Search results for: artificial intelligence in finance

Authors: L. Krpalkova, N. O' Mahony, A. Carvalho, S. Campbell, G. Corkery, E. Broderick, J. Walsh

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Farm management and operations will drastically change due to access to real-time data, real-time forecasting, and tracking of physical items in combination with Internet of Things developments to further automate farm operations. Dairy farms have embraced technological innovations and procured vast amounts of permanent data streams during the past decade; however, the integration of this information to improve the whole farm-based management and decision-making does not exist. It is now imperative to develop a system that can collect, integrate, manage, and analyse on-farm and off-farm data in real-time for practical and relevant environmental and economic actions. The developed systems, based on machine learning and artificial intelligence, need to be connected for useful output, a better understanding of the whole farming issue, and environmental impact. Evolutionary computing can be very effective in finding the optimal combination of sets of some objects and, finally, in strategy determination. The system of the future should be able to manage the dairy farm as well as an experienced dairy farm manager with a team of the best agricultural advisors. All these changes should bring resilience and sustainability to dairy farming as well as improving and maintaining good animal welfare and the quality of dairy products. This review aims to provide an insight into the state-of-the-art of big data applications and evolutionary computing in relation to smart dairy farming and identify the most important research and development challenges to be addressed in the future. Smart dairy farming influences every area of management, and its uptake has become a continuing trend.

Keywords: big data, evolutionary computing, cloud, precision technologies

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Authors: R. J. Kuo, W. C. Cheng, W. C. Lien, T. J. Yang

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Taiwan is a hyper endemic area for the Hepatitis B virus (HBV). The estimated total number of HBsAg carriers in the general population who are more than 20 years old is more than 3 million. Therefore, a case record review is conducted from January 2003 to June 2007 for all patients with a diagnosis of acute hepatitis who were admitted to the Emergency Department (ED) of a well-known teaching hospital. The cost for the use of medical resources is defined as the total medical fee. In this study, principal component analysis (PCA) is firstly employed to reduce the number of dimensions. Support vector regression (SVR) and artificial neural network (ANN) are then used to develop the forecasting model. A total of 117 patients meet the inclusion criteria. 61% patients involved in this study are hepatitis B related. The computational result shows that the proposed PCA-SVR model has superior performance than other compared algorithms. In conclusion, the Child-Pugh score and echogram can both be used to predict the cost of medical resources for patients with acute hepatitis in the ED.

Keywords: acute hepatitis, medical resource cost, artificial neural network, support vector regression

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Authors: Galal Omer, Onisimo Mutanga, Elfatih M. Abdel-Rahman, Elhadi Adam

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Threatened tree species (TTS) play a significant role in ecosystem functioning and services, land use dynamics, and other socio-economic aspects. Such aspects include ecological, economic, livelihood, security-based, and well-being benefits. The development of techniques for mapping and monitoring TTS is thus critical for understanding the functioning of ecosystems. The advent of advanced imaging systems and supervised learning algorithms has provided an opportunity to classify TTS over fragmenting landscape. Recently, vegetation maps have been produced using advanced imaging systems such as WorldView-2 (WV-2) and robust classification algorithms such as support vectors machines (SVM) and artificial neural network (ANN). However, delineation of TTS in a fragmenting landscape using high resolution imagery has widely remained elusive due to the complexity of the species structure and their distribution. Therefore, the objective of the current study was to examine the utility of the advanced WV-2 data for mapping TTS in the fragmenting Dukuduku indigenous forest of South Africa using SVM and ANN classification algorithms. The results showed the robustness of the two machine learning algorithms with an overall accuracy (OA) of 77.00% (total disagreement = 23.00%) for SVM and 75.00% (total disagreement = 25.00%) for ANN using all eight bands of WV-2 (8B). This study concludes that SVM and ANN classification algorithms with WV-2 8B have the potential to classify TTS in the Dukuduku indigenous forest. This study offers relatively accurate information that is important for forest managers to make informed decisions regarding management and conservation protocols of TTS.

Keywords: artificial neural network, threatened tree species, indigenous forest, support vector machines

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Authors: Oscar Covarrubias, Diane Ghanem, Christopher Murdock, Babar Shafiq

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Introduction: ChatGPT is an advanced language AI tool designed to understand and generate human-like text. The aim of this study is to assess the ability of ChatGPT-4 to re-write orthopaedic trauma patient education materials at the recommended 6th-grade level. Methods: Two independent reviewers accessed ChatGPT-4 (chat.openai.com) and gave identical instructions to simplify the readability of provided text to a 6th-grade level. All trauma-related articles by the Orthopaedic Trauma Association (OTA) and American Academy of Orthopaedic Surgeons (AAOS) were sequentially provided. The academic grade level was determined using the Flesh-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE). Paired t-tests and Wilcox-rank sum tests were used to compare the FKGL and FRE between the ChatGPT-4 revised and original text. Inter-rater correlation coefficient (ICC) was used to assess variability in ChatGPT-4 generated text between the two reviewers. Results: ChatGPT-4 significantly reduced FKGL and increased FRE scores in the OTA (FKGL: 5.7±0.5 compared to the original 8.2±1.1, FRE: 76.4±5.7 compared to the original 65.5±6.6, p < 0.001) and AAOS articles (FKGL: 5.8±0.8 compared to the original 8.9±0.8, FRE: 76±5.5 compared to the original 56.7±5.9, p < 0.001). On average, 14.6% of OTA and 28.6% of AAOS articles required at least two revisions by ChatGPT-4 to achieve a 6th-grade reading level. ICC demonstrated poor reliability for FKGL (OTA 0.24, AAOS 0.45) and moderate reliability for FRE (OTA 0.61, AAOS 0.73). Conclusion: This study provides a novel, simple and efficient method using language AI to optimize the readability of patient education content which may only require the surgeon’s final proofreading. This method would likely be as effective for other medical specialties.

Keywords: artificial intelligence, AI, chatGPT, patient education, readability, trauma education

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Authors: Sareh Enjavi

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In recent years, the field of visual communication design has seen a significant shift in the way that art is created and consumed, with the advent of new technologies like virtual reality, augmented reality, and artificial intelligence. This paper explores the ways in which technology is changing the landscape of visual communication design, and how designers are incorporating new technological tools into their artistic practices. The primary objective of this research paper is to investigate the ways in which technology is influencing the creative process of designers and artists in the field of visual communication design. The paper also aims to examine the challenges and limitations that arise from the intersection of art and technology in visual communication design, and to identify strategies for overcoming these challenges. Drawing on examples from a range of fields, including advertising, fine art, and digital media, this paper highlights the exciting innovations that are emerging as artists and designers use technology to push the boundaries of traditional artistic expression. The paper argues that embracing technological innovation is essential for the continued evolution of visual communication design. By exploring the intersection of art and technology, designers can create new and exciting visual experiences that engage and inspire audiences in new ways. The research also contributes to the theoretical and methodological understanding of the intersection of art and technology, a topic that has gained significant attention in recent years. Ultimately, this paper emphasizes the importance of embracing innovation and experimentation in the field of visual communication design, and highlights the exciting innovations that are emerging as a result of the intersection of art and technology, and emphasizes the importance of embracing innovation and experimentation in the field of visual communication design.

Keywords: visual communication design, art and technology, virtual reality, interactive art, creative process

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Authors: Ali Asgary, Haopeng Zhou, Ghassem Tofighi

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Use of virtual (VR), augmented reality (AR), and extended reality technologies for training and education has increased in recent years as more hardware and software tools have become available and accessible to larger groups of users. Similarly, the applications of these technologies in earthquake related training and education are on the rise. Several studies have reported promising results for the use of VR and AR for evacuation behaviour and training under earthquake situations. They simulate the impacts that earthquake has on buildings, buildings’ contents, and how building occupants and users can find safe spots or open paths to outside. Considering that considerable number of earthquake injuries and fatalities are linked to the behaviour, our goal is to use these technologies to reduce the impacts of building contents on people. Building on our artificial intelligence (AI) based indoor earthquake risk assessment application that enables users to use their mobile device to assess the risks associated with building contents during earthquakes, we develop a virtual reality application to demonstrate the behavior of different building contents during earthquakes, their associate moving, spreading, falling, and collapsing risks, and their risk mitigation methods. We integrate realistic seismic models, building contents behavior with and without risk mitigation measures in virtual reality environment. The application can be used for training of architects, interior design experts, and building users to enhance indoor safety of the buildings that can sustain earthquakes. This paper describes and demonstrates the application development background, structure, components, and usage.

Keywords: virtual reality, earthquake damage, building content, indoor risks, earthquake risk mitigation, interior design, unity game engine, oculus

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Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

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In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.

Keywords: anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines

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Authors: Mikko Salminen, Simo Järvelä, Niklas Ravaja

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One of the central models of emotional intelligence (EI) is that of Mayer and Salovey’s, which includes ability to monitor own feelings and emotions and those of others, ability to discriminate different emotions, and to use this information to guide thinking and actions. There is vast amount of previous research where positive links between EI and, for example, leadership successfulness, work outcomes, work wellbeing and organizational climate have been reported. EI has also a role in the effectiveness of work teams, and the effects of EI are especially prominent in jobs requiring emotional labor. Thus, also the organizational context must be taken into account when considering the effects of EI on work outcomes. Based on previous research, it is suggested that EI can also protect managers from the negative consequences of stress. Stress may have many detrimental effects on the manager’s performance in essential work tasks. Previous studies have highlighted the effects of stress on, not only health, but also, for example, on cognitive tasks such as decision-making, which is important in managerial work. The motivation for the current study came from the notion that, unfortunately, many stressed individuals may not be aware of the circumstance; periods of stress-induced physiological arousal may be prolonged if there is not enough time for recovery. To tackle this problem, physiological stress levels of managers were collected using recording of heart rate variability (HRV). The goal was to use this data to provide the managers with feedback on their stress levels. The managers could access this feedback using a www-based learning environment. In the learning environment, in addition to the feedback on stress level and other collected data, also developmental tasks were provided. For example, those with high stress levels were sent instructions for mindfulness exercises. The current study focuses on the relation between the measured physiological stress levels and EI of the managers. In a pilot study, 33 managers from various fields wore the Firstbeat Bodyguard HRV measurement devices for three consecutive days and nights. From the collected HRV data periods (minutes) of stress and recovery were detected using dedicated software. The effects of EI on HRV-calculated stress indexes were studied using Linear Mixed Models procedure in SPSS. There was a statistically significant effect of total EI, defined as an average score of Schutte’s emotional intelligence test, on the percentage of stress minutes during the whole measurement period (p=.025). More stress minutes were detected on those managers who had lower emotional intelligence. It is suggested, that high EI provided managers with better tools to cope with stress. Managing of own emotions helps the manager in controlling possible negative emotions evoked by, e.g., critical feedback or increasing workload. High EI managers may also be more competent in detecting emotions of others, which would lead to smoother interactions and less conflicts. Given the recent trend to different quantified-self applications, it is suggested that monitoring of bio-signals would prove to be a fruitful direction to further develop new tools for managerial and leadership coaching.

Keywords: emotional intelligence, leadership, heart rate variability, personality, stress

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Authors: Binnur Sagbas

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Artificial joint replacements such as total knee and total hip prosthesis have been applied to the patients who affected by osteoarthritis. Although different material combinations are used for these joints, biopolymers are most commonly preferred materials especially for acetabular cup and tibial component of hip and knee joints respectively. The main limitation that shortens the service life of these prostheses is wear. Wear is complicated phenomena and it must be considered with friction and lubrication. In this study, micro wave (MW) induced argon+oxygen plasma surface modification were applied on ultra-high molecular weight polyethylene (UHMWPE) and vitamin E blended UHMWPE (VE-UHMWPE) biopolymer surfaces to improve surface wettability and wear resistance of the surfaces. Contact angel measurement method was used for determination of wettability. Ball-on-disc wear test was applied under 25% bovine serum lubrication conditions. The results show that surface wettability and wear resistance of both material samples were increased by plasma surface modification.

Keywords: artificial joints, plasma surface modification, UHMWPE, vitamin E, wear

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Authors: Wullapa Wongsinlatam

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Back propagation algorithm (BP) is a widely used technique in artificial neural network and has been used as a tool for solving the time series problems, such as decreasing training time, maximizing the ability to fall into local minima, and optimizing sensitivity of the initial weights and bias. This paper proposes an improvement of a BP technique which is called IM-COH algorithm (IM-COH). By combining IM-COH algorithm with cuckoo search algorithm (CS), the result is cuckoo search improved control output hidden layer algorithm (CS-IM-COH). This new algorithm has a better ability in optimizing sensitivity of the initial weights and bias than the original BP algorithm. In this research, the algorithm of CS-IM-COH is compared with the original BP, the IM-COH, and the original BP with CS (CS-BP). Furthermore, the selected benchmarks, four time series samples, are shown in this research for illustration. The research shows that the CS-IM-COH algorithm give the best forecasting results compared with the selected samples.

Keywords: artificial neural networks, back propagation algorithm, time series, local minima problem, metaheuristic optimization

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Authors: Samuel Nderitu Muchiri, Tracy Wangechi Maina

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Truck drivers face a myriad of challenges in their profession. Enhancements in logistics effectiveness can be pivotal in propelling economic developments. The specific objective of the study was to assess the influence of driver distraction on crash risk. The study is significant as it elucidates best practices that truck drivers can embrace in an effort to enhance road safety. These include amalgamating behaviors that enable drivers to fruitfully execute multifaceted functions such as finding and following routes, evading collisions, monitoring speed, adhering to road regulations, and evaluating vehicle systems’ conditions. The analysis involved an empirical review of ten previous studies related to the research topic. The articles revealed that driver distraction plays a substantial role in road accidents and other crucial road security incidents across the globe. Africa depends immensely on the freight transport sector to facilitate supply chain operations. Several studies indicate that drivers who operate primarily on rural roads, such as those found in Sub-Saharan Africa, have an increased propensity to engage in distracted activities such as cell phone usage while driving. The findings also identified the need for digitalization in truck driving operations, including carrier management techniques such as fatigue management, artificial intelligence, and automating functions like cell phone usage controls. The recommendations can aid policymakers and commercial truck carriers in deepening their understanding of driver distraction and enforcing mitigations to foster road safety.

Keywords: truck drivers, distraction, digitalization, crash risk, road safety

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Authors: Marcio Leal, Marta Villamil

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Computacional recognition of sign languages aims to allow a greater social and digital inclusion of deaf people through interpretation of their language by computer. This article presents a model of recognition of two of global parameters from sign languages; hand configurations and hand movements. Hand motion is captured through an infrared technology and its joints are built into a virtual three-dimensional space. A Multilayer Perceptron Neural Network (MLP) was used to classify hand configurations and Dynamic Time Warping (DWT) recognizes hand motion. Beyond of the method of sign recognition, we provide a dataset of hand configurations and motion capture built with help of fluent professionals in sign languages. Despite this technology can be used to translate any sign from any signs dictionary, Brazilian Sign Language (Libras) was used as case study. Finally, the model presented in this paper achieved a recognition rate of 80.4%.

Keywords: artificial neural network, computer vision, dynamic time warping, infrared, sign language recognition

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Authors: Mourad Morsli, Mohamed Tahiri, Azzedine Samdi

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The Cities are the urbanized territories most favorable to the consumption of resources (materials, energy). In Morocco, the economic capital Casablanca is one of them, with its 4M inhabitants and its 60% share in the economic and industrial activity of the kingdom. In the absence of legal status in force, urban development has favored the generation of millions of tons of demolition and construction waste scattered in open spaces causing a significant nuisance to the environment and citizens. Hence the main objective of our work is to valorize concrete waste. The representative wastes are mainly concrete, concrete, and fired clay bricks, ceramic tiles, marble panels, gypsum, and scrap metal. The work carried out includes: geolocation with a combination of artificial intelligence, GIS, and Google Earth, which allowed the estimation of the quantity of these wastes per site; then the sorting, crushing, grinding, and physicochemical characterization of the collected samples allowed the definition of the exploitation ways for each extracted fraction for integrated management of the said wastes. In the present work, we proceeded to the exploitation of the fractions obtained after sieving the representative samples to incorporate them in the manufacture of new ecological materials for construction. These formulations prepared studies have been tested and characterized: physical criteria (specific surface, resistance to flexion and compression) and appearance (cracks, deformation). We will present in detail the main results of our research work and also describe the specific properties of each material developed.

Keywords: demolition and construction waste, GIS combination software, inert waste recovery, ecological materials, Casablanca, Morocco

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Authors: Mthiyane Pretty, Mitsui Toshiake, Nagano Hirohiko, Aycan Murat

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Among the most significant greenhouse gases released from rice fields are methane and carbon dioxide. The primary focus of this research was to quantify CH₄ and CO₂ gas using different 4 rice cultivars, two water regimes, and a recording of soil moisture and temperature. In this study, we hypothesized that paddy field soils may directly affect soil enzymatic activities and physicochemical properties in the rhizosphere soil of paddy fields and subsequently indirectly affect the activity, abundance, diversity, and community composition of methanogens, ultimately affecting CH₄ flux. The experiment was laid out in the randomized block design with two treatments and three replications for each genotype. In two treatments, paddy fields and artificial soil were used. 35 days after planting (DAP), continuous flooding irrigation, Alternate wetting, and drying (AWD) were applied during the vegetative stage. The highest recorded measurements of soil and environmental parameters were soil moisture at 76%, soil temperature at 28.3℃, Bulk EC at 0.99 ds/m, and pore water EC at 1,25, using HydraGO portable soil sensor system. Gas samples were carried out once on a weekly basis at 09:00 am and 12: 00 pm to obtain the mean GHG flux. Gas Chromatography (GC, Shimadzu, GC-2010, Japan) was used for the analysis of CH4 and CO₂. The treatments with paddy field soil had a 1.3℃ higher temperature than artificial soil. The overall changes in Bulk EC were not significant across the treatment. The CH₄ emission patterns were observed in all rice genotypes, although they were less in treatments with AWD with artificial soil. This shows that AWD creates oxic conditions in the rice soil. CO₂ was also quantified, but it was in minute quantities, as rice plants were using CO₂ for photosynthesis. The highest tillering number was 7, and the lowest was 3 in cultivars grown. The rice varieties to be used for breeding are Norin 24, with showed a high number of tillers with less CH₄.

Keywords: greenhouse gases, methane, morphological characterization, alternating wetting and drying

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Authors: Zhaopeng Zhu, Xianzhi Song, Gensheng Li, Shuo Zhu, Shiming Duan, Xuezhe Yao

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Accurate calculation of wellbore pressure is of great significance to prevent wellbore risk during drilling. The traditional mechanism model needs a lot of iterative solving procedures in the calculation process, which reduces the calculation efficiency and is difficult to meet the demand of dynamic control of wellbore pressure. In recent years, many scholars have introduced artificial intelligence algorithms into wellbore pressure calculation, which significantly improves the calculation efficiency and accuracy of wellbore pressure. However, due to the ‘black box’ property of intelligent algorithm, the existing intelligent calculation model of wellbore pressure is difficult to play a role outside the scope of training data and overreacts to data noise, often resulting in abnormal calculation results. In this study, the multi-phase flow mechanism is embedded into the objective function of the neural network model as a constraint condition, and an intelligent prediction model of wellbore pressure under the constraint condition is established based on more than 400,000 sets of pressure measurement while drilling (MPD) data. The constraint of the multi-phase flow mechanism makes the prediction results of the neural network model more consistent with the distribution law of wellbore pressure, which overcomes the black-box attribute of the neural network model to some extent. The main performance is that the accuracy of the independent test data set is further improved, and the abnormal calculation values basically disappear. This method is a prediction method driven by MPD data and multi-phase flow mechanism, and it is the main way to predict wellbore pressure accurately and efficiently in the future.

Keywords: multiphase flow mechanism, pressure while drilling data, wellbore pressure, mechanism constraints, combined drive

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Authors: Yu-Lin Shen, Ming-Kuen Chang

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In addition to a considerable amount of machinery and equipment, intricacies of the transmission pipeline exist in Petrochemical plants. Long term corrosion may lead to pipeline thinning and rupture, causing serious safety concerns. With the advances in non-destructive testing technology, more rapid and long-range ultrasonic detection techniques are often used for pipeline inspection, EMAT without coupling to detect, it is a non-contact ultrasonic, suitable for detecting elevated temperature or roughened e surface of line. In this study, we prepared artificial defects in pipeline for Electromagnetic Acoustic Transducer testing (EMAT) to survey the relationship between the defect location, sizing and the EMAT signal. It was found that the signal amplitude of EMAT exhibited greater signal attenuation with larger defect depth and length. In addition, with bigger flat hole diameter, greater amplitude attenuation was obtained. In summary, signal amplitude attenuation of EMAT was affected by the defect depth, defect length and the hole diameter and size.

Keywords: EMAT, artificial defect, NDT, ultrasonic testing

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Authors: Deepak Singh, Rail Kuliev

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The abstract highlights the critical importance of optimizing data integration and management strategies in the upstream oil and gas industry. With its complex and dynamic nature generating vast volumes of data, efficient data integration and management are essential for informed decision-making, cost reduction, and maximizing operational performance. Challenges such as data silos, heterogeneity, real-time data management, and data quality issues are addressed, prompting the proposal of several strategies. These strategies include implementing a centralized data repository, adopting industry-wide data standards, employing master data management (MDM), utilizing real-time data integration technologies, and ensuring data quality assurance. Training and developing the workforce, “reskilling and upskilling” the employees and establishing robust Data Management training programs play an essential role and integral part in this strategy. The article also emphasizes the significance of data governance and best practices, as well as the role of technological advancements such as big data analytics, cloud computing, Internet of Things (IoT), and artificial intelligence (AI) and machine learning (ML). To illustrate the practicality of these strategies, real-world case studies are presented, showcasing successful implementations that improve operational efficiency and decision-making. In present study, by embracing the proposed optimization strategies, leveraging technological advancements, and adhering to best practices, upstream oil and gas companies can harness the full potential of data-driven decision-making, ultimately achieving increased profitability and a competitive edge in the ever-evolving industry.

Keywords: master data management, IoT, AI&ML, cloud Computing, data optimization

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Authors: Yassine Bouzouita, Oumaima Belghith, Cyrine Zitoun, Charles Bonneau

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Performance measurement framework (PMF) is an essential tool in any organization to assess the performance of its processes. It guides businesses to stay on track with their objectives and benchmark themselves from the market. With the growing trend of the digital transformation of business processes, led by innovations in artificial intelligence (AI) & Big Data applications, developing a mature system capable of capturing the impact of digital solutions across different industries became a necessity. Based on the conducted research, no such system has been developed in academia nor the industry. In this context, this paper covers a variety of methodologies on performance measurement, overviews the major AI and big data applications in the banking sector, and covers an exhaustive list of relevant metrics. Consequently, this paper is of interest to both researchers and practitioners. From an academic perspective, it offers a comparative analysis of the reviewed performance measurement frameworks. From an industry perspective, it offers exhaustive research, from market leaders, of the major applications of AI and Big Data technologies, across the different departments of an organization. Moreover, it suggests a standardized classification model with a well-defined structure of intelligent digital solutions. The aforementioned classification is mapped to a centralized library that contains an indexed collection of potential metrics for each application. This library is arranged in a manner that facilitates the rapid search and retrieval of relevant metrics. This proposed framework is meant to guide professionals in identifying the most appropriate AI and big data applications that should be adopted. Furthermore, it will help them meet their business objectives through understanding the potential impact of such solutions on the entire organization.

Keywords: AI and Big Data applications, impact assessment, metrics, performance measurement

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Authors: Ana Neves, John Leander, Ignacio Gonzalez, Raid Karoumi

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Systematic monitoring and inspection are needed to assess the present state of a structure and predict its future condition. If an irregularity is noticed, repair actions may take place and the adequate intervention will most probably reduce the future costs with maintenance, minimize downtime and increase safety by avoiding the failure of the structure as a whole or of one of its structural parts. For this to be possible decisions must be made at the right time, which implies using systems that can detect abnormalities in their early stage. In this sense, Structural Health Monitoring (SHM) is seen as an effective tool for improving the safety and reliability of infrastructures. This paper explores the decision-making problem in SHM regarding the maintenance of civil engineering structures. The aim is to assess the present condition of a bridge based exclusively on measurements using the suggested method in this paper, such that action is taken coherently with the information made available by the monitoring system. Artificial Neural Networks are trained and their ability to predict structural behavior is evaluated in the light of a case study where acceleration measurements are acquired from a bridge located in Stockholm, Sweden. This relatively old bridge is presently still in operation despite experiencing obvious problems already reported in previous inspections. The prediction errors provide a measure of the accuracy of the algorithm and are subjected to further investigation, which comprises concepts like clustering analysis and statistical hypothesis testing. These enable to interpret the obtained prediction errors, draw conclusions about the state of the structure and thus support decision making regarding its maintenance.

Keywords: artificial neural networks, clustering analysis, model-free damage detection, statistical hypothesis testing, structural health monitoring

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Authors: Satish Kumar, Nisha Goyal

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In the stock market, individual investors exhibit different kinds of behaviour. Traditional finance is built on the notion of 'homo economics', which states that humans always make perfectly rational choices to maximize their wealth and minimize risk. That is, traditional finance has concern for how investors should behave rather than how actual investors are behaving. Behavioural finance provides the explanation for this phenomenon. Although finance has been studied for thousands of years, behavioural finance is an emerging field that combines the behavioural or psychological aspects with conventional economic and financial theories to provide explanations on how emotions and cognitive factors influence investors’ behaviours. These emotions and cognitive factors are known as behavioural biases. Because of these biases, investors make irrational investment decisions. Besides, the emotional and cognitive factors, the social influence of media as well as friends, relatives and colleagues also affect investment decisions. Psychological factors influence individual investors’ investment decision making, but few studies have used qualitative methods to understand these factors. The aim of this study is to explore the behavioural factors or biases that affect individuals’ investment decision making. For the purpose of this exploratory study, an in-depth interview method was used because it provides much more exhaustive information and a relaxed atmosphere in which people feel more comfortable to provide information. Twenty investment advisors having a minimum 5 years’ experience in securities firms were interviewed. In this study, thematic content analysis was used to analyse interview transcripts. Thematic content analysis process involves analysis of transcripts, coding and identification of themes from data. Based on the analysis we categorized the statements of advisors into various themes. Past market returns and volatility; preference for safe returns; tendency to believe they are better than others; tendency to divide their money into different accounts/assets; tendency to hold on to loss-making assets; preference to invest in familiar securities; tendency to believe that past events were predictable; tendency to rely on the reference point; tendency to rely on other sources of information; tendency to have regret for making past decisions; tendency to have more sensitivity towards losses than gains; tendency to rely on own skills; tendency to buy rising stocks with the expectation that this rise will continue etc. are some of the major concerns showed by experts about investors. The findings of the study revealed 13 biases such as overconfidence bias, disposition effect, familiarity bias, framing effect, anchoring bias, availability bias, self-attribution bias, representativeness, mental accounting, hindsight bias, regret aversion, loss aversion and herding bias/media biases present in Indian investors. These biases have a negative connotation because they produce a distortion in the calculation of an outcome. These biases are classified under three categories such as cognitive errors, emotional biases and social interaction. The findings of this study may assist both financial service providers and researchers to understand the various psychological biases of individual investors in investment decision making. Additionally, individual investors will also be aware of the behavioural biases that will aid them to make sensible and efficient investment decisions.

Keywords: financial advisors, individual investors, investment decisions, psychological biases, qualitative thematic content analysis

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Authors: Michael Eickermann, Juergen Junk

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The Western honey (Apis mellifera) is threatened by a number of parasites, especially the devastating Varroa mite (Varroa destructor) is responsible for a high level of mortality over winter, e.g., in Europe and USA. While the use of synthetic pesticides or organic acids has been preferred so far to control this parasite, breeding strategies for less susceptible honey bees are in early stages. Hygienic behavior can be an important tool for controlling Varroa destructor. Worker bees with a high level of this behavior are able to detect infested brood in the cells under the wax lid during pupation and remove them out of the hive. The underlying processes of this behavior are only partly investigated, but it is for sure that hygienic behavior is heritable and therefore, can be integrated into commercial breeding lines. In a first step, breeding lines with a high level of phenotypic hygienic behavior have been identified by using a bioassay for accurate assessment of this trait in a long-term national breeding program in Luxembourg since 2015. Based on the artificial infestation of nucleus colonies with 150 phoretic Varroa destructor mites, the level of phenotypic hygienic behavior was detected by counting the number of mites in all stages, twelve days after infestation. A nucleus with a high level of hygienic behavior was overwintered and used for breeding activities in the following years. Artificial insemination was used to combine different breeding lines. Buckfast lines, as well as Carnica lines, were used. While Carnica lines offered only a low increase of hygienic behavior up to maximum 62.5%, Buckfast lines performed much better with mean levels of more than 87.5%. Some mating ends up with a level of 100%. But even with a level of 82.5% Varroa mites are not able to reproduce in the colony anymore. In a final step, a nucleus with a high level of hygienic behavior were build up to full colonies and located at two places in Luxembourg to build up a drone congregation area. Local beekeepers can bring their nucleus to this location for mating the queens with drones offering a high level of hygienic behavior.

Keywords: agiculture, artificial insemination, honey bee, varroa destructor

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Authors: J.P. Henriques, E. R. Neto, G. Almeida, G. Ribeiro, J.V. Coutinho, A.B. Lugli

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Industry 4.0, or the Fourth Industrial Revolution, is transforming the relationship between people and machines. In this scenario, some technologies such as Cloud Computing, Internet of Things, Augmented Reality, Artificial Intelligence, Additive Manufacturing, among others, are making industries and devices increasingly intelligent. One of the most powerful technologies of this new revolution is the Digital Twin, which allows the virtualization of a real system or process. In this context, the present paper addresses the linear and nonlinear dynamic study of a didactic level plant using Digital Twin. In the first part of the work, the level plant is identified at a fixed point of operation, BY using the existing method of least squares means. The linearized model is embedded in a Digital Twin using Automation Studio® from Famous Technologies. Finally, in order to validate the usage of the Digital Twin in the linearized study of the plant, the dynamic response of the real system is compared to the Digital Twin. Furthermore, in order to develop the nonlinear model on a Digital Twin, the didactic level plant is identified by using the method proposed by Hammerstein. Different steps are applied to the plant, and from the Hammerstein algorithm, the nonlinear model is obtained for all operating ranges of the plant. As for the linear approach, the nonlinear model is embedded in the Digital Twin, and the dynamic response is compared to the real system in different points of operation. Finally, yet importantly, from the practical results obtained, one can conclude that the usage of Digital Twin to study the dynamic systems is extremely useful in the industrial environment, taking into account that it is possible to develop and tune controllers BY using the virtual model of the real systems.

Keywords: industry 4.0, digital twin, system identification, linear and nonlinear models

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Authors: Mohamed Aymen Slim, Afef Abdelkrim, Mohamed Benrejeb

Abstract:

The handwriting is a physical demonstration of a complex cognitive process learnt by man since his childhood. People with disabilities or suffering from various neurological diseases are facing so many difficulties resulting from problems located at the muscle stimuli (EMG) or signals from the brain (EEG) and which arise at the stage of writing. The handwriting velocity of the same writer or different writers varies according to different criteria: age, attitude, mood, writing surface, etc. Therefore, it is interesting to reconstruct an experimental basis records taking, as primary reference, the writing speed for different writers which would allow studying the global system during handwriting process. This paper deals with a new approach of the handwriting system modeling based on the velocity criterion through the concepts of artificial neural networks, precisely the Radial Basis Functions (RBF) neural networks. The obtained simulation results show a satisfactory agreement between responses of the developed neural model and the experimental data for various letters and forms then the efficiency of the proposed approaches.

Keywords: Electro Myo Graphic (EMG) signals, experimental approach, handwriting process, Radial Basis Functions (RBF) neural networks, velocity modeling

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Authors: Hanbey Hazar, Hakan Gul

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In this study, Thermal Barrier Coating (TBC) application is performed in order to reduce the engine emissions. Piston, exhaust, and intake valves of a single-cylinder four-cycle gasoline engine were coated with chromium carbide (Cr3C2) at a thickness of 300 µm by using the Plasma Spray coating method which is a TBC method. Gasoline engine was converted into an LPG system. The study was conducted in 4 stages. In the first stage, the piston, exhaust, and intake valves of the gasoline engine were coated with Cr3C2. In the second stage, gasoline engine was converted into the LPG system and the emission values in this engine were recorded. In the third stage, the experiments were repeated under the same conditions with a standard (uncoated) engine and the results were recorded. In the fourth stage, data obtained from both engines were loaded on Artificial Neural Networks (ANN) and estimated values were produced for every revolution. Thus, mathematical modeling of coated and uncoated engines was performed by using ANN. While there was a slight increase in exhaust gas temperature (EGT) of LPG engine due to TBC, carbon monoxide (CO) values decreased.

Keywords: LPG fuel, thermal barrier coating, artificial neural network, mathematical modelling

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Authors: Hayriye Anıl, Görkem Kar

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In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting

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Authors: G. Ravindranath, S. Savitha

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This paper presents heat transfer analysis of single horizontal bare tube and heat transfer analysis of staggered arrangement of bare tube bundles bare tube bundles in gas-solid (air-solid) fluidized bed and predictions are done by using Artificial Neural Network (ANN) based on experimental data. Fluidized bed provide nearly isothermal environment with high heat transfer rate to submerged objects i.e. due to through mixing and large contact area between the gas and the particle, a fully fluidized bed has little temperature variation and gas leaves at a temperature which is close to that of the bed. Measurement of average heat transfer coefficient was made by local thermal simulation technique in a cold bubbling air-fluidized bed of size 0.305 m. x 0.305 m. Studies were conducted for single horizontal Bare Tube of length 305mm and 28.6mm outer diameter and for bare tube bundles of staggered arrangement using beds of large (average particle diameter greater than 1 mm) particle (raagi and mustard). Within the range of experimental conditions influence of bed particle diameter ( Dp), Fluidizing Velocity (U) were studied, which are significant parameters affecting heat transfer. Artificial Neural Networks (ANNs) have been receiving an increasing attention for simulating engineering systems due to some interesting characteristics such as learning capability, fault tolerance, and non-linearity. Here, feed-forward architecture and trained by back-propagation technique is adopted to predict heat transfer analysis found from experimental results. The ANN is designed to suit the present system which has 3 inputs and 2 out puts. The network predictions are found to be in very good agreement with the experimental observed values of bare heat transfer coefficient (hb) and nusselt number of bare tube (Nub).

Keywords: fluidized bed, large particles, particle diameter, ANN

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Authors: Lu Song Ma Li Zhi

Abstract:

With the rapid advancement of artificial intelligence technology, large language models (LLMs) have become a milestone in the field of natural language processing, demonstrating remarkable capabilities in semantic understanding, intelligent question answering, and text generation. These models are gradually penetrating various industries, particularly showcasing significant application potential in the data analysis domain. However, retraining or fine-tuning these models requires substantial computational resources and ample downstream task datasets, which poses a significant challenge for many enterprises and research institutions. Without modifying the internal parameters of the large models, prompt engineering techniques can rapidly adapt these models to new domains. This paper proposes a prompt design strategy aimed at leveraging the capabilities of large language models to automate the generation of data analysis code. By carefully designing prompts, data analysis requirements can be described in natural language, which the large language model can then understand and convert into executable data analysis code, thereby greatly enhancing the efficiency and convenience of data analysis. This strategy not only lowers the threshold for using large models but also significantly improves the accuracy and efficiency of data analysis. Our approach includes requirements for the precision of natural language descriptions, coverage of diverse data analysis needs, and mechanisms for immediate feedback and adjustment. Experimental results show that with this prompt design strategy, large language models perform exceptionally well in multiple data analysis tasks, generating high-quality code and significantly shortening the data analysis cycle. This method provides an efficient and convenient tool for the data analysis field and demonstrates the enormous potential of large language models in practical applications.

Keywords: large language models, prompt design, data analysis, code generation

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Authors: Ravneet Kaur, Joydeep Acharya, Sudhanshu Gaur

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Artificial Intelligence (AI) has the potential to transform manufacturing by improving shop floor processes such as production, maintenance and quality. However, industrial datasets are notoriously difficult to extract in a real-time, streaming fashion thus, negating potential AI benefits. The main example is some specialized industrial controllers that are operated by custom software which complicates the process of connecting them to an Information Technology (IT) based data acquisition network. Security concerns may also limit direct physical access to these controllers for data acquisition. To connect the Operational Technology (OT) data stored in these controllers to an AI application in a secure, reliable and available way, we propose a novel Industrial IoT (IIoT) solution in this paper. In this solution, we demonstrate how video cameras can be installed in a factory shop floor to continuously obtain images of the controller HMIs. We propose image pre-processing to segment the HMI into regions of streaming data and regions of fixed meta-data. We then evaluate the performance of multiple Optical Character Recognition (OCR) technologies such as Tesseract and Google vision to recognize the streaming data and test it for typical factory HMIs and realistic lighting conditions. Finally, we use the meta-data to match the OCR output with the temporal, domain-dependent context of the data to improve the accuracy of the output. Our IIoT solution enables reliable and efficient data extraction which will improve the performance of subsequent AI applications.

Keywords: human machine interface, industrial internet of things, internet of things, optical character recognition, video analytics

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Authors: Noor Suhaida Kasri

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In the last three decades, Malaysia has developed fundamental legal and regulatory structures that aim to accommodate and facilitate the growth of Islamic banking and finance industry. Important building blocks have been put in place, to cite a few, the elevation of the position of the Malaysian Central Bank Shariah Advisory Council (SAC) as the apex advisory body and the empowerment of their Shariah resolutions through the Central Bank Act 1958; the promulgation of the Islamic Financial Services Act 2013 that regulate and govern Islamic finance market with a robust statutory requirement of Shariah governance and Shariah compliance. Notwithstanding these achievements, enforceability of Shariah-compliant contract remains a contentious subject. The validity of Al Bai Bithaman Ajil concept that was commonly used by the Islamic financial institutions in their financing facilities structures and documentation has been unabatedly challenged by the customers in courts. The challenge was due to the manner in which the Al Bai Bithaman Ajil transactions were carried out. Due to this legal challenge, Al Bai Bithaman Ajil financing structure seems to no longer be the practitioners’ favourite in Malaysia, though its substitute tawarruq and commodity murabahah financing structure may potentially face similar legal challenges. This paper examines the legal challenges affecting the enforceability of these underlying Shariah contracts. The examination of these cases highlights the manner in which these contracts were being implemented and applied by the Malaysian Islamic financial institutions that triggered Shariah and legal concern. The analysis also highlights the approach adopted by the Malaysian courts in determining the Shariah issues as well as the SAC in ascertaining the rulings on the Shariah issues referred to it by the courts. The paper adopts a qualitative research methodology by using textual and documentary analysis approach. The outcome of this study underlines factors that require consideration by industry stakeholder in order to ameliorate the efficacy of the existing building blocks that would eventually strengthens the validity and enforceability of Shariah-compliant contracts. This, in the long run, will further reinforce financial stability and trust into the Islamic banking and finance industry in Malaysia.

Keywords: enforceability of Shariah compliant contract, legal challenge, legal and regulatory framework, Shariah Advisory Council

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Authors: Tahir Nawaz Cheema, Dumitru Baleanu, Ali Raza

Abstract:

In this research work, we design intelligent computing through Bayesian Regularization artificial neural networks (BRANNs) introduced to solve the mathematical modeling of infectious diseases (Covid-19). The dynamical transmission is due to the interaction of people and its mathematical representation based on the system's nonlinear differential equations. The generation of the dataset of the Covid-19 model is exploited by the power of the explicit Runge Kutta method for different countries of the world like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, testing, and validation processes for every frequent update in Bayesian Regularization backpropagation for numerical behavior of the dynamics of the Covid-19 model. The performance and effectiveness of designed methodology BRANNs are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis.

Keywords: mathematical models, beysian regularization, bayesian-regularization backpropagation networks, regression analysis, numerical computing

Procedia PDF Downloads 136