The new fully substituted (1H-1,2,3-triazol-4-yl)acetic acids were synthesized from available precursors (1H-1,2,3-triazol-4-yl)ethanones and 1Н-1,2,3-triazole-4-carboxylic acids, which are easily prepared by the Dimroth reaction from azides and 1,3-dicarbonyl compounds. The practical methods of homologization (the Arndt–Eistert reaction and homologization as a result of nucleophilic substitution by cyanide anion) and the Willgerodt-Kindler reaction were compared. The Willgerodt-Kindler method starting from (5-methyl-1H-1,2,3-triazol-4-yl)ethanones was selected as the most convenient method for the synthesis of 2-(1-aryl-5-methyl-1H-1,2,3-triazol-4-yl)acetic acids, which are promising building blocks for drug discovery. Additionally, (1H-1,2,3-triazol-4-yl)ethanones were studied for the synthesis of alcohols, amines and in the Johnson–Corey–Chaykovsky reaction.

In this study, we attempted to conduct a comprehensive review of the existing and pertinent literature on the topic of factor investment. We performed Scientometric analysis of studies published in reputable finance journals, i.e., The Journal of Portfolio Management, The Financial Analysts Journal, The Journal of Asset Management and others, during the years 2014 to 2023. To obtain the research data for our study, we gathered and examined a collection of 76 bibliographic records sourced from the Web of Science database. This database provided a comprehensive and reliable source of scholarly publications in the field of finance. To analyze the data, we employed Scientometric networks as part of our analytical approach. Scientometric networks allowed us to explore the relationships and connections between different publications, authors, and keywords within the domain of factor investment. To visualize and present the research findings, we utilized the Bibliometrix package for R, a powerful tool specifically designed for bibliometric analysis. This package enabled us to generate insightful visualizations that showcased the key patterns, trends, and interconnections within the literature on factor investment. By employing Scientometric analysis and leveraging the capabilities of the Bibliometrix package, we aimed to provide a comprehensive overview of the existing scholarly research in this field and contribute to the understanding of factor investment.

In today's financial landscape, individuals face challenges when it comes to determining the most effective investment strategies. Cryptocurrencies have emerged as a recent and enticing option for investment. This paper focuses on forecasting the price of Ethereum using two distinct methods: artificial intelligence (AI)-based methods like Genetic Algorithms (GA), and econometric models such as regression analysis and time series models. The study incorporates economic indicators such as Crude Oil Prices and the Federal Funds Effective Rate, as well as global indices like the Dow Jones Industrial Average and Standard and Poor's 500, as input variables for prediction. To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). Furthermore, regression analysis serves as an additional prediction tool. Additionally, we employ the Autoregressive Moving Average (ARMA) model to assess the relationships between variables (dependent and independent variables). To evaluate the performance of our chosen methods, we utilize daily historical data encompassing economic and global indices from the beginning of 2019 until the end of 2021. The results demonstrate the superiority of AI-based approaches over econometric methods in terms of predictability, as evidenced by lower loss functions and increased accuracy. Moreover, our findings suggest that the AI approach enhances computational speed while maintaining accuracy and minimizing errors.

The corrosion inhibition efficiency of the Schiff base N,N’-(5,5-dimethylcyclohexane-1,3-diylidene)dianiline (DmChDa) on mild steel in 1M HCl and 0.5M H₂SO₄ was evaluated using gravimetric, electrochemical impedance spectroscopy, potentiodynamic polarization and electrochemical noise measurement. Experimental results established that DmChDa possess relatively high corrosion inhibition capacity. Langmuir and El-Awady adsorption isotherms were obeyed by the Schiff base in 1M HCl and 0.5M H₂SO₄ respectively. SEM analysis showed that DmChDa was adsorbed on the steel surface during corrosion inhibition. The variation of metal dissolution with temperature was also examined using gravimetric study.

Based on paralyzed and death time, zinc oxide (ZnO) nanoparticles (NPs) were preliminarily investigated for possible biological effectiveness of anthelmintic activity on Pheretima Posthuma. After synthesizing ZnO NPs by low-temperature precipitation method with slight modification, size, morphology, and shape evaluation of these nanoparticles were characterized by scanning electron microscopy and annealing temperature in the range 400-700C. In this anthelmintic assay, the paralyzed time (PT) was gradually shorter with the increase of the sample concentration, for instance, when high concentration (40mg/ml) sample was applied on adult worms the paralyzed time was 35.6 minutes only. The death time (DT) was 450 and 80 minutes when the sample concentration was 20 and 40 mg/ml respectively. These results were compared with albendazole as a reference showed PT and DT 378.6 and 402.3 minutes respectively, and showed maximum dose-dependent anthelmintic activity. Finally, at higher concentration paralytic effect (PE) was showed much earlier and subsequently, it is important to note that the time taken for death observation was shorter.

The objective of this research is to study the reasons for the communication of societal accounting information in Tunisian companies. In fact, we have discussed the questions of the convergence of sustainability accounting models, the questions of the communication of accounting data or even questions of the participation of the actors. The results of qualitative research on a sample of 16 companies show a certain homogeneity in the assessment of the communication policy of societal accounting information of each type of company. It turns out that the social and societal activities identified come under the initiative of companies, their organizational culture and their extra-economic concerns. The social and societal concerns of these companies remain at the heart of the concerns of managers.

Gold and silver nanoparticles are easily synthesized and the most effective nanostructures in clinical practice. Their optical properties, ease of synthesis, colloidal stability and the ability to form any surface for easier conjugation with biological particles, the ability to individual multiple use, make them especially important in oncology. Gold and silver nanoparticles are capable of providing targeted drug delivery, which depends on the photothermal, photodynamic, and antiangiogenic properties of the metal. Due to their exceptional properties, these nanoparticles are considered as a potential tool for the diagnosis of various types of cancer and drug delivery. The non-toxic and non-immunogenic nature of gold and silver nanoparticles, as well as their high permeability and retention effect, provide additional advantages, allowing drugs to easily penetrate and accumulate at tumor sites. The effectiveness of precious metal nanoparticles as radiosensitizers, dose enhancers and contrast agents is gaining increasing recognition. An important problem associated with the use of biogenic metal nanoparticles is the study of their genotoxicity, since they play an important role in the initiation and progression of abnormalities, including genetic ones.

This paper offers a novel multiobjective approach – Multiobjective Ions Motion Optimization (MOIMO) algorithm stimulated by the movements of ions in nature. The main inspiration behind this approach is the force of attraction and repulsion between anions and cations. A storage and leader selection strategy is combined with the single objective Ions Motion Optimization (IMO) approach to estimate the Pareto optimum front for multiobjective optimization. The proposed method is applied to 18 different benchmark test functions to confirm its efficiency in finding optimal solutions. The outcomes are compared with three novel and well-accepted techniques in the literature using five performance parameters quantitatively and obtained Pareto fronts qualitatively. The comparison proves that MOIMO can approximate Pareto optimal solutions with good convergence and coverage with minimum computational time.

In recent years, the rapid transmission of information and interconnectedness of global financial markets have amplified the convergence and influence among them. Consequently, the occurrence of spillover effects in one market can significantly impact other markets. Accurately identifying and understanding these spillover effects is crucial for effectively managing and controlling market fluctuations. This research aims to measure and analyze the spillover effects between China's stock market and selected emerging economies in the Middle East, with a focus on exploring diversification opportunities. The analysis encompasses three distinct time periods, including the overall period from May 1, 2005, to May 31, 2023. The sub-periods consist of the first sub-period from May 1, 2005, to October 31, 2009, and the second sub-period from December 1, 2010, to May 31, 2023. Multivariate Generalized Heterogeneous Autoregression (MGARCH) is employed in this study to examine the spillover effects between China's economy and the emerging economies under consideration. The Granger causality analysis reveals a unidirectional causality running from the Chinese stock market to Jordan, as well as from the UAE to China throughout the entire observation period. However, no spillover effects are found between China and Saudi Arabia in either direction during any of the periods. Notably, a two-way causality is detected between the Chinese and UAE markets in the second sub-period. Furthermore, MGARCH results indicate no spillover effects from China to the emerging economies during the overall period, first sub-period, or second sub-period. The findings of this research offer valuable insights for investment portfolio managers in the Chinese economy, who may consider the examined emerging economies as potential destinations for risk diversification.

Nowadays, with increased demand for aggregates for concrete and an awareness of the need of protecting natural resources, experts are becoming increasingly interested in waste material as a building material substitute. However, the compressive strength is influenced by the composition of concrete. In this study, the compressive strength of concrete under substitution using waste from cockle shells and glass was investigated using Response Surface Methodology (RSM). Central Composite Design (CCD) based on RSM was used to assess the influence of epoxy resin, cockle shells powder, and glass powder on compressive strength responses. RSM developed first-order and second-order mathematical models with findings from experimental design. Analysis of variance was used to determine the correctness of CCD's mathematical models. Desirability analysis was then employed to optimize epoxy resin, cockle shells powder, and glass powder yielding maximum compressive strength. The RSM analysis revealed that the empirical results fit well into linear and quadratic models of concrete compressive strength. The mixing components will produce cement with compressive strength in each formulation of 54.71 MPa (4.88% epoxy resin and 4.0% cockle shells powder), 47.82 MPa (6.85% epoxy resin and 8.0% glass powder), 147.0 MPa, (4% cockle shells powder and 8% glass powder), and 56.08 MPa (4.4% epoxy resin, 4.0% cockle shells powder, and 8.0% glass powder). The results confirmed that a reasonable compressive strength of concrete could be achieved using epoxy resin, cockle shells powder, and glass powder.