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Abstract The widespread use of oxidative hair colouring cosmetics threatens public health. Phenylenediamine derivatives serve as the main pigment in permanent hair colours. They interact with biological macromolecules, altering their functional and structural physiology. The study aimed to investigate the effect of a typical synthetic hair dye pigment, 4-Chloro-orthophenylenediamine (4-Cl-OPD), under a nitrating environment of DEANO on the calf thymus DNA molecule. The results showed single-stranded regions, base/sugar-phosphate backbone alterations, molecular changes, and nitro-oxidative lesions. These modifications are referred to as neo-epitopes on the DNA molecule. IgGs from cancer patients with a history of permanent hair dye use were screened for the recognition of neo-epitopes on DNA molecules. Hepatocellular carcinoma IgG showed the highest binding with 56% inhibition in the competition ELISA. The immune complex formation was observed through electrophoretic mobility shift assay. In conclusion, synthetic hair dye users are likely to present with heightened immunological triggers under elevated nitric oxide levels. The study reports chronic hair dye exposure as one of the factors responsible for altering the intricacies of the DNA’s microarchitectural structure and inducing neo-epitopes on the molecule. The physiological status of NO may define the susceptibility towards 4-Cl-OPD and humoral response in hair dye users. Persistent nitro-oxidative stress due to 4-Cl-OPD and NO may induce a heightened immune response against neoepitopes in the nitro-oxidatively modified DNA. Therefore, chronic hair dye exposure may be identified as a risk to human health. These findings may contribute to a better understanding and reinforcement of hair dye as one of the modifiable risk factors responsible for the pro-inflammatory carcinogenic environment.

Abstract Vaccine acceptance is a crucial component of a viable immunization program in healthcare system, yet the disparities in new and existing vaccination adoption rates prevail across regions. Disparities in the rate of vaccine acceptance result in low immunization coverage and slow uptake of newly introduced vaccines. This research presents an innovative AI-driven predictive model, designed to accurately forecast vaccine acceptance within immunization programs, while providing high interpretability. Primarily, the contribution of this study is to classify vaccine acceptability into Low, Medium, Partial High, and High categories. Secondly, this study implements the Feature Importance method to make the model highly interpretable for healthcare providers. Thirdly, our findings highlight the impact of demographic and socio-demographic factors on vaccine acceptance, providing valuable insights for policymakers to improve immunization rates. A sample dataset containing 7150 data records with 31 demographic and socioeconomic attributes from PDHS (2017–2018) is used in this paper. Using the LightGBM algorithm, the proposed model constructed on the basis of different machine-learning procedures achieved 98% accuracy to accurately predict the acceptability of vaccines included in the immunization program. The association rules suggest that higher SES, region, parents’ occupation, and mother’s education have an association with vaccine acceptability.

Plants are exposed to adverse environmental conditions, including cold, drought, heat, salinity, and heavy metals, which negatively impact plant growth and productivity of edible crops worldwide. Although the previous literature summarized the nanoparticle’s involvement in abiotic stress mitigation, the interaction of nanoparticles with other stress mitigators to overcome abiotic stress from plants remains unclear. Currently, nanotechnology is considered a growing new field in agriculture for understanding plants’ adapted stress tolerance mechanisms. Recent research has shown that nanoparticles can effectively mitigate abiotic stress by interacting synergistically with plant growth regulators. To address this, we comprehensively demonstrated the combined positive potential of nanoparticles in combination with plant growth regulators (signaling molecules, phytohormones, nanoparticles-nanoparticles interaction, fungi, plant growth promoting rhizobacteria and other metal salts) to improve plant growth and mitigate abiotic stresses. Their co-applications augment the plant’s growth, nutrient uptake, antioxidant defense system, water absorption, cell viability, water use efficiency, and photosynthetic and biochemical attributes by reducing oxidative stressors under various abiotic stresses in different plant species. This review provides a comprehensive overview of the combined applications of nanoparticles and plant growth regulators, a novel strategy to reduce the harmful effects of abiotic stress on plants. It identifies research gaps and recommends future studies to overcome their phytotoxicity worldwide.

Abstract The concept of happiness is becoming an increasingly significant area of focus in the evaluation of overall quality of life. This research examines the impact of migration on the well-being of women who remain in their place of residence following the migration of their family members. The study employs data from the Multiple Indicators Cluster Survey (2017–2018) in Punjab, Pakistan, and addresses sample selection bias through Propensity Score Matching (PSM). Moreover, as the dependent variable is ordinal, the study employed ordered logistic regression to examine the correlation between migration and happiness, taking into account migration status and other variables. The findings indicate that women from migrant backgrounds tend to benefit from migrating, primarily in terms of enhanced financial resources. In order to gain insight into the varying effects of migration on women’s happiness, the study employed heterogeneity analysis, which revealed that the influence of migration on women’s happiness differs across various areas. In conclusion, the combination of propensity score matching (PSM) with ordered logistic regression and heterogeneity analysis represents a robust methodology for evaluating the impact of migration on women’s perceptions of happiness. The study demonstrates that migration has the potential to improve the quality of life of women who remain in their home countries, particularly those residing in financially disadvantaged households.

Abstract The disease dynamics affect the human life. When one person is affected with a disease and if it is not treated well, it can weaken the immune system of the body. Human Immunodeficiency Virus (HIV) is a virus that attacks the immune system, of the body which is the defense line against diseases. If it is not treated well then HIV progresses to its advanced stages and it is known as Acquired Immunodeficiency Syndrome (AIDS). HIV is typically a disease that can transferred from one person to another in several ways such as through blood, breastfeeding, sharing needles or syringes, and many others. So, the need of the hour is to consider such important disease dynamics and that will help mankind to save them from such severe disease. For the said purpose the reaction-diffusion HIV CD4 $$^{+}$$ + T cell model with drug therapy under the stochastic environment is considered. The underlying model is numerically investigated with two time-efficient schemes and the effects of various parameters used in the model are analyzed and explained in a real-life scenario. Additionally, the obtained results will help the decision-makers to avoid such diseases. The random version of the HIV model is numerically investigated under the influence of time noise in It $${\hat{o}}$$ o ^ sense. The proposed stochastic backward Euler (SBE) scheme and proposed stochastic Implicit finite difference (SIFD) scheme are developed for the computational study of the underlying model. The consistency of the schemes is proven in the mean square sense and the given system of equations is compatible with both schemes. The stability analysis proves that both schemes and schemes are unconditionally stable. The given system of equations has two equilibria, one is disease-free equilibrium (DFE) and the other is endemic equilibrium. The simulations are drawn for the different values of the parameters. The proposed SBE scheme showed the convergent behavior towards the equilibria for the given values of the parameters but also showed negative behavior that is not biological. The proposed SIFD scheme showed better results as compared with the stochastic SBE scheme. This scheme has convergent and positive behavior towards the equilibria points for the given values of the parameters. The effect of various parameters is also analyzed. Simulations are drawn to evaluate the efficacy of the schemes.

Abstract Background Transcatheter aortic valve implantation (TAVI) is a growing treatment for aortic stenosis, but anatomical variations exist across populations. Asians tend to have smaller aortic annuli and higher bicuspid valve morphology compared to Westerners, potentially impacting TAVI valve selection and outcomes. This study analyzes aortic root parameters in Pakistani patients undergoing TAVI. Results We conducted a retrospective analysis of 78 patients who underwent TAVI at the Peshawar Institute of Cardiology from January 2021 to March 2024. Pre-procedural CT scans were analyzed for aortic annulus diameters, area, and other relevant parameters. The mean age was 72.41 years (SD ± 11.99), and 61.5% were male. Aortic annulus diameters (minimum, mean, maximum) were 21.18 mm (SD ± 3.99), 24.21 mm (SD ± 3.93), and 27.49 mm (SD ± 4.43), respectively. Bicuspid aortic valves were present in 34.61% of patients. Conclusions Our findings suggest that Pakistani patients undergoing TAVI may have aortic root dimensions comparable to Western populations, with a substantial prevalence of bicuspid valves. However, coronary heights were similar to those reported in Asian populations with smaller annuli. Further studies are needed to assess TAVI outcomes in Pakistani patients and determine if tailored valve sizing strategies are required.

Abstract The study investigates the interplay of land use dynamics and climate change on the hydrological regime of the Ravi River using a comprehensive approach integrating Geographic Information System (GIS), remote sensing, and hydrological modeling at the catchment scale. Employing the Soil and Water Assessment Tool (SWAT) model, simulations were conducted to evaluate the hydrological response of the Ravi River to both current conditions and projected future scenarios of land use and climate change. This study differs from previous ones by simulating future land use and climate scenarios, offering a solid framework for understanding their impact on river flow dynamics. Model calibration and validation were performed for distinct periods (1999–2002 and 2003–2005), yielding satisfactory performance indicators (NSE, R2, PBIAS = 0.85, 0.83, and 10.01 in calibration and 0.87, 0.89, and 7.2 in validation). Through supervised classification techniques on Landsat imagery and TerrSet modeling, current and future land use maps were generated, revealing a notable increase in built-up areas from 1990 to 2020 and projections indicating further expansion by 31.7% from 2020 to 2100. Climate change projections under different socioeconomic pathways (SSP2 and SSP5) were derived for precipitation and temperature, with statistical downscaling applied using the CMhyd model. Results suggest substantial increases in precipitation (10.9 − 14.9%) and temperature (12.2 − 15.9%) across the SSP scenarios by the end of the century. Two scenarios, considering future climate conditions with current and future land use patterns, were analyzed to understand their combined impact on hydrological responses. In both scenarios, inflows to the Ravi River are projected to rise significantly (19.4 − 28.4%) from 2016 to 2100, indicating a considerable alteration in seasonal flow patterns. Additionally, historical data indicate a concerning trend of annual groundwater depth decline (0.8 m/year) from 1996 to 2020, attributed to land use and climate changes. The findings underscore the urgency for planners and managers to incorporate climate and land cover considerations into their strategies, given the potential implications for water resource management and environmental sustainability.

Abstract Background Sea buckthorn (Hippophae rhamnoides), a deciduous species plant, is widely distributed around the globe, and native to the cold-temperate regions of Europe and Asia. This medicinal herb contains several bioactive constituents including chlorogenic acid. The conventional methods used for the extraction of phenolic antioxidants from natural herbs often result in low yields, high toxicity, and pose environmental hazards limiting their effectiveness and scalability. Therefore, green extraction techniques using deep eutectic solvents, composed of natural, non-toxic, and biodegradable components were applied for extraction of chlorogenic acid from sea buckthorn weed. Fourteen deep eutectic solvent mixtures were prepared and evaluated for extraction yield of chlorogenic acid. Parameters such as hydrogen bond donor-to-hydrogen bond acceptor ratio, liquid-to-solid ratio, shaking speed, and shaking time were optimized for the best mixture. Results The combination of lactic acid and maltose (1:1) was found to give best extraction yield using response surface methodology. The deep eutectic solvent system under optimum conditions produced 12.2 g/100 g of crude extract sea buckthorn containing 174.7 mg gallic acid equivalents (mg GA)/g) of extract. Moreover, the optimized extract exhibited appreciable radical scavenging capacity (91%), trolox equivalent antioxidant capacity (11.2% of extract), and inhibition of peroxide in linoleic acid (80.6%). High-performance liquid chromatography-based characterization revealed the extracts contained chlorogenic acid (20.1 mg/g of extract) as the major constituent. Conclusions In summary, the adoption of DES for the extraction of bioactive phenolic constituents from sea buckthorn offers multiple benefits, including economic efficiency, enhanced extraction performance, and environmental sustainability. The findings of this study not only advance the understanding of DES in phytochemical extraction but also pave the way for broader application of green solvents in the natural products industry. Future research should focus on further optimizing DES formulations and scaling up the extraction process to fully realize the potential of this innovative extraction method in commercial applications.

Abstract Stochastic delayed modeling has a significant non-pharmaceutical intervention to control transmission dynamics of infectious diseases and its results are close to the reality of nature. The covid-19 has been controlled globally but there is still a threat and appears in different variants like omicron and SARS-CoV-2 etc. globally. This article, considered pattern a mathematical model based on Susceptible, Infected, and recovered populations with highly nonlinear incidence rates. we studied the dynamics of the coronavirus model; a newly proposed version is a stochastic delayed model that is based on nonlinear stochastic delayed differential equations (SDDEs). Transition probabilities and parametric perturbation methods were used for the construction of the stochastic delayed model. The fundamental properties like positivity, boundedness, existence and uniqueness, and stability results of equilibria of the model with certain conditions of reproduction number are studied regularly. Also, the extinction and persistence of disease are studied with the help of well-known theorems. The numerical methods used to find a visualization of results due to the complexity of stochastic delayed differential equations. Furthermore, for computational analysis, we implemented existing methods in the literature and compared their results with the proposed method like nonstandard finite difference for stochastic delayed model. The proposed method restores all dynamical properties of the model with a free choice of time steps.

The gene editing and synthetic biological tools have led to the implementation of diverse metabolic engineering approaches to enhance the production of specific enzymes. Microbial keratinases can convert keratin wastes into valuable compounds for mankind. Since the market for keratinases cannot be satisfied by production from wild hosts, it is obligatory to develop hosts with high keratinase yields. The intention of this review is to evaluate microbial keratinase advancement through protein engineering, breeding techniques, and fermentation optimization. The main aim of protein engineering is to improve the heat resistance ability and catalytic activity of keratinases by employing mutagenesis methods. Moreover, modifying the expression elements and host engineering are also two unique ways to augment the keratinase yield. Intending to accelerate the production of modified keratinase, this review attempts to highlight the optimization of expression elements, such as promoter engineering, UTR, signal peptide, and codon optimization. Moreover, the approaches of host engineering including strengthening precursor supply, membrane surface engineering, and optimization of secretion pathways were also explained here. Furthermore, it is also essential to optimize the medium composition and fermentation condition for high keratinase yield. This review also addressed the present advancements, difficulties, and tendencies in the field of microbial keratinase production, along with its potential.

Delay factors demonstration has a significant role in controlling a strain of infectious disease instead of a pharmaceutical strategy. According to the World Health Organization (WHO), 3–5 million cases are reported annually and approximately 290,000 to 650,000 respiratory deaths annually. So, in the present study, we develop a delayed mathematical model based on delay differential equations (DDEs) for the influenza epidemic using a deterministic approach by introducing double delay parameters. The four distinct sub-populations are considered susceptible, exposed, infected, and recovered. For the rigorous analysis, the fundamental properties of the model like positivity, boundedness, existence, and uniqueness, were studied. The influenza-free equilibrium (IFE) and influenza-existing equilibrium (IEE), are the two nonnegative equilibrium points that the model demonstrates. Both locally and globally, the asymptotic stability of the equilibrium points of the model is established and shown under specific situations of reproduction number. Additionally, investigated the model's parameter sensitivity and determined the relative sensitivity of each parameter. Both standard and nonstandard methods—such as Euler, Runge-Kutta, and nonstandard finite difference with a delayed sense—are presented to make computational analysis support a dynamical analysis and the best visualization of results. The stability of the non-standard finite difference scheme is thoroughly analyzed around the steady states of the model. Additionally, the results show that the nonstandard finite difference approximation is an efficient, cost-effective method, independent of time step size, to solve such highly nonlinear and complex real-world problems.

Abstract Today's banking industry achieving sustainable profitability necessitates by integrating economic, environmental, and social factors due to pressing environmental concerns. This study investigates the impact of sustainability on financial performance in the United Arab Emirates (UAE) banking sector, using the Autoregressive Distributed Lag (ARDL) and Bootstrapping ARDL approaches on data from 2002 to 2022. The analysis revealed that economic sustainability, environmental and social sustainability exhibit a significant impact on financial performance in the short run and the long run at a (p

Abstract Evapotranspiration plays a pivotal role in the hydrological cycle. It is essential to develop an accurate computational model for predicting reference evapotranspiration (RET) for agricultural and hydrological applications, especially for the management of irrigation systems, allocation of water resources, assessments of utilization and demand and water use allocations in rural and urban areas. The limitation of climatic data to estimate RET restricted the use of standard Penman–Monteith method recommended by food and agriculture organization (FAO-PM56). Therefore, the current study used climatic data such as minimum, maximum and mean air temperature (T max, Tmin, T mean), mean relative humidity (RHmean), wind speed (U) and sunshine hours (N) to predict RET using gene expression programming (GEP) technique. In this study, a total of 17 different input meteorological combinations were used to develop RET models. The obtained results of each GEP model are compared with FAO-PM56 to evaluate its performance in both training and testing periods. The GEP-13 model (T max, T min, RHmean, U) showed the lowest errors (RMSE, MAE) and highest efficiencies (R 2, NSE) in semi-arid (Faisalabad and Peshawar) and humid (Skardu) conditions while GEP-11 and GEP-12 perform best in arid (Multan, Jacobabad) conditions during training period. However, GEP-11 in Multan and Jacobabad, GEP-7 in Faisalabad, GEP-1 in Peshawar, GEP-13 in Islamabad and Skardu outperformed in testing period. In testing phase, the GEP models R 2 values reach 0.99, RMSE values ranged from 0.27 to 2.65, MAE values from 0.21 to 1.85 and NSE values from 0.18 to 0.99. The study findings indicate that GEP is effective in predicting RET when there are minimal climatic data. Additionally, the mean relative humidity was identified as the most relevant factor across all climatic conditions. The findings of this study may be used to the planning and management of water resources in practical situations, as they demonstrate the impact of input variables on the RET associated with different climatic conditions.

Background: COVID-19 epidemiology changed with the emergence of SARS-CoV-2 variants of concern (VOC). Pakistan administered mostly inactivated vaccines. We investigated the association between VOC and breakthrough infections in a mixed-vaccination-status population of Karachi. Methods: We investigated SARS-CoV-2 VOC tested in 392 respiratory specimens collected between May and December 2021. Data for age, sex, hospital admission, vaccinations, together with CT values of the diagnostic PCR test were analyzed. Results: The median age of COVID-19 cases tested was 40 (27−57) years and 43.4% were female. Delta variants were most common (56.4%) followed by Alpha (15.9%), Omicron (12.2%), Beta/Gamma (11.3%), and others (4.3%). Eighteen percent of cases were hospitalized whereby, predominant VOC were Beta/Gamma (40.8%), Alpha (35.2%) and Delta (22.5%). Overall, 55.4% of individuals were fully vaccinated, 7.4% were partially vaccinated and 37.2% were unvaccinated. Most (74.6%) inpatients were unvaccinated. Vaccines comprised inactivated (85.34%), single-shot vector (8.62%), two-shot vector (3.02%) and mRNA (3.02%) types. Omicron variants showed lower viral loads as compared to Alpha, Beta/Gamma, and Delta (p = 0.017). The risk of infection with Delta and Omicron variants was higher, 8 weeks after vaccination. The majority of those with breakthrough infections after receiving inactivated vaccines acquired COVID-19 within 4 months of vaccination. Conclusion: Our data highlights the shifting of VOC from Delta to Omicron during 2021 and that COVID-19 vaccinations reduced both hospitalizations and viral transmission. It informs on the increased risk of breakthrough infection within 8 weeks of vaccination, indicating the need for booster vaccinations.

Objective Epileptic seizures are neurological events that pose significant risks of physical injuries characterized by sudden, abnormal bursts of electrical activity in the brain, often leading to loss of consciousness and uncontrolled movements. Early seizure detection is essential for timely treatments and better patient outcomes. To address this critical issue, there is a need for an advanced artificial intelligence approach for the early detection of epileptic seizure disorder. Methods This study primarily focuses on designing a novel ensemble approach to perform early detection of epileptic seizure disease with high performance. A novel ensemble approach consisting of a fast, independent component analysis random forest (FIR) and prediction probability is proposed, which uses electroencephalography (EEG) data to investigate the efficacy of the proposed approach for early detection of epileptic seizures. The FIR model extracts independent components and class prediction probability features, creating a new feature set. The proposed model combined integrated component analysis (ICA) with predicting probability to enhance seizure recognition accuracy scores. Extensive experimental evaluations demonstrate that FIR assists machine learning models to obtain superior results compared to original features. Results The research gap is addressed using combined features to improve the performance of epileptic seizure detection compared to a single feature set. In particular, the ensemble model FIR with support vector machine (FIR + SVM) outperforms other methods, achieving an accuracy of 98.4% for epileptic seizure detection. Conclusions The proposed FIR has the potential for early diagnosis of epileptic seizures and can significantly help the medical industry with enhanced detection and timely interventions.

This study investigates the complex behavior of Jeffrey nanofluid flow in a porous oscillating microchannel under the influence of magnetohydrodynamic (MHD) effects. The research explores how magnetic field distortions lead to diverse accumulation patterns of nanofluid particles, a phenomenon attributed to homogeneous magnetization in fluid dynamics. Nanoparticles ranging from 0.25 % to 0.5 % (low and inexpensive concentrations) are remarkably consistent for best results in most machining procedures. Different concentrations of various nanomaterial's is utilized (φ1 = φ2 = 0.01, 0.02, 0.03, 0.04) to make the simple nanfluid and hybrid nanofluid suspensions. By employing fractal-fractional derivatives governed by power law, a mathematical model developed to describe the time-varying, compressible MHD flow of Jeffrey nanofluid. The model incorporates the effects of heat transfer, pressure, and magnetic fields on the fluid dynamics. A novel fractional approach utilizing the Laplace transform is applied to solve the fractal MHD hybrid-fluid model integrated into a porous medium. The study reveals velocity flow decrease with increasing Reynolds numbers but increase with channel inclination. Additionally, both the Darcy number and magnetic field orientation enhance heat transfer rates. In addition, the velocity profile enhanced by the hybrid nanofluid suspension as compared to simple nanofluid flow. The research validates its findings by demonstrating the convergence of fractional and numerical solution methods. Furthermore, the study compares the performance of different hybrid nanofluids, concluding that water-based (H2O + GO + MoS2) hybrid fluids exhibit slightly superior characteristics compared to (CMC + GO + MoS2) hybrid nanofluids.

Standardized welfare assessment protocols are crucial to enhance animal welfare; up to date, there is no data on the level of welfare of camels kept under pastoralism. A tailored protocol for measuring welfare in dromedary camels kept under nomadic pastoralist conditions was recently developed, drawing from the currently available welfare protocol for dromedary camels kept in intensive systems. This study, therefore, aimed to apply the newly developed tailored protocol and assess the welfare of dromedary camels kept under pastoralism in the Southern Punjab Province of Pakistan. A total of 44 welfare indicators (animal-, resource, and management-based measures) aligning with animal welfare principles (“Good Feeding”, “Good Housing”, “Good Health”, and “Appropriate Behavior”) were gathered into two assessment levels: “Caretaker-Herd level” and “Animal level”. Data were collected in 2023 in the Cholistan desert in the southern Punjab province. Fifty-four herds were evaluated for a total population of 1,186 camels, of which 510 (495 females and 15 males; average age: 5–6 years old) were assessed at the animal level. The indicators were scored and aggregated to obtain Principle Aggregated Indexes (PAIs) and a total Welfare Index (TWI). Using the PAIs classification, 4 herds were categorized as excellent, 42 satisfactory, and 8 unsatisfactory. Total Welfare Index (TWI) varied from 55.7 to 82.2, and the thresholds for classification into tertiles were 65.4 and 70.6. Good feeding and Good housing were the most problematic PAIs, with Good feeding as the most influential variable for classification into welfare categories. As expected, camels kept under pastoralism had a higher level of welfare than those reported in the literature for intensive systems, especially concerning the Appropriate Behavior principle. Our findings are a first step in proposing welfare standards for dromedary in Pakistan and worldwide.

ABSTRACT: The size of the initial primordial follicle pool in the ovary depends on primordial follicle formation, which determines the female reproductive lifespan. However, the molecular regulation of primordial follicle formation in chickens remains unclear. In this study, the left ovaries of chickens were collected at 2 d posthatch (dph), 5.5 dph, and 10.5 dph to examine the formation of primordial follicles. Single-cell mRNA sequencing (scRNA-seq) and spatial transcriptomic analysis were performed to explore the ovarian microenvironment and identify regulatory pathways involved in the formation of primordial follicles in chickens. Histomorphological analysis of chicken ovary tissues revealed the presence of germ cell cysts at 1 dph, which began to disintegrate at 2 dph. Primordial follicles appeared at 5.5 dph and continued to develop into larger-diameter follicles. scRNA-seq and spatial transcriptomic analysis revealed 24 cellular clusters involved in chicken primordial follicle formation. The metabolic pathway of steroid hormone synthesis was found in pregranulosa and pretheca cells. Histological analysis showed that chicken ovaries did not form primordial follicles after the inhibition of the steroid hormone synthesis pathway by simvastatin or tamoxifen. In addition, mRNA transcriptomic and bioinformatics analyses revealed that GREB1 was a downstream gene of the steroid hormone synthesis pathway during the formation of chicken primordial follicles. This study provides a valuable foundation for investigating primordial follicle formation in avian species and optimizing their reproductive performance.

Background: Writing means communication through words whereas academic writing means making careful use of words to communicate ideas to a range of readers and audiences. Therefore, academic writing reflects specificities related to audience, context/discipline and purpose of the use. These specificities result in ample differences in terms of language use. Purpose: This study investigates disciplinary variation in the use of different syntactic (i.e., clausal, intermediate and phrasal) features in academic writing produced by the Pakistani advanced writers of English as an L2 specializing in different disciplines of arts and humanities, life sciences, physical sciences and social sciences. Method: For the said purpose, the corpus has been developed from dissertation texts produced by the Pakistani doctoral candidates from 16 academic disciplines of four disciplinary divisions. The analysis has been performed using AntConc Software after tagging with Multidimensional Analysis, and TagAnt Taggers. Results: The results reveal mixed findings. On the one hand, the results show variation in the use of syntactic features that is observed to be marked by the difference in the frequency of the different types of the said features across disciplines. On the other hand, the results show a similarity in the use of syntactic features that has been evidenced by the finding that the most and least frequently used features are identical across disciplines. Conclusion: These results suggest both heterogeneity and homogeneity in the use of syntactic features by the Pakistani advanced L2 academic writers. The results of this study have implications for educators, policy makers, and syllabus designers to ensure discipline-specific instruction, and incorporation of the discipline-specific syntactic features into the academic curricula for supporting academic writing development skills in the students particularly at the advanced level of education.

Abstract Introduction The annulus fibrosus (AF) of the Intervertebral disc (IVD) is composed of concentric lamellae of helically wound collagen fibers. Understanding the spatial variation of collagen fiber orientations in these lamellae, and the resulting material anisotropy, is crucial to predicting the mechanical behavior of the complete IVD. Methods This study builds on a prior model predicated on path‐independent displacement of fiber endpoints during vertebral body growth to predict a complete, three‐dimensional annulus fibrosus fiber network from a small number of subject‐independent input parameters and vertebral endplate topographies obtained from clinical imaging. To evaluate the model, it was first fit to mid‐plane fiber orientations obtained using polarized light microscopy in a population of bovine caudal discs for which computed tomography images vertebral endplates were also available. Additionally, the model was used to predict the trajectories based on human lumbar disc geometries and results were compared to previously reported data. Finally, the model was employed to investigate potential disc‐related variations in fiber angle distributions. Results The model was able to accurately predict experimentally measured fiber distributions in both bovine and human discs using only endplate topography and three input parameters. Critically, the model recapitulated previously observed asymmetry between the inclinations of right‐ and left‐handed fibers in the posterolateral aspect of the human AF. Level to level variation of disc height and aspect ratio in the human lumbar spine was predicted to affect absolute values of fiber inclination, but not this asymmetry. Conclusion Taken together these results suggest that patient‐specific distributions of AF fiber orientation may be readily incorporated into computational models of the spine using only disc geometry and a small number of subject‐independent parameters.

The electrochemical corrosive processes compromise the passivity of reinforcing steel, potentially leading to structural integrity loss and, in extreme cases, concrete infrastructure failures. While bio-inspired concretes show promise in mitigating strength degradation and enabling self-healing of concrete flaws, their interaction with steel reinforcement remains underexplored. Thus, this investigation aimed to establish a protective strategy by fostering biofilm growth on rebar surfaces. To achieve this, Bacillus subtilis and Escherichia coli bacteria were utilized as biofilm-forming agents, aided by magnetic iron oxide and zeolite micro-nano particles. The study encompassed a thorough assessment of split tensile strength, corrosion resistance of bio-treated embedded steel bars, and a comprehensive biofilm characterization, along with a meticulous examination of the microstructure at the steel-concrete interface. The findings underscored a significant improvement in split tensile strength, demonstrating a remarkable 84.2 % increase when bacterial species were combined with iron oxide nanoparticles, in contrast to the control specimens. Furthermore, the bio-treated bars exhibited an impressive corrosion inhibition potential of 78.5 % relative to their unaltered counterparts. These outcomes are attributed to the discernible refinement of microstructural features surrounding the steel reinforcement and the heightened densification of the inter-transitional zone between steel and concrete.

With the advent of smart grids, advanced information infrastructures, advanced metering facilities, bidirectional exchange of information, and battery storage home area networks have all transformed the electricity consumption and energy efficiency. There is a significant shift in the energy management structure from the traditional centralized infrastructure to the flexible demand side driven cyber-physical power systems with clean energy and energy storage system. These changes have significantly evolved the home energy management (HEM) space. Consequently, stakeholders must define their responsibilities, create efficient regulatory frameworks, and test out novel commercial strategies. P2P energy trading appears to be a feasible solution in these circumstances, allowing users to trade electricity with one another before becoming completely reliant on the utility. P2P energy trading offers a more stable platform for energy trade by facilitating the exchange of energy between prosumers and consumers. This research proposes a novel demand and generation prediction techniques of P2P HEMS for optimal energy using the Multi-Objective Optimization model. An enhanced Wild Horse Optimization technique was first used to summarize historical records' qualities. Then, the Bi-LSTM is used to predict the demand and generation values. Furthermore, a Grasshopper optimization (GHO) approach is employed to fine-tune the model's hyperparameters. The P2P HEMS demand and generation prediction framework is offered with a probabilistic and fault evaluation that upholds load flow balance between need and supply for continuous operations. It results in an intelligent community transforming cities into smart ones, opening new avenues for scientific research in terms of technological developments.

ABSTRACT Malware growth has accelerated due to the widespread use of Android applications. Android smartphone attacks have increased due to the widespread use of these devices. While deep learning models offer high efficiency and accuracy, training them on large and complex datasets is computationally expensive. Hence, a method that effectively detects new malware variants at a low computational cost is required. A transfer learning method to detect Android malware is proposed in this research. Because of transferring known features from a source model that has been trained to a target model, the transfer learning approach reduces the need for new training data and minimizes the need for huge amounts of computational power. We performed many experiments on 1.2 million Android application samples for performance evaluation. In addition, we evaluated how well our framework performed in comparison with traditional deep learning and standard machine learning models. In comparison with state‐of‐the‐art Android malware detection methods, the proposed framework offers improved classification accuracy of 98.87%, a precision of 99.55%, recall of 97.30%, F1‐measure of 99.42%, and a quicker detection rate of 5.14 ms using the transfer learning strategy.

Improper disposal of plastic waste (P-waste) poses significant pollution and health risks to the environment. Additionally, steel rebar corrosion in concrete structures reduces their strength and serviceability. To address these issues, this study explores using glass fiber-reinforced polymer (GFRP) rebars and replacing natural coarse aggregates with P-waste aggregates for sustainable and eco-friendly construction. This work investigates the axial performance of polypropylene structural fiber-reinforced P-waste aggregate concrete (SFPC) compressive components having GFRP-reinforcement (GSFPC) under various loading conditions. For comparison, steel-reinforced SFPC compressive components (SSFPC) were also fabricated. Eighteen circular components with 1200 mm height and 300 mm diameter were tested and compared under different loading conditions. SSFPC components exhibited up to 21.9 % higher axial strength but up to 27.4 % lower ductility compared to GSFPC components. Eccentric loading similarly reduced axial strength in both GSFPC and SSFPC components. A 3-D finite element analysis (FEA) of GSFPC components was proposed using a modified damaged plastic model for SFPC which showed deviations of 2.3 % in axial strength and 7.7 % in equivalent axial shortening, demonstrating a good accuracy of the FEA model.

This work reports the observation of sunlight diffraction through empty spaces in between twigs and leaves in tree crowns. The diffraction patterns are dominated by red wavelength on front followed by the trail of rest of spectral bands of white light rainbow bursts. Tree crowns act as obstacles and apertures causing diffraction/ interference of solar rays. Separation between leaves and twigs (d) is a few inches and distance between crown and camera (D) is in range of a few meters. Empty spaces in tree crown are much longer than red to violet wavelengths in sunlight therefore scattering, dispersion, reflection, diffraction and interference effects cannot be ruled out. Tree crown deflects solar rays into narrow angles all around to enable interference inside camera lens to create grating structure diffracting light into regular patterns. Diffraction pattern look like red petunia flower having 3 × 3 rose petals. Motorola mobile phone camera was used to record the sunlight diffraction patterns. Mobile camera consists of small focal length (≈5 mm) lens, a fixed opening aperture and an image sensor. Lens consists of few plastic or glass elements and sensor consists of charge coupled device (CCD) or CMOS technology. These diffraction/ interference patterns have potential to increase efficiency of Next Gen solar cells. This paper describes possible theoretical background of light diffraction and multiple ray interference through tree twigs and leaves.

Blastomycosis is an endemic disease in North America and commonly manifests with pulmonary symptoms. Blastomycosis should be consider when patients have persistent infiltrates on imaging in an endemic area. We present a case of a 46-year-old male who presented to the pulmonary clinic with fever, cough with production of yellowish-green sputum and culture of BAL-fluid showed growth of Pseudomonas spp. Antimicrobial therapy was started accordingly, but was not effective. A repeat bronchoscopy was performed and BAL-fluid culture was positive for Blastomyces dermatitidis and liposomal amphotericin B was initiated. Unfortunately, the patient died after withdrawing care.

Salinity is a major concern globally and causing reduction in crop growth and development thereby lowering food production. Interestingly, cobalt (Co), a multifunctional non-essential micro-element, has an important role in improving growth and development under salinity stress. In the current study, the effects of Co on the morphological, biochemical, nutritional, and metabolic changes of African marigold plants at two salinity levels were assessed. Two concentrations of Co (C1; 10 mg/L and C2; 20 mg/L) were applied as foliar application to the marigold plants under salinity (S1; 300 mM and S2; 600 mM) stress. The results indicated that salinity substantially reduced the growth by negatively affecting the nutritional and metabolic profile. Interestingly, the C1 application mitigated the salinity effects and improved all the studied parameters including vegetative, nutritional, biochemical and metabolites (amino acids, fatty acids, organic acids, sugars, carotenoids, flavonoids, and terpenoids) by reducing the reactive oxygen species (ROS) and methylglyoxal (MG)-induced oxidative stress, at two salinity levels. The overall results revealed that C1was an ideal dose for marigold plants undergoing salinity stress since C2 application showed some toxicity symptoms under both the salinity levels; with reduced growth and impaired development. Based on the observations, the two different oxidative stress scavenging pathways in marigold are discussed i.e. ROS-scavenging by ascorbate-glutathione (AsA-GSH) cycle and the MG-detoxification by glyoxalase. With the limitations imposed upon the glutathione (GSH) pool size and redox homeostasis, the study indicated that GSH played a critical role in both ROS- and MG-detoxification in marigold plants at both salinity levels.

The tourism industry significantly contributes to a country’s economic growth and creates a positive image of the destination. This study assesses the impact of corruption on international tourism demand. It employs two-dimensional analyses using the gravity model and a pooled ordinary least square estimator to provide a unique recognition of international tourism demand. The study utilized a conditional quantile regression technique to analyze a dataset of 200 destination countries from 1995 to 2022. The empirical results demonstrate a mixed effect of corruption on international tourism demand. The analysis reveals a non-linear relationship between corruption and international tourism demand, with the inverted-U relationship being statistically significant only at the 50th–75th quantiles and not holding at the upper and lower quantiles. The research confirms that income has a positive impact on tourism across quantiles, but the impact is disproportionate. The sub-period of 2006–2022 experienced a significant decline in gross domestic product (GDP) due to the global financial crisis and its aftershocks, which severely impacted the attractiveness of destination countries for tourists. These valuable insights can inform national tourism policies and businesses.

Hydraulic jump is used to dissipate excessive flow energy in stilling basins to control erosion on the downstream side. The literature review revealed that the convergence of the side walls in a USBR type II stilling basin has enhanced energy dissipation by stabilizing the hydraulic jump. Taking this into account, a Computational Fluid Dynamics (CFD) model was created using CFD code to analyze the hydraulic efficiency of a USBR type III stilling basin with varying degrees of side wall convergence. Additionally, alterations were made to the standard Impact Blocks geometry to evaluate their effect on energy dissipation. The side walls of stilling basin were converged from 0.5° to 2.5° (with an increment of 0.5°). Study results indicated an increase in hydraulic jump efficiency from 1.6 to 14.5% due to increase in wall convergence. Modified Friction Blocks also enhanced the energy dissipation up to 2%. Post-jump Froude number values were found in acceptable range of 0.6 to 0.78. The optimal hydraulic performance of stilling basin was noted when wall convergence angle of 2.5° was used along with modified Friction Blocks. Hydraulic performance of modified stilling basin may be investigated during gated operation of the model. HIGHLIGHTS Walls convergence in stilling basin.; Navier–Stokes equations.; Computational Fluid Dynamics (CFD) code.;

Abstract A serious environmental problem that threatens soil quality, agricultural productivity, and food safety is heavy metal pollution in water sources. Heavy metal pollution is the main problem in tehsil Pasrur, Sialkot, Pakistan. Present study was arranged to notice the heavy metals in water, soil, forages and buffalo milk. There are seven sites that were used for this experiment. Highest malondialdehyde (MDA) contents (3.00 ± 0.01) were noticed in barseem roots at site 7. Ascorbate Peroxidase (APX) was reached at its peak (1.93 ± 0.01) at site 7 in the fresh barseem. Maximum protein contents (0.36 ± 0.01) were observed in fresh plant samples at site 2. Site 3's buffalo milk samples had the highest Ni content (7.22 ± 0.33 ppm), while Site 3's soil samples had the lowest Cr content (8.89 ± 0.56 ppm), Site 1's plant shoots had the lowest Cr content (27.75 ± 1.98 ppm), and Site 3's water had the highest Cr content (40.07 ± 0.49 ppm). The maximum fat content (5.38 ± 2.32%) was found in the milk of the animals at site 7. The highest density (31.88 ± 6.501%), protein content (3.64 ± 0.33%), lactose content (5.54 ± 0.320%), salt content (0.66 ± 0.1673%), and freezing point (− 0.5814 ± 0.1827 °C) were also observed in the milk from animals at site 7, whereas site 5 displayed the highest water content (0.66 ± 0.1673%) and peak pH value (11.64 ± 0.09). In selected samples, the pollution load index for Ni (which ranged from 0.01 to 1.03 mg/kg) was greater than 1. Site 7 has the highest conductivity value (5.48 ± 0.48). Values for the health risk index varied from 0.000151 to 1.00010 mg/kg, suggesting that eating tainted animal feed may pose health concerns. Significant health concerns arise from metal deposition in the food chain from soil to feed, with nickel having the highest health risk index.

Abstract In this manuscript, a mathematical model known as the Heimburg model is investigated analytically to get the soliton solutions. Both biomembranes and nerves can be studied using this model. The cell membrane’s lipid bilayer is regarded by the model as a substance that experiences phase transitions. It implies that the membrane responds to electrical disruptions in a nonlinear way. The importance of ionic conductance in nerve impulse propagation is shown by Heimburg’s model. The dynamics of the electromechanical pulse in a nerve are analytically investigated using the Hirota Bilinear method. The various types of solitons are investigates, such as homoclinic breather waves, interaction via double exponents, lump waves, multi-wave, mixed type solutions, and periodic cross kink solutions. The electromechanical pulse’s ensuing three-dimensional and contour shapes offer crucial insight into how nerves function and may one day be used in medicine and the biological sciences. Our grasp of soliton dynamics is improved by this research, which also opens up new directions for biomedical investigation and medical developments. A few 3D and contour profiles have also been created for new solutions, and interaction behaviors have also been shown.

Abstract Monitoring and understanding Land Use/Land Cover (LU/LC) is critical for sustainable development, as it can impact various environmental, social, and economic systems. For example, deforestation and land degradation can lead to soil erosion, loss of biodiversity, and greenhouse gas emissions, affecting the quality of soil, air, and water resources. The present research examined changes in (LU/LC) within the underdeveloped regions of Balochistan and Sindh provinces, which are situated in Pakistan. In order to monitor temporal variations of LU/LC, we employed Geographic Information System (GIS) technique, to conduct an analysis of satellite imagery obtained from the Landsat 8 Operational Land Imager (OLI) during the time period spanning from 2013 to 2023. In order to obtain an accurate LU/LC classification, we used principal component analysis (PCA) and a supervised classification approach using the maximum likelihood algorithm (MLC). According to the results of our study, there was a decrease in the extent of water bodies (− 593.24 km2) and vegetation (− 68.50 km2) by − 3.43% and − 0.40% respectively. In contrast, the area occupied by settlements in the investigated region had a 2.23% rise, reaching a total of 385.66 square kilometers. Similarly, the extent of barren land also expanded by 1.60%, encompassing a total area of 276.04 square kilometers, during the course of the last decade. The overall accuracy (94.25% and 95.75%) and K value (91.75% and 93.50%) were achieved during the year 2013 and 2023 respectively. The enhancement of agricultural output in Pakistan is of utmost importance in order to improve the income of farmers, mitigate food scarcity, stimulate economic growth, and facilitate the expansion of exports. To enhance agricultural productivity, it is recommended that the government undertake targeted initiatives that aimed at enhancing water infrastructure and optimizing land use to foster a sustainable ecological framework. Integrating the sustainable ecological framework provides a foundation for informed decision-making and effective resource management. By identifying areas of urban expansion, agricultural intensification, or alterations in natural LU/LC, stakeholders can design targeted conservation strategies, mitigating potential environmental degradation and promoting biodiversity conservation. In conclusion, the integration of GIS and Remote Sensing (RS) may effectively facilitate the monitoring of land use patterns over a period of time. This combined approach offers valuable insights and recommendations for the judicious and optimal management of land resources, as well as informing policy decisions.

Abstract Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigating the atherogenic, obesogenic, pro-carcinogenic, and pro-diabetogenic effects, normally associated with the free fatty acids bound to FFAR4. In this research, molecular structure-based machine-learning techniques were employed to evaluate compounds as potential agonists for FFAR4. Molecular structures were encoded into bit arrays, serving as molecular fingerprints, which were subsequently analyzed using the Bayesian network algorithm to identify patterns for screening the data. The shortlisted hits obtained via machine learning protocols were further validated by Molecular Docking and via ADME and Toxicity predictions. The shortlisted compounds were then subjected to MD Simulations of the membrane-bound FFAR4-ligand complexes for 100 ns each. Molecular analyses, encompassing binding interactions, RMSD, RMSF, RoG, PCA, and FEL, were conducted to scrutinize the protein–ligand complexes at the inter-atomic level. The analyses revealed significant interactions of the shortlisted compounds with the crucial residues of FFAR4 previously documented. FFAR4 as part of the complexes demonstrated consistent RMSDs, ranging from 3.57 to 3.64, with minimal residue fluctuations 5.27 to 6.03 nm, suggesting stable complexes. The gyration values fluctuated between 22.8 to 23.5 nm, indicating structural compactness and orderliness across the studied systems. Additionally, distinct conformational motions were observed in each complex, with energy contours shifting to broader energy basins throughout the simulation, suggesting thermodynamically stable protein–ligand complexes. The two compounds CHEMBL2012662 and CHEMBL64616 are presented as potential FFAR4 agonists, based on these insights and in-depth analyses. Collectively, these findings advance our comprehension of FFAR4’s functions and mechanisms, highlighting these compounds as potential FFAR4 agonists worthy of further exploration as innovative treatments for metabolic and immune-related conditions.

Electricity demand increasing day by day with the passage of time, fossil fuels and traditional electricity sources are becoming obsolete. The possibility of dealing with a power deficit and importing oil and gas for electricity generation exists for developing nations like Pakistan. One of the most accessible and affordable sources of renewable energy in the world is solar power generating. Photovoltaic (PV) cell performance is influenced by the environment and the technology used to capture the available energy. The Islamabad region of Pakistan is blessed with an average of 300 days of sunshine a year due to its location in the solar band at 33.7215°N latitude and 73.0433°E longitude. The current study compares and contrasts how Pakistan and South Asian Association for Regional Cooperation (SAARC) countries generate electricity using solar systems. Choosing one geographical place from all SAARC nations, PVSyst software is used to develop and estimate the performance study for solar model cell Panasonic 320Wp, 48V and 1.5 kW Inverter Fronius for a 9.6 kWp load. Energy supplied into the grid is studied together with various losses that occur in the system, with the choice of PV arrays and other factors remaining the same for comparison study of all SAARC countries. The approach to minimize losses through the utilization of an adjustable tilt angle has been implemented. This study will be useful in estimating and planning the solar energy output in all SAARC nations for the same photovoltaic system, and it will be simple to trade all PV system components in the region.

Abstract Most of the countries in the world are affected by the coronavirus epidemic that put people in danger, with many infected cases and deaths. The crowding factor plays a significant role in the transmission of coronavirus disease. On the other hand, the vaccines of the covid-19 played a decisive role in the control of coronavirus infection. In this paper, a fractional order epidemic model (SIVR) of coronavirus disease is proposed by considering the effects of crowding and vaccination because the transmission of this infection is highly influenced by these two factors. The nonlinear incidence rate with the inclusion of these effects is a better approach to understand and analyse the dynamics of the model. The positivity and boundedness of the fractional order model is ensured by applying some standard results of Mittag Leffler function and Laplace transformation. The equilibrium points are described analytically. The existence and uniqueness of the non-integer order model is also confirmed by using results of the fixed-point theory. Stability analysis is carried out for the system at both the steady states by using Jacobian matrix theory, Routh–Hurwitz criterion and Volterra-type Lyapunov functions. Basic reproductive number is calculated by using next generation matrix. It is verified that disease-free equilibrium is locally asymptotically stable if $${R}_{0}1$$ R 0 > 1 . Moreover, the disease-free equilibrium is globally asymptotically stable if $${R}_{0}1$$ R 0 > 1 . The non-standard finite difference (NSFD) scheme is developed to approximate the solutions of the system. The simulated graphs are presented to show the key features of the NSFD approach. It is proved that non-standard finite difference approach preserves the positivity and boundedness properties of model. The simulated graphs show that the implementation of control strategies reduced the infected population and increase the recovered population. The impact of fractional order parameter $$\alpha$$ α is described by the graphical templates. The future trends of the virus transmission are predicted under some control measures. The current work will be a value addition in the literature. The article is closed by some useful concluding remarks.

Electricity is establishing ground as a means of energy, and its proportion will continue to rise in the next generations. Home energy usage is expected to increase by more than 40% in the next 20 years. Therefore, to compensate for demand requirements, proper planning and strategies are needed to improve home energy management systems (HEMs). One of the crucial aspects of HEMS are proper load forecasting and scheduling of energy utilization. Energy management systems depend heavily on precise forecasting and scheduling. Considering this scenario, this article was divided into two parts. Firstly, this article gives a thorough analysis of forecasting models in HEMs with the primary goal of determining whichever model is most appropriate in a given situation. Moreover, for optimal utilization of scheduling strategies in HEMs, the current literature has discussed a number of scheduling optimization approaches. Therefore, secondly in this article, these approaches will be examined thoroughly to develop effective operating scheduling and to make wise judgments regarding usage of these techniques in HEMs. Finally, this paper also presents the future technical advancements and research gaps in load forecasting and scheduling and how they affect HEMs activities in the near future.

Abstract Anthrax is a potentially fatal infectious zoonotic disease caused by the spore-forming bacterium Bacillus anthracis. While it is a disease of herbivores which primarily affects livestock and wildlife, it could also lead to serious and lethal infections in humans. Its large-scale outbreak could result in devastating economic impact related to losses in livestock and livestock products. Due to its ability to cause widespread disease and death, Anthrax has also become one of the numerous biological agents that is being considered in biowarfare and bioterrorism. Therefore, the modelling and analysis of Anthrax dynamics is crucial for the proper understanding of its prevention and control. In the present study, we investigate the nonlinear dynamics of Anthrax with delay effects which incorporates the mechanism of its incubation period. The sensitivity of the reproduction number dynamics with the model parameters is studied. The local and global stabilities of the model are studied. It is shown that the delay mechanism plays an important role in the dynamics of disease propagation.

Abstract In this study, we introduce a novel valency-based index, the neighborhood face index (NFI), designed to characterize the structural attributes of benzenoid hydrocarbons. To assess the practical applicability of NFI, we conducted a linear regression analysis utilizing numerous physiochemical properties associated with benzenoid hydrocarbons. Remarkably, the results revealed an extraordinary correlation exceeding 0.9991 between NFI and these properties, underscoring the robust predictive capability of the index. The NFI, identified as the best-performing descriptor, is subsequently investigated within certain infinite families of carbon nanotubes. This analysis demonstrates the index’s exceptional predictive accuracy, suggesting its potential as a versatile tool for characterizing and predicting properties across diverse molecular structures, particularly in the context of carbon nanotubes.

Abstract We demonstrate how using two qubits can drastically improve the estimation of environment parameters compared to using only a single qubit. The two qubits are coupled to a common harmonic oscillator environment, and the properties of the environment are imprinted upon the dynamics of the two qubits. The reduced density matrix of only one of these qubits contains a decoherence factor and an additional factor taking into account the indirect interaction induced between the qubits due to the interaction with their common environment. This additional factor can drastically improve the estimation of the environment parameters, as quantified by the quantum Fisher information. In particular, we investigate the estimation of the cutoff frequency, the coupling strength, and the temperature using our two-qubit scheme compared to simply using a single qubit. We find that the precision of the estimates can be improved by orders of magnitude.

Abstract Background Viral infections have been the main health issue in the last decade. Antiviral peptides (AVPs) are a subclass of antimicrobial peptides (AMPs) with substantial potential to protect the human body against various viral diseases. However, there has been significant production of antiviral vaccines and medications. Recently, the development of AVPs as an antiviral agent suggests an effective way to treat virus-affected cells. Recently, the involvement of intelligent machine learning techniques for developing peptide-based therapeutic agents is becoming an increasing interest due to its significant outcomes. The existing wet-laboratory-based drugs are expensive, time-consuming, and cannot effectively perform in screening and predicting the targeted motif of antiviral peptides. Methods In this paper, we proposed a novel computational model called Deepstacked-AVPs to discriminate AVPs accurately. The training sequences are numerically encoded using a novel Tri-segmentation-based position-specific scoring matrix (PSSM-TS) and word2vec-based semantic features. Composition/Transition/Distribution-Transition (CTDT) is also employed to represent the physiochemical properties based on structural features. Apart from these, the fused vector is formed using PSSM-TS features, semantic information, and CTDT descriptors to compensate for the limitations of single encoding methods. Information gain (IG) is applied to choose the optimal feature set. The selected features are trained using a stacked-ensemble classifier. Results The proposed Deepstacked-AVPs model achieved a predictive accuracy of 96.60%%, an area under the curve (AUC) of 0.98, and a precision-recall (PR) value of 0.97 using training samples. In the case of the independent samples, our model obtained an accuracy of 95.15%, an AUC of 0.97, and a PR value of 0.97. Conclusion Our Deepstacked-AVPs model outperformed existing models with a ~ 4% and ~ 2% higher accuracy using training and independent samples, respectively. The reliability and efficacy of the proposed Deepstacked-AVPs model make it a valuable tool for scientists and may perform a beneficial role in pharmaceutical design and research academia.

This research investigates the synthesis and application of Losod-zeolite (LOS-Z), a crystalline hydrated sodium aluminosilicate (Na12Al12Si12O48.xH2O) as a potential photocatalyst for dye degradation and H2 production from dye-degraded water. The photocatalytic properties are enhanced by impregnating molybdenum (Mo) into LOS-Z (Mo@LOS-Z). XRD, FTIR, SEM, EDX, and elemental mapping were used to study the physicochemical properties of synthesized materials while UV–vis and PL were employed to explore the optoelectronic properties. The photocatalytic activities for dye degradation and H2 production were evaluated using methylene blue (MB) under visible light irradiation. An enhancement in the efficiency of dye degradation (56%–82%) and H2 production rate was demonstrated due to the competitive photocatalytic performance of 10% Mo@LOS-Z compared to the pristine LOS-Z and CFA-BFA blend. It exhibited 2.5 times higher (∼1200 μmol g−1h−1) H2 production than the LOS-Z (∼480 μmol g−1h−1) due to the synergistic effects of narrowed band gap and recombination rate. This study could pave the way forward into the synthesis of waste-derived photocatalysts for efficient dye degradation and H2 production.

Power systems are the most complicated networks that play a crucial role in daily lifestyles. To operate such systems in a stable condition, several control and protection strategies are required. Despite preventative measures, power systems still experience issues and breakdowns like power outages. However, severe and unanticipated conditions might cause damage to the entire system and causes blackout. In this article we studied the analysis of the significant blackouts and cascading failures that have been happened in Pakistan in last few years. The authors proposed a new infrastructure for Pakistan energy sector by introducing advanced energy storage system and cutting edge technology like Vehicle-to-Grid (V2G) technology that make the existing power system robust against these phenomena. The advanced energy storage systems include like solid- state batteries that play a vital role to balance supply and demand, while V2G enables electric vehicles to contribute to grid stability. The potential of these technologies is helpful to improve the grid stability and facilitate a sustainable energy transition. The role of various electric companies in Pakistan power infrastructure has also been discussed after an occurrence of such unplanned events in power system. It has been noted that the main cause of power outages is fluctuation in frequency and voltage collapse. Finally, various solutions have been proposed to overcome these problems and make sure the reliability and stability of power grid against blackout phenomena.

In the last two decades, the surge in carbon emissions has escalated environmental damage and is a major concern globally. Recognized as a significant threat to humanity, unchecked environmental degradation can potentially hinder the achievement of sustainable development. As a result, accurate monitoring of carbon emissions becomes imperative for formulating effective climate policies. Taking into consideration, this study has taken the newly developed consumption-based carbon emissions measure to study the pollution haven hypothesis and examine the link between Greenfield Investment (GFI) inflows to host nations and their environmental impact for 85 developing countries from 1990 to 2020. The results show a positive correlation between Greenfield investment and Consumption-based Carbon Dioxide Emissions (CCO2) in sampled nations. Similarly, energy usage and export damage the environment because developing countries rely on conventional and old methods of energy usage. The results were further analyzed for low, lower middle, and upper middle income countries as well. The subsample outcome shows that Greenfield investment has a more damaged environment in low income countries as compared to lower middle and upper middle income countries. These insights underscore the urgency for developing countries to adopt environmentally conscious policies to attract international investors. It also emphasizes the need for stringent regulations aimed at curbing environmental pollution and complying with the Sustainable Development Goals (SDGs). Similarly, low and lower middle income countries to attract Greenfield investment, may also focus more on strict environmental pollution policies. Industries must be shifted from conventional energy methods to renewable energy sources. Sustainable Development Goals; 7, 12, and 13 can be achieved by host countries, alluring investors to invest in terms of Greenfield in renewable energy resources, which would be used in automobile transportation, to shift industries from conventional energy resources to renewable energy resources. The same Greenfield investment would also be used in bringing efficient machinery for more production in industries with minimal environmental pollution.

Objective: To study the frequency of resistant hypertension in chronic kidney disease patients in our population and to analyse associated risk factors contributing to this problem. Study Design: Cross-sectional study. Place and Duration of Study: Combined Military Hospital, Multan Pakistan from Jun to Dec 2022. Methodology: The study included all patients older than 19 who were already diagnosed with hypertension and CKD and were visiting for the usual routine check-up in the Medical/Nephrology OPD. Results: A total of 325 patients suffering from CKD were included in the study, including 226(69.5%) males and 99(30.5%) females. The mean age of study participants was 56.54±7.15 years. Mean systolic blood pressure was 140.9±19.5 mmHg, whereas mean diastolic blood pressure was 85.12±9.1 mmHg. The average BMI of the participants was measured to be 24.96±4.2 m2/kg. The average/mean eGFR of the case-study participants was calculated to be 47.80±11.6 ml/min. Out of the 325 patients, 141(43.4%) were suffering from resistant hypertension. Conclusion: The frequency of resistant hypertension in CKD patients who have a positive genealogy of hypertension and associated risk factors of diabetes mellitus and hyperlipidaemia is considerable. Worsening of CKD also increases the proportion of treatment-resistant hypertension in hypertensive patients.

Objective: To compare the surgical outcome of Tetralogy of Fallot patients with and without right ventricular diastolic dysfunction after repair. Study Design: Quasi-experimental study. Place and Duration of Study: Department of Cardiology, National Institute of Cardiovascular Disease, Karachi Pakistan, from May to Oct 2021. Methodology: Patients above 5 months of age, of either gender, undergoing surgery for Tetralogy of Fallot were enrolled. Patients were divided into two groups according to the Gatzoulis’ criterion for right ventricular restriction i.e. Group-A had right ventricle with restriction, and Group-B had right ventricle without restriction. Velocities of E and A waves was recorded, and their ratio (E/A) was calculated. The outcome such as aortic cross clamp, cardiopulmonary bypass time, ventilation time, intensive care unit stay, drain time, and transannular patch was noted. Results: Of 54 patients, restrictive physiology patients had significantly higher mean for aortic cross-clamp (p-value 0.004, 95%CI 3.54 - 17.35), ventilation time (p-value

Video content on the web platform has increased explosively during the past decade, thanks to the open access to Facebook, YouTube, etc. YouTube is the second-largest social media platform nowadays containing more than 37 million YouTube channels. YouTube revealed at a recent press event that 30,000 new content videos per hour and 720,000 per day are posted. There is a need for an advanced deep learning-based approach to categorize the huge database of YouTube videos. This study aims to develop an artificial intelligence-based approach to categorize YouTube videos. This study analyzes the textual information related to videos like titles, descriptions, user tags, etc. using YouTube exploratory data analysis (YEDA) and shows that such information can be potentially used to categorize videos. A deep convolutional neural network (DCNN) is designed to categorize YouTube videos with efficiency and high accuracy. In addition, recurrent neural network (RNN), and gated recurrent unit (GRU) are also employed for performance comparison. Moreover, logistic regression, support vector machines, decision trees, and random forest models are also used. A large dataset with 9 classes is used for experiments. Experimental findings indicate that the proposed DCNN achieves the highest receiver operating characteristics (ROC) area under the curve (AUC) score of 99% in the context of YouTube video categorization and 96% accuracy which is better than existing approaches. The proposed approach can be used to help YouTube users suggest relevant videos and sort them by video category.

BackgroundBovine genital campylobacteriosis (BGC) is a globally important venereal disease of cattle caused by Campylobacter fetus subspecies venerealis. Diagnosis of BGC is highly challenging due to the lack of accurate diagnostic tests.MethodsTo characterise the biomarkers for C. fetus venerealis infection, a total of twelve cycling heifers were selected and categorised as vaccinated (n = 6) with Vibrovax® (Zoetis™) and unvaccinated (n = 6). All heifers were oestrous synchronised with a double dose of prostaglandin (PGF2α) 11 days apart and when in oestrous intravaginally challenged with 2.7 x 109 CFU live C. fetus venerealis. DNA extracted from vaginal mucus samples was screened using a C. fetus qPCR and 16S rRNA was characterised using Illumina sequencing (V5-V8 region). Relative abundances of serum proteins were calculated using sequential window acquisition of all theoretical fragment ion spectra coupled to tandem mass spectrometry (SWATH-MS) for all heifers at three timepoints: pre-challenge, post-challenge and post-recovery.ResultsIn 16S rRNA sequencing of vaginal mucus, Campylobacter spp. appeared two days following challenge in unvaccinated compared to 14 days in vaccinated animals, consistent with the qPCR results. Increased relative abundances of Firmicutes and Campylobacterota were identified after C. fetus venerealis challenge and were associated with C. fetus venerealis in vaccinated and unvaccinated heifers. Greater relative abundance of Streptococcus spp. was observed during oestrous rather than dioestrous. In both vaccinated and unvaccinated heifers, Acinetobacter spp. increased after challenge with higher abundance of Corynebacterium spp. in the vaccinated group. A total of 130 unique proteins were identified in SWATH analysis of the serum samples, and the number of differentially abundant proteins found was higher in the vaccinated group after recovery from infection compared to pre-and post-challenge (adjusted P 0.2).ConclusionCoglutinin, clusterin, HP homologs, vitamin D binding protein and fetuin B were identified as potential biomarkers for C. fetus venerealis infection and need further study to validate their efficiency as immune biomarkers for BGC.

A number of thermal devices might benefit from the usage of nanofluids in solar power. In this research, the idea of MHD mixed convective hybrid nanofluids including grapheme oxide (GO) and molybdenum disulfide (MoS2) nanoparticles with water (H2O) and Carboxymethyl Cellulose (CMC) as base fluids in a perpendicular channel to analyze the heat transfer of the flowing fluid with the help of recent definition of fractional derivatives namely Fractal fractional derivative. The problem is expressed as PDEs with beginning and boundary conditions, and it is analyzed analytically using the Laplace transform method. The velocity, temperature, and concentration measurements are also shown in series for their appropriate Laplace inverse. Delays in parameters like nanoparticle volume sharing rate have a significant influence on these techniques. Skin friction, Nusselt, and Sherwood numbers are computed for the longitudinal channel's left and right walls, and the necessary numerical results are presented in tabular format. As a result, it is discovered that the rate of heat transfer reduces as the volume percentage of nanoparticles and the Fractal time fractional value rise. Furthermore, when comparing nanofluids, water-based (H2O + GO + MoS2) hybrid nanofluids have a greater influence on governed model than (CMC + GO + MoS2)-based hybrid nanofluids.

Virtual reality (VR) and immersive technology have emerged as powerful tools with numerous applications. VR technology creates a computer-generated simulation that immerses users in a virtual environment, providing a highly realistic and interactive experience. This technology finds applications in various fields, including gaming, healthcare, education, architecture, and training simulations. Understanding user immersion levels in VR is crucial and challenging for optimizing the design of VR applications. Immersion refers to the extent to which users feel absorbed and engrossed in the virtual environment. This research primarily aims to detect user immersion levels in VR using an efficient machine-learning model. We utilized a benchmark dataset based on user experiences in VR environments to conduct our experiments. Advanced deep and machine learning approaches are applied in comparison. We proposed a novel technique called Polynomial Random Forest (PRF) for feature generation mechanisms. The proposed PRF approach extracts polynomial and class prediction probability features to generate a new feature set. Extensive research experiments show that random forest outperformed state-of-the-art approaches, achieving a high immersion level detection rate of 98%, using the proposed PRF technique. We applied hyperparameter optimization and cross-validation approaches to validate the performance scores. Additionally, we utilized explainable artificial intelligence (XAI) to interpret the reasoning behind the decisions made by the proposed model for user immersion level detection in VR. Our research has the potential to revolutionize user immersion level detection in VR, enhancing the design process.

BackgroundControl of buffalo flies (Haematobia irritans exigua, BFs) relies mainly on chemical methods; however, resistance to insecticides is widespread in BF populations. Breeding for resistance to BFs represents a possible alternative, but direct phenotyping of animals is laborious and often inaccurate. The availability of reliable diagnostic biomarker(s) to identify low BF carrier cattle would facilitate rapid and accurate selection for genetic improvement. However, limited information is available regarding differences amongst cattle in host responses to BF infestation.MethodsThis study investigated the variation in Brangus cattle serum proteomic profiles before (naïve) and after peak BF exposure, in low (LF) and high BF burden (HF) cattle. Cattle were phenotyped for susceptibility based on BF counts on multiple dates using visual and photographic techniques. The relative abundance of serum proteins in cattle before and after exposure to BFs was analysed using sequential window acquisition of all theoretical fragment ion mass spectrometry (SWATH-MS).ResultsExposure to BFs elicited similar responses in HF and LF cattle, with 79 and 70 proteins, respectively, showing significantly different abundances post exposure as compared to their relevant naïve groups. The comparison of serum samples from naïve HF and LF cattle identified 44 significantly differentially abundant (DA) proteins, while 37 significantly DA proteins were identified from the comparison between HF and LF cattle post-exposure to BFs. Proteins with higher abundance in naïve LF cattle were enriched in blood coagulation mechanisms that were sustained after exposure to BFs. Strong immune response mechanisms were also identified in naïve LF cattle, whereas these responses developed in HF cattle only after exposure to BF. High BF cattle also showed active anticoagulation mechanisms in response to BF exposure, including downregulation of coagulation factor IX and upregulation of antithrombin-III, which might facilitate BF feeding.ConclusionUnderlying differences in the abundance of proteins related to blood coagulation and immune response pathways could potentially provide indirect indicators of susceptibility to BF infestation and biomarkers for selecting more BF-resistant cattle.