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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Little is known in the literature about the concept of nonuniform heat source/sink and higher-order chemical reaction for the dynamics of Oldroyd-B nanoparticles. Therefore, the present article addresses the nonuniform heat source/sink and higher-order chemical reaction features in nonlinear mixed convection bidirectional MHD dynamics of Oldroyd-B nanoparticles with thermal radiation aspects through porous space. Stratification effects for both the temperature and concentration setups are also used in the mathematical model with the significance of random movement and thermodiffusion of nanoparticles. Shape-preserving transformations have been employed to convert the transport equations into solvable forms. An innovative analytical tactic, namely, homotopy analysis method, has been adopted to find the solution of the modeled problem. Behaviors of pertinent parameters on thermal and concentration profiles have been discussed through various graphs. Inspection of heat/mass transport against appropriate varieties of pertinent parameters has been made and explained physically. Thermal profile is augmented with the higher estimations of space and temperature-dependent heat source/sink links. Concentration profile is diminished with the augmentation of higher-order chemical reaction parameter. Sherwood number is improved with the estimation of 0 ≤ β t ≤ 100 and is reduced with the growth of 0 ≤ β c ≤ 100. Nusselt number is declined with the upgraded amounts of 0 ≤ N b ≤ 3 and 0 ≤ N t ≤ 5. [ABSTRACT FROM AUTHOR]

The application of graph theory in the study of molecular physical and chemical properties involves theoretical mathematical chemistry. Atoms, represented by vertices, and edges, represented by bonds between them, are detailed in simple graphs called chemical graphs. The mathematical derivation of the numerical value of a graph is called the molecular descriptor of the graph. Any connected graph wherein no edge is contained in exclusive of a single cycle is called a cactus graph. In the research in this article, expressions for various molecular descriptors of line graph of the graph obtained by the rooted product of the cycle and path graphs are constructed. This article obtained the calculation of molecular descriptors for line graphs of chain ortho cactus and chain para cactus graphs. To predict the biological activity of a compound, the generalized Zagreb index, the first Zagreb index M 1 G , the second Zagreb index M 2 G , the F-index, the general Randic index, the symmetric division, the atom bond connectivity (ABC), and the geometric arithmetic (GA) descriptors are created. [ABSTRACT FROM AUTHOR]

In view of the successful applications of graph theory, relationships between the biological activity and chemical structure have been developed. One of the popular topics in graph theory is problems relating to topological indices. Degree-based topological indices, distance-based topological indices, and counting-related topological indices are various types of topological indices. Physiochemical properties such as boiling point and stability of chemical compounds are correlated by these topological indices. A topological index of a graph is a numerical quantity obtained from the graph mathematically. A cactus graph is a connected graph in which no edge lies in more than one cycle. In this study, we have derived certain degree-based topological indices for some families of graphs consisting of graph obtained by the rooted product of paths and cycles and two types of cactus graph (paracactus and orthocactus) with the help of the generalized Zagreb index. [ABSTRACT FROM AUTHOR]

The contribution of amino acids (AAs) to soil nitrogen (N) fluxes is higher than previously thought. The fact that AA uptake is pivotal for N nutrition in boreal ecosystems highlights plant AA transporters as key components of the N cycle. At the same time, very little is known about AA transport and respective transporters in trees. Tree genomes may contain 13 or more genes encoding the lysine histidine transporter (LHT) family proteins, and this complicates the study of their significance for tree N-use efficiency. With the strategy of obtaining a tool to study N-use efficiency, our aim was to identify and characterize a relevant AA transporter in hybrid aspen (Populus tremula L. x tremuloides Michx.). We identified PtrLHT1.2, the closest homolog of Arabidopsis thaliana (L.) Heynh AtLHT1, which is expressed in leaves, stems and roots. Complementation of a yeast AA uptake mutant verified the function of PtrLHT1.2 as an AA transporter. Furthermore, PtrLHT1.2 was able to fully complement the phenotypes of the Arabidopsis AA uptake mutant lht1 aap5 , including early leaf senescence-like phenotype, reduced growth, decreased plant N levels and reduced root AA uptake. Amino acid uptake studies finally showed that PtrLHT1.2 is a high affinity transporter for neutral and acidic AAs. Thus, we identified a functional AtLHT1 homolog in hybrid aspen, which harbors the potential to enhance overall plant N levels and hence increase biomass production. This finding provides a valuable tool for N nutrition studies in trees and opens new avenues to optimizing tree N-use efficiency. [ABSTRACT FROM AUTHOR]

Researchers working in the field of unmanned aerial vehicles are observing rapid developments in the field, such as the use of quadrotors to grasp, manipulate, and transport objects. These advancements are made possible through sophisticated control systems, new types of actuation and sensing technologies, and a greater understanding of the aerodynamic effects and gyroscopic moment generated by the rotating blades of the quadrotor involved. Therefore, this research work proposes four novel optimized control laws, i.e., conditioned adaptive barrier-based double integral super twisting sliding mode controller (CABDIST-SMC), BF double integral SMC (BF-DISMC), BF integral SMC, and barrier function-based SMC control laws, for attitude, heading, position, and altitude trajectory tracking of the quadcopter. The non-linear model of the quadcopter is developed using the Lagrange formalism in MATLAB ODE-45 environment, including the gyroscopic moments and aerodynamic effects, and a Lyapunov stability analysis is performed to ensure the asymptotic stability of the system. A 3D-helical complex trajectory is employed to analyze the performance and consistency of the proposed optimized controllers. For performance comparison, the proposed four variants of the SMC are compared with three traditional control strategies, i.e., backstepping SMC, optimized adaptive SMC via particle swarm optimization algorithm, and improved adaptive SMC. These traditional control strategies have also been developed and tested in MATLAB-Simulink. All the controllers are optimized using the improved grey wolf optimization algorithm, and to determine the best control law, six performance indexes, i.e., mean absolute percentage error (MAPE), root mean square error (RMSE), integral square error (ISE), integral absolute error (IAE), integral time absolute error (ITAE), and integral time square error (ITSE) are used. A hardware-in-loop (HIL) test is also performed to validate the performance of the designed novel control law. The C2000 Delfino MCU F28379D launchpad is used for performing the HIL validation test. The simulated results showed that the performance of optimized CABDIST-SMC surpassed all other control laws for altitude, heading, position, and attitude trajectory tracking. It has achieved MAPE of 0.02%, RMSE of 3.9912e−5, ISE of 3.1859e−8, IAE of 7.9823e−4, ITAE of 0.0080, and ITSE of 3.1849e−7 in case of yaw angle control. The proposed control laws have successfully passed the HIL validation test, and real-time implementation of the proposed control laws indicates no significant variations compared to the simulated results. [ABSTRACT FROM AUTHOR]

This study is based on gray-box modeling for the prediction of optimum mass flow rates of inlet streams of a Plate and Fin Heat Exchanger under uncertainty. A first principle model of the Plate and Fin Heat Exchanger was developed in Aspen Exchanger Design and Rating. Genetic algorithm was integrated with the first principle model to achieve the highest possible exit temperature of the cold process stream under uncertainty. A dataset of uncertain process conditions and their corresponding optimum inlet flow rates derived through the first principle-genetic algorithm integration was used to develop an artificial neural networks model. The artificial neural networks model was then integrated with the first principle model by replacing the genetic algorithm to form a novel gray-box framework. The proposed gray box model, i.e., artificial neural networks and first principle integration, achieved a higher effectiveness and higher outlet temperature than those derived through the straight run first principle model. The performance of the gray box framework was also comparable to the first principle integrated genetic algorithm approach and significantly minimized the computation time needed for estimating the optimum process conditions. First principle integrated genetic algorithm approach enhanced the effectiveness of the straight run model by 3.05%. Performance of the proposed gray-box model was comparable to the integrated framework of the genetic algorithm with the first principle model but was significantly faster. The developed artificial neural networks model was employed as a surrogate in Sobol and FAST sensitivity analysis framework to identify the impact of input variables on output variables. The proposed gray box based method enhanced the capability of the plate and fin heat exchanger to recover energy from the process stream and its robustness to cope with uncertainty. The proposed approach is suitable for real-time application and would contribute to laying a foundation for petroleum refinery 4.0. • A Gray-box (GB) modeling framework was developed for the optimization of process conditions of a plate and fin heat exchanger of a tile industry under uncertainty. • The GB framework was based on the integration of first principle (FP) model and Artificial Neural Networks (ANN) model. • The proposed GB approach attained the optimum conditions in a significantly less period of time than the conventional FP-GA framework. [ABSTRACT FROM AUTHOR]

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

The current study explores a new direction of research in which the magnetohydrodynamic (MHD) effects on two-dimensional nanofluid boundary layer flow under the influence of radiation are investigated in a porous medium using inverse multiquadric (IMQ) radial basis neural networks (RBNNs) i.e. IMQ-RBNNs. A quite new computational approach is used here in which artificial neural networks (ANNs) acquired IMQ radial basis function as an activation function in an amalgam of globally searching solver named genetic algorithms (GAs) together with an efficient local searching sequential quadratic programming (SQP) solver. The basic equations that represent the fluid flow are first obtained in the form of partial differential equations (PDEs) and then converted into dimensionless ordinary differential equations (ODEs) by applying similarity transformations. The dynamical properties of the nanofluid including velocity, temperature along mass concentration are analyzed by varying physical parameters including radiation parameter, magnetic parameter, constant moving parameter, heat source parameter, Brownian motion parameter and Eckert number. Moreover, the escalation in the value of the constant moving parameter diminishes the fluid velocity but this effect is reversed in the case of magnetic parameter. The increasing values of heat source parameter, Brownian motion parameter and Eckert number inflate the nanofluid temperature but a reversal effect is observed in the case of radiation parameter. It is also observed that larger values of heat Brownian motion parameter and radiation parameter amplify the nanofluid concentration but an opposite effect is obtained in the case of heat source parameter. The numerical results of the proposed model obtained through the IMQ-RBNNs based solver optimized with GASQP algorithm. The efficacy and tendency of rapid convergence of the proposed solver are endorsed through graphical illustrations and tables using well-known statistical operators with various fitness analyses on MATLAB are also part of this research. [ABSTRACT FROM AUTHOR]

In this study, an artificial intelligence-based framework was developed to monitor the efficiency of the carbon capture unit, i.e., the Sour Compression Unit (SCU) and a cryogenic unit, in a cement manufacturing plant. Initially, an Aspen Plus-based model of the SCU and the cryogenic unit was developed. The process model was then transformed into dynamic mode through the interfacing of MATLAB and Excel with Aspen Plus to capture real-time process behavior. Five hundred fifty data samples were generated by varying the process conditions, i.e., inlet water flow rate, temperature, pressure, etc. The dataset was then used to develop ensemble models. Output compositions of the models were CO 2 , SO 2 , and NO. The ensemble models were used as surrogates in Sobol and Fourier Amplitude Sensitivity Test (FAST) frameworks to find the most sensitive process conditions that affect the efficiency of the process. The correlation coefficients based on the ensemble models for estimation of the composition of CO 2 , SO 2 , and NO, were 0.9888, 0.9663, and 0.9970, respectively. Based on the sensitivity analysis, the inlet flue gas temperature and pressure were the dominant variables that affect the mole fraction of SO 2 and NO in the flue gas. Besides, the CO 2 recovery was found heavily dependent on temperature of the flash tanks. The proposed framework was highly accurate and will provide a base for real-time implementation of the concept of smart factory in cement manufacturing plants. [ABSTRACT FROM AUTHOR]

In this research-based study, the electro-magneto impacts are numerically observed during Darcy-Forchheimer (DF) viscous fluid flow having nonlinear form of thermal radiation. This two-dimensional fluid flow over a stretchable sheet is exposed to joule heating, thermal convection and viscous dissipation. The entire computational work is successfully performed using reliable neuro computing technique (NCT) through a viable combination of genetic algorithms (GAs) a well-known global solver and a famous local solver named sequential quadratic programming (SQP) i-e NCT-GA-SQP. Partial differential equations (PDEs) that express the DF viscous fluid flow are successfully converted into a third order nonlinear system of ordinary differential equations (ODEs) through manipulation of similarity variables. Numerical illustrations with strong accuracy and effective graphical representations are adopted to visualize the velocity profile, thermal distribution and concentration of fluid flow due to the variational effects of various physical parameters namely the electric field parameter (E), magnetic parameter (M), the porosity parameter (λ), the radiation parameter (Rd), the Prandtl number (Pr), the chemical reaction parameter (γ) and the Schmidt number (Sc). The reference solutions attained through well-known Adams method are used to validate the numerical results gathered through complexity analysis and different statistical operators and are closely matched which guarantees the reliability of the proposed solver. [ABSTRACT FROM AUTHOR]

The presence of high concentrations of metal ions in effluents resulting from industrial metal coatings is a well-known fact. Most of the time, such metal ions, once they reach the environment, significantly contribute to its degradation. Therefore, it is essential that the concentration of metal ions is reduced (as much as possible) before such effluents are discharged into the environment to minimize the negative impact on the quality of the ecosystems. Among all methods that can be used to reduce the concentration of metal ions, sorption is one of the most viable options due to its high efficiency and low cost. Moreover, due to the fact that many industrial wastes have sorbent properties, this method is in accordance with the principles of circular economy. Based on these considerations, in this study, mustard waste biomass (resulting from oil extraction) was functionalized with an industrial polymeric thiocarbamate (METALSORB) and used as a sorbent to remove Cu(II), Zn(II) and Co(II) ions from aqueous media. The best conditions for the functionalization of mustard waste biomass were found to be: mixing ratio biomass: METASORB = 1 g: 1.0 mL and a temperature of 30 °C. The experimental sorption capacities of functionalized sorbent (MET-MWB) were 0.42 mmol/g for Cu(II), 0.29 mmol/g for Zn(II) and 0.47 mmol/g for Co(II), which were obtained under the following conditions: pH of 5.0, 5.0 g sorbent/L and a temperature of 21 °C. The modeling of isotherms and kinetic curves as well as the analysis of the results obtained from desorption processes demonstrate the usefulness of this sorbent in the treatment of effluents contaminated with metal ions. In addition, tests on real wastewater samples highlight the potential of MET-MWB for large-scale applications. [ABSTRACT FROM AUTHOR]

In critical healthcare units, such as operation theaters and intensive care units, healthcare workers require specific temperature environments at different stages of an operation, which depends upon the condition of the patient and the requirements of the surgical procedures. Therefore, the need for a dynamically controlled temperature environment and the availability of the required heating/cooling electric power is relatively more necessary for the provision of a better healthcare environment as compared to other commercial and residential buildings, where only comfortable room temperature is required. In order to establish a dynamic temperature zone, a setpoint regulator is required that can control the zone temperature with a fast dynamic response, little overshoot, and a low settling time. Thus, two zone temperature regulators have been proposed in this article, including double integral sliding mode control (DISMC) and integral terminal sliding mode control (ITSMC). A realistic scenario of a hospital operation theater is considered for evaluating their responses and performance to desired temperature setpoints. The performance analysis and superiority of the proposed controllers have been established by comparison with an already installed Johnson temperature controller (JTC) for various time spans and specific environmental conditions that require setpoints based on doctors' and patients' desires. The proposed controllers showed minimal overshoot and a fast settling response, making them ideal controllers for operation theater (OT) zone temperature control. [ABSTRACT FROM AUTHOR]

Conventional fuels for power systems are now plaguing the environment more than ever before. Efforts to curb the environmental impacts of fossil fuels are now driving modern microgrids (MGs) to utilize green energy sources. In this regard, DC microgrids are well known for their versatility. Considering the rising significance of DC-MGs in future, this paper presents an aggregated DC microgrid model comprising of wind resource, photovoltaics, fuel cell, battery, and a supercapacitor. The model also includes DC–DC power converters and a two-level control structure. Firstly, the high-level of the proposed control structure constitutes an artificial neural network for maximum power point tracking and an intelligent energy management powered by a fuzzy logic controller. At this control level, the unified operation of the fuel cell and storage components under various dynamic conditions is ensured. Furthermore, the low-level control structure is based on an adaptive terminal sliding mode non-linear controller that derives the control signal for the converters. Finally, results showed that the energy management system along with the adaptive terminal sliding mode control successfully regulated the DC bus voltage by keeping the sources within their operational constraints. Moreover, the proposed non-linear controller demonstrated model's robustness, successfully converged the sliding surface to zero in finite time, ensured global asymptotic stability and effectively dealt with the model uncertainties. All the simulation leading to these results were performed in MATLAB/Simulink and further evaluation was carried out by hardware in loop experiments using C2000 Delfino microcontroller F28397D launchpad. • An optimum design of hybrid DC microgrid integrated with two renewable energy resources, an auxiliary source and hybrid energy storage system. • Multi-level control based on ANN, fuzzy Logic and adaptive terminal sliding mode non-linear controller for power balance and control. • HIL verification of controller performance using Delfino Microcontroller F28397D launchpad. [ABSTRACT FROM AUTHOR]

Cardiac disease diagnosis and identification is problematic mostly by inaccurate segmentation of the cardiac left ventricle (LV). Besides, LV segmentation is challenging since it involves complex and variable cardiac structures in terms of components and the intricacy of time-based crescendos. In addition, full segmentation and quantification of the LV myocardium border is even more challenging because of different shapes and sizes of the myocardium border zone. The foremost purpose of this research is to design a precise automatic segmentation technique employing deep learning models for the myocardium border using cardiac magnetic resonance imaging (MRI). The ASPP module (Atrous Spatial Pyramid Pooling) was integrated with a proposed 2D-residual neural network for segmentation of the myocardium border using a cardiac MRI dataset. Further, the ensemble technique based on a majority voting ensemble method was used to blend the results of recent deep learning models on different set of hyperparameters. The proposed model produced an 85.43% dice score on validation samples and 98.23% on training samples and provided excellent performance compared to recent deep learning models. The myocardium border was successfully segmented across diverse subject slices with different shapes, sizes and contrast using the proposed deep learning ensemble models. The proposed model can be employed for automatic detection and segmentation of the myocardium border for precise quantification of reflow, myocardial infarction, myocarditis, and h cardiomyopathy (HCM) for clinical applications. [ABSTRACT FROM AUTHOR]

Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains. [ABSTRACT FROM AUTHOR]

Cancer is a global health concern with a dynamic rise in occurrence and one of the leading causes of mortality worldwide. Among different types of cancer, ovarian cancer (OC) is the seventh most diagnosed malignant tumor, while among the gynecological malignancies, it ranks third after cervical and uterine cancer and sadly bears the highest mortality and worst prognosis. First-line treatments have included a variety of cytotoxic and synthetic chemotherapeutic medicines, but they have not been particularly effective in extending OC patients' lives and are associated with side effects, recurrence risk, and drug resistance. Hence, a shift from synthetic to phytochemical-based agents is gaining popularity, and researchers are looking into alternative, cost-effective, and safer chemotherapeutic strategies. Lately, studies on the effectiveness of phenolic acids in ovarian cancer have sparked the scientific community's interest because of their high bioavailability, safety profile, lesser side effects, and cost-effectiveness. Yet this is a road less explored and critically analyzed and lacks the credibility of the novel findings. Phenolic acids are a significant class of phytochemicals usually considered in the nonflavonoid category. The current review focused on the anticancer potential of phenolic acids with a special emphasis on chemoprevention and treatment of OC. We tried to summarize results from experimental, epidemiological, and clinical studies unraveling the benefits of various phenolic acids (hydroxybenzoic acid and hydroxycinnamic acid) in chemoprevention and as anticancer agents of clinical significance. [ABSTRACT FROM AUTHOR]

Deep learning is expanding and continues to evolve its capabilities toward more accuracy, speed, and cost-effectiveness. The core ingredients for getting its promising results are appropriate data, sufficient computational resources, and best use of a particular algorithm. The application of these algorithms in medical image analysis tasks has achieved outstanding results compared to classical machine learning approaches. Localizing the area-of-interest is a challenging task that has vital importance in computer aided diagnosis. Generally, radiologists interpret the radiographs based on their knowledge and experience. However, sometimes, they can overlook or misinterpret the findings due to various reasons, e.g., workload or judgmental error. This leads to the need for specialized AI tools that assist radiologists in highlighting abnormalities if exist. To develop a deep learning driven localizer, certain alternatives are available within architectures, datasets, performance metrics, and approaches. Informed decision for selection within the given alternative can lead to batter outcome within lesser resources. This paper lists the required components along-with explainable AI for developing an abnormality localizer for X-ray images in detail. Moreover, strong-supervised vs weak-supervised approaches have been majorly discussed in the light of limited annotated data availability. Likewise, other correlated challenges have been presented along-with recommendations based on a relevant literature review and similar studies. This review is helpful in streamlining the development of an AI based localizer for X-ray images while extendable for other radiological reports. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models, and the frequently used input and output variables for each stage. The findings of this article would be beneficial for academia and industry-related professionals involved in model development in different stages of biofuel's life cycle. [ABSTRACT FROM AUTHOR]

The Banded Gneissic Complex (BGC) of the Aravalli Craton (India) comprises Archean BGC‐I (3.3–2.5 Ga) and Proterozoic BGC‐II. The BGC‐II is a mosaic of amphibolite facies namely, (a) Mangalwar Gneissic Complex (MGC), (b) Mangalwar Metasedimentary Complex (MMC), and (c) granulite‐facies Sandmata Metamorphic Complex. Here we present field, petrography and geochemical study of the Proterozoic amphibolites from the MGC and MMC. Based on field and geochemical data, the amphibolites have been characterized into three types related to rift settings (G1, G2 and G3). The G1 type occurs as dykes in the MGC and bears ocean island basalt‐type rare earth element (REE) patterns along with negative Nb and Ti anomalies, negative to positive values of εNd(t) (−0.02 to +3.96) and slightly variable initial 87Sr/86Sr (ISr) ratios. They are derived from deep mantle sources and correspond to the pre‐rift magmatic phase. The G2 type occurs as isolated patches associated with chert and is characterized by light REE (LREE) depleted and almost flat heavy REE (HREE) patterns suggesting that they were emplaced in an oceanic setting and were derived from a shallower mantle bearing positive εNd(t) (+2.87 to +6.27) and ISr = 0.7002–0.7083. This phase corresponds to the opening of the Mangalwar sedimentary basin (MMC). The G3 type occurs intercalated with metasedimentary rocks of the MMC and marked by LREE‐enriched and HREE‐depleted to flat patterns that resemble Upper Continental Crust signature, their εNd(t) mostly negative values and variable ISr also corroborate this explanation. They are believed to be derived from heterogeneous sources and represent syn‐sedimentary volcanic phases. All these signatures indicate that the amphibolites distinctly represent three phases of magmatism that occurred during pre‐rift (1.72 Ga), opening of basin (1.62 Ga) and syn‐sedimentary volcanism (1.6–1.3 Ga) in the rift‐basin and they were formed during the Proterozoic. These rifting events might have been connected with the fragmentation of Columbia. [ABSTRACT FROM AUTHOR]

Context: Coronavirus (COVID-19) is a novel respiratory viral infection, causing a relatively large number of deaths especially in people who underly lung diseases such as chronic obstructive pulmonary and asthma, and humans are still suffering from the limited testing capacity. In this article, a solution is proposed for the detection of COVID-19 viral infections through the analysis of exhaled breath gasses, i.e., nitric oxide, a prominent biomarker released by respiratory epithelial, as a non-invasive and time-saving approach. Here, we designed a novel and low-cost N and P co-doped C60 fullerene-based breathalyzer for the detection of NO gas exhaled from the respiratory epithelial cells. This breathalyzer shows a quick response to the detection of NO gas by directly converting NO to NO2 without passing any energy barrier (0 kcal/mol activation energy). The recovery time of breathalyzer is very short (0.98 × 103 s), whereas it is highly selective for NO sensing in the mixture of CO2 and H2O gasses. The study provides an idea for the synthesis of low-cost (compared to previously reported Au atom decorated nanostructure and metal-based breathalyzer), efficient, and highly selective N and P co-doped C60 fullerene-based breathalyzer for COVID-19 detection. Methods: The geometries of N and P-doped systems and gas molecules are simulated using spin-polarized density functional theory calculations. [ABSTRACT FROM AUTHOR]

A study was conducted to evaluate the influence of various sowing times on flowering, pollen viability and seed yield of zinnia (Zinnia elegans) cultivars 'Lilliput' and 'Super Yoga' under Faisalabad, Pakistan conditions. Seeds of both Zinnia cultivars were sown in 128-cell plug trays at 20 days interval from March 14 to August 20, during 2019. Seedlings were transplanted in thoroughly prepared field beds after 15 days of each sowing, according to factorial arranged randomized complete block design, with three replications. Data were recorded for various growths, flowering traits, pollen viability and seed related parameters. Super Yoga excelled over Lilliput for most the studied traits except number of lateral shoots, flowers per plant, unopened flower buds, pollen viability and seed yield per plant; values of these traits were higher for Lilliput than Super Yoga. Significant variation among various sowing times was recorded in both cultivars. Seeds sown on March 14, 2019 performed best compared to later sowing times for plant height, number of lateral shoots per plant, length of the lateral shoots, leaf area, number of flowers per plant, flower diameter, pollen viability, seed yield per plant and seed viability. However, total number of un-opened flower buds, seed weight per flower and 100-seed weight were more in April 03 sowing, although statistically similar to March 14 sowing. Seed yield declined gradually with delay in sowing; 66% less seed yield in June 20 sowing. Further delay in sowing did not produce seed. It was concluded that Zinnia should be sown by mid-March for getting high seed yield of good quality; later sowing, even during first week of April, resulted in 17.5% decrease in seed yield. [ABSTRACT FROM AUTHOR]

Simulations were performed under continuous processing conditions using the 2D continuum model to describe the internal state of a multi-solid system, comprising solid particles of different sizes and densities at the same time. The feed consisted of 35 types of solid particle species with five different sizes, 1.70, 1.20, 0.85, 0.60 and 0.35 mm, and seven different densities ranging from 1400 to 2000 kg/m3. The simulations results have been used to plot the concentration profiles of solid particles along the bed height. The concentration profiles of the solid particles depicted that the fine dense particles, 0.60 mm, having density equal to 1900 kg/m³ and terminal velocity 0.058 m/s moved downwards and discharged into the underflow. However, the low-density coarse particles, 1.20 mm, having density equal to 1400 kg/m3 and terminal velocity 0.068 m/s moved upwards and conveyed to the overflow, hence, show a separation process based on the density difference. Furthermore, simulation results showed that the particle species having densities close to the value of the separation relative density exhibited higher concentrations along the system height, and the suspension within the system was mainly composed of these species. [ABSTRACT FROM AUTHOR]

Conductive agro-industrial wastes as accelerants in the anaerobic digestion (AD) of organic waste is a good technique for developing a rural circular economy, such as producing bioenergy and biofertilizer. This study disclosed the a role of sugar cane bagasse ash (SCBA) in enhancing the bioenergy (biogas) yield and digestate fertility via anaerobic co-digestion (AcoD) of buffalo dung (BD) and vegetable residue (VR) under mesophilic conditions (37 ᴼC). Firstly, an optimal BD/VR ratio (1:3) was determined based on biogas yield by introducing five different BD/VR ratios (1:0, 3:1, 1:1, 1:3, and 0:1) into AcoD systems. Secondly, the biogas yield was increased further by adding SCBA at five different concentrations (0, 0.5, 1, 1.5, and 2 wt%). Experimental results disclosed that the 1.5 wt% of SCBA gave the highest cumulative biogas yield (153.67 mL/g VS), COD removal rate (31.18%), and fertility (5.08%). Moreover, a framework is suggested to understand the role of SCBA in the enhanced DIET mechanism. This work documents an environmentally friendly and economical technique for developing a rural circular bioeconomy via the AD of organic agro-waste. [Display omitted] • Laterite accelerant can enhance the AD performance of cow-manure. • Laterite (LM 0.3%) increased biogas yield which is comparable BC 0.6 g/L. • Laterite enhanced COD biodegradation of organic-substrate. • Laterite and Biochar increased the nutrient NPK content in digestates. • Direct interspecies elctransfer can be enhanced through Laterite and Biochar. [ABSTRACT FROM AUTHOR]

[Display omitted] • A comprehensive calculation and analysis of defect properties in the corundum, monoclinic, and orthorhombic Ga 2 O 3 phases are presented. • The Ga-rich growth environment in all Ga 2 O 3 phases favors the spontaneous formation of oxygen vacancies with concentrations that can reach as high as 1021 cm−3. • The antisite defects contribution in intrinsic defect density is not negligible in an O-rich environment, and can reach a concentration of 1014 cm−3. • The charge transition levels in all phases are at deep levels within the bandgap. • Raman spectroscopy in an as-grown β-Ga 2 O 3 and a high temperature annealed β-Ga 2 O 3 reveals an increase in point defect concentration, correlating the defect formation energies to experimental observation. Ultrawide bandgap gallium oxide (Ga 2 O 3) is a promising material for power semiconductor devices and deep ultraviolet (UV) solar-blind photodetectors. Understanding the properties of point defects in Ga 2 O 3 is necessary to realize better-performing devices. A comprehensive study based on density functional theory (DFT), using the generalized gradient approximation (GGA): Perdew-Burke-Ernzerhof (PBE) exchange–correlation functional, of point defects in corundum (α), monoclinic (β), and orthorhombic (ε) phases of Ga 2 O 3 is presented. The point defects include vacancies, interstitials, antisites, and extrinsic impurities in various phases of Ga 2 O 3. Defect formation energies, charge transition energy levels, and defect concentrations variation with temperature are listed and presented under both gallium-rich (Ga-rich) and oxygen-rich (O-rich) growth conditions. The formation energy diagrams predict that the Ga 2 O 3 phases favor the formation of Ga and O vacancies and the incorporation of extrinsic impurities. The calculations also show that the charge transition levels are deep inside the bandgap regardless of the Ga 2 O 3 phase and growth environment. The impacts of temperature on the intrinsic point defects are analyzed by probing the vibrational modes of β-Ga 2 O 3 thin films grown in a metal–organic chemical vapor deposition (MOCVD) system at 700 °C temperature and annealed at 1100 °C in an O-rich environment. [ABSTRACT FROM AUTHOR]

The exponentially growing concern of cyber-attacks on extremely dense underwater sensor networks (UWSNs) and the evolution of UWSNs digital threat landscape has brought novel research challenges and issues. Primarily, varied protocol evaluation under advanced persistent threats is now becoming indispensable yet very challenging. This research implements an active attack in the Adaptive Mobility of Courier Nodes in Threshold-optimized Depth-based Routing (AMCTD) protocol. A variety of attacker nodes were employed in diverse scenarios to thoroughly assess the performance of AMCTD protocol. The protocol was exhaustively evaluated both with and without active attacks with benchmark evaluation metrics such as end-to-end delay, throughput, transmission loss, number of active nodes and energy tax. The preliminary research findings show that active attack drastically lowers the AMCTD protocol's performance (i.e., active attack reduces the number of active nodes by up to 10%, reduces throughput by up to 6%, increases transmission loss by 7%, raises energy tax by 25%, and increases end-to-end delay by 20%). [ABSTRACT FROM AUTHOR]

Reusing organic wastes in land applications would enhance the recovery of resources, following the concepts of the circular economy. The sugarcane-based sugar industry produces various by-products (e.g., sugarcane filter mud, molasses, and bagasse) that have the potential to contribute to crop production and soil fertility, owing to their high contents of organic matter and nutrients. Although the agricultural benefits of compost utilization in agriculture have been well-documented, to the best of our knowledge, few scientific data are currently available on the effects of sugarcane filter mud combined with the application of compost for increasing crop production. Thus, a field experiment was carried out to study how sugarcane filter mud, in combination with two compost sources, affected the growth and yield of sweet maize (Zea mays var. saccharata). We compared (i) two types of compost made from brassica residue and household waste applied at a rate of 9 t ha−1, and (ii) two application rates of sugarcane filter mud: 0 and 2 t ha−1 to two controls without any compost application: one with (+SFM) and one without (−SFM) sugar filter mud. The results highlighted that all crop growth and yield parameters benefited more from the domestic waste compost than from the brassica straw compost. Moreover, the addition of sugar filter mud to the compost further boosted the crop performance. Based on the above results, we concluded that the addition of sugarcane filter mud to locally available composts is a feasible approach for more sustainable production of sweet maize, combining efficient waste disposal and the provision of organic matter to the soil. [ABSTRACT FROM AUTHOR]

Acid mine drainage (AMD) is a highly acidic solution rich in heavy metals and produced by mining activities. It can severely inhibit the growth of plants, and microbial communities and disturb the surrounding ecosystem. In recent years, the use of different bioremediation technologies to treat AMD pollution has received widespread attention due to its environment-friendly and low-cost nature. Various active and passive remediation technologies have been developed for the treatment of AMD. The active treatment involves the use of different chemical compounds while passive treatments utilize natural and biological processes like constructed wetlands, anaerobic sulfate-reducing bioreactors, anoxic limestone drains, vertical flow wetlands, limestone leach beds, open limestone channels, and various organic materials. Moreover, different nanomaterials have also been successfully employed in AMD treatment. There are also reports on certain plant growth-promoting rhizobacteria (PGPR) which have the potential to enhance the growth and productivity of plants under AMD-contaminated soil conditions. PGPR applied to plants with phytoremediation potential called PGPR-assisted phytoremediation has emerged as an economical and environment-friendly approach. Nevertheless, various approaches have been tested and employed, all the approaches have certain limitations in terms of efficiency, secondary pollution of chemicals used for the remediation of AMD, and disposal of materials used as sorbents or as phytoextractants as in the case of PGPR-assisted phytoremediation. In the future, more research work is needed to enhance the efficiency of various approaches employed with special attention to alleviating secondary pollutants production and safe disposal of materials used or biomass produced during PGPR-assisted phytoremediation. [Display omitted] • Acid mine drainage (AMD) severely inhibits plant growth and microbial communities. • Active treatment technologies involve the use of different chemical compounds. • Passive treatment technologies utilize natural and biological processes. • Plant growth promoting rhizobacteria (PGPR) enhance plant biomass and phytoextraction. • PGPR-assisted phytoremediation is an economical and eco-friendly approach. [ABSTRACT FROM AUTHOR]

Introduction: The receptor activator NF-κB ligand (RANKL) and Osteoprotegrin (OPG) single nucleotide polymorphisms (SNPs) have been associated with the risk of breast cancer to bone metastasis. This study was designed to investigate the association of RANKL and OPG gene polymorphisms with breast to bone metastasis in Pashtun population of Khyber Pakhtunkhwa, Pakistan. Materials and methods: A total of 215 participants were enrolled containing 106 breast cancer patients, 58 breast to bone metastasis and 51 age and gender matched healthy controls. RANKL (rs9533156) and OPG (rs2073618, rs3102735) polymorphisms were genotyped in genomic DNA, using Tetra-ARMS PCR protocol. The results were analyzed among the three groups and P-value less then 0.05 were considered statistically significant. Results: Our results displayed significant association of OPG (rs3102735) risk allele and corresponding genotypes in breast cancer vs healthy controls, bone metastasis vs healthy controls and breast cancer vs breast to bone metastasis as a disease risk. However, there was no association observed for OPG (rs2073618) risk allele and corresponding genotypes with the diseases risk. Similarly, RANKL (rs9533156) risk allele and corresponding genotypes in breast cancer vs healthy controls, bone metastasis vs healthy controls and breast cancer vs breast to bone metastasis exhibited significant association except for the risk allele carrying genotypes in breast to bone metastasis. Conclusion: OPG (rs3102735) and RANKL (rs9533156) exhibited significant association with breast to bone metastasis while OPG (rs2073618) didn't show significant association with breast to bone metastasis in Pashtun population of Pakistan. However, this study unlocks more questions to investigate the exact scenario of genetic predisposition of breast to bone metastasis. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) is among the deadliest air pollutants due to its negative health impacts and environmental harm. This study reports on monthly and seasonal concentrations of PM10, PM2.5, and PM1, along with their ratios. Twelve-day samples were collected once a month in Mingora city (Swat, Pakistan) from January to December 2019 using a low volume sampler. Maximum average mass concentrations of PM10, PM2.5, and PM1 were recorded in December having values of 78, 56, and 32 μg m−3, respectively. Minimum average values for PM10 (44 μg m−3) and PM2.5 (25.1 μg m−3) were recorded in April, while the lowest PM1 (11 μg m−3) was recorded in August. In comparison to other months, the maximum average mass concentrations were 1.77 times (PM10), 2.23 times (PM2.5), and 2.9 times (PM1) higher in December. During the winter season, average mass concentrations remained high. Substantial correlation coefficients of 0.92, 0.79, and 0.75 were recorded between PM10 and PM2.5, PM2.5 and PM1, and PM2.5 and PM1, respectively. The overall average ratios PM2.5: PM10, PM1: PM2.5, and PM1: PM10 were 68.3, 52.6, and 35.4%, respectively. A moderate negative correlation of PM10, PM2.5, and PM1 with wind speed (−0.34, −0.39, and −0.41), a strong negative correlation with temperature (−0.69, −0.71, and −0.74) and rainfall (−0.63, −0.61, and −0.59), and a weak relationship with relative humidity (−0.32, −0.1, and −0.02) were recorded. [ABSTRACT FROM AUTHOR]