Incorporating citizen science:enhancing hydrological modeling through crowdsourcing

Abstract. General public participating in research design, data collection or analysis process is often referred to as citizen science, and when digital means are involved, it’s defined as crowdsourcing. This thesis project is aimed at examining the feasibility and potential of using citizen science/crowdsourcing for hydrological modelling. The research project revolves around developing a user friendly crowdsourcing mobile application for gathering data from the citizens, which will be specific to urban flooding data, river ice data, lake water quality data and vegetation condition data. The registered users are able to register on the application and upload data in the form of reports, which will be in text form and also attach images of the situation. In the end, we utilize the text reports uploaded by users regarding urban flooding to extract useful hydrological insights, that could be used for updating already existing hydrological models as well as create new hydrological models using NLP. The results indicate that it is possible to extract useful insights from the data reports submitted by the citizen scientists, which could be further used for updating hydrological models or maybe set alerts for the hydrologists in case of important hydrological updates

Impact of Expectancy Based Dispositional Traits of Self Efficacy and Optimism on Job Satisfaction among University Faculty Members: Mediating Role of Affective Organizational Commitment

The study examined expectation based dispositional personality traits of self-efficacy and optimism as predictor of most discussed work attitude of job satisfaction. Moreover affective organizational commitment is tested for mediation in the predictor-outcome relationship of self-efficacy and optimism with job satisfaction. A sample of 293 faculty members from 33 public and private universities from 4 provinces of Pakistan were utilized. Results indicated a significant and positive relationship between dispositional self-efficacy and organizational commitment. Furthermore level of optimism of faculty members significantly and positively related to organizational commitment and job satisfaction. Organizational commitment fully mediated the path between self-efficacy and job satisfaction and partially mediated the path between optimism and job satisfaction. Current research has important theoretical and managerial implications. Current research emphasized the crucial role organizational commitment play in link between expectancy based dispositional traits and job satisfaction. Managers must understand importance of expectancy based dispositional traits as predictors of job satisfaction and adopt appropriate selection and training interventions to enhance self-efficacy and optimism among workforce. Keywords: Disposition, Expectations, Self-efficacy, Optimism, Organizational Commitment, Job Satisfaction

A Detailed Investigation of the Bond Performance of Basalt Fiber-Reinforced Polymer Bars in Geopolymer Concrete

This comprehensive experimental study aimed to determine the bond performance of basalt fiber reinforced polymer (BFRP) bars in geopolymer concrete (GC). The study examined the bond performance of BFRP bars and GC by considering several parameters, including bar diameters of 8, 10, and 12 mm, embedment lengths of 4, 8, and 12 db mm (where db is the diameter of the bar), concrete covers of 20, 40, and 70 mm and compressive strengths of 21.7 and 34.4 MPa. The study also compared the effect of the bar surface and bar type on GC bond performance. Eventually, the results were compared with ordinary concrete (OC). The obtained results indicated that an increase in the BFRP bar diameter results in a decrease in the average bond stress. Similarly, an increase in the length of the bond leads to a reduction in the bond stress. The specimen possessing a short embedment length failed due to bar pullout, while the specimens with a longer embedment length failed as a result of concrete splitting. The outcomes also showed that the strength of bond increases with an increase in compressive strength and cover thickness. Furthermore, the results also indicated that BFRP-reinforced GC has comparable bond performance to steel-reinforced GC and BFRP-reinforced OC and performed better than OC. Last, Comparisons between the existing bond-slip models were offered to demonstrate the best bond stress-slip model for FRP bars and GC for ascending branch up to ultimate bond stress of the bond slip curves and for whole curves

Filtering Dishonest Trust Recommendations in Trust Management Systems in Mobile Ad Hoc Networks

Trust recommendations, having a pivotal role in computation of trust and hence confidence in peer to peer (P2P) environment, if hampered, may entail in colossal attacks from dishonest recommenders such as bad mouthing, ballot stuffing, random opinion etc. Therefore, mitigation of dishonest trust recommendations is stipulated as a challenging research issue in P2P systems (esp in Mobile Ad Hoc Networks). In order to cater these challenges associated with dishonest trust recommendations, a technique named “intelligently Selection of Trust Recommendations based on Dissimilarity factor (iSTRD)” has been devised for Mobile Ad Hoc Networks.  iSTRD exploits  personal experience of an “evaluating node” in conjunction with majority vote of the recommenders. It successfully removes the recommendations of “low trustworthy recommenders” as well as dishonest recommendations of “highly trustworthy recommenders”. Efficacy of proposed approach is evident from enhanced accuracy of “recognition rate”, “false rejection” and “false acceptance”.  Moreover, experiential results depict that iSTRD has unprecedented performance compared to contemporary techniques in presence of attacks asserted

Review of Existing Datasets Used for Software Effort Estimation

The Software Effort Estimation (SEE) tool calculates an estimate of the amount of work that will be necessary to effectively finish the project. Managers usually want to know how hard a new project will be ahead of time so they can divide their limited resources in a fair way. In fact, it is common to use effort datasets to train a prediction model that can predict how much work a project will take. To train a good estimator, you need enough data, but most data owners don’t want to share their closed source project effort data because they are worried about privacy. This means that we can only get a small amount of effort data. The purpose of this research was to evaluate the quality of 15 datasets that have been widely utilized in studies of software project estimation. The analysis shows that most of the chosen studies use artificial neural networks (ANN) as ML models, NASA as datasets, and the mean magnitude of relative error (MMRE) as a measure of accuracy. In more cases, ANN and support vector machine (SVM) have done better than other ML techniques

Evaluating the toxicity of capecitabine-cisplatin versus gemcitabine-cisplatin regimens for palliative chemotherapy in advanced biliary tract carcinoma

Background: Advanced biliary tract carcinoma is a malignancy associated with poor prognosis and limited treatment options. This study aimed to compare the treatment effects in terms of toxicities of Capecitabine-Cisplatin and Gemcitabine-Cisplatin regimens as palliative chemotherapy for ABTC in Bangladesh. Methods: This quasi-experimental study was conducted at the Department of Oncology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh, involving 78 patients with histopathologically confirmed ABTC (AJCC Stage IV). Participants were divided into two groups: Arm-A received Capecitabine-Cisplatin, and Arm-B received Gemcitabine-Cisplatin. Treatment response, hematological and non-hematological toxicities were assessed and compared between the two groups. Results: No significant differences in baseline demographic and clinical characteristics were observed between the two groups. Arm-A demonstrated a higher rate of partial response in the final assessment (51.28% vs. 41.03%, p=0.029). Acute hematological toxicities were more frequent in Arm-B, with a higher incidence of Grade 2 and 3 anemia, neutropenia, and leukopenia (p<0.05). Non-hematological toxicities were comparable, except for Hand-Foot Syndrome, which was significantly higher in Arm-A (p=0.03). Conclusions: The Capecitabine-Cisplatin regimen exhibited a different toxicity profile compared to the Gemcitabine-Cisplatin regimen for palliative chemotherapy in advanced biliary tract carcinoma. While both regimens were generally well-tolerated, the Capecitabine-Cisplatin regimen demonstrated lower incidences of hematological toxicities. These findings emphasize the importance of considering toxicity profiles when selecting treatment options for patients with advanced biliary tract carcinoma

Animal models and natural products to investigate in vivo and in vitro antidiabetic activity

Diabetes mellitus is a chronic disease which has high prevalence. The deficiency in insulin production or impaired insulin function is the underlying cause of this disease. Utilization of plant sources as a cure of diabetes has rich evidence in the history. Recently, the traditional medicinal plants have been investigated scientifically to understand the underlying mechanism behind antidiabetic potential. In this regard, a substantial number of in vivo and in vitro models have been introduced for investigating the bottom-line mechanism of the antidiabetic effect. A good number of methods have been reported to be used successfully to determine antidiabetic effects of plant extracts or isolated compounds. This review encompasses all the possible methods with a list of medicinal plants which may contribute to discovering a novel drug to treat diabetes more efficaciously with the minimum or no side effects

Auto Encoders based Model to Predict Data Breaches in Cloud Computing

We are living in the world of the Internet, due to the increase in the use of the Internet our data is also mostly stored using online storage like cloud storage. Due to the increased use of Cloud Computing, cyber-attacks on cloud storage also increased which is causing data breaches. To mitigate these breaches we need to develop AI-based methods/techniques that can predict attacks for data breaches. Auto Encoders is a Neural network-based technique that can help us in the early prediction of cyber-attacks on cloud storage for breaching of data. Auto Encoders depend upon two functions one is an encoding function that changes the data inputs and a decoding function that recreates the input data from the encoded representation. In this paper, we have proposed an Auto Encoders-based ANN model that can early predict data breaches to save data on cloud storage. We have tested our model on neural network fitting and obtained the accuracy of 100%