Factor 1 Research Articles

Exploring intrinsic and extrinsic sources of academic well-being among university teachers through Q methodology research

PurposeThis paper investigated the intrinsic and extrinsic sources of academic well-being among university teachers at one university in Qatar, to understand how different factors influence their well-being within academia.Design/methodology/approachDrawing on systems theory, this study employs Q methodology research. Data were collected and analyzed using 35 statements (Q-items) related to academic well-being. Twenty-one university teachers participated in the study, providing a range of perspectives on the factors that influenced their academic well-being.FindingsThe analysis revealed two distinct perspectives among the participants concerning the sources of academic well-being. Factor 1 (F-1) emphasized workplace conditions as the primary source of well-being, whereas Factor 2 (F-2) highlighted individual conditions. Additionally, a significant portion of participants did not align strongly with either factor, indicating diverse and individualized sources of well-being that suggest a complex interplay of various elements affecting academic well-being.Research limitations/implicationsThe study’s sample size is limited to twenty-one university teachers at a single institution, which may affect the generalizability of the findings. Future research should consider a larger, more diverse sample to explore the universality of the findings across different academic contexts.Practical implicationsThe implications for university teachers, researchers, leaders and policymakers include a better understanding of the sources that contribute to academic well-being and the need for adopting systems thinking in addressing these sources.Originality/valueThis study employs a unique application of Q methodology within a systems theory framework to explore the sources of academic well-being among university teachers. Unlike previous research that has primarily focused on anxiety, stress and burnout, this study provides a holistic perspective by capturing the complex interplay between organizational structures and individual identities.

Mapping exploration targets through multifractal modelling of soil geochemical data in the Xiaohongshilazi Pb–Zn–(Ag) ore district, Jilin Province, NE China

The Xiaohongshilazi Pb–Zn–(Ag) ore district, located in the Jizhong–Yanbian Metallogenic Belt in NE China, presents challenges in identifying geochemical anomalies and prospecting minerals due to thick covers, complex geological evolution and mining activities. To overcome these obstacles, we adopted an innovative approach that combines multifractal theory with concentration-area (C-A) modelling and local singularity analysis (LSA) to characterize the geochemical signature. Initially, factor analysis of log-transformed data was used to identify ore-forming factors, which were then modelled using C-A and LSA. Subsequently, modelling results were compared to conventional maps generated using inverse distance weighted (IDW) interpolation. Additionally, the C-A model was employed to establish thresholds for singularity indices generated by LSA for target mapping. The results revealed a close relationship between Factor 1 (F1) and mineralization. The C-A model of F1 failed to detect weak anomalies compared to the IDW-interpolated map, while LSA not only identified weak anomalies but also enhanced the detection of reliable anomalies. Three prospecting targets (PT-01, PT-02 and PT-03) were identified. Validation of PT-01 using new drillholes and existing mineralization confirms the effectiveness of targets and the accuracy of the model. Our approach provides a robust alternative for prospecting at ore districts encountering similar challenges.

Genetic associations with mixed neuropathologies in the aging brainGenetic associations with mixed neuropathologies in the aging brain

AbstractBackground““Mixed” neuropathologies (NPs) are common in aged brains. Classic Alzheimer’s disease neuropathologic change (ADNC) proteinopathies are amyloid‐β plaques and tau neurofibrillary tangles (NFTs). In addition, α‐synuclein and TDP‐43 are also key proteinaceous disease markers, and cerebrovascular diseases (CVDs) often coexist. Although the scientific problems regarding mixed NPs are increasingly recognized, genetic associations with mixed NPs have not been well‐investigated. In this study, we applied item response theory (IRT) to optimize the clustering and combination of mixed NPs and examined the genetic associations.MethodNP data were drawn from the National Alzheimer’s Coordinating Center (NACC) NP v10‐11 forms through the March 2022 data freeze, including Thal phase ratings (A score); Braak NFT stage (B score); CERAD neuritic plaque scores (C score); Lewy bodies (LBs); TDP‐43; hippocampal sclerosis (HS); cerebral amyloid angiopathy (CAA); infarct and lacunes; microinfarcts; atherosclerosis of the circle of Willis; and brain arteriolosclerosis. We determined dominant pathology patterns in 1,228 participants who had no missing data using a multidimensional graded response model within the IRT framework. The NACC NP data were linked to the U.S. Alzheimer’s Disease Research Centers genetic data provided by the Alzheimer’s Disease genetic Consortium.ResultFrom the leave one out cross validation simulation, the model fitness tests showed that the 4‐factor model had fewer differences from the data than the other models. As shown in Figure, the predominant NPs were TDP‐43 and HS for factor 1 (F1), CVDs other than CAA for factor 2 (F2), A, B, and C scores and CAA for factor 3 (F3), and LBs for factor 4 (F4). The TMEM106B locus was genome‐wide significant with F1 (TDP‐43/HS) and the APOE locus was genome‐wide significant with F3 (ADNC/CAA) after adjusted for age at death, sex, and other factor scores. The SDK1 and FMNL2 loci were not genome‐wide significant, but had noteworthy associations with F2 (CVDs).ConclusionOur new approach to create mixed NP scores could detect the TMEM106B and APOE loci as TDP‐43/HS related and ADNC related regions, respectively. Although previous studies reported SDK1 as an ADNC related gene, we showed that the gene may be associated with CVDs.

The perceived self-efficacy of senior, middle, and operations managers of the incident command system dealing with emergencies and disasters during the COVID-19 pandemic

BackgroundNatural disasters, health, terrorism, infectious diseases, and social unrest affect more than 200 million people worldwide each year. The present study is an attempt to evaluate the self-efficacy of senior, middle, and operational managers of the Incident Command System (ICS) of Zahedan University of Medical Sciences in Iran.MethodsThe study examined the perceived self-efficacy of 103 senior, middle, and operational managers of the Incidence Command System (ICS) of Zahedan University of Medical Sciences in 2021. Sampling was done by census using a designed questionnaire based on Bandura’s self-efficacy concepts. Based on Factor Analysis, 4 factors were extracted. The factors were labeled and analyzed.ResultsThe number of people who had passed the crisis management course was 57. Seventy-one participants reported their participation in crisis management. The score obtained by men in Factor 3 (F3) was significantly higher than women, but not in other factors. People with stable employment scored far higher in Factor 1 (F1) than those with unsustainable employment conditions. Those who had passed the Crisis Management courses had a higher average score, but only in the three factors 1, 2, and 4, this difference was significant.ConclusionEven training the temporary staff is an organizational investment that can return benefits to the system. This enhances their perceived self-efficacy and promotes their commitment to the organization. Therefore, empowering these managers should be a priority.

Multivariate Analysis of Physical and Chemical Soil Attributes Under Forage Palm Cultivation and Agriculture Reuse in the Semiarid Region

The use of treated effluents (UTE) for irrigation has grown considerably in recent years, especially in arid and semi-arid regions. The objective of this research was to evaluate the effect of soil fertility under cultivation with forage cactus intercropped with different legumes. The experiment was conducted over a period of two years in a research unit that uses irrigation with reuse water (RW) in consortia of cactus species with wood and forage legumes. The soil layers were analyzed in the layers of 0-10 and 10-20 cm in three seasons: T0: without reuse water (RW); T1: dry season + reuse water (RW); T2: wet season + reuse water (RW) under cultivation of three palm varieties or cochineal cactus (mexican elephant ear palm, small palm and baiana). The soil attributes evaluated were granulometry, soil density, pH, salinity, sodicity, macronutrients, organic matter, Mineral N and total organic carbon stock (O.C.S). The plants were periodically irrigated with domestic effluent treated with constant irrigation 2 L h-1. The data were subjected to multivariate statistical analysis, using correlation matrix, cluster analysis, and factorial analysis considering the factors as principal components. According to the factorial analysis, Factor 1 (F1) and Factor 2 (F2)—F1 consisting of Sand, Ca2+, pHs, stock of. K and base saturation (V), and F2 consisting of Soil Organic Matter (SOM), stock of. Mg, CEC, stock of Ca2+, clay and soil density—were essential to differentiate the environments. The cluster analysis formed four groups. The structural groups showed greater similarity, denoting the relationship between source material and land use, followed by the chemical groups pHs, Ac. Pot., V+, CEC, Na+, stock of Na+, PST, and Ds; the structural weighted total porosity, stock of P.; Ksoil, stock of K, and Mg, SB, Te, stock of . Mg, and finally stock of Total, stock of Nitrog, Nit. Inorg.; SOM, TOC, Ca2+ and Tp of soil. This is a local peculiarity due to the climatic pattern of the Brazilian semiarid region in use of domestic sewage treated in agriculture as a forage index and as a social factor. The multivariate statistical analysis through principal component analysis and clustering made it possible to form groups according to soil attributes, which can help in decision making regarding soil fertility and fertilization management in this region with agricultural reuse application mainly in semiarid regions of Brazil.

Sources, transport, and visibility impact of ambient submicrometer particle number size distributions in an urban area of central Taiwan

This study applied positive matrix factorization (PMF) to identify the sources of size-resolved submicrometer (10–1000 nm) particles and quantify their contributions to impaired visibility based on the particle number size distributions (PNSDs), aerosol light extinction (bp), air pollutants (PM10, PM2.5, SO2, O3, and NO), and meteorological parameters (temperature, relative humidity, wind speed, wind direction, and ultraviolet index) measured hourly over an urban basin in central Taiwan between 2017 and 2021. The transport of source-specific PNSDs was evaluated with wind and back trajectory analyses. The PMF revealed six sources to the total particle number (TPN), surface (TPS), volume (TPV), and bp. Factor 1 (F1), the key contributor to TPN (35.0 %), represented nucleation (<25 nm) particles associated with fresh traffic emission and secondary new particle formation, which were transported from the west-southwest by stronger winds (>2.2 m s−1). F2 represented the large Aitken (50–100 nm) particles transported regionally via northerly winds, whereas F3 represented large accumulation (300–1000 nm) particles, which showed elevated concentrations under stagnant conditions (<1.1 m s−1). F4 represented small Aitken (25–50 nm) particles arising from the growth and transport of the nucleation particles (F1) via west-southwesterly winds. F5 represented large Aitken particles originating from combustion-related SO2 sources and carried by west-northwesterly winds. F6 represented small accumulation (100–300 nm) particles emitted both by local sources and by the remote SO2 sources found for F5. Overall, large accumulation particles (F3) played the greatest role in determining the TPV (66.4 %) and TPS (34.8 %), and their contribution to bp increased markedly from 17.3 % to 40.7 % as visibility decreased, indicating that TPV and TPS are better metrics than TPN for estimating bp. Furthermore, slow-moving air masses—and therefore stagnant conditions—facilitate the build-up of accumulation mode particles (F3 + F6), resulting in the poorest visibility.

Adenosine monophosphate deaminase modulates BIN2 activity through hydrogen peroxide-induced oligomerization.

The Arabidopsis thaliana GSK3-like kinase, BRASSINOSTEROID-INSENSITIVE2 (BIN2) is a key negative regulator of brassinosteroid (BR) signaling and a hub for crosstalk with other signaling pathways. However, the mechanisms controlling BIN2 activity are not well understood. Here we performed a forward genetic screen for resistance to the plant-specific GSK3 inhibitor bikinin and discovered that a mutation in the ADENOSINE MONOPHOSPHATE DEAMINASE (AMPD)/EMBRYONIC FACTOR1 (FAC1) gene reduces the sensitivity of Arabidopsis seedlings to both bikinin and BRs. Further analyses revealed that AMPD modulates BIN2 activity by regulating its oligomerization in a hydrogen peroxide (H2O2)-dependent manner. Exogenous H2O2 induced the formation of BIN2 oligomers with a decreased kinase activity and an increased sensitivity to bikinin. By contrast, AMPD activity inhibition reduced the cytosolic reactive oxygen species (ROS) levels and the amount of BIN2 oligomers, correlating with the decreased sensitivity of Arabidopsis plants to bikinin and BRs. Furthermore, we showed that BIN2 phosphorylates AMPD to possibly alter its function. Our results uncover the existence of an H2O2 homeostasis-mediated regulation loop between AMPD and BIN2 that fine-tunes the BIN2 kinase activity to control plant growth and development.

FAKTOR-FAKTOR YANG MEMPENGARUHI TINGKAT KECERDASAN EMOSIONAL ANAK TERHADAP PELAJARAN MATEMATIKA DENGAN MENGGUNAKAN ANALISIS FAKTOR

The purpose of this study was to determine the factors that affect the level of emotional intelegence of children in mathematics at BIMBEL Rumpun Lestari trought a rduction process using the factor analysis method. Data colletion out by using a questionnaire method to 40 respondents. Based on the results of this factor analysis research, there are 2 factors that affect the level of emotional intelligence, namely factor 1 (F1) has a value of 0,630 (63,0%) and factor 2 (F2) has a value of 0,903 (90,3%). The correlation value of (F2) is greater than (F1), but the variance value of (F1) is greater than (F2), where the variand value (F1) =36,49% while (F2) =15,97%. So, the more dominant factor in influencing the level of emotional intelligence of children, namely (F1) with a variand value of 36,49% and is formed by internal and external facrors which greatly affect the level of emotional intelligence of children in mathematicts lessons at BIMBEL Rumpun Lestari

PENGUKURAN KEPUASAN MASYARAKAT TERHADAP PELAYANAN PEMBUATAN AKTA KELAHIRAN DENGAN METODE ANALISIS FAKTOR

This study aims to find out how the level of community satisfaction with birth certificate services. This research encourages curiosity about the manufacture and satisfaction of the community in carrying out the process of making birth certificates. The research method is an analytical method by analyzing factors. The results of this study indicate that there are still shortcomings in the ease of access and processes; discipline, speed and clear service standards; on time and non-discriminatory in service. Factors that influence community satisfaction in the factor of making birth certificates are factor 1 (F1) of 0.711 (71.1%) namely officers, accuracy of service, service of officers and politeness of officers, factor 2 (F2) of 0.862 (86.2%) namely the appearance of the officer, the speed of response and timely guarantee and factor 3 (F3) of 0.828 (82.8%) namely the convenience of the place and the guarantee of legality

Imposed by urbanization on soil heavy metal content of lake wetland and evaluation of ecological risks in East Dongting Lake

The properties of heavy metals in soil hold key to effectively protecting the urban ecosystem and predicting the environmental risks. In this study, we selected the wetland of East Dongting Lake as the study area, and measured the contents of their physicochemical properties. We adopted the methods of multivariate statistical analysis and inverse distance weighted (IDW) interpolation, so as to reveal the sources and distribution characteristics of heavy metal content in soil in the study area. By adopting the potential ecological risk index (PERI) method, we assessed the PERI values of heavy metals. The research findings indicate that: (1) As shown by the analysis of correlation matrix, the elements of As, Hg and Zn are positively correlated with SOC (P < 0.01). (2) As shown by the rotation factor load matrix, Factor 1 (F1) accounts for 18.03% of the total variance, which includes As, Fe, Mn, Zn and soil organic carbon. Factor 2 (F2) mainly includes nitrate ammonia, Pb and soil particles, and the factor load all exceeds 0.50. (3) There is a significantly higher level of contents of the elements of Cd, Zn, Pb and Hg in the agricultural land than other types of land use (P < 0.05). (4) Judging from the potential ecological risk coefficient of single heavy metal Eir, Cd features the highest risk coefficient with the mean value of 292, whereas the sampling points that have their E.56ir ≥ 320 account for 36.51% of the total, indicating strong ecological risks. The average value of the comprehensive PERI in heavy metals amounts to 555.03, representing a strong degree of ecological risks.

Translation and validation of an ageism scale for dental students in Switzerland.

This study aimed to validate an ageism scale for dental students in Switzerland. The original scale was first translated to German and then evaluated by 14 experts for relevance; four items were eliminated. Validation of the resulting questionnaire was performed at three Swiss dental schools. Principal component analysis (PCA) was performed; Cronbach's alpha (α) was used to assess the internal consistency reliability, and Pearson's coefficient to identify any correlations with demographic parameters (P < 0.05). PCA revealed 11 items among 4 factors (Overall: 11 items, α = 0.63, variance = 56.6%; Factor #1 (F1): items = 3, α = 0.64; Factor #2 (F2): items = 3, α = 0.42; Factor #3 (F3): items = 3, α = 0.35; Factor #4 (F4): items = 2, α = 0.37). F1 and F4 were correlated with clinical experience (F1: P = 0.042; F4: P = 0.006) and participation in a gerodontology course (F1: P = 0.021; F4: P = 0.004). F1 was correlated with experience of dealing with the elderly (P = 0.031), while residence locality was correlated with F3 (P = 0.047) and F4 (P = 0.043). F2 was correlated with the presence of elderly in the family (P = 0.047). The translated dental ageism questionnaire for Switzerland resulted in an 11-item scale with acceptable reliability.

Models of multiple regression as an instrument of regional mortgage markets’ functioning effectiveness assessment

The article is dedicated to issues related to the formation of the Russian Federation regions economically justified classifications by housing mortgage lending (HML) systems stage of development, for the implementation of mortgage markets selective government adjustment policy. The purpose of the study is to develop the methodology of regions mortgage potential assessment based on the search of additional application solutions in the area of rapid assessment and forecasting of regional mortgage markets state. To achieve the purpose, an additional version of common methodology of Russian Federation regions systematic ranking by HML systems stage of development is represented, and its main steps are structured. Within the third step of new methodology, a multiple regression model of Factor 1 (F1) is developed, which was interpreted in authors early studies as a performance indicator of regional mortgage markets functioning effectiveness. The models development was based on the statistical processings results of 510 observations in 85 Russian Federation regions in a period from 2014 to 2019. The high accuracy of models approximation has been proved, as well as its statistical reliability. The application capabilities of the model are represented, particularly the results of period 2020-2021 forecasting for Samara region and Saint-Petersburg city. The ways of managing the rating of region through targeted distribution of available regional and federal resources received by the models predictors are outlined. The novelty of this study is contained in the results of adaptation of the new assessment methodology, which is based on the obtainment of multiple regression model for Factor 1. The methodology base of the study consists of systematic analysis methods and multivariate statistics, particularly regressive and correlation analysis methods.

Assessing the Influence of Large-Scale Environmental Conditions on the Rainfall Structure of Atlantic Tropical Cyclones: An Observational Study

AbstractUnderstanding the mechanisms related to the variations in the rainfall structure of tropical cyclones (TCs) is crucial in improving forecasting systems of TC rainfall and its impact. Using satellite precipitation and reanalysis data, we examined the influence of along-track large-scale environmental conditions on inner-core rainfall strength (RS) and total rainfall area (RA) for Atlantic TCs during the TC season (July–November) from 1998 to 2019. Factor analysis revealed three major factors associated with variations in RS and RA: large-scale low and high pressure systems [factor 1 (F1)]; environmental flows, sea surface temperature, and humidity [factor 2 (F2)]; and maximum wind speed of TCs [factor 3 (F3)]. Results from our study indicate that RS increases with an increase in the inherent primary circulation of TCs (i.e., F3) but is less affected by large-scale environmental conditions (i.e., F1 and F2), whereas RA is primarily influenced by large-scale low and high pressure systems (i.e., F1) over the entire North Atlantic and partially influenced by environmental flows, sea surface temperature, humidity, and maximum wind speed (i.e., F2 and F3). A multivariable regression model based on the three factors accounted for the variations of RS and RA across the entire basin. In addition, regional distributions of mean RS and RA from the model significantly resembled those from observations. Therefore, our study suggests that large-scale environmental conditions over the North Atlantic Ocean are important predictors for TC rainfall forecasts, particularly with regard to RA.

Detection of zones based on ore and gangue using fractal and multivariate analysis in Chah Gaz iron ore deposit, Central Iran

Detection of mineralized zones based on ores and gangues is important for mine planning and excavation operation. Major goal of this research is determination of zones based on ores and gangues by combination of fractal and factor analysis in Chah Gaz iron ore (Central Iran). Concentration-Volume (C-V) fractal method was carried out for Fe and S which indicates the main mineralized zones consist of Fe, S and P values ≥57%, ≤0.4% and ≤0.3%, respectively. Factor analysis categorized variables in two groups including factor 1 (F1) and factor 2 (F2) for ore and gangue, respectively. The C-V fractal modeling on the derived factors shows four zones for the F1 and F2. Based on the correlation among results of fractal modeling on the elements and factors, first and second zones of F1 are proper for exploitation. Furthermore, the last and first zones of F1 and F2 can be assumed as main waste for mining excavation.

Validity and Reliability of the Behavior Development Screening for Toddlers-Questionnaire/Parents(BeDevel-Q/P): A Korean Autism Screening Instrument for Infants aged 24-35 Months.

ObjectiveThis study tested the validity and reliability of the Behavior Development Screening for Toddlers-Questionnaire-Parents (BeDevel-Q/P), a new autism spectrum disorder (ASD) screening instrument being developed in South Korea. The parents of 24–35-month-old infants were recruited to complete the questionnaire. MethodsThe participants were 791 infants aged 24–35 months. There were 623 typically developing infants, 88 infants with ASD, and 80 developmentally delayed infants. For test-retest, the participants were surveyed every 1–4 weeks. Participants were recruited nationwide. Subjects’ parents completed the BeDevel-Q/P and concurrent validity questionnaires. The data were used for statistical analysis. ResultsA total of 24 items consisting of 16 items from factor 1 (F1), 6 items from factor 2 (F2), and 2 items from factor 3 (F3), were selected for the final BeDevel-Q/P items. ConclusionThe factors of the screening instrument developed in this study were analyzed, and three factors were extracted, confirming the theoretical foundation of the BeDevel-Q for the parents of 24–35-month-old infants.

Performance evaluation of RBF- and SVM-based machine learning algorithms for predictive mineral prospectivity modeling: integration of S-A multifractal model and mineralization controls

Definition of the efficient ore-forming processes, which are considered as mineralization controls is a fundamental stage in mineral prospectivity modeling. In this contribution, four efficient targeting criteria of geochemical, geological and structural data related to porphyry-type Cu deposits in Varzaghan district, NW Iran, were integrated. For creation of multi-element geochemical layer, a two-stage factor analysis was firstly conducted on ilr-transformed data of 18 selected elements and it was found that factor 1 (F1) is the representative of Cu-Au-Mo-Bi elemental association in the study area. Then, the combined model of multifractal inverse distance weighting (IDW) interpolation technique and spectrum-area (S-A) fractal method of F1 as the significant mineralization-related multi-element geochemical layer was integrated with geological-structural evidence layers. For this purpose, two supervised machine learning algorithms, namely radial basis function (RBF) neural network and support vector machine (SVM) with RBF kernel were used for generating data-driven predictive models of porphyry-Cu mineral prospectivity. Comparison of the generated models demonstrates that the former is more successful in delineating exploration targets than the latter one.

Rasch analysis of the Neck Bournemouth Questionnaire: Turkish version, validity, and reliability study

Background/aimThe multidimensional evaluation of patients with chronic neck pain is important for planning the treatment program. The aim of this study was to investigate the validity and reliability of the Turkish version of the Neck Bournemouth Questionnaire (NBQ).Materials and methods The internal construct validity of the NBQ was examined by the fit of the data to the Rasch measurement model. External validity of the NBQ was evaluated by testing for expected associations of Rasch transformed NBQ score with the corresponding variables through the process of convergent validity. The reliability of the NBQ in terms of both internal consistency and test-retest reliability was assessed by the person separation index (PSI) and differential item functioning (DIF) by time effect.ResultsIt was determined that the questionnaire has 2 factors. None of the items of Factor 1 (F1) and Factor 2 (F2) showed DIF. The reliability of F1 (Cronbach’s alpha = 0.89, PSI = 0.87) and F2 (Cronbach’s alpha = 0.77, PSI = 0.87) was good with Cronbach’s alpha and PSI. There was a good correlation between NBQ/F1 and the Neck Disability Index (NDI) (r = 0.673) and Neck Pain and Disability Scale (NPDS) (r = 0.709). Also, there was a correlation between NBQ/F2 and the Beck Depression Inventory (BDI) (r = 0.552) and Beck Anxiety Inventory (BAI) (r = 0.410).ConclusionThe Turkish version of the Neck Bournemouth Questionnaire is valid and reliable.

Structural and Chemical Attributes of Soil Inorganic Fractions for Environmental Characterization in the Brazilian Semiarid Region

Soil structure affects plant growth factors in agroecosystems. The relationships of these factors with soil texture and chemical attributes are important to differentiate environments. The objective of this work was to evaluate the relationship of soil structural attributes with physical and chemical attributes of different soils to differentiate environments, using multivariate statistics. The research was carried out in the Terra da Esperan&amp;ccedil;a Settlement Project, in Governador Dix-Sept Rosado, RN, semiarid region of Brazil. Ten soil profiles were evaluated: Haplustepts in a conventional area (1CA); Haplustepts in a collective area with (2CA); Haplustepts in an agroecological area with native forest (3AGRO); Eutrustox in a native forest (4NF); Calciustolls in a conventional area of pasture (5CAP); Haplustepts in a native forest (6NF); Haplustepts in an native forest (7NF); Usticfluvents in a permanent conservation area with Licania rigida (8PPA); Haplustepts in a native forest (9MN); and Haplustepts in a collective area with Spondias spp. (10SP). Ten soil samples were collected and analyzed by horizons in volumetric rings and in soil blocks (aggregate analysis), with three replications. Structural, physical, and chemical attributes were evaluated. The data were subjected to multivariate statistical analysis, using correlation matrix, cluster analysis, and factorial analysis considering the factors as principal components. According to the factorial analysis, Factor 1 (F1) and Factor 2 (F2)&amp;mdash;F1 consisting of clay, Ca2+, Na+, sum of bases, and base saturation (V), and F2 consisting of cation exchange capacity (CEC), total organic carbon, macroporosity, particle density, K+, and microporosity&amp;mdash;were essential to differentiate the environments. The cluster analysis formed four groups. The structural groups showed greater similarity, denoting the relationship between source material and land use, followed by the chemical groups P, K+, CEC, Ca2+, V, and pH for the Calciustolls; the structural weighted mean diameter, geometric mean diameter, soil aggregates larger than 2 mm, plasticity index, and plasticity limit for the Eutrustox; and the physical and chemical attributes EC, Na+, Silt, macroporosity, total calculated porosity, aluminum saturation, total sand, volumetric moisture, Mg2+ and particle density for the Haplustepts and the Usticfluvents. The clay fraction presented correlation with chemical attributes (F1) for the distinction of soil classes and land uses. The soil classes presented eutrophic character (V &amp;ge; 50%) as a function of the Na+ and Ca2+ contents, including the Eutrustox (4NF), despite its lower contents when compared to the other soil classes. This is a local peculiarity due to the climatic pattern of the Brazilian semiarid region.

Antioxidant Activity and Spectroscopic Characteristics of Extractable and Non-Extractable Phenolics from Terminalia sericea Burch. ex DC.

The aim of this study was to determine the antioxidant activity of the extractable and non-extractable phenolics of Terminalia. Sericea Burch. Ex DC. Free, ester bound, ether or glycoside bound and insoluble phenolics were extracted from the fruit, leaves, stem, and root samples. Follin Ciocalteu was used to estimate the phenolic content while DPPH (2,2-diphenyl-1-picrylhydrazyl) assay was used to determine the antioxidant activity. The data obtained were subjected to multivariate analysis for relationships. The result indicated that the highest average total phenolic contents and antioxidant activities were found in the free (14.8 mgGAE/g; IC50 6.8 μg/mL) and ester bound (15.1 mgGAE/g; IC50 6.4 μg/mL) extractable phenolics. There was a strong negative correlation between TPC and DPPH (r = −0.828). Agglomerative hierarchical clustering revealed three clusters. Cluster one contained the insoluble and glycoside phenolics while cluster 2 contained only free phenolic acid of the root. The third cluster was predominantly free and ester bound phenolic extracts. The principal component analysis score plot indicated two major clusters with factor 1 (F1) explaining 61% of the variation. The nuclear magnetic resonance spectroscopy spectra indicated that gallic acid and resveratrol are the major phenolic compounds present in the root. This study has demonstrated that extractable phenolics contributed more to the antioxidant activities compared to the non-extractables.

Principal component analysis implementation for brainwave signal reduction based on cognitive activity

Human has the ability to think that comes from the brain. Electrical signals generated by brain and represented in wave form. To record and measure the activity of brainwaves in the form of electrical potential required electroencephalogram (EEG). In this study a cognitive task is applied to trigger a specific human brain response arising from the cognitive aspect. Stimulation is given by using nine types of cognitive tasks including breath, color, face, finger, math, object, password thinking, singing, and sports. Principal component analysis (PCA) is implemented as a first step to reduce data and to get the main component of feature extraction results obtained from EEG acquisition. The results show that PCA succeeded reducing 108 existing datasets to 2 prominent factors with a cumulative rate of 65.7%. Factor 1 (F1) includes mean, standard deviation, and entropy, while factor 2 (F2) includes skewness and kurtosis.