This article analyses studies in the field of digital public administration from a bibliometric perspective. Bibliometrics is a method that evaluates scientific developments by performing statistical and mathematical analyses on scientific publications. In the study, publications on digital public administration were scanned using Web of Science (WoS) scientific databases and bibliometric analyses were performed. As a result of the analyses, important topics in the field of digital public administration, the most cited publications, keywords and researchers in the field were identified. The article aims to contribute to the literature by presenting a bibliometric perspective on research in the field of digital public administration and to provide a basis for future studies. In this context, 1335 publications were identified and analysed with various restrictions. It is expected that the results of the study will serve as a compass for future studies in the field of digital public administration, especially as they aim to identify locomotive topics and niche areas.

Hybridization of oligonucleotides is effectively used to manipulate transcription level of cells. Here we introduce the method of hybridizing complementary single strand DNA (cDNA) to the RNA strands. We call this namely, Anti-Sense Agonist DNA Application to RNA Strands (ASADARS) method. The purpose of our study is to determine the effects of hybridization of single strand DNA oligomer to RNA transcript, to show specific or holistic interference. It is similar to siRNA, but DNA oligomers are replaced for interference instead of small RNA molecules. Identical to the initial protocol in RT-PCR, Hek293 cells were lysed and their RNA transcripts and were separated to synthesize cDNA. The RNA was removed, to leave only complementary ‘single strand DNA’ oligomers. These DNA oligomers were then transfected back to 293 cells. For Controls, SH-Sy5y cDNA, 293 RNA, SH-Sy5y RNA, reagent and blank controls were treated. After 24 hours, the total RNA was decreased but the overall average microarray gene expression was up-regulated in 1.4 fold, in 30,926 genes. The single strand cDNA treatment, in specific gene target condition or holistic miscellaneous gene target was both evaluated. On the oligonucleotide treatment, either RNA or DNA delayed proliferation. Total protein level was not significantly altered. With this approach, primarily, the stability issues of siRNAs can be resolved by replacing it to DNA, which shows long term DNA-RNA hybridization. Cellular homeostasis and RNA compensation response is further postulated as a response of prolonged gene expression inhibitory or regulatory effects.

In a relational database, the embedding of watermarks to integer data using histogram shifting leads to considerable distortion. To solve this problem, a robust watermarking method has been proposed in the past using the gaps that are often present in the histogram. However, in this method, the column with maximum frequency chosen for embedding the watermark cannot guarantee that current distortion-to-robustness ratio is optimal. This paper proposes a new approach that can determine the optimal capacity of watermark embedding, that can maximize the distortion-to-robustness for a given number of records in a given cluster after grouping in the preprocessing step. In the proposed method, we first generate a secret key that reflects the ownership information and characteristics of the database, divide the database into several groups, determine the optimal embedding capacity according to the number of records that the group contains. And determine the location of the watermark embedding in each group, and then embed the watermark based on histogram shifting method. Using the information of the position and surrounding gap of the column stored during the embedding process, we detect the watermark and restore the original database. The experimental results showed that the proposed method provides a higher distortion-to-robustness ratio compared to the NSHGW.

Text recognition for the handwritten overlapping and touching characters has remained a big challenge even after a revolutionary growth in the field of optical character recognition because of the difficult segmentation of such characters. This paper is an attempt to present a novel framework based on word level recognition which doesn’t require the character level segmentation. Unlike the conventional segmentation free recognition techniques, here the word image is considered as an unsegmented sequence which does not require an explicit lexicon. A novel dynamic window selection technique for different aspect ratios of input images is presented in this work to overcome limitation of existing techniques whose performance is subject to the normalized window size and uniform aspect ratio prior to the feature extraction. The combination of spectral features and the structural features through Discrete Wavelet Transform (DWT) and Histogram of Oriented Gradients (HOG) have been used in this work to represent the characteristics of the overlapping and touching characters. Also, the robustness and accuracy in the recognition phase is enhanced in this paper through Bidirectional recurrent configuration of neural network with wavelet transform as kernel function and adaptive tuning laws named as bidirectional adaptive recurrent Wavelet Neural Network (BRWNN). Specially, the gradient descent method with adaptive learning rates (ALRs) is applied to train the parameters of the BRWNN classifier. A comprehensive experimental analysis has been carried out over the publicly available dataset, IAM-DB which reflects that the proposed model is more accurate and robust.

The scientific applications are presently witnessing a challenging landscape in which, the age old constraints of limitations in computing capacity, integration of diverse components of data and problems of interoperability among different resources no longer exist. The modern day cloud infrastructures have made the process of orchestrating the complex and multistage scientific workflows easier and the tasks are executed in fractional time, when compared with the scenarios a decade earlier. The growth of cloud based computing systems is further fueled by the breakthroughs achieved in semi conductor technologies which considerably reduced the hardware costs of setting up such systems. The efficiency of any scientific workflow system depends only on the methodology being adopted to decode the complexity and not on the hardware platform supporting it, as Cloud technologies has broken through all barriers with the trade-off between costs and computations. Cloud computing reigns supreme by virtue of its scalable and elastic capacity to pool additional resources on demand, but suffers desperately in the front of energy efficient management of cloud resources. Several researches have been done in the area of Green Clouds and in this paper, we propose to introduce a novel Fuzzy Bayesian based approach for the energy efficient management of scientific workflows in cloud. The experimental setup is implemented on the WorkflowSim package with the Dynamic Voltage Frequency Scaling add-on and the results show considerable improvements over the existing systems.

In this paper, four degree-based topological indices ABI1index, ABI2 index,ABI3 index & ABI4 index are proposed. Further, the values of molecular structures of eighteen structural octane isomers are calculated using these indices. The correlation coefficients of these indices with twelve physical properties are evaluated and employed for QSPR study through multiple linear regression analysis.

A topological index is a positive real number that is associated with the structure of a simple and connected graph G. It is named as a molecular descriptor which is associated with the chemical compounds in chemical graph theory. These structure-descriptors are closely associated with the Physico-chemical properties of the chemical compounds. Degree-based and distance-based topological indices are the main branches of the topological indices. Neighborhood-degree-based topological indices is the subpart of degree-based topological indices. Inspired by the chemical applicability of degree-based as well as neighborhood degree-sum-based topological indices, we proposed two degree-based topological indices and their neighborhood versions such as First Radical Degree Index RAD1(G), Second Radical Degree Index RAD2(G), First Neighborhood Radical Degree Index NRAD1(G) & Second Neighborhood Radical Degree Index NRAD2(G) of a molecular graph, G. These proposed indices are having a good correlation with the well-known degree-based topological indices and these are showing very good results in structure sensitivity test(Degeneracy Test). And these indices are suitable in the QSPR study for their correlation with elemental Physico-chemical properties of eighteen structural octane isomers prominently. In this paper, the above four indices of the monomer, in linear extension & radical expansion of p-HBC (hexa- peri-hexabenzocoronene) are computed.

Online learning has gained a tremendous popularity in the last decade due to the facility to learn anytime, anything, anywhere from the ocean of web resources available. Especially the lockdown all over the world due to the Covid-19 pandemic has brought an enormous attention towards the online learning for value addition and skills development not only for the school/college students, but also to the working professionals. This massive growth in online learning has made the task of assessment very tedious and demands training, experience and resources. Automatic Question generation (AQG) techniques have been introduced to resolve this problem by deriving a question bank from the text documents. However, the performance of conventional AQG techniques is subject to the availability of large labelled training dataset. The requirement of deep linguistic knowledge for the generation of heuristic and hand-crafted rules to transform declarative sentence into interrogative sentence makes the problem further complicated. This paper presents a transfer learning-based text to text transformation model to generate the subjective and objective questions automatically from the text document. The proposed AQG model utilizes the Text-to-Text-Transfer-Transformer (T5) which reframes natural language processing tasks into a unified text-to-text-format and augments it with word sense disambiguation (WSD), conceptnet and domain adaptation framework to improve the meaningfulness of the questions. Fast T5 library with beam-search decoding algorithm has been used here to reduce the model size and increase the speed of the model through quantization of the whole model by Open Neural Network Exchange (onnx) framework. The keywords extraction in the proposed framework is performed using the Multipartite graphs to enhance the context awareness. The qualitative and quantitative performance of the proposed AQG model is evaluated through a comprehensive experimental analysis over the publicly available Squad dataset.

In a large and multifarious Tamil movie production involving a number of interrelated activities requiring artiste, technicians, cine equipment, sets, costumes and material it is impossible for a production company to make and execute optimum schedule just by intuition, based on capabilities and work experience. A systematic scientific approach has become necessary. This paper introduce a new approach to the pre-production stage of the Tamil movie making by applying network scheduling techniques, Critical Path Method (CPM) and Programme Evaluation and Review Technique (PERT) for shooting scheduling problems. Tamil Movies produced after the year 2010 are considered for this work. Findings of this study indicate that, a marginal reduction in production time in movie making. At the end, this approach is illustrated with numerical example.

The digitalization of an enterprise is a profound transformation benefiting from digital technologies that simplify business processes, increase competitiveness, and improve customer experience. Its importance increases daily, as new developments in information technology (IT) break the boundaries between professional and personal life, and between private and public life. From the viewpoint of an enterprise, digitalization requires investment in IT. Whether it is public or private, many supporting organizations, including policymakers and financial institutions, try to assist enterprises in their endeavors towards digitalization. For both enterprises and the supporting organizations, the most cost-effective technology must be determined. Furthermore, after making investments, enterprises must monitor their results. This requires the measurement of the state of digitalization of an enterprise. The measurement is not an absolute score for an enterprise, since IT is advancing at a rapid speed. An enterprise must ensure that it is not falling behind while competitors employ newer technologies. This measure must be adaptable to the forms of IT that are introduced in the future. In this article, we propose an index to measure the digitalization of an enterprise relative to the other enterprises within the same sector.