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Abstract In this article, we have proposed memory-type exponential and non-exponential estimators for population variance based on exponentially weighted moving average (EWMA) statistic in stratified sampling. We drive mathematical expressions for mean square errors of the proposed estimators using Taylor and exponential expansions. Our analysis demonstrates that the proposed memory-type estimators outperform the conventional estimators under stratification, particularly, when the information of previous sample is utilized. The performances of the proposed estimators are evaluated mathematically by deriving the conditions in which the memory-type estimators would perform better than their corresponding conventional estimators under stratification. Through an extensive simulation, we evaluated the performance of the proposed estimators across various population parameters, revealing their enhanced efficiency in time-scaled survey. Additionally, a real data application is also used to support the mathematical findings, confirming the practical utility of the proposed estimators. The results of numerical study underscore the importance of the use of previous sampled information which significantly improves the accuracy and reliability of the proposed estimator for variance estimation for time-scaled surveys.

The presence of probability-driven models is highly influential in setting the stage for vital decision-making in domains including reliability, engineering, music engineering, and other closely interconnected scenarios. With a deep understanding of the consequential roles played by probability-arisen models, we have developed and implemented a new probabilistic model. This model is constructed by utilizing the sine-based function and the exponentiated Weibull distribution, and it is known as the exponent power sine exponentiated Weibull (EPSE-Weibull) distribution. Point estimators are derived for the EPSE-Weibull distribution. These estimators are then evaluated through a simulation study. The significance of the EPSE-Weibull distribution is demonstrated through the analysis of reliability and music engineering data sets. In addition to the above, we also utilize two deep learning algorithms, namely Artificial Neural Networks (ANN) and Support Vector Regression (SVR), to forecast the same data sets. The findings indicate that the ANN model consistently exhibits higher levels of accuracy, as evidenced by its lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values compared to the SVR model for both data sets. These findings indicate that ANN is better at capturing the fundamental patterns in the underlying data sets. In addition, visual representations, such as bar charts and line charts, further emphasize the superior performance of the ANN across both data sets.

This study presents a novel single-parameter counting model that merges the Poisson distribution with the Agu-Eghwerido distribution. The newly developed model is referred to as the ''Poisson Agu-Eghwerido Distribution.'' We investigate major mathematical and statistical aspects of this distribution, including factorial moments, probability-generating, moment-generating, Shannon entropy, skewness, and kurtosis. The model parameter is estimated using a maximum likelihood estimation and Bayesian estimation approaches. We use Monte Carlo simulation to investigate the behavior of the estimators. Finally, we examine two real-world datasets to determine the effectiveness of the suggested distribution in representing count data. It is concluded that the new model analyzed these two datasets efficiently as compared to the competitive distributions.

Frailty model examines the effect of observable and non-observable factors on time to event data. Presence of collinearity produces unstable estimates of parameters. Therefore, this research focus on the penalized estimation of frailty model and proposed the new estimator which is the extension of ridge and principal component estimators. Simulation is run to reveal the performance of proposed estimator. Moreover, the technique is applied on NFHS (National Family Health Survey) data to examine the infant mortality in India.

Estimating dispersion in populations that are extremely rare, hidden, geographically clustered, and hard to access is a well-known challenge. Conventional sampling approaches tend to overestimate the variance, even though it should be genuinely reduced. In this environment, adaptive cluster sampling is considered to be the most efficient sampling technique as it provides generally a lower variance than the other conventional probability sampling designs for the assessment of rare and geographically gathered population parameters like mean, total, variance, etc. The use of auxiliary data is very common to obtain the precise estimates of the estimators by taking advantage of the correlation between the survey variable and the auxiliary data. In this article, we introduced a generalized estimator for estimating the variance of populations that are rare, hidden, geographically clustered and hard-to-reached. The proposed estimator leverages both actual and transformed auxiliary data through adaptive cluster sampling. The expressions of approximate bias and mean square error of the proposed estimator are derived up to the first-order approximation using Taylor expansion. Some special cases are also obtained using the known parameters associated with the auxiliary variable. The proposed class of estimators is compared with available estimators using simulation and real data applications.

Probability distributions are frequently used for statistical modeling of real-life phenomena in every field of life. Most often, real-life data sets are skewed in nature, and therefore, asymmetrical probability distributions are very competent for such scenarios. In the recent gains in distribution theory, researchers are focusing on developing new symmetrical and asymmetrical trigonometric-based probability distributions for data modeling in various fields. In the literature, however, there is no published work on statistical modeling of the transaction confirmation times on blockchain technology using trigonometric-originated probability distributions. For the first time covering the aforesaid research gap, this paper introduces a new trigonometric-based probabilistic approach for modeling the transaction confirmation times on blockchain technology. The new approach is called a new sine-G family of distributions. Using the proposed method, a new probability distribution called a new sine-Weibull distribution is studied. The proposed model is very flexible and obeys the symmetrical and asymmetrical shapes of its density function. The estimators of the new sine-Weibull distribution are derived, and their evaluation is tested through a simulation study. The applicability of the new sine-Weibull distribution is demonstrated by analyzing the average waiting time until the Bitcoin transaction is confirmed on blockchain technology.

This study introduces an enhanced version of the discrete choice model combining embedded neural architecture to enhance predictive accuracy while preserving interpretability in choice modeling across temporal dimensions. Unlike the traditional architectures, which directly utilize raw data without intermediary transformations, this study introduces a modified approach incorporating temporal embeddings for improved predictive performance. Leveraging the Phones Accelerometer dataset, the model excels in predictive accuracy, discrimination capability and robustness, outperforming traditional benchmarks. With intricate parameter estimates capturing spatial orientations and user-specific patterns, the model offers enhanced interpretability. Additionally, the model exhibits remarkable computational efficiency, minimizing training time and memory usage while ensuring competitive inference speed. Domain-specific considerations affirm its predictive accuracy across different datasets. Overall, the subject model emerges as a transparent, comprehensible, and powerful tool for deciphering accelerometer data and predicting user activities in real-world applications.

Reliability is now widely recognized across various industries, including manufacturing. This study investigates a system composed of five components, one of which is a bridge network. The components are assumed to follow the generalized unit half logistic geometric distribution (GUHLGD) with equal failure rates over time. The following three improvement methods are considered: reduction, cold duplication, and hot duplication. The reliability function and mean time to failure (MTTF) are employers liability equivalence factors (REFs). Additionally, the λ fractiles of both the original and enhanced systems are obtained. Numerical results illustrate the effectiveness of these techniques, with cold duplication shown to be the most effective, offering higher reliability and MTTF compared to hot duplication. The enhanced system outperforms the original system overall.

The Indian Clove (Syzygium aromaticum) is a naturally occurring spice with significant biological properties. It is primarily found in India and Indonesia, but can also be cultivated in other coastal areas. Clove is commonly used as a natural preservative and in the production of natural medicines. One of the notable benefits of clove is its ability to help treat diseases caused by various bacteria and fungi, including E. coli, P. aeruginosa, S. aureus, Z. mobilis, and C. albicans. Additionally, clove possesses antioxidant properties that can help reduce cell damage caused by free radicals. Clove has also shown potential in inhibiting the growth of cancer cells and reducing the activity of enzymes such as amylase, glucosidase, and lipase, which can be beneficial for managing diabetes. The study demonstrated that Silver nanoparticles (AgNPs) synthesized from Indian clove leaf extract exhibited promising antimicrobial, anticancer, antidiabetic, and antioxidant activities. This suggests that these nanoparticles could have versatile applications in antimicrobial therapy, cancer treatment, diabetes management, and antioxidant supplementation. However, further research is needed to understand the underlying mechanisms of these activities and to evaluate the safety and efficacy of these nanoparticles in more complex biological systems. In summary, clove possesses diverse biological properties and has potential applications in various therapeutic areas. The synthesis of AgNPs from clove leaf extract shows promise in multiple fields, but more research is required for a deeper understanding of its mechanisms and broader applications.

In this paper, we proposed a sustainable inventory model with a learning effect for imperfect quality items under different kinds of fuzzy environments like crisp, general fuzzy, cloudy fuzzy, and monsoon fuzzy. We divided the mathematical model into three parts under the learning effect according to the real-time fuzzy components (crisp, cloudy, and monsoon environments) of the demand rate of the items. We minimized the total inventory cost with respect to cycle length in each environment under the proposed assumptions. The non-linear optimization technique is applied for the algorithm and the solution method to find the decision variable. Finally, we compared the total inventory cost under different fuzzy environments and our finding is that the fuzzy monsoon environment is a more effective fuzzy environment than crisp and cloudy fuzzy environments. We have presented a numerical example for the validation of the proposed model and have shown the impact of the inventory input parameters on the cycle length and total inventory fuzzy cost. The managerial insights and future scope of this proposed study have been shown in the sensitivity analysis and conclusion. The limitations, application, future extension and scope, and social implementation have been shown in this research study.

This research presents a model called the ‘Gaussian negative binomial mixture with a latent class choice model’, which serves as a robust and efficient tool for analyzing decisions across different areas. Our innovative model combines elements of mixture models, negative binomial distributions, and latent class choice modeling to create an approach that captures the complexities of decision-making processes. We explain how the model is formulated and estimated, showcasing its effectiveness in analyzing and predicting choices in scenarios. Through the use of a dataset, we demonstrate the performance of this method, marking a significant advancement in choice modeling. Our results highlight the applications of this model and point towards promising directions for future research, especially in exploring symmetrical patterns and structures, within decision-making processes.

The objective of this research is to ascertain the elements that have an impact on and drive domestic violence in Saudi Arabia, a phenomenon that has a prevalence rate of around 35% among women globally. The researchers administered a survey to a sample of 550 individuals and used exploratory factor analysis (EFA) to analyze the collected data. The findings revealed three factors: a lack of familial unity, encouragement of detrimental characteristics, and economic turmoil. The authors examined the consequences of these characteristics on preventive and intervention programs and proposed suggestions for policymakers and researchers. This research enhances the existing body of knowledge on domestic violence by conducting a statistical analysis to examine the factors that lead to it and the resulting outcomes within a particular cultural setting.

Different non-Bayesian and Bayesian techniques were used to estimate the pseudo-Lindley (PsL) distribution’s parameters in this study. To derive Bayesian estimators, one must assume appropriate priors on the parameters and use loss functions such as squared error (SE), general entropy (GE), and linear-exponential (LINEX). Since no closed-form solutions are accessible for Bayes estimates under these loss functions, the Markov Chain Monte Carlo (MCMC) approach was used. Simulation studies were conducted to evaluate the estimators’ performance under the given loss functions. Furthermore, we exhibited the adaptability and practicality of the PsL distribution through real-world data applications, which is essential for evaluating the various estimation techniques. Also, the acceptance sampling plans were developed in this work for items whose lifespans approximate the PsL distribution.

Inflammation is a protective response to a variety of infectious agents. To develop a new anti-inflammatory drug, we explored a pharmacologically important thiazole scaffold in this study. In a multi-step synthetic approach, we synthesized seven new thiazole derivatives (5a–5g). Initially, we examined the in vitro anti-inflammatory potentials of our compounds using COX-1, COX-2, and 5-LOX enzyme assays. After in vitro confirmation, the potential compounds were subjected to in vivo analgesic and anti-inflammatory studies. The hot plate method was used for analgesia, and carrageenan-induced inflammation was also assayed. Overall, all our compounds proved to be potent inhibitors of COX-2 compared to celecoxib (IC50 0.05 μM), exhibiting IC50 values in the range of 0.76–9.01 μM .Compounds 5b, 5d, and 5e were dominant and selective COX-2 inhibitors with the lowest IC50 values and selectivity index (SI) values of 42, 112, and 124, respectively. Similarly, in the COX-1 assay, our compounds were relatively less potent but still encouraging. Standard aspirin exhibited an IC50 value of 15.32 μM. In the 5-LOX results, once again, compounds 5d and 5e were dominant with IC50 values of 23.08 and 38.46 μM, respectively. Standard zileuton exhibited an IC50 value of 11.00 μM. Based on the COX/LOX and SI potencies, the compounds 5d and 5e were subjected to in vivo analgesic and anti-inflammatory studies. Compounds 5d and 5e at concentrations of 5, 10, and 20 mg/kg body weight were significant in animal models. Furthermore, we explored the potential role of compounds 5d and 5e in various phlogistic agents. Similarly, both compounds 5d and 5e were also significantly potent in the anti-nociceptive assay. The molecular docking interactions of these two compounds with the target proteins of COX and LOX further strengthened their potential for use in COX/LOX pathway inhibitions.

Excessive and imbalance of free radicals within the body lead to inflammation. The objective of the current research work was to explore the anti-inflammatory and antioxidant potential of the isolated compounds from Habenaria digitata. In this study, the isolated phenolic compounds were investigated for in vitro and in vivo anti-inflammatory potential along with the antioxidant enzyme. The anti-inflammatory and antioxidant potential of the phenolic compounds was assayed via various enzymes like COX-1/2, 5-LOX and ABTS, DPPH, and H2O2 free radical enzyme inhibitory assay. These compounds were also explored for their in vivo antioxidant activity like examining SOD, CAT, GSH-Px, and MDA levels in the brain, heart, and liver. The anti-inflammatory potential was evaluated using the carrageenan-induced pleurisy model in mice. On the basis of initial screening of isolated compounds, the most potent compound was further evaluated for the anti-inflammatory mechanism. Furthermore, the molecular docking study was also performed for the potent compound. The phenolic compounds were isolated and identified by GC-MS/NMR analysis by comparing its spectra to the library spectra. The isolated phenolic compounds from H. digitata were 5-methylpyrimidine-24,4-diol (1), 3,5-dihydroxy-6-methyl-2,3-dihydropyran-4-one (2), 2-isopropyl-5-methylphenol (3), 3-methoxy-4-vinylphenol (4), and 2,6-dimethoxy-4-vinylphenol (5). In in vitro antioxidant assay, the most potent compound was compound 1 having IC50 values of 0.98, 0.90, and 5 μg/mL against ABTS, DPPH, and H2O2, respectively. Similarly, against COX1/2 and 5-LOX ,compound 1 was again the potent compound with IC50 values of 42.76, 10.70, and 7.40 μg/mL. Based on the in vitro results, compound 1 was further evaluated for in vivo antioxidant and anti-inflammatory potential. Findings of the study suggest that H. digitata contains active compounds with potential anti-inflammatory and antioxidant effects. These compounds could be screened as drug candidates for pharmaceutical research, targeting conditions associated with oxidative stress and inflammatory conditions in medicinal chemistry and support their ethnomedicinal use for inflammation and oxidative stress.

Recently, the Covid-19 pandemic has caused tremendous trauma over the world, leading to psychological and behavioral harm in addition to social and economic instabilities. Even though the pandemic’s statistical analysis is still in progress, it is essential to fit Covid-19 data using statistical models to prevent further harm. In order to model Covid-19 data, the study suggests a novel family of distributions called the exponentiated log-logistic family. The basic Pareto distribution is transformed as a special case, and certain properties of the proposed distribution are discussed. To estimate the model parameters, the maximum likelihood estimation approach is used. Moreover, a simulation study is conducted to ensure the consistency of parameter estimates. Three real-world datasets relevant to the Covid-19 pandemic are examined to demonstrate the applicability of the suggested approach. The proposed model is shown to be more flexible and provides an improved fit to describe the Covid-19 data when compared to various alternative forms of Pareto distribution.

In this article, we offered two ln-type estimators for the population mean estimation of a sensitive study variable by using the auxiliary information under the design of basic probability sampling. The Taylor and log series were used to derive the expressions of mean square error and bias up to the first order. Improved classes of proposed estimators are obtained by using conventional parameters associated with the supplementary variable to obtained precise estimates. Mathematical comparisons of the estimators have been made with the usual mean and ratio estimators using theoretical equations of mean square error. A simulation study is conducted for the evaluation of proposed estimator's implementation using four artificial populations generated through R-software with different choices of mean vectors and variance-covariance matrices. The demonstration of proposed ln-type estimators was implemented through the real data application.

We introduce a new model called the length-biased exponential distribution which become a fascinating new model in a number of research domains in recent years. By adding an extra shape parameter, a new generalised form that is coupled to the length-biased exponential distribution addressed in this work may be constructed, enhancing its utility. The new distribution is known as the new extension length-biased exponential distribution (NELBE). Its density function, as well as its survival and hazard rate curves, are all shown graphically. The study presented the quantile function, linear representations, and some other properties. We displayed and graphed the shapes of the distribution functions. When it came time to calculate the distribution parameters, we employed a total of six distinct estimating strategies. In order to compare and draw conclusions about the performance of the different estimators, a thorough numerical analysis was done. Here, two real data set on COVID-19 mortality rate was examined to show how adaptable and practical the suggested distribution is.

Reliability assessments are pivotal in evaluating system quality and have found extensive application in manufacturing. This research delves into a system comprising five components, one of which is a bridge network. The components are presumed to follow a Bilal lifetime distribution with a failure rate that changes over time. Four distinct methods are employed to enhance the components within the system. This study involves the computation of δ-fractiles and reliability equivalence factors (REFs). Additionally, a numerical case study is provided to elucidate the theoretical findings.

Sample selection is one of the most important factors in estimating the unknown parameters of distributions, as it saves time, saves effort, and gives the best results. One of the challenges is deciding on a suitable distribution estimate technique and adequate sample selection to provide the best results in comparison with earlier research. The method of moments (MOM) was decided on to estimate the unknown parameters of the Gumbel distribution, but with four changes in the sample selection, which were simple random sample (SRS), ranked set sampling (RSS), maximum ranked set sampling (MRSS), and ordered maximum ranked set sampling (OMRSS) techniques, due to small sample sizes. The MOM is a traditional method for estimation, but it is difficult to use when dealing with RSS modification. RSS modification techniques were used to improve the efficiency of the estimators based on a small sample size compared with the usual SRS estimator. A Monte Carlo simulation study was carried out to compare the estimates based on different sampling. Finally, two datasets were used to demonstrate the adaptability of the Gumbel distribution based on the different sampling techniques.

This paper investigates various aspects of the distribution of roots and critical points of a complex polynomial, including their variance and the relationships between their moduli using an inequality due to de Bruijn. Making use of two other inequalities-again due to de Bruijn-we derive two probabilistic results concerning upper bounds for the average moduli of the imaginary parts of zeros and those of critical points, assuming uniform distribution of the zeros over a unit disc and employing the Markov inequality. The paper also provides an explicit formula for the variance of the roots of a complex polynomial for the case when all the zeros are real. In addition, for polynomials with uniform distribution of roots over the unit disc, the expected variance of the zeros is computed. Furthermore, a bound on the variance of the critical points in terms of the variance of the zeros of a general polynomial is derived, whereby it is established that the variance of the critical points of a polynomial cannot exceed the variance of its roots. Finally, we conjecture a relation between the real parts of the zeros and the critical points of a polynomial.

Electroencephalography (EEG) has been used for quite some time as a diagnostic technique in neurology. The goal of this publication is to serve as a resource for researchers interested in applying deep learning methods to EEG data. This paper proposes a unique Hybrid Machine-Deep Learning model that can learn and classify EEG signals on its own. This method allows the model to classify EEG signals of varied sampling frequencies and durations automatically. The proposed model used feature extraction methods from artificial design and performed extensive tests with EEG data collected at varying sample rates to determine how well our suggested model performed. The results show that the Hybrid Machine-Deep Learning strategy significantly improves performance, leading to a remarkable 99.97% classification accuracy. Notably, this method performs exceptionally well when labeling lower-frequency EEG signals (less than 4 Hz). The proposed model has improved consistency and robustness, as shown by this study.

Resveratrol (RSV) is a phytoceutical polyphenolic compound exhibiting a well evidenced wide range of therapeutic activities. Unfortunately, its diminished aqueous solubility and extensive metabolism in gastro intestinal tract (GIT) and liver prohibit its biological activity and systemic availability. Herein the conducted study PEG stabilized emulsomes (PEMLs) were customized to enclose RSV aiming to boost its biological availability and antiviral activity. PEGylating the vesicles not only grant the promoted steric stability of the system but also being beneficial in exaggerating the intestinal permeability and extending the period of circulation of the drug, hence its targeted clinical use. The Investigation of the influence of predetermined variables on the physical characterization of formulae (entrapment efficiency EE%, particle size PS and zeta potential ZP) was implemented utilizing Design Expert® software. (F4) with desirability value (0.772), picked to be the optimal formula, which is fabricated utilizing 35 mg compritol as the lipidic core and 60 mg 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[amino(polyethylene glycol)-2000] (DSPE-Mpeg-2000). The dominance of the F4 relative to RSV dispersion was affirmed by the data acquired from ex-vivo and pharmacokinetic studies. In addition, F4 exhibited significant lower EC50 value (0.0127 µg/mL) relative to that of RSV dispersion(0.338 µg/mL) by around 26 times denoting the capability of the formulation to boost the antiviral activity. To a great extent, F4 was able to significantly suppress the inflammatory response and oxidative stress resulted from MERS-CoV infection on comparison with RSV dispersion. Finally, the potentiality of PEMLs as nano-panel with boosted both antiviral and oral bioavailability for RSV could be deduced based on the outcomes mentioned herein.

Decaprenyl-phosphoryl-ribose 2′-epimerase (DprE1) inhibitors are an innovative and futuristic orally active group of antituberculosis agents. A few DprE1 inhibitors are in the clinical trial for tuberculosis (TB), including macozinone. This review highlights the discovery, developmental status, clinical studies, patents, and prospects of macozinone (MCZ). The patent and non-patent literature search was done by entering keywords such as macozinone; MCZ; PBTZ169; PBTZ-169 in Pubmed, Espacenet, Patentscope, and the USPTO databases. However, data on Sci-Finder was searched using CAS registry number: 1377239–83–2. MCZ clinical trial studies were retrieved from the clinicaltrials.gov database using the exact keywords. The chemical structure of MCZ was disclosed in 2009. Accordingly, patents/patent applications published from 2009 to June 12, 2022, have been discussed herein. MCZ and MCZ hydrochloride salt patents were granted in 2014 and 2019, respectively, in the USA. The patent literature and the clinical trial studies suggest capsule, tablet, and suspension formulations of crystalline MCZ and its hydrochloride salt as the possible and prospective dosage forms to treat TB. Some combinations of MCZ with other drugs (chloroquine, telacebec, tafenoquine, TBI-166, and sanfetrinem) with improved anti-TB efficacy have been documented. Based on the literature covered in this review article on the clinical studies and patents applied/granted to MCZ, it can be inferred that MCZ seems to be a promising DprE1 inhibitor and could help to tackle the emerging dilemma of drug-resistant either as a monotherapy or in combination with additional anti-TB agents. Furthermore, the authors anticipate the development of new combinations, salts, and polymorphs of MCZ as anti-TB agents shortly. This review article might prove beneficial to the scientific community as it summarizes chemistry, pharmacology and provides an update on the clinical studies and patents/patent applications of one of the emerging anti-TB drugs in one place.

Biofilm-formation is one of the most important virulent factors in the pathogenesis of urinary tract infections. Biofilm is a complex microbial population protected by exopolysaccharides (EPS), which presents with adaptive resistance against antimicrobials. In the current study, we aimed to evaluate the efficacy of a methanolic extract of Mangifera indica seed kernels (MISK) against uropathogenic bacteria collected from urine samples. In total, n = 21 uropathogenic Escherichia coli (UPEC) strains were identified from n = 30 urine samples. Among the n = 21 strains, n = 17 E. coli strains were biofilm-producers in different in vitro model systems (tube-based method, microtiter-plate based method, Congo Red Agar, EPS production). Uropathogens were identified by traditional microbiological methods. MISK is a potential natural source of phytochemicals such as carbohydrates, proteins, stearic and linoleic acids. In the present study, 50 μg/mL and 100 μg/mL of methanolic extract of MISK exhibited prominent antibacterial and anti-biofilm activity against the clinical UPEC strains. In addition, qualitative phytochemical characterization of methanolic extract of MISK showed the presence of bioactive compounds such as tannins, saponins, flavonoids, terpenoids, alkaloids, carbohydrates, polyphenols, and glycosides. Moreover, the characterization of the methanolic extract by UV–VIS, FTIR, and GC–MS analysis confirmed the presence of a phenolic profile, namely mangiferin (1,3,6,7-tetrahydroxyxanthone-C2 -β-D-glucoside). Furthermore, in silico analysis was also investigated to validate that mangiferin can bind to the specific active sites of CAMP LpxC in E. coli. Based on the collective findings of our in vitro and in silico analyses, mangiferin may be the principal compound responsible for the anti-bacterial and anti-biofilm effects of the extract.

Kaolin clay-supported Zinc oxide (ZnO/KC) and ZnO NPs nanoparticles (NPs) were prepared by a chemical reduction process and used for the photodegradation of methyl red (MR) dye as a photocatalyst. Due to the interlayered porous structure of the KC, we achieved an extremely good association between ZnO NPs and KC. The product confirmation was conducted by Scanning electron microscopy (SEM), X-Ray diffraction (XRD), energy dispersive X-Ray (EDX), and Fourier transforms infrared (FTIR). SEM showed the irregular morphology of ZnO NPs, while ZnO/KC NCs were predominately round-shaped. Moreover, in both cases, NPs were present in both dispersed as well as agglomerated forms with an average particle size below 100 nm. The results acquired from photodegradation analyses show that ZnO NPs and ZnO/KC NCs degraded about 90 and 99% of MR dye respectively, under UV light in a short irradiation time of 10 min. The recovered and re-recovered ZnO NPs and ZnO/KC NCs also considerably photodegraded MR dye in an aqueous medium. The same NPs also exhibit promising bioactivities against two pathogenic bacteria, i.e., Citrobacter and Providencia. The antioxidant activity of ZnO/KC NCs reached to reasonable 70% compared to the 88% activity of the standard ascorbic acid.

Lung cancer is a pervasive and challenging disease with limited treatment options, with global health challenges often present with complex molecular profiles necessitating the exploration of innovative therapeutic strategies. Single-target drugs have shown limited success due to the heterogeneity of this disease. Multitargeted drug designing is imperative to combat this complexity by simultaneously targeting multiple target proteins and pathways, which can enhance treatment efficacy and overcome resistance by addressing the dynamic nature of the disease and stopping tumour growth and spread. In this study, we performed the molecular docking studies of Drug Bank compounds with a multitargeted approach against crucial proteins of lung cancer such as heat shock protein 5 (BIP/GRP78) ATPase, myosin 9B RhoGAP, EYA2 phosphatase inhibitor, RSK4 N-terminal kinase, and collapsin response mediator protein-1 (CRMP-1) using HTVS, SP with XP algorithms, and poses were filtered using MM\GBSA which identified [3-(1-Benzyl-3-Carbamoylmethyl-2-Methyl-1h-Indol-5-Yloxy)-Propyl-]-Phosphonic Acid (3-1-BenCarMethIn YlPro-Phosphonic Acid) (DB02504) as multitargeted drug candidate with docking and MM\GBSA score ranges from −5.83 to −10.66 and −7.56 to −50.14 Kcal/mol, respectively. Further, the pharmacokinetic and QM-based DFT studies have shown complete acceptance results, and interaction fingerprinting reveals that ILE, GLY, VAL, TYR, LEU, and GLN were among the most interacting residues. The 100 ns MD simulation in the SPC water model with NPT ensemble showed stable performance with deviation and fluctuations

Magnesium oxide nanostructured particles (NP) were prepared using a simple solution combustion technique using different leaf extracts such as Mangifera indica (Mango - Ma), Azadirachta indica (Neem—Ne), and Carica papaya (Papaya—Pa) as surfactants. The highly crystalline phase of MgO nanostructures was confirmed by PXRD and FTIR studies for 2 h 500°C calcined samples. To analyze the characteristics of obtained material–MaNP, NeNP, and PaNP for dosimetry applications, thermoluminescence (TL) studies were carried out for Co-60 gamma rays irradiated samples in the dose range 10–50 KGy; PaNP and NeNP exhibited well-defined glow curve when compared with MaNP samples. In addition, it was observed that the TL intensity decreases, with increase in gamma dose and the glow peak temperature is shifted towards the higher temperature with the increase in heating rate. The glow peak was segregated using glow curve deconvolution and thermal cleaning method. Kinetic parameters estimated using Chen’s method, trap depth (E), and frequency factor (s) were found to be 0.699, 7.408, 0.4929, and 38.71, 11.008, and 10.71 for PaNP, NeNP, and MaNP respectively. The well-resolved glow curve, good linear behavior in the dose range of 10–50, KGy, and less fading were observed in PaNP as compared with MaNP and NeNP. Further, the antibacterial activity was checked against human pathogens such as Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa. A visible zone of clearance was observed at 200 and 100 μg/mL by the PaNP and NeNP, indicating the death of colonies by the nanoparticles. Therefore, PaNP nanomaterial is a potential phosphor material for dosimetry and antibacterial application compared to NeNP and MaNP.

The present study suggested memory-type ratio and product estimators for variance estimation in the presence of measurement errors. We applied the exponentially weighted moving averages statistic which simultaneously utilizes the current and prior information for better estimation in surveys based on the time-scale. The expressions of approximate mean square errors of memory-type estimators are derived using Taylor series up to first order. Mathematical conditions are also obtained for which the suggested memory-type ratio and product estimators perform better than the conventional ratio and product estimators. The efficiency of the proposed estimators is observed using an extensive simulation study in the presence of measurement errors. A real data application is also carried out to support the mathematical expressions. From the results, it is shown that the use of prior sample information significantly increased the efficiency of the proposed estimators.

In this article, we have proposed a generalized estimator for mean estimation by combining the ratio and regression methods of estimation in the presence of auxiliary information using systematic sampling. We incorporated some robust parameters of the auxiliary variable to obtain precise estimates of the proposed estimator. The mathematical expressions for bias and mean square error of proposed the estimator are derived under large sample approximation. Many other generalized ratio and product-type estimators are obtained from the proposed estimator using different choices of scalar constants. Some special cases are also discussed in which the proposed generalized estimator reduces to the usual mean, classical ratio, product, and regression type estimators. Mathematical conditions are obtained for which the proposed estimator will perform more precisely than the challenging estimators mentioned in this article. The efficiency of the proposed estimator is evaluated using four populations. Results showed that the proposed estimator is efficient and useful for survey sampling in comparison to the other existing estimators.

It is well known that polycystic ovarian syndrome (PCOS) may elevate psychological problems in patients, but there is a scarcity of the studies among Saudi Arabian population. This research was designed to investigate the influence of PCOS on the development of psychological load in terms of depression, anxiety, and stress in comparison to normal women who have no PCOS. Further, a correlation of psychological distress in PCOS females was done with their educational level. This is case-control research carried out in one of Riyadh's multispecialty hospitals. In the PCOS patients and control groups (each with 84 samples), samples were collected using convenience sampling and a simple random approach, respectively. The psychological burden was determined using DASS-21. The data obtained were analyzed using SPSS-IBM 25. Most participants (52.9%) were between the ages of 26 and 35 and had a university education (68.4%). A significantly higher percentage of PCOS patients (P = 0.001) had irregular menses, hirsutism, infertility, and acne in comparison to the mothers without PCOS. There was a significantly higher possibility of depression (P = 0.003), anxiety (P = 0.016), and stress (P = 0.001) among PCOS patients than in control subjects. Among the psychological domain tested in the study, the risk of developing stress (odds ratio, OR = 8.32) was high when compared to depression (OR = 3.12) and anxiety (OR = 2.127) in PCOS patients. Furthermore, when compared to PCOS females with less education, a significantly lower number of university-educated PCOS females developed depression. The study demonstrates a high prevalence of psychological burden among the PCOS population. Higher education has been shown to help in alleviating depression in PCOS females. Meeting PCOS women's psychological needs will improve their overall health status.

Bioassay based fractionation of methanolic extract of Berberis baluchistanica (Berberidaceae), used traditionally for internal injuries, led to the isolation of known compounds (1–4). The structure of these compounds was elucidated by different spectroscopic analysis and available literature data. Antidiabetic and antioxidant potentials of B. baluchistanica fractions and isolated compounds were evaluated using in vitro alpha- amylase and DPPH assays. The isolated compounds were identified as obamegine (1), pakistanine (2), 8-oxyberberine (3) and baluchistine (4). Obamegine was reported from many other species of this genus but it is first time isolated from B. baluchistanica in present study. Moreover, in vitro pakistanine (2) was found as bioactive lead molecule for hypoglycemic (IC50:40.26 µg/ml) and antioxidant (IC50:14.15 µg/ml) activities compared to acarbose (IC50:33.68 µg/ml) and ascorbic acid (IC50:0.41 µg/ml). To the best of our knowledge, no previous data were available for these biological activities. Additionally, in silico antidiabetic and antioxidant activity of pakistanine against two proteins, α-amylase (-9.7 kcal/mol) and tyrosinase (-8.7 kcal/mol) are reported here for the first time. The molecular docking binding interactions authenticate and support the above-mentioned activities and are helpful in predicting the mechanism of action of pakistanine (2).

Psychological problems affect a sizable portion of the population, and they require special care. In the current study, we aimed to assess patient satisfaction with the healthcare system at one of the multispecialty hospitals in Riyadh, Saudi Arabia, as well as to identify potential factors that can have an impact on patient satisfaction. A validated pre-tested questionnaire including features to evaluate general hospital services (HS-6 items), nursing services (NS-3 items), pharmacy services (PS-7 items), and a standard patient satisfaction questionnaire (PSQ-18 item) was administered to patients who had been receiving therapy for their psychological disease for the past 3 months. Using binary and multiple regression analysis, the strengths of the associations between sociodemographic factors and patient satisfaction measures were evaluated. The results were expressed as adjusted odds ratios (AOR), which were deemed significant when the P value was < 0.05. Sixty-six percent of the 258 study participants were men, and sixty percent of them were between the ages of 18 and 35 years. The bulk of survey respondents (74%) were employed, married, and well–educated. Our research revealed that those who were employed (AOR, HS-2.5; NS-2.65, PS-2.32), have a higher education (AOR, HS-2.23, NS-2.63, PS-2.82), male gender (AOR, HS-1.12, NS-1.08, PS-1.86) and between the ages of 18 and 35 years (AOR, HS-1.48, NS-1.53, PS-1.67) were more likely to be satisfied with general hospital, nursing, and pharmacy services. Further, those who were married had 1.43 and 1.21 times more chance of satisfaction with the pharmacy and nursing services, respectively, compared to singles. Additionally, those with employment had odds of being satisfied that were 2.4 times higher, highly educated individuals had odds that were 2.1 times higher, participants between the ages of 18 and 35 had odds that were 1.51 times higher, and men had odds that were 1.41 times higher on the patient satisfaction questionnaire scale (PSQ-18). Overall, the study participants' satisfaction with general hospital, nursing, and pharmacy services was 70, 76.3, and 83.3%, respectively, compared to only 61.2% on the PSQ-18. Participants in the survey awarded the hospital amenities, pharmacy services, and nursing care high ratings. The medical care, however, fell short of expectations. The study's findings suggest that action needs to be taken to enhance healthcare system services, particularly in the psychological departments of the medical organization.

Datura metel L. is an important medicinal plant of Solanaceae family which has extensive pharmacological properties. The present investigation was aimed to identify the presence of phytoconstituents and assess in vitro antibacterial, anti-biofilm, anti-diabetic, anti-inflammatory, antioxidant, cytotoxicity, and wound healing efficacy of D. metel leaves extract. Among different solvent extracts, methanolic extract showed higher amount of phenolic (124.61 ± 0.68 mg GAE/g), alkaloid (88.77 ± 1.01 mg AE/g), flavonoids (42.24 ± 0.18 mg QE/g), and tannins contents (38.72 ± 0.51 mg GAE/g). The extract exhibited not only significantly (P 90% of cell viability in the presence of methanolic extract, thereby indicating its non-toxicity effect. Wound healing assay indicated that methanolic extract at 50 µg/mL closed 100% of wound gap after 24 h with high rate of migration and proliferation. Furthermore, GC–MS chromatogram revealed the presence of several components in methanolic extract, including neophytadiene, hexadecanoic acid, and hentriacontane as principal phytoconstituents. In conclusion, methanolic extract of D. metel leaves could be used as potent therapeutic agent not only for treating metabolic diseases but also superficial chronic diabetic wounds.

In this study, a supply chain model is proposed with preservation technology under learning fuzzy theory for deteriorating items where the demand rate depends on the selling price and also treats as a triangular fuzzy number. The deterioration rate of any item cannot be eliminated due to its natural process, but it can be controlled with the help of preservation technology. Some harmful gases are emitted during the preservation process due to deteriorating items that harm the environment. In general, it can be easily seen that most of the sellers offer a trade credit policy to their regular buyers. In this paper, the retailer’s inventory stock reduces due to demand and deterioration. It is also assumed that some units are defective due to machine defects or delivery inefficiency. The retailer accepted the policy of trade credit offered by the seller. The aim of this paper is to enhance the profit of the supply chain partners. We proposed a theorem to get the optimal values of the selling price and cycle length. The retailer’s total profit is a function of selling price and cycle length, and the retailer’s total profit is optimized with respect to selling price and cycle length under trade-credit. Numerical examples are also presented for the validation of the present study, and sensitivity analysis is also discussed to know the robustness of the supply chain model. Managerial insight and observation have been given in the sensitivity section. Limitations and future work of this paper have been presented in the conclusion section.

In this paper, the question of inference of the reliability parameter of fuzzy stress strength ${R_F} = P(Y < X)$ is attached to the difference between stress and strength values when X and Y are independently distributed from inverse Rayleigh random variables. Including fuzziness in the stress-strength interference enables researchers to make more sensitive and precise analyses about the underlying systems. The maximum product of the spacing method for the reliability of fuzzy stress intensity inference has been introduced. As classical estimation methods and Bayesian estimation methods are used to estimate the reliability parameter $R_F$, the maximum product of spacing and maximum likelihood estimation methods is used. The maximum product of spacing under fuzzy reliability of stress strength model is introducing in this paper. Markov Chain Monte Carlo approach is used to obtain Bayesian estimators of traditional and fuzzy reliability of stress strength for inverse Rayleigh model by using the Metropolis-Hastings algorithm. Using an extensive Monte Carlo simulation analysis, the outputs of the fuzzy reliability and traditional reliability estimators are contrasted. Finally, for example, and to verify the efficiency of the proposed estimators, a genuine data application is used.

Background and Objectives: We have recently reported that stains have calcium channel blocking activity in isolated jejunal preparations. In this study, we examined the effects of atorvastatin and fluvastatin on blood vessels for a possible vasorelaxant effect. We also studied the possible additional vasorelaxant effect of atorvastatin and fluvastatin, in the presence of amlodipine, to quantify its effects on the systolic blood pressure of experimental animals. Materials and Methods: Atorvastatin and fluvastatin were tested in isolated rabbits’ aortic strip preparations using 80mM Potassium Chloride (KCl) induced contractions and 1 micro molar Norepinephrine (NE) induced contractions. A positive relaxing effect on 80 mM KCl induced contractions were further confirmed in the absence and presence of atorvastatin and fluvastatin by constructing calcium concentration response curves (CCRCs) while using verapamil as a standard calcium channel blocker. In another series of experiments, hypertension was induced in Wistar rats and different test concentrations of atorvastatin and fluvastatin were administered in their respective EC50 values to the test animals. A fall in their systolic blood pressure was noted using amlodipine as a standard vasorelaxant drug. Results: The results show that fluvastatin is more potent than amlodipine as it relaxed NE induced contractions where the amplitude reached 10% of its control in denuded aortae. Atorvastatin relaxed KCL induced contractions with an amplitude reaching 34.4% of control response as compared to the amlodipine response, i.e., 39.1%. A right shift in the EC50 (Log Ca++ M) of Calcium Concentration Response Curves (CCRCs) implies that statins have calcium channel blocking activity. A right shift in the EC50 of fluvastatin with relatively less EC50 value (−2.8 Log Ca++ M) in the presence of test concentration (1.2 × 10−7 M) of fluvastatin implies that fluvastatin is more potent than atorvastatin. The shift in EC50 resembles the shift of Verapamil, a standard calcium channel blocker (−1.41 Log Ca++ M). Conclusions: Atorvastatin and fluvastatin relax the aortic strip preparations predominantly through the inhibition of voltage gated calcium channels in high molar KCL induced contractions. These statins also inhibit the effects of NE induced contractions. The study also confirms that atorvastatin and fluvastatin potentiate blood pressure lowering effects in hypertensive rats.

Replication of Human Cytomegalovirus (HCMV) requires the presence of a metal-dependent endonuclease at the C-terminus of pUL89, in order to properly pack and cleave the viral genome. Therefore, pUL89 is an attractive target to design anti-CMV intervention. Herein, we used integrated structure-based and ligand-based virtual screening approaches in combination with MD simulation for the identification of potential metal binding small molecule antagonist of pUL89. In this regard, the essential chemical features needed for the inhibition of pUL89 endonuclease domain were defined and used as a 3D query to search chemical compounds from ZINC and ChEMBL database. Thereafter, the molecular docking and ligand-based shape screening were used to narrow down the compounds based on previously identified pUL89 antagonists. The selected virtual hits were further subjected to MD simulation to determine the intrinsic and ligand-induced flexibility of pUL89. The predicted binding modes showed that the compounds reside well in the binding site of endonuclease domain by chelating with the metal ions and crucial residues. Taken in concert, the in silico investigation led to the identification of potential pUL89 antagonists. This study provided promising starting point for further in vitro and in vivo studies.

Fundamentally, newborn items that are used commercially, such as chicken, fish, and small camel, grow day by day in size and also increase their weight. The seller offers a credit policy to the buyer to increase sales for a particular growing item (fish), and in this paper, it is assumed that the buyer accepts the policy of the trade credit. In this paper, the buyer acquires the newborn items (fish) from the seller and then sells them when the newborn items have increased their size and weight. From this point of view, the present paper reveals a fuzzy-based supply chain model that includes carbon emissions and a permissible delay in payment for defective growing items (fish) under the effect of learning where the demand rate is imprecise in nature and is treated as a triangular fuzzy number. Finally, the buyer’s total profit is optimized with respect to the number of newborn items. A numerical example has been presented for the justification of the model. The findings clearly suggest that the presence of trade credit, learning, and a fuzzy environment have an affirmative effect on the ordering policy. The buyer should order more to avoid higher interest charges after the grace period, which eventually increases their profit, while at the same time, it is also beneficial for the buyer to order less to gain the benefit of the trade credit period. The fuzziness theory controls the uncertainty situation of inventory parameters with the help of a de-fuzzified method. The lower and upper deviation of demand affects the total fuzzy profit. The effect of learning gives a positive response concerning the size of the order and the buyer’s total fuzzy profit. This means that the decision-maker should be aware of the size of the newborn items, rate of learning, and trade credit period during the supply chain because these directly affect the buyer’s total fuzzy profit. The impact of the inventory parameter of this model is presented with the help of sensitivity analysis.

Industrial effluents containing dyes are the dominant pollutants, making the drinking water unfit. Among the dyes, methylene orange (MO) dye is mutagenic, carcinogenic and toxic to aquatic organisms. Therefore, its removal from water bodies through effective and economical approach is gaining increased attention in the last decades. Photocatalytic degradation has the ability to convert economically complex dye molecules into non-toxic and smaller species via redox reactions, by using photocatalysts. g-C3N4 is a metal-free n-type semiconductor, typical nonmetallic and non-toxici polymeric photocatalyst. It widely used in photocatalytic materials, due to its easy and simple synthesis, fascinating electronic band structure, high stability and abundant availability. As a photocatalyst, its major drawbacks are its limited efficiency in separating photo-excited electron–hole pairs, high separated charge recombination, low specific surface area, and low absorption coefficient. In this review, we report the recent modification strategies adopted for g-C3N4 for the efficient photodegradation of MO dye. The different modification approaches, such as nanocomposites and heterojunctions, as well as doping and defect introductions, are briefly discussed. The mechanism of the photodegradation of MO dye by g-C3N4 and future perspectives are discussed. This review paper will predict strategies for the fabrication of an efficient g-C3N4-based photocatalyst for the photodegradation of MO dye.

Norovirus (HNoV) is a leading cause of gastroenteritis globally, and there are currently no treatment options or vaccines available to combat it. RNA-dependent RNA polymerase (RdRp), one of the viral proteins that direct viral replication, is a feasible target for therapeutic development. Despite the discovery of a small number of HNoV RdRp inhibitors, the majority of them have been found to possess a little effect on viral replication, owing to low cell penetrability and drug-likeness. Therefore, antiviral agents that target RdRp are in high demand. For this purpose, we used in silico screening of a library of 473 natural compounds targeting the RdRp active site. The top two compounds, ZINC66112069 and ZINC69481850, were chosen based on their binding energy (BE), physicochemical and drug-likeness properties, and molecular interactions. ZINC66112069 and ZINC69481850 interacted with key residues of RdRp with BEs of −9.7, and −9.4 kcal/mol, respectively, while the positive control had a BE of −9.0 kcal/mol with RdRp. In addition, hits interacted with key residues of RdRp and shared several residues with the PPNDS, the positive control. Furthermore, the docked complexes showed good stability during the molecular dynamic simulation of 100 ns. ZINC66112069 and ZINC69481850 could be proven as potential inhibitors of the HNoV RdRp in future antiviral medication development investigations.

Moxifloxacin and gemifloxacin are the two newer broad-spectrum 8-methoxy-quinolone derivatives that are used to treat various bacterial infections in cardiac patients. In this research study, we assessed the impact of moxifloxacin and gemifloxacin on the QT intervals of electrocardiograms in normal adult doses and draw a comparison, in a controlled environment, on healthy volunteers. Additionally, the effect of both test drugs on the QRS complex was checked. Sixty healthy volunteers were randomly assigned to two groups via R-software, and each respectively received moxifloxacin and gemifloxacin for five days. The research ethics committee approved the research, and it was registered for clinical trial under NCT 04692623. The participants’ electrocardiograms were obtained before the start of the dose (baseline) and on the fifth day. Significant prolongation of QT interval was noted in moxifloxacin (p < 0.0001) as compared to gemifloxacin treated groups. There were no cases of QTc prolongation over the usual limits (450–470 ms) in the gemifloxacin-treated group, however, QTc prolongations at the rate of 30 and 60 ms from the baseline were noted, interpreted as per the EMEA guidelines. These findings indicate that moxifloxacin caused significant (p < 0.0001) QT interval prolongation (QTIP) as compared to gemifloxacin. In contrast to the previously reported literature, the prominent effect of moxifloxacin on the widening of the QRS-complex was noted with no such effect on QRS-widening in the gemifloxacin-treated group. It is concluded that both drugs have the potential for considerable QT interval prolongation (QTIP) effects, which is one of the risk factors for developing torsade de pointes (TdPs) in cardiac patients. Thus, clinicians should exercise caution when prescribing moxifloxacin and gemifloxacin to cardiac patients and should consider alternate treatment options.

In this paper, the seller offers a credit period to his buyer for more sales and the buyer accepts the seller’s policy to gain more profit, and it is assumed that the seller has defective and non-defective items. When the seller provides lots for sale to his buyer then, the buyer separates the whole lots with the help of inspection process into defective and perfect quality items. Further, in this scenario, the percentage of defective items present in the lot follows the S-shape learning curve and it is also considered that the demand rate is imprecise in nature. Here, the demand rate assumes a triangular fuzzy number due to the imprecise nature and it is the model assumption. Based on this assumption, we developed an inventory model with the effect of learning and trade credit strategy under a fuzzy environment for the buyer. The buyer’s total profit has been optimized concerning the order quantity in the fuzzy environment where order quantity has been assumed as a decision variable. The results of this model were verified with the help of numerical examples and sensitivity analysis. We compared the buyer’s total profit in a crisp and fuzzy environment and the buyer gained more profit in a fuzzy environment compared to the crisp environment. Moreover, we compared the results with and without the effect of learning and trade credit on the buyer’s ordering policy and obtained a positive effect on the ordering policy in the numerical section. We determined positive results from the sensitivity analysis, which proved that the trade credit policy will be beneficial for both partners of the supply chain.

Introduction: The uses of general anesthesia in outpatient invasive procedures have increased, especially in dermatology. Being uncooperative, children often require general anesthesia since surgical skin operations are mostly painful. Purpose: The aim of this study is to evaluate the safety, significant adverse events, and the complication rates related to general anesthesia when used among pediatric population underwent skin procedures. Methods: A retrospective cohort study of a patient chart review from the period (September 1, 2017, to September 2019). All patients admitted for pediatric skin procedures during this period have participated in our study. We reviewed selected charts to document any unexpected admissions, adverse events, or complications. Surgical outcomes and anesthesia complications were reviewed by three anesthesiologists. We assessed inter-rater reliability. Results: A total of 211 procedures were reported for 211 patients with 19 diagnoses. No adverse events related to anesthesia were recognized, apart from minor complications noticed in 12 patients. Kappa value ranges between 0.78 and 1.00 (95% confidence interval, 0.46809–1.00). Conclusion: Dermatologists and pediatricians can safely do necessary procedures under general anesthesia with the supervision of pediatric-trained anesthesiologists while considering other safety and risk precautions and pediatric age group.

In survey sampling an investigator may be unable to get the complete and correct information at the same time. So non-response and measurement error occur simultaneously and consequently may effect the estimator. Considering this problem, a generalized class of estimators is proposed for estimating the finite population mean for sensitive variable in the presence of measurement error and non-response under stratified random sampling. We conducted a study based on real data set at Quaid-i-Azam University, Islamabad. Simulation and real life data sets are used to observe the performances of the estimators. Bias and MSE values are given for the comparison of estimators.

Assuming the significance of sustainability, it is considered necessary to ensure the conservation of our natural resources, in addition to minimizing waste. To promote significant sustainable effects, factors including production, transportation, energy usage, product control management, etc., act as the chief supports of any modern supply chain model. The buyer performs the firsthand inspection and returns any defective items received from the customer to the vendor in a process that is known as first-level inspection. The vendor uses the policy of recovery product management to obtain greater profit. A concluding inspection is accomplished at the vendor’s end in order to distinguish the returned item as belonging to one of four specific categories, namely re-workable, reusable, recyclable, and disposable, a process that is known as second-level inspection. Then, it is observed that some defective items are suitable for a secondary market, while some are reusable, and some can be disassembled to shape new derived products, and leftovers can be scrapped at the disposal cost. This ensures that we can meet our target to promote a cleaner drive with a lower percentage of carbon emissions, reducing the adverse effects of landfills. The activity of both players in this model is presented briefly in the flowchart shown in the abstract. Thus, our aim of product restoration is to promote best practices while maintaining economic value, with the ultimate goal of removing the surrounding waste with minimum financial costs. In this regard, it is assumed that the demand rate is precise in nature. The learning effect and fuzzy environment are also considered in the present model. The proposed model studies the impacts of learning and carbon emissions on an integrated green supply chain model for defective items in fuzzy environment and shortage conditions. We optimized the integrated total fuzzy profit with respect to the order quantity and shortages. We described the vendor’s strategy and buyer’s strategy through flowcharts for the proposed integrated supply chain model, and here, in the flowchart, R-R-R stands for re-workable, reusable, and recyclable. The demand rate was treated as a triangular fuzzy number. In this paper, a numerical example, sensitivity analysis, limitations, future scope, and conclusion are presented for the validation of the proposed model.

Photodegradation is the chemical conversion of large, toxic, and complex molecules into non-toxic, simpler, and lower molecular weight species due to light exposure. Heterogeneous photocatalysis has sufficient potential to degrade toxic organic pollutants present in wastewater. As industries discharge their effluents containing organic pollutants into natural water bodies, which penetrate into the subsurface through connected pores it is necessary to study this process in natural or tap water. Tap water (TW) is mainly obtained from underground wells having inorganic salts in a minute quantity with a conductivity of 500 μS/cm. TW contains inorganic anions, which affect the photocatalytic activity and photocatalysis process. The aim of this review is to evaluate the effect of TW on the photo-degradation of organic pollutants such as dyes, pharmaceutical products, pesticides, etc., with the support of the literature. The TW had a diverse effect on the photodegradation of organic pollutants; either it may enhance or decrease the rate of pollutants’ photodegradation.

Areca catechu seeds and their extract/s are currently used to treat various ailments and infections including snakebites. The purpose of this investigation was to assess the inhibiting/neutralizing effect of ethyl acetate and aqueous ethanolic seed extracts of A. catechu on Bungarus caeruleus (krait) venom. The enzyme activities and their inhibition were evaluated using standard procedures (in vitro). In vivo studies were conducted using chick embryos and murine models. The extracts inhibited hyaluronidase and phospholipase A2 activities. Protease activity was neutralized by the aqueous ethanolic extract only. The IC50 value of aqueous ethanolic extract for hyaluronidase was 0.001 g/mL, while that for the ethyl acetate extract for phospholipase A2 was 0.006 g/mL. In addition, both the extracts neutralized the indirect hemolysis and fibrinogenolytic activity induced by B. caeruleus venom. The LD50 for the chick embryos was 4.9 µg/egg. The 50 and 100 µg aqueous ethanolic extracts neutralized the LD50 and the challenging dose (3LD50) of venom effectively in the chick embryo model. The LD50 of B. caeruleus venom in mice was 0.1927 µg/kg; the extract extended the survival time of the mice from 25 min to 30 and 35 min in 1:10 and 1:20 ((w/w) venom:extract) ratios, respectively. The extract also neutralized myotoxic activity. The A. catechu seed extract showed promising inhibitory properties against B. caeruleus venom. In this regard, academia and industries should work collaboratively to develop and formulate a cost-effective first-aid drug.

Alzheimer’s disease is the commonest form of dementia associated with short-term memory loss and impaired cognition and, worldwide, it is a growing health issue. A number of therapeutic strategies have been studied to design and develop an effective anti-Alzheimer drug. Curcumin has a wide spectrum of biological properties. In this regard, the antioxidant potentials of mono-carbonyl curcumin analogues (h1–h5) were investigated using in vitro antioxidant assays and hippocampal-based in vivo mouse models such as light–dark box, hole board, and Y-maze tests. In the in vitro assay, mono-carbonyl curcumin analogues h2 and h3 with methoxy and chloro-substituents, respectively, showed promising 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2, 2′-azinobis-3-ethylbenzothiazo-line-6-sulfonate (ABTS) free radical scavenging activities. In the in vivo studies, scopolamine administration significantly (p < 0.001) induced oxidative stress and memory impairment in mice, in comparison to the normal control group. The pretreatment with mono-carbonyl curcumin analogues, specifically h2 and h3, significantly decreased (123.71 ± 15.23 s (p < 0.001), n = 8; 156.53 ± 14.13 s (p < 0.001), n = 8) the duration of time spent in the light chamber and significantly enhanced (253.95 ± 19.05 s (p < 0.001), n = 8, and 239.57 ± 9.98 s (p < 0.001), n = 8) the time spent in the dark compartment in the light–dark box arena. The numbers of hole pokings were significantly (p < 0.001, n = 8) enhanced in the hole board test and substantially increased the percent spontaneous alternation performance (SAP %) in the Y-maze mouse models in comparison to the stress control group. In the biomarker analysis, the significant reduction in the lipid peroxidation (MDA) level and enhanced catalase (CAT), superoxide dismutase (SOD), and glutathione (GSH) activities in the brain hippocampus reveal their antioxidant and memory enhancing potentials. However, further research is needed to find out the appropriate mechanism of reducing oxidative stress in pathological models.

The drug barbital (Bar) has a strong sedative–hypnotic effect. The intermolecular charge transfer compounds associated with the chemical reactions between Bar and some π acceptors, such as 2,6-dibromoquinone-4-chloroimide (DBQ), tetracyanoquinodimethane (TCNQ), chloranil (CHL), and chloranilic acid (CLA), have been synthesized and isolated in solid state. The synthesized products have the molecular formulas (Bar–DBQ), (Bar–TCNQ), (Bar–CHL), and (Bar–CLA) with 1:1 stoichiometry based on Raman, IR, TG, 1H NMR, XRD, SEM, and UV-visible analysis techniques. Additionally, the comparative analysis of molecular docking between the donor reactant moiety, Bar, and its four CT complexes was conducted using two neurotransmitter receptors (dopamine and serotonin). The docking results obtained from AutoDockVina software were investigated by a molecular dynamics simulation technique with 100ns run. The molecular mechanisms behind receptor–ligand interactions were also looked into. The DFT computations were conducted using theory at the B3LYP/6-311G++ level. In addition, the HOMO LUMO electronic energy gap and the CT complex’s optimal geometry and molecule electrostatic potential were examined.