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This research investigates the enhancement of maternal psychological health during pregnancy through the integration of E-health informatics and quantum photonics for security. The study reviews interventions and outcomes related to E-health technologies in maternal mental health, exploring intersections with quantum photonics for enhanced security measures. The primary objective is to assess the efficacy of E-health interventions in promoting maternal well-being, with an innovative link to quantum photonics for security. The study encompasses algorithmic approaches and predictive modeling, exploring potential synergy with quantum photonics for securing healthcare data. Using a systematic approach, publicly available datasets, including Kaggle, are employed. Data preprocessing addresses missing values, encodes categorical variables, and scales features. Eight machine learning algorithms are deployed for predictive modeling. Evaluation reveals distinctive performances among algorithms, with Random Forest leading in accuracy, precision, and recall. Quantum photonics integration is explored, laying the groundwork for securing health data. In conclusion, the study highlights Random Forest's potential in predicting psychological health risks, and integrating quantum photonics introduces innovative security measures. Future directions include refining predictive pathways, exploring additional features, and validating with diverse datasets. Advanced mathematical calculations, algorithmic enhancements, and deeper integration of quantum photonics are suggested to contribute to evolving digital health interventions and innovative studies in health prediction and data security.

Constructed wetlands (CWs) are considered one of the most efficient solutions for treating wastewater and reusing water resources. The present study aimed to monitor the performance of an integrated constructed wetland (ICW) located at NUST, H-12 campus, Islamabad. It has a sedimentation tank for pre-treatment, eight ponds planted with different vegetation (Typha latifolia, Pistia stratiotes, and Centella asiatica), and a FILTER (filtration and irrigated cropping for land treatment and effluent reuse) technology. The objectives of the present study were divided into the analysis of the physicochemical parameters, microbial analysis, and comparison with the past studies on the same ICW. The samples for physicochemical and microbial analysis were collected from four points, i.e., the inlet, sedimentation tank, pond 8, and collection tank. The results indicated that average temperature, pH, and DO range between 23.95-24.20, 6.95-7.20, and 2.25-3.10, respectively. Whereas other parameters showed the removal efficiency as follows: EC 10%, turbidity 73.99%, TSS 79.16%, TDS 32.60%, TS 47.22%, COD 69.01%, NO3-N 52.76%, NO2-N 50.53%, TKN 63.50%, and PO₄³⁻ 41.24%. Gram-negative bacteria with dominating microbial species such as Salmonella, Shigella, and E. coli were found to have a removal rate of up to 95%. The findings indicate that ICW is effective at removing pathogenic microorganisms, organic contaminants, nutrients, and maintaining temperature and pH changes in the water. Moreover, the comparison with past studies showed that the performance of ICW has been maintained over the years.

A 4 × 4 Butler Matrix (BM) without phase shifter is proposed to provide bandwidth enhancement and size reduction by removing a conventional 0 dB/90° crossover, two 0° phase shifters and two 45° phase shifters. Two proposed 3 dB/45° patch couplers replace the combination of two conventional 3 dB/90° branch-line couplers and two 45° phase shifters, whilst a modified 0 dB/0° crossover removes two 0° phase shifters from the conventional BM. The −10 dB fractional bandwidths of reflection coefficients and isolations for the proposed BM are 18.46% in simulation and measurement. The −6 dB ± 1.5 dB average transmission coefficient and +45°, −135°, +135° and −45° of output phase differences with 1.8° average phase imbalance are accomplished at 6.5 GHz. The simulated and measured scattering parameters (S-parameters) and phase differences are in close compliance with each other. Four switched-beams pointed at +15°, −50°, +50° and −15° are developed by integrating the proposed BM with inset feeding patch antennas. The proposed BM is miniaturized by 51.56%, whilst the bandwidth performance is enhanced by 9.7% compared to the conventional BM. Hence, this compact BM is a promising candidate for the fifth generation (5G) communication system.

Wireless sensor networks (WSN) are low-resource devices that run on small batteries. The availability of battery energy, device drive cycles, and environmental conditions all have an impact on node lifetime. The state of charge (SoC) is an important factor in determining the amount of energy available in the batteries. Accurate SoC estimation is critical for device lifetime prediction and safe device operation. We present a novel approach for adaptive SoC estimation based on Gaussian Process Regression in this paper (GPR). The training data was obtained in a climate-controlled laboratory setting by using IEEE 802.15.4-based drive loads at various temperatures for three different batteries such as Lithium-Ion, Nickel-metal hydride, and Lithium-Polymer. To estimate the SoC, battery parameters such as voltage, capacity, and temperature were directly mapped to the corresponding models. For each battery parameter, the GPR model with hyper tuned Radial Bias Filter (RBF) was trained at temperatures ranging from 5 °C to 45 °C. For model accuracy, the proposed scheme was compared to polynomial regression and support vector machines (SVM). In this regard, the proposed model provided Mean Absolute Error (MAE) values of 2.53 percent, 2.54 percent, and 2 percent, respectively, and Root Mean Square Error (RMSE) values of 0.295, 0.292, and 0.35 for Nickel-metal hydride, Lithium-Polymer, and Lithium-Ion batteries at 25 °C. Our proposed lightweight GPR scheme is, to the best of our knowledge, the only active implementation on embedded platforms for SoC estimation of WSN. Finally, the model was rigorously tested on ARM Cortex M4-based microcontrollers to report real-time online SoC estimation on WSN nodes.

Low-speed wind tunnel experiments are conducted to study the aerodynamic performance of a half-span delta wing with 45° leading-edge sweep at subsonic flow regime. The experiments are carried out at a Reynolds number of 8.37 × 105, a free-stream Mach number of 0.1 and angles of attack up to 25°, in steps of 5°. The test model was designed with thirty-two pressure taps fixed on its surfaces (sixteen on each side). Multi-tube manometers were connected to these taps using long tubes to enable recording the pressure readings. Surface pressure distributions and aerodynamic characteristics were calculated at different span-wise locations along the non-dimensional chord-wise distance. Results exhibited that most lift on the studied wing is generated in the region close to the leading edge for all the studied incidence angles. Additional lift is created in the region close to the root chord rather than the tip chord, whereas drag forces increases from tip to root. This can be attributed to the formation of trailing edge vortexes due to the flow separation at the wing leading edge that produces more drag, hence suppressing lift. The study showed also that angle of attack increases the drag coefficient from tip to root, especially at high angle of attack, indicating unfavourable behaviour for manoeuvring. Moreover, the angle of attack increased the pitching moment coefficient up to 10° before it drops sharply until it reaches the tip of the wing model.

In the presented research article, the intelligence based numerical computation of artificial neural network backpropagated with Levenberg-Marquardt algorithm has been developed to analyze the novel ferrofluid flow model in the presence of magnetic dipole. Heat transfer effects are also incorporated along the horizontal. The designed fluid flow model initially represented by system of partial differential equations are converted into system of non-linear ordinary differential equations through suitable similarity transformations. The reference dataset of the possible outcomes is obtained from Adam numerical solver for the different scenarios of flow model by variation of co-efficient of the thermal expansion, Eckert number, suction parameter, magnetization and radiation parameter. The approximated solutions are interpreted for designed model by testing, training and validation process of backpropagated neural networks. Furthermore, the comparative studies and performance analysis of used algorithm is validated through regression analysis, histogram studies, correlation index and results of mean square error.

In this paper, a computational approach based on the operational matrices in conjunction with orthogonal shifted Legendre polynomials (OSLPs) is designed to solve numerically the multi-order partial differential equations of fractional order consisting of mixed partial derivative terms. Our computational approach has ability to reduce the fractional problems into a system of Sylvester types matrix equations which can be solved by using MATLAB builtin function lyap(.). The solution is approximated as a basis vectors of OSLPs. The efficiency and the numerical stability is examined by taking various test examples.

New and novel heavy metal ion selective organic-inorganic composite CNTs reinforced polyaniline@Zn-CuO was expediently obtained by the sol-gel method. Conducting monomer, aniline was polymerized by in situ chemical oxidation. The CNT reinforced polyaniline@Zn-CuO was used for the preparation of ion-selective membrane. The prepared membrane was characterized for porosity, swelling, and find suitable to be used for the preparation of modified ion-selective membrane electrode. The prepared electrode was calibrated with different metal ions and found suitable selective for Hg2+ ions. The electrode showed excellent results as the limit of detection (1 × 10−6 M), working conc range (1 × 10−1 M to 1 × 10−6 M), pH working range (3–6), and response time 17 Sec with room temperature stability two and half month while 30.2 mV is the Nerstian slope obtained by per decade change in metal ion concentration of Hg2+. The practical utility of the proposed membrane sensor assembly was tested by its use as an indicator electrode in the potentiometric titrations.

Breast tumor is one of the most prominent indicators for the diagnosis of breast cancer. The precise segmentation of tumors is crucial for enhancing the accuracy of breast cancer detection. A physician’s assessment of the MRI scan is time-consuming and require a lot of human effort and expertise. Furthermore, traditional medical segmentation approaches frequently need prior information or manual feature extraction, resulting in a subjective diagnosis. Therefore, the development of an automated image segmentation approach is essential for clinical applications. This work presents BTS-GAN, an automatic breast tumor segmentation process using conditional GAN (cGAN) in Magnetic Resonance Imaging (MRI) scans. First, we used an encoder-decoder deep network with skip connections between encoder and decoder for the generator to increase the localization efficiency. Second, we utilized a parallel dilated convolution (PDC) module to retain the features of various sizes of masses and to effectively extract information about the masses’ edges and interior texture. Third, an extra classification-related constraint is included to the loss function of the cGAN for mitigating the hard-to-converge challenge in image-to-image (I2I) translation tasks based on classification. The generator side of our proposed model learns to detect the tumor and construct a binary mask, while the discriminator learns to distinguish between ground truth and synthetic masks, driving the generator to produce masks as genuine as possible. The experimental results demonstrate that our BTS-GAN is more efficient and reliable for breast tumor segmentation and outperform other segmentation techniques in terms of the IoU and Dice coefficient on the publicly available RIDER breast cancer MRI dataset. Our proposed model achieved an average IoU and Dice scores of 77% and 85% respectively.

We scrutinized the influence of nonlinear heat radiation on heat transmission evaluation of Carreau nanofluid and tangent hyperbolic nanofluid streams across a wedge with gyrotactic microorganisms by taking slip situations into consideration in this research article. The necessary nonlinear partial differential formulation is transmuted into non-linear ordinary differential equations by employing appropriate similarity variables, and these equations, including the boundary constraints are resolved in Matlab software utilizing Runge-Kutta fourth order via shooting tactic. A definite description of the framework is achieved by fluctuating the inputs of influential variables of the dependent functions and exhibited via graphs. The inhibiting flow velocity is portrayed by the intensifying inputs of buoyancy ratio, magnetic force, Rayleigh number, and eigenvalue. As a consequence of thermophoresis and Brownian motion of nano-particles, the temperature of the liquids initiates to ascend instantly. Because of differentiated viscous effects, the flow velocity for Carreau nanofluid is slower than that of tangent hyperbolic fluid and the temperature behavior is reversed. Further, the magnitude of skin friction factor for tangent hyperbolic nanofluid is almost half ofs that of Carreau nanofluid.

The present work is focused on enhancing the overall thermo-hydraulic performance of a previously proposed C-shaped printed circuit heat exchanger (PCHEs) using Machine Learning (ML) Algorithms. In this context, CFD analysis is carried out on 81 different channel configurations of the C-shaped channel geometry, and computed data is used to train three ML algorithms. Later, C-shaped channel geometry is optimized by coupling the trained ML model with the multi-objective genetic algorithm (MOGA). Finally, the optimized channel geometry (called optimizedML) is investigated numerically for a wide range of Reynolds numbers. Its performance is compared with the zigzag geometry, C-shaped base geometry, and previously optimized C-shape channel geometry using response surface methodology (RSM). The findings showed that the multilayered approach combining MOGA, CFD, and machine learning techniques is beneficial to accomplish a robust and realistic optimized solution. Comparing the thermo-hydraulic characteristics of the optimizedML channel geometry with zigzag channel geometry shows that the former is up to 1.24 times better than the latter based on the performance evaluation criteria (PEC). Furthermore, the overall performance of the optimizeML channel geometry was found up to 21% and 16% higher than the optimized RSM geometry on the cold and hot sides, respectively.

Asphalt pavement’s surfaces deteriorate over time due to combined effect of traffic and surrounding environment. Fatigue and rutting are the major distresses which cause failures in flexible pavements. Different temperature control computer operated equipment’s are being used worldwide to predict the performance of asphalt mixtures at approximately same condition to those in-service pavements. Similarly, different types of polymers such as elastomer and thermoplastic have been used all over the world in Hot Mix Asphalt (HMA) for the improvement of asphalt mixtures. But little attention has been taken to evaluate the effect of plastomer on hot mix asphalt performance. Moreover, the initial cost of elastomer is higher than other types of polymers such as plastomer. The aim of this research study is to check the effect of various plastomers on high/low temperature performance of asphalt mixture. Four performance tests like Cooper wheel tracker, dynamic modulus, uniaxial repeated load and four-point bending beam test are used to evaluate the effect of different type of plastomers such as polyethylene terephthalate, high density and low density polyethylene with limestone aggregate quarry and 60/70 pen grade asphalt binder. This research study concludes that plastomer increases flexibility and hardness of asphalt mixtures and improves the rut resistance, dynamic modulus and fatigue life of asphalt mixtures. Plastomer modification shows significant benefits as compared to neat binder for high/low temperature performance. Moreover, it can be concluded that plastomer provides an efficient and economical blend of asphalt mixture.

Textile wet processes dispose of huge amounts of water and energy in the form of hot wastewater and exhaust gas emissions. A significant amount of water and energy can be conserved by adopting various waste heat and water recovery methods, such as the use of heat exchanger, recycling hot wastewater, steam condensate, and cooling water recovery and reuse. In this study, various waste energy and water conservation methods are summarized to highlight their importance for water and energy conservation. For practical consideration, a detailed assessment of such practiced methods was done in one textile processing industry. The obtained results revealed that a high amount of energy and water could be conserved by adopting various waste heat and water recovery methods, with the advantage of reducing environmental pollution while minimizing the consumption of energy and water.

Industries tend to manufacture higher quality products with a low cost due to the competitive environmental loop and dynamic customers’ demand. The integration of intelligent manufacturing planning to agricultural operations and products will permit a spike in efficiency of yield, especially in the regions of agriculturally based economies. Though, to gain a vivid exploitation, the old-fashioned agriculture products manufacturing planning and scheduling techniques needs to be revised, especially the methodology in job shop planning must be augmented with efficient operational sequential scheduling. Cellular Manufacturing Systems (CMS) inclined to possess a higher complexity than the traditional manufacturing systems. Three major problems are coping in CMS domain: cell formation, product family selection, and product scheduling. This work deals with the problem of product scheduling in the CMS environment. A mixed integer linear programming mathematical model is introduced for the conflicting performance measures i.e. minimization of work in process (WIP) and maximization of average machine cell utilization. Since the current problem is considered as NP-hard problem, so a modified particle swarm optimization (MPSO) algorithm is proposed to find the optimum scheduling under the given constraint model of conflicting objectives. In the proposed MPSO, basic PSO is integrated with NEH heuristic to achieve better optimal sequence in less computation time. The obtained results are compared with other hybrid PSO algorithms with seed solutions from Gupta and Palmer heuristics. Furthermore, results are also compared against meta-heuristics such as genetic algorithm (GA), standard PSO, and artificial bee colony (ABC) algorithm. It shows that the proposed MPSO algorithm performs better than the existing compared algorithms. A real time case scenario of agriculture-based manufacturing industry is solved to validate the proposed planning algorithm.

Abstrak Beton memiliki kuat tekan yang tinggi yang merupakan sifat paling utama dan mencerminkan kemampuan beton menahan beban tekan yang diberikan kepadanya, namun lingkungan asam yang mengandung unsure kimia asam akan merusak beton secara perlahan- lahan mulai dari tepi dan sudut beton dengan terjadinya pelepasan butiran-butiran partikel beton sehingga beton menjadi keropos dan mengakibatkan terjadinya penurunan kuat tekan beton. Beton dengan bahan tambah fly ash menjadi tijauan khusus untuk lingkungan asam dikarenakan permeabilitas dan porositasnya yang kecil akan mempersulit serangan asam lebih parah lagi sampai ke inti beton. Penelitian ini bertujuan untuk mengetahui nilai kuat tekan beton dengan presentase penambahan butiran fly ash dengan kadar 20% terhadap berat seman, serta pengaruh lingkungan asam terhadap kuat tekan beton yang menggunakan bahan tambah fly ash. Hasil penelitian menunjukan beton normal (BN) mengalami penurunan kuat tekan yang bervariasi yaitu pada waktu 1 bulan sebesar 24,79% dan 2 bulan sebesar 30,58% dari beton normal umur 28 hari, sedangkan perlakuan air asam pada beton fly ash (BFA) juga mengalami penurunan kuat tekan pada waktu yang sama yaitu 1 bulan sebesar 2,61% dan 2 bulan sebesar 26,80% dari beton fly ash (BFA) umur 28 hari, di mana rasio penuruan pada beton fly ash lebih baik dibandingkan beton tanpa fly ash. Dengan demikian terlihat bahwa beton fly ash (BFA) lebih tahan terhadap serangan asam sulfat dibandingkan dengan Beton Normal (BN). Kata Kunci: KuatTekan, Asam Sulfat, Fly Ash Abstract Concrete has a high compressive strength which is the most important characteristic and reflects the ability of the concrete to withstand the compressive load given to it, however an acidic environment that contains acidic chemical elements will slowly damage the concrete starting from the edges and corners of the concrete by releasing the particles. concrete so that the concrete becomes porous and results in a decrease in the compressive strength of theconcrete. Concrete with fly ash added material is of particular importance for acidic environments because its small permeability and porosity will make it more difficult for acid attack to reachthe concrete core. This study aims to determine the value of the compressive strength of concrete with the percentage of adding fly ash granules with a content of 20% to the weight of the cement, as well as the effect of the acidic environment on the compressive strength of concrete using fly ash added. The results showed that normal concrete (BN) experienceda varying decrease in compressive strength, namely at 1 month of 24.79% and 2 months of 30.58% of normal concrete aged 28 days, while the treatment of acid water on fly ash concrete (BFA) also experienced a decrease in compressive strength at the same time, namely 1 month of 2.61% and 2 months of 26.80% of fly ash concrete (BFA) 28 days old. Where the reduction ratio in fly ash concrete is better than concrete without fly ash. Thus it can be seen that fly ash concrete (BFA) is more resistant to sulfuric acid attack than Normal Concrete (BN). Keywords: Compressive Strength, Sulfuric Acid, Fly Ash

In this article, a new collocation technique for numerical solution of Fredholm, Volterra and mixed Volterra-Fredholm integral equations of the second kind is introduced and also developed a numerical integration formula on the basis of linear Legendre multi-wavelets. We also use the linear Legendre multi-wavelets basis for the proposed method. In this technique, the unknown function is approximated by truncated linear Legendre multi-wavelets series. The newly developed numerical technique is applied to both linear and nonlinear benchmark test models from the literature including models with discontinues and non-differentiable exact solutions. The numerical results are compared with the other existing numerical techniques from literature. The comparison among the discussed methods with Monte Carlo method, rationalized Haar functions method, Operational matrix with block-pulse functions method, Bernstein operational matrices method and P-order spline direct method divulge that the present technique is authentic and valid for other physical and engineering problems. The proposed numerical method handle the discontinuity and non-differentiability in the exact solutions very well, whereas the other existence numerical methods are failed to capture these problems. Further, more the comparison of exact and approximate in term of L∞ norms which demonstrate the accuracy, flexibility and robustness of the newly proposed numerical method.

Unsteady magneto-hydrodynamic double-diffusive convection flows between two infinite vertical parallel plates have been investigated in the presence of the inclined magnetic field, heat generation or absorption, and chemical reaction. The dimensionless form of governing equations for momentum, energy and concentration has been solved using the integral transforms (Laplace and finite sine-Fourier transforms). The exact solutions for the velocity, temperature and concentration are obtained without any constraints. Semi analytical solutions of these problems are also established by using the Laplace inversion numerical algorithms Stehfest’s and Tzou’s. Further, we compare our obtained analytical and numerical results in graphical and tabular forms. Finally, the influence of pertinent parameters on the fluid velocity, temperature and concentration have been disused in detailed through graphs.

A mathematical analysis for slip effects on MHD nanofluid in the presence of electromagnetic field and gyrotactic microorganisms is considered. The influence of activation energy and thermal radiation are also discussed. The transportation of nanofluids provides a feasible option for the enhancement of thermal distribution. A rather new aspect of this work is the diffusion and bioconvection of motile microorganisms. It may avoid possible settling of nano-entities. A system of highly non-linear partial differential equations is transformed into Ordinary differential equations by using suitable similarity transformation. The converted ordinary differential equations are then solved numerically by utilizing the shooting technique is built-in function bvp4c solver on the plate form of commercial software Matlab. The results obtained are verified through acceptable agreement with those of the existing one as a special case. The quantities of interest are observed physically. The characteristics of various emerging parameters on the velocity field, temperature distribution and volumetric concentration of nanoparticles, microorganism concentration as well as skin friction coefficient, the gradient of temperature, local Sherwood number, and density number of motile microorganisms are interpreted and reflected in tabulated and graphical form. It is noticed that velocity decreases with the rising values of M. Also, the temperature rises directly with Nb and Nt. With the growing values of Lb, the microorganism profile does down.

Pakistan has proven huge coal reserves but still unable to harvest the energy due to unavailability of ingenious technology. Coal gasification is robust, efficient and environmental friendly technology but it is highly sensitive to the coal characteristics. Scant literature is available on the development of the coal gasification technology which suits the characteristics of indigenous coal reserves. This papers presents the simulation of entrained flow gasifier for using indigenous coal through process modeling software namely Aspen Plus® to evaluate various system flow steps within an entrained flow gasifier. General techniques have also been discussed for creating the equilibrium-bases simulations of gasification systems. Peng-Robinson equation was used to correlate the volume of species with respect to state variables (temperature and pressure). The effects of composition of different indigenous coals like Thar, Lakhra and Sonda were investigated through simulations along with O/C (Oxygen-to-Carbon) ratio. Parametric study revealed that the O/C ratio along with other related system parameters have great influence on the performance. Sulfur could be available in different forms in coal like pyrite, sulfate or organic sulfur so appropriate form of sulfur in feedstocks should be corrected for better accuracy of model results. The highest percentages of CO i.e. 44.2, 37.8, and 46.6% were obtained from Thar coal (air dried form), Lakhra coal and Sonda coal respectively at the 0.3 O/C ratio. The decrease in LHV and HHV (Lower and Higher Heating Values) of syngas was observed on increase of O/C ratio for all coal types. The composition of as received Thar coal gave maximum LHV (1.5x104 KJ/Kg) and HHV (1.78x104 KJ/Kg) at the 0.3 O/C ratio. The future work could be extended by simulating biomass composition in the developed model of gasifier in transient simulations.

The WSNs (Wireless Sensor Networks) lead to great opportunities to explore it scientifically. In this network different numbers of SN (Sensor Nodes) are deployed in a specific area to gather information. The UWSNs (Underwater Wireless Sensor Networks) is a highly distributed network of sensor nodes deployed underwater to gather environmental information. Hence, acquirement of real-time data at enhanced data rate and to reduce power consumption is a key concern while designing routing protocol for UWSNs. In this paper, a cooperation based solution is suggested. The solution proposed here uses the DF (Decode and Forward) strategy for relying the information from the source to the destination using a relay node. The signals coming towards the destination are weighted and combined on the basis of their SNRC (Signal to Noise Ratio Combing). The simulation results verify enhancement in different factors, required for evaluation of a UWSN. After implementation of the proposed solution the stability of the network is increased which maximize the PDR (Packet Delivery Ratio). In our proposed solution the transmission is based on channel estimation, an estimate is made for higher reliable channel, which reduces retransmission of packets. Hence, sink receive the packets with lesser delay and as a result E2E (End-to-End) delay is decreased. Data is forwarded using data forwarding by neighbor nodes. It improves average energy consumption of the system. Hence the overall performance and lifetime of a UWSN is increased.

This manuscript uncovers the heat and mass transfer of an unsteady tangent hyperbolic nanofluid flow across an extensible Riga wedge under the effects of stagnation point, heat source, and activation energy. The flow computations with modified Hartmann numbers are embedded in this investigation particularly in the unsteady tangent hyperbolic liquid stream scenario. The focus pertains to augment heat conduction in the bulk liquid as heat and mass transport media. The implications of controlling parameters on non-dimensional speed, temperature, as well as concentration profiles are visually portrayed. The governing partial differential equations are modified into non-dimensional forms by reducing the number of independent factors, which are then pursued numerically utilizing the Runge-Kutta method with the shooting tool. The velocity of Newtonian fluid improves as the magnitude of wedge angle parameter βw rises, although it is marginally lower than that of tangent hyperbolic fluid, the temperature of Newtonian fluid intensifies substantially faster than that of tangent hyperbolic fluid for higher values of βw. The skin friction factor increases with alterations to the Hartmann parameter, Weissenberg factor, wedge angle parameter as well as suction parameter. The percentage increase in skin friction factor is 13.3 and 21.93 when modified Hartmann number takes input in the range 0 ≤ Mh ≤ 0.2 and unsteady parameter 0.1 ≤ A ≤ 0.5. The Schmidt number, chemical change, and wedge angle parameters are all designed to boost the Sherwood number.

This communication explores the significance of suction/injection for gravity modulation mixed convection in micropolar fluid flow due to an inclined sheet in the presence of magnetic field and thermal radiation. In recent years, very extensive modern technological applications have rise the interest in mixed convection controlled by g-jitter forces associated with microgravity. The novelty of the present study is effects of g-jitter on dynamics of micropolar fluid adjustable inclination to the sheet taken into account. Mathematical formulation based on usual laws of conservation is non dimensionalized with emerging parameters through implementation of similarity transform to yield a corresponding set of partial differential equations. In the face of convective non linearity combined with coupling due to mixed convection and micropolarity, a finite element discretization is harnessed to be coded and run on Matlab platform. The credibility of numerical procedure is assured for its acceptable adoption with the established results. The growing strength of amplitude of modulation is showing proportional decrease and increase in the fluctuation of heat transfer and skin friction coefficients, and the fluctuation of reduced skin friction factor, couple stress, and Nusselt number improves with larger inputs of amplitude. These findings would be helpful to experts dealing with upper space heat transportation, performance of materials such as crystals and effectiveness of chemical catalytic reactors.

An analysis for magnetohydrodynamic impacts on the rotational flow of nanofluids along with microorganisms, binary chemical reaction, and activation energy is considered. The equation of temperature is connected with Brownian motion, the theory of non-Fourier heat flux, and thermophoresis. The time-dependent 3D partially differentiate formulation is simplified in two independent coordinates (ξ, η). Glerikin discretization is used to employ finite element simulation in a MATLAB environment. It is noted that rising contributions of Hartmann number recede secondary as well as primary velocities; however, the primary skin friction attain negatively lower values and secondary skin friction exhibit an opposite trend. The growing input of thermophoresis and Brownian motion uplift the temperature, and the wall temperature gradient attains smaller values. The present solution of FEM has been affirmed with the available literature, indicating an incredible co-relation. The study has noteworthy applications in the industry of food and relevant to biomedical, energy systems, and current advances of aerospace technologies.

The main objective of this manuscript is to glance into the assets of nanoparticles in the stream of generalized micropolar fluid and Carreau fluid against an intensified elongated surface. In order to assess, the heat and mass diffusion occurrences, the Cattaneo-Christov implications are also experienced in the temperature and concentration computations. In contrast to prior studies, rheological attributes on non-Newtonian fluids are depicted by engaging micropolar fluid and Carreau liquid that reflect a clear difference of transport phenomena. By minimizing the number of independent factors, the regulating equations are transmuted into non-dimensional types, that are then tackled numerically using the RK-4 algorithm along with the shooting strategy. For velocity, micro-rotation, temperature and concentration distributions, a visualization evaluation of the entangled flow parameters is executed. It has been revealed that expanding magnetic and micropolar constraints enhance micro-rotation velocity. The unsteadiness parameter enhances all three physical quantities, surface drag force, Nusselt number, and Sherwood number.

The growing demands for mobile broadband application services along with the scarcity of the spectrum have triggered the dense utilization of frequency resources in cellular networks. The capacity demands are coped accordingly, however at the detriment of added inter-cell interference (ICI). Fractional Frequency Reuse (FFR) is an effective ICI mitigation approach when adopted in realistic irregular geometry cellular networks. However, in the literature optimized spectrum resources for the individual users are not considered. In this paper Hungarian Mechanism based Sectored Fractional Frequency Reuse (HMS-FFR) scheme is proposed, where the sub-carriers present in the dynamically partitioned spectrum are optimally allocated to each user. Simulation results revealed that the proposed HMS-FFR scheme enhances the system performance in terms of achievable throughput, average sum rate, and achievable throughput with respect to load while considering full traffic.

The recent study concentrates on the liquid film flow considering Oldroyd-B fluid past a rotating disc in three-dimensional space. The flow pattern has been modeled under the effects of heat absorption/omission and radiation terminologies. The activation energy, bio-convectional, and gyrotactic microorganism phenomena are also included in this study. The Von Karman alterations have been used to alter the basic equations into the set of nonlinear differential equations. The elegant homotopy analysis method (HAM) has been used to obtain the solution of the system of nonlinear differential equations. The influence of the emerging parameters is scrutinized with physical descriptions against the flow pattern, temperature distribution, concentration profile, and gyrotactic microorganism. The detailed outcomes in the heat transfer rate, mass transfer, and motile organism due to the variation of physical parameters have also been shown through bar charts and discussed. It is found that the velocity field decline with the augmentation of magnetic parameter, film thickness parameter, time retardation, and relaxation parameters. The heat transfer enhances with the augmentation of radiation and heat absorption/omission parameters while declining with the Prandtl number. The greater magnitude of the Schmidt number declines the concentration profile.

This study examines the magnetic effects of heat and mass transmission on the flow of micropolar fluid over a permeable stretching geometry with dilute homogeneous dispersion of nano-particles and gyrotactic microorganisms. A system of coupled highly non-linear PDEs is renovated into corresponding ODEs by using similarity functions. These transmuted equations are resolved for a solution with shooting technique accompanied with Runge-Kutta fourth order. The variations of intricate physical quantities such as temperature, micro-motion, concentration, velocity, and motile micro-organism profiles are evaluated under the influence of the emerging parameters. The velocity profile decreases down with upsurge values of magnetic parameter M while micro-rotation is strengthened and its value becomes higher directly with increments in M. The microorganisms profile depict the diminishing behavior with the growing value of bioconvection Lewis number. These results are useful for obtaining better solutions for heat transfer devices and micropolar fuel cells. Additionally, the impact of the parameter of Brownian motion, Rayleigh number, and the parameter of thermophoresis, Peclet number, and buoyancy ratio parameter were discussed numerically and graphically. Moreover, the numerical results were validated by comparing them with previously obtained exact solution for special cases and acceptable compatibility between the two results is achieved. The findings from this work can be utilized for efficient heat exchangers and thermal balance in micro-electronics.

Technology’s expansion has contributed to the rise in popularity of social media platforms. Twitter is one of the leading social media platforms that people use to share their opinions. Such opinions, sometimes, may contain threatening text, deliberately or non-deliberately, which can be disturbing for other users. Consequently, the detection of threatening content on social media is an important task. Contrary to high-resource languages like English, Dutch, and others that have several such approaches, the low-resource Urdu language does not have such a luxury. Therefore, this study presents an intelligent threatening language detection for the Urdu language. A stacking model is proposed that uses an extra tree (ET) classifier and Bayes theorem-based Bernoulli Naive Bayes (BNB) as the based learners while logistic regression (LR) is employed as the meta learner. A performance analysis is carried out by deploying a support vector classifier, ET, LR, BNB, fully connected network, convolutional neural network, long short-term memory, and gated recurrent unit. Experimental results indicate that the stacked model performs better than both machine learning and deep learning models. With 74.01% accuracy, 70.84% precision, 75.65% recall, and 73.99% F1 score, the model outperforms the existing benchmark study.

With the growing popularity of online social networks, one common desire of people is to use of social networking services for establishing social relations with others. The boom of social networking has transformed common users into content (data) contributors. People highly rely on social sites to share their ideas and interests and express opinions. Social network sites store all such activities in a data form and exploit the data for various purposes, e.g., marketing, advertisements, product delivery, product research, and even sentiment analysis, etc. Privacy policies primarily defined in Natural Language (NL) specify storage, usage, and sharing of the user’s data and describe authorization, obligation, or denial of specific actions under specific contextual conditions. Although these policies expressed in Natural Language (NL) allow users to read and understand the allowed (or obliged or denied) operations on their data, the described policies cannot undergo automatic control of the actual use of the data by the entities that operate on them. This paper proposes an approach to systematically translate privacy statements related to data from NL into a controlled natural one, i.e., CNL4DSA to improve the machine processing. The methodology discussed in this work is based on a combination of standard Natural Language Processing (NLP) techniques, logic programming, and ontologies. The proposed technique is demonstrated with a prototype implementation and tested with policy examples. The system is tested with a number of data privacy policies from five different social network service providers. Predominantly, this work primarily takes into account two key aspects: (i) The translation of social networks’ data privacy policy and (ii) the effectiveness and efficiency of the developed system. It is concluded that the proposed system can successfully and efficiently translate any common data policy based on an empirical analysis performed of the obtained results.

This article investigates the impact of radially varying magnetic field during the peristaltically flowing nanofluid in a finite flexible tube. The inner surface of axisymmetric tube is filled with metachronal waves of cilia. Flow is provoked as a result of these metachronal waves, which are formed due to wavy or beating motion cilia. The thermal conduction of water base fluid is enhanced by involvement of carbon nanotubes. Moreover, heat transport analysis is performed with heat generation process. Governing problem is simplified under the long wave length and low Reynolds number approximation. Mathematical formulations have been performed which results into nonlinear partial differential equations. The suitable variables have been employed to obtain coupled ordinary differential system. Numerical solution is obtained by using Adams-Bashforth technique. Error analysis for the obtained results have been presented which show the validity in terms of tolerance level. Graphical results are prepared and analyzed for different sundry parameters showing the real insight of the analysis. The peristaltic pumping region is −1.0

The result for enhanced heat transfer to manage increasing heat density of miniature and several other technical processes have urged to analyze thermal transports of hybrid nanofluids. Molybdenum disulfide (MoS2) and Zinc oxide (ZnO) are hybridized as a very dilute homogenous mixture in the bulk engine oil. The flow of this colloidal fluid with heat transfer occurs through pours medium over a stretching sheet. Moreover, an invariant magnetic field, thermal dissipation, and the heat source are incorporated. Theoretical formulation resulted as, set non-linear partial differential equations. To obtain a numerical solution, similarity transform is hired to yield corresponding ordinary differential equations. Computational software Matlab is availed to run the code for the Runge-Kutta method with shooting technique. A deep insight into the problem is inspected by varying the inputs of the dependent functions influential parameters. It is perceived that the flow speed is hindered by the growing inputs of parameters of magnetic field and porosity. It also comes to know that the velocity f′(η) becomes slower with augmentation of φ2 but the temperature rises. A suitable range of the emerging parameters is tried to observe the variation of physical quantities such as skin fraction facts, velocity, local Nusselt number, and temperature of the fluid.

The safety of water is essential for life on earth. Drinking contaminated water can give rise to many health issues. To highlight the major causes of contamination in water, this study was carried out to determine the physicochemical and bacteriological quality of raw and chlorinated water. Raw and chlorinated water samples were obtained from three locations (zones) of a residential university (National University of Sciences and Technology, NUST). Ten physicochemical parameters were analyzed (pH, Electrical conductivity, Turbidity, Dissolved oxygen, Hardness, Alkalinity, Total suspended solids, Total dissolved solids, Total organic carbon, and Chlorine (free & total)). Bacteriological analysis was performed through the MPN technique. The results showed that all the physicochemical parameters were found within the permissible limits set by WHO and PSDWQ for raw and chlorinated water, except TSS and Chlorine (free & total). The TSS of raw and chlorinated water ranged from 3.3-14.3mg/L and 8.7-12.7mg/L, greater than the permissible limits. The free Chlorine for both raw and chlorinated water ranged from 0.04-0.09mg/L and 0.06-0.07mg/L and total Chlorine for both raw and chlorinated water in the range 0.10-0.09mg/L and 0.22-0.24mg/L, respectively, which were far below the recommended limit required for water disinfection. The MPN index for raw and chlorinated water ranged from 23 to >23MPN/100ml and 16 to >23MPN/100ml, exceeding the permissible limits. The results concluded that the water, whether raw or chlorinated, was not fit for drinking purposes. Proper dosages of Chlorine and filtration techniques are required to make the water fit for consumption.

Modern materials science is aimed towards abandoning Portland cement in the production of building materials. The scientific novelty of this study lies in its being the first time a comprehensive study of the structure formation of alkali-activated materials (AAM) based on thermally treated marl and Na2SiO3 is carried out. The tasks for achieving this goal were to characterize the thermally treated marl as a new binder, and to comprehensively research the microstructure, fresh, physical, and mechanical properties of the AAM based on the binder. Received active particles of marl with a smaller size than Portland cement have a specific surface area of 580–590 m2/kg. The mineral composition of heat-treated marl is characterized by calcium silicates, which guarantee good binding properties. The results of X-ray diffraction analysis of the samples based on the clinker-free binder of alkaline activation using opoka-like marl confirmed the presence of calcite, quartz, and feldspar close to albite, micas, and zeolites. The obtained products of the chemical interaction of the components of the binder confirm the effectiveness of the newly developed AAM. As a result of comparing several binders, it was found that the binder “thermally treated marl—Na2SiO3” is the most effective, since for specimens based on it, a maximum compressive strength of 42.6 MPa, a flexural strength of 4.6 MPa, and minimum setting time were obtained (start 26 min, end 32 min) as well as a water absorption of 10.2 wt.%. The research results will be of interest to specialists in the construction industry, since the proposed recipes for clinker-free cements are an alternative to expensive and energy-intensive Portland cement and provide the creation of strong and durable concrete and reinforced concrete composites.

The rapid and wide proliferation of mobile phones has led to accelerated demand for mobile applications (apps). Consequently, a large number of mobile apps have been developed and deployed on the Google and Apple Play stores. Calling apps hold special importance in this regard by offering the services of sharing messages, making video calls, and sending audio messages, free of cost. Although each app has its own set of features, different apps can provide higher levels of satisfaction for the user, and aspect analysis is often overlooked by existing studies. This study presents an aspect-level analysis of IMO, Skype, Telegram, WeChat, and WhatsApp regarding the services offered for the account, app, call, message, update, video, and working features. A large collected dataset from the Google Play store is utilized for aspect extraction and analysis using the Latent Dirichlet Allocation (LDA) model. Apps are analyzed using LDA-extracted aspects and recommended regarding users’ priorities of call, message, and video requirements. Sentiment analysis is adopted to analyze user sentiments regarding apps as well as to aid in the aspect analysis. For sentiment analysis, a novel ensemble model of a gated recurrent unit and convolutional neural network is presented, which obtains a 94% accuracy score.

Food preservatives and additives are the universal concern of recent days. Particularly, formalin is a chemical compound that is being commonly mingled with food for the preservation purpose. The recurrent ingesting of formalin contaminated food causes uncompromising health sicknesses like chronic cancer. Therefore, identification of formalin in food substances is an extreme need and day by day this need is growing as a general issue in the emerging terrains. In this article, a surface plasmon resonance (SPR) based sensor, comprised of graphene-PtSe2 combined layers associated with a ZnO nano-sheet is presented for the detection of formalin in liquid solution. The performance of the sensor has been analyzed through an analytical approach using MATLAB commercial software. Consequence due to incorporation of the Graphene-PtSe2 combined layer into the sensor structure along with ZnO layer has been studied. The observed sensitivity and quality factor are 155.33° RIU−1 and 88.89 RIU−1, respectively. This sensor senses the presence of formalin molecules by using attenuated total reflection (ATR) approach, assessing the reflectance vs SPR angle. It is found that the sensitivity of the conventional SPR based sensor is 128.33° RIU−1, whereas an improved sensitivity of 155.33° RIU−1 is obtained for this proposed amalgamated sensor structure. Later in time, a comparison of performance for different SPR sensor structures and the proposed sensor has been studied. Finally, another comparison study has been made with performance of the proposed sensor to other reported sensor in the literature.

The Tidore Islands Municipal Steam Power Plant uses coal as fuel to generate electricity. Handling of coal waste in the Rum PLTU is done by storing it on empty land so that it is not effective because it can be potentially dangerous for the environment and surrounding communities. Therefore, it is important to use coal waste. One of them is in the construction field such as in the manufacture of cement bricks. The purpose of this study is to determine the level of water absorption and compressive strength of cement brick using bottom ash base material. Water absorption testing is carried out when the specimen has been immersed for 28 days. The test object is weighed in the SSD state and then inserted into the oven and left for 24 hours. Then the test object is left idle and then weighed. Testing of compressive strength was carried out when the specimen was 28 days old. The results of the study show that the absorption rate of cement brick is increasing when bottom ash is added. Meanwhile, for compressive strength, the cement brick has decreased

During the last decade, the CR (Cognitive Radio) came into view as a major wireless technology to resolve the issue of spectrum secrecy and efficient spectrum utilization. However, due to unlicensed (secondary) users, there are various security threats to the CRN (Cognitive Radio Networks). Some malicious users may access the CRN and mislead the secondary users to vacate the occupied channel, which may stop the communication. In this work, we propose a new cryptographic-based algorithm, CRAES (Cognitive Radio-Advanced Encryption Standard), inspired by the traditional AES to secure the CRN. The data of the primary and secondary users is encrypted at the transmitter and decrypted at the receiver. Unlike the conventional AES, we introduce the data-dependent key-generation and shift-rows process. We also reduce the rounds of AES from 10-6 to improve the computational efficiency without compromising the overall security. The experimental results demonstrate the effectiveness of the proposed CR-AES in terms of better security, reliability, and computational efficiency.

The field of Global Software Development has been an active area of research for the last two decades due to its enormous benefits such as lower labor cost, faster development and easy access to the skilled labor pool. Apart from these benefits, it faces some challenges like communication, coordination, trust and configuration management etc. These challenges arise primarily due to physical, cultural and time zone differences. The empirical studies highlight that the existing Global Software Development solutions do not fully meet the user needs as there are still several gaps in these solutions. Therefore, to fulfill these gaps, there is a need to develop novel frameworks that address outstanding issues. In this paper, we have attempted to address the aforesaid GSD challenges. The practitioners can benefit from our proposed framework during the execution of GSD projects. The proposed framework mainly focuses on the root causes of the two principal challenges namely the communication and cultural differences. We believe that if the team members of a software project can communicate effectively and show considerations for others by imparting due reverence to the cultural norms, then the other residual issues can easily be reduced and minimized.

Nanoparticles when used even in relatively low concentrations, can significantly alter the thermal properties of a base working fluid, thereby substantially enhancing the thermal performance of a power generation system. In the present study, numerical simulations were performed for a solar collector to test the effectiveness of six non-metallic nanoparticles, namely aluminum oxide (Al2O3), cerium oxide (CeO2), copper oxide (CuO), ferric oxide (Fe2O3), titanium dioxide (TiO2) and Silicon dioxide (SiO2). These nanoparticles were dispersed individually in three different base working fluids; therminol VP-1 (at 400 K), water (at 400 K) and molten salt (at 600 K) to form different nanofluids. Each of these nanofluids were then examined with three different volume fractions (2%, 4% and 6%) in addition to the pure base working fluid case for a range of Reynolds number (Re=104-105). For the simulation the Monte Carlo Ray Tracing (MCRT) model was used to represent the non-uniform heat flux around the absorber tube of the Parabolic Trough Collector (PTC). The results show that the enhancement of the thermal and the hydraulic performances depended upon the combination of the nanoparticles and the base working fluid. Silicon dioxide, SiO2, however, was found to be the most efficient nanoparticle regardless of the choice of the base working fluid for all the tested volume fractions. For example, for typical operating conditions for SiO2 with a volume fraction (VF) of 6%, the average Nusselt number of the water-based mixture was enhanced by 32.4%, with a thermal efficiency improvement of 5.11% and performance evaluation criterion (PEC) of 1.313. On the other hand, for a molten salt-based SiO2 mixture, the average Nusselt number was improved by 21.36% and thermal efficiency by 9.92% with a PEC of 1.155. Finally, the corresponding improvements with therminol VP-1-base fluid were 15.6%, 9.18% and 1.21 for the average Nusselt number, thermal efficiency and PEC respectively.

Given recent advances in deep learning, semi-supervised techniques have seen a rise in interest. Generative adversarial networks (GANs) represent one recent approach to semi-supervised learning (SSL). This paper presents a survey method using GANs for SSL. Previous work in applying GANs to SSL are classified into pseudo-labeling/classification, encoder-based, TripleGAN-based, two GAN, manifold regularization, and stacked discriminator approaches. A quantitative and qualitative analysis of the various approaches is presented. The R3-CGAN architecture is identified as the GAN architecture with state-of-the-art results. Given the recent success of non-GAN-based approaches for SSL, future research opportunities involving the adaptation of elements of SSL into GAN-based implementations are also identified.

Research has become increasingly more interdisciplinary over the past few years. Artificial intelligence and its sub-fields have proven valuable for interdisciplinary research applications, especially physical sciences. Recently, machine learning-based mechanisms have been adapted for material science applications, meeting traditional experiments’ challenges in a time and cost-efficient manner. The scientific community focuses on harnessing varying mechanisms to process big data sets extracted from material databases to derive hidden knowledge that can successfully be employed in technical frameworks of material screening, selection, and recommendation. However, a plethora of underlying aspects of the existing material discovery methods needs to be critically assessed to have a precise and collective analysis that can serve as a baseline for various forthcoming material discovery problems. This study presents a comprehensive survey of state-of-the-art benchmark data sets, detailed pre-processing and analysis, appropriate learning model mechanisms, and simulation techniques for material discovery. We believe that such an in-depth analysis of the mentioned aspects provides promising directions to the young interdisciplinary researchers from computing and material science fields. This study will help devise useful modeling in the materials discovery to positively contribute to the material industry, reducing the manual effort involved in the traditional material discovery. Moreover, we also present a detailed analysis of experimental and computation-based artificial intelligence mechanisms suggested by the existing literature.

Unitary decomposition is a widely used method to map quantum algorithms to an arbitrary set of quantum gates. Efficient implementation of this decomposition allows for the translation of bigger unitary gates into elementary quantum operations, which is key to executing these algorithms on existing quantum computers. The decomposition can be used as an aggressive optimization method for the whole circuit, as well as to test part of an algorithm on a quantum accelerator. For the selection and implementation of the decomposition algorithm, perfect qubits are assumed. We base our decomposition technique on Quantum Shannon Decomposition, which generates O(344n) controlled-not gates for an n-qubit input gate. In addition, we implement optimizations to take advantage of the potential underlying structure in the input or intermediate matrices, as well as to minimize the execution time of the decomposition. Comparing our implementation to Qubiter and the UniversalQCompiler (UQC), we show that our implementation generates circuits that are much shorter than those of Qubiter and not much longer than the UQC. At the same time, it is also up to 10 times as fast as Qubiter and about 500 times as fast as the UQC.

In the current work, an investigation has been carried out for the Bingham fluid flow in a channel-driven cavity with a square obstacle installed near the inlet. A square cavity is placed in a channel to accomplish the desired results. The flow has been induced using a fully developed parabolic velocity at the inlet and Neumann condition at the outlet, with zero no-slip conditions given to the other boundaries. Three computational grids, C1, C2, and C3, are created by altering the position of an obstacle of square shape in the channel. Fundamental conservation and rheological law for viscoplastic Bingham fluids are enforced in mathematical modeling. Due to the complexity of the representative equations, an effective computing strategy based on the finite element approach is used. At an extra-fine level, a hybrid computational grid is created; a very refined level is used to obtain results with higher accuracy. The solution has been approximated using P2 − P1 elements based on the shape functions of the second and first-order polynomial polynomials. The parametric variables are ornamented against graphical trends. In addition, velocity, pressure plots, and line graphs have been provided for a better physical understanding of the situation Furthermore, the hydrodynamic benchmark quantities such as pressure drop, drag, and lift coefficients are assessed in a tabular manner around the external surface of the obstacle. The research predicts the effects of Bingham number (Bn) on the drag and lift coefficients on all three grids C1, C2, and C3, showing that the drag has lower values on the obstacle in the C2 grid compared with C1 and C3 for all values of Bn. Plug zone dominates in the channel downstream of the obstacle with augmentation in Bn, limiting the shear zone in the vicinity of the obstacle.

Histopathological image analysis is an examination of tissue under a light microscope for cancerous disease diagnosis. Computer-assisted diagnosis (CAD) systems work well by diagnosing cancer from histopathology images. However, stain variability in histopathology images is inevitable due to the use of different staining processes, operator ability, and scanner specifications. These stain variations present in histopathology images affect the accuracy of the CAD systems. Various stain normalization techniques have been developed to cope with inter-variability issues, allowing standardizing the appearance of images. However, in stain normalization, these methods rely on the single reference image rather than incorporate color distributions of the entire dataset. In this paper, we design a novel machine learning-based model that takes advantage of whole dataset distributions as well as color statistics of a single target image instead of relying only on a single target image. The proposed deep model, called stain acclimation generative adversarial network (SA-GAN), consists of one generator and two discriminators. The generator maps the input images from the source domain to the target domain. Among discriminators, the first discriminator forces the generated images to maintain the color patterns as of target domain. While second discriminator forces the generated images to preserve the structure contents as of source domain. The proposed model is trained using a color attribute metric, extracted from a selected template image. Therefore, the designed model not only learns dataset-specific staining properties but also image-specific textural contents. Evaluated results on four different histopathology datasets show the efficacy of SA-GAN to acclimate stain contents and enhance the quality of normalization by obtaining the highest values of performance metrics. Additionally, the proposed method is also evaluated for multiclass cancer type classification task, showing a 6.9% improvement in accuracy on ICIAR 2018 hidden test data.

The load pressure on electrical power system is increased during last decade. The installation of new power generators (PGs) take huge time and cost. Therefore, to manage current power demands, the solar plants are considered a fruitful solution. However, critical caring and balance output power in solar plants are the highlighted issues. Which needs a proper procedure in order to minimize balance output power and caring issues in solar plants. This paper investigates artificial neural network (ANN) and hybrid boost converter (HBC) based MPPT for improving the output power of solar plants. The proposed model is analyzed in two steps, the offline step and the online step. Where the offline status is used for training various terms of ANNs in terms of structure and algorithm while in the online step, the online procedure is applied with optimum ANN for maximum power point tracking (MPPT) using traditional converter and hybrid converter in solar plants. Moreover, a detail analytical framework is studied for both proposed steps. The mathematical and simulation approaches show that the presented model efficiently regulate the output of solar plants. This technique is applicable for current installed solar plants which reduces the cost per generation.

Web systems evolve by adding new functionalities or modifying them to meet users’ requirements. Web systems require retesting to ensure that existing functionalities are according to users’ expectations. Retesting a web system is challenging due to high cost and time consumption. Existing ‘systematic literature review’ (SLR) studies do not comprehensively present the ontology-based regression testing approaches. Therefore, this study focuses on ontology-based regression testing approaches because ontologies have been a growing research solution in regression testing. Following this, a systematic search of studies was performed using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines. A total of 24 peer-reviewed studies covering ontologies (semantic and inference rules) and regression testing, published between 2007 and 2019, were selected. The results showed that mainly ontology-based regression testing approaches were published in 2011–2012 and 2019 because ontology got momentum in research in other fields of study during these years. Furthermore, seven challenges to ontology-driven regression testing approaches are reported in the selected studies. Cost and validation are the main challenges examined in the research studies. The scalability of regression testing approaches has been identified as a common problem for ontology-based and other benchmark regression testing approaches. This SLR presents that the safety of critical systems is a possible future research direction to prevent human life risks.

As (Arsenic) is one of the lethal element present at the various locations of the world, putting human beings in danger by polluting the water. Arsenic Kit and atomic absorption spectrometer were used to determine As in ground water of Sindh province, Pakistan. Twenty-Four (24) districts both on the left and right bank of RI (River Indus) were analyzed. It was observed from the results that highest As concentration 200 ppb (parts per billion) i.e. above the WHO (World Health Organization) limit (10 ppb) was observed in Sakrand, district Shaheed Benazirabad followed by Hala, Matairi, TMK (Tando Mohammad Khan) and Nasarpur regions. It was further found that ground water of regions on the left bank of RIwas more contaminated than the right bank. Contour map was created using OriginPro and coordinate systems to highlight the elevated arsenic in the studied area. HRA (Health Risk Assessment) of these areas was carried out to calculate EDI (Estimated Daily Intake), TQH (Target Hazard Quotient) and CR (Cancer Risk). 45% of the total ground water samples analyzed were above the permissible limit for As in water and mostly these are located on the left bank of RI. The local wells in Sindh have never been tested for metal concentration former to use. These results provide baselines for researchers, NGO's (Non-Governmental Organizations) and government to apply arsenic treatment technologies in those areas

Amid the worldwide COVID-19 pandemic lockdowns, the closure of educational institutes leads to an unprecedented rise in online learning. For limiting the impact of COVID-19 and obstructing its widespread, educational institutions closed their campuses immediately and academic activities are moved to e-learning platforms. The effectiveness of e-learning is a critical concern for both students and parents, specifically in terms of its suitability to students and teachers and its technical feasibility with respect to different social scenarios. Such concerns must be reviewed from several aspects before e-learning can be adopted at such a larger scale. This study endeavors to investigate the effectiveness of e-learning by analyzing the sentiments of people about e-learning. Due to the rise of social media as an important mode of communication recently, people’s views can be found on platforms such as Twitter, Instagram, Facebook, etc. This study uses a Twitter dataset containing 17,155 tweets about e-learning. Machine learning and deep learning approaches have shown their suitability, capability, and potential for image processing, object detection, and natural language processing tasks and text analysis is no exception. Machine learning approaches have been largely used both for annotation and text and sentiment analysis. Keeping in view the adequacy and efficacy of machine learning models, this study adopts TextBlob, VADER (Valence Aware Dictionary for Sentiment Reasoning), and SentiWordNet to analyze the polarity and subjectivity score of tweets’ text. Furthermore, bearing in mind the fact that machine learning models display high classification accuracy, various machine learning models have been used for sentiment classification. Two feature extraction techniques, TF-IDF (Term Frequency-Inverse Document Frequency) and BoW (Bag of Words) have been used to effectively build and evaluate the models. All the models have been evaluated in terms of various important performance metrics such as accuracy, precision, recall, and F1 score. The results reveal that the random forest and support vector machine classifier achieve the highest accuracy of 0.95 when used with Bow features. Performance comparison is carried out for results of TextBlob, VADER, and SentiWordNet, as well as classification results of machine learning models and deep learning models such as CNN (Convolutional Neural Network), LSTM (Long Short Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional-LSTM). Additionally, topic modeling is performed to find the problems associated with e-learning which indicates that uncertainty of campus opening date, children’s disabilities to grasp online education, and lagging efficient networks for online education are the top three problems.

The first-ever use of halloysite nanotube (HNT), a relatively low-cost nanomaterial abundantly available with minor toxicity for removing brilliant green dye from aqueous media, is reported. The factors affecting adsorption were studied by assessing the adsorption capacity, kinetics, and equilibrium thermodynamic properties. All the experiments were designed at a pH level of around 7. The Redlich-Peterson isotherm model fits best amongst the nine isotherm models studied. The kinetic studies data confirmed a pseudo model of the second order. Robotic investigations propose a rate-controlling advance being overwhelmed by intraparticle dispersion. The adsorbent features were interpreted using infrared spectroscopy and electron microscopy. Process optimization was carried out using Response Surface Methodology (RSM) through a dual section Fractional Factorial Experimental Design to contemplate the impact of boundaries on the course of adsorption. The examination of fluctuation (ANOVA) was utilized to consider the joined impact of the boundaries. The possibilities of the use of dye adsorbing HNT (“sludge”) for the fabrication of the composites using plastic waste are suggested.

High temperature severely affects the nature of the ingredients used to produce concrete, which in turn reduces the strength properties of the concrete. It is a difficult and time-consuming task to achieve the desired compressive strength of concrete. However, the application of supervised machine learning (ML) approaches makes it possible to initially predict the targeted result with high accuracy. This study presents the use of a decision tree (DT), an artificial neural network (ANN), bagging, and gradient boosting (GB) to forecast the compressive strength of concrete at high temperatures on the basis of 207 data points. Python coding in Anaconda navigator software was used to run the selected models. The software requires information regarding both the input variables and the output parameter. A total of nine input parameters (water, cement, coarse aggregate, fine aggregate, fly ash, superplasticizers, silica fume, nano silica, and temperature) were incorporated as the input, while one variable (compressive strength) was selected as the output. The performance of the employed ML algorithms was evaluated with regards to statistical indicators, including the coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual models using DT and ANN gave R2 equal to 0.83 and 0.82, respectively, while the use of the ensemble algorithm and gradient boosting gave R2 of 0.90 and 0.88, respectively. This indicates a strong correlation between the actual and predicted outcomes. The k-fold cross-validation, coefficient correlation (R2), and lesser errors (MAE, MSE, and RMSE) showed better performance than the ensemble algorithms. Sensitivity analyses were also conducted in order to check the contribution of each input variable. It has been shown that the use of the ensemble machine learning algorithm would enhance the performance level of the model.