for classification. Percentage splitting (PER, 66–34%) and k-fold (k = 10) cross-validation techniques were used when dividing the data set into training and test data. The results obtained were evaluated with accuracy, recall, F1 score, precision, and AUC metrics. German data set was used in this study. The effect of data splitting and dimension reduction on the classification of CRA systems was examined. The highest ACC in PER and CV data splitting was obtained with the RF algorithm. Using data splitting methods and PCA, the highest accuracy was observed with RF and the highest AUC with NB, with 13 PCs in which 80% of the variance was obtained. As a result, the data set consisting of a total of 20 features, expressed by 13 PCs, achieved similar or higher success than the results obtained from the original data set. [ABSTRACT FROM AUTHOR]

Salinity is one of the main factors influencing the early life stages of fish along an estuary, which shows great temporal and spatial changes. Recently, remote sensing has been widely applied to map salinity changes and fuzzy logic is identified as a suitable and strong tool for modeling complex systems. Based on collections of fish larvae and juveniles and water parameters in May, September, November, and December during 2019 along the Ba Lat estuary of the Red River, northern Vietnam, the present study attempts to predict the mapping and monitoring of the salinity using multispectral satellite imagery from Landsat 8 OLI satellite. The study determined that the NFS machine learning model, when improved by PCA, achieved a higher performance in displaying different salinity levels. The present study also confirms that using high spatial resolution or hyperspectral images would have increased the accuracy of spatial variation in similar modeling and mapping. Fuzzy rule–based modelling suggests that the occurrence of fish larvae and juveniles depended on salinity levels, with an R2 > 0.9, compared to the field surveys. This finding further confirms the accuracy data obtained by artificial neural network models. [ABSTRACT FROM AUTHOR]