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Even to date, the excessive mining of natural river sand is an unattempted issue. It is a global problem that impacts the river's ecosystem and the environment. This study has attempted to develop a new technique to modify vacant desert sand to replace 100% natural river sand. A technique is introduced in this study to modify desert sand by adding manufactured and recycled sand separately. Four new concrete mixes were prepared with developed modified sand. Additional four mixes were obtained when 12.5% ordinary Portland cement was replaced with silica fume. A detailed compressive strength was evaluated on 8 prepared mixes at curing ages of 3, 7, 28, 56, 91, and 360 days. The physical properties, such as density, absorption, and porosity, were evaluated at 28 days. The sulphuric acid attack was also studied to evaluate durability at 28, 56, 91, and 120 days. The developed concrete mixes with 12.5% silica fume at all curing phases revealed higher compressive strength than those without. The developed sustainable modified sand with 12.5% silica fume filled the pores and absorbed less water than the samples without silica fume. The modified sand with 12.5% silica fume revealed better density, water absorption, and voids and showed better resistance against 5% sulphuric acid solution than those without silica fume. It is concluded that all newly developed mixes showed satisfactory results. Moreover, the mix of (50% desert sand + 50% recycled sand) and 12.5% silica fume is the best sustainable mix for replacing 100% river sand and 12.5% cement from the concrete industry. Highlights A novel methodology has been developed to utilize 50% desert sand in the concrete industry. This study recommends the amendment in ACI-211.1-91 using fine desert sand in concrete. The modified sand concrete that was developed passed the strength and durability criteria. The mix (50%RS + 50%DS2 + 12.5%SF) is the best sustainable concrete for this study.

Biosynthesized nanoparticles have played vital role recently, as suggested to be alternative to physical and chemical methods. In this study, biosynthesis of zinc oxide nanoparticles (ZnO NPs) were carried out using leaf extracts of Phoenix dactylifera L. and Zinc nitrate. The effect of ZnO nanoparticles on biomass and biochemical parameters was investigated. Biosynthesized ZnO nanostructure was characterized using X-ray diffraction (XRD), transmission electron microscopy (TEM), UV–visible spectrophotometer and Fourier transform infrared spectroscopy (FTIR). Which resulted in spherical shape with size ranging between 16 to 35 nm of Biosynthesized ZnO nanoparticles and UV absorption beak at 370.5 nm with clear peaks of functional groups. The impact of different concentrations (0.0 mg/L, 80 mg/L and 160 mg/L) of biosynthesized ZnO nanoparticles on biomass and bioactive compounds production of Juniperus procera in vitro was investigated. The results showed that, biosynthesized ZnO NPs (80 mg/L and 160 mg/L) concentrations were boosted the growth of J. Procera with significantly compared to non-treated plants in vitro. The highest concentration (160 mg/L) of ZnO NPs was enhanced the growth of plant at beginning period, one month later shoots became yellow and callus turned to be brownish. Moreover, the influence of ZnO NPs on phytochemical compounds in callus of Juniperus procera was examined using GC–MS analysis. The differences among treatments were recoded . Overall, zinc oxide nanoparticles substantially improved the growth of shoots and callus with increasing of biochemical parameters such as chlorophyll a, total phenolic and flavonoids contents, besides the total protein and, SOD, CAT and APX activity. ZnO NPs might be induced some phytochemical compounds as well as inhibit.

Chat Generative Pre-trained Transformer (ChatGPT) has gained significant interest and attention since its launch in November 2022. It has shown impressive performance in various domains, including passing exams and creative writing. However, challenges and concerns related to biases and trust persist. In this work, we present a comprehensive review of over 100 Scopus-indexed publications on ChatGPT, aiming to provide a taxonomy of ChatGPT research and explore its applications. We critically analyze the existing literature, identifying common approaches employed in the studies. Additionally, we investigate diverse application areas where ChatGPT has found utility, such as healthcare, marketing and financial services, software engineering, academic and scientific writing, research and education, environmental science, and natural language processing. Through examining these applications, we gain valuable insights into the potential of ChatGPT in addressing real-world challenges. We also discuss crucial issues related to ChatGPT, including biases and trustworthiness, emphasizing the need for further research and development in these areas. Furthermore, we identify potential future directions for ChatGPT research, proposing solutions to current challenges and speculating on expected advancements. By fully leveraging the capabilities of ChatGPT, we can unlock its potential across various domains, leading to advancements in conversational AI and transformative impacts in society.

Metaheuristics represent an evolving problem-solving methodology that has proven to be effective in addressing optimization problems across various fields. Over the years, researchers have continuously strived to enhance the framework of metaheuristics in order to broaden their applicability, bolster their robustness, and ensure greater flexibility. In line with these objectives, we propose a novel framework called the Confluence Metaheuristic, which incorporates the concept of river confluence into the metaheuristic approach. The Confluence Metaheuristic operates by dividing the initial population into multiple streams, each subject to a different stochastic algorithm. These streams eventually converge at a single point and undergo further evolution through a specified number of iterations, yielding optimal results eventually. The proposed framework is of a general nature and has been implemented using two stochastic algorithms: particle swarm optimization (PSO) and genetic algorithm (GA). To validate the effectiveness of our proposed approach, we conducted extensive testing on ten benchmark functions. Furthermore, to substantiate the efficacy of our proposed idea, we subjected it to evaluation through three classical engineering problems. Statistical analysis was performed using Friedman and Wilcoxon tests, while sensitivity analysis was also conducted. The results obtained from this comprehensive evaluation were found to be highly satisfactory. Additionally, we compared the outcomes of our approach with the latest concept of ‘Multipopulation’. Moreover, our study contributes to improving various aspects of the metaheuristic framework, including the development of a more effective initialization strategy, a suitable parameter tuning approach, and an enhanced search technique.

Juniperus procera is a natural source of bioactive compounds with the potential of antitumor, antimicrobial, insecticidal, antifungal, and antioxidant activities. An optimization method was developed for total phenolic content (TPC), total flavonoid content (TFC), and total tannin content (TTC) in leaf and seed extract of Juniperus procera. Organic solvents (methanol (99.8%), ethanol (99%), and acetone (99.5%)), and deionized water (DI) were used for extraction. The estimation of TPC, TFC, and TTC in plant materials was carried out using UV-spectrophotometer and HPLC with the standards gallic acid, quercetin, and tannic acid. Recovery of TPC in leaf extract ranged from 2.9 to 9.7 mg GAE/g DW, TFC from 0.9 to 5.9 mg QE/g DW, and TTC ranged from 1.5 to 4.3 mg TA/g DW while the TPC value in the seed extract ranged from 0.53 to 2.6 mg GAE/g DW, TFC from 0.5 to 1.6 mg QE/g DW, and TTC ranged from 0.5 to 1.4 mg TA/g DW. This result revealed that methanol is the best solvent for recovery of the TPC value (9.7 mg) from leaf extract in comparison to other solvents. Ethanol recorded the highest result of TFC (5.9 mg) in leaf extract among the solvents whereas acetone was the best for TTC yield recovery from leaf extract (4.3 mg). In the case of the seed extract, ethanol was the best solvent for both TPC (2.6 mg), and TFC (1.6 mg) recovery in comparison to other solvents. Total tannin content in methanol resulted in significant recovery from seed extract (1.4 mg). Separation and quantification of gallic acid, quercetin, and tannic acid in plant materials were undertaken using HPLC. Gallic acid in leaf and seed of J. procera ranged from 6.6 to 9.2, 6.5 to 7.2 µg/g DW, quercetin from 6.3 to 18.2, 0.9 to 4.2 µg/g DW, and tannic acid from 16.2 to 29.3, 6.6 to 9.3 µg/g DW, respectively. Solvents have shown a significant effect in the extraction of phenolic compounds. Moreover, phytochemicals in plant materials were identified using GC-MS and resulted in very important bioactive compounds, which include anti-inflammatory, antibacterial, and antitumor agents such as ferruginol, phenanthrene, and n-hexadecanoic acid. In conclusion, the optimal solvent for extraction depends on the part of the plant material and the compounds that are to be isolated.

A thrombus straddling a patent foramen ovale (TSPFO) is a rare condition that presents significant health risks, including stroke or myocardial infarction, and can be life-threatening if not promptly addressed. We report the case of a 42-year-old female with morbid obesity who presented with sudden shortness of breath due to a bilateral pulmonary embolism. Imaging revealed a thrombus extending from the right atrium to the left atrium through the patent foramen ovale (PFO). Further evaluation showed a new diagnosis of dilated cardiomyopathy, resulting in biventricular heart failure, confirmed by cardiac MRI. Given her relatively stable hemodynamics during her hospital stay, the patient was successfully treated with anticoagulation, leading to complete resolution of the clot, thus avoiding the need for thrombolysis or surgical intervention. In managing TSPFO, treatment decisions should be based on factors such as hemodynamic stability, coexisting conditions, the acute nature of the diagnosis, and the risk of embolism. While some literature reviews and case studies suggest that surgery may lower the risk of complications compared to thrombolysis or conservative medical management, the lack of level I and II evidence highlights the need for an individualized, evidence-informed approach to effective risk management.

Decoding signals from the CNS brain activity is done by a computer-based communication device called a BCI. In contrast, the system is considered compelling communication equipment enabling command, communication, and action without using neuromuscular or muscle channels. Various techniques for automatic emotion identification based on body language, speech, or facial expressions are nowadays in use. However, the monitoring of exterior emotions, which are easily manipulated, limits the applicability of these procedures. EEG-based emotion detection research might yield significant benefits for enhancing BCI application performance and user experience. To overcome these issues, this study proposed a novel EKO-ALSTM for emotion detection in EEG-based brain–computer interfaces. The proposed study comprises EEG-based signals that record the electrical activity of the brain connected to various emotional states, which are gathered as real-time acquired EEG signals for emotion detection. The data was pre-processed using a bandpass filter to remove unwanted frequency noise for the obtained data. Then, feature extraction is performed using DWT from pre-processed data. Specifically, the proposed approach is implemented using Python software. The proposed system and existing algorithms are compared using a variety of evaluation criteria, including specificity, F1 score, accuracy, recall or sensitivity, and positive predictive values or precision. The results demonstrated that the proposed method achieved better performance in EEG-based BCI emotion detection with an accuracy of 97.93%, a positive predictive value of 96.24%, a sensitivity of 97.81%, and a specificity of 97.75%. This study emphasizes that innovative approaches have significantly increased the accuracy of emotion identification when applied to EEG-based emotion recognition systems. Additionally, the findings suggest that integrating advanced machine learning techniques can further enhance the effectiveness and reliability of these systems in real-world applications, paving the way for more responsive and intuitive BCI technologies.

Bioactive compounds of medicinal plants present as natural ingredients provide health benefits beyond the basic nutritional value of these products. However, the availability of bioactive compounds in the current natural sources is limited. Hence, the induction of bioactive compound production from medicinal plants through nanoparticles (NPs) might play a vital role in industrially important medicinal compounds. Therefore, this study aimed to synthesize silver nanoparticles (AgNPs) biologically and to investigate their effect on phytochemical compound production from the callus of Juniperus procera . AgNPs were synthesized biologically using aqueous leaf extract of Phoenix dactylifera , which acted as a reducing and capping agent, and silver nitrate solution. The formation of AgNPs has been confirmed through different analytical techniques such as UV-Visible spectroscopy (UV), Fourier-transform infrared spectroscopy (FTIR), dynamic light scattering (DLS), and scanning electron microscope (SEM). The impact of different concentrations (0.0, 5, 20, and 50 mg/L) of AgNPs on enzymatic and non-enzymatic antioxidants of the callus of J. procera was investigated. The obtained results showed a significant effect of AgNPs on biomass accumulation and non-enzymatic antioxidants (phenol, tannin, and flavonoid content). Additionally, total protein content and superoxide dismutase (SOD) activity were increased in response to AgNPs. Furthermore, bioactive compounds like gallic acid, tannic acid, coumarin, hesperidin, rutin, quercetin, and ferruginol were chromatographically separated and quantified using high-performance liquid chromatography (HPLC) with reference standards. These compounds were increased significantly in response to AgNPs treatments. We concluded that AgNPs could be a promising elicitor for improving the production of phytochemical compounds in medicinal plants. This work can serve asa good model for improving the production of bioactive compounds from medicinal plants in vitro. This molecular investigation should be done to understand better the metabolic mechanism leading to bioactive compound production scaling.

The distinctive characteristics of nanoparticles and their potential applications have been given considerable attention by scientists across different fields, particularly agriculture. However, there has been limited effort to assess the impact of copper nanoparticles (CuNPs) in modulating physiological and biochemical processes in response to salt-induced stress. This study aimed to synthesize CuNPs biologically using Solenostemma argel extract and determine their effects on morphophysiological parameters and antioxidant defense system of barley ( Hordeum vulgare ) under salt stress. The biosynthesized CuNPs were characterized by (UV–vis spectroscopy with Surface Plasmon Resonance at 320 nm, the crystalline nature of the formed NPs was verified via XRD, the FTIR recorded the presence of the functional groups, while TEM was confirmed the shape (spherical) and the sizes (9 to 18 nm) of biosynthesized CuNPs. Seeds of barley plants were grown in plastic pots and exposed to different levels of salt (0, 100 and 200 mM NaCl). Our findings revealed that the supplementation of CuNPs (0, 25 and 50 mg/L) to salinized barley significantly mitigate the negative impacts of salt stress and enhanced the plant growth-related parameters. High salinity level enhanced the oxidative damage by raising the concentrations of osmolytes (soluble protein, soluble sugar, and proline), malondialdehyde (MDA) and hydrogen peroxide (H 2 O 2 ). In addition, increasing the activities of enzymatic antioxidants, total phenol, and flavonoids. Interestingly, exposing CuNPs on salt-stressed plants enhanced the plant-growth characteristics, photosynthetic pigments, and gas exchange parameters. Furthermore, CuNPs counteracted oxidative damage by lowering the accumulation of osmolytes, H 2 O 2 , MDA, total phenol, and flavonoids, while simultaneously enhancing the activities of antioxidant enzymes. In conclusion, the application of biosynthesized CuNPs presents a promising approach and sustainable strategy to enhance plant resistance to salinity stress, surpassing conventional methods in terms of environmental balance.

Large language models (LLMs) have revolutionized natural language processing with their ability to generate human-like text through extensive training on large data sets. These models, including Generative Pre-trained Transformers (GPT)-3.5 (OpenAI), GPT-4 (OpenAI), and Bard (Google LLC), find applications beyond natural language processing, attracting interest from academia and industry. Students are actively leveraging LLMs to enhance learning experiences and prepare for high-stakes exams, such as the National Eligibility cum Entrance Test (NEET) in India. This comparative analysis aims to evaluate the performance of GPT-3.5, GPT-4, and Bard in answering NEET-2023 questions. In this paper, we evaluated the performance of the 3 mainstream LLMs, namely GPT-3.5, GPT-4, and Google Bard, in answering questions related to the NEET-2023 exam. The questions of the NEET were provided to these artificial intelligence models, and the responses were recorded and compared against the correct answers from the official answer key. Consensus was used to evaluate the performance of all 3 models. It was evident that GPT-4 passed the entrance test with flying colors (300/700, 42.9%), showcasing exceptional performance. On the other hand, GPT-3.5 managed to meet the qualifying criteria, but with a substantially lower score (145/700, 20.7%). However, Bard (115/700, 16.4%) failed to meet the qualifying criteria and did not pass the test. GPT-4 demonstrated consistent superiority over Bard and GPT-3.5 in all 3 subjects. Specifically, GPT-4 achieved accuracy rates of 73% (29/40) in physics, 44% (16/36) in chemistry, and 51% (50/99) in biology. Conversely, GPT-3.5 attained an accuracy rate of 45% (18/40) in physics, 33% (13/26) in chemistry, and 34% (34/99) in biology. The accuracy consensus metric showed that the matching responses between GPT-4 and Bard, as well as GPT-4 and GPT-3.5, had higher incidences of being correct, at 0.56 and 0.57, respectively, compared to the matching responses between Bard and GPT-3.5, which stood at 0.42. When all 3 models were considered together, their matching responses reached the highest accuracy consensus of 0.59. The study's findings provide valuable insights into the performance of GPT-3.5, GPT-4, and Bard in answering NEET-2023 questions. GPT-4 emerged as the most accurate model, highlighting its potential for educational applications. Cross-checking responses across models may result in confusion as the compared models (as duos or a trio) tend to agree on only a little over half of the correct responses. Using GPT-4 as one of the compared models will result in higher accuracy consensus. The results underscore the suitability of LLMs for high-stakes exams and their positive impact on education. Additionally, the study establishes a benchmark for evaluating and enhancing LLMs' performance in educational tasks, promoting responsible and informed use of these models in diverse learning environments.