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Abstract A straightforward and sensitive spectrofluorimetric approach was established for the determination of heptaminol hydrochloride (HTM-HCl) based on the derivatization of the drug through its reaction with 5-dimethylaminonaphthalene-1-sulfonyl chloride (Dansyl chloride). The reagent underwent a nucleophilic substitution of its chlorine atom with HTM to give N-(5-dimethylaminonaphthalene-1-sulfonyl)-6-amino-2-methylheptan-2-ol. The highly luminescent derivative was extracted using methylene chloride and subjected to analysis at an excitation wavelength of 345 nm and an emission wavelength of 490 nm. The chemical reaction occurred within an aqueous environment buffered with a 0.1 M borate buffer solution adjusted to pH 10.5. Experimental findings indicate that the proposed method displays sensitivity and linearity across a concentration range from 0.03 to 2 µg mL− 1. The method achieves lower detection and quantification limits of 0.016 and 0.048 µg mL− 1, respectively. The analytical validation of this method followed the guidelines outlined by the International Council of Harmonization (ICH). This approach was applied effectively for quantifying the medication in both tablet and oral drops formulations available on the market, demonstrating excellent recovery of 98.95 ± 0.45 for tablets and 99.37 ± 0.24 for oral drops with no interference from excipients.

Abstract Artificial intelligence (AI) transforms the educational landscape by radically changing how lessons are taught and students are evaluated. In light of these facts, it is critical to consider learners’ emotional and mental wellness as the building blocks of education. Given these facts, it is imperative to consider learners’ emotional and mental well-being as the fundamental components of education. The key goal of this investigation was to investigate the relationships between learner autonomy (LA), academic buoyancy (AB), psychological well-being (PW), and academic success (AS) in Intelligent Computer-Assisted Language Assessment (ICALA). The current study was conducted on a cohort of 391 students registered in courses at private language institutions in Al-Kharj, Saudi Arabia, who were at the intermediate proficiency level. The following instruments were administered: the Academic Buoyancy Scale, the learner autonomy instrument, the learner psychological well-being, and the academic assessment. Then, to evaluate data and quantify the interaction between LA, AB, PW, and AS, confirmatory factor analysis (CFA) and structural equation modeling (SEM) were applied. The findings underscored the significance of LA and AB in achieving a balance in the PW of EFL pupils, particularly when utilizing ICALA for language acquisition. Additionally, the instructional implications of this study and potential avenues for future research are extensively investigated.

Abstract A novel, environmentally friendly, and sensitive HPTLC method has been developed and validated for simultaneous quantification of duloxetine (DLX) and tadalafil (TDL) in their pure state, laboratory-prepared mixtures, and spiked human plasma. This method is particularly important for patients dealing with depression and sexual issues, as it allows for the measurement of these co-administered antidepressant and sexual stimulant drugs in biological fluids. The separation process employed a stationary phase of pre-coated silica gel 60 F254 and a mobile phase consisting of ethyl acetate, acetonitrile, and 33% ammonia (8:1:1, v/v). The optimized mobile phase resulted in well-defined bands for DLX and TDL, with Rf values of 0.3 and 0.8, respectively with dual-wavelength detection at 232 nm for DLX and 222 nm for TDL. Polynomial regression analysis revealed exceptional linearity for both drugs, with correlation coefficients of 0.9999 over concentration ranges of 10–900 ng/band for DLX and 10-1200 ng/band for TDL. The quantitation limits were 8.2 ng/band for DLX and 8.6 ng/band for TDL, while the detection limits (LOD) were 2.7 ng/band for DLX and 2.8 ng/band for TDL. The validation of this method followed the guidelines set by the International Council for Harmonization (ICH). Additionally, the suggested method’s greenness was assessed by means of four up-to-date ecological tools, namely the Eco-Scale, the National Environmental Method Index (NEMI), the Green Analytical Procedure Index (GAPI), and the Analytical Greenness metric approach (AGREE). The proposed method was also assessed using the Blue Applicability Grade Index (BAGI), a recently developed metric for assessing the practicality (blueness) of procedures.

Abstract Among various natural hazards, those induced by hydrological and meteorological factors have emerged as the most frequent and damaging. India is at the second from the top, after Bangladesh, of flood hazard-affected countries of the world, causing immense damage to life and property every year. The Middle Ganga Plain in the country is one of the most severely affected regions due to the convergence of Himalayan rivers in the Ganga (Ganges) plain. This study focuses on optimizing novel ensemble models to enhance the mapping of flood hazard susceptibility in specific topoclimatic setting, characterized by a low-altitude-range, sub-tropical monsoonal climate, and a riverine floodplain environment of the tectonically active Middle Ganga Plain. This region forms part of the Ganga Foreland Basin prone to frequent floods. We employ a comprehensive flood inventory and twelve selected flood conditioning factors and is the first study that has developed these ensemble models in low relief riverine floodplain environmental setting with high resolution geomorphology as one of the conditioning factors. Four artificial intelligence ensemble models were employed in this work: Logistic Regression-Evidential Belief Function (LR-EBF), Logistic Regression-Frequency Ratio (LR-FR), Multi-Layer Perceptron-Evidential Belief Function (MLP-EBF), and Multi-Layer Perceptron-Frequency Ratio (MLP-FR). Results showed that LR-based ensembles outperformed MLP-based ensembles in the studied topoclimatic setting, with LR-EBF model achieving 87.2% and 84.7% success and prediction rates respectively. Our results reveal consistent performance differences between LR-based ensembles with FR and EBF models across different environmental settings. MLP-based ensembles, particularly with Frequency Ratio, displayed more significant performance variations. The findings underscore the importance of considering specific topoclimatic and altitudinal range environments when applying flood hazard susceptibility models. Our study also provides valuable insights for planning and policymaking in flood management practices in similar topoclimatic settings worldwide.

This study focuses on the biosorption of harmful metals from aqueous solutions using Enteromorpha compressa macroalgal biomass nanoparticles as the biosorbent. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction analysis (XRD) were employed to characterize the biosorbent. The effects of pH, initial metal ion concentration, biosorbent dosage, and contact time on the biosorption process were investigated. The maximum biosorption capacity for metals was observed at a pH of 5.0. The experimental equilibrium data were analyzed using three-parameter isotherm models, namely Freundlich, Temkin, and Langmuir equations, which provided better fits for the equilibrium data. A contact time of approximately 120 min was required to achieve biosorption equilibrium for various initial metal concentrations. Cr(III), Co(II), Ni(II), Cu(II), and Cd(II) demonstrated distinct maximum biosorption capacities of 24.99375 mg/g, 25.06894 mg/g, 24.55796 mg/g, 24.97502 mg/g, and 25.3936 mg/g, respectively. Different kinetic models were applied to fit the kinetic data, including intraparticle diffusion, pseudo-second-order, and pseudo-first-order versions. The pseudo-second-order model showed good agreement with the experimental results, indicating its suitability for describing the kinetics of the biosorption process. Based on these findings, it can be stated that E. compressa nanoparticle demonstrates potential as an effective biosorbent for removing targeted metals from water.

Abstract A novel antipsychotic medication named brexpiprazole (BRX) is currently employed for the treatment of schizophrenia and other psychotic disorders. Because BRX’s molecular structure includes a benzothiophene ring, it natively fluoresces. To detect BRX with precision and speed, a flow injection-fluorometric method, which is both sensitive and selective, is recommended. The fluorescence detection was conducted at 364 nm following excitation at 326 nm to capture the strong intrinsic fluorescence of BRX. The carrier solution employed was a mixture of phosphate buffer (pH 4, 10 mM) and acetonitrile (50: 50, v/v), with a flow rate of 0.5 mL min− 1. The calibration curve, based on peak areas, exhibited linearity within the concentration range of 20–350 ng mL− 1, with a remarkable correlation coefficient (r2) of 0.9999. The limit of quantitation was 9.7 ng mL− 1, and the limit of detection was found to be 3.2 ng mL− 1. This method was applied to quantify BRX in Neopression® tablets, achieving recovery within an acceptable range without interference from the tablet’s additives. Additionally, the proposed approach was successfully utilised to quantify the drug in spiked human plasma. The approach underwent validation following ICH requirements.

Abstract Electrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers (Albert River and Barratta Creek) was forecasted for up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method was used to determine the significant inputs (time series lagged data) to the model. To compare the performance of Boruta-XGB-CNN-LSTM models, three machine learning approaches—multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) were used. Different statistical metrics, such as correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error, were used to assess the models' performance. From 10 years of data in both rivers, 7 years (2012–2018) were used as a training set, and 3 years (2019–2021) were used for testing the models. Application of the Boruta-XGB-CNN-LSTM model in forecasting one day ahead of EC showed that in both stations, Boruta-XGB-CNN-LSTM can forecast the EC parameter better than other machine learning models for the test dataset (R = 0.9429, RMSE = 45.6896, MAPE = 5.9749 for Albert River, and R = 0.9215, RMSE = 43.8315, MAPE = 7.6029 for Barratta Creek). Considering the better performance of the Boruta-XGB-CNN-LSTM model in both rivers, this model was used to forecast 3–10 days ahead of EC. The results showed that the Boruta-XGB-CNN-LSTM model is very capable of forecasting the EC for the next 10 days. The results showed that by increasing the forecasting horizon from 3 to 10 days, the performance of the Boruta-XGB-CNN-LSTM model slightly decreased. The results of this study show that the Boruta-XGB-CNN-LSTM model can be used as a good soft computing method for accurately predicting how the EC will change in rivers.

Scouring around bridge piers is a critical concern, as the risk of bridge failures poses significant economic and safety threats to the public. Traditional models often struggle to accurately estimate the equilibrium scour depth (deq) at bridge piers due to the complexity of scour processes and their reliance on simple regression methods. This study combines two metaheuristic optimization techniques—Siberian tiger optimization (STO) and brown-bear optimization algorithms (BOA)—with artificial neural networks (ANNs) to enhance deq prediction accuracy for both round- and sharp-nosed piers using both field and laboratory data. The findings indicate that BOA and STO effectively optimize ANN hyperparameters, resulting in improved prediction accuracy. Furthermore, both BOA–ANN and STO–ANN outperformed empirical equations and other machine learning techniques. An explicit equation was also derived from the BOA–ANN model. The influence of independent variables on deq was further examined using SHAP, revealing that pier width has the greatest impact on deq estimates.

Accurate runoff forecasting is crucial for effective water resource management, yet existing models often face challenges due to the complexity of hydrological systems. This study addresses these challenges by introducing a novel bio-inspired metaheuristic algorithm, Artificial Rabbits Optimization (ARO), integrated with various machine learning (ML) models for runoff forecasting in the Carson and Chehalis River basins. The ARO-ML models are rigorously evaluated, demonstrating their superior ability to enhance forecasting accuracy compared to widely used ML techniques, including gradient boosting (GB), multi-layer perceptron (MLP), and K-nearest neighbor regression (KNN). In the Carson River, the GB model achieves the highest forecasting accuracy, which is significantly improved by ARO, resulting in a 24.8 % reduction in root mean square error (RMSE). The MLP model also benefits notably from ARO, with RMSE improvements of 4.8 % and a substantial 48.9 % reduction in mean absolute error (MAE). The ARO-optimized KNN model shows exceptional performance, surpassing the baseline KNN during testing. In the Chehalis River Basin, ARO integration leads to substantial improvements in both GB and MLP models across various performance metrics during training and testing. Crucially, the results indicate that in both river basins, rainfall in preceding days has the most significant influence on streamflow forecasts, followed by air temperature. This study advances hydrological forecasting by validating the efficacy of ARO-ML models, offering a robust framework for enhanced predictive accuracy and providing deeper insights into the key drivers of streamflow in diverse hydrological contexts.

Study region: The Miocene Fluvial Moghra Aquifer in the North Western Desert of Egypt Study focus: The study integrates stable isotope analyses with aeromagnetic and hydrogeological datasets to accomplish the following: (1) define the primary source(s) of recharge to the Moghra aquifer, (2) assess aquifer connectivity with the Nile River aquifer, and (3) investigate the vertical connectivity of the Moghra aquifer with the underlying Nubian Aquifer System (NAS) through subvertical faults. New hydrological insights for the region: The findings reveal (1) significant heterogeneity in groundwater isotopic compositions (Group A: δ18O: 1.1–13.8 ‰; δD: 4.6–73.5 ‰, B: δ18O:−0.99 to 0.85; δD: −7.58 to 4.38 ‰; and C: δ18O:−1.1 to −3.4; δD: − 6.3 to −18.2 ‰) indicative of variability in recharge sources. (2) Groundwater compositions west (up to 30 km) of the Nile River (Group A) resemble enriched modern Nile waters following the Aswan High Dam (AHD) construction that give way further west to relatively depleted groundwater (Group B) resembling historical pre-AHD Nile water compositions. (3) Further west from Group B depleted Group C samples occur along intersections of multiple fault systems (NW and NE-oriented faults) interpreted as mixtures of rising highly-depleted paleo Nubian Aquifer System (NAS) waters and pre-AHD Nile waters. (4) The advocated structural control is reported in similar settings in Egypt, suggesting that intersections of multiple fault systems provide regional connections between deep and shallow aquifers in northeast Africa, recharge overlying shallow aquifers, and should be considered in groundwater management scenarios.

Valproic acid and its derivatives are common drugs used in the treatment of epilepsy, a result of CNS disorders. Because the molecular structure of valproic acid, as a fatty acid, does not contain any chromophore groups, its peak response demanded special conditions to be detected in HPLC, ensuring the best precision results. Still, the current approach illustrates simple, validated procedures for conduction. The conducted chromatographic system consists of the BDS Hypersil C8, 150 × 4.6 mm, 5 µm column using a mobile phase of acetonitrile: phosphate buffer (4:6) at a detection wavelength of 215 nm at room temperature. A full method validation study was conducted and approved to ensure precise, repeatable, and accurate results by implementing system suitability parameters. In microbiological terms, a suitability test is used to validate microbiological testing methods, ensuring their efficacy despite potential interference from antimicrobial properties in the tested materials. The HPLC-UV developed and validated analytical method was evaluated, and it was found to be sensitive for use in the detection of low concentrations of methylparaben, propylparaben, and valproate with an optimum run time of six minutes. limit of detection (LODs) were statistically estimated and found to be 2.27 µg/mL, 39.77 ng/mL, and 1.84 µg/mL for methylparaben, propylparaben, and valproate, respectively. Additionally, the method demonstrated a high recovery of methylparaben, propylparaben, and valproate with an excellent closed-accuracy range (98.84% - 101.34%). An excellent regression coefficient (r) of 0.99924, 0.99998, and 0.99997 for methylparaben, valproate, and propylparaben, respectively, was achieved. Assay determination of various pharmaceutical dosage forms in the local market was implemented, yielding admirable results.

The study focuses on green methodologies in synthetic organic chemistry, emphasizing the use of thiamine hydrochloride (Vitamin B1) as an eco-friendly catalyst for synthesizing hydrazono-thiazolones. These compounds serve as versatile core units with various therapeutic activities, including anti-HIV, anti-tubercular, antifungal, antiviral, and anticancer properties. Building on prior research involving sustainable barium oxide-chitosan nanocomposite catalysts, this novel green synthetic approach contributes to the sustainable synthesis of bioactive heterocycles. The synthesized compounds 6a-l were tested in vitro against different bacterial species, revealing notable activity against Gram-positive and Gram-negative bacteria. The MIC investigation showed that compounds 6d, 6e, 6j, and 6k have broad-spectrum antibacterial potential, with 6j and 6k being especially effective, their MICs near those of ciprofloxacin, highlighting their strong antibacterial capabilities. Molecular docking studies predicting the binding mode of the most potent substances to Escherichia coli DNA gyrase (PDB ID 4DUH) aligned well with in vitro studies, confirming the new ligands’ potency as potential antimicrobial agents. In silico investigations for the most potent substances revealed a favorable ADME profile, suggesting promising pharmacological properties. This research contributes to both sustainable synthesis and potential therapeutic applications of hydrazono- thiazolone derivatives.

The potential of advanced tree-based models and optimized deep learning algorithms to predict fluvial bedload transport was explored, identifying the most flexible and accurate algorithm, and the optimum set of readily available and reliable inputs. Using 926 datasets for 20 rivers, the performance of three groups of models was tested: (1) standalone tree-based models Alternating Model Tree (AMT) and Dual Perturb and Combine Tree (DPCT); (2) ensemble tree-based models Iterative Absolute Error Regression (IAER), ensembled with AMT and DPCT; and (3) optimized deep learning models Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) ensembled with Grey Wolf Optimizer. Comparison of the predictive performance of the models with that of commonly used empirical equations and sensitivity analysis of the driving variables revealed that: (i) the coarse grain-size percentile D90 was the most effective variable in bedload transport prediction (where Dx is the xth percentile of the bed surface grain size distribution), followed by D84, D50, flow discharge, D16, and channel slope and width; (ii) all tree-based models and optimized deep learning algorithms displayed ‘very good’ or ‘good’ performance, outperforming empirical equations; and (iii) all algorithms performed best when all input parameters were used. Thus, a range of different input variable combinations must be considered in the optimization of these models. Overall, ensemble algorithms provided more accurate predictions of bedload transport than their standalone counterpart. In particular, the ensemble tree-based model IAER-AMT performed most strongly, displaying great potential to produce robust predictions of bedload transport in coarse-grained rivers based on a few readily available flow and channel variables.

A commonly wasted agricultural waste, pomegranate peel has attracted attention as a useful biomaterial with a wide range of potential uses. Fruit and vegetable wastes create environmental difficulties, as well as a loss of valuable biomass and a financial cost to enterprises, due to its high biodegradability. This study focuses on the extraction and characterization of cellulose from both the outer and inner layers of pomegranate peel. The cellulose extraction process involves a multi-step approach, encompassing delignification and mercerization. Fourier transform infrared spectroscopy (FTIR) revealed characteristic peaks associated with cellulose, confirming its presence in the extracted material. Field emission scanning electron microscopy (FESEM) depicted the morphological transformation of cellulose as non-cellulosic components were removed during the extraction process. Particle size analysis demonstrated that alkaline treatment reduced the cellulose particle size. X-ray diffraction (XRD) analysis indicated a significant increase in cellulose crystallinity due to the elimination of hemicellulose and lignin following the extraction process. The chemical composition analysis revealed that treated pomegranate peels contained higher cellulose content compared to their untreated counterparts. This study offers insightful information about the possible uses of pomegranate peel cellulose and its adaptability to a different application.

Accurate prediction of daily river flow (Qt) remains a challenging yet essential task in hydrological modeling, particularly crucial for flood mitigation and water resource management. This study introduces an advanced M5 Prime (M5P) predictive model designed to estimate Qt as well as one- and two-day-ahead river flow forecasts (i.e. Qt+1 and Qt+2). The predictive performance of M5P ensembles incorporating Bootstrap Aggregation (BA), Disjoint Aggregating (DA), Additive Regression (AR), Vote (V), Iterative classifier optimizer (ICO), Random Subspace (RS), and Rotation Forest (ROF) were comprehensively evaluated. The proposed models were applied to a case study data in Tuolumne County, US, using a dataset comprising measured precipitation (Pt), evaporation (Et), and Qt. A wide range of input scenarios were explored for predicting Qt, Qt+1, and Qt+2. Results indicate that Pt and Qt significantly influence prediction accuracy. Notably, relying solely on the most correlated variable (e.g., Qt-1) does not guarantee robust prediction of Qt. However, extending the forecast horizon mitigates the influence of low-correlation input variables on model accuracy. Performance metrics indicate that the DA-M5P model achieves superior results, with Nash-Sutcliff Efficiency of 0.916 and root mean square error of 23 m3/s, followed by ROF-M5P, BA-M5P, AR-M5P, AR-M5P, RS-M5P, V-M5P, ICO-M5P, and the standalone M5P model. The ensemble M5P modeling framework enhanced the predictive capability of the stand-alone M5P algorithm by 1.2 %–22.6 %, underscoring its efficacy and potential for advancing hydrological forecasting.

Abstract The paper presents and addresses the concept of “spontaneous sustainability”, the informal and unplanned sustainable development of communities, as the basis and framework to support the notion, drives, and agendas of “formal sustainability”. It follows the interdependence between the “spontaneous sustainability” of “distinct” communities and their “cultural” characteristics, including history, spatial continuity, values and behaviors, the associated physical settings, and tangible and intangible products, which enabled them to continue, survive, and develop. The research aims at formulating and validating a “framework” for monitoring and enhancing the “spontaneous sustainability” of “distinct” communities to enable, support and link it, to the broader frameworks of “sustainable development”. The proposed “framework” combines “sustainability” dimensions: environmental, economic, and socio-cultural; and the levels of community contexts, and spatial impact. The “framework” is formulated by integrating selected “urban sustainability” approaches, and systems, with later research that developed the key aspects, criteria, and indicators of those systems, together with research addressing “social and cultural sustainability”, and assessment aspects, criteria, and indicators. The “framework” is developed and validated through a limited questionnaire, involving local experts and specialists, academics, and practitioners, to point out limitations and potential, and to suggest the relative importance of its components, key aspects, and criteria. The validation supported the research propositions, the formulation, and development of the proposed “spontaneous sustainability framework” and emphasized its flexibility to include other approaches and agendas, and potential for further development and application in distinct local communities.

Abstract Background The Berlin definition of acute respiratory distress syndrome (ARDS) includes only clinical characteristics. Understanding unique patient pathobiology may allow personalized treatment. We aimed to define and describe ARDS phenotypes/endotypes combining clinical and pathophysiologic parameters from a Canadian ARDS cohort. Methods A cohort of adult ARDS patients from multiple sites in Calgary, Canada, had plasma cytokine levels and clinical parameters measured in the first 24 h of ICU admission. We used a latent class model (LCM) to group the patients into several ARDS subgroups and identified the features differentiating those subgroups. We then discuss the subgroup effect on 30 day mortality. Results The LCM suggested three subgroups (n 1 = 64, n 2 = 86, and n 3 = 30), and 23 out of 69 features made these subgroups distinct. The top five discriminating features were IL-8, IL-6, IL-10, TNF-a, and serum lactate. Mortality distinctively varied between subgroups. Individual clinical characteristics within the subgroup associated with mortality included mean PaO2/FiO2 ratio, pneumonia, platelet count, and bicarbonate negatively associated with mortality, while lactate, creatinine, shock, chronic kidney disease, vasopressor/ionotropic use, low GCS at admission, and sepsis were positively associated. IL-8 and Apache II were individual markers strongly associated with mortality (Area Under the Curve = 0.84). Perspective ARDS subgrouping using biomarkers and clinical characteristics is useful for categorizing a heterogeneous condition into several homogenous patient groups. This study found three ARDS subgroups using LCM; each subgroup has a different level of mortality. This model may also apply to developing further trial design, prognostication, and treatment selection.

Abstract The simultaneous assay of duloxetine hydrochloride (DLX) and avanafil (AVN) in their pure forms, synthetic mixtures, and spiked human plasma was achieved using a novel, eco-friendly, sensitive, and specific HPTLC methodology that have been established and validated. Measuring the levels of co-administered antidepressants and sexual stimulants in biological fluids is an important step for individuals with depression and sexual problems. Separation was performed successfully using pre-coated silica gel 60-F254 as a stationary phase and a mobile phase composed of methanol, acetone, and 33% ammonia (8:2:0.05, v/v/v). Compact bands were produced by the optimized mobile phase that was chosen for development (Rf values were 0.23 and 0.75 for DLX and AVN, individually) after dual-wavelength detection for DLX and AVN at 232 and 253 nm, respectively. The results of polynomial regression analysis were exceptional (r = 0.9999 for both medicines) over concentration ranges of 5-800 and 10-800ng/spot for DLX and AVN, respectively. The quantitation limits were 4.69 and 9.53 ng/spot (0.31 and 0.94 µg/mL), whereas the detection limits were 1.55 and 3.15 ng/spot (0.63 and 1.91 µg/mL), for DLX and AVN, respectively. The International Council for Harmonization (ICH) criteria served as the basis for validating the established approach. Moreover, the proposed technique was evaluated in terms of greenness using four contemporary ecological metrics: The Analytical Greenness software (AGREE), the Green Analytical Procedure Index (GAPI), Eco-Scale, and the National Environmental Method Index (NEMI). Additionally, the Blue Applicability Grade Index (BAGI), a newly developed tool for evaluating the practicality (blueness) of procedures, was taken into consideration when evaluating the sustainability levels of the established approach.

Abstract The paper addresses the emerging notion of “happiness” and “community happiness” as means and an objective in site planning and community design highlighting its complexities and illusive nature in terms of scope, aspects, and components to formulate a conceptual “framework” and a design tool for monitoring, enhancing, and developing community happiness, in limited scale urban settings. The research presents and follows the proposition that “happiness” is inherently addressed in site planning and design processes and the closely related notions and drives of “sustainable development” and “quality of life”. The design for the “community happiness” framework is formulated through a sequence of relational matrices, for “happiness”, “sustainable development”, and “quality of life”, each comprising selected international approaches and agendas, related indicators, and key aspects, physical, and non-physical, emphasizing similarities and overlapping. The proposed “framework” and underlying propositions were validated through a pilot questionnaire, targeting a sample of specialists, and practicing academics. The participants generally accepted the research key propositions, adopted the method and the proposed “framework”, and interacted with them, emphasizing the relative weights of the selected “happiness” key indicators and relations to site planning elements, and criteria. The relative importance of the design criteria, the related “happiness” indicators, and the likely products, as well as the cost of achieving “happiness” deserve to be further addressed, in future research.

Abstract Rational design of efficient pH‐universal hydrogen evolution reaction catalysts to enable large‐scale hydrogen production via electrochemical water splitting is of great significance, yet a challenging task. Herein, Ru atoms in the Ru2P structure were replaced with M = Co, Ni, or Mo to produce M2−xRuxP nanocrystals. The metals show strong site preference, with Co and Ni occupying the tetrahedral sites and Ru the square pyramidal sites of the CoRuP and NiRuP Ru2P‐type structures. The presence of Co or Ni in the tetrahedral sites leads to charge redistribution for Ru and, according to density functional theory calculations, a significant increase in the Ru d‐band centers. As a result, the intrinsic activity of CoRuP and NiRuP increases considerably compared to Ru2P in both acidic and alkaline media. The effect is not observed for MoRuP, in which Mo prefers to occupy the pyramidal sites. In particular, CoRuP shows state‐of‐the‐art activity, outperforming Ru2P with Pt‐like activity in 0.5 M H2SO4 (η10 = 12.3 mV; η100 = 52 mV; turnover frequency (TOF) = 4.7 s−1). It remains extraordinarily active in alkaline conditions (η10 = 12.9 mV; η100 = 43.5 mV) with a TOF of 4.5 s−1, which is 4x higher than that of Ru2P and 10x that of Pt/C. Further increase in the Co content does not lead to drastic loss of activity, especially in alkaline medium, where, for example, the TOF of Co1.9Ru0.1P remains comparable to that of Ru2P and higher than that of Pt/C, highlighting the viability of the adopted approach to prepare cost‐efficient catalysts.

Background In non-small cell lung cancer (NSCLC), chemoradiotherapy (CRT) yields pathological complete response (pCR) rates of approximately 30%. We investigated using ipilimumab plus nivolumab (IPI-NIVO) with neoadjuvant CRT in resectable, and borderline resectable NSCLC.Methods This single-arm, phase-II trial enrolled operable T3-4N0–2 patients with NSCLC without oncogenic drivers. Primary study endpoints were safety, major pathological response (MPR) and pCR. Treatment encompassed platinum-doublet concurrent CRT, IPI 1 mg/kg intravenous and NIVO 360 mg intravenous on day-1, followed by chemotherapy plus NIVO 360 mg 3 weeks later. Thoracic radiotherapy was 50 or 60 Gy, in once-daily doses of 2 Gy. Resections were 6 weeks post-radiotherapy.Results In a total of 30 patients in the intention-to-treat (ITT) population, grades 3–4 treatment-related adverse events (TRAEs) occurred in 70%, one TRAE grade 5 late-onset pneumonitis on day 96 post-surgery (1/30, 3.3%) occurred, and one non-TRAE COVID-19 death (1/30, 3.3%). pCR and MPR were achieved in 50% (15/30) and 63% (19/30) of the ITT; and in 58% (15/26) and 73% (19/26) of the 26 patients who underwent surgery, respectively. Postoperative melanoma was seen in one non-pCR patient. The R0 rate was 100% (26/26), and no patient failed surgery due to TRAEs. In peripheral blood, proliferative CD8+ T cells were increased, while proliferative regulatory T cells (Tregs) were not. On-treatment, pCR-positives had higher CD8+CD39+ T cells and lower HLA-DR+ Tregs.Conclusions Neoadjuvant IPI-NIVO-CRT in T3-4N0–2 NSCLC showed acceptable safety with pCR and MPR in 58% and 73% of operated patients, respectively. No patient failed surgery due to TRAEs.Trial registration number NCT04245514.

Sensing trivalent chromium ion (Cr(III)) is widely applied in different areas, such as clinical analysis, marine, environmental monitoring, or even chemical industry applications. Cr(III) has a significant role in the physiological process of human life. It is classified as an essential micronutrient for living organisms. Herein, we developed and designed a novel optical Cr(III) ions sensor film. The investigated sensor has a relatively small dynamic range of 1.24 × 10−3 to 0.5 μM. We report a highly sensitive optical sensor film for Cr(III) ions based on diethyl 3,4-diaminothieno[2,3-b]thiophene-2,5-dicarboxylate (3D) probe. The optical characteristics of the chemical probe exhibit substantial emission at 460 nm under 354 nm excitation. Besides, the interaction of the Cr(III) ions with 3D involves a complex formation with a 2:1 (metal: ligand) ratio, which is convoyed by the main peak enhancement that centered at 460 nm of 3D, and the main peak is red-shifted to 480 nm. The easily discernible fluorescence enhancement effect is a defining characteristic of the complexation reaction between the 3D probe and Cr(III). On the basis of the substantial fluorescence mechanism caused by the formation of a (Cr(III)-3D complex, which inhibits the photo-induced electron transfer (PET) process, the devised optical sensor was proposed. This film exhibits exceptional sensitivity and selectivity due to its notable fluorescence properties, stock shift of less than 106 nm, and detection capabilities at a significantly low detection limit of 0.37 × 10−3 μM. The detection procedure is executed by utilizing a physiological pH medium (pH = 7.4) with a relative standard deviation RSDr (1 %, n = 3). In addition, the 3D sensor demonstrates a high degree of affinity for Cr(III), as determined by the calculation of its binding constant to be 1.40 × 106. We present an impressive optical sensor that is constructed upon a three-dimensional molecule.

The Al-Qassim region, a prominent agricultural hub in Saudi Arabia, significantly contributes to the national production of vegetables and fruits. This study validated the standard EN-QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) method in conjunction with liquid chromatography–tandem mass spectrometry (LC–MS/MS) to determine 90 multiple pesticide residues in three categories of peppers: green bell, green hot and red chilli peppers. Validation criteria, including linearity range, accuracy, precision, limit of detection (LOD), and limit of quantification (LOQ), were within the acceptance range of the SANTE/11312/2021 guideline. The validated method was then used to analyse 536 pepper samples collected in 2023 from the Al-Qassim region of Saudi Arabia. The analysis of 536 pepper samples revealed that 394 samples (73.51 %) contained pesticide residues, with 126 (23.51 %) exceeding the established maximum residue limits (MRLs). The most frequently identified pesticide was imidacloprid (171 samples, 31.9 %) and acetamiprid (94 samples, 17.54 %), followed by bifenazate and difenoconazole, which were each detected in 66 samples (12.31 %). Among the remaining 32 pesticides, 24 were detected in 1%–10 % of the samples, whereas 8 were detected in

This study examines the complex relationships necessary for a sustainable economic recovery, focusing on the interplay between contracts for renewable energy, natural resource use, corporate social responsibility (CSR), and rights frameworks. Motivated by the increasing scrutiny of environmental practices, this research aims to highlight the need for sustainable business models during the transition to a more environmentally sensitive economy. The study area encompasses diverse sectors where CSR goals can be aligned with renewable energy project frameworks through natural resource utilization. Methodologies include a novel composite CSR evaluation indicator designed to complement industry rankings and a thorough analysis of CSR within the mining industry. Results demonstrate how aligning CSR with renewable energy initiatives can reshape profit models for stakeholders and emphasize the changing green product market as a catalyst for economic resurgence. Recommendations in the area of policies focus on the reasoned utilization of natural resources and the application of innovations following the principles of CSR. This research provides critical guidance to relevant authorities and institutions charged with ethical responsibility, ensuring the proper utilization and implementation of renewable energy sources to create a more ecological future based on green technology and sustainable resource management.

Biomass created by algae-based water purification has several potential applications outside the conventional energy, agriculture, and chemical industries. Herein, we use a specific type of algae, Enteromorpha intestinalis Ei, to remove significant metal ions. The Ei particles was converted into nanoparticles mechanically and their surface was chemically modified based on amino silane linker. The effect of utilizing dried Enteromorpha intestinalis nanoparticles, EiNPs, and silica-coated Enteromorpha intestinalis nanoparticles, Si-EiNPs, was examined for removing arsenic ions from an aqueous solution. Fourier transform infrared spectroscopy (FTIR) studied the chemical structure and shape of dry and silica-coated Enteromorpha intestinalis nanoparticles. Transmission electron microscope (TEM), Brunauer Emmett Teller (BET), and scanning electron microscopy (SEM). Batch experiments used the adsorbent dose, pH, temperature, contact time, and initial content of arsenic ion. The optimum conditions were exhibited by As3+ and As5+ concentrations of 10 ppm, contact time of 40 h, biosorbent dosage of 200 mg, Temperature 25 ± 1 °C, pH 6 for As3+ and pH 3 for As5+ and agitation rate 150 rpm. Thermodynamic experiments confirmed that the process is both spontaneous and exothermic. Kinetic models comprising pseudo-first-order, pseudo-second-order, and intra-particle diffusion models were implemented to examine the adsorption process of arsenic ions on the surface of the studied nanoparticles. Langmuir, Freundlich, Dubinin-Radushkevich (D-R), and Temkin isotherm models were examined to define equilibrium isotherms. However, the data were deemed to be better explained by the Freundlich isotherm model. The results indicate that the examined adsorbents, particularly EiNPs and Si-EiNPs, possess a strong capacity to adsorb and desorb arsenic ions, effectively purifying a contaminated effluent.

Sensing devices has been an interesting area of study because of their immense application in practical life. In the present work, the extraordinary optical transmission (EOT) along with surface plasmon polaritons (SPPs) excitation at the metal/ dielectric interface is investigated RF-module of COMSOL Multiphysics 5.3a has been used to investigate copper (Cu) nanograting structure on the glass substrate in periodic arrangement of 1-dimensional (1D). The visible-infrared electromagnetic wavelength of 400–900 nm has been used to excite the SPPs at the interface and a light port is provided from the substrate side. The optimum EOT has been investigated at transmission spectra of 0th order. During this process thickness of the slit is fixed at 50 nm, the periodicity of the unit cell is fixed at 700 nm, and the width of the slit changed to check its effect on the EOT. Additionally, phenomena of near field investigation have also used to explore the transmission-based performance of field at the specific boundary of copper (Cu) and air which confirm spectra outcomes of transmission thorough the fabricated device. The optimum value of EOT found when the width of slit is at 250 nm for the Cu/air interface. The device used for this purpose is modeled in COMSOL. Along the EOT the SPPs phenomena is also investigated by using present modeled device. These phenomena are observed by studying the electric and magnetic parts of the device that models the Cu/Air interface. We tested it with different slit widths ranging from 50-450 nm. The coupling efficiency and sensitivity of Cu/Air 1D device design at optimum slit width of 250 nm calculated. Such devices are increasingly applicable in bio photonics sensing of DNA structure, in vivo study of the internal structure of the body, imaging, surface chemical reaction, environmental remediation, and in various solar cell industries (plasmonic solar cell).

Abstract This study investigates the impact of Philosophy-Based Language Teaching (PBLT) on the critical thinking skills and learner engagement of English as a Foreign Language (EFL) students. A concurrent mixed-methods approach, including semi-structured interviews, document analysis, and academic achievement tests, was employed with an experimental group (EG) exposed to PBLT and a control group (CG) receiving traditional instruction. The results reveal a substantial positive effect of PBLT on the critical thinking abilities of EFL learners, as evidenced by thematic analyses of interviews and document content. Themes include heightened critical thinking awareness, collaborative knowledge construction, and increased learner engagement. Academic achievement tests further demonstrate significant improvement in the experimental group’s performance. A comparison with existing literature underscores the novelty of our mixed-methods approach. Implications for language teachers, materials developers, syllabus designers, and policy-makers are discussed, highlighting the potential of PBLT in cultivating critical thinking and learner engagement.

Abstract This research investigated the impact of planning time, working memory (WM), and cognitive styles on language learning outcomes within the framework of Task-Based Language Teaching (TBLT). Drawing on a diverse sample of language learners, the study employed a pretest-posttest control group quasi-experiment to examine the effects of providing pre-task planning time on language performance. Participants engaged in communicative tasks, with a focus on vocabulary acquisition and task complexity, while their cognitive processes were assessed through measures of WM and cognitive styles. The findings revealed significant interactions between planning time and cognitive styles, particularly field dependence, influencing language production and proficiency such that learners with planning time outperformed learners without planning time; high-WM learners outperformed their low-WM peers, and field independent learners outstripped their field-dependent counterparts. Moreover, the study contributes to the broader understanding of the nuanced relationship between planning time, WM, and cognitive styles in the context of TBLT. The implications of these findings for language teachers, materials developers, syllabus designers, curriculum developers, and policymakers are discussed, offering insights into the design of effective language learning environments. Despite certain limitations, the study provides a foundation for further research exploring cross-cultural variations, longitudinal effects, and the integration of technology in language education, with the aim of advancing pedagogical practices tailored to diverse learner profiles.

Abstract Four sensitive and fast analytical approaches relied on ion pairing with eosin Y were built up and evaluated using spectroscopy for determination of Alcaftadine and Olopatadine hydrochloride with high sensitivity and selectivity. Two spectrofluorimetric techniques were employed to observe the quenching effect of Alcaftadine or Olopatadine hydrochloride on the intrinsic fluorescence of eosin Y in a 0.1 M acetate buffer solution at pH 3.8 and 3.3 for Alcaftadine and Olopatadine hydrochloride, respectively. Those methods are considered the first spectrofluorimetric methods for Alcaftadine and Olopatadine hydrochloride assay. The fluorescence quenching effect was linear with concentration ranging from 150 to 2000 and 200 to 2000 ng mL−1 for Alcaftadine and Olopatadine hydrochloride, respectively. In the two spectrophotometric techniques, the absorbance of the produced ion-pair was monitored at 548 and 547 nm in aqueous buffered solution at pH 3.8 and 3.3 for Alcaftadine and Olopatadine hydrochloride, respectively. Beer's law was obeyed in the concentrations range of 0.8–8.0 and 1.0–10.0 µg mL−1. The four techniques were evaluated in accordance with ICH requirements and were effectively used to analyze dosage forms with a high percent recovery.

Egypt’s geographic location and natural resources make it an ideal candidate for Renewable Energy (RE) projects. The government has implemented initiatives and policies to promote RE development, resulting in notable projects such as the Benban Solar Park and wind farms in the Gulf of Suez. These endeavors aim to combat climate change, reduce greenhouse gas emissions, and foster sustainable development. Multicriteria decision making techniques are increasingly favored due to their ability to accommodate diverse criteria for selecting RE sources. This paper evaluates numerous RE options in Egypt, including solar, wind, hydropower, and biomass energy, and proposes a hybrid strategy for classifying RE sources for power generation based on criteria such as finance, environment, technology, and community. The study utilizes four multicriteria decision making methods: the analytical hierarchy process, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), Fuzzy Technique for Order Preference by Similarity to an Ideal Solution (FTOPSIS), and fuzzy analytical hierarchy process. The key findings indicate that solar and wind energy are the most viable options for Egypt’s renewable energy strategy, with the fuzzy technique for order preference by similarity to an ideal solution method proving to be the most effective for ranking these sources by providing a comprehensive evaluation framework that aligns well with related studies. These findings have significant implications for policy and investment, as they provide a robust decision-making framework that ensures balanced consideration of financial, environmental, technological, and social factors. The study underscores the value of using advanced multicriteria decision making techniques to guide RE development in Egypt, contributing to the global effort to combat climate change.

Abstract Enhancing heat transfer rates within enclosures is a topic of considerable interest since it has several technical applications. Most heat transfer research projects focus on increasing the heat transfer rates of thermal systems since this will raise the systems' total efficiency. The geometry of the enclosure might have a substantial impact on heat transfer rates. This research studies quantitatively the natural convection of a nanofluid in a complicated form geometry with many baffle configurations. The system's governing equations were addressed by the Galerkin Finite Element Method (GFEM). The main consideration was given to the effects of the following factors: The Darcy number (Da), which ranges from 10–2 to 10–5; the Hartmann number (Ha), which ranges from 0 to 100; the volumetric fraction (ϕ), which ranges from 0 to 0.08, and the Rayleigh number (Ra) (102 to 106). The results suggested that raising Ra increases heat transfer discharge, whereas raising Ha and Da decreases it. In terms of heat transmission, case 1 (the case with a wavenumber of 1 and the zigzag pointing outward) is determined to be the optimum cavity structure, as it obtained the highest mean Nusselt (Nuavg) number when compared to other cases. At the highest studied Ra number, growing (ϕ) from 0 to 0.8 improved Nu avg by 25%, while growing Da from 10–2 to 10–5 and Ha from 0 to 100 declined Nu avg by 57% and 48%, respectively. The reason for the improvement in the values of the (Nu) is due to the speed of fluid movement within the compartment. Also, the shape of fins plays a major role in strengthening and weakening thermal activity.

Abstract For the treatment of rhinitis and asthma, a combination of Montelukast sodium and Bilastine has just been approved. Based on the first derivative of synchronous fluorescence, the current work developed a green, highly accurate, sensitive, and selective spectroscopic approach for estimating Montelukast sodium and Bilastine in pharmaceutical dosage form without previous separation. The selected technique focuses on measuring the synchronized fluorescence of the studied medications at a fixed wavelength range (Δλ) = 110 nm, and using the amplitude of the first derivative's peak at 381 and 324 nm, for quantitative estimation of Montelukast sodium and Bilastine, respectively. The impacts of different factors on the referred drugs' synchronized fluorescence intensity were investigated and adjusted. The calibration plots for were found to be linear over concentration ranges of 50–2000 ng mL−1 for Montelukast sodium and 50–1000 ng mL−1 for Bilastine. Montelukast sodium and Bilastine have LODs of 16.5 and 10.9 ng mL−1, respectively. In addition, LOQs were: 49.9 and 33.0 ng mL−1, for both drugs, respectively. The developed method was successfully employed to quantify the two drugs in synthetic tablets mixture and in laboratory prepared mixtures containing varied Montelukast and Bilastine ratios. To compare the results with the published analytical approach, a variance ratio F-test and a student t-test were used, which revealed no significant differences.

Sandy soil is poor in organic matter and nutrients. Animal production shortage due to the animal feed shortage needs an increased production of fodder crops. This experiment aims to study the effect of single and/or combined treatments of inorganic mineral, organic and bio-fertilization on the growth and macronutrients content of successive four cuts of sudan grass (sorghum vulgare var. sudanense) plants grown on sandy soil as well as the macronutrients’ availability in soil. A pot experiment was carried-out under greenhouse conditions at the experimental farm, Faculty of Agriculture, Zagazig University (summer season of 2019) in a randomized complete block design with four replicates. The organic fertilization (Org.) was four types of manures 1. chicken manure (CM), 2. farmyard manure (FYM), 3. quail manure (QM), and 4. rabbit manure (RM). Individual and combined treatments of 0.5 and/or 1% organic manures (CM, FYM, QM, and RM) with/without the mineral fertilizers (N-P-K, 50 and/or 100% RD recommended dose) with/without the bio-fertilizer Microbein (Mic) were mixed with the soil before planting. A control CL (without addition) was used. The results has showed that adding any organic sources at the rate of 0.5% with 50% N-P-K RD plus inoculation by Mic gave the highest significant values of four cuts and accumulative fresh and dry weight as well as N, P and K content (%) of Sudan grass compared with the CL treatment. The N and P of soil (mg kg−1) after harvesting were improved using 50% N-P-K with any organic wastes plus Mic inoculation. Organic and bio-fertilizer used with 50% N-P-K improved the Sudan grass cultivation in the sandy soil and can reduce the pollution of underground water.

Abstract Test anxiety is a combination of a confluence of physiological hyperarousal, tension, and bodily manifestations, with apprehension, trepidation, fear of inadequacy, and the tendency to magnify negative outcomes, which manifest before or during evaluative assessments. Online evaluation might potentially exacerbate anxiety and demotivation among learners. When students’ psychological and emotional well-being are taken into account, it may be possible to improve their educational and evaluative experiences. This study set out to shed light on the interplay between anxiety, demotivation, academic buoyancy, and autonomy in online assessment. To achieve this objective, printed copies of the related questionnaires were distributed among 392 EFL university students in China. The association between anxiety, demotivation, academic buoyancy, and autonomy in online assessment was assessed by data screening utilizing confirmatory factor analysis (CFA) and structural equation modeling (SEM). The findings indicated that students who experienced less anxiety and demotivation were more buoyant and autonomous. Possible improvements in language education and assessment are considered, as are the study’s broader implications.

In this study, two deep learning approaches, bidirectional long short-term memory (BiLSTM) and long short-term memory (LSTM), were used along with adaptive boosting and general regression neural network to forecast multi-step-ahead pan evaporation in two arid climate stations in Iran (Ahvaz and Yazd). Lagged time series of meteorological data and pan evaporation data were input to the machine learning models. Two feature selection methods, i.e., the Boruta extra tree and XGBoost, were used to select significant inputs to reduce the number of inputs and model complexity. Different statistical metrics were used to investigate the model performance. The results demonstrated that Boruta-extra-tree-based models were more accurate than XGBoost-based models. Compared with the machine learning techniques, the combination of Boruta extra tree and BiLSTM enabled more accurate one-day-ahead forecasting of pan evaporation for both sites (Root Mean Square Error (RMSE) = 1.6857, for the Ahvaz station, and RMSE = 1.3996 for the Yazd station). The proposed model was used to forecast up to 30 days ahead of pan evaporation in both stations. The results showed that the Boruta-BiLSTM model could accurately forecast the pan evaporation for up to 30 days in both stations.

Two brachiopod species, Terebratulina tenuistriata (Leymerie, 1846) and Orthothyris pectinoides (von Koenen, 1984), have been recorded for the first time from the Middle Eocene (Late Lutetian) nummulitic limestone beds in the Darnah Formation at the Wadi Darnah area in Northeast Libya. These brachiopod species are associated here with Nummulites discorbinus (Schlotheim), Nummulites praelyelli (Boukhary and Kamal), and Nummulites bullatus (Schaub) and are widely distributed on this Middle Eocene Nummulites carbonate platform. The two recorded species are common in the Eocene rocks of Europe and the Arabian Gulf. In northern Africa, the brachiopod species Terebratulina tenuistriata (Leymerie) was only recorded from the Middle Eocene (Bartonian) of Egypt, while Orthothyris pectinoides (von Koenen) is firstly recorded from the Middle Eocene of the southern Tethyan Province (NE Libya) in the present work.

Numerical methods in the area of nonlinear systems are extensively implemented for computing their approximate solutions because these systems are very difficult to tackle analytically. There are various numerical techniques available in the literature to find the solutions of nonlinear oscillators. Variational iteration method (VIM) is one of these approaches which is convenient to implement for these kinds of problems. In this work, our study aims to identify the numerical solution of nonlinear oscillator by making use of variational iteration method associated with Formable transformation. For the smooth utilization of this approach, we have to formulate the Lagrange multiplier through variational theory. Furthermore, we develop a new unified iterative scheme for the correction functional of VIM, considering the Formable transformation. Several new schemes of correction functional can be deduced from the newly proposed method considering the duality relation of Formable transform. In support of our primary finding, we discuss numerical example as application. A number of Physical applications of nonlinear oscillators are available in the field of vibrations and oscillations but in recent times nonlinear oscillators are used to describe complicated systems or to address mechanical, electrical, and other engineering phenomenon.

The designed and synthesized multifunctional properties of bis-hydrazono[1,3,4]thiadiazoles became important for developing an efficient medication for Alzheimer’s disease. Thiadiazoles were characterized and studied towards this target as an acetylcholinesterase inhibitor (ChEIs). In this study, one-pot synthetic strategy was applied for the synthesis of bis-hydrazono[1,3,4]thiadiazole series from 2,2′-(1,2-diphenylethane-1,2-diylidene)bis(hydrazine-1-carbodithioic acid) and hydrazonoyl chlorides. The greener pastures, in silico, an environmentally-friendly and free computer-aided method, bioactivity studies of the bis-hydrazono[1,3,4]thiadiazoles 4a-h exhibited various possible interesting inhibitory activities against AChE showing similar behaviour to the approved drugs, Donepezil, Galantamine and Rivastigmine. For a fair comparison, the superpositions of 4a-h, and Donepezil, Galantamine and Rivastigmine docked together into the functional domain (binding pocket) of 1EVE to reveal interesting different molecular laydowns of the compounds. On the other side, the display of the molecules before and after docking was discussed. The binding affinity slightly differs between Donepezil, Galantamine, Rivastigmine, and the ligands 4a-h. The range of the recorded binding affinity for 4a-h is from (−9.4 to −8.4 kcal/mol−1), whereas the binding affinity for Donepezil, Galantamine and Rivastigmine is (−11.1, −9.8 and −8.0 kcal/mol−1 respectively), which is higher than all the prepared ligands.Furthermore, the binding amino acids also varied among the studied compounds in this study. Phe290, Phe330, Phe331, Trp279, Tyr70, Tyr121 and Tyr334 are the common amino acids binding with the FDA-approved AChE inhibitors for treating AD, Donepezil, Galantamine and Rivastigmine and ligands. Notably, the number of hydrophobic and hydrogen interactions studied between the ligands 4a-h and the Donepezil, Galantamine and Rivastigmine drugs were compared as a preliminary indicator towards a successful inhibitor. The comparative study in this research work aims to rank our compounds with respect to the approved medications. This is a friendly environmental preliminary helpful indication before leaping into time and energy-consuming experimental work. Organ toxicological endpoints and toxicity-predicted activity were obtained using the ProTox-II web server for both the approved medication and ligands. Later, in-vitro acetylcholinesterase inhibition assays were conducted to assess the efficacy of bis-hydrazono[1,3,4]thiadiazoles 4a-h as inhibitors of acetylcholinesterase (AChE) in comparison to Donepezil. Results indicated promising inhibition activities for example 4 h, 4 g and 4d to the testing drug Donepezil for breaking down the neurotransmitter acetylcholine.

The objective of the manuscript is to model and analyze nonlinear waves dynamics through fuzzy-fractional calculus. Since fuzzy logic facilities the waves dynamics to be uncertain, while fractional calculus captures the memory effect inherent in wave propagation. The current study focuses on modeling and analysis of fuzzy-fractional KdV equations namely Burgers KdV, Caudrey-Dodd-Gibbon KdV, and generalized KdV. To include uncertainty in the models, symmetric Gaussian fuzzy numbers are utilized in three different cases at upper and lower bounds in fractional environment. For numerical simulations, hybrid of Mahgoub transformation with homotopy perturbation is proposed and successfully implemented in fuzzy-fractional sense. Validity and competence of proposed methodology is confirmed theoretically by proving existence, uniqueness and convergence. The crest and trough in waves are analyzed in 2D and 3D simulations with respect to time, space, fractional parameter, and k-level sets. The obtained results highlight the accuracy of proposed methodology in case of nonlinear fuzzy-fractional waves dynamics and can be extended to other models in science and engineering.

Abstract Task-based instruction (TBI) is a pedagogical approach that centers on engaging learners in meaningful and communicative tasks to promote language acquisition, active participation, and practical language use. The primary objective of this investigation was to assess the influence of TBI on reading comprehension, motivation for L2 reading, anxiety, and L2 grit. Thus, two pre-existing classes from a large university in China were selected and one of them was assigned as the treatment and the other as the comparison group. An Oxford Quick Placement Test determined that the subjects possessed an intermediate level of proficiency. The experimental group received reading comprehension-based TBI, while the control group received traditional lecture-based strategy training. Each of the dependent variables was tested at two points of time. Based on a conducted t-test, while both groups knew exactly the same as time 1, the comparison group did less on the posttest in terms of reading comprehension skills than its treatment counterpart. The effect size was large. Furthermore, the results of a series of chi-squares showed that TBI had facilitating effects on motivation for L2 reading, language learning anxiety, and L2 grit, all with large effect sizes. That is, on the posttest, the number of motivated learners and grittier learners in the treatment group increased, while the number of high-anxiety learners in the experimental group decreased sharply. However, the participants’ motivation for L2 reading, anxiety, and L2 grit did not significantly change after the treatment. The implications of the study are explained.

Abstract In this study, the Sobolev-type equation is considered analytically to investigate the solitary wave solutions. The Sobolev-type equations are found in a broad range of fields, such as ecology, fluid dynamics, soil mechanics, and thermodynamics. There are two novel techniques used to explore the solitary wave structures namely as; generalized Riccati equation mapping and modified auxiliary equation (MAE) methods. The different types of abundant families of solutions in the form of dark soliton, bright soliton, solitary wave solutions, mixed singular soliton, mixed dark-bright soliton, periodic wave, and mixed periodic solutions. The linearized stability of the model has been investigated. Solitons behave differently in different circumstances, and their behaviour can be better understood by building unique physical problems with particular boundary conditions (BCs) and starting conditions (ICs) based on accurate soliton solutions. So, the choice of unique physical problems from various solutions is also carried out. The 3D, line graphs and corresponding contours are drawn with the help of the Mathematica software that explains the physical behavior of the state variable. This information can help the researchers in their understanding of the physical conditions.

Background: Hydroponic feeding system enables more effective utilization of the entire plant than typical grazing, which only consumes the plant's shoot. Aim: This study evaluated the effects of feeding maize hydroponic fodder on growth performance, nitrogen balance, nutrient digestibility, hematology and blood metabolites of buffalo calves. Methods: Twelve water buffalo calves, weighing an average of 112 +1.18 kg and between 8 and 10 months old, were divided into three treatments, each with four calves. Each group received one of the treatment diets: T1: the basal diet (BD) at 100%; T2: the BD plus hydroponic feed meal (HFM) at 80%: 20%; and T3: the BD plus HFM at 60%: 40%. For 100 days, each animal was fed ad libitum; the first ten days were used for nutritional adaptation, and the final ten days were used for collection. In addition to their basal diet, each animal received 200 g/day of a normal concentrate mixture to meet their maintenance needs. The basal diet included Green Hay (Lucerne) 80% and Wheat straw 20%. Each animal's daily feed consumption was noted. Calves were weighed biweekly to track growth. Upon completion of the experiment, blood samples were obtained. Results: The amount of DM consumed by ruminants fed diets, including hydroponic fodder, was considerably higher (P< 0.05). Similar trends were seen in CP, ADF, and NDF intake. Ingesting of CP was highest in animals fed T3. Animals fed diets comprising Basal diet 60% + HFM 40% had the highest levels of DM and CP digestibility. Animals fed the T3 diet (Basal diet 60% + HFM 40%) showed the best feed conversion values (P˂0.05). Blood metabolites like blood urea nitrogen, creatinine, and glucose showed non-significant variations in all experimental animals. In haematology, a similar trend was seen. Conclusion: Therefore, it can be said that supplementing the diet with more HFM helped growing buffalo calves gain weight, have a lower FCR, and digest their food more efficiently. [Open Vet J 2023; 13(12.000): 1607-1613]

Abstract A professional training or staff development endeavor is known as an in-service program. During these programs, professionals get training and have the opportunity to discuss their work with others who are in their peer group. In the context of the field of education, the term "in-service classes" refers to the process by which instructors participate in additional education or training over the duration of their job in order to maintain or improve their level of professional knowledge, abilities, and practices. In spite of the fact that publicizing in-service programs for language teachers is of utmost significance, not enough focus has been placed on elaborating how they contribute to teacher-related traits. In this study, an effort was made to compare and contrast the outcomes and progress of language instructors who attended or did not attend an in-service program, as well as the effects of this decision on the teachers' resilience, immunity, L2-teacher grit, and reflective teaching. This research was of a quasi-experimental type, and it included participation from 103 English as a Foreign Language (EFL) teachers. During this research project, members of the first group, known as the control group, were not required to attend any in-service courses, whereas members of the second group, known as the experimental group, were. According to the findings of the MANOVA, EFL teachers who participated in in-service training courses had a significantly better level of resilience, immunity, L2-teacher grit, and reflectivity. It is hoped that the outcomes of this study will shed light on the significant contributions offered by in-service training programs and open up new avenues for the mental well-being of EFL teachers.

Abstract Teachers' psychological wellbeing is defined as their evaluation and contentment with their own fulfillment, wellness, and profession as a concept best observable in positive psychology. Therefore, the psychological well-being of teachers is intertwined with a variety of other concepts. In the past, investigations mirrored that emotional regulation, reflective teaching, self-efficacy, and identity play important roles in the psychological and mental health of language instructors. Despite the enormous impact they play, there has never been any study that specifically investigated the contribution of emotion regulation, reflective teaching, self-efficacy, or identity to the psychological well-being of language teachers. As a result, the purpose of this sturdy was to provide emphasis on the aforementioned interpersonal dynamics within an EFL context. The Language Teacher Emotion Regulation Inventory, the English Language Teacher Reflective Inventory, the Teachers' Sense of Efficacy Scale, the Professional Identity Scale, and the Psychological Well-Being at Work were all part of an online survey that 433 EFL teachers from China responded to. The data analysis conducted with Structural Equation Modeling as well as Confirmatory Factor Analysis via LISREL 8.80 suggested that the status of language instructors' emotion control, reflective teaching, self-efficacy, and identity may be expected to be a significant indicator for their psychological well-being. This was shown to be the case after they were subjected to a questionnaire. The results highlight the need of adding reflective practices, skills for emotional management, self-efficacy beliefs, and identity reconstruction within the curriculum of teacher training programs.

Abstract The steady two-dimension (2D) ternary nanofluid (TNF) flow across an inclined permeable cylinder/plate is analyzed in the present study. The TNF flow has been examined under the consequences of heat source/sink, permeable medium and mixed convection. For the preparation of TNF, the magnesium oxide (MgO), cobalt ferrite (CoFe2O4) and titanium dioxide (TiO2) are dispersed in water. The rising need for highly efficient cooling mechanisms in several sectors and energy-related processes ultimately inspired the current work. The fluid flow and energy propagation is mathematically described in the form of coupled PDEs. The system of PDEs is reduced into non-dimensional forms of ODEs, which are further numerically handled through the Matlab package (bvp4c). It has been observed that the results display that the porosity factor advances the thermal curve, whereas drops the fluid velocity. The effect of heat source/sink raises the energy field. Furthermore, the plate surface illustrates a leading behavior of energy transport over cylinder geometry versus the variation of ternary nanoparticles (NPs). The energy dissemination rate in the cylinder enhances from 4.73 to 11.421%, whereas for the plate, the energy distribution rate boosts from 6.37 to 13.91% as the porosity factor varies from 0.3 to 0.9.

Introduction: Flow dynamics due to the peristaltic pumping has been the topic of great interest for the researchers. But numerical and analytical analyses for the peristaltic motion are limited where flow domain is deformed real-time. Research on peristalsis has a limitation where theoretical aspects of walls motion are considered, neglecting the real time deformation of the walls.Objectives: This paper aims to propose a more reliable and accurate numerical methodology for peristaltic motions to address the above-mentioned challenge. Stream traces, velocities, and pressure drops along the tube is to be visualized more accurately.Methods: In present study a finite volume based dynamic mesh motion method is adopted to analyze the peristaltic motion of a non-Newtonian Quemada fluid in an axisymmetric channel. The walls and interior domain of the channel is dynamically deformed for a sinusoidal wave traveling on boundary.Results: Simulation of unsteady flow behavior for time t=0s to 2s and amplitude ratio Φ=0.2,0.4,and0.6. predicts fluid trapping phenomenon. Rotation of fluid particles is more prominent for higher amplitude ratios. Pressure gradient increases with increasing amplitude ratios.Conclusion: A novel dynamic mesh method is proposed for peristaltic pumping. It provides more accurate and more physical results for stream traces; pressure drops and velocities along the tube. A limited case of the study validates the theoretical and analytical results already presented in literature; hence the method is reliable.

Accurate predictions of sea surface temperature (SST) are crucial due to the significant impact of SST on the global ocean-atmospheric system and its potential to trigger extreme weather events. Many existing machine-learning-based SST predictions adapt the traditional iterative point-wise prediction mechanism, whose predicting horizons and accuracy are limited owing to the high sensitivity to cumulative errors during iterative predictions. Therefore, this paper proposes a novel granulation-based long short-term memory (LSTM)-random forest (RF) combination model that can fully capture the feature dependencies involved in the fluctuation of SST sequences, reduce the cumulative error in the iteration process, and extend the prediction horizons, which includes two sub-models (adaptive granulation model and hybrid prediction model). They can restack the one-dimensional SST time-series into multidimensional feature variables, and achieve a strong forecasting ability. The analysis shows that the proposed model can achieve more accurate prediction-hours in nearly all prediction ranges from 1 to 125 h. The average prediction error of the proposed model in 25–125 h is 0.07 K, similar to that (0.067 K) in the first 24 h, which exhibits a high generalization performance and robustness and isthus a promising platform for the medium- and long-term forecasting of hourly SSTs.

ABSTRACTBackground A wide range of drugs are used for sedation in gastrointestinal (GI) endoscopy procedures, including midazolam, dexmedetomidine, and ketamine. Therefore, this study aimed to compare the effects of these drugs in combination with propofol among cancer patients undergoing GI endoscopy.Methods This randomized, double-blinded study was carried out on 75 cancer patients who underwent GI endoscopy. Patients were categorized into three equal groups. Group D: received dexmedetomidine 0.5 µg/kg bolus infusion over 10 min. Group K: received ketamine 0.5 mg/kg. Group M: received midazolam 0.05 mg/kg. With these drugs, 0.5 mg/kg propofol was administered intravenously with incremental 20 mg till achievement of Ramsey sedation score (RSS) 3–4. After that, 0.5 mg/kg propofol boluses were offered for rescue sedation.Results The endoscopy duration was comparable in the three groups. Time of RSS 3–4 achievement and total propofol dose (P

Abstract The effects of thermal radiation and thermophoretic particles deposition (TPD) on the hybrid nanofluid (HNF) flow across a circling sphere have momentous roles in research and engineering. Such as electrical devices, projectiles, thermal conveyance, sheet production, renewable energy, and nuclear-powered plants. Therefore, the current study presents the stagnation point flow of HNF flows about an orbiting sphere. The HNF is organized with the accumulation of aluminum alloys (AA70772 and AA7075) nanoparticles in the water. The HNF flow model equations are changed into the non-dimensional form of ODEs through the similarity variables and then numerically solved through the parametric simulation. It has been perceived that the significance of the rotation factor boosts the velocity curve, while the flow motion drops with the increasing numbers of AA7072 and AA7075 nanoparticles. Furthermore, the addition of AA7072 and AA70775 nano particulates in water lessens with the temperature profile. The energy distribution rate in case of hybrid nanoliquid enhances from 3.87 to 13.79%, whereas the mass dissemination rate enhances from 4.35 to 11.24% as the nanoparticles concentration varies from 0.01 to 0.03.

Abstract The heat and mass transfer through the third grade fluid (TGF) flow over an inclined elongating sheet with the consequences of magnetic field and chemical reaction is reported. The impact of activation energy, heat source/sink, and thermal radiation is considered on the TGF flow. Fluid that demonstrate non-Newtonian (NN) properties such as shear thickening, shear thinning, and normal stresses despite the fact that the boundary is inflexible is known as TGF. It also has viscous elastic fluid properties. In the proposed model, the TGF model is designed in form of nonlinear coupled partial differential equations (PDEs). Before employing the numerical package bvp4c, the system of coupled equations are reduced into non-dimensional form. The finite-difference code bvp4c, in particular, executes the Lobatto three-stage IIIa formula. The impacts of flow constraints on velocity field, energy profile, Nusselt number and skin friction are displayed through Tables and Figures. For validity of the results, the numerical comparison with the published study is performed through Table. From graphical results, it can be perceived that the fluid velocity enriches with the variation of TGF factor and Richardson number. The heat source parameter operational as a heating mediator for the flow system, its influence enhances the fluid temperature.