Cloud-Based Retrieval Information System Using Concept for Multi-Format Data

The need of effective and efficient method to retrieving non-Web-enabled and Web-enabled information entities is essential, due to the fact of inaccuracy of the existing search engines that still use traditional term-based indexing for text documents and annotation text for images, audio and video files. Previous works showed that incorporating the knowledge in the form of concepts into an information retrieval system may increase the effectiveness of the retrieving method. Unfortunately, most of the works that implemented the concept-based information retrieval system still focused on one information format. This paper proposes a multi-format (text, image, video and, audio) concept-based information retrieval method for Cloud environment. The proposed method is implemented in a laboratory-scale heterogeneous cloud environment using Eucalyptus middleware.  755 multi-format information is experimented and the performance of the proposed method is measured

Experimental approximation of breast tissue permittivity and conductivity using NN-based UWB imaging

This paper presents experimental study to distinguish between malignant and benign tumors in early breast cancer detection using Ultra Wide Band (UWB) imaging. The contrast between dielectric properties of these two tumor types is the main key. Mainly water contents control the dielectric properties. Breast phantom and tumor are fabricated using pure petroleum jelly and a mixture of wheat flour and water respectively. A complete system including Neural Network (NN) model is developed for experimental investigation. Received UWB signals through the tumor embedded breast phantom are fed into the NN model to train, test and determine the tumor type. The accuracy of the experimental data is about 98.6% and 99.5% for permittivity and conductivity respectively. This leads to determine tumor dielectric properties accurately followed by distinguish between malignant and benign tumors. As malignant tumors need immediate further medical action and removal, this findings could contribute to save precious file in near future

Sustainability Reporting, Global Uncertainty, Cost of Capital and Firm Performance: The Case of Global Energy Industry

This study examines the impact of ESG (Environmental, Social, and Governance) performance on financial metrics within the energy sector, focusing on cost of capital and firm performance, with moderating factors such as the World Uncertainty Index (WUI) and Climate Vulnerability Index (CVI). The first study investigates how ESG performance affects the cost of capital measured as weighted average cost of capital (WACC), cost of equity, and cost of debt in energy firms. Using ordinary least squares regressions and longitudinal data from the LSEG database, findings reveal that higher ESG scores, including individual pillar performance (Environmental, Social, Governance), consistently reduce all three cost-of-capital measures. The WUI significantly moderates this relationship, amplifying ESG’s cost-lowering effect amid global uncertainty, offering energy managers a pathway to optimize capital structure while enhancing sustainability. The second study explores ESG’s impact on firm performance proxied by return on assets (ROA), return on equity (ROE), and earnings per share (EPS), across 700 energy firms from 2007–2023, analyzed through panel regression. Results indicate that robust ESG practices, particularly the Social Pillar (e.g., employee relations), strongly enhance ROA and ROE, while the Environmental Pillar drives EPS, underscoring the financial benefits of sustainable practices. Midstream and Downstream energy sectors show the strongest ESG performance links, with the CVI revealing that climate-vulnerable firms with high ESG scores maintain profitability during environmental stress. Collectively, these findings highlight ESG’s transformative potential in reducing financing costs and boosting performance, moderated by uncertainty and climate risks. For practitioners, integrating ESG offers a dual benefit of financial efficiency and resilience, while policymakers can leverage these insights to strengthen ESG reporting and address climate vulnerabilities like biodiversity loss and extreme weather. This research bridges gaps in ESG literature, emphasizing its critical role in shaping energy sector stability and sustainability

Characterization of Population Pharmacokinetics and Associated Interindividual Variability of Drugs Used in Mass Drug Administration against Lymphatic Filariasis

Lymphatic filariasis (LF) is a mosquito-borne filarial infection that affects the lymphatic system. The progression of the disease is associated with severe comorbidities and social stigma among patients. The World Health Organization has categorized the disease as a public health problem, and it has been targeted by mass drug administration (MDA) campaigns over the past two decades. Several preventive chemotherapies have been used in MDA campaigns against LF, including a combination of three anti-filarial drugs (IDA therapy): albendazole (ALB), diethylcarbamazine (DEC), and ivermectin (IVM). Other antiparasitic drugs, such as moxidectin (MOX), are still under investigation for their activity against LF. High inter-individual variability (IIV) in pharmacokinetics has been reported with anti-filarial drugs used in MDA. Addressing IIV, especially for drugs that exhibit significant variability, is critical for optimal dosing to ensure maximum drug efficacy and limiting toxicities. This dissertation aims to characterize the population pharmacokinetics (PopPK) of anti-filarial drugs used in MDA against LF, investigate the reported high IIV, and evaluate different dosing regimens to simplify the existing weight-based dosing. Utilizing drug exposure data from populations living in areas where LF is prevalent, PopPK models for IDA therapy and MOX were developed using non-linear mixed-effect modeling. The presented models showed a good ability to describe the PopPK of MDA drugs, which was evident by the goodness of fit criteria. The covariates analysis showed that sex was a significant explaining part of IIV observed with IVM and DEC. Moreover, the clearance of albendazole sulfoxide (ALB-OX), the active metabolite of ALB, was different between men and women. Weight and microfilaria count at baseline significantly explained part of the variability associated with the volume of distribution of DEC and MOX, respectively. Models-based simulation findings demonstrated that those factors have also affected drug exposure parameters, including maximum plasma concentration (Cmax) and area under the curve (AUC) of IDA and MOX. In MDA campaigns, IVM and DEC are dose-based on body weight, and this dosing regimen could be challenging due to logistic concerns. We have performed a model-based simulation to simulate IVM and DEC exposure after various non-weight-based dosing regimens to simplify the dosing in MDA campaigns. For IVM, the model-based simulation showed higher drug exposure with the experimental 18 mg fixed dose regimen compared with weight-based dosing; however, the increase in drug exposure was relatively small and remained below the plasma threshold associated with emergent side effects. Height-based DEC dosing decreased variability in drug exposure observed with weight-based dosing. In conclusion, the presented work uniquely described PopPK and IIV linked with IDA and MOX therapy in MDA-eligible populations. The developed models can be used for future dose-informing studies to optimize drug dosing and help achieve LF eradication goals

A UWB imaging system to detect early breast cancer in heterogeneous breast phantom

This paper presents an experimental early breast cancer detection system in terms of heterogeneous breast phantom. The system consists of commercial Ultrawide-Band (UWB) transceivers and our developed Neural Network (NN) based Pattern Recognition (PR) software for imaging. A simple way to construct cancer- tissue and heterogeneous breast phantom using available low cost materials and their mixtures is also proposed here. The materials are: (i) A mixture of petroleum jelly, soy oil, wheat flour and water as heterogeneous tissue; (ii) A particular glass as skin; and (iii) A specific mixture of water and wheat flour as cancer- tissue. All the materials and their mixtures are considered according to the ratio of the dielectric properties of the breast tissues. To experimentally detect cancer, the UWB signals are transmitted from one side of the breast phantom and received from opposite side diagonally. By using discrete cosine transform (DCT) of the received signals, a Neural Network (NN) is trained, tested and interfaced with the UWB transceiver to form the complete system. The achieved detection rate of cancer cell's existence, size and location are approximately 100%, 93.1% and 93.3% respectively

THE EFFECT OF INCREASING AWARENESS ABOUT THE USE OF SOCIAL MEDIA ON SPORT FANATICISM FOR SAUDI SOCCER FANS

Background: In Saudi Arabia, sport fanaticism has become one of problems for the occurrence of riots and violence in competitive sports, particularly in soccer. Moreover, one of the main causes for sport fanaticism is media in general and social media in particular. This study tried to explore the issue, find solutions to reduce this problem, and develop an awareness of the good values for sports and focused on the effect of increasing awareness for soccer fans who use media platforms in their daily lives.Purpose: The purpose of this study was to determine the effect of increasing awareness about the use of social media on sports fanaticism of Saudi soccer fans.Procedures: The participants were given three survey documents with coding numbers and without asking them their names: “Pretest, Posttest 1, and Posttest 2”. Participants complete the demographic information such as age, marital status, education level, and other types of identifiable information. These demographic information were optional for the participants which means that each participant has rights to not respond to these demographic questions. Moreover, the participants have completed the sport fanaticism survey which was presented to the participants as a single document written in Arabic, “Pretest”. This determined the initial sport fanaticism level. After that, the participants were given a lecture about the role of social media such as Facebook, Twitter, and YouTube videos in influencing sports fanaticism in Saudi Arabia. Immediately after hearing the lecture the participants were asked to complete the sport fanaticism survey a second time, “Posttest 1”. As the final measure, two weeks after the lecture, the participants were asked to complete the sport fanaticism survey once more, “Posttest 2”.Methods: The subjects were categorized into three groups based on their scores on the pretest-lecture survey. These groups represented High, Medium, and Low levels of sports fanaticism. Basic descriptive statistics used to describe the demographic information of the participants, independent variables and dependent variables. All of the research questions investigated used a series of repeated-measures analyses of variance, each used one of the independent variables (Fanaticism Level, Age, Education Level, or Marital Status) as a grouping variable. In each case the repeated variable was the three scores for sports fanaticism for each student. With post hoc tests, the repeated-measures ANOVAs can assess differences between groups, changes in sports fanaticism over time, the interaction between groups and time, and other necessary comparisons.Results: The participants’ mean level of fanaticism on Posttest 1 was significantly lower compared to the Pretest. Mean scores on Posttest 2 were higher than on Posttest 1, but lower than scores on the Pretest. The change from Pretest to Posttest 1 was significant, change from Posttest 1 to Posttest 2 was significant, and change from Pretest to Posttest 2 was significant.Conclusion: According to the findings, social media can be negative if used in increasing sport fanaticism, at the same time it can be positive if used in increasing awareness in use of social media correctly. Additionally, the positive effects of the awareness lecture about the use of social media on sport fanaticism could reduce sport fanaticism.Ph.D

Cross-cultural Adaption and Psychometric Properties Testing of The Arabic Anterior Knee Pain Scale

Patellofemoral pain syndrome (PFP) is a common condition affecting the musculoskeletal system and has a tendency of becoming chronic and is problematic in the affected people. It is the commonest cause of anterior knee pain. In over 2/3 of the patients affected it has been successfully treated through the use of rehabilitation protocols which are designed in pain reduction and returning the functionality to an individual. Many cases of patellofemoral pain syndrome can be avoided only if a clinician can make a pre-diagnosis. Preparation Screening Evaluation testing done by a certified athletic trainer can also help in prevention of this syndrome. The purpose of this topic is to be able to review the anatomy of the knee, the risk factors predisposing to patellofemoral pain syndrome, soft tissue, arterial system, innervation of the patellofemoral joint and strategies for rehabilitation. This will enable reviewing the anatomy of the knee, relationships between arterial collateralization, nerve supply and alignment of soft tissues in explaining the mechanisms that lead to this syndrome. By doing so, it will help in the future whereby using different treatments that will be aiming at the non-soft tissue that cause patellofemoral pain syndrome

Deep Learning for Electricity Forecasting Using Time Series Data

The complexity and nonlinearities of the modern power grid render traditional physical modeling and mathematical computation unrealistic. AI and predictive machine learning techniques allow for accurate and efficient system modeling and analysis. Electricity consumption forecasting is highly valuable in energy management and sustainability research. Furthermore, accurate energy forecasting can be used to optimize energy allocation. This thesis introduces Deep Learning models including the Convolutional Neural Network (CNN), the Recurrent neural network (RNN), and Long Short-Term memory (LSTM). The Hourly Usage of Energy (HUE) dataset for buildings in British Columbia is used as an example for our investigation, as the dataset contains data from residential customers of BC Hydro, a provincial power utility company. Due to the temporal dependency in time-series observation data, data preprocessing is required before a model can be created. The LSTM model is utilized to create a predictive model for electricity consumption as output. Approximately 63% of the data is used for training, and the remaining 37% is used for testing. Various LSTM parameters are tested and tuned for best performance. Our LSTM predictive model can facilitate power companies’ resource management decisions

The Influence of Mathematics Teachers' Knowledge in Technology, Pedagogy and Content (TPACK) on their Teaching Effectiveness in Saudi Public Schools

Many researchers including (Hill et al., 2008; McCray & Chen, 2012) have found that teachers' understanding of the mathematics content knowledge and their expertise in teaching methods "pedagogy" are largely responsible for how effective they are as teachers. More recent research (Lyublinskaya & Tournaki, 2012; Polly, 2011) suggests that teachers' ability to integrate technology into their teaching is also critical to their mathematics teaching effectiveness. This study investigated the validity of these assumptions for 7-12 grade mathematics teachers in Saudi Arabia and how their expertise in Technological Pedagogical And Content Knowledge (TPACK) influences their teaching effectiveness. The central question for grade 7-12 Saudi Arabian mathematics teachers is: Does expertise in technology integration, pedagogy and content relate to teaching effectiveness? The TPACK expertise of 347 secondary male mathematics teachers in Riyadh public schools was measured by self-evaluation questionnaires. Principals from 109 schools rated their teachers by using a 14 item "Teacher Effectiveness" survey. Descriptive statistics, bivariate correlations, ANOVA, Paired-Samples t-test and MANOVA were used to evaluate the relationship between the teachers' TPACK knowledge and teaching effectiveness. Results showed that teachers evaluated their TPACK at a high level. On the TPACK 1-5 Likert scale survey (5 = highly competent), the teachers rated their general mathematics content knowledge (CK) at M=3.7 (SD=.67), their general pedagogy knowledge (PK) at M=4.1 (SD =.55), their general technology knowledge (TK) at M=3.6 (SD=.70), their pedagogical knowledge within mathematics content (PCK) at M=4 (SD =.60), their technological knowledge within mathematics content (TCK) at M=3.7 (SD=.69), their technological knowledge within pedagogical knowledge (TPK) at M=3.6 (SD=.74), their technological pedagogical and content knowledge at M=3.7 (SD=.71), and their cumulative knowledge of technology, pedagogy and content at M=3.8 (SD=.52). The teachers also rated their professional preparation to integrate technology. They reported that their university courses prepared them to integrate digital technologies (M=3.51, SD=.88) better than professional development workshop and training (M=3.07, SD=1.7); t(346)= 8.17, p<.01. Principals rated the overall effectiveness of their teachers at M=3.11 (SD=.59) on the 14 item scale and their usage of technology at M=2.84 (SD=1.06). Correlations between mathematics teachers' 7 TPACK self-efficacy and the principals' rating of teacher effectiveness were not significantly different. Negative correlations were found between principals' ratings of teaching effectiveness and the teachers' evaluation of their professional preparedness in university courses (r=-.125, p<.05) and professional development training programs (r=-.129, p<.05). This discrepancy may point to differences between the way these principals and the higher education institutions value teacher preparation curriculum. Further studies may consider comparing teachers' TPACK self-efficacy to student achievement