Search

Enhancement in the visible light absorption and efficient interfacial charge transfer is crucial for optimizing photocatalytic efficiency in the degradation of pollutants such as methyl orange (MO) and formaldehyde. This study focuses on the properties of a TiO2/C/g-C3N4 hybrid efficient photocatalyst, which is developed using an air calcination method to deposit graphitic nitride (g-C3N4) onto a carbon-modified TiO2 surface. The characterization techniques, including high-resolution transmission electron Microscopy (HRTEM), X-ray diffraction (XRD), Raman spectroscopy, thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS), were used to provide a comprehensive understanding of the material’s structural, morphological, thermal, and chemical properties. This hybrid catalyst is specifically engineered for the efficient decomposition of methyl orange (MO) and formaldehyde, demonstrating a significant increase in photocatalytic activity. The TiO2/C/g-C3N4 photocatalyst also exhibits an enhanced specific surface area of 181.2 m2/g, which facilitates increased physical adsorption and photo-catalytic active sites. Experimental results confirm that this catalyst effectively adsorbs MO physically even in the dark without degradation. Combining physical and photo-catalytic functions, this catalyst degrades 94 % of MO within 180 min with the initial concentration 0.2 mol/L of MO, and achieves almost 100 % decolorization of MO under visible light irradiation. Notably, the catalyst retains its high activity after 4 cycles of MO degradation, underscoring its durability and consistent performance. Additionally, the hybrid catalyst features a staggered type-II energy level configuration, which effectively enhances charge separation and boosts photocatalytic efficacy. The incorporation of an ultrafine carbon layer further augments electron mobility towards the surface, crucial for effective catalytic reactions. This study paves the way for future development of highly efficient photocatalytic materials for environmental purification.

Herein, five Schiff’s base analogs (designated as AK1 to AK5) were synthesized and screened for their analgesic and anti-inflammatory efficacies. All compounds exhibited analgesic activity at selected doses from 45.31 % to 75.75 %, while the standard drug diclofenac sodium produced result as 86.33 %. Compounds AK1, AK3, AK4 and AK5 were found significantly P

The growing attractiveness of Artificial Neural Networks (ANNs) derives from their exceptional effectiveness in handling difficult and exceptionally nonlinear mathematical ideas. In complicated disciplines such as fluid mechanics, biological computation, and the field of biotechnology ANNs provide a diverse computing framework that is extremely valuable. This article's major aim is to harness the capabilities of the Levenberg-Marquardt technique with backpropagation intelligent neural networks (LM- BPINNs) to study there is still a lack of clarity regarding the mechanics underlying the increased heat transfer caused by dispersed nanoparticles. The using proposed LM-BPINNs to improve the heat transmission use activation energy and Hall current phenomena in nanofluid (HTAHCNF). The data set is obtained by using Lobatto-III. A method and then ANNs is applied. The LM- BPINNs technique is applied by utilizing reference datasets, with 80% of the dataset devoted to training, 10% to testing, and 10% to verification. The precision/accuracy and converging of developed LM- BPINNs are validated based on the obtained reliability via efficient fitness achieved on mean squared error (MSE), comprehensive regression analysis, and appropriate error histogram visualizations. A diminished MSE indicates that the model's predictions are more reliable. The outcome is consistent with getting a minimal absolute error close to zero, exhibiting the effectiveness of the proposed approach.

Neurodegenerative diseases (NDDs) have received considerable interest from scientists for a long time due to their multifactorial nature. Alzheimer’s disease (AD) is of particular importance among pathologies, and despite approved drugs for its treatment, there is no effective pharmacological therapy to stop, halt, or repair neurodegeneration. The U.S. Food and Drug Administration (FDA) has approved certain medications to treat AD, including galantamine, donepezil, rivastigmine, memantine, tacrine, suvorexant, brexpiprazole, butein, and Licochalcone A. Topological indices and quantitative structure-property relationships (QSPRs) are indispensable in drug discovery. They allow researchers to analyze, compare, and predict the properties of chemical compounds, thereby expediting the identification of promising drug candidates while minimizing experimental costs and efforts. Regression models are vital in QSPR analysis, especially when dealing with topological indices. They facilitate quantifying the relationship between chemical structure and properties, thereby facilitating drug design, material discovery, and other chemistry-related applications. The objective of this study is to examine the efficacy of descriptors in correlating the physicochemical features of molecules associated with Alzheimer’s disease. We will use the computational method to calculate degree-related, distance-related, and eccentricity-based topological indices for any chemical graph. QSPR models are developed utilizing degree-based, distance-based, and eccentricity-based topological indices to estimate some physicochemical properties of AD drugs, including molecular weight (MW), boiling point (BP), topological polar surface area (TPSA), complexity (C), polarizability (P), and refractive index (R). The QSPRS studies are obtained using the linear regression technique. The present study found that the topological indices Randic index R, first Zagreb index M1, and atom-bond connectivity index ABC provide valuable insights into the structure-activity relationships of different drugs and help in designing more effective combinations for treating Alzheimer’s disease.

The construction industry strongly relies on concrete and clay bricks for various applications. The escalating demand for these materials, driven by rapid population growth, has led to resource depletion and increased construction and demolition waste (CDW). Recycling CDW into construction materials, particularly in the form of recycled concrete aggregates (RCAs) and recycled brick aggregates (RBAs), has emerged as a promising solution. This study deals with the structural performance of concrete incorporating RCAs and RBAs. The experimental program encompasses material characterization, concrete mix design, and several tests to assess density, compressive strength, bond behavior, and flexural properties. The results indicate that the replacement of fine natural aggregate (NA) with fine RCAs or RBAs has a negligible impact on density, while the partial replacement of coarse NAs with RAs yields modest reductions in compressive strength. Notably, the bond strength between steel rebar and concrete is influenced by the type and content of RA, with specimens containing RCAs exhibiting a higher bond strength than those with RBAs. Empirical models used to predict bond strength generally align with experimental results, with conservative predictions by some models, such as ACI 318, and overestimation by others, such as models proposed by AS-3600 and CEB-FIB. The flexural tests of beams highlight the variation in stiffness and load-bearing capacity with the proportion of NAs replaced by RAs. While beams with 50% NA replacement demonstrate comparable performance to control beams, those with 100% RA replacement exhibit lower cracking and yielding stiffness. Cracking patterns in beams with RAs differ from control beams, with RA-containing beams showing more cracks and an altered crack distribution. The findings underscore the feasibility of using recycled aggregates in construction, with partial NA replacement offering a balance between sustainable material usage and desired structural properties.

The current study presents an analysis of an inclined magnetized unsteady Cross fluid flowing over a coated disk with buoyancy effects and energy loss. The flow is modeled using the Navier-Stokes equations, including buoyancy, magnetic field, and energy loss effects based on the coated disk. The governing equations are solved numerically by applying the process of bvp4c to analyze the effects of inclination angle, magnetic field strength, and coating thickness using the flow characteristics. The results indicate that the buoyancy effects have a significant impact on the flow along with the results of flow velocity increment along with static pressure decrement. The magnetic field also has significant effects on the flow, which shows the decreasing velocity by increasing the magnetic field. Additionally, the coating thickness has significant effects on energy loss that decrease by increasing the coating thickness. The purpose of this work is to provide the valuable insight using the buoyancy, magnetic field, and coating thickness effects on the flow characteristics and energy loss based on the inclined magnetic unsteady cross flow passing over a coated disk.

Nanofinishing is the process by which ultrafine dispersion of nanomaterials is applied to a textile for the development of functionalities. The utilization of nanometal oxides as antimicrobial agents have shown a substantial antimicrobial property in cotton. In the present study, previously synthesized powder containing ZnO nanoflowers (ZnO NFs) was characterized for morphology, surface composition, roughness, and charge using Transmission electron microscopy (TEM), Scanning transmission electron microscopy (STEM), Atomic force microscopy(AFM) and Zeta potential. Optical properties of crystalline ZnO were determined by Photoluminescence (PL), Diffused reflectance Spectroscopy (DRS), and bandgap energy determination. Highly crystalline, ZnO NFs bearing crystal defects and high surface charge were loaded onto the pristine cotton by a dip coating method using Triton X-100 as dispersant and iSys MTX fabric binder. The pristine cotton fabric of 125 g/m2 was nano finished by loading 20,42 and 58 µg/cm2 (1–3 dip cycles) ZnO NFs respectively. The loading of ZnO NFs onto the surface of cotton fabric was confirmed by SEM and used for antibacterial activity against E. coli as a photocatalytic reactor. The prepared samples were irradiated with a UV lamp of λmax = 254 nm (15 min, 30 min, 45 min) and D65 artificial sunlight (60 min, 120 min, 180 min) to investigate their photocatalytic activity against pathogenic E. coli using modified Breed Smear’s method. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of ZnO NFs@ cotton were determined as 19.53 µg/ml and 39.06 µg/ml respectively after exposure to UV light. After exposure to sunlight MIC and MBC observed were higher i.e. 156.25 µg/ml and 312.5 µg/ml respectively showing lesser activity in sunlight as compared to ionizing UV radiations. To verify the photocatalytic activity, hydroxyl radicals generated by ZnO NFs@ cotton were also determinedtime-resolved PL on exposure to a UV lamp and D65 artificial sunlight. This nano-finished cotton is a promising candidate to be used as a medical textile with high antibacterial activity even after 20 washing cycles with only a 5% decrease in efficiency.

Abstract Quinolone is a privileged scaffold in medicinal chemistry and 4-Quinolone-3-Carboxamides have been reported to harbor vast therapeutic potential. However, conversion of N-1 substituted 4-Quinolone 3-Carboxylate to its corresponding carbamates is highly restrictive. This motivated us to adopt a much simpler, scalable and efficient methodology for the synthesis of highly pure N-1 substituted 4- Quinolone-3-Carboxamides with excellent yields. Our adopted methodology not only provides a robust pathway for the convenient synthesis of N-1 substituted 4- Quinolone-3-Carboxamides which can then be explored for their therapeutic potential, this may also be adaptable for the derivatization of other such less reactive carboxylate species.

Utilizing zeolitic imidazolate frameworks (ZIFs) poses a significant challenge that demands a facile synthesis method to produce uniform and nanometer-scale materials with high surface areas while achieving high yields. Herein, we demonstrate a facile and cost-effective strategy to systematically produce ZIF8 nanocrystals. Typically, ZIF8 nanocrystal synthesis involves a wet chemical route. As the reaction time decreased (150, 120, and 90 min), the size of the ZIF8 crystals decreased with uniform morphology, and productivity reached as high as 89%. The composition of the product was confirmed through XRD, FE-SEM, TEM, EDS, and Raman spectroscopy. The ZIF8 synthesized with different reaction time was finally employed for catalyzing the electrochemical hydrogen evaluation reaction (HER). The optimized ZIF8-3 obtained at 90 min of reaction time exhibited a superior catalytic action on the HER in alkaline medium, along with a remarkably long-term stability for 24 h compared with the other ZIF8 nanocrystals obtained at different reaction times. Specifically, the optimized ZIF8-3 sample revealed an HER overpotential of 172 mV and a Tafel slope of 104.15 mV·dec−1. This finding, thus, demonstrates ZIF8 as a promising electrocatalyst for the production of high-value-added green and sustainable hydrogen energy.

Considering the importance of cotton products, timely identification of pests (flying moths—being a significant threat to cotton crops) helps to protect cotton crops and improve their production and quality. This study proposes real-time detection of Cotton Flying Moths (CFMs) with the assistance of an Internet of Things (IoT)-based system in the agricultural field. The proposed prototype contains a group of sharp infrared sensors, a Zigbee-based communication module, an Arduino 2560 Mega board, a lithium polymer battery (to power the mote), a gateway device, and an unmanned aerial vehicle (UAV) to respond as a pesticide-sprayer against the detected pest. The proposed pest detection algorithm detects the flying insects’ presence by monitoring variations in the reflected light. Based on this, it sends a detection alert to the gateway device. The gateway device sends detection coordinates to the drone/UAV to respond by spraying pesticide in the detection region. A real testbed and simulation scenarios were implemented to evaluate the effectiveness of the proposed detection system. The results of the testbed implementation suggest the effectiveness of the sensor design and CFM detection. Initial results from the simulation study indicate the suitability of the proposed prototype deployment in the agricultural field. The proposed prototype would not only help minimize the use of pesticides but also maintain the quality and quantity of cotton products. The originality of this study is the custom-made and cost-effective IoT prototype for CFM detection in the agricultural field.

Modern means of communication, economic crises, and political decisions play imperative roles in reshaping political and administrative systems throughout the world. Twitter, a micro-blogging website, has gained paramount importance in terms of public opinion-sharing. Manual intelligence of law enforcement agencies (i.e., in changing situations) cannot cope in real time. Thus, to address this problem, we built an alert system for government authorities in the province of Punjab, Pakistan. The alert system gathers real-time data from Twitter in English and Roman Urdu about forthcoming gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.). To determine public sentiment regarding upcoming anti-government gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.), the alert system determines the polarity of tweets. Using keywords, the system provides information for future gatherings by extracting the entities like date, time, and location from Twitter data obtained in real time. Our system was trained and tested with different machine learning (ML) algorithms, such as random forest (RF), decision tree (DT), support vector machine (SVM), multinomial naïve Bayes (MNB), and Gaussian naïve Bayes (GNB), along with two vectorization techniques, i.e., term frequency–inverse document frequency (TFIDF) and count vectorization. Moreover, this paper compares the accuracy results of sentiment analysis (SA) of Twitter data by applying supervised machine learning (ML) algorithms. In our research experiment, we used two data sets, i.e., a small data set of 1000 tweets and a large data set of 4000 tweets. Results showed that RF along with count vectorization performed best for the small data set with an accuracy of 82%; with the large data set, MNB along with count vectorization outperformed all other classifiers with an accuracy of 75%. Additionally, language models, e.g., bigram and trigram, were used to generate the word clouds of positive and negative words to visualize the most frequently used words.

The novelty of this paper is to propose a numerical method for solving ordinary differential equations of the first order that include both linear and nonlinear terms (ODEs). The method is constructed in two stages, which may be called predictor and corrector stages. The predictor stage uses the dependent variable’s first- and second-order derivative in the given differential equation. In literature, most predictor–corrector schemes utilize the first-order derivative of the dependent variable. The stability region of the method is found for linear scalar first-order ODEs. In addition, a mathematical model for boundary layer flow over the sheet is modified with electrical and magnetic effects. The model’s governing equations are expressed in partial differential equations (PDEs), and their corresponding dimensionless ODE form is solved with the proposed scheme. A shooting method is adopted to overcome the deficiency of the scheme for solving only first-order boundary value ODEs. An iterative approach is also considered because the proposed scheme combines explicit and implicit concepts. The method is also compared with an existing method, producing faster convergence than an existing one. The obtained results show that the velocity profile escalates by rising electric variables. The findings provided in this study can serve as a helpful guide for investigations into fluid flow in closed-off industrial settings in the future.

The unprecedented growth in Augmented Reality (AR) has captured the focus of researchers and the industrial sector. The development of AR applications and their implementation in various domains is broadening. One of the advancements in the field of AR is Collaborative AR, which provides ample opportunities for the members of a team to work on a particular project remotely. The various activities carried out remotely, in a collaborative fashion, are based on the active interaction and transmission of data and applications across a communication channel that constitutes a mesh of frequently interacting applications, thus providing a real feeling of working together physically in the purportedly same demographic area. However, in the integration of different roles, remotely working in collaborative AR has a great chance of being intruded upon and manipulated. Consequently, the intrusion may explore novel vulnerabilities to various sensitive collaborative projects. One of the security concerns for collaborative and interconnected remote applications is to have pristine environments, where the participants of the collaborative AR can reliably trust each other during the execution of the various processes. This paper presents an integrity-aware CAR-Tourist (Collaborative Augmented reality for Tourism) framework wherein the unauthorized user’s access is denied and the remote participants of the network are provided with a secure environment through the state-of-the-art Blockchain architecture. This study further provides a use-case implementation of a tourism application. Each tourist has the chance to hire a remote guide for collaborative guidance over a blockchain-trusted network. Moreover, the proposed framework is lightweight, as the only necessary communication between the tourist and guide is recorded in the blockchain network. Each user has to register on a permission blockchain to be allowed to perform certain activities on our proposed CAR-Tourist framework. The decentralized Blockchain approach provides a consensus mechanism based on which not every participant is free to intrude on ongoing communication. Thus, through the proposed framework, all the participants in the collaborative Augmented Reality will have the essential trust of working remotely without external intrusion.

Facial emotion recognition (FER) is an important and developing topic of research in the field of pattern recognition. The effective application of facial emotion analysis is gaining popularity in surveillance footage, expression analysis, activity recognition, home automation, computer games, stress treatment, patient observation, depression, psychoanalysis, and robotics. Robot interfaces, emotion-aware smart agent systems, and efficient human–computer interaction all benefit greatly from facial expression recognition. This has garnered attention as a key prospect in recent years. However, due to shortcomings in the presence of occlusions, fluctuations in lighting, and changes in physical appearance, research on emotion recognition has to be improved. This paper proposes a new architecture design of a convolutional neural network (CNN) for the FER system and contains five convolution layers, one fully connected layer with rectified linear unit activation function, and a SoftMax layer. Additionally, the feature map enhancement is applied to accomplish a higher detection rate and higher precision. Lastly, an application is developed that mitigates the effects of the aforementioned problems and can identify the basic expressions of human emotions, such as joy, grief, surprise, fear, contempt, anger, etc. Results indicate that the proposed CNN achieves 92.66% accuracy with mixed datasets, while the accuracy for the cross dataset is 94.94%.

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.

The Internet of Things (IoT) is a swiftly developing technology in all sectors, with the number of devices that connect to the Internet has increased remarkably in recent years. However, most of these devices use cheap hardware and lack a concrete security defence system. This may encourage hackers to recruit these devices and use them to launch Distributed Denial of Service (DDoS) attacks, which is one of the main causes of concern among security engineers. This paper investigates the possibility of using a matrix profile to detect DDoS attacks in an IoT-based environment. According to our empirical experiments, the preliminary findings illustrate that the matrix profile algorithm can efficiently detect IoT-based DDoS attacks.

This paper proposes a numerical scheme for solving linear and nonlinear differential equations obtained from the mathematical modeling of a flow phenomenon. The scheme is constructed on two grid points. It is a two-stage, or predictor–corrector type, scheme whose first stage (the predictor stage) comprises a forward Euler scheme. The stability region of the proposed scheme is larger than that of the first-order forward Euler scheme. A problem is constructed, comprised of a mathematical model for the Darcy–Forchheimer flow of micropolar fluid over a stretching sheet, and is modified using partial differential equations (PDEs) by incorporating the effects of homogeneous–heterogeneous reactions. A set of PDEs is further reduced into ordinary differential equations (ODEs) by several transformations and is solved using the proposed numerical scheme. By comparing the results obtained using the proposed scheme with those obtained using the existing forward Euler scheme, it can be observed that the proposed scheme achieved a smaller absolute error. The obtained results show that the angular velocity profile displayed dual behavior according to increases in the values of the microrotation and coupling constant parameters. As part of our research, we conducted a comparison with other existing schemes. The findings of this study can serve as a helpful guide for future investigations into fluid flow in closed-off industrial settings.

In this work, silver-entecavir nanoparticles (Ag-ETR) were successfully synthesized using the green synthetic procedure and caped by entecavir that worked as a reducing agent, as it has amino and hydroxy functional groups. Synthesis of NPs was confirmed by UVS spectrophotometry and thin-layer chromatography while surface functionalization was studied using Fourier transform infrared spectrometer and scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS). Morphological studies of synthesized NPs were performed using the scanning electron microscopy, atomic force microscopy, and zetasizer. Structure of self-assembly Ag-ETR nano-clusters was proposed by UV–vis spectroscopy, FT-IR, H-NMR, X-ray diffraction, and SEM-EDS. The stoichiometry of Ag-ETR NPs was found to be 8:2 (ETR and Ag) and 120–188 nm in size, while having a surface charge –13.7 mv. Decomposition kinetics of Ag-ETR NPs was determined by Horowitz-Metzger method and allied kinetic parameter including Gibb’s free energy (ΔG), change in entropy (ΔS) and heat of enthalpy (ΔH) were determined. ETR conjugated Ag NPs exhibited significantly increased stability and aqueous solubility. Ag-ETR NPs could be a promising agent to have better synergistic effect, are expectedly reduce drug resistance which is corroborated to different cellular uptake mechanisms and may increase bioavailability as NPs had high water solubility.

A measurement of the molecular topology of graphs is known as a topological index, and several physical and chemical properties such as heat formation, boiling point, vaporization, enthalpy, and entropy are used to characterize them. Graph theory is useful in evaluating the relationship between various topological indices of some graphs derived by applying certain graph operations. Graph operations play an important role in many applications of graph theory because many big graphs can be obtained from small graphs. Here, we discuss two graph operations, i.e., double graph and strong double graph. In this article, we will compute the topological indices such as geometric arithmetic index GA, atom bond connectivity index ABC, forgotten index F, inverse sum indeg index ISI, general inverse sum indeg index ISIα,β, first multiplicative-Zagreb index PM1 and second multiplicative-Zagreb index PM2, fifth geometric arithmetic index GA5, fourth atom bond connectivity index ABC4 of double graph, and strong double graph of Dutch Windmill graph D3p.

In the fields of mathematical chemistry, a topological index, also known as a connectivity index, is a type of a molecular descriptor that is calculated based on the molecular graph of a chemical compound. Topological indices are an analytical framework of a graph which portray its topology and are mostly equal graphs. Topological indices (TIs) are numeral quantities that are used to foresee the natural correlation among the physicochemical properties of the chemical compounds in their fundamental network. TIs show an essential role in the theoretical abstract and environmental chemistry and pharmacology. In this paper, we compute many latest developed degree-based TIs. An analogy among the computed different versions of the TIs with the help of the numerical values and their graphs is also included .In this article, we compute the first Zagreb index, second Zagreb index, hyper Zagreb index, ABC Index, GA Index, and first Zagreb polynomial and second Zagreb polynomial of chemical graphs polythiophene, nylon 6,6, and the backbone structure of DNA.

Current work is focused on fabrication of MOF based hydrophobic sponge using single step in-situ coating of Zeolitic Immidazole Framework-8 (ZIF-8) on polyurethane sponge. The physicochemical characterizations have confirmed the growth of ZIF-8 on sponge skeleton. Effect of ZIF-8 loading on hydrophobicity of sponge is studied by contact angle analysis for all samples. It is found that the optimized molar ratio 1:8.08: 124.93 of Zn+2, 2-methylimidazole (Hmim) and MeOH forms a uniform layer of ZIF-8 on sponge skeleton that gives highest water contact angle of 129.2o and good absorption capacity for different oils/organic solvents ranging from 28 to 79 times of its own weight. Facile fabrication without requiring pre-modification of sponge by surfactants have made MOF sponge easy to handle and reliable for oily water treatment.

This contribution aims to propose a compact numerical scheme to solve partial differential equations (PDEs) with q-spatial derivative terms. The numerical scheme is based on the q-Taylor series approach, and an operator is proposed, which is useful to discretize second-order spatial q-derivative terms. The compact numerical scheme is constructed using the proposed operator, which gives fourth-order accuracy for second-order q-derivative terms. For time discretization, Crank–Nicolson, and Runge–Kutta methods are applied. The stability for the scalar case and convergence conditions for the system of equations are provided. The mathematical model for the heat transfer of boundary layer flow under the effects of non-linear mixed convection is given in form of PDEs. The governing equations are transformed into dimensionless PDEs using suitable transformations. The velocity and temperature profiles with variations of mixed convection parameters and the Prandtl number are drawn graphically. From considered numerical experiments, it is pointed out that the proposed scheme in space and Crank–Nicolson in time is more effective than that in which discretization for the time derivative term is performed by applying the Runge–Kutta scheme. A comparison with existing schemes is carried out as part of the research. For future fluid-flow investigations in an enclosed industrial environment, the results presented in this study may serve as a useful guide.

Wireless sensor networks (WSNs) have been applied in networking devices, and a new problem has emerged called source-location privacy (SLP) in critical security systems. In wireless sensor networks, hiding the location of the source node from the hackers is known as SLP. The WSNs have limited battery capacity and low computational ability. Many state-of-the-art protocols have been proposed to address the SLP problems and other problems such as limited battery capacity and low computational power. One of the popular protocols is random path routing (RPR), and in random path routing, the system keeps sending the message randomly along all the possible paths from a source node to a sink node irrespective of the path’s distance. The problem arises when the system keeps sending a message via the longest route, resulting because of high battery usage and computational costs. This research paper presents a novel networking model referred to as calculated random path routing (CRPR). CRPR first calculates the top three shortest paths, and then randomly sends a token to any of the top three shortest calculated paths, ensuring the optimal tradeoff between computational cost and SLP. The proposed methodology includes the formal modeling of the CRPR in Colored Petri Nets. We have validated and verified the CRPR, and the results depict the optimal tradeoff.

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

Underground utilities are important assets that provide basic services for society’s daily life. They are generally very complex and remain unnoticed until they fail due to any particular reason. The stakeholders involved in the design, construction, and maintenance of utility infrastructure face many problems due to the traditional underground utility management system, resulting in injuries, loss of life, disruptions, project delays, and financial loss. The problem with the traditional system is that it uses 2D drawings and keeps unreliable information or a lack of updated information, which makes it an inefficient utility management system. With the advancement in construction information technology, we can address this effectively by integrating BIM and GIS. In this paper, a novel integrated BIM-GIS framework for underground utility management systems was developed on the basis of IFC to CityGML mapping. It provides an effective underground utility management system that facilitates designers in optimization of the design, assists in the excavator’s operator by providing real-time three-dimensional spatial information during the construction process, and acts as an as-built information database for utility facility management. For validation, a real-time project case study indicated that the proposed system can effectively provide comprehensive underground utility information at different project stages.

The increase in population and urbanization needs attention towards intense construction activities to meet the social and economic needs. Soil excavation is a primary step in every construction project that needs proper surface and subsurface information modeling since it is vulnerable to construction hazards. Geographic information system (GIS) provides significant information about the existing contextual surface information while building information modeling (BIM) gives information about the asset in a great detail that has been integrated into the construction industry for many applications. However, the integration of BIM and GIS for the subsurface geotechnical property modeling and classification into zones has been rarely explored. This paper presents the integration of BIM and GIS for modeling geotechnical properties and safe construction zones based on soil type. The use of open standard IFC classes such as IfcBorehole, IfcGeoslice, and IfcGeomodel enhances the collaboration and allows the exchange of geotechnical information among different stakeholders. The method has been applied to the in-situ and laboratory test dataset of the Peshawar, region, to validate the proof of concept. The results demonstrate that the proposed method successfully integrates BIM and GIS providing a three-dimensional surface and subsurface model. The 3D digital geotechnical model has excellent potential to provide information about soil type, properties, depth, and volume of each available soil layer that can be used by construction planners and managers to identify best construction practices and plan for safe construction.

A series of schiff base derivatives of 4-aminophenazone (4APZ-1,5-dimethyl-2-phenyl-1,2-dihydro-3H-pyrazol-3-one) with different aldehydes were synthesized. The synthetic compounds were screened for their anti-inflammatory, analgesic and antipyretic activities. The characterization of synthesized compounds was carried out by 1H NMR, 13C NMR and MS. Carrageenan-induced paw oedema (CIPO) and histamine induced paw oedema (HIPO) methods were used to determine the anti-inflammatory activity of commercial sample of 4APZ and its synthesized schiff bases in mice. The anti-inflammatory activity was in the order of 4APZAB > 4APZBB > 4APZCB > 4APZVn and all the test compounds exhibited considerable dose dependent inhibition of the paw oedema. The effect of the compounds on membrane stabilization was also determined which showed that compounds 4APZ (120 and 240 mg/kg doses), 4APZAB (160 mg/kg) and 4APZVn (600 mg/kg) produced highly significant inhibition (P 4APZBB > 4APZVn > 4APZCB. Moreover, phenobarbitone-induced sleeping time (PIST) in mice was also studied but only 600 mg/kg of 4APZVn significantly increased the duration of induced sleep which also suggested its sedative property. Brewer’s yeast was used to induce fever in rabbits and analysed the compounds for their antipyretic activity. Different doses of 4APZ for different time durations (240 mg/kg-after 1 h, 120 and 240 mg/kg doses-after 2 h) produced highly significant (P