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.

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.

2-Indolinone nucleus is considered one of a promising heterocyclic core in medicinal chemistry that showed numerous range of activity among which antimicrobial, antioxidant, antiviral, antitubercular and anticancer activities. Cancer targeting is still an issue so there is a need for developing new agents that inhibit cancer growth without or low effect on normal body cells. Some derivatives of indolin-2-one are known to be a critical structure in some inhibitors of receptor tyrosine kinases (RTKs); a cancer target therapy, for example, Sunitinib. Herein in this review we focus on 2-indolinone derivatives as RTKs inhibitors as cancer targeting therapy.

This research aims to gain an overview of the evaluation results and the many challenges to implementing machining competency test certification (CTC) in Vocational High Schools (VHS). The research approach to evaluating this program is a qualitative method using the analysis of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The program evaluation design in this study uses the Assessment Evaluability and Performance Monitoring (AEPM) model, which has four evaluation components: Context, Inputs, Activities, and Performance Monitoring. The subjects involved in data collection through the distribution of questionnaires of five VHS in the Special Capital Region of Jakarta. The technique of determining all subjects using the Purposive Sampling technique. The results showed the level of effectiveness of the implementation of the machining CTC program. Some dimensions need to be strengthened, especially for the “less and “very lacking” category. Finally, the approach presented in this research using the AEPM model is a step forward in the analysis of the CTC program. This approach can easily be replicated in other countries with similar aims as this research.