Changes are occurring in customers demanding that companies produce environmentally friendly products. Apart from that, there is pressure from the government so that companies carrying out business activities can follow the rules and regulations set. The company has developed a system that uses technology to support production process activities properly and adequately. Blockchain technology makes green supply chain implementation faster and more flexible to continue improving supply chain performance. Data is collected on companies implementing blockchain technology and environmentally friendly programs. Data was collected at 512 manufacturing companies in Indonesia using Google Forms, which was distributed via email and social media. Data analysis used PLS to answer all research hypotheses. The research results showed that blockchain technology significantly influenced green supply chain management of 0.816, green practice of 0.370, supply chain flexibility of 0.115, and green supply chain performance of 0.150. Green supply chain management is a commitment for companies that have become top management commitments to adopt by reducing consumption of materials that impact the environment, which affects green supply practices of 0.473, supply chain flexibility of 0.244, and green supply chain performance of 0.247. Green supply practice, as a form of company best practice application, can influence supply chain flexibility by 0.428 and green supply chain performance by 0.214. Supply chain flexibility that has been running in manufacturing companies has had a significant impact on green supply chain performances of 0.331. This research provides a practical contribution to top management's commitment to running a green supply chain to increase company performance. Contribution for regulators and rule makers to continue to carry out continuous monitoring of business actors. Theoretical contributions to enrich theories about the green environment, green supply chain management, and blockchain technology.

Several models have been developed to predict financial difficulties and corporate bankruptcy. In this research various models were employed, including the Altman model (referred to as the Z-Score), the Springate model (known as the S-Score), the Zmijewski model (designated as the X-Score), and the Grover model (referred to as the G-Score). These techniques serve the purpose of evaluating the likelihood of encountering financial difficulties, which in turn determines the probability of PT Garuda Indonesia (Persero) Tbk going bankrupt. The study utilized secondary data sourced from financial statements spanning the years from 2020 to 2022. The application of the Altman model for bankruptcy prediction revealed that PT Garuda Indonesia (Persero), Tbk experienced financial distress throughout the period from 2020 to 2022. According to the Springate model, the company was in a state of distress and declared bankruptcy in 2020 and 2022, while 2021 fell into a grey area. The Zmijewski model indicated that the company was on the brink of bankruptcy, with financial difficulties and a potential risk of bankruptcy within the next three years. Grover's model predicted bankruptcy for the company in 2020 and 2022, but indicated safety in 2021. Notably, the Taffler model emerged as the most accurate in forecasting bankruptcy, boasting a 100% accuracy rate with no errors. Meanwhile, the Zmijewski model achieved an 81.25% accuracy rate with an error rate of 18.75%, and the Springate model exhibited the lowest accuracy in bankruptcy prediction, scoring only 12.50% accuracy with an error rate of 87.50%.

In this work, two organic materials as dyes, namely, methylene blue (MB) and methyl red (MR), have been proposed to play the role of the electron donor in organic photovoltaic (OPV) cells. We use the PCBM as a well-known electron acceptor. The density functional theory (DFT) method has been used to determine the electrostatic potential and the frontier molecular orbitals (FMO), of the methylene blue (MB) and the methyl red (MR) compounds. Nonlinear optical (NLO) descriptors have been determined for the two compounds. The potential energy surface analysis has been performed by the DFT method using the exchange and correlation of Becke, Lee, Yang, and Parr Gradient Corrected Functional (B3LYP) with the standard 6-31G(d) base. We have performed another theoretical study using quantum time-dependent density functional theory (TD-DFT) on both MB and MR as organic dyes to determine their UV-Vis spectra. The results of the energy gap, chemical hardness, dipole moment, and hyperpolarizability show that MB may be chemically more reactive Than MR. The present work has proposed a bilayer organic photovoltaic (OPV) cell to contribute to the valorization of the two dyes as solar materials. The developed photovoltaic cell project has used electrical and energetic parameters that can describe the OPV cell based on ([MB or MR]: PCBM). Open-circuit voltage (Voc), excitation energy, and oscillator strength have been theoretically determined. The results of the present work showed a remarkably high open circuit voltage, especially in the case of methyl red (1.55 V) more than in the case of the methylene blue (0.84 V) so that both of the two dyes can be a good candidate for organic solar cells.

This study applies the Motivation - Opportunity – Ability model on social networking platforms to study tourists’ selection of green hotels for their participation in sports events. The study analyzed the intention to choose green hotels of 324 Vietnamese tourists staying at 4- and 5-star hotels with green labels in Vietnam. The research results show that the Motivations, Opportunity, and Ability factors in using social networks have a positive impact on the belief in green hotels, thereby promoting the intention to choose green hotels when tourists participate in sports events in Vietnam. This study also provides some practical implications for green hotels in using social networks to enhance the trust and intention to choose a hotel of tourists participating in sports events. At the same time, the study also proposes some suggestions for directing human resources training towards the organization of tourism-integrated sports events to create responsible tourism products.

Materialism has emerged as a growing global drift in the past decade. This study highlights the influence of materialism on consumers' attitudes and actions regarding the purchase of environmentally sustainable products (ESPs) in a country such as Pakistan, which environmental orientations are in the developing stage. Materialistic perspectives can hinder the formation of positive attitudes towards environmentally sustainable products, especially when consumers engage in product comparisons. To evaluate the effects of materialism and product comparisons on purchasing intentions and behaviors, a survey was conducted among university students. The results revealed that consumers tend to avoid purchasing experience goods primarily because these items require a higher degree of involvement and risk assessment due to their intangible and experiential nature. This avoidance is particularly pronounced when the subjective norms and attitudes toward these goods are influenced by materialistic values, which often prioritize tangible, ownership-oriented products.

Condition-based maintenance (CBM) of industrial machines depends on the continuous, real-time monitoring of the machine’s operational condition via smart sensors attached to different components on the machine. The problem of regularly spaced missing data, which can occur due to a variety of hardware or software issues, is one that is often overlooked in the literature surrounding CBM in industrial machines. Such missing data can cause issues in interpreting the true operational state of the machine, which can reduce the effectiveness of CBM processes. In this paper, we examine the capabilities of five data imputation techniques for handling this regular missing data and examine the impact these techniques have on machine learning (ML) classification algorithms for machine fault diagnosis. We examine the following techniques: simple mean imputation, mean imputation with outliers removed, best and worst-case imputation, and previous day imputation. Each of these methods is configured with the specific parameters that they will only consider data from the previous 24 hours, to ensure that the data is recent, and adequately represents the current status of the machine. The efficacy of each method at accurately reconstructing the missing data and the impact they have on ML classification is recorded in the results. The models are evaluated on a real-world dataset and are evaluated on a variety of common performance metrics.

The pandemic COVID-19 has been spreading around the globe from December 2019 onwards and is considered the most infectious disease of this century. To date, there is no effective drug against SARS-CoV-2 discovered by pharmaceutical scientists, and the research is going rigorously all over the world. In this work, we examined the interaction of the already existing antivirals (Lopinavir, Atazanavir, and Remdesivir) with the structural proteins of SARS-CoV-2 using computational methods. Pharmacophore modeling of these drugs was conducted using molecular databases to determine the lead compounds from molecular databases. Pharmagist Webserver and Zinc Molecular Database were used to find out the pharmacophore and lead compounds, respectively. The drug-likeness properties of the compounds were evaluated by the SwissADME webserver. In silico studies showed that the binding affinities of the drugs followed the order Remdesivir > Atazanavir > Lopinavir. Docking and pharmacological studies revealed the potency and drug-likeness of the synthetic molecules.

This review provides a comprehensive overview of the development and applications of conjugated polymer semiconductors. The review summarizes the current state of research in this field, including the synthesis and characterization of these materials, their properties, and potential applications. The scope covers a wide range of topics, including organic photovoltaics, organic field-effect transistors, and conjugated polymer nanoparticles. The main findings highlight the significant progress that has been made in this field, with conjugated polymers exhibiting favorable electronic and optical properties, as well as high charge mobility. These materials have shown great potential for use in various applications, including solar cells, light-emitting diodes, and field-effect transistors. The applications are far-reaching, with the potential to revolutionize the electronics industry by providing low-cost, flexible, and environmentally friendly alternatives to traditional inorganic materials. One of the key challenges facing this field is the need to improve the efficiency and stability of conjugated polymer-based devices. While significant progress has been made in this area, there is still much work to be done to develop materials and device architectures that can achieve high performance over extended periods of time. Another challenge is the need to develop scalable manufacturing processes that can produce large quantities of high-quality conjugated polymers at a reasonable cost. The continued advancement of this field holds great promise for the development of new technologies that can improve our lives and help to address some of the world’s most pressing challenges.

Sukawati Art Market is one of the distinguished art markets in Bali for decades, which is also close to the Sukawati Public Market. The government is making efforts to revitalize the Sukawati Public Market to become the Sukawati Art Market, while the Sukawati Public Market is being relocated to a new location far from settlements. So that during the two years of the market revitalization and reallocation program it was quiet and triggered the emergence of traders around the old market area which disturbed public order. The purpose of this research is to analyze the polemic of stakeholders objectives regarding the Sukawati Market revitalization and relocation program, using MACTOR analysis. The results of the analysis show that local government, village government, and custom village are regulatory actors, while traditional markets' sellers are the main target of the project and serve as mediation to other actors. Actors still have polemic interests, especially traders outside the market. Therefore the local government needs to control, educate, and make innovation to facilitate the sellers around outside the traditional market. The revitalization program and market relocation are successful, the traditional markets’ sellers return to selling, and the market will be busy again.

This paper proposes an original view to determine the effect of P2P loans on MSME and non-MSME bank loans after the COVID-19 pandemic as a whole and then focuses on the island of Java (more developed areas) and outside Java (areas which are still undeveloped). The approach used in this study uses panel data regression from 33 provinces in Indonesia during Jan-Dec 2022 after the COVID-19 pandemic. The results of this study confirm that P2P lending is not a disrupter for bank credit, the details of the results are: (1) P2P lending has a significant positive effect on overall MSME banking credit, but has no significant effect on overall non-MSME banking credit; (2) P2P lending has no significant effect on MSME banking credit in Java, but has a significant positive effect on non-MSME banking credit in Java after the COVID-19 pandemic; (3) P2P lending has a significant positive effect on MSME banking credit outside Java after the COVID-19 pandemic, but has no significant effect on non-MSME banking credit in Java post the COVID-19 pandemic.