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In order to investigate the stability of qualitative and quantitative traits and choose the most appropriate cultivation method and irrigation regime in rice plants, the experiment was conducted in the form of a split-plot design based on a randomized complete block design in three replications and in two cropping years. The results of compound analysis indicated that the effect of year × irrigation regimes in terms of traits, grain yield, rainfall, productivity 2, number of full grains, number of hollow grains, harvest index, percentage of crushed grains and yield of white rice, the effect of year × Cultivation method characteristics in terms of water consumption, rainfall, productivity 1, number of tillers, plant height, spike length, number of hollow grains, thousand seed weight, small grain percentage and white rice yield and the effect of irrigation regime × cultivation methods in terms of all Traits except traits productivity 2 were significant. The results of the mean comparison of the interaction effect of irrigation regimes and cultivation methods also showed that treatments T1W1, T2W1 and T1W3 are favorable for all evaluated traits. Based on the table of correlation coefficients, correlation diagram and map of the intensity of the correlation in the years of the experiment, it is possible to report the correlation of the grain yield trait with the traits of water consumption, rainfall, plant height, 100- seed weight, full grain number and white rice yield. Also, traits productivity 1, productivity 2 and small grain percentage showed a positive correlation and a negative correlation of these three traits with most of the traits evaluated in the experiment was observed. Based on the analysis of the main components, the first four main components explained the most data variance, and T2W2 and T2W3 treatments were identified as suitable treatments for rice cultivation in terms of the first and second main components. Resumo Para investigar a estabilidade dos caracteres qualitativos e quantitativos e escolher o método de cultivo e regime de irrigação mais adequados em plantas de arroz, o experimento foi conduzido em esquema de parcelas subdivididas em blocos completos em três repetições e em dois anos de colheita. Os resultados da análise composta indicaram os seguintes fatores significativos: o efeito dos regimes de irrigação do ano X em termos de características, rendimento de grãos, pluviosidade, produtividade 2, número de grãos cheios, número de grãos ocos, índice de colheita, porcentagem de grãos esmagados e rendimento de arroz branco, o efeito do ano X, características do método de cultivo relativamente ao consumo de água, precipitação, produtividade 1, número de perfilhos, altura da planta, comprimento da espiga, número de grãos ocos, peso de 1000 sementes, porcentagem de grãos pequenos e rendimento de arroz branco e o efeito da irrigação no regime X, métodos de cultivo em relação a todos os caracteres, exceto os caracteres produtividade 2. Já os resultados da comparação média do efeito de interação entre regimes de irrigação e métodos de cultivo também mostraram que os tratamentos T1W1, T2W1 e T1W3 são favoráveis para todas as características avaliadas. Com base na tabela de coeficientes de correlação, diagrama de correlação e mapa da intensidade da correlação nos anos do experimento, é possível relatar a correlação da característica produtividade de grãos com as características consumo de água, pluviosidade, altura de planta, 100 - peso da semente, número de grãos cheios e rendimento do arroz branco. Além disso, os caracteres produtividade 1, produtividade 2 e porcentagem de grãos pequenos apresentaram correlação positiva e foi observada correlação negativa desses 3 caracteres com a maioria dos caracteres avaliados no experimento. Por fim, com base na análise dos componentes principais, os quatro primeiros componentes principais explicaram a maior variância dos dados, e os tratamentos T2W2 e T2W3 foram identificados como tratamentos adequados para o cultivo de arroz quanto aos primeiro e segundo componentes principais.

The lack of diagnostic taxonomic characteristics in some species complexes leave the species delimitation of Oxytropis DC. unresolved. Seed morphological features have proved to be useful diagnostic and taxonomic characteristics in Fabaceae. However, there are few systematic studies on the seed characteristics of Oxytropis. Here, we used scanning electron and stereoscopic microscopy to investigate the seed characteristics of 35 samples obtained from 21 Oxytropis species from northwest China. Our examination showed two main types of hilum positions, terminal and central, and five different types of seed shapes: prolonged semielliptic, reniform, prolonged reniform, quadratic, and cardiform. Seven different sculpturing patterns were identified: scaled, regulated, lophate with stellated testa cells, simple reticulate, rough, compound reticulate, and lophate with rounded testa cells. The seeds ranged from 1.27 to 2.57 mm in length and from 1.18 to 2.02 mm in width, and the length-to-width ratio ranged from 0.89 to 1.55 mm. The seed shape was constant within species and was useful for species delimitation within the genus Oxytropis when combined with other macroscopic traits. In contrast, the sculpturing patterns were highly variable at the species level and could not be used for species identification. Results of the cluster analysis and principal component analysis (PCA) indicated that the seed traits of Oxytropis species are useful for taxa identification at the species level, but have low taxonomic value at the section level.

In many manufacturing systems, anomaly detection is critical to identifying process errors and ensuring product quality. This paper proposes three semi-supervised solutions to detect anomalies in Direct Current (DC) Nut Runner engine assembly processes. The nut runner process is a challenging anomaly detection problem due to the manual nature of the process inducing high variability and ambiguity of the anomalous class. These characteristics lead to a scenario where anomalies are not outliers, and the normal operating conditions are difficult to define. To address these challenges, a Gaussian Mixture Model (GMM) was trained using a semi-supervised approach. Three dimensionality reduction methods were compared in pre-processing: PCA, t-SNE, and UMAP. These approaches are demonstrated to outperform the current approaches used by a major automotive company on two real-world datasets. Furthermore, a novel approach to labelling real-world data is proposed, including the concept of an ‘Anomaly No Concern’ class, in addition to the traditional labels of ‘Anomaly’ and ‘Normal’. Introducing this new term helped address knowledge gaps between data scientists and domain experts, as well as providing new insights during model development and testing. This represents a major advancement in identifying anomalies in manual production processes that use handheld tools.

In this paper, the dataset of real incidents that occurred in Turkey between 2013 and 2017 and are regarded as acts of terrorism without any doubt according to Global Terrorism Database (GTD) are used to predict the group names responsible for unknown attacks. Principal Component Analysis (PCA) technique was used for feature selection. A novel voting method between five classification algorithms such as Random Forests, Logistic Regression, AdaBoost, Neural Network, and Support Vector Machine was used to predict the names. The results clearly demonstrate that the classification accuracy of all classifiers studied in this paper improved when PCA was used to select features as compared to selecting features without using PCA. The prediction of terrorist group names with PCA based feature reduction and the original features is carried out and the results are compared.

Biometrics deals with the recognition of humans based on their unique physical characteristics. It can be based on face identification, iris, fingerprint and DNA. In this paper, we have considered the iris as a source of biometric verification as it is the unique part of eye which can never be altered, and it remains the same throughout the life of an individual. We have proposed the improved iris recognition system including image registration as a main step as well as the edge detection method for feature extraction. The PCA-based method is also proposed as an independent iris recognition method based on a similarity score. Experiments conducted using our own developed database demonstrate that the first proposed system reduced the computation time to 6.56 sec, and it improved the accuracy to 99.73, while the PCA-based method has less accuracy than this system does.

The adaptability of two peach cultivars (‘Royal Glory’ and ‘Caldesi 2000’) to different environmental conditions in four localities in Serbia (Grocka, Rača, Niš and Bela Crkva) was examined during the two-year period (2020-2021). Cultivar significantly influenced the variation of fruit weight, stone weight, flesh rate, soluble solids content, total acid content and the ratio of total sugar content and total acid content (TSC/TAC). For most traits, significant differences were observed among the localities, while the year had a significant impact only on the variation of fruit weight, stone weight and flowering time. The response of cultivars to climatic conditions was different, which could be explained by differences in genotype stability. A greater effect of climatic factors on the studied traits was found in ‘Royal Glory’ cultivar. In this cultivar, significant correlations were found between temperatures and fruit weight and flesh rate. Total precipitation was significantly correlated with fruit weight and total acid content. In ‘Caldesi 2000’ cultivar, significant correlations were found between total precipitation and fruit weight and TSC/TAC ratio. Four principal components with eigenvalues higher than 1, were obtained from principal component analysis (PCA). Traits with higher scores on PC1 are stone weight, soluble solids content, total sugar content and TSC/TAC. The highest contribution of PC2 corresponded to total acid content, flowering time and fruit development period. The PCA showed that peach cultivars were differentiated according to stability to climatic conditions. ‘Royal Glory’ was homogenously distributed, while ‘Caldesi 2000’ cultivar was dispersed on scatter plot. The obtained results showed the importance of cultivar adaptability testing before recommendation for planting in a particular region. © 2022 International Society for Horticultural Science. All rights reserved.

Principal component approaches are often used in the construction of composite indicators to summarize the information of input variables. The gain of dimension reduction comes at the cost of difficulties in interpretation, inaccurate targeting, and possible conflicts with the theoretical framework when the signs in the loading are not aligned with the expected direction of impact. In this study, we propose an adjustment in the construction of principal component approaches to avoid these problems. The effectiveness of the proposed approach is illustrated in defining the Food and Agriculture Organization of the United Nations’ Resilience Capacity Index, which is used to measure household-level resilience to food insecurity. We conclude that the robustness gain of using the new method improves the reliability of the composite indicator.

Cosmetics in the world have various characteristics, but differences in their effects on skin-domesticated bacteria were not known. In this study, to obtain insights into their effect on skin microbiota, we established an anaerobic culture and short-chain fatty acid measurement system using Staphylococcus epidermidis, a representative bacterium of indigenous skin bacteria. This system was used to extract the characteristics of nine cosmetics and classify them. Cosmetics containing ferments of rice and soy were clustered in the vicinity, although one cosmetic containing soy ferment was clustered distantly from other similar cosmetics. Cosmetics from South Asia and those containing natural plant extracts were clustered in the vicinity. This trial could be utilized as a pattern map of cosmetics in terms of their effects on skin microbiota.

Sampling efforts on the Saya de Malha Bank (part of the Mascarene Plateau, western Indian Ocean) unveiled three unusual small juvenile angel shark specimens, that were a much paler color than the only known western Indian Ocean species, Squatina africana Regan, 1908. However, it took many years before further specimens, including adults of both sexes, and tissue samples were collected. The present manuscript contains a redescription of S. africana based on the holotype and additional material, as well as the formal description of the new species of Squatina. All specimens of the new species, hereafter referred to as Squatina leae sp. nov., were collected in the western Indian Ocean off southwestern India and on the Mascarene Plateau at depths of 100–500 m. The new species differs from S. africana in a number of characteristics including its coloration when fresh, smaller size at birth, size at maturity, and adult size, genetic composition, and distribution. Taxonomic characteristics include differences in the morphology of the pectoral skeleton and posterior nasal flap, denticle arrangement and morphology, vertebral counts, trunk width, pectoral–pelvic space, and clasper size. A key to the species of Squatina in the Indian Ocean is provided.

Ekspresija gena odnosi se na proces kojim se informacije kodirane u DNA pretvaraju u funkcionalne molekule poput proteina. Razvojem tehnika analize ekspresije gena u posljednjim godinama omogućen je uvid u uzroke i liječenje različitih bolesti, uključujući karcinom. Cilj rada bio je razviti modele strojnog učenja za klasifikaciju karcinoma primijenjujući tehnike nadziranog i nenadziranog strojnog učenja, kao i duboko učenje, te usporediti njihove performanse. Implementirani su algoritmi logističke regresije, stroja potpornih vektora, dubokog učenja i grupiranja uz pomoć programskih biblioteka sklearn i keras. Za postizanje boljih rezultata kod modela dubokog učenja istražili smo tehniku poznatu kao Analiza glavnih komponenti (PCA) i model ranog zaustavljanja te usporedili rezultate. Algoritme smo primijenili na skupu podataka za klasifikaciju leukemije i skupu podataka za klasifikaciju raka dojke. Uspješno smo klasificirali pojednine karcinome modelima strojnog učenja i dobili rezultate u skladu s očekivanjima. Gene expression refers to the process by which information encoded in DNA is transformed into functional molecules such as proteins. The development of gene expression analysis techniques in recent years has provided insights into the causes and treatment of various diseases, including cancer. The aim of this study was to develop machine learning models for cancer classification by applying supervised and unsupervised learning techniques, as well as deep learning, and to compare their performances. The implemented algorithms included logistic regression, support vector machine, deep learning, and clustering using the sklearn and keras programming libraries. To improve the results of the deep learning models, we experimented with a technique known as Principal Component Analysis (PCA) and early stopping, and compared the results. We applied the algorithms to datasets intended for leukemia and breast cancer classification. Using machine learning models and obtained results in line with expectations, we successfully classified different types of cancer.

Rastuća količina podataka koja se generira iz godine u godinu zahtijeva učinkovite metode za obradu i analizu. U ovom radu implementiran je i istražen algoritam analize glavnih komponenti. Opisane su matematičke metode te model autoenkodera potrebnog za razumijevanje algoritma. Implementacija algoritma ostvarena je na dva načina: maksimizacijom varijance u prostoru projekcije te minimizacijom rekonstrukcijske pogreške. Uspoređeni su rezultati dviju implementacija nad stvarnim i umjetnim podacima te su prikazane prednosti i mane svake od implementacija. Završno, uspoređena je učinkovitost implementacija s postojećim rješenjima. The growing amount of data generated year after year requires efficient methods for processing and analysis. In this work, the principle component analysis algorithm was implemented and researched. Mathematical methods and the autoencoder model were described to understand the algorithm. The algorithm was implemented in two ways: maximization of the variance in the projection space and minimization of the reconstruction error. The results of two implementations on real and artificial data were compared, and the advantages and disadvantages of each implementation were presented. Finally, the effectiveness of the implementations was compared with already implemented solutions.

With the spread of Internet technologies, the use of social media has increased exponentially. Although social media has many benefits, it has become the primary source of disinformation or fake news. The spread of fake news is creating many societal and economic issues. It has become very critical to develop an effective method to detect fake news so that it can be stopped, removed or flagged before spreading. To address the challenge of accurately detecting fake news, this paper proposes a solution called Statistical Word Embedding over Linguistic Features via Deep Learning (SWELDL Fake), which utilizes deep learning techniques to improve accuracy. The proposed model implements a statistical method called “principal component analysis” (PCA) on fake news textual representations to identify significant features that can help identify fake news. In addition, word embedding is employed to comprehend linguistic features and Bidirectional Long Short-Term Memory (Bi-LSTM) is utilized to classify news as true or fake. We used a benchmark dataset called SWELDL Fake to validate our proposed model, which has about 72,000 news articles collected from different benchmark datasets. Our model achieved a classification accuracy of 98.52% on fake news, surpassing the performance of state-of-the-art deep learning and machine learning models.

Kyasanur Forest Disease virus (KFDV), a neglected human pathogenic virus, is a Flavivirus that causes severe hemorrhagic fever in humans. KFDV is transmitted to humans by the bite of the hard tick (Haemaphysalis spinigera), which acts as a reservoir of KFDV. The recent expansion of the endemic area of KFDV is of concern and requires the development of new preventive measures against KFDV. Currently, there is no antiviral therapy against KFDV, and the existing vaccine has limited efficacy. To develop a new antiviral therapy against KFDV, we focused on the nonstructural proteins NS2B and NS3 of KFDV, which are responsible for serine protease activity. Viral proteases have shown to be suitable therapeutic targets in the development of antiviral drugs against many diseases. However, success has been limited in flaviviruses, mainly because of the important features of the active site, which is flat and highly charged. In this context, the present study focuses on the dynamics of NS2B and NS3 to identify potential allosteric sites in the NS2B/NS3 protease of KDFV. To our knowledge, there are no reports on the dynamics of NS2B and NS3 in KFDV, and the crystal structure of the NS2B/NS3 protease of KFDV has not yet been solved. Overall, we created the structure of the NS2B/NS3 protease of KFDV using AlphaFold and performed molecular dynamics simulations with and without NS2B cofactor to investigate structural rearrangements due to cofactor binding and to identify alternative allosteric sites. The identified allosteric site is promising due to its geometric and physicochemical properties and druggability and can be used for new drug development. The applicability of the proposed allosteric binding sites was verified for the best-hit molecules from the virtual screening and MD simulations.

The SARS-CoV-2 main protease (Mpro) is essential for the life cycle of the COVID-19 virus. It cleaves the two polyproteins at 11 positions to generate mature proteins for virion formation. The cleavage site on these polyproteins is known to be Leu-Gln↓(Ser/Ala/Gly). A range of hexapeptides that follow the known sequence for recognition and cleavage was constructed using RDKit libraries and complexed with the crystal structure of Mpro (PDB ID 6XHM) through extensive molecular docking calculations. A subset of 131 of these complexes underwent 20 ns molecular dynamics simulations. The analyses of the trajectories from molecular dynamics included principal component analysis (PCA), and a method to compare PCA plots from separate trajectories was developed in terms of encoding PCA progression during the simulations. The hexapeptides formed stable complexes as expected, with reproducible molecular docking of the substrates given the extensiveness of the procedure. Only Lys-Leu-Gln*** (KLQ***) sequence complexes were studied for molecular dynamics. In this subset of complexes, the PCA analysis identified four classifications of protein motions across these sequences. KLQ*** complexes illustrated the effect of changes in substrate on the active site, with implications for understanding the substrate recognition of Mpro and informing the development of small molecule inhibitors.

In China, Codonopsis Radix (CR) is frequently consumed both as food and medicine. Here, a comprehensive strategy based on fingerprinting and chemometric approaches was created to explore the influence of origins, storage time and kneading processing on the quality of CR. Firstly, high-performance liquid chromatography with diode array detection was used to obtain the fingerprints of 35 batches of CR from six different origins and 33 batches of CR from varying storage times or kneading procedures. Secondly, chemometric methods including similarity analysis (SA), principal component analysis (PCA), hierarchical clustering analysis (HCA), and two-way orthogonal partial least square with discriminant analysis (O2PLS—DA) were used to evaluate the differences of chemical components in CR so as to identify its source and reflect its quality. Moreover, 13 and 16 major compounds were identified as marker compounds for the discrimination of CR from different origins, storage time and kneading processing, respectively. Furthermore, the relative content of the marker components and the exact content of Lobetyolin were measured, indicating that the contents of these components vary significantly between various CR samples. Meanwhile, the chemical components of CR were identified using Mass spectrometry. According to the findings of our investigation, the quality of CR from Gansu was the best, followed by Shanxi and then Sichuan. The quality of CR from Chongqing and Guizhou was poor. At the same time, the quality of CR was the best when it was kneaded and stored for 0 years, indicating that the traditional kneading process of CR is of great significance. Conclusively, HPLC fingerprint in conjunction with chemical pattern recognition and component content determination can be employed to differentiate the raw materials of different CR samples. Additionally, it is also a reliable, comprehensive and prospective method for quality control and evaluation of CR.

This work addresses the chemical characterization of atmospheric aerosols and precipitation in the period from May to November 2019 at Monte Fenton (53.16° S, 71.05° W, 612 m.a.s.l.), 9 km west of Punta Arenas, to study the contribution and distribution of emission sources and chemical enrichment. The main ions (Ca2+, Cl−, K+, Mg2+, Na+, NH4+, NO3− and SO42−) were studied using ion chromatography, and trace elements (Al, Br, Ca, Cl, Cr, Fe, K, Mg, Mn, Na, Ni, P, Pb, S, Se, Si, Ti, V and Zn) using energy dispersive X-ray fluorescence. Ions concentration ranged from 5.0 × 10−1 to 2.9 × 104 mg/m3 for Ca2+ and Cl−, respectively; whilst the concentration of elements varied between 8.8 × 10−11 and 2.1 × 10−2 mg/m3, for crZn (crustal Zn) and Fe, respectively. The electrical conductivity (EC, mean = 32.5 µS/cm) and the pH (mean = 6.8), showed the atmosphere of the study site was relatively neutral compared to the standard pH for rain (or snow) without contamination (pH = 5.6), and presented relatively low levels of conductivity compared to the EC standards for distilled water (0.5 to 3 µS/cm) and seawater (30,000 to 60,000 µS/cm). The main contribution to aerosols in the atmosphere of Monte Fenton came from marine and lithospheric sources, followed by local anthropogenic sources such as burning firewood and/or urban waste for heating production, etc., that led to the enrichment of aerosols with high Fe, K, Mn and V content. The results of this study contribute to filling a gap in knowledge of the chemistry of atmospheric aerosols in Southern Patagonia.

Osteoarthritis (OA) is the most common arthritis and the leading cause of lower extremity disability in older adults. Understanding OA progression is important in the development of patient-specific therapeutic techniques at the early stage of OA rather than at the end stage. Histopathology scoring systems are usually used to evaluate OA progress and the mechanisms involved in the development of OA. This study aims to classify the histopathological images of cartilage specimens automatically, using artificial intelligence algorithms. Hematoxylin and eosin (HE)- and safranin O and fast green (SafO)-stained images of human cartilage specimens were divided into early, mild, moderate, and severe OA. Five pre-trained convolutional networks (DarkNet-19, MobileNet, ResNet-101, NasNet) were utilized to extract the twenty features from the last fully connected layers for both scenarios of SafO and HE. Principal component analysis (PCA) and ant lion optimization (ALO) were utilized to obtain the best-weighted features. The support vector machine classifier was trained and tested based on the selected descriptors to achieve the highest accuracies of 98.04% and 97.03% in HE and SafO, respectively. Using the ALO algorithm, the F1 scores were 0.97, 0.991, 1, and 1 for the HE images and 1, 0.991, 0.97, and 1 for the SafO images for the early, mild, moderate, and severe classes, respectively. This algorithm may be a useful tool for researchers to evaluate the histopathological images of OA without the need for experts in histopathology scoring systems or the need to train new experts. Incorporating automated deep features could help to improve the characterization and understanding of OA progression and development.

The coronavirus disease 2019 (COVID-19) pandemic has not only brought considerable and permanent changes to economies and healthcare systems, but it has also greatly changed the habits of almost the entire society. During the lockdowns, people were forced to stay in their dwellings, which served as a catalyst for the initiation of a survey on the estimation of the metallic element content in residential indoor dust in different parts of Latvia. This article presents the study results obtained through the analysis of collected dust samples from 46 dwellings, both in the capital of Latvia, Riga, and in smaller cities. Two methods were employed for indoor dust collection: vacuum sampling and manual sampling with a brush and plastic spatula. After microwave-assisted acid extraction, the samples were analyzed using inductively coupled plasma mass spectrometry (ICP-MS) in terms of the major (Na, K, Ca, Mg, Al and Fe) and minor (Mn, Ni, Co, Pb, Cr, As, Ba, Li, Be, B, V, Cu, Zn, Se, Rb, Sr, Cd, La, Ce and Bi) elements. For the data analysis, principal component analysis was performed. Among the measured metals, the highest values were determined for the macro and most abundant elements (Na > K > Ca > Fe > Mg > Al). The concentration ranges of the persistently detected elements were as follows: Pb, 0.27–1200 mg kg−1; Cd, 0.01–6.37 mg kg−1; Ni, 0.07–513 mg kg−1; As, 0.01–69.2 mg kg−1; Cu, 5.71–1900 mg kg−1; Zn, 53.6–21,100 mg kg−1; and Cr, 4.93–412 mg kg−1. The critical limit values of metallic elements in soil defined by the legislation of the Republic of Latvia (indicating the level at or above which the functional characteristics of soil are disrupted, or pollution poses a direct threat to human health or the environment) were exceeded in the following numbers of dwellings: Pb = 4, Ni = 2, As = 1, Cu = 16, Cr = 1 and Zn = 28.

Cities have been increasingly involved in the development of food policies, becoming key points in achieving food security and fostering the transition to sustainable agri-food systems. The aim of this paper is to identify citizens’ profiles by performing segmentation and profiling according to their socio-economic variables and perception of key factors affecting food security. This is to define appropriate strategies to guide policy makers in a more effective creation of urban food policies. An online survey was filled out by citizens of the Metropolitan City of Bari from July to November 2022. Descriptive analysis, principal component analysis and K-means cluster analysis were applied to the collected data. Four clusters of citizens were obtained and labelled based on socio-economic characteristics and key factors affecting food security perception. Specifically, the “Law-confident” (45% of citizens) and “Hedonist” (36%) clusters revealed the greatest trust in “governance” and “quality certification” aspects. The “Capitalist” (15%) and “Conservatory” (4%) clusters were relatively small groups, characterized respectively by a positive perception of the standardization of food production and governance power, with a focus on strategies regarding food policy implementation, reduction of food loss and waste (FLW) and improvement of food quality certifications systems. The proposed approach and results may support EU policy makers in identifying key macro-areas and matters toward which to direct public funding in order to improve food security in urban areas, and to put in place actions enhancing citizens’ knowledge and awareness of key issues of food security.

Broadly distributed seaweeds, such as the boreal-temperate kelp species Saccharina latissima, contain a multitude of metabolites supporting acclimation to environmental changes, such as temperature and salinity. In Europe, S. latissima occurs along the coasts from Spitsbergen to Portugal, including the Baltic Sea, exhibiting great morphological plasticity. We investigated the morphological and biochemical traits of field-collected sporophytes from 16 different locations across the species entire distributional range in relation to local abiotic conditions (sea surface temperature, salinity). By statistically linking morphological and biochemical data to geographic information that also took into account the respective sampling depth, we aimed to obtain first insights into the site-specific adaptive features of this species. Frond length and width, mannitol and phlorotannin contents, and molar C:N ratio showed strong intraspecific variability among S. latissima sporophytes dependent on individual local abiotic drivers. Despite the conspicuous impact of local abiotic factors on specimens’ morphology, we could not determine habitat-specific signatures in the biochemical phenotypes. Even though our findings are based on a relative small sample size per site, they cover a broad biogeographical range and support a high plasticity of S. latissima sporophytes. The study provides a first base for studying separation processes of populations across latitudes and conservation ecology.

Ground deformations in urban areas can be the result of a combination of multiple factors and pose several hazards to infrastructures and human lives. In order to monitor these phenomena, Interferometric Synthetic Aperture Radar (InSAR) techniques are applied. The obtained signals record the overlapping of the phenomena, and their separation is a relevant issue. In this framework, we explored a new multi-method approach based on the combination of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Hierarchal Clustering (HC) on the standardized results to distinguish the main trends and seasonal signals embedded in the time series of ground displacements, to understand spatial-temporal patterns, to correlate ground deformation phenomena with geological and anthropogenic factors, and to recognize the specific footprints of different ground deformation phenomena. This method allows us to classify the ground deformations at the site scale in the metropolitan area of Naples, which is affected by uplift cycles, subsidence, cavity instabilities and sinkholes. At the local scale, the results allow a kinematic classification using the extracted components and considering the effect of the radius of influence generated by each cavity, as it is performed from a theoretical point of view when the draw angle is considered. According to the results, among the classified cavities, 2% were assigned to subsidence and 11% to uplift kinematics, while the remaining were found to be stable. Furthermore, our results show that the centering of the Spatial-PCA (S-PCA) is representative of the region’s main trend, whereas Temporal-PCA (T-PCA) gives information about the displacement rates identified by each component.

The thorough search (TS) method was introduced to solve the problems in a conventional extended X-ray absorption fine structure analysis method. The TS method gives all possible and rational structures (structure candidates) which can reproduce the experimental data well in the parameter space. However, it still has difficulties how to decide the number of structure candidates without discretion and visualize the parameter sets with dimensions of more than 3. In this paper, we have applied the K-means method and principal component analysis and discussed its merits and drawbacks.

Even though waste pickers play a crucial role in the urban solid waste management system in developing countries, their social, familial, and labor conditions remain uncertain. In this study, we analyzed the profiles of waste pickers in Colombia using multivariate statistical methods and cluster analysis. Our findings indicate that the majority of waste pickers have only completed elementary or high school education, and most of them are not associated with any organization and belong to the subsidized health regime. We identified five profiles of waste pickers in the population. The first consists of the most experienced individuals, while the second comprises individuals with the lowest salaries. The third includes older individuals who work more hours per day. The fourth is made up of individuals who work in the informal sector, and the fifth includes individuals who work in the formal sector. Our study highlights specific actions to be taken for each profile. Improving family and household conditions, social and educational status, and job conditions can have a significant impact on their lives.

Resumen Este trabajo presenta la aplicación de técnicas relacionadas con Análisis de Componentes Principales (ACP) que buscó caracterizar en limitadas dimensiones a los 81 cantones de Costa Rica con base en indicadores de exposición a heridas leves, graves o fallecimiento (en sitio) en accidentes de tránsito de distintos usuarios de la vía, con el fin de analizar posibles variables que podrían estar relacionadas a su incidencia. Con el análisis de agrupamientos jerárquicos se hallaron tres grupos de cantones con condiciones diferenciadas en dos componentes construidos y en términos de las tasas de incidencia en análisis. Al asociar variables físicas, se halló que los cantones con menores índices de desarrollo y mayores carencias presentan mayor riesgo de victimización en ocupantes de automóviles de cuatro o más ruedas. Abstract This work presents the application of techniques related to Principal Component Analysis (PCA) to characterize the 81 cantons of Costa Rica in limited dimensions, based on indicators of road users' exposure to minor, serious injuries, or death (on-site) in traffic accidents, to analyze possible variables that could be related to their incidence. Through the analysis of hierarchical groupings, three groups of cantons were found with differentiated conditions in two constructed components, and in terms of the incidence rates in analysis. With the association of physical and sociodemographic variables, it was found that the cantons with the lowest development indices, the greatest deficiencies, and the highest proportion of young population have a higher risk of road victimization in occupants of cars with four or more wheels. Resumo Este trabalho apresenta a aplicação de técnicas relacionadas à Análise de Componentes Principais (ACP) que buscaram caracterizar em dimensões limitadas os 81 cantões da Costa Rica com base em indicadores de exposição a ferimentos leves ou graves ou morte (no local) em acidentes de trânsito de diferentes usuários da via, a fim de analisar possíveis variáveis que possam estar relacionadas à sua incidência. Com a análise de agrupamento hierárquico, foram encontrados três grupos de cantões com condições diferenciadas em dois componentes construídos e em termos das taxas de incidência em análise. Ao associar variáveis físicas, verificou-se que os cantões com menores índices de desenvolvimento e maiores deficiências apresentam maior risco de vitimização em ocupantes de carros com quatro ou mais rodas.

Structural damage detection is an important issue in conservation. In this research, principal component analysis (PCA) has been applied to the temporal variation of modal frequencies obtained from a dynamic test of a scaled steel structure subjected to different damages and different temperatures. PCA has been applied in order to reduce, as much as possible, the number of variables involved in the problem of structural damage detection. The aim of the PCA study is to determine the minimum number of principal components necessary to explain all the modal frequency variation. Three estimators have been studied: T2 (the square of the vector norm of the projection in the principal component plan), Q (the square of the norm of the residual vector), and the variance explained. In the study, the results related to the undamaged structure needed one principal component to explain the modal frequency variation. However, the high damage configurations need five principal components to explain the modal frequency. The T2 and Q estimators have been arranged in order of increasing damage for all the performed experimental tests. The results indicate that these estimators could be useful to detect damage and to distinguish among a range of intensities of structural damage. This research has been partially funded by the Spanish Research Agency (AEI) of the Spanish Ministry of Science and Innovation (MICIN) through project with reference: PID2020-117374RB-I00/AEI/10.13039/501100011033.

The peach stones and kernels are easily available biowaste which could be useful for the extraction of nutritionally important compounds such as fatty acids. Except in industry, characterization of stones and kernels could be useful in pomology to describe different cultivars, and for selecting new parents in a breeding program. A total of 25 samples of stones and kernels from various peach cultivars that differed in origin and ripening time, but growing in the same climatic conditions, were characterized by fatty acids composition and physical properties. This work confirmed that unsaturated fatty acids (oleic and linoleic fatty acids) were the most represented in peach kernel oil and their content depended of peach genotype. Additionally, the fatty acids in combination with length, weight, and moisture of peach kernels could be used as a parameter of authenticity assessment. This research may contribute for the peach cultivar discrimination and recommendation of cultivars/genotypes with high kernel quality which could be used for the extractions of oil rich in unsaturated fatty acids and further use in food, pharmaceutical and cosmetic industry. Besides, selected cultivars could be used in breeding programs, for creating new genotypes for oil production. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

The term “Business intelligence” is described as a plan or a strategy where the operations like reporting, data analysis, data mining, event processing are performed to improve the production and growth of a business enterprise or a business entity. And on the other hand, the “Knowledge management” is explained as well-organized management of resources and information within a commercial organization it can be a business too. Almost all business will have limitations and challenges which can be also known as the business problems. One of the main business problem is demand, the business plans must work according to the demand of the consumers. Analyzing the demand would provide the solutions for queries like what is the business trend? What is the need of the users? What should be the improvement make in the production? Where is the current position of the enterprise? And who all will be the competitors? For the predictive analysis a dataset of bitcoin is taken. The major aim of the study is to implement the strategies to overcome the business problems mainly the demand prediction. And the objective is to find out the relevant issues and the remedies by using knowledge management and business intelligence to the common business problems. The dataset has columns called lowest price, highest price, open price, close price, trading volume and market capital. The research methodology used is predictive analysis using PCA and K-means clustering algorithm. By this dataset predictive plots are developed as achieved results for easy analysis by using research methodology. PCA and K-means are the algorithm used for accurate prediction. The importance of study is to predict the future sale, as it is very essential for a business enterprise to find future demand so that the organization can improve production.

Indonesia diperkirakan akan menghadapi bonus demografi pada tahun 2035. Sebagai upaya untuk menghadapi hal tersebut, negara Indonesia membutuhkan sumber daya manusia yang berkualitas salah satunya melalui pendidikan. Namun ditinjau dari Angka Partisipasi Kasar (APK) untuk tingkat pendidikan tinggi di Kabupaten Semarang masih tergolong rendah, yaitu 19,25 persen. Oleh karena itu, penelitian ini bertujuan untuk mengelompokkan tingkat pendidikan berdasarkan jenis kelamin, umur, dan status individu dalam keluarga dengan menggunakan algoritma K-Means dan reduksi dimensi melalui metode Principal Component Analysis (PCA). Penelitian ini dilakukan dengan mengolah data hasil Survei Sosial Ekonomi Nasional 2021 Maret (KOR) Wilayah Kabupaten Semarang yang bersumber dari data Badan Pusat Statistik (BPS). Setelah dilakukan analisis, dihasilkan dua komponen utama dengan proporsi kumulatif mencapai 70%. Kemudian, setelah dilakukan reduksi dimensi dengan menggunakan metode PCA, analisis klaster dilakukan dengan menggunakan algoritma K-means yang menghasilkan empat kelompok dengan karakteristik yang berbeda untuk masing-masing klaster.

The number of inbound tourists in Japan has been increasing steadily in recent years. However, due to the COVID-19 pandemic, the number of inbound tourists decreased in 2020. This is particularly worrisome for Japan, as the number of inbound tourists is expected to reach 60 million per year by 2030. In order to help Japan’s tourism industry to recover from the pandemic, we propose a method of identifying elements that attract the attention of inbound tourists (focus points) by analyzing reviews on tourist sites. We focus on Hokkaido, a popular area in Japan for tourists from China. Our proposed method extracts high-frequency n-gram patterns from reviews written by Chinese inbound tourists, showing which aspects are mentioned most often. We then use seven types of motivational factors for tourists and principal component analysis to quantify the focus points of each tourist destination. Finally, we estimate the focus points by clustering the n-gram patterns extracted from the tourists’ reviews. The results show that our method successfully identifies the features and focus points of each tourist spot.

In polder-type land, water dynamics are heavily influenced by the artificial maintenance of water levels. Polders are low-lying areas of land that have been reclaimed from the sea or from freshwater bodies and are protected from flooding by dikes or other types of flood-protection structures. The water regime in polders is typically managed using a system of canals, pumps, and sluices to control the flow of water in and out of the area. In this study, the temporal changes in water salinity in the polder-type agricultural floodplain within the Neretva River Delta (NRD), Croatia, were analyzed by applying multivariate statistics and forecast modelling. The main aim of the study was to test the model that can be used in practice to forecast, primarily, water suitability for irrigation in a coastal low-lying agricultural catchment. The specific aim of this study was to use hydrochemistry data series to explain processes in water salinity dynamics and to test the model which may provide accurate salinity prediction, or finally select the conditions in which the model can be applied. We considered the accuracy of the model, and it was validated using independent data sets. To describe different patterns of chemical changes in different water classes due to their complex hydrological connectivity, multivariate statistics (PCA) were coupled with time-series analysis and Vector Autoregression (VAR) model forecasting. The multivariate statistics applied here did not indicate a clear connection between water salinity of the surface-water bodies and groundwater. The lack of correlation lies in the complex hydrological dynamics and interconnectivity of the water bodies highly affected by the artificial maintenance of the groundwater level within the polder area, as well as interventions in the temporal release of freshwater into the drainage canal network. Not all individual water classes contributed equally to the dominant patterns of ionic species identified by PCA. Apparently, land use and agricultural management practices in the different polders lead to uneven water chemistry and the predominant contributions of specific ions, especially nutrients. After applying the Granger causality test to reveal the causal information and explain hidden relationships among the variables, only two surface-water and two groundwater monitoring locations displayed a strong causal relationship between water electrical conductivity (ECw) as an effect and sea level as a possible cause. The developed models can be used to evaluate and emphasize the unique characteristics and phenomena of low-lying land and to communicate their importance and influence to management authorities and agricultural producers in managing and planning irrigation management in the wider Mediterranean area.

Les Malformacions Arteriovenoses cerebrals (MAVs) són considerades la principal causa d’hemorràgia cerebral en pacients pediàtrics. Per tal d’avaluar l’efectivitat del tractament de les MAVs cerebrals, diverses tècniques d’imatge mèdica no invasives han sigut proposades, incloent seqüències com la la pseudo-continuous Spin Labelling (ASL). L’obliteració total de una MAV tarda habitualment un període de dos anys, pel que és necessari un seguiment del o la pacient, sent així d’alta importància que les tècniques d’imatge siguin el menys invasives possible, requerint per tant, que siguin estudiades les alternatives no invasives. Per tal d’avaluar la resposta al tractament de les MAVs, és crucial identificar les zones del cervell específiques que participen de l’activitat d’aquestes, per així poder obtenir informació sobre la condició de la malformació i estudiar l’eficàcia del tractament. Per aquesta raó, s’ha dissenyat i implementat un algoritme de segmentació en forma d’aplicació web que ha sigut validat per tal de crear un "ground-truth atlas" del procés de segmentació. Las Malformaciones Arteriovenosas cerebrales (MAVs) son consideradas la principal causa de hemorragia intracraneal en pacientes pediátricos. Para evaluar la efectividad de los tratamientos de las MAVs cerebrales, han sido propuestas varias técnicas de imagen no invasivas, incluyendo secuencias como la pseudo-continuous Spin Labelling (ASL). La obliteración total de una MAV suele tardar unos dos años, por lo que es necesario un seguimiento del paciente, siendo así de alta importancia que las técnicas de imagen sean lo menos invasivas posible, requiriendo el estudio de alternativas no invasivas. Para evaluar la respuesta que tienen las MAVs al tratamiento, es crucial identificar las zonas específicas del cerebro que participan de la actividad de estas, para poder así obtener información sobre la condición de la malformación y de este modo estudiar la eficacia del tratamiento. Por consiguiente, un algoritmo de segmentación implementado en forma de aplicación web ha sido diseñado, desarrollado y validado para crear un "ground-truth atlas" del proceso de segmentación. Cerebral Arteriovenous Malformations (AVMs) are considered the primary cause of paediatric intracranial haemorrhage. To evaluate the effectiveness of cerebral AVMs treatment, various non-invasive imaging techniques have been suggested, including the pseudo-continuous Arterial Spin-Labelling (ASL) sequence. Since the complete obliteration of AVMs may take up to two years, a subsequent follow-up is necessary. Hence, it is imperative to investigate non-invasive imaging techniques to minimize the invasiveness of the diagnostic process. To evaluate the response to treatment of Brain Arteriovenous Malformations, it is crucial to identify the specific brain regions that provide information on the AVM's condition and study them to assess the treatment's performance. Accordingly, a segmentation algorithm in the form of a web application has been designed, developed, and validated to create a "ground-truth atlas" from the segmentation process.

Aim of study: To determine the amount of diversity in pea breeding materials with the objective to classify a set of potential parents carrying novel/economic variations that could be used in future breed pea varieties. Area of study: ICAR–Indian Institute of Vegetable Research, Varanasi. Material and methods: A total of 45 pea accessions were analysed for phenotypic and molecular diversity using 17 agro-morphological traits and 52 SSR markers. Main results: All traits under investigation showed considerable genetic variation. The genotypes exhibited 6.7, 2.7 and 12-fold variation for traits viz., pods/plant, 10-pod weight and yield/plant, respectively. Among 52 SSR markers, 22 were found to be polymorphic. A total of 90 allelic variants were detected, with an average of 2.7 alleles/locus. PIC and D-values for markers AA135 (0.79 and 0.81) and PSMPSAD51 (0.7 and 0.74) were the highest, while AB40 (0.19 and 0.2) had the lowest. Two principal components PC1 and PC2 explained 46.96 and 23.96% of total variation, respectively. The clustering based on agro-morphological traits differentiated 45 individuals into three mega clusters, while SSR markers-based clustering classified these accessions into four groups. Research highlights: Based on their uniqueness, we identified a set of genotypes (VRPD-2, VRPD-3, PC-531, ‘Kashi Nandini’, ‘Kashi Udai’, ‘Kashi Mukti’, ‘Arkel’, VRPE-101, ‘Azad Pea-3’, EC865944, VRPM-901 and VRP-500) harbouring genes for various economic traits. The findings presented here will be extremely useful to breeders who are working on improvement of peas through selective introgression breeding.

Mungbean [Vigna radiata (L.) Wilczek] is an important food, feed, and cash crop in rice-based agricultural ecosystems in Southeast Asia and other continents. It has the potential to enhance livelihoods due to its palatability, nutritional content, and digestibility. We evaluated 166 diverse mungbean genotypes in two seasons using multivariate and multi-traits index approaches to identify superior genotypes. The total Shannon diversity index (SDI) for qualitative traits ranged from moderate for terminal leaflet shape (0.592) to high for seed colour (1.279). The analysis of variances (ANOVA) indicated a highly significant difference across the genotypes for most of the studied traits. Descriptive analyses showed high diversity among genotypes for all morphological traits. Six components with eigen values larger than one contributed 76.50% of the variability in the principal component analysis (PCA). The first three PCs accounted for the maximum 29.90%, 15.70%, and 11.20% of the total variances, respectively. Yield per plant, pod weight, hundred seed weight, pod length, days to maturity, pods per plant, harvest index, biological yield per plant, and pod per cluster contributed more to PC1 and PC2 and showed a positive association and positive direct effect on seed yield. The genotypes were grouped into seven clusters with the maximum in cluster II (34) and the minimum in cluster VII (10) along with a range of intra-cluster and inter-cluster distances of 5.15 (cluster II) to 3.60 (cluster VII) and 9.53 (between clusters II and VI) to 4.88 (clusters I and VII), suggesting extreme divergence and the possibility for use in hybridization and selection. Cluster III showed the highest yield and yield-related traits. Yield per plant positively and significantly correlated with pod traits and hundred seed weight. Depending on the multi-trait stability index (MTSI), clusters I, III, and VII might be utilized as parents in the hybridization program to generate high-yielding, disease-resistant, and small-seeded mungbean. Based on all multivariate-approaches, G45, G5, G22, G55, G143, G144, G87, G138, G110, G133, and G120 may be considered as the best parents for further breeding programs.

This study aimed to show the importance and functionality of the tools of multivariate statistics in the environmental area, and more specifically on 13 water collection points of the Subaé River in the municipality of Feira de Santana, Bahia, an important Industrial Pole in the region, carried out with 3 samplings. To achieve this objective, applied research of exploratory nature and quantitative approach was carried out. As method and technical procedures were adopted, respectively, the bibliographic review, and a case study. As a contribution of this research, it is pointed out the holistic view of opportunities to carry out new research on the subject in question with the tools presented.

The accessibility of railway stations plays a crucial role in assessing service quality, predicting travel patterns, and developing infrastructure in the surrounding areas. This paper proposes a railway station accessibility index (RsAI) (external) that incorporates various parameters, including network performance, into a weighted measure. We reviewed different methods for measuring accessibility levels for transit systems to identify the most suitable models for this study. The primary objective of this paper is to classify railway stations into different hierarchies based on their accessibility levels and to develop an external accessibility index to measure their performance. With increasing urbanization and congestion, accessing railway stations has become more challenging, impacting railway efficiency and leading to modal shifts to other transportation systems. This paper not only identifies critical parameters but also emphasizes the need to measure and improve last-mile network performance to enhance station accessibility, thereby benefiting both passengers and the railway industry.

Machine learning is a burgeoning technology where the machine learns from the historical data. Predictions are made using a variety of algorithms based on past data. Braille is a fundamental system of raised dots that enables the visually challenged and impaired (VI) to write and read by touch. In the digital age, the VI are facing great challenges in accessing digital content, integrating speech ware as output will assist them in gaining more knowledge. The existing papers, uses SVM classifier, Braille script, keypad and microphone as input and the relevant English alphabets are produced as output. This paper proposes a novel approach to convert English braille alphabets along with special characters to English text using various different pre-processing steps using SVM, RF and KNN Classifier. Braille images are captured by digital camera and pre-processed. The features of the scanned images are extracted by Segmentation. Support Vector Machine(SVM) Classifier with PCA feature extraction were used for Braille character recognition.

To explore the application of a traditional machine learning model in the intelligent management of pigs, in this paper, the influence of PCA pre-treatment on pig face identification with RF is studied. By this testing method, the parameters of two testing schemes, one adopting RF alone and the other adopting RF + PCA, were determined to be 65 and 70, respectively. With individual identification tests carried out on 10 pigs, accuracy, recall, and f1-score were increased by 2.66, 2.76, and 2.81 percentage points, respectively. Except for the slight increase in training time, the test time was reduced to 75% of the old scheme, and the efficiency of the optimized scheme was greatly improved. It indicates that PCA pre-treatment positively improved the efficiency of individual pig identification with RF. Furthermore, it provides experimental support for the mobile terminals and the embedded application of RF classifiers.

The processed discharges from Tangier Automotive City’s (TAC) Chrafate Wastewater Treatment Plant (WWTP) contaminate the Jouamaa Hakama groundwater and the Ouljat Echatt river. We aimed to study the unknown interactions between surface water (SW) and groundwater (GW). A total of nine Jouamaa Hakama GW samples and eleven Ouljat Echatt SW samples were taken and analyzed in 2021 and 2022 to determine 16 physical and chemical parameters (pH, temperature (T), electrical conductivity (EC), dissolved oxygen (DO), total hardness (TH), turbidity (TURB), and total dissolved solids (TDS), cations: Na+, K+, Mg2+ and Ca2+, anions: Cl−, CO32−, HCO3−, NO3−, and SO42−). For exploitation of the data, we used a methodology based on hydrochemical modeling (HM), principal component analysis (PCA), Water Quality Index (WQI), Irrigation Water Quality Index (IWQI), inverse distance weighted interpolation (IDW) using Geographic Information Systems (GIS), and regression analysis (RA). We studied the interaction of the surface water of the river (contaminated by discharges from the WWTP) with the shallow groundwater on a strip of 100 m on either side of the river to understand the transverse and longitudinal dispersion of this pollution The investigations indicated that the major ions found in GW and SW were characterized in a different order in the anion list order Cl− > CO32− > NO3− > HCO3− > SO42− and Cl− > SO42− > CO32− > NO3− > HCO3−, respectively, while the concentrations of cations showed the same order for both: Na+ > Ca2+ > Mg2+ > K+. As a result, GW showed in the Piper diagram the type of sodium chloride to magnesium carbonate, while SW belongs to the sodium chloride to magnesium sulfate type. The WQI showed that the river waters are all unsuitable for use (WQI > 100), while the GW is of poor quality (WQI > 76). Moreover, the results of the GW–SW interaction along the river revealed a significant relationship (R2 = 0.85), which means that strong circulation and the infiltration of contaminated SW into shallow GW occur in this area. The approaches followed have been proven effective in evaluating water quality for human and animal uses. These results can help decision-makers in the region take suitable management measures to mitigate this environmental problem.

Sorghum (Sorghum bicolor L. Moench) is ranked fifth as a cereal crop after maize, rice, wheat, and barley based on global cultivation area. However, heat and drought stresses cause improper fertility restoration and inefficient pollination, severely affecting sorghum productivity. The discovery of cytoplasmic male sterility (CMS) is a milestone for commercializing hybrids. This study assessed the pollen fertility and in vitro pollen germination percentage of male and female lines and F1 hybrids of sorghum using two years of pooled data with multivariate analysis. The principal component analysis (PCA) of female and male lines showed that PC1 represented 82.8% of the variation, whereas PCA of hybrids revealed a significant genetic divergence of 97.1%. Agglomerative hierarchical clustering marked that cluster II genotypes have a high pollen fertility contribution, which can generate superior and high-yielding hybrids. Three male-sterile lines exhibited 100% pollen sterility, with morphological attributes, viz., pinpointed, flattened, low anther extrusion, and starch-digested pollens. Pollen fertility restoration behavior revealed that nine hybrids were fully fertile, eighteen were partially fertile, and three were completely sterile amongst thirty hybrids. The findings of this study will facilitate the identification of potential restorers for the exploitation of high-yielding hybrids in sorghum breeding programs.

Raman and infrared spectroscopy, used as individual and low-level fused datasets, were evaluated to identify and quantify the presence of adulterants (palm oil, PO; ω-3 concentrates in ethyl ester, O3C and fish oil, FO) in krill oil. These datasets were qualitatively analysed with principal component analysis (PCA) and classified as adulterated or unadulterated using support vector machines (SVM). Using partial least squares regression (PLSR), it was possible to identify and quantify the adulterant present in the KO mixture. Raman spectroscopy performed better (r2 = 0.98; RMSEP = 2.3%) than IR spectroscopy (r2 = 0.91; RMSEP = 4.2%) for quantification of O3C in KO. A data fusion approach further improved the analysis with model performance for quantification of PO (r2 = 0.98; RMSEP = 2.7%) and FO (r2 = 0.76; RMSEP = 9.1%). This study demonstrates the potential use of Raman and IR spectroscopy to quantify adulterants present in KO.

An accurate estimation of ionospheric variables such as the total electron content (TEC) is important for many space weather, communication, and satellite geodetic applications. Empirical and physics-based models are often used to determine TEC in these applications. However, it is known that these models cannot reproduce all ionospheric variability due to various reasons such as their simplified model structure, coarse sampling of their inputs, and dependencies to the calibration period. Bayesian-based data assimilation (DA) techniques are often used for improving these model’s performance, but their computational cost is considerably large. In this study, first, we review the available DA techniques for upper atmosphere data assimilation. Then, we will present an empirical decomposition-based data assimilation (DDA), based on the principal component analysis and the ensemble Kalman filter. DDA considerably reduces the computational complexity of previous DA implementations. Its performance is demonstrated by updating the empirical orthogonal functions of the empirical NeQuick and the physics-based TIEGCM models using the rapid global ionosphere map (GIM) TEC products as observation. The new models, respectively, called ‘DDA-NeQuick’ and ‘DDA-TIEGCM,’ are then used to predict TEC values for the next day. Comparisons of the TEC forecasts with the final GIM TEC products (that are available after 11 days) represent an average 42.46% and 31.89% root mean squared error (RMSE) reduction during our test period, September 2017.

Gasoline is a complex mixture of hydrocarbons whose detailed study of its chemical profiles is a challenging task. This type of product of great commercial importance has its quality measured in Brazil from classical physical-chemical tests. Although robust, physical-chemical analyses generally require large amounts of assays, solvents, and samples, in addition to not being indicated for the detection of small compositional changes. In this work, NMR and chemometrics were used in the quantitative study of different samples of gasoline with varying ratings of octane and ethanol contents. The results demonstrated the usefulness of NMR spectroscopy both to characterize as well as to distinguish the chemical profiles of regular and premium gasoline. With the application of the ERETIC, the chemical profiles mentioned were quantitatively differentiated and the proportions between hydrocarbons were determined for both types of gasoline. The application of the ERETIC was also successful in the quantitative determination of ethanol contents in regular and premium gasoline. The aforementioned method, which dispenses with the use of quantification standards inserted directly into the sample of interest, was unequivocal in accounting for the volume of ethanol, in addition to showing excellent agreement with the method currently adopted by the Brazilian government.

Global navigation satellite system (GNSS) positions include various useful signals and some unmodeled errors. In order to enhance the accuracy and extract the features of the GNSS daily time sequence, an improved method of complete ensemble empirical mode decomposition (CEEMD) and multi-PCA (MPCA) based on correlation coefficients and block spatial filtering was proposed. The results showed that the mean standard deviations of the raw residual time sequence were 1.09, 1.20 and 4.79 mm, while those of the newly proposed method were 0.15, 0.20 and 2.86 mm in north, east and up directions, respectively. The proposed method outperforms wavelet decomposition (WD)-PCA and empirical mode decomposition (EMD)-PCA in effectively eliminating low- and high-frequency noise, and is suitable for denoising nonlinear and nonstationary GNSS position sequences. Furthermore, feature extraction of the denoised GNSS daily time series was based on CEEMD, which is superior to WD and EMD. Results of noise analysis suggested that the noise components in the original and denoised GNSS time sequence are complex. The advantages of the proposed method are the following: (i) it fully exploits the merits of CEEMD and WD, where CEEMD is first used to obtain the limited intrinsic modal functions (IMFs) and then to extract seasonal and trend features; (ii) it has good adaptive processing ability via WD for noise-dominant IMFs; and (iii) it fully considers the correlation between the different components of each station and the non-uniform behavior of common mode error on a spatial scale.

In this research work a novel two-step system for anomaly detection is presented and tested over several real datasets. In the first step the novel Exploratory Projection Pursuit, Beta Hebbian Learning algorithm, is applied over each dataset, either to reduce the dimensionality of the original dataset or to face nonlinear datasets by generating a new subspace of the original dataset with lower, or even higher, dimensionality selecting the right activation function. Finally, in the second step Principal Component Analysis anomaly detection is applied to the new subspace to detect the anomalies and improve its classification capabilities. This new approach has been tested over several different real datasets, in terms of number of variables, number of samples and number of anomalies. In almost all cases, the novel approach obtained better results in terms of area under the curve with similar standard deviation values. In case of computational cost, this improvement is only remarkable when complexity of the dataset in terms of number of variables is high. CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund and the Secretaría Xeral de Universidades (ref. ED431G 2019/01). info:eu-repo/semantics/publishedVersion

Changes in cultivation conditions, in particular salinity and temperature, affect the production of secondary fungal metabolites. In this work, the extracts of fungus previously described as Penicillium dimorphosporum cultivated in various salinity and temperature conditions were investigated using HPLC UV/MS techniques, and their DPPH radical scavenging and cytotoxicity activities against human prostate cancer PC-3 cells and rat cardiomyocytes H9c2 were tested. In total, 25 compounds, including 13 desoxyisoaustamide-related alkaloids and eight anthraquinones, were identified in the studied extracts and their relative amounts were estimated. The production of known neuroprotective alkaloids 5, 6 and other brevianamide alkaloids was increased in hypersaline and high-temperature conditions, and this may be an adaptation to extreme conditions. On the other hand, hyposalinity stress may induce the synthesis of unidentified antioxidants with low cytotoxicity that could be very interesting for future investigation. The study of secondary metabolites of the strain KMM 4689 showed that although brevianamide-related alkaloids and anthraquinone pigments are widely distributed in various fungi, these metabolites have not been described for P. dimorphosporum and related species. For this reason, the strain KMM 4689 was re-sequenced using the β-tubulin gene and ITS regions as molecular markers and further identified as P. hispanicum.

Lung and colon cancer are among humanity’s most common and deadly cancers. In 2020, there were 4.19 million people diagnosed with lung and colon cancer, and more than 2.7 million died worldwide. Some people develop lung and colon cancer simultaneously due to smoking which causes lung cancer, leading to an abnormal diet, which also causes colon cancer. There are many techniques for diagnosing lung and colon cancer, most notably the biopsy technique and its analysis in laboratories. Due to the scarcity of health centers and medical staff, especially in developing countries. Moreover, manual diagnosis takes a long time and is subject to differing opinions of doctors. Thus, artificial intelligence techniques solve these challenges. In this study, three strategies were developed, each with two systems for early diagnosis of histological images of the LC25000 dataset. Histological images have been improved, and the contrast of affected areas has been increased. The GoogLeNet and VGG-19 models of all systems produced high dimensional features, so redundant and unnecessary features were removed to reduce high dimensionality and retain essential features by the PCA method. The first strategy for diagnosing the histological images of the LC25000 dataset by ANN uses crucial features of GoogLeNet and VGG-19 models separately. The second strategy uses ANN with the combined features of GoogLeNet and VGG-19. One system reduced dimensions and combined, while the other combined high features and then reduced high dimensions. The third strategy uses ANN with fusion features of CNN models (GoogLeNet and VGG-19) and handcrafted features. With the fusion features of VGG-19 and handcrafted features, the ANN reached a sensitivity of 99.85%, a precision of 100%, an accuracy of 99.64%, a specificity of 100%, and an AUC of 99.86%.

Breast cancer (BC) is a type of cancer suffered by adult females worldwide. A late diagnosis of BC leads to death, so early diagnosis is essential for saving lives. There are many methods of diagnosing BC, including surgical open biopsy (SOB), which however constitutes an intense workload for pathologists to follow SOB and additionally takes a long time. Therefore, artificial intelligence systems can help by accurately diagnosing BC earlier; it is a tool that can assist doctors in making sound diagnostic decisions. In this study, two proposed approaches were applied, each with two systems, to diagnose BC in a dataset with magnification factors (MF): 40×, 100×, 200×, and 400×. The first proposed method is a hybrid technology between CNN (AlexNet and GoogLeNet) models that extracts features and classify them using the support vector machine (SVM). Thus, all BC datasets were diagnosed using AlexNet + SVM and GoogLeNet + SVM. The second proposed method diagnoses all BC datasets by ANN based on combining CNN features with handcrafted features extracted using the fuzzy color histogram (FCH), local binary pattern (LBP), and gray level co-occurrence matrix (GLCM), which collectively is called fusion features. Finally, the fusion features were fed into an artificial neural network (ANN) for classification. This method has proven its superior ability to diagnose histopathological images (HI) of BC accurately. The ANN algorithm based on fusion features achieved results of 100% for all metrics with the 400× dataset.

With the implementation of air pollution control measures, the concentration of air pollutants in the North China Plain has exhibited a downward trend, but severe fine particulate matter (PM2.5) pollution remains. PM2.5 is harmful to human health, and the exploration of its source characteristics and potential hazards has become the key to mitigating PM2.5 pollution. In this study, PM2.5 samples were collected in Beijing and Gucheng during the summer of 2019. PM2.5 components, its oxidative potential (OP), and health risks were characterized. The average PM2.5 concentrations in Beijing and Gucheng during the sampling period were 34.0 ± 6.1 μg/m3 and 37.1 ± 6.9 μg/m3, respectively. The principal component analysis (PCA) results indicated that the main sources of PM2.5 in Beijing were vehicle exhaust and secondary components and that the main sources in Gucheng were industrial emissions, dust and biomass combustion. The OP values were 91.6 ± 42.1 and 82.2 ± 47.1 pmol/(min·m3), respectively, at these two sites. The correlation between the chemical components and the OP values varied with the PM2.5 sources at these two locations. The health risk assessment results demonstrated that Cr and As were potentially carcinogenic to all populations at both sites, and Cd posed a potential carcinogenic risk for adults in Gucheng. Regional cooperation regarding air pollution control must be strengthened to further reduce PM2.5 pollution and its adverse health effects.