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This paper presents a new method for calculating the insulation health index (HI) of oil-paper transformers rated under 110 kV to provide a snapshot of health condition using binary logistic regression. Oil breakdown voltage (BDV), total acidity of oil, 2-Furfuraldehyde content, and dissolved gas analysis (DGA) are singled out in this method as the input data for determining HI. A sample of transformers is used to test the proposed method. The results are compared with the results calculated for the same set of transformers using fuzzy logic. The comparison results show that the proposed method is reliable and effective in evaluating transformer health condition. [ABSTRACT FROM AUTHOR]

This paper focuses on the differences in crowding perception among different types of passengers in trains, aiming to optimize passenger experience and improve the level of service of urban rail transit. Based on data from a passenger survey on the Beijing subway, this paper introduces the concept of Crowding Perception Threshold (CPT) and analyzes the principle of passenger spatiotemporal crowding. Considering factors such as gender, age, travel purpose, and standing density of passengers, the paper constructs a quantitative model of crowding perception using the ordered logit model and proposes a method for classifying the level of service accordingly. The study results indicate that the CPTs for all types of passengers range from 91.8% to 101.6%, with the females, elderly individuals, and noncommuters showing greater sensitivity to crowding. In the temporal dimension, all types of passengers have higher CPTs during peak hours than during off‐peak hours, influenced by passengers' crowding expectations. In the spatial dimension, the level of service for most types of passengers is considered crowded at standing densities of 6‐7 pax/m2 during peak hours, while the level of service for all types of passengers is deemed to be very crowded at 8 pax/m2, at which point additional passengers are not recommended. [ABSTRACT FROM AUTHOR]

Background. Social and organisational changes in businesses have led to the appearance of "emergent risks" which affect workers but are less evident and hard to quantify such as psychosocial risks. Linked to psychosocial risks, issues such as work‐related stress and workplace violence are major challenges to occupational health and safety. This paper analyses the trends in the concerns expressed by wage‐earners in Spain regarding psychosocial factors that may affect them in the workplace. Methods. A causal analysis based on the application of binary logistic regression is presented, covering certain social and occupational characteristics of survey respondents and the psychosocial factors included in the Spanish National Surveys of Working Conditions for 2007 and for 2011‐2012. Binary logistic regression is a multivariate statistical tool that serves as a classification technique, identifying the variables that affect the probability of the event to be studied (dichotomous variable). This technique has the advantage that it does not require that the explanatory variables follow a normal distribution. The aim is to estimate the influence of the explanatory variables on the probability of the occurrence of the event under study, represented by the explained variable. Results. During the economic crisis of 2008, workers became more concerned about losing their jobs and about factors related to personnel cutbacks and decreases in investment in the prevention of occupational risks. Downsizing due to the crisis led to increased workloads for many of those still in work. Thus, in 2011, the likelihood of workers being concerned about working hours was greater, especially among respondents aged 25–34 and those working in commerce and transport. Workload was found to be a particular concern among respondents aged 25–34 and among workers in transport, communication, financial, professional, and administrative activities; health‐related activities; and industry. Conclusions. Policy should also be directed towards improving the structural aspects of psychosocial variations, in terms of work conditions, employment protection, and employment security to protect workers against income fluctuations as a result of job loss. [ABSTRACT FROM AUTHOR]

This paper investigated the factors in the technology–organization–environment (TOE) framework that affect the decision of whether to adopt electronic commerce (EC) or not within small‐ and medium‐sized enterprises (SMEs). To this end, a questionnaire‐based survey was conducted to collect data from 60 managers or owners of manufacturing SMEs in Tunisia. Unlike the traditional regression approaches, we referred to novel machine learning (ML) techniques and reveal that ML techniques reach a higher level of performance in forecasting driving factors to EC adoption compared to the traditional logistic regression approach. The achieved results also indicate that EC adoption within SMEs is significantly affected by eight factors, namely, IT vendors' support, the adopted technology complexity degree, chief executive officer (CEO) innovativeness, technology readiness, customers' pressure, firm size, infrastructure compatibility, and the innovative technology‐perceived relative advantage. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common and deadly type of cancer in the world. Based on machine learning algorithms such as XGBoost, random forest, logistic regression, and K-nearest neighbor, this paper establishes different models to classify and predict breast cancer, so as to provide a reference for the early diagnosis of breast cancer. Recall indicates the probability of detecting malignant cancer cells in medical diagnosis, which is of great significance for the classification of breast cancer, so this article takes recall as the primary evaluation index and considers the precision, accuracy, and F1-score evaluation indicators to evaluate and compare the prediction effect of each model. In order to eliminate the influence of different dimensional concepts on the effect of the model, the data are standardized. In order to find the optimal subset and improve the accuracy of the model, 15 features were screened out as input to the model through the Pearson correlation test. The K-nearest neighbor model uses the cross-validation method to select the optimal k value by using recall as an evaluation index. For the problem of positive and negative sample imbalance, the hierarchical sampling method is used to extract the training set and test set proportionally according to different categories. The experimental results show that under different dataset division (8 : 2 and 7 : 3), the prediction effect of the same model will have different changes. Comparative analysis shows that the XGBoost model established in this paper (which divides the training set and test set by 8 : 2) has better effects, and its recall, precision, accuracy, and F1-score are 1.00, 0.960, 0.974, and 0.980, respectively. [ABSTRACT FROM AUTHOR]

FET is to resuscitate the endometrium and transfer the embryo into the uterus after the endometrium is ready. The quality of transferred embryos is an important factor affecting the outcome of assisted reproductive technology. This paper aims to explore the feasibility of D4 frozen-thaw embryo transfer and analysis of related factors affecting the outcome of freeze-thaw embryo transfer. A retrospective analysis of the clinical data of 2925 patients who received frozen-thaw embryo transfer (FET) in the Department of Reproductive Medicine, General Hospital of Northern Theater Command from January 1, 2017 to July 31, 2019. Including the woman's age, body mass index (BMI), endometrial thickness on the day of transplantation, number of embryos to be transferred, and type of embryos to be transferred. A single factor, multivariate logistic regression and nomogram were used to analyze the influence of different factors on the clinical outcome of FET. Nanomedicines and related nanomedicines are rapidly developing and establishing their importance in embryo transfer. This paper uses nanomaterials to explore the feasibility of D4 frozen-thawed embryo transfer. The woman's age, endometrial thickness on the day of transplantation, BMI, the number of embryos transferred, and the type of embryos transferred all affect the outcome of FET. The pregnancy rate of the D5 and D4 transplantation groups was, respectively, higher than that of the D3 transplantation group, with statistically significant differences. In the FET cycle, the age of the woman, endometrial thickness on the day of transplantation, the number of embryos transferred, and the type of embryos transferred are all independent factors influencing the outcome of FET. D5 blastocyst is the easiest to get pregnant, and that has the best clinical outcome which is better than the D6 blastocyst group; D4 morula and D5 blastocyst FET have little difference in clinical pregnancy outcomes, but both of them are significantly better than D3 cell embryos, so D4 morula can be considered for transplantation in the FET cycle. In conclusion, whether it is a patient who has failed the fresh cycle transplantation or the whole embryo freezing cycle whose transplantation is canceled due to high hormone levels on the transplantation day, FET is required. [ABSTRACT FROM AUTHOR]

Hypertensive disorders of pregnancy are a group of pregnancy-related diseases characterized by the coexistence of pregnancy and elevated blood pressure, which seriously endanger the health of mothers and infants, and are one of the main causes of maternal and perinatal deaths. The purpose of this paper is to investigate the clinical analysis of vitamin E and astragalus in the adjuvant treatment of hypertensive disorders in pregnancy and to describe the learning model. This paper puts forward the problem of clinical treatment, which is established on the basis of adjuvant therapy, then narrates around the clinical characteristics of gestational hypertension, and designs and analyzes the experimental design and analysis of adjuvant therapy with vitamin E and astragalus. The experimental results showed that the delivery methods of the three groups of patients were compared P < 0.05. Compared with the traditional Chinese medicine control group and the vitamin E control group, there were more vaginal births in the experimental group, 36 patients in total. It shows that astragalus and vitamin E can alleviate the disease in different aspects and can effectively intervene in gestational hypertension. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth study of occupational therapy and the prevention of common sports injuries in special physical training. The issue of sports injuries and rehabilitation has always been a hot topic in special training. With the continuous development of sports, the increasing intensity of competition, and more stringent requirements for special techniques, the increase in difficulty and intensity of training has led to the increasing frequency of sports injuries, so how to prevent injuries in special physical training and rehabilitation and recovery of athletes after the injury is particularly important. Since the most common musculoskeletal injuries occur in the lower quadrant, this paper proposes a lower extremity functional test (LEFT) model as a means of identifying injury risk and guiding the implementation of training programs to prevent sports injuries. In this paper, a knee injury is used as an example, and an occupational therapy program of TCM physical therapy + aquatic rehabilitation is adopted for the already occurred sports injuries. Through interviews and clinical examinations of athletes, coaches, and medical personnel, this paper summarizes the sites, types, characteristics, and probability of occurrence of common sports injuries in special physical training. Experiments were conducted through clinical rehabilitation of common sports injuries with the addition of TCM manual massage. A series of effects of this modality on the rehabilitation of sports injuries were examined by monitoring physiological and biochemical indexes and by comparative analysis before and after testing physical function indexes using the Omega Wave system. Sports injuries are diverse. Traditional Chinese medicine physical therapy + water rehabilitation therapy is an effective physical therapy method. According to the relevant theories of traditional Chinese medicine treatment, diagnosis and treatment through meridians and related acupuncture points have significant curative effects. Traditional Chinese medicine, massage, and acupuncture have irreplaceable roles in the rehabilitation and treatment of sports injuries and can effectively improve and cure sports injuries. [ABSTRACT FROM AUTHOR]

With the economic development, smart communities have been widely studied and applied. However, the system in this field is not perfect, and there are still a series of problems, such as high construction cost, low level of intelligence, mutual independence of different systems, difficulty in unified management, and so on. To solve the above problems, this paper proposes the smart community big data dynamic analysis model based on logistic regression model. First, this paper constructs the big data research architecture of smart community based on IOT technology, including IAAs, DAAS, PAAS, and SaaS layers and the virtual service layer of resource scheduling of spatiotemporal information cloud platform optimized by spatiotemporal law. And the IoT platform is designed to collect data to lay the foundation for research. Second, this paper is oriented to the big data application requirements with distribution and mobility as the main technical characteristics. Based on the distributed data flow, this paper designs mining operator to provide technical support for the data mining algorithm; at the same time, this paper constructs a high-dimensional random matrix model for measuring big data and then deduces its abnormal data detection theory and method to detect high-dimensional abnormal data. Finally, this paper uses logistic regression model to predict the development trend of smart community and provide guarantee for smart community service. The simulation results show the efficiency and accuracy of prediction can be improved based on logistic regression model. Furthermore, it effectively avoid repeated construction and waste of resources in the community and form a new community management model based on intelligent and information-based social management and service. [ABSTRACT FROM AUTHOR]

With the continuous updating and advancement of artificial intelligence technology, it gradually begins to shine in various industries, especially playing an increasingly important role in incremental music teaching and assisted therapy systems. This study designs artificial intelligence models from the perspectives of attention mechanism, contextual information guidance, and distant dependencies combined with incremental music teaching for the segmentation of MS (multiple sclerosis) lesions and achieves the automatic and accurate segmentation of MS lesions through the multidimensional analysis of multimodal magnetic resonance imaging data, which provides a basis for physicians to quantitatively analyze MS lesions, thus assisting them in the diagnosis and treatment of MS. To address the highly variable characteristics of MS lesion location, size, number, and shape, this paper firstly designs a 3D context-guided module based on Kronecker convolution to integrate lesion information from different fields of view, starting from lesion contextual information capture. Then, a 3D spatial attention module is introduced to enhance the representation of lesion features in MRI images. The experiments in this paper confirm that the context-guided module, cross-dimensional cross-attention module, and multidimensional feature similarity module designed for the characteristics of MS lesions are effective, and the proposed attentional context U-Net and multidimensional cross-attention U-Net have greater advantages in the objective evaluation index of lesion segmentation, while being combined with the incremental music teaching approach to assist treatment, which provides a new idea for the intelligent assisted treatment approach. In this paper, from algorithm design to experimental validation, both in terms of accuracy, the operational difficulty of the experiment, consumption of arithmetic power, and time cost, the unique superiority of the artificial intelligence attention-based combined with incremental music teaching adjunctive therapy system proposed in this paper can be seen in the MS lesion segmentation task. [ABSTRACT FROM AUTHOR]

In the fierce market competition, companies are constantly facing the threat of falling into GFC. A global financial crisis refers to a crisis in global financial assets or financial institutions or financial markets. However, the threat of a global financial crisis (GFC) is not helpless, but can be predicted in advance. Therefore, building a GFC prediction model is of great significance to the development of the company. This article mainly studies the GFC prediction model of listed companies based on statistics and AI methods. This paper chooses to determine the number of training samples and test samples as 40 and 16 respectively, that is, 8 companies are randomly selected as test samples from financial health companies and GFC companies respectively, and the remaining 40 become training samples. According to the primary selection of characteristic indicators, this paper adopts the frequency statistics method, that is, the higher frequency is selected through the previous research, and the indicator selection is made on this basis. This article will use the Kolmogorov–Smimov (K-S test) goodness-of-fit test method. Each of the early warning indicators selected in this article should be able to distinguish between GFC and non-GFC companies, so the selection should be made by indicators one by one. Bring the indicators of each year into the factor function formula obtained by factor analysis, and get a new variable group. Then SPSS16.0 was used for binomial logistic regression analysis for each year. This article uses KMO and Bartlett identification. The assumption of the sphericity test of the Bartlett test is that the correlation coefficient matrix is an identity matrix, and statistics are obtained according to the matrix formula of the correlation coefficient matrix. The prediction accuracy of the nonlinear combination discriminant method has been improved in the first three years of the GFC, and in the year (t − 3), which is a little far away from the crisis time, the accuracy rate has reached 83%. The results show that the combination of statistics and AI has a significant effect on improving the prediction accuracy of the GFC prediction model of listed companies. [ABSTRACT FROM AUTHOR]

Human resource management involves a variety of data processing, and the process is complicated. In order to improve the effect of human resource management, this paper combines BP neural network and logistic regression analysis to construct an intelligent human resource management system and uses backpropagation learning to adjust training errors and determine connection weights. Moreover, this paper estimates the probability of a certain event through regression analysis, predicts and analyzes the human resource management process, and builds an intelligent human resource management system with the support of joint algorithms. In order to explore the reliability of the joint algorithm proposed in this paper, the effectiveness of the algorithm proposed in this paper is verified through simulation tests. The experimental research results show that the human resource management system based on BP neural network and logistic regression proposed in this paper has good practical effects. [ABSTRACT FROM AUTHOR]

Background. China will inevitably enter a medium, severe, or deep aging society in the future, and the number of elderly people living alone is also increasing. Mental health is a major issue for older people living alone. With the deepening of aging, social participation has become an important way to promote mental health and improve the quality of life of the elderly. Methods. This study uses data from Chinese Longitudinal Health Longevity Survey (CLHLS). Based on the CLHLS data of 2018, this paper uses multiple ordered logistic models to measure the mental health level of elderly people living alone through two dimensions of depression and anxiety and carries out a heterogeneity analysis on the mental health level of elderly people living alone. Results. The analysis of 2477 elderly people living alone shows that the increase of social participation in simple communication can reduce the degree of depression and anxiety of elderly people living alone, and the decrease of social participation in self-recreation can reduce the degree of depression and anxiety of elderly people living alone. In addition, the heterogeneity analysis found that the heterogeneity of social participation was more significant among the elderly living alone with different genders, ages, places of residence, and self-care abilities. Limitations. This study has some limitations, and CES-D-10 is a screening tool and cannot fully determine the presence of depression in high-rise older adults living alone. Conclusions. In the future, primary healthcare-targeted interventions can be provided according to the different degrees of depression and anxiety of elderly people living alone. [ABSTRACT FROM AUTHOR]

The "innovation-driven" development approach plays a crucial role in transforming and developing current China's national economy. Many innovation-driven mergers and acquisitions (M&As) emerge in the M&As activities of firms, and the acquiring firm's business strategy of conducting innovation-driven M&As has drawn significant attention. To study the impact of business strategy on innovation-driven M&As, this paper adopts the probit model to conduct logistic regression analysis on the 223 M&As data samples, exploring which business strategy is inclined to perform innovation-driven M&As. The results show that prospective firms are more likely to conduct innovation-driven M&As. Besides, the firms that conduct prospective business strategies are more likely to have a higher innovation level than firms operating defensive business strategies. The innovation level plays the role of a mediator variable in the model of business strategies influencing innovation-driven M&As. This paper analyzes the innovation-driven motivation from the perspective of business strategy, enriching the research of M&A and offering a method to predict the possibility of innovation-driven M&A by measuring business strategy. [ABSTRACT FROM AUTHOR]

What is known and objective: Australian hospitals have undergone a transformation with both a review and expansion of traditional roles of healthcare professionals and the implementation of an ieMR. The implementation of an ieMR brings large scale organizational change within the health system especially for staff with direct patient contact. This is changing the future of healthcare and the roles of healthcare professionals. There is minimal research on the impact of these electronic systems on the people and processes required to realise the improvements in patient care such as therapeutic drug monitoring (TDM) and the role of the pharmacist within the TDM process. The literature has discussed the use of computerised programs to assist with the interpretation of results and calculating of doses but the impact of an ieMR on the TDM process has not been discussed. This study undertook a retrospective analysis at an Australian tertiary hospital to investigate the impact of a digital hospital system on TDM within the facility. Methods: A 2‐year retrospective audit was conducted on TDM at an Australian Tertiary Hospital. The periods were 2016 (a paper‐based hospital) and 2018 (ieMR). Patients were identified using the pathology database. Patients were excluded if under the age of 18, in an outpatient setting or the emergency department. Progress notes, medication charts, ieMR and other relevant pathology were reviewed. They were assessed for appropriateness of the timing of collection, compliance to recommended TDM guidelines, and pharmacist documentation. Results and Discussion: A total of 2926 observations were included in the analysis. There was as similar percentage of appropriately collected samples between the paper‐based system (2016) and the digital hospital system (2018) with 59% and 58% respectively. Results of logistic regression analysis models show the effect of year was not significant with regards to TDM for either a sample being appropriate or the dose adjustment being appropriate. Samples for TDM were more likely to be appropriate if the pharmacist had documented advice but less likely with regards to appropriate dose adjustment. This study considered the effect of introducing a hospital wide digital system on TDM processes. Overall, the results indicate no difference between the paper‐based system and ieMR for appropriate samples and doses adjustments. What is new and conclusion: To our knowledge, this is the first study of this kind looking at the impact of a digital hospital system on TDM. The introduction of a digital hospital system does not appear to have made improvement on the effective use of TDM. Inappropriate sampling as seen in this study can lead to ineffective clinical management of patients, inefficient use of time, and waste of financial resources. Further work is required to incorporate specific guidance and recommendations within the digital system to optimize TDM. [ABSTRACT FROM AUTHOR]

A strong First Nations health workforce is necessary to meet community needs, health rights, and health equity. This paper reports the findings from a national survey of Australia's First Nations people employed in health services to identify enablers and barriers to career development, including variations by geographic location and organisation type. A cross-sectional online survey was undertaken across professions, roles, and jurisdictions. The survey was developed collaboratively by Aboriginal and non-Aboriginal academics and Aboriginal leaders. To recruit participants, the survey was promoted by key professional organisations, First Nations peak bodies and affiliates, and national forums. In addition to descriptive statistics, logistic regression was used to identify predictors of satisfaction with career development and whether this varied by geographic location or organisation type. Of the 332 participants currently employed in health services, 50% worked in regional and remote areas and 15% in Aboriginal Community-Controlled Health Organisations (ACCHOs) with the remainder in government and private health services. All enablers identified were associated with satisfaction with career development and did not vary by location or organisation type. "Racism from colleagues" and "lack of cultural awareness," "not feeling supported by their manager," "not having role models or mentors," and "inflexible human resource policies" predicted lower satisfaction with career development only for those employed in government/other services. First Nations people leading career development were strongly supported. The implications for all workplaces are that offering even a few career development opportunities, together with supporting leadership by Aboriginal and Torres Strait Islander staff, can make a major difference to satisfaction and retention. Concurrently, attention should be given to building managerial cultural capabilities and skills in supporting First Nations' staff career development, building cultural safety, providing formal mentors and addressing discriminatory and inflexible human resources policies. [ABSTRACT FROM AUTHOR]

Logistic regression model is widely used in ecology and in the analysis of social economic systems, because of its good adaptability. In order to improve the measurement accuracy of logistic model, this paper proposes a new method. A compound grey-logistic model is developed to carry out the grey transformation of the original data. Practice shows that the grey transformation data has better simulation accuracy; at the same time, grey transformation can reduce the observation noise of the original data. Mean absolute percentage error index has been used to evaluate the accuracy of prediction model, and information entropy can be used to evaluate the change of information entropy of forecasting data. In this paper, three cases are used to verify the applicability of grey-logistic model. From the perspective of the type of original data, the three cases represent three different data conditions: sufficient data, insufficient data, and fragmentary data. The cases represent different related fields: market share data, economic growth data, and R&D output data. The results show that the proposed grey-logistic method can effectively carry out the population growth analysis. [ABSTRACT FROM AUTHOR]

As the main component of an electric impact drill, the gearbox is used to decelerate and transmit power; damage and failure to the gears often lead to the transmission system's failure. Therefore, as the core component of power transmission, the fault detection and diagnosis of gearbox devices have attracted increasing attention. This paper presents a psychoacoustic-based fault diagnosis method for gears of electric impact drills. The proposed methods employ acoustic signals and the time-varying loudness theory of psychoacoustic parameters. Two states of electric impact drills were analyzed: an electric impact drill with healthy gears and an electric impact drill with faulty gears. A feature extraction peak-to-average ratio (PAR) method based on the time-varying loudness spectrum was described and implemented to compute the feature vectors. The classification was carried out by applying logistic regression (LR). This paper provides the results of an acoustic analysis of electric impact drills. The results had a good recognition rate and the total accuracy of recognition of EIDs based on the PAR with LR was 97%. This method simulates the human auditory perception to detect the gear components of an electric impact drill, which can replace the traditional artificial listening detection method. [ABSTRACT FROM AUTHOR]

In transport demand analysis, the calibration of a model means estimation of its (endogenous) parameters from observed data with an inference statistical estimator. Indeed, these considerations apply to any choice behaviour model, such as those derived from Random Utility Theory or any other choice modelling theory. Calibration of choice models can be carried out from disaggregate vs. aggregate data, while inference statistical estimators can be specified through Bayesian vs. Classic (or Frequentist) approaches. In this paper, the resulting Bayesian or Classic disaggregate or aggregate calibration methods are discussed, analysed in detail, and compared from the mathematical point of view. These methods are applied to calibrate Logit choice models for describing path choice behaviour at national scale on a small sample. The Logit choice model can be derived from Random Utility Theory (or be considered an instance of the Bradley–Terry model). Path choice set definition is also discussed, and specialised indicators are used for result comparison. The main contributions of this study concern the use of two different estimation approaches, Bayesian vs. Classic, adopting and introducing some indicators of goodness of estimation. The results of this work, relating to the sample of users adopted, show that the Bayesian approach provides a better estimate than the Classic approach because the calibrated parameters are more stable, the specific constants of the alternatives decrease, and the resulting models show better values of clearly right indicator. [ABSTRACT FROM AUTHOR]

Understanding the temporal trends in the place of death among patients in receipt of home‐based palliative care can help direct health policies and planning of health resources. This paper aims to assess the temporal trends in place of death and its determinants over the past decade for patients receiving home‐based palliative care. This paper also examines the impact of early referral to home‐based palliative care services on patient's place of death. Survey data collected in a home‐based end‐of‐life care program in Toronto, Canada from 2005 to 2015 were analysed using a multivariate logistic model. The results suggest that the place of death for patients in receipt of home‐based palliative care has changed over time, with more patients dying at home over 2006–2015 when compared to 2005. Also, early referral to home‐based palliative care services may not increase a patient's likelihood of home death. Understanding the temporal shifts of place of death and the associated factors is essential for effective improvements in home‐based palliative care programs and the development of end‐of‐life care policies. [ABSTRACT FROM AUTHOR]

Pulp washing process has the features of multivariate, time delay, nonlinearity. Considering the difficulties of modeling and optimal control in pulp washing process, a data-driven operational-pattern optimization method is proposed to model and optimize the pulp washing process in this paper. The most important quality indexes of pulp washing performance are residual soda in the washed pulp and Baume degree of extracted black liquor. Considering the difficulties of modeling, online measurement of these indexes, two-step neural networks, and multivariate logistic regression are used to establish the prediction models of residual soda and Baume degree. The mathematical model of the washing process can be identified, and the indexes can meet the production requirements. In the target of better product quality, low cost, and low energy consumption, a multiobjective problems is solved by ant colony optimization algorithm based on the optimized operational-pattern database. It shows that the theoretical analyses are correct and the practical applications are feasible, optimization control system has been designed for the pulp washing process, and the practical results show that pulp production increased by 20% and water consumption decreased by nearly 30%. This method is effective in the pulp washing process. [ABSTRACT FROM AUTHOR]

Objective. This study aims to explore the mediation role of perceived stress between nonrestorative sleep (NRS) and emotional distress, as well as the moderation role of resilience among NRS, perceived stress, and emotional distress in university students. Method. We recruited 851 students from the Be Resilient to Nursing Career program (BRNC, registration number: NFYKDX002) in June 2022. Nonrestorative sleep scale (NRSS), 10-item perceived stress scale (PSS-10), 10-item Kessler psychological distress scale (K10), and 10-item Connor–Davidson resilience scale (CD-RISC-10) were administered through a paper questionnaire. Latent profile analysis and moderated mediation analysis were performed. Results. Three profiles of perceived stress were identified: high ability-low stress (24.5%), middle ability-high stress (65.0%), and low ability-middle stress (10.5%). The mediation role of perceived stress between NRS and emotional distress was significant (SE = 0.025; 95% confidence interval = −0.369, −0.269). The moderation role of resilience among NRS, perceived stress, and emotional distress was not significant. Conclusion. Heterogeneity exists in freshmen students' perceived stress. Perceived stress plays a significant mediating role between NRS and emotional distress, while resilience cannot significantly moderate the associations among NRS, perceived stress, and emotional distress. The trial is registered with ChiCTR2000038693. [ABSTRACT FROM AUTHOR]

Objective. The purpose is to understand the depression status of the elderly in the community, explore its influencing factors, formulate a comprehensive psychological intervention plan according to the influencing factors, implement demonstration psychological intervention, and evaluate and feedback the effect, so as to provide a reference for improving the mental health of the elderly. Method. In order to make the output of different emotional data in LSTM more discriminative, a method to dynamically filter the output of LSTM is proposed. Combining the methods of Attention-LSTM, time-dimensional AI attention, and feature-dimensional AI attention, the best model in this paper is obtained. The multistage stratified cluster sampling method was used to conduct a questionnaire survey on the elderly aged 60 and above in a certain area, including the general demographic characteristics questionnaire of the elderly, the self-rating scale of mental health symptoms, and the health self-management ability of adults. All data were entered into a database using Excel software, and SPSS 19.0 statistical software was used for statistical analysis. Results/Discussion. The detection rate of depression (GDS ≥ 11 points) among the elderly in a community in a certain area was 39.38%. Multivariate logistic regression analysis showed that family history of mental illness, more negative life events, decreased ability of daily living, living alone, and suffering from physical diseases in the past six months were the risk factors for depression in the elderly. Community health education can partially alleviate depression in the elderly. The detection rate and degree of depression of the elderly in the comprehensive psychological intervention group were significantly lower than those in the control group, and the difference was statistically significant (P < 0.05). [ABSTRACT FROM AUTHOR]

This paper aims to investigate Chinese people's willingness to stay in the city where they work when the Spring Festival meets the COVID-19 pandemic. Specifically, a stated choice experiment about intercity travel including three homecoming trips (i.e., trips carried by conventional railway, high-speed railways, and private car) and the option "stay in place" was designed. Respondents were requested to choose the most preferred alternative in the context of the current situation of the COVID-19 pandemic and relevant policies. Based on the data collected from 800 respondents, a latent class mixed logit model was developed and estimated to capture the potential correlations within alternatives and respondents and the preference heterogeneity between respondents. Two latent classes were identified, one of which paid more attention to epidemic prevention policies while the other cared more about the characteristics of homecoming trips. Results show that people's willingness to stay in the city of work is largely dependent on epidemic prevention policies in their hometowns and decisions of social network members. [ABSTRACT FROM AUTHOR]

Building an efficient and effective credit scorer for enterprises is an important and urgent demand in the cross-border e-commerce industry. In this paper, we present a framework to build a credit scorer using e-commerce data integrated from various sources. First, an improved dependency graph approach is proposed to recognize distinct records in the dataset. Then, we apply logistic regression using a prejudice remover regularizer to train the model, preceded by predictor preparation through binning and evaluating their information value. Lastly, we build the credit scorer according to the coefficients of the model. We implement our framework on a dataset from the official customs database and a large cross-border e-commerce platform. The empirical results demonstrate that the scorer built by our methodology can be used to effectively evaluate enterprises, while also removing prejudice against small and medium enterprises to a certain extent. [ABSTRACT FROM AUTHOR]

Mode choice behaviour is often modelled by discrete choice models, in which the utility of each mode is characterized by mode-specific parameters reflecting how strongly the utility of that mode depends on attributes such as travel speed and cost, and a mode-specific constant value. For new modes, the mode-specific parameters and the constant in the utility function of discrete choice models are not known and are difficult to estimate on the basis of stated preferences data/choice experiments and cannot be estimated on the basis of revealed preference data. This paper demonstrates how revealed preference data can be used to estimate a discrete mode choice model without using mode-specific constants and mode-specific parameters. This establishes a method that can be used to analyze any new mode using revealed preference data and discrete choice models and is demonstrated using the OViN 2017 dataset with trips throughout the Netherlands using a multinomial and nested logit model. This results in a utility function without any alternative specific constants or parameters, with a rho-squared of 0.828 and an accuracy of 0.758. The parameters from this model are used to calculate the future modal split of shared autonomous vehicles and electric steps, leading to a potential modal split range of 24–30% and 37–44% when using a multinomial logit model, and 15–20% and 33–40% when using a nested logit model. An overestimation of the future modal split occurs due to the partial similarities between different transport modes when using a multinomial logit model. It can therefore be concluded that a nested logit model is better suited for estimating the potential modal split of a future mode than a multinomial logit model. To the authors' knowledge, this is the first time that the future modal split of shared autonomous vehicles and electric steps has been calculated using revealed preference data from existing modes using an unlabelled mode modelling approach. [ABSTRACT FROM AUTHOR]

P53 protein tumor suppressor gene plays a guiding role in the treatment and prognosis of colorectal cancer (CRC). This paper aimed at proposing a feature selection method based on variable clustering to improve positive and negative discrimination of P53 protein in CRC patients. In this approach, we cluster the preprocessed dataset with variables, and then find the features with the largest information value (IV) for each cluster to form a feature subset. We call this method as IV_Cluster. In the actual medical data test, compared with the information value feature selection method, the accuracy of the 10-fold cross-validation logistic regression model increased by 4.4%, 2.0%, and 5.8%, and Kappa values increased by 21.8%, 8.6%, and 22.4%, respectively, under 5, 10, and 15 feature sets. [ABSTRACT FROM AUTHOR]

In this paper, we study the deployment potential of automated minibuses (AmBs) on the first-mile part of public transport (PT) trips or short (sub)urban commutes by comparing "regular" (fixed route and fixed schedule) and "flexible" (door-to-door and on-demand) service types. For reaching that goal, we run a stated choice experiment in the Netherlands. The participants have assessed the referred two AmB alternatives compared to their current travel mode (car, PT, or active modes (AM) – bicycle and walking) used as the main mode for unimodal travellers or as access travel mode to transit lines for multimodal travellers. The results of a joint mixed logit model estimation based on data obtained from Dutch travellers show that there are similarities and differences in the preferences for the AmBs service type within and between the segments of travellers (car, PT, and AM) and that these are mostly in instrumental variables (cost and time) and attitudes. Current PT users prefer the flexible service to regular service based on their perception of in-vehicle travel time and waiting time, while current users of car and AM do not show a difference in preference between the two services concerning these variables. Moreover, their perception of in-vehicle travel time and waiting time is not significantly different from PT users' perception of those variables in the regular service. This may mean that for non-PT users (car and AM), AmB's flexibility of door-to-door transport is not seen as offering a significant advantage over what they think about public transport. When looking at the preferences of potential users explained by underlying psychological factors, we conclude that a positive attitude towards riding in AmBs is a significant factor in all three segments of travellers. Trust, usefulness, and enjoyment in using AmBs are important prerequisites for car and PT users to choose either service type. The experience with technology positively influences the preferences of current PT users for both AmB services. [ABSTRACT FROM AUTHOR]

In this paper, we study the deployment potential of automated minibuses (AmBs) on the first-mile part of public transport (PT) trips or short (sub)urban commutes by comparing "regular" (fixed route and fixed schedule) and "flexible" (door-to-door and on-demand) service types. For reaching that goal, we run a stated choice experiment in the Netherlands. The participants have assessed the referred two AmB alternatives compared to their current travel mode (car, PT, or active modes (AM) – bicycle and walking) used as the main mode for unimodal travellers or as access travel mode to transit lines for multimodal travellers. The results of a joint mixed logit model estimation based on data obtained from Dutch travellers show that there are similarities and differences in the preferences for the AmBs service type within and between the segments of travellers (car, PT, and AM) and that these are mostly in instrumental variables (cost and time) and attitudes. Current PT users prefer the flexible service to regular service based on their perception of in-vehicle travel time and waiting time, while current users of car and AM do not show a difference in preference between the two services concerning these variables. Moreover, their perception of in-vehicle travel time and waiting time is not significantly different from PT users' perception of those variables in the regular service. This may mean that for non-PT users (car and AM), AmB's flexibility of door-to-door transport is not seen as offering a significant advantage over what they think about public transport. When looking at the preferences of potential users explained by underlying psychological factors, we conclude that a positive attitude towards riding in AmBs is a significant factor in all three segments of travellers. Trust, usefulness, and enjoyment in using AmBs are important prerequisites for car and PT users to choose either service type. The experience with technology positively influences the preferences of current PT users for both AmB services. [ABSTRACT FROM AUTHOR]

Since the diversity of urban transport modes and the growth of public transport demands recently, it is essential to consider the multiple mode options in the network capacity problem. This paper derives a comprehensive network capacity model from a single-mode transportation network with only route choice to a multimodal transportation network with both mode choice and route choice. To avoid biases in the evaluation of the multimodal network capacity, two characteristics of the multimodal transportation system are considered in modeling and formulating the problem: (1) the mode interaction between cars and buses is explicitly reflected when they share the same link; (2) the correlation of travel alternatives (modes or routes) is measured by developing a combined modal split and traffic assignment (CMSTA) problem, in which the nested logit (NL) model is employed to account for mode similarity in mode split, while the path-size logit model (PSL) is employed to account for route overlapping in traffic assignment. Numerical experiments demonstrate the characteristics of the new model. It also shows how planning schemes or management strategies affect the multimodal transportation network capacity via a real network case. [ABSTRACT FROM AUTHOR]

At present, percutaneous nephrolithotomy has become an option for hospitals increasingly to treat calculus. However, due to the certain risk of percutaneous nephrolithotomy, it may lead to postoperative complications. Analyzing the preoperative factors of septic shock after percutaneous nephrolithotomy can provide guidance for reducing the incidence of septic shock after PCNL. This article establishes a logistic regression model based on preoperative factors, uses statistical methods, and uses the professional statistical software SPSS to create a database and analyze the data. The method of classification data analysis was used to determine various influencing factors including patient factors, stone factor, and preoperative factors and combined B-ultrasound guidance and percutaneous nephrolithotomy to reduce the risk of surgery. The experimental results found that the percutaneous nephroscope laser lithotripsy guided by the smart Internet of Things can effectively perform lithotripsy; in this paper, the preoperative data of different complications are obtained, and the risk of surgery is closely related to the operation time. The operation time exceeds 90 minutes and causes the risk of severe bleeding after PCNL. It is twice the operation time of less than 90 minutes and 5 times the operation time of less than 60 minutes. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is the second most common disease after cardiovascular diseases and malignant tumors. With the continuous acceleration of people's living standards and life rhythm, the number of diabetic patients is rapidly increasing and showing a trend of youthfulness. A recent study found that 114 million adults in China have diabetes and have a high prevalence rate, but the awareness rate, treatment rate, and compliance rate are low. If diabetes is not treated and controlled in time, various complications will occur, such as cardiovascular, cerebrovascular, and diabetic foot, which will not only have a great impact on the survival of the patient, but also cause a lot of pressure on the family and society. Therefore, prevention and control of diabetes is an important strategy to save medical resources and reduce medical costs. In this paper, we mainly read a lot of literature and accumulate some important theoretical knowledge to clarify the basic principles and methods of data mining and refer to the research results of other scholars to select a new combined algorithm model combining K-means algorithm and logistic regression algorithm to construct a prediction model of diabetes and explore the law of medication for diabetic patients based on this analysis. [ABSTRACT FROM AUTHOR]

In this paper, in-depth research and analysis of juvenile delinquency prevention and occupational therapy education guidance using artificial intelligence are conducted, and its response mechanism is designed in this way. Two crime type prediction algorithms based on time-crime type count vectorization and dense neural network and crime type prediction based on the fusion of dense neural network and long- and short-term memory neural network are proposed. The outputs of both are fed into a new neural network for training to achieve the fusion of the two neural networks. Among them, the use of the dense neural network can effectively fit the relationship between the constructed features and crime types. The behavioral manifestations and causes of the formation of deviant behavior in adolescents are discussed. They can only read numerical data, but there is a lot of information in the textual data that is closely related to the training effect. When experimenting, it is necessary to extract knowledge and build applications. The practical work with adolescents with deviant behaviors is again carried out from group work and casework, respectively, with problem diagnosis, needs assessment, and service plan development for specific clients, to carry out relevant practical service work. The causes of juvenile delinquency in the Internet culture are discussed in terms of the Internet environment, juvenile use of the Internet, Internet supervision, and crime prevention education, respectively. The fourth chapter focuses on the analysis of the prevention and control measures for juvenile delinquency in cyberculture. In response to the above-mentioned causes of juvenile delinquency in cyberculture, the prevention and control measures are discussed in four aspects, namely, strengthening the construction of cyberculture and building a healthy cyber environment, strengthening the capacity building of guiding juveniles to use cyber correctly, building a prevention and supervision system to promote the improvement of the legal system, and improving and innovating the crime prevention education in the cyber era. [ABSTRACT FROM AUTHOR]

In this paper, in-depth research analysis of anti-hepatocellular carcinoma molecular targets for hepatocellular carcinoma diagnosis was conducted using artificial intelligence. Because BRD4 plays an important role in gene transcription for cell cycle regulation and apoptosis, tumor-targeted therapy by inhibiting the expression or function of BRD4 has received increasing attention in the field of antitumor research. Study subjects in small samples were used as the validation set for validating each diagnostic model constructed based on the training set. The diagnostic effect of each model in the validation set is evaluated by calculating the sensitivity, specificity, and compliance rate, and the model with the best and most stable diagnostic value is selected by combining the results of model construction, validation, and evaluation. The total sample was divided into a training set and test set by using a stratified sampling method in the ratio of 7 : 3. Logistic regression, weighted k -nearest neighbor, decision tree, and BP artificial neural network were used in the training set to construct diagnostic models for early-stage liver cancer, respectively, and the optimal parameters of the corresponding models were obtained, and then, the constructed models were validated in the test set. To evaluate the diagnostic efficacy, stability, and generalization ability of the four classification methods more robustly, a 10-fold crossover test was performed for each classification method. BRD4 is an epigenetic regulator that is associated with the upregulation of expression of various oncogenic drivers in tumors. Targeting BRD4 with pharmacological inhibitors has emerged as a novel approach for tumor treatment. However, before we implemented this topic, there were no detailed studies on whether BRD4 could be used for the treatment of HCC, the role of BRD4 in HCC cell proliferation and apoptosis, and the ability of small molecule BRD4 inhibitors to induce apoptosis in hepatocellular carcinoma cells. [ABSTRACT FROM AUTHOR]

In recent years, the increase of customer loan risk and the aggravation of the epidemic have led to the increase of customer default risk. Therefore, identifying high-risk customers has become an important research hotspot for banks. The customer's credit is the standard to evaluate the loan amount and interest rate, and the ability to quickly identify customer information has become a research hotspot. Based on the bank credit application scenario, this paper realizes function extraction and data processing for customer basic attribute data and download transaction data. Then, a linear regression model with penalty and a neural network prediction model are proposed to improve the accuracy of bankruptcy assessment and achieve local optimization. In this way, the implicit risk prediction and control of customer credit are improved, and the default risk of bank loans is significantly reduced. According to the characteristics of the collected sample data, the most appropriate penalty linear regression prediction algorithm is selected and the experimental analysis is carried out to improve the risk management level of banks. The experimental results show that the improved logistic regression and neural network model has obvious advantages in the prediction effect for four models. [ABSTRACT FROM AUTHOR]

Researchers in the past discussed the psychological issue like stress, anxiety, depression, phobias on various forms, and cognitive issues (e.g., positive thinking) together with personality traits on traditional research methodologies. These psychological issues vary from one human to other human based on different personality traits. In this paper, we discussed both psychological issues together with personality traits for predicting the best human capital that is mentally healthy and strong. In this research, we replace the traditional methods of research used in the past for judging the mental health of the society, with the latest artificial intelligence techniques to predict these components for attaining the best human capital. In the past, researchers have point out major flaws in predicting psychological issue and addressing a right solution to the human resource working in organizations of the world. In order to give solution to these issues, we used five different psychological issues pertinent to human beings for accurate prediction of human resource personality that effect the overall performance of the employee. In this regard, a sample of 500 data has been collected to train and test on computer through python for selecting the best model that will outperform all the other models. We used supervised AI techniques like support vector machine linear, support vector machine radial basis function, decision tree model, logistic regression, and neural networks. Results proved that psychological issue data from employee of different organizations are better means for predicting the overall performance based on personality traits than using either of them alone. Overall, the novel traditional techniques predicted that sustainable organization is always subject to the control of psychological illness and polishing the personality traits of their human capital. [ABSTRACT FROM AUTHOR]

Heart disease is among the leading causes of mortality globally. Predicting cardiovascular disease is a major difficulty in clinical data analysis. AI has been demonstrated to be powerful in deciding and anticipating an enormous measure of information created by the health domain. We provide a unique method for finding essential traits employing machine learning approaches in this paper, which enhances the effectiveness of identifying heart diseases. Decision tree (DT), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN) are the classification techniques used to create the proposed system. Ensemble stacking integrates the four classification models to create a single best-fit predictive model using logistic regression. Many explorations have been directed at the identification of cardiac infection; however, the exactness of the outcomes is poor. Accordingly, to further enhance the efficiency, Moth-Flame Optimization (MFO) algorithm is proposed. The feature selection strategies are used to improve the classification accuracy while shortening the execution time of the classification system. Medical data are used to assess the probability of heart disease based on BP, age, gender, chest ache, cholesterol, blood sugar, and other variables. Results revealed that the proposed system excelled other existing models, obtaining 99% accuracy in the Cleveland dataset. [ABSTRACT FROM AUTHOR]

Intimate partner homicides are often situated within the context of domestic abuse, and although less prevalent than domestic abuse, there have been several multi‐agency approaches to understanding the risk for these fatal crimes. Domestic Homicide Reviews (DHRs) were introduced in 2011 to provide information to help with assessing such risk. This paper aims to analyse DHRs in England and Wales to investigate/determine risk factors for domestic homicide following intimate partner abuse. All publicly available DHRs published between July 2011 and November 2020 where the victim and perpetrator were or had been intimate partners (N = 263) were retrieved from Community Safety Partnership websites in England and Wales. A quantitative design was used to extract data from DHRs, and descriptive and inferential statistics were generated by SPSS 26. Findings identified risk factors relating to domestic abuse, including stalking, separation, and the victim being in a new relationship. Sociodemographic risk factors included higher levels of deprivation, lower income and higher barriers to housing and services. This highlights the role of both individual and sociodemographic factors in domestic homicides, and particularly the need for greater socioeconomic security for victims of domestic abuse. In conclusion, though much of the data is in line with previous research, our analysis highlights the pivotal role of regional poverty, with comfortable socioeconomic conditions offering protection against intimate partner homicides. This research suggests important directions for future research and makes a valuable contribution to a more in‐depth understanding of the relationship between domestic abuse and intimate partner homicide. [ABSTRACT FROM AUTHOR]

The Internet plays a significant role in health information searching, sharing and emotional support. However, scholars have devoted little attention to the complementary and substitute value of online health information from diseases, especially chronic diseases, health insurance, barriers to health resources and their interaction effects with income. This research uses data from the 2020 Health Information National Trends Survey (HINTS 2020), the latest HINTS survey that includes seeking online health information questions critical to this research. This paper proposes that the factors contributing to seeking online health information can be categorized into two modalities – complementary and substitutive. Concerning the complementary value, I argue that individuals with certain health conditions use online health information as a complementary health resource in addition to traditional health resources such as doctors to understand their health issues better. Online health information also functions as substitute information sources for individuals who have experienced more barriers to typical health information resources. [ABSTRACT FROM AUTHOR]

An infinite number of fitness apps are available on various app stores. However, hardly any of them are fitted to the needs and requirements of care‐dependent people. This paper investigates the effectiveness of a customised fitness‐app prototype for increasing physical activity in home care service users. Home care service users from Austria and Italy were randomly assigned to two groups. In total, 216 participants were involved in the field trial, 104 received a tablet with the fitness app and an activity tracker (treatment group), 112 did not (control group). Regularity of physical activity, frequency of fitness exercises and walking behaviour were self‐reported by participants at baseline, after 4 months and after 8 months. In addition, the frequency of using the prototype was assessed based on the fitness app's logged usage data. We estimated multilevel mixed‐effects ordered logistic models to examine the effects of the intervention. After 4 months, the intervention increased the home care users' probability of agreeing strongly with being physically active on a regular basis by 28 percentage points (p < 0.001; 95% CI: 0.20, 0.36) and their probability of reporting to exercise more than once a week by 45 percentage points (p < 0.001; 95% CI: 0.32, 0.57). Walking behaviour was not affected on group‐level but improved for frequent users of the activity tracker. Frequent and regular users of the fitness app benefited most and effects persisted until the end of the 8 months controlled trial. Tailoring a fitness‐app prototype to the needs of care‐dependent people has the potential to support people with functional limitations to engage in a more active lifestyle. Future research is encouraged to seek further insights into how new technologies can support physical activities in people with long‐term care needs. [ABSTRACT FROM AUTHOR]

At present, there is a huge gap between supply and demand of old-age services in rural areas of China. Developing rural mutual old-age services is of great significance to remedy the gap. Based on the survey data of 1200 rural residents in Hubei and Henan provinces, this paper adopts binary logistic regression model to analyze the influence of special trust and general trust on rural residents' willingness to participate in mutual care for the aged. The results show that both special trust and general trust have an impact on rural residents' willingness to participate in mutual support for the elderly, but the effect of special trust on rural residents' willingness to participate in mutual support for the elderly is not significant. General trust has a significant promoting effect on rural residents' willingness to participate in mutual care for the aged. Chinese rural residents' trust in village cadres has a significant promoting effect on their willingness to participate in mutual assistance for the aged. The trust of ordinary friends significantly inhibited their willingness to participate. The educational level, living style, and economic status of Chinese rural residents have a positive impact on their willingness to participate in mutual care for the aged. Age, marital status, health status, and intergenerational relationship are inversely correlated with willingness to participate. [ABSTRACT FROM AUTHOR]

In this paper, the random matrix of multivariate statistical analysis is used to conduct in-depth research and analysis of the university coordination utility management and online repair platform. Considering that the chunking of variables based on mechanistic knowledge is not easy to achieve, firstly, the maximum correlation and minimum redundancy algorithm is used to portray the correlation more accurately between process variables and remove the redundancy between variables to provide the optimal variable input for the base model. The multivariate mean control chart was used to calculate the offset between the data of each test group of the contact network and the overall mean and standard values of the contact network parameters under different correlations among the contact network parameters. Based on the daily work research and process document sampling of the university coordination utilities management department, the requirement analysis and design of the target system were completed, and a university coordination utility management system based on BS architecture was developed. Student information is lost, data statistics are wrong, etc., so that the business work of other departments of the school cannot be carried out smoothly. The whole platform can be divided into several submodules according to the functions: super administrator module, administrator module, staff module, and user module, and the detailed design scheme of each module is described in detail. At the same time, the logistic regression model is trained using the collected data sets, and the training scheme of the model is designed. The mathematical model of logistic regression and the related algorithm are used to decide whether to purchase maintenance equipment at this stage and the quantity of purchase. Finally, a new monitoring index is proposed to monitor the process status. MNPE-GMM not only maintains most of the local structural information of the window dataset in the feature subspace but also reduces the computational complexity of GMM in the fault detection process. The MNPE-GMM method can effectively improve the fault detection rate of multimodal intermittent processes by introducing new statistics. [ABSTRACT FROM AUTHOR]

Given the benefits both individuals and collectivity have achieved over the past few years thanks to Mobility-as-Service (MaaS) systems, various studies were conducted to predict the level of acceptance of MaaS bundles from different territorial scales and in different countries. Results obtained are in some cases contradictory. Literature is lacking in the study of small-to-medium-sized urban contexts and in the effects of the ongoing COVID-19 pandemic. This paper aims to understand (1) what factors influence respondents' preferences between their usual transportation means and a possible MaaS alternative and (2) what leads a user to prefer one MaaS bundle to another. A logistic regression and a mixed logit model were developed to reach the two aims, respectively. These models were calibrated using questionnaires administered to employees of the Municipality of Padua, a medium-sized city in Italy. Aspects concerning the perception of health safety in relation to the COVID-19 pandemic were included in the analyses. In 37% of the cases, users stated they would be willing to adopt at least one of the proposed MaaS bundles. The results suggest that MaaS solutions can be a useful tool for managing mobility even in medium-sized cities, provided users' biosecurity concerns are addressed by appropriate countermeasures. [ABSTRACT FROM AUTHOR]

Objective. Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare. In conventional approach, the biggest challenge faced by healthcare professionals is to predict a disease by observing the symptoms, monitoring the remote area patient, and also attending to the patient all the time after being hospitalised. IoT provides real-time data, makes decision-making smarter, and provides far superior analytics, and all these to help improve the quality of healthcare. The main objective of the work was to create an IoT-based automated system using machine learning models for symptom-based COVID-19 prognosis. Methods. Comparative analysis of predictive microbiology of COVID-19 from case symptoms using various machine learning classifiers like logistics regression, k-nearest neighbor, support vector machine, random forest, decision trees, Naïve Bayes, and gradient booster is reported here. For the sake of the validation and verification of the models, performance of each model based on the retrieved cloud-stored data was measured for accuracy. Results. From the accuracy plot, it was concluded that k-NN was more accurate (97.97%) followed by decision tree (97.79), support vector machine (97.42), logistics regression (96.50), random forest (90.66), gradient boosting classifier (87.77), and Naïve Bayes (73.50) in COVID-19 prognosis. Conclusion. The paper presents a health monitoring IoT framework having high clinical significance in real-time and remote healthcare monitoring. The findings reported here and the lessons learnt shall enable the healthcare system worldwide to counter not only this ongoing COVID but many other such global pandemics the humanity may suffer from time to come. [ABSTRACT FROM AUTHOR]

Investigating the relationship between the months and traffic crashes is a foremost task for the safety improvement of mountainous freeways. Taking a mountainous freeway located in China as an example, this paper proposed a combined modeling framework to identify the relationships between months and different crash types. K-means and Apriori were initially used to extract the monthly distribution patterns of different types of crashes. A graphical approach and a risk calculation equation were developed to assess the output of K-means and Apriori. Then, using the assessment results as the input, a logistic regression model was constructed to quantify the effects of each month on crashes. The results indicate that the monthly distribution patterns of different crash types are inconsistent, i.e., for a specific month, the high risk of a certain crash type may be covered up if experts only focus on the total number of crashes. Moreover, when identified as high-risk months by K-means and Apriori, the crash-proneness will significantly increase several times than months identified as high-risk by only one of K-means and Apriori, thereby illustrating the superior performance of the mix-method. The conclusions can assist local relevant organizations in formulating strategies for preventing different types of traffic crashes in different months (e.g., the risk of rear-end crashes in August, the risk of fixed-object hitting crashes in February, and the risk of overturning crashes in October) and provide a methodological reference for relevant studies in other regions. [ABSTRACT FROM AUTHOR]

The employment of university graduates has always been a matter of great concern to educational administrative departments and governments at all levels. EPI (employment prosperity index) is a weather vane that reflects the situation of the labor market, and it is also an important reference for the national economic situation. This paper constructs college students' EPI from different aspects, such as employment environment, employability, employment status, and public service, focuses on the calculation method of college students' EPI, and obtains the general formula for calculating college students' EPI. Based on the data obtained from a university, the EPI system is optimized by using MLM (multinomial logit model). Through empirical research, it is found that EPI, which is optimized by the system, has the function of evaluating the employment environment of college students, which not only provides macroemployment policies for the government but also provides scientific and effective employment service policies for universities. [ABSTRACT FROM AUTHOR]

Small, medium, and micro enterprises play an important role in the development of the national economy and are of great significance in promoting technological innovation, relieving employment pressure, facilitating people's lives, and maintaining social stability. But in China, small, medium, and micro enterprises generally exist in the phenomenon of "financing difficulties." Therefore, we need to find a method to forecast its credit risk. By using Python, SPSS, and other software, based on a two-component logistic regression model, assisted by multievaluation model and supported by game theory, this paper establishes an innovative comprehensive credit risk assessment model for small, medium, and micro enterprises. [ABSTRACT FROM AUTHOR]

Objective. Therapeutic intervention can improve the overall level of mental health of American postmodern fiction writers by improving their social communication skills and overall well-being. This paper discusses the application of art therapy in the mental health education of American postmodern novel creators and proves that expressive art therapy intervention is effective in improving the mental health level of American postmodern novel creators. Method. This article attempts to help American postmodernist novel creators understand their own mental health status by means of individualized assessment and therapeutic intervention and to analyze and discover their own potential mental health problems. The writers of postmodernist novels in the USA were measured and screened by means of scales and questionnaires, and the members who met the experimental intervention were divided into experimental group and control group, and the experimental group received a 30-day reception music therapy intervention. After the intervention, the data will be counted, and the quantitative and qualitative aspects will be analyzed to comprehensively evaluate the effect of the intervention. Results/Discussion. Receptive music therapy intervention has a significant effect in relieving the anxiety of American postmodernist novel creators and plays a positive role in helping American postmodernist novel creators better adapt to study life and build good emotional psychology and interpersonal relationships. Psychological counseling relies more on external forces to correct cognition and adjust psychology, while music therapy intervention, as a nonsemantic art, can arouse inner functions and mobilize the self-healing potential of American postmodern novel creators. Practice has proven that when the language intervention of traditional psychotherapy encounters resistance and silence, music as a special language plays a vital role. The nonlinguistic nature of music, through a mode of reproduction of sounds in nature, makes the beautiful and soothing melody stimulate a pleasant and peaceful psychological experience through human hearing. [ABSTRACT FROM AUTHOR]

Stroke is an acute cerebrovascular disease caused by the rapid rupture or blockage of intracranial blood vessels for a variety of reasons, preventing blood from flowing into the brain and causing damage to brain tissue. The global burden of stroke disease is quickly increasing, and ischemic stroke (IS) accounts for 60 percent to 70 percent of all strokes, owing to the prevalence of people's bad lifestyles and the intensity of global ageing. Although most IS patients have received effective treatment, many patients still have certain dysfunction or death after treatment, and the recurrence rate is about 18%, which brings a heavy economic burden to society and families. Therefore, it is urgent to build a postoperative prediction model for IS, so as to take targeted clinical intervention measures, which has extremely important practical significance for improving the prognosis of IS. The following work has been done in this paper: (1) the theoretical background for the BP prediction model and logistic regression prediction model suggested in this work is offered, as well as the research progress and related technologies of IS recurrence prediction by domestic and foreign academics. (2) The basic principles of BPNN and logistic regression are introduced, and the logistic multifactor predictor is constructed. (3) The experimental results show that the consistency rate, sensitivity, and specificity of the prediction results of BPNN are higher than those of logistic regression, indicating that for diseases such as IS, which have many pathogenic factors and complex relationships between factors, the fitting effect of BPNN model is better than that of the logistic regression model. [ABSTRACT FROM AUTHOR]

In order to solve the complex and recurrent problem of chronic pelvic inflammation disease (CPID) in the process of the clinical treatment, a method of understanding the influencing factors of CPID by investigating the actual situation of clinical cases and using logistics regression analysis was proposed in this study. A total of 204 outpatients were selected from a certain hospital. The ratio of the cases in the experimental group to those in the control group stands at 1 : 1. The results were obtained as follows. According to the data of CPID patients collected in the paper, the majority of patients had a high school education background or below technical secondary school education background, accounting for 66.7%. And the majority of patients were manual workers, accounting for 69.1%. All the exp (B) values of the frequency of sex life per month ≥ 9 times, frequent sex life during menstruation, IUD contraception, no contraception, abortion ≥ 3 times, vaginal irrigation per week ≥ 1 time, and intrauterine surgery ≥ 3 times were more than 1. These seven factors were the risk factors for chronic pelvic inflammation. Oral contraceptives were a weak protective factor of chronic pelvic inflammation. These factors including early drug withdrawal (53.1%), without understanding the condition of the disease (35.7%), no time to review the disease (24.5%), and irregular medication (21.4%) accounted for a large proportion. They were associated with the recurrence of CPID. This method is aimed at providing some foundations for establishing effective prevention and control measures for chronic pelvic inflammation and providing a recognized clinical diagnosis and efficacy evaluation criteria for the treatment of chronic pelvic inflammation. [ABSTRACT FROM AUTHOR]