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.

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.

The study aimed to explore the anticancer efficacy of indazole pharmacophore by analyzing a series of 109 derivatives of indazole as Tyrosine Threonine Kinase (TTK) inhibitors through quantitative activity relationship analysis using 2D and 3D QSAR techniques. The best 2D-QSAR model was generated by the MLR method, showing a high correlation coefficient (r²) of 0.9512, and good internal (q²), and external (pred_r²) cross-validation regression coefficients of 0.8998, and 0.8661, respectively. The residual values were modest, indicating good agreement between the observed and predicted pIC50 values, which suggested that the chosen model was predictably accurate. The 3D QSAR model, built using the SWF kNN approach, displayed a high internal cross-validation regression coefficient (q²) of 0.9132. Essential structural features/considerations in developing indazole as prospective anticancer medicines have been suggested. The study provides a reliable and predictive model for the prediction of anticancer activity of indazole derivatives. The identified essential structural features/considerations may be useful for the development of prospective anticancer medicines.

In the contemporary business landscape, effective interpretation of customer sentiment, accurate demand forecasting, and precise price prediction are pivotal in making strategic decisions and efficiently allocating resources. Harnessing the vast array of data available from social media and online platforms, this paper presents an integrative approach employing machine learning, deep learning, and probabilistic models. Our methodology leverages the BERT transformer model for customer sentiment analysis, the Gated Recurrent Unit (GRU) model for demand forecasting, and the Bayesian Network for price prediction. These state-of-the-art techniques are adept at managing large-scale, high-dimensional data and uncovering hidden patterns, surpassing traditional statistical methods in performance. By bridging these diverse models, we aim to furnish businesses with a comprehensive understanding of their customer base and market dynamics, thus equipping them with insights to make informed decisions, optimize pricing strategies, and manage supply chain uncertainties effectively. The results demonstrate the strengths and areas for improvement of each model, ultimately presenting a robust and holistic approach to tackling the complex challenges of modern supply chain management.