In silico study of the essential oil compounds of ginger and thyme on Coronavirus-2 receptors

Coronavirus-2 (SARS-Cov-2) is a virus that attacks the respiratory system and causes the Covid-19 pandemic. After the pandemic, prevention and appropriate therapy research continue to be carried out to anticipate the emergence of more dangerous viruses. In line with the culture of consuming herbs that has arisen due to the effects of the pandemic, in this study, an insilico screening was carried out for essential oil compounds produced by ginger and thyme herbs which have been widely consumed by the public. The aim of the research was to find the essential oil content that has the most potential as an antiviral against coronavirus-2. The research method was carried out in silico, including ligand preparation, receptor and method validation, and analysis of ligand-receptor binding interactions using the AutoDoc 4.2.6 program. As a comparison, a study was conducted on remdesivir and favipiravir, which have been used as antivirals. The three components that have the most potential based on the calculation of the free energy value, were determined by the ADMET parameters using the Admet lab 2.0 program. The results showed that the three components in the essential oil exhibited better interactions when compared to remdesivir and favipiravir at the 3-Cl protease and spike glycoprotein receptors. The results of the insilico study and ADMET prediction test showed that of the three most potent compounds, lamda-farnesen was the most potent and safe to us

XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting

Investment in the capital market has become a lifestyle for millennials in Indonesia as seen from the increasing number of SID (Single Investor Identification) from 2.4 million in 2019 to 10.3 million in December 2022. The increase is due to various reasons, starting from the Covid-19 pandemic, which limited the space for social interaction and the easy way to invest in the capital market through various e-commerce platforms. These investors generally use fundamental and technical analysis to maximize profits and minimize the risk of loss in stock investment. These methods may lead to problem where subjectivity and different interpretation may appear in the process. Additionally, these methods are time consuming due to the need in the deep research on the financial statements, economic conditions and company reports. Machine learning by utilizing historical stock price data which is time-series data is one of the methods that can be used for the stock price forecasting. This paper proposed XGBoost optimized by Particle Swarm Optimization (PSO) for stock price forecasting. XGBoost is known for its ability to make predictions accurately and efficiently. PSO is used to optimize the hyper-parameter values of XGBoost. The results of optimizing the hyper-parameter of the XGBoost algorithm using the Particle Swarm Optimization (PSO) method achieved the best performance when compared with standard XGBoost, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and Random Forest. The results in RSME, MAE and MAPE shows the lowest values in the proposed method, which are, 0.0011, 0.0008, and 0.0772%, respectively. Meanwhile, the  reaches the highest value. It is seen that the PSO-optimized XGBoost is able to predict the stock price with a low error rate, and can be a promising model to be implemented for the stock price forecasting. This result shows the contribution of the proposed method

Interprofessional education applied in first-year and third-year health students: cross-sectional study

The purpose of this study was to compare Interprofessional Education (IPE) results from first and third year batches. The two batches of IPE included 345 first-year students and 460 third-year students, from three different health disciplines, including medicine, pharmacy, and public health, respectively. These students met for four weeks in an effort to increase interprofessional collaboration, improve communication skills, foster respect and increase knowledge of the various roles each discipline plays on the health care team, especially in case management, conflict management and team work. Before the IPE program began, the students were given a pre-questionnaire to assess their prior understanding of IPE. Each group of first-year students presented the outcomes of their discussions in the fourth week, while the third-year students created a poster about the subject and presented it in the second week. The students complete the IPE program and pre-post-questionnaire after giving their presentation. The International Collaborative Competencies Attainment Survey served as a model for the development of the IPE questionnaire (ICCAS). The result of pre-IPE domains’ score revealed substantial disparities in the team work domain, with third-year students score was lower than the first students, whereas first-year students had the highest score in the most of IPE categories, unless collaboration and conflict management (p>0.05). Despite this, the post-IPE domains’ score showed significant differences in all of the domains. Most of the IPE domains had higher score in first year students, excluding communication and team work

Design Blockchain Architecture for Population Data Management to Realize a Smart City in Cimahi, West Java, Indonesia

Smart city as a concept of city development which integrates information and communication technology with the intention of optimizing city management becomes a major goal for Indonesia, especially through the movement towards 100 Smart Cities. However, population data management is crucial in achieving this for optimal planning and management. Personal data protection becomes a crucial challenge with the rapid population growth and mobility in cities. The need for a more reliable protection system is very necessary. This research proposes a blockchain architecture that not only manages digital identities but also population data. The focus is population administration in Cimahi City, West Java, with the hope of providing security, transparency, and a strong audit trail for all population data. The contribution of this research is to design a blockchain architecture specifically for population data management, meeting the needs of population administration in cities, especially the city of Cimahi. Through a blockchain architecture development approach, this research considers the diverse administrative needs of the population and applies a blockchain model that enables data security and integrity. This implementation of blockchain architecture provides promising results in maintaining the security and integrity of population data, enabling greater transparency and auditability. This implementation of blockchain architecture provides promising results in maintaining the security and integrity of population data, enabling greater transparency and auditability. This research also shows that the use of blockchain technology specifically for population data management can be a reliable and innovative solution in ensuring the security and reliability of data important for smart city development.However, this research has limited access to central data, so the data obtained is still very limited. Therefore, further research is needed to follow up on these limitations. Apart from that, this research is also expected to provide knowledge and solutions in securing data, especially population data in government environments

PEMAHAMAN KONSEP OPERASI HITUNG BILANGAN BULAT MELALUI PENDEKATAN PMRI PADA SISWA SMP

Pendidikan merupakan kebutuhan penting bagi kehidupan manusia. Diperlukannya sebuah pendidikan ialah untuk meningkatkan pengetahuan dan mengembangkan potensi diri setiap manusia. Terdapat banyak cabang ilmu pengetahuan, salah satunya adalah matematika. Matematika adalah program studi wajib untuk semua mahasiswa dan siswa sekolah. Matematika adalah argumen logis, cara berpikir, dan bahasa yang terdiri dari frasa yang didefinisikan secara tepat, diungkapkan secara simbolis, dan padat. Ini lebih merupakan bahasa simbolis tentang konsep matematis. Namun, banyak menganggap pelajaran matematika adalah pelajaran yang sulit dan menyeramkan. Sehingga peserta didik selalu merasa kesal ataupun malas saat mata pelajaran matematika. Peserta didik merasa bahwa materi-materi yang ada didalam matematika merupakan musuh bagi kehidupan terutama pada materi operasi hitung bilangan bulat. Tujuan dari penelitian ini adalah untuk mengkaji pemahaman siswa SMP terhadap pengertian operasi penghitungan bilangan bulat yang diukur dengan metode PMRI. Penelitian kualitatif ini menggunakan desain penelitian deskriptif dan dilakukan dengan tiga belas siswa SMP. Penelitian ini mengumpulkan datanya melalui penggunaan wawancara dan hasil tes. Analisis data dalam penelitian ini menganut Miles dkk. analisis, yang terdiri dari reduksi data, penyajian data, dan penarikan kesimpulan. Berdasarkan hasil analisis menunjukkan bahwa kemampuan pemahaman konsep peserta didik sudah cukup baik yang didasarkan pada indikator kemampuan pemahaman konsep matematika yang telah di ukur dari 2 kelompok yang sudah mampu mengimplementasikan semua indikator pemahaman konsep yang digunakan oleh peneliti

Design Human Object Detection Yolov4-Tiny Algorithm on ARM Cortex-A72 and A53

Currently, many object detection systems still use devices with large sizes, such as using PCs, as supporting devices, for object detection. This makes these devices challenging to use as a security system in public facilities based on human object detection. In contrast, many Mini PCs currently use ARM processors with high specifications. In this research, to detect human objects will use the Mini PC Nanopi M4V2 device that has a speed in processing with the support of CPU Dual-Core Cortex-A72 (up to 2.0 GHz) + Cortex A53 (Up to 2.0 GHz) and 4 Gb DDR4 Ram. In addition, for the human object detection system, the author uses the You Only Look Once (YOLO) method with the YoloV4-Tiny type, With these specifications and methods, the detection rate and FPS score are seen which are the feasibility values for use in detecting human objects. The simulation for human object recognition was carried out using recorded video, simulation obtained a detection rate of 0,9845 or 98% with FPS score of 3.81-5.55.  These results are the best when compared with the YOLOV4 and YOLOV5 models. With these results, it can be applied in various human detection applications and of course robustness testing is needed

Advanced Control for Quadruple Tank Process

In the realm of control systems, the last three decades have witnessed significant advancements in model predictive control (MPC), an advanced technique renowned for its ability to optimize processes with constraints, handle multivariate systems, and incorporate future references when feasible. This paper introduces an innovative offset-free MPC approach tailored for the control of a complex nonlinear system—the quadruple tank process (QTP). The QTP, known for its deceptively simple yet challenging multivariate behavior, serves as an ideal benchmark for evaluating the efficacy of the proposed algorithm. In this work, we rigorously compare the performance of the PID and MPC controller when applied to both linear and nonlinear models of the QTP. Notably, our research sheds light on the advantages of MPC, particularly when confronted with constant disturbances. Our novel algorithm demonstrates exceptional capabilities, ensuring error-free tracking even in the presence of persistent load disturbances for both linear and nonlinear QTP models. Compared to the PID control, the proposed method can reduce the overall set point tracking error up to 32.1%, 27.6%, and 38.54% using the performance indices ISE, ITAE, and IAE, respectively, for the linear case. Furthermore, for the nonlinear case, the overall set point tracking error reduction is up to 93.4%, 94.9%, and 91.5%. This work contributes to bridging the gap in effective control strategies for nonlinear systems like the QTP, highlighting the potential of offset-free MPC to enhance control and stability in a challenging process industry involving automatic liquid level control.In the realm of control systems, the last three decades have witnessed significant advancements in model predictive control (MPC), an advanced technique renowned for its ability to optimize processes with constraints, handle multivariate systems, and incorporate future references when feasible. This paper introduces an innovative offset-free MPC approach tailored for the control of a complex nonlinear system—the quadruple tank process (QTP). The QTP, known for its deceptively simple yet challenging multivariate behavior, serves as an ideal benchmark for evaluating the efficacy of the proposed algorithm. In this work, we rigorously compare the performance of the PID and MPC controller when applied to both linear and nonlinear models of the QTP. Notably, our research sheds light on the advantages of MPC, particularly when confronted with constant disturbances. Our novel algorithm demonstrates exceptional capabilities, ensuring error-free tracking even in the presence of persistent load disturbances for both linear and nonlinear QTP models. Compared to the PID control, the proposed method can reduce the overall set point tracking error up to , , and  using the performance indices ISE, ITAE, and IAE, respectively, for the linear case. Furthermore, for the nonlinear case, the overall set point tracking error reduction is up to , , and . This work contributes to bridging the gap in effective control strategies for nonlinear systems like the QTP, highlighting the potential of offset-free MPC to enhance control and stability in a challenging process industry involving automatic liquid level control

Cytotoxicity of Zingiber officinale var. rubrum on HeLa cells and prediction of anti-proliferative activity via the jak2/stat3 and hedgehog pathways using a molecular docking approach

Cervical cancer is one of the second-leading causes of death in women. The discovery of cancer drug candidates continues to be carried out due to the resistance that occurs in cervical cancer therapy. Plant metabolite compounds are one of the sources used to explore new drug candidates. Red ginger rhizome is a candidate plant that has anti-cervical cancer activity. This study aims to determine the cytotoxicity of an ethanol extract of red ginger rhizomes on the growth of HeLa cancer cells and predict anti-proliferative activity via the jak2/stat3 and hedgehog pathways. The sample (red ginger rhizome simplicia) was extracted by remaceration using 75% ethanol. The MTT assay method is used to test the cytotoxicity and anti-proliferation of metabolite compounds using Autodock 4.2 software. The receptors used in the jak2, stat3, and smo pathways were obtained from the Protein Data Bank with the codes 6VGL, 6NUQ, and 5L7I, respectively. The ethanol extract produced is a thick yellowish brown extract with an aromatic smell and spicy taste, with an extract yield of 18.63% w/w. 75% ethanol extract of red ginger rhizomes has cytotoxic activity in HeLa cancer with an IC50 of 104.22 ± 6.18 µg/mL and an IC50 of cisplatin of 38.61 ± 3.66. Prediction of antiproliferative activity via the jak2 pathway shows a binding energy and Ki value of -7.47 kcal/mol, -7.48 kcal/mol, and 3.33 uM, 3.27 uM, as shown by alpha-cedrol and beta-eudesmol compounds. The highest inhibition on the stat3 and smo pathways was shown by the beta compound eudesmol, with binding energy and Ki values of -6.05 kcal/mol, -7.57 kcal/mol, and 36.48 uM, respectively; 2.81 uM

Analysis of Self-Regulation in the Rencong Telang Islamic Society Perspective of Social Cognitive Theory

The Rencong Telang Islamic society reflects distinctive cultural characteristics and unique values, particularly in the self-regulation system involving religious norms and local traditions. The individual's ability to control behavior and adapt to societal values is a key element in understanding behavior, especially in social and religious contexts. This research aims primarily to delve into and comprehensively analyze the phenomenon of self-regulation in the Rencong Telang Islamic society. Specific objectives include exploring the concepts and practices of self-regulation, involving religious norms, local traditions, and cultural values that shape self-regulation. This article also aims to analyze the role of self-regulation in various aspects of community life, ranging from worship to marriage customs and communal land management. Focus is also given to the social challenges and changes faced by the Rencong Telang Islamic society to understand how self-regulation can uphold traditional values. The method used is literature review, encompassing an analysis of relevant literature on self-regulation, Islamic society, and the specific context of the Rencong Telang Islamic society. The research findings indicate that self-regulation in this society encompasses various aspects of daily life, involving customs, Islamic principles, and governance. The ability to control behavior in worship, marriage customs, and communal land management is an integral part of self-regulation. The community employs self-regulation to preserve traditional values, adapt to changes, address social challenges, and manage social interactions. In the context of Social Cognitive Theory, self-regulation is explained as the result of behavioral control in customs and Islamic beliefs, embodied in Undang as the daily norm reflecting the influence of nature, Islamic teachings, and local traditions. Overall, self-regulation in this society is not solely individual but reflects social dynamics involving the entire community, with significant impacts on aspects of life, customs, Islam, and governance, playing a crucial role in preserving cultural values and identity

Review of Peer-to-Peer (P2P) Lending Based on Blockchain

Peer-to-Peer (P2P) lending is a financing business model that has gained popularity in recent years due to the ease of loan application, disbursement, and repayment processes. The volume of Peer-to-Peer (P2P) Lending transactions have a significant growth. One of the reasons for the popularity of Peer-to-Peer (P2P) lending is its utilization of technology in both the application and loan repayment processes. One such technology gaining traction in Peer-to-Peer (P2P) lending is blockchain technology. The popularity of blockchain technology lies in its ability to enhance the transparency of the transaction process. This literature study aims to address three main questions: What are the characteristics of blockchain suitable for Peer-to-Peer (P2P) lending , the benefits of implementing blockchain technology in Peer-to-Peer (P2P) lending and the challenges of Peer-to-Peer (P2P) lending based on blockchain. The findings reveal that there are characteristics of blockchain that can be applied to Peer-to-Peer (P2P) lending, bringing numerous benefits to the overall Peer-to-Peer (P2P) lending process. However, challenges persist in the implementation of blockchain technology in Peer-to-Peer (P2P) lending. The insights gained from this literature review are intended to guide researchers interested in studying the application of blockchain technology in the context of Peer-to-Peer (P2P) lending