Search

We rely on a total of 23 (cluster size, 8 structural, and 14 connectivity) descriptors to investigate structural patterns and connectivity motifs associated with water cluster aggregation. In addition to the cluster size n (number of molecules), the 8 structural descriptors can be further categorized into (i) one-body (intramolecular): covalent OH bond length (rOH) and HOH bond angle (θHOH), (ii) two-body: OO distance (rOO), OHO angle (θOHO), and HOOX dihedral angle (ϕHOOX), where X lies on the bisector of the HOH angle, (iii) three-body: OOO angle (θOOO), and (iv) many-body: modified tetrahedral order parameter (q) to account for two-, three-, four-, five-coordinated molecules (qm, m = 2, 3, 4, 5) and radius of gyration (Rg). The 14 connectivity descriptors are all many-body in nature and consist of the AD, AAD, ADD, AADD, AAAD, AAADD adjacencies [number of hydrogen bonds accepted (A) and donated (D) by each water molecule], Wiener index, Average Shortest Path Length, hydrogen bond saturation (% HB), and number of non-short-circuited three-membered cycles, four-membered cycles, five-membered cycles, six-membered cycles, and seven-membered cycles. We mined a previously reported database of 4 948 959 water cluster minima for (H2O)n, n = 3–25 to analyze the evolution and correlation of these descriptors for the clusters within 5 kcal/mol of the putative minima. It was found that rOH and % HB correlated strongly with cluster size n, which was identified as the strongest predictor of energetic stability. Marked changes in the adjacencies and cycle count were observed, lending insight into changes in the hydrogen bond network upon aggregation. A Principal Component Analysis (PCA) was employed to identify descriptor dependencies and group clusters into specific structural patterns across different cluster sizes. The results of this study inform our understanding of how water clusters evolve in size and what appropriate descriptors of their structural and connectivity patterns are with respect to system size, stability, and similarity. The approach described in this study is general and can be easily extended to other hydrogen-bonded systems. [ABSTRACT FROM AUTHOR]

Hardness of thin films is a noteworthy property in the electronic and mechanical industry and is generally observed to be dependent on the degree of roughening facilitated from surface heights' surface spatial heterogeneity at the mesoscopic observation scale. Nonetheless, owing to enhanced scale fluctuations and higher-order central moments, conventional parameters provide limitations and errors in capturing the spatial heterogeneity of surfaces. Herein, we have utilized scale-independent fractal parameters to analyze the spatial heterogeneity of surface heights in Ti1−xSixN ternary hard films deposited with varying Si doping concentrations using sputtering technique. The fractal dimension, lacunarity coefficient, Moran index, surface entropy, Otsu's separability, and fractal succolarity were computed to provide an overarching understanding of the surface heights' spatial heterogeneity. Principal component analysis was employed on the data sets to identify the parameter(s) accounting for the maximum variance and accordingly, the structure–property relation between spatial heterogeneity of surface and hardness is analyzed and discussed in the context of the fractal dimension of surface heights. The results indicate the possibility of mesoscopic surface engineering and, consequently, tuning of hardness and modulus of elasticity in Ti1−xSixN hard films by mere changing of surface spatial heterogeneity facilitated by the fractal dimension of surface heights. [ABSTRACT FROM AUTHOR]

We have reanalyzed the rich plethora of ground state configurations of the asymmetric Wigner bilayer system that we had recently published in a related diagram of states [Antlanger et al., Phys. Rev. Lett. 117, 118002 (2016)], comprising roughly 60 000 state points in the phase space spanned by the distance between the plates and the charge asymmetry parameter of the system. In contrast to this preceding contribution where the classification of the emerging structures was carried out "by hand," we have used for the present contribution machine learning concepts, notably based on a principal component analysis and a k-means clustering approach: using a 30-dimensional feature vector for each emerging structure (containing relevant information, such as the composition of the configuration as well as the most relevant order parameters), we were able to reanalyze these ground state configurations in a considerably more systematic and comprehensive manner than we could possibly do in the previously published classification scheme. Indeed, we were now able to identify new structures in previously unclassified regions of the parameter space and could considerably refine the previous classification scheme, thereby identifying a rich wealth of new emerging ground state configurations. Thorough consistency checks confirm the validity of the newly defined diagram of states. [ABSTRACT FROM AUTHOR]

Torsional and bending deformations of DNA molecules often occur in vivo and are important for biological functions. DNA "under stress" is a conformational state, which is by far the most frequent state during DNA–protein and gene regulation. In DNA minicircles of length < 100 base pairs (bp), the combined effect of torsional and bending stresses can cause local unusual conformations, with certain base pair steps often absorbing most of the stress, leaving other steps close to their relaxed conformation. To better understand the superhelical dynamics of DNA under stress, molecular simulations of 94 bp minicircles with different torsional linking numbers were interpreted using Fourier analyses and principal component analyses. Sharp localized bends of nearly 90° in the helical axis were observed, which in turn decreased fluctuations of the rotational register and helped redistribute the torsional stress into writhe, i.e., superhelical turn up to 360°. In these kinked minicircles, only two-thirds of the DNA molecule bends and writhes and the remaining segment stays close to straight and preserves a conformational flexibility typical of canonical B-DNA (bending of 39° ± 17° distributed parsimoniously across 36 bp), which was confirmed and visualized by principal component analysis. These results confirm that stressed DNA molecules are highly heterogeneous along their sequence, with segments designed to locally store and release stress so that nearby segments can stay relaxed. [ABSTRACT FROM AUTHOR]

In the Internet of Medical Things (IoMT)-based real-time telemedicine systems, patients can utilize a wide range of medical devices and sensors, which leads to the continuous generation of massive amounts of data. The high speed of data generation poses challenges in collecting, organizing, processing, and making decisions about patients' emergency levels. Existing methods for classifying (triaging) patients in such environments often yield inaccurate triage levels, necessitating a computational approach to enhance accuracy. This research aims to handle data from multiple heterogeneous sources in IoMT-based real-time telemedicine systems, analyze the data to accurately triage patients with the most urgent cases, and provide swift healthcare services. The proposed solution, the Data Processing with Triaging Model (DPTM), employs a hybrid approach that combines principal component analysis and decision tree algorithms. The model was tested using 55,680 patients with two chronic diseases: heart disease and hypertension. The computational results demonstrate the promising effectiveness of DPTM in accommodating and managing the requests of 55,680 patients. The proposed system achieves an impressive accuracy of 93%, surpassing four other algorithms. In conclusion, DPTM enhances medical services, reduces hospital overcrowding, and ensures accurate services for all patients, with a 93% accuracy rate. [ABSTRACT FROM AUTHOR]

BACKGROUND: The relevance of several characteristics for the acceptability of steamed East Africa bananas (matooke) was assessed using consumer‐preferred characteristics, the overall liking scores, check‐all‐that‐apply (CATA) and the Just About Right scale. The study was conducted in rural and urban locations in three banana growing regions of Uganda. Two landraces and two hybrids were processed into matooke. Twelve trained panellists evaluated color, taste and texture sensory characteristics. RESULTS: Consumers scored matooke from landraces as the most liked. The CATA test showed that the most important characteristics were: smooth mouthfeel, soft to the touch, not sticky, moldable, deep yellow color, attractive, good matooke taste and smell. Principal component analysis confirmed that most of the preferred sensory characteristics were associated with the local genotypes, whereas the less preferred characteristics were associated with hybrids. Correlation analysis revealed strong positive correlations between the consumer assessed characteristics, hardness by touch, softness to touch and yellowness, as well as quantitative laboratory characteristics (moldable, hardness by touch, softness and yellowness) of the steamed matooke. Color assessed by consumers was strongly correlated with the laboratory‐assessed color indicators. CONCLUSION: The strong associations observed between laboratory‐assessed and consumer‐based characteristics (moldable by touch and yellowness) suggest the possibility of predicting consumer characteristics using quantitative laboratory sensory assessments. Matooke taste as assessed by consumer panel is strongly associated with smooth texture and deep yellow color, which were the characteristics associated with landraces in the laboratory sensory assessment. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: Food quality traits related to the genetics of yam influence the acceptability for its consumption. This study aimed at identifying genetic factors underlying sensory and textural quality attributes of boiled and pounded yam, the two dominant food products from white Guinea yam. RESULTS: A genome‐wide association study (GWAS) of a panel of 184 genotypes derived from five multi‐parent crosses population was conducted. The panel was phenotyped for the qualities of boiled and pounded yam using sensory quality and instrument‐based textural profile assays. The genotypes displayed significant variation for most of the attributes. Population differentiation and structure analysis using principal component analysis (PCA) and population structure‐based Bayesian information criteria revealed the presence of four well‐defined clusters. The GWAS results from a multi‐random mixed linear model with kinship and PCA used as covariate identified 13 single‐nucleotide polymorphic (SNP) markers significantly associated with the boiled and pounded yam food qualities. The associated SNP markers explained 7.51–13.04% of the total phenotypic variance with a limit of detection exceeding 4. CONCLUSION: Regions on chromosomes 7 and 15 were found to be associated with boiled and pounded yam quality attributes from sensory and instrument‐based assays. Gene annotation analysis for the regions of associated SNPs revealed co‐localization of several known putative genes involved in glucose export, hydrolysis and glycerol metabolism. Our study is one of the first reports of genetic factors underlying the boiled and pounded yam food quality to pave the way for marker‐assisted selection in white Guinea yam. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: It has recently become increasingly evident that banana projects in Uganda need to consider consumer preferences as part of the breeding process to increase the acceptability of new cultivars. A trained panel used quantitative descriptive analysis (QDA) as a tool to assess the sensory characteristics of 32 cooking bananas (matooke). The aim was to investigate which sensory characteristics best describe matooke. RESULTS: Fourteen descriptors were generated. The preferred attributes of matooke were high‐intensity yellow color, homogeneous distribution of yellow color, good matooke aroma, highly moldable by touch, moist and smooth in the mouth. Analysis of variance revealed significant differences in the yellowness, homogeneity of color, firmness, moistness, smoothness, matooke aroma, hardness, and moldability across the genotypes (P < 0.05). Principal component analysis (PCA) showed strong positive correlations between yellowness and homogeneity of the color (R = 0.92). Smoothness in the mouth and moldability by touch were strongly and positively correlated (R = 0.88). Firmness in the mouth was well predicted by hardness to touch (R2 = 0.85). The matooke samples were ranked into two sensory clusters by agglomerative hierarchical clustering (AHC). CONCLUSION: The study showed attribute terms that could be used to describe matooke and also revealed that QDA may be used as a tool during the assessment and selection of new cooking banana hybrids to identify relevant sensory attributes because of its ability to discriminate among the banana hybrids. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: Gari and eba, forms of cassava semolina, are mainly consumed in Nigeria and other West African countries. This study aimed to define the critical quality traits of gari and eba, to measure their heritability, to define medium and high throughput instrumental methods for use by breeders, and to link the traits with consumer preferences. The definition of a food product's profiles, including its biophysical, sensory, and textural qualities, and the identification of the characteristics that determine its acceptability, are important if new genotypes are to be adopted successfully. RESULTS: Eighty cassava genotypes and varieties (three different sets) from the International Institute of Tropical Agriculture (IITA) research farm were used for the study. Participatory processing and consumer testing data on different types of gari and eba products were integrated to prioritize the traits preferred by processors and consumers. The color, sensory, and instrumental textural properties of these products were determined using standard analytical methods, and standard operating protocols (SOPs) developed by the RTBfoods project (Breeding Roots, Tubers, and Banana Products for End‐user Preferences, https://rtbfoods.cirad.fr). There were significant (P < 0.05) correlations between instrumental hardness and sensory hardness and between adhesiveness and sensory moldability. Principal component analysis showed broad discrimination amongst the cassava genotypes and the association of the genotypes concerning the color and textural properties. CONCLUSIONS: The color properties of gari and eba, together with instrumental measures of hardness and cohesiveness, are important quantitative discriminants of cassava genotypes. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI)-based IDS systems are susceptible to adversarial attacks and face challenges such as complex evaluation methods, elevated false positive rates, absence of effective validation, and time-intensive processes. This study proposes a WCSAN-PSO framework to detect adversarial attacks in IDS based on a weighted conditional stepwise adversarial network (WCSAN) with a particle swarm optimization (PSO) algorithm and SVC (support vector classifier) for classification. The Principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) are used for feature selection and extraction. The PSO algorithm optimizes the parameters of the generator and discriminator in WCSAN to improve the adversarial training of IDS. The study presented three distinct scenarios with quantitative evaluation, and the proposed framework is evaluated with adversarial training in balanced and imbalanced data. Compared with existing studies, the proposed framework accomplished an accuracy of 99.36% in normal and 98.55% in malicious traffic in adversarial attacks. This study presents a comprehensive overview for researchers interested in adversarial attacks and their significance in computer security. [ABSTRACT FROM AUTHOR]

Given the growing urge for plastic management and regulation in the world, recent studies have investigated the problem of plastic material identification for correct classification and disposal. Recent works have shown the potential of machine learning techniques for successful microplastics classification using Raman signals. Classification techniques from the machine learning area allow the identification of the type of microplastic from optical signals based on Raman spectroscopy. In this paper, we investigate the impact of high-frequency noise on the performance of related classification tasks. It is well-known that classification based on Raman is highly dependent on peak visibility, but it is also known that signal smoothing is a common step in the pre-processing of the measured signals. This raises a potential trade-off between high-frequency noise and peak preservation that depends on user-defined parameters. The results obtained in this work suggest that a linear discriminant analysis model cannot generalize properly in the presence of noisy signals, whereas an error-correcting output codes model is better suited to account for inherent noise. Moreover, principal components analysis (PCA) can become a must-do step for robust classification models, given its simplicity and natural smoothing capabilities. Our study on the high-frequency noise, the possible trade-off between pre-processing the high-frequency noise and the peak visibility, and the use of PCA as a noise reduction technique in addition to its dimensionality reduction functionality are the fundamental aspects of this work. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are the leading cause of death globally, attributed to a complex etiology involving metabolic, genetic, and protein-related factors. Lipoprotein(a) (Lp(a)), identified as a genetic risk factor, exhibits elevated levels linked to an increased risk of cardiovascular diseases. The lipoprotein(a) kringle domains have recently been identified as a potential target for the treatment of CVDs, in this study we utilized a fragment-based drug design approach to design a novel, potent, and safe inhibitor for lipoprotein(a) kringle domain. With the use of fragment library (61,600 fragments) screening, combined with analyses such as MM/GBSA, molecular dynamics simulation (MD), and principal component analysis, we successfully identified molecules effective against the kringle domains of Lipoprotein(a). The hybridization process (Breed) of the best fragments generated a novel 249 hybrid molecules, among them 77 exhibiting superior binding affinity (≤ -7 kcal/mol) compared to control AZ-02 (-6.9 kcal/mol), Importantly, the top ten molecules displayed high similarity to the control AZ-02. Among the top ten molecules, BR1 exhibited the best docking energy (-11.85 kcal/mol), and higher stability within the protein LBS site, demonstrating the capability to counteract the pathophysiological effects of lipoprotein(a) [Lp(a)]. Additionally, principal component analysis (PCA) highlighted a similar trend of motion during the binding of BR1 and the control compound (AZ-02), limiting protein mobility and reducing conformational space. Moreover, ADMET analysis indicated favorable drug-like properties, with BR1 showing minimal violations of Lipinski's rules. Overall, the identified compounds hold promise as potential therapeutics, addressing a critical need in cardiovascular medicine. Further preclinical and clinical evaluations are needed to validate their efficacy and safety, potentially ushering in a new era of targeted therapies for CVDs. [ABSTRACT FROM AUTHOR]

Citrus is considered to have originated in north-eastern India which is recognized for having a wide variety of Citrus plants. Assam (India) is also a rich biodiversity hotspot of different Citrus species. The geographic region of this state is favored by a very suitable climatic condition for the growth of different crop plants including Citrus. But, being a flood prone area, every year Assam faces a destruction of large scale of crop plants. The increasing problem of climate change also affects this region to a greater extent from the past few years. For this reason, some unpredictable environmental conditions are experienced by this area which has become a cause of unfavorable growth of plants. Since Citrus is a very significant fruit crop of Assam, therefore, study on the effect of abiotic stress on this species is necessary. The present investigation includes a study on the morpho-physiological response of five different Citrus species to three different abiotic stresses including submergence, drought and low pH. Growth parameters like leaf fresh weight and dry weight, root fresh weight and dry weight were measured which revealed decreasing value in the stress conditions followed by an increased rate in the recovery. Similarly, the stress conditions were found to affect the physiological parameters including total chlorophyll content, relative water content (RWC), membrane stability index (MSI), relative electrolytic conductance (REC). Maximum quantum efficiency (Fv/Fm) of the Citrus plants under stress was found to be notably reduced which further increased in recovery. The key parameters mostly affected by stress were found in principal component analysis (PCA). Hydrogen peroxide (H2O2) and malondialdehyde (MDA) content of leaves and roots were highly affected by low pH and drought stress, whereas maximum quantum efficiency (Fv/Fm), membrane stability index (MSI) and relative water content (RWC) were come out to be the principal factors affected by submergence stress. Nevertheless, a good recovery rate was observed in the Citrus species. From this study the traditional Citrus species of Assam were discovered to possess significant tolerance ability against abiotic stresses. [ABSTRACT FROM AUTHOR]

A hydro-geochemical study for water-quality assessment was conducted in the Tuticorin region of Tamil Nadu in southern India during the pre- and post-monsoon seasons of 2022 and the water samples are examined to comply with BIS and WHO guidelines. In the present study, the values of TDS (75%), Mg (37.5%), and TH (25%) have exceeded the permissible limits namely 500, 100, and 600 mg/L, respectively. The WQI proposed by CCME is used to analyze water suitability. The Pearson correlation and principal component analysis are used to study the relationship between various physicochemical parameters and principal components of water quality, respectively. Piper & Gibbs diagrams are plotted to identify the water types and the main water type predicted by the piper plot is Ca-Mg-Cl-SO4 which makes up 74.3% of the the samples studied. WQI suggests that 22.5% and 50.0% specimens have excellent water quality during pre and post-monsoon seasons, respectively. [ABSTRACT FROM AUTHOR]

Micropollutants of priority have gained significant attention due to their rising concentrations and persistence in aquatic environments. Among these significant micro-pollutants is benzene, released into the environment because of mining activities, thermal power plants, and certain industrial operations, thereby contributing to environmental contamination. This study monitored benzene concentrations in the Orhaneli River over a year, employing an artificial neural network (ANN) approach to formulate a predictive model for benzene micropollutant concentrations. To provide input to the ANN, Principal Component Analysis (PCA) was employed, utilizing parameters such as precipitation, temperature, humidity, pH, flow rate, dissolved oxygen, conductivity, total organic carbon, and total nitrogen. Eleven backpropagation algorithms were compared, with the Levenberg-Marquardt algorithm identified as the optimal training algorithm. For the selected training algorithm, an optimized number of 6 neurons in the hidden layer provided the best predictions for benzene concentrations. The three-layered optimized ANN model achieved an impressive correlation coefficient (R2) of 0.9889. [ABSTRACT FROM AUTHOR]

Background: Research has demonstrated the anti‐photoaging properties of glabridin and bakuchiol. Methods: The impact of glabridin, glabridin + bakuchiol, and bakuchiol on the levels of tumor necrosis factor alpha (TNF‐α) and interleukin‐1 beta (IL‐1β) in mice skin fibroblasts was observed. Furthermore, we investigated the potential roles of fibronectin (FN), interferon‐γ (IFN‐γ), interleukin‐22 (IL‐22), and transforming growth factor‐β (TGF‐β) in the tissues, and evaluated their impact on the enzymatic levels in the skin. In conjunction with transcriptomic analysis, metabolomic profiling, and network pharmacology, all samples underwent comprehensive metabolomic and principal component analysis. The Venny2.1 method was utilized to identify variances in shared metabolites between the treatment group and the UVB group, as well as between the UVB group and the control group. Subsequently, a cluster heat map was generated to forecast and analyze metabolic pathways and targets. Results: The outcomes from the hematoxylin and eosin and toluidine blue staining revealed that glabridin and bakuchiol markedly decreased dermal thickness and suppressed mast cell infiltration in photoaged mice. Immunohistochemistry and Elisa analysis revealed that glabridin and bakuchiol effectively attenuated the levels of pro‐inflammatory factors, including IL‐1β, tumor necrosis factor‐α, IL‐22, and IFN‐γ. Furthermore, an increase in the levels of anti‐inflammatory factors such as FN and TGF‐β was also observed. The determination of the contents of superoxide dismutase, hydroxypropyltransferase and malondialdehyde in mice dorsal skin revealed that glabridin and bakuchiol not only elevated the levels of superoxide dismutase and hydroxyproline, but also reduced malondialdehyde content. Due to the limited number of shared differential metabolites exclusively within Kyoto Encyclopedia of Genes and Genomes, comprehensive pathway enrichment analysis was not feasible. Conclusion: This study demonstrates that glabridin and bakuchiol effectively impede photoaging and alleviate skin inflammation in mice. [ABSTRACT FROM AUTHOR]

This article proposes a framework for the damage assessment of and effect of temperature variations in laminated composites using Lamb waves and unsupervised autonomous features. A network of piezoelectric transducers is employed to generate data for 18 health states of a laminated composite plate. The data is processed with sparse autoencoder (SAE) for unsupervised autonomous features. The discriminative capabilities of the extracted features are confirmed by processing the feature space in the supervised and unsupervised frameworks of machine learning. The confusion matrices of supervised learning provided physical insights into the problem. The feature space was also visualized in two dimensions in an unsupervised manner through principal component analysis (PCA), which revealed physically consistent results for the effect of temperature variations, damage of different severity levels, and the undamaged paths between the actuator and sensors. The healthy state data and information on the paths between the actuator and sensors was processed via SAE for damage localization. The proposed approach can be employed for the autonomous assessment of composite structures for the presence of damage and variations of operating temperatures while using both supervised and unsupervised machine learning algorithms. [ABSTRACT FROM AUTHOR]

With the permeation of digital media into all spheres of life, individual-level efforts to manage information abundance and constant availability have become more common. To date, information on the prevalence of the motivations and strategies for such disconnection practices and how different sociodemographic groups experience digital disconnection is scarce. We surveyed a national sample of 1163 Swiss Internet users in November 2020. Thematic coding of open-text responses demonstrated people's understandings of "balanced digital media use" as primarily concerned with subjectively appropriate amounts of use, purposeful use, social connections, non-addiction, and time for "real life." Through principal components analysis, we provide a classification of the types of motivations people have for disconnecting and strategies people use to disconnect. Persistent age differences suggest that life-span approaches to studying digital disconnection are imperative. We formulate implications for disconnection research in the context of digital inequality and provide an outlook for evolving digital habits in future digital societies. [ABSTRACT FROM AUTHOR]

Objectives: The Middle Pleistocene (MP) saw the emergence of new species of hominins: Homo sapiens in Africa, H. neanderthalensis, and possibly Denisovans in Eurasia, whose most recent common ancestor is thought to have lived in Africa around 600 ka ago. However, hominin remains from this period present a wide range of morphological variation making it difficult to securely determine their taxonomic attribution and their phylogenetic position within the Homo genus. This study proposes to reconsider the phenetic relationships between MP hominin fossils in order to clarify evolutionary trends and contacts between the populations they represent. Materials and Methods: We used a Geometric Morphometrics approach to quantify the morphological variation of the calvarium of controversial MP specimens from Africa and Eurasia by using a comparative sample that can be divided into 5 groups: H. ergaster, H. erectus, H. neanderthalensis, and H. sapiens, as well as individuals from current modern human populations. We performed a Generalized Procrustes Analysis, a Principal Component Analysis, and Multinomial Principal Component Logistic Regressions to determine the phenetic affinities of the controversial Middle Pleistocene specimens with the other groups. Results: MP African and Eurasian specimens represent several populations, some of which show strong affinities with H. neanderthalensis in Europe or H. sapiens in Africa, others presenting multiple affinities. Discussion: These MP populations might have contributed to the emergence of these two species in different proportions. This study proposes a new framework for the human evolutionary history during the MP. [ABSTRACT FROM AUTHOR]

Objectives: Dental anthropological investigations into sexual dimorphism have conventionally concentrated on evaluating the dimensions and configuration of the enamel cap of canines. However, the morphology of the crown dentine surface can be closely linked to that of the enamel surface. This link can facilitate examination of crown morphology even when the enamel surface is slightly worn. Here, we determine if the morphology of the enamel‐dentine junction (EDJ) differs within (maxillary vs. mandibular) and between a sample of male (n = 26) and female (n = 21) contemporary human permanent canines from Europe. Methods: The morphological data of the EDJ were gathered employing a template comprising 96 landmarks and sliding semilandmarks. Subsequently, the data underwent analysis through form space principal component analysis following Procrustes registration, utilizing standard 3D geometric morphometric techniques. Results: Significant differences in the morphology of the EDJ were observed between the sexes, particularly concerning the overall shape of the crown, the symmetry of the mesial and distal edges, and the development of the distal accessory ridge. Conclusions: Sex differences in the morphology of the EDJ could relate in part to retention of the canine‐premolar honing complex in males. Our results indicate that analyses of the permanent canine EDJ may potentially provide a novel method for estimating the sex of adult and nonadult skeletons. [ABSTRACT FROM AUTHOR]

Exploratory analysis and visualization of multiple time series data are essential for discovering the underlying dynamics of a series before attempting modeling and forecasting. This study extends two dimension reduction methods - principal component analysis (PCA) and sliced inverse regression (SIR) - to multiple time series data. This is achieved through the innovative path point approach, a new addition to the symbolic data analysis framework. By transforming multiple time series data into time-dependent intervals marked by starting and ending values, each series is geometrically represented as successive directed segments with unique path points. These path points serve as the foundation of our novel representation approach. PCA and SIR are then applied to the data table formed by the coordinates of these path points, enabling visualization of temporal trajectories of objects within a reduced-dimensional subspace. Empirical studies encompassing simulations, microarray time series data from a yeast cell cycle, and financial data confirm the effectiveness of our path point approach in revealing the structure and behavior of objects within a 2D factorial plane. Comparative analyses with existing methods, such as the applied vector approach for PCA and SIR on time-dependent interval data, further underscore the strength and versatility of our path point representation in the realm of time series data. [ABSTRACT FROM AUTHOR]

Preadolescence is a critical period for the onset of puberty and eating‐related psychopathology. More advanced pubertal status is associated with elevated eating pathology. However, it was unclear whether this association was moderated by self‐referential processing, an important, modifiable cognitive risk for various forms of psychopathology, including eating problems. Further, no study has examined the neural correlates of self‐referential processing in relation to eating pathology. To address these gaps, we examined how the association between pubertal status and disordered eating was moderated by self‐referential processing in a community sample of 115 nine‐to‐12‐year‐old preadolescents (66 girls; mean age/SD = 10.98/1.18 years; 87.5% White). Youths reported their pubertal status and disordered eating behaviors and completed an ERP version of the Self‐Referent Encoding Task (SRET) to assess self‐referential processing. A Principal Component Analysis of the ERP data identified an anterior late positive potential (LPP) in both the positive and negative SRET conditions. The LPP in the positive condition moderated the positive association between pubertal status and disordered eating behaviors, such that this association was significant for youths with a smaller LPP toward positive self‐referential cues, but non‐significant for those showing a larger LPP toward positive self‐referential cues. These results suggest that a deeper processing of positive self‐referential information, indicated by a potentiated LPP, may weaken the negative impact of pubertal status on disordered eating. Our findings also suggest that enhancing positive self‐referential processing may be a useful tool in preventing the development of eating pathology in preadolescents, especially for those with more advanced pubertal status. This study for the first time examined the neurophysiological correlates of self‐referential processing in association with puberty and eating pathology in preadolescents. In particular, we provided novel evidence on the moderating effect of the late positive potential (LPP) underlying positive self‐referential processing on the relationship between pubertal status and eating disordered behaviors in youths. [ABSTRACT FROM AUTHOR]

Disease is a serious problem in tomato plants, causing huge economic losses. Disease detection, as the premise of protection, is important. This paper employed Electronic nose (E-nose) and Gas Chromatography–Mass Spectrometer (GC–MS), as an auxiliary technique, to predict disease type and its severity in the tomato plant. Twenty-five volatile constituents were identified using GC–MS. Their concentrations were calculated and showed the difference in different groups. Furthermore, the results of E-nose and GC–MS were compared and showed a good correlation. Moreover, the possibility of E-nose in classifying tomato plants infected with different types and severities of disease either respectively or together was proved based on either Principal Component Analysis (PCA) or Discriminant Functions Analysis (DFA). Then, Backpropagation neural network (BPNN) was introduced and showed that the correct classification rates were 98.3% for the training set and 97.5% for the testing set for predicting disease type and severity. Moreover, 100% correct classification rate was obtained for the diseased groups, which was very meaningful for the prevention of disease spread and met actual application needs. This study demonstrates the feasibility of E-nose in predicting tomato plants infected with disease in different types and severities. [ABSTRACT FROM AUTHOR]

Summary: In today's consumer landscape, the health benefits of preserved foods, particularly pickled and salted mustard greens, are gaining recognition on a global scale. To compare health‐promoting compounds between fresh and preserved mustard greens, a comprehensive assessment using proximate composition analysis and untargeted metabolomics was conducted. The sub‐metabolome data revealed distinct profiles for each type of mustard greens, clearly seen in their separation through principal component analysis. The metabolite profiling identified 37 metabolites with significant differences (P‐value < 0.05, −log10 P‐value > 4). Fresh mustard greens were found to be rich in 16 identified metabolites, including sulfonic acids, purines, oxepanes, monosaccharides, fatty acids, amino acids and tripeptides, offering antioxidants, gut enhancers and immune boosters. In contrast, pickled mustard greens exhibited an abundance of 20 identified metabolites in the classes of flavonoids, amino acids and dipeptides, providing antioxidant properties. Salted mustard greens, on the other hand, primarily showcased a high level of uridine diphosphate glucose (a pyrimidine class metabolite), supporting glycoconjugate synthesis among all samples. This study highlights the prominent presence of various classes of metabolites associated with health‐benefit compounds in fresh and preserved mustard greens. These insights contribute to the evaluation of health benefits in the food industry when choosing between pickled and salted preservation methods. [ABSTRACT FROM AUTHOR]

This paper analyzes and identifies the binders used in the jewel settings of the Baodian and Diexie belts excavated from the tomb of Murongzhi in Wuwei, Gansu, China. Fourier transform infrared spectroscopy (FTIR) indicated the presence of protein in the binder samples. The result of gas chromatography–mass spectrometry (GC‐MS) shows that samples from both contained hydroxyproline, a characteristic amino acid for animal glue. Principal component analysis was performed on one of the samples, and it was concluded that the binder was a mixture of hide glue and egg glue. Further analysis using scanning electron microscopy with energy‐dispersive X‐ray (SEM‐EDX) and laser Raman spectroscopy (Raman) found that a red particulate matter visually identified in the binder was cinnabar, which was apparently added to binder in order to modify the color of the jewels. This study provides a basis for the conservation and restoration of future finds of elite jewelry. It also shows how imperial jewelry was made during the Tang Dynasty. Finally, the analysis of these heraldic belts provides a key insight the life and role of an exiled prince in the Tang court. [ABSTRACT FROM AUTHOR]

Eucalyptus scab and shoot malformation is an emerging disease and a serious threat to the global plantation forestry industry. The disease appeared in North Sumatra (Indonesia) in the early 2010s and the causal agent was recently described as a novel species, Elsinoe necatrix. Nothing is known regarding its possible origin or why it emerged rapidly to cause a serious local epidemic. To investigate its population biology, we developed 15 polymorphic microsatellite markers as well as mating‐type markers using genome sequences for two E. necatrix isolates. Isolates of the pathogen were collected from different host varieties at four locations in the Lake Toba region of North Sumatra and characterized using these markers. A high level of genotypic diversity was observed for all populations with little to no genetic differentiation between sampling areas. Discriminant analysis of principal components, genotype networks and analysis of molecular variance all showed a lack of population structure and a high level of gene flow among sampling regions. Mating‐type ratios and linkage disequilibrium analyses suggest that sexual recombination is likely to be occurring, although a sexual state has not been found for the pathogen. The results of this study highlight the fact that new genotypes of E. necatrix, probably arising from cryptic sexual recombination, will challenge efforts to manage the disease, and that breeding and selection for tolerance will require substantial host genetic diversity. [ABSTRACT FROM AUTHOR]

Summary: Background: There is limited evidence on the associations of dietary factors and patterns with risk of later‐onset ulcerative colitis (UC) in Chinese adults. Aims: To investigate the associations of dietary factors and patterns with risk of later‐onset UC in Chinese. Methods: The prospective China Kadoorie Biobank cohort study recruited 512,726 participants aged 30–79. Dietary habits were assessed using food frequency questionnaires. Dietary patterns were derived by factor analysis with a principal component method. Cox regression analysis was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results: During a median follow‐up of 12.1 years, 312 cases of newly diagnosed UC were documented (median age of diagnosis 60.1 years). Egg consumption was associated with higher risk of UC (HR for daily vs. never or rarely: 2.29 [95% CI: 1.26–4.16]), while spicy food consumption was inversely associated with risk of UC (HR: 0.63 [0.45–0.88]). The traditional northern dietary pattern, characterised by high intake of wheat and low intake of rice, was associated with higher risk of UC (HR for highest vs. lowest quartile of score: 2.79 [1.93–4.05]). The modern dietary pattern, characterised by high intake of animal‐origin foods and fruits, was associated with higher risk of UC (HR: 2.48 [1.63–3.78]). Population attributable fraction was 13.04% (7.71%–19.11%) for daily/almost daily consumption of eggs and 9.87% (1.94%–18.22%) for never/rarely consumption of spicy food. Conclusions: The findings highlight the importance of evaluating dietary factors and patterns in the primary prevention of later‐onset UC in Chinese adults. [ABSTRACT FROM AUTHOR]

From a conservation standpoint, species that are managed without consideration of their population sizes and connectivity have the potential to be over‐exploited and/or incur population decline. The burrowing shrimp, Trypaea australiensis, is an important ecosystem engineer and fishery resource caught in large numbers for which population information is unknown for properly managing the species. Here, we determined the level of population structure of T. australiensis across three locations along the East Coast of New South Wales, Australia, using genome‐wide single nucleotide polymorphisms (SNPs) obtained through double digest Restriction‐site Associated DNA‐sequencing (ddRAD‐seq). Analysis of population structure, including pairwise Fst (−0.003 to −0.001), STRUCTURE (K = 2) and Discriminant Analysis of Principal Components (DAPC) showed no evidence of structure among locations. Our findings provide crucial preliminary population genetic data for a key cryptic species, that also suggests gene flow among sampling locations enables the management of fisheries throughout the study area as a single unit. [ABSTRACT FROM AUTHOR]

Qaidam cattle are a typical Chinese native breed inhabiting northwest China. They bear the characteristics of high cold and roughage tolerance, low‐oxygen adaptability and good meat quality. To analyze the genetic diversity of Qaidam cattle, 60 samples were sequenced using whole‐genome resequencing technology, along with 192 published sets of whole‐genome sequencing data of Indian indicine cattle, Chinese indicine cattle, North Chinese cattle breeds, East Asian taurine cattle, Eurasian taurine cattle and European taurine cattle as controls. It was found that Qaidam cattle have rich genetic diversity in Bos taurus, but the degree of inbreeding is also high, which needs further protection. The phylogenetic analysis, principal component analysis and ancestral component analysis showed that Qaidam cattle mainly originated from East Asian taurine cattle. Qaidam cattle had a closer genetic relationship with the North Chinese cattle breeds and the least differentiation from Mongolian cattle. Annotating the selection signals obtained by composite likelihood ratio, nucleotide diversity analysis, integrated haplotype score, genetic differentiation index, genetic diversity ratio and cross‐population extended haplotype homozygosity methods, several genes associated with immunity, reproduction, meat, milk, growth and adaptation showed strong selection signals. In general, this study provides genetic evidence for understanding the germplasm characteristics of Qaidam cattle. At the same time, it lays a foundation for the scientific and reasonable protection and utilization of genetic resources of Chinese local cattle breeds, which has great theoretical and practical significance. [ABSTRACT FROM AUTHOR]

The study of groundwater samples from Patna, which is in the middle Ganga River basin in the state of Bihar, was done to evaluate the hydrogeochemical nature of groundwater and figure out the controlling factors that affect its ionic chemistry. This study's groundwater samples were taken seasonally from 20 Patna, Bihar locations from 2019 to 2020 and analyzed for their physical and chemical properties using APHA's standard methods. Multivariate statistical techniques, such as Correlation matrix, Principal Component Analysis and Hierarchical cluster analysis, were carried out on the analytical variables, followed by interpreting the groundwater's geochemical nature using Durov Plot, Piper plot, ion-scatter plots and Gibb's diagram. The results obtained were compared with water quality standards prescribed by the Bureau of Indian Standards. The study revealed that groundwater's alkaline nature and water's total hardness varied from hard to very hard categories. Total dissolved solids range from 158 to 575 mg/l, where 31.66% of samples have exceeded the desirable TDS limit for drinking water. The abundance of anions was observed in decreasing order (HCO3− > Cl− > SO42−) during all seasons and cations as Ca2+ > Na+ > Mg2+ > K+ in pre-monsoon and post-monsoon, whereas in monsoon season, it was observed as Mg2+ > Na+ > Ca2+ > K+. Ca2+–Mg2+–HCO3− type water was revealed as the significant hydrogeochemical facie, and the reverse ion exchange process was observed to be the governing factor in 77% of the total groundwater samples. Gibbs's diagram showed rock–water dominance as a major factor in groundwater ionic composition during all seasons. [ABSTRACT FROM AUTHOR]

The conversion of forest to agricultural land has been a major worldwide concern and a cause of deterioration of soil quality. The study examined the influence of land uses on the soil quality index (SQI) using minimum datasets (MDS) constructed from soil profiles through two methods namely, additive (SQIa) and weighted (SQIw). In this study, 29 geo-referenced soil profiles (116 layers) up to a depth of 150 cm were collected in Northeastern India, representing forest, jhum, jhum fallow, and agricultural land uses. Principal component analysis (PCA) with multiple correlation analysis is found to be a suitable statistical method for selecting the key soil quality indicators. From PCA, organic carbon (OC), sand, clay, available P and available S were selected as soil quality indicators in MDS. Our results showed that these MDS significantly varied within the profiles depending on the land-uses. The estimated values of SQIs showed that SQIa was higher than SQIw across the soil depths and land-uses. For the surface soil, SQIa values were in the order of forest (2.97) > jhum fallow (2.82) > agriculture (2.68) > jhum land (1.97) and for profile soil jhum fallow (2.38) > forest (2.37) > agriculture (2.33) > jhum (1.88). Similarly, SQIw followed the trend of forest > jhum fallow > agriculture > jhum for all the three studied depths. The statistical analysis showed that the surface, subsurface, and profile soil had no significant impact on SQIw for agriculture, fallow jhum, and forest. The overall data indicated that jhum land had a significantly lower SQI than other land-uses for all soil depths. From this study, the information generated can be useful to assess the soil quality in different land-uses for managing soil health in similar areas of the Northeastern region of India. The findings are also useful in the ecosystems in which soils develop in an acid edaphic environment. [ABSTRACT FROM AUTHOR]

In this paper, a streak camera system is used for obtaining the spectral images in laser-induced breakdown spectroscopy. Streak images are time-resolved, thus enabling tracking of the temporal development of the atomic and ionic emission lines from the plasma plume. To make our study more comprehensive, we have used a machine-learning approach to data analysis. We propose how to reduce the level of noise on streak images by applying the principal component analysis technique. The usual method of improving the signal-to-noise ratio by taking a large number of expositions in streak camera photon counting mode is often not practical. [ABSTRACT FROM AUTHOR]

Mediation has been promoted as a complementary alternative to traditional adjudicatory resolution methods for settling construction disputes. The use of mediation has been promoted for its time- and cost-saving advantages. The voluntary approach is also considered suitable to the spirit of disputing parties settling their own disputes by themselves and is thus considered a prerequisite design of mediation. Nevertheless, the power asymmetry between disputing parties may hinder bona fide mediation. This study examined the importance of voluntary participation in fostering collaborative mediating behaviors. The impact of power asymmetry on the use of voluntary construction dispute mediation was also explored. With data collected from mediation practitioners and the application of principal component factor analysis, constructs for voluntary participation (VP), collaborative mediating behaviors (CMB), and power asymmetry (PA) were developed. Partial least squares structural equation modeling analyses were then used to test the hypothesized relationships. It was found that (1) the practice of CMB would bring about positive mediation outcomes; (2) VP would engender collaborative mediating behaviors; and (3) PA between the disputing parties would have no significant negative impacts on voluntary attempts at mediation or the practice of CMB. These findings were validated by real mediation cases. This study contributes to construction mediation studies by affirming the importance of keeping mediation a voluntary process. With a flexible process, voluntary mediation can be a tenable option for resolving construction disputes notwithstanding the power differentials between the mediating parties. [ABSTRACT FROM AUTHOR]

Semen cryopreservation represents a promising technology utilized for preserving high-quality chicken varieties in husbandry practices. However, the efficacy of this methodology is significantly impeded by the diminished quality of sperm. Metabolites, as the end products of metabolic reactions, serve as indicators of biological processes and offer insights into physiological conditions. In this study, we investigaged the sperm quality and alteration in metabolic profiles during the cryopreservation of Longyou Partridge Chicken semen. Following artificial semen collection, four groups of semen samples were established based on four points of the cryopreservation process (Ⅰ, fresh semen; Ⅱ, semen added extender and chilled at 4 °C for 30 min; Ⅲ, semen added cryoprotectants; Ⅳ, semen gradient freezed and stored in liquid nitrogen). Semen cryopreservation has a negative effect on the percentage of sperm in a straight-line trajectory (LIN), has no significant effect on total motile sperms (TM) or the proportion of sperm with typical morphology (NM). Metabolites were identified using LC-MS technique and analyses including Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), Univariate statistical analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database were employed to identify metabolites. A total of 2471 metabolites had been identified, with the majority of the list being made up of amino acids and their metabolites as well as benzene and substituted derivatives. Group II exhibits 882 metabolites with significantly elevated abundance relative to Group I, alongside 37 metabolites displaying decreased abundance. In Group III, 836 metabolites demonstrate notably augmented abundance compared to Group II, while 87 metabolites exhibit reduced abundance. Furthermore, Group IV showcases 513 metabolites with markedly heightened abundance in comparison to Group III, and 396 metabolites with decreased abundance. Specific metabolites such as 5-Hydroxylysine, Phosphocholine, and alpha- d -glucose-6-phosphate exhibited a progressive decline during the cryopreservation process, correlating with either dilution and chilling, cryoprotectant addition, or freezing. In conclusion, our investigation systematically examined the changes of seminal metabolome and sperm quality throughout the cryopreservation process of rooster semen. • The alterations in metabolites during cryopreservation of rooster semen were investigated through four pivotal stages. • It demonstrates correlations between the semen metabolome and sperm quality. • Certain metabolites were newly found to decline progressively during semen cryopreservation. [ABSTRACT FROM AUTHOR]

With the rapid development of sensor and communication technology, multi-sensor data is available to monitor the degradation of complex systems and predict the failure modes. However, two huge challenges remain to be resolved: (i) how to predict the failure modes with limited failure mode labeled systems to alleviate the heavy dependence on expert experience; (ii) how to effectively fuze the useful information from the multi-sensor data to achieve an accurate estimation of the degradation status automatically. To address these issues, we propose a novel semi-supervised prognostic model for the systems with partially observable failure modes, where only a small fraction of the systems in the training set are known for their failure modes. First, we develop a graph-based semi-supervised learning method to extract features characterizing the failure modes. Then, we input these features as well as the multi-sensor streams into an elastic net functional regression model to predict the residual useful lifetime. The proposed model is validated by extensive simulation studies and a case study of aircraft turbofan engines available from the NASA repository. [ABSTRACT FROM AUTHOR]

Thymoquinone, a significant component in medicine and the spice industry due to its potent antioxidant properties, is the focus of this paper. The development of a titanium oxide-modified graphite paste electrode is presented for the detection of thymoquinone in black cumin. The electrochemical characteristics of the thymoquinone molecule were explored in a 0.1 M phosphate buffer solution of pH of 6. The modified graphite paste electrode exhibits excellent linearity across the concentration range of 0.1-200 μ M with limit of detection is 0.02 μ M. The developed electrode exhibits commendable repeatability, reproducibility and exceptional sensitivity towards thymoquinone. The separation of various concentrations of thymoquinone samples such as Mousumi, Priya Gold, Sunrise and DNV using Principal Component Analysis (PCA) with the Separability Index (SI) of 67.89 utilized to differentiate between different thymoquinone concentrations. [ABSTRACT FROM AUTHOR]

Background: A thorough understanding of lameness prevalence is essential for evaluating the impact of this condition on the dairy industry and assessing the effectiveness of preventive strategies designed to minimize its occurrence. Therefore, this cross-sectional study aimed to ascertain the prevalence of lameness and identify potential risk factors associated with lameness in Holstein Friesian crossbred cows across both commercial and smallholder dairy production systems in Bengaluru Rural District of Karnataka, India. Methods: The research encompassed six commercial dairy farms and 139 smallholder dairy farms, involving a total of 617 Holstein Friesian crossbred cattle. On-site surveys were conducted at the farms, employing a meticulously designed questionnaire. Lameness in dairy cattle was assessed subjectively using a locomotion scoring system. Both bivariate and binary logistic regression models were employed for risk assessment, while principal components analysis (PCA) was conducted to address the high dimensionality of the data and capture the underlying structure of the explanatory variables. Results: The overall lameness prevalence of 21.9% in commercial dairy farms and 4.6% in smallholder dairy farms. Various factors such as age, body weight, parity, body condition score (BCS), floor type, hock and knee injuries, animal hygiene, provision of hoof trimming, and the presence of hoof lesions were found to be significantly associated with lameness. Binary logistic regression analysis indicated that the odds of lameness in crossbred cows increased with higher parity, decreased BCS, presence of hard flooring, poor animal hygiene, and the existence of hoof lesions. These factors were identified as potential risk factors for lameness in dairy cows. Principal component analysis unveiled five components explaining 71.32% of the total variance in commercial farms and 61.21% in smallholder dairy farms. The extracted components demonstrated higher loadings of housing and management factors (such as hoof trimming and provision of footbath) and animal-level factors (including parity, age, and BCS) in relation to lameness in dairy cows. Conclusions: The findings suggest that principal component analysis effectively reduces the dimensionality of risk factors. Addressing these identified risk factors for lameness is crucial for the strategic management of lameness in dairy cows. Future research in India should investigate the effectiveness of management interventions targeted at the identified risk factors in preventing lameness in dairy cattle across diverse environments. [ABSTRACT FROM AUTHOR]

For nearly two decades, the Ozone Monitoring Instrument (OMI) aboard the NASA Aura spacecraft (launched in 2004) and the Ozone Mapping and Profiler Suite (OMPS) aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite (launched in 2011) have been providing global monitoring of SO2 column densities from both anthropogenic and volcanic activities. Here, we describe the version 1 NOAA-20 (N20)/OMPS SO2 product, aimed at extending the long-term climate data record. To achieve this goal, we apply a principal component analysis (PCA) retrieval technique, also used for the OMI and SNPP/OMPS SO2 products, to N20/OMPS. For volcanic SO2 retrievals, the algorithm is identical between N20 and SNPP/OMPS and produces consistent retrievals for eruptions such as the 2018 Kilauea and 2019 Raikoke. For anthropogenic SO2 retrievals, the algorithm has been customized for N20/OMPS, considering its greater spatial resolution and reduced signal-to-noise ratio as compared with SNPP/OMPS. Over background areas, N20/OMPS SO2 slant column densities (SCD) show relatively small biases, comparable retrieval noise with SNPP/OMPS (after aggregation to the same spatial resolution), and remarkable stability with essentially no drift during 2018–2023. Over major anthropogenic source areas, the two OMPS retrievals are generally well-correlated but N20/OMPS SO2 is biased low especially for India and the Middle East, where the differences reach ~20 % on average. The reasons for these differences are not fully understood but are partly due to algorithmic differences. Better agreement (typical differences of ~10–15 %) is found over degassing volcanoes. SO2 emissions from large point sources, inferred from N20/OMPS retrievals, agree well with those based on OMI, SNPP/OMPS, and TROPOspheric Monitoring Instrument (TROPOMI), with correlation coefficients > 0.98 and overall differences < 10 %. The ratios between the estimated emissions and their uncertainties offer insights into the ability of different satellite instruments to detect and quantify SO2 sources. While TROPOMI has the highest ratios among all four sensors, ratios from N20/OMPS are slightly greater than OMI and substantially greater than SNPP/OMPS. Overall, our results suggest that the version 1 N20/OMPS SO2 product will successfully continue the long-term OMI and SNPP/OMPS SO2 data records. Efforts currently underway will further enhance the consistency of retrievals between different instruments, facilitating the development of multi-decade, coherent global SO2 datasets across multiple satellites. [ABSTRACT FROM AUTHOR]

Background: Kiwifruit (Actinidiaceae family) is an economically important fruit tree in China and New Zealand. It is a typical dioecious plant that has undergone frequent natural hybridization, along with chromosomal ploidy diversity within the genus Actinidia, resulting in higher genetic differences and horticultural diversity between interspecific and intraspecific traits. This diversity provides a rich genetic base for breeding. China is not only the original center of speciation for the Actinidia genus but also its distribution center, housing the most domesticated species: A. chinensis var. chinensis, A. chinensis var. deliciosa, A. arguta, and A. polygama. However, there have been relatively few studies on the application of DNA markers and the genetic basis of kiwifruit plants. By combining information from chloroplast-specific SNPs and nuclear SCoT (nSCoT) markers, we can uncover complementary aspects of genetic variation, population structure, and evolutionary relationships. In this study, one chloroplast DNA (cpDNA) marker pair was selected out of nine cpDNA candidate pairs. Twenty nSCoT markers were selected and used to assess the population structure and chloroplast-specific DNA haplotype diversity in 55 kiwifruit plants (Actinidia), including 20 samples of A. chinensis var. chinensis, 22 samples of A. chinensis var. deliciosa, 11 samples of A. arguta, and two samples of A. polygama, based on morphological observations collected from China. Results: The average genetic distance among the 55 samples was 0.26 with chloroplast-specific SNP markers and 0.57 with nSCoT markers. The Mantel test revealed a very small correlation (r = 0.21). The 55 samples were categorized into different sub-populations using Bayesian analysis, the Unweighted Pair Group Method with the Arithmetic Mean (UPGMA), and the Principal Component Analysis (PCA) method, respectively. Based on the analysis of 205 variable sites, a total of 15 chloroplast-specific DNA haplotypes were observed, contributing to a higher level of polymorphism with an Hd of 0.78. Most of the chloroplast-specific DNA haplotype diversity was distributed among populations, but significant diversity was also observed within populations. H1 was shared by 24 samples, including 12 of A. chinensis var. chinensis and 12 of A. chinensis var. deliciosa, indicating that H1 is an ancient and dominant haplotype among the 55 chloroplast-specific sequences. H2 may not have evolved further.The remaining haplotypes were rare and unique, with some appearing to be exclusive to a particular variety and often detected in single individuals. For example, the H15 haplotype was found exclusively in A. polygama. Conclusion: The population genetic variation explained by chloroplast-specific SNP markers has greater power than that explained by nSCoTs, with chloroplast-specific DNA haplotypes being the most efficient. Gene flow appears to be more evident between A. chinensis var. chinensis and A. chinensis var. deliciosa, as they share chloroplast-specific DNA haplotypes, In contrast, A.arguta and A. polygama possess their own characteristic haplotypes, derived from the haplotype of A. chinensis var. chinensis. Compared with A. chinensis, the A.arguta and A. polygama showed better grouping. It also seems crucial to screen out, for each type of molecular marker, especially haplotypes, the core markers of the Actinidia genus. [ABSTRACT FROM AUTHOR]

This study presents an analysis of soil contamination caused by Ni, Zn, Cd, Cu, and Pb at municipal solid waste (MSW) landfills, with a focus on ecological risk assessment. The approach aims to assess how different landfill practices and environmental conditions affect soil contamination with potentially toxic elements (PTEs) and associated environmental risks. Soil samples were collected from MSW landfills in Poland and the Czech Republic. The research included a comprehensive assessment of PTEs in soils in the context of global environmental regulations. The degree of soil contamination by PTEs was assessed using indices: Geoaccumulation Index (Igeo), Single Pollution Index (Pi), Nemerow Pollution Index (PN), and Load Capacity of a Pollutant (PLI). The ecological risk was determined using the Risk of PTEs (ERi) and Sum of Individual Potential Risk Factors (ERI). The maximum values of the indicators observed for the Radiowo landfill were as follows: Igeo = 4.04 for Cd, Pi = 24.80 for Cd, PN = 18.22 for Cd, PLI = 2.66, ERi = 744 for Cd, ERI = 771.80. The maximum values of the indicators observed for the Zdounky landfill were as follows: Igeo = 1.04 for Cu, Pi = 3.10 for Cu, PN = 2.52 for Cu, PLI = 0.27, ERi = 25 for Cd, ERI = 41.86. The soils of the tested landfills were considered to be non-saline, with electrical conductivity (EC) values less than 2,000 μS/cm. Varying levels of PTEs were observed, and geostatistical analysis highlighted hotspots indicating pollution sources. Elevated concentrations of Cd in the soil indicated potential ecological risks. Concentrations of Cu and lead Pb were well below the thresholds set by the environmental legislation in several countries. In addition, Ni concentrations in the soils of both landfills indicated that the average levels were within acceptable limits. Principal Component Analysis (PCA) revealed common sources of PTEs. The identification of specific risk points at the Radiowo and Zdounky sites contributes to a better understanding of potential hazards in landfill environments. By establishing buffer zones and implementing regular maintenance programs, emerging environmental problems can be addressed in a timely manner. [ABSTRACT FROM AUTHOR]

Digital rural construction is a key strategic direction to promote China's rural revitalization and alleviate global climate problems. In order to put forward feasible suggestions for the subsequent development and ensure the smooth development of digital village construction, how to reflect the development level of the digital village through scientific and reasonable comprehensive evaluation has become an urgent problem to be solved. This paper establishes a comprehensive evaluation index system through the Delphi method and principal component analysis method, then assigns weights to the evaluation indicators based on the improved CRITIC-G1 method, and then grades the development level of digital villages according to the extension matter element method. Finally, taking Jiangxi Province in China as an example, the overall development level of digital villages in Jiangxi Province is evaluated from the provincial level according to the proposed method. And put forward the corresponding countermeasures and suggestions. Results: Firstly, the development level of digital villages in Jiangxi Province is good, and there is a trend of excellent development level. Secondly, from different aspects of digital rural development, the digitalization of infrastructure, services, economy, and green production in Jiangxi Province is at a good level, and the digitalization of life has reached an excellent level. Thirdly, from the perspective of development trends, the digitization of infrastructure has a progressive trend towards an excellent level of development, while the digitization of services, economy and green production has signs of development regression. According to the analysis results, the relevant countermeasures and suggestions are put forward from four aspects: talent, capital, governance system and development planning. Other regions can evaluate the development level of the digital village according to the evaluation model proposed in this paper so as to analyze the existing problems and put forward targeted solutions to promote the construction of the digital village. [ABSTRACT FROM AUTHOR]

Objectives: Soccer heading is adversely associated with neurocognitive performance, but whether greater neck strength or anthropometrics mitigates these outcomes is controversial. Here, we examine the effect of neck strength or anthropometrics on associations of soccer heading with neurocognitive outcomes in a large cohort of adult amateur players. Methods: 380 adult amateur league soccer players underwent standardized measurement of neck strength (forward flexion, extension, left lateral flexion, right lateral flexion) and head/neck anthropometric measures (head circumference, neck length, neck circumference and neck volume). Participants were assessed for heading (HeadCount) and cognitive performance (Cogstate) on up to 7 visits over a period of two years. Principal components analysis (PCA) was performed on 8 neck strength and anthropometric measures. We used generalized estimating equations to test the moderation effect of each of the three PCs on 8 previously identified adverse associations of 2-week and 12-month heading estimates with cognitive performance (psychomotor speed, immediate verbal recall, verbal episodic memory, attention, working memory) and of unintentional head impacts on moderate to severe central nervous system symptoms. Results: 3 principal components (PC's) account for 80% of the variance in the PCA. In men, PC1 represents head/neck anthropometric measures, PC2 represents neck strength measures, and PC3 represents the flexor/extensor (F/E) ratio. In women, PC1 represents neck strength, PC2 represents anthropometrics, and PC3 represents the F/E ratio. Of the 48 moderation effects tested, only one showed statistical significance after Bonferroni correction, which was not robust to extensive sensitivity analyses. Conclusion: Neither neck strength nor anthropometrics mitigate adverse associations of soccer heading with cognitive performance in adult amateur players. [ABSTRACT FROM AUTHOR]

Hydrogen is an ideal energy carrier manufactured mainly by the natural gas steam reforming hydrogen production process. The concentrations of  CH 4 ,  CO ,  CO 2 , and  H 2  in this process are key variables related to product quality, which thus need to be controlled accurately in real-time. However, conventional measurement methods for these concentrations suffer from significant delays or huge acquisition and upkeep costs. Virtual sensors effectively compensate for these shortcomings. Unfortunately, previously developed virtual sensors have not fully considered the complex characteristics of the hydrogen production process. Therefore, a virtual sensor model, called "moving window-based dynamic variational Bayesian principal component analysis (MW-DVBPCA)" is developed for key gas concentration estimation. The MW-DVBPCA considers complicated characteristics of the hydrogen production process, involving dynamics, time variations, and transportation delays. Specifically, the dynamics are modeled by the finite impulse response paradigm, the transportation delays are automatically determined using the differential evolution algorithm, and the time variations are captured by the moving window method. Moreover, a comparative study of data-driven virtual sensors is carried out, which is sporadically discussed in the literature. Meanwhile, the performance of the developed MW-DVBPCA is verified by the real-life natural gas steam reforming hydrogen production process. [ABSTRACT FROM AUTHOR]

The main aim of this study was to utilize remote sensing data to establish regression models through machine learning to predict locust density in the upcoming year. First, a dataset for monitoring grassland locust density was constructed based on meteorological data and multi-source remote sensing data in the study area. Subsequently, an SVR (support vector regression) model, BP neural network regression model, random forest regression model, BP neural network regression model with the PCA (principal component analysis), and deep belief network regression model were built on the dataset. The experimental results show that the random forest regression model had the best prediction performance among the five models. Specifically, the model achieved a coefficient of determination (R2) of 0.9685 and a root mean square error (RMSE) of 1.0144 on the test set, which were the optimal values achieved among all the models tested. Finally, the locust density in the study area for 2023 was predicted and, by comparing the predicted results with actual measured data, it was found that the prediction accuracy was high. This is of great significance for local grassland ecological management, disaster warning, scientific decision-making support, scientific research progress, and sustainable agricultural development. [ABSTRACT FROM AUTHOR]

The co-processing of different wastes as fuels in the manufacture of cement clinker not only meets the objectives of a circular economy but also contributes to the reduction in CO2 emissions in the manufacture of Portland cement. However, waste used as alternative fuels, such as sludge or organic-rich residues, may contain naturally occurring radionuclides that can be concentrated during the combustion process. In this study, the presence of natural radionuclides (radioactive series of uranium, thorium, and 40K) and anthropogenic radionuclides (137Cs) in these wastes has been investigated by gamma spectrometry. Possible relationships between the radioactive content and the obtained chemical composition, determined by X-ray fluorescence, have also been studied by applying a principal component analysis (PCA). The results showed that the wastes with the highest radioactive content were sewage sludge with activity concentrations of 238U and 210Pb of 321 ± 38 Bq kg−1 and 110 ± 14 Bq kg−1, respectively. A correlation between radioactive content and Fe2O3 concentration was also observed. The annual effective dose rates to workers for the ashes estimated from the ash content ranged from 0.0033 mSv to 0.092 mSv and therefore do not pose a risk to workers as they are lower than the 1 mSv per year limit for the general public (DIRECTIVE 2013/59/EURATOM). [ABSTRACT FROM AUTHOR]

In recent decades several methods have been developed to extract coastlines from remotely sensed images. In fact, this is one of the principal fields of remote sensing research that continues to receive attention, as testified by the thousands of scientific articles present in the main databases, such as SCOPUS, WoS, etc. The main issue is to automatize the whole process or at least a great part of it, so as to minimize the human error connected to photointerpretation and identification of training sites to support the classification of objects (basically soil and water) present in the observed scene. This article proposes a new fully automatic methodological approach for coastline extraction: it is based on the unsupervised classification of the most decorrelated fictitious band derived from Principal Component Analysis (PCA) applied to the satellite images. The experiments are carried out on datasets characterized by images with different geometric resolution, i.e., Landsat 9 Operational Land Imager (OLI) multispectral images (pixel size: 30 m), a Sentinel-2 dataset including blue, green, red and Near Infrared (NIR) bands (pixel size: 10 m) and a Sentinel-2 dataset including red edge, narrow NIR and Short-Wave Infrared (SWIR) bands (pixel size: 20 m). The results are very encouraging, given that the comparison between each extracted coastline and the corresponding real one generates, in all cases, residues that present a Root Mean Squared Error (RMSE) lower than the pixel size of the considered dataset. In addition, the PCA results are better than those achieved with Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) applications. [ABSTRACT FROM AUTHOR]