Search results for: multimodal transformer autoencoder

Authors: J. S. Dhillon, K. K. Dhaliwal

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In almost all the digital filtering applications the digital infinite impulse response (IIR) filters are preferred over finite impulse response (FIR) filters because they provide much better performance, less computational cost and have smaller memory requirements for similar magnitude specifications. However, the digital IIR filters are generally multimodal with respect to the filter coefficients and therefore, reliable methods that can provide global optimal solutions are required. The artificial bee colony (ABC) algorithm is one such recently introduced meta-heuristic optimization algorithm. But in some cases it shows insufficiency while searching the solution space resulting in a weak exchange of information and hence is not able to return better solutions. To overcome this deficiency, the opposition based learning strategy is incorporated in ABC and hence a modified version called oppositional artificial bee colony (OABC) algorithm is proposed in this paper. Duplication of members is avoided during the run which also augments the exploration ability. The developed algorithm is then applied for the design of optimal and stable digital IIR filter structure where design of low-pass (LP) and high-pass (HP) filters is carried out. Fuzzy theory is applied to achieve maximize satisfaction of minimum magnitude error and stability constraints. To check the effectiveness of OABC, the results are compared with some well established filter design techniques and it is observed that in most cases OABC returns better or atleast comparable results.

Keywords: digital infinite impulse response filter, artificial bee colony optimization, opposition based learning, digital filter design, multi-parameter optimization

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Authors: Raffaello Furlan

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In the 21st century, the sustainable urban development of GCC-cities is challenged by inhabitants’ over-dependency on private-use vehicles. In turn, this habit has generated problems of urban inefficiency, contributing to traffic congestion, pollution, urban sprawling, fragmentation of the urban fabric, and various environmental and social challenges. In the context of Doha, the capital city of the State of Qatar, the over-dependency on private-use vehicles is justified by the lack of alternative public modes of transportation that support the need to connect fragmented urban districts and provide an effective solution to urban sprawl. Therefore, the current construction of the Qatar Metro Rail is offering the potential for investigating and defining a strategy for the sustainable urban development and/or urban regeneration of transit villages (TODs) in Qatar. Namely, the aim of this research study is (i) to investigate the development of transit villages (TODs) in the cultural-heritage district of Msheireb, Downtown Doha, (ii) to explore how the introduction of the new public transport system of Doha Metro can be effectively utilized as means of urban regeneration of the cultural core of the city, (iii) to propose a masterplan for TOD suitable for the district, suiting and responding to regional cultural and societal values. The findings reveal that the strategies for the sustainable urban regeneration of Msheireb are based on (i) the integration of land-use and multimodal transportation systems, (ii) the implementation of the public realm, and (iii) conservation of culture and urban identity.

Keywords: sustainable urbanism, smart growth, TODs, cultural district, Msheireb Downtown Doha

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Authors: Vishwanath Pethri Kamath, Jayantha Gowda Sarapanahalli, Vishal Mishra, Siddhesh Balwant Bandgar

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Recognition of emotional information is essential in any form of communication. Growing HCI (Human-Computer Interaction) in recent times indicates the importance of understanding of emotions expressed and becomes crucial for improving the system or the interaction itself. In this research work, textual data for emotion recognition is used. The text being the least expressive amongst the multimodal resources poses various challenges such as contextual information and also sequential nature of the language construction. In this research work, the proposal is made for a neural architecture to resolve not less than 8 emotions from textual data sources derived from multiple datasets using google pre-trained word2vec word embeddings and a Multi-head attention-based bidirectional LSTM model with a one-vs-all Multi-Level Classification. The emotions targeted in this research are Anger, Disgust, Fear, Guilt, Joy, Sadness, Shame, and Surprise. Textual data from multiple datasets were used for this research work such as ISEAR, Go Emotions, Affect datasets for creating the emotions’ dataset. Data samples overlap or conflicts were considered with careful preprocessing. Our results show a significant improvement with the modeling architecture and as good as 10 points improvement in recognizing some emotions.

Keywords: text emotion recognition, bidirectional LSTM, multi-head attention, multi-level classification, google word2vec word embeddings

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Authors: Aseel Zibin, Abdel Rahman Altakhaineh

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This study analyses 30 cartoons depicting THE DEAL OF THE CENTURY as envisaged by two Jordanian cartoonists, namely, EmadHajjaj and Osama Hajjaj. Conceptual Blending Theory (CBT) and Multimodal Metaphor Theory (MMT) are adopted as a theoretical framework to interpret the metaphors and metonymies used in the target cartoons. The results reveal that the target domain THE DEAL OF THE CENTURY was conceptualized mainly through layered metaphors that have metonymic basis and event metaphors\allegories. Specifically, 6 groups were identified: OBJECT or a situation involving OBJECTS, situations involving HUMANS\HYBRIDS of HUMANS and OBJECTS, an ANIMAL OR situation involving an ANIMAL, hybrids of WEAPONS and humans, and event metaphors used to build a story\allegory. The target domain was also depicted via event metaphors used to build a story; some of which are embedded in the Jordanian culture, while others could be perceivable cross-culturally. The results also demonstrate that the most widely used configurations to construe the metaphors was the pictorial source–verbal target in line with Lan and Zuo (2016); the motivation was probably the greater conceptual density and concreteness of visual representation since the target is better captured verbally because of its abstractness. The use of cross-modal mappings of this type was attributed to the abstractness of the target domain, THE DEAL OF THE CENTURY, which makes it more construable via verbal cues rather than visual ones. In contrast, the source domains used were mainly concrete and thus perceivable pictorially rather than verbally.

Keywords: semiotics, cognitive semantics, metaphor, culture, blending, cartoon

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Authors: Toktam Khatibi

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Self-supervised learning (SSL) includes machine learning models that are trained on one aspect and/or one part of the input to learn other aspects and/or part of it. SSL models are divided into two different categories, including pre-text task-based models and contrastive learning ones. Pre-text tasks are some auxiliary tasks learning pseudo-labels, and the trained models are further fine-tuned for downstream tasks. However, one important disadvantage of SSL using pre-text task solving is defining an appropriate pre-text task for each image dataset with a variety of image modalities. Therefore, it is required to design an appropriate pretext task automatically for each dataset and each downstream task. To the best of our knowledge, the automatic designing of pretext tasks for image analysis has not been considered yet. In this paper, we present a framework based on In-context learning that describes each task based on its input and output data using a pre-trained image transformer. Our proposed method combines the input image and its learned description for optimizing the pre-text task design and its hyper-parameters using Meta-learning models. The representations learned from the pre-text tasks are fine-tuned for solving the downstream tasks. We demonstrate that our proposed framework outperforms the compared ones on unseen tasks and image modalities in addition to its superior performance for previously known tasks and datasets.

Keywords: in-context learning (ICL), meta learning, self-supervised learning (SSL), vision-language domain, transformers

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Authors: James Jin, Weisi Xia, Runzhe Gao, Alain Vandal, Darren Svirkis, Andrew Hill

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Background: Post-haemorrhoidectomy pain is concerned by patients/clinicians. Minimizing the postoperation pain is highly interested clinically. Combinations of topical cream targeting three hypothesised post-haemorrhoidectomy pain mechanisms were developed and their effectiveness were evaluated. Specifically, a multi-centred double-blinded randomized clinical trial (RCT) was conducted in adults undergoing excisional haemorrhoidectomy. The primary analysis was conveyed on the data collected to evaluate the effectiveness of the combinations of topical cream targeting three hypothesized pain mechanisms after the operations. Methods: 192 patients were randomly allocated to 4 arms (each arm has 48 patients), and each arm was provided with pain cream 10% metronidazole (M), M and 2% diltiazem (MD), M with 4% lidocaine (ML), or MDL, respectively. Patients were instructed to apply topical treatments three times a day for 7 days, and record outcomes for 14 days after the operations. The primary outcome was VAS pain on day 4. Covariates and models were selected in the blind review stage. Multiple imputations were applied for the missingness. LMER, GLMER models together with natural splines were applied. Sandwich estimators and Wald statistics were used. P-values < 0.05 were considered as significant. Conclusions: The addition of topical lidocaine or diltiazem to metronidazole does not add any benefit. ML had significantly better pain and recovery scores than combination MDL. Multimodal topical analgesia with ML after haemorrhoidectomy could be considered for further evaluation. Further trials considering only 3 arms (M, ML, MD) might be worth exploring.

Keywords: RCT, primary analysis, multiple imputation, pain scores, haemorrhoidectomy, analgesia, lmer

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Authors: Heba M. Jahin, Ali F. Bakr, Zeyad T. Elsayad

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As smart spaces can be defined as any working environment which integrates embedded computers, information appliances and multi-modal sensors to remain focused on the interaction between the users, their activity, and their behavior in the space; hence, smart space must be aware of their contexts and automatically adapt to their changing context-awareness, by interacting with their physical environment through natural and multimodal interfaces. Also, by serving the information used proactively. This paper suggests a dynamic framework through the architectural design process of the space based on the principles of computational methods and context-awareness principles to help in creating a field of changes and modifications. It generates possibilities, concerns about the physical, structural and user contexts. This framework is concerned with five main processes: gathering and analyzing data to generate smart design scenarios, parameters, and attributes; which will be transformed by coding into four types of models. Furthmore, connecting those models together in the interaction model which will represent the context-awareness system. Then, transforming that model into a virtual and ambient environment which represents the physical and real environments, to act as a linkage phase between the users and their activities taking place in that smart space . Finally, the feedback phase from users of that environment to be sure that the design of that smart space fulfill their needs. Therefore, the generated design process will help in designing smarts spaces that can be adapted and controlled to answer the users’ defined goals, needs, and activity.

Keywords: computational methods, context-awareness, design process, smart spaces

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Authors: M. Aghili, S. Tabarestani, C. Freytes, M. Shojaie, M. Cabrerizo, A. Barreto, N. Rishe, R. E. Curiel, D. Loewenstein, R. Duara, M. Adjouadi

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A major challenge in medical studies, especially those that are longitudinal, is the problem of missing measurements which hinders the effective application of many machine learning algorithms. Furthermore, recent Alzheimer's Disease studies have focused on the delineation of Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) from cognitively normal controls (CN) which is essential for developing effective and early treatment methods. To address the aforementioned challenges, this paper explores the potential of using the eXtreme Gradient Boosting (XGBoost) algorithm in handling missing values in multiclass classification. We seek a generalized classification scheme where all prodromal stages of the disease are considered simultaneously in the classification and decision-making processes. Given the large number of subjects (1631) included in this study and in the presence of almost 28% missing values, we investigated the performance of XGBoost on the classification of the four classes of AD, NC, EMCI, and LMCI. Using 10-fold cross validation technique, XGBoost is shown to outperform other state-of-the-art classification algorithms by 3% in terms of accuracy and F-score. Our model achieved an accuracy of 80.52%, a precision of 80.62% and recall of 80.51%, supporting the more natural and promising multiclass classification.

Keywords: eXtreme gradient boosting, missing data, Alzheimer disease, early mild cognitive impairment, late mild cognitive impair, multiclass classification, ADNI, support vector machine, random forest

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Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: Salah Al-Enezi, Rashed Al-Zufairi, Naseer Ahmad

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A Thermo-mechanical technique was developed to determine softening point temperature/glass transition temperature (Tg) of polystyrene exposed to high pressures. The design utilizes the ability of carbon dioxide to lower the glass transition temperature of polymers and acts as plasticizer. In this apparatus, the sorption of carbon dioxide to induce softening of polymers as a function of temperature/pressure is performed and the extent of softening is measured in three-point-flexural-bending mode. The polymer strip was placed in the cell in contact with the linear variable differential transformer (LVDT). CO₂ was pumped into the cell from a supply cylinder to reach high pressure. The results clearly showed that full softening point of the samples, accompanied by a large deformation on the polymer strip. The deflection curves are initially relatively flat and then undergo a dramatic increase as the temperature is elevated. It was found that increasing the pressure of CO₂ causes the temperature curves to shift from higher to lower by increment of about 45 K, over the pressure range of 0-120 bars. The obtained experimental Tg values were validated with the values reported in the literature. Finally, it is concluded that the defection model fits consistently to the generated experimental results, which attempts to describe in more detail how the central deflection of a thin polymer strip affected by the CO₂ diffusions in the polymeric samples.

Keywords: softening, high-pressure, polystyrene, CO₂ diffusions

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Authors: Jalal M. Abdallah

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There is a great problem in testing and investigations the reliability of different type of transformers insulation materials. It summarized in how to create and simulate the real conditions of working transformer and testing its insulation materials for Partial Discharge PD, typically as in the working mode. A lot of tests may give untrue results as the physical behavior of the insulation material differs under tests from its working condition. In this work, the real working conditions were simulated, and a large number of specimens have been tested. The investigations first stage, begin with choosing samples of different types of insulation materials (papers, pressboards, etc.). The second stage, the samples were dried in ovens at 105 C⁰and 0.01bar for 48 hours, and then impregnated with dried and gasless oil (the water content less than 6 ppm.) at 105 C⁰and 0.01bar for 48 hours, after so specimen cooling at room pressure and temperature for 24 hours. The third stage is investigating PD for the samples using ICM PD measuring device. After that, a continuous test on oil-impregnated insulation materials (paper, pressboards) was developed, and the phase resolved partial discharge pattern of PD signals was measured. The important of this work in providing the industrial sector with trusted high accurate measuring results based on real simulated working conditions. All the PD patterns (results) associated with a discharge produced in well-controlled laboratory condition. They compared with other previous and other laboratory results. In addition, the influence of different temperatures condition on the partial discharge activities was studied.

Keywords: transformers, insulation materials, voids, partial discharge

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Authors: Peng, Chun-Yi, Chen, Wei-Hsuan, Ueng, Shyh-Kuang

Abstract:

This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: B. Wattanakul, G. Ngamwongwan, S. Ngamkham

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Exposure to different learning experiences contributes to changing in learning style. Addressing students’ learning preference could help teachers provide different learning activities that encourage the student to learn effectively. Purpose: The purpose of this descriptive study was to describe learning styles of nursing students at Boromarajonani College of Nursing Chon Buri. Sample: The purposive sample was 463 nursing students who were enrolled in a nursing program at different academic levels. The 16-item VARK questionnaire with 4 multiple choices was administered at one time data collection. Choices have consisted with modalities of Visual, Aural, Read/write, and Kinesthetic measured by VARK. Results: Majority of learning preference of students at different levels was visual and read/write learning preference. Almost 67% of students have a multimodal preference, which is visual learning preference associated with read/write or kinesthetic preference. At different academic levels, multimodalities are greater than single preference. Over 30% of students have one dominant learning preference, including visual preference, read/write preference and kinesthetic preference. Analysis of Variance (ANOVA) with Bonferroni adjustment revealed a significant difference between students based on their academic level (p < 0.001). Learning style of the first-grade nursing students differed from the second-grade nursing students (p < 0.001). While learning style of nursing students in the second-grade has significantly varied from the 1st, 3rd, and 4th grade (p < 0.001), learning preference of the 3rd grade has significantly differed from the 4th grade of nursing students (p > 0.05). Conclusions: Nursing students have varied learning styles based on their different academic levels. Learning preference is not fixed attributes. This should help nursing teachers assess the types of changes in students’ learning preferences while developing teaching plans to optimize students’ learning environment and achieve the needs of the courses and help students develop learning preference to meet the need of the course.

Keywords: learning preference, VARK, learning style, nursing

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Authors: Zeenat Parveen, Ashiq Hussain

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This review paper consists of applications of PDMS (polydimethylsiloxane) materials for enhanced performance, optical fiber sensors in acousto-ultrasonic, mechanical measurements, current applications, sensing, measurements and interferometric optical fiber sensors. We will discuss the basic working principle of fiber optic sensing technology, various types of fiber optic and the PDMS as a coating material to increase the performance. Optical fiber sensing methods for detecting dynamic strain signals, including general sound and acoustic signals, high frequency signals i.e. ultrasonic/ultrasound, and other signals such as acoustic emission and impact induced dynamic strain. Optical fiber sensors have Industrial and civil engineering applications in mechanical measurements. Sometimes it requires different configurations and parameters of sensors. Optical fiber current sensors are based on Faraday Effect due to which we obtain better performance as compared to the conventional current transformer. Recent advancement and cost reduction has simulated interest in optical fiber sensing. Optical techniques are also implemented in material measurement. Fiber optic interferometers are used to sense various physical parameters including temperature, pressure and refractive index. There are four types of interferometers i.e. Fabry–perot, Mach-Zehnder, Michelson, and Sagnac. This paper also describes the future work of fiber optic sensors.

Keywords: fiber optic sensing, PDMS materials, acoustic, ultrasound, current sensor, mechanical measurements

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Authors: Aderonke Oluseun Akinwumi, Olusola A. Komolaf

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This paper gives information about the wide array of challenges facing both the electric utilities and consumers in the distribution system in developing countries, using Obafemi Awolowo University, Ile-Ife Nigeria as a case study. It also proffers cost-effective solution through remote monitoring, diagnostic and operation of distribution networks without compromising the system reliability. As utilities move from manned and unintelligent networks to completely unmanned smart grids, switching activities at substations and feeders will be managed and controlled remotely by dedicated systems hence this design. The Substation Remote Diagnostic and Operation Solution (sRDOs) would remotely monitor the load on Medium Voltage (MV) and Low Voltage (LV) feeders as well as distribution transformers and allow the utility disconnect non-paying customers with absolutely no extra resource deployment and without interrupting supply to paying customers. The aftermath of the implementation of this design improved the lifetime of key distribution infrastructure by automatically isolating feeders during overload conditions and more importantly erring consumers. This increased the ratio of revenue generated on electricity bills to total network load.

Keywords: electric utility, consumers, remote monitoring, diagnostic, system reliability, manned and unintelligent networks, unmanned smart grids, switching activities, medium voltage, low voltage, distribution transformer

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Authors: Tzu-Hao Li, Shie-Liang Hsieh, Han-Chieh Lin, Ying-Ying Yang

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TNF superfamily-stimulated pathogenic cascades and macrophage (M1)/kupffer cells (KC) polarization are important in the pathogenesis of ischemia-reperfusion (IR) liver injury in animals with hepatic steatosis (HS). Decoy receptor 3 (DcR3) is a common upstream inhibitor of the above-mentioned pathogenic cascades. The study evaluated whether modulation of these DcR3-related cascades was able to protect steatotic liver from IR injury. Serum and hepatic DcR3 levels were lower in patients and animals with HS. Accordingly, the effects of pharmacologic and genetic DcR3 replacement on the IR-related pathogenic changes were measured. Significantly, DcR3 replacement protected IR-Zucker(HS) rats and IR-DcR3-Tg(HS) mice from IR liver injury. The beneficial effects of DcR3 replacement were accompanied by decreased serum/hepatic TNF, soluble TNF-like cytokine 1A (TL1A), Fas ligand (Fas-L) and LIGHT, T-helper-cell-1 cytokine (INF) levels, neutrophil infiltration, M1 polarization, neutrophil-macrophage/KC-T-cell interaction, hepatocyte apoptosis and improved hepatic microcirculatory failure among animals with IR-injured steatotic livers. Additionally, TL1A, Fas-L, LIGHT and TLR4/NFB signals were found to mediate the DcR3-related protective effects of steatotic livers from IR injury. Using multimodal in vivo and in vitro approaches, we found that DcR3 was a potential agent to protect steatotic livers from IR injury by simultaneous blocking the multiple IR injury-related pathogenic changes.

Keywords: Decoy 3 receptor, ischemia-reperfusion injury, M1 polarization, TNF superfamily

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Authors: Khairul Zahreen Mohd Arof, Rahimah Muhamad

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Oil palm fiber (OPF) is a fibrous material produced from the waste of palm oil industry which is suitable to be used in construction industry. The applications of OPF in concrete can reduce the material costs and enhance concrete behavior. Dog-bone test provides significant results for investigating the behavior of fiber reinforced concrete under tensile loading. It is able to provide stress-strain profile, modulus of elasticity, stress at cracking point and total crack width. In this research, dog-bone tests have been conducted to analyze total crack width, stress-strain profile, and modulus of elasticity of OPFC. Specimens are in a dog-bone shape with a long notch in the middle as compared to the end, to ensure cracks occur only within the notch. Tests were instrumented using a universal testing machine Shimadzu 300kN, a linear variable differential transformer and two strain gauges. A total of nine specimens with different fibers at fiber volume fractions of 0.75%, 1.00%, and 1.25% have been tested to analyze the behavior under tensile loading. Also, three specimens of plain concrete fiber have been tested as control specimens. The tensile test of all specimens have been carried out for concrete age exceed 28 days. It shows that OPFC able to reduce total crack width. In addition, OPFC has higher cracking stress than plain concrete. The study shows plain concrete can be improved with the addition of OPF.

Keywords: cracks, crack width, dog-bone test, oil palm fiber concrete

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Authors: Tiffany Yung Hsuan Ma

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The Whole Language Approach, which gained prominence in the 1980s, and the increasing emphasis on multimodal resources in educational research have elevated the utilization of picture books in English as a foreign language (EFL) instruction. This approach underscores real-world language application, providing EFL learners with a range of sensory stimuli, including visual elements. Additionally, the substantial impact of picture books on fostering prosocial behaviors in children has garnered recognition. These narratives offer opportunities to impart essential values such as kindness, fairness, and respect. Examining how picture books enhance vocabulary acquisition can offer valuable insights for educators in devising engaging language activities conducive to a positive learning environment. This research entails a case study involving two kindergarten-aged EFL learners and employs qualitative methods, including worksheets, observations, and interviews with parents. It centers on three pivotal inquiries: (1) The extent of young learners' acquisition of essential vocabulary, (2) The influence of these books on their behavior at home, and (3) Effective teaching strategies for the seamless integration of picture storybooks into EFL instruction for young learners. The findings can provide guidance to parents, educators, curriculum developers, and policymakers regarding the advantages and optimal approaches to incorporating picture books into language instruction. Ultimately, this research has the potential to enhance English language learning outcomes and promote moral education within the Taiwanese EFL context.

Keywords: EFL, vocabulary acquisition, young learners, picture book, moral education

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Authors: Himanshu J. Bahirat, Apoorva S. Janawlekar

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Plasma actuators are essential for aerodynamic flow separation control due to their lack of mechanical parts, lightweight, and high response frequency, which have numerous applications in hypersonic or supersonic aircraft. The working of these actuators is based on the formation of a low-temperature plasma between a pair of parallel electrodes by the application of a high-voltage AC signal across the electrodes, after which air molecules from the air surrounding the electrodes are ionized and accelerated through the electric field. The high-frequency operation is required in dielectric discharge barriers to ensure plasma stability. To carry out flow separation control in a hypersonic flow, the optimal design and construction of a power supply to generate dielectric barrier discharges is carried out in this paper. In this paper, it is aspired to construct a simplified circuit topology to emulate the dielectric barrier discharge and study its various frequency responses. The power supply can generate high voltage pulses up to 20kV at the repetitive frequency range of 20-50kHz with an input power of 500W. The power supply has been designed to be short circuit proof and can endure variable plasma load conditions. Its general outline is to charge a capacitor through a half-bridge converter and then later discharge it through a step-up transformer at a high frequency in order to generate high voltage pulses. After simulating the circuit, the PCB design and, eventually, lab tests are carried out to study its effectiveness in controlling flow separation.

Keywords: aircraft propulsion, dielectric barrier discharge, flow separation control, power source

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Authors: Yuan-Chung Tsai, Masao Kamimura, Kohei Soga, Hsin-Cheng Chiu

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In order to enhance the photodynamic/photothermal therapeutic efficacy on glioblastoma, the functionized upconversion nanoparticles with the capability of converting the deep tissue penetrating near-infrared light into visible wavelength for activating photochemical reaction were developed. The drug-loaded nanoparticles (NPs) were obtained from the self-assembly of oleic acid-coated upconversion nanoparticles along with maleimide-conjugated poly(ethylene glycol)-cholesterol (Mal-PEG-Chol), as the NP stabilizer, and hydrophobic photosensitizers, IR-780 (for photothermal therapy, PTT) and mTHPC (for photodynamic therapy, PDT), in aqueous phase. Both the IR-780 and mTHPC were loaded into the hydrophobic domains within NPs via hydrophobic association. The peptide targeting ligand, angiopep-2, was further conjugated with the maleimide groups at the end of PEG adducts on the NP surfaces, enabling the affinity coupling with the low-density lipoprotein receptor-related protein-1 of tumor endothelial cells and malignant astrocytes. The drug-loaded NPs with the size of ca 80 nm in diameter exhibit a good colloidal stability in physiological conditions. The in vitro data demonstrate the successful targeting delivery of drug-loaded NPs toward the ALTS1C1 cells (murine astrocytoma cells) and the pronounced cytotoxicity elicited by combinational effect of PDT and PTT. The in vivo results show the promising brain orthotopic tumor targeting of drug-loaded NPs and sound efficacy for brain tumor dual-modality treatment. This work shows great potential for improving photodynamic/photothermal therapeutic efficacy of brain cancer.

Keywords: drug delivery, orthotopic brain tumor, photodynamic/photothermal therapies, upconversion nanoparticles

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Authors: Anne T. Herr, Clemens Lorei

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Employees of organizations with security tasks, such as emergency services, public order services, or police forces, work every day to ensure people's safety. Violence against police is, therefore, a relevant topic both socially and politically. An increase in violence is often discussed without there being any verifiable and generally valid data. So far, scientific research has mainly focused on offender characteristics and crime statistics. These surveys are mostly subjective, retrospective, and neglect the dynamics and interactions in concrete violent situations. Therefore, more recent research methods attempt to capture the issue of violence against emergency forces more comprehensively. However, the operationalization of the constructs and the methodological approach pose particular challenges. This contribution provides an overview of new perspectives on the understanding of violent assaults and identifies current research gaps. In addition, a new research project of the Hessian University of Police and Administration in Germany is presented. In the 'AMBOSafe' study, different theoretical backgrounds for understanding violence against police and emergency services personnel will be combined in order to capture as many different perspectives of violent assaults as possible in a multimodal research approach. In a retrospective as well as in a longitudinal survey, the conditions of escalation dynamics in the assaults are recorded and supplemented by the current and valid prevalence of physical and verbal assaults in a period of four months. In addition, qualitative interviews with those affected will be used to record more detailed descriptions of and the feelings during the assaults, as well as possible causes and connections between the different groups of people. In addition to the reports of the police forces, the motives of the attackers will be collected and supplemented by analyzing the criminal case files. This knowledge can contribute to a more comprehensive understanding of violent assaults against police forces in order to be able to derive scientifically based preventive measures.

Keywords: assaults, crime statistics, escalation dynamics, police

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Authors: Ali Kazemi

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The digital evolution of financial services, while offering unprecedented convenience and accessibility, has also escalated the vulnerabilities to fraudulent activities. In this study, we introduce a distinct approach to real-time anomaly detection in financial transactions, aiming to fortify the defenses of banking and financial institutions against such threats. Utilizing unsupervised machine learning algorithms, specifically autoencoders and isolation forests, our research focuses on identifying irregular patterns indicative of fraud within transactional data, thus enabling immediate action to prevent financial loss. The data we used in this study included the monetary value of each transaction. This is a crucial feature as fraudulent transactions may have distributions of different amounts than legitimate ones, such as timestamps indicating when transactions occurred. Analyzing transactions' temporal patterns can reveal anomalies (e.g., unusual activity in the middle of the night). Also, the sector or category of the merchant where the transaction occurred, such as retail, groceries, online services, etc. Specific categories may be more prone to fraud. Moreover, the type of payment used (e.g., credit, debit, online payment systems). Different payment methods have varying risk levels associated with fraud. This dataset, anonymized to ensure privacy, reflects a wide array of transactions typical of a global banking institution, ranging from small-scale retail purchases to large wire transfers, embodying the diverse nature of potentially fraudulent activities. By engineering features that capture the essence of transactions, including normalized amounts and encoded categorical variables, we tailor our data to enhance model sensitivity to anomalies. The autoencoder model leverages its reconstruction error mechanism to flag transactions that deviate significantly from the learned normal pattern, while the isolation forest identifies anomalies based on their susceptibility to isolation from the dataset's majority. Our experimental results, validated through techniques such as k-fold cross-validation, are evaluated using precision, recall, and the F1 score alongside the area under the receiver operating characteristic (ROC) curve. Our models achieved an F1 score of 0.85 and a ROC AUC of 0.93, indicating high accuracy in detecting fraudulent transactions without excessive false positives. This study contributes to the academic discourse on financial fraud detection and provides a practical framework for banking institutions seeking to implement real-time anomaly detection systems. By demonstrating the effectiveness of unsupervised learning techniques in a real-world context, our research offers a pathway to significantly reduce the incidence of financial fraud, thereby enhancing the security and trustworthiness of digital financial services.

Keywords: anomaly detection, financial fraud, machine learning, autoencoders, isolation forest, transactional data analysis

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Authors: Jawaher F. Alsharef, Abdullah A. Ghaddaf, Mohammed S. Alomari, Abdullah A. Al Qurashi, Ahmed S. Abdulhamid, Mohammed S. Alshehri, Majed Alosaimi

Abstract:

Botulinum toxin type A (BTX-A) is the most popular therapeutic agent for muscle relaxation and pain control. Lately, BTX-A injection received great interest as a part of multimodal pain management for lower limb lengthening and deformity correction. This systematic review aimed to determine the role of BTX-A injection in pain management for during lower limb lengthening and/or deformity correction. We searched Medline, Embase, and CENTRAL. We included randomized controlled trials (RCTs) that compared the BTX-A injection to placebo for individuals undergoing lower limb lengthening and/or deformity correction. We sought to evaluate the following outcomes: pain on visual analogue scale (VAS), range of motion parameters, average opioid consumption, and adverse events. The standardized mean difference (SMD) was used to represent continuous outcomes while risk ratio (RR) was used to represent dichotomous outcomes. A total of 4 RCTs that enrolled 257 participants (337 limbs) deemed eligible. Adjuvant BTX-A injection showed a significant reduction in post-operative pain compared to placebo (SMD=–0.28, 95% CI –0.53 to –0.04). No difference was found between BTX-A injection and placebo in terms of range of motion parameters, average opioid consumption, or adverse events after surgical limb lengthening and/or deformity correction (RR= 0.77, 95% CI –0.58 to 1.03). Conclusions: Adjuvant BTX-A injection conferred a discernible reduction in post-operative pain during surgical limb lengthening and/or deformity without increasing the risk of adverse events.

Keywords: botulinum toxin type A, limb lengthening, distraction osteogenesis, deformity correction, pain management

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Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

Abstract:

Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

Procedia PDF Downloads 175

Authors: Apostolos Kalatzis, Ashish Teotia, Vishnunarayan Girishan Prabhu, Laura Stanley

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Emotions are functional behaviors influenced by thoughts, stimuli, and other factors that induce neurophysiological changes in the human body. Understanding and classifying emotions are challenging as individuals have varying perceptions of their environments. Therefore, it is crucial that there are publicly available databases and virtual reality (VR) based environments that have been scientifically validated for assessing emotional classification. This study utilized two commercially available VR applications (Guided Meditation VR™ and Richie’s Plank Experience™) to induce acute stress and calm state among participants. Subjective and objective measures were collected to create a validated multimodal dataset and classification scheme for affective state classification. Participants’ subjective measures included the use of the Self-Assessment Manikin, emotional cards and 9 point Visual Analogue Scale for perceived stress, collected using a Virtual Reality Assessment Tool developed by our team. Participants’ objective measures included Electrocardiogram and Respiration data that were collected from 25 participants (15 M, 10 F, Mean = 22.28  4.92). The features extracted from these data included heart rate variability components and respiration rate, both of which were used to train two machine learning models. Subjective responses validated the efficacy of the VR applications in eliciting the two desired affective states; for classifying the affective states, a logistic regression (LR) and a support vector machine (SVM) with a linear kernel algorithm were developed. The LR outperformed the SVM and achieved 93.8%, 96.2%, 93.8% leave one subject out cross-validation accuracy, precision and recall, respectively. The VR assessment tool and data collected in this study are publicly available for other researchers.

Keywords: affective computing, biosignals, machine learning, stress database

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Authors: Patience Muchini, Electdom Matandiroya, Emmanuel Mashonjowa

Abstract:

A ground-end manifestation of space weather phenomena is known as geomagnetically induced currents (GICs). GICs flow along the electric power transmission cables connecting the transformers and between the grounding points of power transformers during significant geomagnetic storms. Geomagnetically induced currents have been studied in other regions and have been noted to affect the power grid network. In Zimbabwe, grid failures have been experienced, but it is yet to be proven if these failures have been due to GICs. The purpose of this paper is to characterize geomagnetically induced currents with a power grid network. This paper analyses data collected, which is geomagnetic data, which includes the Kp index, DST index, and the G-Scale from geomagnetic storms and also analyses power grid data, which includes reactive power, relay tripping, and alarms from high voltage substations and then correlates the data. This research analysis was first theoretically analyzed by studying geomagnetic parameters and then experimented upon. To correlate, MATLAB was used as the basic software to analyze the data. Latitudes of the substations were also brought into scrutiny to note if they were an impact due to the location as low latitudes areas like most parts of Zimbabwe, there are less severe geomagnetic variations. Based on theoretical and graphical analysis, it has been proven that there is a slight relationship between power system failures and GICs. Further analyses can be done by implementing measuring instruments to measure any currents in the grounding of high-voltage transformers when geomagnetic storms occur. Mitigation measures can then be developed to minimize the susceptibility of the power network to GICs.

Keywords: adverse space weather, DST index, geomagnetically induced currents, KP index, reactive power

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Authors: Vaishakh Medikeri

Abstract:

Energy in this Universe is the most basic characteristic of every particle. Since the birth of life on this planet, there has been a quest undertaken by the living beings to analyze, understand and harness the precious natural facts of the nature. In this quest, one of the greatest undertaken is the process of harnessing the naturally available energy. Scientists around the globe have discovered many ways to harness the freely available energy. But even today we speak of “Power Crisis”. Nikola Tesla once said “Nature has stored up in this universe infinite energy”. Energy is everywhere around us in unlimited quantities; all of it waiting to be harnessed by us. Here in this paper a method has been proposed to harness earth's electric field and transmit the stored electric energy using strong magnetic fields and electric fields. In this paper a new technique has been proposed to harness earth's electric field which is everywhere around the world in infinite quantities. Near the surface of the earth there is an electric field of about 120V/m. This electric field is used to charge a capacitor with high capacitance. Later the energy stored is allowed to pass through a device which converts the DC stored into AC. The AC so produced is then passed through a step down transformer to magnify the incoming current. Later the current passes through the RLC circuit. Later the current can be transmitted wirelessly using the principle of resonant inductive coupling. The proposed apparatus can be placed in most of the required places and any circuit tuned to the frequency of the transmitted current can receive the energy. The new source of renewable energy is of great importance if implemented since the apparatus is not costly and can be situated in most of the required places. And also the receiver which receives the transmitted energy is just an RLC circuit tuned to the resonant frequency of the transmitted energy. By using the proposed apparatus the energy losses can be reduced to a very large extent.

Keywords: capacitor, inductive resonant coupling, RLC circuit, transmission of electricity

Procedia PDF Downloads 350

Authors: M. Lu

Abstract:

The paper presents part of the results of EU-funded projects: SoCool@EU (Sustainable Organisation between Clusters Of Optimized Logistics @ Europe), DG-RTD (Research and Innovation), Regions of Knowledge Programme (FP7-REGIONS-2011-1). It will provide an in-depth review of emerging technologies for further improving urban mobility and freight transport systems, such as (information and physical) infrastructure, ICT-based Intelligent Transport Systems (ITS), vehicles, advanced logistics, and services. Furthermore, the paper will provide an analysis of the barriers and will review business models for the market uptake of innovations. From a perspective of science and technology, the challenges of urbanization could be mainly handled through adequate (human-oriented) solutions for urban planning, sustainable energy, the water system, building design and construction, the urban transport system (both physical and information aspects), and advanced logistics and services. Implementation of solutions for these domains should be follow a highly integrated and balanced approach, a silo approach should be avoided. To develop a sustainable urban transport system (for people and goods), including inter-hubs and intra-hubs, a holistic view is needed. To achieve a sustainable transport system for people and goods (in terms of cost-effectiveness, efficiency, environment-friendliness and fulfillment of the mobility, transport and logistics needs of the society), a proper network and information infrastructure, advanced transport systems and operations, as well as ad hoc and seamless services are required. In addition, a road map for an enhanced urban transport system until 2050 will be presented. This road map aims to address the challenges of urban transport, and to provide best practices in inter-city and intra-city environments from various perspectives, including policy, traveler behaviour, economy, liability, business models, and technology.

Keywords: synchromodality, multimodal transport, logistics, Intelligent Transport Systems (ITS)

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Authors: C. von Essen

Abstract:

This study outlines and evaluates a constructivist design approach to the creation of educational video on postgraduate business degree programmes. Many online courses are tapping into the educational affordances of video, as this form of online learning has the potential to create rich, multimodal experiences. And yet, in many learning contexts video is still being used to transmit instruction to passive learners, rather than promote learner engagement and knowledge creation. Constructivism posits the notion that learning is shaped as students make connections between their experiences and ideas. This paper pivots on the following research question: how can we design educational video in ways which promote constructivist learning and stimulate analytic viewing? By exploring and categorizing over two thousand educational videos created since 2014 for over thirty postgraduate courses in business, economics, mathematics and statistics, this paper presents and critically reflects on a taxonomy of video styles and features. It links the pedagogical intent of video – be it concept explanation, skill demonstration, feedback, real-world application of ideas, community creation, or the cultivation of course narrative – to specific presentational characteristics such as visual effects including diagrammatic and real-life graphics and aminations, commentary and sound options, chronological sequencing, interactive elements, and presenter set-up. The findings of this study inform a framework which captures the pedagogical, technological and production considerations instructional designers and educational media specialists should be conscious of when planning and preparing the video. More broadly, the paper demonstrates how learning theory and technology can coalesce to produce informed and pedagogical grounded instructional design choices. This paper reveals how crafting video in a more conscious and critical manner can produce powerful, new educational design.

Keywords: educational video, constructivism, instructional design, business education

Procedia PDF Downloads 208

Authors: Vladimir Klepikov

Abstract:

Countries of Central and Southern Europe have a typical feature: oil consumption in the region exceeds own commodity production capacity by far. So crude oil import prevails in the region’s crude oil consumption structure. Transportation using marine and pipeline transport is a common method of the imported oil delivery in the region. For certain refineries, in addition to possible transportation by oil pipelines from seaports, oil is delivered from Russian oil fields. With the view to these specific features and geographic location of the region’s refineries, three ways of imported oil delivery can be singled out: oil delivery by tankers to the port and subsequent transportation by pipeline transport of the port and the refinery; oil delivery by tanker fleet to the port and subsequent transportation by oil trunk pipeline transport; oil delivery from the fields by oil trunk pipelines to refineries. Oil is also delivered by road, internal water, and rail transport. However, the volumes transported this way are negligible in comparison to the three above transportation means. Multimodal oil transportation to refineries using the pipeline and marine transport is one of the biggest cargo flows worldwide. However, in scientific publications this problem is considered mainly for certain modes of transport. Therefore, this study is topical. To elaborate an efficient transportation policy of crude oil supply to Central and Southern Europe, in this paper the geographic concentration of oil refineries was determined and the capacities of the region’s refineries were assessed. The quantitative analysis method is used as a tool. The port infrastructure and the oil trunk pipeline system capacity were assessed in terms of delivery of raw materials to the refineries. The main groups of oil consuming countries were determined. The trends of crude oil production in the region were reviewed. The changes in production capacities and volumes at refineries in the last decade were shown. Based on the revealed refining trends, the scope of possible crude oil supplies to the refineries of the region under review was forecast. The existing transport infrastructure is able to handle the increased oil flow.

Keywords: European region, infrastructure, oil terminal capacity, pipeline capacity, refinery capacity, tanker draft

Procedia PDF Downloads 131