Prediction of Credit Card Approval

Credit risk as the board in banks basically centers around deciding the probability of a customer's default or credit decay and how expensive it will end up being assuming it happens. It is important to consider major factors and predict beforehand the probability of consumers defaulting given their conditions. Which is where a machine learning model comes in handy and allows the banks and major financial institutions to predict whether the customer, they are giving the loan to, will default or not. This project builds a machine learning model with the best accuracy possible using python. First we load and view the dataset. The dataset has a combination of both mathematical and non-mathematical elements, that it contains values from various reaches, in addition to that it contains a few missing passages. We preprocess the dataset to guarantee the AI model we pick can make great expectations. After the information is looking great, some exploratory information examination is done to assemble our instincts. Finally, we will build a machine learning model that can predict if an individual's application for a credit card will be accepted. Using various tools and techniques we then try to improve the accuracy of the model. This project uses Jupyter notebook for python programming to build the machine learning model. Using Data Analysis and Machine Learning, we attempted to determine the most essential parameters for obtaining credit card acceptance in this project. The machine learning model we built gave an 86 % accuracy for predicting whether the credit card will be approved or not, considering the various factors mentioned in the application of the credit card holder. Even though we achieved an accuracy of 86%, we conducted a grid search to see if we could increase the performance even further. However, using both the machine learning models: random forest and logistic regression, the best we could get from this data was 86 percent.

Classifying Forest Structure of Red-Cockaded Woodpecker Habitat Using Structure from Motion Elevation Data De-Rived from sUAS Imagery

Small unmanned aerial systems (sUAS) and relatively new photogrammetry software solutions are creating opportunities for forest managers to perform spatial analysis more efficiently and cost-effectively. This study aims to identify a method for leveraging these technologies to analyze vertical forest structure of red-cockaded woodpecker habitat in Montgomery County, Texas. Traditional sampling methods would require numerous hours of ground surveying and data collection using various measuring techniques. Structure from Motion (SfM), a photogrammetric method for creating 3-D structure from 2-D images, provides an alternative to relatively expensive LIDAR sensing technologies and can accurately model the high level of complexity found within our study area’s vertical structure. DroneDeploy, a photogrammetry processing app service, was used to post-process and create a point cloud, which was later further processed into a Canopy Height Model (CHM). Using supervised, object-based classification and comparing multiple classifier algorithms, classifications maps were generated with a best overall accuracy of 84.8% using Support Vector Machine in ArcGIS Pro software. Appropriately sized training sample datasets, correctly processed elevation data, and proper image segmentation were among the major factors impacting classification accuracy during the numerous classification iterations performed.

Immobilization of β-Galactosidase by Encapsulation of Enzyme-Conjugated Polymer Nanoparticles Inside Hydrogel Microparticles

Increasing the shelf life of enzymes and making them reusable is a prominent topic in biotechnology. The encapsulation inside hydrogel microparticles (HMPs) can enhance the enzyme’s stability by preserving its native conformation and facilitating continuous biocatalytic processes and enzyme recovery. In this study, we present a method to immobilize β-galactosidase by, first, conjugating the enzyme onto the surface of polymer nanoparticles, and then encapsulating these enzyme-conjugated nanoparticles (ENPs) inside HMPs using microfluidic device paired with UV-LEDs. Polymer nanoparticles act as anchors for enzyme molecules, potentially preventing their leaching through the hydrogel network especially during swelling. The affinity binding (through streptavidin-biotin interaction) was used as an immobilization technique of β-galactosidase on the surface of polymer nanoparticles. The hydrogel microparticles of roughly 400 μm in size (swollen state) containing unbound enzyme and ENPs were produced. The effects of encapsulation and storage in different conditions were evaluated. It was discovered that the encapsulation in acrylamide (AcAm) microparticles caused an almost complete loss of enzymatic activity. Encapsulation in poly(ethylene glycol) (PEG)-diacrylate microparticles, on the other hand, showed a residual activity of 15–25%, presumably due to a protective effect of PEG during polymerization. One of the major factors that affected the enzyme activity was presence of photoinitiator exposed to UV-irradiation. Storage studies were carried out at room temperature, in the fridge and in the freezer throughout 1, 7 and 28 days. The polymer nanoparticles showcased excellent immobilization properties and preserved the activity of the conjugated enzyme at room temperature (115% residual activity after 28 days), while a slight decrease was observed for the unbound enzyme (94% after 28 days). Similar trends were observed for encapsulated ENPs and unbound enzyme. Nevertheless, storage at −26°C resulted in an almost complete loss of enzymatic activity for all samples.

Effects of Government's Policy in Stock Price: A Case of Nepse

The purpose of this study is to find out the relationship of government's policy on the price movement of Nepal stock exchange (NEPSE). This study followed a case study research design, because it offers a deeper perspective and clearer understanding of the stock price movement of Nepalese joint venture banks. The sample size of this study consists of five joint venture commercial Banks, economic analysis and survey reports conducted by central bank of Nepal (Nepal Rastra Bank).The judgmental sampling method has been applied for selection of joint venture banks. The study was totally based on secondary data. in order to make proper analysis descriptive and inferential statistics were used using SPSS software version 26. The finding of this study revealed that the GDP and import are inversely associated with stock price movement and CRR, export, interest rate and inflation are positively associated with stock price movement. Further, it is found that the macroeconomic variables are key factors to determine the Nepalese stock price movement. More importantly, stock market has been found to respond significantly to changes in the government policy. It is recommended that CRR, EXPORT, INTEREST RATE and INFLATION are major factors which largely affect the stock price movement of NEPSE. GDP and IMPORT are not compliance with the stock price movement as they produce negative association with the stocks volatility.

Long Non-Coding RNAs as Emerging Regulators of Pathogen Response in Plants

Long non-coding RNAs (lncRNAs) are transcripts without protein-coding potential that contain more than 200 nucleotides that play important roles in plant survival in response to different stresses. They interact with molecules such as DNA, RNA, and protein, and play roles in the regulation of chromatin remodeling, RNA metabolism, and protein modification activities. These lncRNAs regulate the expression of their downstream targets through epigenetic changes, at the level of transcription and post-transcription. Emerging information from computational biology and functional characterization of some of them has revealed their diverse mechanisms of action and possible roles in biological processes such as flowering time, reproductive organ development, as well as biotic and abiotic stress responses. In this review, we have mainly focused on the role of lncRNAs in biotic stress response due to the limited availability of knowledge in this domain. We have discussed the available molecular mechanisms of certain known lncRNAs against specific pathogens. Further, considering that fungal, viral, and bacterial diseases are major factors in the global food crisis, we have highlighted the importance of lncRNAs against pathogen responses and the progress in plant research to develop a better understanding of their functions and molecular mechanisms.

Comparing Push and Pull Factors Affecting Migration

The purpose of this research was to conduct a comparison of the push and pull factors affecting migration between Poland and Romania. The study aimed to find out which among the push and pull factors have a greater effect overall and individually on the migration activities. The study was conducted using primary data collected from migrants in both countries using a structured questionnaire. There were data from 298 and 288 surveys for Poland and Romania, respectively. The push and pull migration framework was applied to guide the study. The model suitability was confirmed satisfactory on validity, reliability and factor analysis. The hypothesis was analyzed and evaluated using multiple regression analysis. The findings of the study indicated that pull factors have a greater influence on migration in these two countries as compared to the push factors. Five out of six (economic, political and social in Poland and economic and political in Romania) pull factors were found significant as compared to two (social in Poland and in Romania) out of six push factors. Pull economic factors were significant determinants of migration in all the countries. Pull political factors were found to have the highest effect in both countries, because they influenced migrants in Romania. Economic factors are the major factors that influence migration, including the hope of finding better jobs and better life in the foreign countries, and these factors should be addressed in the effort to reduce migration. In addition, political issues such as unfair legal system, violent conflicts, underdevelopment, poverty, political instability and corruption should be addressed to control the issue of migration.

Evolution of policy for the treatment of substance use disorders in Qatar

There is a marked paucity of published evidence on the extent and nature of substance use disorders in the State of Qatar. This is mirrored by a dearth of information on the policy for the treatment of substance use disorders in the public domain. Between 2007 and 2017, substance use disorders have risen from the third to leading cause of disability in Qatar. More recently, Qatar has shifted from applying a punitive only paradigm in managing substance use problems to recognizing the role of treatment and care for people with substance use disorders. Recently published official documents in Qatar define addiction as a disease and as a chronic condition where people with substance use disorders should be treated as patients who need care and assistance. This shifts the onus of providing, and developing services, for individuals with substance use disorders with healthcare providers rather than purely with the criminal justice system. Following cabinet approval, the recently established Permanent Committee for Addiction Treatment headed by the Minister of Public Health, signals the need to institutionalize systems and structures to upscale demand reduction programmes in the country. This article is a descriptive examination of the shifts in substance abuse treatment policy in Qatar, the major factors influencing this evolution, and will utilise some of the policy science theories to describe and analyse policy outcomes. The article will also frame the substance use problem in Qatar for the first time, based on documents published by various government organisations.

A REVIEW OF TREATMENT, RISK FACTORS, AND INCIDENCE OF COLORECTAL CANCER

Colorectal cancer (CRC) is considered as the third most frequent cancer in the world and the incidence increases with increasing age. CRC accounts for nearly 9 % of all cancer incidence, with an estimated 1.4 million cases happening in 2012. The aim of this paper is to provide a review of incidence, risk factors, screening strategies, and treatment of colorectal cancer. We searched the studies in five English databases, including Web of Science, PubMed, Scopus, EMBASE, and Google Scholar with no limitation in publication time to find all papers regarding colorectal cancers. Papers with any language were included in the first step of search if they had an English abstract. We used the following words and terms including colorectal cancer, treatment, risk factor, diagnosis, chemotherapy, radiotherapy, surgery. Geographical variations and different time courses in the CRC incidence indicate that environmental factors and lifestyle are major factors in the development of this disease. The main preventable risk factors for CRC are nutrition, a high-fat diet, a low-fiber diet, obesity and physical inactivity, smoking and alcohol consumption, aspirin and nonsteroidal anti-inflammatory drugs, and some non-preventable risk factors such as age, gender, race, and diabetes mellitus. Colonoscopy remains the study of choice to diagnose colorectal cancer. Prior to any treatment, CT imaging of chest, abdomen and pelvis with contrast is needed for staging the patient’s CRC. The preferred option for localized colorectal cancer is surgery (etc, laparoscopic surgery, colostomy for rectal cancer); whereas the adjuvant chemotherapy is generally recommended for patients with lymph node metastases. Targeted treatment of colorectal cancer by monoclonal antibodies are important bioengineered proteins that can help the body's natural immune response to detect, attack, and kill cancer cells. Monoclonal antibodies may be used alone or in combination with other treatments such as chemotherapy. CRC accounts an important health problem worldwide that is estimated to increase because of the growth and aging of the population, and because of the adoption of at-risk manners and lifestyles, particularly in economically less developed countries. Screening has been confirmed to significantly decrease mortality and can prevent the onset of the disease. More international efforts are required to situate into practice targeted prevention approaches that might reduce the burden of CRC worldwide.

Uncrewed Surface Vessel Technological Diffusion Depends on Cross-Sectoral Investment in Open-Ocean Archetypes: A Systematic Review of USV Applications and Drivers

Accessing the world's oceans is essential for monitoring and sustainable management of the maritime domain. Difficulty in reaching remote locations has resulted in sparse coverage, undermining our capacity to deter illegal activities and gather data for physical and biological processes. Uncrewed Surface Vessels (USVs) have existed for over two decades and offer the potential to overcome difficulties associated with monitoring and surveillance in remote regions. However, they are not yet an integral component of maritime infrastructure. We analyse 15 years of non-autonomous and semi-autonomous USV-related literature to determine the factors limiting technological diffusion into everyday maritime operations. We systematically categorised over 1,000 USV-related publications to determine how government, academia and industry sectors use USVs and what drives their uptake. We found a striking overlap between these sectors for 11 applications and nine drivers. Low cost was a consistent and central driver for USV uptake across the three sectors. Product ‘compatibility' and lack of ‘complexity' appear to be major factors limiting USV technological diffusion amongst early adopters. We found that the majority (21 of 27) of commercially available USVs lacked the complexity required for multiple applications in beyond the horizon operations. We argue that the best value for money to advance USV uptake is for designs that offer cross-disciplinary applications and the ability to operate in an unsheltered open ocean without an escort or mothership. The benefits from this technological advancement can excel under existing collaborative governance frameworks and are most significant for remote and developing maritime nations.

Sexual Harassment AND Its effects on Mental Health OF THE Teenage School Girls in Lalitpur and rupandehi district

A cross-sectional study was conducted to find the prevalence of mental health on teenage girl students and the effect of sexual harassment on their mental health: depression, anxiety, and stress. A concurrent mixed method was used and the study was conducted at both community and institutional schools of Lalitpur and Rupendehi districts. A semi-structured questionnaire was used for quantitative data collection along with DASS (42 points) test to measure depression, anxiety, and depression. Focus group discussion (FGD), in-depth interviews (IDI), and key informant information (KII) were used for qualitative data collection. Poor and ill mental health were found among the respondents and the prevalence of depression, anxiety and stress were 45%, 52%, and 35% respectively among the sexually harassed girl. As sexual harassment was found as one of the major factors that responsible for the poor mental health status of girl students, it is necessary for every school to adopt anti-sexual harassment policies and to take action against it to create a healthy learning environment.