Active Path Updation for Layered Routing (APULAR) in Wireless Mesh Networks

One of the major research issues in the Wireless Mesh Network (WMN) is routing. The routing protocols of ad-hoc networks can be applied for WMN, but have limited success because, ad-hoc networks are mainly structure less networks with highly dynamic topology and harmonized nodes, where WMN are relatively static network with two types of nodes, one fixed mesh routers and mobile clients. In layered routing protocol, source node initiates a path establishing process whenever path breaks. It will cause huge control packets and increase packet loss. This is not an ideal method in WMN where every nodes rather than source and destination in the path are motionless. One way of overcoming this is by initiating the local route repair by destination node.. In this paper, we propose an active path updating procedure (APULAR) for quickly updating the broken path to recover from packet loss. Moreover, to improve throughput and to reduce the co-channel interference, we use multiple interface with multi channels. We are considering 4-hop as an interference range and will use fixed channel assignment within the mesh routers to reduce the inter flow interference. Our procedure is simulated in NS2 and compared with AODV and Infrastructure Wireless Mesh Routing Architecture (IWMRA). Simulation results show that our protocol performs better than IWMRA and AODV in key performance metrics like packet delivery ratio, control overhead, average throughput and end-to-end delay. Keywords: Active path repair, channel assignment, multi channel routing, and wireless mesh network routing

Framing Research Question and Formulating Hypothesis for Testing: Critical Step in Research

Science is so dynamic that every moment some new advances are happening in each and every field. Medical science is also one of them. To keep pace with the advancements, we always need to update our knowledge to improve our decisions and actions relating to various aspects of medical science. A successful research will largely depend upon how well an investigator frames the research question. To comment upon the research with a degree of certainty, we need to develop a hypothesis, so that, it can be tested statistically by applying required tests of significance

Trie-NLG: Trie Context Augmentation to Improve Personalized Query Auto-Completion for Short and Unseen Prefixes

Query auto-completion (QAC) aims at suggesting plausible completions for a given query prefix. Traditionally, QAC systems have leveraged tries curated from historical query logs to suggest most popular completions. In this context, there are two specific scenarios that are difficult to handle for any QAC system: short prefixes (which are inherently ambiguous) and unseen prefixes. Recently, personalized Natural Language Generation (NLG) models have been proposed to leverage previous session queries as context for addressing these two challenges. However, such NLG models suffer from two drawbacks: (1) some of the previous session queries could be noisy and irrelevant to the user intent for the current prefix, and (2) NLG models cannot directly incorporate historical query popularity. This motivates us to propose a novel NLG model for QAC, Trie-NLG, which jointly leverages popularity signals from trie and personalization signals from previous session queries. We train the Trie-NLG model by augmenting the prefix with rich context comprising of recent session queries and top trie completions. This simple modeling approach overcomes the limitations of trie-based and NLG-based approaches and leads to state-of-the-art performance. We evaluate the Trie-NLG model using two large QAC datasets. On average, our model achieves huge ~57% and ~14% boost in MRR over the popular trie-based lookup and the strong BART-based baseline methods, respectively. We make our code publicly available.Comment: Accepted at Journal Track of ECML-PKDD 202

Unsupervised Threat Hunting using Continuous Bag of Terms and Time (CBoTT)

Threat hunting is sifting through system logs to detect malicious activities that might have bypassed existing security measures. It can be performed in several ways, one of which is based on detecting anomalies. We propose an unsupervised framework, called continuous bag-of-terms-and-time (CBoTT), and publish its application programming interface (API) to help researchers and cybersecurity analysts perform anomaly-based threat hunting among SIEM logs geared toward process auditing on endpoint devices. Analyses show that our framework consistently outperforms benchmark approaches. When logs are sorted by likelihood of being an anomaly (from most likely to least), our approach identifies anomalies at higher percentiles (between 1.82-6.46) while benchmark approaches identify the same anomalies at lower percentiles (between 3.25-80.92). This framework can be used by other researchers to conduct benchmark analyses and cybersecurity analysts to find anomalies in SIEM logs

Study of vertical transmission of COVID-19 infection in COVID-19 positive obstetrical patients by comparing amniotic fluid and immediate neonatal COVID-19 RT-PCR

Background: The objective of this study was to estimate the incidence of vertical transmission COVID-19 by RT-PCR.Methods: In this hospital based prospective study, all COVID-19 positive pregnant women admitted in COVID ward in Muzaffarnagar medical college and hospital from April 2020 to January 2021 were included. A detailed history and examination was done and all routine investigations were done as per protocol. Samples were taken from amniotic fluid during vaginal delivery or caesarean section and collected in viral transport medium. Sample were also collected as nasal and oropharyngeal swab from neonate immediately after birth and sent for COVID 19 RT-PCR.Results: Out of total 50 cases; 43 (86%) neonates were delivered via LSCS and 7 (14%) by normal delivery, out of these 41 (82%) neonates were normal; 4 (8%) were IUGR, 2 (4%) were IUD, 2 (4%) were pre-term and 1 (2%) neonatal death. All 50 amniotic fluid as well as nasal and oropharyngeal samples of all neonates were negative by RT-PCR.Conclusions: Low vertical transmission may be due to the fact that antibodies are produced by mother that crosses the placenta and saves the fetus or there are highly specific immunological mediators in the placenta that do not allow the infection to pass to the fetus

In-silico molecular docking for Potential herbal leads from Withaniasomnifera L. Dunal for the treatment of Alzheimer’s disease

Alzheimer's disease (AD) poses a significant global health challenge, necessitating novel therapeutic interventions. Withaniasomnifera L. Dunal, commonly known as Ashwagandha, has been historically utilized in traditional medicine for its neuroprotective properties. This study employs computational techniques to explore the potential of W. somnifera compounds in targeting key proteins associated with AD. The reported phytoconstituents of W. somnifera were identified and subjected to molecular docking studies against 5NUU (Torpedo californica acetylcholinesterase in complex with a chlorotacrine-tryptophan hybrid inhibitor), as crucial targets. The results revealed several phytoconstituents of W. somnifera exhibiting favorable binding affinities and promising interactions with the target proteins. These findings provide a valuable foundation for further experimental validation and the development of novel therapeutic agents derived from natural sources for the treatment of Alzheimer's

Molecular Docking studies of chemical constituents of Rauwolfia serpentina on hypertension

Hypertension is still a prevalent cardiovascular disorder which remains a major global health concern. Rauwolfia serpentina, renowned for its therapeutic potential in managing hypertension, harbors a diverse array of bioactive compounds. This study aimed to elucidate the molecular interactions of chemical constituents derived from Rauwolfia serpentina with key hypertensive targets through molecular docking simulations. Utilizing computational tool, a comprehensive library of phytoconstituents obtained from Rauwolfia serpentina was constructed and subjected to molecular docking analyses against human angiotensin receptor (4ZUD) as target protein. The results revealed significant binding affinities between the chemical constituents of Rauwolfia serpentina and the active sites of these molecular targets. This study bridges the knowledge gap regarding the molecular mechanisms underlying the antihypertensive effects of Rauwolfia serpentina's constituents through computational simulations. The identified compounds exhibiting strong binding affinities and favorable interactions serve as promising candidates for further in vitro and in vivo studies, offering avenues for the development of novel therapeutic agents for hypertension management

Exploring the Potential of Chemical Constituents of Datura metel in Breast Cancer from Molecular Docking Studies

Breast cancer remains a pervasive health challenge worldwide, prompting the exploration of novel therapeutic prospects. Datura metel has long been recognized for its pharmacological properties, particularly in containing various bioactive compounds like alkaloids, flavonoids, and terpenoids. This review focuses on the potential of chemical constituents sourced from Datura metel, a traditional medicinal plant, in combating breast cancer, primarily through molecular docking studies. The review meticulously scrutinizes the chemical composition of Datura metel, emphasizing the identified compounds known for their therapeutic attributes. Through an extensive analysis of molecular docking studies, the interactions between these Datura metel constituents and crucial molecular targets associated with breast cancer are elucidated. The phytoconstituents (compound 1-13) were found to be more potent as compare to Tomoxifen citrate as standard anticancer drug. The findings presented herein beckon for further exploration, highlighting a promising avenue in the pursuit of effective and targeted treatments for breast cancer. In conclusion, this review emphasizes the synergistic integration of computational approaches with traditional knowledge, accelerating the discovery and development of innovative breast cancer therapies

Gender Correlation of Stress in Mbbs Students in Relation to Covid 19 Pandemic

Objective: Since December 2019, the COVID-19 pandemic has posed a substantial threat with its associated high mortality, infection, and risk of psychological stress. A large number of students were affected because of a prolonged break from academic activities and staying at home. The focus of this study is to understand the stress levels of M.B.B.S. students, any psychological imbalances, and their major hurdles during the COVID-19 lockdown. Methods: The study was designed to determine the prevalence of stress levels in 150 medical students to explore the sources of stress, 64 were male students and 86 were female students & it ‘s relationship across the male and female students. Demographic information and Qualitative data from investigator tailored Medical Student Stress Questionnaire (MSSQ) by self-rating under supervision of investigators were subjected to analysis. Results: We observed that medical students were generally stressed during lockdown and the pandemic. Stress perceived was more in males’ students 49/64(76.5%) as compared to females 51/86(59.3%) and their MSSQ index score was significantly different. Moderate to high academic stress was present among 79% of students more so in males with the academic domain score significantly different from that of females. Females perceived more stress in inter personal domain (12.7%) with the score significantly different from males. Group and Teaching stress was equally present in males and females. Conclusion: During the pandemic, students’ mental health needs to be continually monitored as they are stressed owing to fear as well as about their studies and future careers