Enabling High Reversibility of Zn anode via Interfacial Engineering Induced by Amino acid Electrolyte Additive.

Despite possessing substantial benefits of enhanced safety and cost-effectiveness, the aqueous zinc ion batteries (AZIBs) still suffers with the critical challenges induced by inherent instability of Zn metal in aqueous electrolytes. Zn dendrites, surface passivation, and corrosion are some of the key challenges governed by water-driven side reactions in Zn anodes. Herein, a highly reversible Zn anode is demonstrated via interfacial engineering of Zn/electrolyte driven by amino acid D-Phenylalanine (DPA) additions. The preferential adsorption of DPA and the development of compact SEI on the Zn anode suppressed the side reactions, leading to controlled and uniform Zn deposition. As a result, DPA added aqueous electrolyte stabilized Zn anode under severe test environments of 20.0 mA cm-2 and 10.0 mAh cm-2 along with an average plating/stripping Coulombic efficiency of 99.37%. Under multiple testing conditions, the DPA-incorporated electrolyte outperforms the control group electrolyte, revealing the critical additive impact on Zn anode stability. This study advances interfacial engineering through versatile electrolyte additive(s) toward development of stable Zn anode, which may lead to its practical implementation in aqueous rechargeable zinc batteries.

Uncovering Potentially Therapeutic Phytochemicals, In silico Analysis, and Biological Assessment of South-Chinese Red Dragon Fruit (Hylocereus polyrhizus).

Red dragon fruit is gaining popularity globally due to its nutritional value and bioactive components. The study aimed to assess the phytochemical, nutritional composition, antioxidant, antibacterial, and cytotoxic properties of extracts from the South Chinese red dragon fruit peel, flesh, and seeds. Extract fractions with increasing polarity (ethyl acetate

High-Current Capable and Non-Flammable Protic Organic Electrolyte for Rechargeable Zn Batteries.

Rechargeable Zinc batteries (RZBs) are considered a potent competitor for next-generation electrochemical devices, due to their multiple advantages. Nevertheless, traditional aqueous electrolytes may cause serious hazards to long-term battery cycling through fast capacity fading and poor Coulombic efficiency (CE), which happens due to complex reaction kinetics in aqueous systems. Herein, we proposed the novel adoption of the protic amide solvent, N-methyl formamide (NMF) as a Zinc battery electrolyte, which possesses a high dielectric constant and high flash point to promote fast kinetics and battery safety simultaneously. Dendrite-free and granular Zn deposition in Zn-NMF electrolyte assures ultra-long lifespan of 2000 h at 2.0 mA cm-2 /2.0 mAh cm-2 , high CE of 99.57 %, wide electrochemical window (≈3.43 V vs. Zn2+ /Zn), and outstanding durability up to 10.0 mAh cm-2 . This work sheds light on the efficient performance of the protic non-aqueous electrolyte, which will open new opportunities to promote safe and energy-dense RZBs.

On the Computational Study of a Fully Wetted Longitudinal Porous Heat Exchanger Using a Machine Learning Approach.

The present study concerns the modeling of the thermal behavior of a porous longitudinal fin under fully wetted conditions with linear, quadratic, and exponential thermal conductivities surrounded by environments that are convective, conductive, and radiative. Porous fins are widely used in various engineering and everyday life applications. The Darcy model was used to formulate the governing non-linear singular differential equation for the heat transfer phenomenon in the fin. The universal approximation power of multilayer perceptron artificial neural networks (ANN) was applied to establish a model of approximate solutions for the singular non-linear boundary value problem. The optimization strategy of a sports-inspired meta-heuristic paradigm, the Tiki-Taka algorithm (TTA) with sequential quadratic programming (SQP), was utilized to determine the thermal performance and the effective use of fins for diverse values of physical parameters, such as parameter for the moist porous medium, dimensionless ambient temperature, radiation coefficient, power index, in-homogeneity index, convection coefficient, and dimensionless temperature. The results of the designed ANN-TTA-SQP algorithm were validated by comparison with state-of-the-art techniques, including the whale optimization algorithm (WOA), cuckoo search algorithm (CSA), grey wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and machine learning algorithms. The percentage of absolute errors and the mean square error in the solutions of the proposed technique were found to lie between 10-4 to 10-5 and 10-8 to 10-10, respectively. A comprehensive study of graphs, statistics of the solutions, and errors demonstrated that the proposed scheme's results were accurate, stable, and reliable. It was concluded that the pace at which heat is transferred from the surface of the fin to the surrounding environment increases in proportion to the degree to which the wet porosity parameter is increased. At the same time, inverse behavior was observed for increase in the power index. The results obtained may support the structural design of thermally effective cooling methods for various electronic consumer devices.

How inclusive leadership paves way for psychological well-being of employees during trauma and crisis: A three-wave longitudinal mediation study.

AIMS: Nurses are at the forefront of public health emergencies facing psychological pressures ensuing from the loss of patients and potential risk of infection while treating the infected. This study examines whether inclusive leadership has a causal relationship with psychological distress and to assess the mediation effect of psychological safety on this relationship in the long run. The hypotheses are developed and interpreted with the help of theoretical underpinnings from job demands resources theory and the theory of shattered assumptions. DESIGN: Three-wave longitudinal study. METHODS: Questionnaire was used to carry out three waves of data collection from 405 nurses employed at five hospitals in Wuhan during the COVID-19 outbreak between the months of January-April 2020. Partial least square structural equation modelling (PLS-SEM) was used to analyze data while controlling for age, gender, education, experience, and working hours. RESULTS: Results supported the hypothesized relationships where inclusive leadership indicated significant inverse causal relationship with psychological distress and a positive causal relationship with psychological safety. Mediation effect of psychological safety was found significant, while the model explained 73.9% variance in psychological distress. CONCLUSION: Inclusive leadership, through its positive and supportive characteristics, can pave way for such mechanisms that improve the psychological safety of employees in the long run and curbs psychological distress. IMPACT: This is the first longitudinal study to examine the relationship between inclusive leadership and psychological distress in health care and also examines the mediating mechanism of psychology safety. There is scarcity of empirical research on factors that determine and affect behavioural mechanism of healthcare workers during traumatic events and crisis. Clinical leaders and healthcare policy makers must invest in and promote inclusive and supportive environment characterized with open and accessible leaders at workplace to improve psychological safety; it helps reduce levels of psychological distress.

Theoretical Analysis on Absorption of Carbon Dioxide (CO2) into Solutions of Phenyl Glycidyl Ether (PGE) Using Nonlinear Autoregressive Exogenous Neural Networks.

In this paper, we analyzed the mass transfer model with chemical reactions during the absorption of carbon dioxide (CO2) into phenyl glycidyl ether (PGE) solution. The mathematical model of the phenomenon is governed by a coupled nonlinear differential equation that corresponds to the reaction kinetics and diffusion. The system of differential equations is subjected to Dirichlet boundary conditions and a mixed set of Neumann and Dirichlet boundary conditions. Further, to calculate the concentration of CO2, PGE, and the flux in terms of reaction rate constants, we adopt the supervised learning strategy of a nonlinear autoregressive exogenous (NARX) neural network model with two activation functions (Log-sigmoid and Hyperbolic tangent). The reference data set for the possible outcomes of different scenarios based on variations in normalized parameters (α1, α2, ß1, ß2, k) are obtained using the MATLAB solver "pdex4". The dataset is further interpreted by the Levenberg-Marquardt (LM) backpropagation algorithm for validation, testing, and training. The results obtained by the NARX-LM algorithm are compared with the Adomian decomposition method and residual method. The rapid convergence of solutions, smooth implementation, computational complexity, absolute errors, and statistics of the mean square error further validate the design scheme's worth and efficiency.

Mathematical Analysis of Reaction-Diffusion Equations Modeling the Michaelis-Menten Kinetics in a Micro-Disk Biosensor.

In this study, we have investigated the mathematical model of an immobilized enzyme system that follows the Michaelis-Menten (MM) kinetics for a micro-disk biosensor. The film reaction model under steady state conditions is transformed into a couple differential equations which are based on dimensionless concentration of hydrogen peroxide with enzyme reaction (H) and substrate (S) within the biosensor. The model is based on a reaction-diffusion equation which contains highly non-linear terms related to MM kinetics of the enzymatic reaction. Further, to calculate the effect of variations in parameters on the dimensionless concentration of substrate and hydrogen peroxide, we have strengthened the computational ability of neural network (NN) architecture by using a backpropagated Levenberg-Marquardt training (LMT) algorithm. NNs-LMT algorithm is a supervised machine learning for which the initial data set is generated by using MATLAB built in function known as "pdex4". Furthermore, the data set is validated by the processing of the NNs-LMT algorithm to find the approximate solutions for different scenarios and cases of mathematical model of micro-disk biosensors. Absolute errors, curve fitting, error histograms, regression and complexity analysis further validate the accuracy and robustness of the technique.

Curbing nurses' burnout during COVID-19: The roles of servant leadership and psychological safety.

AIMS: This study examines the role of servant leadership through the mechanism of psychological safety in curbing nurses' burnout during the COVID-19 pandemic. BACKGROUND: During the COVID-19 pandemic, studies have shown an increased level of stress and burnout among health care workers, especially nurses. This study responds to the call for research to explore the mechanisms of servant leadership in predicting nurses' burnout by employing the perspective of conservation of resources theory. METHODS: Through a cross-sectional quantitative research design, data were collected in three waves from 443 nurses working in Pakistan's five public sector hospitals. Data were analysed by employing the partial least squares path modelling (PLS-PM) technique. RESULTS: Servant leadership (ß = -0.318; 95% CI = 0.225, 0.416) and psychological safety (ß = -0.342; CI = 0.143, 0.350) have an inverse relationship with nurses' burnout and explain 63.1% variance. CONCLUSIONS: Servant leadership significantly reduces nurses' burnout, and psychological safety mediates this relationship. IMPLICATIONS FOR NURSING MANAGEMENT: Human resource management policies in health care must emphasize training nursing leaders in servant leadership behaviour.

Study of Nonlinear Models of Oscillatory Systems by Applying an Intelligent Computational Technique.

In this paper, we have analyzed the mathematical model of various nonlinear oscillators arising in different fields of engineering. Further, approximate solutions for different variations in oscillators are studied by using feedforward neural networks (NNs) based on the backpropagated Levenberg-Marquardt algorithm (BLMA). A data set for different problem scenarios for the supervised learning of BLMA has been generated by the Runge-Kutta method of order 4 (RK-4) with the "NDSolve" package in Mathematica. The worth of the approximate solution by NN-BLMA is attained by employing the processing of testing, training, and validation of the reference data set. For each model, convergence analysis, error histograms, regression analysis, and curve fitting are considered to study the robustness and accuracy of the design scheme.

Application of Euler Neural Networks with Soft Computing Paradigm to Solve Nonlinear Problems Arising in Heat Transfer.

In this study, a novel application of neurocomputing technique is presented for solving nonlinear heat transfer and natural convection porous fin problems arising in almost all areas of engineering and technology, especially in mechanical engineering. The mathematical models of the problems are exploited by the intelligent strength of Euler polynomials based Euler neural networks (ENN's), optimized with a generalized normal distribution optimization (GNDO) algorithm and Interior point algorithm (IPA). In this scheme, ENN's based differential equation models are constructed in an unsupervised manner, in which the neurons are trained by GNDO as an effective global search technique and IPA, which enhances the local search convergence. Moreover, a temperature distribution of heat transfer and natural convection porous fin are investigated by using an ENN-GNDO-IPA algorithm under the influence of variations in specific heat, thermal conductivity, internal heat generation, and heat transfer rate, respectively. A large number of executions are performed on the proposed technique for different cases to determine the reliability and effectiveness through various performance indicators including Nash-Sutcliffe efficiency (NSE), error in Nash-Sutcliffe efficiency (ENSE), mean absolute error (MAE), and Thiel's inequality coefficient (TIC). Extensive graphical and statistical analysis shows the dominance of the proposed algorithm with state-of-the-art algorithms and numerical solver RK-4.

Highly Reversible and Rechargeable Safe Zn Batteries Based on a Triethyl Phosphate Electrolyte.

Zinc metal is an attractive anode material for next-generation batteries. However, dendrite growth and limited Coulombic efficiency (CE) during cycling are the major roadblocks towards the widespread commercialization of batteries employing Zn anodes. In this work we report the novel adoption of triethyl phosphate (TEP) as a solvent and co-solvent with aqueous electrolytes to obtain a highly stable and dendrite-free Zn anode. Stable Zn plating/stripping for over 3000 h was obtained, accompanied by a CE of 99.68 %. SEM images of the Zn anodes revealed highly porous interconnected dendrite-free Zn deposits. The electrolyte displayed good compatibility with both Zn anodes and potassium copper hexacyanoferrate (KCuHCf) cathodes for Zn ion batteries (ZIBs). The full cell showed a long cycling stability and high rate capability. The present work is a contribution towards cost-effective and safe battery systems.

An Intrinsic Flame-Retardant Organic Electrolyte for Safe Lithium-Sulfur Batteries.

Safety concerns pose a significant challenge for the large-scale employment of lithium-sulfur batteries. Extremely flammable conventional electrolytes and dendritic lithium deposition cause severe safety issues. Now, an intrinsic flame-retardant (IFR) electrolyte is presented consisting of 1.1 m lithium bis(fluorosulfonyl)imide in a solvent mixture of flame-retardant triethyl phosphate and high flashpoint solvent 1,1,2,2-tetrafluoroethyl-2,2,3,3-tetrafluoropropyl (1:3, v/v) for safe lithium-sulfur (Li-S) batteries. This electrolyte exhibits favorable flame-retardant properties and high reversibility of the lithium metal anode (Coulombic efficiency >99 %). This IFR electrolyte enables stable lithium plating/stripping behavior with micro-sized and dense-packing lithium deposition at high temperatures. When coupled with a sulfurized pyrolyzed poly(acrylonitrile) cathode, Li-S batteries deliver a high composite capacity (840.1 mAh g-1 ) and high sulfur utilization of 95.6 %.

Exploring the Transition of Human α-Synuclein from Native to the Fibrillar State: Insights into the Pathogenesis of Parkinson's Disease.

The etiology of Parkinson's disease involves the interplay between the environmental and genetic factors. Here in this study human α-synuclein upon exposure to 100 µM pendimethalin for 12 h in vitro passes through a partially folded state which proceeds to the aggregated state and terminally ends in the fibrillar phase. Variations in the ANS fluorescence intensities led to the detection of intermediate and aggregated states at 6 and 10 h respectively. Far-UV CD analysis depicted significant α-helical content for intermediate state at 6 h in presence of 100 µM pendimethalin. Further increasing the incubation time to 12 h resulted in a predominant ß-sheet content which was confirmed to be fibrillar by TEM. Turbidity, Rayleigh scattering analysis, Congo red assay and ThT measurements supported the TEM data i.e. the formation of fibrillar structure of human α-synuclein upon 12 h incubation. Thus, our observation could suggest a possible underlying molecular basis for Parkinson's disease. Graphical Abstract Schematic elucidation of the factors involved in the fibrillation of α-Synuclein during Parkinson's pathogenesis.

Analysing Cytochrome c Aggregation and Fibrillation upon Interaction with Acetonitrile: an in Vitro Study.

The propensity of native state to form aggregated and fibrillar assemblies is a hallmark of amyloidosis. Our study was focused at analyzing the aggregation and fibrillation tendency of cytochrome c in presence of an organic solvent i.e. acetonitrile. In vitro analysis revealed that the interaction of cytochrome c with acetonitrile facilitated the oligomerization of cytochrome c via the passage through an intermediate state which was obtained at 20 % v/v concentration of acetonitrile featured by a sharp hike in the ANS fluorescence intensity with a blue shift of 20 nm compared to the native state. Oligomers and fibrils were formed at 40 and 50 % v/v concentration respectively as indicated by a significant hike in the ThT fluorescence intensity, red shift of 55 nm in congo red binding assay and an increase in absorbance at 350 nm. They possess ß-sheet structure as evident from appearance of peak at 217 nm. Finally, authenticity of oligomeric and fibrillar species was confirmed by TEM imaging which revealed bead like aggregates and a meshwork of thread like fibrils respectively. It could be suggested that the fibrillation of bovine cytchrome c could serve as a model protein to unravel the general aggregation and fibrillation pattern of heme proteins. Graphical abstract ᅟ.

Contemporary advances in photocatalytic CO2 reduction using single-atom catalysts supported on carbon-based materials.

The persistent issue of CO2 emissions and their subsequent impact on the Earth's atmosphere can be effectively addressed through the utilization of efficient photocatalysts. Employing a sustainable carbon cycle via photocatalysis presents a promising technology for simultaneously managing the greenhouse effect and the energy dilemma. However, the efficiency of energy conversion encounters limitations due to inadequate carrier utilization and a deficiency of reactive sites. Single-atom catalysts (SACs) have demonstrated exceptional performance in efficiently addressing the aforementioned challenges. This review article commences with an overview of SAC types, structures, fundamentals, synthesis strategies, and characterizations, providing a logical foundation for the design and properties of SACs based on the correlation between their structure and efficiency. Additionally, we delve into the general mechanism and the role of SACs in photocatalytic CO2 reduction. Furthermore, we furnish a comprehensive survey of the latest advancements in SACs concerning their capacity to enhance efficiency, long-term stability, and selectivity in CO2 reduction. Carbon-structured support materials such as covalent organic frameworks (COFs), graphitic carbon nitride (g-C3N4), metal-organic frameworks (MOFs), covalent triazine frameworks (CTFs), and graphene-based photocatalysts have garnered significant attention due to their substantial surface area, superior conductivity, and chemical stability. These carbon-based materials are frequently chosen as support matrices for anchoring single metal atoms, thereby enhancing catalytic activity and selectivity. The motivation behind this review article lies in evaluating recent developments in photocatalytic CO2 reduction employing SACs supported on carbon substrates. In conclusion, we highlight critical issues associated with SACs, potential prospects in photocatalytic CO2 reduction, and existing challenges. This review article is dedicated to providing a comprehensive and organized compilation of recent research findings on carbon support materials for SACs in photocatalytic CO2 reduction, with a specific focus on materials that are environmentally friendly, readily accessible, cost-effective, and exceptionally efficient. This work offers a critical assessment and serves as a systematic reference for the development of SACs supported on MOFs, COFs, g-C3N4, graphene, and CTFs support materials to enhance photocatalytic CO2 conversion.

Nitrogen-vacancy-rich molybdenum nitride nanosheets as highly efficient electrocatalysts for nitrogen reduction reaction.

The development of low-cost earth-abundant electrocatalysts to produce ammonia (NH3) with high efficiency for the nitrogen (N2) reduction reaction (NRR) remains challenging. Herein, we propose the development of highly efficient ultrathin nitrogen-vacancy-rich molybdenum nitride nanosheets (MoN-NV) for NRR using basic electrolytes under ambient conditions. In 0.1 M KOH, this catalyst attained a high faradaic efficiency (FE) of ∼14% with an NH3 yield of 22.5 µg h-1 mg-1cat at -0.3 V vs. a reversible hydrogen electrode under ambient conditions. The characterization results and electrochemical studies disclosed that nitrogen vacancies in the MoN-NV nanosheets played a critical role in the enhanced electrocatalytic activity for NRR. Furthermore, the recycling tests confirmed the stability of the catalyst during NRR electrolysis.

Evaluation of Heavy Metal Concentration in Different Organs of Labeo rohita in Water and Sediment of Marala Ravi Link Canal (MRL) and Upper Chenab Canal (UCC) of Chenab River, Pakistan, and Their Health Implications.

Heavy metals (HMs) increasing rate in riverine water by human activities cause serious hazards to the health and sustainability of aquatic environments. The present study examines the accumulation of HMs (Ni, Mn, Pb, Cr) in water, sediments, and Labeo rohita fish of MRL and UCC, Chenab River Pakistan, and its implications on human health. Fish 36 specimens were collected with an average body weight of 170 ± 20 g. The results revealed that HM concentration in fish organs of MRL and UCC found in gills: Ni 8.57 ± .01, Mn 7.57 ± .01, Cr 5.20 ± .01, Pb 2.56 ± .01; Ni 8.20 ± .01, Mn 7.19 ± .01, Cr 4.78 ± .01, Pb 2.19 ± .01; liver: Mn 9.54 ± .01, Ni 6.98 ± .01, Cr 4.75 ± .01, Pb 4.66 ± .01; Mn 9.15 ± .01, Ni 6.48 ± .01, Pb 4.26 ± .01, Cr 4.22 ± .01; and muscle: Ni 4.94 ± .01, Mn 4.86 ± .02, Cr 1.73 ± .01, Pb 1.50 ± .01; Ni 4.48 ± .02, Mn 4.29 ± .01, Cr 1.28 ± .01, Pb 1.25 ± .02, respectively. BCF in gills, liver, and muscle found MN > Ni > Pb > Cr; Mn > Ni > Cr > Pb; Mn > Ni > Cr > Pb, respectively. THQ value for individual metal observed THQ < 1, which signifies no adverse effects, while the combined THQ value of investigated HMs found (1.094, 1.149) THQ > 1, which signifies expected adverse effects on human health during lifetime. HI values 2.23 and 2.16 observed HI > 1 indicated that consumption of studied fish contaminated with HMs cause a possible health risk. HM concentration was also observed higher than the permissible limits of TRV/USEPA/WHO in water and sediments. Therefore, consumption of investigated fish L. rohita can accumulate an estimated concentration of 1 × 14.10-4 to 4.50 × 10-4 (mg/kg, ww)/day of Cr, Mn, Ni, and Pb which exceeded the permissible limit of 1 × 10-4 to 1 × 10-6 of FAO/WHO and specifying possible carcinogenic threats for humans.

Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials.

This paper presents a study investigating the performance of functionally graded material (FGM) annular fins in heat transfer applications. An annular fin is a circular or annular structure used to improve heat transfer in various systems such as heat exchangers, electronic cooling systems, and power generation equipment. The main objective of this study is to analyze the efficiency of the ring fin in terms of heat transfer and temperature distribution. The fin surfaces are exposed to convection and radiation to dissipate heat. A supervised machine learning method was used to study the heat transfer characteristics and temperature distribution in the annular fin. In particular, a feedback architecture with the BFGS Quasi-Newton training algorithm (trainbfg) was used to analyze the solutions of the mathematical model governing the problem. This approach allows an in-depth study of the performance of fins, taking into account various physical parameters that affect its performance. To ensure the accuracy of the obtained solutions, a comparative analysis was performed using guided machine learning. The results were compared with those obtained by conventional methods such as the homotopy perturbation method, the finite difference method, and the Runge-Kutta method. In addition, a thorough statistical analysis was performed to confirm the reliability of the solutions. The results of this study provide valuable information on the behavior and performance of annular fins made from functionally graded materials. These findings contribute to the design and optimization of heat transfer systems, enabling better heat management and efficient use of available space.

Nutrition, phytochemical profiling, in vitro biological activities, and in silico studies of South Chinese white pitaya (Hylocereus undatus).

Background: White pitaya, a popular tropical fruit, is known for its high nutritional value. It is commercially cultivated worldwide for its potential use in the food and pharmaceutical industries. This study aims to assess the nutritional and phytochemical contents and biological potential of the South Chinese White Pitaya (SCWP) peel, flesh, and seed extracts. Methods: Extract fractions with increasing polarity (ethyl acetate < acetone < ethanol < methanol < aqueous) were prepared. Antibacterial potential was tested against multidrug-resistant (MDR) bacteria, and antioxidant activity was determined using, 2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-Azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) radical scavenging assays, and cytotoxic activity against human keratinocyte cells using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay. Pharmacological screening and molecular docking simulations were conducted to identify potential antibacterial compounds with druggable characteristics. Molecular dynamics simulation (MDS) was employed to validate the binding stability of the promising ligand-protein complexes. Results: All parts of the fruit exhibited a substantial amount of crucial nutrients (minerals, sugars, proteins, vitamins, and fatty acids). The ethanol (ET) and acetone (AC) fractions of all samples demonstrated notable inhibitory effects against tested MDR bacteria, with MIC50 ranges of 74-925 µg/mL. Both ET and AC fractions also displayed remarkable antioxidant activity, with MIC50 ranges of 3-39 µg/mL. Cytotoxicity assays on HaCaT cells revealed no adverse effects from the crude extract fractions. LC-MS/MS analyses identified a diverse array of compounds, known and unknown, with antibacterial and antioxidant activities. Molecular docking simulations and pharmacological property screening highlighted two active compounds, baicalein (BCN) and lenticin (LTN), showing strong binding affinity with selected target proteins and adhering to pharmacological parameters. MDS indicated a stable interaction between the ligands (BCN and LTN) and the receptor proteins over a 100-ns simulation period. Conclusion: Our study provides essential information on the nutritional profile and pharmacological potential of the peel, flesh, and seeds of SCWP. Furthermore, our findings contribute to the identification of novel antioxidants and antibacterial agents that could be capable of overcoming the resistance barrier posed by MDR bacteria.

Mathematical modeling and machine learning-based optimization for enhancing biofiltration efficiency of volatile organic compounds.

Biofiltration is a method of pollution management that utilizes a bioreactor containing live material to absorb and destroy pollutants biologically. In this paper, we investigate mathematical models of biofiltration for mixing volatile organic compounds (VOCs) for instance hydrophilic (methanol) and hydrophobic ( α -pinene). The system of nonlinear diffusion equations describes the Michaelis-Menten kinetics of the enzymic chemical reaction. These models represent the chemical oxidation in the gas phase and mass transmission within the air-biofilm junction. Furthermore, for the numerical study of the saturation of α -pinene and methanol in the biofilm and gas state, we have developed an efficient supervised machine learning algorithm based on the architecture of Elman neural networks (ENN). Moreover, the Levenberg-Marquardt (LM) optimization paradigm is used to find the parameters/ neurons involved in the ENN architecture. The approximation to a solutions found by the ENN-LM technique for methanol saturation and α -pinene under variations in different physical parameters are allegorized with the numerical results computed by state-of-the-art techniques. The graphical and statistical illustration of indications of performance relative to the terms of absolute errors, mean absolute deviations, computational complexity, and mean square error validates that our results perfectly describe the real-life situation and can further be used for problems arising in chemical engineering.