Search results for: multi-agent double deep Q-network (MADDQN)

Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

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Authors: Clotilde Guyon, Nada Jmari, Yen-Chin Li, Jean Denoyel, Noriyuki Fujikado, Christophe Blanchet, David Root, Matthieu Giraud

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Aire induces ectopic expression of a large repertoire of tissue-specific antigen (TSA) genes in thymic medullary epithelial cells (MECs), driving immunological self-tolerance in maturing T cells. Although important mechanisms of Aire-induced transcription have recently been disclosed through the identification and the study of Aire’s partners, the fine transcriptional functions underlied by a number of them and conferred to Aire are still unknown. Alternative cleavage and polyadenylation (APA) is an essential mRNA processing step regulated by the termination complex consisting of 85 proteins, 10 of them have been related to Aire. We evaluated APA in MECs in vivo by microarray analysis with mRNA-spanning probes and RNA deep sequencing. We uncovered the preference of Aire-dependent transcripts for short-3’UTR isoforms and for proximal poly(A) site selection marked by the increased binding of the cleavage factor Cstf-64. RNA interference of the 10 Aire-related proteins revealed that Clp1, a member of the core termination complex, exerts a profound effect on short 3’UTR isoform preference. Clp1 is also significantly upregulated in the MECs compared to 25 mouse tissues in which we found that TSA expression is associated with longer 3’UTR isoforms. Aire-dependent transcripts escape a global 3’UTR lengthening associated with MEC differentiation, thereby potentiating the repressive effect of microRNAs that are globally upregulated in mature MECs. Consistent with these findings, RNA deep sequencing of actinomycinD-treated MECs revealed the increased stability of short 3’UTR Aire-induced transcripts, resulting in TSA transcripts accumulation and contributing for their enrichment in the MECs.

Keywords: Aire, central tolerance, miRNAs, transcription termination

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Authors: Hardisiswo Soedjana, Inne Caroline

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Deep dermal or full thickness burns are inevitably lead to post-burn contractures. These contractures remain to be one of the most concerning late complications of burn injuries. Surgical management includes releasing the contracture followed by resurfacing the defect accompanied by post-operative rehabilitation. Optimal treatment of post-burn contractures depends on the characteristics of the contractures. This study is aimed to describe clinical characteristics, problems, and management of post-burn contractures in adults and children. A retrospective analysis was conducted from medical records of patients suffered from contractures after burn injuries admitted to Hasan Sadikin general hospital between January 2016 and January 2018. A total of 50 patients with post burn contractures were included in the study. There were 17 adults and 33 children. Most patients were male, whose age range within 15-59 years old and 5-9 years old. Educational background was mostly senior high school among adults, while there was only one third of children who have entered school. Etiology of burns was predominantly flame in adults (82.3%); whereas flame and scald were the leading cause of burn injury in children (11%). Based on anatomical regions, hands were the most common affected both in adults (35.2%) and children (48.5%). Contractures were identified in 6-12 months since the initial burns. Most post-burn hand contractures were resurfaced with full-thickness skin graft (FTSG) both in adults and children. There were 11 patients who presented with recurrent contracture after previous history of contracture release. Post-operative rehabilitation was conducted for all patients; however, it is important to highlight that it is still challenging to control splinting and exercise when patients are discharged and especially the compliance in children. In order to improve quality of life in patients with history of deep burn injuries, prevention of contractures should begin right after acute care has been established. Education for the importance of splinting and exercise should be administered as comprehensible as possible for adult patients and parents of pediatric patients.

Keywords: burn, contracture, education, exercise, splinting

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Authors: Balakrishna Shetty

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Introduction: Stem Cell Imaging is a challenging field since the advent of Stem Cell treatment in humans. Series of research on tagging and tracking the stem cells has not been very effective. The present study is an effort by the authors to track the stem cells injected into calf muscles by Magnetic Resonance Diffusion Weighted Imaging. Materials and methods: Stem Cell injection deep into the calf muscles of patients with peripheral vascular disease is one of the recent treatment modalities followed in our institution. 5 patients who underwent deep intramuscular injection of stem cells as treatment were included for this study. Pre and two hours Post injection MRI of bilateral calf regions was done using 1.5 T Philips Achieva, 16 channel system using 16 channel torso coils. Axial STIR, Axial Diffusion weighted images with b=0 and b=1000 values with back ground suppression (DWIBS sequence of Philips MR Imaging Systems) were obtained at 5 mm interval covering the entire calf. The invert images were obtained for better visualization. 120ml of autologous bone marrow derived stem cells were processed and enriched under c-GMP conditions and reduced to 40ml solution containing mixture of above stem cells. Approximately 40 to 50 injections, each containing 0.75ml of processed stem cells, was injected with marked grids over the calf region. Around 40 injections, each of 1ml normal saline, is injected into contralateral leg as control. Results: Significant Diffusion hyper intensity is noted at the site of injected stem cells. No hyper intensity noted before the injection and also in the control side where saline was injected conclusion: This is one of the earliest studies in literature showing diffusion hyper intensity in intramuscularly injected stem cells. The advantages and deficiencies in this study will be discussed during the presentation.

Keywords: stem cells, imaging, DWI, peripheral vascular disease

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Authors: Tahani Hallek, Dhaou Akrout, Riadh Ahmadi, Mabrouk Montacer

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The present work is an organic geochemical study of the Fahdene Formation outcrops at the Mahjouba region belonging to the Eastern part of the Kalaat Senan structure in northwestern Tunisia (the Kef-Tedjerouine area). The analytical study of the organic content of the samples collected, allowed us to point out that the Formation in question is characterized by an average to good oil potential. This fossilized organic matter has a mixed origin (type II and III), as indicated by the relatively high values of hydrogen index. This origin is confirmed by the C29 Steranes abundance and also by tricyclic terpanes C19/(C19+C23) and tetracyclic terpanes C24/(C24+C23) ratios, that suggest a marine environment of deposit with high plants contribution. We have demonstrated that the heterogeneity of organic matter between the marine aspect, confirmed by the presence of foraminifera, and the continental contribution, is the result of an episodic anomaly in relation to the sequential stratigraphy. Given that the study area is defined as an outer platform forming a transition zone between a stable continental domain to the south and a deep basin to the north, we have explained the continental contribution by successive forced regressions, having blocked the albian transgression, allowing the installation of the lowstand system tracts. This aspect is represented by the incised valleys filling, in direct contact with the pelagic and deep sea facies. Consequently, the Fahdene Formation, in the Kef-Tedjerouine area, consists of transgressive system tracts (TST) brutally truncated by extras of continental progradation; resulting in a mixed influence deposition having retained a heterogeneous organic material.

Keywords: molecular geochemistry, biomarkers, forced regression, deposit environment, mixed origin, Northern Tunisia

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Authors: Suleyman Ogul Ertugrul, Mustafa Turgut, Serkan Inandı, Mustafa Gorkem Coban, Mustafa Kıgılı, Ali Mert, Oguzhan Kesmez, Murat Ozancı, Caglar Uyulan

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In high-performance automotive engineering, the realistic simulation of suspension systems is crucial for enhancing vehicle dynamics and handling. This study focuses on the double wishbone suspension system, prevalent in racing vehicles due to its superior control and stability characteristics. Utilizing MATLAB and Adams Car simulation software, we conduct a comprehensive analysis of displacement behaviors and damper sizing under various dynamic conditions. The initial phase involves using MATLAB to simulate the entire suspension system, allowing for the preliminary determination of damper size based on the system's response under simulated conditions. Following this, manual calculations of wheel loads are performed to assess the forces acting on the front and rear suspensions during scenarios such as braking, cornering, maximum vertical loads, and acceleration. Further dynamic force analysis is carried out using MATLAB Simulink, focusing on the interactions between suspension components during key movements such as bumps and rebounds. This simulation helps in formulating precise force equations and in calculating the stiffness of the suspension springs. To enhance the accuracy of our findings, we focus on a detailed kinematic and dynamic analysis. This includes the creation of kinematic loops, derivation of relevant equations, and computation of Jacobian matrices to accurately determine damper travel and compression metrics. The calculated spring stiffness is crucial in selecting appropriate springs to ensure optimal suspension performance. To validate and refine our results, we replicate the analyses using the Adams Car software, renowned for its detailed handling of vehicular dynamics. The goal is to achieve a robust, reliable suspension setup that maximizes performance under the extreme conditions encountered in racing scenarios. This study exemplifies the integration of theoretical mechanics with advanced simulation tools to achieve a high-performance suspension setup that can significantly improve race car performance, providing a methodology that can be adapted for different types of racing vehicles.

Keywords: FSAE, suspension system, Adams Car, kinematic

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Authors: J. Fernandez de Canete, S. Fernandez-Calvo, I. García-Moral

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This paper presents and discusses the application of the object-oriented modelling software SIMSCAPE to hydraulic systems, with particular reference to multivariable proportional-integral-derivative (PID) control. As a result, a particular modelling approach of a double cylinder-piston coupled system is proposed and motivated, and the SIMULINK based PID tuning tool has also been used to select the proper controller parameters. The paper demonstrates the usefulness of the object-oriented approach when both physical modelling and control are tackled.

Keywords: object-oriented modeling, multivariable hydraulic system, multivariable PID control, computer simulation

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Authors: Hsin-Yi Huang, Ming-Sheng Liu, Jiun-Yan Shiau

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Planning the order picking lists of warehouses to achieve when the costs associated with logistics on the operational performance is a significant challenge. In e-commerce era, this task is especially important productive processes are high. Nowadays, many order planning techniques employ supervised machine learning algorithms. However, the definition of which features should be processed by such algorithms is not a simple task, being crucial to the proposed technique’s success. Against this background, we consider whether unsupervised algorithms can enhance the planning of order-picking lists. A Zone2 picking approach, which is based on using clustering algorithms twice, is developed. A simplified example is given to demonstrate the merit of our approach.

Keywords: order picking, warehouse, clustering, unsupervised learning

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Authors: Rona Lee Suelo-Calanao, Mahmoud Loubani

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Objective: Sternal wound infection (SWI) following cardiac operation has a significant impact on patient morbidity and mortality. It also contributes to longer hospital stays and increased treatment costs. SWI management is mainly focused on treatment rather than prevention. This study looks at the effect of closed incision negative pressure therapy (ciNPT) dressing to help reduce the incidence of superficial SWI in high-risk patients after cardiac surgery. The ciNPT dressing was evaluated at 3 cardiac hospitals in the United Kingdom". Methods: All patients who had cardiac surgery from 2013 to 2021 were included in the study. The patients were classed as high risk if they have two or more of the recognised risk factors: obesity, age above 80 years old, diabetes, and chronic obstructive pulmonary disease. Patients receiving standard dressing (SD) and patients using ciNPT were propensity matched, and the Fisher’s exact test (two-tailed) and unpaired T-test were used to analyse categorical and continuous data, respectively. Results: There were 766 matched cases in each group. Total SWI incidences are lower in the ciNPT group compared to the SD group (43 (5.6%) vs 119 (15.5%), P=0.0001). There are fewer deep sternal wound infections (14(1.8%) vs. 31(4.04%), p=0.0149) and fewer superficial infections (29(3.7%) vs. 88 (11.4%), p=0.0001) in the ciNPT group compared to the SD group. However, the ciNPT group showed a longer average length of stay (11.23 ± 13 days versus 9.66 ± 10 days; p=0.0083) and higher mean logistic EuroSCORE (11.143 ± 13 versus 8.094 ± 11; p=0.0001). Conclusion: Utilization of ciNPT as an approach to help reduce the incidence of superficial and deep SWI may be effective in high-risk patients requiring cardiac surgery.

Keywords: closed incision negative pressure therapy, surgical wound infection, cardiac surgery complication, high risk cardiac patients

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Authors: Dipo Dunsin, Mohamed C. Ghanem, Karim Ouazzane

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Digital investigators often have a hard time spotting evidence in digital information. It has become hard to determine which source of proof relates to a specific investigation. A growing concern is that the various processes, technology, and specific procedures used in the digital investigation are not keeping up with criminal developments. Therefore, criminals are taking advantage of these weaknesses to commit further crimes. In digital forensics investigations, artificial intelligence is invaluable in identifying crime. It has been observed that an algorithm based on artificial intelligence (AI) is highly effective in detecting risks, preventing criminal activity, and forecasting illegal activity. Providing objective data and conducting an assessment is the goal of digital forensics and digital investigation, which will assist in developing a plausible theory that can be presented as evidence in court. Researchers and other authorities have used the available data as evidence in court to convict a person. This research paper aims at developing a multiagent framework for digital investigations using specific intelligent software agents (ISA). The agents communicate to address particular tasks jointly and keep the same objectives in mind during each task. The rules and knowledge contained within each agent are dependent on the investigation type. A criminal investigation is classified quickly and efficiently using the case-based reasoning (CBR) technique. The MADIK is implemented using the Java Agent Development Framework and implemented using Eclipse, Postgres repository, and a rule engine for agent reasoning. The proposed framework was tested using the Lone Wolf image files and datasets. Experiments were conducted using various sets of ISA and VMs. There was a significant reduction in the time taken for the Hash Set Agent to execute. As a result of loading the agents, 5 percent of the time was lost, as the File Path Agent prescribed deleting 1,510, while the Timeline Agent found multiple executable files. In comparison, the integrity check carried out on the Lone Wolf image file using a digital forensic tool kit took approximately 48 minutes (2,880 ms), whereas the MADIK framework accomplished this in 16 minutes (960 ms). The framework is integrated with Python, allowing for further integration of other digital forensic tools, such as AccessData Forensic Toolkit (FTK), Wireshark, Volatility, and Scapy.

Keywords: artificial intelligence, computer science, criminal investigation, digital forensics

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Authors: Kim Kennedy, Daren Gibson, Stephanie Flukes, Chandra Diwakarla, Lisa Spalding, Leanne Pilkington, Andrew Redfern

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Introduction: Head and neck cancers (HNC) are often associated with the development of non-HNC primaries, as the risk factors that predispose patients to HNC are often risk factors for other cancers. Aim: We sought to evaluate whether there was an increased risk of smoking and alcohol-related cancers and also other cancers in HNC patients and to evaluate whether there is a difference between the rates of non-HNC primaries in Aboriginal compared with non-Aboriginal HNC patients. Methods: We performed a retrospective cohort analysis of 320 HNC patients from a single center in Western Australia, identifying 80 Aboriginal and 240 non-Aboriginal patients matched on a 1:3 ratio by sites, histology, rurality, and age. We collected data on the patient characteristics, tumour features, treatments, outcomes, and past and subsequent HNCs and non-HNC primaries. Results: In the overall study population, there were 86 patients (26.9%) with a metachronous or synchronous non-HNC primary. Non-HNC primaries were actually significantly more common in the non-Aboriginal population compared with the Aboriginal population (30% vs. 17.5%, p=0.02); however, half of these were patients with cutaneous squamous or basal cell carcinomas (cSCC/BCC) only. When cSCC/BCCs were excluded, non-Aboriginal patients had a similar rate as Aboriginal patients (16.7% vs. 15%, p=0.73). There were clearly more cSCC/BCCs in non-Aboriginal patients compared with Aboriginal patients (16.7% vs. 2.5%, p=0.001) and more patients with melanoma (2.5% vs. 0%, p value not significant (p=NS). Rates of most cancers were similar between non-Aboriginal and Aboriginal patients, including prostate (2.9% vs. 3.8%), colorectal (2.9% vs. 2.5%), kidney (1.2% vs. 1.2%), and these rates appeared comparable to Australian Age Standardised Incidence Rates (ASIR) in the general community. Oesophageal cancer occurred at double the rate in Aboriginal patients (3.8%) compared with non-Aboriginal patients (1.7%), which was far in excess of ASIRs which estimated a lifetime risk of 0.59% in the general population. Interestingly lung cancer rates did not appear to be significantly increased in our cohort, with 2.5% of Aboriginal patients and 3.3% of non-Aboriginal patients having lung cancer, which is in line with ASIRs which estimates a lifetime risk of 5% (by age 85yo). Interestingly the rate of Glioma in the non-Aboriginal population was higher than the ASIR, with 0.8% of non-Aboriginal patients developing Glioma, with Australian averages predicting a 0.6% lifetime risk in the general population. As these are small numbers, this finding may well be due to chance. Unsurprisingly, second HNCs occurred at an increased incidence in our cohort, in 12.5% of Aboriginal patients and 11.2% of non-Aboriginal patients, compared to an ASIR of 17 cases per 100,000 persons, estimating a lifetime risk of 1.70%. Conclusions: Overall, 26.9% of patients had a non-HNC primary. When cSCC/BCCs were excluded, Aboriginal and non-Aboriginal patients had similar rates of non-HNC primaries, although non-Aboriginal patients had a significantly higher rate of cSCC/BCCs. Aboriginal patients had double the rate of oesophageal primaries; however, this was not statistically significant, possibly due to small case numbers.

Keywords: head and neck cancer, synchronous and metachronous primaries, other primaries, Aboriginal

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Authors: Samaneh Yavari, Christiane Pferrer, Elisabeth Engelke, Alexander Starke, Juergen Rehage

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Background: Nowadays, bovine limb interventions, especially any claw surgeries, raises selection of the most qualified and appropriate local anesthesia technique applicable for any superficial or deep interventions of the limbs. Currently, two local anesthesia methods of Intravenous Regional Anesthesia (IVRA), as well as Nerve Blocks, have been routine to apply. However, the lack of studies investigating the quality and duration as well as quantity and onset of full (complete) local anesthesia, is noticeable. Therefore, the aim of our study was comparing the onset and quality of both IVRA and our modified NBA at the hind limb of dairy cows. For this abstract, only the onset of full local anesthesia would be consider. Materials and Methods: For that reason, we used six healthy non pregnant non lactating Holestein Frisian cows in a cross-over study design. Those cows divided into two groups to receive IVRA and our modified four-point NBA. For IVRA, 20 ml procaine without epinephrine was injected into the vein digitalis dorsalis communis III and for our modified four-point NBA, 10-15 ml procaine without epinephrine preneurally to the nerves, superficial and deep peroneal as well as lateral and medial branches of metatarsal nerves. For pain stimulation, electrical stimulator Grass S48 was applied. Results: The results of electrical stimuli revealed the faster onset of full local anesthesia (p < 0.05) by application of our modified NBA in comparison to IVRA about 10 minutes. Conclusion and discussion: Despite of available references showing faster onset of foot local anesthesia of IVRA, our study demonstrated that our modified four point NBA not only can be well known as a standard foot local anesthesia method applicable to desensitize the hind limb of dairy cows, but also, selection of this modified validated local anesthesia method can lead to have a faster start of complete desensitization of distal hind limb that is remarkable in any bovine limb interventions under time constraint.

Keywords: IVRA, four point NBA, dairy cow, hind limb, full onset

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Authors: Kylie Mueller, Nijole Bernaitis, Shailendra Anoopkumar-Dukie

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Background: Vitamin K antagonists particularly warfarin is the most frequently used oral medication for deep vein thrombosis (DVT) treatment and prophylaxis. Time in therapeutic range (TITR) of the international normalised ratio (INR) is widely accepted as a measure to assess the quality of warfarin therapy. Multiple factors can affect warfarin control and the subsequent adverse outcomes including thromboembolic and bleeding events. Predictor models have been developed to assess potential contributing factors and measure the individual risk of these adverse events. These predictive models have been validated in atrial fibrillation (AF) patients, however, there is a lack of literature on whether these can be successfully applied to other warfarin users including DVT patients. Therefore, the aim of the study was to assess the ability of these risk models (HAS BLED and CHADS2) to predict haemorrhagic and ischaemic incidences in DVT patients treated with warfarin. Methods: A retrospective analysis of DVT patients receiving warfarin management by a private pathology clinic was conducted. Data was collected from November 2007 to September 2014 and included demographics, medical and drug history, INR targets and test results. Patients receiving continuous warfarin therapy with an INR reference range between 2.0 and 3.0 were included in the study with mean TITR calculated using the Rosendaal method. Bleeding and thromboembolic events were recorded and reported as incidences per patient. The haemorrhagic risk model HAS BLED and ischaemic risk model CHADS2 were applied to the data. Patients were then stratified into either the low, moderate, or high-risk categories. The analysis was conducted to determine if a correlation existed between risk assessment tool and patient outcomes. Data was analysed using GraphPad Instat Version 3 with a p value of <0.05 considered to be statistically significant. Patient characteristics were reported as mean and standard deviation for continuous data and categorical data reported as number and percentage. Results: Of the 533 patients included in the study, there were 268 (50.2%) female and 265 (49.8%) male patients with a mean age of 62.5 years (±16.4). The overall mean TITR was 78.3% (±12.7) with an overall haemorrhagic incidence of 0.41 events per patient. For the HAS BLED model, there was a haemorrhagic incidence of 0.08, 0.53, and 0.54 per patient in the low, moderate and high-risk categories respectively showing a statistically significant increase in incidence with increasing risk category. The CHADS2 model showed an increase in ischaemic events according to risk category with no ischaemic events in the low category, and an ischaemic incidence of 0.03 in the moderate category and 0.47 high-risk categories. Conclusion: An increasing haemorrhagic incidence correlated to an increase in the HAS BLED risk score in DVT patients treated with warfarin. Furthermore, a greater incidence of ischaemic events occurred in patients with an increase in CHADS2 category. In an Australian population of DVT patients, the HAS BLED and CHADS2 accurately predicts incidences of haemorrhage and ischaemic events respectively.

Keywords: anticoagulant agent, deep vein thrombosis, risk assessment, warfarin

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Authors: Wen-Bao Qiao, Jean-Charles Créput

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To accelerate the solution for large scale traveling salesman problems (TSP), a parallel 2-opt local search algorithm with simple implementation based on Graphics Processing Unit (GPU) is presented and tested in this paper. The parallel scheme is based on technique of data decomposition by dynamically assigning multiple K processors on the integral tour to treat K edges’ 2-opt local optimization simultaneously on independent sub-tours, where K can be user-defined or have a function relationship with input size N. We implement this algorithm with doubly linked list on GPU. The implementation only requires O(N) memory. We compare this parallel 2-opt local optimization against sequential exhaustive 2-opt search along integral tour on TSP instances from TSPLIB with more than 10000 cities.

Keywords: parallel 2-opt, double links, large scale TSP, GPU

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Authors: Srilaxmi Gangula, MahaLakshmi Vinukonda, Neeraj Rao

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A wide band polarization insensitive metamaterial absorber with bandwidth enhancement in X and C band is proposed. The structure proposed here consists of a periodic unit cell of resonator arrangements in double layer. The proposed structure shows near unity absorption at frequencies of 6.21 GHz and 10.372 GHz spreading over a bandwidth of 1 GHz and 6.21 GHz respectively in X and C bands. The proposed metamaterial absorber is designed so as to increase the bandwidth. The proposed structure is also independent for TE and TM polarization. Because of its simple implementation, near unity absorption and wide bandwidth this dual band polarization insensitive metamaterial absorber can be used for EMI/EMC applications.

Keywords: absorber, C-band, metamaterial, multilayer, X-band

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Authors: Beata Bilska, Marzena Tomaszewska, Danuta Kolozyn-Krajewska

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Food loss and food waste are a global problem of the modern economy. The research undertaken aimed to analyze how food is handled in catering establishments when it comes to food waste and to demonstrate the main ways of management with foods/dishes not served to consumers. A survey study was conducted from January to June 2019. The selection of catering establishments participating in the study was deliberate. The study included establishments located only in Mazowieckie Voivodeship (Poland). Forty-two completed questionnaires were collected. In some questions, answers were based on a 5-point scale of 1 to 5 (from "always" / "every day" to "never"). The survey also included closed questions with a suggested cafeteria of answers. The respondents stated that in their workplaces, dishes served cold and hot ready meals are discarded every day or almost every day (23.7% and 20.5% of answers respectively). A procedure most frequently used for dealing with dishes not served to consumers on a given day is their storage at a cool temperature until the following day. In the research, 1/5 of respondents admitted that consumers "always" or "usually" leave uneaten meals on their plates, and over 41% "sometimes" do so. It was found additionally that food not used in the foodservice sector is most often thrown into a public container for rubbish. Most often thrown into the public container (with communal trash) were: expired products (80.0%), plate waste (80.0%) and inedible products (fruit and vegetable peels, eggshells) (77.5%). Most frequently into the container dedicated only to food waste were thrown out used deep-frying oil (62.5%). 10% of respondents indicated that inedible products in their workplaces are allocated for animal feeds. Food waste in the foodservice sector remains an insufficiently studied issue, as owners of these objects are often unwilling to disclose data about the subject. Incorrect ways of management with foods not served to consumers were observed. There is a need to develop educational activities for employees and management in the context of food waste management in the foodservice sector.

Keywords: food waste, inedible products, plate waste, used deep-frying oil

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Authors: Wei Wang, Yuan Hu

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Epoxy resins (EP), one of the most important thermosetting polymers, is widely applied in various fields due to its desirable properties, such as excellent electrical insulation, low shrinkage, outstanding mechanical stiffness, satisfactory adhesion and solvent resistance. However, like most of the polymeric materials, EP has the fatal drawbacks including inherent flammability and high yield of toxic smoke, which restricts its application in the fields requiring fire safety. So, it is still a challenge and an interesting subject to develop new flame retardants which can not only remarkably improve the flame retardancy, but also render modified resins low toxic gases generation. In recent work, polymer nanocomposites based on nanohybrids that contain two or more kinds of nanofillers have drawn intensive interest, which can realize performance enhancements. The realization of previous hybrids of carbon nanotubes (CNTs) and molybdenum disulfide provides us a novel route to decorate layered double hydroxide (LDH) nanosheets on the surface of β-FeOOH nanorods; the deposited LDH nanosheets can fill the network and promote the work efficiency of β-FeOOH nanorods. Moreover, the synergistic effects between LDH and β-FeOOH can be anticipated to have potential applications in reducing fire hazards of EP composites for the combination of condense-phase and gas-phase mechanism. As reported, β-FeOOH nanorods can act as a core to prepare hybrid nanostructures combining with other nanoparticles through electrostatic attraction through layer-by-layer assembly technique. In this work, LDH nanosheets wrapped β-FeOOH nanorods (LDH-β-FeOOH) hybrids was synthesized by a facile method, with the purpose of combining the characteristics of one dimension (1D) and two dimension (2D), to improve the fire resistance of epoxy resin. The hybrids showed a well dispersion in EP matrix and had no obvious aggregation. Thermogravimetric analysis and cone calorimeter tests confirmed that LDH-β-FeOOH hybrids into EP matrix with a loading of 3% could obviously improve the fire safety of EP composites. The plausible flame retardancy mechanism was explored by thermogravimetric infrared (TG-IR) and X-ray photoelectron spectroscopy. The reasons were concluded: condense-phase and gas-phase. Nanofillers were transferred to the surface of matrix during combustion, which could not only shield EP matrix from external radiation and heat feedback from the fire zone, but also efficiently retard transport of oxygen and flammable pyrolysis.

Keywords: fire hazards, toxic gases, self-assembly, epoxy

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Authors: Ankit Kamboj, Shikha Talwar, Nilesh Powar

Abstract:

To successfully deliver our products in the market, the employees need to be in a safe environment, especially in an industrial and manufacturing environment. The consequences of delinquency in wearing safety glasses while working in industrial plants could be high risk to employees, hence the need to develop a real-time automatic detection system which detects the persons (violators) not wearing safety glasses. In this study a convolutional neural network (CNN) algorithm called faster region based CNN (Faster RCNN) with rotated bounding box has been used for detecting safety glasses on persons; the algorithm has an advantage of detecting safety glasses with different orientation angles on the persons. The proposed method of rotational bounding boxes with a convolutional neural network first detects a person from the images, and then the method detects whether the person is wearing safety glasses or not. The video data is captured at the entrance of restricted zones of the industrial environment (manufacturing plant), which is further converted into images at 2 frames per second. In the first step, the CNN with pre-trained weights on COCO dataset is used for person detection where the detections are cropped as images. Then the safety goggles are labelled on the cropped images using the image labelling tool called roLabelImg, which is used to annotate the ground truth values of rotated objects more accurately, and the annotations obtained are further modified to depict four coordinates of the rectangular bounding box. Next, the faster RCNN with rotated bounding box is used to detect safety goggles, which is then compared with traditional bounding box faster RCNN in terms of detection accuracy (average precision), which shows the effectiveness of the proposed method for detection of rotatory objects. The deep learning benchmarking is done on a Dell workstation with a 16GB Nvidia GPU.

Keywords: CNN, deep learning, faster RCNN, roLabelImg rotated bounding box, safety goggle detection

Procedia PDF Downloads 124

Authors: Abdelhalem Shahen

Abstract:

The world is now experiencing a digital revolution and greatly accelerated technological developments, in addition to the transition from the economy in its traditional form to the digital economy, which has resulted in the emergence of new tools that are appropriate to those developments, and from this, this paper attempts to explore the economic characteristics of the bitcoin currency that circulated recently. Due to the many advantages that distinguish it from money in its traditional forms, which have a range of economic effects. The study found that Bitcoin is among the technological innovations, which contain a set of characteristics that are worth studying, those that make it the focus of attention, such as the digital currency, the peer-to-peer property, Lower and Faster Transaction Costs, transparency, decentralized control, privacy, and Double-Spending, as well as security and Cryptographic, and finally mining.

Keywords: Digital Economics, Digital Currencies, Bitcoin, Features of Bitcoin

Procedia PDF Downloads 131

Authors: N. Pitcheswara Rao

Abstract:

In the process of conveying the information there may be a chance of signal being corrupted which leads to the erroneous bits in the message. The message may consist of single, double and multiple bit errors. In high-reliability applications, memory can sustain multiple soft errors due to single or multiple event upsets caused by environmental factors. The traditional hamming code with SEC-DED capability cannot be address these types of errors. It is possible to use powerful non-binary BCH code such as Reed-Solomon code to address multiple errors. However, it could take at least a couple dozen cycles of latency to complete first correction and run at a relatively slow speed. In order to overcome this drawback i.e., to increase speed and latency we are using reed-Muller code.

Keywords: SEC-DED, BCH code, Reed-Solomon code, Reed-Muller code

Procedia PDF Downloads 421

Authors: Gholba Niranjan Dilip, Anil Kumar

Abstract:

Conduct of accurate reliable mapping of oil palm plantations and census of individual palm trees is a huge challenge. This study addresses this challenge and developed an optimized solution implemented deep learning techniques on remote sensing data. The oil palm is a very important tropical crop. To improve its productivity and land management, it is imperative to have accurate census over large areas. Since, manual census is costly and prone to approximations, a methodology for automated census using panchromatic images from Cartosat-2, SkySat and World View-3 satellites is demonstrated. It is selected two different study sites in Indonesia. The customized set of training data and ground-truth data are created for this study from Cartosat-2 images. The pre-trained model of Single Shot MultiBox Detector (SSD) Lite MobileNet V2 Convolutional Neural Network (CNN) from the TensorFlow Object Detection API is subjected to transfer learning on this customized dataset. The SSD model is able to generate the bounding boxes for each oil palm and also do the counting of palms with good accuracy on the panchromatic images. The detection yielded an F-Score of 83.16 % on seven different images. The detections are buffered and dissolved to generate polygons demarcating the boundaries of the oil palm plantations. This provided the area under the plantations and also gave maps of their location, thereby completing the automated census, with a fairly high accuracy (≈100%). The trained CNN was found competent enough to detect oil palm crowns from images obtained from multiple satellite sensors and of varying temporal vintage. It helped to estimate the increase in oil palm plantations from 2014 to 2021 in the study area. The study proved that high-resolution panchromatic satellite image can successfully be used to undertake census of oil palm plantations using CNNs.

Keywords: object detection, oil palm tree census, panchromatic images, single shot multibox detector

Procedia PDF Downloads 157

Authors: Esmat Kamel

Abstract:

This paper aims to shed light on the extent to which the Agadir Association agreement has fostered inter regional trade between the E.U_26 and the Agadir_4 countries; once that we control for the evolution of Agadir agreement’s exports to the rest of the world. The next valid question will be regarding any remarkable variation in the spatial/sectoral structure of exports, and to what extent has it been induced by the Agadir agreement itself and precisely after the adoption of rules of origin and the PANEURO diagonal cumulative scheme? The paper’s empirical dataset covering a timeframe from [2000 -2009] was designed to account for sector specific export and intermediate flows and the bilateral structured gravity model was custom tailored to capture sector and regime specific rules of origin and the Poisson Pseudo Maximum Likelihood Estimator was used to calculate the gravity equation. The methodological approach of this work is considered to be a threefold one which starts first by conducting a ‘Hierarchal Cluster Analysis’ to classify final export flows showing a certain degree of linkage between each other. The analysis resulted in three main sectoral clusters of exports between Agadir_4 and E.U_26: cluster 1 for Petrochemical related sectors, cluster 2 durable goods and finally cluster 3 for heavy duty machinery and spare parts sectors. Second step continues by taking export flows resulting from the 3 clusters to be subject to treatment with diagonal Rules of origin through ‘The Double Differences Approach’, versus an equally comparable untreated control group. Third step is to verify results through a robustness check applied by ‘Propensity Score Matching’ to validate that the same sectoral final export and intermediate flows increased when rules of origin were relaxed. Through all the previous analysis, a remarkable and partial significance of the interaction term combining both treatment effects and time for the coefficients of 13 out of the 17 covered sectors turned out to be partially significant and it further asserted that treatment with diagonal rules of origin contributed in increasing Agadir’s_4 final and intermediate exports to the E.U._26 on average by 335% and in changing Agadir_4 exports structure and composition to the E.U._26 countries.

Keywords: agadir association agreement, structured gravity model, hierarchal cluster analysis, double differences estimation, propensity score matching, diagonal and relaxed rules of origin

Procedia PDF Downloads 312

Authors: Deboraj Muchahary, Amlan Deep Borah Abir J. Mondal, Alak Majumder

Abstract:

A discrete cosine transform (DCT) is described and a technique to compute it using fast Fourier transform (FFT) is developed. In this work, DCT of a finite length sequence is obtained by incorporating CORDIC methodology in radix-2 FFT algorithm. The proposed methodology is simple to comprehend and maintains a regular structure, thereby reducing computational complexity. DCTs are used extensively in the area of digital processing for the purpose of pattern recognition. So the efficient computation of DCT maintaining a transparent design flow is highly solicited.

Keywords: DCT, DFT, CORDIC, FFT

Procedia PDF Downloads 471

Authors: Pitcheswara Rao Nelapati

Abstract:

In the process of conveying the information there may be a chance of signal being corrupted which leads to the erroneous bits in the message. The message may consist of single, double and multiple bit errors. In high-reliability applications, memory can sustain multiple soft errors due to single or multiple event upsets caused by environmental factors. The traditional hamming code with SEC-DED capability cannot be address these types of errors. It is possible to use powerful non-binary BCH code such as Reed-Solomon code to address multiple errors. However, it could take at least a couple dozen cycles of latency to complete first correction and run at a relatively slow speed. In order to overcome this drawback i.e., to increase speed and latency we are using reed-Muller code.

Keywords: SEC-DED, BCH code, Reed-Solomon code, Reed-Muller code

Procedia PDF Downloads 420

Authors: Raquel Zydeck, Karina Azzolin, Luis Kosteski, Alisson Milani

Abstract:

This work presents numerical models in plane deformations (2D), using the Discrete Element Method formedbybars (LDEM) andtheFiniteElementMethod (FEM), in structuralmasonrywallswith horizontal chasesof 20%, 30%, and 50% deep, located in the central part and 1/3 oftheupperpartofthewall, withcenteredandeccentricloading. Differentcombinationsofboundaryconditionsandinteractionsbetweenthemethodswerestudied.

Keywords: chases in structural masonry walls, discrete element method formed by bars, finite element method, numerical models, boundary condition

Procedia PDF Downloads 159

Authors: P. Chantarawitoon, H. Askarinejad

Abstract:

The steel beams with web opening including the cellular and castellated I-beams are fabricated from a solid web I-beam through a double cutting method to a specific shape and size along the beam. The two halves of the beams are then welded together, increasing the overall depth of the web section. In this paper, the deflection of the cellular and castellated beams subject to uniform vertical loads are investigated using Finite Element Autodesk simulation package. The structural response of the beams with web opening are compared with regular solid beams. Additionally, parametric studies are carried out to study the influence of the geometric properties of a cellular beam to its structural responses.

Keywords: beams with web opening, cellular and castellated beams, finite element analysis, vertical deflection

Procedia PDF Downloads 235

Authors: K. R. Roopesh Bharatwaj, Avinash Maharana, Favour Tobi Aborisade, Roger Young

Abstract:

Presently, there are few cases of complete automation of drones and its allied intelligence capabilities. In essence, the potential of the drone has not yet been fully utilized. This paper presents feasible methods to build an intelligent drone with smart capabilities such as self-driving, and obstacle avoidance. It does this through advanced Reinforcement Learning Techniques and performs object detection using latest advanced algorithms, which are capable of processing light weight models with fast training in real time instances. For the scope of this paper, after researching on the various algorithms and comparing them, we finally implemented the Deep-Q-Networks (DQN) algorithm in the AirSim Simulator. In future works, we plan to implement further advanced self-driving and object detection algorithms, we also plan to implement voice-based speech recognition for the entire drone operation which would provide an option of speech communication between users (People) and the drone in the time of unavoidable circumstances. Thus, making drones an interactive intelligent Robotic Voice Enabled Service Assistant. This proposed drone has a wide scope of usability and is applicable in scenarios such as Disaster management, Air Transport of essentials, Agriculture, Manufacturing, Monitoring people movements in public area, and Defense. Also discussed, is the entire drone communication based on the satellite broadband Internet technology for faster computation and seamless communication service for uninterrupted network during disasters and remote location operations. This paper will explain the feasible algorithms required to go about achieving this goal and is more of a reference paper for future researchers going down this path.

Keywords: convolution neural network, natural language processing, obstacle avoidance, satellite broadband technology, self-driving

Procedia PDF Downloads 242

Authors: Husnain Yousaf, Rudy Swennen, Hannes Claes, Muhammad Amjad

Abstract:

In recent years, the Lower Triassic Main Buntsandstein sandstones in the southern Netherlands Basins have become a point of interest for their deep geothermal potential. To identify the most suitable reservoir for geothermal exploration, the diagenesis and factors affecting reservoir quality, such as porosity and permeability, are assessed. This is done by combining point-counted petrographic data with conventional core analysis. The depositional environments play a significant role in determining the distribution of lithofacies, cement, clays, and grain sizes. The position in the basin and proximity to the source areas determine the lateral variability of depositional environments. The stratigraphic distribution of depositional environments is linked to both local topography and climate, where high humidity leads to fluvial deposition and high aridity periods lead to aeolian deposition. The Middle Buntsandstein Sandstones in the southern part of the Netherlands shows high porosity and permeability in most sandstone intervals. There are various controls on reservoir quality in the examined sandstone samples. Grain sizes and total quartz content are the primary factors affecting reservoir quality. Conversely, carbonate and anhydrite cement, clay clasts, and intergranular clay represent a local control and cannot be applied on a regional scale. Similarly, enhanced secondary porosity due to feldspar dissolution is locally restricted and minor. The analysis of textural, mineralogical, and petrophysical data indicates that the aeolian and fluvial sandstones represent a heterogeneous reservoir system. The ephemeral fluvial deposits have an average porosity and permeability of <10% and <1mD, respectively, while the aeolian sandstones exhibit values of >18% and >100mD.

Keywords: reservoir quality, diagenesis, porosity, permeability, depositional environments, Buntsandstein, Netherlands

Procedia PDF Downloads 58

Authors: Varsha Prasad, S. Sandya

Abstract:

Cosmic Ray effects in microelectronics such as single event effect (SET) and total dose ionization (TID) have been of major concern in space electronics since 1970. Advanced CMOS technologies have demonstrated reduced sensitivity to TID effect. However, charge pump Phase Locked Loop is very much vulnerable to single event transient effect. This paper presents an SET analysis model, where the SET is modeled as a double exponential pulse. The time domain analysis reveals that the settling time of the voltage controlled oscillator (VCO) depends on the SET pulse strength, setting the time constant and the damping factor. The analysis of the proposed SET analysis model is confirmed by the simulation results.

Keywords: charge pump, phase locked loop, SET, VCO

Procedia PDF Downloads 430