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Small and medium enterprises are imperative for the growth of a striving economy because they cater for a huge level of manpower and vast resources. Therefore, it is crucial to guarantee their continuous performance in order to support Somaliland’s economic development. The purpose of this study was to examine the effect of financial risks on profitability of SMEs in 26-June District in Hargeisa, Somaliland. The study used descriptive research design; using convenient non-probability sampling method, a suitable sample of 206 SME was selected from the population of study. Logistic regression model was used in this study since the dependent variable was binary. The study found out that credit risk and market risk are associated with profitability and are statistically significant. The odds ratio for credit risk and market risk indicates that every unit increase in credit risk and market risk respectively is associated with decrease in the odds of profitability in SMEs in June District as per the sample while liquidity risk was statistically insignificant with the odds ratio of liquidity risk indicating that every unit increase in liquidity risk is associated with increase in the odds of profitability in SMEs in June District as per the sample. The study recommends that the SMEs should put in a place system that can provide them with latest information about both credit risk sources which are suppliers and customer’s hazards so as they can refine their collection process and improve their unstable cash flow. The study on market risk recommends that, to optimize earnings and mitigate financial damages, it is crucial for the SMEs to monitor market information regularly as to apply any modifications to its products or services so that they can reduce their exposure to market risk. The study further recommends creation of specialized training and development programs that are intended to help businesses increase their overall knowledge of the financial risks, as this aligns with Somaliland vision 2030 that seeks to create a well-developed private sector that provides investment, employment, sustained livelihoods and vital services to strengthen the economic foundation of peace. Keywords: Financial risks, Credit Risk, Market Risk, Liquidity Risk, Profitability, Logistic Regression Model, Small Medium Enterprises (SMEs). Title: Effect of Financial Risk on Profitability of Small and Medium Enterprises in 26-June District in Hargeisa Somaliland Author: Isra Ismail Ahmed, Dr. Nathan Wamalwa Wafula International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 11, Issue 1, April 2023 - September 2023 Page No: 278-290 Research Publish Journals Website: www.researchpublish.com Published Date: 08-July-2023 DOI: https://doi.org/10.5281/zenodo.8126092 Paper Download Link (Source) https://www.researchpublish.com/papers/effect-of-financial-risk-on-profitability-of-small-and-medium-enterprises-in-26-june-district-in-hargeisa-somaliland, International Journal of Management and Commerce Innovations, ISSN 2348-7585 (Online), Research Publish Journals, Website: www.researchpublish.com

To reduce the dependence on fossil fuels to fulfill energy needs and give rural areas better access to energy, biomethane generation technology (BG-TECH) can help in these situations. However, several crucial factors might influence BG-TECH’s acceptance by households. In order to eradicate the barriers to and strengthen the driving forces of BG-TECH acceptance, it becomes inevitable to explore those crucial factors. Therefore, the core objective of this research is to analyze the factors impacting BG-TECH acceptance by households in rural Pakistan. This research will enrich the existing literature by comprehensively analyzing factors driving or impeding BG-TECH acceptance. To collect relevant data, a questionnaire was developed and distributed in four districts of Pakistan. In this respect, 150 users and 150 non-users of biomethane were selected through stratified random sampling. To analyze the factors affecting the acceptance of BG-TECH, a logistic regression model was utilized. As per our empirical results, age, ownership of cattle, aftersales service, education, household income, and loan accessibility were revealed to be the driving forces of BG-TECH. However, small farmers’ age and household size impeded the acceptance of BG-TECH. However, occupation, ownership of land, and working experience did not influence the acceptance of BG-TECH. Thus, for BG-TECH approval, it is recommended that governments bring about a change in approaches as well as the development of aftersales services, improved promotions, the qualification of adults, and better loan facilities. On the one hand, our theoretical implications would prove powerful tools for the follow-up studies to dig deeper into the crucial factors of BG-TECH acceptance and transcribe those implications for other green energy technologies. On the other hand, our practical implications would empower policymakers and practitioners to guide improved policy implementation to realize the green energy revolution.

In China, the majority of mountainous regions are characterized by complex topography and a delicate, sensitive geological environment. These areas, which exhibit insufficient infrastructure and widespread irrational human engineering activities, are often susceptible to geological hazards such as slope instability and soil mass movements. These geological hazards pose substantial threats to human lives and property, hindering the progress of mountainous areas. Therefore, conducting research on evaluating the vulnerability of slope rock and soil mass movement geological hazards (hereinafter referred to as geological hazards) is of utmost importance for hazard prevention, emergency management, and economic advancement in these regions. This study focuses on Xuanwei City and selects eight factors for evaluation, including elevation, gradient, slope aspect, normalized vegetation index, stratigraphic lithology, distance from faults, distance from rivers, and distance from roads. These factors are chosen based on a comprehensive analysis of the spatial and temporal distribution of geological hazards and hazard incubation conditions. Two paired models, the deterministic coefficient model + logistic regression model (CF+LR) and the information quantity model + logistic regression model (I+LR), were employed to assess the study area quantitatively. The performance of these models was assessed by employing receiver operating characteristic (ROC) curves and calculating the corresponding area under curve (AUC) values. The results indicate that: (1) The AUC values for the coupled CF+LR and I+LR models are 0.799 and 0.772, respectively. These results indicate that both models provide an objective and reliable assessment of the vulnerability to geological hazards, specifically slope rock and soil mass movements, in the study area. (2) Based on the CF+LR model calculations, the geological hazard susceptibility of Xuanwei City can be categorized into four zones: extremely high susceptibility (6.09%), high susceptibility (31.08%), medium susceptibility (32.26%), and low susceptibility (30.57%). (3) The CF+LR model more accurately represents the evaluation results and offers a strong reference value.

During the Coronavirus disease (COVID-19) period, the world witnessed a complete closure of all aspects of social life, and people must go for home quarantine to limit the spread of Corona virus disease. This study aims to use multiple logistic regression model (MLR) to identify the impact of home quarantine on psychological stability of individuals and its relationship to various factors. Maximum likelihood is used to estimate the parameters of MLR. Hosmer-Lemeshow method and Wald statistic are calculated to assess the significance of MLR. Cross sectional study was carried out and MLR was fitted to the collected data. This study applied to 846 residents of Makkah region during the COVID-19 period. High percentage of respondents felt psychological stability during the period of home quarantine. Logistic regression model showed that education level and psychological disorders during home quarantine were the significant risk factors for psychological stability during COVID-19. In addition, the positive impact of the home quarantine that people became more attention to their health, more interested to self-development and the families become close to each other. On the other hand, people on private employee or unemployed had psychological disorders more than others due to home quarantine and decrease their income.

This research analyzes the spatiotemporal patterns of wildfire regime attributes (e.g., seasonality, size, frequency, and burn rate) across the Golestan National Park (GNP), northeast Iran over the last two decades. We used a variety of data, including existing vegetation data, current vegetation survey, and historical wildfire data, and then data were processed through ArcMap. We also predicted fire exposure profiles (burn probability (BP), conditional flame length (CFL (m)), and fire size (FS (ha)) by the application of the minimum travel time (MTT) fire spread algorithm. The kernel density estimation (KDE) method was used to estimate wildfire likelihood, based on recent wildfires (2000–2020) that occurred in the GNP. Finally, we developed a logistic regression model to investigate how independent variables such as weather, fuel, and topographic data influence wildfires in the park. Wildfires in the landscape have not been constant in either space or time. Their extent, seasonality, frequency, and other wildfire regime characters varied considerably across the landscape. Our results highlighted that shrublands in the southern part of the park showed, in general, the highest values in terms of the wildfire regime attributes. Large fires (10–100 ha, 51%) and very large fires (>100 ha, 24%), fire intervals greater than 10 years (90%), and high burn rates (>1% y−1, 35%) are all characteristics that contribute to high wildfire activity in shrublands. Similarly, areas predicted to have high wildfire exposure levels (average BP = 0.004; average CFL = 1.60 m; average FS = 840 ha) are found in the fuel models of high-load grass and medium-load shrub. Finally, the regression model results revealed that weather and fuel were the most influential parameters (R2 ≥ 0.2), while topography had comparatively less influence in the study area. In light of these results, we suggest proactively incorporating this information into fire and fuel management which can help develop a fire prevention plan, predict fire ignition probability and frequency, and finally address altered fire regimes threatening the park.

Multicollinearity negatively affects the efficiency of the maximum likelihood estimator (MLE) in both the linear and generalized linear models. The Kibria and Lukman estimator (KLE) was developed as an alternative to the MLE to handle multicollinearity for the linear regression model. In this study, we proposed the Logistic Kibria-Lukman estimator (LKLE) to handle multicollinearity for the logistic regression model. We theoretically established the superiority condition of this new estimator over the MLE, the logistic ridge estimator (LRE), the logistic Liu estimator (LLE), the logistic Liu-type estimator (LLTE) and the logistic two-parameter estimator (LTPE) using the mean squared error criteria. The theoretical conditions were validated using a real-life dataset, and the results showed that the conditions were satisfied. Finally, a simulation and the real-life results showed that the new estimator outperformed the other considered estimators. However, the performance of the estimators was contingent on the adopted shrinkage parameter estimators.

Agritourism can enhance rural development and is a useful alternative activity to increase farm incomes. It has attracted the attention of agricultural experts and researchers around the world in recent years. As a result of rapid growth worldwide, agritourism has become one of the most significant supplementary income sources for farmers. The main purpose of this study is to identify factors influencing participation of fermer in agritourism in the Samarkand region of Uzbekistan, using scientific studies and statistical data, which indicate that there are great opportunities for agritourism activities. Increasing the economic potential of the rural areas by introducing innovative activities on the Samarkand region’s medium-sized farms (fermers) and ensuring sustainable development are essential. In Samarkand, there are a lot of conditions for the development of tourism, especially agritourism. The study examined all 10 agritourism fermers and 100 randomly selected fermers in Samarkand that do not carry out agritourism activities. Based on this data collection and analysis using a logistic regression model, the results showed several factors influencing the participation of fermers in the Samarkand region in agritourism activities, which was analyzed using the logistic regression model. According to the results of this model, each fermer’s income, employment, fermer’s education, and distance from the city had a significantly positive effect on the fermer’s initiation of agritourism.

The value of power data credit reporting in the social credit system continues to increase, and the government, users and the whole society have deep expectations and support for power data credit reporting. This paper will combine the data labeling theory as the support, define the power data label and explain its labeling implementation. Based on the construction of knowledge graph, the method of labeling power data is introduced in detail: demand analysis method, index selection method, data cleaning method and data desensitization method. Use the sorted data labels to establish a label system for power data, and through its system, visualize the comprehensive situation of enterprise power data credit information to meet the development of power data credit business. This paper takes shell enterprises as the main representatives of credit risk enterprises, analyzes the power data in the three stages before and after loans, and builds a value mining model for power credit data. In the future, the data labeling technology and value mining model of the power data credit business will be comprehensively applied, and the power data label library and credit model library will be established and continuously improved, so as to facilitate the evaluation of the operation of the enterprise at different stages.

The purpose of this paper is to analyze the demographic andbehavioral factors that significantly affect the credit card Non-Performing Loan (NPL). This study is carried out to providemanagerial recommendations for controlling credit card NPL. Thisstudy uses secondary data from Indonesia’s most significant privatebank with 100,000 samples of cardholder data. Demographicfactors and cardholder behavior that significantly influence creditcard NPL can be used to improve the credit scoring system fornew cardholders and as indicators for a behavior scoring system forexisting cardholders. This research uses a probability stratified randomsampling technique. Logistic regression uses demographic factors andcardholder behavior significantly affected credit card NPL. Accordingto the logistic regression model, cardholder behavior was more likelyto NPL than demographic characteristics. The number of credit cardsshowed the highest credit card NPL probability.How to Cite:Achsan, Wahid, Achsani, N. A, & Bandono, Bayu. (2022). The Demographic and Behavior Determinant of Credit Card Default in Indonesia. Signifikan: Jurnal Ilmu Ekonomi, 11(1), 43-56. https://doi.org/10.15408/sjie.v11i1.20215.

The present study explores one of the biggest causes of the lack of organizational knowledge creation: knowledge hiding (KH). KH can be provoked by the deviant and detached behaviours of leaders and/or the motivations of employees. In this context, leaders assume a key role in reducing the effect of KH. Through the lens of transformational leadership (TL), a sample of 758 European SMEs with a total number of 2,232 employees operating in a knowledge-intensive sector is investigated. The scope is to evaluate the correlation between the three main characteristics of transformational leadership (i.e., trust, a collaborative environment, and the involvement of employees) and the phenomenon of KH through a logistic regression analysis. It emerges that TL can influence the organizational context and redefine the behaviours related to KH. In addition, empathic leadership can provide added value for companies since a collaborative environment and common objectives reduce the level of KH.

Student-teachers’ dropout is a complicated and serious issue in the learning process, with its attendant negative implications on students, academic institutions, economic resources, and society. This study investigated the composite and relative impact of personal (student), academic and socioeconomic predictive variables on student-teacher dropout. The study improves the early identification of at-risk student-teachers by developing a model that optimizes predictability. We used questionnaires and adopted a four-step logistic regression procedure on a sample of 1723 student-teachers in public teachers training colleges (TTCs) of a least-developed country (LDC). The study confirmed twin academic performance and aspirations factors as the highest predictors of student-teacher attrition. Academic reasons for choosing TTC were significant, as vocational motivation and goals established by student-teachers early in their education help prevent dropout. Contrary to expectations, student-teachers’ cultural values, parents’ level of education, and cost of financing education had no significant impact on dropout decisions. This is most likely due to the Government’s financial support for student-teachers in LDCs and the widespread belief that higher education can improve one’s social and economic status. The findings indicate that early identification and dropout prevention efforts should integrate various support services to foster a healthy learning and retention environment.

Analysis through logistic regression explored to investigate the relationship between binary or multivariable ordinal response probability and in one or more explanatory variables. The main objectives of this study to investigate advanced prediction risk factor of Coronary Heart Disease (CHD) using a logit model. Attempts made to reduce risk factors, increase public or professional awareness. Logit model used to evaluate the probability of a person develop CHD, considering any factors such as age, gender, high low-density lipoprotein (LDL) cholesterol, low high-density lipoprotein (HDL) cholesterol, high blood pressure, family history of CHD younger than 45, diabetes, smoking, being post-menopausal for women and being older than 45 for men. Logit concept of brief statistics described with slight modification to estimate the parameters testing for the significance of the coefficients, confidence interval fits the simple, multiple logit models. Besides, interpretation of the fitted logit regression model introduced. Variables showing best results within the scientific context, good explanation data assessed to fit an estimated logit model containing chosen variables, this present experiment used the statistical inference procedure; chi-square distribution, likelihood ratio, Score, or Wald test and goodness-of-fit. Health promotion started with increased public or professional awareness improved for early detection of CHD, to reduce the risk of mortality, aimed to be Saudi vision by 2030.

Supplemental digital content is available in the text., Purpose This study aimed to investigate the value of magnetic resonance (MR) characteristics in differentiating the subtypes of growth hormone pituitary adenomas. Materials and Methods The clinical and MR imaging data of 70 patients with growth hormone pituitary adenoma confirmed by surgery and pathology were retrospectively analyzed. The tumors were divided into dense granular (DG; 36 cases) and sparse granular subtypes (SG; 34 cases). The tumors' MR features were analyzed, including the mean and maximum diameters, T2 signal intensity, T2 relative signal intensity (rSI), homogeneity, enhancement degree, and invasiveness (Knosp grade). Mann-Whitney U test and χ2 test were used to analyze MR characteristics between the 2 groups. The independent predictors and predictive probabilities of tumor subtypes were obtained via a logistic regression model, and the efficacy was compared by receiver operating characteristic curve. Results The mean and maximum diameters of growth hormone adenoma in DG and SG were 1.77 versus 2.45 and 1.95 versus 3.00 cm (median, P < 0.05), respectively. There was a significant difference between the 2 groups in T2 signal intensity and rSI (P values were 0.02 and 0.001, respectively). Most DG adenomas (86.1%) appeared as hypointense on T2 images, and 38.2% of SG adenomas were hyperintense. There was no significant difference in tumor homogeneity (P = 0.622). A significant difference was found in the Knosp grade between the 2 subtypes (P = 0.004). In addition, the enhancement degree of SG adenomas was significantly higher than that of DG adenomas (P = 0.001). Logistic regression analysis showed that high T2 rSI value and marked contrast enhancement were independent predictors of the 2 subtypes, and the odds ratios were 4.811 and 4.649, respectively. The multivariate logistic model obtained relatively high predicting efficacy, and the area under the curve, sensitivity, and specificity were 0.765, 0.882, and 0.500, respectively. Conclusions There are significant differences in tumor size, T2 signal intensity, T2 rSI, enhancement degree, and invasiveness between DG and SG adenomas. The logistic model based on the marked contrast enhancement and high T2 rSI value has an important value in predicting the subtype of growth hormone adenoma.

Yan Liu,1 Zhenglu Wang,2 Lin Zhao1 1Department of Obstetrics, Tianjin First Central Hospital, Nankai University, Tianjin, 300192, People’s Republic of China; 2Biobank, Tianjin First Central Hospital, Nankai University, Tianjin, 300192, People’s Republic of ChinaCorrespondence: Lin ZhaoDepartment of Obstetrics, Tianjin First Central Hospital, Nankai University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, People’s Republic of ChinaTel +86 22-85358245Email 5020200076@nankai.edu.cnBackground: Hypertensive disorders of pregnancy affect approximately 5– 10% of all pregnancies, and this study aims to identify potential diagnostic signatures.Methods: We downloaded the mRNA profiles of GSE75010 (placenta samples) and GSE48424 (blood samples) datasets with or without hypertension in pregnancy from the Gene Expression Omnibus database. Differential expression analysis was performed on the placenta samples using limma package of R language. GO terms and KEGG pathways enrichment analyses were performed on the placenta samples by the clusterProfiler package of R language. Infiltrating immune cell proportion of the placenta samples was evaluated using CIBERSORT software. The key genes involved in hypertension in pregnancy were screened from protein–protein interaction (PPI) network constructed based on the differentially expressed genes (DEGs). The logistic regression model was constructed by the glm package of R language, and receiver operating characteristic (ROC) curve was plotted to determine the accuracy of the model.Results: For the placenta samples, a total of 104 DEGs were identified, and 39 GO terms and 7 KEGG pathways were significantly enriched based on these 104 genes. Furthermore, the analysis of infiltrating immune cells indicated that the difference in the amount of immune cells might be the potential cause of hypertension in pregnancy. The logistic regression model was constructed based on three optimal genes (LEP, PRL and IGFBP1) screened from PPI network and could efficiently separate patients with hypertension in pregnancy from healthy subjects.Conclusion: A predictive model based on three potential genes LEP, PRL and IGFBP1 was obtained, suggesting that these genes might be potential diagnostic signatures for hypertension in pregnancy.Keywords: hypertension in pregnancy, diagnostic signature, GO terms and KEGG pathways analyses, PPI network, logistic regression model

Yan Liu,1 Zhenglu Wang,2 Lin Zhao1 1Department of Obstetrics, Tianjin First Central Hospital, Nankai University, Tianjin, 300192, People’s Republic of China; 2Biobank, Tianjin First Central Hospital, Nankai University, Tianjin, 300192, People’s Republic of ChinaCorrespondence: Lin ZhaoDepartment of Obstetrics, Tianjin First Central Hospital, Nankai University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, People’s Republic of ChinaTel +86 22-85358245Email 5020200076@nankai.edu.cnBackground: Hypertensive disorders of pregnancy affect approximately 5– 10% of all pregnancies, and this study aims to identify potential diagnostic signatures.Methods: We downloaded the mRNA profiles of GSE75010 (placenta samples) and GSE48424 (blood samples) datasets with or without hypertension in pregnancy from the Gene Expression Omnibus database. Differential expression analysis was performed on the placenta samples using limma package of R language. GO terms and KEGG pathways enrichment analyses were performed on the placenta samples by the clusterProfiler package of R language. Infiltrating immune cell proportion of the placenta samples was evaluated using CIBERSORT software. The key genes involved in hypertension in pregnancy were screened from protein–protein interaction (PPI) network constructed based on the differentially expressed genes (DEGs). The logistic regression model was constructed by the glm package of R language, and receiver operating characteristic (ROC) curve was plotted to determine the accuracy of the model.Results: For the placenta samples, a total of 104 DEGs were identified, and 39 GO terms and 7 KEGG pathways were significantly enriched based on these 104 genes. Furthermore, the analysis of infiltrating immune cells indicated that the difference in the amount of immune cells might be the potential cause of hypertension in pregnancy. The logistic regression model was constructed based on three optimal genes (LEP, PRL and IGFBP1) screened from PPI network and could efficiently separate patients with hypertension in pregnancy from healthy subjects.Conclusion: A predictive model based on three potential genes LEP, PRL and IGFBP1 was obtained, suggesting that these genes might be potential diagnostic signatures for hypertension in pregnancy.Keywords: hypertension in pregnancy, diagnostic signature, GO terms and KEGG pathways analyses, PPI network, logistic regression model

Rural households depend on forest resources for cash and subsistence needs. Thus, forests represent a valuable natural capital for the rural economy, particularly in developing countries. However, depending on various factors, there are dissimilarities in the rural livelihoods’ reliance on forests. Therefore, this study attempted to determine and characterize the level of forest dependence and identify the demographic and socioeconomic factors influencing the households’ dependence on forests in the Rubi-Tele Hunting Domain (RTHD)/Democratic Republic of the Congo (DR Congo). Demographic and socioeconomic data of forest dependents and other qualitative information were collected through structured household-level surveys, focus group discussions, and key informant interviews. Descriptive statistical analysis, Kruskal–Wallis Test, χ2 test of independence, and binary logistic regression model were used for data analysis. The findings revealed that the forest dependence index varied from 0.01 to 1 (Mean = 0.46, SD = 0.30). The distribution of forest-dependent households by wealth status (income tercile) and level of forest dependence differs significantly. Logistic regression revealed that household size and non-forest income were significant determinants of forest dependence and had the theoretically expected signs. The household size was in a positive association with forest dependence. Large families tended to depend more on forest resources. On the other hand, the non-forest income was inversely related to forest dependence, implying that forest-dependent households with non-forest income sources were less dependent on forest resources extraction. Other selected variables were not statistically significant while showing positive and negative associations with the reliance on forests. Achieving the balance between forest dependence and conservation requires promoting incentive policies to diversify livelihood opportunities and environmental education.

In order to improve the rescue efficiency of the advanced automatic crash notification (AACN) system,a driver injury prediction algorithm was proposed,and the overall design of the AACN system terminal was conducted based on this algorithm.First,the amount of speed change,the direction of the accident,the driver′s age,gender,whether to wear the seat belt,and whether the driver′s side airbag inflated were selected as the influencing factors of the driver′s injury.Next,a Logistic regression model was analyzed and developed based on traffic accident data.The effectiveness of the model was verified by using the Hosmer-Lemeshow test table,and the best trigger threshold was obtained through sensitivity analysis.Then,the AACN system terminal was designed.Finally,an actual case was used to test the accuracy of the injury prediction algorithm and the effectiveness of AACN system terminal.The results of the case study show that the proposed driver injury prediction algorithm and the AACN system are highly accurate,which can effectively predict the driver′s injury and help the rescue center work out an active rescue plan.The research results can be used to solve the problem that existing centralized AACN systems′ efficiency is not high and their accuracy is greatly affected by human factors.Consequently,they provide a reference for the design of driver injury prediction algorithm in the decentralized AACN system.

Forest fire is an ecosystem regulating factor and affects the stability, renewal, and succession of forest ecosystems. However, uncontrolled forest fires can be harmful to the forest ecosystem and to the public at large. Although Yunnan, China is regarded as a global hotspot for forest fires, a general lack of understanding prevails there regarding the mechanisms and interactions that cause forest fires. A logistic regression model based on fire points in Yunnan detected by satellite in 2005–2019 was used to estimate how environmental factors in local areas affect forest fire events. The results show that meteorology is the dominant cause of the frequent forest fires in the area. Other factors of secondary importance are the daily minimum relative humidity and the daily maximum temperature. When using the logistic regression model based on the data of fire points in Yunnan over the period 2005–2019, the key threshold for the daily minimum relative humidity is 28.07% ± 11.85% and the daily maximum temperature is 21.23 ± 11.15 °C for a forest fire probability of 50%. In annual and monthly dynamic trends, the daily minimum relative humidity also plays a dominant role in which combustible substance load remains relatively stable from January to March, and the impact on forest fire becomes greater in April, May, and June, which plays a secondary role compared with the interannual climate. The maximum daily temperature ranks third in importance for forest fires. At the county level, minimum relative humidity and maximum temperature are the top two factors influencing forest fires, respectively. Meanwhile, the differences in forest fire points between counties correspond to the pathways of the two monsoons. This study applies quantitative expressions to reveal the important environmental factors and mechanisms that cause forest fires. The results provide a reference for monitoring and predicting forest fires.

The need to search for new measures describing the classification of a logistic regression model stems from the difficulty in searching for previously unknown factors that predict the occurrence of a disease. A classification quality assessment can be performed by testing the change in the area under the receiver operating characteristic curve (AUC). Another approach is to use the Net Reclassification Improvement (NRI), which is based on a comparison between the predicted risk, determined on the basis of the basic model, and the predicted risk that comes from the model enriched with an additional factor. In this paper, we draw attention to Cohen’s Kappa coefficient, which examines the actual agreement in the correction of a random agreement. We proposed to extend this coefficient so that it may be used to detect the quality of a logistic regression model reclassification. The results provided by Kappa‘s reclassification were compared with the results obtained using NRI. The random variables’ distribution attached to the model on the classification change, measured by NRI, Kappa, and AUC, was presented. A simulation study was conducted on the basis of a cohort containing 3971 Poles obtained during the implementation of a lower limb atherosclerosis prevention program.

Journal of Statistics on Health Decision, vol. 4 n.º 1 (2022): Special Issue - Statistics on Health Decision Making: Real World Data

State departments of transportation recognize the need to incorporate pavement structural condition in their pavement performance models and/or decision processes used to select candidate projects for preservation, rehabilitation, or reconstruction at the network level. However, pavement structural condition data are costly to obtain. To this end, this paper develops and evaluates the effectiveness of two machine learning methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), for predicting a flexible pavement’s structural condition. The aim is to be able to predict whether a pavement section’s structural condition is poor or not based on Annual Average Daily Traffic (AADT), truck percentage, and speed limit. The structural condition of a pavement is considered poor if the Surface Curvature Index (SCI12) is above 3.3. The models are developed using 950 miles of Traffic Speed Deflectometer (TSD) data collected along 8 primary routes in South Carolina. The performance of the machine learning models was compared with that of a logistic regression model. When the trained models are applied to the test data, the prediction results indicated that the XGBoost and RF models outperform the logistic regression model by 12% and 8%, respectively. XGBoost outperformed RF by 4%. With XGBoost found to be the best among the three models evaluated, its performance was examined using other poor structural condition threshold values; its prediction accuracy is found to be robust across the different scenarios. AADT and truck percentages are found to be significant factors whereas speed limit has no effect on a pavement’s structural condition.

Background: The incidence of depression in patients with systemic lupus erythematosus (SLE) is high and leads to a lower quality of life than that in undepressed SLE patients and healthy individuals. The causes of SLE depression are still unclear. Methods: A total of 94 SLE patients were involved in this study. A series of questionnaires (Hospital Depression Scale, Social Support Rate Scale and so on) were applied. Flow cytometry was used to test the different stages and types of T cells and B cells in peripheral blood mononuclear cells. Univariate and multivariate analyses were conducted to explore the key contributors to depression in SLE. Support Vector Machine (SVM) learning was applied to form the prediction model. Results: Depressed SLE patients showed lower objective support, severer fatigue, worse sleep quality and higher percentages of ASC%PBMC, ASC%CD19+, MAIT, TEM%Th, TEMRA%Th, CD45RA+CD27-Th, TEMRA%CD8 than non-depressed patients. A learning-based SVM model combining objective and patient-reported variables showed that fatigue, objective support, ASC%CD19+, TEM%Th and TEMRA%CD8 were the main contributing factors to depression in SLE. With the SVM model, the weight of TEM%Th was 0.17, which is the highest among objective variables, and the weight of fatigue was 0.137, which was the highest among variables of patients’ reported outcomes. Conclusions: Both patient-reported factors and immunological factors could be involved in the occurrence and development of depression in SLE. Scientists can explore the mechanism of depression in SLE or other psychological diseases from the above perspective.

Background and Aims It remains difficult to forecast the 180-day prognosis of patients with hepatitis B virus-acute-on-chronic liver failure (HBV-ACLF) using existing prognostic models. The present study aimed to derive novel-innovative models to enhance the predictive effectiveness of the 180-day mortality in HBV-ACLF. Methods The present cohort study examined 171 HBV-ACLF patients (non-survivors, n=62; survivors, n=109). The 27 retrospectively collected parameters included the basic demographic characteristics, clinical comorbidities, and laboratory values. Backward stepwise logistic regression (LR) and the classification and regression tree (CART) analysis were used to derive two predictive models. Meanwhile, a nomogram was created based on the LR analysis. The accuracy of the LR and CART model was detected through the area under the receiver operating characteristic curve (AUROC), compared with model of end-stage liver disease (MELD) scores. Results Among 171 HBV-ACLF patients, the mean age was 45.17 years-old, and 11.7% of the patients were female. The LR model was constructed with six independent factors, which included age, total bilirubin, prothrombin activity, lymphocytes, monocytes and hepatic encephalopathy. The following seven variables were the prognostic factors for HBV-ACLF in the CART model: age, total bilirubin, prothrombin time, lymphocytes, neutrophils, monocytes, and blood urea nitrogen. The AUROC for the CART model (0.878) was similar to that for the LR model (0.878, p=0.898), and this exceeded that for the MELD scores (0.728, p

The reliability of disc cutters has a significant influence on the safety and working efficiency of tunnel boring machines (TBMs). To investigate the reliability of disc cutters under different geological and operational conditions, we conducted a series of novel rolling cutting tests on intact and jointed sandstone blocks using different dip angles and interlayers. Different normal forces and rotational speeds of the cutterhead were also applied during the experiment. A novel reliability estimation method, based on a logistic regression model, was then proposed, and the influence of dip angle, strata, normal force, and rotational speed on the reliability of the disc cutter were analyzed. The reliability estimation method consisted of data acquisition regarding the normal force and cutter wear, feature extraction using wavelet packet transform and correlation analysis, and the estimation of the logistic regression model. To obtain the spectrum and normalized wavelet energy for each frequency band, we decomposed the time domain of the normal force by the wavelet packet transform. A correlation analysis was employed to determine the feature frequency bands that were sensitive to wear loss. On the basis of salient feature parameters and wear loss, the logistic regression model was established to evaluate the reliability of disc cutters. The analytical results indicated that the optimal dip angle for rock cutting was 30°. In the presence of mixed-face and single ground, the reliability of disc cutters was primarily affected by the difficulty of TBM excavation and wear loss, respectively. An increase in normal force and rotational speed aggravated wear on the cutter, thus reducing reliability. Furthermore, compared with Rabinowicz’s formula, the proposed method considers various geological and operational conditions, making the proposed method more applicable to estimate the reliability of disc cutters.

This study aims to determine the effect of borrowing costs, leverage ratios and firm sizes on the audit quality of enterprises. In this context, data on the profit/loss statement and financial position statements of the companies traded in the BIST and the independent audit reports published at the end of the period for 2012 and 2021 were used. The data obtained in this context were analyzed with the logistic regression method. According to the results obtained from the research, it was determined that there was positive and significant relationship between the audit quality of enterprises and their borrowing costs. According to this relationship, it had been determined that the borrowing costs of the companies audited by BIG 4 audit companies were hıgher than the companies that were not audited. While it was observed that there was a negative and significant relationship between audit quality and firm size, no significant relationship was found between audit quality and leverage ratios., Bu çalışmada BIST’te işlem gören işletmelerin denetim kaliteleri üzerinde borçlanma maliyetleri, kaldıraç oranı ve işletme büyüklüklerinin etkisinin tespit edilmesi amaçlanmıştır. Bu kapsamda BIST’te işlem gören işletmelerin 2012 ve 2021 yıllarına ilişkin dönem sonunda yayımladıkları bağımsız denetim raporları ile birlikte kar/zarar tablosu ve finansal durum tablolarına ilişkin veriler kullanılmıştır. Bu kapsamda elde edilen veriler lojistik regresyon yöntemi ile analiz edilmiştir. Araştırmadan elde edilen sonuçlara göre işletmelerin denetim kaliteleri ile borçlanma maliyetleri arasında pozitif ve anlamlı bir ilişki olduğu belirlenmiştir. Bu ilişkiye göre “BIG 4” denetim şirketleri tarafından denetlenen işletmelerin, denetlenmeyen işletmelere göre borçlanma maliyetlerinin daha yüksek olduğu tespit edilmiştir. Denetim kalitesi ile işletme büyüklüğü arasında negatif ve anlamlı bir ilişki olduğu gözlemlenirken, denetim kalitesi ile kaldıraç oranı arasında ise anlamlı bir ilişki bulunamamıştır.

Non-Zero crossing point detection in a sinusoidal signal is essential in case of various power system and power electronics applications like power system protection and power converters controller design. In this paper 96 data sets are created from a distorted sinusoidal signal based on MATLAB simulation. Distorted sinusoidal signals are generated in MATLAB with various noise and harmonic levels. In this paper, logistic regression model is used to predict the non-zero crossing point in a distorted signal based on input features like slope, intercept, correlation and RMSE. Logistic regression model is trained and tested in Google Colab environment. As per simulation results, it is observed that logistic regression model is able to predict all non-zero-crossing point in a distorted signal.

If sufficient historical failure life data exist, the failure distribution of the system can be estimated to identify the system initial hazard function. The conventional proportional covariate model (PCM) can reveal the dynamic relationship between the response covariates and the system hazard rate. The system hazard rate function can be constantly updated by the response covariates through the basic covariate function (BCF). Under the circumstances of sparse or zero failure data, the key point of the PCM reliability assessment method is to determine the proportional factor between covariates and the hazard rate for getting BCF. Being devoid of experiments or abundant experience of the experts, it is very hard to determine the proportional factor accurately. In this paper, an improved PCM (IPCM) is put forward based on the logistic regression model (LRM). The salient features reflecting the equipment degradation process are extracted from the existing monitoring signals, which are considered as the input of the LRM. The equipment state data defined by the failure threshold are considered as the output of the LRM. The initial reliability can be first estimated by LRM. Combined with the responding covariates, the initial hazard function can be calculated. Then, it can be incorporated into conventional PCM to implement the reliability estimation process on other equipment. The conventional PCM and the IPCM methods are respectively applied to aero-engine rotor bearing reliability assessment. The comparative results show that the assessing accuracy of IPCM is superior to the conventional PCM for small failure sample. It provides a new method for reliability estimation under sparse or zero failure data conditions.

Speeding is one of the most relevant risk behaviours for serious and fatal road traffic accidents, particularly among young drivers, being the cause of approximately every third road traffic accident. Due to this background, many road traffic safety campaigns are aimed at reducing speeding among young drivers. However, the effects of campaigns aimed at complying with speed limits for young drivers have significantly fewer effects than other campaigns. For these reasons, an experimental study was conducted to examine how young drivers react to the speeding campaign, which was shown to them on a smartwatch while driving in a driving simulator. Speeding results were compared for three scenarios: no campaign, a billboard campaign and a smartwatch campaign. The experiment involved 102 participants with an average age of 21 years. The results showed that participants were six times more likely to comply with the speed limit if a campaign was shown on a smartwatch than when shown on billboards.

To explore the evolution trajectories of cultivated land in the middle and lower reaches of the Yellow River(considering the areas along the Yellow River and areas located away from the Yellow River within the Henan Province as research areas), a comparative analysis of the evolution trajectories and their driving mechanisms was carried out using the change trajectory method, a landscape pattern index and a Logistic regression model. The results showed that the instances of expansion and shrinkage of cultivated land along the Yellow River were more frequent than those in the areas located away from the Yellow River. Reduced levels of cultivated land in areas located away from the Yellow River were typically found toward the centers of their respective administrative regions. Meanwhile, along the Yellow River, reduced levels of cultivated land tended to be concentrated in the Zhengzhou region. Cultivated land was the dominant landscape type in Henan Province. The expansion and shrinkage of the accumulation and connectivity of cultivated land in the areas along the Yellow River were higher than in the areas located away from the river; However, the growth rate of connected agglomeration was lower in the areas along the Yellow River. Logistic regression analysis showed that the driving factors of cultivated land trajectories in areas along the Yellow River were slightly different to those of areas located away from the river. The expansion of cultivated land in areas along the Yellow River was highly dependent on the water of the Yellow River, while the expansion of cultivated land in areas located away from the river was affected more by economic development and social life. These factors had different contributions to the shrinkage of cultivated land in two areas. This research provides a theoretical basis for ensuring national food security and responding to the high-quality development of areas surrounding the Yellow River.

Global warming increases the frequency and intensity of extreme rainfall, putting many areas at risk of landslides. Landslide susceptibility assessment is essential to understand the threats and to predict, prevent, and mitigate landslides. In this study, a soil landslide inventory was constructed based on satellite images, topological maps, and extensive field studies. Subsequently, eight different GIS layers, which were geomorphology, elevation, slope angle, slope aspect, slope structure, slope curvature, antecedent rainfall, and cumulative rainfall on 16 September, were produced as control factors of soil planar slides for the susceptibility mapping. Landslide susceptibility mapping was performed using two different methods, logistic regression model and backpropagation (BP) neural network. Landslide susceptibility in the study area is divided into four levels, which are high, moderate, low, and no susceptibility in both the logistic regression model and the BP neural network model. In both the two models, most of the observed soil planar slides were located in areas with high or moderate susceptibility. For the logistic regression model, total 605 soil planar slides locate in the area with high susceptibility, of which the area is 800.56 km2, accounting for 40.31% of the total area. Finally, the validation of two models was evaluated. The AUC value of the logistic regression model was 0.878 and the parameters of BP neural network has the correlation coefficient of 0.880, which shows the two models are both reliable and reasonable for predicting the spatial susceptibility of soil planar slides. According to field checks, the BP neural network model is verified to have more accurate spatial prediction performance than the logistic regression model.

Under the Bayesian framework, this study proposes a Tweedie compound Poisson partial linear mixed model on the basis of Bayesian P-spline approximation to nonparametric function for longitudinal semicontinuous data in the presence of nonignorable missing covariates and responses. The logistic regression model is simultaneously used to specify the missing response and covariate mechanisms. A hybrid algorithm combining the Gibbs sampler and the Metropolis–Hastings algorithm is employed to produce the joint Bayesian estimates of unknown parameters and random effects as well as nonparametric function. Several simulation studies and a real example relating to the osteoarthritis initiative data are presented to illustrate the proposed methodologies.

We often meet the case in data analysis that the explanatory variables can be occasionally divided into two groups. One group comprises the variables that researchers consider controllable, and the other group comprises those they do not. We call them controllable and uncontrollable variables, respectively. In the study, we deal with binary response data and aim to estimate the relationship between the binary response and controllable variables. Logistic regression model is typically used in binary response data. In addition to that, AIM (Adaptive Index Model; (Tian and Tibshirani Biostatics 12:68-86, 2010)) can also be used in binary response data. Contrast with logistic regression model, AIM can explain the result easier using binary rules but the prediction accuracy of AIM is shown worse than that of logistic regression model. Considering the interpretability and accuracy, it is better to apply AIM to controllable variables and adjust the effect of uncontrollable variables using logistic regression model. Therefore, we propose the method combining AIM and logistic regression model, called hybrid adaptive index model (HAIM), to give best solution.

With the maturity of the online food delivery (OFD) industry in China, the growth of the market in recent years is mainly driven by the increase of the usage frequency of existing users rather than the number of new users. The usage frequency of users is affected by various factors, with the delivery charge as one of the most significant ones. The purpose of this study is to examine the impact of delivery charge and other factors on the probability of consumers choosing to use OFD service. In this study, 391 questionnaire records from China were collected, based on which a logistic regression model was established. The results of the model show that age, occupation, monthly income, city tier of residence, location and time period of usage, and delivery charges all play a role on the probability of consumers using the service, and the delivery charge has the greatest impact. For every one yuan increase in the delivery charge, consumers will be less likely to choose “certainly” of using OFD (OR: 0.435; 95% CI: 0.415, 0.455). Sensitivity analysis shows that when the delivery charge changes between 2~5 yuan, it has the greatest impact on the probability of consumers using the service. The analysis further shows that delivery charge has different impacts under different scenarios composed of three key factors, i.e., the city tier of residence, locations of usage, and time period of usage. From a management perspective, these findings help to understand the behavior of OFD consumers and provide insights for the OFD operators to establish best pricing strategies for long-term economic sustainability.

Genel mutluluk, bir şehrin farklı lokasyonlarında yaşanabilirliğin bir göstergesidir. Kategorik olmayan bir sosyal gösterge, bir şehirde genel mutluluk ve yaşanabilirlik için herhangi bir kanıt göstermeyebilir. Bununla birlikte, daha yüksek sosyal göstergeler, şehrin bazı bölgelerinde daha yüksek mutluluğu yakalayabilir. Bu makale, sosyal göstergelerin ve yaşanılan yerlerin genel mutluluk üzerindeki anlamlı etkileşim etkilerini bulmayı amaçlamaktadır. Bu nedenle, Adana'nın tabakalı lokasyonlarında uygulanan bir sosyal ankete dayalı olarak, sosyal göstergelerin ve lokasyonların genel mutluluk üzerindeki etkileşim etkileri tahmin edilmiştir. Sonuçlar, sosyal göstergelerin ve konumların genel mutluluk üzerinde önemli etkileşim etkilerine sahip olduğunu göstermektedir. Kanıtlar, araç bağımlı mahallelerin daha iyi sağlık koşullarına sahip bireyler için daha yaşanabilir olduğunu göstermektedir. İkincil yaya bölgeleri boşanmış-ayrı kalmış ve dul kişiler için daha yaşanabilirdir. Kırsal mahalleler, orta ve ileri yaş grupları ile daha mutlu görünmektedir, dolayısıyla kentsel Adana'nın diğer yerlerine göre daha yüksek yaşanabilirliğe sahiptir. Kırsal mahalleler boşanmış veya ayrılmışlar için daha az yaşanabilirken, dullar için bekârlara göre daha yaşanabilirdir., General happiness is an indicator of livability in different locations of a city. A noncategorical social indicator may not show any evidence for general happiness and livability in a city. However, higher social indicators may capture higher happiness in some parts of the city. This article aims to find significant interaction effects of social indicators and locations of living on general happiness. Hence, based on a social survey administered in stratified locations of Adana, interaction effects of social indicators and locations over general happiness were estimated. Results indicate that social indicators and locations have significant interaction effects over general happiness. The evidences suggest that vehicle dependent neighborhoods are more livable for individuals with better health conditions. Secondary pedestrian zones are more livable for divorced-separated and widowed persons. Rural neighborhoods are happier with moderate to older age groups, thus has higher degree of livability compared to other locations of urban Adana. Rural neighborhoods are less livable for divorced or separated whereas more livable for widowed persons compared to single persons.

To know whether a given public transport vehicle will observe COVID-19 Ministry of Health Protocols and Guidelines in Kenya, a logistic expression was developed based on gender, type of vehicle, distance covered and the possibility of contracting COVID-19 by passengers while on board a given vehicle. Data collection was done by use 112 questionnaires administered on line. The respondents were first year students attending online courses in Kenyan University. Results showed majority of the respondents (41%) used 8-seater vehicle to travel. As for the distance travelled, majority of the respondents (37.3%) covered less than 25 km to reach their home County Headquarters. Possibility of infection in 8-seater and 32-seater vehicle was concentrated in level 3 while that for 14-seater was concentrated in Level 4. For 45-seater and 63-seater vehicles the possibility of infection was in level 2. A model efficiency of 84.8% was found indicating that the model is good. The results of this study provided information on behaviour of public transport sector in Kenya.

Over the last three decades, traffic crashes have been one of the leading causes of fatalities and economic losses in the U.S.; compared with other age groups, this is especially concerning for the youth population (those aged between 16 and 24), mostly due to their inexperience, greater inattentiveness, and riskier behavior while driving. This research intends to investigate this issue around selected Florida university campuses. We employed three methods: (1) a comparative assessment for three selected counties using both planar Euclidean Distance and Roadway Network Distance-based Kernel Density Estimation methods to determine high-risk crash locations, (2) a crash density ratio difference approach to compare the maxima-normalized crash densities for the youth population and those victims that are 25 and up, and (3) a logistic regression approach to identify the statistically significant factors contributing to young-driver-involved crashes. The developed GIS maps illustrate the difference in spatial patterns of young-driver crash densities compared to those for other age groups. The statistical findings also reveal that intersections around university areas appear to be significantly problematic for youth populations, regardless of the differences in the general perspective of the characteristics of the selected counties. Moreover, the speed limit countermeasures around universities could not effectively prevent young-driver crash occurrences. Hence, the results of this study can provide valuable insights to transportation agencies in terms of pinpointing the high-risk locations around universities, assessing the effectiveness of existing safety countermeasures, and developing more reliable plans with a focus on the youth population.

This study aims to determine the variables affecting the cigarette consumption of households by employing logistic regression analysis based on the data of the Turkish Statistical Institute (TUIK) 2017 Household Budget Survey (HBA). In this model, the variables that affect cigarette consumption, namely, gender, age, education level, marital status, occupation, and the total disposable income earned by the household head in a year, are used. The results of this study indicate that variables of income, occupation and education of the household head affect cigarette consumption in Turkey. In this context, it is expected that focusing on young people, low educated and income groups in the fight against smoking will increase the success of the policies implemented.

This study aims to investigate the contributing factors affecting the occurrence of crashes while lane-changing maneuvers of drivers. Two different data sets were used from the same drivers' population. The first data set was collected from the traffic police crash reports and the second data set was collected through a questionnaire survey that was conducted among 429 drivers. Two different logistic regression models were developed by employing the two sets of the collected data. The results of the crash occurrence model showed that the drivers' factors (gender, nationality and years of experience in driving), location and surrounding condition factors (non-junction locations, light and road surface conditions) and roads feature (road type, number of lanes and speed limit value) are the significant variables that affected the occurrence of lane-change crashes. About 57.2% of the survey responders committed that different sources of distractions were the main reason for their sudden or unsafe lane change including 21.2% was due to mobile usage. The drivers' behavior model results showed that drivers who did sudden lane change are more likely to be involved in traffic crashes with 2.53 times than others. The drivers who look towards the side mirrors and who look out the windows before lane-change intention have less probability to be involved in crashes by 4.61 and 3.85 times than others, respectively. Another interesting finding is that drivers who reported that they received enough training about safe lane change maneuvering during issuing the driving licenses are less likely to be involved in crashes by 2.06 times than other drivers.

The profitability of farms in the northeastern U.S. is hampered by the high cost of production in the region. Production of high-value crops such as organic produce is one of the solutions to keep these farms viable in the region. However, production challenges and limited information related to marketing are likely to hamper the potential growth of organic agriculture in the northeastern U. S. A logit model was developed to predict who is more likely to buy certified organic produce using 1,100 respondents in the Mid-Atlantic region. About 87% of the consumers were willing to buy certified organic produce, and the remaining 13% were not. While 6% of respondents stated that they would not pay slightly more for organic produce, nearly 43 % of the respondents stated that they would pay more than 10 cents premium for a dollar’s worth of organic produce. The results indicate that those who have a graduate degree, those who are influenced by an eco-friendly label, country of origin label, or pesticide-free label are more likely to purchase certified organic produce. Producers and marketers of certified organic vegetables and fruits can target customers with these specific traits to enhance their sales and profitability.

In this work, a statistical permafrost distribution model for coarse-blocky deposits in the Dry Andes of Argentina (28-33°S) is presented. The empiric mathematical formulation was based on a logistic regression. The final model is a combination of two independent occurrence probability models: a) a mean annual air temperature-terrain ruggedness model and, b) a mean annual air temperature-potential incoming solar radiation model. For all cases, calibration was made according the complete geomorphological characterization of a periglacial basin with 250 km2. Lately, the results of probabilistic model were extrapolated to the whole study area in the Dry Andes and compared with the Argentine rock glacier inventory data base. High permafrost likelihood, in coarse debris, is expected above 4200 and 5700 m a.s.l., from south to north in the study area and covers a surface of approximately 1200 km2. Medium permafrost likelihood is expected above 3400 and 4200 m a.s.l. with a surface of 6178 km2 while low permafrost likelihood, occurs between 3000 and 3400 m a.s.l. with an area of 11.060 km2. These findings indicate that permafrost may occur in several types of coarse-blocky deposits in the Dry Andes, not only restricted to rock glaciers. Thermal properties of the ground in coarse-blocky deposits allow permafrost permanence, even under unfavourable climatic conditions.The performance of the permafrost model was also tested, considering the transition from cold paleoclimate Tardiglacial to present climatic conditions. During the warming, likely permafrost surface reduced from 56 to 13%. In the same way, rock glaciers with high and medium permafrost likelihood decrease from 62 to 30%, respectively while, rock glaciers with low likelihood and no permafrost category, increased 75% and 474%, respectively. Moreover, we identified some sites in which permafrost degradation is arguably expected. About that, 0.9% of the rock glaciers in the study area display possible permafrost degradation and 33% of them, likely permafrost degradation.

The issue of decision-making is one of the important issues in modern management because of its impact on individuals, communities and countries, as most of the problems faced by individuals, communities and countries result from making incorrect decisions. The evolution of management science and the use of quantitative methods of treatment are also in a continuous growth as decisions are influenced by a variety of factors or variables. In this research, we considered the variables or factors related to the illusion of excellence and the level of ambition through the use of logistic regression technique to identify the variables that have a significant moral effect in the decision-making and are arranged according to their importance. A model by which to predict the extent to which the decision is affected by the environment. The result of the analysis demonstrated that out of ten factors that were identified and studied, three of them had a significant effect while the others had a non-significant effect, and the model achieves a correct classification of 81%.

There is a lack of evidence on the levels of investment literacy among South African university students in business-related courses. Being an investment literate is pivotal in making informed financial decisions that affect long term financial well-being. This study surveyed 344 students studying business degrees at a South African university to assess their investment literacy and the relationship between their investment literacy and their gender, age, discipline, major field of study, race and monthly allowance. The 2012 Financial Services Board survey revealed poor levels of financial literacy among South Africans, especially in areas of savings and investments. This study utilised ANOVA and a logistic regression model to analyse and explore relationships between the students’ mean percentage scores for each investment literacy question vis-à-vis their sociodemographic diversities. The findings suggest that business students are investment literate. However, it was found that female business students are less knowledgeable than male business students and finance-related students are more knowledgeable than non-finance related students. The differences between these groups are statistically significant and imply that not all business students and eventual business graduates are investment literate. Hence, this study advocate for the inclusion of personal investment modules in the students’ curriculum.

Objective: to study the incidence rate of personality and anxiety disorders and their interrelationship in seniors of the medical university.Materials and methods. The screening study for detection of personality and anxiety disorders using the Generalized Anxiety Disorder-7 (GAD-7) and Standardized Assessment of Personality – Abbreviated Scale (SAPAS) scales included 104 senior-year students of the medical university. In the studied sample, males (n = 46; 44.2%) and females (n = 58; 55.8%) (p < 0.05) were equally represented, the average age of men was 23 (23; 25) years, women – 23 (23; 23) years. Statistical processing was performed using the Pearson’s χ2 test, Mann–Whitney U test to compare independent samples, and the Spearman correlation analysis (r) with a preliminary estimate of the normal distribution.Results. The data on the statistically significant differences in the level of anxiety and personality disorders depending on gender and age were obtained. Using the GAD-7 scale, the presence of moderate and average level of anxiety in male students was revealed in 26.1%, in female students – in 48.3% (ð = 0.001). The use of SAPAS scale revealed statistically insignificant differences in the studied groups: in males – 4 (4; 6) points, in the group of female students – 4 (3; 5) points (ð = 0.2).Conclusion. A logistic regression model was obtained that predicted the highest likelihood of development of anxiety disorders.

This paper addresses the use in different stages of pregnancy of ultrasound imaging and to examine the tumors diagnosed during lactation or pregnancy. There are recent advancements in the application of obstetric ultrasound and imaging techniques helpful for improving the outcome of the pregnancy using various Learning techniques. This paper addresses the need to implement sustainable ultrasound standards with an acceptably high maternal and perinatal mortality rates to provide better and more affordable, quality Ultrasonic Flaw (UT) equipment which can improve Obstetric health care. The state-of-the-art learning approach for obstetric ultrasound is a category of methods in machine learning that are gaining popularity and attracting interest in various fields, including image processing and computer vision. In this paper advanced Machine learning processes map a raw input image to the desired output image using logistic regression classifier(LRC) and Convolution neural networks (CNNs) are of particular interest among all Machine learning methods. Furthermore, we have utilized the Internet of Medical Things (IoMT) for obstetric tumor image segmentation and identification of tumors for the medical experts. The experimental results show the LRC based on CNN can be utilized to predict the output of the ultrasound of obstetric with increased maternal and perinatal mobility rates.

Pregnancy status is usually not included in ecological studies because it is difficult to evaluate. The use of non-invasive methods to determine pregnancy, without physically restraining individuals, would enable pregnancy to be included in population studies. In this study, we evaluated sex steroid hormones in plasma and fecal samples from pregnant and non-pregnant females to develop a pregnancy predictive model for guanacos (Lama guanicoe (Müller, 1776)). Samples were obtained during live-shearing management (i.e., capture, shear, and release) of guanacos. Enzyme immunoassays were used to evaluate progesterone (P4) and estradiol (E2) concentrations in plasma and pregnanediol glucuronides (PdG) and conjugated estrogens (EC) in feces. Mean hormonal and fecal metabolite concentrations were significantly higher in pregnant females than in non-pregnant females. A linear relationship was found between each hormone and its fecal metabolite. Finally, hormonal data were combined with an independent source of pregnancy diagnosis such as abdominal ballottement to develop a logistic regression model to diagnose pregnancy in non-handled individuals. The use of predictive models and non-invasive methods might be suitable to incorporate pregnancy information in large-scale population studies on guanaco and other free-ranging ungulates. L’état de gestation n’est généralement pas inclus dans les études écologiques en raison de la difficulté que présente son évaluation. L’utilisation de méthodes non effractives pour déterminer l’état de gestation sans imposer de restriction physique aux spécimens permettrait l’intégration de l’état de gestation aux études démographiques. Nous avons évalué les hormones stéroïdiennes sexuelles dans des échantillons de plasma et de fèces de femelles gravides et non gravides afin d’élaborer un modèle pour prédire l’état de gestation chez les guanacos (Lama guanicoe (Müller, 1776)). Des échantillons ont été prélevés durant la gestion de la tonte (c.-à-d., capture, tonte et lâcher) de guanacos vivants. Des épreuves immunoenzymatiques ont été utilisées pour évaluer les concentrations de progestérone (P4) et d’estradiol (E2) dans le plasma et de glucuronides de prégnandiol (GdP) et d’estrogènes conjugués (EC) dans les fèces. Les concentrations moyennes de métabolites hormonaux et fécaux étaient significativement plus grandes chez les femelles gravides que chez les femelles non gravides. Une relation linéaire est relevée entre chaque hormone et ses métabolites fécaux. Enfin, les données hormonales ont été combinées à une épreuve diagnostique de l’état de gestation indépendante comme le ballottement abdominal afin de produire un modèle de régression logistique pour diagnostiquer la gestation chez des spécimens non manipulés. L’utilisation de modèles prédictifs et de méthodes non effractives pourrait se prêter à l’intégration d’information sur la gestation dans des études démographiques à grande échelle sur les guanacos et d’autres ongulés vivant en liberté. Fil: Marozzi, Antonela Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue. Asentamiento Universidad San Martín de Los Andes; Argentina Fil: Cantarelli, Verónica Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Ciencias de la Salud. Universidad Nacional de Córdoba. Instituto de Investigaciones en Ciencias de la Salud; Argentina Fil: Gomez, Facundo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Comahue. Asentamiento Universidad San Martín de Los Andes; Argentina Fil: Panebianco, Antonella. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue. Asentamiento Universidad San Martín de Los Andes; Argentina Fil: Leggieri, Leonardo Ramón. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue. Asentamiento Universidad San Martín de Los Andes; Argentina Fil: Gregorio, Pablo Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue. Asentamiento Universidad San Martín de Los Andes; Argentina Fil: Ponzio, Marina Flavia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Ciencias de la Salud. Universidad Nacional de Córdoba. Instituto de Investigaciones en Ciencias de la Salud; Argentina Fil: Carmanchahi, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue. Asentamiento Universidad San Martín de Los Andes; Argentina

Housing affordability represents a challenge everyone faces when covering the costs of their current or potential housing on the one hand and costs unrelated to their housing within the limits of their own income on the other hand. At the international level, two approaches are used to measuring the housing affordability: the ratio approach and the residual approach. According to Eurostat’s definition, a household is considered “overburdened” when the total housing costs (“net” of housing allowances) represent more than 40% of disposable income. The primary objective of this paper was to define the relevant factors affecting the household cost burden in the Slovak Republic and quantifying the intensity of their influence. For this purpose, a logistic regression model and a classification tree model were created, using the sample of the cross-component of the data of the statistical survey EU SILC 2016. The analysis was completed by using SAS Enterprise Guide (SAS EG) and SAS Enterprise Miner (SAS EM).

Estonia mainly experienced urban expansion after regaining independence in 1991. Employing the CORINE Land Cover dataset to analyze the dynamic changes in land use/land cover (LULC) in Estonia over 28 years revealed that urban land increased by 33.96% in Harju County and by 19.50% in Tartu County. Therefore, after three decades of LULC changes, the large number of shifts from agricultural and forest land to urban ones in an unplanned manner have become of great concern. To this end, understanding how LULC change contributes to urban expansion will provide helpful information for policy-making in LULC and help make better decisions for future transitions in urban expansion orientation and plan for more sustainable cities. Many different factors govern urban expansion; however, physical and proximity factors play a significant role in explaining the spatial complexity of this phenomenon in Estonia. In this research, it was claimed that urban expansion was affected by the 12 proximity driving forces. In this regard, we applied LR and MLP neural network models to investigate the prediction power of these models and find the influential factors driving urban expansion in two Estonian counties. Using LR determined that the independent variables “distance from main roads (X7)”, “distance from the core of main cities of Tallinn and Tartu land (X2)”, and “distance from water land (X11)” had a higher negative correlation with urban expansion in both counties. Indeed, this investigation requires thinking towards constructing a balance between urban expansion and its driving forces in the long term in the way of sustainability. Using the MLP model determined that the “distance from existing residential areas (X10)” in Harju County and the “distance from the core of Tartu (X2)” in Tartu County were the most influential driving forces. The LR model showed the prediction power of these variables to be 37% for Harju County and 45% for Tartu County. In comparison, the MLP model predicted nearly 80% of variability by independent variables for Harju County and approximately 50% for Tartu County, expressing the greater power of independent variables. Therefore, applying these two models helped us better understand the causative nature of urban expansion in Harju County and Tartu County in Estonia, which requires more spatial planning regulation to ensure sustainability.

Previous studies have shown that the miR-17-92 cluster is involved in the occurrence and development of bladder cancer. However, the role of serum miR-17-92 cluster in the diagnosis of bladder cancer has not been studied. In the present study, we evaluated the expression of miR-17-92 cluster members in bladder cancer tissues by analyzing 428 cases from TCGA database. Next, we collected the sera of 74 bladder cancer patients and 90 controls, and used qRT-PCR to detect the relative expression of the cluster. The results showed that the expression of the cluster members in the sera of patients were significantly higher than that of the controls, and they were positively correlated with the clinical stage and pathological grade of the patients. We evaluated their ability to diagnose bladder cancer using ROC, of which miR-92a-3p (AUC = 0.902), miR-17-5p (AUC = 0.845) and miR-20a-5p (AUC = 0.806) were the most prominent. Finally, we established a diagnostic model by logistic regression (AUC = 0.969). We further validated the results of the study using another dataset from the GEO database. Moreover, we evaluated the prognostic value of the cluster. The results revealed that miR-20a-5p was correlated with recurrence of bladder cancer. In summary, the present study validated the overexpression of serum miR-17-92 cluster in bladder cancer. The model composed of the three cluster members were confirmed to be a promising noninvasive biomarker for bladder cancer diagnosis.

Students’ commitment and engagement in the educational process are shaped by a dense combination of factors, with effects on educational attainment and on the length of their educational careers. Decisions of prolonging education by enrolling in master’s degrees are beneficial for both individuals and societies, as such programs provide higher levels of specialized skills Longer educational careers are favored by a mix of factors acting at the level of individual, university, or wider environment. We focus our study on exploring factors conducive for students’ intentions to pursue master’s degrees considering longer educational careers as desirable outcomes. Thus, this article investigates how the individual and environmental factors interplay and shape the predisposition of students to prolong their educational career by enrolling in master’s degrees. For this, we applied three-level logistic regression models for a sample of 502 students enrolled in their final year of bachelor studies grouped by universities and universities grouped by counties. The empirical results revealed that the final grade, the father level of education, the type of working contract, and job seniority are individual-level determinants influencing the decision of enrolment in a master’s program. At the university level, the type of university and the university performance score positively impact the students’ decision to enroll in a master’s program. At the county level, the empirical evidence pointed out the significance of determinants such as the proportion of students enrolled in bachelor studies; participation rate in education and training; employment level in high-technology sectors (HTC), total-knowledge intensive sectors (KIS), and knowledge-intensive high-technology sectors (KIS_HTC); proportion of persons with tertiary education employed in science and technology; proportion of scientists and engineers; local development; R&D expenditure, personnel, and researchers in the business sector; average gross earnings; density of active firms; birth rate of companies; proportion of innovative enterprises or those introducing product innovations on the decision to enroll in a master’s program.

Objective Caries risk assessment tools are essential for identifying and providing treatment for individuals at high risk of developing caries. We aimed to develop a nomogram for the assessment and evaluation of caries risk among Chinese children. Methods We enrolled schoolchildren age 7 years from a primary school in Shanghai. Baseline information of participants was collected using a questionnaire completed by children’s caregivers. A nomogram of a novel prediction scoring model was established based on predictors detected in univariate and multivariate analyses. Predictive accuracy and discriminative ability of the nomogram were calculated using the concordance index (C index). The bootstrap method (1000 samples) was used to decrease overfitting. The net benefit of the model was validated using decision curve analysis. Results Overall, 406 children with complete information and two completed dental examinations were included in the final analysis. The nomogram based on logistic regression model coefficients demonstrated a C index of 0.766 (95% confidence interval: 0.761–0.771) for caries risk. The net benefit of the decision curve analysis was 38.6% at 55% threshold probability. Conclusion This nomogram model, derived using dietary habits, oral hygiene status, and caries experience, showed promising predictive ability to assess the caries risk among Chinese children.