Search results for: vulnerability intelligence

Authors: Daniel Schimmelpfennig

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This paper attempts to delineate the idea to curate the leveling up for whole-systems change. Curation is the act fo select, organize, look after, or present information from a professional point of view through expert knowledge. The trans-paradigmatic, trans-contextual, trans-disciplinary, trans-perspective of trans-media futures studies hopes to enable a move from a monochrome intellectual pursuit towards breathing a higher dimensionality. Progressing to the next level to equip actors for whole-systems change is in consideration of the commonly known symptoms of our time as well as in anticipation of future challenges, both a necessity and desirability. Systems of collective intelligence could potentially scale regenerative, adaptive, and anticipatory capacities. How could such a curation then be enacted and implemented, to initiate the process of leveling-up? The suggestion here is to focus on the metasystem transition, the bio-digital fusion, namely, by merging neurosciences, the ontological design of money as our operating system, and our understanding of the billions of years of time-proven permutations in nature, biomimicry, and biological metaphors like symbiogenesis. Evolutionary cybernetics accompanies the process of whole-systems change.

Keywords: bio-digital fusion, evolutionary cybernetics, metasystem transition, symbiogenesis, transmedia futures studies

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Authors: Bassma A. Abdel Haleem, Ehab K. Emam, George E. Yacoub, Ashraf M. Salem

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Background: Obesity is an increasingly common problem in childhood. Childhood obesity is considered the main risk factor for the development of metabolic syndrome (MetS) (diabetes type 2, dyslipidemia, and hypertension). Recent studies estimated that among children with obesity 30-60% will develop MetS. Visceral fat thickness is a valuable predictor of the development of MetS. Computed tomography and dual-energy X-ray absorptiometry are the main techniques to assess visceral fat. However, they carry the risk of radiation exposure and are expensive procedures. Consequently, they are seldom used in the assessment of visceral fat in children. Some studies explored the potential of ultrasound as a substitute to assess visceral fat in the elderly and found promising results. Given the vulnerability of children to radiation exposure, we sought to evaluate ultrasound as a safer and more cost-efficient alternative for measuring visceral fat in obese children. Additionally, we assessed the correlation between visceral fat and obesity indicators such as insulin resistance. Methods: A cross-sectional study was conducted on 46 children with obesity (aged 6–16 years). Their visceral fat was evaluated by ultrasound. Subcutaneous fat thickness (SFT), i.e., the measurement from the skin-fat interface to the linea alba, and visceral fat thickness (VFT), i.e., the thickness from the linea alba to the aorta, were measured and correlated with anthropometric measures, fasting lipid profile, homeostatic model assessment for insulin resistance (HOMA-IR) and liver enzymes (ALT). Results: VFT assessed via ultrasound was found to strongly correlate with the BMI, HOMA-IR with AUC for VFT as a predictor of insulin resistance of 0.858 and cut off point of >2.98. VFT also correlates positively with serum triglycerides and serum ALT. VFT correlates negatively with HDL. Conclusions: Ultrasound, a safe and cost-efficient technique, could be a useful tool for measuring the abdominal fat thickness in children with obesity. Ultrasound-measured VFT could be an appropriate prognostic factor for insulin resistance, hypertriglyceridemia, and elevated liver enzymes in obese children.

Keywords: metabolic syndrome, pediatric obesity, sonography, visceral fat

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Authors: Javier Barrachina, Piedad Garrido, Manuel Fogue, Julio A. Sanguesa, Francisco J. Martinez

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It is very common to observe, especially in Computer Science studies that students have difficulties to correctly understand how some mechanisms based on Artificial Intelligence work. In addition, the scope and limitations of most of these mechanisms are usually presented by professors only in a theoretical way, which does not help students to understand them adequately. In this work, we focus on the problems found when teaching Evolutionary Algorithms (EAs), which imitate the principles of natural evolution, as a method to solve parameter optimization problems. Although this kind of algorithms can be very powerful to solve relatively complex problems, students often have difficulties to understand how they work, and how to apply them to solve problems in real cases. In this paper, we present two interactive graphical applications which have been specially designed with the aim of making Evolutionary Algorithms easy to be understood by students. Specifically, we present: (i) TSPS, an application able to solve the ”Traveling Salesman Problem”, and (ii) FotEvol, an application able to reconstruct a given image by using Evolution Strategies. The main objective is that students learn how these techniques can be implemented, and the great possibilities they offer.

Keywords: education, evolutionary algorithms, evolution strategies, interactive learning applications

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Authors: Ruijuan Bi

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Discrimination in insurance pricing is a topic of increasing concern, particularly in the context of the rapid development of big data and artificial intelligence. There is a need to explore the various forms of discrimination, such as direct and indirect discrimination, proxy discrimination, algorithmic discrimination, and unfair discrimination, and understand their implications in insurance pricing models. This paper aims to analyze and interpret the definitions of discrimination in insurance pricing and explore measures to reduce discrimination. It utilizes a textual analysis methodology, which involves gathering qualitative data from relevant literature on definitions of discrimination. The research methodology focuses on exploring the various forms of discrimination and their implications in insurance pricing models. Through textual analysis, this paper identifies the specific characteristics and implications of each form of discrimination in the general insurance industry. This research contributes to the theoretical understanding of discrimination in insurance pricing. By analyzing and interpreting relevant literature, this paper provides insights into the definitions of discrimination and the laws and regulations surrounding it. This theoretical foundation can inform future empirical research on discrimination in insurance pricing using relevant theories of probability theory.

Keywords: algorithmic discrimination, direct and indirect discrimination, proxy discrimination, unfair discrimination, insurance pricing

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Authors: John Onyima, Ikechukwu Ezepue

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Virtualization and cloud computing are among the fast-growing computing innovations in recent times. Organisations all over the world are moving their computing services towards the cloud this is because of its rapid transformation of the organization’s infrastructure and improvement of efficient resource utilization and cost reduction. However, this technology brings new security threats and challenges about safety, reliability and data confidentiality. Evidently, no single security technique can guarantee security or protection against malicious attacks on a cloud computing network hence an integrated model of intrusion detection and prevention system has been proposed. Anomaly-based and signature-based detection techniques will be integrated to enable the network and its host defend themselves with some level of intelligence. The anomaly-base detection was implemented using the local deviation factor graph-based (LDFGB) algorithm while the signature-based detection was implemented using the snort algorithm. Results from this collaborative intrusion detection and prevention techniques show robust and efficient security architecture for cloud computing networks.

Keywords: anomaly-based detection, cloud computing, intrusion detection, intrusion prevention, signature-based detection

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Authors: Victor O. Abegunde, Melusi Sibanda

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There is need for a great deal of attention on small-scale agriculture for livelihood and food security because of the expanding global population. Small-scale agriculture has been identified as a major driving force of agricultural and rural development. However, the high dependence of the sector on natural and climatic resources has made small-scale farmers highly vulnerable to the adverse impact of climatic change thereby necessitating the need for embracing practices or concepts that will help absorb shocks from changes in climatic condition. This study examines the strategic position of small-scale farming in South African agriculture and in ensuring food security in the country, the vulnerability of small-scale agriculture to climate change and the potential of the concept of climate-smart agriculture to tackle the challenge of climate change. The study carried out a systematic review of peer-reviewed literature touching small-scale agriculture, climate change, food security and climate-smart agriculture, employing the realist review method. Findings revealed that increased productivity in the small-scale agricultural sector has a great potential of improving the food security of households in South Africa and reducing dependence on food purchase in a context of high food price inflation. Findings, however, also revealed that climate change affects small-scale subsistence farmers in terms of productivity, food security and family income, categorizing the impact on smallholder livelihoods into three major groups; biological processes, environmental and physical processes and impact on health. Analysis of the literature consistently showed that climate-smart agriculture integrates the benefits of adaptation and resilience to climate change, mitigation, and food security. As a result, farming households adopting climate-smart agriculture will be better off than their counterparts who do not. This study concludes that climate-smart agriculture could be a very good bridge linking small-scale agricultural sector and agricultural productivity and development which could bring about the much needed food security.

Keywords: climate change, climate-smart agriculture, food security, small-scale

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Authors: Igwenagu Chinelo Mercy

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This study is on the concept of Neurostatistics in relation to neuroscience. Neuroscience also known as neurobiology is the scientific study of the nervous system. In the study of neuroscience, it has been noted that brain function and its relations to the process of acquiring knowledge and behaviour can be better explained by the use of various interrelated methods. The scope of neuroscience has broadened over time to include different approaches used to study the nervous system at different scales. On the other hand, Neurostatistics based on this study is viewed as a statistical concept that uses similar techniques of neuron mechanisms to solve some problems especially in the field of life science. This study is imperative in this era of Artificial intelligence/Machine leaning in the sense that clear understanding of the technique and its proper application could assist in solving some medical disorder that are mainly associated with the nervous system. This will also help in layman’s understanding of the technique of the nervous system in order to overcome some of the health challenges associated with it. For this concept to be well understood, an illustrative example using a brain associated disorder was used for demonstration. Structural equation modelling was adopted in the analysis. The results clearly show the link between the techniques of statistical model and nervous system. Hence, based on this study, the appropriateness of Neurostatistics application in relation to neuroscience could be based on the understanding of the behavioural pattern of both concepts.

Keywords: brain, neurons, neuroscience, neurostatistics, structural equation modeling

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Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

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Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet

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Authors: Miron Wolnicki

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In the past 75 years the world was either influenced by, competing with or learning from U.S. corporations. This is no longer true. As the economic power shifts from the West to the East, U.S. corporations are lagging behind Asian competitors. Moreover, U.S. statecraft fails to address this decline. In a world dominated by interventionist and neo-mercantilist states, having an ineffective non-activist government becomes a costly neoclassic delusion which weakens the world’s largest economy. American conservative economists continue talking about the superiority of the free market system in generating new technologies. The reality is different. The U.S. is sliding further into an overregulated, over-taxed, anti-business state. This paper argues that in order to maintain its economic strength and technological leadership, the U.S. must reform federal institutions to increase support for artificial intelligence and other cutting-edge technologies. The author outlines a number of institutional reforms, under one umbrella, which he calls the American Entrepreneurial State (AES). The AES will improve productivity and bring about coherent business strategies for the next 10-15 years. The design and inspiration for the AES come from the experience of successful statecraft examples in Asia and also other parts the global economy.

Keywords: post-neoliberal system, entrepreneurial state, government and economy, American entrepreneurial state

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Authors: Baylasan Al-Amoudi, Hala Abdulmajeed, Amjad Jilani

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This project explores the role of artificial intelligence (AI) in enhancing user engagement on TikTok by examining the app’s AI-driven features. Through a structured survey of 4 main questions and experimental analysis, we tried to examine how TikTok’s recommendations, algorithms, search engine, and filter tools influence user interactions and satisfaction. A diverse cohort of 20 participants, including casual users and content creators, were involved to provide a broad perspective on user experiences. The examination highlights the recommendation algorithm’s ability to deliver highly personalized content, creating a seamless and engaging experience. TikTok’s search engine is shown to simplify content discovery by enabling users to find specific topics or trends related to their preferences. Meanwhile, the filter tools are found to encourage creativity, particularly for content creators, by offering versatile options to enhance video quality and visual appeal. By evaluating the unique roles of these AI features, the project underscores their significance in maintaining TikTok’s appeal and driving consistent user engagement.

Keywords: TikTok, hashtags, filters, viral sounds, for you page

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Authors: Divya Palaniappan

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Early Childhood assessment tools must measure the quality and the appropriateness of a curriculum with respect to culture and age of the children. Preschool assessment tools lack psychometric properties and were developed to measure only few areas of development such as specific skills in music, art and adaptive behavior. Existing preschool assessment tools in India are predominantly informal and are fraught with judgmental bias of observers. The World of Discovery TM curriculum focuses on accelerating the physical, cognitive, language, social and emotional development of pre-schoolers in India through various activities. The curriculum caters to every child irrespective of their dominant intelligence as per Gardner’s Theory of Multiple Intelligence which concluded "even students as young as four years old present quite distinctive sets and configurations of intelligences". The curriculum introduces a new theme every week where, concepts are explained through various activities so that children with different dominant intelligences could understand it. For example: The ‘Insects’ theme is explained through rhymes, craft and counting corner, and hence children with one of these dominant intelligences: Musical, bodily-kinesthetic and logical-mathematical could grasp the concept. The child’s progress is evaluated using an assessment tool that measures a cluster of inter-dependent developmental areas: physical, cognitive, language, social and emotional development, which for the first time renders a multi-domain approach. The assessment tool is a 5-point rating scale that measures these Developmental aspects: Cognitive, Language, Physical, Social and Emotional. Each activity strengthens one or more of the developmental aspects. During cognitive corner, the child’s perceptual reasoning, pre-math abilities, hand-eye co-ordination and fine motor skills could be observed and evaluated. The tool differs from traditional assessment methodologies by providing a framework that allows teachers to assess a child’s continuous development with respect to specific activities in real time objectively. A pilot study of the tool was done with a sample data of 100 children in the age group 2.5 to 3.5 years. The data was collected over a period of 3 months across 10 centers in Chennai, India, scored by the class teacher once a week. The teachers were trained by psychologists on age-appropriate developmental milestones to minimize observer’s bias. The norms were calculated from the mean and standard deviation of the observed data. The results indicated high internal consistency among parameters and that cognitive development improved with physical development. A significant positive relationship between physical and cognitive development has been observed among children in a study conducted by Sibley and Etnier. In Children, the ‘Comprehension’ ability was found to be greater than ‘Reasoning’ and pre-math abilities as indicated by the preoperational stage of Piaget’s theory of cognitive development. The average scores of various parameters obtained through the tool corroborates the psychological theories on child development, offering strong face validity. The study provides a comprehensive mechanism to assess a child’s development and differentiate high performers from the rest. Based on the average scores, the difficulty level of activities could be increased or decreased to nurture the development of pre-schoolers and also appropriate teaching methodologies could be devised.

Keywords: child development, early childhood assessment, early childhood curriculum, quantitative assessment of preschool curriculum

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Authors: Mohammad Saleh Vahdatpour, Yanqing Zhang

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Understanding the neural mechanisms underlying motion detection in the human visual system has long been a fascinating challenge in neuroscience and artificial intelligence. This paper presents a spiking neural network model inspired by the processing of motion information in the primate visual system, particularly focusing on the Middle Temporal (MT) area. In our study, we propose a multi-layer spiking neural network model to perform motion detection tasks, leveraging the idea that synaptic delays in neuronal communication are pivotal in motion perception. Synaptic delay, determined by factors like axon length and myelin insulation, affects the temporal order of input spikes, thereby encoding motion direction and speed. Overall, our spiking neural network model demonstrates the feasibility of capturing motion detection principles observed in the primate visual system. The combination of synaptic delays, learning mechanisms, and shared weights and delays in SMD provides a promising framework for motion perception in artificial systems, with potential applications in computer vision and robotics.

Keywords: neural network, motion detection, signature detection, convolutional neural network

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Authors: Yakubu Bala Mohammed, Nadire Chavus, Mohammed Bulama

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Generative Pre-trained Transformer (ChatGPT) represents an artificial intelligence (AI) tool capable of swiftly generating comprehensive responses to prompts and follow-up inquiries. This emerging AI tool was introduced in November 2022 by OpenAI firm, an American AI research laboratory, utilizing substantial language models. This present study aims to delve into the potential future consequences of ChatGPT usage in education using AI-based algorithms. The paper will bring forth the likely potential risks of ChatGBT utilization, such as academic integrity concerns, unfair learning assessments, excessive reliance on AI, and dissemination of inaccurate information using four machine learning algorithms: eXtreme-Gradient Boosting (XGBoost), Support vector machine (SVM), Emotional artificial neural network (EANN), and Random forest (RF) would be used to analyze the study collected data due to their robustness. Finally, the findings of the study will assist education stakeholders in understanding the future implications of ChatGPT usage in education and propose solutions and directions for upcoming studies.

Keywords: machine learning, ChatGPT, education, learning, implications

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Authors: Oussama Hafferssas, Hiba Benyahia, Amina Madani, Nassima Zeriri

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Although data nowadays has multiple forms; from text to images, and from audio to videos, yet text is still the most used one at a public level. At an academical and research level, and unlike other forms, text can be considered as the easiest form to process. Therefore, a brunch of Data Mining researches has been always under its shadow, called "Text Mining". Its concept is just like data mining’s, finding valuable patterns in data, from large collections and tremendous volumes of data, in this case: Text. Named entity recognition (NER) is one of Text Mining’s disciplines, it aims to extract and classify references such as proper names, locations, expressions of time and dates, organizations and more in a given text. Our approach "Octopub" does not aim to find new ways to improve named entity recognition process, rather than that it’s about finding a new, and yet smart way, to use NER in a way that we can extract sentiments of millions of people using Social Networks as a limitless information source, and Marketing for product promotion as the main domain of application.

Keywords: textmining, named entity recognition(NER), sentiment analysis, social media networks (SN, SMN), business intelligence(BI), marketing

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Authors: Vijaya Subramaniam, Loh Soh Kheang, Astimar Abdul Aziz

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Water footprint (WFP) has gained importance due to the increase in water scarcity in the world. This study analyses the WFP for an agriculture sector, i.e., the oil palm supply chain, which produces oil palm fresh fruit bunch (FFB), crude palm oil, palm kernel, and crude palm kernel oil. The water accounting and vulnerability evaluation (WAVE) method was used. This method analyses the water depletion index (WDI) based on the local blue water scarcity. The main contribution towards the WFP at the plantation was the production of FFB from the crop itself at 0.23m³/tonne FFB. At the mill, the burden shifts to the water added during the process, which consists of the boiler and process water, which accounted for 6.91m³/tonne crude palm oil. There was a 33% reduction in the WFP when there was no dilution or water addition after the screw press at the mill. When allocation was performed, the WFP reduced by 42% as the burden was shared with the palm kernel and palm kernel shell. At the kernel crushing plant (KCP), the main contributor towards the WFP 4.96 m³/tonne crude palm kernel oil which came from the palm kernel which carried the burden from upstream followed by electricity, 0.33 m³/tonne crude palm kernel oil used for the process and 0.08 m³/tonne crude palm kernel oil for transportation of the palm kernel. A comparison was carried out for mills with biogas capture versus no biogas capture, and the WFP had no difference for both scenarios. The comparison when the KCPs operate in the proximity of mills as compared to those operating in the proximity of ports only gave a reduction of 6% for the WFP. Both these scenarios showed no difference and insignificant difference, which differed from previous life cycle assessment studies on the carbon footprint, which showed significant differences. This shows that findings change when only certain impact categories are focused on. It can be concluded that the impact from the water used by the oil palm tree is low due to the practice of no irrigation at the plantations and the high availability of water from rainfall in Malaysia. This reiterates the importance of planting oil palm trees in regions with high rainfall all year long, like the tropics. The milling stage had the most significant impact on the WFP. Mills should avoid dilution to reduce this impact.

Keywords: life cycle assessment, water footprint, crude palm oil, crude palm kernel oil, WAVE method

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Authors: Telma Fontao de Castro, Kylee Miller, Maria Xavier Araújo, Isabel Brandao, Sandra Torres

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Objective: There is a dearth of research investigating the long-term emotional functioning of individuals recovered from anorexia nervosa (AN). This 15-year longitudinal study aimed to examine whether difficulties in cognitive processing of emotions persisted after long-term AN recovery and its link to anxiety and depression. Method: Twenty-four females, who were tested longitudinally during their acute and recovered AN phases, and 24 healthy control (HC) women, were screened for anxiety, depression, alexithymia, and emotion regulation difficulties (ER; only assessed in recovery phase). Results: Anxiety, depression, and alexithymia levels decreased significantly with AN recovery. However, scores on anxiety and difficulty in identifying feelings (alexithymia factor) remained high when compared to the HC group. Scores on emotion regulation difficulties were also lower in HC group. The abovementioned differences between AN recovered group and HC group in difficulties in identifying and accepting feelings and lack of emotional clarity were no longer present when the effect of anxiety and depression was controlled. Conclusions: Findings suggest that emotional dysfunction tends to decrease in AN recovered phase. However, using an HC group as a reference, we conclude that several emotional difficulties are still increased after long-term AN recovery, in particular, limited access to emotion regulation strategies, and difficulty controlling impulses and engaging in goal-directed behavior, thus suggesting to be a trait vulnerability. In turn, competencies related to emotional clarity and acceptance of emotional responses seem to be state-dependent phenomena linked to anxiety and depression. In sum, managing emotions remains a challenge for individuals recovered from AN. Under this circumstance, maladaptive eating behavior can serve as an affect regulatory function, increasing the risk of relapse. Emotional education and stabilization of depressive and anxious symptomatology after recovery emerge as an important avenue to protect from long-term AN relapse.

Keywords: alexithymia, anorexia nervosa, emotion recognition, emotion regulation

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Authors: Hamid Rostami Jaz, Kamran Ameri Siahooei

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Up to now different methods have been used to forecast the index returns and the index rate. Artificial intelligence and artificial neural networks have been one of the methods of index returns forecasting. This study attempts to carry out a comparative study on the performance of different Radial Base Neural Network and Feed-Forward Perceptron Neural Network to forecast investment returns on the index. To achieve this goal, the return on investment in Tehran Stock Exchange index is evaluated and the performance of Radial Base Neural Network and Feed-Forward Perceptron Neural Network are compared. Neural networks performance test is applied based on the least square error in two approaches of in-sample and out-of-sample. The research results show the superiority of the radial base neural network in the in-sample approach and the superiority of perceptron neural network in the out-of-sample approach.

Keywords: exchange index, forecasting, perceptron neural network, Tehran stock exchange

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Authors: Chan Ka Lok Sobel

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The rapid evolution of digital media platforms has given rise to new forms of narrative engagement, particularly through multi-channel storytelling. This research focuses on exploring the teaching pedagogy and effectiveness of multi-channel storytelling for social media, cinema, games, and streaming platforms. The study employs case studies of the popular series "Squid Game" to investigate the diverse pedagogical approaches and strategies used in teaching multi-channel storytelling. Through qualitative research methods, including interviews, surveys, and content analysis, the research assesses the effectiveness of these approaches in terms of student engagement, knowledge acquisition, critical thinking skills, and the development of digital literacy. The findings contribute to understanding best practices for incorporating multi-channel storytelling into educational contexts and enhancing learning outcomes in the digital media landscape.

Keywords: digital literacy, game-based learning, artificial intelligence, animation production, educational technology

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Authors: Teh Kean Kheng, Low Soon Yee, Burra Venkata Durga Kumar

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In 2022, as the future world becomes increasingly computer-related, more individuals are attempting to study coding for themselves or in school. This is because they have discovered the value of learning code and the benefits it will provide them. But learning coding is difficult for most people. Even senior programmers that have experience for a decade year still need help from the online source while coding. The reason causing this is that coding is not like talking to other people; it has the specific syntax to make the computer understand what we want it to do, so coding will be hard for normal people if they don’t have contact in this field before. Coding is hard. If a user wants to learn bash code with bash prompt, it will be harder because if we look at the bash prompt, we will find that it is just an empty box and waiting for a user to tell the computer what we want to do, if we don’t refer to the internet, we will not know what we can do with the prompt. From here, we can conclude that the bash prompt is not user-friendly for new users who are learning bash code. Our goal in writing this paper is to give an idea to implement a user-friendly Bash prompt in Ubuntu OS using Artificial Intelligent (AI) to lower the threshold of learning in Bash code, to make the user use their own words and concept to write and learn Bash code.

Keywords: user-friendly, bash code, artificial intelligence, threshold, semantic similarity, lexical similarity

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Authors: Sangeeta Palekar, Neeraj Rangwani, Akash Poddar, Jayu Kalambe

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The bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia, which can cause severe medical problems like nerve damage or kidney diseases. This paper presents a method for predicting the glucose concentration in blood samples using image processing and machine learning algorithms. The glucose solution is prepared by the glucose oxidase (GOD) and peroxidase (POD) method. An experimental database is generated based on the colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using image processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like multiple linear regression, decision tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. The multiple linear regression algorithm predicts the results with 97% accuracy. The image processing and machine learning-based approach reduce the hardware complexities of existing platforms.

Keywords: artificial intelligence glucose detection, glucose oxidase, peroxidase, image processing, machine learning

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Authors: Julio Jesus Salazar, Max Jesus De Lama

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The challenges of water management are aggravated by global change, which implies high complexity and associated uncertainty; water management is difficult because water networks cross domains (natural, societal, and political), scales (space, time, jurisdictional, institutional, knowledge, etc.) and levels (area: patches to global; knowledge: a specific case to generalized principles). In this context, we need to apply natural and non-natural measures to manage water and soil. The Basin Management Methodology considers multifunctional measures of natural water retention and erosion control and soil formation to protect water resources and address the challenges related to the recovery or conservation of the ecosystem, as well as natural characteristics of water bodies, to improve the quantitative status of water bodies and reduce vulnerability to floods and droughts. This method of water management focuses on the positive impacts of the chemical and ecological status of water bodies, restoration of the functioning of the ecosystem and its natural services; thus, contributing to both adaptation and mitigation of climate change. This methodology was applied in 7 interventions in the sub-basin of the Shullcas River in Huancayo-Junín-Peru, obtaining great benefits in the framework of the participation of alliances of actors and integrated planning scenarios. To implement the methodology in the sub-basin of the Shullcas River, a process called Climate Smart Territories (CST) was used; with which the variables were characterized in a highly complex space. The diagnosis was then worked using risk management and adaptation to climate change. Finally, it was concluded with the selection of alternatives and projects of this type. Therefore, the CST approach and process face the challenges of climate change through integrated, systematic, interdisciplinary and collective responses at different scales that fit the needs of ecosystems and their services that are vital to human well-being. This methodology is now replicated at the level of the Mantaro river basin, improving with other initiatives that lead to the model of a resilient basin.

Keywords: climate-smart territories, climate change, ecosystem services, natural measures, Climate Smart Territories (CST) approach

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Authors: Melissa R. Quispe-Zuniga, Daniel Callo-Concha, Christian Borgemeister, Klaus Greve

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The Peruvian Andes have a high potential for mining, but many of the mining areas overlay with campesino community lands, being these key actors for agriculture and livestock production. Lead by economic incentives, some communities are renting their lands to mining companies for exploration or exploitation. However, a growing number of campesino communities, usually social and economically marginalized, have developed resistance, alluding consequences, such as water pollution, land-use change, insufficient economic compensation, etc. what eventually end up in Socio-Environmental Conflicts (SEC). It is hypothesized that disclosing the information on environmental pollution and enhance the involvement of communities in the decision-making process may contribute to prevent SEC. To assess whether such complains are grounded on the environmental impact of mining activities, we measured the heavy metals concentration in 24 indicative samples from rivers that run across mining exploitations and farming community lands. Samples were taken during the 2016 dry season and analyzed by inductively-coupled-plasma-atomic-emission-spectroscopy. The results were contrasted against the standards of monitoring government institutions (i.e., OEFA). Furthermore, we investigated the water/environmental complains related to mining in the neighboring 14 communities. We explored the relationship between communities and mining companies, via open-ended interviews with community authorities and non-participatory observations of community assemblies. We found that the concentrations of cadmium (0.023 mg/L), arsenic (0.562 mg/L) and copper (0.07 mg/L), surpass the national water quality standards for Andean rivers (0.00025 mg/L of cadmium, 0.15 mg/L of arsenic and 0.01 mg/L of copper). 57% of communities have posed environmental complains, but 21% of the total number of communities were receiving an annual economic benefit from mining projects. However, 87.5% of the communities who had posed complains have high concentration of heavy metals in their water streams. The evidence shows that mining activities tend to relate to the affectation and vulnerability of campesino community water streams, what justify the environmental complains and eventually the occurrence of a SEC.

Keywords: mining companies, campesino community, water, socio-environmental conflict

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Authors: Samaneh Abedini, Diana Coholic

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The transition from adolescence into adulthood involves many changes that result in increased vulnerability to psychological challenges. This developmental stage can be especially stressful for female youth living in underserviced regions. If mental health problems are left untreated in socially marginalized youth, these challenges can extend into adulthood. We know that a lack of access to mental health services and supports can influence adolescents’ psycho-social development and well-being, while resilience and emotion regulation can help them cope with these challenges. Feasible therapeutic programs can play a significant role in assisting youth in developing these characteristics and skills. Mindfulness-Based Cognitive Therapy for Children (MBCT-C) and Holistic Art-Based Program (HAP) are two examples of mindfulness-based interventions (MBIs) that address emotion regulation, coping strategies, and resilience in marginalized youth. While each program’s beneficial effects have been documented, there is a lack of research comparing MBIs with youth, within underserviced geographical locations, and across different cultures. In this study, the sample was 42 female youth between the ages of 12 and 17 years from Iran. 42 female youth from the Elm o Honar High School, located in rural parts of Iran, Isfahan province, have been enrolled in the study. The participants were assigned to one of the MBIs (three MBCT-C experimental groups (n=20) and three HAP experimental groups (n=22)). All participants completed measures including the Child and Youth Resilience Measure-28 (CYRM-28), Child and Adolescent Mindfulness Measure (CAMM), and Difficulties in Emotion Regulation Scale (DERS) at baseline and post-intervention. At the end of intervention, the MBCT-C and HAP experimental groups showed significant changes in resilience and emotion regulation. However, the changes in resilience in HAP groups were not significant; the participants in MBCT-C experimental groups showed significant improvement in resilience. The study provided initial evidence that mindfulness-based intervention can be potentially beneficial for improving mental health status in marginalized Iranian female youth living in the middle east culture.

Keywords: benefits, female, marginalized, mindfulness, youth

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Authors: K. O'Malley

Abstract:

Background: The emergence and rapid evolution of large language models (LLMs) (i.e., models of generative artificial intelligence, or AI) has been unprecedented. ChatGPT is one of the most widely used LLM platforms. Using natural language processing technology, it generates customized responses to user prompts, enabling it to mimic human conversation. Responses are generated using predictive modeling of vast internet text and data swathes and are further refined and reinforced through user feedback. The popularity of LLMs is increasing, with a growing number of students utilizing these platforms for study and revision purposes. Notwithstanding its many novel applications, LLM technology is inherently susceptible to bias and error. This poses a significant challenge in the educational setting, where academic integrity may be undermined. This study aims to evaluate the performance of the latest iteration of ChatGPT (ChatGPT4.0) in standardized state medical licensing examinations. Methods: A considered search strategy was used to interrogate the PubMed electronic database. The keywords ‘ChatGPT’ AND ‘medical education’ OR ‘medical school’ OR ‘medical licensing exam’ were used to identify relevant literature. The search included all peer-reviewed literature published in the past five years. The search was limited to publications in the English language only. Eligibility was ascertained based on the study title and abstract and confirmed by consulting the full-text document. Data was extracted into a Microsoft Excel document for analysis. Results: The search yielded 345 publications that were screened. 225 original articles were identified, of which 11 met the pre-determined criteria for inclusion in a narrative synthesis. These studies included performance assessments in national medical licensing examinations from the United States, United Kingdom, Saudi Arabia, Poland, Taiwan, Japan and Germany. ChatGPT 4.0 achieved scores ranging from 67.1 to 88.6 percent. The mean score across all studies was 82.49 percent (SD= 5.95). In all studies, ChatGPT exceeded the threshold for a passing grade in the corresponding exam. Conclusion: The capabilities of ChatGPT in standardized academic assessment in medicine are robust. While this technology can potentially revolutionize higher education, it also presents several challenges with which educators have not had to contend before. The overall strong performance of ChatGPT, as outlined above, may lend itself to unfair use (such as the plagiarism of deliverable coursework) and pose unforeseen ethical challenges (arising from algorithmic bias). Conversely, it highlights potential pitfalls if users assume LLM-generated content to be entirely accurate. In the aforementioned studies, ChatGPT exhibits a margin of error between 11.4 and 32.9 percent, which resonates strongly with concerns regarding the quality and veracity of LLM-generated content. It is imperative to highlight these limitations, particularly to students in the early stages of their education who are less likely to possess the requisite insight or knowledge to recognize errors, inaccuracies or false information. Educators must inform themselves of these emerging challenges to effectively address them and mitigate potential disruption in academic fora.

Keywords: artificial intelligence, ChatGPT, generative ai, large language models, licensing exam, medical education, medicine, university

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Authors: Rahul Mohite, Lahcen Ouarbya

Abstract:

This paper focuses on the interplay between humans and computer systems and the ability of these systems to understand and respond to human emotions, including non-verbal communication. Current emotion recognition systems are based solely on either facial or verbal expressions. The limitation of these systems is that it requires large training data sets. The paper proposes a system for recognizing human emotions that combines both speech and emotion recognition. The system utilizes advanced techniques such as deep learning and image recognition to identify facial expressions and comprehend emotions. The results show that the proposed system, based on the combination of facial expression and speech, outperforms existing ones, which are based solely either on facial or verbal expressions. The proposed system detects human emotion with an accuracy of 86%, whereas the existing systems have an accuracy of 70% using verbal expression only and 76% using facial expression only. In this paper, the increasing significance and demand for facial recognition technology in emotion recognition are also discussed.

Keywords: facial reputation, expression reputation, deep gaining knowledge of, photo reputation, facial technology, sign processing, photo type

Procedia PDF Downloads 123

Authors: Xuhui Lin, Qiuchen Lu, Yi An, Tao Yang

Abstract:

As global climate change intensifies, extreme weather events such as floods increasingly threaten urban infrastructure, making the vulnerability of urban road networks a pressing issue. Existing static repair strategies fail to adapt to the rapid changes in road network conditions during flood events, leading to inefficient resource allocation and suboptimal recovery. The main research gap lies in the lack of repair strategies that consider both the dynamic characteristics of networks and the progression of flood propagation. This paper proposes a topology-based dynamic repair strategy that adjusts repair priorities based on real-time changes in flood propagation and traffic demand. Specifically, a novel method is developed to assess and enhance the resilience of urban road networks during flood events. The method combines road network topological analysis, flood propagation modelling, and traffic flow simulation, introducing a local importance metric to dynamically evaluate the significance of road segments across different spatial and temporal scales. Using London's road network and rainfall data as a case study, the effectiveness of this dynamic strategy is compared to traditional and Transport for London (TFL) strategies. The most significant highlight of the research is that the dynamic strategy substantially reduced the number of stranded vehicles across different traffic demand periods, improving efficiency by up to 35.2%. The advantage of this method lies in its ability to adapt in real-time to changes in network conditions, enabling more precise resource allocation and more efficient repair processes. This dynamic strategy offers significant value to urban planners, traffic management departments, and emergency response teams, helping them better respond to extreme weather events like floods, enhance overall urban resilience, and reduce economic losses and social impacts.

Keywords: Urban resilience, road networks, flood response, dynamic repair strategy, topological analysis

Procedia PDF Downloads 37

Authors: Steve Haberlin

Abstract:

United States college students are experiencing record-high stress and anxiety rates, and due to technological advances, there are more distractions in the classroom. With these challenges comes the need to explore additional, non-traditional pedagogical strategies that can help students de-stress, become centered, and feel more deeply connected to content. In addition, embedding contemplative practices, such as mindfulness meditation, in the higher education classroom could assist faculty in presenting a more holistic education that encourages students to develop self-awareness, emotional intelligence, compassion, interconnectedness, and other “non-academic” qualities. Brief meditation may help students de-stress, focus, and connect. A facilitation guide could also help faculty implement classroom meditation practices; however, additional research is needed to determine how to best train faculty, what meditation techniques work best with students, and how to handle resistance. In this paper, a two-phase study is presented that involves a mindfulness meditation intervention with 180 undergraduate students at a private college in the southeastern United States. Data were collected through qualitative surveys and journaling and analyzed for themes. Findings included a majority of students reporting improved calm, reduced stress, and increased focus and ability to transition to classroom instruction.

Keywords: college students, higher education, mindfulness meditation, stress

Procedia PDF Downloads 64

Authors: Adel Atta Youssef Rezkalla

Abstract:

Russia's invasion of Ukraine tested the international community and prompted not only states but also non-state actors to take deterrent measures in response. In fact, international sports federations, notably FIFA and UEFA, have managed to shift the power dynamic quite effectively by imposing a blanket ban on Russian national teams and clubs. The purpose of this article is to examine the human rights consequences of such actions by international sports organizations. First, the article moves away from assessing the legal status of FIFA and UEFA under international law and examines the question of how a legal connection can be established with their human rights obligations. Secondly, the human rights aspects of the controversial FIFA and UEFA measures against Russian athletes are examined and these are analyzed in more detail using the proportionality test than the principle of non-discrimination under international human rights law. Finally, the main avenues for redress for possible human rights violations related to the actions taken by these organizations are identified and the challenges of arbitration and litigation in Switzerland are highlighted.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security.

Procedia PDF Downloads 77

Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

Abstract:

In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

Procedia PDF Downloads 146

Authors: Yuichi Kida, Takuro Kida

Abstract:

In a new smart society supported by the recent development of 5G and 6G Communication systems, the im- portance of wireless power transmission is increasing. These systems contain discrete sampling systems in the middle of the transmission path and equivalent circuit representation of lossless or lossy power transmission through these systems is an important issue in circuit theory. In this paper, for the given weight function, we show that a lossless power transmission system with the given weight is expressed by an equivalent circuit representation of the Kida’s optimal signal prediction system followed by a reactance multi-port circuit behind it. Further, it is shown that, when the system is lossy, the system has an equivalent circuit in the form of connecting a multi-port positive-real circuit behind the Kida’s optimal signal prediction system. Also, for the convenience of the reader, in this paper, the equivalent circuit expression of the reactance multi-port circuit and the positive- real multi-port circuit by Cauer and Ohno, whose information is currently being lost even in the world of the Internet.

Keywords: signal prediction, pseudo inverse matrix, artificial intelligence, power transmission

Procedia PDF Downloads 123