Risk of publication in worthless journals

oai:nepjol:article/22217Implementing research and publishing results is a crucial for a professional development, scientific communication and collaboration of any academicians, scholars, and researchers in science around the world. The timely dissemination of knowledge and scientific information in the global scientific community helps the development of science and worldwide recognition. The researchers working on scientific community cannot appreciate the value of evidence generated without publishing their work in right and quality journals. Therefore, authors should be careful about predatory or fake journals/publishers for communicating their scientific works. The objective of this study is to raise awareness on predatory or fake publishers/journals and of their dishonest publishing practices. In general, the predatory journal publishes without peer review and true editorial board, often publish mediocre or even worthless papers on charging high publication cost, citing fake and non-existing impact factors and mostly focused on private business motives. On the other hand, publishing in a high impact quality journals undoubtedly enhances the future career prospects, communication ability of authors and deliver concise research messages in the scientific field. Researcher of various disciplines and academic experience should aware with the lists of predatory journals/publishers which are available on Beall’s list in internet before publishing any research articles. Therefore, publishing in predatory/fake journals not only spoil or degrade academic reputations but also waste the time, resources and research message too

A Multi Criteria Recommendation Engine Model for Cloud Renderfarm Services

Cloud services that provide a complete platform for rendering the animation files using the resources in the cloud are known as cloud renderfarm services. This work proposes a multi criteria recommendation engine model for recommending these Cloud renderfarm services to animators. The services are recommended based on the functional requirements of the animation file to be rendered like the rendering software, plug-in required etc and the non functional Quality of Service (QoS) requirements like render node cost, time taken to upload animation files etc. The proposed recommendation engine model uses a domain specific ontology of renderfarm services to identify the right services that could satisfy the functional requirements of the user and ranks the identified services using the popular Multi Criteria Decision Analysis method like Simple Additive Weighting (SAW). The ranked list of services is provided as recommended services to the animators in the ranking order. The Recommendation model was tested to rank and recommend the cloud renderfarm services in multi criteria requirements by assigning different QoS criteria weight for each scenario. The ranking based recommendations were generated for six different scenarios and the results were analyzed. The results show that the services recommended for each scenario were different and were highly dependent on the weights assigned to each criterion

Chronotherapy of Janus Kinase Inhibitor against Complete Freund’s Adjuvant-Induced Rheumatoid Arthritis in Wistar Rats

INTRODUCTION: Rheumatoid arthritis (RA) is a chronic systemic auto immune disease that arises more frequently in females than males, being predominantly observed in the elderly. The prevalence rate reported in 2002 ranged from 0.5% to 1% of the population and had regional variation. RA primarily affects the lining of the synovial joints and can cause progressive disability, premature death, and socioeconomic burdens. The clinical manifestations of symmetrical joint involvement include arthralgia, swelling, redness, and even limiting the range of motion. Early diagnosis is considered as the key improvement index for the most desirable outcomes (i.e., reduced joint destruction, less radiologic progression, no functional disability, and disease modifying antirheumatic drugs (DMARD)-free remission) as well as cost effectiveness as the first 12 weeks after early symptoms occur is regarded as the optimal therapeutic window. AIM OF THE STUDY: To explore the circadian rhythm of serum CRP in complete Freund’s adjuvant (CFA) rats and effectiveness of tofacitinib administered via chronotherapy. METHODS: CFA rat models were immunized with mycobacterium butyricum. Serum CRP levels in normal and CFA rats were measured at 4,10,16, or 22 h ZT0/ZT12.Tofacitinib was administered to ZT8/ZT20 experimental groups of Wistar rats once daily according to the circadian rhythm. The positive control was given with methotrexate, for ZT0 and ZT12 was given with tofacitinib and normal control given (0.5% CMC) once daily simultaneously. Arthritis score, paw volume, body weight was measured on 1st, 5th ,7th ,14th and 21st. Rheumatoid factor and C reactive protein (CRP) levels in the serum were measured by semiauto-analyser using turbilatex method. Histological changes in the ankle joint were analyzed. RESULTS: After 3 weeks of treatment, arthritis scores in the experimental group were lower than in the Negative control group. The expression of CRP was lower in the ZT0 treated group than in the negative control or ZT12 treated groups. Histopathology scores in the experimental groups shows less inflammatory cells than in the control group. CONCLUSION: The serum CRP levels in CFA rats were higher than in normal rats and showed significant circadian rhythm. Daily dose time dependent administration of tofacitinib is more potent than traditional administration. The therapeutic index of rheumatoid arthritis (RA) may be improved with tofacitinib therapy based on the serum CRP circadian rhythm

Low Power and Efficient Re-Configurable Multiplier for Accelerator

Deep learning is a rising topic at the edge of technology, with applications in many areas of our lives, including object detection, speech recognition, natural language processing, and more. Deep learning's advantages of high accuracy, speed, and flexibility are now being used in practically all major sciences and technologies. As a result, any efforts to improve the performance of related techniques are worthwhile. We always have a tendency to generate data faster than we can analyse, comprehend, transfer, and reconstruct it. Demanding data-intensive applications such as Big Data. Deep Learning, Machine Learning (ML), the Internet of Things (IoT), and high- speed computing are driving the demand for "accelerators" to offload work from general-purpose CPUs. An accelerator (a hardware device) works in tandem with the CPU server to improve data processing speed and performance. There are a variety of off-the-shelf accelerator architectures available, including GPU, ASIC, and FPGA architectures. So, this work focus on designing a multiplier unit for the accelerators. This increases the performance of DNN, reduced the area and increasing the training speed of the system

In Silico Drug Repurposing: An Effective Tool to Accelerate the Drug Discovery Process

Repurposing “old” drugs to treat both common and rare diseases is increasingly emerging as an attractive proposition due to the use of de-risked compounds, with potential for lower overall development costs and shorter development timelines. This is due to the high attrition rates, significant costs, and slow pace of new drug discovery and development. Drug repurposing is the process of finding new, more efficient uses for already-available medications. Numerous computational drug repurposing techniques exist, there are three main types of computational drug-repositioning methods used on COVID-19 are network-based models, structure-based methods and artificial intelligence (AI) methods used to discover novel drug–target relationships useful for new therapies. In order to assess how a chemical molecule can interact with its biological counterpart and try to find new uses for medicines already on the market, structure-based techniques made it possible to identify small chemical compounds capable of binding macromolecular targets. In this chapter, we explain strategies for drug repurposing, discuss about difficulties encountered by the repurposing community, and suggest reported drugs through the drug repurposing. Moreover, metabolic and drug discovery network resources, tools for network construction, analysis and protein–protein interaction analysis to enable drug repurposing to reach its full potential