Personality Correlates Research Articles

Axons of retinal ganglion cells on the optic nerve disc following vessel density correction at different IOP values.

The present study aimed to determine how the vascular density (VD) in each segment peripapillary influences the retinal nerve fiber layer (RNFL) and to eliminate its contribution to RNFL in pathological intraocular pressure (IOP). In a cohort of 69 subjects (mean age, 45±6 years old) with untreated ocular hypertension (122 eyes in total) enrolled in this study, Ocular Response Analyser IOP was measured during routine outpatient care. Its value was >21 (range, 21-36) mmHg in all eyes. Furthermore, peripapillary VD and RNFL were measured using optical coherence tomography in the following eight segments: Inferior temporal (segment 1); temporal inferior (segment 2); temporal superior (segment 3); superior temporal (segment 4); superior nasal (segment 5); nasal superior (segment 6); nasal inferior (segment 7); and inferior nasal (segment 8). The visual field examination was performed with the fast threshold glaucoma program using the Medmont M 700. The overall defect was evaluated. Person's correlation coefficient was used to assess the correlation between VD and IOP. The largest changes were observed in peripapillary segments 1, 4, 5, 6, 7 and 8. The second part of the work was to eliminate the contribution of VD to RNFL. The partial correlation coefficient r was used to adjust RNFL from VD to assess the dependence between the selected parameters. The largest changes in RNFL were in segments 5 and 8 after they had been 'cleaned' of peripapillary VD. In conclusion, the present study revealed that the largest changes in RNFL after VD adjustment were observed for the incipient hypertensive glaucoma in segments 5 and 8.

Prediction for the Extent of Cancer based on Multiple Machine Learning Algorithms

Machine learning is a very important method for predicting the extent of cancer. In recent years, there has been considerable progress in detecting tiny features of the body, such as the specific surface area of cancer cells, the nuclear volume and perimeter of cancer cells. There is not development in measuring macro symptoms, such as Coughing of Blood, Chest Pain. Because in some remote and backward areas, detailed data are difficult to obtain, macro features need to be considered. In this paper, we use some relevant characteristics and environmental factors to predict the degree of cancer. During data preprocessing, some irrelevant contents are deleted, and the training set and the test set are divided. At the same time, some text data are digitized. Then, Naive Bayes, Decision Tree, Random Forest and Support Vector Machine were used in turn to make predictions and record their respective results. As a result, Naive Bayes, Decision Tree, Random Forest and Support Vector Machine achieved a fairly high prediction rate. More relevant features are obtained through the Person correlation coefficient and Feature Importance. On this basis, the prediction will not only maintain a high prediction rate, but also greatly reduce the memory consumption and training time.

DESENVOLVIMENTO E VALIDAÇÃO DE MÉTODO ANALÍTICO PARA DETERMINAR O DOSEAMENTO DE CINARIZINA EM CÁPSULAS MAGISTRAIS

Background: Cinnarizine is a brain vasodilator used in labyrinth diseases, a disorder characterized by dizziness, gait deviations, or falling. The quality control of compounded capsules containing cinnarizine is limited. Because of the difficulty accessing official pharmacopeia monographs of this drug, there is a need to describe an analytical method that is reliable and safe to perform the quantitative determination of cinnarizine in capsules. Aim: This work aims to develop and validate an analytical method to determine cinnarizine assay in capsules by spectrophotometry in the UV region that is easy to perform, low cost, and offers reliability in the dosage results. Methods: The RDC nº. 166, of 24 July 2017, of the National Health Surveillance Agency, was used as the guide for validating analytical methods. Results and Discussion: The proposed method was linear in the range of 5.833 to 10.833 μg mL-1 and presented similar results of linear and Person correlation coefficient (0.999), selectivity/specificity (0.14 %), detection limits (130, 20 ng/mL), and quantification (260, 40 ng/mL) at 251 nm. The accuracy results (DPR Rep. accuracy: 2.89 %; DPR Inter. accuracy: 1.07 %) and precision (CQB= 99.96 %, CQM= 100.02 %, and CQA= 100.06 %) complied with the validation parameters. The robustness has been proven, and the analytical method does not vary significantly from small deliberations. After validation, the assay of cinnarizine in capsules of pharmacies of the retail trade of Barra do Garças - MT was determined. The results were found to comply with Farmacopéia Brasileira 6th Edition requirements. Conclusions: The validated analytical method proved to be linear, specific, accurate, and robust and allows the determination of the dosage of cinnarizine in compounded capsules in a simple way, with low cost, and provides reliable results.

Oceanographic connectivity explains the intra-specific diversity of mangrove forests at global scales

The distribution of mangrove intra-specific biodiversity can be structured by historical demographic processes that enhance or limit effective population sizes. Oceanographic connectivity (OC) may further structure intra-specific biodiversity by preserving or diluting the genetic signatures of historical changes. Despite its relevance for biogeography and evolution, the role of oceanographic connectivity in structuring the distribution of mangrove's genetic diversity has not been addressed at global scale. Here we ask whether connectivity mediated by ocean currents explains the intra-specific diversity of mangroves. A comprehensive dataset of population genetic differentiation was compiled from the literature. Multigenerational connectivity and population centrality indices were estimated with biophysical modeling coupled with network analyses. The variability explained in genetic differentiation was tested with competitive regression models built upon classical isolation-by-distance (IBD) models considering geographic distance. We show that oceanographic connectivity can explain the genetic differentiation of mangrove populations regardless of the species, region, and genetic marker (significant regression models in 95% of cases, with an average R-square of 0.44 ± 0.23 and Person's correlation of 0.65 ± 0.17), systematically improving IBD models. Centrality indices, providing information on important stepping-stone sites between biogeographic regions, were also important in explaining differentiation (R-square improvement of 0.06 ± 0.07, up to 0.42). Wefurther show that ocean currents produce skewed dispersal kernels for mangroves, highlighting the role of rare long-distance dispersal events responsible for historical settlements. Overall, we demonstrate the role of oceanographic connectivity in structuring mangrove intra-specific diversity. Our findings are critical for mangroves' biogeography and evolution, but also for management strategies considering climate change and genetic biodiversity conservation.

Abnormal characteristics capture for thermal power unites before unexpected breakdown based on correlation analysis of operation parameters

Thermal power generation is the main force of power industry. With the development of communications technologies, more and more power plants are equipped with high-precision sensors, so operating data can be gotten in real time. Therefore, unexpected breakdown detection based on operating data has become an urgent demand under the condition of digitalization. In this paper, an abnormal characteristics capture method for thermal power units before unexpected breakdown based on correlation of operating parameters is proposed. First, the Person correlation coefficient and the Box-Cox transformation are introduced to extract the correlation of the features, and the mutual information is used to screen out the correlation features used as indictor for abnormal characteristics. Secondly, a method based on three-sigma rule and Johnson transform is presented for dynamical feature threshold calculating. At last, a case study is used to illustrate the effectiveness of the method. The results show that the dynamic threshold method can predict the breakdown earlier.

Predictable Factors that Help Students Engage in Online EFL Classroom and their Relationship to Self-Management

Online teaching has become a decisive factor to keep growing in higher education settings. The present analytical correlational study examined the predictable factors that would help EFL Jordanian students engage in online learning in English courses after COVID-19 pandemic. To achieve the objective of the study, a validated and a reliable survey was administered to investigate the students’ perceptions toward various factors that would affect their success and engagement in online EFL classroom. Additionally, the relationship among some factors was investigated. Fifty-nine EFL students in an English Language Skills course in a technical university college in Jordan were randomly selected to respond to a questionnaire which was adapted from Bernard [7]. Data of the study were analyzed using the descriptive statistics (means and standard deviations) and Person Correlation Coefficient. Findings of the study confirmed that relationship among some factors has uncovered the importance of having basic skills in building other skills. A positive relationship was also found between the factors that help students engage in online learning and self- management. Findings of the study would be helpful for teachers, instructors, and course designers when designing and delivering online courses as well as policy makers

A Time Series Regression Model via Improved PCA and Bagging Algorithms

Considering the case that the prediction variable is a time series and the response variable is a continuous scalar, we propose a time series regression model based on improved PCA and Bagging Algorithms. Compared with PCA dimension reduction, the proposed method uses distance correlation coefficient matrix instead of Person correlation coefficient matrix, which makes the distribution assumption of original variables more free. Considering that PCA is an unsupervised dimension reduction technique and the connection functions between principal components and response variables are unknown, we propose to use Bagging Algorithmss to capture information of principal components related to response variables. In the actual data analysis, the comparative methods are LASSO and PCA-based linear models, and the empirical results show that the proposed method has certain competitiveness compared with the comparison method.Finally, because the base-model of Bagging Algorithms is model-free, some machine learning methods with higher precision and flexibility can be used as the base-model for data tasks with different complexity.

A Study on Investment Behaviour of Working Women with Reference to Irinjalakuda Municipality

Working women want to be financially independent & secure. They want to invest their money & savings to handle critical situations at any stage of their lives. This research study is based on the analysis of income and investment behaviour of the working women in India. The study was conducted on working women in both government and private sectors in India. The data has been analysed using frequency analysis, descriptive statistics, percentage and person correlation coefficient analysis with the help of statistical software.

Optimization Analysis Model Determining PNMP Mandiri Loan Status Based on Pearson Correlation

PNPM Mandiri is an organization engaged in financing small and medium enterprises in the community. The problem that always occurs is an error in determining the loan status resulting in bad credit. This study aims to present a classification analysis model for determining loan status at PNPM Mandiri. The classification analysis model was built using the Perceptron algorithm artificial neural network. The analysis model will later be optimized using the Person Correlation (PC) method to measure the accuracy of the variables used. The research dataset is based on historical data from the last 2 years as many as 67 data samples. The analysis variables consist of Business Type (X1), Loan Amount (X2), Collateral (X3), Income (X4), and Expenses (X5). The results of the analysis show that the model built can provide optimal classification results. These results can be seen based on the results of variable measurements using the PC method indicating that variable X2 has no significant relationship. With the results of these measurements, the performance of the artificial neural network presents maximum results in determining loan status. Overall, the results of this study can provide an effective analytical model as well as an alternative solution for determining loan status.

Reliability and validity of an adapted Saudi Arabic version of scoliosis research society outcomes questionnaire (SRS-30) in Saudi Arabia.

To perform a cross-cultural adaptation and validation of Saudi Arabic (SA) version of SRS-30 and test it on Saudi patients with adolescent idiopathic scoliosis (AIS). A retrospective analysis of prospective collected data of 322 patients with AIS. The cross-cultural adaptation process was performed using the American Association of Orthopedic Surgeons (AAOS) Outcomes Committee method. A pre-test analysis was conducted on 30 patients. All patients had completed the SRS-22r and SF-12 preoperatively and 198 patients had completed the SA version of SRS-30 and SF-12 postoperatively twice at six weeks interval. The validity and internal consistency were tested using the interclass correlation coefficient (ICC), Cronbach alpha, person correlation coefficient, and confidence interval (CI). There are 288 (89.4%) girls and 34 (10.6%) boys and the average age is 15 ± 2.3years (range from 10-21years). The majority of patients have a Lenke type 1 curve (41%) and only 14% have a Lenke type 5 and 6. The overall Cronbach alpha (CA) of all SRS-30 domains is 0.87. However, the Cronbach alpha for the satisfaction, and function domains slightly lower than the original English questionnaire. The Saudi Arabic version of SRS-30 is a reliable and valid instrument and can be applied to evaluate the quality of life in patients with AIS.

Trace metal geochemistry of metalliferous black shales in the Mamfe basin (South West Cameroon): Implication for heavy metal assessment

This study is focused on assessing the contamination levels of heavy metal elements (Cu, Cd, Cr, Ni, and Zn) in black metalliferous shales of the Mamfe basin in South Western Cameroon and identifying the possible pollution sources. Forty (40) black shale samples collected from seven outcrops from different sites in the Mamfe basin were subjected to geochemical analysis by inductively coupled plasma-mass spectrometry (ICP-MS). The results obtained from these analyses were used to calculate the contamination factor (CF), geo-accumulation index (Igeo), and Pollution Load Index (PLI). Person's correlation coefficient was applied in this work to assess the metals origin. The values of heavy metals in the shales were compared with experimental values of TEL (Threshold Effects Level), PEL (Probable Effects Level), ERL (Effects Range Low), and ERM (Effects Range Medium) to assess the long-term adverse biota effect if the shales weathers and release the hosted heavy metals. The average heavy metal concentrations in the studied shales were 35 mg/kg, 89 mg/kg, 0.24 mg/kg, 82 mg/kg and 37.3 mg/kg for Cu, Zn, Cd, Cr and Ni respectively with some above the threshold values of United State Environment Protection Authority (USEPA), average shale (ASV), toxicity reference values (TRV) and Indian River system (IRS). The shales are poorly to moderately enriched in heavy metals which may cause a long-term effect on the surrounding soils and waters after degradation. The heavy metal values of the studied shales compared with the experimental results of TEL, PEL, ERL, and ERM indicate that a little adverse biota effect of Ni, Cu and Cr to the ecosystem after degradation of the studied shales. The geo-accumulation index ranged between 0 and 0.06 indicating that the black Mamfe shales are uncontaminated to moderately contaminated and may have adverse effects on the freshwater ecology and surrounding soils. The pollution load index (between 0.0 to 0.05) and contamination factor (0.18-1.84) indicate that the shales have a low level of contamination and their long-term degradation and weathering will have a limited effect on the soils and water ecosystems. The positive correlation between Cu, and Zn, suggest the same source of contamination input. The positive correlation between Zn, Cu, Cr, and Ni indicates a natural origin of these elements in the shales. A rapid weathering/degradation of large quantity of theses shales may increase the concentration of heavy metals in the surrounding rivers and soils in the near future.

Single-phase grounding fault line selection method based on dynamic time warping algorithm

Aiming at the problems of the complex line selection process and slow line selection speed of the existing intelligent classification line selection methods, a single-phase grounding fault line selection method based on the dynamic time warping (DTW) algorithm is proposed. The wavelet modulus maximum truncates the original signal to extract the temporary part. Then the DTW distance matrix between feeders is figured by the DTW algorithm. The fault line selection is realized by comparing the difference between the maximum, sub maximum, and minimum values with the threshold value. The simulation shows that the proposed approach can recognize proper line selection under various fault states and partial loss of recorded data, has high accuracy and has better robustness than the Euclidean distance and person correlation coefficient methods.

Insomnia attenuates response inhibition: Evidence from Go/NoGo research

ObjectiveVaried cognitive dysfunctions including memory, attention, inputs, processing and filtration have been found in insomnia. Meanwhile, evidence from functional neuroimaging have revealed that the abnormal metabolism in prefrontal cortex was associated with probable deficit of executive function. And in our study, we have detected the response inhibition in insomnia patients by Go/NoGo,an Event-related potentials (ERPs) study, in order to explore the impaired executive function, because response inhibition is a hallmark of executive function. MethodsWe used polysomnography (PSG) to record such objective PSG parameters. Go/NoGo was performed in sequence, different ERP components have been analyzed such as latency or amplitude between insomnia group and control group. And we used Person correlation coefficient R to make analysis between different ERP components and gender, duration, education, BMI and sleep characteristics. ResultsOn the behavioral level, we found a little poor performance insomnia participants. On the electrophysiological level, under Go condition, insomnia participants have prolonged latency and smaller amplitude of N2 or P3. While, under NoGo condition, insomnia participants also have longer latency, but higher amplitude of N2 or P3. another major finding was that different correlation was found between gender,anxiety-depression,duration, education,BMI,sleep characteristics and N2 or P3. DiscussionOur study has revealed that sleep loss may influence the response inhibition ability in insomnia, not only on behavior level, but also on the electrophysiological level. Abnormal changes in inhibition process or inhibition supervision can be represented as N2/P3 components under Go/No-go condition. Additionally, correlation analysis has been found between gender, anxiety-depression, BMI, education, and sleep structure. Thus, sleep loss attenuates response inhibition and impairs executive function in insomnia participants.

A Dynamic Prediction Neural Network Model of Cross-Border e-Commerce Sales for Virtual Community Knowledge Sharing.

In this paper, a neural network algorithm is used to conduct in-depth research and analysis on the sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce. Both the expected returns and the social network structure are analyzed, and both have positive effects on knowledge sharing in the actual development process, but the degree of them also possesses certain variability. A model of the factors influencing the quality of knowledge shared by users is constructed to explore the relationship between the dimensions in social capital and how they affect the community users' perceptions of knowledge quality. Exploring the strong influencing factors of product repurchase rate has key implications for promoting product sales and sales forecasting. The scale of this paper has undergone several minor revisions, and the content validity is very good. Criterion validity is generally reflected by Person correlation coefficient, and construct validity includes exploratory factor analysis and confirmatory factor analysis. First, the values were clustered, and the optimal variables were selected using stepwise regression and fitted with Poisson regression models to explore the relationship between the repurchase rate of different products and the factors that strongly influence the repurchase rate in the case. To predict the sales of goods, the advantages of the BP neural network, LSTM neural network, and Verhulst gray model are combined: BP neural network can predict sales by combining the current data corresponding to independent variables, LSTM neural network can explore the influence of historical data on sales, and Verhulst model can predict sales based on the growth trend of variables. A BP-LSTM-Verhulst nested neural network based on the AP algorithm is constructed to predict sales volume, and the accuracy of this method is proved by example. Finally, it is found that the proposed sales prediction method has higher accuracy than exponential regression and shallow neural networks. The deep learning prediction method combining unstructured data such as images proposed in the paper not only provides a more accurate sales prediction method for short life cycle products in e-commerce but also provides an effective deep learning method for management practices. The KMO value obtained after the test is less than 0.5, which is not suitable for factor analysis. Using SPSS22.0 to expand the KMO value and Bartlett's sphericity test, the KMO value is significantly greater than the minimum requirement of 0.5, the KMO value of self-efficacy is 0.826, and the KMO value of expected return is 0.870.

치위생(학) 전공 학생들의 임상실습 만족도와 전문직 자아개념과의 관련성 연구

Objectives: This study is to investigate the factors that affect the self-concept satisfaction of clinical practice of students on their satisfaction of clinical practice among Students Majoring in Dental Hygiene. Methods: The subject of this study was a self-reported survey of 179 students in the 3rd grade dental hygiene department residing in Gwangju Metropolitan City and Jeollabuk-do. The data were analyzed using SPSS/PC WIN 23.0 program with frequency, mean(standard deviatio), t-test, ANOVA, Person's correlation coefficients(Duncan') and multiple liner regression. Results: The results from multiple liner regression analysis showed that satisfaction of clinical practice among the students significantly influenced professional self-concept. In particular, clinical practice contents, clinical practice instruction, clinical practice environment, and interpersonal relationship were statistically significant with professional self-concept(p<0.001). Conclusions: Therefore, It is necessary to develop educational programs to form positive dental hygiene professional self-concept.

Social interaction and its relationship to Psychological Adjustment among Kindergarten children from the Perspective of Kindergarten teachers

This research aims to Identify the relationship between social interaction with Psychological adjustment among the children of the kindergarten from the teacher's Perspective and to know the level of social interaction and mental adjustment among children in kindergartens According to these variables (gender ,age, number of children, child sequencing, parent’s education level)
 This research used associative method research society is kindergarten children and kindergarten teachers in the center of Halabja Government. The research Sample is 400 kindergarten children, The research tool is the measure of (social interaction and psychological adjustment) of kindergarten children from the teacher’s Perceptive which was created by the researchers, among the most important statistical tools used (person's correlation with Person, Spearman, Alpha Cronbach , T- test is one example and two examples, the statistical equation of discrimination, One away ANOVA, Colmogrov-Sminroo). One of the most important result of the research was that the level of Social interaction and psychological adjustment among kindergarten children does not differ according to the variables (age, number of children, child sequencing, Father's educational level), but it does differ according to the variable (gender , mother's educational level).and it was also found that the social interaction and mental adjustment among children in kindergarten from kindergarten teachers perspective to have a weak positive correlation

임상간호사의 역할갈등과 조직효과성, 간호업무수행에 관한 연구

Objectives The purpose of this study is to investigate the correlation between role conflict, organizational effectiveness, and differences in nursing job performance and variables according to the general characteristics of clinical nurses. Methods A structured questionnaire was distributed to 520 clinical nurses working at 3 secondary medical institutions with 250 beds or more and 1 tertiary medical institution in U city, and 477 copies of data were used for the final analysis. For data analysis, the SPSS 22.0 program was used for descriptive statistics, independent sample t-test, one-way ANOVA, Scheffe post-test, Person's correlation analysis, and reliability of the tool. Results In the difference in role conflict according to general characteristics, it was found that there was a statistically significant difference according to the current departmental experience and position. 3.340, p<.05), there was no difference between groups in role conflict. In terms of organizational effectiveness, it can be seen that there is a high level of organizational effectiveness with more than 40 years of experience, more than 15 years of total experience, full-time employees, and more than a head nurse, suggesting the importance of role management according to working conditions, careers, and positions. In nursing job performance, the difference in nursing job level improvement according to work type was found to be statistically significant (t=2.085, p<.05), and the improvement of nursing job level was higher than that of having no job change experience. According to educational background, the differences in nursing job performance ability, nursing job performance attitude, nursing job level improvement, nursing support function, and nursing process application were found to be statistically significant. Organizational effectiveness showed a significant negative (-) correlation result with role conflict, and nursing job performance did not show a significant correlation with role conflict, and it was significant with role isolation and role ambiguity, which are sub-factors of role conflict. A negative (-) correlation result was done. Conclusions Effective organizational management and leadership are needed to reduce role conflict of clinical nurses and improve organizational effectiveness and nursing work performance, and it is considered that various nursing intervention programs need to be developed so that they can adapt to the organization.

Application of Correlation Matrix and Analysis of Variance (Anova) in Profiling Surface Water in Nembe Creek of Niger Delta, Nigeria

Water resource contamination is a major concern in several regions, especially in the Niger Delta, in which the oil exploration activities pose serious risks to human health, water resource and the environment. This present study evaluates the properties of surface water in Nembe Creek. Surface water samples were collected from three (3) control stations and twenty-four (24) sampling stations around the creek during the wet and dry seasons. The samples were subjected to analysis following the standard operating procedures of ASTM and APHA analytical methods. The relationship between the parameters and variations of surface water quality of Nembe creek were determined by using descriptive statistics, Analysis of variance (ANOVA) and Pearson correlation analysis. The results indicated pH had a mean value of 6.67± 0.33 (wet season) and 7.21 ± 0.3 (dry season), Electrical Conductivity ranged 200 – 3950 µS/cm (wet season) and 17900 – 25800 µS/cm (dry season), DO ranged between 4.3 – 5.8 (wet season) and 3.9 – 5.6 (dry season), Nitrate had values of 0.007- 0.068 mg/L (wet season) and 0 – 0.007 mg/L (dry season), Sulphate values range 130.58- 158.76 mg/L (wet season) and 215.12 – 657.35 mg/L (dry season). Iron range 3.23 – 5.6 mg/L (wet season) and 0.26 – 7.87 mg/L (dry season), Zinc ranged 0.02 – 0.47 mg/L (wet season) and 0.13 – 2.22 mg/L (dry season). Across all sampling stations and control areas during the wet season, the values for Total Petroleum Hydrocarbon (TPH), total hydrocarbon Content (THC) and Petroleum Aromatic Hydrocarbon (PAH) were below the detection limit; however, the values varied during the dry season. The Pearson Correlation matrix revealed positive, negative and significant correlations between the pairs of parameters at 95% confidence level. Amongst the sampling and control stations, some parameters had no significant difference (p > 0.05), however some differences were statistically significant (p < 0.05). In conclusion, Nembe Creek is marginally more polluted during the dry season than during the wet season. Most parameters on the Person's Correlation matrix were connected with each other based on the metrics. However, some parameters such as metals, do not significantly correlate with one another, indicating that the sources of contamination may have originated from various places. It is possible that the source may not predominantly originate from oil exploration activities but may involve other natural, geological and anthropogenic activities.

Sex-differences in hikikomori traits as predictors of problematic internet use in Italian university students

BackgroundDespite a confirmed association between Problematic Internet use (PIU) and hikikomori-like social withdrawal, few studies investigated differences between sexes. Hence, the main objective was identifying psychopathological predictors (including hikikomori-like traits) associated with PIU (vs. non-PIU) across both sexes. MethodsA total of 1141 Italian university students were assessed with the Hikikomori Questionnaire-11 (HQ-11), Toronto Alexithymia Scale (TAS-20), Internet Addiction Test (IAT) and Depression Anxiety Stress Scale-21 (DASS-21). Student's T-test, bivariate Person's correlations, linear regression and a stepwise binary logistic regression analysis were post-hoc run. CHERRIES guidelines were followed for data reporting. ResultsPIU university students, independently of sex, had greater psychological problems (p < 0.001) and greater hikikomori traits, compared to non-PIU (respectively, p = 0.010 and p < 0.001). PIU females had a better social quality of life (p = 0.007), and higher anxiety (p = 0.013) and stress (p = 0.051) levels, compared to PIU males. Linear regression analysis showed that depressive (p = 0.001) and stress levels (p = 0.003) as well as Hikikomori traits (p < 0.001) significantly predicted IAT total score in females (p < 0.001). Sex-specific binary logistic analyses showed that hikikomori, psychological distress, alexithymia, and compromised social quality of life predicted PIU in females. In males, PIU is predicted by family annual income, psychopathological distress, alexithymia, and compromised social quality of life, but not by Hikikomori traits. ConclusionMain psychopathological predictors of PIU in Italian university students showed differences across sexes, as hikikomori-like traits are predictors of the emergence of a PIU only in the female sample. Further research studies should better investigate whether female Hikikomori-like young people may display better functioning compared to their male counterparts.

Assessment of Heavy Metal Content in Soil and Lycopersicon esculentum (Tomato) and Their Health Implications.

In this study, the content of lead (Pb), cadmium (Cd), chromium (Cr) and manganese (Mn) was evaluated in soils and tomatoes (Lycopersicon esculentum) collected from rural areas of Dobrogea province, South-East of Romania. The risk to human health due to the heavy metal exposure via tomato consumption was also assessed.The results suggest that based on the contamination factor, the soils are moderately contaminated with Cd and Mn (Cf values of 1.266. and 1.40) and poorly contaminated with Pb and Cr. The bioconcentration factor (BAF) was below 1 and indicated that the studied species of Lycopersicon esculentum did not accumulate the monitored elements. Person's correlation analysis showed that there were significant relations between soil pH and BCF values of Cd, Pb, Cr and Mn in analysed tomatoes. The estimated daily intake of each metal was below the oral reference dose. The hazard quotient (HQ) and hazard index (HI) were below the acceptable level (< 1), and the cancer risk (CR) for Pb, Cd and Cr was found within acceptable levels (1.0 × 10-6-1.0 × 10-4). Based on health guidance values, it may be concluded that the analysed tomatoes do not present health risks to consumers in terms of content and accumulation of heavy metals. It is important to monitor the other toxic metals as well, in order to evaluate the heavy metal accumulation variation and the toxicity value of each metal in agricultural soils from both rural and industrial areas.