Heteroatom-doped carbons are emerging candidates for metal-free catalysis, photocatalysis, and pollutant removal. Herein, we report the synthesis of N-doped solid (CS) and hollow carbon spheres (CNB) via a modified Stöber’s method. TEM, Raman, and XPS characterization techniques demonstrated a well-constructed N-doped sp2/sp3 hybridized carbons scaffold. Both CNB and CS nanospherical carbons have shown a high surface area of 360 and 400 m2/g, respectively, making them ideal candidates for the adsorption of pharmaceutical pollutants (ciprofloxacin and ibuprofen) and heavy metals (Pb and Cr) from wastewater. These materials have good interfacial interaction with the adsorbate and generate the proper medium to facilitate fast and efficient remediation. The highest surface adsorption (96% in 30 min) was observed for ibuprofen by CS. Interestingly, CNB was more selective for heavy metals at lower concentrations, while CS showed high surface adsorption at higher concentrations.

A field experiment was conducted during rabi 2016-17 and 2017-18, at the Research Farm of Department of Agronomy, Punjab Agricultural University, Ludhiana, to study the phenological behaviour of gobhi sarson (Brassicanapus L.) and thermal indices as influenced by drip irrigation (60, 80 and 100% of cumulative pan-evaporation, CPE) and fertigation schedules (60, 80 and 100 % recommended dose of fertilizers, RDF) in comparison with conventional flood irrigation and manual application of fertilizers i.e. absolute control. The pooled data revealed that Brassica irrigated through drip at 100 % of CPE took maximum number of days to attain 50% flowering, 50% siliqua formation and physiological maturity, followed by 80 and 60% of CPE. Higher fertigation levels also delayed the number of days taken to attain various phenological stages. Maximum seed yield was observed at 100% of CPE with 100% RDF which was statistically at par with 100% of CPE with 80% RDF and 80% of CPE with 80 or 100% RDF, but significantly higher than absolute control. Maximum accumulation of heat units along with heat use efficiency (1.49 kg grains ha-1 °C day hour-1) was also obtained at 100% of CPE with 100% RDF.

The pandemic (COVID-19), which emerged in late December 2019 in Wuhan, China, is still continuing to ravage every country in the world. As a result of the outbreak, the world has experienced tough times. To date, only 24% of the world's population has been vaccinated yet, and over 18 million cases are still active. In many places, the hospital is running short of beds and oxygen cylinders. With many undetected cases and casual approaches by people, this third wave is predicted to be a big hit. Conventional methods of diagnosis, such as antigen analysis, serological tests, and polymerase chain reactions, although widely used, are time-consuming. The use of Deep Learning (DL) and a convolutional neural network (CNN) to examine chest CT (computerized tomography) or chest x-ray images has been shown to be a promising technique for early diagnosis of COVID-19. In this study, a multi-level analysis method is proposed to detect COVID-19 from chest radiographs of a human. Through the model, a fast and accurate diagnosis of coronavirus can be made at the reach of fingertips. Based on patient clinical data, an ANN is used to calculate the likelihood of the patient becoming infected with COVID-19. The proposed model makes use of 8 layers of Convolutional Neural Network which are trained on the dataset (80-10-10 train-validate-test split) which gives an accuracy of 97% on the training data and 98.7% on the validation data.

Drip irrigation and fertigation, being advocated for higher crop and water productivity require optimization of irrigation and fertilization schedules. Field experiments were conducted during 2016–2018 to evaluate water and energy-efficient drip irrigation and fertigation schedules for higher productivity and profitability from oilseed rape. The treatments comprised of a combination of three levels of drip irrigation {60, 80 and 100% of cumulative pan-evaporation (CPE)} and three levels of fertigation {60, 80 and 100% recommended dose of fertilizers (RDF)} along with one absolute control (flood irrigation and soil application of RDF). The yields of crop under drip irrigation at 100 and 80% of CPE were statistically similar but significantly higher than irrigation at 60% of CPE. The irrigation use efficiency (IUE) and water use efficiency (WUE) were maximum at 80% of CPE followed by 100 and 60% of CPE. Drip irrigation at 80% of CPE resulted in 18% higher yield along with a water-saving of 35.4% than absolute control. Energy use efficiency (EUE) was higher at 100% of CPE followed by 80 and 60% of CPE. Whereas, energy productivity at 100 and 80% of CPE being statistically similar but significantly higher than 60% of CPE. Highest benefit: cost (B:C) ratio and net returns were obtained with drip irrigation at 100% of CPE and lowest at 60% of CPE. Fertigation at 100 and 80% RDF recorded significantly higher seed yield than 60% RDF and absolute control.

A field experiment was conducted to study the effect of drip irrigation and fertigation levels on performance of gobhi sarson (Brassica napus L.) during rabi 2016-18. The results revealed that application of irrigation through drip at 100% of CPE (I1.0) recorded highest values for growth components, viz. periodic plant height, dry matter accumulation and leaf area index than I0.8 (80% of CPE) and I0.6 (60% of CPE). Maximum seed and oil yield were recorded at I1.0, which was statistically at par with I0.8 but significantly higher than I0.6 and absolute control. Utilization of water-soluble fertilizers through drip at 100% RDF (F1.0) recorded highest growth components along with seed and oil yield than that produced by 80 and 60% RDF (F0.8 and F0.6). Among treatment combinations, drip-fertigation at I1.0 or I0.8 with F1.0 or F0.8 significantly enhanced seed and oil yield of gobhi sarson than absolute control (flood irrigation and manual application of RDF).

It has remained to be the cause of misery for millions of businesses and lives throughout 2020 and into 2021 after the outbreak of Coronavirus Disease 2019 (COVID-19). Almost everyone, especially those planning to resume in-person activity, is feeling anxious while the world is recovering from the pandemic and prepares to return to a normal condition. Face masks are proven to be the only prominent way of reducing the risk of transfusion of viral agents, as well as provide a sense of protection. But, due to the negligence and casual attitude of people, strict policies must be enacted. Manual tracking of this policy, while possible, is ineffective and time-consuming. This is where technology plays a critical role and that's why in this paper, we propose a Deep Learning-based system that uses Convolutional Neural Network (CNN) architecture to detect unmasked as well as masked faces and can interface with security cameras installed. This architecture is trained by using 1923 images. It was found that a high rate of accuracy (99.13%) and validation was achieved with the proposed model, more accurate than other models. As a result, safety violations can be tracked, face masks can be encouraged, and safe working conditions can be ensured.

Nanometer and subnanometer particles and films are becoming an essential and integral part of new technologies and inventions in different areas. Some of the most common areas include the microelectronic industry, magnetic recordings, photovoltaic applications, and optical coatings. Because of the ultrasmall size at atomic levels, the effect of quantum size becomes prominent, and the sensitivity of size is defined even by a difference of a single atom. Additionally, the effect is of utmost importance as the single-atom catalysts are far more advantageous than conventional catalysts as they tend to anchor easily because of their low coordination. Also, the presence of a single-atom catalyst in reactions creates efficient charge transfer as it forms a strong interaction with the support. Furthermore, catalysts in the subnanometer regime exhibit different electronic states and adsorption capabilities compared to traditional catalysts. Therefore, to fully appreciate the subnanometer catalysis reactions, it is essential to study the means of characterizing the prepared subnano catalysts, in order to characterize the materials in their as-synthesized form, to obtain a precise and accurate analysis; these are some of the fundamental requirements for achieving an optimum performance. The physical properties of many interesting materials for advanced technological usage are highly governed by the distribution and placement of atoms. Superior techniques such as high-resolution transmission electron microscopy and high-angle annular dark-field scanning transmission electron microscopy and infrared and X-ray absorption spectroscopic techniques provide electronic and geometric configurations and also reveal the transformation of the subnano catalysts on the support material. Modeling methods such as density functional theory can successfully predict the electronic structure and geometric configuration of the catalyst, which in turn influence the selectivity and activity of the catalyst. Thus, understanding the characterization techniques gives the ability to understand, identify, and measure the local environment of individual atoms and the interaction with the surface support, which will give fundamental knowledge and insights in the realms of nanoscience and technology, materials science, chemistry, and physics. Therefore, detection and enhanced measurement of individual atoms is inherently challenging and is a prerequisite to the development of new technology and better performing materials. In this chapter, we have discussed various important methods of characterization for characterizing subnanometer and atomic catalysts.

Peripheral nerve sheath tumors (PNST) are a group of heterogeneous, often benign and a rare condition that originates from the neuroectodermal or neural crest and display features that mirror the elements of the nerve. Schwannomas are one such peripheral nerve sheath tumors which entirely are made up of benign neoplastic Schwann cells. The objective of this case report is to highlight the diverse clinical presentations of these swellings. In this presentation, reporting three cases of PNST in which two presented with neurological symptoms of paraesthesia and pain and one who was asymptomatic swelling over his neck. All of whom were diagnosed with an alternate soft tissue swelling post clinical examination and taken up for excision as there were no significant clinical evidence for imaging. Intra-operatively we noted that all were closely related to the peripheral nerve of that anatomical region. Histopathological study revealed it to be PNST. PNST and schwannoma in particular although an entity that is not so common to come across in the surgical clinic we need to have and high indices of suspicion when associated close to peripheral nerves and symptomatic of a nerve involvement as we discuss here below.

Well-defined nano- and atomic-sized heterogeneous catalysts with extremely high catalytic activities and unique selectivities show promise in addressing the critical energy- and environment-related challenges of this century. The exceptional properties of these catalysts, such as their electronic and geometric structures and the effective interactions between metals and supports, give rise to unprecedented catalytic efficiency over that of conventional catalysts. The facile prospects for tuning the active sites of these catalysts pave the way to optimizing their activities, selectivities, and stabilities, thus offering extensive application possibilities in significant industry-related catalytic reactions. A prerequisite for synthesizing nano- and atomic-sized catalyst is to prepare extremely disperse nano- and subnanoscale atoms on suitable supports. This book chapter summarizes various synthesis methods employed to synthesize nano- and atomic-scale catalysts.

Multi-tenancy is an essential feature in cloud computing and is a major component to achieve scalability and energy-efficient solution to gain high level of economic benefits. As the cloud, computing is gaining more audiences and high user base, the problem of scheduling the computational workflow for multi-tenant cloud scheduling is becoming a difficult task to achieve. In this study, we present a learning-based scheduler for catering heterogeneous software and hardware resources in the context of multi-tenant cloud computing. The experimentation has been carried out with the help of green cloud simulator and the results are compared with the state of the art techniques like minimum completion time, first come first serve and backfilling. The experimental results exhibit that the presented algorithm provides an effective means of utilizing cloud resources in addition with drastic reduction in cost of operation.

An analytical solution of an MHD free convective thermal diffusive flow of a viscous, incompressible, electrically conducting and heat-absorbing fluid past a infinite vertical permeable porous plate in the presence of radiation and chemical reaction is presented. The flow is considered under the influence of a magnetic field applied normal to the flow. The plate is assumed to move with a constant velocity in the direction of fluid flow in slip flow regime, while free stream velocity is assumed to follow the exponentially increasing small perturbation law. The velocity, temperature, concentration, skin friction, Nusselt number and Sherwood number distributions are derived and have shown through graphs and tables by using the simple perturbation technique.

CD147/Basigin/EMMPRIN (Extracellular Matrix Metalloproteinase inducer) is a single pass type1 transmembrane protein playing a central role in developmental process, wound healing, nutrient transport, inflammation, arthritis and also in microbial pathologies. It is also found to be a potent stimulator of MMP (matrix metalloproteinases) and has been considered as a prognostic marker in cancer. Dysregulation of CD147 is reported in several types of cancer. It activates cell proliferation, invasion, metastasis and inhibits tumor cell apoptosis under hypoxic condition. Thus, CD147 serves as a hub protein in cancer, as it is involved in several homophilic and heterophilic cellular interactions spanning the major hallmarks of cancer. Targeting these interactions is considered to be an efficient therapeutic modality in cancerous conditions. Hence, by this review we intend to collate the structure-function relationships of CD147, with an exclusive thrust on potential druggable hotspots based on its intra and inter molecular interactions.

The pathways controlling cilium biogenesis in different cell types have not been fully elucidated. We recently identified peptidylglycine α-amidating monooxygenase (PAM), an enzyme required for generating amidated bioactive signaling peptides, in Chlamydomonas and mammalian cilia. Here, we show that PAM is required for the normal assembly of motile and primary cilia in Chlamydomonas, planaria and mice. Chlamydomonas PAM knockdown lines failed to assemble cilia beyond the transition zone, had abnormal Golgi architecture and altered levels of cilia assembly components. Decreased PAM gene expression reduced motile ciliary density on the ventral surface of planaria and resulted in the appearance of cytosolic axonemes lacking a ciliary membrane. The architecture of primary cilia on neuroepithelial cells in Pam-/- mouse embryos was also aberrant. Our data suggest that PAM activity and alterations in post-Golgi trafficking contribute to the observed ciliogenesis defects and provide an unanticipated, highly conserved link between PAM, amidation and ciliary assembly.