Search results for: trained athletes

Authors: Denis Duagi, Ben Carter, Maria Farrelly, Stephen Lisk, June S. L. Brown

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Background: Adolescence represents a distinctive phase of development, and variables linked to this developmental period could affect the efficiency of prevention and treatment for depression and anxiety, as well as the long-term prognosis. The objectives of this study were to investigate the long-term effectiveness of psychosocial interventions for adolescents on depression and anxiety symptoms and to assess the influence of different intervention parameters on the long-term effects. Methods: Searches were carried out on the 11ᵗʰ of August 2022 using five databases (Cochrane Library, Embase, Medline, PsychInfo, Web of Science), as well as trial registers. Randomized controlled trials of psychosocial interventions targeting specifically adolescents were included if they assessed outcomes at 1-year post-intervention or more. The Cochrane risk of bias-2 quality assessment tool was used. The primary outcome was depression, and studies were pooled using a standardised mean difference, with an associated 95% confidence interval, p-value, and I². The study protocol was pre-registered (CRD42022348668). Findings: A total of 57 reports (n= 46,678 participants) were included in the review. Psychosocial interventions led to small reductions in depressive symptoms, with a standardised mean difference (SMD) at 1-year of -0.08 (95%CI -0.20, -0.03, p=0.002, I²=72%), 18-months SMD=-0.12, 95% CI -0.22, -0.01, p=0.03, I²=63%) and 2-years SMD=-0.12 (95% CI -0.20, -0.03, p=0.01, I²=68%). Sub-group analyses indicated that targeted interventions produced stronger effects, particularly when delivered by trained mental health professionals (K=18, SMD=-0.24, 95% CI -0.38, -0.10, p=0.001, I²=60%). No effects were detected for anxiety at any assessment. Conclusion: Psychosocial interventions specifically targeting adolescents were shown to have small but positive effects on depression symptoms but not anxiety symptoms, which were sustained for up to 2 years. These findings highlight the potential population-level preventive effects if such psychosocial interventions become widely implemented in accessible settings such as schools.

Keywords: psychosocial, adolescent, interventions, depression, anxiety, meta-analysis, randomized controlled trial

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Authors: Ram Sharan Mehta, Gayanandra Malla, Anita Gurung, Anu Aryal, Divya Labh, Hricha Neupane

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Objectives: Lack of resuscitation skills of nurses and doctors in basic life support (BLS) and advanced life support (ALS) has been identified as a contributing factor to poor outcomes of cardiac arrest victims. The objective of this study was to examine retention of life support measures (BLS/ALS) knowledge and skills of nurses following education intervention programme. Materials and Methods: Pre-experimental research design was used to conduct the study among the nurses working in medical units of B.P Koirala Institute of Health Sciences, where CPR is very commonly performed. Using convenient sampling technique total of 20 nurses agreed to participate and give consent were included in the study. The theoretical, demonstration and re-demonstration were arranged involving the trained doctors and nurses during the three hours educational session. Post-test was carried out after two week of education intervention programme. The 2010 BLS & ALS guidelines were used as guide for the study contents. The collected data were analyzed using SPSS-15 software. Results: It was found that there is significant increase in knowledge after education intervention in the components of life support measures (BLS/ALS) i.e. ratio of chest compression to ventilation in BLS (P=0.001), correct sequence of CPR (p <0.001), rate of chest compression in ALS (P=0.001), the depth of chest compression in adult CPR (p<0.001), and position of chest compression in CPR (P=0.016). Nurses were well appreciated the programme and request to continue in future for all the nurses. Conclusions: At recent BLS/ALS courses (2010), a significant number of nurses remain without any such training. Action is needed to ensure all nurses receive BLS training and practice this skill regularly in order to retain their knowledge.

Keywords: pre-experimental, basic and advance life support, nurses, sampling technique

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Authors: Loyce Nahwera, Joy Wachira, Edwin Kiptolo, Constance Nsibambi, Mshilla Maghanga, Timothy Makubuya

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Introduction: Hypertension is one of the most prominent risk factors for cardiovascular diseases globally, and it can be modified through lifestyle interventions such as exercise. The objective of this study was to investigate the effects of a 12-week aerobic dance programme on systolic blood pressure (SBP) in stage 1 hypertensive individuals. Methods: This study employed an experimental research design. A total of 36 stage 1 hypertensive individuals who were randomly assigned into experimental and control groups completed the study. Systolic BP was measured using a mercury sphygmomanometer at baseline, mid-point and after the program. The experimental group participants trained 3 days a week, 45 minutes per session, at a moderate intensity of 40-60% of maximum oxygen consumption (VO2max) monitored by Garmin heart rate monitors. Data were analyzed using SPSS version 20. The significance level was set at p<0.05. A paired sample t-test was used to compare mean differences within the groups. Results: Data from the 36 participants (22 males and 14 females) (experimental; n=18, control; n=18) show that the experimental group had a mean SBP of 143.83±6.382 mmHg at baseline while the control had a mean of 137.61±6.400 mmHg. Following the end of a 6-week aerobic dance, the mean SBP of the experimental group reduced to 138.06±9.539 mmHg while that of the control marginally decreased to 137.00±8.073 mmHg. At the completion of a 12-week program, the mean SBP of the experimental group reduced to 136.33±9.191 mmHg, while that of the control marginally increased to 139.56±9.954 mmHg. This implies that both the 6-week and 12-week aerobic dance program reduced the SBP of the experimental group by 5.77±7.133 mmHg and 7.50±8.487 mmHg, respectively, while the control group fast reduced marginally by 0.61 before ultimately increasing by 1.95±7.974 mmHg at 12-weeks. The changes were statistically significant (p<0.05) at both 6 and 12 weeks of an aerobic dance program. Conclusion: The study concluded that aerobic dance is an effective non-pharmacological method for managing SBP of stage 1 hypertensive individuals both in the short-term (6 weeks) and long-term (12 weeks).

Keywords: aerobic dance, blood pressure, stage 1 hypertension, systolic blood pressure.

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Authors: Emilia A. Campayo-Munoz, Alberto Cabedo-Mas

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Music students have to face the challenges of musical practice -such as discipline in study, competitiveness, or performance anxiety- that require good emotional management to enable successful performance. However, few rigorous implementations focused on studying the influence of emotional skills in student's musical performance. Responding to this gap in the literature, this study aims to explore the relationship between emotional skills and musical performance in the context of elementary music education in conservatoires. Given the individual nature of the instrumental studies and the difficult availability of teachers to be trained in emotional education, it was decided to conduct a multiple case study in a Spanish music conservatoire. Author 1 carried out the implementation of the research with three 10-year-old students who were selected from her piano class. All of them attended the third year of their piano studies. The research processes consisted of the implementation of a set of specific and cross-sectional activities designed 'ad hoc' to be articulated in the subjects of individual instrument -piano- and ensemble in parallel to the contents of musical nature. The CE-360º questionnaire was used to measure different aspects of the students' emotional skills from a multi-angle perspective, each of the questionnaires being responded by oneself, three teachers and three peers, before and after the implementation. The data from the questionnaire were compared with the grades that the students obtained during the first and last quarter of the school year in the attended subjects. Acknowledging the complexity of emotional development, the results indicate possible relations between emotional skills and musical performance in music education in conservatoires. The results show that for the cases explored; there exists a relationship between emotional skills and musical performance. Although generalizations cannot be made, this study reinforces the need to further explore emotional development in instrumental teaching and suggest the importance of inviting teachers to reflect on the pedagogical practices extended in the conservatoires and to develop and implement those that promote the work of the students' emotions.

Keywords: conservatoires, emotional skills, music education, musical performance

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Authors: Richard O’Riordan, Saritha Unnikrishnan

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Autonomous Vehicles (AVs) are incorporating an increasing number of ADAS features, including automated lane-keeping systems. In recent years, many research papers into lane detection algorithms have been published, varying from computer vision techniques to deep learning methods. The transition from lower levels of autonomy defined in the SAE framework and the progression to higher autonomy levels requires increasingly complex models and algorithms that must be highly reliable in their operation and functionality capacities. Furthermore, these algorithms have no room for error when operating at high levels of autonomy. Although the current research details existing computer vision and deep learning algorithms and their methodologies and individual results, the research also details challenges faced by the algorithms and the resources needed to operate, along with shortcomings experienced during their detection of lanes in certain weather and lighting conditions. This paper will explore these shortcomings and attempt to implement a lane detection algorithm that could be used to achieve improvements in AV lane detection systems. This paper uses a pre-trained LaneNet model to detect lane or non-lane pixels using binary segmentation as the base detection method using an existing dataset BDD100k followed by a custom dataset generated locally. The selected roads will be modern well-laid roads with up-to-date infrastructure and lane markings, while the second road network will be an older road with infrastructure and lane markings reflecting the road network's age. The performance of the proposed method will be evaluated on the custom dataset to compare its performance to the BDD100k dataset. In summary, this paper will use Transfer Learning to provide a fast and robust lane detection algorithm that can handle various road conditions and provide accurate lane detection.

Keywords: ADAS, autonomous vehicles, deep learning, LaneNet, lane detection

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Authors: Khairani Binti Supyan, Fatimah Khalid, Mas Rina Mustaffa, Azreen Bin Azman, Amirul Azuani Romle

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Perennial plant classification plays a significant role in various agricultural and environmental applications, assisting in plant identification, disease detection, and biodiversity monitoring. Nevertheless, attaining high accuracy in perennial plant image classification remains challenging due to the complex variations in plant appearance, the diverse range of environmental conditions under which images are captured, and the inherent variability in image quality stemming from various factors such as lighting conditions, camera settings, and focus. This paper proposes an adaptation approach to optimize perennial plant image classification by fine-tuning the pre-trained DNNs model. This paper explores the efficacy of fine-tuning prevalent architectures, namely VGG16, ResNet50, and InceptionV3, leveraging transfer learning to tailor the models to the specific characteristics of perennial plant datasets. A subset of the MYLPHerbs dataset consisted of 6 perennial plant species of 13481 images under various environmental conditions that were used in the experiments. Different strategies for fine-tuning, including adjusting learning rates, training set sizes, data augmentation, and architectural modifications, were investigated. The experimental outcomes underscore the effectiveness of fine-tuning deep neural networks for perennial plant image classification, with ResNet50 showcasing the highest accuracy of 99.78%. Despite ResNet50's superior performance, both VGG16 and InceptionV3 achieved commendable accuracy of 99.67% and 99.37%, respectively. The overall outcomes reaffirm the robustness of the fine-tuning approach across different deep neural network architectures, offering insights into strategies for optimizing model performance in the domain of perennial plant image classification.

Keywords: perennial plants, image classification, deep neural networks, fine-tuning, transfer learning, VGG16, ResNet50, InceptionV3

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Authors: Kuo-Chun Chien, Chia-Hui Chang, Ren-Der Sun

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Legal Judgment Prediction (LJP) is a subtask for legal AI. Its main purpose is to use the facts of a case to predict the judgment result. In Taiwan's criminal procedure, when prosecutors complete the investigation of the case, they will decide whether to prosecute the suspect and which article of criminal law should be used based on the facts and evidence of the case. In this study, we collected 305,240 indictments from the public inquiry system of the procuratorate of the Ministry of Justice, which included 169 charges and 317 articles from 21 laws. We take the crime facts in the indictments as the main input to jointly learn the prediction model for law source, article, and charge simultaneously based on the pre-trained Bert model. For single article cases where the frequency of the charge and article are greater than 50, the prediction performance of law sources, articles, and charges reach 97.66, 92.22, and 60.52 macro-f1, respectively. To understand the big performance gap between articles and charges, we used a bipartite graph to visualize the relationship between the articles and charges, and found that the reason for the poor prediction performance was actually due to the wording precision. Some charges use the simplest words, while others may include the perpetrator or the result to make the charges more specific. For example, Article 284 of the Criminal Law may be indicted as “negligent injury”, "negligent death”, "business injury", "driving business injury", or "non-driving business injury". As another example, Article 10 of the Drug Hazard Control Regulations can be charged as “Drug Control Regulations” or “Drug Hazard Control Regulations”. In order to solve the above problems and more accurately predict the article and charge, we plan to include the article content or charge names in the input, and use the sentence-pair classification method for question-answer problems in the BERT model to improve the performance. We will also consider a sequence-to-sequence approach to charge prediction.

Keywords: legal judgment prediction, deep learning, natural language processing, BERT, data visualization

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Authors: Md. Mizanoor Rahman, Md. Zakir Hossain Talukder, M. Mahruf C. Shohel, Prithvi Shrestha

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In Bangladesh, classroom practice and English Learning (EL) competencies acquired both by the teacher and learner in primary and secondary schools are still very weak. Therefore, English is the most commonly failed examination subject at the school level; in addition, there are severe problems in communicative English by the Bangladeshi nationals– this has been characterized as a constraint to economic development. Job applicants and employees often lack English language skills necessary to work effectively. As a result; both government and its international development partners such as DFID, UNESCO, and CIDA have been very active to uplift the quality of the English language learning and implementing projects with innovative approaches. Recently; the economy has been increasing and in line with this, the technology has been deployed in English learning to improve reading, writing, speaking and listening skills. Young Bangladeshi creative, from a variety of backgrounds including film, animation, photography, and digital media are being trained to develop ideas for English Language Teaching (ELT) media. They are being motivated to develop a wide range of ideas for low cost English learning media products. English Language education policy in Bangladesh supports communicative language teaching practices and accordingly, actors have been influencing curriculum, textbook, deployment of technology and assessment changes supporting communicative ELT. The various projects are also being implemented to reform the curriculum, revise the textbook and adjust the assessment mechanism so that the country can increase in proficiency in communicative English among the population. At present; the numbers of teachers, students and adult learners classified at higher levels of proficiency because of deployment of technology and motivation for learning and using English among school population of Bangladesh. The current paper discusses the various interventions in Bangladesh with appropriate media to improve the competencies of the ELT among population.

Keywords: English learning, technology, education, psychological sciences

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Authors: Hadj Sahraoui Omar, Kebir Lahcen Wahib, Bennia Ahmed

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Interferometric Synthetic Aperture Radar (InSAR) coherence is a crucial parameter for accurately monitoring ground deformation and environmental changes. However, coherence can be degraded by various factors such as temporal decorrelation, atmospheric disturbances, and geometric misalignments, limiting the reliability of InSAR measurements (Omar Hadj‐Sahraoui and al. 2019). To address this challenge, we propose an innovative hybrid approach that combines artificial intelligence (AI) with advanced filtering techniques to optimize interferometric coherence in InSAR data. Specifically, we introduce a Convolutional Neural Network (CNN) integrated with the Lee filter to enhance the performance of radar interferometry. This hybrid method leverages the strength of CNNs to automatically identify and mitigate the primary sources of decorrelation, while the Lee filter effectively reduces speckle noise, improving the overall quality of interferograms. We develop a deep learning-based model trained on multi-temporal and multi-frequency SAR datasets, enabling it to predict coherence patterns and enhance low-coherence regions. This hybrid CNN-SAR with Lee filtering significantly reduces noise and phase unwrapping errors, leading to more precise deformation maps. Experimental results demonstrate that our approach improves coherence by up to 30% compared to traditional filtering techniques, making it a robust solution for challenging scenarios such as urban environments, vegetated areas, and rapidly changing landscapes. Our method has potential applications in geohazard monitoring, urban planning, and environmental studies, offering a new avenue for enhancing InSAR data reliability through AI-powered optimization combined with robust filtering techniques.

Keywords: CNN-SAR, Lee Filter, hybrid optimization, coherence, InSAR phase unwrapping, speckle noise reduction

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Authors: Joseph Lillington, Tom Gout, Mike Harrison, Ian Farnan

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The vitrification of high-level nuclear waste within borosilicate glass and its incorporation within a multi-barrier repository deep underground is widely accepted as the preferred disposal method. However, for this to happen, any safety case will require validation that the initially localized radionuclides will not be considerably released into the near/far-field. Therefore, accurate mechanistic models are necessary to predict glass dissolution, and these should be robust to a variety of incorporated waste species and leaching test conditions, particularly given substantial variations across international waste-streams. Here, machine learning is used to predict glass material properties (viscosity, density) and glass leaching model parameters from large-scale industrial data. A variety of different machine learning algorithms have been compared to assess performance. Density was predicted solely from composition, whereas viscosity additionally considered temperature. To predict suitable glass leaching model parameters, a large simulated dataset was created by coupling MATLAB and the chemical reactive-transport code HYTEC, considering the state-of-the-art GRAAL model (glass reactivity in allowance of the alteration layer). The trained models were then subsequently applied to the large-scale industrial, experimental data to identify potentially appropriate model parameters. Results indicate that ensemble methods can accurately predict viscosity as a function of temperature and composition across all three industrial datasets. Glass density prediction shows reliable learning performance with predictions primarily being within the experimental uncertainty of the test data. Furthermore, machine learning can predict glass dissolution model parameters behavior, demonstrating potential value in GRAAL model development and in assessing suitable model parameters for large-scale industrial glass dissolution data.

Keywords: machine learning, predictive modelling, pattern recognition, radioactive waste glass

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Authors: Leandros A. Maglaras, Jianmin Jiang

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The protection of the national infrastructures from cyberattacks is one of the main issues for national and international security. The funded European Framework-7 (FP7) research project CockpitCI introduces intelligent intrusion detection, analysis and protection techniques for Critical Infrastructures (CI). The paradox is that CIs massively rely on the newest interconnected and vulnerable Information and Communication Technology (ICT), whilst the control equipment, legacy software/hardware, is typically old. Such a combination of factors may lead to very dangerous situations, exposing systems to a wide variety of attacks. To overcome such threats, the CockpitCI project combines machine learning techniques with ICT technologies to produce advanced intrusion detection, analysis and reaction tools to provide intelligence to field equipment. This will allow the field equipment to perform local decisions in order to self-identify and self-react to abnormal situations introduced by cyberattacks. In this paper, an intrusion detection module capable of detecting malicious network traffic in a Supervisory Control and Data Acquisition (SCADA) system is presented. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automates SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detects anomalies in the system real time. The module is part of an IDS (intrusion detection system) developed under CockpitCI project and communicates with the other parts of the system by the exchange of IDMEF messages that carry information about the source of the incident, the time and a classification of the alarm.

Keywords: cyber-security, SCADA systems, OCSVM, intrusion detection

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Authors: Rabbya Ashrafi, Mohammad Tarikul Islam , Al-Amin Bhuiyan, Aminur Rahman

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Injury is the leading killer of children in Bangladesh. Anchal (community crèche) is an effective intervention to prevent injuries among children under 5. Through the SoLiD project, 1,600 Anchals are in place in three sub-districts in Bangladesh. The objectives of the Anchal are to provide supervision and early childhood development stimulations (ECD) to the children. A locally trained caregiver supervises 20-25 children, 9 to 59 months old, from 9 a.m. to 1 p.m., six days a week. Although it was found effective, during its implementation phase several challenges were noticed. To identify challenges and means to overcome those to improve the Anchal activities. In-depth interviews were conducted with Anchal caregivers, their supervisors, and trainers. Focus group discussions were conducted with the mothers of the Anchal children. The study was conducted in the Manohardi sub-district in November 2015. Decay of knowledge and skills after 2-3 months of training, lack of formal certification and inappropriate selection of women as Anchal caregivers, and enrollment of small children (less than 12 months) were the important challenges. The reluctance of parents to send children to the Anchal at the proper time, failure to engage children in various ECD activities, ineffective conduction of parents and community leaders meeting by the Anchal caregivers, insufficient accommodation, and poor supply of logistics for children were also the important challenges. The suggestion for improvement was to recruit caregivers as per standard criteria, provide them refreshers training at three months intervals, train them on effective conduction of parents and community leaders meetings, provide a formal certificate, and ensure regular supply of logistics. The identified challenges are needed to be addressed by utilizing the suggestions obtained from the IDIs and FGDs to make the Anchal intervention more effective in preventing childhood injuries.

Keywords: comunity crech, earlychildhood development, measures for improvement, childhood injury

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Authors: Maha Salah Abdullah Ismail, Manal S. Ismail, Amir M. Saleh

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Reflexology treatment is a method for enhancing body relaxation. It is a widely recognized as an alternative therapy, effective for many health conditions. This study aimed to evaluate the effect of reflexology treatment on arterial blood gases among mechanically ventilated patients. A quasi-experimental (pre and post-test) research design was used. Research hypothesis was mechanically ventilated patients who will receive the reflexology treatment will have improvement in their arterial blood gases than those who will not. The current study was carried out in different Intensive Care Units at the Cairo University Hospitals. A purposeful sample of 100 adults’ mechanically ventilated patients was recruited over a period of three months of data collection. The participants were divided into two equally matched groups; (1) The study group who has received the routine care, in addition, two reflexology sessions on the feet, (2) The control group who has received only the routine care. One tool was utilized to collect data pertinent to the study; mechanically ventilated patients' data sheet that consists of demographic and medical data. Result: Majority (58% of the study group and 82% of the control group) were males, with mean age of 50.9 years in both groups. Patients who received the reflexology treatment significantly increase in the oxygen saturation pre second session (t=5.15, p=.000), immediate post sessions (t=4.4, p=.000) and post two hours (t= 4.7, p= .000). The study group was more likely to have lower PaO2 (F=5.025, p=.015), PaCo2 (F=4.952, p=.025) and higher HCo3 (F=15.211, p=.000) than the control group. Conclusion: This study results support the positive effect of reflexology treatment in improving some arterial blood gases among mechanically ventilated patients’ with the conventional therapy as in the study group there was increase in the oxygen saturation. In differences between groups there decrease PaO2, PaCo2 and increase HCo3 in the study group. Recommendation: Nurses should be trained how to demonstrate the foot reflexology among mechanically ventilated patients.

Keywords: arterial blood gases, foot, mechanical ventilated patient, reflexology

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Authors: Anne N. Heirman, Song Duimel, Rob van Son, Lisette van der Molen, Richard Dirven, Gyorgi B. Halmos, Julia van Weert, Michiel W.M. van den Brekel

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Objectives: Treatments for head-neck cancer are drastic and often significantly impact the quality of life and appearance of patients. Shared decision-making (SDM) beholds a collaboration between patient and doctor in which the most suitable treatment can be chosen by integrating patient preferences, values, and medical information. SDM has a lot of advantages that would be useful in making difficult treatment choices. The objective of this study was to determine the current level of SDM among patients and head-and-neck surgeons. Methods: Consultations of patients with a non-cutaneous head-and-neck malignancy facing a treatment decision were selected and included. If given informed consent, the consultation was recorded with an audio recorder, and the patient and surgeon filled in a questionnaire immediately after the consultation. The SDM level of the consultation was scored objectively by independent observers who judged audio recordings of the consultation using the OPTION5-scale, ranging from 0% (no SDM) to 100% (optimum SDM), as well as subjectively by patients (using the SDM-Q-9 and Control preference scale) and clinicians (SDM-Q-Doc, modified control preference scale) percentages. Preliminary results: Five head-neck surgeons have each at least seven recorded conversations with different patients. One of them was trained in SDM. The other four had no experience with SDM. Most patients were male (74%), and oropharyngeal carcinoma was the most common diagnosis (41%), followed by oral cancer (33%). Five patients received palliative treatment of which two patients were not treated recording guidelines. At this moment, all recordings are scored by the two independent observers. Analysis of the results will follow soon. Conclusion: The current study will determine to what extent there is a discrepancy between the objective and subjective level of shared decision-making (SDM) during a doctor-patient consultation in Head-and-Neck surgery. The results of the analysis will follow shortly.

Keywords: head-and-neck oncology, patient involvement, physician-patient relations, shared decision making

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Authors: Ankit Kamboj, Shikha Talwar, Nilesh Powar

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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

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Authors: Tristan R. Fraser, Christopher D. Steadman, Christopher J. Boos

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Myopericarditis is an inflammation syndrome characterised by clinical diagnostic criteria for pericarditis, such as chest pain, combined with evidence of myocardial involvement, such as elevation of biomarkers of myocardial damage, e.g., troponins. It can rarely be a complication of therapeutics used for dysregulated immune-mediated diseases such as inflammatory bowel disease (IBD), for example, mesalazine. The infrequency of mesalazine-induced myopericarditis adds to the challenge in its recognition. Rapid diagnosis and the early introduction of treatment are crucial. This case report follows a 24-year-old professional footballer with a past medical history of ulcerative colitis, recently started on mesalazine for disease control. Three weeks after mesalazine was initiated, he was admitted with fever, shortness of breath, and chest pain worse whilst supine and on deep inspiration, as well as elevated venous blood cardiac troponin T level (cTnT, 288ng/L; normal: <13ng/L). Myocarditis was confirmed on initial inpatient cardiac MRI, revealing the presence of florid myocarditis with preserved left ventricular systolic function and an ejection fraction of 67%. This was a longitudinal case study following the progress of a single individual with myopericarditis over four acute hospital admissions over nine weeks, with admissions ranging from two to five days. Parameters examined included clinical signs and symptoms, serum troponin, transthoracic echocardiogram, and cardiac MRI. Serial measurements of cardiac function, including cardiac MRI and transthoracic echocardiogram, showed progressive deterioration of cardiac function whilst mesalazine was continued. Prior to cessation of mesalazine, transthoracic echocardiography revealed a small global pericardial effusion of < 1cm and worsening left ventricular systolic function with an ejection fraction of 45%. After recognition of mesalazine as a potential cause and consequent cessation of the drug, symptoms resolved, with cardiac MRI performed as an outpatient showing resolution of myocardial oedema. The patient plans to make a return to competitive sport. Patients suffering from myopericarditis are advised to refrain from competitive sport for at least six months in order to reduce the risk of cardiac remodelling and sudden cardiac death. Additional considerations must be taken in individuals for whom competitive sport is an essential component of their livelihood, such as professional athletes. Myopericarditis is an uncommon, however potentially serious medical condition with a wide variety of aetiologies, including viral, autoimmune, and drug-related causes. Management is mainly supportive and relies on prompt recognition and removal of the aetiological process. Mesalazine-induced myopericarditis is a rare condition; as such increasing awareness of mesalazine as a precipitant of myopericarditis is vital for optimising the management of these patients.

Keywords: myopericarditis, mesalazine, inflammatory bowel disease, professional athlete

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Authors: Altilio Rosa, Chirico Francesco, Foglia Goffredo

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In recent years, the number of Unmanned Aerial Vehicles (UAVs) has significantly increased. The rapid development of commercial and recreational drones makes them an important part of our society. Despite the growing list of their applications, these vehicles pose a huge threat to civil and military installations: detection, classification and neutralization of such flying objects become an urgent need. Radar is an effective remote sensing tool for detecting and tracking flying objects, but scenarios characterized by the presence of a high number of tracks related to flying birds make especially challenging the drone detection task: operator PPI is cluttered with a huge number of potential threats and his reaction time can be severely affected. Flying birds compared to UAVs show similar velocity, RADAR cross-section and, in general, similar characteristics. Building from the absence of a single feature that is able to distinguish UAVs and birds, this paper uses a multiple features approach where an original feature selection technique is developed to feed binary classifiers trained to distinguish birds and UAVs. RADAR tracks acquired on the field and related to different UAVs and birds performing various trajectories were used to extract specifically designed target movement-related features based on velocity, trajectory and signal strength. An optimization strategy based on a genetic algorithm is also introduced to select the optimal subset of features and to estimate the performance of several classification algorithms (Neural network, SVM, Logistic regression…) both in terms of the number of selected features and misclassification error. Results show that the proposed methods are able to reduce the dimension of the data space and to remove almost all non-drone false targets with a suitable classification accuracy (higher than 95%).

Keywords: birds, classification, machine learning, UAVs

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Authors: Anshika Kankane, Dongshik Kang

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Marine debris has a significant influence on coastal environments, damaging biodiversity, and causing loss and damage to marine and ocean sector. A functional cost-effective and automatic approach has been used to look up at this problem. Computer vision combined with a deep learning-based model is being proposed to identify and categorize marine debris of seven kinds on different beach locations of Japan. This research compares state-of-the-art deep learning models with a suggested model architecture that is utilized as a feature extractor for debris categorization. The model is being proposed to detect seven categories of litter using a manually constructed debris dataset, with the help of Mask R-CNN for instance segmentation and a shape matching network called HOGShape, which can then be cleaned on time by clean-up organizations using warning notifications of the system. The manually constructed dataset for this system is created by annotating the images taken by fixed KaKaXi camera using CVAT annotation tool with seven kinds of category labels. A pre-trained HOG feature extractor on LIBSVM is being used along with multiple templates matching on HOG maps of images and HOG maps of templates to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the warning notifications using live recorded beach debris data. The suggested network results in the improvement of misclassified debris masks of debris objects with different illuminations, shapes, viewpoints and litter with occlusions which have vague visibility.

Keywords: computer vision, debris, deep learning, fixed live camera images, histogram of gradients feature extractor, instance segmentation, manually annotated dataset, multiple template matching

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Authors: Petrus Louis Nolte

Abstract:

University sport in South Africa is not isolated from the complexity of globalization and professionalization of sport, as it forms an integral part of the sport development environment in South Africa. In order to align their sport programmes with global and professional requirements, several universities opted to develop elite sport programmes; recruit specialized personnel such as coaches, administrators and athletes; provide expert coaching; scientific and medical services; sports testing; fitness, technical and tactical expertise; sport psychological and rehabilitation support; academic guidance and career assistance; and student-athlete accommodation. In addition, universities provide administrative support and high-quality physical resources (training facilities) for the benefit of the overall South African sport system. Although it is not compulsory for universities to develop elite sport programmes to prepare their teams for competitions, elite competitions such as the annual Varsity Sport, University Sport South Africa (USSA) and local club competitions and leagues within university international competitions where universities not only compete but also deliver players for representative national netball teams. The aim of this study is therefore to describe the perceptions of players of the university elite netball programmes they were participating in. This study adopted a descriptive design with a quantitative approach, utilizing a self-structured questionnaire as research technique. As this research formed part of a national research project for NSA with a population of 172 national and provincial netball players, a sample of 92 university netball players from the population was selected. Content validity of the self-structured questionnaire was secured through a test-retest process, with construct validity through a member of the Statistical Consultation Services (STATCON) of the University of Johannesburg that provided feedback on the structural format of the questionnaire. Reliability was measured utilising Cronbach Alpha on p<0.005 level of significance. A reliability score of 0.87 was measured. The research was approved by the Board of Netball South Africa and ethical conduct implemented according to the processes and procedures approved by the Ethics Committees of the Faculty of Health Sciences, University of Johannesburg with clearance number REC-01-30-2019. From the results it is evident that university elite netball programmes are professional, especially with regards to the employment of knowledgeable and competent coaches and technical officials such as team managers and sport sciences staff. These professionals have access to elite training facilities, support staff, and relatively large groups of elite players, all elements of an elite programme that could enhance the national federation’s (Netball South Africa) system. Universities could serve the dual purpose of serving as university netball clubs, as well as providing elite training services and facilities as performance hubs for national players.

Keywords: elite sport programmes, university netball, player experiences, Varsity Sport netball

Procedia PDF Downloads 149

Authors: Ruth Nsibirano

Abstract:

Online mode of delivery in higher institutions of learning, popularly known in some circles as e-Learning or distance education is a new phenomenon that is steadily taking root in African universities but specifically at Makerere University. For slightly over a decade, the Department of Open and Distance Learning has been offering the first generation mode of distance education. In this, learning and teaching experiences were based on the use of hard copy materials circulated through postal services in a rather correspondence mode. There were more challenges to this including high dropout rates, limited support to the learners and sustainability issues. Fortunately, the Department was supported by the Norwegian Government through a NORHED grant to “leapfrog” to the fifth generation of distance education that makes more use of educational technologies and tools. The capacity of faculty staff was gradually enhanced through a series of training to handle the upgraded structure of fifth generation distance education. The trained staff was then tasked to develop modules befitting an online delivery mode, for use on the program. This paper will present attitudes, experiences of the course writers with a view of sharing the good practices that enabled them leap from e-faculty trainees to distinct online course writers. This perspective will hopefully serve as building blocks to enhance the capacity of other upcoming distance education programs in low capacity universities and also promote the uptake of e-Education on the continent and beyond. Methodologically the findings were collected through individual interviews with the 30 course writers. In addition, semi structured questionnaires were designed to collect data on the profile, challenges and lessons from the writers. Findings show that the attitudes of course writers on project supported activities are so much tagged to the returns from their committed efforts. In conclusion, therefore, it is strategically useful to assess and selectively choose which individual to nominate for involvement at the initial stages.

Keywords: distance education, online course content, staff attitudes, best practices in online learning

Procedia PDF Downloads 253

Authors: Joselito Medina-Marin, Maria G. Serna-Diaz, Alejandro Tellez-Jurado, Juan C. Seck-Tuoh-Mora, Eva S. Hernandez-Gress, Norberto Hernandez-Romero, Iaina P. Medina-Serna

Abstract:

Trichoderma harzianum is a fungus that has been utilized as a low-cost fungicide for biological control of pests, and it is important to determine the optimal conditions to produce the highest amount of conidiospores of Trichoderma harzianum. In this work, the conidiospore production of Trichoderma harzianum is modeled and optimized by using Artificial Neural Networks (AANs). In order to gather data of this process, 30 experiments were carried out taking into account the number of hours of culture (10 distributed values from 48 to 136 hours) and the culture humidity (70, 75 and 80 percent), obtained as a response the number of conidiospores per gram of dry mass. The experimental results were used to develop an iterative algorithm to create 1,110 ANNs, with different configurations, starting from one to three hidden layers, and every hidden layer with a number of neurons from 1 to 10. Each ANN was trained with the Levenberg-Marquardt backpropagation algorithm, which is used to learn the relationship between input and output values. The ANN with the best performance was chosen in order to simulate the process and be able to maximize the conidiospores production. The obtained ANN with the highest performance has 2 inputs and 1 output, three hidden layers with 3, 10 and 10 neurons in each layer, respectively. The ANN performance shows an R2 value of 0.9900, and the Root Mean Squared Error is 1.2020. This ANN predicted that 644175467 conidiospores per gram of dry mass are the maximum amount obtained in 117 hours of culture and 77% of culture humidity. In summary, the ANN approach is suitable to represent the conidiospores production of Trichoderma harzianum because the R2 value denotes a good fitting of experimental results, and the obtained ANN model was used to find the parameters to produce the biggest amount of conidiospores per gram of dry mass.

Keywords: Trichoderma harzianum, modeling, optimization, artificial neural network

Procedia PDF Downloads 159

Authors: Donna Erika E. De Jesus

Abstract:

Introduction: Cardiac arrest occurs when abrupt cessation of cardiac function results in loss of effective circulation and complete cardiovascular collapse. For every minute of cardiac arrest without early intervention (cardiopulmonary resuscitation [CPR], defibrillation), chances of survival drop by 7-10%. It is crucial that CPR be initiated within 4-6 minutes to avoid brain death. Most out-of-hospital cardiac arrests (OHCA) occur in a residential setting where access to trained personnel and equipment is not readily available, resulting in poor victim outcomes. Methods: This is a descriptive study done from August to November 2021 using a prospective cohort design. Participants of the study include adult patients aged 18 years and above brought to the emergency room who suffered from out-of-hospital cardiac arrest. Out of the total 102 cases of OHCA, 63 participants were included in the study. Descriptive statistics were used to summarize the demographic and clinical characteristics of the patients. Results: 43 were male patients, comprising the majority at 73.02%. Hypertension was identified as the top co-morbidity, followed by diabetes mellitus, heart failure, and chronic kidney disease (CKD). Medical causes of arrest were identified in 96.83% of the cases. 90.48% of cardiac arrests occurred at home. Only 26 patients (41.27%) received pre-hospital intervention prior to ER arrival, which comprised only hands-only CPR. Twenty-three of which were performed by individuals with background knowledge of CPR. 60.32% were brought via self-conduction, the remainder by ambulances, which were noted to have no available equipment necessary to provide proper resuscitation. The average travel time from dispatch to ER arrival is 20 minutes. Conclusion: Overall survival of OHCA in our local setting remains dismal, as a return of spontaneous circulation was not achieved in any of the patients. The small number of patients having pre-hospital CPR indicates the need for emphasis on training and community education.

Keywords: out-of-hospital cardiac arrest, cardiopulmonary resuscitation, basic life support, emergency medical services

Procedia PDF Downloads 106

Authors: T. Bryan , V. Kepuska, I. Kostnaic

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A method of data compression and denoising is introduced that is based on atomic decomposition of audio data using “basis vectors” that are learned from the audio data itself. The basis vectors are shown to have higher data compression and better signal-to-noise enhancement than the Gabor and gammatone “seed atoms” that were used to generate them. The basis vectors are the input weights of a Sparse AutoEncoder (SAE) that is trained using “envelope samples” of windowed segments of the audio data. The envelope samples are extracted from the audio data by performing atomic decomposition with Gabor or gammatone seed atoms. This process identifies segments of audio data that are locally coherent with the seed atoms. Envelope samples are extracted by identifying locally coherent audio data segments with Gabor or gammatone seed atoms, found by matching pursuit. The envelope samples are formed by taking the kronecker products of the atomic envelopes with the locally coherent data segments. Oracle signal-to-noise ratio (SNR) verses data compression curves are generated for the seed atoms as well as the basis vectors learned from Gabor and gammatone seed atoms. SNR data compression curves are generated for speech signals as well as early American music recordings. The basis vectors are shown to have higher denoising capability for data compression rates ranging from 90% to 99.84% for speech as well as music. Envelope samples are displayed as images by folding the time series into column vectors. This display method is used to compare of the output of the SAE with the envelope samples that produced them. The basis vectors are also displayed as images. Sparsity is shown to play an important role in producing the highest denoising basis vectors.

Keywords: sparse dictionary learning, autoencoder, sparse autoencoder, basis vectors, atomic decomposition, envelope sampling, envelope samples, Gabor, gammatone, matching pursuit

Procedia PDF Downloads 253

Authors: Wim Hollander, Petrus Louis Nolte

Abstract:

University sport in South Africa is not isolated from the complexity of globalization and professionalization of sport, as it forms an integral part of the sports development environment in South Africa. In order to align their sports programs with global and professional requirements, several universities opted to develop elite sports programs; recruit specialized personnel such as coaches, administrators, and athletes; provide expert coaching; scientific and medical services; sports testing; fitness, technical and tactical expertise; sport psychological and rehabilitation support; academic guidance and career assistance; and student-athlete accommodation. In addition, universities provide administrative support and high-quality physical resources (training facilities) for the benefit of the overall South African sport system. Although it is not compulsory for universities to develop elite sports programs to prepare their teams for competitions, elite competitions such as the annual Varsity Sport, University Sport South Africa (USSA) and local club competitions and leagues within international university competitions where universities not only compete but also deliver players for representative national netball teams. The aim of this study is, therefore, to describe the perceptions of players of the university elite netball programs they were participating in. This study adopted a descriptive design with a quantitative approach, utilizing a self-structured questionnaire as a research technique. As this research formed part of a national research project for NSA with a population of 172 national and provincial netball players, a sample of 92 university netball players from the population was selected. Content validity of the self-structured questionnaire was secured through a test-retest process, with construct validity through a member of the Statistical Consultation Services (STATCON) of the University of Johannesburg that provided feedback on the structural format of the questionnaire. Reliability was measured utilizing Cronbach Alpha on p < 0.005 level of significance. A reliability score of 0.87 was measured. The research was approved by the Board of Netball South Africa and ethical conduct implemented according to the processes and procedures approved by the Ethics Committees of the Faculty of Health Sciences, the University of Johannesburg with clearance number REC-01-30-2019. From the results, it is evident that university elite netball programs are professional, especially with regards to the employment of knowledgeable and competent coaches and technical officials such as team managers and sport sciences staff. These professionals have access to elite training facilities, support staff, and relatively large groups of elite players, all elements of an elite program that could enhance the national federation’s (Netball South Africa) system. Universities could serve the dual purpose of serving as university netball clubs, as well as providing elite training services and facilities as performance hubs for national players.

Keywords: elite sport programmes, university netball, player experiences, varsity sport netball

Procedia PDF Downloads 166

Authors: Sunho Seo, Younghwan Shin, Jong-Hong Park, Sooeun Song, Junsung Kim, Jusik Yun, Yongkyun Kim, Jong-Moon Chung

Abstract:

In order to effectively cope with large and complex disasters, disaster response training is needed. Recently, disaster response training led by the ROK (Republic of Korea) government is being implemented through a 4 year R&D project, which has several similar functions as the HSEEP (Homeland Security Exercise and Evaluation Program) of the United States, but also has several different features as well. Due to the unpredictiveness and diversity of disasters, existing training methods have many limitations in providing experience in the efficient use of disaster incident response and recovery resources. Always, the challenge is to be as efficient and effective as possible using the limited human and material/physical resources available based on the given time and environmental circumstances. To enable repeated training under diverse scenarios, an AR (Augmented Reality) and VR (Virtual Reality) combined simulator is under development. Unlike existing disaster response training, simulator based training (that allows remote login simultaneous multi-user training) enables freedom from limitations in time and space constraints, and can be repeatedly trained with different combinations of functions and disaster situations. There are related systems such as ADMS (Advanced Disaster Management Simulator) developed by ETC simulation and HLS2 (Homeland Security Simulation System) developed by ELBIT system. However, the ROK government needs a simulator custom made to the country's environment and disaster types, and also combines the latest information and communication technologies, which include AR, VR, and MPEG-DASH (Moving Picture Experts Group - Dynamic Adaptive Streaming over HTTP) technology. In this paper, a new disaster response training simulator is proposed to overcome the limitation of existing training systems, and adapted to actual disaster situations in the ROK, where several technical features are described.

Keywords: augmented reality, emergency response training simulator, MPEG-DASH, virtual reality

Procedia PDF Downloads 301

Authors: Aderemi, Moses Aderibigbe

Abstract:

The supply chain of a manufacturing company can be classified into three categories, namely: 1) supplier chain, these are a network of suppliers of raw materials, machinery, and other requirements for daily operations for the company; 2) internal chain, which are departmental or functional relationships within the organization like production, finance, marketing, logistic and quality control departments all interacting together to achieve the goals and objective of the company; and 3) customer chain; these are networks used for products distribution to the final consumer which includes the product distributors and retailers in the marketplace as may be applicable. In a developing country like Nigeria, where government infrastructures are poor or, in some cases, none in existence, the survival of a small-scale manufacturing company often depends on how effectively its supply chain is managed. In Nigeria, suppliers of machinery and raw materials to most manufacturing companies are from low-cost but high-tech countries like China or India. The problem with the supply chain from these countries apart from the language barrier between these countries and Nigeria, is also that of product quality and after-sales support services. The internal chain also requires funding to employ an experienced and trained workforce to deliver the company’s goals and objectives effectively and efficiently, which is always a challenge for small-scale manufacturers, including product marketing. In Nigeria, the management of the supply chain by small-scale manufacturers is further complicated by unfavourable government policies. This empirical research is a review and analysis of the supply chain management of a small-scale manufacturing company located in Lagos, Nigeria. The company's performance for the past five years has been on the decline and company management thinks there is a need for a review of its supply chain management for business survival. The company’s supply chain is analyzed and compared with best global practices in this research, and recommendations are made to the company management. The research outcome justifies the company’s need for a strategic change in its supply chain management for business sustainability and provides a learning point to small-scale manufacturing companies from developing countries in Africa

Keywords: management, small scale, supply chain, companies, leaders

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Authors: Sadhana Sharma, Neha Sharma, Hardik Khandelwal, Arti Sharma

Abstract:

Background: It is widely acknowledged globally that parents must advocate for their children's drug and substance abuse prevention. However, many parents find it difficult to advocate due to systemic and logistical barriers. Alternatives to introducing advocacy, awareness, and support for the prevention of drug and substance abuse to children could occur in schools. However, little research has been conducted on the development of advocates for substance abuse in school settings. Objective: to evaluate the effectiveness of a school-based addiction prevention and control created as part of the Rajasthan Addiction Prevention Project (a partnership between state-community initiative). Methods: We conducted an evaluation in this study to determine the impact of a RAPP on a primary outcome (substance abuse knowledge) and other outcomes (family–school partnership, empowerment, and support). Specifically, between September-December 2022, two schools participated in the intervention group (advocacy training), and two schools participated in the control group (waiting list). The RAPP designed specialised 2-hrs training to equip teachers-parents with the knowledge and skills necessary to advocate for their own children and those of other families. All participants were required to complete a pre- and post-survey. Results: The intervention group established school advocates in schools where trained parents volunteered to lead support groups for high-risk children. Compared to the participants in the wait list control group, those in the intervention group demonstrated greater education knowledge, P = 0.002, and self-mastery, P = 0.04, and decreased family–school partnership quality, P = 0.002.Conclusions: The experimental evaluation of school-based advocacy programme revealed positive effects on substance abuse that persist over time. The approach wa s deemed feasible and acceptable by both parents and the school.

Keywords: prevention, school based, addiction, advocacy

Procedia PDF Downloads 96

Authors: Ejaz Ashraf, Raheel Babar, Muhammad Yaseen, Hafiz Khurram Shurjeel, Nosheen Fatima

Abstract:

Main purpose of this study was to assess adoption level of farmers for quinoa cultivation after they had been taught through training and visit extension approach. At this time of the 21^st century, population structure, climate change, food requirements and eating habits of people are changing rapidly. In this scenario, farmers must play their key role in sustainable crop development and production through adoption of new crops that may also be helpful to overcome the issue of food insecurity as well as reducing poverty in rural areas. Its cultivation in Pakistan is at the early stages and there is a need to raise awareness among farmers to grow quinoa crops. In the middle of the 2015, a training and visit extension approach was used to raise awareness and convince farmers to grow quinoa in the area. During training and visit extension program, 80 farmers were randomly selected for the training of quinoa cultivation. Later on, these farmers trained 60 more farmers living into their neighborhood. After six months, a survey was conducted with all 140 farmers to assess the impact of the training and visit program on adoption level of respondents for the quinoa crop. The survey instrument was developed with the help of literature review and other experts of the crop. Validity and reliability of the instrument were checked before complete data collection. The data were analyzed by using SPSS. Multiple regression analysis was used for interpretation of the results from the survey, which indicated that factors like information/ training, change in agronomic and plant protection practices play a key role in the adoption of quinoa cultivation by respondents. In addition, the model explains more than 50% of variation in the adoption level of respondents. It is concluded that farmers need timely information for improved knowledge of agronomic and plant protection practices to adopt cultivation of the quinoa crop in the area.

Keywords: farmers, quinoa, adoption, contact, training and visit

Procedia PDF Downloads 357

Authors: Velimir Monty Vesselinov, Trais Kliplhuis, Hope Jasperson

Abstract:

Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

Procedia PDF Downloads 53

Authors: Meriem Taleb, Ghania Tail, Fatma Zohra Kara, Brahim Djedouani, T. Moussa

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Forensic entomology is the use of insects and their arthropod relatives as silent witnesses to aid legal investigations by interpreting information concerning a death. The main purpose of forensic entomology is to establish the postmortem interval or PMI Postmortem interval is a matter of crucial importance in the investigations of homicide and other untimely deaths when the body found is after three days. Forensic entomology has grown immensely as a discipline in the past thirty years. In Algeria, forensic entomology was introduced in 2010 by the National Institute for Criminalistics and Criminology of the National Gendarmerie (NICC). However, all the work that has been done so far in this growing field in Algeria has been unknown at both the national and international levels. In this context, the aim of this paper is to describe the state of forensic entomology in Algeria. The Laboratory of Entomology of the NICC is the only one of its kind in Algeria. It started its activities in 2010, consisting of two specialists. The main missions of the laboratory are estimation of the PMI by the analysis of entomological evidence, and determination if the body was moved. Currently, the laboratory is performing different tasks such as the expert work required by investigators to estimate the PMI using the insects. The estimation is performed by the accumulated degree days method (ADD) in most of the cases except for those where the cadaver is in dry decay. To assure the quality of the entomological evidence, crime scene personnel are trained by the laboratory of Entomology of the NICC. Recently, undergraduate and graduate students have been studying carrion ecology and insect activity in different geographic locations of Algeria using rabbits and wild boar cadavers as animal models. The Laboratory of Entomology of the NICC has also been involved in some of these research projects. Entomotoxicology experiments are also conducted with the collaboration of the Toxicology Department of the NICC. By dint of hard work that has been performed by the Laboratory of Entomology of the NICC, official bodies have been adopting more and more the use of entomological evidence in criminal investigations in Algeria, which is commendable. It is important, therefore, that steps are taken to fill in the gaps in the knowledge necessary for entomological evidence to have a useful future in criminal investigations in Algeria.

Keywords: forensic entomology, corpse, insects, postmortem interval, expertise, Algeria

Procedia PDF Downloads 407