Search results for: transfer learning

Authors: Wedad Albalawi

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The mathematical inequalities have been the core of mathematical study and used in almost all branches of mathematics as well in various areas of science and engineering. The inequalities by Hardy, Littlewood and Polya were the first significant composition of several science. This work presents fundamental ideas, results and techniques and it has had much influence on research in various branches of analysis. Since 1934, various inequalities have been produced and studied in the literature. Furthermore, some inequalities have been formulated by some operators; in 1989, weighted Hardy inequalities have been obtained for integration operators. Then, they obtained weighted estimates for Steklov operators that were used in the solution of the Cauchy problem for the wave equation. They were improved upon in 2011 to include the boundedness of integral operators from the weighted Sobolev space to the weighted Lebesgue space. Some inequalities have been demonstrated and improved using the Hardy–Steklov operator. Recently, a lot of integral inequalities have been improved by differential operators. Hardy inequality has been one of the tools that is used to consider integrity solutions of differential equations. Then dynamic inequalities of Hardy and Coposon have been extended and improved by various integral operators. These inequalities would be interesting to apply in different fields of mathematics (functional spaces, partial differential equations, mathematical modeling). Some inequalities have been appeared involving Copson and Hardy inequalities on time scales to obtain new special version of them. A time scale is defined as a closed subset contains real numbers. Then the inequalities of time scales version have received a lot of attention and has had a major field in both pure and applied mathematics. There are many applications of dynamic equations on time scales to quantum mechanics, electrical engineering, neural networks, heat transfer, combinatorics, and population dynamics. This study focuses on double integrals to obtain new time-scale inequalities of Copson driven by Steklov operator. They will be applied in the solution of the Cauchy problem for the wave equation. The proof can be done by introducing restriction on the operator in several cases. In addition, the obtained inequalities done by using some concepts in time scale version such as time scales calculus, theorem of Fubini and the inequality of H¨older.

Keywords: time scales, inequality of Hardy, inequality of Coposon, Steklov operator

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Authors: H. Zidoum, A. AlShareedah, S. Al Sawafi, A. Al-Ansari, B. Al Lawati

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Systemic lupus erythematosus (SLE) is an autoimmune disease with genetic and environmental components. SLE is characterized by a wide variability of clinical manifestations and a course frequently subject to unpredictable flares. Despite recent progress in classification tools, the early diagnosis of SLE is still an unmet need for many patients. This study proposes an interpretable disease classification model that combines the high and efficient predictive performance of CatBoost and the model-agnostic interpretation tools of Shapley Additive exPlanations (SHAP). The CatBoost model was trained on a local cohort of 219 Omani patients with SLE as well as other control diseases. Furthermore, the SHAP library was used to generate individual explanations of the model's decisions as well as rank clinical features by contribution. Overall, we achieved an AUC score of 0.945, F1-score of 0.92 and identified four clinical features (alopecia, renal disorders, cutaneous lupus, and hemolytic anemia) along with the patient's age that was shown to have the greatest contribution on the prediction.

Keywords: feature selection, classification, systemic lupus erythematosus, model interpretation, SHAP, Catboost

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Authors: DEVASHISH KHULBE, MANU PATHAK

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The rise in popularity of major social media platforms have enabled people to share photos and textual information about their daily life. One of the popular topics about which information is shared is food. Since a lot of media about food are attributed to particular locations and restaurants, information like spatio-temporal popularity of various cuisines can be analyzed. Tracking the popularity of food types and retail locations across space and time can also be useful for business owners and restaurant investors. In this work, we present an approach using off-the shelf machine learning techniques to identify trends and popularity of cuisine types in an area using geo-tagged data from social media, Google images and Yelp. After adjusting for time, we use the Kernel Density Estimation to get hot spots across the location and model the dependencies among food cuisines popularity using Bayesian Networks. We consider the Manhattan borough of New York City as the location for our analyses but the approach can be used for any area with social media data and information about retail businesses.

Keywords: Web Mining, Geographic Information Systems, Business popularity, Spatial Data Analyses

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Authors: Riccardo Cacciotti, Jiri Frankl, Benjamin Wolf, Michael Machacek

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Moisture fluctuations govern the occurrence of fungi-related problems in buildings, which may impose significant health risks for users and even lead to structural failures. Several numerical engineering models attempt to capture the complexity of mold growth on building materials. From real life observations, in cases with suppressed daily variations of boundary conditions, e.g. in crawlspaces, mold growth model predictions well correspond with the observed mold growth. On the other hand, in cases with substantial diurnal variations of boundary conditions, e.g. in the ventilated cavity of a cold flat roof, mold growth predicted by the models is significantly overestimated. This study, founded by the Grant Agency of the Czech Republic (GAČR 20-12941S), aims at gaining a better understanding of mold growth behavior on solid wood, under varying boundary conditions. In particular, the experimental investigation focuses on the response of mold to changing conditions in the boundary layer and its influence on heat and moisture transfer across the surface. The main results include the design and construction at the facilities of ITAM (Prague, Czech Republic) of an innovative device allowing for the simulation of changing environmental conditions in buildings. It consists of a square section closed circuit with rough dimensions 200 × 180 cm and cross section roughly 30 × 30 cm. The circuit is thermally insulated and equipped with an electric fan to control air flow inside the tunnel, a heat and humidity exchange unit to control the internal RH and variations in temperature. Several measuring points, including an anemometer, temperature and humidity sensor, a loading cell in the test section for recording mass changes, are provided to monitor the variations of parameters during the experiments. The research is ongoing and it is expected to provide the final results of the experimental investigation at the end of 2022.

Keywords: moisture, mold growth, testing, wood

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Authors: Joshua W. Edefo, Shafiul Azam, Murray D. Smith

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Introduction: In April 2022, the Welsh Government introduced the six goals for urgent and emergency care programs. One of these goals was to provide access to clinically safe alternatives, leading to the establishment of the Same Day Emergency Care (SDEC) program. The SDEC initiative aims to offer viable options that maintain patient safety while avoiding unnecessary hospital stays. The aim of the study is to determine the duration of patient stay in SDEC and compare it with that of Emergency department (ED) stay to ascertain if one of the objectives of SDEC is achieved. Methods: Patient stays and attendance datasets were constructed from Withybush SDEC and ED patient records. These records were provided by Hywel Dda University Health Board Informatics. Some hypothetical pathways were identified, notably SDEC visits involving a single attendance and ED visits then immediately transferred to SDEC. Descriptive statistics were used to summarise the data, and hypothesis tests for mean differences used the student t-test. Propensity scoring was employed to match a set of ED patient stays to SDEC patient stays which were then used to determine the average treatment effect (ATE) to compare durations of stay in SDEC with ED. Regression methods were used to model the natural logarithm of the duration of SDEC attendance, and the level of statistical significance was set to 0.05. Results: SDEC visits involving a single attendance (170 of 384; 44.3%) is the most frequently observed pathway with patient length of stay at 256 minutes (95%CI 237.4 - 275.1). The next most frequently observed pathway of patient stay was SDEC attendance after presenting to ED (80 of 384; 20.8%) and gave the length of stay of 440 minutes (95%CI 351.6 - 529.2). Time spent in this pathway significantly increased by 184 minutes (95%CI 118.0 - 250.2, support for no difference p<0.001) compared to the most seen pathway. When SDEC data were compared with ED, the estimate for the ATE from SDEC single attendance was -272 minutes (95%CI -334.1 - -210.5; p<0.001), while that of ED then SDEC pathway was -50.6 min (95%CI -182.7-81.5; p=0.453). Conclusion: When patients are admitted to SDEC and successfully discharged, their stays are significantly shorter, approximately 4.5 hours, compared to patients who spend their entire stay in the Emergency Department. That difference vanishes when the patient stay includes a period spent previously in ED before transfer to SDEC.

Keywords: attendance, emergency-department, patient-stay, same-day-emergency-care

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Authors: Mirain Rhys, Kevin Smith

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In 2017, the Welsh Government introduced an ambitious, new strategy to increase the number of Welsh speakers in Wales to 1 million by 2050. The Welsh education system is a vitally important feature of this strategy. All children attending state schools in Wales learn Welsh as a second language until the age of 16 and are assessed at General Certificate of Secondary Education (GCSE) level. In 2013, a review of Welsh second language instruction in Key Stages 3 and 4 was completed. The report identified considerable gaps in teachers’ preparation and training for teaching Welsh; poor Welsh language ethos at many schools; and a general lack of resources to support the instruction of Welsh. Recommendations were made across a number of dimensions including curriculum content, pedagogical practice, and teacher assessment, training, and resources. With a new national curriculum currently in development, this study builds on this review and provides unprecedented detail into pupils’ and teachers’ perceptions of Welsh language instruction. The current research built on data taken from an existing capacity building research project on Welsh education, the Wales multi-cohort study (WMS). Quantitative data taken from WMS surveys with over 1200 pupils in schools in Wales indicated that Welsh language lessons were the least enjoyable subject among pupils. The current research aimed to unpick pupil experiences in order to add to the policy development context. To achieve this, forty-four pupils and four teachers in three schools from the larger WMS sample participated in focus groups. Participants from years 9, 11 and 13 who had indicated positive, negative and neutral attitudes towards the Welsh language in a previous WMS survey were selected. Questions were based on previous research exploring issues including, but not limited to pedagogy, policy, assessment, engagement and (teacher) training. A thematic analysis of the focus group recordings revealed that the majority of participants held positive views around keeping the language alive but did not want to take on responsibility for its maintenance. These views were almost entirely based on their experiences of learning Welsh at school, especially in relation to their perceived lack of choice and opinions around particular lesson strategies and assessment. Analysis of teacher interviews highlighted a distinct lack of resources (materials and staff alike) compared to modern foreign languages, which had a negative impact on student motivation and attitudes. Both staff and students indicated a need for more practical, oral language instruction which could lead to Welsh being used outside the classroom. The data corroborate many of the review’s previous findings, but what makes this research distinctive is the way in which pupils poignantly address generally misguided aims for Welsh language instruction, poor pedagogical practice and a general disconnect between Welsh instruction and its daily use in their lives. These findings emphasize the complexity of incorporating the educational sector in strategies for Welsh language maintenance and the complications arising from pedagogical training, support, and resources, as well as teacher and pupil perceptions of, and attitudes towards, teaching and learning Welsh.

Keywords: bilingual education, language maintenance, language revitalisation, minority languages, Wales

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Authors: Krishna Prasad Sharma

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Nepal’s financial sector has become deeper and wider, and the number and types of financial intermediaries have grown rapidly over the past two decades. However, access to financial services remains limited for many people in many parts of rural Nepal. While financial institutions have been expanding rapidly in an urban area in recent years, the access to the rural poor is excessively inadequate due to financial illiteracy and limited numbers of financial institutions that confined only to the district headquarters. Based on the focus group discussion, semi-structured interview of key people and literature review, this paper aims to examine the supply of and demand for financial services in Nepal and the constraints to increasing access to them, and offers way forward for making the financial sector work for all of Nepal’s people, especially the rural poor. While Nepal’s government has tried to increase access to formal financial services for small businesses and low-income households through directed lending programs for small businesses and low-income households, created specialized wholesale and retail institutions, and lowered market entry requirements, formal financial services are declining, and financial intermediation is stagnating. Supply and demand indicators show that, despite government efforts, formal financial institutions do not serve the needs of most of the Nepalese population. While access to and use of formal financial services are limited, in general, the problem is acute for small businesses and low-income households. Indeed, both access and use are closely correlated with business loan size and household income. This study concludes that banks and microfinance institutions with the use of mobile phones can connect hundreds of millions of unbanked and low-income people, especially rural poor to financial services at low costs. While there are many challenges ahead in expanding the service to rural areas, the mobile financial services will be beneficial that makes payments faster and cheaper, more convenient and accessible to a greater number of senders and recipients in rural areas. In rural areas, clients will benefit from money transfer and other mobile and online services.

Keywords: financial inclusion, financial enabling environment, microfinance, branchless banking, rural poor

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Authors: Jayson Cheyne, David Butler, Iain Bomphray

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In recent years, lithium-ion batteries have been used increasingly for electric vehicles and large energy storage systems due to their high-power density and long lifespan. Despite this, thermal runaway remains a significant safety problem because of its uncontrollable and irreversible nature - which can lead to fires and explosions. In large-scale lithium-ion packs and modules, thermal runaway propagation between cells can escalate fire hazards and cause significant damage. Thus, safety measures are required to mitigate thermal runaway propagation. The current research explores composite phase change materials (PCM) containing expanded graphite (EG) for thermal runaway mitigation. PCMs are an area of significant interest for battery thermal management due to their ability to absorb substantial quantities of heat during phase change. Moreover, the introduction of EG can support heat transfer from the cells to the PCM (owing to its high thermal conductivity) and provide shape stability to the PCM during phase change. During the research, a thermal model was established for an array of 16 cylindrical cells to simulate heat dissipation with and without the composite PCM. Two conditions were modeled, including the behavior during charge/discharge cycles (i.e., throughout regular operation) and thermal runaway. Furthermore, parameters including cell spacing, composite PCM thickness, and EG weight percentage (WT%) were varied to establish the optimal material parameters for enabling thermal runaway mitigation and effective thermal management. Although numerical modeling is still ongoing, initial findings suggest that a 3mm PCM containing 15WT% EG can effectively suppress thermal runaway propagation while maintaining shape stability. The next step in the research is to validate the model through controlled experimental tests. Additionally, with the perceived fire safety concerns relating to PCM materials, fire safety tests, including UL-94 and Limiting Oxygen Index (LOI), shall be conducted to explore the flammability risk.

Keywords: battery safety, electric vehicles, phase change materials, thermal management, thermal runaway

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Authors: R. Biondi, G. Dys, G. Ferone, T. Renard, M. Zysman

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This paper describes an automated implementable system for impulsive signals detection and recognition. The system uses a Digital Signal Processing device for the detection and identification process. Here the system analyses the signals in real time in order to produce a particular response if needed. The system analyses the signals in real time in order to produce a specific output if needed. Detection is achieved through normalizing the inputs and comparing the read signals to a dynamic threshold and thus avoiding detections linked to loud or fluctuating environing noise. Identification is done through neuronal network algorithms. As a setup our system can receive signals to “learn” certain patterns. Through “learning” the system can recognize signals faster, inducing flexibility to new patterns similar to those known. Sound is captured through a simple jack input, and could be changed for an enhanced recording surface such as a wide-area recorder. Furthermore a communication module can be added to the apparatus to send alerts to another interface if needed.

Keywords: sound detection, impulsive signal, background noise, neural network

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Authors: Saima Nazir, Mustansar Ali Ghazanfar, Sanay Muhammad Umar Saeed, Muhammad Awais Azam, Saad Ali Alahmari

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Terrorism is an important and challenging concern. The entire world is threatened by only few sophisticated terrorist groups and especially in Gulf Region and Pakistan, it has become extremely destructive phenomena in recent years. Predicting the pattern of attack type, attack group and target type is an intricate task. This study offers new insight on terrorist group’s attack type and its chosen target. This research paper proposes a framework for prediction of terrorist attacks using the historical data and making an association between terrorist group, their attack type and target. Analysis shows that the number of attacks per year will keep on increasing, and Al-Harmayan in Saudi Arabia, Al-Qai’da in Gulf Region and Tehreek-e-Taliban in Pakistan will remain responsible for many future terrorist attacks. Top main targets of each group will be private citizen & property, police, government and military sector under constant circumstances.

Keywords: data mining, counter terrorism, machine learning, SVM

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Authors: Mohammad Parvaresh, Mahdi Babaee, Bahareh Arghand, Davood Nourmohammadi

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Environmental degradation is caused by social inequalities and the inappropriate use of nature and a factor in the violation of human rights. Indeed, the right to a safe, healthy and ecologically-balanced environment is an independent human right. Therefore, the relationship between human rights and environmental protection is crucial for the study of social justice and sustainable development, and environmental problems are a result of the failure to realize social and economic justice. In this regard, 'article 50 of the constitution of the Islamic Republic of Iran as a general principle have many of the concepts of sustainable development, including: the growth and improvement of human life, the rights of present and future generations, and the integrity of the inner and outer generation, the prohibition of any environmental degradation'. Also, Charter on Citizen’s Rights, which was conveyed by the President of the Islamic Republic of Iran, Mr. Rouhani refers to the right to a healthy environment and sustainable development. In this regard in 2013, Tehran Province Water and Wastewater Co. defined a plan called 'Tehran’s Health Line' was includes Western and Eastern part by about 26 kilometers of water transferring pipelines varied 1000 to 2000 mm diameters. This project aims to: (1) Transfer water from the northwest water treatment plant to the southwest areas, which suffer from qualitative and quantitative water, in order to mix with the improper wells’ water; (2) Reducing the water consumption provided by harvesting from wells which results in improving the underground water resources, causing the large settlements and stopping the immigrating slums into the center or north side of the city. All of the financial resources accounted for 53,000,000 US$ which is mobilized by Tehran Province Water and Wastewater Co. to expedite the work. The present study examines the Tehran Health Line plan and the purpose of implementation of this plan to achieve environmental protection, environmental justice and citizenship rights for all people who live in Tehran.

Keywords: environmental justice, international environmental law, erga omnes, charter on citizen's rights, Tehran health line

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Authors: Iman Dwi Almunandar, Nellawaty A. Tewu, Anshari Al Ghaniyy

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The emergence of academic procrastination behavior among students in Indonesian, especially the students of Faculty of Psychology at YARSI University becomes a habit to be underestimated, so often interfere with the effectiveness of learning process. The lecturers at the Faculty of Psychology YARSI University have very often warned students to be able to do and collect assignments accordance to predetermined deadline. However, they are still violated it. According to researchers, this problem needs to do a proper training for the solution to minimize academic procrastination behavior on students. In this study, researchers conducted analyze needs for deciding whether need the training or not. Number of sample is 30 respondents which being choose with a simple random sampling. Measurement of academic procrastination behavior is using the theory by McCloskey (2011), there are six dimensions: Psychological Belief about Abilities, Distractions, Social Factor of Procrastination, Time Management, Personal Initiative, Laziness. Methods of analyze needs are using Questioner, Interview, Observations, Focus Group Discussion (FGD), Intelligence Tests. The result of analyze needs shows that psychology students generation of 2015 at the Faculty of Psychology YARSI University need for training on Time Management.

Keywords: procrastination, psychology, analyze needs, behavior

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Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modelling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: sentiment analysis, social media, Twitter, Amazon, data mining, machine learning, text mining

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Authors: Laura Brennan, Ram Balakumar

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ChatGPT has been developed by Open AI (Nov 2022) as a powerful artificial intelligence (AI) language model which has been designed to produce human-like text from user written prompts. To gain the most from the system, user written prompts must give context specific information. This article aims to give guidance on how to optimise the ChatGPT system in the context of education for otolaryngology. Otolaryngology is a specialist field which sees little time dedicated to providing education to both medical students and doctors. Additionally, otolaryngology trainees have seen a reduction in learning opportunities since the COVID-19 pandemic. In this article we look at these various barriers to medical education in Otolaryngology training and suggest ways that ChatGPT can overcome them and assist in simulation-based training. Examples provide how this can be achieved using the Authors’ experience to further highlight the practicalities. What this article has found is that while ChatGPT cannot replace traditional mentorship and practical surgical experience, it can serve as an invaluable supplementary resource to simulation based medical education in Otolaryngology.

Keywords: artificial intelligence, otolaryngology, surgical training, medical education

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Authors: M. A. Talha, M. Osman Gani, M. Ferdows

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This paper focuses on the study of two dimensional magnetohydrodynamic (MHD) steady incompressible viscous Williamson nanofluid with exponential internal heat generation containing gyrotactic microorganism over a stretching sheet. The governing equations and auxiliary conditions are reduced to a set of non-linear coupled differential equations with the appropriate boundary conditions using similarity transformation. The transformed equations are solved numerically through spectral relaxation method. The influences of various parameters such as Williamson parameter γ, power constant λ, Prandtl number P_r, magnetic field parameter M, Peclet number P_e, Lewis number Le, Bioconvection Lewis number Lb, Brownian motion parameter Nb, thermophoresis parameter Nt, and bioconvection constant σ are studied to obtain the momentum, heat, mass and microorganism distributions. Moment, heat, mass and gyrotactic microorganism profiles are explored through graphs and tables. We computed the heat transfer rate, mass flux rate and the density number of the motile microorganism near the surface. Our numerical results are in better agreement in comparison with existing calculations. The Residual error of our obtained solutions is determined in order to see the convergence rate against iteration. Faster convergence is achieved when internal heat generation is absent. The effect of magnetic parameter M decreases the momentum boundary layer thickness but increases the thermal boundary layer thickness. It is apparent that bioconvection Lewis number and bioconvection parameter has a pronounced effect on microorganism boundary. Increasing brownian motion parameter and Lewis number decreases the thermal boundary layer. Furthermore, magnetic field parameter and thermophoresis parameter has an induced effect on concentration profiles.

Keywords: convection flow, similarity, numerical analysis, spectral method, Williamson nanofluid, internal heat generation

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Authors: Ming-Jen Huang, Chun-Fang Huang, Chiching Wei

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With the recent advance of the deep neural network, we observe new applications of NLP (natural language processing) and CV (computer vision) powered by deep neural networks for processing business documents. However, creating a real-world document processing system needs to integrate several NLP and CV tasks, rather than treating them separately. There is a need to have a unified approach for processing documents containing textual and graphical elements with rich formats, diverse layout arrangement, and distinct semantics. In this paper, a framework that fulfills this unified approach is presented. The framework includes a representation model definition for holding the information generated by various tasks and specifications defining the coordination between these tasks. The framework is a blueprint for building a system that can process documents with rich formats, styles, and multiple types of elements. The flexible and lightweight design of the framework can help build a system for diverse business scenarios, such as contract monitoring and reviewing.

Keywords: document processing, framework, formal definition, machine learning

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Authors: Sezin Ekşioğlu

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Multilabel classification has a huge importance for several applications, it is also a challenging research topic. It is a kind of supervised learning that contains binary targets. The distance between multilabel and binary classification is having more than one class in multilabel classification problems. Features can belong to one class or many classes. There exists a wide range of applications for multi label prediction such as image labeling, text categorization, gene functionality. Even though features are classified in many classes, they may not always be properly classified. There are many ensemble methods for the classification. However, most of the researchers have been concerned about better multilabel methods. Especially little ones focus on both efficiency of classifiers and pairwise relationships at the same time in order to implement better multilabel classification. In this paper, we worked on modified ensemble methods by getting benefit from k-Nearest Neighbors and neural network structure to address issues within a beneficial way and to get better impacts from the multilabel classification. Publicly available datasets (yeast, emotion, scene and birds) are performed to demonstrate the developed algorithm efficiency and the technique is measured by accuracy, F1 score and hamming loss metrics. Our algorithm boosts benchmarks for each datasets with different metrics.

Keywords: multilabel, classification, neural network, KNN

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Authors: Max Neu

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A large variety of studies on the utilization of games in classroom settings promote positive effects on students motivation for learning. Still, most of those studies rarely can give any specifics about the factors that might lead to changes in students motivation. The undertaken study has been conducted in tandem with the development of a highly classroom-optimized serious game, with the intent of providing a subjectively positive initial contact with the subject of political participation and to enable the development of personal motivation towards further engagement with the topic. The goal of this explorative study was to Identify the factors that influence students motivation towards the subject when serious games are being used in classroom education. Therefor, students that have been exposed to a set of classes in which a classroom optimized serious game has been used. Afterwards, a selection of those have been questioned in guided interviews that have been evaluated through Qualitative Content Analysis. The study indicates that at least 23 factors in the categories, mechanics, content and context potentially influence students motivation to engage with the classes subject. The conclusions are of great value for the further production of classroom games as well as curricula involving digital games in general.

Keywords: formal education, games in classroom, motivation, political education

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Authors: Hossam Abdelrahman, Sama Rostom, Reem Yassein, Yara Mohamed, Salma Salah, Nour Awny

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In today’s environment, people are becoming increasingly interested in their appearance. However, they are afraid of their unknown appearance after a plastic surgery or treatment. Accidents, burns and genetic problems such as bowing of body parts of people have a negative impact on their mental health with their appearance and this makes them feel uncomfortable and underestimated. The approach presents a revolutionary deep learning-based image inpainting method that analyses the various picture structures and corrects damaged images. In this study, A model is proposed based on the in-painting of medical images with Stable Diffusion Inpainting method. Reconstructing missing and damaged sections of an image is known as image inpainting is a key progress facilitated by deep neural networks. The system uses the input of the user of an image to indicate a problem, the system will then modify the image and output the fixed image, facilitating for the patient to see the final result.

Keywords: generative adversarial network, large mask inpainting, stable diffusion inpainting, plastic surgery

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Authors: Cesar Leines

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The social effects of the pandemic are significant and diverse, most of those effects have widened the gap of economic inequality. Without a doubt, each country faces difficulties associated with the strengths and weaknesses of its own institutions that can address these causes and consequences. Around the world, pricing practices that have no connection to production costs have been used extensively in numerous markets beyond those relating to the supply of essential goods and services, and although it is not unlawful to adjust pricing considering the increased demand of certain products, shortages and disruption of supply chains, illegitimate pricing practices may arise and these tend to transfer wealth from consumers to producers that affect the purchasing power of the former, making people worse off. High prices with no objective justification indicate a poor state of the competitive process in any market and the impact of those underlying competition issues leading to inefficiency is increased when national competition agencies are weak and ineffective in enforcing competition in law and policy. It has been observed that in those countries where competition authorities are perceived as weak or ineffective, price increases of a wide range of products and services were more significant during the pandemic than those price increases observed in countries where the perception of the effectiveness of the competition agency is high. When a perception is created of a highly effective competition authority, one which enforces competition law and its non-enforcement activities result in the fulfillment of its substantive functions of protecting competition as the means to create efficient markets, the price rise observed in markets under its jurisdiction is low. A case study focused on the effectiveness of the national competition agency in Mexico (COFECE) points to institutional weakness as one of the causes leading to excessive pricing. There are many factors that contribute to its low effectiveness and which, in turn, have led to a very significant price hike, potentiated by the pandemic. This paper contributes to the discussion of these factors and proposes different steps that overall help COFECE or any other competition agency to increase the perception of effectiveness for the benefit of the consumers.

Keywords: agency effectiveness, competition, institutional weakness, price gouging

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Authors: Sally A. Brown

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Over the last 30 years, researchers have developed models or frameworks to provide a more structured understanding of the reading comprehension process. Cognitive information processing models and social cognitive theories both provide frameworks to inform reading comprehension instruction. The purpose of this paper is to (a) provide an overview of the historical development of reading comprehension theory, (b) review the literature framed by cognitive information processing, social cognitive, and integrated reading comprehension theories, and (c) demonstrate how these frameworks inform instruction. As integrated models of reading can guide the interpretation of various factors related to student learning, an integrated framework designed by the researcher will be presented. Results indicated that features of cognitive processing and social cognitivism theory—represented in the integrated framework—highlight the importance of the role of the teacher. This model can aid teachers in not only improving reading comprehension instruction but in identifying areas of challenge for students.

Keywords: explicit instruction, integrated models of reading comprehension, reading comprehension, teacher’s role

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Authors: Mohammad Anwar, Shah Waliullah

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The development of science and technology in the present era has an urgent demand for the training of thinking of undergraduates. This requirement actively promotes research and teaching of basic theories, beneficial to the career development of students. This study clarified the dialectical relation between the thermodynamic equilibrium constant and reaction rate constant through the contrast thinking method. Findings reveal that both the isobaric Van't Hoff equation and the Arrhenius equation had four similar forms, and the change in the trend of both constants showed a similar law. By the derivation of the formation rate constant of the product (KY) and the consumption rate constant of the reactant (KA), the ratio of both constants at the end state indicated the nature of the equilibrium state in agreement with that of the thermodynamic equilibrium constant (K^θ (T)). This study has thus presented that the thermodynamic equilibrium constant contained the characteristics of microscopic dynamics based on the analysis of the reaction mechanism, and both constants are organically connected and unified. The reaction enthalpy and activation energy are closely related to each other with the same connotation.

Keywords: thermodynamic equilibrium constant, reaction rate constant, PBL teaching, dialectical relation, innovative thinking

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Authors: Jinchen Cao, Tiantian Du

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As waxy crude oil is easy to crystallization and deposition in the pipeline wall, it causes pipeline clogging and leads to the reduction of oil and gas gathering and transmission efficiency. In this paper, a mesoscopic scale dissipative particle dynamics method is employed, and constructed four pipe wall models, including smooth wall (SW), hydroxylated wall (HW), rough wall (RW), and single-layer graphene wall (GW). Snapshots of the simulation output trajectories show that paraffin molecules interact with each other to form a network structure that constrains water molecules as their nucleation sites. Meanwhile, it is observed that the paraffin molecules on the near-wall side are adsorbed horizontally between inter-lattice gaps of the solid wall. In the pressure range of 0 - 50 MPa, the pressure change has less effect on the affinity properties of SS, HS, and GS walls, but for RS walls, the contact angle between paraffin wax and water molecules was found to decrease with the increase in pressure, while the water molecules showed the opposite trend, the phenomenon is due to the change in pressure, leading to the transition of paraffin wax molecules from amorphous to crystalline state. Meanwhile, the minimum crystalline phase pressure (MCPP) was proposed to describe the lowest pressure at which crystallization of paraffin molecules occurs. The maximum number of crystalline clusters formed by paraffin molecules at MCPP in the system showed NSS (0.52 MPa) > NHS (0.55 MPa) > NRS (0.62 MPa) > NGS (0.75 MPa). The MCPP on the graphene surface, with the least number of clusters formed, indicates that the addition of graphene inhibited the crystallization process of paraffin deposition on the wall surface. Finally, the thermal conductivity was calculated, and the results show that on the near-wall side, the thermal conductivity changes drastically due to the occurrence of adsorption crystallization of paraffin waxes; on the fluid side the thermal conductivity gradually tends to stabilize, and the average thermal conductivity shows: ĸRS(0.254W/(m·K)) > ĸRS(0.249W/(m·K)) > ĸRS(0.218W/(m·K)) > ĸRS(0.188W/(m·K)).This study provides a theoretical basis for improving the transport efficiency and heat transfer characteristics of waxy crude oil in terms of wall type, wall roughness, and MCPP.

Keywords: waxy crude oil, thermal conductivity, crystallization, dissipative particle dynamics, MCPP

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Authors: Thummalapalli CSM Gupta, Banti Sidhiwala

Abstract:

Mineral oils of naphthenic and paraffinic type have been traditionally been used as insulating liquids in the transformer applications to protect the solid insulation from moisture and ensures effective heat transfer/cooling. The performance of these type of oils have been proven in the field over many decades and the condition monitoring and diagnosis of transformer performance have been successfully monitored through oil properties and dissolved gas analysis methods successfully. Different type of gases representing various types of faults due to components or operating conditions effectively. While large amount of data base has been generated in the industry on dissolved gas analysis for mineral oil based transformer oils and various models for predicting the fault and analysis, oil specifications and standards have also been modified to include stray gassing limits which cover the low temperature faults and becomes an effective preventative maintenance tool that can benefit greatly to know the reasons for the breakdown of electrical insulating materials and related components. Natural esters have seen a rise in popularity in recent years due to their "green" credentials. Some of its benefits include biodegradability, a higher fire point, improvement in load capability of transformer and improved solid insulation life than mineral oils. However, the Stray gases evolution like hydrogen and hydrocarbons like methane (CH4) and ethane (C2H6) show very high values which are much higher than the limits of mineral oil standards. Though the standards for these type esters are yet to be evolved, the higher values of hydrocarbon gases that are available in the market is of concern which might be interpreted as a fault in transformer operation. The current paper focuses on developing a natural ester based transformer oil which shows very levels of stray gassing by standard test methods show much lower values compared to the products available currently and experimental results on various test conditions and the underlying mechanism explained.

Keywords: biodegadability, fire point, dissolved gassing analysis, stray gassing

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Authors: Hassan Attahiru Gwandu

Abstract:

This study aims at surveying and analyzing the contribution of Sokoto scholars or Sokoto Caliphate in the development of science and technology in West Africa. Today, it is generally accepted that the 19^th century Islamic revivalism in Hausaland was a very important revolution in the history of Hausa society and beyond. It is therefore, as a result of this movement or Jihad; the Hausaland (West Africa in general) witnessed several changes and transformations. These changes were in different sectors of life from politics, economy to social and religious aspect. It is these changes especially on religion that will be given considerations in this paper. The jihad resulted is the establishment of an Islamic state of Sokoto Caliphate, the revival Islam and development of learning and scholarship. During the existence of this Caliphate, a great deal of scholarship on Islamic laws were revived, written and documented by mostly, the three Jihad leaders; Usmanu Danfodiyo, his brother Abdullahi Fodiyo and his son Muhammad Bello. The trio had written more than one thousand books and made several verdicts on Islamic medicine. This study therefore, seeks to find out the contributions of these scholars or the Sokoto caliphate in the development of science in West Africa.

Keywords: Sokoto caliphate, scholarship, science and technology, West Africa

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Authors: Souhir Ben Amor, Heni Boubaker, Lotfi Belkacem

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This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts.

Keywords: electricity price, k-factor GARMA, LLWNN, G-GARCH, forecasting

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Authors: Paulo Cesar Pereira Junior, Alexandre Monteiro, Rafael da Luz Ribeiro, Antonio Carlos Sobieranski, Aldo von Wangenheim

Abstract:

In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models.

Keywords: convolutional neural networks, deep learning, digital image processing, precision agriculture, semantic segmentation, unmanned aerial vehicles

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Authors: Thanakrit Taweesoontorn, Sarucha Yanyong, Poom Konghuayrob

Abstract:

Robotic arms have gained popularity in various industries due to their accuracy and efficiency. This research proposes a method for bin-picking tasks using the Cobot, combining the YOLOv5 CNNs model for object detection and pose estimation with traditional feature detection (FAST), feature description (BRISK), and matching algorithms. By integrating these algorithms and utilizing a small-scale depth sensor camera for capturing depth and color images, the system achieves real-time object detection and accurate pose estimation, enabling the robotic arm to pick objects correctly in both position and orientation. Furthermore, the proposed method is implemented within the ROS framework to provide a seamless platform for robotic control and integration. This integration of robotics, cameras, and AI technology contributes to the development of industrial robotics, opening up new possibilities for automating challenging tasks and improving overall operational efficiency.

Keywords: robotic vision, image processing, applications of robotics, artificial intelligent

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Authors: Hamnah Nisar

Abstract:

As food is something which is particularly important for survival, in fact, it improves the quality of life and promotes health. Quality food is a key to a healthy life. Consumption of food depends on the knowledge we have regarding the nutrients it contains. Moreover, the awareness and knowledge about something is an initial stage for its improvement. We cannot work on anything unless we have knowledge about it. The pros and cons, effects, causes, dos, and don'ts, especially for an important things like food, are a necessity to learn. That is why my research would be all about analyzing what difference awareness makes on people and how making people more aware about a certain thing can help them improve their lifestyles and bring a positive change for them. The research would be done through questionnaires and interviews among two classes, one would be the upper class and the other would-be lower class. Because the upper class can easily access learning facilities and can know about the new things than the lower class. The questions would be related to what kind of food do they consume, what health issues they face, or what health issues are common among their regions. The results of the research would be helpful to know firstly the effects of awareness and education regarding food on health, how a basic thing like knowledge can have a significant effect on health and can be the cause of several diseases.

Keywords: nutrition, awareness, quality food, knowledge

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Authors: Sonay Ödemiş

Abstract:

Movement Staff Notation System (MSNS) is a movement transcription, analyzing method, and it's been constantly improved since it was first developed in 2005. This method is based on human anatomy, is being used and applied in the lessons at The Department of Turkish Folk Dances in Istanbul Technical University, nowadays. In this research, it is aimed to discover, how MSNS can help to participants about learning the basic movements of lower extremity. This experiment has six volunteers who were randomly selected. Each volunteer has been graded for their dance backgrounds and all the volunteers have been studied for six weeks. Each week has included different topic and examples such as contacts on foot, jumps, timing, directions and basic symbols of MSNS. Examples have changed from easy to hard. On conclusion, 6 volunteer subjects were tested in final test. The tests were recorded with the camera. In this presentation, it will be explained and detailed the results of the reading test on MSNS. Some of important video records will be watched and interpreted after the test. As a conclusion, all the scores will be interpreted and assessed from different perspectives.

Keywords: dance notation, Turkish dances, reading test, Education

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