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In an operating system, disk scheduling is the process of managing the I/O request to the secondary storage devices such as hard disk. The speed of the processor and primary memory has increased in a rapid way than the secondary storage. Seek time is the important factor in an operating system to get the best access time. For the better performance, speedy servicing of I/O request for secondary memory is very important. The goal of the disk-scheduling algorithm is to minimize the response time and maximize throughput of the system. This work analyzed and compared various basic disk scheduling techniques like First Come First Serve (FCFS), Shortest Seek Time First (SSTF), SCAN, LOOK, Circular SCAN (C-SCAN) and Circular LOOK (C-LOOK) along with the corresponding seek time. From the comparative analysis, the result show that C-LOOK algorithm give the least head movement and seek time in different cases as compared to other algorithm. Therefore, it maximizes the throughput for the storage devices.

The rapid rise of platforms like YouTube and Facebook is due to the spread of tablets, smartphones, and other electronic devices. Massive volumes of data are collected every second on such a platform, demanding large-scale data processing. Because these data come in a variety of modalities, including text, audio, and video, sentiment categorization in various modalities and emotional computing are the most researched fields in today's scenario. Companies are striving to make use of this information by developing automated systems for a variety of purposes, such as automated customer feedback collection from user assessments, where the underlying challenge is to mine user sentiment connected to a specific product or service. The use of efficient and effective sentiment analysis tools is required to solve such a complex problem with such a big volume of data. The sentiment analysis of videos is investigated in this study, with data available in three modalities: audio, video, and text. In today's world, modality fusion is a major problem. This study introduces a novel approach to speaker-independent fusion: utilizing deep learning to fuse in a hierarchical fashion. The work tried to obtain improvement over simple concatenation-based fusion.

In order to facilitate the collection and analysis of accounting information related with the competitor in the context of strategic management accounting, the term competitor accounting has been developed. However, no empirical results are available as to the extent to which such information influences strategic decision behavior. Main purpose of this study is to explore the effect of competitor accounting on performance of the joint venture banks, and compare the effectiveness of competitor accounting between those banks. For this structured questionnaire survey with 133 staffs of the concerned banks as well as unstructured interview with 4 senior managers were conducted. Various published and unpublished reports like annual reports of the concerned banks were also used. From the study it is found out that levels of CA formalised application appear limited, especially when compared with a widely held managerial perception that significant benefits could derive from CA. The CA practices noted were conducted in an unstructured and ad hoc manner. CA does not have any effect on performance of Nepalese joint venture banks because of not applying any formalised CA application. The study had /some of the limitations generally associated with a qualitative as well as quantitative study. These limitations include the degree of subjectivity that is invoked when researchers interpret qualitative data, scarcity of various resources, and accuracy of secondary data. The study clarifies the notion of CA and provides an outline of CA management issues arising in the context of a joint venture banks. An outline is provided of those parts of a bank operation that are most likely to be more active in CA, together with empirically informed suggestions with respect to CA uses in a joint venture banks.

The growth of social data on the web is increasing rapidly nowadays. This leads to researchers to access the data and information for the research purpose. During the global COVID-19 outbreak, many individuals as well as organizations and government agencies are posting their viewpoints regarding the coronavirus. The study focuses on the sentiment analysis of tweets of the Twitter social media using Python programming language with Tweepy and TextBlob library. The tweets have been collected, preprocessed and then used for text mining and sentiment analysis using Google Colab. The graphical representation has been provided on the data after sentiment analysis based on two specified hashtags keywords : #COVID-19 and #coronavirus. The data are collected from the users who shared their location as ‘Nepal’ between 21st May 2020 and 31st May 2020. The result of the study concluded that while majority of the people of Nepal are taking a positive and hopeful approach, there are instances of fear, sadness and disgust exhibited too.

The main objective of this research is to analyze the general impact of television advertising on consumer behavior with reference to Sunsilk Shampoo. Many people use advertising at some time in their life. Some may use it privately while others may use in business. And many people respond to advertisements. They enjoy the choices available to them in every sphere of life (Kotler, 1994:24). It is one of the major tools that companies use to persuade target buyers and publics to buy their products or services. In this scenario researcher takes Survey research design as a methodology. In this study media of advertising and impact of television advertising have been evaluated. Data used in this study are only primary in nature. The required data are collected through the questionnaire survey among the people of different age groups, gender, different education group etc. covering the consumer of urban area. According to the findings it can be concluded that majority of the people have television watching habits and out of them women are more curious in television then men which indicated that television advertisement is more popular means of advertisement.

Enteric fever is an invasive life-threatening systemic disease caused by the Salmonella enterica human-adapted serovars typhi and paratyphi. Increased incidence of infection with S. enterica serovar paratyphi A poses a significant health concern in some areas of the world. In this study, the incidence of enteric fever confirmed by isolation of Salmonella paratypi A or S. typhi from blood cultures of patients presenting with clinical symptoms was 5.1%. Of the total isolates, 64.13% were S. paratyphi A, and 35.87% were Salmonella typhi. All isolates were susceptible to amoxicillin, ceftriaxone and cefixime. Conventional antibiotics (ampicillin, cloramphenicol and co-trimoxazole) showed 100% sensitivity rate towards S. paratyphi A and 96.9% towards S. typhi. Overall nalidixic acid-resistance (NAR) rate was extremely high (92.39%). Nalidixic acid resistant (MIC ≥ 32 µg/ml) S. paratyphi A showed increased MICs of the fluoroquinolone than nalidixic acid resistant S. typhi ranges from 0.125-8 µg/ml with ciprofloxacin and 0.25-4 µg/ml with levofloxacin and was statistically significant (p≤0.001). Immunization with currently available vaccines against typhoid fever does not provide cross protection against paratyphoid fever. This may contribute to the emergence of paratyphoid fever as the major cause of enteric fever in Nepal and possibly other geographical locations. Key words: Enteric fever, Salmonella paratyphi A, Salmonella typhi, fluoroquinolones (FQs), Nepal.