Guru Gobind Singh Indraprastha University

Climate change and the consumption of non-renewable resources are considered as the greatest problems facing humankind. Because of this, photocatalysis research has been rapidly expanding. TiO2 nanoparticles have been extensively investigated for photocatalytic applications including the decomposition of organic compounds and production of H2 as a fuel using solar energy. This article reviews the structure and electronic properties of TiO2, compares TiO2 with other common semiconductors used for photocatalytic applications and clarifies the advantages of using TiO2 nanoparticles. TiO2 is considered close to an ideal semiconductor for photocatalysis but possesses certain limitations such as poor absorption of visible radiation and rapid recombination of photogenerated electron/hole pairs. In this review article, various methods used to enhance the photocatalytic characteristics of TiO2 including dye sensitization, doping, coupling and capping are discussed. Environmental and energy applications of TiO2, including photocatalytic treatment of wastewater, pesticide degradation and water splitting to produce hydrogen have been summarized.

Nearly half a century ago, telemedicine was disregarded for being an unwieldy, unreliable, and unaffordable technology. Rapidly evolving telecommunications and information technologies have provided a solid foundation for telemedicine as a feasible, dependable, and useful technology. Practitioners from a variety of medical specialties have claimed success in their telemedicine pursuits. Gradually, this new modality of healthcare delivery is finding its way into the mainstream medicine. As a multidisciplinary, dynamic, and continually evolving tool in medicine, researchers and users have developed various definitions for telemedicine. The meaning of telemedicine encapsulated in these definitions varies with the context in which the term was applied. An analysis of these definitions can play an important role in improving understanding about telemedicine. In this paper we present an extensive literature review that produced 104 peer-reviewed definitions of telemedicine. These definitions have been analyzed to highlight the context in which the term has been defined. The paper also suggests a definition of modern telemedicine. The authors suggest that telemedicine is a branch of e-health that uses communications networks for delivery of healthcare services and medical education from one geographical location to another. It is deployed to overcome issues like uneven distribution and shortage of infrastructural and human resources. We expect that this study will enhance the level of understanding and meaning of telemedicine among stakeholders, new entrants, and researchers, eventually enabling a better quality of life.

Hippocampus is a complex brain structure embedded deep into temporal lobe. It has a major role in learning and memory. It is a plastic and vulnerable structure that gets damaged by a variety of stimuli. Studies have shown that it also gets affected in a variety of neurological and psychiatric disorders. In last decade or so, lot has been learnt about conditions that affect hippocampus and produce changes ranging from molecules to morphology. Progresses in radiological delineation, electrophysiology, and histochemical characterization have made it possible to study this archicerebral structure in greater detail. Present paper attempts to give an overview of hippocampus, both in health and diseases.

There is a growing need to develop clean, nontoxic and environmentally friendly ("green chemistry") procedures for synthesis and assembly of nanoparticles. The use of biological organisms in this area is rapidly gaining importance due to its growing success and ease of formation of nanoparticles. Presently, the potential of bio-organisms ranges from simple prokaryotic bacterial cells to eukaryotic fungus and even live plants. In this article we have reviewed some of these biological systems, which have revolutionized the art of nano-material synthesis.

With the increased momentum of knowledge generation in the field of research, systematic reviews are essential to epitomise the state of extant literature and for theory building.In this article, we discuss the advantages of synthesising and reporting findings using a more impactful type of systematic review, the framework-based review.Additionally, we list and summarise some of the popularly used frameworks, TCCM (theories-contexts-characteristics-methods;

Chitin is among the most abundant biomass present on Earth. Chitinase plays an important role in the decomposition of chitin and potentially in the utilization of chitin as a renewable resource. During the previous decade, chitinases have received increased attention because of their wide range of applications. Chito-oligomers produced by enzymatic hydrolysis of chitin have been of interest in recent years due to their broad applications in medical, agricultural, and industrial applications, including antibacterial, antifungal, hypocholesterolemic, and antihypertensive activity, and as a food quality enhancer. Microorganisms, particularly bacteria, form one of the major sources of chitinase. In this article, we have reviewed some of the chitinases produced by bacterial systems that have gained worldwide research interest for their diverse properties and potential industrial uses.

Molecular markers, due to their stability, cost-effectiveness and ease of use provide an immensely popular tool for a variety of applications including genome mapping, gene tagging, genetic diversity diversity, phylogenetic analysis and forensic investigations. In the last three decades, a number of molecular marker techniques have been developed and exploited worldwide in different systems. However, only a handful of these techniques, namely RFLPs, RAPDs, AFLPs, ISSRs, SSRs and SNPs have received global acceptance. A recent revolution in DNA sequencing techniques has taken the discovery and application of molecular markers to high-throughput and ultrahigh-throughput levels. Although, the choice of marker will obviously depend on the targeted use, microsatellites, SNPs and genotyping by sequencing (GBS) largely fulfill most of the user requirements. Further, modern transcriptomic and functional markers will lead the ventures onto high-density genetic map construction, identification of QTLs, breeding and conservation strategies in times to come in combination with other high throughput techniques. This review presents an overview of different marker technologies and their variants with a comparative account of their characteristic features and applications.

Cobalt doped ZnO nanodisks and nanorods were synthesized by a facile wet chemical method and well characterized by X-ray diffraction, field emission scanning electron microscopy (FESEM), high resolution transmission electron microscopy (HRTEM) with energy dispersive X-ray spectroscopy, photoluminescence spectroscopy, Raman spectroscopy and UV-visible absorption spectroscopy. The photocatalytic activities were evaluated for sunlight driven degradation of an aqueous methylene blue (MB) solution. The results showed that Co doped ZnO nanodisks and nanorods exhibit highly enhanced photocatalytic activity, as compared to pure ZnO nanodisks and nanorods. The enhanced photocatalytic activities of Co doped ZnO nanostructures were attributed to the combined effects of enhanced surface area of ZnO nanodisks and improved charge separation efficiency due to optimal Co doping which inhibit recombination of photogenerated charge carriers. The possible mechanism for the enhanced photocatalytic activity of Co doped ZnO nanostructures is tentatively proposed.

The area of metaheuristics has grown immensely in the past two decades as a solution to real-world optimisation problems. They are able to perform well in situations where exact optimisation techniques fail to deliver satisfactory results. For complex optimisation problems (Nondeterministic polynomial time-hard problems), metaheuristic techniques are able to generate good quality solution in relatively much less time than traditional optimisation techniques. Metaheuristics find applications in a wide range of areas including finance, planning, scheduling and engineering design. This paper presents a review of various metaheuristic algorithms, their methodology, recent trends and applications.

In now days, deep learning has become a prominent and emerging research area in computer vision applications. Deep learning permits the multiple layers models for computation to learn representations of data by processing in their original form while it is not possible in conventional machine learning. These methods surprisingly improved the accuracy of various image processing domains such as speech recognition, face recognition, object detection and in biomedical applications. Deep neural networks (DNN) such as convolutional neural network (CNN) provide tremendous results in processing of images and videos, while another approach of deep network i.e. recurrent neural network (RNN) gives better performance with sequential data such as text and speech.

BACKGROUND: Important developmental processes in both plants and animals are partly regulated by genes whose expression is modulated at the post-transcriptional level by processes such as RNA interference (RNAi). Dicers, Argonautes and RNA-dependent RNA polymerases (RDR) form the core components that facilitate gene silencing and have been implicated in the initiation and maintenance of the trigger RNA molecules, central to process of RNAi. Investigations in eukaryotes have revealed that these proteins are encoded by variable number of genes with plants showing relatively higher number in each gene family. To date, no systematic expression profiling of these genes in any of the organisms has been reported. RESULTS: In this study, we provide a complete analysis of rice Dicer-like, Argonaute and RDR gene families including gene structure, genomic localization and phylogenetic relatedness among gene family members. We also present microarray-based expression profiling of these genes during 14 stages of reproductive and 5 stages of vegetative development and in response to cold, salt and dehydration stress. We have identified 8 Dicer-like (OsDCLs), 19 Argonaute (OsAGOs) and 5 RNA-dependent RNA polymerase (OsRDRs) genes in rice. Based on phylogeny, each of these genes families have been categorized into four subgroups. Although most of the genes express both in vegetative and reproductive organs, 2 OsDCLs, 14 OsAGOs and 3 OsRDRs were found to express specifically/preferentially during stages of reproductive development. Of these, 2 OsAGOs exhibited preferential up-regulation in seeds. One of the Argonautes (OsAGO2) also showed specific up-regulation in response to cold, salt and dehydration stress. CONCLUSION: This investigation has identified 23 rice genes belonging to DCL, Argonaute and RDR gene families that could potentially be involved in reproductive development-specific gene regulatory mechanisms. These data provide an insight into probable domains of activity of these genes and a basis for further, more detailed investigations aimed at understanding the contribution of individual components of RNA silencing machinery during reproductive phase of plant development.

Blockchain is an emerging technology with a wide array of potential applications. This technology, which underpins cryptocurrency, provides an immutable, decentralised, and transparent distributed database of digital assets for use by firms in supply chains. However, not all firms are appropriately suited to adopt blockchain in the existing supply chain primarily due to their lack of knowledge on the benefits of this technology. Using Total Interpretive Structural Modelling (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC), this paper identifies the adoption barriers, examines the interrelationships between them to the adoption of blockchain technology, which has the potential to revolutionise supply chains. The TISM technique supports developing a contextual relationship-based structural model to identify the influential barriers. MICMAC classifies the barriers in blockchain adoption based on their strength and dependence. The results of this research indicate that the lack of business awareness and familiarity with blockchain technology on what it can deliver for future supply chains, are the most influential barriers that impede blockchain adoption. These barriers hinder and impact businesses decision to establish a blockchain-enabled supply chain and that other barriers act as secondary and linked variables in the adoption process.

New antimicrobial agents are desperately needed to combat the increasing number of antibiotic resistant strains of pathogenic microorganisms. Natural products remain the most propitious source of novel antibiotics. It is widely accepted that actinobacteria are prolific producers of natural bioactive compounds. We argue that the likelihood of discovering a new compound having a novel chemical structure can be increased with intensive efforts in isolating and screening rare genera of microorganisms. Screening rare actinomycetes and their previously under-represented genera from unexplored environments in natural product screening collections is one way of achieving this. Rare actinomycetes are usually regarded as the actinomycete strains whose isolation frequency is much lower than that of the streptomycete strains isolated by conventional methods. Many natural environments are still either unexplored or under-explored and thus, can be considered as a prolific resource for the isolation of less exploited microorganisms. More and different ecological niches need to be studied as sources of a greater diversity of novel microorganisms. In this review, we wish to update our understanding of the potential of the rare actinomycetes by focusing on the ways and means of enhancing their bio-discovery potential.

To understand the genetic basis of tolerance to drought and heat stresses in chickpea, a comprehensive association mapping approach has been undertaken. Phenotypic data were generated on the reference set (300 accessions, including 211 mini-core collection accessions) for drought tolerance related root traits, heat tolerance, yield and yield component traits from 1-7 seasons and 1-3 locations in India (Patancheru, Kanpur, Bangalore) and three locations in Africa (Nairobi, Egerton in Kenya and Debre Zeit in Ethiopia). Diversity Array Technology (DArT) markers equally distributed across chickpea genome were used to determine population structure and three sub-populations were identified using admixture model in STRUCTURE. The pairwise linkage disequilibrium (LD) estimated using the squared-allele frequency correlations (r2; when r2<0.20) was found to decay rapidly with the genetic distance of 5 cM. For establishing marker-trait associations (MTAs), both genome-wide and candidate gene-sequencing based association mapping approaches were conducted using 1,872 markers (1,072 DArTs, 651 single nucleotide polymorphisms [SNPs], 113 gene-based SNPs and 36 simple sequence repeats [SSRs]) and phenotyping data mentioned above employing mixed linear model (MLM) analysis with optimum compression with P3D method and kinship matrix. As a result, 312 significant MTAs were identified and a maximum number of MTAs (70) was identified for 100-seed weight. A total of 18 SNPs from 5 genes (ERECTA, 11 SNPs; ASR, 4 SNPs; DREB, 1 SNP; CAP2 promoter, 1 SNP and AMDH, 1SNP) were significantly associated with different traits. This study provides significant MTAs for drought and heat tolerance in chickpea that can be used, after validation, in molecular breeding for developing superior varieties with enhanced drought and heat tolerance.

Social entrepreneurship has been recognized as a tool to attain sustainable development. This paper highlights the role of social entrepreneurship in triggering social change and attaining sustainable development. The paper contributes significantly to the existing literature by conducting a systematic review of extant works. To this end, we analyzed and reviewed 173 research papers from the Web of Science database. The results are presented in the form of descriptive findings and thematic discussion. The paper concludes by setting up the agenda for future researchers in the field.

Internet of Things (IoT) is an enabler for the intelligence appended to many central features of the modern world, such as hospitals, cities, grids, organizations, and buildings. The security and privacy are some of the major issues that prevent the wide adoption of Internet of Things. In this paper, with example scenarios, we are presenting review of security attacks from the perspective of layers that comprises IoT. In addition, a review of methods that provide solutions to these issues is presented along with their limitations. To overcome these limitations, we have provided future work recommendations with a framework. Further research and implementation of the framework and our recommendations will further enhance the robustness and reliability of the IoT and their applications against a variety of known attacks.

Abstract TiO2 can be prepared in the form of powder, crystals, or thin films. Liquid-phase processing is one of the most convenient and utilized methods of synthesis. It has the advantage of allowing control over the stoichiometry, production of homogeneous materials, formation of complex shapes, and preparation of composite materials. However, there may be some disadvantages such as expensive precursors, long processing times, and the presence of carbon as an impurity. In comparison, the physical production techniques, although environment friendly, are limited by the size of the produced samples which is not sufficient for a large-scale production. The most commonly used solution routes in the synthesis of TiO2 are reviewed.

The objective of classifier is to classify objects of a data set into one or more classes based on its characteristics. In real life applications, classifiers are applied on data sets which are unbalanced i.e. some classes having very less number of instances known as minority classes as compared to other classes known as majority classes. Classification algorithms are highly accurate for the majority classes but significantly less accurate for the minority classes. Unbalanced data sets have a negative effect on classification performance of traditional classification algorithms. Analyzing such problem is called class imbalance problem. To solve Class Imbalance Problem different techniques have been proposed at the Data level, Algorithm level and at the Hybrid level. Most commonly used data balancing techniques are over and under sampling for handling the class imbalance problem. In our paper we compare various oversampling techniques which are SMOTE (Synthetic minority oversampling approach), ADASYN, Borderline-SMOTE, Safe-Level SMOTE by applying different classifiers to the problem and observing various performance metrics.

The paper provides an overview of how Artificial Intelligence (AI) is applied in different government sectors. Our methodology is based on a systematic review of 74 papers retrieved from Web of Science and Scopus databases. We find that the extant literature is less focused on healthcare, ICT, education, social and cultural services, and fashion sector; while ignoring the practical implementation of AI in these sectors. We present an organizing framework stating different areas related to governance and throws light on research gaps in the extant literature that can be further worked upon for promoting the research in digital governance.

In this paper, we propose two general frameworks for deriving several software reliability growth models based on a non-homogeneous Poisson process (NHPP) in the presence of imperfect debugging and error generation. The proposed models are initially formulated for the case when there is no differentiation between failure observation and fault removal testing processes, and then extended for the case when there is a clear differentiation between failure observation and fault removal testing processes. During the last three decades, many software reliability growth models (SRGM) have been developed to describe software failures as a random process, and can be used to evaluate development status during testing. With SRGM, software engineers can easily measure (or forecast) the software reliability (or quality), and plot software reliability growth charts. It is not easy to select the best model from a plethora of models available. There are few SRGM in the literature of software engineering that differentiates between failure observation and fault removal processes. In real software development environments, the number of failures observed need not be the same as the number of faults removed. Due to the complexity of software systems, and an incomplete understanding of software, the testing team may not be able to remove the fault perfectly on observation of a failure, and the original fault may remain, resulting in a phenomenon known as imperfect debugging, or get replaced by another fault causing error generation. In the case of imperfect debugging, the fault content of the software remains the same; while in the case of error generation, the fault content increases as the testing progresses. Removal of observed faults may result in the introduction of new faults.