Search results for: post classification change detection

Authors: Corinne Kowalski, Lea de Jong, Rainer Sauerborn, Niamh Herlihy, Anneliese Depoux, Jale Tosun

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Climate change is considered one of the biggest threats to human health of the 21st century. The link between climate change and health has received relatively little attention in the media, in research and in policy-making. A long term and broad overview of how health is represented in the legislation on climate change is missing in the legislative literature. It is unknown if or how the argument for health is referred in legal clauses addressing climate change, in national and European legislation. Integrating scientific based evidence into policies regarding the impacts of climate change on health could be a key step to inciting the political and societal changes necessary to decelerate global warming. This may also drive the implementation of new strategies to mitigate the consequences on health systems. To provide an overview of this issue, we are analyzing the Global Climate Legislation Database provided by the Grantham Research Institute on Climate Change and the Environment. This institution was established in 2008 at the London School of Economics and Political Science. The database consists of (updated as of 1st January 2015) legislations on climate change in 99 countries around the world. This tool offers relevant information about the state of climate related policies. We will use the database to systematically analyze the 829 identified legislations to identify how health is represented as a relevant aspect of climate change legislation. We are conducting explorative research of national and supranational legislations and anticipate health to be addressed in various forms. The goal is to highlight how often, in what specific terms, which aspects of health or health risks of climate change are mentioned in various legislations. The position and recurrence of the mention of health is also of importance. Data will be extracted with complete quotation of the sentence which mentions health, which will allow for second qualitative stage to analyze which aspects of health are represented and in what context. This study is part of an interdisciplinary project called 4CHealth that confronts results of the research done on scientific, political and press literature to better understand how the knowledge on climate change and health circulates within those different fields and whether and how it is translated to real world change.

Keywords: climate change, explorative research, health, policies

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Authors: Gabriel Dolabella, Henrique Rangel, Stella Araújo, Carlos Bolonha, Igor de Lazari

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Public security is a common subject on the Brazilian political agenda. The seventh largest economy in the world has high crime and insecurity rates. Specialists try to explain this social picture based on poverty, inequality or public policies addressed to drug trafficking. This excerpt approaches State measures to handle that picture. Therefore, the public security - law enforcement institutions - is at the core of this paper, particularly the relationship among federal and state law enforcement agencies, mainly ruled by a system of urgency. The problems are informal changes on law enforcement management and public opinion collaboration to these changes. Whenever there were huge international events, Brazilian armed forces occupied streets to assure law enforcement - ensuring the order. This logic, considered in the long time, could impact the federal structure of the country. The post-madisonian theorists verify that urgency is often associated to delegation of powers, which is true for Brazilian law enforcement, but here there is a different delegation: States continuously delegate law enforcement powers to the federal government throughout the use of Armed Forces. Therefore, the hypothesis is: Brazil is under a political process of federalization of public security. The political framework addressed here can be explained by the disrespect of legal constraints and the failure of rule of law theoretical models. The methodology of analysis is based on general criteria. Temporally, this study investigates events from 2003, when discussions about the disarmament statute begun. Geographically, this study is limited to Brazilian borders. Materially, the analysis result from the observation of legal resources and political resources (pronouncements of government officials). The main parameters are based on post-madisonianism and federalization of public security can be assessed through credibility and popularity that allow evaluation of this political process of constitutional change. The objective is to demonstrate how the Military Forces are used in public security, not as a random fact or an isolated political event, in order to understand the political motivations and effects that stem from that use from an institutional perspective.

Keywords: public security, governability, rule of law, federalism

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Authors: Abubakar Maikudi

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Contrastive analysis is the method of analyzing the structure of any two languages with a view to determining the possible differential aspects of their systems irrespective of their genetic affinity or level of development. Contrastive analysis of two languages becomes useful when it is adequately describing the sound structure and grammatical structure of two languages, with comparative statements giving emphasis to the compatible items in the two systems. This research work uses comparative analysis theory to analyze adjective and adjectival phrases in Hausa and Yorùbá languages. The Hausa language belongs to the Chadic family of the Afro-Asiatic phylum, while the Yorùbá language belongs to the Benue-Congo family of the Niger-Congo phylum. The findings of the research clearly demonstrated that there are significant similarities in the adjectival phrase constructions of the two languages, i.e., nominal (Head) and post-nominal (Post-Head) use of the adjective, predicative function of an adjective, use of the reduplicative adjective, use of the comparative and superlative adjective, etc. However, there are dissimilarities in the adjectival phrase of the two languages in gender/number agreement and pre-nominal (Post-Head) use of adjectives.

Keywords: genetic affinity, contrastive analysis, phylum, pre-head, post-head

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Authors: T. C. Ng, R. Radzi, T. K. Ng, H. C. Chen

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The analgesic effects of lidocaine-bupivacaine block in cats undergoing routine orchiectomy were determined in this controlled, randomized, and blinded study. Twelve cats were randomly assigned to two groups. Cats in local block group received subcutaneous infiltration of 1 mg/kg of 2% lidocaine and 1 mg/kg of 0.5% bupivacaine into the scrotal sac. Cats in control group received equivolume of saline. Both groups were induced with mixture of ketamine (15 mg/kg) and acepromazine (0.1 mg/kg) intramuscularly and maintained on sevoflurane via facemask. Non-invasive blood pressures (BP), heart (HR), and respiratory rate (RR) were measured intra-operatively at specific events. Post-operatively, all cats received meloxicam, 0.2 mg/kg subcutaneously. Pain scores were determined at 4, 8, and 24 hours postoperatively. Mechanical pressure thresholds (MPT) at the perineum and metatarsus were determined at 2, 4, 8, and 24 hours postoperatively. Intra-operatively, the BP and HR tended to be higher in the control group. The increment in HR peaked during traction and autoligation of the spermatic cord in the control group. There was no treatment difference in RR. Post-operatively, pain scores in the group given local blocks were lower than the control group at 4 hour post-operation. There was no treatment difference in the post-operative HR, RR, BP and MPT values. In conclusion, subcutaneous infiltration of lidocaine-bupivacaine into the scrotal sac before orchiectomy improved intra-operative hemodynamic stability and provided better analgesia up to 4 hours post-surgery.

Keywords: analgesia, bupivacaine, cat, lidocaine, local block, orchiectomy

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Authors: Mustafa Kaya

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After the 1999 Marmara earthquake in Turkey research by the Turkish Precast Union stated that 24.50% of the precast structures were damaged with some of this damage being observed in the beam to column connections of the structures. Since it is essential to provide those rendered homeless by the earthquake with safe, habitable accommodation repairing medium and slight levels of damage at the connection parts should be undertaken. In order to prove that a repaired connection was sufficiently strong, a precast beam to column post tensioned connection was tested in three phases. In phase one, the middle level damage was observed at 6% drift at these connections. As a result of the extra loads applied, little damage was observed. In the last phase, the four connections tested in the first phase were repaired using epoxy resin and then retested. The results from the tests on the repaired precast and the undamaged specimens showed that the repaired specimens were sufficiently strong, thus proving that repair to damaged precast beam to column post tensioned connections can be undertaken.

Keywords: precast beam to column connection, moment-resisting connection, post-tensioned connections, repair of precast connections

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Authors: S. Korkut, M. S. Kilic, E. Erhan

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In order to detect and quantify the phenolic contents of a wastewater with biosensors, two working electrodes based on modified Poly (Pyrrole) films were fabricated. Enzyme horseradish peroxidase was used as biomolecule of the prepared electrodes. Various phenolics were tested at the biosensor. Phenol detection was realized by electrochemical reduction of quinones produced by enzymatic activity. Analytical parameters were calculated and the results were compared with each other.

Keywords: carbon nanotube, phenol biosensor, polypyrrole, poly (glutaraldehyde)

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Authors: Paul M. H. J. Roekaerts

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In recent years, it became clear that much intensive care (ICU) survivors develop a post-intensive care syndrome (PICS) consisting of psychiatric, cognitive and physical problems for a prolonged period after their ICU stay. Physical inactivity and delirium during the ICU stay are the main determinants of the post-ICU PICS. This presentation will focus on delirium, its epidemiology, prevalence, effect on outcome, risk factors and the current standard of care for managing delirium. Because ICU delirium is a predictor of prolonged length-of-stay in the ICU and of death, the use of a delirium prevention bundle (DPB) becomes mandatory in every ICU. In this presentation, a DPB bundle will be discussed consisting of six components: pain, sedation, sleep, sensory and intellectual stimulation, early mobilization, and hydration. For every of the six components, what to do and what not to do will be discussed. The author will present his own institutional policy on pharmacological and non-pharmacological interventions in the management of delirium. The component ‘early mobilization’ will be discussed more in detail, as this component is extremely important in the prevention of delirium as well as in the prevention of the PICS. The author will conclude his presentation with the remaining areas of uncertainties/work and research to be done.

Keywords: delirium, delirium prevention bundle, early mobilisation in intensive care (ICU), post-intensive care syndrome (PICS)

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Authors: Mouli Banga, Chaya Ravindra

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Wound classification is a crucial step in wound diagnosis. An effective classifier can aid wound specialists in identifying wound types with reduced financial and time investments, facilitating the determination of optimal treatment procedures. This study presents a deep neural network-based classifier that leverages wound images and their corresponding locations to categorize wounds into various classes, such as diabetic, pressure, surgical, and venous ulcers. By incorporating a developed body map, the process of tagging wound locations is significantly enhanced, providing healthcare specialists with a more efficient tool for wound analysis. We conducted a comparative analysis between two prominent convolutional neural network models, ResNet50 and MobileNetV2, utilizing a dataset of 730 images. Our findings reveal that the RestNet50 outperforms MovileNetV2, achieving an accuracy of approximately 90%, compared to MobileNetV2’s 83%. This disparity highlights the superior capability of ResNet50 in the context of this dataset. The results underscore the potential of integrating deep learning with spatial data to improve the precision and efficiency of wound diagnosis, ultimately contributing to better patient outcomes and reducing healthcare costs.

Keywords: wound classification, MobileNetV2, ResNet50, multimodel

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Authors: A. Nachour, L. Ouzizi, Y. Aoura

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Recent developments on multi-agent system have brought a new research field on image processing. Several algorithms are used simultaneously and improved in deferent applications while new methods are investigated. This paper presents a new automatic method for edge detection using several agents and many different actions. The proposed multi-agent system is based on parallel agents that locally perceive their environment, that is to say, pixels and additional environmental information. This environment is built using Vector Field Convolution that attract free agent to the edges. Problems of partial, hidden or edges linking are solved with the cooperation between agents. The presented method was implemented and evaluated using several examples on different synthetic and medical images. The obtained experimental results suggest that this approach confirm the efficiency and accuracy of detected edge.

Keywords: edge detection, medical MRImages, multi-agent systems, vector field convolution

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Authors: Rhea Kapoor

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Fractal dimension provides a way to characterize non-geometric shapes like those found in nature. The purpose of this research is to estimate Minkowski fractal dimension of human lung images for early detection of lung cancer. Lung cancer is the leading cause of death among all types of cancer and an early histopathological analysis will help reduce deaths primarily due to late diagnosis. A Python application program, PathoPy2.0, was developed for analyzing medical images in pixelated format and estimating Minkowski fractal dimension using a new box-counting algorithm that allows windowing of images for more accurate calculation in the suspected areas of cancerous growth. Benchmark geometric fractals were used to validate the accuracy of the program and changes in fractal dimension of lung images to indicate the presence of issues in the lung. The accuracy of the program for the benchmark examples was between 93-99% of known values of the fractal dimensions. Fractal dimension values were then calculated for lung images, from National Cancer Institute, taken over time to correctly detect the presence of cancerous growth. For example, as the fractal dimension for a given lung increased from 1.19 to 1.27 due to cancerous growth, it represents a significant change in fractal dimension which lies between 1 and 2 for 2-D images. Based on the results obtained on many lung test cases, it was concluded that fractal dimension of human lungs can be used to diagnose lung cancer early. The ideas behind PathoPy2.0 can also be applied to study patterns in the electrical activity of the human brain and DNA matching.

Keywords: fractals, histopathological analysis, image processing, lung cancer, Minkowski dimension

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Authors: Retno Supriyanti, Ahmad Chuzaeri, Yogi Ramadhani, A. Haris Budi Widodo

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The use of ultrasonography in the medical world has been very popular including the diagnosis of pregnancy. In determining pregnancy, ultrasonography has many roles, such as to check the position of the fetus, abnormal pregnancy, fetal age and others. Unfortunately, all these things still need to analyze the role of the obstetrician in the sense of image raised by ultrasonography. One of the most striking is the determination of gestational age. Usually, it is done by measuring the length of the fetus manually by obstetricians. In this study, we developed a computer-aided diagnosis for the determination of gestational age by measuring the length of the fetus automatically using edge detection method and image morphology. Results showed that the system is sufficiently accurate in determining the gestational age based image processing.

Keywords: computer aided diagnosis, gestational age, and diameter of uterus, length of fetus, edge detection method, morphology image

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Authors: S. Zolghadri, M. Naderi, H. Yousefnia, B. Alirzapour, A. R. Jalilian, A. Ramazani

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Nowadays, 68Ga-DOTATOC has been known as a potential agent for the detection of neuroendocrine tumours and it has indicated higher sensitivity compared with the 111In-Octeroetide. The aim of this study was to evaluate the effectiveness of this new agent in the diagnosis of adenocarcinoma breast cancer. 68Ga-DOTATOC was prepared with the radiochemical purity of higher than 98% and by the specific activity of 39.6 TBq/mmol. 37 MBq of the complex was injected intravenously into the BULB/c mice with adenocarcinoma breast cancer. PET/CT images were acquired after 30, 60 and 90 min post injection demonstrated significant accumulation in the tumour sites. Also, considerable activity was observed in the kidney and bladder as the main routs of excretion. Generally, the results showed that 68Ga-DOTATOC can be considered as a suitable complex for diagnosis of the adenocarcinoma breast cancer using PET procedure.

Keywords: adenocarcinoma breast cancer, 68Ga, octreotide, imaging

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Authors: Alden Boby, Dane Brown, James Connan

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Character recognition is a well-researched topic across disciplines. Regardless, creating a solution that can cater to multiple situations is still challenging. Vehicle licence plates lack an international standard, meaning that different countries and regions have their own licence plate format. A problem that arises from this is that the typefaces and designs from different regions make it difficult to create a solution that can cater to a wide range of licence plates. The main issue concerning detection is the character recognition stage. This paper aims to create an object detection-based character recognition model trained on a custom dataset that consists of typefaces of licence plates from various regions. Given that characters have featured consistently maintained across an array of fonts, YOLO can be trained to recognise characters based on these features, which may provide better performance than OCR methods such as Tesseract OCR.

Keywords: computer vision, character recognition, licence plate recognition, object detection

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The widespread use of machine learning applications in production is significantly accelerated by improved computing power and increasing data availability. Predictive quality enables the assurance of product quality by using machine learning models as a basis for decisions on test results. The use of real Bosch production data based on geometric gauge blocks from machining, mating data from assembly and hydraulic measurement data from final testing of directional valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: machine learning, classification, predictive quality, hydraulics, supervised learning

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Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: Weiwei Cui, Dejian Lin, Leigang Zhang, Yao Wang, Zheng Sun, Lianfeng Li

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Built-in test (BIT) is an important technology in testability field, and it is widely used in state monitoring and fault diagnosis. With the improvement of modern equipment performance and complexity, the scope of BIT becomes larger, and it leads to the emergence of false alarm problem. The false alarm makes the health assessment unstable, and it reduces the effectiveness of BIT. The conventional false alarm suppression methods such as repeated test and majority voting cannot meet the requirement for a complicated system, and the intelligence algorithms such as artificial neural networks (ANN) are widely studied and used. However, false alarm has a very low frequency and small sample, yet a method based on ANN requires a large size of training sample. To recognize the false alarm, we propose a method based on multi-classification support vector machine (SVM) in this paper. Firstly, we divide the state of a system into three states: healthy, false-alarm, and faulty. Then we use multi-classification with '1 vs 1' policy to train and recognize the state of a system. Finally, an example of fault injection system is taken to verify the effectiveness of the proposed method by comparing ANN. The result shows that the method is reasonable and effective.

Keywords: false alarm, fault diagnosis, SVM, k-means, BIT

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Authors: Alshehri Sultan

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Organizational change has remained an important subject for many researchers in the field of organizations theory. We propose the importance of organizational liability of renewal through a model that examines how an organization can overcome potential rigidities in organizational capabilities from learning by changing capabilities. We examine whether an established organization can overcome liability of renewal by changes in organizational capabilities and how the organizational renewal process reflect on the balance between the dynamic aspect of organizational learning as demonstrated by changes in capabilities and the stabilizing aspects of organizational inertia. We found both positive relationship between organizational learning and performance, and between legitimacy and performance. Performance and legitimacy have, however, a negative relationship on the pressure for change.

Keywords: organizational capabilities, organizational liability, liability of renewal, pressure for change

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Authors: Niloofar Yousefi, Marie Alaghband, Ivan Garibay

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With the increase of credit card usage, the volume of credit card misuse also has significantly increased, which may cause appreciable financial losses for both credit card holders and financial organizations issuing credit cards. As a result, financial organizations are working hard on developing and deploying credit card fraud detection methods, in order to adapt to ever-evolving, increasingly sophisticated defrauding strategies and identifying illicit transactions as quickly as possible to protect themselves and their customers. Compounding on the complex nature of such adverse strategies, credit card fraudulent activities are rare events compared to the number of legitimate transactions. Hence, the challenge to develop fraud detection that are accurate and efficient is substantially intensified and, as a consequence, credit card fraud detection has lately become a very active area of research. In this work, we provide a survey of current techniques most relevant to the problem of credit card fraud detection. We carry out our survey in two main parts. In the first part, we focus on studies utilizing classical machine learning models, which mostly employ traditional transnational features to make fraud predictions. These models typically rely on some static physical characteristics, such as what the user knows (knowledge-based method), or what he/she has access to (object-based method). In the second part of our survey, we review more advanced techniques of user authentication, which use behavioral biometrics to identify an individual based on his/her unique behavior while he/she is interacting with his/her electronic devices. These approaches rely on how people behave (instead of what they do), which cannot be easily forged. By providing an overview of current approaches and the results reported in the literature, this survey aims to drive the future research agenda for the community in order to develop more accurate, reliable and scalable models of credit card fraud detection.

Keywords: Credit Card Fraud Detection, User Authentication, Behavioral Biometrics, Machine Learning, Literature Survey

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Authors: Sunil Mittal, Sukhpreet Singh, Hardeep Kaur

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The affinity of thiol group with heavy metals is a well-established phenomenon. The present investigation has been focused on electrochemical response of cysteine and thioredoxin against arsenite (As III) on indium tin oxide (ITO) electrodes. It was observed that both the compounds produce distinct response in free and immobilised form at the electrode. The SEM, FTIR, and impedance studies of the modified electrode were conducted for characterization. Various parameters were optimized to achieve As (III) effect on the reduction potential of the compounds. Cyclic voltammetry and linear sweep voltammetry were employed as the analysis techniques. The optimum response was observed at neutral pH in both the cases, at optimum concentration of 2 mM and 4.27 µM for cysteine and thioredoxin respectively. It was observed that presence of As (III) increases the reduction current of both the moieties. The linear range of detection for As (III) with cysteine was from 1 to 10 mg L⁻¹ with detection limit of 0.8 mg L⁻¹. The thioredoxin was found more sensitive to As (III) and displayed a linear range from 0.1 to 1 mg L⁻¹ with detection limit of 10 µg L⁻¹.

Keywords: arsenite, cyclic voltammetry, cysteine, thioredoxin

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Authors: M. R. Ghasemi, R. Ghiasi, H. Varaee

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Model updating method has received increasing attention in damage detection structures based on measured modal parameters. Therefore, a probability-based damage detection (PBDD) procedure based on a model updating procedure is presented in this paper, in which a one-stage model-based damage identification technique based on the dynamic features of a structure is investigated. The presented framework uses a finite element updating method with a Monte Carlo simulation that considers the uncertainty caused by measurement noise. Enhanced ideal gas molecular movement (EIGMM) is used as the main algorithm for model updating. Ideal gas molecular movement (IGMM) is a multiagent algorithm based on the ideal gas molecular movement. Ideal gas molecules disperse rapidly in different directions and cover all the space inside. This is embedded in the high speed of molecules, collisions between them and with the surrounding barriers. In IGMM algorithm to accomplish the optimal solutions, the initial population of gas molecules is randomly generated and the governing equations related to the velocity of gas molecules and collisions between those are utilized. In this paper, an enhanced version of IGMM, which removes unchanged variables after specified iterations, is developed. The proposed method is implemented on two numerical examples in the field of structural damage detection. The results show that the proposed method can perform well and competitive in PBDD of structures.

Keywords: enhanced ideal gas molecular movement (EIGMM), ideal gas molecular movement (IGMM), model updating method, probability-based damage detection (PBDD), uncertainty quantification

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Authors: Rahil Bahrami, Kaveh Ashenayi

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This paper presents a systematic classification and comparative analysis of non-isolated bi-directional DC-DC converters. The increasing demand for efficient energy conversion in diverse applications has spurred the development of various converter topologies. In this study, we categorize bi-directional converters into three distinct classes: Inverting, Non-Inverting, and Interleaved. Each category is characterized by its unique operational characteristics and benefits. Furthermore, a practical comparison is conducted by evaluating the results of simulation of each bi-directional converter. BDCs can be classified into isolated and non-isolated topologies. Non-isolated converters share a common ground between input and output, making them suitable for applications with minimal voltage change. They are easy to integrate, lightweight, and cost-effective but have limitations like limited voltage gain, switching losses, and no protection against high voltages. Isolated converters use transformers to separate input and output, offering safety benefits, high voltage gain, and noise reduction. They are larger and more costly but are essential for automotive designs where safety is crucial. The paper focuses on non-isolated systems.The paper discusses the classification of non-isolated bidirectional converters based on several criteria. Common factors used for classification include topology, voltage conversion, control strategy, power capacity, voltage range, and application. These factors serve as a foundation for categorizing converters, although the specific scheme might vary depending on contextual, application, or system-specific requirements. The paper presents a three-category classification for non-isolated bi-directional DC-DC converters: inverting, non-inverting, and interleaved. In the inverting category, converters produce an output voltage with reversed polarity compared to the input voltage, achieved through specific circuit configurations and control strategies. This is valuable in applications such as motor control and grid-tied solar systems. The non-inverting category consists of converters maintaining the same voltage polarity, useful in scenarios like battery equalization. Lastly, the interleaved category employs parallel converter stages to enhance power delivery and reduce current ripple. This classification framework enhances comprehension and analysis of non-isolated bi-directional DC-DC converters. The findings contribute to a deeper understanding of the trade-offs and merits associated with different converter types. As a result, this work aids researchers, practitioners, and engineers in selecting appropriate bi-directional converter solutions for specific energy conversion requirements. The proposed classification framework and experimental assessment collectively enhance the comprehension of non-isolated bi-directional DC-DC converters, fostering advancements in efficient power management and utilization.The simulation process involves the utilization of PSIM to model and simulate non-isolated bi-directional converter from both inverted and non-inverted category. The aim is to conduct a comprehensive comparative analysis of these converters, considering key performance indicators such as rise time, efficiency, ripple factor, and maximum error. This systematic evaluation provides valuable insights into the dynamic response, energy efficiency, output stability, and overall precision of the converters. The results of this comparison facilitate informed decision-making and potential optimizations, ensuring that the chosen converter configuration aligns effectively with the designated operational criteria and performance goals.

Keywords: bi-directional, DC-DC converter, non-isolated, energy conversion

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Authors: Mitchell Stanton

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Background: Endovascular management is well established as a mainstay treatment option for cerebral aneurysms. It is also well established that immediate post procedural imaging can be difficult to interpret due to the presence of contrast material. However, through the use of Dual-Energy computed tomography, it has become possible to differentiate contrast extravasation and intracranial haemorrhage. This case illustrates the importance of this technology following endovascular treatment of an unruptured cerebral aneurysm. Case Presentation: A 79-year-old female was found to have an unruptured large intracavernous ICA fusiform aneurysm on CT Brain Angiogram after presenting with acute ophthalmoplegia. This ophthalmoplegia was caused by mass effect from the aneurysm and subsequently the aneurysm was treated with an endovascular flow diverting stent. CT brain was performed post operatively due to a reduced level of consciousness and this showed diffuse subarachnoid hyperdensity of the left hemisphere. The use of Dual-Energy CT allowed accurate differentiation and illustrated diffuse contrast material extravasation, allowing patient to continue on dual-antiplatelets and therapeutic anticoagulation to reduce the risk of ischaemic injury post endovascular stent. Conclusion: Endovascular treatment options for management of intracranial aneurysms are constantly evolving. The use of Dual-Energy CT therefore has an integral role in accurately diagnosing any post-operative complications. Specifically, differentiating between subarachnoid haemorrhage and contrast extravasation is vital in these patients due to the significant consequences to their ongoing management in regards to continuation or cessation of antiplatelets or anticoagulation. With increasing access to this technology, its use should become standard practice in the post-operative investigation of these patients undergoing endovascular treatment.

Keywords: aneurysm, computed tomography, contrast extravasation, dual-energy CT, endovascular, subarachnoid haemorrhage

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Authors: Vaibhav Singh Rajput, Ravi Kumar Jatoth, Nagu Bhookya, Bhasker Boda

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In this paper proportional, integral, derivative (PID) controller is used to control the pitch angle of the aircraft when the elevation angle is changed or modified. The pitch angle is dependent on elevation angle; a change in one corresponds to a change in the other. The PID controller helps in restricted change of pitch rate in response to the elevation angle. The PID controller is dependent on different parameters like Kp, Ki, Kd which change the pitch rate as they change. Various methodologies are used for changing those parameters for getting a perfect time response pitch angle, as desired or wished by a concerned person. While reckoning the values of those parameters, trial and guessing may prove to be futile in order to provide comfort to passengers. So, using some metaheuristic techniques can be useful in handling these errors. Hybrid GA-PSO is one such powerful algorithm which can improve transient and steady state response and can give us more reliable results for PID gain scheduling problem.

Keywords: pitch rate, elevation angle, PID controller, genetic algorithm, particle swarm optimization, phugoid

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Authors: Paul Campbell, Nicholas Biddle

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There is a complex, endogenous relationship between individual circumstances, attitudes, and behaviour. This study uses longitudinal panel data to assess changes in social and political attitudes over a 7-year period. Attitudes are captured with the question 'what is the most important issue facing Australia today', collected at multiple time points in a longitudinal survey of 2200 Australians. Consistency of attitudes, and factors predicting change over time, are assessed. The consistency of responses has methodological implications for data collection, specifically how often such questions ought to be asked of a population. When change in attitude is observed, this study assesses the extent to which individual demographic characteristics, personality traits, and broader societal events predict change.

Keywords: attitudes, longitudinal survey analysis, personality, social values

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Authors: Mohsen Hasirian, Amir Shahab Shahabi

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Modern societies, marked by expansive Internet connectivity and the rise of e-commerce, are now integrated with digital platforms at an unprecedented level. The efficiency, speed, and accessibility of e-commerce have garnered a substantial consumer base. Against this backdrop, electronic banking has undergone rapid proliferation within the realm of online activities. However, this growth has inadvertently given rise to an environment conducive to illicit activities, notably electronic payment fraud, posing a formidable challenge to the domain of electronic banking. A pivotal role in upholding the integrity of electronic commerce and business transactions is played by electronic fraud detection, particularly in the context of credit cards which underscores the imperative of comprehensive research in this field. To this end, our study introduces an Attentional Bi-Stream Sequence Learner (AttBiSeL) framework that leverages attention mechanisms and recurrent networks. By incorporating bidirectional recurrent layers, specifically bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, the proposed model adeptly extracts past and future transaction sequences while accounting for the temporal flow of information in both directions. Moreover, the integration of an attention mechanism accentuates specific transactions to varying degrees, as manifested in the output of the recurrent networks. The effectiveness of the proposed approach in automatic credit card fraud classification is evaluated on the European Cardholders' Fraud Dataset. Empirical results validate that the hybrid architectural paradigm presented in this study yields enhanced accuracy compared to previous studies.

Keywords: credit card fraud, deep learning, attention mechanism, recurrent neural networks

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Authors: Caroline Hoorn

Abstract:

Post-matriculants in disadvantaged communities such as Douglas encounter a number of career challenges. The transition to the democratic dispensation in 1994, coupled with the rapid changes in the information domain that are characteristic of post-industrial life, complicate the career development trajectories of disadvantaged youth. The career development stories and experiences of disadvantaged youth in provinces such as the Northern Cape have not been told, leading to their marginalisation. It is against this background that the study explored the gendered dimensions of career development narratives, experiences, and choices of post-matriculants in the Douglas community in the Northern Cape. Using a qualitative, narrative approach, the researcher elicited career development stories from 23 participants in Douglas using semi-structured interviews. Two main themes were highlighted through the narratives; (1) willingness to challenge the traditional male dominated career script (2) breaking gender barriers. The study showed that gender did not have any influence on the career choices of the post-matriculants. The perceptions around career choices and gender were being challenged partly by the urge to affirm equality and the constant reminder of the poverty-stricken conditions prevalent in the households. A preferred gender is not required to be attached to the fulfilment of outcomes in a knowledge-based economy. Thus, it is not an issue of gender or masculinity but knowledge and skills. Furthermore, the study revealed that the career choices being considered are still the traditionally stereotypical careers like nursing, teaching, and social work, which demonstrates a lack of information to a broader pool of career options to select from.

Keywords: career development, gender, narratives, post-matriculants

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Authors: Daniel M. Wolff, Patricia Gray, Rafael de la Parra Venegas

Abstract:

An unsupervised machine listening system is constructed and applied to a dataset of 17,195 30-second marine hydrophone recordings. The system is then heuristically supplemented with anecdotal listening, contextual recording information, and supervised learning techniques to reduce the number of false positives. Features for classification are assembled by extracting the following data from each of the audio files: the spectral centroid, root-mean-squared values for each frequency band of a 10-octave filter bank, and mel-frequency cepstral coefficients in 5-second frames. In this way both time- and frequency-domain information are contained in the features to be passed to a clustering algorithm. Classification is performed using the k-means algorithm and then a k-nearest neighbors search. Different values of k are experimented with, in addition to different combinations of the available feature sets. Hypothesized class labels are 'primarily anthrophony' and 'primarily biophony', where the best class result conforming to the former label has 104 members after heuristic pruning. This demonstrates how a large audio dataset has been made more tractable with machine learning techniques, forming the foundation of a framework designed to acoustically monitor and gauge biological and anthropogenic activity in a marine environment.

Keywords: anthrophony, hydrophone, k-means, machine learning

Procedia PDF Downloads 170

Authors: Mehak Masood

Abstract:

Education is critical for promoting sustainable development and improving the capacity of people to address environment and development issues. It is also critical for achieving environmental and ethical awareness, values and attitudes, skills and behaviour consistent with sustainable development and for effective public participation in decision-making. In this regard, The British Council Pakistan have conducted a need assessment study conducted during the training sessions with three different groups of educationists belonging to both government and public sectors on the topic of Climate Change and Environmental Education (CCEE). This study aims to review perceptions about climate change and environmental education and analyze its need and importance according to educationists of Pakistan.

Keywords: environmental education, climate change, resilience development, awareness

Procedia PDF Downloads 424

Authors: S. Nandagopalan, N. Pradeep

Abstract:

The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, bayesian, echocardiographic image, feature vector

Procedia PDF Downloads 421

Authors: Gigih Supriyatno

Abstract:

SQL injection is one of the most common types of attacks and has a very critical impact on web servers. In the worst case, an attacker can perform post-exploitation after a successful SQL injection attack. In the case of forensics web servers, web server analysis is closely related to log file analysis. But sometimes large file sizes and different log types make it difficult for investigators to look for traces of attackers on the server. The purpose of this paper is to help investigator take appropriate steps to investigate when the web server gets attacked. We use attack scenarios using SQL injection attacks including PHP backdoor injection as post-exploitation. We perform post-mortem analysis of web server logs based on Hypertext Transfer Protocol (HTTP) POST and HTTP GET method approaches that are characteristic of SQL injection attacks. In addition, we also propose structured analysis method between the web server application log file, database application, and other additional logs that exist on the webserver. This method makes the investigator more structured to analyze the log file so as to produce evidence of attack with acceptable time. There is also the possibility that other attack techniques can be detected with this method. On the other side, it can help web administrators to prepare their systems for the forensic readiness.

Keywords: web forensic, SQL injection, investigation, web shell

Procedia PDF Downloads 148