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.

A pressurized liquid extraction (PLE) technique was evaluated in this study and optimized using response surface methodology (RSM) for the determination of polycyclic aromatic hydrocarbons and their nitro derivatives from diesel particulate matter. A central composite design (CCD) was applied for the study of three parameters (temperature, nature of solvent and static time) and the optimal conditions were determined for a time of 9 min and a temperature of 145 °C and dichloromethane as extraction solvent. With the GC/MS analytical technique, the limits of detection (LOD) obtained range from 0.3 to 1.5 µg.L-1 for PAHs and from 4.6 to 8 µg.L-1 for nitrated PAHs. Also, this method allowed excellent recoveries of NPAH adsorbed on diesel soot particles (R ≥ 90%) using a mixture of solvents (1% acetic acid in pyridine). The PLE process developed in this research is quantitative and reproducible while using much less solvent than conventional extraction methods.

In this research, the photocatalytic effect was studied for sol-gel synthesized Mn/Mg co-doped metal oxide (TiO2) nanoparticles (NPs), which are encapsulated with anionic Gemini surfactant. The physicochemical properties of Mn/Mg co-doped TiO2 were investigated using a variety of characterization techniques, FT-IR, UV Vis-DRS, XRD, SEM, EDX, TEM, BET and PL. Characterization results reveal that Mn/Mg dopants stabilize TiO2 in the form of anatase phase and reduce the bandgap energy. Presence of strong chemical bonding and functional groups at the interface of dopant and TiO2 NPs was confirmed with FT-IR. The photocatalytic activity of these catalysts was assessed by the degradation of Orange II (AO7) using visible light irradiation. Of the variable nanomaterials MMT5-GS2 showed remarkable results. The photoluminescence studies revealed that OH radicals are the reactive species and responsible for oxidative photocatalytic degradation of Orange II.

This study aimed to identify the impact of entrepreneurship on the sustainability of small enterprises and productivity financed by the ministry of social development of Jordan. To achieve the objectives of the study, a descriptive analytical method was adopted, and a questionnaire was designed and distributed to members of the study sample consisting of owners of productive projects funded by the Ministry of Social Development in Jordan. The results show that Entrepreneurship has a significant impact on the sustainability of small enterprises and productivity financed by the ministry of social development of Jordan.

The study aimed to identify the Impact of Digital Leadership on Kuwaiti hospitals’ employee performance by using a descriptive and analytical methodology. According to statistics provided to us by the Kuwaiti Ministry of Health, the study population consists of administrators in Kuwaiti hospitals, numbering 12006. A simple random sample commensurate with the study's objectives was drawn, numbering 372 administrators to distribute the study questionnaire to them, and 277 valid questionnaires were retrieved for analysis. It was also found that there is a statistically significant impact at the significance level (α≤0.05) of digital leadership with its dimensions (innovation, persuasion, and knowledge) on Kuwaiti hospitals' employees' performance. The researchers recommended that it is essential to strengthen the factors that lead to enhancing the concept of digital leadership under its essential elements in the work environment. as well as paying attention to the priorities of the impact of the digital leadership elements on the performance of employees in order to obtain a practical impact.

The purpose of this study was to investigate the relationship between organizational health and the performance of employees in the extractive industry. The quantitative approach was employed as the research methodology by the researchers. Participants in the study are individuals who are employed by companies that are involved in the extraction of minerals and other resources. Three hundred forty-three valid questionnaires were recovered for further analysis from a sample of 360 employees who were found to be an appropriate fit for delivering the study questionnaire to them. The findings of the study indicate that organizational health, as measured by its dimensions (communication, trust, cohesiveness, targeting, and creativity), had a statistically significant impact on the employees’ performance at the extractive sectors in Jordan. The researchers provided a number of recommendations, including the necessity to build unified and fair administrative accountability mechanisms, as well as the need for extractive companies’ decision makers to collaborate in order to understand the demands and requirements of employees.

The relationship between behavioural characteristics (both rational and irrational measures) and capital structure determinants has been empirically validated. This study examines the influence of the behavioural traits of overconfidence and optimism on capital structure determinations by IDX-listed public Indonesian firms’ (Tbks) management. This is statistically tested via a comprehensive hypothesis modelling construct that includes empirically validated capital structure determinants (market timing, profitability, tangibility, size and their impacts on stock price). Panel regression PLS and path analysis were performed on stock price data and financial metrics extracted from the 2013–2020 financial statements of 55 Tbks from the LQ-45 and Kompas-100 stock indices. This study found that Optimism, Market Timing and Adjusted Debt on Market Timing are not determinants of capital structure for Tbks, while Overconfidence and the control variables Firm Profitability, Firm’s Asset Tangibility and Firm Size were statistically validated as capital structure determinants. Overconfidence (as a behavioural bias) is observed to have significant negative influence on management’s capital structure determinations, while Optimism has insignificant positive influence. The less aggressive leveraged models adopted by the sampled Tbks may indicate that implemented good principles of corporate governance have played a role in preventing capital structure determinations skewed by managements’ behavioural biases or psychological tendencies.

India had broken all the records and counts of confirmed COVID-19 cases per day and daily death toll reached over thousands. India is way far from other developed nations in the number of vaccine doses per 100 population. Although vaccination is an effective measure to be followed to overcome this grave situation, still certain misconceptions and rumors throughout the country have pulled a decent part of the population from being vaccinated. Another big challenge is production and supply of vaccines to meet the demand. COVID-19 pandemic will not end until the entire population gets vaccinated that would protect them from this deadly disease. Therefore, this paper aims at clearly identifying the factors and subsequently prioritizing them as barriers in effective immunization against COVID-19 in India following multi-criteria decision making (MCDM) technique. In this study, a fuzzy decision-making trail and evaluation laboratory (F-DEMATEL) approach is applied for understanding the contextual relationship among the barriers for effective immunization against COVID-19. The methodology is followed in a fuzzy environment to address the issue of uncertainty in the data gathered. The result suggests that the ‘Misinformation/ Misconceptions/ Lack of vaccine education in underserved communities’, ‘Lack of information regarding a vaccination center close to home’, ‘Difficulties in getting appointments’, ‘Supply chain issues in the distribution of vaccine’, and ‘Lack of access for marginalized communities’ are the important barriers in effective immunization against COVID-19. Recommendations have been made to overcome this situation and help to immunize the population and drag COVID-19 down to earth.