Search results for: 2023 vision

Authors: Mary H Moen, Lauren H. Mandel

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Creating a sense of community in online learning is important for student engagement and success. The integration of discussion forums within online learning environments presents an opportunity to explore how this computer mediated communications format can cultivate a sense of community among students in accelerated master’s degree programs. This research has two aims, to delve into the ways instructors utilize this communications technology to create community and to understand the feelings and experiences of graduate students participating in these forums in regard to its effectiveness in community building. This study is a two-phase approach encompassing qualitative and quantitative methodologies. The data will be collected at an online accelerated Master of Library and Information Studies program at a public university in the northeast of the United States. Phase 1 is a content analysis of the syllabi from all courses taught in the 2023 calendar year, which explores the format and rules governing discussion forum assignments. Four to six individual interviews of department faculty and part time faculty will also be conducted to illuminate their perceptions of the successes and challenges of their discussion forum activities. Phase 2 will be an online survey administered to students in the program during the 2023 calendar year. Quantitative data will be collected for statistical analysis, and short answer responses will be analyzed for themes. The survey is adapted from the Classroom Community Scale Short-Form (CSS-SF), which measures students' self-reported responses on their feelings of connectedness and learning. The prompts will contextualize the items from their experience in discussion forums during the program. Short answer responses on the challenges and successes of using discussion forums will be analyzed to gauge student perceptions and experiences using this type of communication technology in education. This research study is in progress. The authors anticipate that the findings will provide a comprehensive understanding of the varied approaches instructors use in discussion forums for community-building purposes in an accelerated MLIS program. They predict that the more varied, flexible, and consistent student uses of discussion forums are, the greater the sense of community students will report. Additionally, students’ and instructors’ perceptions and experiences within these forums will shed light on the successes and challenges faced, thereby offering valuable recommendations for enhancing online learning environments. The findings are significant because they can contribute actionable insights for instructors, educational institutions, and curriculum designers aiming to optimize the use of discussion forums in online accelerated graduate programs, ultimately fostering a richer and more engaging learning experience for students.

Keywords: accelerated online learning, discussion forums, LIS programs, sense of community, g

Procedia PDF Downloads 52

Authors: Shuruq Ghazi Nahhas

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Islamic art is a decorative art that depends on repeating motifs in various shapes to cover different surfaces. Each motif has its own characteristics and style that may reflect different Islamic periods, such as Umayyad, Abbasid, Fatimid, Seljuk, Nasrid, Ottoman, and Safavid. These periods were the most powerful periods which played an important role in developing the Islamic motifs. Most of these motifs of the Islamic heritage were not used in new applications. This research focused on reviving the vegetal Islamic motifs found on Islamic heritage and redesign them in a new format to decorate various products, including scarfs, cushions, coasters, wallpaper, wall art, and boxes. The scarf is chosen as one element of these decorative products because it is used as accessories to add aesthetic value to fashion. A descriptive-analytical method is used for this research. The process started with extracting and analyzing the original motifs. Then, creating the new motifs by simplifying, deleting, or adding elements based on the original structure. Then, creating repeated patterns and applying them to decorative products. The findings of this research indicated: repeating patterns based on different structures creates unlimited patterns. Also, changing the elements of the motifs of a pattern adds new characteristics to the pattern. Also, creating frames using elements from the repeated motifs adds aesthetic and contemporary value to decorative products. Finally, using various methods of combining colors creates unlimited variations of each pattern. At the end, reviving the Islamic motifs in contemporary vision enriches decorative products with aesthetic, artistic, and historical values of different Islamic periods. This makes the decorative products valuable that adds uniqueness to their surroundings.

Keywords: Islamic motifs, contemporary patterns, scarfs, decorative products

Procedia PDF Downloads 142

Authors: Mahmoud Ahmed Khater

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This study aimed to improve the deleterious effects of salinity stress in chickpeas using both proline and glycine betaine as osmoregulants. The aim was achieved using foliar spraying with different concentrations of proline (5 mM and 10 mM) and glycinebetaine (10 mM and 20 mM) to chickpea plants grown in pots under salinity stress (3000 mg/l NaCl) at the greenhouse of the National Research Centre, Egypt, during two successive seasons 2021/2022 and 2022/2023. Results indicated that all applied treatments caused significant increases in most of the investigated parameters of chickpea plants irrigated with either tap water or saline solution relative to the corresponding control. It is worth mentioning that proline treatments were more effective than glycine betaine treatments in increasing the salinity tolerance of chickpea plants, reflected in their quality and quantity. Moreover, proline treatment at 5mM was the most pronounced treatment in alleviating the deleterious effect of salinity on chickpea plants.

Keywords: cicer arietinum L., osmoprotectant, proline, glycinebetaine salinity tolerance

Procedia PDF Downloads 18

Authors: Iqraq Kamal, Akmal Razif, Sivadas Chandra Sekaran, Ahmad Syazwan Hisaburi

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Painting plays a crucial role in the aerospace manufacturing industry, serving both protective and cosmetic purposes for components. However, the traditional manual painting method is time-consuming and labor-intensive, posing challenges for the sector in achieving higher efficiency. Additionally, the current automated robot path planning has been a bottleneck for spray painting processes, as typical manual teaching methods are time-consuming, error-prone, and skill-dependent. Therefore, it is essential to develop automated tool path planning methods to replace manual ones, reducing costs and improving product quality. Focusing on flat panel painting in aerospace manufacturing, this study aims to address issues related to unreliable part identification techniques caused by the high-mixture, low-volume nature of the industry. The proposed solution involves using a spray gun and a UR10 robotic arm with a vision system that utilizes one-shot object detection (OS2D) to identify parts accurately. Additionally, the research optimizes path planning by concentrating on the region of interest—specifically, the identified part, rather than uniformly covering the entire painting tray.

Keywords: aerospace manufacturing, one-shot object detection, automated spray painting, vision-based path optimization, deep learning, automation, robotic arm

Procedia PDF Downloads 55

Authors: Tingwei Shu, Dong Zhou, Chengjun Guo

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Semantic communication is an emerging form of communication that realize intelligent communication by extracting semantic information of data at the source and transmitting it, and recovering the data at the receiving end. It can effectively solve the problem of data transmission under the situation of large data volume, low SNR and restricted bandwidth. With the development of Deep Learning, semantic communication further matures and is gradually applied in the fields of the Internet of Things, Uumanned Air Vehicle cluster communication, remote sensing scenarios, etc. We propose an improved semantic communication system for the situation where the data volume is huge and the spectrum resources are limited during the transmission of remote sensing images. At the transmitting, we need to extract the semantic information of remote sensing images, but there are some problems. The traditional semantic communication system based on Convolutional Neural Network cannot take into account the global semantic information and local semantic information of the image, which results in less-than-ideal image recovery at the receiving end. Therefore, we adopt the improved vision-Transformer-based structure as the semantic encoder instead of the mainstream one using CNN to extract the image semantic features. In this paper, we first perform pre-processing operations on remote sensing images to improve the resolution of the images in order to obtain images with more semantic information. We use wavelet transform to decompose the image into high-frequency and low-frequency components, perform bilinear interpolation on the high-frequency components and bicubic interpolation on the low-frequency components, and finally perform wavelet inverse transform to obtain the preprocessed image. We adopt the improved Vision-Transformer structure as the semantic coder to extract and transmit the semantic information of remote sensing images. The Vision-Transformer structure can better train the huge data volume and extract better image semantic features, and adopt the multi-layer self-attention mechanism to better capture the correlation between semantic features and reduce redundant features. Secondly, to improve the coding efficiency, we reduce the quadratic complexity of the self-attentive mechanism itself to linear so as to improve the image data processing speed of the model. We conducted experimental simulations on the RSOD dataset and compared the designed system with a semantic communication system based on CNN and image coding methods such as BGP and JPEG to verify that the method can effectively alleviate the problem of excessive data volume and improve the performance of image data communication.

Keywords: semantic communication, transformer, wavelet transform, data processing

Procedia PDF Downloads 57

Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

Procedia PDF Downloads 42

Authors: M. A. Zaki Ewiss, S. Afifi

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Nahda University (NUB) believes that internationalisation of higher educational is able to provide global society with an education that meets current needs and that can respond efficiently to contemporary demands and challenges, which are characterized by globalisation, interdependence, and multiculturalism. In this paper, we will discuss the the challenges of the Egyptian Higher Education system and the future vision to improve this system> In this report, the following issues will be considered: Increasing knowledge on the development of specialized programs of study at the university. Developing international cooperation programs, which focus on the development of the students and staff skills, and providing academic culture and learning opportunities. Increasing the opportunities for student mobility, and research projects for faculty members. Increased opportunities for staff, faculty and students to continue to learn foreign universities, and to benefit from scholarships in various disciplines. Taking the advantage of the educational experience and modern teaching methods; Providing the opportunities to study abroad without increasing the period of time required for graduation, and through greater integration in the curricula and programs; More cultural interaction through student exchanges.Improving and providing job opportunities for graduates through participation in the global labor market. This document sets out NUB strategy to move towards that vision. We are confident that greater explicit differentiation, greater freedom and greater collaboration are the keys to delivering the further improvement in quality we shall need to retain and strengthen our position as one of the world’s leading higher education systems.

Keywords: technology transfer higher education, knowledge transfer, internationalisation, mobility

Procedia PDF Downloads 420

Authors: Xi Liu, Fan Yuan

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In the evolving landscape of physics-based machine learning (PBML), particularly within fluid dynamics and its applications in electromechanical engineering, robot vision, and robot learning, achieving precision and alignment with researchers' specific needs presents a formidable challenge. In response, this work proposes a methodology that integrates neural transformation with a modified smoothed particle hydrodynamics model for generating transformed 3D fluid simulations. This approach is useful for nanoscale science, where the unique and complex behaviors of viscoelastic medium demand accurate neurally-transformed simulations for materials understanding and manipulation. In electromechanical engineering, the method enhances the design and functionality of fluid-operated systems, particularly microfluidic devices, contributing to advancements in nanomaterial design, drug delivery systems, and more. The proposed approach also aligns with the principles of PBML, offering advantages such as multi-fluid stylization and consistent particle attribute transfer. This capability is valuable in various fields where the interaction of multiple fluid components is significant. Moreover, the application of neurally-transformed hydrodynamical models extends to manufacturing processes, such as the production of microelectromechanical systems, enhancing efficiency and cost-effectiveness. The system's ability to perform neural transfer on 3D fluid scenes using a deep learning algorithm alongside physical models further adds a layer of flexibility, allowing researchers to tailor simulations to specific needs across scientific and engineering disciplines.

Keywords: physics-based machine learning, robot vision, robot learning, hydrodynamics

Procedia PDF Downloads 41

Authors: Shariful Islam, Sharun Akter Khushbu, S. M. Shaqib, Shahriar Sultan Ramit

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In the construction sector, ensuring worker safety is of the utmost significance. In this study, a deep learning-based technique is presented for identifying safety gear worn by construction workers, such as helmets, goggles, jackets, gloves, and footwear. The suggested method precisely locates these safety items by using the YOLO v7 (You Only Look Once) object detection algorithm. The dataset utilized in this work consists of labeled images split into training, testing and validation sets. Each image has bounding box labels that indicate where the safety equipment is located within the image. The model is trained to identify and categorize the safety equipment based on the labeled dataset through an iterative training approach. We used custom dataset to train this model. Our trained model performed admirably well, with good precision, recall, and F1-score for safety equipment recognition. Also, the model's evaluation produced encouraging results, with a [email protected] score of 87.7%. The model performs effectively, making it possible to quickly identify safety equipment violations on building sites. A thorough evaluation of the outcomes reveals the model's advantages and points up potential areas for development. By offering an automatic and trustworthy method for safety equipment detection, this research contributes to the fields of computer vision and workplace safety. The proposed deep learning-based approach will increase safety compliance and reduce the risk of accidents in the construction industry.

Keywords: deep learning, safety equipment detection, YOLOv7, computer vision, workplace safety

Procedia PDF Downloads 47

Authors: Amrita Mishra, Tushar Kanti Das

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Agriculture is the backbone of the economy of Odisha. Sustainability of agriculture holds the key for the development of Odisha. The Sustainable Development Goals are a framework of 17 goals and 169 targets across social, economical and environmental areas of sustainable development. Among all the seventeen goals the second goal is focusing on the promotion of Sustainable Agriculture. In this research our main aim is also to contribute an understanding of effectiveness of sustainable agriculture as a tool for rural development in the selected tribal district (i.e. Koraput and Malkangiri) of Odisha. These two districts are comes under KBK districts of Odisha which are identified as most backward districts of Odisha. The objectives of our study are to investigate the effect of sustainable agriculture on the lives of tribal farmers, to study whether the farmers are empowered by their participation in sustainable agriculture initiatives to move towards their own vision of development and to study the investment and profit ratio in sustainable agriculture. This research will help in filling the major gaps in sociological studies of sustainable agriculture. This information will helpful for farmers, development organisations, donors and policy makers in formulating the development of effective initiatives and policies to support the development of sustainable agriculture. In this study, we have taken 210 respondents and used various statistical techniques like chi-square test, one-way ANOVA and percentage analysis. This research shows that sustainable agriculture is an effective development strategy that benefits the tribal farmers to move towards their own vision of Good Fortune. The poor farmers who struggle to feed their families and maintain viable livelihoods on shrinking land for them sustainable agriculture are really benefited. The farmers are using homemade pesticides, manure and also getting the seeds from different development organisations and Government. So the investment in Sustainable Agriculture is very less. All farmers said their lives are now better than before. The creation of farmers groups for training and marketing for the produces was shown to be very important for empowerment.

Keywords: sustainable, agriculture, tribal farmers, development, empowerment

Procedia PDF Downloads 146

Authors: Manjitsingh Rajput

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Visual communication plays an important role in artificial intelligence (AI) because it enables machines to understand and interpret visual information, similar to how humans do. This abstract explores the importance of visual communication in AI and emphasizes the importance of various applications such as computer vision, object emphasis recognition, image classification and autonomous systems. In going deeper, with deep learning techniques and neural networks that modify visual understanding, In addition to AI programming, the abstract discusses challenges facing visual interfaces for AI, such as data scarcity, domain optimization, and interpretability. Visual communication and other approaches, such as natural language processing and speech recognition, have also been explored. Overall, this abstract highlights the critical role that visual communication plays in advancing AI capabilities and enabling machines to perceive and understand the world around them. The abstract also explores the integration of visual communication with other modalities like natural language processing and speech recognition, emphasizing the critical role of visual communication in AI capabilities. This methodology explores the importance of visual communication in AI development and implementation, highlighting its potential to enhance the effectiveness and accessibility of AI systems. It provides a comprehensive approach to integrating visual elements into AI systems, making them more user-friendly and efficient. In conclusion, Visual communication is crucial in AI systems for object recognition, facial analysis, and augmented reality, but challenges like data quality, interpretability, and ethics must be addressed. Visual communication enhances user experience, decision-making, accessibility, and collaboration. Developers can integrate visual elements for efficient and accessible AI systems.

Keywords: visual communication AI, computer vision, visual aid in communication, essence of visual communication.

Procedia PDF Downloads 59

Authors: Abdelsalam Gomaa, Mahjabeen Ramzan, Tooba Ali Akbar, Huma Nadeem

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The use of tobacco and the health risks linked to it are well known in this day and age due to the presence of easily available information through the internet. The media is a powerful platform that is used by many anti-smoking awareness campaigns to reach their target audience; yet, it has been found that adolescents are taking up smoking every passing day. Half of this smoking population of youngsters resides in Asia alone, which includes Qatar, the focus country of this study. As smoking happens to be one of the largest avoidable causes of serious diseases like cancers and heart problems, children are taking up smoking at an alarming rate everywhere including Qatar. Importance of the health of the citizens of Qatar is one of the pillars of the Qatar vision 2030, which is to ensure a healthy population, both physically and mentally. Since the youth makes up a significant percentage of the population and in order to achieve the health objectives of the Qatar vision 2030, it is essential to ensure the health and well-being of this part of the population of the country as they are the future of Qatar. Children, especially boys who tend to be more aggressive by nature, are highly likely to develop behavioral and health issues due to smoking at an early age. Research conducted around the world has also emphasized on this association between the smokers developing a bad behaviour as well as poor social communication skills. However, due to lack of research into this association, very little is known about the extent to which smoking impacts the children’s academics, health and behaviour. Moreover, a study of this nature has not yet been conducted in Qatar previously as most of the studies focus on adult smokers and ways to minimize the number of smoking habits in universities and workplaces. This study solely focuses on identifying a relationship between smoking and its impacts on the adolescents by conducting a research on different schools across Qatar.

Keywords: adolescents, modelling techniques, Qatar, smoking

Procedia PDF Downloads 217

Authors: Prodipta Bhowmik, Poulami Saha, Preety Mehra, Yogesh Soni, Triloki Nath Jha

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In India, the smart city concept is growing day by day. So, for smart city development, a better traffic management and monitoring system is a very important requirement. Nowadays, road accidents increase due to more vehicles on the road. Reckless driving is mainly responsible for a huge number of accidents. So, an efficient traffic management system is required for all kinds of roads to control the traffic speed. The speed limit varies from road to road basis. Previously, there was a radar system but due to high cost and less precision, the radar system is unable to become favorable in a traffic management system. Traffic management system faces different types of problems every day and it has become a researchable topic on how to solve this problem. This paper proposed a computer vision and machine learning-based automated system for multiple vehicle detection, tracking, and speed estimation of vehicles using image processing. Detection of vehicles and estimating their speed from a real-time video is tough work to do. The objective of this paper is to detect vehicles and estimate their speed as accurately as possible. So for this, a real-time video is first captured, then the frames are extracted from that video, then from that frames, the vehicles are detected, and thereafter, the tracking of vehicles starts, and finally, the speed of the moving vehicles is estimated. The goal of this method is to develop a cost-friendly system that can able to detect multiple types of vehicles at the same time.

Keywords: OpenCV, Haar Cascade classifier, DLIB, YOLOV3, centroid tracker, vehicle detection, vehicle tracking, vehicle speed estimation, computer vision

Procedia PDF Downloads 59

Authors: Rachelle McFarlane

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The Second Decade of Action Road Safety has begun with increased focus on countries who are disproportionately affected by road fatalities. Researchers highlight the low effectiveness of road safety campaigns in Latin America and the Caribbean (LAC) still reporting approximately 130,000 deaths and six million injuries annually. The regional fatality rate 19.2 per 100,000 with heightened concern for persons 15 to 44 years. In 2021, 483 Jamaicans died in 435 crashes, with 33% of these fatalities occurring during Covid-19 curfew hours. The study objective is to conduct a systemic safety review of Jamaican road crashes and provide a framework for its use in complementing traditional methods. The methodology involves the use of the FHWA Systemic Safety Project Selection Tool for analysis. This tool reviews systemwide data in order to identify risk factors across the network associated with severe and fatal crashes, rather that only hotspots. A total of 10,379 crashes with 745 fatalities and serious injuries were reviewed. Of the focus crash types listed, 50% of ‘Pedestrian Accidents’ resulted in fatalities and serious injuries, followed by 32% ‘Bicycle’, 24% ‘Single’ and 12% of ‘Head-on’. This study seeks to understand the associated risk factors with these priority crash types across the network and recommend cost-effective countermeasures across common sites. As we press towards Vision Zero, the inclusion of the systemic safety review method, complementing traditional methods, may create a wider impact in reducing road fatalities and serious injury by targeting issues across network with similarities; focus crash types and contributing factors.

Keywords: systemic safety review, risk factors, road crashes, crash types

Procedia PDF Downloads 69

Authors: Yuri Katz, Kun Liu, Arunram Atmacharan

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Due to illiquidity, mispricing on Credit Markets is inevitable. This creates huge challenges to banks and investors as they seek to find new ways of risk valuation and portfolio management in a post-credit crisis world. Here, we analyze the difference in behavior of the spread-to-maturity in investment and high-yield categories of US corporate bonds between 2014 and 2023. Deviation from the theoretical dependency of this measure in the universe under study allows to identify multiple cases of mispriced credit risk. Remarkably, we observe mispriced bonds in both categories of credit ratings. This identification is supported by the application of the state-of-the-art machine learning model in more than 90% of cases. Noticeably, the ML-driven model-based forecasting of a category of bond’s credit ratings demonstrate an excellent out-of-sample accuracy (AUC = 98%). We believe that these results can augment conventional valuations of credit portfolios.

Keywords: credit risk, credit ratings, bond pricing, spread-to-maturity, machine learning

Procedia PDF Downloads 58

Authors: Ingy Ibrahim El-Darwish, Neveen Youssef Azmy

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The endowment of a healthy environment can be implemented by endorsing sustainable fundamentals. Design of sustainable buildings through recycling of waste, can reduce health problems, provide good environments and contribute to the aesthetically pleasing entourage. Such environments can help in providing energy-saving alternatives to consolidate the principles of sustainability. The poor community awareness and the absence of laws and legislation in Egypt for waste management specifically in residential areas have led to an inability to provide an integrated system for waste management in urban and rural areas. Many problems and environmental challenges face the Egyptian urban environments. From these problems, is the lack of a cohesive vision for waste collection and recycling for energy-saving. The second problem is the lack public awareness of the short term and long term vision of waste management. Bad practices have adversely affected the efficiency of environmental management systems due to lack of urban legislations that codify collection and recycling of residential communities in Egyptian urban environments. Hence, this research tries to address residents on waste management matters to facilitate legislative process on waste collection and classification within residential units and outside them in a preparation phase for recycling in the Egyptian urban environments. In order to achieve this goal, one of the Egyptian communities has been addressed, analyzed and studied. Waste collection, classification, separation and access to recycling places in the urban city are proposed in preparation for a legislation ruling and regulating the process. Hence, sustainable principles are to be achieved.

Keywords: recycling, residential buildings, sustainability, waste

Procedia PDF Downloads 298

Authors: Shinta Stri Ayuda Nur Setyaningsih

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Anterior uveitis is often triggered by viral infections such as herpes simplex virus (HSV) and cytomegalovirus (CMV). This study aims to provide an overview of the demographic and clinical characteristics of patients with anterior uveitis caused by CMV and HSV infection at PMN Cicendo Eye Hospital Bandung. This study used a retrospective observational method. Data were collected from the medical records of patients who visited the PMN Infection and Immunology Polyclinic at Cicendo Eye Hospital between February and July 2023. The results showed that anterior uveitis associated with HSV and CMV viruses often occurs in the elderly and more in women. The most common clinical symptoms are red eyes and decreased visual acuity, with a gradual onset of symptoms. Complications that often arise are cataracts and glaucoma. This study provides a deeper understanding of anterior uveitis caused by infection with HSV and CMV viruses.

Keywords: uveitis anterior, cytomegavirus, herpes simplex virus type I ELISA

Procedia PDF Downloads 51

Authors: Tarun Kumar Dalakoti, Debajyoti Majumder, Aditya Prasad Das, Samir Chandra Saxena

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India’s ambitious target to achieve a 50 percent share of energy from non-fossil fuels and the 500-gigawatt (GW) renewable energy capacity before the deadline of 2030, coupled with the global pursuit of sustainable development, will compel the nation to embark on a rapid clean energy transition. As a result, electricity market reforms will emerge as critical policy instruments to facilitate this transition and achieve ambitious environmental targets. This paper will present a comprehensive analysis of the various electricity market reforms to be introduced in the Indian Electricity sector to facilitate the integration of clean energy sources and will assess their impact on the overall energy landscape. The first section of this paper will delve into the policy mechanisms to be introduced by the Government of India and the Central Electricity Regulatory Commission to promote clean energy deployment. These mechanisms include extensive provisions for the integration of renewables in the Indian Electricity Grid Code, 2023. The section will also cover the projection of RE Generation as highlighted in the National Electricity Plan, 2023. It will discuss the introduction of Green Energy Market segments, the waiver of Inter-State Transmission System (ISTS) charges for inter-state sale of solar and wind power, the notification of Promoting Renewable Energy through Green Energy Open Access Rules, and the bundling of conventional generating stations with renewable energy sources. The second section will evaluate the tangible impact of these electricity market reforms. By drawing on empirical studies and real-world case examples, the paper will assess the penetration rate of renewable energy sources in India’s electricity markets, the decline of conventional fuel-based generation, and the consequent reduction in carbon emissions. Furthermore, it will explore the influence of these reforms on electricity prices, the impact on various market segments due to the introduction of green contracts, and grid stability. The paper will also discuss the operational challenges to be faced due to the surge of RE Generation sources as a result of the implementation of the above-mentioned electricity market reforms, including grid integration issues, intermittency concerns with renewable energy sources, and the need for increasing grid resilience for future high RE in generation mix scenarios. In conclusion, this paper will emphasize that electricity market reforms will be pivotal in accelerating the global transition towards clean energy systems. It will underscore the importance of a holistic approach that combines effective policy design, robust regulatory frameworks, and active participation from market actors. Through a comprehensive examination of the impact of these reforms, the paper will shed light on the significance of India’s sustained commitment to a cleaner, more sustainable energy future.

Keywords: renewables, Indian electricity grid code, national electricity plan, green energy market

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Authors: Shef Amir Arasy

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In 2023, the world's population will be 7.8 billion, which has increased significantly in the last 20 years. Every country in the world is experiencing the impacts of climate change directly and indirectly. However, the community still needs to build massive infrastructure and buildings. The massive CO2 emissions which lead to climate change come from cement usage in construction activity. Bamboo is one of the most sustainable materials for reducing carbon emissions and releasing more than 30% oxygen compared to the mass of trees. Besides, bamboo harvest time is faster than other sustainable materials, around 3-4 years. Furthermore, Bamboo has a high tensile strength, which can provide ductility effectively to prevent damage to buildings during an earthquake. By the finite element method, this research analyzes bamboo configuration and connection for tall building structures under different earthquake frequencies and fire. The aim of this research is to provide proper design and connection of bamboo buildings that can be more reliable than concrete structures.

Keywords: bamboo, concrete, ductility, earthquake.

Procedia PDF Downloads 44

Authors: Reetika Sharma, Lalit Tejwani, Himanshu Shekhar, Arun Singhvi

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Purpose: Advanced Glaucoma with Failed trab is not an uncommon sight in glaucoma clinic, and such cases usually tend to present with high intraocular pressure (IOP) and advanced cupping, or even glaucomatous atrophy stage. Re-surgery is needed for such cases, and wound modulation poses a major challenge in these cases. We share our experience in this case series with the use of Ologen (collagen matrix implant) along with MMC 0.04% used in surgery. The purpose of the study was to evaluate the efficacy and outcome of collagen matrix implant in re-trabeculectomy in advanced glaucoma cases. Methodology: Eleven eyes of 11 patients (one eye of one patient) underwent re-trabeculectomy surgery with MMC and Ologen. Ologen implant was used in sub scleral and subconjunctival space, as a spacer and wound modulator. In five cases, triple modulation with implant soaked in anti-VEGF was used. Results: All patients had cupping more than 0.9, and one case was GOA. All cases were on maximal medication at presentation and majority were on systemic anti-glaucoma therapy also. Post-surgery, follow-up ranged from 13 – 34 months, and all cases had a follow longer than the gap between previous surgery (which was failed) and re-trab. One case needed AC reformation and one needling was done. Phaco was done at same sitting in four cases. All cases had their IOP lowered post surgery, and vision was maintained in all, however one case was considered as failed re-surgery case. Topical medication was needed in seven cases post-surgery also. Conclusion: Ologen as adjuvant should be considered in all re-trab cases and all high risk and advanced cases, and triple modulation can be next step in these cases. Aggressive IOP control and non- reluctance to continue topical medications post second surgery should be considered in such cases, to give them best possible vision.

Keywords: failed trabeculectomy, ologen, trabeculectomy, advanced glaucoma

Procedia PDF Downloads 312

Authors: Hokuma Isgandarova, Khalil Aryanfar

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The patient's rights include all care items that the patient has the right to receive. Considering the growing importance of this important issue and its effect on improving treatment results and customer satisfaction, the present study was conducted with the aim of investigating the level of respect for patient rights in the physiotherapy clinic of the Faculty of Medicine University of Medical Sciences in 2023. In this study, the patients or companions who were referred to the clinic answered questions about the performance status of the clinic with respect to various aspects of the patient's rights. The aspects that were studied: choosing the service provider, having authority, respect, safety, prevention and access were found to have inappropriate performance scores. However, communication and interaction, continuity of service, quality of basic facilities and facilities, timely and immediate attention and trust had appropriate performance. Also, the results of the data analysis showed that there is no significant relationship between the total performance score and any of the demographic variables.

Keywords: compliance, patients' rights, physiotherapy clinic, performance level

Procedia PDF Downloads 38

Authors: Ian Omung'a

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Breast cancer remains to be one of the deadliest cancers for women worldwide, with the risk of developing tumors being as high as 50 percent in Sub-Saharan African countries like Kenya. With as many as 42 percent of these cases set to be diagnosed late when cancer has metastasized and or the prognosis has become terminal, Full Field Digital [FFD] Mammography remains an effective screening technique that leads to early detection where in most cases, successful interventions can be made to control or eliminate the tumors altogether. FFD Mammograms have been proven to multiply more effective when used together with Computer-Aided Detection and Diagnosis [CADe] systems, relying on algorithmic implementations of Deep Learning techniques in Computer Vision to carry out deep pattern recognition that is comparable to the level of a human radiologist and decipher whether specific areas of interest in the mammogram scan image portray abnormalities if any and whether these abnormalities are indicative of a benign or malignant tumor. Within this paper, we review emergent Deep Learning techniques that will prove relevant to the development of State-of-The-Art FFD Mammogram CADe systems. These techniques will span self-supervised learning for context-encoded occlusion, self-supervised learning for pre-processing and labeling automation, as well as the creation of a standardized large-scale mammography dataset as a benchmark for CADe systems' evaluation. Finally, comparisons are drawn between existing practices that pre-date these techniques and how the development of CADe systems that incorporate them will be different.

Keywords: breast cancer diagnosis, computer aided detection and diagnosis, deep learning, whole mammogram classfication, ultrasound classification, computer vision

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Authors: Christolyn Raj, Lewis Levitz

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Introduction: Two cases of late presentation ( > five years ) of bilateral pseudophakic macula edema related to oral tyrosine kinase inhibitors are described. These cases are the first of their type in the published literature. A review of ocular inflammatory complications of tyrosine kinase inhibitors in the current literature is explored. Case Presentations(s): Case 1 is an 83-year-old female who has been stable on Ibrutinib (Imbruvica ®) for chronic lymphocytic leukemia (CLL). She presented with bilateral blurred vision from severe cystoid macula edema seven years following routine cataract surgery. She was treated with intravitreal steroids with complete resolution without relapse. Case 2 is a 76-year-old female who was on therapy for polycythemia vera with Ruxolitinib (Jakafi®). She presented with bilateral blurred vision from mild cystoid macula edema six years following routine cataract surgery. She responded well to topical steroids without relapse. In both cases, oral tyrosine kinase inhibitor agents were presumed to be the underlying cause and were ceased. Over the last five years, there have been increasing reports in the literature of the inflammatory effects of tyrosine kinase inhibitors on the retina, uvea and optic nerve. Conclusion: Late presentation of pseudophakic macula edema following routine cataract surgery is rare. Such presentations should prompt investigation of the chronic use of systemic medications, especially oral kinase inhibitors. Patients who must remain on these agents require ongoing ophthalmologic assessment in view of their long-term inflammatory side effects.

Keywords: macula edema, oral kinase inhibitors, retinal toxicity, pseudo-phakia

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Authors: Yuta Nakano, Kozo Kajiwara, Atsushi Hori, Takeshi Fujita

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In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score.

Keywords: event camera, dynamic vision sensor, deep learning, data representation, object recognition, low energy consumption

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Authors: Sandra Cardoso, Gaspar Pais, Judite Neves, Sandra Cavaca, Fernando Araújo

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In 2021, Portugal has 63 emergency department (ED) visits per 100 people annually, the highest numbers in Europe. While EDs provide a critical service, high use is indicative of inappropriate and inefficient healthcare. In Portugal, all ED have the Manchester Triage System (MTS), a clinical risk management tool to enable that patients are seen in order of clinical priority. In 2023, more than 40% of the ED visits were of non-urgent conditions (blue and green), that could be better managed in primary health care (PHC), meaning wrong use of resources and lack of health literacy. From 2017, the country has a phone line, SNS24 (Contact Centre of the National Health Service), for triage, counseling, and referral service, 24 hours/7 days a week. The pilot project ‘Call before, save lives’ was implemented in the municipalities of Póvoa de Varzim and Vila do Conde (around 150.000 residents), in May 2023, by the executive board of the Portuguese Health Service, with the support of the Shared Services of the Ministry of Health, and local authorities. This geographical area has short travel times, 99% of the population a family doctor and the region is organized in a health local unit (HLU), integrating PHC and the local hospital. The purposes of this project included to increase awareness to contact SNS 24, before going to an ED, and non-urgent conditions oriented to a family doctor, reducing ED visits. The implementation of the project involved two phases, beginning with: i) development of campaigns using local influencers (fishmonger, model, fireman) through local institutions and media; ii) provision of telephone installed on site to contact SNS24; iii) establishment of open consultation in PHC; iv) promotion of the use of SNS24; v) creation of acute consultations at the hospital for complex chronic patients; and vi) direct referral for home hospitalization by PHC. The results of this project showed an excellent level of access to SNS24, an increase in the number of users referred to ED, with great satisfaction of users and professionals. The second phase, initiated in January 2024, for access to the ED, the need for prior referral was established as an admission rule, except for certain situations, as trauma patients. If the patient refuses, their registration in the ED and subsequent screening in accordance with the MTS must be ensured. When the patient is non-urgent, shall not be observed in the ED, provided that, according to his clinical condition, is guaranteed to be referred to PHC or to consultation/day hospital, through effective scheduling of an appointment for the same or the following day. In terms of results, 8 weeks after beginning of phase 2, we assist of a decrease in self-reported patients to ED from 59% to 15%, and a reduction of around 7% of ED visits. The key for this success was an effective public campaign that increases the knowledge of the right use of the health system, and capable of changing behaviors.

Keywords: contact centre of the national health service, emergency department visits, public campaign, health literacy, SNS24

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Authors: Oumayma Hilal

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The devastating earthquake in Marrakech-El Haouz in September 2023 underscores the critical need for sustainable tourism practices. This study proposes innovative approaches to territory tourism, prioritizing resilient and sustainable development to aid recovery and empower local communities. Using a mixed-methods approach, the research evaluates post-earthquake tourism impacts, gathers local perspectives, and benchmarks global models for disaster recovery through tourism. The paper aims to offer practical, community-centric tourism initiatives, integrated with strategic communication strategies, to enhance socio-economic welfare and ensure long-term resilience. The findings are expected to contribute significantly to sustainable tourism literature in post-disaster contexts and provide actionable strategies for the revitalization of the Marrakech-El Haouz region.

Keywords: sustainable tourism, community development, Marrakech El Haouz, communication strategies, territory tourism, sustainable tourism, community development

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Authors: Igone Guerra, Xabier Barandiaran

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The deep crisis (economic, social and cultural) in which Europe and Gipuzkoa, in the Basque Country (Spain), have been immersed in since 2008 forces governments to face a necessary transformation. These challenges demand different solutions and answers to meet the needs of the citizens. Adapting to continuous and sometimes abrupt changes in the social and political landscape requires an undeniable will to reinvent the way in which governments practice politics. This reinvention of government should help us build different organizations that, first, develop challenging public services, second, respond effectively to the needs of the citizens, and third, manage scarce resources, ultimately offering a contemporary concept of public value. In this context, the Etorkizuna Eraikiz initiative was designed to face the future challenges of the territory in a collaborative way. The aim of the initiative is to promote an alternative form of governance to generate common good and greater public value. In Etorkizuna Eraikiz democratic values, such as collaboration, participation, and accountability are prominent. This government approach is based on several features such as the creation of relational spaces to design and deliberate about the public politics or the promotion of a team-working approach, breaking down the silos between and within organizations, as an exercise in defining a shared vision regarding the Future of the Territory. A future in which the citizens are becoming actors in the problem-solving process and in the construction of a culture of participation and collective learning. In this paper, the Etorkizuna Eraikiz initiative will be presented (vision and methodology) as a model of a local approach to public policy innovation resulting in a way of governance that is more open and collaborative. Based on this case study, this paper explores the way in which collaborative governance leads to better decisions, better leadership, and better citizenry. Finally, the paper also describes some preliminary findings of this local approach, such as the level of knowledge of the citizenry about the projects promoted within Etorkizuna Eraikiz as well as the link between the challenges of the territory, as identified by the citizenry, and the political agenda promoted by the provincial government. Regarding the former, the Survey on the socio-political situation of Gipuzkoa showed that 27.9% of the respondents confirmed that they knew about the projects promoted within the initiative and gave it a mark of 5.71. In connection with the latter, over the last three years, 65 millions of euros have been allocated for a total of 73 projects that have covered socio-economic and political challenges such as aging, climate change, mobility, participation in democratic life, and so on. This governance approach of Etorkizuna Eraikiz has allowed the local government to match the needs of citizens to the political agenda fostering in this way a shared vision about the public value.

Keywords: collaborative governance, citizen participation, public good, social listening, public innovation

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Authors: Helen S. Ghali, Haidy S. Ghali, Salma Ibrahim, Ossama Hosny, Hatem S. Elbehairy

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Infrastructure systems are essential assets in any community; accordingly, authorities aim to maximize its life span while minimizing the life cycle cost. This requires studying the asset conditions throughout its operation and forming a cost-efficient maintenance strategy plan. The objective of this study is to develop a highway management system that provides medium-term maintenance plans with the minimum life cycle cost subject to budget constraints. The model is applied to data collected for the highway network in India with the aim to output a 5-year maintenance plan strategy from 2019 till 2023. The main element considered is the surface coarse, either rigid or flexible pavement. The model outputs a 5-year maintenance plan for each segment given the budget constraint while maximizing the new pavement condition rating and minimizing its life cycle cost.

Keywords: infrastructure, asset management, optimization, maintenance plan

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Authors: Sultan Samah A Almjlad

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The translation industry in Saudi Arabia is experiencing significant shifts driven by Vision 2030, which aims to diversify the economy and enhance international engagement. This change highlights the need for translators who are skilled in various languages and cultures, playing a crucial role in the nation's global integration efforts. However, there's a notable gap between the skills taught in academic institutions and what the job market demands. Many translation programs in Saudi universities don't align well with industry needs, resulting in graduates who may not meet employer expectations. To tackle this challenge, it's essential to thoroughly analyze the market to identify the key skills required, especially in sectors like legal, medical, technical, and audiovisual translation. At the same time, existing translation programs need to be evaluated to see if they cover necessary topics and provide practical training. Involving stakeholders such as translation agencies, professionals, and students is crucial to gather diverse perspectives. Identifying discrepancies between academic offerings and market demands will guide the development of targeted strategies. These strategies may include enriching curricula with industry-specific content, integrating emerging technologies like machine translation and CAT tools, and establishing partnerships with industry players to offer practical training opportunities and internships. Industry-led workshops and seminars can provide students with valuable insights, and certification programs can validate their skills. By aligning academic programs with industry needs, Saudi Arabia can build a skilled workforce of translators, supporting its economic diversification goals under Vision 2030. This alignment benefits both students and the industry, contributing to the growth of the translation sector and the overall development of the country.

Keywords: translation industry, briging gap, labor market, requirements

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Authors: Jim Weese

Abstract:

The Kinesiology, Exercise and Sport Science environment remain excellent venues for leadership research. Prescribed leadership (coaching), emergent leadership (players and organizations), and executive leadership are all popular themes in the research literature. Leadership remains a popular area of inquiry in the sport management domain as well as an interesting area for practitioners who wish to heighten their leadership practices and effectiveness. The need for effective leadership in these areas given competing demands for attention and resources may be at an all-time high. The presenter has extensive research and practical experience in the area and has developed his concept based on the latest leadership literature. He refers to this as the Five ’C’s of Leadership. These components, noted below, have been empirically validated and have served as the foundation for extensive consulting with academic, sport, and business leaders. Credibility (C1) is considered the foundation of leadership. There are two components to this area, namely: (a) leaders being respected for having the relevant knowledge, insights, and experience to be seen as credible sources of information, and (b) followers perceiving the leader as being a person of character, someone who is honest, reliable, consistent, and trustworthy. Compelling Vision (C2) refers to the leader’s ability to focus the attention of followers on a desired end goal. Effective leaders understand trends and developments in their industry. They also listen attentively to the needs and desires of their stakeholders and use their own instincts and experience to shape these ideas into an inspiring vision that is effectively and continuously communicated. Charismatic Communicator (C3) refers to the leader’s ability to formally and informally communicate with members. Leaders must deploy mechanisms and communication techniques to keep their members informed and engaged. Effective leaders sprinkle in ‘proof points’ that reinforce the vision’s relevance and/or the unit’s progress towards its attainment. Contagious Enthusiasm (C4) draws on the emotional intelligence literature as it relates to exciting and inspiring followers. Effective leaders demonstrate a level of care, commitment, and passion for their people and feelings of engagement permeate the group. These leaders genuinely care about the task at hand, and for the people working to make it a reality. Culture Builder (C5) is the capstone component of the model and is critical to long-term success and survival. Organizational culture refers to the dominant beliefs, values and attitudes of members of a group or organization. Some have suggested that developing and/or imbedding a desired culture for an organization is the most important responsibility for a leader. The author outlines his Five ‘C’s’ of Leadership concept and provide direct application to executive leadership in Kinesiology, Exercise and Sport Science.

Keywords: effectiveness, leadership, management, sport

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