Handgrip strength and risk of cognitive outcomes: new prospective study and meta-analysis of 16 observational cohort studies

Handgrip strength (HGS), a measure of muscular strength, might be a risk indicator for cognitive functioning, but the evidence is not consistent. Using a new prospective study and meta-analysis of published observational cohort studies, we aimed to evaluate the prospective associations of HGS with poor cognitive outcomes including cognitive impairment, dementia and Alzheimer’s disease (AD). Handgrip strength, measured using a Martin-Balloon-Vigorimeter, was assessed at baseline in a population-based sample of 852 men and women with good cognitive function in the Kuopio Ischemic Heart Disease cohort. Hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated for cognitive outcomes. Relevant published studies were sought in MEDLINE, Embase and Web of Science from inception until October 2021 and pooled using random effects meta-analysis. During a median follow-up of 16.6 years, 229 dementia cases were recorded. Comparing extreme tertiles of HGS, the multivariable adjusted HR (95% CI) for dementia, AD and vascular dementia was 0.77 (0.55–1.07), 0.75 (0.52–1.10) and 0.49 (0.16–1.48), respectively. In a meta-analysis of 16 population-based prospective cohort studies (including the current study) comprising 180,920 participants, the pooled multivariable adjusted relative risks (95% CIs) comparing the top vs bottom thirds of HGS levels were as follows: 0.58 (0.52–0.65) for cognitive impairment; 0.37 (0.07–1.85) for cognitive decline; 0.73 (0.62–0.86) for dementia; 0.68 (0.53–0.87) for AD; and 0.48 (0.32–0.73) for vascular dementia. GRADE quality of evidence ranged from low to very low. Meta-analysis of aggregate prospective data suggests that HGS may be a risk indicator for poor cognitive outcomes such as cognitive impairment, dementia and AD. Systematic review registration: PROSPERO 2021: CRD42021237750.

Risk Analysis of Textile Industry Foreign Investment Based on Deep Learning

With the decline of China’s economic growth rate and the uproar of antiglobalization, the textile industry, one of the business cards of China’s globalization, is facing a huge impact. When the economic model is undergoing transformation, it is more important to prevent enterprises from falling into financial distress. So, the financial risk early warning is one of the important means to prevent enterprises from falling into financial distress. Aiming at the risk analysis of the textile industry’s foreign investment, this paper proposes an analysis method based on deep learning. This method combines residual network (ResNet) and long short-term memory (LSTM) risk prediction model. This method first establishes a risk indicator system for the textile industry and then uses ResNet to complete deep feature extraction, which are further used for LSTM training and testing. The performance of the proposed method is tested based on part of the measured data, and the results show the effectiveness of the proposed method.

ARID1A Is a Prognostic Biomarker and Associated with Immune Infiltrates in Hepatocellular Carcinoma

Objective. ARID1A has been discovered as a potential cancer biomarker. But its role in hepatocellular carcinoma (HCC) is subject to considerable dispute. Methods. The relationship between ARID1A and clinical factors was investigated. Clinicopathological variables related to overall survival in HCC subjects were identified using Cox and Kaplan–Meier studies. The connection between immune infiltrating cells and ARID1A expression was investigated using the tumor Genome Atlas (TCGA) dataset for gene set enrichment analysis (GSEA). Finally, a cell experiment was used to confirm it. Results. The gender and cancer topography (T) categorization of HCC were linked to increased ARID1A expression. Participants with advanced levels of ARID1A expression had a worse prognosis than someone with lower levels. ARID1A was shown to be a risk indicator of overall survival on its own. ARID1A expression is inversely proportional to immune cell infiltration. In vitro, decreasing ARID1A expression substantially slowed the cell cycle and decreased HCC cell proliferation, migration, and invasion. Conclusion. The expression of ARID1A could be used to predict the outcome of HCC. It is closely related to tumor immune cell infiltration.

Development of a hybrid AHP and Dempster-Shafer theory of evidence for project risk assessment problem

In this paper, a new hybrid AHP and Dempster-Shafer Theory of Evidence is presented for solving the problem of choosing the best project among a list of available alternatives while uncertain risk factors are taken into account. The aim is to minimize overall risks. For this purpose, four groups of risk factors, including Properties, Operational and Technological, Financial, Strategic risk factors, are considered. Then using an L24 Taguchi method, several experiments with various dimensions have been designed and solved by the proposed algorithm. The outcomes are then analyzed using the Validating Index (VI), Reduced Risk Indicator (R.R.I%), and Solving time. The findings indicated that, compared to the classic AHP, the results of the proposed hybrid method were different in most cases due to uncertainty of risk factors. It was observed that the method could be safely used for selecting project problems in real industries.

Evaluation on Risk Factors of Elderly Services From the Perspective of Integrated SCOR Model

Analysing and identifying the risk factors of elderly services is conducive to improving the risk management capabilities of the elderly care industry and maintaining the safety and stability of the elderly care service industry chain. Based on the integrated Supply Chain Operations Reference (SCOR) model, a risk indicator system for elderly services supply chain was established from plan, design, supply, implementation, and customer service. The DEA method with Entropy-AHP mixed constraint was introduced to deal with the weight freedom of traditional DEA method. Taking the likelihood, exposure and consequence of risk occurrence as decision variables, the risk evaluation and ranking of the indicators were carried out. According to the empirical analysis based on the data of elderly care institutions in the Pearl River Delta of China, the biggest Pareto risk factors in the first-level and second-level indicators, the risk growth and reduction ratios of the first-level indicators were obtained.

Risk Evaluation on China Government Bonds with EWMAVaR and SVM Methods

This research studies the strategy of risk evaluation of China government bonds with the latest data. The angle of evaluation focuses on the interest rate and the stability risk, employing the EWMAVaR and SVM methods. The weights of each risk indicator are determined by the entropy method. Experimental results show that the risk of government bonds is stable in recent years. However, the impact of COVID-19 cannot be ignored because the risk level increased in the year 2020. The issuing of one trillion special antipandemic bonds could explain the fluctuation of the market because the fiscal incomes of Chinese government decreased in 2020 and could not be recovered in a short time. The experimental results show that the method proposed in this paper has a better performance than the existing methods, and it can help well in realizing the risk assessment of government bonds.

Fasting Glucose Variability as a Risk Indicator for End-Stage Kidney Disease in Patients with Diabetes: A Nationwide Population-Based Study

Given the fact that diabetes remains a leading cause of end-stage kidney disease (ESKD), multi-aspect approaches anticipating the risk for ESKD and timely correction are crucial. We investigated whether fasting glucose variability (FGV) could anticipate the development of ESKD and identify the population prone to the harmful effects of GV. We included 777,192 Koreans with diabetes who had undergone health examinations more than three times in 2005–2010. We evaluated the risk of the first diagnosis of ESKD until 2017, according to the quartile of variability independent of the mean (VIM) of FG using multivariate-adjusted Cox proportional hazards analyses. During the 8-year follow-up, a total of 7290 incidents of ESKD were found. Subjects in the FG VIM quartile 4 had a 27% higher risk for ESKD compared to quartile 1, with adjustment for cardiovascular risk factors and the characteristics of diabetes. This effect was more distinct in patients aged <65 years; those with a long duration of diabetes; the presence of hypertension or dyslipidemia; and prescribed angiotensin-converting enzyme inhibitors, metformin, sulfonylurea, α-glucosidase inhibitors, and insulin. In contrast, the relationship between baseline FG status and ESKD risk showed a U-shaped association. FGV is an independent risk factor for kidney failure regardless of FG.

Rules of incidental operation risk propagation in metro networks under fully automatic operations mode

The frequent interruptions of network operation due to any incident suggest the necessity to study the rules of operational risk propagation in metro networks, especially under fully automatic operations mode. In this study, risk indicator computation models were developed by analyzing risk propagation processes within transfer stations and metro networks. Moreover, indicator variance rules for a transfer station and different structural networks were discussed and verified through simulation. After reviewing the simulation results, it was concluded that under the impacts of both sudden incident and peak passenger flow, the more the passengers coming from platform inlets, the longer the non-incidental line platform total train operation delay and the higher the crowding degree. However, train headway has little influence on non-incidental line platform risk development. With respect to incident risk propagation in a metro network, the propagation speed varies with network structure, wherein an annular-radial network is the fastest, a radial is moderately fast, and a grid-type network is the slowest. The conclusions are supposed to be supports for metro operation safety planning and network design.

A New Hybrid AHP and Dempster—Shafer Theory of Evidence Method for Project Risk Assessment Problem

In this paper, a new hybrid AHP and Dempster—Shafer theory of evidence is presented for solving the problem of choosing the best project among a list of available alternatives while uncertain risk factors are taken into account. The aim is to minimize overall risks. For this purpose, a three-phase framework is proposed. In the first phase, quantitative research was conducted to identify the risk factors that can influence a project. Then, a hybrid PCA-agglomerative unsupervised machine learning algorithm is proposed to classify the projects in terms of Properties, Operational and Technological, Financial, and Strategic risk factors. In the third step, a hybrid AHP and Dempster—Shafer theory of evidence is presented to select the best alternative with the lowest level of overall risks. As a result, four groups of risk factors, including Properties, Operational and Technological, Financial, and Strategic risk factors, are considered. Afterward, using an L2^4 Taguchi method, several experiments with various dimensions have been designed which are then solved by the proposed algorithm. The outcomes are then analyzed using the Validating Index, Reduced Risk Indicator, and Solving Time. The findings indicated that, compared to classic AHP, the results of the proposed hybrid method were different in most cases due to uncertainty of risk factors. It was observed that the method could be safely used for selecting project problems in real industries.