Search results for: urea deep placement

Authors: Dajana Gašo-Sokač, Valentina Bušić

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Deep eutectic solvents (DES) are liquids composed of two or three safe, inexpensive components, often interconnected by noncovalent hydrogen bonds which produce eutectic mixture whose melting point is lower than that of each component. No data in literature have been found on the quaternization reaction in DES. The use of DES have several advantages: they are environmentally benign and biodegradable, easy for purification and simple for preparation. An environmentally sustainable method for preparing quaternary salts of izonicotinamide and substituted 2-bromoacetophenones was demonstrated here using choline chloride-based DES. The quaternization reaction was carried out by three synthetic approaches: conventional method, microwave and ultrasonic irradiation. We showed that the highest yields were obtained by the microwave method.

Keywords: deep eutectic solvents, izonicotinamide salts, microwave synthesis, ultrasonic irradiation

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Authors: Hamid Hatami

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This experiment was included two cultivars i.e. Habbit and L17 (Main factor) with six fertilizer treatments i.e. control, seed inoculated with rhyzobium, base nitrogen + top-dress urea at R2 stage, base nitrogen + seed inoculated with rhyzobium + top-dress nitrogen at R2 stage, seed treated with humax + top-dress humax at R2 stage, base nitrogen + seed treated with humax + top-dress humax at R2 stage (sub factors ), as split-plot on the basis of RCBD with 3 replications at 2014. Treatment fertilizer of base nitrogen + seed treated with humax + top- dress humax at R2 stage and base nitrogen + top-dress urea in R2 stage had a significant superiority than the other fertilizer treatment in biological yield. L17 and Habbit with base nitrogen + seed treated with humax + top-dress humax in R2 stage and yield economical 5600 and 5767 kg/ha respectively, showed the most economical yield and Habbit cultivar with control and economical yield 3085 kg/ha showed the least economical yield among all the treatments. Results showed that fertilizer treatment of base nitrogen + seed treated with humax + top-dress humax in R2 stage and Habbit variety were suitable in this study.

Keywords: soybean, humax, rhyzobium, habbit

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Authors: Lidia Dzierzbicka-Glowacka, Anna Lemieszek, Mariusz Figiela

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In 2010-2011, the research on zooplankton was conducted in the southern part of the Baltic Sea to determine seasonal variability in changes occurring throughout the zooplankton in 2010 and 2011, both in the region of Gdańsk Deep, and in the western part of Gdańsk Bay. The research in the sea showed that the taxonomic composition of holoplankton in the southern part of the Baltic Sea was similar to that recorded in this region for many years. The maximum values of abundance and biomass of zooplankton both in the Deep and the Bay of Gdańsk were observed in the summer season. Copepoda dominated in the composition of zooplankton for almost the entire study period, while rotifers occurred in larger numbers only in the summer 2010 in the Gdańsk Deep as well as in May and July 2010 in the western part of Gdańsk Bay, and meroplankton – in April 2011.

Keywords: Baltic Sea, composition, Gdańsk Bay, zooplankton

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Authors: Khitem Amiri, Mohamed Farah

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Image classification is a challenging task and is gaining lots of interest since it helps us to understand the content of images. Recently Deep Learning (DL) based methods gave very interesting results on several benchmarks. For Hyperspectral images (HSI), the application of DL techniques is still challenging due to the scarcity of labeled data and to the curse of dimensionality. Among other approaches, Deep Belief Network (DBN) based approaches gave a fair classification accuracy. In this paper, we address the problem of the curse of dimensionality by reducing the number of bands and replacing the HSI channels by the channels representing radiometric indices. Therefore, instead of using all the HSI bands, we compute the radiometric indices such as NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), etc, and we use the combination of these indices as input for the Deep Belief Network (DBN) based classification model. Thus, we keep almost all the pertinent spectral information while reducing considerably the size of the image. In order to test our image representation, we applied our method on several HSI datasets including the Indian pines dataset, Jasper Ridge data and it gave comparable results to the state of the art methods while reducing considerably the time of training and testing.

Keywords: hyperspectral images, deep belief network, radiometric indices, image classification

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Authors: Taha Benarbia

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The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metrics

Keywords: automated vehicles, connected vehicles, deep learning, smart transportation network

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Authors: Srinidhi. A, Naveed Ahmed, Twinkle Hareendran, Vriksha Prakash

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This work reviews some of the advanced image processing techniques for deep learning applications. Object detection by template matching, image denoising, edge detection, and super-resolution modelling are but a few of the tasks. The paper looks in into great detail, given that such tasks are crucial preprocessing steps that increase the quality and usability of image datasets in subsequent deep learning tasks. We review some of the methods for the assessment of image quality, more specifically sharpness, which is crucial to ensure a robust performance of models. Further, we will discuss the development of deep learning models specific to facial emotion detection, age classification, and gender classification, which essentially includes the preprocessing techniques interrelated with model performance. Conclusions from this study pinpoint the best practices in the preparation of image datasets, targeting the best trade-off between computational efficiency and retaining important image features critical for effective training of deep learning models.

Keywords: image processing, machine learning applications, template matching, emotion detection

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Ali Ravangard, S. Mohammadi

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Shunt Capacitors have several uses in the electric power systems. They are utilized as sources of reactive power by connecting them in line-to-neutral. Electric utilities have also connected capacitors in series with long lines in order to reduce its impedance. This is particularly common in the transmission level, where the lines have length in several hundreds of kilometers. However, this post will generally discuss shunt capacitors. In distribution systems, shunt capacitors are used to reduce power losses, to improve voltage profile, and to increase the maximum flow through cables and transformers. This paper presents a new method to determine the optimal locations and economical sizing of fixed and/or switched shunt capacitors with a view to power losses reduction and voltage stability enhancement. For solving the problem, a new enhanced cuckoo optimization algorithm is presented.The proposed method is tested on distribution test system and the results show that the algorithm suitable for practical implementation on real systems with any size.

Keywords: capacitor placement, power losses, voltage stability, radial distribution systems

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Authors: Mary Ann Cristine R. Olgado

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This tracer study aimed to track down the TVL-ICT and EIM graduates of Las Piñas City Technical-Vocational High School as they pursue their career path and incorporated the evidence-based insights from this study as the basis for informed career pathing program and the implementation of placement exams for incoming senior high school students. The researcher utilized a graduate tracer study (GTS) using a descriptive research method employing a quanti-qualitative research design to gather data on the demographic and academic profiles of the respondents, chosen career paths, and the relevance and significance of their senior high school (SHS) track/strand to their chosen career paths. Findings revealed a diverse range of career paths pursued by SHS graduates, with a majority opting for higher education. However, there was a notable mismatch between SHS tracks/strands and higher education programs, highlighting the need for improved career paths and guidance. The study also assessed the relevance of SHS education to graduates' current jobs, with mixed results indicating areas for improvement. Despite challenges, graduates generally perceived their SHS education positively, particularly in providing hands-on experiences and relevant skills. The study concluded by emphasizing the importance of aligning SHS tracks/strands with future career paths by enhancing career pathing and guidance services to better support students in making informed decisions about their careers and incorporating assessments for personal interests and aptitudes could assist students in making more informed decisions about their career paths.

Keywords: career pathing, EIM, graduate tracer study, ICT, placement exam, TVL

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Authors: Leah Lee

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This paper examines the correlation between deep learning models and emotions with noises to see whether or not noises mask emotions. The deep learning models used are plain convolutional neural networks (CNN), auto-encoder, long short-term memory (LSTM), and Visual Geometry Group-16 (VGG-16). Emotion datasets used are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Crowd-sourced Emotional Multimodal Actors Dataset (CREMA-D), Toronto Emotional Speech Set (TESS), and Surrey Audio-Visual Expressed Emotion (SAVEE). To make it four times bigger, audio set files, stretch, and pitch augmentations are utilized. From the augmented datasets, five different features are extracted for inputs of the models. There are eight different emotions to be classified. Noise variations are white noise, dog barking, and cough sounds. The variation in the signal-to-noise ratio (SNR) is 0, 20, and 40. In summation, per a deep learning model, nine different sets with noise and SNR variations and just augmented audio files without any noises will be used in the experiment. To compare the results of the deep learning models, the accuracy and receiver operating characteristic (ROC) are checked.

Keywords: auto-encoder, convolutional neural networks, long short-term memory, speech emotion recognition, visual geometry group-16

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Authors: Boumaraf Malak

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The development of intelligent transportation systems (ITS) and artificial intelligence (AI) are spurring us to pave the way for the widespread adoption of autonomous vehicles (AVs). This is open again opportunities for smart roads, smart traffic safety, and mobility comfort. A highly intelligent decision-making system is essential for autonomous driving around dense, dynamic objects. It must be able to handle complex road geometry and topology, as well as complex multiagent interactions, and closely follow higher-level commands such as routing information. Autonomous vehicles have become a very hot research topic in recent years due to their significant ability to reduce traffic accidents and personal injuries. Using new artificial intelligence-based technologies handles important functions in scene understanding, motion planning, decision making, vehicle control, social behavior, and communication for AV. This paper focuses only on deep reinforcement learning-based methods; it does not include traditional (flat) planar techniques, which have been the subject of extensive research in the past because reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. The DRL algorithm used so far found solutions to the four main problems of autonomous driving; in our paper, we highlight the challenges and point to possible future research directions.

Keywords: deep reinforcement learning, autonomous driving, deep deterministic policy gradient, deep Q-learning

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Authors: Ramezani M., Neitzert T.

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This paper investigates mechanical properties and formability of the AZ61 magnesium alloy at high temperatures. Tensile tests were performed at elevated temperatures of up to 400ºC. The results showed that as temperature increases, yield strength and ultimate tensile strength decrease significantly, while the material experiences an increase in ductility (maximum elongation before break). A finite element model has been developed to further investigate the formability of the AZ61 alloy by deep drawing a square cup. Effects of different process parameters such as punch and die geometry, forming speed and temperature as well as blank-holder force on deep drawability of the AZ61 alloy were studied and optimum values for these parameters are achieved which can be used as a design guide for deep drawing of this alloy.

Keywords: AZ61, formability, magnesium, mechanical properties

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

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In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

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Authors: Niklas Panten, Eberhard Abele

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This paper presents a novel approach for real-time and near-optimal control of industrial smart grids by deep reinforcement learning (DRL). To achieve highly energy-efficient factory systems, the energetic linkage of machines, technical building equipment and the building itself is desirable. However, the increased complexity of the interacting sub-systems, multiple time-variant target values and stochastic influences by the production environment, weather and energy markets make it difficult to efficiently control the energy production, storage and consumption in the hybrid industrial smart grids. The studied deep reinforcement learning approach allows to explore the solution space for proper control policies which minimize a cost function. The deep neural network of the DRL agent is based on a multilayer perceptron (MLP), Long Short-Term Memory (LSTM) and convolutional layers. The agent is trained within multiple Modelica-based factory simulation environments by the Advantage Actor Critic algorithm (A2C). The DRL controller is evaluated by means of the simulation and then compared to a conventional, rule-based approach. Finally, the results indicate that the DRL approach is able to improve the control performance and significantly reduce energy respectively operating costs of industrial smart grids.

Keywords: industrial smart grids, energy efficiency, deep reinforcement learning, optimal control

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Authors: P. Panwalkar, K. Veravalli, R. Gwynn, M. Tofighi, R. Clement, A. Mofidi

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When operating on tibial plateau fracture especially medial tibial plateau, it has regularly been said “where do I put my thumb to reduce the fracture”. This refers to the ideal placement of the buttress device to hold the fracture till union. The aim of this study was to see if one can identify this sweet spot using a CT scan. Methods: Forty-five tibial plateau fractures with medial plateau involvement were identified and included in the study. The preoperative CT scans were analysed and the medial plateau involvement pattern was classified based on modified radiological classification by Yukata et-al of stress fracture of medial tibial plateau. The involvement of part of plateau was compared with position of buttress plate position which was classified as medial posteromedial or both. Presence and position of the buttress was compared with ability to achieve and hold the reduction of the fracture till union. Results: Thirteen fractures were type-1 fracture, 19 fractures were type-2 fracture and 13 fractures were type-3 fracture. Sixteen fractures were buttressed correctly according to the potential deformity and twenty-six fractures were not buttressed and three fractures were partly buttressed correctly. No fracture was over butressed! When the fracture was buttressed correctly the rate of the malunion was 0%. When fracture was partly buttressed 33% were anatomically united and 66% were united in the plane of buttress. When buttress was not used, 14 were malunited, one malunited in one of the two planes of deformity and eleven anatomically healed (of which 9 were non displaced!). Buttressing resulted in statistically significant lower mal-union rate (x2=7.8, p=0.0052). Conclusion: The classification based on involvement of medial condyle can identify the placement of buttress plate in the tibial plateau. The correct placement of the buttress plate results in predictably satisfactory union. There may be a correlation between injury shape of the tibial plateau and the fracture type.

Keywords: knee, tibial plateau, trauma, CT scan, surgery

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Authors: Jun Ming Moey, Zhiyaun Chen, David Nicholson

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Bad actors are often hard to detect in data that imprints their behaviour patterns because they are comparatively rare events embedded in non-bad actor data. An unsupervised machine learning framework is applied here to detect bad actors in financial crime datasets that record millions of transactions undertaken by hundreds of actors (<0.01% bad). Specifically, the framework combines ‘shallow’ (PCA, Isolation Forest) and ‘deep’ (Autoencoder) methods to detect outlier patterns. Detection performance analysis for both the individual methods and their combination is reported.

Keywords: detection, machine learning, deep learning, unsupervised, outlier analysis, data science, fraud, financial crime

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: Ayoola, Simeon Oluwatoyin, Omogoriola Hannah Omoloye

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The effects of 17α-Methyltestosterone Hormone on blood composition of the Sex Reversed Sarotherodon melanotheron were investigated. S. melanotheron fry were reared in six (6) plastic tanks for three (3) months, of which three (3) tanks served as treatment tanks while the other three (3) served as the control. The fry were fed with 17α-methyl testosterone enzyme, which functions as a sex reversal hormone. The fry were administered this hormone for 30 days, to ensure complete sex reversal. All the S. melanotheron fry were reared to table size for duration of three (3) months, after which, blood samples were taken from both the control and treatment fishes. The blood parameters showed no significant differences with the same values of White Blood Cell count (WBC) and Total plasma protein for the control and experimental fishes. A total protein value for sex reversed specimens was 3.99g/dL, while urea and creatinine values were 0.2g/dL. Alkaline Phosphatase, Aspartate transaminase and Alanine transaminase for the treatment specimen were 183nm/mg protein/min, 98nm/mg protein/min and 105nm/mg protein/min respectively. A total protein value for control specimens was 2.81g/dL, while urea and creatinine values were 0.2g/dL. Alkaline Phosphatase, Aspartate transaminase and Alanine transaminase for the control species were 174nm/mg protein/min, 93nm/mg protein/min and 106nm/mg protein/min respectively. The safety of MT on S. melanotheron is therefore proved since there is no adverse effect on the fish.

Keywords: 17α-Methyltestosterone, haematology, sex reversal, sarotherodon melanotheron

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Authors: Gaokai Liu

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Automatic defect detection for nanomaterial images is widely required in industrial scenarios. Deep learning approaches are considered as the most effective solutions for the great majority of image-based tasks. In this paper, an edge guidance network for defect segmentation is proposed. First, the encoder path with multiple convolution and downsampling operations is applied to the acquisition of shared features. Then two decoder paths both are connected to the last convolution layer of the encoder and supervised by the edge and segmentation labels, respectively, to guide the whole training process. Meanwhile, the edge and encoder outputs from the same stage are concatenated to the segmentation corresponding part to further tune the segmentation result. Finally, the effectiveness of the proposed method is verified via the experiments on open nanofibrous datasets.

Keywords: deep learning, defect detection, image segmentation, nanomaterials

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Authors: Ling Ai Wong, Hussain Shareef, Azah Mohamed, Ahmad Asrul Ibrahim

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The installation of photovoltaic based distributed generation (PVDG) in active distribution system can lead to voltage fluctuation due to the intermittent and unpredictable PVDG output power. This paper presented a method in mitigating the voltage rise by optimally locating and sizing the battery energy storage system (BESS) in PVDG integrated distribution network. The improved firefly algorithm is used to perform optimal placement and sizing. Three objective functions are presented considering the voltage deviation and BESS off-time with state of charge as the constraint. The performance of the proposed method is compared with another optimization method such as the original firefly algorithm and gravitational search algorithm. Simulation results show that the proposed optimum BESS location and size improve the voltage stability.

Keywords: BESS, firefly algorithm, PVDG, voltage fluctuation

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Authors: Li Qingjian, Li Ke, He Chun, Huang Yong

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In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples.

Keywords: DBN, SOM, pattern classification, hyperspectral, data compression

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Authors: Mardiana Madjid, Nurpudji Astuti Taslim, Suryani As'ad, Haerani Rasyid, Agussalim Bukhari

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The high level of Urine Urea Nitrogen (UUN) indicates hypercatabolism occurs in hospitalized patients. High levels of Total Lymphocyte Count (TLC) indicates the immune system condition, adequate wound healing, and limit complication. Adequate dietary intake affects to decrease of hypercatabolism status in treated patient’s hospitals. Nitrogen Balance (NB) is simply the difference between nitrogen (N₂) intake and output. If more N₂ intake than output, then positive NB or anabolic will occur. This study aims to evaluate the effect of dietary intake in influencing balance nitrogen and total lymphocyte count. Method: A total of 43 patients admitted to a Wahidin Sudirohusodo Hospital between 2018 and 2019 for 10 days' treats are included. The inclusion criteria were patients who were treated for 10 days and receives food from the hospital orally. Patients did not experience gastrointestinal disorders such as vomiting and diarrhea and experience impair kidney function and liver function and expressed approval to participate in this study. During hospitalization, food intake, UUN, albumin serum, balance nitrogen, and TLC was assessed twice on day 1 and day 10. There is no Physician Clinical Nutritional intervention to correct food intake. UUN is 24 hours of urine collected on the second day after admission and the tenth day. Statistical analysis uses SPSS 24 with observational cohort methods. Result: The Forty-three participants completed the follow-up (27 men and 18 women). The age of fewer than 4 years is 22 people, 45 to 60 years is 16 people, and over 60 years is 4 people. The result of the study on day 1 obtained SGA score A, SGA score B, SGA score C are 8, 32, 3 until day 10 are 8, 31, 4, respectively. According to 24h dietary recalls, the energy intake during observation was from 522.5 ± 400.4 to 1011.9 ± 545.1 kcal/day P < 0.05, protein intake from 20.07 ± 17.2 to 40.3 ± 27.3 g/day P < 0.05, carbohydrates from 92.5 ± 71.6 to 184.8 ± 87.4 g/day, and fat from 5.5 ± 3.86 to 13.9 ± 13.9 g/day. The UUN during the observation was from 6.6 ± 7.3 to 5.5 ± 3.9 g/day, TLC decreased from 1622.9 ± 897.2 to 1319.9 ± 636.3/mm³ value target 1800/mm³, albumin serum from 3.07 ± 0.76 to 2.9 ± 0.57 g/day, and BN from -7.5 ± 7.2 to -3.1 ± 4.86. Conclusion: The high level of UUN needs to correct adequate dietary intake to improve NB and TLC status on hospitalized patients.

Keywords: adequate dietary intake, balance nitrogen, total lymphocyte count, urine urea nitrogen

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Authors: Agoubi Belgacem, Adel Kharroubi

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Geothermal groundwater is the main water source to supply various sectors in El Hamma city, southeastern Tunisia. This region was long the destination of thousands of people from Tunisia and neighboring countries for care and bathing. The main objective of this study is to understand the groundwater mineralization origins and factors that control. The second goal is the appraisal of radon in geothermal groundwater in the study area. For this aim, geothermal groundwater was sampled and collected from different locations (thermal baths and deep wells). Physical parameters were measured and major ions were analyzed. Results reveal three water types. The water first type has Na-Mg-Ca-SO4-Cl facies and T>55°C. The second water type dominated by Na-Ca-Cl-SO4 facies with a temperature < 45 °C. However the third water type is dominated by Ca-SO4-Na-Cl-Mg. The three water types may be controlled by depth and geology. The first represent groundwater from deep aquifer (lower cretaceous), the second type was the shallow aquifer and the first is mixed water from deep and shallow water with a temperature ranging from 45 to 55°C. Measured Radon shows that shallow aquifer has a higher 222Rn concentration (677 to 2903 Bq.m-3) than deep water (203 to 1100 Bq.m-3). R-222 in El Hamma thermal aquifer was controlled by structures, porosity and permeability of aquifers. Geostatistical analyses of hydrogeological data and radon activities confirm the vertical flow and communication between deep and shallow aquifers through vertical faults system.

Keywords: Radon-222, geothermal, water, environment, Tunisia

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Authors: Mohsen Abotalebi Esfahani, Amin Rahimi

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Maintenance and management of route and roads infrastructure is one of the most important and the most fundamental principles of the countries. Several methods have been under investigation as preventive proceedings for the maintenance of asphalt pavements for many years. Using a mixture of salt, sand and gravel is the most common method of deicing, which could have numerous harmful consequences. Icy or snow-covered road is one of the major reasons of accidents in rainy seasons, which causes substantial damages such as loss of time and energy, environmental pollution, destruction of buildings, traffic congestion and rising possibility of accidents. Regarding this, every year the government incurred enormous costs to secure traverses. In this study, asphalt pavements have been cured, in terms of compressive strength, tensile strength and resilient modulus of asphalt samples, under the influence of Magnesium Chloride, Calcium Chloride, Sodium Chloride, Urea and pure water; and showed that de-icing with the calcium chloride solution and urea have the minimum negative effect and de-icing with pure water has most negative effect on laboratory specimens. Hence some simple techniques and new equipment and less use of sand and salt, can reduce significantly the risks and harmful effects of excessive use of salt, sand and gravel and at the same time use the safer roads.

Keywords: maintenance, sodium chloride, icyroad, calcium chloride

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Authors: Ya-Ching Fu, An-Kuo Chou, Boon-Fatt Tan, Chi-Nien Chen, Wen-Chien Yang, Pou-Leng Cheong

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Blind oro-/naso-pharyngeal suction and feeding tube placement are very common practices in neonatal intensive care unit. Though esophageal perforation is a rare complication of these instrumentations, its prevalence is highest in extremely premature neonates. Due to its association with significant morbidity (including respiratory deterioration, pneumothorax, and sepsis) and even mortality, it is an important issue to prevent this iatrogenic complication in the field of premature care. We demonstrate an esophageal perforation in an extreme-low-birth-weight neonate after oro-gastric tube placement. This female baby weighing 680 grams was delivered by caesarean section at 25 weeks of gestational age. She initially received oro-tracheal intubation with mechanical ventilation which was smoothly weaned to non-invasive positive-pressure ventilation at 7-day-old. However, after insertion of a 5-French oro-gastric tube, the baby’s condition suddenly worsened with apnea requiring mechanical ventilation. Her chest radiogram showed the oro-gastric tube in right pleural space, and thus another oro-gastric tube was replaced, and its position was radiographically confirmed. The malpositioned tube was then removed. The baby received 2-week course of intravenous antibiotics for her esophageal perforation. Feeding was then reintroduced and increased to full feeds in a smooth course. She was discharged at 107-day-old. Esophageal perforation in newborn is very rare. Sudden respiratory deterioration in a neonate after naso-/oro-gastric tube placement should alarm us to consider esophageal perforation, and further radiological investigation is required for the diagnosis. Tube materials, patient condition, and age are major risk factors of esophageal perforation. The use of softer tube material, such as silicone, in extreme premature baby might prevent this fetal complication.

Keywords: esophageal perforation, preterm, newborn, feeding tube

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Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao

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Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.

Keywords: artificial intelligence and office, NLP, deep learning, text classification

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Authors: Brinda Sharma, Saakshi S. Sarpotdar

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Deep-fat frying is popular standard method that has been studied basically to clarify the complicated mechanisms of fat decomposition at high temperatures and to assess their effects on human health. The aim of this paper is to point out the application of method engineering that has been recently improved our understanding of the fundamental principles and mechanisms concerned at different scales and different times throughout the process: pretreatment, frying, and cooling. It covers the several aspects of deep-fat drying. New results regarding the understanding of the frying method that are obtained as a results of major breakthroughs in on-line instrumentation (heat, steam flux, and native pressure sensors), within the methodology of microstructural and imaging analysis (NMR, MRI, SEM) and in software system tools for the simulation of coupled transfer and transport phenomena. Such advances have opened the approach for the creation of significant information of the behavior of varied materials and to the event of latest tools to manage frying operations via final product quality in real conditions. Lastly, this paper promotes an integrated approach to the frying method as well as numerous competencies like those of chemists, engineers, toxicologists, nutritionists, and materials scientists also as of the occupation and industrial sectors.

Keywords: frying, cooling, imaging analysis (NMR, MRI, SEM), deep-fat frying

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Authors: Gizem Eser Erdek

Abstract:

This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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Authors: Dingyang Hu, Dan Liu

Abstract:

DNN (Deep Neural Network) deep learning models are widely used in classification, prediction, and other task scenarios. To address the difficulties of generic adversarial perturbation generation for deep learning models under black-box conditions, a generic adversarial ingestion generation method based on a saliency map (CJsp) is proposed to obtain salient image regions by counting the factors that influence the input features of an image on the output results. This method can be understood as a saliency map attack algorithm to obtain false classification results by reducing the weights of salient feature points. Experiments also demonstrate that this method can obtain a high success rate of migration attacks and is a batch adversarial sample generation method.

Keywords: adversarial sample, gradient, probability, black box

Procedia PDF Downloads 104

Authors: Virakti Dhaval Shah

Abstract:

Advances in technology and the changes in lifestyle and work expectations amid the COVID-19 pandemic are requiring changes to vocational assessment, provision of vocational training, and identification of job matches for individuals with intellectual disability. Vocational assessment involves the assessment of interests, skills, abilities, and strengths/weaknesses, as well as a detailed understanding of healthcare, familial, social, community, workplace-related and policy-level protective and risk factors impacting the individual. In India, vocational assessment procedures such as interviews play a major role in vocational placement today. Unfortunately, many of the most widely used vocational assessment instruments in India were developed in the 1970s to 2000s and have not been revised since. Hence, there is a dire need to update existing tools and prepare a structured approach for vocational service providers to meet the unique employment needs of individuals with intellectual disabilities. To address this need, this project designed a multi-domain assessment toolkit for a structured approach to vocational assessment, training, and job placement for individuals with intellectual disabilities in India. Methods included conducting an empirical review of the available tools currently in use in India for the vocational assessment of persons with intellectual disabilities. Domains addressed in the instrument review were organized into a structured system, and additional items related to contemporary technology, pandemic-related experiences of persons with disabilities, and changes in lifestyle and work expectations due to the pandemic were added. Items assessing behaviors, provision of vocational training, and identification of job matches for individuals with intellectual disabilities were developed. The proposed tool has the potential to benefit organizations working with and preparing individuals with intellectual disabilities to find successful employment by undertaking a structured approach to vocational assessment, rehabilitation, training, and placement. It can be particularly useful for guiding new professionals doing vocational rehabilitation in India.

Keywords: intellectual disability, rehabilitation, vocational assessment, vocational rehabilitation

Procedia PDF Downloads 109