Results Of Principal Component Analysis Research Articles

Design E-Learning User Interface On Website-Based Edspert.Id With Kansei Engineering Methods

The development of information technology has encouraged people to rely on information systems, especially through websites. Websites provide easy access to information and learning with various educational materials. Although e-learning has been implemented in many educational websites, Edspert. id, a company in the education sector, has not implemented it yet. User interface design development is one of the important processes in e-learning website development. A user interface that is easy to use will improve the learning experience of learners. This research proposes a solution to design the user interface of Edspert.id e-learning website by using the Kansei Engineering method. This approach has been done beforefor web-based e-learning based on users' emotions. Principal component analysis (PCA) is used to reduce Kansei Word variables that are relevant to users' emotions. The e-learning website element design was then designed based on the PCA results. The next step is to determine the design elements in the e-learning design. Then, partial Least Square (PLS) was used to analyze the relationship between Kansei Word and element design. The results show that there multiuser interface design has two concepts whose element designs are in accordance with user needs.

Geochemistry of Cenozoic coals from Sarawak Basin, Malaysia: implications for paleoclimate, depositional conditions, and controls on petroleum potential

Forty Tertiary coals from Mukah-Balingian and Merit-Pila coalfields of the Sarawak Basin, Malaysia were investigated using bulk and molecular geochemical techniques such as proximate analysis, gas chromatography-mass spectrometry, elemental analyser, isotope ratio mass spectrometry, and inductively coupled plasma mass spectrometry to reconstruct their paleovegetation, paleoclimate, and environments of deposition. In addition, principal component analysis (PCA) of selected geochemical parameters was carried out to determine the controlling influences on the petroleum potential of the humic coals. δ13C values and the abundance of terpenoids imply the predominant contribution of angiosperms to the paleoflora. Bimetal proxies (Sr/Ba, Sr/Cu, and C-value), and δD values are generally suggestive of a warm and humid climate during the accumulation of the paleopeats. However, n-alkane proxies (Pwax, Paq, n-C23/n-C29, etc.) and polycyclic aromatic hydrocarbons (PAHs) distribution suggest that Balingian coals accumulated under relatively drier and strongly seasonal paleoclimate in the Late Pliocene. When compared with published global average abundances, the investigated coals are mostly depleted in major oxides and trace elements, suggesting peat accumulation in freshwater-influenced environments. Nonetheless, higher (> 0.5 wt%) total sulfur content in some Mukah-Balingian coals suggests some degree of epigenetic marine influence. Furthermore, the low to moderately-high ash contents of the Sarawak Basin coals indicate the presence of ombrotrophic and rheotrophic peat deposits. PCA result of selected geochemical proxies suggests that source input, paleoflora, and marine incursions are not major controlling influences on the petroleum potential. However, climatic, and depositional conditions appear to slightly influence the petroleum potential of the studied humic coals.

Coastline Automatic Extraction from Medium-Resolution Satellite Images Using Principal Component Analysis (PCA)-Based Approach

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.

Quality markers of Polygala fallax Hemsl decoction based on qualitative and quantitative analysis combined with network pharmacology and chemometric analysis.

Polygala fallax Hemsl (PFH) is a widely used herbal medicine in Guangxi, China. At present, research on PFH mainly focuses on extraction technology and cultivation, lacking quality control standards for systematic evaluation. The study aimed to assess the quality of PFH from different sources and to predict markers that would help assess quality. Fingerprinting of 15 batches of PFH samples was performed by ultra-high performance liquid chromatography (UPLC) and similarity was assessed using hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discrimination (OPLS-DA). Differential components were screened by mathematical analysis, and a "component-target-pathway" network map was constructed in combination with network pharmacology, quality markers (Q-markers) of PFH were predicted, and quantitative analysis was performed. Fifteen batches were fingerprinted for PFH, with 11 common peaks, and peak 5 was identified as 4-hydroxybenzoic acid, which was generally consistent with the results of HCA, PCA, and OPLS-DA. Network pharmacology screened 18 potential compounds, 45 core targets, and 20 key pathways, integrating fingerprinting, pattern recognition, and network pharmacology methods. One of the potential Q-markers that can identify the principle of testability, efficacy, and specificity is 4-hydroxybenzoic acid, whose content ranges from 0.0188 to 1.4517 mg/g. The potential Q-markers of PFH were predicted by integrating fingerprinting, pattern recognition, and network pharmacological analysis, which provided a scientific basis for the overall control and evaluation of the quality of PFH and a theoretical reference for the study of the quality standard of multi-base traditional Chinese medicine.

Reversed-phase thin-layer chromatography and ultra-performance liquid chromatography/mass spectrometry to estimate the drug likeness of phosphodiesterase 10A inhibitors with phthalimide core

AbstractLipophilicity is a physicochemical parameter well known as a decisive factor for predicting the successful development of a drug. Thus, a balance between potency and physicochemical properties during medicinal chemistry optimization is needed. In this study, the lipophilicity of isoindole-1,3(2H)-dione derivatives designed as phosphodiesterase 10A (PDE10A) inhibitors was determined by chromatographic [reversed-phase thin-layer chromatography (RP-TLC) and ultra-performance liquid chromatography/mass spectrometry (UPLC/MS)] and in silico methods. To assess the correlation between the obtained lipophilicity parameters, principal component analysis (PCA) was performed. logP values obtained by chromatographic (logPRP-TLC and logPUPLC/MS) and in silico methods were compared using the PCA method. The results of PCA revealed that logPUPLC/MS and in silico clogP provided by the ChemDraw program were highly correlated. Compounds’ drug likeness was screened, and the pharmacokinetic properties were predicted. All the investigated compounds displayed drug-likeness properties, and they met the criteria of Lipinski’s rule of five, which predicted the oral bioavailability of drug candidates. Analysis of the influence of physicochemical properties on the biological activity showed that the compounds with increased potency on PDE10A had significantly higher topological polar surface area (TPSA) values. The blood‒brain barrier permeability and the hemolytic activity of model compound 18 were examined. The model compound 18 displayed no toxicity effect on erythrocytes in the hemolytic assay and good parallel artificial membrane permeability. The results showed that phthalimide compounds with benzimidazole moiety are a source of compound-targeted inhibition of PDE10A with balanced physicochemical and drug-likeness properties.

VCF2PCACluster: a simple, fast and memory-efficient tool forprincipal component analysis of tens of millions of SNPs.

Principal component analysis (PCA) is an important and widely used unsupervised learning method that determines population structure based on genetic variation. Genome sequencing of thousands of individuals usually generate tens of millions of SNPs, making it challenging for PCA analysis and interpretation. Here we present VCF2PCACluster, a simple, fast and memory-efficient tool for Kinship estimation, PCA and clustering analysis, and visualization based on VCF formatted SNPs. We implemented five Kinship estimation methods and three clustering methods for its users to choose from. Moreover, unlike other PCA tools, VCF2PCACluster possesses a clustering function based on PCA result, which enabling users to automatically and clearly know about population structure. We demonstrated the same accuracy but a higher performance of this tool in performing PCA analysis on tens of millions of SNPs compared to another popular PLINK2 software, especially in peak memory usage that is independent of the number of SNPs in VCF2PCACluster.

Description of The Phenotypic Characteristics of Some Tomato Genotypes

This study was carried out in order to determine some morphological characteristics of 240 tomato genotypes at S2 level and to demonstrate the usability of these materials in breeding programs. Accordingly, the average fruit weight was between 553.11 gr and 74.83 gr. The mean pericarp thickness, carpel number, and soluble dry matter content of the genotypes were 7.38 mm, 3.6 pieces and 4.04% brix, respectively. Based on these measurements and observations, tomato genotypes were also investigated using the Cluster and Principal Component Analysis (PCA) methods. According to the PCA results, the first component explained 24.1% of the total variance, and the number of fruit carpels, average fruit weight, and fruit width were the most highly explained parameters. The second and third main components constituted 13.06% and 11.16% of the total. Based on the results of fruit characterization studies, genotypes 233, 447, 126, 22, 391, 118, 71, 100, 340, 102 were determined as superior. Overall, A high degree of morphological variation was detected among the characterized tomato genotypes.

Pollution Characteristics and Risk Assessment of Heavy Metals in the Sediments of the Inflow Rivers of Dianchi Lake, China.

To explore the contamination status and identify the source of the heavy metals in the sediments in the major inflow rivers of Dianchi Lake in China, sediment samples were collected and analyzed. Specifically, the distribution, source, water quality, and health risk assessment of the heavy metals were analyzed using correlation analysis (CA), principal component analysis (PCA), the heavy metal contamination factor (Cf), the pollution load index (PLI), and the potential ecological risk index (PERI). Additionally, the chemical fractions were analyzed for mobility characteristics. The results indicate that the average concentration of the heavy metals in the sediment ranked in the descending order of Zn > Cr > Cu > Pb > As > Ni > Cd > Hg, and most of the elements existed in less-mobile forms. The Cfwas in the order of Hg > Zn > Cd > As > Pb > Cr > Ni; the accumulation of Hg, Zn, Cd, and As was obvious. Although the spatial variability of the heavy metal contents was pronounced, the synthetical evaluation index of the PLI and PERI both reached a high pollution level. The PCA and CA results indicate that industrial, transportation, and agricultural emissions were the dominant factors causing heavy metal pollution. These results provide important data for improving water resource management efficiency and heavy metal pollution prevention in Dianchi Lake.

What makes neighbourhood-level commercial centres attractive for neighbourhood residents?

ABSTRACT Neighbourhood commercial centres (NCC) are dynamic hubs that provide essential goods, promote community bonds and drive economic growth. Despite numerous studies on shopping behaviour, there remains a notable gap in understanding how transportation and shopping centre-related factors collectively shape the attractiveness of the NCCs, especially in developing countries like India. This research aims to understand why some NCCs outperform others in terms of attractiveness. This study proposes the neighbourhood commercial centre attractiveness index (NCCAI), which is developed by collecting 455 samples from 13 NCCs with the help of a primary survey to understand how different factors influence the attractiveness of NCCs in the Indian planned city context. The study used principal component analysis (PCA) and identified five major components that significantly contribute to NCC’s attractiveness. Further, the weightage of the components was determined through two distinct approaches: one based on PCA results and the other utilising the analytic hierarchy process (AHP) with expert opinion data. Finally, the more reliable weights from these two methodologies were selected using analysis of covariance (ANCOVA). The NCCAI can help urban planners, store owners and retail managers, providing data-driven insights to enhance overall NCC attractiveness. The decomposable nature of NCCAI allows for component score breakdown, enabling planners and policymakers to allocate resources strategically.

The Effect of Fermentation on the Chemical Constituents of Gastrodia Tuber Hallimasch Powder (GTHP) Estimated by UHPLC-Q-Orbitrap HRMS and HPLC.

To compare the effect of fermentation on the chemical constituents of Gastrodia Tuder Halimasch Powder (GTHP), to establish its fingerprinting and multicomponent content determination, and to provide a basis for the processing, handling, and clinical application of this herb. Ultra-high-performance liquid chromatography-quadrupole-Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) was used to conduct a preliminary analysis of the chemical constituents in GTHP before and after fermentation. High-performance liquid chromatography (HPLC) was used to determine some major differential components of GTHP and establish fingerprints. Cluster analysis (CA), and principal component analysis (PCA) were employed for comprehensive evaluation. Seventy-nine compounds were identified, including flavonoids, organic acids, nucleosides, terpenoids, and others. The CA and PCA results showed that ten samples were divided into three groups. Through standard control and HPLC analysis, 10 compounds were identified from 22 peaks, namely uracil, guanosine, adenosine, 5-hydroxymethylfurfural (5-HMF), daidzin, genistin, glycitein, daidzein, genistein, and ergosterol. After fermentation, GTHP exhibited significantly higher contents of uracil, guanosine, adenosine, 5-hydroxymethylfurfural, and ergosterol and significantly lower genistein and daidzein contents. The UHPLC-Q-Orbitrap HRMS and HPLC methods can effectively identify a variety of chemical components before and after the fermentation of GTHP. This study provides a valuable reference for further research on the rational clinical application and quality control improvement of GTHP.

The fruit quality and nutrient content of kiwifruit produced by organic versus chemical fertilizers.

Currently, organic farming has become a feasible approach for the production of high-quality fruits. To evaluate the response of fruit quality and mineral nutrition contents of Hayward Kiwifruit affected by different organic and inorganic fertilizers, the present study was conducted in Citrus and Subtropical Fruits Research Center, Iran, in 2017-2021, as a randomized block design with three replications. The studied treatments were organic fertilizers (cow, vermicompost and Azolla) and chemical fertilizers. After 4 years of fertilization, the fruit's nutritional elements content and some fruit bioactive compounds were evaluated after 3 months of cold storage and then analyzed by the principal component analysis (PCA) method. The use of organic amendments boosted the calcium, phosphorus, potassium and iron content of the kiwifruits compared to chemical fertilizers. The highest fruit potassium and phosphorus content were recorded in the cow manure treatment. The lowest amount of nitrate and the highest calcium, zinc, copper and manganese accumulation were recorded in the fruits treated with vermicompost. In addition to mineral nutrients, the dry matter, total soluble solids, total phenolic and antioxidant capacity of kiwifruit were improved by the application of vermicompost amendment compared to the other fertilizer sources. However, the highest fruit vitamin C and total soluble carbohydrates were measured in the cow manure treatment. The PCA results of the fruit quality indices indicated that fertilization treatments were ranked as vermicompost (1.88) > cow manure (1.63) = chemical (1.60) > Azolla (1.54). It is concluded that the application of 40 kg of vermicompost or 40 kg of cow manure in the next rank in Hayward kiwifruit orchards in March (growth stage beginning of bud swelling) may be a more suitable approach for improving the nutritional quality of the fruit. © 2024 Society of Chemical Industry.

Electronic nose for detecting Colletotrichum coccodes causing anthracnose fruit rots in tomatoes

Electronic nose (EN) technology has several applications targeting various study areas, and food spoilage is one of these sections. This study investigated the ability of the EN, with metal-oxide sensors (MOS), to sense the presence of Colletotrichum coccodes, which cause anthracnose fruit rots, on tomato fruit and to distinguish between different stages and doses of infection. C. coccodes were identified by an appropriate polymerase chain reaction (PCR) program and sequencing, using two different primer sets: ITS1F/ITS4 and Cc1F1/Cc2R1. Three intact homogeneous tomato samples were injected with three different concentrations of conidial fungal suspension: 3 × 104, 3 × 105 and 3 × 106 conidia/ml, and three others were kept as negative controls. Principal component analysis (PCA) was used in analyzing the data acquired from the EN sensors. The results of PCA showed that EN was able to detect the presence of C. coccodes pathogen at early stages (two days of infection and before any symptoms appear). Moreover, EN was able to differentiate between different infection stages. In addition, the concentrations of infected samples on the sixth, eighth and tenth days were clearly differentiated. The use of EN is a promising technique for determining quality assurance and control.

142 Validation of the Mentoring Competency Assessment to evaluate the mentorship skills and competencies of mentees

OBJECTIVES/GOALS: The purpose of this study was to assess if the Mentoring Competency Assessment (MCA) could be used to capture mentee gains in mentorship skills and how the mentorship competencies may vary structurally for mentees compared to mentors, while the original MCA was shown to be a validated measure to assess mentor skills. METHODS/STUDY POPULATION: The mentee training survey data were collected nationally from 2015 to 2022. The survey data set included 401 respondents who consented to participate after 59 mentee training events hosted by 34 institutions/organizations who participated in face-to-face and online training as well as completed the Mentoring Competency Assessment (MCA) in their surveys. We conducted principal component analysis (PCA) with varimax rotation to investigate the internal structure of the MCA and Hatcher’s criteria were applied. After a team of mentoring experts independently interpreted the PCA results and reached a consensus on the interpretations of the components, factor analysis and internal consistency reliability analysis were applied to assess the construct validity and the reliability. RESULTS/ANTICIPATED RESULTS: There were significant component loadings of the eight components with varimax rotation and 22 of the total 26 items were loaded into components. Four items, (5) pursuing strategies to improve communication, (6) coordinating with other mentors, (11) developing strategies to meet goals, and (23) setting career goals, were excluded from the factor analysis and Cronbach’s alpha analysis since these items were not significantly loaded into any components. The eight-component structure was validated (χ2=313.209, p<.001, RMSEA=.083, CFI=.907, TLI=.881, SRMR=.073) and the hypothesized model of the eight components resulted in an acceptable fit to the data with standardized factor loadings ranging from 0.58 to 0.93. The alpha coefficient is from 0.58 to 0.90, suggesting the items have high internal consistency. DISCUSSION/SIGNIFICANCE: Based upon the findings we recommend that the full revised MCA for mentees is used to capture mentees’ mentorship skill gains even if not all of the competency modules are used in the training. The development and validation of measures such as the MCA are important as we move toward the use of common measures across programs such as the CTSAs.

Data-Driven Classification and Logging Prediction of Mudrock Lithofacies Using Machine Learning: Shale Oil Reservoirs in the Eocene Shahejie Formation, Bonan Sag, Bohai Bay Basin, Eastern China

As the world’s energy demand continues to expand, shale oil has a substantial influence on the global energy reserves. The third submember of the Mbr 3 of the Shahejie Fm, characterized by complicated mudrock lithofacies, is one of the significant shale oil enrichment intervals of the Bohai Bay Basin. The classification and identification of lithofacies are key to shale oil exploration and development. However, the efficiency and reliability of lithofacies identification results can be compromised by qualitative classification resulting from an incomplete workflow. To address this issue, a comprehensive technical workflow for mudrock lithofacies classification and logging prediction was designed based on machine learning. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were conducted to realize the automatic classification of lithofacies, which can classify according to the internal relationship of the data without the disturbance of human factors and provide an accurate lithofacies result in a much shorter time. The PCA and HCA results showed that the third submember can be split into five lithofacies: massive argillaceous limestone lithofacies (MAL), laminated calcareous claystone lithofacies (LCC), intermittent lamellar argillaceous limestone lithofacies (ILAL), continuous lamellar argillaceous limestone lithofacies (CLAL), and laminated mixed shale lithofacies (LMS). Then, random forest (RF) was performed to establish the identification model for each of the lithofacies and the obtained model is optimized by grid search (GS) and K-fold cross validation (KCV), which could then be used to predict the lithofacies of the non-coring section, and the three validation methods showed that the accuracy of the GS–KCV–RF model were all above 93%. It is possible to further enhance the performance of the models by resampling, incorporating domain knowledge, and utilizing the mechanism of attention. Our method solves the problems of the subjective and time-consuming manual interpretation of lithofacies classification and the insufficient generalization ability of machine-learning methods in the previous works on lithofacies prediction research, and the accuracy of the model for mudrocks lithofacies prediction is also greatly improved. The lithofacies machine-learning workflow introduced in this study has the potential to be applied in the Bohai Bay Basin and comparable reservoirs to enhance exploration efficiency and reduce economic costs.

Estimation of soil erodibility in Peninsular Malaysia: A case study using multiple linear regression and artificial neural networks

Soil erodibility (K) is an essential component in estimating soil loss indicating the soil's susceptibility to detach and transport. Data Computing and processing methods, such as artificial neural networks (ANNs) and multiple linear regression (MLR), have proven to be helpful in the development of predictive models for natural hazards. The present case study aims to assess the efficiency of MLR and ANN models to forecast soil erodibility in Peninsular Malaysia. A total of 103 samples were collected from various sites and K values were calculated using the Tew equation developed for Malaysian soil. From several extracted parameters, the outcomes of correlation and principal component analysis (PCA) revealed the influencing factors to be used in the development of ANN and MLR models. Based on the correlation and PCA results, two sets of influencing factors were employed to develop predictive models. Two MLR (MLR-1 and MLR-2) models and four neural networks (NN-1, NN-2, NN-3, and NN-4) optimized using Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) were developed and evaluated. The model performance validation was conducted using the coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE). The analysis showed that ANN models outperformed MLR models. The R2 values of 0.446 (MLR-1), 0.430 (MLR-2), 0.894 (NN-1), 0.855 (NN-2), 0.940 (NN-3), and 0.826 (NN-4); MSE values of 0.0000306 (MLR-1), 0.0000315 (MLR-2), 0.0000158 (NN-1), 0.0000261 (NN-2), 0.0000318 (NN-3), and 0.0000216 (NN-4) suggested the higher accuracy and lower modelling error of ANN models as compared with MLR. This study could provide an empirical basis and methodological support for K factor estimation in the region.

Principal components analysis and K-means clustering of till geochemical data: Mapping and targeting of prospective areas for lithium exploration in Västernorrland Region, Sweden

To achieve the demand for elements used for the green transition energy, such as lithium, it is necessary to recognize the spatial distribution of the concentrations of these elements in different earth materials such as bedrock and soil and to identify areas with anomalous concentrations of such elements (i.e., mineralization) for further exploration and hopefully exploitation. This study carried out multivariate statistical analyses on compositional (i.e., element concentration) data from till samples to recognize areas that likely contain lithium pegmatite mineralization in the Västernorrland region, central Sweden. We applied principal components analysis (PCA) and K-means clustering techniques to reveal regional-scale patterns in the till geochemical data. We demonstrate that these two methods have potential for recognition of geochemical anomalies related to the underlying bedrock geology as well as to mineralization. The results of PCA- and K-means clustering were validated using known occurrences of lithium mineralization. Two different datasets were compared; one containing all available geochemical data and the second containing only available trace elements in the dataset and it was found that anomalous clusters of samples defined by K-means clustering have anomalous multi-element signatures defined by robust PCA. This demonstrated that principal components are the continuous solutions to the discrete cluster members for the K-means clustering. The results show that both PCA and K-means clustering of till geochemical datasets at the early stages of exploration and target generation may reveal useful information that can be used to identify potential areas for more detailed mapping or exploration activities.

Assessment of Genetic Diversity in Aromatic Short Grain Rice (Oryza sativa L.) Genotypes using PCA and Cluster Analysis

A population panel of 90 aromatic short grain rice accessions were evaluated for 26 agro-morphological and quality traits using principal component analysis (PCA) and cluster analysis for the determination of genetic variation pattern, and identification of the major traits contributing to the diversity. First six principal components (PCs) exhibited Eigenvalue more than one with 74.4 per cent of total variability among the 26 characters. The PC1 showed 24.55% while, PC2, PC3, PC4, PC5 and PC6 exhibited 15.48 %, 11.48 %, 9.96 %, 7.89 % and 5.12 % variability, respectively among the accessions for the traits under study. The results of PCA suggested that characters such as effective tillers per plant, number of spikelets per panicle, number of filled spikelets per panicle, spikelet fertility %, milling %, head rice recovery %, kernel length and kernel length after cooking were the principal discriminatory characteristics of aromatic short grain accessions of rice. Seven divergent clusters were formed by UPGMA clustering method. The pattern of group constellation proved the existence of significant amount of variability. The intra cluster distance ranged from 0.00 (cluster VI) to 6.33 (cluster V). The inter cluster distance was maximum between cluster VI and VII (18.854) and minimum between cluster II and cluster IV (7.673). To realize much variability and high heterotic effect, parents should be selected from two clusters having wider inter-cluster distance. Considering the importance of genetic distance and relative contribution of characters towards total divergence, the present study indicated that parental lines selected from cluster VI (IGSR -3-1-5) for number of spikelets per panicle, number of filled spikelets per panicle, grain length, kernel length and length breadth ratio, and from cluster VII (Khasakani, Kolijoha) for effective tillers per plant, 1000 grain weight, grain yield per plant, harvest index, grain breadth, length breadth ratio after cooking and elongation index could be used in crossing programmes to achieve desired segregants.

Relationships between current climate and deforestation on citrus productivity in Northeastern Pará (Eastern Amazon)

This study analyzed the temporal and spatial patterns of the precipitation (PRP) and maximum temperature (TMAX) time series of 37 municipalities proportionally distributed in the northeastern part of Pará (Eastern Amazon), from 1981 to 2018 (37 years). Environmental data on citrus productivity-PROD (oranges and lime) (IBGE), evapotranspiration (ET) and deforestation (PRODES) were used for the last 15 years to identify the impacts of climate change. The non-parametric approach, specifically the Mann-Kendall (MK) test and delta variation in the period 1989-2003 and 2004-2018 (15 years), were applied to estimate annual and seasonal trends and detect variations after using heatmap and boxplots. To compose the climate change study, Principal Component Analysis (PCA) was applied to environmental variables and Principal Component Regression (PCR) was used to test the hypothesis. The results showed an increasing positive trend in the annual and seasonal TMAX time series over 37 years, with the most significant trends in the dry season. The trend results in PRP, on the other hand, showed regions with both negative and positive trend significance. On annual and seasonal scales, regions such as R5 in northeastern Pará showed positive trends. On the other hand, negative trends of decreasing PRP were observed in the coastal areas of Pará (R1 and R3). Based on PCA and PCR results, deforestation data are associated with higher maximum temperatures and orange productivity follows correlated with PRP and ET. In contrast, PRP and ET are considered limiting factors for lime productivity.

Multivariate analysis for data mining to characterize poultry house environment in winter

The processing and analysis of massive high-dimensional datasets are important issues in precision livestock farming (PLF). This study explored the use of multivariate analysis tools to analyze environmental data from multiple sensors located throughout a broiler house. An experiment was conducted to collect a comprehensive set of environmental data including particulate matter (TSP, PM10, and PM2.5), ammonia, carbon dioxide, air temperature, relative humidity, and in-cage and aisle wind speeds from 60 locations in a typical commercial broiler house. The dataset was divided into three growth phases (weeks 1–3, 4–6, and 7–9). Spearman's correlation analysis and principal component analysis (PCA) were used to investigate the latent associations between environmental variables resulting in the identification of variables that played important roles in indoor air quality. Three cluster analysis methods; k-means, k-medoids, and fuzzy c-means cluster analysis (FCM), were used to group the measured parameters based on their environmental impact in the broiler house. In general, the Spearman and PCA results showed that the in-cage wind speed, aisle wind speed, and relative humidity played critical roles in indoor air quality distribution during broiler rearing. All three clustering methods were found to be suitable for grouping data, with FCM outperforming the other two. Using data clustering, the broiler house spaces were divided into three, two, and two subspaces (clusters) for weeks 1–3, 4–6, and 7–9, respectively. The subspace in the center of the house had a poorer air quality than other subspaces.

Principal component analysis of mineral and element composition of ores from the Bayan Obo Nb-Fe-REE deposit: Implication for mineralization process and ore classification

The Bayan Obo Nb-Fe-REE polymetallic deposit in northern China is the world’s largest light REE deposit. The deposit was formed through multiperiodic alteration and mineralization, and thus mineral species and assemblages are extremely complex. To date, there is still no consensus on the classification of ore types, and research on the paragenetic association of minerals is still inadequate. Eighty ore samples are divided into seven types according to altered minerals, including dolomite, iron oxide, aegirine, riebeckite, biotite, fluorite, and barite types. These samples were analyzed by a TESCAN Integrated Mineral Analyzer (TIMA) for their mineral and element composition. The data set was investigated by principal component analysis (PCA) to reveal the mineralization and alteration information and classify the ores.PCM1 to PCM3 and PCE1 to PCE3 represent the first three principal components for PCA results of minerals and elements, respectively. The results indicate that PCM1 may distinguish rare earth mineralization from barite and silicification alteration that is associated with the latest fluids. PCM2 can distinguish mineralization and alteration associated with Nb from those associated with REE. The combination of PCM2 and PCE1 indicates that the precipitation of REE may be closely related to fluorination and phosphate, while the combination of PCM2 and PCE3 indicates that the Nb mineralization may be more closely related to alteration of riebeckite and biotite, as well as to Ti-rich fluids. In addition, PCM3 distinguishes the Th mineralization and associated fluorination from other alteration and mineralization. Based on PCA results, the above seven ore types can be classified into three types: dolomite REE ore, Fe-REE-F ore, and Nb-rich ore, which provides a simplified and comprehensive classification for Bayan Obo ores. PCA-based approach combining geochemical and mineralogical data can quickly identify ore types, which is helpful in mineral exploration.