Feasibility and Co-Benefits of Biomass Co-Firing: Case in Utah

This research examines the physical and economic feasibility of 5% biomass co-firing in the coal-fired power plants of Utah. Transportation models is used to find out the physical feasibility of 5% biomass co-firing, as well as locate the supply zone for each power plant that would minimize the transportation cost. Additional cost required for 5% biomass co-firing and the economic benefits associated with biomass co-firing are calculated. The additional cost required for 5% biomass co-firing is estimated to be $34.84 million. Previous studies on CO2 emission reduction are used to compute the economic benefit attain from CO2 reduction by selling carbon credits in the carbon trading market. Based on 2010 emission record in Utah, 5$11.37~$34.10 million assuming$16/ton of CO2 in the emission trading market. The regression model is used to find the relationship between PM emission and the human health damage. The regression results show that decreases in 1% of PM25 emission improves the human health in U.S. by 0.65%~0.67% in value. Five percent biomass co-firing generates annual economic benefits of $6.72~$9.93 million in Utah depending on the emission reduction scenarios. Note that these might not be the precise economic benefit from the biomass co-firing in Utah because elasticities estimated in the regression are expected to be lower in Utah. This is because most of power plants in Utah are located in open areas. Altogether, the economic benefit from 5% biomass co-firing is estimated to be $38.55 million assuming the medium emission reduction scenario, moderate carbon price ($16/ton of CO2) which is higher than the additional cost of biomass co-firing to generate electricity ($34.84 million). The benefit cost ratio is calculated as 1.107. Five percent biomass co-firing is economically feasible when benefits from all the positive externalities are included. The findings of the research suggest that in order to make 5$16 and $20, respectively

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and they have their own advantages and difficulties. Rule-based reasoning is accurate and explainable but rule learning with searching over the graph always suffers from efficiency due to huge search space. Embedding-based reasoning is more scalable and efficient as the reasoning is conducted via computation between embeddings, but it has difficulty learning good representations for sparse entities because a good embedding relies heavily on data richness. Based on this observation, in this paper we explore how embedding and rule learning can be combined together and complement each other's difficulties with their advantages. We propose a novel framework IterE iteratively learning embeddings and rules, in which rules are learned from embeddings with proper pruning strategy and embeddings are learned from existing triples and new triples inferred by rules. Evaluations on embedding qualities of IterE show that rules help improve the quality of sparse entity embeddings and their link prediction results. We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently. Experiments show that iteratively learning embeddings and rules benefit each other during learning and prediction.Comment: This paper is accepted by WWW'1

Digital learning Initiatives, Challenges and Achievement in Higher Education in Nepal Amidst COVID-19

The COVID-19 pandemic has affected higher education institutions (HEIs) worldwide and reshaped the existing educational system. Due to travel constraints and physical separation, there has been a global shift toward distance learning, and Nepal is no exception. This research intends to assess the practicality of online education by evaluating learners' experiences amidst COVID-19. A cross-sectional study was directed among HEIs students in Nepal using self-structured questionnaires. Our study revealed that 64.6% of the respondents were unsatisfied with online classes. More than half of the respondents (53.4%) use cell phones for online studies. Online education was reported to be unappealing to 28.8% of respondents. Variables such as age group (p = 0.05), enjoying class (p < 0.001), hours spent for an online class in a day (p = 0.05), and period for educational work using an electronic device (p = 0.1) were found significant with satisfaction level using both bivariate test and inferential test of univariate binary logistics regression. The challenges and opportunities encountered among students and faculties are highlighted along with the recommendations for fortifying communication in online-based teaching/learning

Severity and Clinical Outcome of COVID-19 Patients Admitted at a Provincial Infectious and Communicable Disease Hospital of Nepal: A Cross-Sectional Study

Background: This study provides information regarding severity and clinical outcome of people admitted with the diagnosis of COVID-19 infection during the global pandemic at a provincial infectious disease hospital in Gandaki Province in Nepal. The evidence from this study will be helpful to compare the clinical outcome of people admitted with COVID-19 during the outbreak. Methods: Cross-sectional study was conducted from March 2023 to August 2023 after approval from NHRC (ref. no. 1448) with sample size of 1366 at the hospital. Structured questionnaire was used to collect secondary data (electronic and paper records) retrospectively from hospital records with a diagnosis of COVID-19 infection. Total enumeration technique was used with enlisting of all cases of COVID-19 to the hospital. The collected data was analyzed using SPSS version 11.5. Results: The hospital admitted the highest number of cases between April to September 2021. Among the 1366 admitted cases, 791 (57.91%) were males and 575 (42.09%) were females, the most common age group affected was 31 to 40 years (22.99%); 1092 (79.94%) were from Kaski district. As per disease severity, 884 (64.71%) were moderate cases followed by 391 (28.62%) mild cases and 91 (6.67%) severe cases. A total of 1205 (88.21%) patients were discharged, 105(7.69%) patients were referred and 56 (4.10%) patients died of COVID -19. Conclusions: Almost 3/4th of the admitted cases came from same district, majority had moderate disease and the hospital cure rate was almost 8/9th. As the majority of cases are from active age group (21 years to 60 years old), public health measures can be targeted to these groups including surrounding population to stop transmission and spread of COVID-19 or similar infectious diseases. The information from this study can guide for the preparation and planning of in-patient and isolation departments of similar other provincial infectious disease hospitals

Fear, risk perception, and engagement in preventive behaviors for COVID-19 during nationwide lockdown in Nepal

The world has faced huge negative effects from the COVID-19 pandemic between early 2020 and late 2021. Each country has implemented a range of preventive measures to minimize the risk during the COVID-19 pandemic. This study assessed the COVID-19-related fear, risk perception, and preventative behavior during the nationwide lockdown due to COVID-19 in Nepal. In a cross-sectional study, conducted in mid-2021 during the nationwide lockdown in Nepal, a total of 1484 individuals completed measures on fear of COVID-19, COVID-19 risk perception, and preventive behavior. A multiple linear regression analysis was used to identify factors associated with COVID-19 fear. The results revealed significant differences in the fear of COVID-19 in association with the perceived risk of COVID-19 and preventive behaviors. Age, risk perception, preventive behavior, and poor health status were significantly positively related to fear of COVID-19. Perceived risk and preventive behaviors uniquely predicted fear of COVID-19 over and above the effects of socio-demographic variables. Being female and unmarried were the significant factors associated with fear of COVID-19 among study respondents. Higher risk perception, poor health status, and being female were strong factors of increased fear of COVID-19. Targeted interventions are essential to integrate community-level mental health care for COVID-19 resilience

Improving recommendation diversity and identifying cultural biases for personalized ranking in large networks

Personalized ranking and filtering algorithms, also known as recommender systems, form the backbone of many modern web applications. They are used to tailor and rank suggestions for users in search engines, e-commerce sites, social networks, and news aggregators. As such systems gain prevalence in people’s day-to-day lives, they also affect people’s behavior in several ways. Of the several concerns regarding these systems, the diversity of choices they offer to users is one of the important ones. Exposure to diverse items is considered important for many reasons: for improving user-experience by adding richness, novelty and variety, reducing polarization and helping improve political participation through exposure to diverse viewpoints. It is therefore important to investigate ways to make recommender algorithms serve more diverse content. In this thesis, we present three new recommender algorithms for increasing the diversity of suggestions. We also present a new method to detect biases in knowledge bases, which are often used as input data source by recommender systems. The first algorithm uses a local exploration of the user-item feedback graph to increase the long-tail diversity of items. Long-tail items form a bulk of many product catalogs but compared to the few popular items that dominate recommendation lists, they are not recommended often. Our random-walk based method of promoting such long-tail items results in both more accurate and more diverse recommendations. In the second algorithm, we use a probabilistic latent-factor model to differentiate between positive and negative items in recommender systems. We find that the state-of-the-art algorithms not only have more negative items at the top of their recommendations, they also have low diversity and coverage. The recommendations produced by our approach is able to put fewer negative items at the top, and are also more diverse. In the third strategy, we look into the problem of diversifying political content recommendation. We collected data from the popular social network Twitter and created datasets that can be used to study political content recommendations. Based on these datasets, we first develop a new method to identify the ideological positions of not just users and political elites, but also of web-content. Then we used the identified ideological positions to diversify the recommendations based on diversification strategies that can be specified by the service provider. Our method is able to correctly identify political ideologies and to diversify recommendation of political content. Finally, since knowledge bases are used as input in many systems including recommender algorithms, we investigate them for the presence of human-like biases related to gender and race. We develop a new method based on cultural dimensions that can identify such biases in knowledge bases. Using our approach, it is possible to develop methods that can learn unbiased representations from knowledge bases, which can then be used by recommender algorithms. With our work, we present new ways to diversify and de-bias the output of recommender systems and we hope this will enable them to better serve the diverse needs of our societies