Search results for: deep acting

Authors: Maryam Fallahpoor, Biswajeet Pradhan

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Background: Artificial intelligence (AI) serves as a valuable tool in mitigating the scarcity of human resources required for the evaluation and categorization of vast quantities of medical imaging data. When AI operates with optimal precision, it minimizes the demand for human interpretations and, thereby, reduces the burden on radiologists. Among various AI approaches, deep learning (DL) stands out as it obviates the need for feature extraction, a process that can impede classification, especially with intricate datasets. The advent of DL models has ushered in a new era in medical imaging, particularly in the context of COVID-19 detection. Traditional 2D imaging techniques exhibit limitations when applied to volumetric data, such as Computed Tomography (CT) scans. Medical images predominantly exist in one of two formats: neuroimaging informatics technology initiative (NIfTI) and digital imaging and communications in medicine (DICOM). Purpose: This study aims to employ DL for the classification of COVID-19-infected pulmonary patients and normal cases based on 3D CT scans while investigating the impact of image format. Material and Methods: The dataset used for model training and testing consisted of 1245 patients from IranMehr Hospital. All scans shared a matrix size of 512 × 512, although they exhibited varying slice numbers. Consequently, after loading the DICOM CT scans, image resampling and interpolation were performed to standardize the slice count. All images underwent cropping and resampling, resulting in uniform dimensions of 128 × 128 × 60. Resolution uniformity was achieved through resampling to 1 mm × 1 mm × 1 mm, and image intensities were confined to the range of (−1000, 400) Hounsfield units (HU). For classification purposes, positive pulmonary COVID-19 involvement was designated as 1, while normal images were assigned a value of 0. Subsequently, a U-net-based lung segmentation module was applied to obtain 3D segmented lung regions. The pre-processing stage included normalization, zero-centering, and shuffling. Four distinct 3D CNN models (ResNet152, ResNet50, DensNet169, and DensNet201) were employed in this study. Results: The findings revealed that the segmentation technique yielded superior results for DICOM images, which could be attributed to the potential loss of information during the conversion of original DICOM images to NIFTI format. Notably, ResNet152 and ResNet50 exhibited the highest accuracy at 90.0%, and the same models achieved the best F1 score at 87%. ResNet152 also secured the highest Area under the Curve (AUC) at 0.932. Regarding sensitivity and specificity, DensNet201 achieved the highest values at 93% and 96%, respectively. Conclusion: This study underscores the capacity of deep learning to classify COVID-19 pulmonary involvement using real 3D hospital data. The results underscore the significance of employing DICOM format 3D CT images alongside appropriate pre-processing techniques when training DL models for COVID-19 detection. This approach enhances the accuracy and reliability of diagnostic systems for COVID-19 detection.

Keywords: deep learning, COVID-19 detection, NIFTI format, DICOM format

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Authors: Fatma Ahmed, Heba Mostafa, Bassem Ramdan, Azza El-Soussi

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Background: World Health Organization stated that by 2020 cardiac disease will be the number one cause of death worldwide and estimates that 25 million people per year will suffer from heart disease. Cardiac surgery is considered an effective treatment for severe forms of cardiovascular diseases that cannot be treated by medical treatment or cardiac interventions. In spite of the benefits of cardiac surgery, it is considered a major stressful experience for patients who are candidate for surgery. Prehabilitation can decrease incidences of postoperative complications as it prepares patients for surgical stress through enhancing their defenses to meet the demands of surgery. When patients anticipate the postoperative sequence of events, they will prepare themselves to act certain behaviors, identify their roles and actively participate in their own recovery, therefore, anxiety levels are decreased and functional capacity is enhanced. Prehabilitation programs can comprise interventions that include physical exercise, psychological prehabilitation, nutritional optimization and risk factor modification. Physical exercises are associated with improvements in the functioning of the various physiological systems, reflected in increased functional capacity, improved cardiac and respiratory functions and make patients fit for surgical intervention. Prehabilitation programs should also prepare patients psychologically in order to cope with stress, anxiety and depression associated with postoperative pain, fatigue, limited ability to perform the usual activities of daily living through acting in a healthy manner. Notwithstanding the benefits of psychological preparations, there are limited studies which investigated the effect of psychological prehabilitation to confirm its effect on psychological, quality of life and physiological outcomes of patients who had undergone cardiac surgery. Aim of the study: The study aims to determine the effect of prehabilitation interventions on outcomes of patients undergoing cardiac surgeries. Methods: Quasi experimental study design was used to conduct this study. Sixty eligible and consenting patients were recruited and divided into two groups: control and intervention group (30 participants in each). One tool namely emotional, physiological, clinical, cognitive and functional capacity outcomes of prehabilitation intervention assessment tool was utilized to collect the data of this study. Results: Data analysis showed significant improvement in patients' emotional state, physiological and clinical outcomes (P < 0.000) with the use of prehabilitation interventions. Conclusions: Cardiac prehabilitation in the form of providing information about surgery, circulation exercise, deep breathing exercise, incentive spirometer training and nutritional education implemented daily by patients scheduled for elective open heart surgery one week before surgery have been shown to improve patients' emotional state, physiological and clinical outcomes.

Keywords: emotional recovery, clinical recovery, coronary artery bypass grafting patients, prehabilitation

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Authors: Yahya Almurtadha, Mukhtar Ghaleb, Ahmed M. Shamsan Saleh

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The world witnessed a fierce attack from the Covid-19 virus, which affected public life socially, economically, healthily and psychologically. The world's governments tried to confront the pandemic by imposing a number of precautionary measures such as general closure, curfews and social distancing. Scientists have also made strenuous efforts to develop an effective vaccine to train the immune system to develop antibodies to combat the virus, thus reducing its symptoms and limiting its spread. Artificial intelligence, along with researchers and medical authorities, has accelerated the vaccine development process through big data processing and simulation. On the other hand, one of the most important negatives of the impact of Covid 19 was the state of anxiety and fear due to the blowout of rumors through social media, which prompted governments to try to reassure the public with the available means. This study aims to proposed using Sentiment Analysis (AKA Opinion Mining) and deep learning as efficient artificial intelligence techniques to work on retrieving the tweets of the public from Twitter and then analyze it automatically to extract their opinions, expression and feelings, negatively or positively, about the symptoms they may feel after vaccination. Sentiment analysis is characterized by its ability to access what the public post in social media within a record time and at a lower cost than traditional means such as questionnaires and interviews, not to mention the accuracy of the information as it comes from what the public expresses voluntarily.

Keywords: deep learning, opinion mining, natural language processing, sentiment analysis

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Authors: Sophia Shi

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Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

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Authors: Surjit Angra, Manisha Kumari, Vinod Kumar

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The objective of the Hydro Turbine Head Cover is to support the guide bearing, guide vane regulating mechanism and even in some design for generator thrust bearing support. Mechanical design of head cover deals with high static as well as fluctuating load acting on the structure. In the present work structural analysis of hydro turbine Head-cover using ANSYS software is carried out. Finite element method is used to calculate stresses on head cover. These calculations were done for the maximum possible loading under operating condition “LCI Quick Shut Down”. The results for equivalent Von-Mises stress, total deformation and directional deformation have been plotted and compared with the existing results whether the design is safe or not.

Keywords: ANSYS, head cover, hydro-turbine, structural analysis, total deformation, Von-Mises stress

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Authors: Kedar Nath Nepal, Dyutiman Choudhary, Naba Raj Pandit, Yam Gahire

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Crop fertilizer recommendations are outdated as these are based on the response trails conducted over half a century ago. Further, these recommendations were based on the response trials conducted over large geographical area ignoring the large spatial variability in indigenous nutrient supplying capacity of soils typical of most smallholder systems. Application of fertilizer following such blanket recommendation in fields with varying native nutrient supply capacity leads to under application in some places and over application in others leading to reduced nutrient-use-efficiency (NUE), loss of profitability, and increased environmental risks associated with loss of unutilized nutrient through emissions or leaching. Opportunities exist to further increase yield and profitability through a significant gain in fertilizer use efficiency with commercialization of affordable and precise application technologies. We conducted participatory trails in Maize (Zea Mays), Cauliflower (Brassica oleracea var. botrytis) and Tomato (Solanum lycopersicum) in Mid Hills of Nepal to evaluate the efficacy of Urea Deep Placement (UDP and Polymer Coated Urea (PCU);. UDP contains 46% of N having individual briquette size 2.7 gm each and PCU contains 44% of N . Both PCU and urea briquette applied at reduced amount (100 kg N/ha) during planting produced similar yields (p>0.05) compared with regular urea (200 Kg N/ha). . These fertilizers also reduced N fertilizer by 35 - 50% over government blanket recommendations. Further, PCU and urea briquette increased farmer’s net income by USD 60 to 80.

Keywords: high efficiency fertilizers, urea deep placement, briquette polymer coated urea, zea mays, brassica, lycopersicum, Nepal

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Authors: Mangala Kohli, P. A. Athira, Reeha Mahajan

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The subthalamic nucleus (STN) is a biconvex nucleus situated in the diencephalon. The knowledge of the morphometry of the subthalamic nucleus is essential for accurate targeting of the nucleus during Deep Brain Stimulation. The present study aims to note the morphometry of the subthalamic nucleus in both the cerebral hemispheres which will prove to be of great value to radiologists and neurosurgeons. A cross‐sectional observational study was conducted in the Departments of Anatomy and Forensic Medicine, Lady Hardinge Medical College & Associated Hospitals, New Delhi on thirty adult cadaveric brain specimens of unclaimed and donated corpses. The specimens were categorized into 3 age groups: 20-35, 35-50 and above 50 years. All samples were collected after following the standard protocol for ethical clearance. The morphometric study of 60 subthalamic nucleus was thus conducted. Transverse section of the brain was made at a plane 4mm ventral to the plane containing mid commissural point. The dimensions of the subthalamic nucleus were measured bilaterally with the aid of digital Vernier caliper and magnifying glass. In the present study, the mean length and width and AC-PC length of the subthalamic nucleus was recorded on the right and left side in Group A, B and C. On comparison of mean of subthalamic nucleus dimensions between the right and left side in Group C, no statistically significant difference was observed. The length and width of subthalamic nucleus measured in the 3 age groups were compared with each other and the p value calculated. There was no statistically significant difference between the dimensions of Group A and B, Group B and C as well as Group A and C. The present study reveals that there is no significant reduction in the size of the nucleus was noted with increasing age. Thus, the values obtained in the present study can be used as a reference for various invasive and non-invasive procedures on subthalamic nucleus.

Keywords: cerebral hemisphere, deep brain stimulation, morphometry, subthalamic nucleus

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Authors: Jinan Fiaidhi, Hashmath Shaik

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Breaking the barrier for practicing evidence-based medicine relies on effective methods for rapidly identifying relevant evidence from the body of biomedical literature. An important challenge confronted by medical practitioners is the long time needed to browse, filter, summarize and compile information from different medical resources. Deep learning can help in solving this based on automatic question answering (Q&A) and transformers. However, Q&A and transformer technologies are not trained to answer clinical queries that can be used for evidence-based practice, nor can they respond to structured clinical questioning protocols like PICO (Patient/Problem, Intervention, Comparison and Outcome). This article describes the use of deep learning techniques for Q&A that are based on transformer models like BERT and GPT to answer PICO clinical questions that can be used for evidence-based practice extracted from sound medical research resources like PubMed. We are reporting acceptable clinical answers that are supported by findings from PubMed. Our transformer methods are reaching an acceptable state-of-the-art performance based on two staged bootstrapping processes involving filtering relevant articles followed by identifying articles that support the requested outcome expressed by the PICO question. Moreover, we are also reporting experimentations to empower our bootstrapping techniques with patch attention to the most important keywords in the clinical case and the PICO questions. Our bootstrapped patched with attention is showing relevancy of the evidence collected based on entropy metrics.

Keywords: automatic question answering, PICO questions, evidence-based medicine, generative models, LLM transformers

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Authors: Somayeh Komeylian

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The direction of arrival (DoA) estimation is the crucial aspect of the radar technologies for detecting and dividing several signal sources. In this scenario, the antenna array output modeling involves numerous parameters including noise samples, signal waveform, signal directions, signal number, and signal to noise ratio (SNR), and thereby the methods of the DoA estimation rely heavily on the generalization characteristic for establishing a large number of the training data sets. Hence, we have analogously represented the two different optimization models of the DoA estimation; (1) the implementation of the decision directed acyclic graph (DDAG) for the multiclass least-squares support vector machine (LS-SVM), and (2) the optimization method of the deep neural network (DNN) radial basis function (RBF). We have rigorously verified that the LS-SVM DDAG algorithm is capable of accurately classifying DoAs for the three classes. However, the accuracy and robustness of the DoA estimation are still highly sensitive to technological imperfections of the antenna arrays such as non-ideal array design and manufacture, array implementation, mutual coupling effect, and background radiation and thereby the method may fail in representing high precision for the DoA estimation. Therefore, this work has a further contribution on developing the DNN-RBF model for the DoA estimation for overcoming the limitations of the non-parametric and data-driven methods in terms of array imperfection and generalization. The numerical results of implementing the DNN-RBF model have confirmed the better performance of the DoA estimation compared with the LS-SVM algorithm. Consequently, we have analogously evaluated the performance of utilizing the two aforementioned optimization methods for the DoA estimation using the concept of the mean squared error (MSE).

Keywords: DoA estimation, Adaptive antenna array, Deep Neural Network, LS-SVM optimization model, Radial basis function, and MSE

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Authors: Ricky Saputra, Rashmi Walvekar, Mohammad Khalid, Kaveh Shahbaz, Suganti Ramarad

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This particular study of interest aims to study the effect of coupling ultrasonic treatment with eutectic solvents in devulcanization process of waste rubber tyre. Specifically, three different types of Deep Eutectic Solvents (DES) were utilized, namely ChCl:Urea (1:2), ChCl:ZnCl₂ (1:2) and ZnCl₂:urea (2:7) in which their physicochemical properties were analysed and proven to have permissible water content that is less than 3.0 wt%, degradation temperature below 200ᵒC and freezing point below 60ᵒC. The mass ratio of rubber to DES was varied from 1:20-1:40, sonicated for 1 hour at 37 kHz and heated at variable time of 5-30 min at 180ᵒC. Energy dispersive x-rays (EDX) results revealed that the first two DESs give the highest degree of sulphur removal at 74.44 and 76.69% respectively with optimum heating time at 15 minutes whereby if prolonged, reformation of crosslink network would be experienced. Such is supported by the evidence shown by both FTIR and FESEM results where di-sulfide peak reappears at 30 minutes and morphological structures from 15 to 30 minutes change from smooth with high voidage to rigid with low voidage respectively. Furthermore, TGA curve reveals similar phenomena whereby at 15 minutes thermal decomposition temperature is at the lowest due to the decrease of molecular weight as a result of sulphur removal but increases back at 30 minutes. Type of bond change was also analysed whereby it was found that only di-sulphide bond was cleaved and which indicates partial-devulcanization. Overall, the results show that DES has a great potential to be used as devulcanizing solvent.

Keywords: crosslink network, devulcanization, eutectic solvents, reformation, ultrasonic

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Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

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Authors: Vignesh Ratnaraj, Jaewon Chang

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Background: Frailty is associated with poorer outcomes in cardiac surgery, but the heterogeneity in frailty assessment tools makes it difficult to ascertain its true impact in cardiac surgery. Slow gait speed is a simple, validated, and reliable marker of frailty. We performed a systematic review and meta-analysis to examine the effect of slow gait speed on postoperative cardiac surgical patients. Methods: PubMED, MEDLINE, and EMBASE databases were searched from January 2000 to August 2021 for studies comparing slow gait speed and “normal” gait speed. The primary outcome was in-hospital mortality. Secondary outcomes were composite mortality and major morbidity, AKI, stroke, deep sternal wound infection, prolonged ventilation, discharge to a healthcare facility, and ICU length of stay. Results: There were seven eligible studies with 36,697 patients. Slow gait speed was associated with an increased likelihood of in-hospital mortality (risk ratio [RR]: 2.32; 95% confidence interval [CI]: 1.87–2.87). Additionally, they were more likely to suffer from composite mortality and major morbidity (RR: 1.52; 95% CI: 1.38–1.66), AKI (RR: 2.81; 95% CI: 1.44–5.49), deep sternal wound infection (RR: 1.77; 95% CI: 1.59–1.98), prolonged ventilation >24 h (RR: 1.97; 95% CI: 1.48–2.63), reoperation (RR: 1.38; 95% CI: 1.05–1.82), institutional discharge (RR: 2.08; 95% CI: 1.61–2.69), and longer ICU length of stay (MD: 21.69; 95% CI: 17.32–26.05). Conclusion: Slow gait speed is associated with poorer outcomes in cardiac surgery. Frail patients are twofold more likely to die during hospital admission than non-frail counterparts and are at an increased risk of developing various perioperative complications.

Keywords: cardiac surgery, gait speed, recovery, frailty

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Authors: Mourad Bédir, Sami Khomsi, Hakim Gabtni, Hajer Azaiez, Ramzi Gharsalli, Riadh Chebbi

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Eastern Tunisia is a semi-arid area; located in the northern Africa plate; southern Mediterranean side. It is facing water scarcity, overexploitation, and decreasing of water quality of phreatic water table. Water supply and storage will not respond to the demographic and economic growth and demand. In addition, only 5 109 m3 of rainwater from 35 109 m3 per year renewable rain water supply can be retained and remobilized. To remediate this water deficiency, researches had been focused to near new subsurface deep aquifers resources. Among them, Upper Miocene sandstone deposits of Béglia, Saouaf, and Somaa Formations. These sandstones are known for their proven Hydrogeologic and hydrocarbon reservoir characteristics in the Tunisian margin. They represent semi-confined to confined aquifers. This work is based on new integrated approaches of seismic stratigraphy, seismic tectonics, and hydrogeology, to highlight and characterize these reservoirs levels for aquifer exploitation in semi-arid area. As a result, five to six third order sequence deposits had been highlighted. They are composed of multi-layered extended sandstones reservoirs; separated by shales packages. These reservoir deposits represent lowstand and highstand system tracts of these sequences, which represent lowstand and highstand system tracts of these sequences. They constitute important strategic water resources volumes for the region.

Keywords: Tunisia, Hydrogeology, sandstones, basin, seismic, aquifers, modeling

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Authors: Ardiansyah I. Ryan, Ashsholih K. R., Fathurrohman G. R., Kurniadi M. R., Huda P. A

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The burden of brain-related disease is very high. There are a lot of brain-related diseases with limited treatment result and thus raise the burden more. The Parkinson Disease (PD), Mental Health Problem, or Paralysis of extremities treatments had risen concern, as the patients for those diseases usually had a low quality of life and low chance to recover fully. There are also many other brain or related neural diseases with the similar condition, mainly the treatments for those conditions are still limited as our understanding of the brain function is insufficient. Brain Implant Technology had given hope to help in treating this condition. In this paper, we examine the current update of the brain implant technology. Neurotechnology is growing very rapidly worldwide. The United States Food and Drug Administration (FDA) has approved the use of Deep Brain Stimulation (DBS) as a brain implant in humans. As for neural implant both the cochlear implant and retinal implant are approved by FDA too. All of them had shown a promising result. DBS worked by stimulating a specific region in the brain with electricity. This device is planted surgically into a very specific region of the brain. This device consists of 3 main parts: Lead (thin wire inserted into the brain), neurostimulator (pacemaker-like device, planted surgically in the chest) and an external controller (to turn on/off the device by patient/programmer). FDA had approved DBS for the treatment of PD, Pain Management, Epilepsy and Obsessive Compulsive Disorder (OCD). The target treatment of DBS in PD is to reduce the tremor and dystonia symptoms. DBS has been showing the promising result in animal and limited human trial for other conditions such as Alzheimer, Mental Health Problem (Major Depression, Tourette Syndrome), etc. Every surgery has risks of complications, although in DBS the chance is very low. DBS itself had a very satisfying result as long as the subject criteria to be implanted this device based on indication and strictly selection. Other than DBS, there are several brain implant devices that still under development. It was included (not limited to) implant to treat paralysis (In Spinal Cord Injury/Amyotrophic Lateral Sclerosis), enhance brain memory, reduce obesity, treat mental health problem and treat epilepsy. The potential of neurotechnology is unlimited. When brain function and brain implant were fully developed, it may be one of the major breakthroughs in human history like when human find ‘fire’ for the first time. Support from every sector for further research is very needed to develop and unveil the true potential of this technology.

Keywords: brain implant, deep brain stimulation (DBS), deep brain stimulation, Parkinson

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Authors: N. Singh, G. K. Darbha

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Since the last decade, a wave of research has begun to study the prevalence and impact of ever-increasing plastic pollution in the environment. The wide application and ubiquitous distribution of plastic have become a global concern due to its persistent nature. The disposal of plastics has emerged as one of the major challenges for waste management landfills. Microplastics (MPs) have found its existence in almost every environment, from the high altitude mountain lake to the deep sea sediments, polar icebergs, coral reefs, estuaries, beaches, and river, etc. Microplastics are fragments of plastics with size less than 5 mm. Microplastics can be classified as primary microplastics and secondary microplastics. Primary microplastics includes purposefully introduced microplastics into the end products for consumers (microbeads used in facial cleansers, personal care product, etc.), pellets (used in manufacturing industries) or fibres (from textile industries) which finally enters into the environment. Secondary microplastics are formed by disintegration of larger fragments under the exposure of sunlight, mechanical abrasive forces by rain, waves, wind and/or water. A number of factors affect the quantity of microplastic present in freshwater environments. In addition to physical forces, human population density proximal to the water body, proximity to urban centres, water residence time, and size of the water body also affects plastic properties. With time, other complex processes in nature such as physical, chemical and biological break down plastics by interfering with its structural integrity. Several studies demonstrate that microplastics found in wastewater sludge being used as manure for agricultural fields, thus having the tendency to alter the soil environment condition influencing the microbial population as well. Inadequate data are available on the fate and transport of microplastics under varying environmental conditions that are required to supplement important information for further research. In addition, microplastics have the tendency to absorb heavy metals and hydrophobic organic contaminants such as PAHs and PCBs from its surroundings and thus acting as carriers for these contaminants in the environment system. In this study, three kinds of microplastics (polyethylene, polypropylene and expanded polystyrene) of different densities were chosen. Plastic samples were placed in sand with different aqueous media (distilled water, surface water, groundwater and marine water). It was incubated at varying temperatures (25, 35 and 40 °C) and agitation levels (rpm). The results show that the number of plastic fragments enhanced with increase in temperature and agitation speed. Moreover, the rate of disintegration of expanded polystyrene is high compared to other plastics. These results demonstrate that temperature, salinity, and mechanical abrasion plays a major role in degradation of plastics. Since weathered microplastics are more harmful as compared to the virgin microplastics, long-term studies involving other environmental factors are needed to have a better understanding of degradation of plastics.

Keywords: environmental contamination, fragmentation, microplastics, temperature, weathering

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Authors: Michael Karnes, Alper Yilmaz

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This paper presents the application of the omni-modeler towards pedestrian redetection. The pedestrian redetection task creates several challenges when applying deep neural networks (DNN) due to the variety of pedestrian appearance with camera position, the variety of environmental conditions, and the specificity required to recognize one pedestrian from another. DNNs require significant training sets and are not easily adapted for changes in class appearances or changes in the set of classes held in its knowledge domain. Pedestrian redetection requires an algorithm that can actively manage its knowledge domain as individuals move in and out of the scene, as well as learn individual appearances from a few frames of a video. The Omni-Modeler is a dynamically learning few-shot visual recognition algorithm developed for tasks with limited training data availability. The Omni-Modeler adapts the knowledge domain of pre-trained deep neural networks to novel concepts with a calculated localized language encoder. The Omni-Modeler knowledge domain is generated by creating a dynamic dictionary of concept definitions, which are directly updatable as new information becomes available. Query images are identified through nearest neighbor comparison to the learned object definitions. The study presented in this paper evaluates its performance in re-identifying individuals as they move through a scene in both single-camera and multi-camera tracking applications. The results demonstrate that the Omni-Modeler shows potential for across-camera view pedestrian redetection and is highly effective for single-camera redetection with a 93% accuracy across 30 individuals using 64 example images for each individual.

Keywords: dynamic learning, few-shot learning, pedestrian redetection, visual recognition

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Authors: Jarmila Zolnova, Lucia Hrebenarova, Veronika Palkova

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The interconnections between supportive sources of authorities at school and students have been proved. Lacking research in this field in Slovakia translates into absenting perception of social support by students with special educational needs. The aim of this paper (presented by the poster) is to reveal and interpret the perception of frequency and importance of authorities at school from students' perspective. The sample included 718 students aged 10 years and 1 month on average. Eighty nine students of this count were students with special educational needs. Data were obtained from the Child and Adolescent Social Support Scale (CASSS) for students. Mutual relations between teachers acting as the source of support and students were not significant. Neither was significant the support of other school employees. Both groups of students assessed the frequency and importance of social support from teachers more positively than the support from other school employees.

Keywords: intact student, pedagogue, pupil with special education needs, school employee, social support

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Authors: Aurela Jahja, Katarzyna Wątor, Arjan Beqiraj, Piotr Rusiniak, Nevton Kodhelaj

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Albanian geological terrains represent an important node of the Alpine – Mediterranean mountain belt and are divided into several predominantly NNW - SSE striking geotectonic units, which, based on the presence or lack of Cretaceous transgression and magmatic rocks, belong to Internal or External Albanides. The internal (Korabi, Mirdita and Gashi) units are characterized by the Lower Cretaceous discordance and the presence of abundant magmatic rocks whereas in the external (Alps, Krasta-Cukali, Kruja, Ionian, Sazani and Peri Adriatic Depression) units an almost continuous sedimentation from Triassic to Paleogene is evidenced. The internal and external units show relevant differences in both geothermal and heat flow density values. The gradient values vary from 15-21.3 to 36 mK/m, while the heat flow density ranges from 42 to 60 mW/m2, in the external (Preadriatic Depression) and internal (ophiolitic belt) units, respectively. The geothermal fluids, which are found in natural springs and deep oil wells of Albania, are located in four thermo-mineral provinces: a) Peshkopi (Korabi) province; b) Kruja province; c) Preadriatic basin province, and d) South Ionian province. Thirteen geothermal waters were sampled from 11 natural springs and 2 deep wells, of which 6 springs and 2 wells from Kruja, 1 spring from Peshkopia, 2 springs from Preadriatic basin and 2 springs South Ionian province. Temperature, pH and Electrical Conductivity were measured in situ, while in laboratory were analyzed by ICP method major anions and cations and several trace elements (B, Li, Sr, Rb, I, Br, etc.). The measured values of temperature, pH and electrical conductivity range within 17-63°C, 6.26-7.92 and 724- 26856µS/cm intervals, respectively. The chemical type of the Albania thermal waters is variable. In the Kruja province prevail the Cl-SO4-NaCa and Cl-Na-Ca water types; while SO4-Ca, HCO3-Ca and Cl-HCO3-Na-Ca, and Cl-Na are found in the provinces of Peshkopi, Ionian and Preadriatic basin, respectively. In the Cl-SO4-HCO3 triangular diagram most of the geothermal waters are close to the chloride corner that belong to “mature waters”, typical of geothermal deep and hot fluids. Only samples from the Ionian province are located within the region of high bicarbonate concentration and they can be classified as peripheral waters that may have mixed with cold groundwater. In the Na-Ca-Mg and Na-K-Mg triangular diagram the majority of waters fall in the corner of sodium, suggesting that their cation ratios are controlled by mineral-solution equilibrium. There is a linear relationship between Cl and B which indicates the mixing of geothermal water with cold water, where the low-chlorine thermal waters from Ionian basin and Preadriatic depression provinces are distinguished by high-chlorine thermal waters from Kruja province. The Cl/Br molar ration of the thermal waters from Kruja province ranges from 1000 to 2660 and separates them from the thermal waters of Ionian basin and Preadriatic depression provinces having Cl/Br molar ratio lower than 650. The apparent increase of Cl/Br molar ratio that correlates with the increasing of the chloride, is probably related with dissolution of the Halite.

Keywords: geothermal fluids, geotectonic units, natural springs, deep wells, mature waters, peripheral waters

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Authors: Mohammad Saleem, Neeta Kumar, Anita Sharma, Sazina Muzammil

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It is observed that some of the dental students in our setting take less interest in medical subjects. Various teaching methods are focus of research interest currently and being tried to generate interest among students. An approach of interactive integrated tutorial was used to assess its feasibility in teaching medical subjects to dental undergraduates. The aim was to generate interest and promote active self-learning among students. The objectives were to (1) introduce the integrated interactive learning method through two departments, (2) get feedback from the students and faculty on feasibility and effectiveness of this method. Second-year students in Bachelor of Dental Surgery course were divided into two groups. Each group was asked to study physiology and pathology of a common and important condition (anemia and hypertension) in a week’s time. During the tutorial, students asked questions on physiology and pathology of that condition from each other in the presence of teachers of both physiology and pathology departments. The teachers acted only as facilitators. After the session, the feedback from students and faculty on this alternative learning method was obtained. Results: Majority of the students felt that this method of learning is enjoyable, helped to develop reasoning skills and ability to correlate and integrate the knowledge from two related fields. Majority of the students felt that this kind of learning led to better understanding of the topic and motivated them towards deep learning. Teachers observed that the study promoted interdepartmental cross-discipline collaboration and better students’ linkages. Conclusion: Interactive integrated tutorial is effective in motivating dental students for better and deep learning of medical subjects.

Keywords: active learning, education, integrated, interactive, self-learning, tutorials

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Authors: Dalila Chaid, Bennameur Fedilli, Mohammed Amine Bellelou

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The study focuses on the experience of lower-limb amputees, who face both physical and psychological challenges due to their disability. Chronic neuropathic pain and various types of limb pain are common in these patients. They often require orthopaedic interventions for issues such as dressings, infection, ulceration, and bone-related problems. Research Aim: The aim of this study is to determine the most suitable anaesthetic technique for lower-limb amputees, which can provide them with the greatest comfort and prolonged analgesia. The study also aims to demonstrate the effectiveness and cost-effectiveness of ultrasound-guided local regional anaesthesia (LRA) in this patient population. Methodology: The study is an observational analytical study conducted over a period of eight years, from 2010 to 2018. It includes a total of 955 cases of revisions performed on lower limb stumps. The parameters analyzed in this study include the effectiveness of the block and the use of sedation, the duration of the block, the post-operative visual analog scale (VAS) scores, and patient comfort. Findings: The study findings highlight the benefits of ultrasound-guided LRA in providing comfort by optimizing post-operative analgesia, which can contribute to psychological and bodily repair in lower-limb amputees. Additionally, the study emphasizes the use of alpha2 agonist adjuvants with sedative and analgesic properties, long-acting local anaesthetics, and larger volumes for better outcomes. Theoretical Importance: This study contributes to the existing knowledge by emphasizing the importance of choosing an appropriate anaesthetic technique for lower-limb amputees. It highlights the potential of ultrasound-guided LRA and the use of specific adjuvants and local anaesthetics in improving post-operative analgesia and overall patient outcomes. Data Collection and Analysis Procedures: Data for this study were collected through the analysis of medical records and relevant documentation related to the 955 cases included in the study. The effectiveness of the anaesthetic technique, duration of the block, post-operative pain scores, and patient comfort were analyzed using statistical methods. Question Addressed: The study addresses the question of which anaesthetic technique would be most suitable for lower-limb amputees to provide them with optimal comfort and prolonged analgesia. Conclusion: The study concludes that ultrasound-guided LRA, along with the use of alpha2 agonist adjuvants, long-acting local anaesthetics, and larger volumes, can be an effective approach in providing comfort and improving post-operative analgesia for lower-limb amputees. This technique can potentially contribute to the psychological and bodily repair of these patients. The findings of this study have implications for clinical practice in the management of lower-limb amputees, highlighting the importance of personalized anaesthetic approaches for better outcomes.

Keywords: neuropathic pain, ultrasound-guided peripheral nerve block, DN4 quiz, EMG

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Authors: Christina Killian, Katie Wall, Séamus Fanning, Guerrino Macori

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Deep-level metagenomics offers a useful technical approach to explore the three dynamic One Health axes: healthcare, domiciliary and veterinary. There is currently limited understanding of the composition of complex biofilms, natural abundance of AMR genes and gene transfer occurrence in these ecological niches. By using a newly established small-scale complex biofilm model, COMBAT has the potential to provide new information on microbial diversity, antimicrobial resistance (AMR)-encoding gene abundance, and their transfer in complex biofilms of importance to these three One Health axes. Shotgun metagenomics has been used to sample the genomes of all microbes comprising the complex communities found in each biofilm source. A comparative analysis between untreated and biocide-treated biofilms is described. The basic steps include the purification of genomic DNA, followed by library preparation, sequencing, and finally, data analysis. The use of long-read sequencing facilitates the completion of metagenome-assembled genomes (MAG). Samples were sequenced using a PromethION platform, and following quality checks, binning methods, and bespoke bioinformatics pipelines, we describe the recovery of individual MAGs to identify mobile gene elements (MGE) and the corresponding AMR genotypes that map to these structures. High-throughput sequencing strategies have been deployed to characterize these communities. Accurately defining the profiles of these niches is an essential step towards elucidating the impact of the microbiota on each niche biofilm environment and their evolution.

Keywords: COMBAT, biofilm, metagenomics, high-throughput sequencing

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Authors: Shehryar Afzal

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Gender is one of the organizing principles of the society. Gender relations are based on the ideology of sexual division of labors. Consequently, women tend to have a lower level of education, vocational and professional skills then men in a conservative area. In Swabi women, overall take part in their daily work, either it is home management. I-e cooking, sewing. Their Economic roles are selling daily used commodities I-e poultry, embroidery Selling, etc. Their Social roles are participation in traditional ceremonies’ like Death, marriages, etc. The aim is to introduce the Society a new range of communal and recreational spaces acting as a community center for women and children, while developing plans for the community women and children, Providing recreational and communal activities for which the community strive and urge, having a sense of freedom and openness. Already interacting spaces are present where they have a social and communal gathering, but there is no such facilities to celebrate these activities.

Keywords: social sitting, communal spaces, tradition, freedom

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Authors: Jixiang Lei, Alexander Fuchs, Franz Pernkopf, Katrin Ellermann

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Within the Space@Sea project, funded by the Horizon 2020 program, an island consisting of multiple platforms was designed. The platforms are connected by ropes and fenders. The connection is critical with respect to the safety of the whole system. Therefore, fault detection systems are investigated, which could detect early warning signs for a possible failure in the connection elements. Previously, a model-based method called Extended Kalman Filter was developed to detect the reduction of rope stiffness. This method detected several types of faults reliably, but some types of faults were much more difficult to detect. Furthermore, the model-based method is sensitive to environmental noise. When the wave height is low, a long time is needed to detect a fault and the accuracy is not always satisfactory. In this sense, it is necessary to develop a more accurate and robust technique that can detect all rope faults under a wide range of operational conditions. Inspired by this work on the Space at Sea design, we introduce a fault diagnosis method based on deep neural networks. Our method cannot only detect rope degradation by using the acceleration data from each platform but also estimate the contributions of the specific acceleration sensors using methods from explainable AI. In order to adapt to different operational conditions, the domain adaptation technique DANN is applied. The proposed model can accurately estimate rope degradation under a wide range of environmental conditions and help users understand the relationship between the output and the contributions of each acceleration sensor.

Keywords: fault diagnosis, deep learning, domain adaptation, explainable AI

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Authors: Alessandra Aparecida Vieira França, Adriana de Paula Lacerda Santos, Mauro Lacerda Santos Filho

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The purpose of this research i to study the behavior of precast prestressed hollow core slabs subjected to shear. In order to achieve this goal, shear tests were performed using hollow core slabs 26,5cm thick, with and without a concrete cover of 5 cm, without cores filled, with two cores filled and three cores filled with concrete. The tests were performed according to the procedures recommended by FIP (1992), the EN 1168:2005 and following the method presented in Costa (2009). The ultimate shear strength obtained within the tests was compared with the values of theoretical resistant shear calculated in accordance with the codes, which are being used in Brazil, noted: NBR 6118:2003 and NBR 14861:2011. When calculating the shear resistance through the equations presented in NBR 14861:2011, it was found that provision is much more accurate for the calculation of the shear strength of hollow core slabs than the NBR 6118 code. Due to the large difference between the calculated results, even for slabs without cores filled, the authors consulted the committee that drafted the NBR 14861:2011 and found that there is an error in the text of the standard, because the coefficient that is suggested, actually presents the double value than the needed one! The ABNT, later on, soon issued an amendment of NBR 14861:2011 with the necessary corrections. During the tests for the present study, it was confirmed that the concrete filling the cores contributes to increase the shear strength of hollow core slabs. But in case of slabs 26,5 cm thick, the quantity should be limited to a maximum of two cores filled, because most of the results for slabs with three cores filled were smaller. This confirmed the recommendation of NBR 14861:2011which is consistent with standard practice. After analyzing the configuration of cracking and failure mechanisms of hollow core slabs during the shear tests, strut and tie models were developed representing the forces acting on the slab at the moment of rupture. Through these models the authors were able to calculate the tensile stress acting on the concrete ties (ribs) and scaled the geometry of these ties. The conclusions of the research performed are the experiments results have shown that the mechanism of failure of the hollow-core slabs can be predicted using the strut-and-tie procedure, within a good range of accuracy. In addition, the needed of the correction of the Brazilian standard to review the correction factor σcp duplicated (in NBR14861/2011), and the limitation of the number of cores (Holes) to be filled with concrete, to increase the strength of the slab for the shear resistance. It is also suggested the increasing the amount of test results with 26.5 cm thick, and a larger range of thickness slabs, in order to obtain results of shear tests with cores concreted after the release of prestressing force. Another set of shear tests on slabs must be performed in slabs with cores filled and cover concrete reinforced with welded steel mesh for comparison with results of theoretical values calculated by the new revision of the standard NBR 14861:2011.

Keywords: prestressed hollow core slabs, shear, strut, tie models

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Authors: Tamou Nasser, Ahmed Essadki, Ali Boukhriss

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This paper presents the control of doubly fed induction generator (DFIG) used in the wind energy conversion systems. Maximum power point tracking (MPPT) strategy is used to extract the maximum of power during the conversion and taking care that the system does not exceed the operating limits. This is done by acting on the pitch angle to control the orientation of the turbine's blades. Having regard to its robustness and performance, active disturbance rejection control (ADRC) based on the extended state observer (ESO) is employed to achieve the control of both rotor and grid side converters. Simulations are carried out using matlab simulink.

Keywords: active disturbance rejection control, extended state observer, doubly fed induction generator, maximum power point tracking

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Authors: Tamou Nasser, Ahmed Essadki, Ali Boukhriss

Abstract:

This paper presents the control of doubly fed induction generator (DFIG) used in the wind energy conversion systems. Maximum power point tracking (MPPT) strategy is used to extract the maximum of power during the conversion and taking care that the system does not exceed the operating limits. This is done by acting on the pitch angle to control the orientation of the turbine's blades. Having regard to its robustness and performance, active disturbance rejection control (ADRC) based on the extended state observer (ESO) is employed to achieve the control of both rotor and grid side converters. Simulations are carried out using matlab simulink.

Keywords: active disturbance rejection control, extended state observer, doubly fed induction generator, maximum power point tracking

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Authors: Mohammad Ghiasvand, Babak Khorsandi, Morteza Kolahdoozan

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Under the influence of waves, oil in the sea is subject to vertical scattering in the water column. Scientists' knowledge of how oil is dispersed in the water column is one of the lowest levels of knowledge among other processes affecting oil in the marine environment, which highlights the need for research and study in this field. Therefore, this study investigates the distribution of oil in the water column in a turbulent environment with zero velocity characteristics. Lack of laboratory results to analyze the distribution of petroleum pollutants in deep water for information Phenomenon physics on the one hand and using them to calibrate numerical models on the other hand led to the development of laboratory models in research. According to the aim of the present study, which is to investigate the distribution of oil in homogeneous and isotropic turbulence caused by the oscillating Grid, after reaching the ideal conditions, the crude oil flow was poured onto the water surface and oil was distributed in deep water due to turbulence was investigated. In this study, all experimental processes have been implemented and used for the first time in Iran, and the study of oil diffusion in the water column was considered one of the key aspects of pollutant diffusion in the oscillating Grid environment. Finally, the required oscillation velocities were taken at depths of 10, 15, 20, and 25 cm from the water surface and used in the analysis of oil diffusion due to turbulence parameters. The results showed that with the characteristics of the present system in two static modes and network motion with a frequency of 0.8 Hz, the results of oil diffusion in the four mentioned depths at a frequency of 0.8 Hz compared to the static mode from top to bottom at 26.18, 57 31.5, 37.5 and 50% more. Also, after 2.5 minutes of the oil spill at a frequency of 0.8 Hz, oil distribution at the mentioned depths increased by 49, 61.5, 85, and 146.1%, respectively, compared to the base (static) state.

Keywords: homogeneous and isotropic turbulence, oil distribution, oscillating grid, oil spill

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Authors: Shivani Thakur, Jasmin Dominguez Cervantes, Ahmed Zabiba, Fatima Zabiba, Sandhini Agarwal, Kamalpreet Kaur, Hussein Maatouk, Shae Chand, Omar Madriz, Tiffany Huang, Saloni Bansal

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Contraceptives are an effective public health achievement, allowing for family planning and reducing the risk of sexually transmitted diseases (STDs). California’s rural Central Valley has high rates of teenage pregnancy and STDs. Factors affecting contraceptive usage here may include religious concerns, financial issues, and regional variations in the accessibility and availability of contraceptives. The increasing population and diversity of the Central Valley make the understanding of the determinants of unintended pregnancy and STDs increasingly nuanced. Patients in California’s Central Valley were surveyed at 6 surgical clinics to assess attitudes toward contraceptives. The questionnaire consisted of demographics and 14 Likert-scale statements investigating patients’ feelings regarding contraceptives. Parametric and non-parametric analysis was performed on the Likert statements. A correlation matrix for the Likert-scale statements was used to evaluate the strength of the relationship between each question. 76 patients aged 18-75 years completed the questionnaire. 90% of the participants were female, 76% Hispanic, 36% married, 44% with an income range between 30-60K, and 83% were between childbearing ages. 60% of participants stated they are currently using or had used some type of contraceptive. 25% of participants had at least one unplanned pregnancy. The most common type of contraceptives used were oral contraceptives(28%) and condoms(38%). The top reasons for patients’ contraceptive usage were: prevention of pregnancy (72%), safe sex/prevention of STDs (32%), and regulation of menstrual cycle (19%). Further analysis of Likert responses revealed that contraception usage increased due to approval of contraceptives (x̄=3.98, σ =1.02); partner approval of contraceptives (x̄=3.875, σ =1.16); and reduced anxiety about pregnancy (x̄=3.875, σ =1.23). Younger females (18-34 years old) agreed more with the statement that the cost of contraceptive supplies is too expensive than older females (35-75 years old), (x̄=3.2, σ = 1.4 vs x̄=2.8, σ =1.3, p<0.05). Younger females (44%) were also more likely to use short-acting contraceptive methods (oral and male condoms) compared to older females (64%) who use long-acting methods (implants/ intrauterine devices). 51% of Hispanic females were using some type of contraceptive. Of those Hispanic females who do not use contraceptives, 33% stated having no children, and all plan to have at least one child in the future. 35% of participants had a bachelor's degree. Those with bachelor’s degrees were more likely to use contraceptives, 58% vs 51%, p<0.05, and less likely to have unplanned pregnancy, 50% vs. 12%, p<0.01. There is increasing use and awareness among patients in rural settings concerning contraceptives. Our finding shows that younger women and women with higher educational attainment tend to have more positive attitudes towards the use of contraceptives. This work gives physicians an understanding of patients’ concerns about contraceptive methods and offers insight into culturally competent intervention programs that respect individual values.

Keywords: contraceptives, public health, rural california, women of child baring age

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Authors: Federica Ceccoli

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Italy is undergoing one of its largest migratory waves, and Italian schools are reporting the highest numbers of multilingual students coming from immigrant families and speaking minority languages. For these pupils, who have not perfectly acquired their mother tongue yet, learning a second language may represent a burden on their linguistic development and may have some repercussions on their school performances and relational skills. These are some of the reasons why they have turned out to be those who have the worst grades and the highest school drop-out rates. However, despite these negative outcomes, it has been demonstrated that multilingual immigrant students frequently act as translators or language brokers for their peers or family members who do not speak Italian fluently. This activity has been defined as Child Language Brokering (hereinafter CLB) and it has become a common practice especially in minority communities as immigrants’ children often learn the host language much more quickly than their parents, thus contributing to their family life by acting as language and cultural mediators. This presentation aims to analyse the data collected by a research carried out during the school year 2014-2015 in the province of Ravenna, in the Northern Italian region of Emilia-Romagna, among 126 immigrant students attending junior high schools. The purpose of the study was to analyse by means of a structured questionnaire whether multilingualism matched with language brokering experiences or not and to examine the perspectives of those students who reported having acted as translators using their linguistic knowledge to help people understand each other. The questionnaire consisted of 34 items roughly divided into 2 sections. The first section required multilingual students to provide personal details like their date and place of birth, as well as details about their families (number of siblings, parents’ jobs). In the second section, they were asked about the languages spoken in their families as well as their language brokering experience. The in-depth questionnaire sought to investigate a wide variety of brokering issues such as frequency and purpose of the activity, where, when and which documents young language brokers translate and how they feel about this practice. The results have demonstrated that CLB is a very common practice among immigrants’ children living in Ravenna and almost all students reported positive feelings when asked about their brokering experience with their families and also at school. In line with previous studies, responses to the questionnaire item regarding the people they brokered for revealed that the category ranking first is parents. Similarly, language-brokering activities tend to occur most often at home and the documents they translate the most (either orally or in writing) are notes from teachers. Such positive feelings towards this activity together with the evidence that it occurs very often in schools have laid the foundation for further projects on how this common practice may be valued and used to strengthen the linguistic skills of these multilingual immigrant students and thus their school performances.

Keywords: immigration, language brokering, multilingualism, students' points of view

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Authors: Marlin Mubarak

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Background: Endometriosis is a complex disabling disease affecting young females in the reproductive period mainly. The aim of this project is to generate a diagnostic model to predict severity and stage of endometriosis prior to Laparoscopic surgery. This will help to improve the pre-operative diagnostic accuracy of stage 3 & 4 endometriosis and as a result, refer relevant women to a specialist centre for complex Laparoscopic surgery. The model is based on the British Society of Gynaecological Endoscopy (BSGE) pain questionnaire, clinical examination and ultrasound scan. Design: This is a prospective, observational, study, in which women completed the BSGE pain questionnaire, a BSGE requirement. Also, as part of the routine preoperative assessment patient had a routine ultrasound scan and when recto-vaginal and deep infiltrating endometriosis was suspected an MRI was performed. Setting: Luton & Dunstable University Hospital. Patients: Symptomatic women (n = 56) scheduled for laparoscopy due to pelvic pain. The age ranged between 17 – 52 years of age (mean 33.8 years, SD 8.7 years). Interventions: None outside the recognised and established endometriosis centre protocol set up by BSGE. Main Outcome Measure(s): Sensitivity and specificity of endometriosis diagnosis predicted by symptoms based on BSGE pain questionnaire, clinical examinations and imaging. Findings: The prevalence of diagnosed endometriosis was calculated to be 76.8% and the prevalence of advanced stage was 55.4%. Deep infiltrating endometriosis in various locations was diagnosed in 32/56 women (57.1%) and some had DIE involving several locations. Logistic regression analysis was performed on 36 clinical variables to create a simple clinical prediction model. After creating the scoring system using variables with P < 0.05, the model was applied to the whole dataset. The sensitivity was 83.87% and specificity 96%. The positive likelihood ratio was 20.97 and the negative likelihood ratio was 0.17, indicating that the model has a good predictive value and could be useful in predicting advanced stage endometriosis. Conclusions: This is a hypothesis-generating project with one operator, but future proposed research would provide validation of the model and establish its usefulness in the general setting. Predictive tools based on such model could help organise the appropriate investigation in clinical practice, reduce risks associated with surgery and improve outcome. It could be of value for future research to standardise the assessment of women presenting with pelvic pain. The model needs further testing in a general setting to assess if the initial results are reproducible.

Keywords: deep endometriosis, endometriosis, minimally invasive, MRI, ultrasound.

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