مطالب

Loss of balance and postural control are the main reasons for falls among the elderly adults that are considered as serious elderly health challenges with high impacts on the quality of life. The cerebellum is involved in postural control as one of the key neural structures that receives many inputs from the sensory systems. Cerebellar transcranial direct current stimulation (ctDCS) is a popular noninvasive and safe method enabling the adjustment of cerebellar activity. Therefore, we investigated the effect of ctDCS on postural control variables among elderly population with a history of stroke. Thirty elderly individuals (60-75 years) participated in this study. After preliminary assessments of postural control strategies by post urography at six sensory conditions, they received five 20-min sessions of active ctDCS at 2mA (intervention, n = 15) or sham stimulation (control, n = 15) in a random manner. At the end of the stimulation process, postural control strategies were reassessed. After adjustment for pre-test values using the ANCOVA test, an improvement of the subjects' performance in the first four conditions was observed in the intervention group compared with the control group (P < 0.05). However, in the fifth (P = 0.24) and sixth (P = 0.58) conditions, there were no significant differences between the groups. The results of this study showed the significant effect of ctDCS on the postural control strategies. These improvements were reflected by normalization of strategy of postural control in elderly adults.

Pension funds are major concerns and future threats to the economy of de-veloping countries. In general, pension funds are among the most sensitive and complicated modern financial institutions in today’s world, as well as an important component of the national economy to achieve the goals of the social security system. The present study was only focused on the dimensions and prioritization of the influential factors in evaluating the impact of pension funds. This could be an important step toward accurate policymaking in this regard within a shorter period and with decreased costs by recognizing the important factors. This was an applied research in terms of the objectives and involved a mixed method in terms of the design. Data were collected in two consecutive stages using the descriptive survey method. The first stage involved a qualitative assessment using the content analysis method, and the second stage was carried out using a decision-making mathematical model known as the fuzzy DEMATEL method. The two stages facilitated the identification of the influ-ence and interaction of the factors and prioritized the identified indexes. The fuzzy DEMATEL technique is a simple and efficient model applied to rec-ognize and rank the most important factors. The factors were calculated based on the opinions of experts using the fuzzy DEMATEL method. Ac-cording to the results, the three most important factors in this regard were upstream rules and regulations, inflation, and investment strategies.

In customs management, the main problem is balancing the needs of trade facilita-tion as a process of simplifying and accelerating foreign business on the one hand and countering illegal trade, reducing government revenue, capital sleep and the level of controls and interventions on the other. Also, due to the financial crisis in recent years, risk management has been reconsidered, although this attention is related to various financial branches. Since risk analysis and identification is the main component of risk management, developing a suitable model for data analysis is of particular importance. The purpose of this study was to use data data analysis techniques to develop an intelligent model to timely predict the risk of import declarations in customs and thus prevent irreparable losses. In this study, data analysis techniques have been used according to the statistical population which is data-driven. Statistical data were extracted from www.eplonline.ir with 575006 import declarations of all Iranian customs during 2019-2020. having pre-processed and prepared the data using PCA, LDA and FastICA methods, attribute reduction and effective attribute extraction were performed using 14 data analysis algorithms. Using Python software, algorithms were trained and modeled with 80% of the final data. Then, 14 obtained models were tested and validated with 20% of the data. Finally, the results of these models were compared with each other and the model obtained from the random forest algorithm was selected as a comprehensive model for predicting and determining the level of risk of import declarations at customs.