Search results for: deep hole

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Crop fertilizer recommendations are outdated as these are based on the response trails conducted over half a century ago.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: Marzie Shahini, Alireza Bagheri Sabbagh, Rasoul Mirghaderi, Paul C. Davidson

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The focus of this paper is to incorporating the slotted bolted connection into the cold-formed steel (CFS) beams with aim of increasing inelastic bending capacity through bolt slip. An extensive finite element analysis was conducted on the through plate CFS bolted connections which are equipped with the slotted hole. The studied parameters in this paper included the following: CFS beam section geometry, the value of slip force, CFS beam thickness. The numerical results indicate that CFS slotted bolted connection exhibit higher inelastic capacity in terms of ductility compare to connection with standards holes. Moreover, the effect of slip force was analysed by comparing the moment-rotation curves of different models with different slip force value. As a result, as the slip force became lower, there was a tendency for the plastic strain to extend from the CFS member to the connection region.

Keywords: slip-critical bolted connection, inelastic capacity, slotted holes, cold-formed steel, bolt slippage, slip force

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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: Indu Gupta

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Climate change and global warming is most burning issue for “our common future”. For this common global interest. Countries organize conferences of government and nongovernment type. Human being destroying the non-renewable resources and polluting the renewable resources of planet for economic growth. Air pollution is mainly responsible for global warming and climate change .Due to global warming ice glaciers are shrinking and melting. Forests are shrinking, deserts expanding and soil eroding. The depletion of stratospheric ozone layer is depleting and hole in ozone layer that protect us from harmful ultra violet radiation. Extreme high temperature in summer and extreme low temperature and smog in winters, floods in rainy season. These all are indication of climate change. The level of carbon dioxide and other heat trapping gases in the atmosphere is increasing at high speed. Nation’s are worried about environmental degradation.

Keywords: environmental degradation, global warming, soil eroding, ultra-Violate radiation

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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: 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: F. A. Rodríguez-Prada, W Gutierrez, I. D. Mikhailov

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We study the spectral properties of one-electron non-uniform crater-shaped quantum dot whose thickness is increased linearly with different slopes in different radial directions between the central hole and the outer border and which is deposited over thin wetting layer in the presence of the external vertically directed magnetic field. We show that in the adiabatic limit, when the crater thickness is much smaller than its lateral dimension, the one-particle wave functions of the electron confined in such structure in the zero magnetic field case can be found exactly in an analytical form and they can be used subsequently as the base functions in framework of the exact diagonalization method to study the effect of the wetting layer and an external magnetic field applied along of the grown axis on energy levels of one-electron non-uniform quantum dot. It is shown that both the structural non-uniformity and the increase of the thickness of the wetting layer provide a quenching of the Aharonov-Bohm oscillations of the lower energy levels.

Keywords: electronic properties, quantum rings, volcano shaped, wetting layer

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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: 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: 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: 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: Nuray Güy, Mahmut Özacar

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Semiconductor nanoparticles such as TiO₂ and ZnO as photocatalysts are very efficient catalysts for wastewater treatment by the chemical utilization of light energy, which is capable of converting the toxic and nonbiodegradable organic compounds into carbon dioxide and mineral acids. ZnO semiconductor has a wide bandgap energy of 3.37 eV and a relatively large exciton binding Energy (60 meV), thus can absorb only UV light with the wavelength equal to or less than 385 nm. It exhibits low efficiency under visible light illumination due to its wide band gap energy. In order to improve photocatalytic activity of ZnO under visible light, band gap of ZnO may be narrowed by doping such as N, C, S nonmetal ions and coupled two separate semiconductors possessing different energy levels for their corresponding conduction and valence bands. ZnS has a wider band gap (Eg=3.7 eV) than ZnO and generates electron–hole pairs by photoexcitation rapidly. In the present work, N doped ZnO/ZnS nano photocatalysts with visible-light response were synthesized by microwave-hydrothermal method using thiourea as N source. The prepared photocatalysts were characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM) and UV–visible (UV–vis). The photocatalytic activities samples and undoped ZnO have been studied for the degradation of dye, and have also been compared with together.

Keywords: photocatalyst, synthesis, visible light, ZnO/ZnS

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Authors: Marlin Mubarak

Abstract:

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.

Procedia PDF Downloads 339

Authors: M. Seguini, D. Nedjar

Abstract:

An accuracy nonlinear analysis of a deep beam resting on elastic perfectly plastic soil is carried out in this study. In fact, a nonlinear finite element modeling for large deflection and moderate rotation of Euler-Bernoulli beam resting on linear and nonlinear random soil is investigated. The geometric nonlinear analysis of the beam is based on the theory of von Kàrmàn, where the Newton-Raphson incremental iteration method is implemented in a Matlab code to solve the nonlinear equation of the soil-beam interaction system. However, two analyses (deterministic and probabilistic) are proposed to verify the accuracy and the efficiency of the proposed model where the theory of the local average based on the Monte Carlo approach is used to analyze the effect of the spatial variability of the soil properties on the nonlinear beam response. The effect of six main parameters are investigated: the external load, the length of a beam, the coefficient of subgrade reaction of the soil, the Young’s modulus of the beam, the coefficient of variation and the correlation length of the soil’s coefficient of subgrade reaction. A comparison between the beam resting on linear and nonlinear soil models is presented for different beam’s length and external load. Numerical results have been obtained for the combination of the geometric nonlinearity of beam and material nonlinearity of random soil. This comparison highlighted the need of including the material nonlinearity and spatial variability of the soil in the geometric nonlinear analysis, when the beam undergoes large deflections.

Keywords: finite element method, geometric nonlinearity, material nonlinearity, soil-structure interaction, spatial variability

Procedia PDF Downloads 405

Authors: Farzad Hosseini Hossein Abadi, Cristina Prieto Sierra, Cesar Álvarez Díaz

Abstract:

Predicting streamflow accurately in flashy catchments prone to floods is a major research and operational challenge in hydrological modeling. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown to be promising in achieving accurate hydrological predictions at daily and hourly time scales. In this work, a multi-timescale LSTM (MTS-LSTM) network was applied to the context of regional hydrological predictions at an hourly time scale in flashy catchments. The case study includes 40 catchments allocated in the Basque Country, north of Spain. We explore the impact of hyperparameters on the performance of streamflow predictions given by regional deep learning models through systematic hyperparameter tuning - where optimal regional values for different catchments are identified. The results show that predictions are highly accurate, with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) metrics values as high as 0.98 and 0.97, respectively. A principal component analysis reveals that a hyperparameter related to the length of the input sequence contributes most significantly to the prediction performance. The findings suggest that input sequence lengths have a crucial impact on the model prediction performance. Moreover, employing catchment-scale analysis reveals distinct sequence lengths for individual basins, highlighting the necessity of customizing this hyperparameter based on each catchment’s characteristics. This aligns with well known “uniqueness of the place” paradigm. In prior research, tuning the length of the input sequence of LSTMs has received limited focus in the field of streamflow prediction. Initially it was set to 365 days to capture a full annual water cycle. Later, performing limited systematic hyper-tuning using grid search, revealed a modification to 270 days. However, despite the significance of this hyperparameter in hydrological predictions, usually studies have overlooked its tuning and fixed it to 365 days. This study, employing a simultaneous systematic hyperparameter tuning approach, emphasizes the critical role of input sequence length as an influential hyperparameter in configuring LSTMs for regional streamflow prediction. Proper tuning of this hyperparameter is essential for achieving accurate hourly predictions using deep learning models.

Keywords: LSTMs, streamflow, hyperparameters, hydrology

Procedia PDF Downloads 41

Authors: H. M. Haytham, E. A. Azza, E.S. Mohamed, E. G. Nesreen

Abstract:

Thoracotomy is a great surgery that has serious pulmonary complications, so purpose of this study was to determine the response of diaphragmatic excursion to inspiratory muscle trainer post thoracotomy. Thirty patients of both sexes (16 men and 14 women) with age ranged from 20 to 40 years old had done thoracotomy participated in this study. The practical work was done in cardiothoracic department, Kasr-El-Aini hospital at faculty of medicine for individuals 3 days Post operatively. Patients were assigned into two groups: group A (study group) included 15 patients (8 men and 7 women) who received inspiratory muscle training by using inspiratory muscle trainer for 20 minutes and routine chest physiotherapy (deep breathing, cough and early ambulation) twice daily, 3 days per week for one month. Group B (control group) included 15 patients (8 men and 7 women) who received the routine chest physiotherapy only (deep breathing, cough and early ambulation) twice daily, 3 days per week for one month. Ultrasonography was used to evaluate the changes in diaphragmatic excursion before and after training program. Statistical analysis revealed a significant increase in diaphragmatic excursion in the study group (59.52%) more than control group (18.66%) after using inspiratory muscle trainer post operatively in patients post thoracotomy. It was concluded that the inspiratory muscle training device increases diaphragmatic excursion in patients post thoracotomy through improving inspiratory muscle strength and improving mechanics of breathing and using of inspiratory muscle trainer as a method of physical therapy rehabilitation to reduce post-operative pulmonary complications post thoracotomy.

Keywords: diaphragmatic excursion, inspiratory muscle trainer, ultrasonography, thoracotomy

Procedia PDF Downloads 309

Authors: A. M. Nasr, W. R. Azzam, K. E. Ebeed

Abstract:

For bored piles, careful cleaning must be used to reduce the amount of material trapped in the drilled hole; otherwise, the debris' presence might cause the soft toe effect, which would affect the axial resistance. There isn't much comprehensive research on bored piles with debris. In order to investigate the behavior of a single pile, a pile composite foundation, a two pile group, a three pile group and a four pile group investigation conducts, forty-eight numerical tests in which the debris is simulated using foam rubber.1m pile diameter and 10m length with spacing 3D and depth of foundation 1m used in this study. It is found that the existence of debris causes a reduction of bearing capacity by 64.58% and 33.23% for single pile and pile composite foundation, respectively, 23.27% and 24.24% for the number of defective piles / total number of pile =1/2 and 1 respectively for two group pile, 10.23%, 19.42% and 28.47% for the number of defective piles / total number of pile =1/3,2/3 and 1 respectively for three group pile and, this reduction increase with the increase in a number of defective piles / a total number of piles and 7.1%, 13.32%,19.02% and 26.36 for the number of defective piles / total number of pile =1/4,2/4,3/4 and 1 respectively for four group pile and decreases with an increase of number of pile duo to interaction effect.

Keywords: debris, Foundation, defective, interaction, board pile

Procedia PDF Downloads 80

Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

Abstract:

In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring. SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, polarimetric SAR image, convolutional neural network, deep learnig, deep neural network

Procedia PDF Downloads 59

Authors: T. Garlan, I. Gabelotaud, S. Lucas, E. Marchès

Abstract:

Production of a global sedimentological seabed map has been initiated in 1995 to provide the necessary tool for searches of aircraft and boats lost at sea, to give sedimentary information for nautical charts, and to provide input data for acoustic propagation modelling. This original approach had already been initiated one century ago when the French hydrographic service and the University of Nancy had produced maps of the distribution of marine sediments of the French coasts and then sediment maps of the continental shelves of Europe and North America. The current map of the sediment of oceans presented was initiated with a UNESCO's general map of the deep ocean floor. This map was adapted using a unique sediment classification to present all types of sediments: from beaches to the deep seabed and from glacial deposits to tropical sediments. In order to allow good visualization and to be adapted to the different applications, only the granularity of sediments is represented. The published seabed maps are studied, if they present an interest, the nature of the seabed is extracted from them, the sediment classification is transcribed and the resulted map is integrated in the world map. Data come also from interpretations of Multibeam Echo Sounder (MES) imagery of large hydrographic surveys of deep-ocean. These allow a very high-quality mapping of areas that until then were represented as homogeneous. The third and principal source of data comes from the integration of regional maps produced specifically for this project. These regional maps are carried out using all the bathymetric and sedimentary data of a region. This step makes it possible to produce a regional synthesis map, with the realization of generalizations in the case of over-precise data. 86 regional maps of the Atlantic Ocean, the Mediterranean Sea, and the Indian Ocean have been produced and integrated into the world sedimentary map. This work is permanent and permits a digital version every two years, with the integration of some new maps. This article describes the choices made in terms of sediment classification, the scale of source data and the zonation of the variability of the quality. This map is the final step in a system comprising the Shom Sedimentary Database, enriched by more than one million punctual and surface items of data, and four series of coastal seabed maps at 1:10,000, 1:50,000, 1:200,000 and 1:1,000,000. This step by step approach makes it possible to take into account the progresses in knowledge made in the field of seabed characterization during the last decades. Thus, the arrival of new classification systems for seafloor has improved the recent seabed maps, and the compilation of these new maps with those previously published allows a gradual enrichment of the world sedimentary map. But there is still a lot of work to enhance some regions, which are still based on data acquired more than half a century ago.

Keywords: marine sedimentology, seabed map, sediment classification, world ocean

Procedia PDF Downloads 224

Authors: Asmat Nawaz, Ceylan Zafer, Ali K. Erdinc, Kaiying Wang, M. Nadeem Akram

Abstract:

Hybrid halide Perovskites with the general formula ABX₃, where X = Cl, Br or I, are considered as an ideal candidates for the preparation of photovoltaic devices. The most commonly and successfully used hybrid halide perovskite for photovoltaic applications is CH₃NH₃PbI₃ and its analogue prepared from lead chloride, commonly symbolized as CH₃NH₃PbI₃_ₓClₓ. Some researcher groups are using lead free (Sn replaces Pb) and mixed halide perovskites for the fabrication of the devices. Both mesoporous and planar structures have been developed. By Comparing mesoporous structure in which the perovskite materials infiltrate into mesoporous metal oxide scaffold, the planar architecture is much simpler and easy for device fabrication. In a typical perovskite solar cell, a perovskite absorber layer is sandwiched between the hole and electron transport. Upon the irradiation, carriers are created in the absorber layer that can travel through hole and electron transport layers and the interface in between. We fabricated inverted planar heterojunction structure ITO/PEDOT/ Perovskite/PCBM/Al, based solar cell via two-step spin coating method. This is also called Sequential deposition method. A small amount of cheap additive H₂O was added into PbI₂/DMF to make a homogeneous solution. We prepared four different solution such as (W/O H₂O, 1% H₂O, 2% H₂O, 3% H₂O). After preparing, the whole night stirring at 60℃ is essential for the homogenous precursor solutions. We observed that the solution with 1% H₂O was much more homogenous at room temperature as compared to others. The solution with 3% H₂O was precipitated at once at room temperature. The four different films of PbI₂ were formed on PEDOT substrates by spin coating and after that immediately (before drying the PbI₂) the substrates were immersed in the methyl ammonium iodide solution (prepared in isopropanol) for the completion of the desired perovskite film. After getting desired films, rinse the substrates with isopropanol to remove the excess amount of methyl ammonium iodide and finally dried it on hot plate only for 1-2 minutes. In this study, we added H₂O in the PbI₂/DMF precursor solution. The concept of additive is widely used in the bulk- heterojunction solar cells to manipulate the surface morphology, leading to the enhancement of the photovoltaic performance. There are two most important parameters for the selection of additives. (a) Higher boiling point w.r.t host material (b) good interaction with the precursor materials. We observed that the morphology of the films was improved and we achieved a denser, uniform with less cavities and almost full surface coverage films but only using precursor solution having 1% H₂O. Therefore, we fabricated the complete perovskite solar cell by sequential deposition technique with precursor solution having 1% H₂O. We concluded that with the addition of additives in the precursor solutions one can easily be manipulate the morphology of the perovskite film. In the sequential deposition method, thickness of perovskite film is in µm and the charge diffusion length of PbI₂ is in nm. Therefore, by controlling the thickness using other deposition methods for the fabrication of solar cells, we can achieve the better efficiency.

Keywords: methylammonium lead iodide, perovskite solar cell, precursor composition, sequential deposition

Procedia PDF Downloads 231

Authors: Judith Partouche-Sebban, Saeedeh Rezaee Vessal

Abstract:

Because of the rising number of new cases of cancer, especially among women, it is more than essential to better understand how women experience cancer in order to bring them adapted to support and care and enhance their well-being and patient experience. Cancer stands for a traumatic experience in which the diagnosis, its medical treatments, and the related side effects lead to deep physical and psychological changes that may arouse considerable stress and anxiety. In order to reduce these negative emotions, women tend to use various defense mechanisms, among which denial has been defined as the most frequent mechanism used by breast cancer patients. This study aims to better understand the consequences of the experience of cancer and their link with the adoption of a denial strategy. The empirical research was done among female cancer survivors in France. Since the topic of this study is relatively unexplored, a qualitative methodology and open-ended interviews were employed. In total, 25 semi-directive interviews were conducted with a female with different cancers, different stages of treatment, and different ages. A systematic inductive method was performed to analyze data. The content analysis enabled to highlight three different denial-related behaviors among women with cancer, which serve a self-protective function. First, women who expressed high levels of anxiety confessed they tended to completely deny the existence of their cancer immediately after the diagnosis of their illness. These women mainly exhibit many fears and a deep distrust toward the medical context and professionals. This coping mechanism is defined by the patient as being unconscious. Second, other women deliberately decided to deny partial information about their cancer, whether this information is related to the stages of the illness, the emotional consequences, or the behavioral consequences of the illness. These women use this strategy as a way to avoid the reality of the illness and its impact on the different aspects of their life as if cancer does not exist. Third, some women tend to reinterpret and give meaning to their cancer as a way to reduce its impact on their life. To this end, they may use magical thinking or positive reframing, or reinterpretation. Because denial may lead to delays in medical treatments, this topic deserves a deep investigation, especially in the context of oncology. As denial is defined as a specific defense mechanism, this study contributes to the existing literature in service marketing which focuses on emotions and emotional regulation in healthcare services which is a crucial issue. Moreover, this study has several managerial implications for healthcare professionals who interact with patients in order to implement better care and support for the patients.

Keywords: cancer, coping mechanisms, denial, healthcare services

Procedia PDF Downloads 74

Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo ferraioli

Abstract:

In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring . SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, deep learning, convolutional neural network, deep neural network, SAR polarimetry

Procedia PDF Downloads 74

Authors: Yuta Nakano, Kozo Kajiwara, Atsushi Hori, Takeshi Fujita

Abstract:

In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score.

Keywords: event camera, dynamic vision sensor, deep learning, data representation, object recognition, low energy consumption

Procedia PDF Downloads 82