Integrated Management for Patients with Diabetes Mellitus: A Literature Review

Background: Many factors contribute to DM, such as age, heredity, high-calorie meals, an unhealthy lifestyle, obesity, and stress.  Inadequate glycemic control contributes to elevated blood glucose levels and potential complications. However, many people of DM had unmet proper DM management regularly due to many factors.Objective: This study reviewed published articles about integrated management for people with DM through three databases.Methods: We identified 1902 articles, and 13 articles were selected for full-text analysis.Results: We found that in integrated DM management for patients with DM, they highly recommend to monitoring their blood glucose, doing exercise, dietary plan, coping strategy, and continuous health education were the most effective DM management strategies to lowering HbA1c levels.Conclusion: Nurses as healthcare providers should engage with people with DM to ensure they have good knowledge and understanding of how to maintain their disease

Investigation Impact Literacy Finance and Socialization Financial to Satisfaction Finance Through Attitude Risk Finance: Perspective Generation Millennials

This study investigates financial literacy and socialization's impact on financial satisfaction through financial risk attitudes. The researchers collected data from 240 respondents aged 24–39 through a web-based self-administered questionnaire. The data were analyzed using Structural Equation Modeling (SEM) with Stata 14 for hypothesis testing. The results showed that financial literacy positively and significantly affected financial satisfaction. In contrast, financial socialization had no significant impact on financial satisfaction. Financial risk attitudes had a significant positive effect, facilitating the relationship between financial literacy and financial satisfaction. Financial risk attitudes also significantly impacted the moderating of financial literacy and financial socialization on financial satisfaction. The findings of this study contribute to the literature on financial satisfaction and encourage the successful delivery of financial services to the millennial generation, followed by recommendations for providing financial literacy for individuals. The results of this study can also be a reference for public policy development

A Performance Evaluation of Repetitive and Iterative Learning Algorithms for Periodic Tracking Control of Functional Electrical Stimulation System

Functional electrical stimulation (FES) is a medical device that delivers electrical pulses to the muscle, allowing patients with spinal cord injuries to perform activities such as walking, cycling, and grasping. It is critical for the FES to generate stimuli with the appropriate controller so that the desired movements can be precisely tracked. By considering the repetitive nature of the movements, the learning-based control algorithms are utilized for regulating the FES. Iterative learning control (ILC) and repetitive control (RC) are two learning algorithms that can be used to accomplish accurate repetitive motions. This study investigates a variety of ILC and RC designs with distinct learning functions; this constitutes our contribution to the field. The FES model of ankle angle, which is in a class of discrete-time linear systems is considered in this study. Two learning functions, i.e., proportional, and zero-phase learning functions, are simulated for the second-order FES model running at a sampling time of 0.1 s. The results indicate the superior performance of the ILC and RC in terms of convergence rate using the zero-phase learning function. ILC and RC with a zero-phase learning function can reach a zero root-mean-square error in two iterations if the model of the plant is correct. This is faster than proportional-based ILC and RC, which takes about 40 iterations. This indicates that the zero-phase learning function requires two iterations to ensure that the patient's ankle angle precisely tracks the intended trajectory. However, the tracking performance is degraded for both control methods, especially when the model is subject to uncertainties. This specific problem can lead to future research directions

Optimal Integral Sliding Mode Controller Design for Micro Gyroscope Based on Time Delay Estimation

Controlling Micro-Electro-Mechanical Systems (MEMS) gyroscopes often involves dealing with uncertainties and external disturbances, which can complicate control strategies. This article proposes a novel control strategy that integrates Integral Sliding Mode Control (ISMC) with Time Delay Estimation (TDE) and Arithmetic Optimization Algorithm (AOA) to enhance control performance. The proposed controller, OTDISMC, is designed to eliminate chattering and improve robustness against disturbances without relying on system dynamics. Contrary to the conventional controllers structures which depended on the system dynamic in their schemes, a model free controller is formulated without using system dynamics in its formulation. Time delay estimation technique has been undertaken as an efficient approximating strategy to approximate and compensate the lumped uncertain dynamics of the system. AOA has been undertaken to determine the optimum solutions of the coefficients of proposed control approach. The stability has been analyzed and investigated using the Lyapunov stability criterion. To show the effectiveness and validity of the developed controller, computer simulations in nominal and robustness scenarios have been carried out and compared with TDISMC that tuned by trial and error and PSO-TDISMC that tuned by particle swarm optimization (PSO). Simulation results demonstrate that OTDISMC significantly reduces tracking errors and improves robustness. The results indicate the superiority of the proposed controller as compared with traditional TDISMC tuned by classical methods and PSO-TDISMC tuned by particle swarm optimization (PSO)

A literature review on work stress and audit quality reduction behavior: trend and future challenges

Research aims: This research aims to identify research development on work stress and its impact on audit quality reduction behavior.Design/Methodology/Approach: This qualitative study used the systematics of the PRISMA protocol review to analyze relevant articles.Research findings: Some research has focused more on the causes of job stress and audit quality reduction behavior. However, recent research has begun to harness the positive potential of auditors to reduce work stress and audit quality reduction behavior.Theoretical contribution/Originality: This study contributes to the literature review on work stress and audit quality reduction behavior.Practitioner/Policy implication: This review is expected to help organizations understand developments and findings related to work stress and audit quality reduction behavior, as well as support the development of effective stress management programs in the auditor's work environment.Research limitation/Implication: This literature review focuses only on the impact of job stress on audit quality reduction behavior. Nevertheless, it is still possible that work stress can also impact other aspects beyond audit quality reduction behavior, such as auditor performance and auditor judgment

The Influence of Gross Regional Domestic Product Per Capita and Foreign Direct Investment on Income Inequality: An Empirical Study of 34 Provinces in Indonesia

The study investigates the correlation between Gross Regional Domestic Product (GRDP) per capita, Foreign Direct Investment (FDI), and the Gini coefficient using panel data from 34 provinces in Indonesia from 2015 to 2023. A panel data regression analysis method was employed for data processing to optimize estimation results. The results obtained from the analysis using the Fixed Effects Model (FEM) consistently show strong positive outcomes. The findings imply a direct connection between higher GRDP per capita, increased FDI, and a higher Gini coefficient, suggesting that more significant foreign investment and higher per capita GDP contribute to increased income inequality across the Indonesian provinces. The study emphasizes the critical role of regional governments in addressing these disparities. It suggests strategies such as implementing regional policies to boost investment, enhancing infrastructure, creating more business opportunities, and promoting tourism. The proposal to establish the National Capital City (IKN) in East Kalimantan is presented as a long-term solution to reduce income inequality among the provinces

Sectoral Herding During Global Rare Events: Evidence from the Indonesian Capital Market

Research Aims: This research aims to examine the effects of increased levels of herding on abnormal returns during rare events.Design/Methodology/Approach: Time series regression including all stocks across 9 sectors in the Indonesia Stock Exchange from 1997 to 2020, totaling 5,615 observations is used. The primary model is predicated on three factors derived from Fama-French and prospect theory to incorporate herding as a primary risk factor in assessing the impact of on abnormal returns during rare events.Research Results: The results show that various events produce impacts on herding behavior across different sectors. During bearish market conditions, this behavior manifests significant negative effect leading to greater abnormal returns. Conversely, positive and significant anti-herding behavior is observed in bullish market conditions. Rare events do not necessarily induce herding behavior but may lead to anti-herding behavior.Theoretical Contribution/Originality: In this research, the variables are developed from the Efficient Market Hypothesis, Capital Asset Pricing Model, prospect theory, and market integration theory. The estimation model is grounded in prospect theory and the contribution addresses the research gaps.Practitioners/Policy Implications: The provision of insights to stakeholders in the capital market regarding the impact of rare events on financial behaviors influences investors' decision-making processes in stock investments.Research Limitations/Implications: The measurement of herding refers to Chang et al. (2000) due to the availability of aggregate data from the Indonesian Stock Exchange. Comprehensive micro-level data is not unavailable and the accessibility of complete micro-level data can be conducted. The presence of these data in the capital markets of other countries should be investigated

Who emits more emission? the association between CO2 emissions and socio-economics characteristics of Indonesian household

Much research has been done on identifying socio-economic household links in developed countries. However, the study of household carbon emission (HCE) levels and related variables still needs to be examined, especially in developing countries. The study uses an ordinary least squares model to pinpoint the socio-economic elements that affect a household's carbon emission levels. SUSENAS (National Socio-economic Survey) data from March 2019 and 2021, covering 655,694 households, were used. This study used ordinary least squares (OLS) for the regression and dominance analyses (DA) to determine the most crucial factors affecting the HCE. The household characteristics, individuals, and residential conditions are used to measure socio-economic situations. The DA analysis shows that income and household size are the most crucial determinants of HCE. The OLS analysis reveals that the income variable exhibits a non-linear relationship with HCE as an inverted U-shape in the total HCE and most consumption categories. Wealthier households generate higher levels of household carbon emissions than poorer households. The variable of household size demonstrates a positive relationship with the HCE. The composition of household members also significantly affects household carbon emission levels, where the presence of working members and toddlers tends to increase household carbon emissions. The research also finds differences in consumption patterns between urban and rural households, resulting in varying levels of carbon emissions. The findings of this study can assist policymakers in formulating targeted policies to reduce household carbon emissions

Naive Bayes for Diabetes Prediction: Developing a Classification Model for Risk Identification in Specific Populations

Depending on persuasive statistics, the increasing prevalence of diabetes worldwide is a huge challenge for individuals, families, and nations. According to International Diabetes Federation (IDF) projections, the number of adults with diabetes is expected to rise by an astounding 46% by 2045, to reach 783 million, or one in eight. In response to this growing concern, this research explores the implementation of the Naive Bayes algorithm for predicting diabetes, employing comprehensive data cleansing and randomization techniques. A systematic evaluation of the model's performance is conducted using several training and testing split ratios (65:35, 75:25, 85:15). The outcome showed that the model performed best at the 65:35 split ratio, with accuracy reaching its maximum of 88.16%, precision 0.883, recall 0.881, and f1-score 0.882

The Effect of Benzyl Amino Purin and NaphtalenaAcetic Acid Applications on Direct Shoot Organogenesis in Porang (Amorphophallus muelleri B)

Porang (Amorphophallus muelleri B.) is a tuberous plant with the potential to be the main source of carbohydrates and is rich in benefits. Porang proliferation is limited by a 6-month dormancy period per year, and generative propagation is unlikely due to the seeds being apomictic triploid. The aimed of my research to analyze the application of BAP and NAA in culture media for direct propagation of porang shoots. The explant used in this research was young leaves. The research was arranged in a completely randomized design with a combination of BAP and NAA hormones added to the MS medium. There were three BAP treatments, namely 1.0 mg/L, 2.0 mg/L, and 3.0 mg/L, while NAA treatments consisted of 2 levels, namely 2.0 mg/L and 4.0 mg/L. The addition of 1.0 mg/L BAP combined with 4.0 mg/L NAA was the best treatment that produced seven shoots with an average shoot length of 2.14 cm and root length of 3.6 cm, with the earliest bud emergence (9.7 weeks after planting)