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Abstract Independent control of electromagnetic (EM) waves by metasurfaces for multiple tasks are highly desired and is the recent hot topic of research. In this work we contribute a polarization insensitive frequency multiplexed 2-bit coding metasurface to control the Terahertz (THz) waves in the two operating bands independently. In this regard, as a first step a cascaded meta-atom composed of square rings and/or square metallic patches separated by two polyimide substrates is designed and optimized that provides sixteen independent distinct discrete phases in the reflection geometry. These meta-atoms are then distributed with distinct coding sequences in the two-dimensional spatial plane to realize various bi-functional metasurfaces. As a proof of the concept various full structures are designed and simulated to realize a series of bi-functionalities including anomalous reflection/beam shaping, beam shaping/anomalous reflection, beam deflection/Orbital angular momentum (OAM) beam generation with distinct modes and propagating wave to surface wave (PW–SW) conversion/PW beam manipulation in the lower and higher THz bands, respectively. All the simulation results are in excellent agreement with their theoretical equivalents. We envision that the proposed meta-designs have potential applications for the multi-spectral control of EM waves in THz band. The idea can be further extended to design frequency dependent tri-functional and multi-functional THz meta-devices.

Desalination processes are energy consuming and it is required to apply clean energy sources for supplying them to prevent environmental issues. Solar energy is one of the attractive clean energy sources for desalination. In solar thermal desalination systems, different thermal components could be used for heat transfer purpose. In solar desalination technologies, heat pipe as efficient heat transfer mediums could be employed to transfer absorbed and/or stored thermal energy. The objective of this study is to review applications of heat pipes in solar energy desalination systems. Regarding the performance dependency of these thermal systems on the variety of factors, scholars have investigated these systems by consideration of the effect of different influential factors. Based on the results, it is concluded that use of heat pipes could lead to proper performance of solar desalination systems. Aside from direct transfer of absorbed heat from solar radiation, heat pipes can be applied in the storage units of solar desalination systems to keep the systems active in night-hours or low solar irradiation conditions. The overall performance of the solar desalinations systems with heat pipes can be influenced by some factors such as filling ratio and operating fluid that affect the performance of heat pipes. HIGHLIGHTS Applications of heat pipes as efficient heat transfer tools are reviewed.; Solar desalination system utilizing heat pipes for heat transfer are introduced.; Some recommendations are provided for the upcoming studies on the use of heat pipes in solar desalination systems.;

In this paper, the extended direct algebraic approach with the general fractional derivative is employed to attain various novel wave structures of the non-linear fractional Tzitzéica type non-linear evolution equations in the form of kink, singular, periodic singular, dark, bright and dark-bright combine solitons. For the purpose to illustrate the physical behavior of the acquired solutions, some of the extracted results are sketched in the pattern of 3-D and 2-D plots which show the efficiency and authenticity of the proposed method. The under consideration method can also utilize to any other non-linear model appears in optics and engineering.

The transport of heat in an unsteady flow of nanofluid has been discussed in this paper. The flow is maintained over a non-flat, variably porous, and moving sheet in the present simulation. Moreover, the sheet has both constant temperature and nanoparticle concentration at its surface. Unsteadiness in all field variables (i.e. the flow, temperature, and nanoparticles concentration) has been produced by the time-dependent quantities, specified at the surface of the sheet (i.e. kinematics of sheet). The well-known Buongiorno model of nanofluid has been undertaken in the present studies. Furthermore, the simulation includes the particular cases of uniform and linear (polynomial & algebraic functions) stretching/shrinking and injection/suction velocities. Besides that observations of the same nature have been recorded for the non-flat sheet. The boundary layer form of the governing PDEs has been utilized to evaluate the exact status of field variables in the flow domain. Appropriate conditions are imposed by considering the geometry of the flow problem. The problem at hand is simplified by introducing a new set of functions for the field variables because of boundary inputs. After this treatment, the system of boundary value ODEs, which contains several dimensionless numbers (parameters), has appeared, and the problem is solved numerically for various values of parameters, involved in it. More precisely, effects of unsteadiness, wall roughness, surface deformation parameters, Prandtl, Schmidt, thermophoretic, and Brownian motion numbers have been seen on profiles of four field variables. Besides that, the numerical results are also obtained for the skin friction coefficient, Nusselt, and Sherwood numbers. The present modelled problem and its solution are compared with classical models of that particular nature and with their solutions.

ABSTRACTThis study conducts a comprehensive numerical investigation into enhancing thermal transfer within square enclosures filled with water-based oxide nanoparticle suspensions, subjected to central sinusoidal heating. Further flow configuration, influenced by an inclined magnetic field, is designed with a focus on enhancing thermal efficiency for engineering applications. Key innovations include the application of sinusoidal heating elements to enhance thermal performance significantly. Computational analysis supported by finite element analysis, quantifies the impact of these parameters on flow dynamics and thermal transmission, presenting a substantial advance in the understanding of nanofluid-filled enclosure thermal management. The study reveals that the undulation of the heating element plays a crucial role in the heat transfer rate, with improvements observed as undulation increases. The introduction of magnetic fields further controls flow distribution and buoyancy effects, as demonstrated by our findings that an increase in the Rayleigh number correlates with enhanced convection, dominating the cavity's thermal dynamics. Additionally, the report outlines the conditions under which the Nusselt number increases, indicating enhanced thermal performance. These insights are pivotal for designing optimized heat transfer systems and energy-efficient applications, setting a new benchmark for thermal management strategies in practical engineering contexts.

This work provides a brief comparative analysis of the influence of heat creation on micropolar blood-based unsteady magnetised hybrid nanofluid flow over a curved surface. The Powell–Eyring fluid model was applied for modelling purposes, and this work accounted for the impacts of both viscous dissipation and Joule heating. By investigating the behaviours of Ag and TiO2 nanoparticles dispersed in blood, we aimed to understand the intricate phenomenon of hybridisation. A mathematical framework was created in accordance with the fundamental flow assumptions to build the model. Then, the model was made dimensionless using similarity transformations. The problem of a dimensionless system was then effectively addressed using the homotopy analysis technique. A cylindrical surface was used to calculate the flow quantities, and the outcomes were visualised using graphs and tables. Additionally, a study was conducted to evaluate skin friction and heat transfer in relation to blood flow dynamics; heat transmission was enhanced to raise the Biot number values. According to the findings of this study, increasing the values of the unstable parameters results in increase of the blood velocity profile.

Nanofluids, which are fluid suspensions containing nanoparticles, exhibit remarkable property enhancements even at low nanoparticle concentrations. Much of the research on nanofluids aims to understand their behavior for applications where heat transfer enhancement is crucial, spanning industrial, nuclear, transportation, electronics, biomedical, and food sectors. Notably, nanofluids can act as smart fluids, allowing the controlled modulation of heat transfer. This paper presents an overview of recent advances in nanofluid research, covering preparation techniques and a wide range of existing and potential applications in energy, mechanical engineering, and biomedicine. Furthermore, the paper highlights avenues for future research in this field. In light of the findings, this paper concludes that a comprehensive hybrid model for understanding nanofluid thermophysical properties is lacking. Further exploration of heat transfer in nanofluids and additional research on unexplored aspects, particularly the applications of nanoparticles, are deemed essential. To capture the entirety of our study, the graphical abstract provides a comprehensive representation.

Abstract Background The oral health care-seeking behavior among prison inmates is an overlooked area, often leading to deteriorating general health due to the prisoners’ limited awareness of oral hygiene practices. It is crucial to address this issue and understand the factors associated with oral healthcare-seeking behavior in prisons. Objective To assess the oral healthcare-seeking behavior of prison inmates at Central Prisoner Jail, Peshawar Pakistan and to look the factors associated with their dental care utilization. Material and Methods This cross-sectional study was conducted at Central Prisoner Jail, Peshawar Khyber Pakhtunkhwa, Pakistan from November 2021 to April 2022. A consecutive sampling technique was used to collect data from both convicted and under-trial prisoners by using a pre-tested WHO Basic Oral Health Survey 2013 tool. Our outcome variable was “Visit to a dentist in the last 12 months (Never/Once or more than one visit). Chi-square test was used to determine univariate association with other explanatory variables while multivariable logistic regression was performed to adjust for potential confounders. Result A total of 225 participants were recruited to the study with a mean (SD) age of 32.9(11.4). More than two-thirds of 200(88.9%) of the participants were males. One-third of the sample never visited the dentist75(33.3). Participants who completed college/university education and never visited the dentist in the last 12 months constituted a smaller proportion (17.6%) compared to those who visited the dentist once or more than once n = 28(82.4%, p-value = 0.003). Individuals who were using toothbrushes were most frequently visiting the dentist n = 130(72.6%=p value = 0.001) as compared to never visitors. Multivariate logistic regression analysis revealed that Participants who experienced teeth pain or discomfort had 0.42 times lower odds of visiting the dentist compared to those who did not experience any pain or discomfort [AOR 0.42 (95% CI 0.17–0.80), p = 0.04]. Similarly, Participants who do not use any denture have 4.06 times higher odds[AOR 4.06(95% CI 1.76–9.36), p = 0.001] of visiting the dentist compared to those who use a denture. Conclusion Our result demonstrates that those prisoners who were experiencing tooth pain or discomfort and not using dentures were the strong predictors with lower dental visit frequency to seek oral health care.

Abstract Background The COVID-19 pandemic has revealed vulnerabilities in healthcare systems worldwide, emphasizing the importance of healthcare worker safety through adequate utilization of personal protective equipment (PPE). This study aims to assess the impact of pre-pandemic PPE training on the practices and other associated factors among frontline healthcare workers during the COVID-19 pandemic in Pakistan and provide insights into the implications of such training programs for future initiatives. Methods A cross-sectional study from May 9th to June 5th, 2020 was conducted among the frontline healthcare workers against COVID-19 in Pakistan, utilizing an online structured questionnaire shared via WhatsApp and Facebook by using purposive sampling. Statistical analyses, including chi-square tests for proportion and logistic regression for the association while multi-logistic regression for potential confounders, were performed using SPSS version 22. Results A total of 453 healthcare staff participated, with 68.9% (n = 312) reporting no prior PPE training and 31.1% (n = 141) having received training. Significant associations were found between prior training and healthcare group distribution (p = 0.006), with doctors exhibiting the highest proportion of training 82 (37.61%), followed by nurses 50 (27.32%) and paramedics 9 (17.31%). Those who didn’t receive any prior training in PPEs showed a higher perceived professional risk of 216 (69.23%) compared to those who received prior PPE training 96 (30.77%, p-value 0.005). Similarly, a higher frequency 137 (63.72%) of Perceived Personal risk was observed in those who didn’t receive training, labeled as “high risk” compared to those who were trained 78 (36.28%, P value 0.02). Multi-logistic regression analysis identified paramedics as 0.26 times less likely to have received prior PPE training (Adjusted OR 0.26, 95% CI 0.10–0.65, p = 0.01) compared to medical doctors. Healthcare workers in tertiary care hospitals were 0.46 times less likely to undergo PPE training (Adjusted OR 0.46, 95% CI 0.25–0.87,p = 0.01) compared to those working at COVID-19 facilities/hospitals/quarantine centers. Likewise, individuals who doffed disposable gowns [Adjusted OR 3.86, (95% CI, 1.23–12.08, p = 0.02] were 3.86 times more interested in getting prior training in PPE compared to those who don’t have skills to wear them. Conclusion Our findings highlight that healthcare levels, type of healthcare, and doffing skills are important predictors of whether healthcare workers have taken prior training in PPE. These findings imply developing effective training programs for healthcare workers to ensure safety while providing care during pandemics like COVID-19.

An ideal metasurface design encompasses the control of all the degrees of freedom of electromagnetic (EM) waves. Here, we present a digital coding metasurface optimized for dual-band operations (in K and Ka band) for orthogonal polarizations. The proposed design allows for independent and the simultaneous control of the phases and amplitudes of cross-polarized transmitted waves in both (±z) direction. We employ the design to demonstrate distinct functionalities, which include single and multiple vortex beams carrying orbital angular momentum (OAM) of orders ‘l=+1’ and ‘l=−1’, and beam shaping with the feature of intensity modulations in distinct operating bands. We tested a prototype of the proposed design in the standard EM measurement environment, and the measured results agree well with the corresponding simulations. We remark that the digital coding metasurfaces presented here should improve the capacity of multiple-input multiple-output (MIMO) optical communication systems for power allocations in multi-band operations.

As for spintronic applications, all information is encoded in the spin degree of freedom, so the key issues are the high spin-polarization, large half-metallic (HM) bandgap, large magnetic moment and higher Curie temperature than room temperature. In this paper, we studied halogen based CsYZ2 (Y = V or Cr; Z = F, Cl, Br or I) full-Heusler alloys. Our study reveals that CsVF2, CsVCl2, CsVI2 and CsCrI2 alloys are all HM ferromagnets while remaining alloys exhibits magnetic semiconductor behavior. The electronic structures indicate that these alloys open very large bandgaps in the spin-down channel. Following the Slater-Pauling rule Mt=Zt-16, these alloys exhibit very large total magnetic moments Mt of 4.00 μBf.u. when Y = V and 5.00 μBf.u. when Y = Cr. The high Curie temperatures indicate these alloys are appropriate for spintronic applications at room temperature. Furthermore, the absorption coefficient α(ω) of the CsVF2 alloy is better from other alloys in the visible region. The maximum ZT = 0.7 is obtained for CsCrBr2 and is an ideal candidate for thermoelectric (TE) applications.

Abstract Extraintestinal pathogenic Escherichia coli (ExPEC) producing extended-spectrum β-lactamases (ESBL) cause serious human infections due to their virulence and multidrug resistance (MDR) profiles. We characterized 144 ExPEC strains (collected from a tertiary cancer institute) in terms of antimicrobial susceptibility spectrum, ESBL variants, virulence factors (VF) patterns, and Clermont’s phylogroup classification. The developed multiplex recombinase polymerase amplification and thermophilic helicase-dependent amplification (tHDA) assays for bla CTX-M, bla OXA, bla SHV, and bla TEM detection, respectively, were validated using PCR-sequencing results. All ESBL-ExPEC isolates carried bla CTX-M genes with following prevalence frequency of variants: bla CTX-M-15 (50.5%) > bla CTX-M-55 (17.9%) > bla CTX-M-27 (16.8%) > bla CTX-M-14 (14.7%). The multiplex recombinase polymerase amplification assay had 100% sensitivity, and specificity for bla CTX-M, bla OXA, bla SHV, while tHDA had 86.89% sensitivity, and 100% specificity for bla TEM. The VF genes showed the following prevalence frequency: traT (67.4%) > ompT (52.6%) > iutA (50.5%) > fimH (47.4%) > iha (33.7%) > hlyA (26.3%) > papC (12.6%) > cvaC (3.2%), in ESBL-ExPEC isolates which belonged to phylogroups A (28.4%), B2 (28.4%), and F (22.1%). The distribution of traT, ompT, and hlyA and phylogroup B2 were significantly different (P

Abstract Background Diabetes Mellitus and periodontitis are chronic diseases with known reciprocal association. Studies have shown that uncontrolled diabetes increases the risk of development and progression of periodontal disease. This study aimed to explore the association and severity of periodontal clinical parameters and oral hygiene with HbA1c levels in non-diabetics and T2DM patients. Materials and methods In this cross-sectional study, the periodontal status of 144 participants, categorized into non-diabetics, controlled T2DM, and uncontrolled T2DM and were assessed via the Community Periodontal Index (CPI), Loss of Attachment Index (LOA index), and the number of missing teeth, while oral hygiene was measured by utilizing the Oral Hygiene Index Simplified (OHI-S). SPSS was used for data analysis. Chi-square test was used to find out the association of different independent variables with HbA1c groups, while ANOVA and post-hoc tests were run for inter-group and intra-group comparison respectively. Results Out of 144 participants, the missing dentition was prevalent in uncontrolled T2DM with mean 2.64 ± 1.97 (95% CI 2.07–3.21; p = 0.01) followed by controlled T2DM 1.70 ± 1.79 (95% CI 1.18–2.23; p = 0.01) and non-diabetics 1.35 ± 1.63 (95% CI 0.88–1.82; p = 0.01) respectively. Furthermore, non-diabetics had a higher proportion of CPI score 0 (Healthy) [30 (20.8%); p = 0.001] as compared to uncontrolled T2DM [6 (4.2%); p = 0.001], while CPI score 3 was more prevalent in uncontrolled T2DM in comparison to non-diabetics. Loss of attachment (codes-2,3 and 4) was also frequently observed in uncontrolled T2DM compared to non-diabetics (p = 0.001). Similarly, based on Oral Hygiene Index- Simplified (OHI-S), the result showed that poor oral hygiene was most commonly observed in uncontrolled T2DM 29 (20.1%) followed by controlled T2DM patients 22 (15.3%) and non-diabetic [14 (9.7%); p = 0.03]. Conclusion This study showed that periodontal status and oral hygiene status were deteriorated in uncontrolled T2DM patients compared to non-diabetic participants and controlled T2DM.

In this paper, our aim is to construct the novel wave structures for two non-linear evolution equations which are rising in non-linear optics, mathematical biological models, fluid dynamics, waves theory, mechanics, quantum mechanics, and many more. We employed an efficient analytical technique namely, the Sardar-subequation method to build the wave solutions for the modified Benjamin-Bona-Mahony equation and the Coupled Klein–Gordon equations. We have built the numerous type of soliton wave structures of modified Benjamin-Bona-Mahony equation and the Coupled Klein–Gordon equations via the Sardar-subequation method. Acquired results reveal the dynamics behavior of waves structures including the bright, singular, dark, and periodic singular solitons solutions. To illustrate the behavior of these solutions some selected solutions are sketched in two-, and three-dimensional graphs. On the basis of these results, our technique is suitable, up-to-date, and powerful. The obtained solutions are very efficacious and influential in non-linear optics, mathematical biology, mechanics, fluid mechanics, plasma physics, and many more. This study will assist to predict some new hypothesis and theories in the field of mathematical physics.

Magneto hydrodynamic flow over a wedge-shaped channel is often seen in geothermal applications, microelectronic, energy systems, and pumping systems. The slip condition is a normally recognized condition for the fluid flow occurs along lubricated, coated, wall channels. The best examples of such flows are the blood flow in arteries with plaque layer arteries. Flow configuration within channel is anticipated by using Carreau nanofluid injection at inlet. The intersecting wall of channel is inclined with respect to horizontal plane to make convergent angle of −α and divergent angle of α. The mathematical equations for energy and nanofluid concentration are established using well-known Navier–Stokes Equation with conservation equations invoking modified Buongiorno model. The temperature differences between the wall and core region leads to entropy. By using distinguishing amendments, system of equations are remodeled into system of ordinary differential equations (ODE's), which are numerically solved by Bvp4c technique of MATLAB for realistic conditions of under discussed phenomena. Simple shear close to the wall admit compression in local flow topology in zones of channel (divergent). The temperature field increases along thermal radiation, Brinkman number, nanoscale effects and heat source. Entropy is minimal and can be minimized with optimal angle and slip velocity.

The present study investigates heat transfer characteristics of power law fluid flow and energy transfer in a crown cavity with adjustment of the heated cylinder. The flow distribution is generated by the response of the heated cylinder. Additionally, the enclosure is partially adiabatic and the influence of cold interfaces is also taken. This whole setup generates a partial differential system in its mathematical translation. The numerical simulation is performed for the mathematical formulation by adopting the Galerkin Finite element method. The obtained results are analyzed against pertinent parameters of interest and presented via line graphs and contour maps for better explanation. The findings indicate that the K.E (energy motion) and Nu (Nusselt number) are higher for shear thinning While K.E energy motion) and Nu (Nusselt number) are higher is minimum for shear thickening.

IntroductionRecently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. MethodThis research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications.ResultsThe proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively.DiscussionThe experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases.

Background: Percutaneous access to the kidney with the patient in supine position was described in 1998. Although not frequently used in Pakistan there is a constant effort to adopt the supine position, primarily because it offers certain advantages over prone position. Objective: To present our experience in performing supine percutaneous nephrolithotomy and to compare our results with those reported in the international literature. Study type, settings & duration: A retrospective study was conducted at Federal Government Polyclinic hospital Islamabad from January 2015 to January 2020. Methodology: The medical records of 63 patients that underwent supine percutaneous nephrolithotomy were reviewed. The evaluated variables were: age, gender, body mass index, stone location and size, surgery duration, success rate, and complications. Results: A total of 63 patients and 70 kidneys were treated. The mean age of the patients was 41.7 years (16-76) with predominance of males 37 (58%) while there were 26 (42%) females. Mean body mass index was 25 m2 (18-34), mean stone size was 31 mm (12-80).Topography of the calculi was pelvic, 17 (24.2%); caliceal system; inferior 16 (22.8%), middle 3 (4.2%), superior 5 (7.14%). and mean duration of surgery was 80 min (30-160). The first intervention success rate was 90%, the second intervention success rate was 95%, and the complication rate was 11% (Clavien: I and II). Conclusion: Supine percutaneous nephrolithotomy is safe and efficacious, with a high success rate, low complication rate, and undisputable advantages of anesthesia management.

Fluid flows occur due to internal or external forces such as wind, gravity, pressure gradients, side-wall motion, MHD, and free convection. This study examines how meanders impact heat transfer by studying the behavior of viscous fluid flow with streamwise vortices in a sinusoidal wavy meandering channel of non-uniform radius. The study simplifies the motion and energy equations governing the fluid flow using novel transformations and a regular perturbation method. By plotting graphs for different parameter values, such as Pr, Re, and Ec, it reveals that decreasing the wavelength leads to flow separation near the channel surface. However, the stream moves forward with a sudden meander disturbance, causing the flow to become rectilinear and independent of vertex-generating centrifugal forces. The study identifies a stream function using standard and established relations. The fluid flow patterns and temperature distribution behavior are shown in various plots, highlighting the significant impact of meanders on fluid flow.

The present article emphasizes on Bioconvection (Cu– Al2O3–TiO2)/H2O modified nanoliquid flow in the crown cavity considering the impact of gyrotactic microorganisms. The bioconvection flow is examined in a crown cavity containing a square obstacle, oxytactic bacteria and occupied by modified nonliquid. In order to simulate the partial differential equation of the model, the finite element method is used. The results of the study are exhibited in the form of streamlines, isothermals and isoconcentrations of oxygen and microorganism. The Kinetic energy, Nusselt number, Sherwood number and density of motile microorganisms are also discussed. It has been noticed that the density of motile microorganisms is enhanced with the effect of the Bioconvection parameter, Lewis and Peclet numbers.

Coronavirus disease (COVID-19) has significantly affected the daily life activities of people globally. To prevent the spread of COVID-19, the World Health Organization has recommended the people to wear face mask in public places. Manual inspection of people for wearing face masks in public places is a challenging task. Moreover, the use of face masks makes the traditional face recognition techniques ineffective, which are typically designed for unveiled faces. Thus, introduces an urgent need to develop a robust system capable of detecting the people not wearing the face masks and recognizing different persons while wearing the face mask. In this paper, we propose a novel DeepMasknet framework capable of both the face mask detection and masked facial recognition. Moreover, presently there is an absence of a unified and diverse dataset that can be used to evaluate both the face mask detection and masked facial recognition. For this purpose, we also developed a largescale and diverse unified mask detection and masked facial recognition (MDMFR) dataset to measure the performance of both the face mask detection and masked facial recognition methods. Experimental results on multiple datasets including the cross-dataset setting show the superiority of our DeepMasknet framework over the contemporary models.

Background: Leishmaniasis is the second and fourth highest cause of mortality and morbidity respectively among all tropical diseases. Recurrence in the onset of leishmaniasis is a major problem that needs to be addressed to reduce the case fatality rate and ensure timely clinical intervention. Here we are investigating the association of risk factors with recurrent cutaneous leishmaniasis to address this issue. Material and methods: Patients received by Nasser Ullah Khan Babar Hospital in Peshawar, Pakistan from March 2019 to July 2020 were enrolled in this study. Those patients who developed symptoms after completion of treatment were included in Group-A while those who had atypical scars like leishmaniasis but were negative for cutaneous leishmaniasis were included in the comparison group tagged as Group B. All those individuals who had completed six weeks of treatment for CL but had normal complete blood counts (CBC) were included to avoid other underlying immunological pathologies, while we excluded those participants who had co-morbidities like diabetes, liver disease, cardiac disease, and pregnant and lactating women through their history Association was tested between Group-A and Group-B with other explanatory variables through chi-square test. The regression model was proposed to determine the predictors. Result: A total of 48 participants of both sexes were included in the study with a mean age of 32.2 ± 15.10. The data suggest that females are overrepresented among the patients with recurrent leishmaniasis [21(53.8 %,); p = 0.07]. Compared to patients; healthy participants had a higher proportion of adults (19–59 years) versus adolescents (13–18 years) [26(66.7 %) vs 07(17.9), p = 0.004]. Multivariate logistic regression analysis shows that females are 2.1 times more prone to infections among cases as compared to healthy individuals [unadjusted OR 2.20, 95 % confidence interval (CI) 1.5–10.6, p = 0.02; adjusted OR 2.1, 95 % CI 1.50–10.69, p = 0.02]. We propose that patients receiving intradermal were less likely to be infected as compared to those receiving intralesional injections [unadjusted OR 0.07.0, 95 % confidence interval (CI) 1.18–3.37, p = 0.03; adjusted OR 0.06, 95 % CI 1.18–3.38, p = 0.03]. Conclusion: Old age (adults) and sex (females) were the strongest predictors to be associated with recurrent leishmaniasis. Similarly, the choice of intradermal as compared to intralesional injection and the prolonged treatment duration were strongly associated with greater chances of recurrence.

This paper retrieves the optical solitons to the Biswas-Arshed equation (BAE), which is examined with the lack of self-phase modulation by applying the extended hyperbolic function (EHF) method. Novel constructed solutions have the shape of bright, singular, periodic singular, and dark solitons. The achieved solutions have key applications in engineering and physics. These solutions define the wave performance of the governing models. The outcomes show that our scheme is very active and reliable. The acquired results are illustrated by 3-D and 2-D graphs to understand the real phenomena for such sort of non-linear models.

Flow and heat transfer dynamics are considered quite complex phenomenon in different enclosures. Heat transport mechanism and its improvement due to the presence of nanoscale particles is vital topic of research. Owing to the widespread importance of free convection through fluids with nanoparticle in various configuration and constraint, we have addressed in this article the natural convection nanofluid flow in a hexagon shaped enclosure with heated cylinder. Simulation has been performed in the influence of porous medium and inclined magnetic field. The physical model is translated in terms of differential systems. For solutions purpose, the finite element method is utilized. The quantities of interest include the fluid flow and heat transfer aspects. The obtained results against flow controlling parameters are offered by means of contour plots and line graphs. It is noticed that the at lower Darcy number the heat transfer rates has diminishing behavior because of the higher porosity of medium. At higher Hartman number the velocity profile tends to decrease due to resistive nature of Lorentz forces. At maximum radius of heat source, the Nusselt number and velocity profile has higher values.

Several types of meandering channels and their mathematical simulation have been proposed and discussed widely in the open literature. In the present study, the impact of a novel meandering tube geometry on streamwise vortices and pressure losses have been determined. Using a simplified Poiseuille flow simulation approach with a sinusoidal wavy meandering tube of non-uniform radius, the onset flow separation, vertex formation, and the impact of Reynolds number on field variables and stream function has been analyzed. Moreover, the linear stability theory has been implemented to trace the vertex formation. A decrease in wavelength leads to flow separation near the tube’s surface, but the flow becomes rectilinear with a sudden disturbance caused by the meander, becoming independent of vertex generating centrifugal forces. Novel insights are provided on the impact of meandering tube geometry on fluid flow and potential applications for enhancing flow conditions are suggested.

In this study, our focus is on the construction of novel soliton solutions of non-linear Schrödinger equation with parabolic law and non-local law non-linearities via new extended hyperbolic function method. The acquired solutions are original and could be helpful in the field of nonlinear optics, fluid dynamics, and plasma physics. From these outcomes, it is believed that this method is a promising technique to handle a wide variety of such type of equations. For physical understanding and better realization of our constructed solutions, some of the obtained solutions under appropriate parameters are demonstrated by 2D and 3D plots. Analysis discloses that the proposed scheme is consistent and can be applied to more advanced models in engineering and physics.

Objective: The aim of current study was to examine the frequency of meningitis in infants having late onset sepsis (LOS). Bacterial sepsis and meningitis endure to be leading causes of illness and mortality in neonates around the world, predominantly in preterm neonates. They must be discovered and cured as soon as possible to avoid death or disability. Study Design: The Cross-Sectional method was used to conduct a study. Duration and Place of Study: The study period was July to December 2021. Data was collected from Pak Emirates Military Hospital (PEMH) NICU Rawalpindi. Material and Method: The study comprised a total of 110 individuals, both male and female. All of them were older than 3 days and had been hospitalized and diagnosed with late onset sepsis. A neonate was diagnosed with meningitis if his or her cerebrospinal fluid (CSF) had > 30 leukocytes/mm3 and any of these two: >200 mg/dl protein or 40 mg/dl glucose. Results: Out of 110 infants having LOS 38 were diagnosed with meningitis in which 21(55.2%) were male and 17(44.8%) were female. Overall mean weight of neonates was 2.38±1.72. A total of 24(63.1%) having low body weight and 22(57.8%) were premature. Meningitis was found to have a significant association with low bod weight and pre-term birth (p value

Background: Thalassemia is a set of inherited hematological illnesses caused by mutations in the genes that code for globin. Regular blood transfusions extend the lives of thalassemia patients, but they also increase the danger of blood-borne illness infections. Objective: The current study was conducted to check the frequency and risk factors of hepatitis B virus (HBV) infections among β thalassemia major patients. Methods: The study was conducted over a period of 3 months that is from November 2021 to February 2022 in the department of Thalassemia center CMH Rawalpindi. The study consists of thalassemia patients aged 6 months to 15 years including both male and female patients. Their venous blood was taken and transmitted to the laboratory, Armed force institute of pathology CMH Rawalpindi, for hepatitis B surface antigen test (HbsAg). Their demographic information was gathered, including their age, gender, number of blood transfusions received each year and Hepatitis B virus (HBV) vaccination status. Results: Out of 100 patients there were 45 patients who were male and 55 patients who were female. The patients' ages span from 6 months to 15 years, with a mean age difference of 11.20 years and a standard deviation of 5.56. In three months, the mean difference in the number of blood transfusions received by patients was 22.7 6.6 SD. Patients under the age of ten were 50 percent of the time, and patients beyond ten were also 50 percent of the time (50 percent). Five (5%) of the total 100 patients tested positive for hepatitis B. In three months, the mean difference in the number of blood transfusions received by patients was 18.2 5.6 SD. Thirty (30%) patients out of a total of 100 were not immunized. Conclusion: Frequency of hepatitis B in beta thalassemia major patients is minimal (less than 5 per 100) is seen in our study. To reduce the future risk of TTI, development of strong national and regional policies on safe blood transfusion procedures, VNRD-based transfusions, and universal quality-assured donor screening are recommended.

The SARS COV-2 and its variants are spreading around the world at an alarming speed, due to its higher transmissibility and the conformational changes caused by mutations. The resulting COVID-19 pandemic has imposed severe health consequences on human health. Several countries of the world including Pakistan have studied its genome extensively and provided productive findings. In the current study, the mCSM, DynaMut2, and I-Mutant servers were used to analyze the effect of identified mutations on the structural stability of spike protein however, the molecular docking and simulations approaches were used to evaluate the dynamics of the bonding network between the wild-type and mutant spike proteins with furin. We addressed the mutational modifications that have occurred in the spike protein of SARS-COV-2 that were found in 215 Pakistani's isolates of COVID-19 patients to study the influence of mutations on the stability of the protein and its interaction with the host cell. We found 7 single amino acid substitute mutations in various domains that reside in spike protein. The H49Y, N74K, G181V, and G446V were found in the S1 domain while the D614A, V622F, and Q677H mutations were found in the central helices of the spike protein. Based on the observation, G181V, G446V, D614A, and V622F mutants were found highly destabilizing and responsible for structural perturbation. Protein-protein docking and molecular simulation analysis with that of furin have predicted that all the mutants enhanced the binding efficiency however, the V622F mutant has greatly altered the binding capacity which is further verified by the KD value (7.1 E−14) and therefore may enhance the spike protein cleavage by Furin and increase the rate of infectivity by SARS-CoV-2. On the other hand, the total binding energy for each complex was calculated which revealed −50.57 kcal/mol for the wild type, for G181V −52.69 kcal/mol, for G446V −56.44 kcal/mol, for D614A −59.78 kcal/mol while for V622F the TBE was calculated to be −85.84 kcal/mol. Overall, the current finding shows that these mutations have increased the binding of Furin for spike protein and shows that D614A and V622F have significant effects on the binding and infectivity.

Breast cancer is a frequently encountered and potentially lethal illness that can affect not only women but also men. It is the most common disease affecting women globally, and is the main cause of morbidity and death. Early and accurate detection of this risky disease is crucial. A timely and precise identification of breast cancer can decrease death rate and can also protect people from additional damage. The traditional methods used for breast cancer detection are very expensive in term of time and cost. The goal of this study is to develop a system which can detect the breast cancer accurately and at an early stage. The primary objective of this research study is to make use of histopathological images to identify breast cancer correctly and faster. In the proposed research work, we have developed a model with the name Breast Cancer Detection Network (BCDecNet), which comprises eleven learnable layers, i.e., eight convolution layers and three fully connected (FC) layers. The architecture has a total of twenty-nine layers, including one input layer, seven leaky ReLu (LR) layers, four ReLu layers, five maximum-pooling layers, six batch-normalization (BN) layers, one cross-channel normalization layer and three dropout layers, a softmax layer, and a classification layer. The proposed work uses image-based data taken from the Kaggle online repository. The suggested model achieved 97.33% accuracy, 96% precision, 96.5% recall and a 96.25% F1 score. Furthermore, the result of the proposed model was compared with other hybrid approaches used for diagnosis of breast cancer at early stages. Our model achieved a more satisfactory result than all other approaches used for breast cancer detection. Additionally, the proposed BCDecNet model can be generally applied to other medical-image datasets for diagnosis of various diseases.

A significant issue in developing metal-catalyzed plastic polymer materials is obtaining distinctive catalytic characteristics to compete with current plastics in industrial commodities. We performed first-principle DFT calculations on the key insertion steps for industrially important monomers, vinyl fluoride (VF) and 3,3,3-trifluoropropene (TFP), to explain how the ligand substitution patterns affect the complex’s polymerization behaviors. Our results indicate that the favorable 2,1-insertion of TFP is caused by less deformation in the catalyst moiety of the complexes in contrast to the 1,2-insertion mode. In contrast to the VF monomer, the additional interaction between the fluorine atoms of 3,3,3-trifluoropropene and the carbons of the catalyst ligands also contributed to favor the 2,1-insertion. It was found that the regioselectivity of the monomer was predominated by the progressive alteration of the catalytic geometry caused by small dihedral angles that were developed after the ligand–monomer interaction. Based on the distribution of the 1,2- and 2,1-insertion products, the activity and selectivity were influenced by the steric environment surrounding the palladium center; thus, an increased steric bulk visibly improved the selectivity of the bulkier polar monomer (TFP) during the copolymerization mechanism. In contrast, better activity was maintained through a sterically less hindered Pd metal center; the calculated moderate energy barriers showed that a catalyst with less steric hindrance might provide an opportunity for a wide range of prospective industrial applications.

The current communication, manifest mathematical modelling and numerical computations of Sutterby nanofluids with radiant heat assessment subject to heat generation/absorption. The thermophoresis and Brownian motion effects are incorporated via the Buongiorno model in flow governing equations. Moreover, the present analysis reveals the impacts of thermal stratification, velocity slip, and a magnetic field on flow phenomena. The non-Newtonian nature is modelled using Sutterby fluid. The proposed model is formulated mathematically through basic partial differential equations relating mass, momentum, energy, and nanoparticle concentration conservations using boundary layer theory. We adapted the generated governed equations to ordinary differential equations utilizing similarity variables mechanism. Numerical treatment for the reduced system of ordinary differential equations is performed using the built-in MATLAB code bvp4c. The impacts of distinct characterizing parameters on velocity, temperature, and concentration profiles are determined and analyzed via graphs. The existence of velocity slip parameter, fluid flow is significantly dwindle, while the surface friction growth is sophisticated. Brownian and thermophoresis mechanisms degrade the heat transmission rate and escalate the mass flux. The thermal and solutal stratification exhibits opposite conduct for thermal and concentration of the nanoparticles.

Thin film flows over porous and moving sheets of variable thickness have been reconsidered here. New and multiple dimensions of classical problems of such type flow are analyzed. Here, we categorically emphasized on the nature and kinds of injection (suction) and moving velocities of the sheet, whereas, variable size of the sheet is also taken into account. We formed and investigated different cases and checked different options for kinematics of sheet, variable sizes of thin film and that of sheet. All possible cases of exact similarities are noticed by which the system of partial differential equations and boundary conditions are exactly transformed into ordinary differential equations. The final systems of exact equations are solved by bvp4c technique. The present simulations are exactly matched with the previously published analyses for special choices of functions and parameters. Strict behavioral changes have been observed in the velocity profiles by changing the nature of sheet’s kinematics.

Purpose: To timely manage supply chain disruptions, experts have focused their attention on the impact of COVID-19 on industries worldwide. Epidemic outbursts are a specific supply chain risk with long disruption propagation, disruption persistence, and high uncertainty. This study aimed to investigate the role of R&D investment and firm performance in mediating the relationship between disruption risk and supply chain performance in Pakistani manufacturing industries and supply chain employees during the recovery phase of the COVID-19 pandemic via the application of the dynamic capability theory.Methodology: From 21 July 2020 to 23 August 2020, 318 employees from supply chains of manufacturing industries in Rawalpindi and Islamabad, Pakistan, participated in this cross-sectional online web-based survey. The four standard research scales were used to examine the research and development, disruption risk, firm, and supply chain performance. The response link was distributed to respondents via Facebook, WhatsApp, and email. The study analyzed the data using structural equation modeling and a partial least squares technique.Results: The study’s findings suggest that disruption risk, research and development investment, and firm performance all improve supply chain performance, but the mediation effect is unsupported by the data. These measures help plan a better supply chain in the face of disruption risk. They provide one of the timely empirical conclusions on the role of R&D investment in mitigating risk disruptions and improving supply chain performance.

OBJECTIVES The study’s objective was to implement a methodological approach, "Acute Physiological and Chronic Health Evaluation II (APACHE-II),” to classify critically ill patients based on severity. METHODOLOGY A retrospective study design was conducted at Shaukat Khanum Memorial Cancer Hospital Lahore, Pakistan, from May 2019 to May 2020. A pilot study of 6 months was conducted by reviewing the medical record of 30 adult patients following convenient sampling after the approval of the proposal by ASRB/IRB of Shaukat Khanum hospital. The record of both male and female patients was studied, while no record of paediatric or adult patients outside the ICU was studied. Each patient’s severity score was obtained using the patient’s parameters with the help of the APACHE-II table. RESULTS Among the patients, three out of 30 scored 25, 29 and 30 on APACHE-II. These patients later passed away in ICU. This indicates that the mortality rate increases with an increase in the APACHE-II score. Thus, the scoring system is very beneficial in predicting adult ICU patients' mortality rate. CONCLUSION It is concluded that APACHE II is one of the best severity scoring systems in predicting the critical condition of patients.

In this paper, we work on the Cauchy problem of the three-dimensional micropolar fluid equations. For small initial data, in the variable-exponent Fourier–Besov spaces, we achieve the global well-posedness result. The Littlewood–Paley decomposition method and the Fourier-localization technique are main tools to obtain the results. Moreover, the results discussed in our work show the Gevrey class regularity of solution to the Cauchy problem of micropolar fluid equations.

Accurate diagnosis of the brain tumor type at an earlier stage is crucial for the treatment process and helps to save the lives of a large number of people worldwide. Because they are non-invasive and spare patients from having an unpleasant biopsy, magnetic resonance imaging (MRI) scans are frequently employed to identify tumors. The manual identification of tumors is difficult and requires considerable time due to the large number of three-dimensional images that an MRI scan of one patient's brain produces from various angles. Moreover, the variations in location, size, and shape of the brain tumor also make it challenging to detect and classify different types of tumors. Thus, computer-aided diagnostics (CAD) systems have been proposed for the detection of brain tumors. In this paper, we proposed a novel unified end-to-end deep learning model named TumorDetNet for brain tumor detection and classification. Our TumorDetNet framework employs 48 convolution layers with leaky ReLU (LReLU) and ReLU activation functions to compute the most distinctive deep feature maps. Moreover, average pooling and a dropout layer are also used to learn distinctive patterns and reduce overfitting. Finally, one fully connected and a softmax layer are employed to detect and classify the brain tumor into multiple types. We assessed the performance of our method on six standard Kaggle brain tumor MRI datasets for brain tumor detection and classification into (malignant and benign), and (glioma, pituitary, and meningioma). Our model successfully identified brain tumors with remarkable accuracy of 99.83%, classified benign and malignant brain tumors with an ideal accuracy of 100%, and meningiomas, pituitary, and gliomas tumors with an accuracy of 99.27%. These outcomes demonstrate the potency of the suggested methodology for the reliable identification and categorization of brain tumors.

Abstract This report is devoted to the study of the flow of MHD nanofluids through a vertical porous plate with a temperature-dependent surface tension using the Cattaneo–Christov heat flow model. The energy equation was formulated using the Cattaneo–Christov heat flux model instead of Fourier’s law of heat conduction. The Tiwari–Das model was used to take into account the concentration of nanoparticles when constructing the momentum equation. The problem is described mathematically using the boundary layer approach as a PDE, which is then converted into an ODE with the help of the transformation process. The solution finding process was completed by running the bvp4c code in MATLAB. A quantitative analysis of the influence of some newly occurring parameters on physical quantities was carried out using graphics. The addition of nanoparticles to the base fluid leads to an increase in both skin friction and thermal conductivity. The increase in thermal conductivity is the advantage, while the increase in skin friction is the disadvantage of the nanoparticle concentration. Marangoni convection has proven to be one of the most cost-effective tools available that can reduce skin friction. Marangoni convection improves the heat transfer coefficient during suction but decreases the heat transfer coefficient during the injection.

In wave theory, the higher dimensional non-linear models are very important to define the physical phenomena of waves. Herein study we have built the various solitons solutions of (4+1)-dimensional fractional-order Fokas equation by using two analytical techniques that is, the Sardar-subequation method and new extended hyperbolic function method. Different types of novel solitons are attained such as, singular soliton, bright soliton, dark soliton, and periodic soliton. To understand the physical behavior, we have plotted 2D and 3D graphs of some selected solutions. From results we concluded that the proposed methods are straightforward, simple, and efficient. Moreover, this paper offers a hint, how we can convert the fractional-order PDE into an ODE to acquire the exact solutions. Also, the proposed methods and results can be help to examine the advance fractional-order models which seem in optics, hydrodynamics, plasma and wave theory etc.

Crop pests are to blame for significant economic, social, and environmental losses worldwide. Various pests have different control strategies, and precisely identifying pests has become crucial to pest control and is a significant difficulty in agriculture. Many agricultural professionals are interested in deep learning (DL) models since they have shown significant promise in image recognition. Pest identification approaches in literature have relatively low accuracy in pest recognition and classification due to the complexity of their algorithms and limited data availability. Misclassification of insect pests sometimes leads to using the wrong pesticides, causing harm to agricultural yields and the surrounding environment. It necessitates developing an automated system capable of more accurate pest identification and classification. This paper presents a novel end-to-end DeepPestNet framework for pest recognition and classification. The proposed model has 11 learnable layers, including eight convolutional and three fully connected (FC) layers. We used image rotations techniques to increase the size of the dataset and image augmentations techniques to test the generalizability of the proposed DeepPestNet approach. We used the popular Deng’s crops data set to assess the proposed DeepPestNet framework. We used the proposed method to recognize and classify crop pests into 10-class pests, i.e., Locusta migratoria, Euproctis pseudoconspersa strand, chrysochus Chinensis, empoasca flavescens, Spodoptera exigua, larva of laspeyresia pomonella, parasa lepida, acrida cinerea, larva of S. exigua, and L.pomonella types of insects pests. The proposed method achieved optimal accuracy of 100%. We compared the proposed DeepPestNet approach with traditional pre-trained deep learning (DL) models. To verify the general adaptability of this model, we tested the proposed model on the standard Kaggle dataset “Pest Dataset” to recognize nine types of pests: aphids, armyworm, beetle, bollworm, grasshopper, mites, mosquito, sawfly, and stem borer and achieved an accuracy of 98.92%. The proposed model can provide specialists and farmers with immediate and effective aid in recognizing pests, potentially reducing economic and crop yield losses.

Looking for the exact solutions in the form of optical solitons of nonlinear partial differential equations has become very famous to analyze the core structures of physical phenomena. In this paper, we have constructed some various type of optical solitons solutions for the Kaup-Newell equation (KNE) and Biswas-Arshad equation (BAE) via the generalized Kudryashov method (GKM). The conquered solutions help to understand the dynamic behavior of different physical phenomena. These solutions are specific, novel, correct and may be beneficial for edifying precise nonlinear physical phenomena in nonlinear dynamical schemes. Graphical recreations for some of the acquired solutions are offered.

Abstract The current study characterizes the effects of Hall current, Arrhenius activation energy and binary chemical reaction on the rotating flow of hybrid nanofluid in two double disks. By the use of suitable similarity transformations, the system of partial differential equations and boundary conditions for hybrid nanofluid are transformed to ordinary differential equations which are solved through optimal homotopy analysis method. The intensified magnetic field and hybrid nanofluid performances are represented in three dimensional model with flow, heat and mass transfer. Radial velocity decreases and tangential velocity increases with the Hall parameter. Temperature rises with high values of rotation parameter while it decreases with the Prandtl number. Nanoparticles concentration enhances with the increments in Arrhenius activation energy parameter and stretching parameter due to lower disk. There exists a close and favorable harmony in the results of present and published work.

Background: Camel is vital to daily life as a source of food and means of transportation; and just as importantly, its milk and urine have been used as medicines for diverse ailments since ancient times for people in the desert region. Aim: Current study was planned to investigate the effect of different watering regimes on physiological and haematological parameters of lactating Marecha camel during the summer season. Methods: For this purpose, 12 lactating she-camels of almost similar age (8-13 years) and parity (2-5) were selected in a completely randomized design as four animals per treatment at Camel Breeding and Research Station (CBRS) Rakh Mahni District Bhakkar. The trial was completed in 60 days, while seven days were given as an acclimatization period for adaptation. The treatments were: Group-1 (G-1) considered as control having access to water once in a day; Group-2 (G-2) access to water once in four days and Group-3 (G-3) access to water once in six days. Animals were housed individually for feeding and watering in an intensive management system (IMS). The same feed consisting of green fodder (Lucerne) and gram straw (Cicer arientinum) was offered to all animals. Average ambient temperature and relative humidities were 39-41°C and 55-63%, respectively. Result: The mean values (P

Corporate governance is a set of rules, regulations, procedures, processes, and practices through which an organization is controlled and directed. The present study aimed to examine the monitoring methods used in Islamic banking, including standardized measures for better performance, an individual’s aptitude towards Islamic financial markets, risk propensity, and the level of efficiency of the Islamic banking industry in Pakistan and Malaysia. There is room to improve monitoring systems for Islamic banking operations and standardized measures could improve efficiency, leading to more sustainable performance. The study uses a self-developed semi-structured scale based on literature and expert interviews, after content and context validity to gain a wide range of diverse information. In Pakistan and Malaysia, individuals’ perceptions are different because of differences in the banking environment and preferences. Eventually, the Islamic banking growth rate may differ in Pakistan and Malaysia. Thus, there should be regular monitoring to improve banking performance. Similarly, standardized measures for Islamic banking operations and governance performance in Pakistan and Malaysia will result in more sustainable performance. The antecedents of Islamic corporate governance could be improved to enhance banking performance, which helps individuals make decisions based on available product information. The business growth of the banking industry is based on convenient monitoring policies, standardized performance measures, and, most importantly, excellent corporate governance mechanisms. Improved monitoring measures will further enhance these business operations.

Objective: To determine the vaccination refusal rate, associated factors and perceptions of parents who refused routine immunization for their children using the World Health Organization (WHO) SAGE Working Group on Vaccine Hesitancy Survey Tool. Materials and methods: A cross-sectional survey was conducted using multi-stage cluster sampling in Bannu District, Khyber Pakhtunkhwa Province, Pakistan from March 2019 to July 2019. A WHO validated questionaire was used. The outcome variable was parental refusal of routine immunization of their children. Logistic regression was performed for associations, and multi-regression was applied to identify any confounders. Results: Of 610 parents, 170 (27.9%) refused vaccination of their children. Of these, the majority of mothers had no education [n = 145 (85.3%); p = 0.03], and mothers were less likely to own a mobile phone than fathers [24 (14.1%) vs 152 (89.4%); p ≤ 0.001]. The vaccination refusal rate was higher in parents with food security [n = 88 (51.8%)] compared with parents with minimal food insecurity [n = 62 (36.5%)] and high food insecurity [20 (11.8%); p ≤ 0.05)]. On multi-variate logistic regression, fathers who were employed [adjusted odds ratio (OR) 0.59, 95% confidence interval (CI) 0.37–0.94; p = 0.02] and had a high level of education (adjusted OR 0.21, 95% CI 0.08–0.50; p ≤ 0.001) were less likely to refuse vaccination of their children. Parents with high food insecurity were more likely to refuse vaccination of their children (adjusted OR 2.2, 95% CI 1.0–0.50; p = 0.04) compared with parents with minimal food insecurity (adjusted OR 1.6, 95% CI 1.0–2.5; p = 0.02). Conclusion: The vaccination refusal rate was very high among parents, and this was associated with inability to read or write, no education, owning a mobile phone, unemployment and food security.

The study aimed to analyze the role of information & communication technologies in teachers’ motivation, professional skills and performance at public sector universities in Pakistan. The study was survey type and descriptive in nature. Probability sampling technique was adopted for data collection. Population of the study comprised of lecturers, assistant professors, associate professors and professor from 5 public sector universities. Sample of the study consisted of 20 professors, 40 associate professors, 80 assistant professors and 160 lecturers’ total 300 faculty members randomly selected from 5 public sector universities of Pakistan. The questionnaire and observation sheet as research tools were used and finalized after expert’s opinions and its pilot testing. The collected data was analyzed through SPSS. It was found that 59% of respondents expressed that latest information & communication technologies are available for university teachers in public sector universities, majority of respondents stated that digital technologies have significant role in developing teachers’ motivation, professional skills and performance.

In this article, we investigate novel optical solitons solutions for the Lakshmanan–Porsezian–Daniel (LPD) equation, along with group velocity dispersion and spatio-temporal dispersion, via three altered analytical techniques. A variety of bright, singular, dark, periodic singular, and kink solitons solutions are constructed via the Kudryashov method, the generalized tanh method and the Sardar-subequation method. The dynamical behavior of the extracted solutions is demonstrated in graphical form such as 3D plots, 2D plots, and contour plots. The originality of the obtained solutions is recognized by comparison with each other and solutions previously stated in the literature for the LPD model, which displays the efficiency of the methods under consideration.

Breast cancer causes hundreds of women’s deaths each year. The manual detection of breast cancer is time-consuming, complicated, and prone to inaccuracy. For Breast Cancer (BC) detection, several imaging methods are explored. However, sometimes misidentification leads to unnecessary treatment and diagnosis. Therefore, accurate detection of BC can save many people from unnecessary surgery and biopsy. Due to recent developments in the industry, deep learning’s (DL) performance in processing medical images has significantly improved. Deep Learning techniques successfully identify BC from ultrasound images due to their superior prediction ability. Transfer learning reuses knowledge representations from public models built on large-scale datasets. However, sometimes Transfer Learning leads to the problem of overfitting. The key idea of this research is to propose an efficient and robust deep-learning model for breast cancer detection and classification. Therefore, this paper presents a novel DeepBraestCancerNet DL model for breast cancer detection and classification. The proposed framework has 24 layers, including six convolutional layers, nine inception modules, and one fully connected layer. Also, the architecture uses the clipped ReLu activation function, the leaky ReLu activation function, batch normalization and cross-channel normalization as its two normalization operations. We observed that the proposed model reached the highest classification accuracy of 99.35%. We also compared the performance of the proposed DeepBraestCancerNet approach with several existing DL models, and the experiment results showed that the proposed model outperformed the state-of-the-art. Furthermore, we validated the proposed model using another standard, publicaly available dataset. The proposed DeepBraestCancerNet model reached the highest accuracy of 99.63%.

Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs, computed tomography (CT) scan, and electrocardiogram (ECG) trace images are the most widely known for early discovery and analysis of the coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i.e., binary, three-class, and four-class classifications. In addition, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i.e., binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 Radiography Database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms state-of-the-art DL approaches, such as Squeezenet, Alexnet, and Darknet19, by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.

Abstract Background In recent years, ecofriendly compounds such as chitosan has been used to alleviate the destructive effects of salt stress. Chitosan is a natural biodegradable compound with no toxicity in nature and act as a stress tolerance inductor involved in physiological processes and prevent water loss through transpiration. Tomato cv. Rio Grande grown in pots was subjected with salinity stress in the form of 4 levels (0, 50, 100 and 150 mM) whose effect was mediated by treating it with different concentration of chitosan (0, 50, 100 and 150 mg L−1). Results The data revealed that various application of salinity had a negative effect on almost all the studied parameters. Tomato plants treated with distilled water having no salinity (control) recorded maximum plant height (cm), average number of compound leaves plant−1, leaf area (cm2), stem diameter (mm), number of fruits plant−1, fruit firmness (kg cm−2), leaf chlorophyll content (SPAD), fruit juice pH, yield plant−1 (kg) and minimum total soluble solids (Brix°). Whereas, minimum plant height (cm), average number of compound leaves plant−1, leaf area (cm2), stem diameter (mm), number of fruits plant−1, fruit firmness (kg cm−2), leaf chlorophyll content (SPAD), fruit juice pH, yield plant−1 (kg) and maximum total soluble solids (Brix°) were found in plants treated with salinity level of 150 mM. Chitosan concentration of 150 mg L−1 significantly mediated the effect of salinity stress and recorded maximum plant height (cm), average number of compound leaves plant−1, leaf area (cm2), stem diameter (mm), number of fruits plant−1, fruit firmness (kg cm−2), leaf chlorophyll content (SPAD), total soluble solids (Brix°) and yield plant−1 (kg) with minimum fruit juice pH. Conclusion It is concluded that foliar application of chitosan at the rate of 150 mg L−1 and salinity stress 150 mM could have positive impact on performance of tomato.