Collinearity Test Research Articles

Determinants of early marriage in rural areas Aceh Province Indonesia

Early marriage often occurs in the low and middle income countries because of thetradition and cultural belief that girls must be married off immediately. The data was obtained from the Aceh Regional Office of the Ministry of Religion, from a population of all married women under the age of 19 years, totaling 507 respondents. The study used a total sampling technique. Data analysis consisted of the use of collinearity tests, chi-square, and multiple logistic regression analysis. The results showed that parental and economic factors have significant relationships to early marriage. The poorest women are 2.23 times more likely to experience early marriages than the richest women. Poor women are 1.68 times more vulnerable to early marriage than the richest women. Women who have parents with a very strong role have a risk of early marriage of 10.34 times compared to women whose parents do not play much of a role, namely 9.10 times. We conclude that parental factors play dominant in the incidence of early marriage in Aceh Province in Indonesia.

Nerve conduction, latency, and its association with hand function in young men.

The median and ulnar nerves have been suggested to play a significant role in hand function; however, there are insufficient data to determine the strength of this association. This study aimed to investigate the correlation between hand function as measured with the Grooved pegboard test (GPT) and conduction velocity and latency of the median and ulnar nerves. We collected convenience samples in the College of Medicine, KSAU-HS. We used GPT to characterize hand function and performed measured nerve conduction velocity (NCV) and latency of the ulnar and median nerves of both hands. We used the Edinburgh handedness inventory (EHI) to determine hand dominance. We recruited 28 healthy medical students aged 20-29 years (mean: 21.46 ± 1.62 years). Most were right-handed (n = 25, 89.3%), with a mean EHI score of 302 ± 210. The mean GPT time was significantly faster in the dominant (65.5 ± 6.4 s) than in the non-dominant (75.0 ± 9.6 s) hand. The NCV for the ulnar nerve of the dominant hand was significantly correlated with GPT (r = -0.52, p = 0.005) while median nerve was not correlated (0.24, p = 0.21). Regression analysis and collinearity test showed that the ulnar NCV explained 20% of the variance in GPT of the dominant hand (R2 = 0.203, p = 0.016). The ulnar nerve conduction velocity, explained 20% of the variance in GPT times of the young men. Performance on this biomarker of neurological health seems to be more influenced by other factors in healthy young individuals.

Predicting Forest Fire Area Growth Rate Using an Ensemble Algorithm

Due to its unique geographical and climatic conditions, the Liangshan Prefecture region is highly prone to large fires. There is an urgent need to study the growth rate of fire-burned areas to fill the research gap in this region. To address this issue, this study uses the Grey Wolf Optimizer (GWO) algorithm to optimize the hyperparameters in the eXtreme Gradient Boosting (XGBoost) model, constructing a GWO-XGBoost model. Finally, the optimized ensemble model (GWO-XGBoost) is used to create a fire growth rate warning map for the Liangshan Prefecture in Sichuan Province, China, filling the research gap in forest fire studies in this area. This study comprehensively selects factors such as monthly climate, monthly vegetation, terrain, and socio–economic aspects and incorporates monthly reanalysis data from forest fire assessment systems in Canada, the United States, and Australia as features to construct the forest fire dataset. After collinearity tests to filter redundant features and Pearson correlation analysis to explore features related to the burned area growth rate, the Synthetic Minority Oversampling Technique (SMOTE) is used to oversample the positive class samples. The GWO algorithm is used to optimize the hyperparameters in the XGBoost model, constructing the GWO-XGBoost model, which is then compared with XGBoost, Random Forest (RF), and Logistic Regression (LR) models. Model evaluation results showed that the GWO-XGBoost model, with an AUC value of 0.8927, is the best-performing model. Using the SHapley Additive exPlanations (SHAP) value analysis method to quantify the contribution of each influencing factor indicates that the Ignition Component (IC) value from the United States National Fire Danger Rating System contributes the most, followed by the average monthly temperature and the population density. The growth rate warning map results indicate that the southern part of the study area is the key fire prevention area.

Costing Techniques And Profitability Trade Off: An Investigation On The Agricultural Firms Listed In The Nairobi Securities Exchange.

Costing techniques have great impact on life and global economy especially the profitability levels of Agricultural companies in the Nairobi Securities Exchange (NSE). The profitability levels in the Agricultural firms in Kenya have declined, stagnated or suffered losses in the recent past leading to price fluctuations hence low profitability levels. The drive of this study was to investigate the costing techniques and profitability trade off of agricultural firms listed in the NSE. Specifically the study sought to establish the effect of job costing on determination of the level of profitability in the quoted agricultural firms in NSE. The study was steered by transaction cost theory and applied descriptive research design. The target population of seven (7) listed agricultural firms listed in the NSE, hence adopting the census technique where all the seven respondents were used. Data collection sheet was used to obtain secondary data from audited published financial statements of listed agricultural firms in NSE. The period of the study was five years from 2018 to 2022. The data collected was analyzed using descriptive statistics that were summarized using mean and standard deviation. Diagnostic tests used to test the research hypothesis included tests of normality, autocorrelation, collinearity and linearity tests. Correlation, regression analysis and multiple linear regression models was carried out to show whether and how strongly changes in costing techniques were related to firms’ profitability and to determine the association amongst costing techniques and profitability. The findings were presented in figures and tables and prose form for its discussions. Job costing had a significant effect on the profitability of agricultural firms listed in the NSE when measured alongside other costing methods. Both the coefficient is positive (β1=0.565) and the p-value is lower than 0.05, indicating statistical significance. The recommendation to the industry is to prioritize Job Costing given its significant positive effect on profitability of agricultural firms should consider implementing and expanding its use. Firms using other costing methods should reassess their effectiveness and consider integrating Job Costing elements into their existing systems. For academia, conduct more studies to authenticate these findings across different contexts, firm sizes and geographical locations within the agricultural sector. Emphasize on Job Costing method in accounting and agricultural management curricula while still maintaining a balanced approach to other costing methods. For policy formulators, consider policies that encourage or incentive the adoption of Job Costing method in agricultural companies like, tax benefits or grants for implementation and developing guidelines or standards of costing.

PLS-SEM for Multivariate Analysis: A Practical Guide to Educational Research using SmartPLS

Implementation of PLS-SEM in educational research has developed significantly, but there are variations in the presentation of the analysis results. This study aims to provide practical understanding to researchers who intend to utilize PLS-SEM in multivariate analysis to enhance the recognition and validity of the resultant research outcomes using SmartPLS. This research is a literature study that conducts content analysis of relevant books and publications. The research results present PLS-SEM analysis using SmartPLS on reflective and formative research models with first-order and second-order approaches through measurement model evaluation (outer model) and structural model evaluation (inner model) with various criteria. Evaluation of the reflective measurement model consists of reflective indicator loadings, internal consistency reliability, convergent validity, and discriminant validity. The review of the formative measurement model consists of convergent validity, collinearity, and statistical significance of weights. The structural model evaluation consists of the collinearity test, significance value, f square, R square, Q square, SRMR, PLSpredict, and robustness checks. Therefore, this study can provide guidance using SmartPLS in conducting PLS-SEM analysis and presenting acceptable analysis results.

Coupled InVEST–MGWR modeling to analyze the impacts of changing landscape patterns on habitat quality in the Fen River basin

The present study employed remote sensing images of the Fen River Basin from 2005, 2010, 2015, and 2020 as the primary data source. The software ENVI, ArcGIS, and Fragstats 4.2 were utilized to measure the landscape pattern index of the Fen River Basin. A collinearity test was conducted to remove any redundant landscape pattern indices. Based on the selected landscape indices, the landscape pattern index values were ascertained as follows. Using the shifting window method, the landscape pattern index of the Fen River Basin was obtained. Second, the habitat quality in the Fen River Basin was assessed using the InVEST model, and the spatial autocorrelation approach was employed to confirm that the habitat quality was spatially autocorrelated. Finally, the spatial impacts of landscape pattern indices on habitat quality were examined using the MGWR model. The results show that (1) the Fen River Basin's overall habitat quality declined between 2005 and 2020; however, the deterioration slowed with time and had a typical "poor in the middle and high around the margins" spatial distribution. The habitat quality of the low-value area continued to increase, the habitat quality of the lower-value area decreased annually, the habitat quality of the middle-value area decreased and then increased, the habitat quality of the higher-quality area tended to increase, decrease, and then increase again, and the habitat quality of the high-quality area decreased annually. (2) The fit of the MGWR model was greater than those of the OLS and traditional GWR models, and it was able to more clearly illustrate the various roles that landscape pattern indices and habitat quality play in one another. (3) Changes in landscape patterns had a major impact on habitat quality; habitat quality was positively impacted by PD and AI, negatively impacted by MESH, and had positive and negative bidirectional effects from CONTAG and AI.

Can smart supply chain bring agility and resilience for enhanced sustainable business performance?

PurposeSupply chains need to be made viable in this volatile and competitive market, which could be possible through digitalization. This study is an attempt to explore the role of Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business performance from the lens of natural resource-based view.Design/methodology/approachThe study tests the proposed model using a covariance-based structural equation modelling and further investigates the ranking of each construct using the artificial neural networks approach in AMOS and SPSS respectively. A total of 234 respondents selected using purposive sampling aided in capturing the industry practices across supply chains in the UK. The full collinearity test was carried out to study the common method bias and the content validity was carried out using the item content validity index and scale content validity index. The convergent and discriminant validity of the constructs and mediation study was carried out in SPSS and AMOS V.23.FindingsThe results are overtly inferring the significant impact of Industry 4.0 practices on creating smart and ultimately sustainable supply chains. A partial relationship is established between Industry 4.0 and supply chain agility through a smart supply chain. This work empirically reinstates the combined significance of green practices, Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business value. The study also uses the ANN approach to determine the relative importance of each significant variable found in SEM analysis. ANN determines the ranking among the significant variables, i.e. supply chain resilience > green practices > Industry 4.0> smart supply chain > supply chain agility presented in descending order.Originality/valueThis study is a novel attempt to establish the role of digitalization in SCs for attaining sustainable business value, providing empirical support to the mediating role of supply chain agility, supply chain resilience and smart supply chain and manifests a significant integrated framework. This work reinforces the integrated model that combines all the constructs dealt with in silos so far in prior literature.

Impact of territorial spatial landscape pattern on PM2.5 and O3 concentrations in the Yangtze River delta urban agglomeration: Exploration and planning strategies

PM2.5-O3 composite pollution has become a pressing atmospheric challenge in China. Assessing the impact of territorial spatial landscape pattern on PM2.5 and O3 concentrations within the context of territorial spatial planning will provide ideas for atmospheric governance and spatial planning. Based on the agricultural-urban-ecological space system, this study first reclassifies territorial space within the Yangtze River Delta urban agglomeration (YRDUA) and calculates multilevel and multidimensional landscape metrics. Then, variables are screened by the random forest model and collinearity test. Furthermore, the spatiotemporally heterogeneous impact of selected landscape metrics on PM2.5 and O3 concentrations is explored using the geographically and temporally weighted regression (GTWR) model, with a tradeoff-synergy analysis being conducted to propose planning strategies. The conclusions show: (1) Two pollutant concentrations have both numerical and spatial correlations. The temporal stability of concentrations' responses to metrics is increasing, while the spatial heterogeneity continues to be significant. (2) Aggregation index of agricultural production space (AP_AI) and percentage of forest area (FO_PLAND) are trade-off and synergy factors of coordinated control, respectively. Largest patch index of grassland (GR_LPI) and patch density of other ecological spaces (OT_PD) are individual factors of PM2.5 reduction. (3) Spatial zoning supported by intra-zonal and inter-zonal cooperation policies, and coordinated control guidelines centered around the utilization of agricultural production space, have been advocated. This study aims to provide theoretical supplement and practical support for urban agglomeration's atmospheric governance and territorial spatial landscape pattern optimization.

Identification of the ERF gene family of Mangifera indica and the defense response of MiERF4 to Xanthomonas campestris pv. mangiferaeindicae

An important regulatory role for ethylene-responsive transcription factors (ERFs) is in plant growth and development, stress response, and hormone signaling. However, AP2/ERF family genes in mango have not been systematically studied. In this study, a total of 113 AP2/ERF family genes were identified from the mango genome and phylogenetically classified into five subfamilies: AP2 (28 genes), DREB (42 genes), ERF (33 genes), RAV (6 genes), and Soloist (4 genes). Of these, the ERF family, in conjunction with Arabidopsis and rice, forms a phylogenetic tree divided into seven groups, five of which have MiERF members. Analysis of gene structure and cis-elements showed that each MiERF gene contains only one AP2 structural domain, and that MiERF genes contain a large number of cis-elements associated with hormone signaling and stress response. Collinearity tests revealed a high degree of homology between MiERFs and CsERFs. Tissue-specific and stress-responsive expression profiling revealed that MiERF genes are primarily involved in the regulation of reproductive growth and are differentially and positively expressed in response to external hormones and pathogenic bacteria. Physiological results from a gain-of-function analysis of MiERF4 transiently overexpressed in tobacco and mango showed that transient expression of MiERF4 resulted in decreased colony count and callose deposition, as well as varying degrees of response to hormonal signals such as ETH, JA, and SA. Thus, MiERF4 may be involved in the JA/ETH signaling pathway to enhance plant defense against pathogenic bacteria. This study provides a basis for further research on the function and regulation of MiERF genes and lays a foundation for the selection of disease-resistant genes in mango.

Modelling climate change impacts on the spatial distribution of anthrax in Zimbabwe

BackgroundIn Zimbabwe, anthrax is endemic with outbreaks being reported almost annually in livestock, wildlife, and humans over the past 40 years. Accurate modelling of its spatial distribution is key in formulating effective control strategies. In this study, an Ensemble Species Distribution Model was used to model the current and future distribution of anthrax occurrence in Zimbabwe.MethodsBioclimatic variables derived from the Beijing Climate Centre Climate System Model were used to model the disease. Collinearity testing was conducted on the 19 bioclimatic variables and elevation to remove redundancy. Variables that had no collinearity were used for anthrax habitat suitability modelling. Two future climate change scenarios for different Representative Concentration Pathways (RCP), RCP4.5 and RCP8.5 were used. Model evaluation was done using true skill, Kappa statistics and receiver operating characteristics.ResultsThe results showed that under current bioclimatic conditions, eastern and western districts of Zimbabwe were modelled as highly suitable, central districts moderately suitable and southern parts marginally suitable for anthrax occurrence. Future predictions demonstrated that the suitable (8%) and highly suitable (7%) areas for anthrax occurrence would increase under RCP4.5 scenario. In contrast, a respective decrease (11%) and marginal increase (0.6%) of suitable and highly suitable areas for anthrax occurrence were predicted under the RCP8.5 scenario. The percentage contribution of the predictors varied for the different scenarios; Bio6 and Bio18 for the current scenario, Bio2, Bio4 and Bio9 for the RCP4.5 and Bio3 and Bio15 for the RCP8.5 scenarios.ConclusionsThe study revealed that areas currently suitable for anthrax should be targeted for surveillance and prevention. The predicted future anthrax distribution can be used to guide and prioritise surveillance and control activities and optimise allocation of limited resources. In the marginally to moderately suitable areas, effective disease surveillance systems and awareness need to be put in place for early detection of outbreaks. Targeted vaccinations and other control measures including collaborative ‘One Health’ strategies need to be implemented in the predicted highly suitable areas. In the southern part where a high decrease in suitability was predicted, continued monitoring would be necessary to detect incursions early.

Risk factors of electrical status epilepticus during sleep in children with benign childhood epilepsy with centro-temporal spikes.

To explore risk factors of electrical status epilepticus during sleep in children with benign childhood epilepsy with centro-temporal spikes (BECT). This is a clinical comparative study. The subjects of study were 67 children with BECT from the Outpatient Department of Pediatric Neurology in Xingtai People's Hospital from January 2019 to January 2022. According to the occurrence of ESES, the enrolled children were divided into control group which included BECT children without ESES and the observation group which included BECT children with ESES. Compared differences of the two groups in the age of first seizure, the frequency of seizures before treatment, the classification of treatment drugs, cranial MRI, and discharge side of electroencephalogram (EEG). There was no statistical difference between the two groups in the frequency of seizures before treatment, the classification of treatment drugs, cranial MRI, and the distribution of EEG discharges in the left and right cerebral areas(P>0.05). Statistical differences were observed in the age of the first seizure, whether the seizures occurred after treatment, and EEG discharges in unilateral/bilateral cerebral areas (P<0.05). Furthermore, the collinearity test and Logistic regression analysis showed that the age of the first seizure, the frequency of seizures before treatment, and whether the seizures occurred after treatment were independent risk factors for the occurrence of ESES in BECT (P<0.05). Clinically, the occurrence of ESES in children with BECT may be related to the younger age of the first seizure, higher frequency of seizures before treatment, and the occurrence of seizures after treatment.

Construction and Validation of a Risk Prediction Model for Mild Cognitive Impairment in Non-Dialysis Chronic Kidney Disease Patient.

The aims of this study are to explore the factors affecting mild cognitive impairment in patients with chronic kidney disease (CKD) who are not undergoing dialysis and to construct and validate a nomogram risk prediction model. Using a convenience sampling method, 383 non-dialysis CKD patients from two tertiary hospitals in Chengdu were selected between February 2023 and August 2023 to form the modeling group. The patients were divided into a mild cognitive impairment group (n = 192) and a non-mild cognitive impairment group (n = 191), and factors such as demographics, disease data, and sleep disorders were compared between the two groups. Univariate and multivariate binary logistic regression analyses were used to identify independent influencing factors, followed by collinearity testing, and construction of the regression model. The final risk prediction model was presented through a nomogram and an online calculator, with internal validation using Bootstrap sampling. For external validation, 137 non-dialysis CKD patients from another tertiary hospital in Chengdu were selected between October 2023 and December 2023. In the modeling group, 192 (50.1%) of the non-dialysis CKD patients developed mild cognitive impairment, and in the validation group, 56 (40.9%) patients developed mild cognitive impairment, totaling 248 (47.7%) of all sampled non-dialysis CKD patients. Age, educational level, Occupation status, Use of smartphone, sleep disorders, hemoglobin, and platelet count were independent factors influencing the occurrence of mild cognitive impairment in non-dialysis CKD patients (all p &lt; 0.05). The model evaluation showed an area under the ROC curve of 0.928, 95% CI (0.902, 0.953) in the modeling group, and 0.897, 95% CI (0.844, 0.950) in the validation group. The model's Youden index was 0.707, with an optimal cutoff value of 0.494, sensitivity of 0.853, and specificity of 0.854, indicating good predictive performance; calibration curves, Hosmer-Lemeshow test, and clinical decision curves indicated good calibration and clinical benefit. Internal validation results showed a consistency index (C-index) of 0.928, 95% CI (0.902, 0.953). The risk prediction model developed in this study shows excellent performance, demonstrating significant predictive potential for early screening of mild cognitive impairment in non-dialysis CKD patients. The application of this model will provide a reference for healthcare professionals, helping them formulate more targeted intervention strategies to optimize patient treatment and management outcomes.

Research on Food security Model of Rural Revitalization Based on Computer Artificial Intelligence Model

In this paper, grain demand and supply models were established based on data collected from the China Statistical Yearbook and the website of the National Bureau of Statistics of China. This paper uses stepwise regression method to establish linear regression, residual autoregressive model and C-D production function, residual autoregressive model to analyze various factors that may affect grain yield. Then, R software was used to conduct collinearity test and residual analysis on the results, and cluster analysis method was used to conduct in-depth analysis on the causes of grain yield fluctuation. Finally, the regression equation of grain yield was obtained. Through MATLAB simulation curve, the values of the four main influencing factors are predicted respectively to get the future grain yield of China.

Pengaruh Kebijakan Dividen, Ukuran Perusahaan, Kepemilikan Manajerial, dan Kebijakan Hutang Terhadap Nilai Perusahaan Pada Indeks Perusahaan LQ45 di BEI

This study aims to identify the effect of dividend policy, company size and management ownership or debt policy on company value on Indonesia's LQ45 index (IDX). This research is also very quantitative using secondary data with an analytical descriptive approach. The analysis was carried out using Smart PLS 4 to carry out Discriminant Validity Test, Outer Model Collinearity Test, Reliability and Validity Test, and Path Coefficients Mean, STDEV, T Values, P Values Test. All companies whose shares were listed in the LQ45 index on the IDX during the period December 2021-December 2022 became the study population. Target sampling is used to select a sample of research participants. Based on the research, it was found that the IDX LQ45 company index is strongly influenced by various factors such as dividend policy, company size, management ownership, and debt policy.

Evaluation of Freeze–Thaw Erosion Intensity in the Ecological Function Reserve of the Greater Hinggan Mountains Based on Geographic Information Systems and a Geographic Detector

Freeze–thaw erosion is one of the three major soil erosions in China, including wind erosion and hydraulic erosion, which leads to the destruction of the natural environment, the imbalance of economic development, a threat to personal safety, and irreversible disaster to the country and people. China’s permafrost area accounts for about one-fifth of the country’s land area, and the seasonal permafrost area accounts for half of China’s land area, mainly concentrated in the Qinghai–Tibet Plateau, Xinjiang Province, Heilongjiang Province, Gansu Province, and other regions. In order to establish an evaluation system for the ecological function reserve of the Greater Hinggan Mountains, nine evaluation indicators were selected from the perspectives of topography, climate, soil, and vegetation conditions. GIS technology, a multiple collinearity test, and principal component analysis were used to comprehensively evaluate the freeze–thaw erosion in the ecological function reserve of the Greater Hinggan Mountains. This study categorized the evaluation results into five intensity levels, from micro to severe. Finally, the degree of influence of different influencing factors on freeze–thaw erosion and the interactions between the factors were analyzed using a geographic detector. The results showed the following: (1) The intensity of freeze–thaw erosion in the study area gradually increased from west to east, and the comprehensive evaluation results were −0.2552 to 0.7581. Overall, moderate, severe, and mild erosion accounted for 29.83%, 25.9%, and 21.54% of the total area of the freeze–thaw zone, respectively. (2) Soil moisture content and the annual range in temperature were the main factors contributing to freeze–thaw erosion. The degree of influence of the two effects on freeze–thaw erosion (q = 0.5997) was better than that of the single-factor effect.

Genomic evolution of island birds from the view of the Swinhoe's pheasant (Lophura swinhoii).

Island endemic birds account for the majority of extinct vertebrates in the past few centuries. To date, the evolutionary characteristics of island endemic bird's is poorly known. In this research, we de novo assembled a high-quality chromosome-level reference genome for the Swinhoe's pheasant, which is a typical endemic island bird. Results of collinearity tests suggest rapid ancient chromosome rearrangement that may have contributed to the initial species radiation within Phasianidae, and a role for the insertions of CR1 transposable elements in rearranging chromosomes in Phasianidae. During the evolution of the Swinhoe's pheasant, natural selection positively selected genes involved in fecundity and body size functions, at both the species and population levels, which reflect genetic variation associated with island adaptation. We further tested for variation in population genomic traits between the Swinhoe's pheasant and its phylogenetically closely related mainland relative the silver pheasant, and found higher levels of genetic drift and inbreeding in the Swinhoe's pheasant genome. Divergent demographic histories of insular and mainland bird species during the last glacial period may reflect the differing impact of insular and continental climates on the evolution of species. Our research interprets the natural history and population genetic characteristics of the insular endemic bird the Swinhoe's pheasant, at a genome-wide scale, provides a broader perspective on insular speciation, and adaptive evolution and contributes to the genetic conservation of island endemic birds.

Susceptibility assessment of soil-water hazard chain on a small catchment in gully region of Loess Plateau: Implications for artificially-induced mountaintop removal filling valley and geoheritage

Soil-water hazards are the most common hazards in Loess Plateau, China, and they can impart devastating effects on living environments of human beings, land planning, and geoheritage sites. A typical study area was selected to elucidate the relationship between soil-water hazards chain susceptibility and human activities, geoheritage sites. Landslide and gully erosion susceptibility was obtained by using gradient boosting and support vector machines, and then, hazard matrix was carried out to couple landslide and gully erosion susceptibility. Ultimately, the high susceptibility classes of landslides and gully erosion was selected as the material source of soil-water hazards chain. Meanwhile, Flow-R software was applied to acquire soil-water hazards chain susceptibility. Thereafter, different magnification times of soil-water hazards chain was quantified under different scenarios. Fourteen conditioning factors were considered in different susceptibility mapping after a collinearity test. The medium-high susceptibility of landslide, gully erosion, coupling soil-water hazards, soil-water hazards chain accounted for 41.40 %, 46.96 %, 49.11 %, and 51.14 % of the entire area, respectively. The proportion of magnification times were a trend of decreasing from first scenarios to third scenarios when the soil-water hazards magnification times was <5, however, there was a trend of increasing that of different scenarios when the magnification times was >5. Moreover, a series of geomorphological factors and human activities factors were highly important for the occurrence of geohazards, especially in artificially-induced mountaintop removal filling valley obviously amplified the harm of soil-water hazards. In addition, many geoheritage sites were distributed in medium-high susceptibility areas for different soil-water hazards, and their proportion exceeded 40 %. This study provides a novel and useful paradigm for the relationship between human activities and soil-water hazards can attain an optimal state, and a harmonious balance can be established between human beings and nature.

Computed Tomography-Based Radiomics Using Tumor and Vessel Features to Assess Resectability in Cancer of the Pancreatic Head.

The preoperative prediction of resectability pancreatic ductal adenocarcinoma (PDAC) is challenging. This retrospective single-center study examined tumor and vessel radiomics to predict the resectability of PDAC in chemo-naïve patients. The tumor and adjacent arteries and veins were segmented in the portal-venous phase of contrast-enhanced CT scans, and radiomic features were extracted. Features were selected via stability and collinearity testing, and least absolute shrinkage and selection operator application (LASSO). Three models, using tumor features, vessel features, and a combination of both, were trained with the training set (N = 86) to predict resectability. The results were validated with the test set (N = 15) and compared to the multidisciplinary team's (MDT) performance. The vessel-features-only model performed best, with an AUC of 0.92 and sensitivity and specificity of 97% and 73%, respectively. Test set validation showed a sensitivity and specificity of 100% and 88%, respectively. The combined model was as good as the vessel model (AUC = 0.91), whereas the tumor model showed poor performance (AUC = 0.76). The MDT's prediction reached a sensitivity and specificity of 97% and 84% for the training set and 88% and 100% for the test set, respectively. Our clinician-independent vessel-based radiomics model can aid in predicting resectability and shows performance comparable to that of the MDT. With these encouraging results, improved, automated, and generalizable models can be developed that reduce workload and can be applied in non-expert hospitals.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUASAN KONSUMEN PRODUK SUSU MURNI KPUB SAPI JAYA MENGGUNAKAN METODE REGRESI LINEAR BERGANDA

Customer satisfaction became one major priority for business agent to attract customer buying interest. Customer satisfaction is the feeling of happiness or sadness which appear after buying and comparing the product with other brands. The process of getting good Multiple Linear Regression analysis result in this research, then it has to be examined in these following steps: (1) Testing the validity and reliability of questionnaire measuring instruments. (2) Describing variables of the research. (3) Testing the classical assumptions such as heterokedasticity, multicollinearity, collinearity and normality test. (4) Analyzing Multiple Linear Regression, which includes the F_test (joint test), t_test (partial test), and the coefiicient of detemination (R-squared) test. Identification values of the factors that affect customer satisfaction towards KPUB Sapi Jaya pure milk products obtained from the result of the linear regression equation, where the value of the influence constituent is 2,117 units, the value of the influence of X1. of 0.367 units, the value of influence X3. Product features of 0.671 units, the value of influence X4. The product beauty is 0.259, the value of influence X5. The perceived quality is 0.814 units and the value of influence X6. Service is 0.211 units. Furthermore, the results of the significance test, which significantly affects customer satisfaction of KPUB Sapi Jaya pure milk products, it is concluded that there are 2 factors, the first is product durability factor (X1) and the second is perceived quality factor (X5). Meanwhile 4 other factors are not significant, such as product suitability factor (X2); product features (X3); beauty products (X4) and services (X6).

Flood‐oriented watershed health and ecological security conceptual modeling using pressure, state, and response (PSR) approach for the Sharghonj Watershed, South Khorasan Province, Iran

AbstractToday, watersheds have changed under the influence of various environmental and human factors, and their expected performance has decreased in terms of meeting human needs, providing natural responses, and reducing environmental risks. However, evaluating health status and its impact on various factors has not been given enough attention. To this end, an attempt has been made to evaluate the health and ecological security of the subwatersheds of the Sharghonj Watershed using the pressure‐state‐response approach emphasizing flood‐related issues in the region. Toward that, 21 preliminary hydrological, anthropogenic, natural, and climatic criteria were selected. The preliminary variables were then finalized based on the results of the collinearity test and the data availability. The final variables were appropriately allocated to pressure, state, and response (PSR) indices, and associated health and ecological security indices were determined at the subwatershed scale. The results showed that the PSR ranged from 0.56 to 0.86, 0.46 to 0.84, and 0.35 to 0.96, respectively. Health and ecological security also varied from 0.53 to 0.83 and 0.27 to 1.01 in different subwatersheds. Eventually, the weighted mean PSR indices for the Sharghonj Watershed were 0.72, 0.67, and 0.73, respectively. The weighted mean health and ecological security indices were 0.70 and 0.68, respectively. The research results indicated that the dynamic of various variables related to floods considerably affects the health of the Sharghonj Watershed. It could be concluded from the results that the Sharghonj Watershed is highly flood‐susceptible, whose health and ecological security status was mainly controlled by the damages due to the destruction of residences and orchards by floods in the past few years. The results of the current study provide an appropriate roadmap to the decision makers and planners for the better management of the watershed resources in the region through allocating adequate financial support and implementation measures to priority subwatersheds.