Dr. K.N.Modi University

Abstract: Vascular dementia (VaD) is a progressive neurodegenerative disorder characterized by cognitive decline and anxiety-related symptoms, primarily associated with cerebrovascular dysfunction. Preclinical models frequently employ scopolamine, a muscarinic acetylcholine receptor antagonist, to induce cholinergic deficits and replicate key features of both Alzheimer's disease and VaD. This model reliably demonstrates cognitive impairment, heightened anxiety-like behaviors, and cerebrovascular abnormalities. Recent interest has emerged in Cyclovirobuxine (CVB), a bioactive alkaloid derived from Buxus species, due to its potential neuroprotective properties. CVB exhibits vasodilatory, antiinflammatory, and anti-apoptotic effects, making it a compelling candidate for mitigating neurovascular dysfunction central to VaD pathogenesis. Theoretical frameworks suggest that CVB may counteract scopolamine-induced neurotoxicity by enhancing cerebral perfusion, attenuating oxidative stress, and modulating cholinergic and other neurotransmitter systems. These mechanisms align with established therapeutic targets in VaD, such as neuroinflammation reduction, cholinergic enhancement, and synaptic plasticity modulation. While preclinical evidence supports these roles, the precise molecular pathways and translational relevance of CVB remain insufficiently defined. Rodent models of scopolamineinduced cognitive dysfunction provide a valuable platform for elucidating these mechanisms and assessing the therapeutic potential of CVB and related compounds. Future investigations should focus on delineating the molecular cascades modulated by CVB and evaluating its efficacy in clinically relevant models. Clarifying these pathways may inform the development of targeted interventions for VaD and related neurodegenerative conditions.

The technique of multimodal sentiment analysis enables researchers to examine human emotions through its capability to process data from three different sources which include text and audio as well as visual signals. The research intro- duces a new multimodal framework which uses transformer technology to build its system via vision-language models and large language models that enable the system to analyze multiple modal links while maintaining its ability to under- stand contextual information. The model uses cross-modal attention together with feature fusion methods to increase the accuracy of sentiment predictions. Researchers used CMU-MOSEI dataset for testing purposes because it contains over 23000 annotated video segments from more than 1000 speakers who presented their opinions about different topics with sentiment intensity labels that ranged from 3 to +3. The experimental results show that the proposed model out- performs both traditional unimodal methods and early fusion methods because it achieves better accuracy and F1-score results. The system now uses explainable AI techniques to enhance model interpretability which enables its applications in real-world scenarios involving social media analytics and human-computer interaction as well as affective computing.

Background: E-commerce is an important means of trade, which allows quicker and efficient order management, particularly in such developing nations as Nepal. It is critical to understand the view of trade professionals regarding the online management of orders in order to embrace the concept of digital transformation in trade practices. Methods: This research surveyed 51 trade professionals in Nepal in order to determine how they saw e-commerce in order management. The data were obtained with the help of structured questionnaires and analyzed with descriptive statistics and independent samples t-test in order to compare the two genders. Findings: The majority of the respondents perceived e-commerce systems to be useful in easing order handling, accelerating cross-border orders, minimizing shipping mistakes, boosting tracking and communication, as well as customer satisfaction. Even though there was a marginally better positive perceptions among females as compared to the males, the difference was not significant. Conclusion: Nepalese trade practitioners overall are positive opinions about e-commerce using to manage orders, and there are no significant differences between the genders. These results indicate the need to encourage digital solutions in enhancing trade efficiency and competitiveness in Nepal.

Abstract A series of novel 3-substituted quinazoline-2,4(1H,3H)-dione derivatives was designed, synthesized, and evaluated for their anticonvulsant, antibacterial, and antifungal activities. Anticonvulsant efficacy was assessed in vivo using the maximal electroshock (MES) seizure model, while antibacterial and antifungal activities were screened against selected Gram-positive, Gram-negative bacterial, and fungal strains. To support the experimental findings and elucidate possible binding interactions, molecular docking studies were carried out against relevant biological targets for antibacterial (PDB ID: 6BPP), antifungal (PDB ID: 7VPR), and anticonvulsant activity (PDB ID: 2Z5X). Among the synthesized compounds, compound 4h exhibited significant anticonvulsant activity by effectively inhibiting extensor seizures in the MES model at a dose of 30 mg/kg, showing comparable efficacy to the reference drug phenytoin sodium. Compound 4f demonstrated broad-spectrum antibacterial activity against Gram-positive bacteria ( Bacillus subtilis , Staphylococcus aureus ) and Gram-negative bacteria ( Escherichia coli , Pseudomonas aeruginosa ), inhibiting microbial growth at 100 µg/mL. Additionally, compound 4b displayed potent antifungal activity against Candida albicans and Aspergillus niger at 100 µg/mL, surpassing the activity of the standard antifungal agent griseofulvin. The study highlights the quinazoline-2,4-dione heterocyclic scaffold as a promising pharmacophore for the development of multifunctional bioactive agents with potential therapeutic relevance and reduced adverse effects. The combined in silico and experimental findings provide a valuable foundation for further structural optimization and pharmacological exploration.

The speed of the advancement of digital technologies has led to a remarkable rise in the scale, complexity, and sophistication of cyber-attacks. Traditional security approaches based on extensive use of static rules and signature-based detections are simply not adequate for contemporary threats such as zero-day exploits, advanced persistent threats (APTs), and adversarial attacks. As a radical shift in approach, deep learning has emerged, providing intelligent, adaptive, and automated features for cyber defenses. This review paper provides an inclusive overview of deep learning technologies to mitigate various forms of cyber-attacks. Various deep learning architectures are investigated in this paper, such as convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), autoencoders, generative adversarial networks (GANs), and graph neural networks (GNN), and the applications of each technology in intrusion detection, malware classification, phishing detection, anomaly detection, and network traffic analysis. In addition, we discuss mainstream datasets, metrics for evaluation, and methodology as they relate to cyber security. Issues such as limited data, adversarial robustness, and usability, will be critically discussed. We also discuss new directions such as hybrid models, privacy-preserving learning, and federated defenses. By aggregating recent research, this review highlights the promise of deep models to serve as robust defenders against deep and evolving cyber-attacks, which serves as impetus for next-generation intelligent cybersecurity systems.

Background: Smaller entrepreneurs are considered indispensable in the developmental economy of emerging countries such as Nepal, but a substantial portion of them are left out of the financial system. Objectives: To examine financial literacy among small entrepreneurs. To highlight major obstacles in getting access to banking institutions. To analyze the viability of digital financial solutions and microfinance institutions. To comprehend awareness and effectiveness of government schemes. Methods: A cross-sectional study using a structured format with a population of 50 small shopkeepers in Sukedhara, Kathmandu, was used. Data analysis included descriptive statistics, Cronbach's Alpha Test in SPSS for reliability, and a Pearson Correlation Test for four major hypotheses. Findings: Lack of financial literacy (r=0.68, p<0.01) and geographical restrictions (r=0.72, p<0.01) were strongly related to financial exclusion and informal financial participation. M-banking had a great potential for financial inclusion (r=0.61, p<0.01) but this was conditional on digital literacy. Awareness of government schemes was a critical factor where no important relationship with better access existed (r=0.19, p>0.05). Conclusion: The financial gap is perpetuated by interwoven demand side (literacy, perception) and supply side (access, product) barriers. Implementation: To make this vision a reality, a synergistic approach can be adopted, which will include the following: (1) Financial and digital literacy programs at a national level; (2) Improving mobile banking infrastructure and trust; (3) Improving public-private credit guarantee schemes and awareness thereof; and (4) Banks developing new products based on an alternative credit.

Congestion control is essential to provide a smooth transmission of data in a current network especially in latency sensitive applications like video streaming. Existing traditional congestion control algorithms fail to cope with dynamic network conditions causing suboptimal use of bandwidth, high latency and packet loss. This paper introduces an improved OLIA to real-time adaptive streaming. The given framework presents a dynamic congestion window adaptation algorithm that is grounded on real-time network feedback and a rational adaptive retransmission to enhance bandwidth use and minimize unnecessary retransmissions. Adaptive window adjustment relies on the feedback-based scaling factor based on the occupancy of the queue and lost packets, and the retransmission mechanism rearranges the priority of the lost packets on the network conditions. NS-3 $\mathbf{5 G}$ traffic datasets were used in experiments with different network topologies. Findings indicate that network performance improved significantly: throughput improved to $\mathbf{1 5. 8} \mathrm{Mbps}$, packet loss dropped to 1.8 percent, latency dropped to 25.3 ms, jitter dropped to 3.6 ms and fairness index was also enhanced to 0.98. The suggested improvements allow playing video faster, less buffering and better user experience, which indicates the efficiency of OLIA in high bandwidth, latency sensitive streaming.

Introduction: Mumps, a viral infection caused by a member of the Paramyxoviridae family, remains a public health concern despite the success of global vaccination programs. The disease typically presents with parotitis and systemic symptoms such as fever and malaise, with complications including orchitis, meningitis, and sensorineural deafness. Methods: This review synthesizes current literature on mumps epidemiology, pathogenesis, clinical features, diagnostic approaches, and control strategies. Information was gathered from peerreviewed journals, public health reports, and molecular diagnostics studies. Results: The global incidence of mumps has significantly declined since the introduction of the measles-mumps-rubella vaccine in the 1970s. However, recent outbreaks in both high- and lowincome countries are attributed to waning immunity, genetic mismatch between vaccine and circulating strains, and suboptimal vaccine coverage. Diagnostic advancements, especially RT-PCR and serological assays, have enhanced outbreak detection and management. Discussion: Persistent mumps outbreaks highlight the challenges of long-term immunity and virus evolution. Current control strategies emphasize high vaccination coverage, rapid outbreak response, and continuous epidemiological surveillance. Research efforts are underway to develop mRNA-based and genotype-specific vaccines to address these challenges. However, gaps remain in understanding immunity duration, disease progression, and asymptomatic transmission. Conclusion: Effective control of mumps requires strengthened public health infrastructure, improved diagnostics, and efforts to counter vaccine hesitancy. Further research is crucial to overcome existing limitations and to inform future vaccination and outbreak management policies.

Workplace stress is a significant issue, particularly for female employees in high-pressure environments like banking. The background of this research stems from the growing concern over how stressors such as workload, job security, shift scheduling, and role ambiguity affect the performance and well-being of female employees in the banking sector. The main goal of this study is to find the main sources of stress at work and look at how they affect the work performance of Nepalese female bank workers. A structured questionnaire was used to get information from 250 female employees as part of a quantitative study design. We used both correlation and regression analyses to find the link between stressors and job performance. The findings reveal that all identified stressors—workload, job insecurity, shift scheduling, and role ambiguity—have a significant negative effect on employee performance. Shift scheduling explained 23.5% of the variation in performance, while role ambiguity accounted for 16.7%. The results highlight that higher levels of stress are associated with lower job performance, leading to the rejection of null hypotheses related to the impact of shift scheduling and role ambiguity. The originality of this study lies in its focus on the specific challenges faced by female bank employees in Nepal, providing valuable insights into how workplace stressors impact job performance in this context. The results make it clear that effective stress management methods are needed to boost the health and productivity of workers.