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In this article, we analyze the dynamics of the non-linear tumor-immune delayed (TID) model illustrating the interaction among tumor cells and the immune system (cytotoxic T lymphocytes, T helper cells), where the delays portray the times required for molecule formation, cell growth, segregation, and transportation, among other factors by exploiting the knacks of soft computing paradigm utilizing neural networks with back propagation Levenberg Marquardt approach (NNLMA). The governing differential delayed system of non-linear TID, which comprised the densities of the tumor population, cytotoxic T lymphocytes and T helper cells, is represented by non-linear delay ordinary differential equations with three classes. The baseline data is formulated by exploiting the explicit Runge-Kutta method (RKM) by diverting the transmutation rate of Tc to Th of the Tc population, transmutation rate of Tc to Th of the Th population, eradication of tumor cells through Tc cells, eradication of tumor cells through Th cells, Tc cells' natural mortality rate, Th cells' natural mortality rate as well as time delay. The approximated solution of the non-linear TID model is determined by randomly subdividing the formulated data samples for training, testing, as well as validation sets in the network formulation and learning procedures. The strength, reliability, and efficacy of the designed NNLMA for solving non-linear TID model are endorsed by small/negligible absolute errors, error histogram studies, mean squared errors based convergence and close to optimal modeling index for regression measurements. [ABSTRACT FROM AUTHOR]

The realization of Janus monolayers presents an exciting opportunity to disrupt structural symmetry, opening up novel avenues in the realm of layered materials. While several ternary systems, comprising the group-III monochalcogenides, has been proposed for this purpose, the impact of oxygen interaction on the electronic, thermoelectric and optical characteristics of gallium and indium monochalcogenides has been observed. Interestingly, the concept of incorporating oxygen as a third element in ternary systems has not yet been explored extensively. Here we embark on the design and exploration of 2D A2OX (A = Ga, In and X = S, Se) monolayer through first-principles calculations. Our investigation includes the analysis of electronic band structure, optical and thermoelectric properties, revealing that Ga2OS, Ga2OSe and In2OS exhibit direct band nature. However, In2OSe is found to be metallic in unstrained condition. Moreover, the band gap in these materials can be fine-tuned through the application of tensile or compressive strain. Additionally, our analysis suggests strong optical absorption within the visible, and/or ultraviolet regions, depending on the specific system under consideration. Our findings reveal that Ga2OS and In2OS monolayers elevated power factors with application of tensile strain, rendering them as compelling candidates for applications in thermoelectric devices. [ABSTRACT FROM AUTHOR]

The current challenge faced by the global research community is how to effectively address, manage, and control the spread of infectious diseases. This research focuses on conducting a dynamic system analysis of a stochastic epidemic model capable of predicting the persistence or extinction of the dengue disease. Numerical methodology on deterministic procedures, i.e. Adams method and stochastic/probabilistic schemes, i.e. stochastic Runge–Kutta method, is employed to simulate and forecast the spread of disease. This study specifically employs two nonlinear mathematical systems, namely the deterministic vector-borne dengue epidemic (DVBDE) and the stochastic vector-borne dengue epidemic (SVBDE) models, for numerical treatment. The objective is to simulate the dynamics of these models and ascertain their dynamic behavior. The VBDE model segmented the population into the following five classes: susceptible population, infected population, recovered population, susceptible mosquitoes, and the infected mosquitoes. The approximate solution for the dynamic evolution for each population is calculated by generating a significant number of scenarios varying the infected population’s recovery rate, human population birth rate, mosquitoes birth rate, contaminated people coming into contact with healthy people, the mortality rate of people, mosquitos population death rate and infected mosquito contact rate with population that is not infected. Comparative evaluations of the deterministic and stochastic models are presented, highlighting their unique characteristics and performance, through the execution of numerical simulations and analysis of the results. [ABSTRACT FROM AUTHOR]

Oncolytic viral immunotherapy is gaining considerable prominence in the realm of chronic diseases treatment and rehabilitation. Oncolytic viral therapy is an intriguing therapeutic approach due to its low toxicity and dual function of immune stimulations. This work aims to design a soft computing approach using stupendous knacks of neural networks (NNs) optimized with Bayesian regularization (BR), i.e. NNs-BR, procedure. The constructed NNs-BR technique is exploited in order to determine the approximate numerical treatment of the nonlinear multi-delayed tumor virotherapy (TVT) models in terms of the dynamic interactions between the tumor cells free of viruses, tumor cells infected by viruses, viruses, and cytotoxic T-lymphocytes (CTLs). The strength of state-of-the-art numerical approach is incorporated to develop the reference dataset for the variation in the infection rate for tumor cells, virus-free tumor cell clearance rate by CTLs, CTLs clearance rate for infectious tumor cells, the natural lifecycle of infectious tumor cells, the natural lifecycle of viral cell, the natural lifecycle of CTLs cells, tumor cells free of viruses' maximum proliferation rate, production of tumor cells with an infection, CTLs simulated ratio for infectious tumor cells, CTLs simulated ratios for virus-free cells and delay in time. The dataset is randomly chosen/segmented for training-testing-validation samples to construct the NNs models optimized with backpropagated BR representing the approximate numerical solutions of the dynamic interactions in the TVT model. The performance of the designed NNs-BR technique is accessed/evaluated and outcomes are found in good agreement with the reference solutions having the range of accuracy from 10 − 9 to 10 − 1 6 . The efficacy of NNs-BR paradigm is further substantiated after rigorous analysis on regression metrics, learning curves on MSE, and error histograms for the dynamics of TVT model. [ABSTRACT FROM AUTHOR]

This research uses the Sinc Collocation method to numerically examine the Degasperis–Procesi and Benjamin–Bona–Mahony equations, achieving a high level of precision and accuracy on computational grounds with a variety of mesh points. The proposed technique involves global collocation using Sinc bases function (SBF) as an activation function. Initially, the time derivative discretization has been performed through finite difference scheme and the remaining spatial derivatives are approximated by θ -weighted scheme to minimize the complexity in numerical computations. The main contribution of research is in modifying the proposed scheme to overcome the complexity involved in the whole process of finding solutions with a cardinal expansion of Sinc functions in nonlinear and singular systems. The solutions of Degasperis–Procesi and Benjamin–Bona–Mahony stiff nonlinear systems are illustrated numerically and graphically and the validation and verification of the obtained results are examined through stability analysis by calculating the spectral radius by bounding the value of θ. A comparative study is presented with some of the well-known numerical techniques, which assured the accuracy and reliability of our technique. [ABSTRACT FROM AUTHOR]

Alcohol abuse is a substantial cause of various health and societal issues, as well as a significant factor in global disease. Once alcohol is consumed in the gastrointestinal tract, it undergoes metabolism in the liver and lungs. In this investigation, the nonlinear deterministic and stochastic differential frameworks are analyzed numerically to predict the dynamic evolution of the virus in the drinker alcohol model. The framework for apprehending drinking patterns is categorized into three distinct groups: the susceptible population, risk drinkers, and moderate drinkers. The approximate solution for each population group is determined by exhaustively creating scenarios that vary the probability ratio of infection in susceptible individuals who do not consume alcohol, the increasing rate of alcohol consumption, the rate at which individuals transition from acute to chronic drinking categories, the rate at which new non-drinking consumers are attracted, the death rate of the population, the ratio affecting the rate of sociability in heavy drinkers, and the overall population rate. The Euler–Maruyama approach for the stochastic framework and the Adams method for the deterministic framework are utilized, respectively, to determine the solutions of the alcohol drinker model. This study compares deterministic and stochastic frameworks to underscore their distinct characteristics and efficiency, achieved through comprehensive simulations and in-depth analysis of the numerical outcomes. [ABSTRACT FROM AUTHOR]

Recently, the number of Internet of Things (IoT)-connected devices has increased daily. Consequently, cybersecurity challenges have increased due to the natural diversity of the IoT, limited hardware resources, and limited security capabilities. Intrusion detection systems (IDSs) play a substantial role in securing IoT networks. Several researchers have focused on machine learning (ML) and deep learning (DL) to develop intrusion detection techniques. Although ML is good for classification, other methods perform better in feature transformation. However, at the level of accuracy, both learning techniques have their own certain compromises. Although IDSs based on ML and DL methods can achieve a high detection rate, the performance depends on the training dataset size. Incidentally, collecting a large amount of data is one of the main drawbacks that limits performance when training datasets are lacking, and such methods can fail to detect novel attacks. Few-shot learning (FSL) is an emerging approach that is employed in different domains because of its proven ability to learn from a few training samples. Although numerous studies have addressed the issues of IDSs and improved IDS performance, the literature on FSL-based IDSs is scarce. Therefore, an investigation is required to explore the performance of FSL in IoT IDSs. This work proposes an IoT intrusion detection model based on a convolutional neural network as a feature extractor and a prototypical network as an FSL classifier. The empirical results were analyzed and compared with those of recent intrusion detection approaches. The accuracy results reached 99.44%, which shows a promising direction for involving FSL in IoT IDSs. [ABSTRACT FROM AUTHOR]

In this communication, a new stochastic numerical paradigm is introduced for thorough scrutinization of the magnetohydrodynamic (MHD) boundary layer flow of Prandtl–Eyring fluidic model along a stretched sheet impact on thermal radiation as well as convective heating scenarios by exploiting the accurate approximation knacks of ANNs (artificial‐neural‐networks) modeling by using the inverse multiquadric (IMQ) function optimized/trained with well‐reputed global search with genetic algorithms (GAs) and local search efficacy of sequential quadratic programming (SQP), that is, ANNs‐IMQ‐GA‐SQP. The mathematical model of the Prandtl–Eyring fluid flow is portrayed in the form of PDEs and then converted in the form of a nonlinear system of ODEs by utilizing suitable similarity transformation to calculate/analyze the solution dynamics. The kinematic and thermodynamic properties of MHD Prandtl–Eyring fluid flow are examined/observed by varying the sundry physical parameters. The obtained intelligent computing designed solver‐based numerical results are consistently found in good agreement with reference results obtained through the Adams numerical technique. First and second‐order cumulant‐based statistical assessments are extensively exploited to endorse trustily the efficacy of ANNs‐IMQ‐GA‐SQP solver based on exhaustive simulations for the Prandtl–Eyring fluidic system. [ABSTRACT FROM AUTHOR]

Entropy generation with activation energy has garnered worldwide attention from researchers due to its extensive applications in thermodynamic design, chemical engineering, and optimization processes. One of the primary reasons for focusing on entropy is the global energy crisis, driven by massive consumption against limited resources. This study elucidates the double diffusion process in the dynamics of a viscous fluid over a stretching cylinder, incorporating considerations of entropy generation and activation energy. The non‐Fourier heat flux model is employed to describe the thermodynamics of the thermal system, while non‐Fick's law is used to elucidate the mass diffusion process. The unsteady phases of axisymmetric flow of the viscous fluid are thoroughly discussed. Transport equations in cylindrical configuration are transformed into ordinary differential equations, and an efficient mathematical method (i.e., homotopy analysis method) is applied to solve the transformed system. The graphical examination reveals the impact of flow parameters on velocity configuration, entropy configuration, temperature configurations, and concentration configurations. Additionally, a comparative benchmark is established with the results from previously published work for authentication and validation purposes. It is noted that concentration and temperature configurations are directly related to the unsteady stretching parameter. Entropy generation exhibits an inverse relationship with the unsteady stretching parameter when close to the stretching cylinder, whereas it shows a direct relationship when the fluid is flowing away from the surface. [ABSTRACT FROM AUTHOR]

The proposed research work analyzes the bio-inspired problem through artificial neural networks with a feed-forward approach utilized to approximate the numerical results for singular nonlinear bio-heat equation (BHE) with boundary conditions based on four different scenarios created on the variation of environmental temperature to illustrate the effects of temperature on the human dermal region. The log-sigmoid function is used to construct the fitness function, while the optimization solvers: pattern search and genetic algorithm, are then hybridized with the active set technique, interior point technique, sequential quadratic programming for accurate and reliable results of the proposed BHE with various scenarios where the convergence of the numerical results is also analyzed. Moreover, a comparison of the proposed technique is expressed through residual error that reveals the nature of the numerical results and their efficiency. Additionally, a comprehensive statistical analysis is presented for the designed technique to better illustrate the accuracy, reliability, and efficiency of the obtained results. [ABSTRACT FROM AUTHOR]

The improved thermal assessment of nano‐particles in presence of magnetic force, thermal radiation and activation energy involve dynamic applications in thermal engineering, industrial processes, and modern technologies. The bioconvection pattern in various nanoparticles attributes novel bio‐technology applications like bio‐fuels petroleum engineering, enzymes, bio‐sensors, and many more. On this end, present theoretical analysis endeavors to examine the thermal characteristics of time dependent Williamson nanofluid confined by an unsteady stretched radioactive Riga plate. Here, nanofluid bioconvection is developed by the combined aspects of buoyancy forces and magnetic field with collaboration of motile microorganisms and nanoparticles. Present analysis is further carried out in presence of activation energy, nonlinear thermal radiation and chemical reaction. The whole exploration is exposed to convective Nield constraints on boundary. The dimensionless form of formulated nonlinear system is achieved with utilization of apposite transformations and then approximate solution is elucidated by shooting technique. The graphical interpretation for numerous relevant parameters on velocity, concentration, temperature and motile microorganism profiles are displayed. The results reveal that an increasing variation in velocity is noticed with modified Hartmann. An increasing nanofluid temperature is associated with Williamson parameter and temperature ratio constant. Moreover, the motile microorganism distribution enhances with bioconvected Lewis and mixed convection constant. [ABSTRACT FROM AUTHOR]

Using the power of numerical computation through Levenberg Marquardt backpropagation ($LMB$LMB), the fluidic issue in the present study has features of heat transfer by convection in the hybrid nanofluid suspension of magnesium oxide and gold nanoparticles. The influence of slip effects on boundary layer flow with viscous dissipation at the nodal/saddle stagnation point is examined using a mathematical model. The current study considers water to be the base fluid, $MgO$MgO and $Au$Au as nanoparticles, and the circular cylinder to be the medium’s surface. Non-linear partial differential equations are converted into ordinary differential equations via similarity transformations. The dataset for Levenberg Marquardt backpropagation is generated in Mathematica software using an explicit RK-4 approach by varying different parameters such as the slip parameter ($\lambda $λ), convective parameter ($Bi$Bi), Eckert number ($E{c^*}$Ec∗) and volume fraction (${\phi _2}$ϕ2), Bi with Ec. Validation, training, and testing are utilized for network modeling through the Levenberg-Marquardt backpropagation approach for various scenarios of the fluidic model. The result shows that the base fluid’s thermal behavior is quickly increased by the $MgO - Au$MgO−Au/water hybrid nanofluid. The reliability of the findings is evaluated using autocorrelation, regression, mean square error, absolute error, and error histogram analyses. [ABSTRACT FROM AUTHOR]

A comprehensive theoretical study has been carried out to examine the electronic and thermoelectric properties of AsXY (where X = S, Se; Y = Cl, Br, and I) monolayers. The lattice constants of these monolayers are optimized to determine their most stable configurations. The electronic and thermoelectric characteristics of these monolayers are calculated using state-of-the-art computational methods. Specifically, the first-principles calculations in combination with semiclassical Boltzmann transport theory were employed to gain insights into their behavior. One of the crucial findings of the study is the observation of an indirect band nature in all the studied monolayers. This characteristic provides valuable information about the materials’ electronic behavior and potential applications. Furthermore, the impacts of tensile and compressive strains on these monolayers are investigated. Interestingly, we observed changes in the band value when strain is applied, which opens up exciting possibilities for engineering their electronic properties. Importantly, despite these changes, the band nature of the monolayers remains consistent. In particular, it is found that the AsSI monolayer exhibits a remarkable enhancement in the Seebeck coefficient, both in the unstrained state and under a compressive strain of 4% in the p-type region. This enhancement leads to a higher power factor (PF), suggesting that AsSI monolayers could be promising candidates for efficient thermoelectric devices. Overall, these findings highlight the potential of strain engineering to tailor the electronic properties of AsXY monolayers, offering exciting opportunities for their application in thermoelectric devices. This research contributes valuable insights into the design and optimization of novel materials for future energy conversion and electronic applications. [ABSTRACT FROM AUTHOR]

Context: The software engineering researchers and practitioners echoed the need for investigations to better understand the engineers developing software and services. In light of current studies, there are significant associations between the personalities of software engineers and their work preferences. However, limited studies are using psychometric measurements in software engineering. Objective: We aim to evaluate attitudes of early‐stage software engineers and investigate link between their personalities and work preferences. Method: We collected extensive psychometric data from 303 graduate‐level students in Computer Science programs at four Pakistani and one Swedish university using Five‐Factor Model. The statistical analysis investigated associations between various personality traits and work preferences. Results: The data support the existence of two clusters of software engineers, one of which is more highly rated across the board. Numerous correlations exist between personality qualities and the preferred types of employment for software developers. For instance, those who exhibit greater levels of emotional stability, agreeableness, extroversion, and conscientiousness like working on technical activities on a set timetable. Similar relationships between personalities and occupational choices are also evident in the earlier studies. More neuroticism is reported in female respondents than in male respondents. Higher intelligence was demonstrated by those who worked on the "entire development process" and "technical components of the project." Conclusion: When assigning project tasks to software engineers, managers might use the statistically significant relationships that emerged from the analysis of personality attributes. It would be beneficial to construct effective teams by taking personality factors like extraversion and agreeableness into consideration. The study techniques and analytical tools we use may identify subtle relationships and reflect distinctions across various groups and populations, making them valuable resources for both future academic research and industrial practice. [ABSTRACT FROM AUTHOR]

In this study, a novel neuro heuristic approach is designed to investigate the flow properties of magnetohydrodynamic (MHD) nanofluid along an exponentially extending sheet with a permeable medium with the impact of radiation as well as fluctuating heat source/sink. The designed scheme to handle the suggested problem is established through the well‐known biologically inspired neural networks (BINNs) by exploiting the inverse multiquadric kernel (IMQK), that is, BINNs‐IMQK which is quite a new approach. The partial differential equations (PDEs) which govern the fluidic flow are reformed into a nonlinear system of ordinary differential equations (ODEs) using the most fitted similarity transformations rules and numerically solved by varying the parametric values including unsteady parameter, Brownian motion parameter, suction/injection parameter, radiation parameter, Schmidt number together with Prandtl number to visualize the velocity, thermal gradient, and mass transfer in the suggested fluid problem. It is noticed that nanofluid temperature hikes by uplifting the value of the Brownian motion parameter but this effect is reversed in case of unsteady parameter. The obtained numerical results are verified through reference solution using the well‐known Adams method and the efficacy of the suggested solver is endorsed using a variety of statistical operators. [ABSTRACT FROM AUTHOR]

This paper presents a novel neuro-stochastic adaptive processing to investigate the dynamic behavior of the nonlinear SIS epidemic delayed model with the impact of double disease (SIS-EDDM) using the knack of prediction and modeling of artificial neural networks (ANNs) optimized through Levenberg-Marquardt technique (LMT), i.e. ANNs-LMT. The model captures the dynamic progression of individuals in the population, differentiating between those susceptible to infection and affected by two unique viruses. The inclusion of time delays in the differential equations introduces a critical temporal aspect to the model, enhancing its precision in portraying the transmutation process. The reference dataset for ANNs-LMT is produced using the explicit Runge-Kutta method, with variations in the transmission coefficients between susceptible and infective populations, the death rate of infective population caused by diseases, the cure rate of treatment for infected population, vaccination proportional coefficient for the susceptible population, white noise of the environment, and time delay. The designed computing framework of ANNs-LMT is used to determine the numerical solutions of the variants of SIS-EDDM by incorporating the training, testing, and validation samples based adaptive learning processing. The neuro-stochastic adaptive processing of ANNs-LMT is validated by minimal absolute and mean squared errors, coupled with the attainment of nearly optimal regression measures, demonstrating its accuracy and effectiveness. [ABSTRACT FROM AUTHOR]

Purpose: The present study aims to encompass the bidirectional magnetized flowing of a hybrid-nanofluid over an unsteady stretching device with the inclusion of thermal radiation and entropy generation. Brick-shaped nanoparticles (zinc-oxide and ceria) are suspended in water, serving as the base-fluid to observe the performance of the hybrid mixture. The Maxwell thermal conductivity relation is employed to link the thermophysical attributes of the hybrid mixture with the host liquid. Additionally, a heat source/sink term is incorporated in the energy balance to enhance the impact of the investigation. Both prescribed-surface-temperature (PST) and prescribed-heat-flux (PHF) conditions are applied to inspect the thermal performance of the hybrid nanofluid. Design/methodology/approach: The transport equations in Cartesian configuration are transformed into ordinary differential equations (ODEs), and an efficient method, namely the Keller-Box method (KBM), is utilized to solve the transformed system. Postprocessing is conducted to visually represent the velocity profile, thermal distribution, skin-friction coefficients, Bejan number, Nusselt number and entropy generation function against the variations of the involved parameters. Findings: It is observed that more entropy is generated due to the increases in temperature difference and radiation parameters. The Bejan number initially declines but then improves with higher estimations of unsteadiness and Hartmann number. Overall, the thermal performance of the system is developed for the PST scenario than the PHF scenario for different estimations of the involved constraints. Originality/value: To the best of the authors' knowledge, no investigation has been reported yet that explains the bidirectional flow of a CeO2-ZnO/water hybrid nanofluid with the combined effects of prescribed thermal aspects (PST and PHF) and entropy generation. [ABSTRACT FROM AUTHOR]

Curcumin is a natural compound derived from turmeric and can target malignant tumor molecules involved in cancer propagation. It has potent antioxidant activity, but its effectiveness is limited due to poor absorption and rapid elimination from the body. Various curcumin derivatives have also shown anticancer potential in in‐vitro and in‐vivo models. Curcumin can target multiple signaling pathways involved in cancer development/progression or induce cancer cell death through apoptosis. In addition, curcumin and its derivatives could also enhance the effectiveness of conventional chemotherapy, radiation therapy and reduce their associated side effects. Lately, nanoparticle‐based delivery systems are being developed/explored to overcome the challenges associated with curcumin's delivery, increasing its overall efficacy. The use of an imaging system to track these formulations could also give beneficial information about the bioavailability and distribution of the nano‐curcumin complex. In conclusion, curcumin holds significant promise in the fight against cancer, especially in its nanoform, and could provide precise delivery to cancer cells without affecting normal healthy cells. Significance statement: The current article highlights the usage of natural compounds, especifically curcumin, as an anticancer agent. Due to their inherent property, such as poor absorption and solubility, low bioavailability, and rapid elimination, this potent compound has yet to be exploited to its potential. To overcome the above‐mentioned issues, several nanoformulations have been developed that overcome these limitations and enhance the efficacy of this compound in different in‐vitro and in‐vivo cancer models. [ABSTRACT FROM AUTHOR]

The quality and marketing of horticultural crops is affected by the feeding of thrips on plant tissues. Thrips commonly feed upon the gladiolus and different vegetative and floral parts of gladioli are attacked by them. Application of chemical insecticides provides effective control of insect pest in short period of time. The present investigation was carried out on Jasminum sambac against flower thrips for efficacy of insecticides. To conduct this experiment, six insecticides viz., imidacloprid 20Sl, Spinosad 240SC, Spintoram 120SC, Chlophenapyre, Imidacloprid+fipronil and abamectin were applied. Thrips population was observed on jasmine flower before application of insecticides and thrips mortality data was noted after 24, 72 and 168 hours of insecticides spray. The maximum mortality of thrips was recorded in Imidacloprid+fipronil (68.45%) and Spinosad (65.45%) after 24 hours of insecticides spray. The lowest mortality of thrips was counted in abamectin and imidacloprid. Population of thrips was reduced after 72 and 168 hours as compared to 24 hours after insecticides spray. It is concluded from the findings of investigation that the insecticide (Imidacloprid+fipronil) was found effective against the population of thrips after 24, 72 and 168 hours in years, 2021 and 2022, respectively when compared to other insecticides. [ABSTRACT FROM AUTHOR]

Simple Summary: Dermacentor ticks have a wide geographic range with an uneven distribution in the globe. They are not scientifically well known because they survive in hard topographic and harsh climatic regions along with elevated mountains. Many mammals serve as a primary host for Dermacentor ticks, like many other tick species. The present study aimed to provide the first morphological and molecular confirmation of Dermacentor marginatus and its related pathogens like Anaplasma marginale and Rickettsia raoultii in Pakistan. In this study, a total of 26 specimens (19 males and 7 females) were collected from goats and morphologically identified. A subset of 18 specimens were subjected for the molecular characterization of ticks and associated pathogen detection. In the BLAST and phylogenetic analyses, D. marginatus and their associated pathogen sequences showed close resemblance with their corresponding species. In the present study, we reported the first genetic characterization of D. marginatus and associated A. marginale and R. raoultii in Pakistan. Due to the difficult access and harsh climate, it is important to investigate the ticks and related pathogens in the northern parts of Pakistan due to their zoonotic threats. Ticks of the genus Dermacentor Koch, 1844 (Acari: Ixodidae) are poorly known systematically due to their habitation in harsh topographic environments and high mountains. Dermacentor ticks are diversely distributed in the Palearctic, Nearctic, and Oriental regions. There is no available information on the occurrence of Dermacentor marginatus in Pakistan; thus, the current investigation aimed the first morphological and molecular confirmation of this species and associated Anaplasma marginale and Rickettsia raoultii. Ticks were collected from goats (Capra hircus) and morphologically identified. Genomic DNA was extracted from 18/26 (69.23%) tick specimens, including 11 males and 7 females (1 unfed and 6 fed females). Extracted DNA was subjected to PCR for the amplification of genetic markers like 16S rDNA and cox1 for ticks, 16S rDNA for Anaplasma spp., and gltA and ompB for Rickettsia spp. A total of 26 D. marginatus ticks composed of 19 males (73.07%) and 7 females (26.9%) [1 (3.84%) unfed and 6 (23.07%) fed females] were collected from goats. According to amplicons via BLAST analysis, the 16S rDNA sequence showed 97.28–98.85% identity and the cox1 sequence showed 95.82–98.03% identity with D. marginatus. Additionally, the 16S rDNA sequence for Anaplasma sp. was detected in D. marginatus that showed 100% identity with Anaplasma marginale. Rickettsial gltA and ompB sequences for Rickettsia sp. showed 100% identity with Rickettsia raoultii. In phylogenetic analysis, ticks' 16S rDNA and cox1 sequences clustered with the same species. In phylogenetic analysis, A. marginale based on 16 rDNA clustered with A. marginale, while gltA and ompB sequences clustered with R. raoultii. This is the first study on the genetic characterization of D. marginatus and associated A. marginale and R. raoultii in Pakistan. The northern areas of Pakistan, which need to be explored in terms of ticks and associated pathogens due to their zoonotic threats, have been neglected due to the inaccessible climatic conditions. [ABSTRACT FROM AUTHOR]

This study is designed to analyze the fuzzy dynamical model of vibrating mass system by using neural networks (NNs). Stochastic numerical solvers are implemented with Levenberg–Marquardt backpropagation (LMB) algorithm. The proposed solvers handle the uncertainties of fuzzy differential system and minimize the error consistently. Validation, constancy and verification of NNs with LMB algorithm are endorsed by attaining reasonable accuracy through statistical measures in terms of error histograms, regression and mean squared errors for different values of fuzzy parameters. [ABSTRACT FROM AUTHOR]

Corals are sessile invertebrates living underwater in colorful structures known as reefs. Unfortunately, coral's temperature sensitivity is causing color bleaching, which hosts organisms that are crucial and consequently affect marine pharmacognosy. To address this problem, many researchers are developing cures and treatment procedures to restore bleached corals. However, before the cure, the researchers need to precisely localize the bleached corals in the Great Barrier Reef. The researchers have developed various visual classification frameworks to localize bleached corals. However, the performance of those techniques degrades with variations in illumination, orientation, scale, and view angle. In this paper, we develop highly noise-robust and invariant robust localization using bag-of-hybrid visual features (RL-BoHVF) for bleached corals by employing the AlexNet DNN and ColorTexture handcrafted by raw features. It is observed that the overall dimension is reduced by using the bag-of-feature method while achieving a classification accuracy of 96.20% on the balanced dataset collected from the Great Barrier Reef of Australia. Furthermore, the localization performance of the proposed model was evaluated on 342 images, which include both train and test segments. The model achieved superior performance compared to other standalone and hybrid DNN and handcrafted models reported in the literature. [ABSTRACT FROM AUTHOR]

Detecting cyber intrusions in network traffic is a tough task for cybersecurity. Current methods struggle with the complexity of understanding patterns in network data. To solve this, we present the Hybrid Deep Learning Intrusion Detection Model (HD-IDM), a new way that combines GRU and LSTM classifiers. GRU is good at catching quick patterns, while LSTM handles long-term ones. HD-IDM blends these models using weighted averaging, boosting accuracy, especially with complex patterns. We tested HD-IDM on four datasets: CSE-CIC-IDS2017, CSE-CIC-IDS2018, NSL KDD, and CIC-DDoS2019. The HD-IDM classifier achieved remarkable performance metrics on all datasets. It attains an outstanding accuracy of 99.91%, showcasing its consistent precision across the dataset. With an impressive precision of 99.62%, it excels in accurately categorizing positive cases, crucial for minimizing false positives. Additionally, maintaining a high recall of 99.43%, it effectively identifies the majority of actual positive cases while minimizing false negatives. The F1-score of 99.52% emphasizes its robustness, making it the top choice for classification tasks requiring precision and reliability. It is particularly good at ROC and precision/recall curves, discriminating normal and harmful network activities. While HD-IDM is promising, it has limits. It needs labeled data and may struggle with new intrusion methods. Future work should find ways to handle unlabeled data and adapt to emerging threats. Also, making HD-IDM work faster for real-time use and dealing with scalability challenges is key for its broader use in changing network environments. [ABSTRACT FROM AUTHOR]

Process uncertainties have been a big challenge to the stable operation and control of process industries. The current study is based on the use of an artificial intelligence model as a surrogate in the online optimization of process conditions of reactive units of a petroleum refinery under uncertainty. Initially, a steady-state Aspen model was used to perform exergy analysis for quantifying the exergy efficiency, irreversibility, and improvement potential of the plant. The process model was then transformed to a dynamic mode by inserting ±5 % uncertainty in process conditions, i.e., mass flow rate, pressure, and temperature, to generate a dataset of 216 samples. An artificial neural network (ANN) model was developed using the dataset to predict exergy efficiency. The ANN model was used as a surrogate in GA and PSO environments to achieve higher exergy efficiency under uncertainty. The optimized process condition derived through GA and PSO based approaches were fed to Aspen model for cross-validation. The Fourier Amplitude Sensitivity Test (FAST) was performed to identify the most influential process conditions in terms of their effect on process exergy efficiency. The overall plant had an exergy efficiency, irreversibility, and improvement potential of 50.57 %, 34,955.55 kW, and 17,276.98 kW, respectively. The correlation coefficient of ANN model was 0.97432. The use of ANN as a surrogate in the optimization frameworks outperformed the standalone (SA) Aspen model of the process in attaining the highest exergy efficiency. Overall the performance of both the GA and the PSO based approaches were comparable. Based on sensitivity analysis, inlet temperatures of reactors were the most sensitive variables affecting the process exergy efficiency. The current study helps in laying a foundation for the simulation of Refinery 4.0. [Display omitted] [ABSTRACT FROM AUTHOR]

Cancer and diabetes mellitus (DM) are among the leading causes of mortality and morbidity in the global arena. Lately, several studies demonstrated that DM could promote cancer. However, the exact mechanism(s) highlighting this association are largely untouched and require comprehensive detailing. In the present review, we aimed to explore and decipher the possible mechanism of DM an cancer association. Hyperglycemia could be a subordinate plausible explanation of carcinogenesis in the diabatic patient. It is well known that high glucose levels may help in cancer proliferation. In addition, chronic inflammation, the other well‐known factor of diabetes, could also play a role in carcinogenesis. Moreover, the numerous medicines to treat diabetes either increase or reduce cancer risk. Insulin is one of the potent growth factors that promotes cell propagation and induces cancer directly or via insulin like growth factor‐1. On the other hand, hyperinsulinemia leads to an increased activity of growth factor‐1 by inhibiting growth factor binding protein‐1. To improve cancer prognosis, individuals with diabetes should be screened to discover cancer at an early stage and treated appropriately. Significance statement: The current article highlights the possible linkage between various forms of cancer and diabetes mellitus based on experimental animal models and epidemiological studies.Different factors such as hyperglycemia, inflammation, and antidiabetic medicine have been suggested to play the interlinking role.Hyperinsulinemia could also increase the chances of cancer through hyperactivations of insulin like growth factor‐1. [ABSTRACT FROM AUTHOR]

Plant performance is mainly estimated based on plant architecture, leaf features and internal microstructural changes. Olive (Olea europaea L.) is a drought tolerant, oil yielding, and medium sized woody tree that shows specific structural and functional modifications under changing environment. This study was aimed to know the microstructural alteration involving in growth and yield responses of different Olive cultivars. Eleven cultivars were collected all over the world and were planted at Olive germplasm unit, Barani Agricultural Research Institute, Chakwal (Punjab) Pakistan, during September to November 2017. Plant material was collected to correlate morpho-anatomical traits with yield contributing characteristics. Overall, the studied morphological characters, yield and yield parameters, and root, stem and leaf anatomical features varied highly significantly in all olive cultivars. The most promising cultivar regarding yield was Erlik, in which plant height seed weight and root anatomical characteristics, i.e., epidermal thickness and phloem thickness, stem features like collenchymatous thickness, phloem thickness and metaxylem vessel diameter, and leaf traits like midrib thickness, palisade cell thickness a phloem thickness were the maximum. The second best Hamdi showed the maximum plant height, fruit length, weight and diameter and seed length and weight. It also showed maximum stem phloem thickness, midrib and lamina thicknesses, palisade cell thickness. Fruit yield in the studied olive cultivars can be more closely linked to high proportion of storage parenchyma, broader xylem vessels and phloem proportion, dermal tissue, and high proportion of collenchyma. [ABSTRACT FROM AUTHOR]

The present research, a numerical approach to examine magnetohydrodynamics (MHD) Casson nanofluid flow in a porous medium along a stretchable surface with different slips using artificial neural networks (ANNs) by taking inverse multiquadric (IMQ) radial basis function (RBF) as an activation function. i.e. ANNs-IMQ-RBF. The hybridization of genetic algorithms (GAs) and sequential quadratic programming (SQP) is used for learning in ANNs-IMQ-RBF. The PDEs representing the fluid flow are converted into a nonlinear system of dimensionless form of ODEs through an appropriate transformation while effects of variation in the values of Casson parameter (β), Brownian motion parameter (Nb), Prandtl number (Pr), stretching parameter (n), porosity parameter (P), Lewis number (Le) along with temperature slip parameter (λ 2) on velocity, temperature and nanofluid concentration are depicted through graphs. The effectiveness, convergence and accuracy of the proposed solver are validated evidently through boxplot analysis, histograms and cumulative distribution function (CDF) plots. [Display omitted] • A new ANNs-IMQ-RBF solver is designed for MHD Casson nanofluid problem in the shape of a system of nonlinear ODEs. • Velocity, temperature and concentration profiles are calculated for sundry cases of physical parameters of the interest. • Results are compared with the reference solutions of Adams numerical technique to analyze the accuracy and convergence. • Statistical operators via box plot, CDF and histograms analyses are used to verify the effectiveness and stability. [ABSTRACT FROM AUTHOR]

Accelerating cosmic expansion is a challenging issue faced by cosmology in the present times. Modified gravity could present a promising choice in order to understand and explain it in its framework. In this context, f (R) models of modified gravity look apparently the most compatible and viable scenario. In this work, we investigate the cosmic dynamics of the late times using a dynamical system approach in f (R) cosmology. The properties associated with the critical points are investigated to understand the system stability by analyzing the dynamical system which describes the cosmological evolution from the perspective of the model under consideration. It is observed that the accelerated cosmic expansion following the phase of matter domination is arrived at in a particular model f (R) = R p exp (q R) under discussion. The geometric curve m (r) also helps figure out some significant properties of the model upon plotting in the (r , m) plane. It also assists substantially to form the dynamical system for the model in question. The analysis of system stability is carried out by finding out the critical points of the dynamical system whose dynamic characteristics are responsible for the stability of the model. It is extended afterward by considering the cosmological constant as dark energy, which proves, however, redundant from the viewpoint of modified gravity in f (R) models. Two cases regarding linear and nonlinear interactions between cosmic fluids are also discussed. At some points, as the analysis shows, we see that accelerated expansion is attained by yielding a viable epoch of matter domination. The results which came out through stability analysis show that the universe is currently subject to accelerating expansion regardless of the dark energy to remain in existence. [ABSTRACT FROM AUTHOR]

Cancer development is associated with the deregulation of various cell signaling pathways brought on by certain genetic and epigenetic alterations. Therefore, novel therapeutic strategies have been developed to target those pathways. The phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) (PI3K/Akt/mTOR) pathway is one major deregulated pathway in various types of cancer. Several anticancer drug candidates are currently being investigated in preclinical and/or clinical studies to target this pathway. Natural bioactive compounds provide an excellent source for anticancer drug development. Curcumin and plumbagin are two potential anticancer compounds that have been shown to target the PI3K/Akt/mTOR pathway individually. However, their combinatorial effect on cancer cells is still unknown. This study aims to investigate the synergistic effect of these two compounds on the PI3K/Akt/mTOR pathway by employing a sequential molecular docking and molecular dynamics (MD) analysis. An increase in binding affinity and a decrease in inhibition constant have been observed when curcumin and plumbagin were subjected to sequential docking against the key proteins PI3K, Akt, and mTOR. The MD simulations and molecular mechanics combined with generalized Born surface area (MM-GBSA) analyses validated the target proteins' more stable conformation when interacting with the curcumin and plumbagin combination. This indicates the synergistic role of curcumin and plumbagin against cancer cells and the possible dose advantage when used in combination. The findings of this study pave the way for further investigation of their combinatorial effect on cancer cells in vitro and in vivo models. [ABSTRACT FROM AUTHOR]

Granitoids and orthogneisses (~ 1.7 Ga) from the northern part of the Banded Gneissic Complex (BGC-II domain), Aravalli Craton (NW India) are geochemically analyzed to understand the geodynamic condition and crustal evolution processes. The samples are metaluminous to peraluminous (molar A/CNK: 0.74–2.12), and characterized by Eu-anomaly ranging from 0.17 to 1.06, Fe2O3T/(Fe2O3T + MgO) from 0.8 to 0.9 and high magmatic zircon saturation temperature (> 830 °C) which are the features suggestive of A-type granite affinity. Tectonic discrimination diagrams classify the samples as post-collisional extensional A2-type granites. Geochemical characteristics along with trace element ratios [(Y/Nb)N = 0.15 to 4.33 (avg. 0.76), (Th/Nb)N = 4.63 to 255.47 (avg. 63.13), (Th/Ta)N = 1.37 to 9.84 (avg. 8.86), (Ce/Pb)N = 0.05 to 3.05 (avg. 1.43)] indicate that the rocks were derived from a plagioclase-rich and garnet-free crustal source under low-pressure conditions. Further, it is also proposed that tonalite-trondhjemite-granodiorite (TTG) rocks which occur dominantly in southern Rajasthan (BGC-I) are precursors and their partial melting led to the generation of the studied A-type granite samples. The studied samples also bear close geochemical similarity with A-type granites of similar age (~1.7 Ga) near the Sakhun-Ladera region of northern BGC-II. The studied A-type granites are believed to be coeval to similar aged A-type granites of the Khetri and Alwar sub-basins of the North Delhi Fold Belt (NDFB). They are comparable in age and magmatic history to recorded A-type magmatism in North America and parts of the Chinese craton. The large geographical extents of synchronous A-type granites are proposed to be related to the Columbia Supercontinent assembly (ca. 1.7 Ga; post-collisional granites). Thus, based on the studied extensional granites, we surmise that BGC-II was part of the Columbia Supercontinent. [ABSTRACT FROM AUTHOR]

Cyperus niveus Retz., also known as snow white sedge grows in dry mountainous regions of Himalayas. Eight populations of C. niveus were collected from different locations in the Salt Range (Punjab) and Kaghan Valley (Khyber Pakhtoonkhwa) to evaluate role of plasticity in adaptability potential through modification in anatomical traits. This species developed xeric anatomical modifications in roots at higher elevations. These were thick endodermis, formation of sclerenchymatous cells outside endodermis, numerous metaxylem vessels, and increased area of central vessel in roots. Roots were more developed in the Kaghan (K) population, showing the largest root radius (774.7 µm), sclerenchymatous cell area (1429.1 µm2), cortical thickness (147.1 µm), and endodermal cell area (7701.4 µm). Stomatal density (21.7 per mm2) was also the highest in the K population. Formation of aerenchymatous cavities, numerous large vascular bundles with wide metaxylem vessels, and large phloem tissue in stems were the prominent features in the K population at the highest elevation (2078 m a.s.l.). All C. niveus populations showed sclerification around and outside of the vascular bundles, larger adaxial epidermal cell area as compared to abaxial epidermal cells, hypostomatal leaves, and increased storage parenchyma in leaves. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has provided significant help in many fields of life. This study proposed a framework that helped in understanding customers' attitudes about the adoption of Robo-advisors. The role of the Technology Readiness Index moderated as one of the primary relationships. A total of 208 potential users of Robo-advisor services provided the data that confirmed the validity of the model. This model provided the input for structural equation modeling and analysis of the study hypotheses. The results indicated that consumers showed positive attitudes about Robo-advisor services, with the moderating effect of Technology Readiness Index dimensions, namely, contributors and inhibitors. Perceived ease of use, perceived usefulness, and perceived convenience influenced consumers in developing positive attitudes about this service. Financial businesses can design better AI Robo-advisor services to fulfill the requirements of a wide range of consumers. This proposed framework contributes to the consumers' understanding of behavioral intentions for the use of Robo-advisors in FinTech. [ABSTRACT FROM AUTHOR]

The aim of this paper is to determine analytic solutions to the steady/unsteady 2-D flows of a Newtonian fluid passing through a porous media. Using the stream function, the analytical general solutions of the non-linear equations corresponding to boundary-layer flows are determined by employing the extended approach of variable separation. Some specific flow problems, described by given initial and boundary conditions, are investigated using the obtained general analytic solutions. [ABSTRACT FROM AUTHOR]

To overcome environmental problems and the energy crisis, water splitting through solar energy for the production of hydrogen fuel is a promising technique. Efficient and low-cost photocatalyst are highly desired. In the recent era, van der Waal (vdW) heterostructures have gained considerable attention due to their promising properties. Hybrid density functional theory is used to calculate the electronic and photocatalytic properties of BX-MSSe (X = P, As and M = Mo, W) vdW heterostructures. Most stable stacking is figured out among different stacking from their relaxation energy. The stability of the stable stacking is confirmed through phonon calculations. Calculated band structure shows that BAs-MoSSe and BP-WSSe have direct band gap nature while the BP-MoSSe and BAs-WSSe have indirect band gap nature. BAs-MoSSe has Type-I band alignment while BP-MoSSe, BAs-WSSe and BP-WSSe have type-II band alignment. The creation of heterostructures broadened the optical absorption from the infrared to the ultraviolet region of the solar spectrum. To tune the properties of these heterostructures compressive and tensile strain is applied. Switching in band alignment is predicted in these heterostructures with the applications of strain. Based on valence and conduction band edges potentially the photocatalytic behavior for pH = 0 is investigated, which confirms that in unstrained conditions BP-MSSe (M = Mo, W) heterostructures are efficient photocatalyst. [ABSTRACT FROM AUTHOR]

Objective: To find the magnitude of clinically overt hypocalcaemia post-total thyroidectomy and its relation with the age of the patient. Study Design: Cross-sectional study. Place and Duration of Study: Combined Military Hospital, Bahawalpur Pakistan, from Jan 2017 to Dec 2019. Methodology: Sixty patients who were to undergo total thyroidectomy were included in the study in strict compliance with the inclusion and exclusion criteria. Patients were divided into two groups according to their ages. In Group-A, patients younger than 50 and Group-B, patients older than 50 years were included. All the patients were followed up by clinical examination for the development of clinically overt hypocalcaemia and confirmed by measurement of serum calcium. Results: Mean age of the patients was 42.75±13.05 years, with an age range of 20 to 68 years. Of all the patients, 14(23.3%) were male, and 46(76.7%) were females. Seven patients in Group-A developed post-thyroidectomy hypocalcaemia clinically, whereas, in Group-B, only one developed this condition. Clinical post-thyroidectomy hypocalcaemia developed more often in the younger age group and these results were statistically significant (p= 0.023). Conclusion: Clinically overt hypocalcaemia develops more often in younger patients after total thyroidectomy. [ABSTRACT FROM AUTHOR]

Investigation of expanding sheet flow in the existence of tiny particles is an interesting field of research and this type of mathematical model is usually expressed in the form of partial differential equations. Present exploration has been made to describe the unsteady 3D dynamics of MHD tangent-hyperbolic fluid impinging over an expanded sheet with the characteristics of haphazard movement and thermo migration of nanoparticles. Moreover, securitization has been accomplished in the environment of prescribed temperature and prescribed concentration restrictions at the stretchable surface. Novel combination of scaling transformations has been accustomed to exchange the governing equations into complicated system of ordinary differential equation and then solved by an innovative analytical scheme, namely, homotopy analysis method. The significance of involved scientific factors on temperature phase, concentration phase, condensed Nusslet number and condensed Sherwood number has been explained through various contours. It has been noticed that increasing choices of random movement 0 ≤ N b ≤ 1.0 and thermo-migration of nanoparticles 0 ≤ N t ≤ 3.0 reduce the estimate of heat transaction along with the estimate of mass transaction. Moreover, temperature and concentration circulations have improved with the advanced choices of Weissenberg number 0 ≤ W e x ≤ 10.0 and flow behavior index 0 ≤ n ≤ 2.1. [ABSTRACT FROM AUTHOR]

A stochastic computing approach is implemented in the present work to solve the nonlinear nanofluidics system that occurs in the model of atomic physics. The process converts the partial differential nanofluidics system with suitable level of similarities transformation into nonlinear systems of differential equations. For the construction of datasets, finite difference scheme (Lobatto IIIA) is applied through different selection of collocation points for nonlinear nanofluidics system having accuracy of order four. Lobatto IIIA has a strong point to tackle extremely nonlinear systems of ordinary differential equations in smooth way. For different scenarios, datasets are well trained through computing scheme to investigate the heat transfer and thermal performance of nanofluidic transportation system of nanofluids and hybrid nanofluids toward stretching surfaces with variation of Biot number, Nusselt number and skin fraction. Furthermore, the reliability, accuracy and efficiency are endorsed through various statistical analysis and graphical illustrations of proposed computing scheme. [ABSTRACT FROM AUTHOR]

To improve yield and shelf life of stock can enhance economic value to flowers growers globally. A study was conducted to evaluate the efficacy of various bio-stimulants on yield and quality of stock. Three cultivars of stock, viz. 'Cheerful White', 'Iron Rose' and 'Iron White' foliarly sprayed by bio-stimulants (Isabion and Tecamin Max) with three different concentrations (1 ppm, 3 ppm, and 5 ppm) with the different number of sprays (1, 2, or 3 sprays). The first spray was applied after one month of transplanting, while the other two were applied at fortnight intervals after the first spray. Fisher's Analysis of variance revealed that Isabion at 1 ppm as 1 spray, 3 ppm as 3 sprays, and 5 ppm as 3 sprays produced the highest plant height for three cultivars of stock. 'Cheerful White' had the highest stem length when plants were sprayed once at 1 ppm Isabion. Among cultivars, 'Iron Rose', produced highest leaf area (42.8 cm2) with 3 ppm Isabion application, when sprayed only one time. Among the number of sprays, plants sprayed three times with Isabion produced the thickest stem diameter followed by 2 and 1 sprays. 'Cheerful White' had maximum leaf total chlorophyll contents, followed by 'Iron White' and 'Iron Rose' (69.1 SPAD and 68.8 SPAD, respectively). Among the number of sprays, plants sprayed three times had the longest vase life. Cheerful White proved early cultivar of stock while Iron White were late cultivars when grown in agro-climatic conditions of Faisalabad. Tecamoin Mix is recommended biostimulant for quality production of Mathiola incoma L. rather than isabion. [ABSTRACT FROM AUTHOR]

An intrusion detection system is one of the main defense lines used to provide security to data, information, and computer networks. The problems of this security system are the increased processing time, high false alarm rate, and low detection rate that occur due to the large amount of data containing various irrelevant and redundant features. Therefore, feature selection can solve this problem by reducing the number of features. Choosing appropriate feature selection methods that can reduce the number of features without a negative effect on the classification accuracy is a major challenge. This challenge motivated us to investigate the application of different wrapper feature selection techniques in intrusion detection. The performance of the selected techniques, such as the genetic algorithm (GA), sequential forward selection (SFS), and sequential backward selection (SBS), were analyzed, addressed, and compared to the existing techniques. The efficiency of the three feature selection techniques with two classification methods, including support vector machine (SVM) and multi perceptron (MLP), was compared. The CICIDS2017, CSE-CIC-IDS218, and NSL-KDD datasets were considered for the experiments. The efficiency of the proposed models was proved in the experimental results, which indicated that it had highest accuracy in the selected datasets. [ABSTRACT FROM AUTHOR]

To improve yield and shelf life of stock can enhance economic value to flowers growers globally. A study was conducted to evaluate the efficacy of various bio-stimulants on yield and quality of stock. Three cultivars of stock, viz. ‘Cheerful White’, ‘Iron Rose’ and ‘Iron White’ foliarly sprayed by bio-stimulants (Isabion and Tecamin Max) with three different concentrations (1 ppm, 3 ppm, and 5 ppm) with the different number of sprays (1, 2, or 3 sprays). The first spray was applied after one month of transplanting, while the other two were applied at fortnight intervals after the first spray. Fisher’s Analysis of variance revealed that Isabion at 1 ppm as 1 spray, 3 ppm as 3 sprays, and 5 ppm as 3 sprays produced the highest plant height for three cultivars of stock. ‘Cheerful White’ had the highest stem length when plants were sprayed once at 1 ppm Isabion. Among cultivars, ‘Iron Rose’, produced highest leaf area (42.8 cm² ) with 3 ppm Isabion application, when sprayed only one time. Among the number of sprays, plants sprayed three times with Isabion produced the thickest stem diameter followed by 2 and 1 sprays. ‘Cheerful White’ had maximum leaf total chlorophyll contents, followed by ‘Iron White’ and ‘Iron Rose’ (69.1 SPAD and 68.8 SPAD, respectively). Among the number of sprays, plants sprayed three times had the longest vase life. Cheerful White proved early cultivar of stock while Iron White were late cultivars when grown in agro-climatic conditions of Faisalabad. Tecamoin Mix is recommended biostimulant for quality production of Mathiola incoma L. rather than isabion. [ABSTRACT FROM AUTHOR]

A formidable challenge in suburban agriculture is the sustainability of soil health following the use of wastewater for irrigation. The wastewater irrigation likely toxifies the crop plants making them unconsumable. We used a multivariate, completely randomized design in a greenhouse, comparing the phytoextraction capacities of Brassica juncea, Eruca sativa, Brassica rapa, and Brassica napus—all grown on silt loam soil irrigated with industrial wastewater, canal water, and a 1:1 mixture, during 2018. The studied Brassica plants were generally closely efficient in remediating toxic metals found in wastewater irrigated soil. Substantial differences between Brassica and Eruca plants/parts were recorded. For example, B. napus had significantly higher metal extraction or accumulation compared to E. sativa for Zn (71%), Cu (69%), Fe (78%), Mn (79%), Cd (101%), Cr (57%), Ni (92%). and Pb (49%). While the water and plant were the main predictors of metal extraction or accumulation, an interaction between the main effects substantially contributed to Cu, Mn, and Fe extractions from soil and accumulations in plants. Significant correlations between biological accumulation coefficient and biological transfer coefficient for many metals further supported the metal extraction or accumulation efficiencies as: B. napus > B. juncea > B. rapa > E. sativa. Root-stem mobility index correlation with stem-leaf mobility index indicated the metal translocation along the root-stem-leaf continuum. Therefore, we suggest that these crops may not be used for human or animal consumption when grown with industrial wastewater of toxic metal concentrations ≥ permissible limits. Rather these plants may serve as effective remediators of toxic metal-polluted soil. [ABSTRACT FROM AUTHOR]

Cybersecurity has been widely used in various applications, such as intelligent industrial systems, homes, personal devices, and cars, and has led to innovative developments that continue to face challenges in solving problems related to security methods for IoT devices. Effective security methods, such as deep learning for intrusion detection, have been introduced. Recent research has focused on improving deep learning algorithms for improved security in IoT. This research explores intrusion detection methods implemented using deep learning, compares the performance of different deep learning methods, and identifies the best method for implementing intrusion detection in IoT. This research is conducted using deep learning models based on convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs). A standard dataset for intrusion detection in IoT is considered to evaluate the proposed model. Finally, the empirical results are analyzed and compared with the existing approaches for intrusion detection in IoT. The proposed method seemed to have the highest accuracy compared to the existing methods. [ABSTRACT FROM AUTHOR]

The present mathematical model discloses the effects of Boussinesq and Rosseland approximations on unsteady 3D dynamics of water-driven hybridized nanomaterial with the movements of nanoplatelets (molybdenum disulfide, MoS 2 and graphene oxide, GO). Variable thermal conditions, namely, VST (variable surface temperature) and VHF (variable heat flux), are opted to provide temperature to the surface. MHD effects have also been used additionally to make the study more versatile. In order to transmute the transportation equations into nondimensionlized forms, similarity transformations have been adopted. The Keller-Box technique has been applied to obtain a numerical simulation of the modeled problem. The convergence of the solution for both VST and VHF cases is presented via the grid independence tactic. Thermal setup against escalating choices of power indices and nonlinear thermal radiation parameter is discussed via graphical illustrations. The rate of heat transaction has been discussed with the growing choices of mixed convection, thermal radiation, and unsteady parameters through various tabular arrangements. It is observed through the present analysis that mixed convection parameter, radiation parameter, temperature maintaining indices r , s , and unsteady parameter magnify the rate of heat transference under the control of platelet-shaped nanoparticles. [ABSTRACT FROM AUTHOR]

Wire coating is a commercial method to insulate wires for mechanical intensity and environmental protection. In this experimental study, the technique of computational intelligence is used for nonlinear wire coating analysis by soaking the wires in Oldroyd 8-constant fluid under a constant pressure gradient with the help of feed forward artificial neural networks (ANNs). The system of partial differential equations generated for the process of wire coating is transformed into a nonlinear dimensionless ordinary differential equation. One of the salient features of ANNs is the mathematical modeling of transformed equations by exploring the unsupervised error. A new scheme based on inverse multiquadric neural networks (IMQNNs) is used with a hybridization process of well-known genetic algorithms (GAs) and sequential quadratic programming (SQP) to obtain expeditious convergence, i.e., IMQNNs-GA-SQP. The applicability of the problem is investigated by altering the values of the dilatant constant, pressure gradient, shear stress, and pseudo-plastic constant, the outcome of which is in the form of varying polymer coating thickness. Comparison of highly accurate results in the shape of error analysis based on absolute errors of obtained results with those of the reference solution, calculated using the Adams numerical technique using MATHEMATICA software, statistical analysis such as root-mean-square error, Theil's coefficient for inequality, E-R2 (error function based on the coefficient of determination), E-VAF (variance accounted for), E-NSE (Nash–Sutcliffe efficiency), mean absolute error, box plot analyses, and the cumulative distributive function through histogram analyses, is also carried out in this research, which guarantees the effectiveness of the used scheme. [ABSTRACT FROM AUTHOR]

Application of wastewater to agricultural soils not only enhances economic benefits but is also considered as a safe disposal option by the administrators. Worldwide, peri-urban horticulture is a common practice for growing vegetables. When agricultural soils are irrigated with wastewater, numerous potentially toxic elements (PTEs) contained therein are bioaccumulated and pose health risks. The presented study aimed to reveal the PTEs, i.e., copper (Cu), cadmium (Cd), nickel (Ni) and lead (Pb) concentration in the agricultural soils irrigated with wastewater for longer times. Zeolite, a natural mineral was used to immobilize these in contaminated soils to reduce its availability to brinjal (Solanum melongena L.). During a pot study, zeolite was applied at four different levels, i.e., 0.25, 0.50, 1.00 and 2.00% in contaminated soil, keeping one control. The results revealed that growth as well as biochemical and physiological characters were found best with treatment receiving zeolite at 2.00%. In edible parts (fruit), PTE contents were found lowest in the same treatment. Relative to the control, ~121, 87, 120 and 140% less DTPA-extracted Cu, Cd, Ni and Pb in soil was found with this treatment. Based on the results, it was revealed that zeolite effectively immobilized Cu, Cd, Ni and Pb in the soil. Although all the applied levels of zeolite had positive potential to immobilize PTEs in wastewater-contaminated soil, zeolite applied at 2.00% proved most effective. [ABSTRACT FROM AUTHOR]

Tetralogy of Fallot is one of the frequently encountered cyanotic heart diseases. We describe how we handled anesthesia for a pregnant woman with uncorrected Tetralogy of Fallot during a cesarean section. Less than 10% of patients with Congenital Heart Disease survive for more than 10 years. Among the survivors, very few of them reach adulthood in countries like Pakistan with no previous surgical interventions. The occurrence of such case is quite rare. [ABSTRACT FROM AUTHOR]

In this exploration, we decided to investigate the significance of prescribed thermal conditions on unsteady 3D dynamics of water-based radiative hybrid nanofluid with the impact of cylindrical-shaped nanosized particles (alumina ( Al 2 O 3 ) and titania (Ti O 2 )). For physical relevancy, the impact of the Lorentz force is also included. The combination of suitable variables has been used to transform the transport equations into the system of ordinary differential equations and then numerically solved via the Keller-Box approach. Graphical illustrations have been used to predict the impact of the involved parameters on the thermal setup. Convergence analysis is presented via the grid independence approach. Skin frictions and local Nusselt numbers against various choices of involved parameters are plotted and arranged in tabular forms. It is observed through the present investigation that temperature distribution is increased with the higher choices of radiation parameter 0.0 ≤ R d ≤ 2.0 and decreased with the improvement in the choices of temperature maintaining indices (i.e., − 2.0 ≤ r , s ≤ 2.0). Moreover, the thermophysical properties except specific heat for hybrid nanofluid are improved with the involvement of cylindrical-shaped nanoparticles. The temperature of the hybrid nanofluid is observed to be higher for variable thermal conditions as compared to uniform thermal conditions. Outfalls for a limited version of the report have been compared with a previous published paper. [ABSTRACT FROM AUTHOR]

An investigation of the thermal performance of water-conveying nanospheres (magnetite ( Fe 3 O 4 ) and silver (Ag)) subject to variable thermal controls, namely, variable surface temperature and variable surface normal heat flux, has been made. A bidirectionally elongating surface is used to generate an unsteady flow mechanism with the action of the Lorentz force. Derived equations of basic laws are firstly nondimensionalized and then numerically solved by applying the Keller-Box method. The local Nusselt number for both the thermal cases is calculated and discussed. Percent-wise enhancement in the rate of heat transport has also been included in the analysis. It was concluded through the present exploration that at lower volume fractions of magnetite and silver, the rate of heat transport is observed to be dominant. The rate of heat transference has attained identical values for both the provided thermal conditions at the surface. Moreover, intensities of velocity and thermal profiles diminish with the appreciation of the choice of unsteadiness. The temperature-controlling indices also affect the thermal profile, and it is reduced with the intensification in the considerations of these indices. The values of thermal conductivity, density, and electrical conductivity have been improved with the inclusion of nanospheres (magnetite ( Fe 3 O 4 ) and silver (Ag)), whereas the value of specific heat is reduced with the mixture of these nanospheres. The Nusselt number is increased up to 5% with the involvement of magnetite nanospheres, and it is enhanced up to 4% with the involvement of silver nanospheres. [ABSTRACT FROM AUTHOR]

The ultra-high significances of thermal radiation, magnetic field and activation energy in thermal enhancement processes allow significant applications in chemical and mechanical engineering, modern technology and various thermal engineering eras. The improvement in energy resources and production became one of the major challenges for researchers and scientists for sustained development in industrial growths. Beside this, the bioconvection assessment in nanomaterials conveys prestigious applications in biotechnology like bio-sensors, enzymes, petroleum industry, bio-fuels and many more. In view of such renewable applications, present exploration discloses unsteady two-dimensional flow of third-grade nanomaterial accommodating gyrotactic microorganisms induced by unsteady stretched Riga sheet in porous medium. The formulated flow problem is further scrutinized by utilizing the chemical reaction, activation energy, thermal radiation and magnetic aspects. The convective Nield constraints are further subjected in the current investigation. Apposite transformations are used to condense the nonlinear developed problem into dimensionless ordinary form. The numerical solution of such similar flow problem is presented via shooting technique. The detailed graphical illustrations of the dimensionless temperature, nanoparticles concentration, velocity and motile microorganisms for physical significance of diverse relevant parameters are deliberated. Furthermore, numerical data of local Sherwood, Nusselt and motile density numbers is designated in tabular form. Study accentuated that velocity increases for higher modified Hartmann and material constants, while the effects of buoyancy ratio and bioconvected Rayleigh numbers are rather opposite. The temperature, microorganism and concentration distributions were enhanced for unsteady parameter. It is also acknowledged that the concentration distribution is enhanced for activating the energy number. Moreover, the microorganism distribution enhances for concentration difference and magneto-porous constants, while bioconvected Lewis and Peclet numbers show conflicting trend. [ABSTRACT FROM AUTHOR]