This study investigates machine learning-based quantitative structure-property relationship (QSPR) models for predicting the thermal stability of zinc metal-organic frameworks (Zn-MOF). Utilizing a dataset comprising 151 Zn-MOF compounds with relevant molecular descriptors, ridge (R) and kernel ridge (KR) regression models were developed and evaluated. The results demonstrate that the R model outperforms the KR model in terms of prediction accuracy, with the R model exhibiting exceptional performance (R² = 0.999, RMSE = 0.0022). While achieving high accuracy, opportunities for further improvement exist through hyperparameter optimization and exploration of polynomial functions. This research underscores the potential of ML-based QSPR models in predicting the thermal stability of Zn-MOF compounds and highlights avenues for future investigation to enhance model accuracy and applicability in materials science.

The purpose of this study is to use quantitative structure-property relationship (QSPR)-based machine learning (ML) to examine the corrosion inhibition capabilities of benzimidazole compounds. The primary difficulty in ML development is creating a model with a high degree of precision so that the predictions are correct and pertinent to the material's actual attributes. We assess the comparison between the extra trees regressor (EXT) as an ensemble model and the decision tree regressor (DT) as a basic model. It was discovered that the EXT model had better predictive performance in predicting the corrosion inhibition performance of benzimidazole compounds based on the coefficient of determination (R2) and root mean square error (RMSE) metrics compared DT model. This method provides a fresh viewpoint on the capacity of ML models to forecast potent corrosion inhibitors.

Empirical studies of possible compound corrosion inhibitors require a lot of money, time, and resources. Therefore, we used a machine learning (ML) paradigm based on quantitative structure-property relationship (QSPR) models to evaluate ensemble algorithms as predictors of corrosion inhibition efficiency (CIE) values. Our investigation reveals that the gradient boosting (GB) regressor model outperforms other ensemble-based models. This advantage is evaluated objectively using the metrics root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). In summary, our research provides a new perspective on how well machine learning algorithms in particular ensembles work to identify organic molecules such as pyridazine that have the potential to prevent corrosion on the surfaces of metals such as iron and its alloys.

Corrosion in materials is a significant concern for the industrial and academic fields because corrosion causes enormous losses in various fields such as the economy, environment, society, industry, security, safety, and others. Currently, material damage control using organic compounds has become a popular field of study. Pyridine and quinoline stand out as corrosion inhibitors among a myriad of organic compounds because they are non-toxic, inexpensive, and effective in a variety of corrosive environments. Experimental investigations in developing various candidate potential inhibitor compounds are time and resource-intensive. In this work, we use a quantitative structure-property relationship (QSPR)-based machine learning (ML) approach to investigate support vector machine (SVR), random forest (RF), and k-nearest neighbors (KNN) algorithms as predictive models of inhibition performance. (Inhibition efficiency) corrosion of pyridine-quinoline derivative compounds as corrosion inhibitors on iron. We found that the RF model showed the best predictive ability based on the coefficient of determination (R2) and root mean squared error (RMSE) metrics. Overall, our study provides new insights regarding the ML model in predicting corrosion inhibition on iron surfaces.

Investigating potential corrosion inhibitors via empirical research is a labor- and resource-intensive process. In this work, we evaluated various linear and non-linear algorithms as predictive models for corrosion inhibition efficiency (CIE) values using a machine learning (ML) paradigm based on the quantitative structure-property relationship (QSPR) model. In the quinoxaline compound dataset, our analysis showed that the XGBoost model performed the best predictor of other ensemble-based models. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean squared error (RMSE) metrics were used to objectively assess this superiority. To sum up, our study offers a fresh viewpoint on the effectiveness of machine learning algorithms in determining the ability of organic compounds like quinoxaline to suppress corrosion on iron surfaces.

This research explains the procedure for implementing electronic mortgage registration and discusses legal protection for creditors in mortgage registration through SKMHT in the event of the principal's death. This normative legal research utilizes a prescriptive approach based on legislation, cases, and conceptual analysis Primary data sources include relevant laws and regulations related to mortgages, supplemented by interviews with Notaries and PPAT, as well as secondary legal materials such as books, legal research papers, and previous studies in the form of journals, theses, and dissertations related to the research. Data collection techniques are carried out through interviews, observations, and literature studies Meanwhile, the analysis technique used by the author is qualitative model. The research results indicate that electronic registration of Mortgage Rights is carried out by interested parties through the website https://mitra.atr.bpn.go.id. However, in practice, there are still shortcomings in electronic Mortgage Rights registration, particularly when it is registered using SKMHT (Power of Attorney to Burden Mortgage Rights), and then the certificate owner passes away, the registration of Mortgage Rights cannot be continued. The solution to this problem is to add an optional refinement formulation in the Electronic Mortgage Rights System that allows for the replacement of the Identity Card Number (NIK) of the grantor with the NIK of the recipient if the Mortgage Rights are registered first with a SKMHT.

Ensuring the preservation of human rights and the coherence of law enforcement's tasks are predicated on the pre-trial provisions of the criminal procedure legislation. As part of its mission to ensure that the criminal justice system is fair, the pretrial facility has put safeguards in place to monitor the effectiveness with which police officers carry out their tasks. All eyes are on the judge's evaluation of the law's applicability in light of pre-trial judgment 4/Pid.Pra/2022/PN.Kla. Normative and empirical theories of law are both used in this study's methodology. Primary data collected from the field and secondary data collected from various relevant legal sources are both used in this study. The study team included a law school professor, a prosecutor from the South Lampung District Prosecutor's office, and a judge from the Kalianda District Court. The court considered the following factors in rendering pretrial decision 4/Pid.Pra/2022/PN.Kla, in accordance with the study's conclusions: Instead of using the seizure reports and records that initially connected the suspect to the South Lampung District Prosecutor's Office ( Respondent) as evidence, the judgment was based on legal, social, and philosophical factors. Beyond that, the applicant was not suspected of being the subject of an official audit by the State Financial Loss Calculation Audit (PKKN). In order to establish a monetary loss to the state, the investigator must explicitly recognize a critical component that forms the foundation or risk of the inquiry. All of the applicant's pretrial requests were therefore granted by the court. The officials tasked with carrying out the pretrial decision's enforcement have finished their task. Kla is based on the multi-stage execution of criminal legislation.

This case study investigates the implementation of Occupational Health and Safety (K3) in the PT Gunbuster Nickel Industry area. Through in-depth analysis of policies, practices, challenges, strategies and impacts of K3 implementation, this research aims to provide comprehensive insight into the company's efforts to ensure a safe and healthy work environment for its workers. The research results show that PT Gunbuster Nickel Industry has adopted various comprehensive K3 policies and practices, including routine K3 training, work environment monitoring, and safety campaigns. However, several challenges such as employee resistance and complex working conditions remain obstacles in implementing K3. To overcome these challenges, the company has implemented proactive strategies, including increasing K3 communication and awareness, regular evaluation of working environment conditions, and active involvement of employees in efforts to prevent work-related accidents and diseases. The impact of implementing effective K3 policies at PT Gunbuster Nickel Industry includes reducing the number of work accidents, increasing productivity, and improving the company's image. This research makes an important contribution to the understanding of best practices in K3 management in the industrial sector, as well as becoming a reference for other companies in their efforts to create a safe and healthy work environment.

This study describes and examines the issue of the legal responsibility of online gambling promoters to people who lose due to influencers, considering the situation and conditions that are in need, of course, people will be tempted to obtain instant wealth. This study aims as an effort so that victims get proportionate attention in fulfilling their rights as victims to apply for restitution against online gambling influencers who advertise by providing false or untrue reviews. This research is a normative legal research that is perscriptive. Secondary data types include primary and secondary legal materials. The technique of collecting legal materials is carried out by literature study, then the analytical technique used is the deductive method. The results of this study show that victims can hold criminal and civil liability contained in Article 27 paragraph (2) jo. Article 45 paragraph (3) of Law number 1 of 2024 concerning online gambling, Law, Law 8 of 1999 concerning Consumer Protection, Article 1365 of the Civil Code concerning PMH, Article 1320 of the Civil Code concerning the legal terms of the agreement, and the Witness and Victim Protection Agency (LPSK).
 
 

This research aims to describe the process of Ro’a mu’u ceremony in Pogon village and also To Analyzing meaning of the Ro’a mu’u ceremony in Pogon village. The research used qualitative method and data collection techniques are, interview, and documentation. Data analysis in this research used data reduces, data display, and drawing conclusion. The result of this research showed that Ro'a Mu’u is a marriage ritual as a symbol of the second opportunity for the bride and groom.Ro'amu'u is performed at the bride's house, after the bride and groom carry out a church wedding. Ro'amu'u is carried out home from the church in accordance with the agreement of both parties, namely the male side (me pu) and the female side (inaama). In this process, the woman will plant a banana tree in front of the house and replace the traditional Sikka sarong, in general, ro’amu’u in Sikka district is one of the traditional ceremonies marriage that is prioritized in traditional wedding ceremonies, Meaning of ro'amu'u is that the bride and groom feel that marriage is a very sacred value with the aim of continuing offspring so that their marriage is symbolized by meaning and social values. specialy in Pogaon village must preserve the customs and culture of ro'a mu'u the younger generation because this ceremony is a sacred wedding ceremony with the aim of continuing the lineage so that the younger generation must learn and carry out this ceremony for generations.