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

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

Prediction Manufacturing Industry Potential Output and Growth: Case Study Indonesia’s Manufacturing Industry 2000-2021

The analysis of the output gap, which refers to the disparity between the actual and potential output, serves as a diagnostic tool to evaluate a country's economic position. The outcomes of this analysis provide insights into the current state of the country's business cycle. It is valuable as an initial framework for policy scenarios. Moreover, this study conducted an analysis on the estimation of potential output and output gaps in Indonesia's non-oil and gas processing industry sector. Three methodologies were employed: the HP (Hodrick-Prescott) Filter, the BP (Band-Pass) Filter, and the Production Function Approach. These approaches collectively indicate that the non-oil and gas processing industry in Indonesia fell slightly below its potential between 2020 and 2021. Consequently, policymakers should take this into account in both the short and long term. In the short term, inflationary pressures were brought about by a positive output gap, which required the government to prioritize implementing measures to control inflation. Meanwhile, medium-term structural reforms should continue to enhance potential output, including increasing labor force participation, sustainable investment for capital factors, and improving human capital quality and technological expertise for productivity factor

Analisis Hukum Pidana dan Kriminologi Terhadap Sukarelawan Pengatur Lalu Lintas di Kabupaten Bantul

The problem formulation of this research how the social control theory of criminology analysis to factor exist SUPELTAS in Bantul Regency ? and whether the SUPELTAS deed in Bantul Regency can be categorized as a criminal act of begging ?. This research is an empirical study that uses a statutory approach in which to get a review of a legal event in the community based on statutory regulations. The research data were collected by conducting interview with the Head of Traffic Police of the Bantul Police, Banguntapan Police Traffic Head, 5 SUPELTAS and 3 road users in Bantul district. Based on the result of this research, showing that The economic difficulties, limited numbers of police and legal instruments which does not cause a deterrent effect, as well as personal control and social bonds which does not work effectively within SUPELTAS were the factors that causing the SUPELTAS in Bantul district exists and the SUPELTAS deed in Bantul district can be categorized as a criminal act of begging and it is violating Article 37 paragraph (1) jo. Article 22 letter a Bantul Regency Regional Regulation Number 4 of 2018 concerning the Implementation of Tranquility and Public Orde

Economic Imperatives Amidst a Global Pandemic: The Resilience of Kangean Migrant Workers in Pursuing Employment Opportunities in Malaysia

Pandemi COVID-19 telah membawa era transformatif terhadap dunia global, yang sangat berdampak pada kehidupan dan aspirasi para pekerja migran Kangean yang mencari pekerjaan di Malaysia. Meski terjadi secara global, para pekerja migran Kangean tetap teguh dan bertekad untuk tetap mendapatkan kesempatan kerja di Malaysia. Penelitian ini berupaya mengungkap faktor-faktor utama yang memicu aspirasi para pekerja migran Kangean untuk bertahan menghadapi rintangan berat dalam upaya mereka untuk bekerja di Malaysia selama pandemi COVID-19. Dengan menggunakan metodologi penelitian kualitatif, penelitian ini menggunakan pengumpulan data primer melalui wawancara dan observasi terhadap warga Kangean yang pernah, sedang, atau berencana bekerja di Malaysia. Selain itu, data sekunder dikumpulkan melalui analisis ekstensif terhadap literatur yang bersumber dari jurnal, buku, artikel, dan situs web imigrasi terkemuka seperti Badan Perlindungan Pekerja Migran Indonesia (BP2MI), Dinas Tenaga Kerja (DISNAKER), dan DATABOKS Tenaga Kerja Indonesia (TKI) . Untuk mendukung analisis kami, penelitian ini mengadopsi Teori Ketergantungan, yang mencakup tingkat ketergantungan yang dilakukan oleh pekerja migran Kangean terhadap Malaysia. Temuan penelitian ini menggarisbawahi bahwa faktor ekonomi sebagai pendorong utama yang mendorong calon pekerja migran Kangean mencari pekerjaan di Malaysia. Lebih jauh lagi, penelitian ini menyoroti dua pendekatan berbeda yang digunakan oleh para pekerja ini dalam upaya mereka untuk masuk ke Malaysia, yaitu jalur legal dan ilegal. Namun demikian, karena ketatnya peraturan perjalanan internasional yang diberlakukan selama pandemi, upaya-upaya ini menghadapi hambatan yang signifikan. Oleh  karena itu, penelitian di masa depan harus fokus tidak hanya pada peningkatan pemahaman kita tentang pola migrasi internasional yang dilakukan oleh penduduk Kangean, namun juga mengeksplorasi penerapan yang lebih luas dari tren migrasi tersebut, tidak hanya Malaysia tetapi juga mencakup negara-negara lain.Kata Kunci : Pekerja Migran Kangean, faktor ekonomi, migrasi internasional, Pandemi COVID-19, teori ketergantungan

Addressing Challenges in Dynamic Modeling of Stewart Platform using Reinforcement Learning-Based Control Approach

In this paper, we focus on enhancing the performance of the controller utilized in the Stewart platform by investigating the dynamics of the platform. Dynamic modeling is crucial for control and simulation, yet challenging for parallel robots like the Stewart platform due to closed-loop kinematics. We explore classical methods to solve its inverse dynamical model, but conventional approaches face difficulties, often resulting in simplified and inaccurate models. To overcome this limitation, we propose a novel approach by replacing the classical feedforward inverse dynamic block with a reinforcement learning (RL) agent, which, to our knowledge, has not been tried yet in the context of the Stewart platform control. Our proposed methodology utilizes a hybrid control topology that combines RL with existing classical control topologies and inverse kinematic modeling. We leverage three deep reinforcement learning (DRL) algorithms and two model-based RL algorithms to achieve improved control performance, highlighting the versatility of the proposed approach. By incorporating the learned feedforward control topology into the existing PID controller, we demonstrate enhancements in the overall control performance of the Stewart platform. Notably, our approach eliminates the need for explicit derivation and solving of the inverse dynamic model, overcoming the drawbacks associated with inaccurate and simplified models. Through several simulations and experiments, we validate the effectiveness of our reinforcement learning-based control approach for the dynamic modeling of the Stewart platform. The results highlight the potential of RL techniques in overcoming the challenges associated with dynamic modeling in parallel robot systems, promising improved control performance. This enhances accuracy and reduces the development time of control algorithms in real-world applications. Nonetheless, it requires a simulation step before practical implementations

Editorial Foreword

Editorial Forewor

Monitoring DC Motor Based On LoRa and IOT

Electrical energy efficiency is a dynamic in itself that continues to be driven by electrical energy providers. In this work, long-range (LoRa) technology is used to monitor DC motors. In the modern world, IoT is becoming increasingly prevalent. Embedded systems are now widely used in daily life. More can be done remotely in terms of control and monitoring. LoRa is a new technology discovered and developing rapidly. LoRa technology addresses the need for battery-operated embedded devices. LoRa technology is a long-range, low-power technology. In this investigation, a LoRa transmitter and a LoRa receiver were employed. This study employed a range of cases to test the LoRa device. In the first instance, there are no barriers, whereas there are in the second instance. The results of the two trials showed that the LoRa transmitter and receiver had successful communication. In this study, the room temperature is used to control DC motors. So that the DC motor's speed adjusts to fluctuations in the room's temperature. Additionally, measuring tools and the sensors utilised in this investigation were contrasted. The encoder sensor and the INA 219 sensor were the two measured sensors employed in this study. According to the findings of the experiment, the tool was functioning properly

A Multi Representation Deep Learning Approach for Epileptic Seizure Detection

Epileptic seizures, unpredictable in nature and potentially dangerous during activities like driving, pose significant risks to individual and public safety. Traditional diagnostic methods, which involve labour-intensive manual feature extraction from Electroencephalography (EEG) data, are being supplanted by automated deep learning frameworks. This paper introduces an automated epileptic seizure detection framework utilizing deep learning to bypass manual feature extraction. Our framework incorporates detailed pre-processing techniques: normalization via L2 normalization, filtering with an 80 Hz and 0,5 Hz Butterworth low-pass and high-pass filter, and a 50 Hz IIR Notch filter, channel selection based on standard deviation calculations and Mutual Information algorithm, and frequency domain transformation using FFT or STFT with Hann windows and 50% hop. We evaluated on two datasets: the first comprising 4 canines and 8 patients with 2.299 ictal, 23.445 interictal, and 32.915 test data, 400-5000Hz sampling rate across 16-72 channels; the second dataset, intended for testing, 733 icatal, 4.314 interictal, and 1908 test data, each 10 minutes long, recorded at 400Hz across 16 channels. Three deep learning architectures were assessed: CNN, LSTM, and a hybrid CNN-LSTM model-stems from their demonstrated efficacy in handling the complex nature of EEG data. Each model offers unique strengths, with the CNN excelling in spatial feature extraction, LSTM in temporal dynamics, and the hybrid model combining these advantages.  The CNN model, comprising 31 layers, yielded highest accuracy, achieving 91% on the first dataset (precision 92%, recall 91%, F1-score 91%) and 82% on the second dataset using a 30-second threshold. This threshold was chosen for its clinical relevance. The research advances epileptic seizure detection using deep learning, indicating a promising direction for future medical technology. Future work will focus on expanding dataset diversity and refining methodologies to build upon these foundational results