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Measurement errors may significantly distort the properties of an estimator. In this paper, estimators of the finite population variance using the information on first and second raw moments of the study variable are developed under stratified random sampling that incorporate the variance of a measurement error component. Additionally, combined and separate estimators are also developed for estimating the finite population variance using supplementary information in terms of one or two auxiliary variables. An empirical study is carried out to study the effect of measurement error on the relative efficiencies of the proposed estimators.

Abstract Background Potassium nitrate poisoning is a rare but potentially serious condition that can result in methemoglobinemia and subsequent cyanosis. This case report presents a unique instance of rapid-onset methemoglobinemia resulting from the ingestion of a traditional medicine preparation containing potassium nitrate, known as “kalmi shora.” Case presentation A 14-year-old Pakistani boy reported to the emergency department with a history of sudden-onset headache, drowsiness agitation, irritability, and generalized cyanosis. Pulse oximetry showed a concerning oxygen saturation level of 58%, whereas arterial blood gas analysis revealed a normal partial pressure of oxygen (90 mmHg). The profile of abrupt onset of symptoms, generalized cyanosis, and the discrepancy between the partial pressure of oxygen and oxygen saturation readings necessitated a comprehensive assessment including inquiries into potential toxins. The peculiar appearance of the blood, resembling chocolate in color, further indicated the possibility of methemoglobinemia. The patient was successfully treated with methylene blue, leading to a prompt resolution of symptoms. Conclusion This case highlights the significance of considering toxin exposures, such as traditional-medicine-induced poisoning, in emergency settings. The report contributes to the medical literature by highlighting the potential risks associated with traditional remedies and emphasizes the critical role of prompt diagnosis and intervention in optimizing patient outcomes. Recognition of the specific etiology of methemoglobinemia, in this case, traditional medicine ingestion, is essential for effective management in emergency medicine.

Abstract Cystic fibrosis transmembrane conductance regulator (CFTR) protein is an ion channel found in numerous epithelia and controls the flow of water and salt across the epithelium. The aim of our study to find natural compounds that can improve lung function for people with cystic fibrosis (CF) caused by the p.Gly628Arg (rs397508316) mutation of CFTR protein. The sequence of CFTR protein as a target structure was retrieved from UniProt and PDB database. The ligands that included Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and one Trikafta (R*) reference drug were screened out from PubChem database. Autodock vina software carried out docking, and binding energies between the drug and the target were included using docking-score. The following tools examined binding energy, interaction, stability, toxicity, and visualize protein-ligand complexes. The compounds having binding energies of −6.4, −5.1, −6.6, −5.1, and − 6.5 kcal/mol for Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and R*-drug, respectively with mutated CFTR (Gly628Arg) structure were chosen as the most promising ligands. The ligands bind to the mutated CFTR protein structure active sites in hydrophobic bonds, hydrogen bonds, and electrostatic interactions. According to ADMET analyses, the ligands Armepavine and Quercetin also displayed good pharmacokinetic and toxicity characteristics. An MD simulation for 200 ns was also established to ensure that Armepavine and Quercetin ligands attached to the target protein favorably and dynamically, and that protein-ligand complex stability was maintained. It is concluded that Armepavine and Quercetin have stronger capacity to inhibit the effect of mutated CFTR protein through improved trafficking and restoration of original function.

Abstract Wheat is an important staple crop not only in Pakistan but all over the globe. Although the area dedicated to wheat cultivation expands annually, the quantity of wheat harvested is declining due to various biotic and abiotic factors. Global wheat production and output have suffered as a result of the drought, which is largely driven by a lack of water and environmental factors. Organic fertilizers have been shown to reduce the severity of drought. The current research was conducted in semi-arid climates to mitigate the negative effects of drought on wheat during its critical tillering (DTS), flowering (DFS), and grain filling (DGFS) stages through the application of three different abscisic acid treatments: ABA0 (0 mgL−1) control, ABA1 (100 mgL−1) and ABA2 (200 mgL−1). Wheat growth and yield characteristics were severely harmed by drought stress across all critical development stages, with the DGFS stage being particularly vulnerable and leading to a considerable loss in yield. Plant height was increased by 24.25%, the number of fertile tillers by 25.66%, spike length by 17.24%, the number of spikelets per spike by 16.68%, grain count per spike by 11.98%, thousand-grain weight by 14.34%, grain yield by 26.93% and biological yield by 14.55% when abscisic acid (ABA) was applied instead of the control treatment. Moreover, ABA2 increased the more physiological indices (water use efficiency (36.12%), stomatal conductance (44.23%), chlorophyll a (24.5%), chlorophyll b (29.8%), transpiration rate (23.03%), photosynthetic rate (24.84%), electrolyte leakage (− 38.76%) hydrogen peroxide (− 18.09%) superoxide dismutase (15.3%), catalase (20.8%), peroxidase (− 18.09%), and malondialdehyde (− 13.7%)) of drought-stressed wheat as compared to other treatments. In the case of N, P, and K contents in grain were maximally improved with the application of ABA2. Through the use of principal component analysis, we were able to correlate our results across scales and provide an explanation for the observed effects of ABA on wheat growth and production under arid conditions. Overall, ABA application at a rate of 200 mgL−1 is an effective technique to boost wheat grain output by mitigating the negative effects of drought stress.

Abstract Omniphobic membranes, due to their exceptional properties, have drawn significant attention for overcoming the bottleneck in membrane distillation (MD) technology. This study demonstrates an innovative method for fabricating an omniphobic membrane that is simple and facile compared to other methods such as wet/dry etching and photolithography. The surface morphology of springtails was imitated using electrospraying technique to coat a polyvinylidene fluoride substrate with concave-shaped polystyrene beads that were successfully developed by controlling the electrical traction (voltage) and air resistance (humidity). Then, the lipid coating of springtail surfaces was mimicked by dip-coating the membrane in a low-toxicity short-chain perfluoropolyether lubricant. The concave structure’s tiny air pockets increased membrane hydrophobicity significantly, indicated by the fact that the first round of water bouncing took only 16.3 ms. Finally, in MD treatment of seawater containing 1.0 mM sodium dodecyl sulfate, the optimized omniphobic membrane maintained a stable 99.9% salt rejection rate.

Abstract This paper investigates the disparity between matter and antimatter in the universe with the help of gravitational baryogenesis. This phenomenon commenced shortly after the big bang, resulting in a predominance of matter over antimatter. We analyze the mechanism of gravitational baryogenesis (baryon to entropy ratio) under the framework of f(Q, C) gravity, where Q indicates non-metricity scalar and C denotes the boundary term. This Phenomenon depends on the charge parity violation interaction and for this paper we produce it with the coupling between baryon matter current ( $$j^{\nu }$$ j ν ) and $$\partial _{\nu }(Q+C)$$ ∂ ν ( Q + C ) . In the present work, we evaluate he baryon to entropy ratio ( $$\frac{\eta _{_B}}{S}$$ η B S ) by proposing two models of f(Q, C) with the assumption of power-law scale factor for each model and the universe contains perfect fluid throughout. We find that under optimal choice of model parameters, the results of $$\frac{\eta _{_B}}{S}$$ η B S of propose models in f(Q, C) are compatible with the observational bound. The crux of the current work is that the outcomes of our propose models for generalized case of gravitational baryogenesis are consist with its observational constraint in different eras of the Universe.

Abstract Anion exchange membrane water electrolysis (AEMWE) has seen rapid advancements over the past decade due to its promising role in green hydrogen production. Ensuring long-term functionality is as crucial as optimizing performance to achieve commercial viability and industrial integration. However, few studies have systematically discussed the degradation issues of this technology. Therefore, a thorough understanding of AEMWE degradation is needed to guide the design, assembly, operation, and maintenance of the device over its lifetime. To address this gap, this review systematically overviewed the heterogeneous degradation of AEMWE across different material and interface levels, focusing on several key components including catalysts, ionomers, membranes, and gas diffusion layers. The influences of these components and their interfaces on the catalytic efficiency, active site density, and mass and electron transfer capabilities were discussed. Moreover, the impacts of operation conditions, including temperature, electrolyte composition, and clamping pressure, on the stable operation of AEMWE were assessed. Accordingly, current mitigation strategies to resolve these degradation phenomena were rigorously evaluated. By offering insights into optimizing operations, designing materials, and improving assessment protocols for AEMWE, this work will contribute to enhancing its stability for large-scale hydrogen production.

Abstract Background Trifarotene is effective for treating acne and other skin issues. To ensure its quality and meet regulatory standards, a reverse phase liquid chromatography (RP-LC) stability-indicating method was developed and validated for its quantification in pure and topical dosage forms. An isocratic elution chromatographic method was employed, using an octadecylsilyl silica gel-packed column (150 mm × 4.6 mm, 3 µm particle size). The mobile phase was a mixture of phosphate buffer and acetonitrile (40:60 v/v). Chromatographic conditions included a flow rate of 0.5 mL/min, column temperature of 40 °C, detection at 265 nm, and injection volume of 20 µL. Results The developed analytical method reports the retention time of trifarotene 11.2 min, higher theoretical plate count, asymmetric peak, and good resolution between the peaks of trifarotene, phenoxyethanol, and environmentally generated impurities. Conclusion The analytical method has been found to be linear, accurate, robust, specific, and selective for impurities produced during forced degradation studies. The proposed analytical method can be utilized for routine pharmaceutical analysis of trifarotene to judge its quality and safety.

Abstract QSPR mathematically links physicochemical properties with the structure of a molecule. The physicochemical properties of chemical molecules can be predicted using topological indices. It is an effective method for eliminating costly and time-consuming laboratory tests. We established a QSPR between mev-degree and mve-degree-based indices and the physical properties of benzenoid hydrocarbons. To compute these indices, we designed a program using Maple software and the correlation between indices and physical properties was developed using the SPSS software. Our study reveals that the mve-degree-based sum-connectivity $$(\chi ^{mve})$$ ( χ mve ) and atom bond connectivity ( $$ABC^{mve}$$ A B C mve ) index, mev-degree-based Randić ( $$R^{mev}$$ R mev ) and Zagreb ( $$M^{mev}$$ M mev ) index are the three most significant parameters and have good prediction ability for the physicochemical properties. We examined that $$R^{mev}$$ R mev predicts the molar refractivity and boiling point, $${\chi }^{mve}$$ χ mve predicts the LogP and enthalpy, $$ABC^{mve}$$ A B C mve predicts the molecular weight, $$M^{mev}$$ M mev predicts the Gibb’s energy, Pie-electron energy and Henry’s law. Moreover, we computed the indices for the linear [n]-phenylen.

Abstract Independent component analysis (ICA) and dictionary learning (DL) are the most successful blind source separation (BSS) methods for functional magnetic resonance imaging (fMRI) data analysis. However, ICA to higher and DL to lower extent may suffer from performance degradation by the presence of anomalous observations in the recovered time courses (TCs) and high overlaps among spatial maps (SMs). This paper addressed both problems using a novel three-layered sparse DL (TLSDL) algorithm that incorporated prior information in the dictionary update process and recovered full-rank outlier-free TCs from highly corrupted measurements. The associated sequential DL model involved factorizing each subject’s data into a multi-subject (MS) dictionary and MS sparse code while imposing a low-rank and a sparse matrix decomposition restriction on the dictionary matrix. It is derived by solving three layers of feature extraction and component estimation. The first and second layers captured brain regions with low and moderate spatial overlaps, respectively. The third layer that segregated regions with significant spatial overlaps solved a sequence of vector decomposition problems using the proximal alternating linearized minimization (PALM) method and solved a decomposition restriction using the alternating directions method (ALM). It learned outlier-free dynamics that integrate spatiotemporal diversities across brains and external information. It differs from existing DL methods owing to its unique optimization model, which incorporates prior knowledge, subject-wise/multi-subject representation matrices, and outlier handling. The TLSDL algorithm was compared with existing dictionary learning algorithms using experimental and synthetic fMRI datasets to verify its performance. Overall, the mean correlation value was found to be $$26\%$$ 26 % higher for the TLSDL than for the state-of-the-art subject-wise sequential DL (swsDL) technique.