Search results for: ring deep beam

Authors: Muneeb Ullah, Daishihan, Xiadong Young

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Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively.

Keywords: classification, deep learning, medical images, CXR, GAN.

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Authors: Sneha Thakur, Sanjeev Karmakar

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This paper proposed hybrid Particle Swarm Optimization (PSO) – Long-Short Term Memory (LSTM) model for groundwater level prediction. The evaluation of the performance is realized using the parameters: root mean square error (RMSE) and mean absolute error (MAE). Ground water level forecasting will be very effective for planning water harvesting. Proper calculation of water level forecasting can overcome the problem of drought and flood to some extent. The objective of this work is to develop a ground water level forecasting model using deep learning technique integrated with optimization technique PSO by applying 29 years data of Chhattisgarh state, In-dia. It is important to find the precise forecasting in case of ground water level so that various water resource planning and water harvesting can be managed effectively.

Keywords: long short-term memory, particle swarm optimization, prediction, deep learning, groundwater level

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Authors: Gorkem Algan, Ilkay Ulusoy, Saban Gonul, Banu Turgut, Berker Bakbak

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Computer vision systems recently made a big leap thanks to deep neural networks. However, these systems require correctly labeled large datasets in order to be trained properly, which is very difficult to obtain for medical applications. Two main reasons for label noise in medical applications are the high complexity of the data and conflicting opinions of experts. Moreover, medical imaging datasets are commonly tiny, which makes each data very important in learning. As a result, if not handled properly, label noise significantly degrades the performance. Therefore, a label-noise-robust learning algorithm that makes use of the meta-learning paradigm is proposed in this article. The proposed solution is tested on retinopathy of prematurity (ROP) dataset with a very high label noise of 68%. Results show that the proposed algorithm significantly improves the classification algorithm's performance in the presence of noisy labels.

Keywords: deep learning, label noise, robust learning, meta-learning, retinopathy of prematurity

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Authors: Mahima D, Sini T

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The use of geocell in subgrade has been previously studied by various researchers in the past. It was observed that the infill material used could affect the performance of the geocell reinforced subgrade. So, the use of waste materials as infill in geocell reinforced subgrade may prove to be more effective, economical, and environment-friendly. The performance of demolition waste as an infill was studied using flexure testing, and we compared the results with that of the other infill materials; soil and sand. Flexural behaviour is very important to the geosynthetic application in pavements as it acts as a the geocell reinforcement acts as flexible layer embedded in pavements and leads to an improvement in stress distribution and reduction in stress on the soil subgrade. The flexural behaviour was determined using four-point bending tests and results were expressed in terms of modulus improvement factor (MIF) and load-deflection behaviour. The geocell reinforced subgrade with different infill materials was tested for flexural behaviour in a polywood-polywood three-layered beam model. The deflections of the three-layered model beam were measured for the corresponding load increments. Elastic modulus of the soil-geocell composite was calculated using closed-form solutions. Geocells were prepared from geonets with three different aspect ratios 0.45, 0.67, and 1. The demolition waste infilled geocell mattress with aspect ratio 0.67 showed improved flexural behavior with MIF of 2.67 followed by soil and sand. Owing to its improved flexural resistance as seen from the MIF and load-deflection behivour, crushed demolition waste can be effectively used as infill material for geocell reinforced subgrade, thereby reducing the difficulties in the management of demolition waste and improving the load distribution of weaker subgrade.

Keywords: demolition waste, flexural behavior, geocell, modulus improvement factor

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Authors: Kripesh Adhikari, Hamid Bouchachia, Hammadi Nait-Charif

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Falls are one of the major causes of injury and death among elderly people aged 65 and above. A support system to identify such kind of abnormal activities have become extremely important with the increase in ageing population. Pose estimation is a challenging task and to add more to this, it is even more challenging when pose estimations are performed on challenging poses that may occur during fall. Location of the body provides a clue where the person is at the time of fall. This paper presents a vision-based tracking strategy where available joints are grouped into three different feature points depending upon the section they are located in the body. The three feature points derived from different joints combinations represents the upper region or head region, mid-region or torso and lower region or leg region. Tracking is always challenging when a motion is involved. Hence the idea is to locate the regions in the body in every frame and consider it as the tracking strategy. Grouping these joints can be beneficial to achieve a stable region for tracking. The location of the body parts provides a crucial information to distinguish normal activities from falls.

Keywords: fall detection, machine learning, deep learning, pose estimation, tracking

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Authors: Cheng Yang Kwa, Yoke Rung Wong

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Laminated composite materials are widely employed in automotive, aerospace, and other industries. These materials provide distinct benefits due to their high specific strength, high specific modulus, and ability to be customized for a specific function. However, delamination of laminated composite materials is one of the main defects which can occur during manufacturing, regular operations, or maintenance. Delamination can bring about considerable internal damage, unobservable by visual check, that causes significant loss in strength and stability, leading to composite structure catastrophic failure. Structural health monitoring (SHM) is known to be the automated method for monitoring and evaluating the condition of a monitored object. There are several ways to conduct SHM in aerospace. One of the effective methods is to monitor the natural frequency shift of structure due to the presence of defect. This study investigated the mechanical resonance frequency shift of a multi-layer composite cantilever beam due to interlaminar delamination. ANSYS Workbench® was used to create a 4-plies laminated composite cantilever finite element model with [90/0]s fiber setting. Epoxy Carbon UD (230GPA) Prepreg was chosen, and the thickness was 2.5mm for each ply. The natural frequencies of the finite element model with various degree of delamination were simulated based on modal analysis and then validated by using literature. It was shown that the model without delamination had natural frequency of 40.412 Hz, which was 1.55% different from the calculated result (41.050 Hz). Thereafter, the various degree of delamination was mimicked by changing the frictional conditions at the middle ply-to-ply interface. The results suggested that delamination in the laminated composite cantilever induced a change in its stiffness which alters its mechanical resonance frequency.

Keywords: structural health monitoring, NDT, cantilever, laminate

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Authors: R. G. D Jayawardena

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The Buddha was known by great wise men from 6th century B.C up to date as a superhuman being born in the world beyond the omnipotent. The Buddha’s doctrinal descriptions reflect his deep enlightenment about imperial and metaphysical knowledge. Buddhology undertaken for this study is an unexposed subject in metaphysical points. The Buddhist wall painting in Sri Lanka depicts deep metaphysical meaning than its simple perspective of estheticism. Buddhology, in some perspectives, has been interpreted as a complete natural science discovered by the Buddha to teach the way of honorable living in perfect happiness and peace of mind till death. Such interpretations which emphasized are based on textual studies. The Buddhology conducted through literal tradition is depicted in wall paintings in Sri Lanka are in visual art with specific techniques rules. The Buddhology, which is investigated on wall paintings, portrays the Buddha in the form of a superhuman being and as an unparalleled person among the Devas, Brahmas, Yakshas, Maras, and humans. The Buddha concept is known to Sri Lankan Buddhists as a person attained to full awakening of wisdom. In personality, the Buddha is depicted as a supernormal person in the world and a rare birth. In brief, the paper will discuss and illustrate the Buddha’s transcendental position and the reality of what he experienced and its authenticity.

Keywords: Buddhology, Metaphysic, Sri Lanka, paintings

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Authors: Satya P. Dubey, Hrushikesh A Abhyankar, Veronica Marchante, James L. Brighton, Björn Bergmann

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Aims: To develop a mathematical model that simulates the ROP of PLA taking into account the effect of alternative energy to be implemented in a continuous reactive extrusion production process of PLA. Introduction: The production of large amount of waste is one of the major challenges at the present time, and polymers represent 70% of global waste. PLA has emerged as a promising polymer as it is compostable, biodegradable thermoplastic polymer made from renewable sources. However, the main limitation for the application of PLA is the traces of toxic metal catalyst in the final product. Thus, a safe and efficient production process needs to be developed to avoid the potential hazards and toxicity. It has been found that alternative energy sources (LASER, ultrasounds, microwaves) could be a prominent option to facilitate the ROP of PLA via continuous reactive extrusion. This process may result in complete extraction of the metal catalysts and facilitate less active organic catalysts. Methodology: Initial investigation were performed using the data available in literature for the reaction mechanism of ROP of PLA based on conventional metal catalyst stannous octoate. A mathematical model has been developed by considering significant parameters such as different initial concentration ratio of catalyst, co-catalyst and impurity. Effects of temperature variation and alternative energies have been implemented in the model. Results: The validation of the mathematical model has been made by using data from literature as well as actual experiments. Validation of the model including alternative energies is in progress based on experimental data for partners of the InnoREX project consortium. Conclusion: The model developed reproduces accurately the polymerisation reaction when applying alternative energy. Alternative energies have a great positive effect to increase the conversion and molecular weight of the PLA. This model could be very useful tool to complement Ludovic® software to predict the large scale production process when using reactive extrusion.

Keywords: polymer, poly-lactic acid (PLA), ring opening polymerization (ROP), metal-catalyst, bio-degradable, renewable source, alternative energy (AE)

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Authors: Vivek Garg, Brajendra S. Sengar, Gaurav Siddharth, Nisheka Anadkat, Amitesh Kumar, Shailendra Kumar, Shaibal Mukherjee

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CuInGaSe (CIGSe) is the dominant thin film solar cell technology. The band alignment of Buffer/CIGSe interface is one of the most crucial parameters for solar cell performance. In this article, the valence band offset (VBOff) and conduction band offset (CBOff) values of Cu(In0.70Ga0.30)Se/ 1 at.% Ga: Mg0.25Zn0.75O (GMZO) heterojunction, grown by dual ion beam sputtering system (DIBS), are calculated to understand the carrier transport mechanism at the heterojunction for the realization of all sputtered buffer-less solar cells. To determine the valence band offset (VBOff), ∆E_V at GMZO/CIGSe heterojunction interface, the standard method based on core-level photoemission is utilized. The value of ∆E_V can be evaluated by considering common core-level peaks. In our study, the values of (Valence band onset)VBOn, obtained by linear extrapolation method for GMZO and CIGSe films are calculated to be 2.86 and 0.76 eV. In the UPS spectra peak positions of Se 3d is observed in UPS spectra at 54.82 and 54.7 eV for CIGSe film and GMZO/CIGSe interface respectively, while the peak position of Mg 2p is observed at 50.09 and 50.12 eV for GMZO and GMZO/CIGSe interface respectively. The optical band gap of CIGSe and GMZO are obtained from absorption spectra procured from spectroscopic ellipsometry are 1.26 and 3.84 eV respectively. The calculated average values of ∆E_v and ∆E_C are estimated to be 2.37 and 0.21 eV, respectively, at room temperature. The calculated positive conduction band offset termed as a spike at the absorber junction is the required criterion for the high-efficiency solar cells for the efficient charge extraction from the junction. So we can conclude that the above study confirms GMZO thin films grown by the dual ion beam sputtering system are the suitable candidate for the CIGSe thin films based ultra-thin buffer-less solar cells. We investigated the band-offset properties at the GMZO/CIGSe heterojunction to verify the suitability of the GMZO for the realization of the buffer-less solar cells. The calculated average values of ∆E_V and ∆E_C are estimated to be 2.37 and 0.21 eV, respectively, at room temperature. The calculated positive conduction band offset termed as a spike at the absorber junction is the required criterion for the high-efficiency solar cells for the efficient charge extraction from the junction. So we can conclude that the above study confirms GMZO thin films grown by the dual ion beam sputtering system are the suitable candidate for the CIGSe thin films based ultra-thin buffer-less solar cells. Acknowledgment: We are thankful to DIBS, EDX, and XRD facility equipped at Sophisticated Instrument Centre (SIC) at IIT Indore. The authors B.S.S and A.K acknowledge CSIR and V.G acknowledge UGC, India for their fellowships. B.S.S is thankful to DST and IUSSTF for BASE Internship Award. Prof. Shaibal Mukherjee is thankful to DST and IUSSTF for BASE Fellowship and MEITY YFRF award. This work is partially supported by DAE BRNS, DST CERI, and DST-RFBR Project under India-Russia Programme of Cooperation in Science and Technology. We are thankful to Mukul Gupta for SIMS facility equipped at UGC-DAE Indore.

Keywords: CIGSe, DIBS, GMZO, solar cells, UPS

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Authors: Jaspreet Singh, Gurvinder Singh, Prabhsimran Singh, Rajinder Singh, Prithvipal Singh, Karanjeet Singh Kahlon, Ravinder Singh Sawhney

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Morphological evaluation of Indian languages is one of the burgeoning fields in the area of Natural Language Processing (NLP). The evaluation of a language is an eminent task in the era of information retrieval and text mining. The extraction and classification of knowledge from text can be exploited for sentiment analysis and morphological evaluation. This study coalesce morphological evaluation and sentiment analysis for the task of classification of farmer suicide cases reported in Punjab state of India. The pre-processing of Punjabi text involves morphological evaluation and normalization of Punjabi word tokens followed by the training of proposed model using deep learning classification on Punjabi language text extracted from online Punjabi news reports. The class-wise accuracies of sentiment prediction for four negatively oriented classes of farmer suicide cases are 93.85%, 88.53%, 83.3%, and 95.45% respectively. The overall accuracy of sentiment classification obtained using proposed framework on 275 Punjabi text documents is found to be 90.29%.

Keywords: deep neural network, farmer suicides, morphological processing, punjabi text, sentiment analysis

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Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

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In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: data estimation, link data, machine learning, road network

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Authors: Hushyar Azari

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Aim and background: Deep brain stimulation (DBS) is regarded as an important therapeutic choice for Parkinson's disease (PD). The two most common targets for DBS are the subthalamic nucleus (STN) and globus pallidus (GPi). This review was conducted to compare the clinical effectiveness of these two targets. Methods: A systematic literature search in electronic databases: Embase, Cochrane Library and PubMed were restricted to English language publications 2010 to 2021. Specified MeSH terms were searched in all databases. Studies which evaluated the Unified Parkinson's Disease Rating Scale (UPDRS) III were selected by meeting the following criteria: (1) compared both GPi and STN DBS; (2) had at least three months follow-up period; (3)at least five participants in each group; (4)conducted after 2010. Study quality assessment was performed using the Modified Jadad Scale. Results: 3577 potentially relevant articles were identified, of these, 3569 were excluded based on title and abstract, duplicate and unsuitable article removal. Eight articles satisfied the inclusion criteria and were scrutinized (458 PD patients). According to Modified Jadad Scale, the majority of included studies had low evidence quality which was a limitation of this review. 5 studies reported no statistically significant between-group difference for improvements in UPDRS ш scores. At the same time, there were some results in terms of pain, action tremor, rigidity, and urinary symptoms, which indicated that STN DBS might be a better choice. Regarding the adverse effects, GPi was superior. Conclusion: It is clear that other larger randomized clinical trials with longer follow-up periods and control groups are needed to decide which target is more efficient for deep brain stimulation in Parkinson’s disease and imposes fewer adverse effects on the patients. Meanwhile, STN seems more reasonable according to the results of this systematic review.

Keywords: brain stimulation, globus pallidus, Parkinson's disease, subthalamic nucleus

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Authors: Ricky Saputra, Rashmi Walvekar, Mohammad Khalid

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Landfills, water contamination, and toxic gas emission are a few impacts faced by the environment due to the increasing number of αof waste rubber tires (WRT). In spite of such concerning issue, only minimal efforts are taken to reclaim or recycle these wastes as their products are generally not-profitable for companies. Unlike the typical reclamation process, devulcanization is a method to selectively cleave sulfidic bonds within vulcanizates to avoid polymeric scissions that compromise elastomer’s mechanical and tensile properties. The process also produces devulcanizates that are re-processable similar to virgin rubber. Often, a devulcanizing agent is needed. In the current study, novel and sustainable ammonium chloride-based ternary deep eutectic solvents (TDES), with a different number of α-hydrogens, were utilised to devulcanize ground rubber tire (GRT) as an effort to implement green chemistry to tackle such issue. 40-mesh GRT were soaked for 1 day with different TDESs and sonicated at 37-80 kHz for 60-120 mins and heated at 100-140oC for 30-90 mins. Devulcanizates were then filtered, dried, and evaluated based on the percentage of by means of Flory-Rehner calculation and swelling index. The result shows that an increasing number of α-Hs increases the degree of devulcanization, and the value achieved was around eighty-percent, thirty percent higher than the typical industrial-autoclave method. Resulting bondages of devulcanizates were also analysed by Fourier transform infrared spectrometer (FTIR), Horikx fitting, and thermogravimetric analyser (TGA). The earlier two confirms only sulfidic scissions were experienced by GRT through the treatment, while the latter proves the absence or negligibility of carbon-chains scission.

Keywords: ammonium, sustainable, deep eutectic solvent, α-hydrogen, waste rubber tire

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Authors: Aditi Jain

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Aim: To compare the canal transportation, centering ability and remaining dentin thickness of OneShape and WaveOne system using CBCT. Objective: To identify rotary system which respects original canal anatomy. Materials and Methods: Forty extracted human single-rooted premolars were used in the present study. Pre-instrumentation scans of all teeth were taken, canal curvatures were calculated, and the samples were randomly divided into two groups with twenty samples in each group, where Group 1 included WaveOne system and Group 2 Protaper rotary system. Post-instrumentation scans were performed, and the two scans were compared to determine canal transportation, centering ability and remaining dentin thickness at 1, 3, and 5 mm from the root apex. Results: Using Student’s unpaired t test results were as follows; for canal transportation Group 1 showed statistical significant difference at 3mm, 6mm and non-significant difference was obtained at 9mm but for Group 2 non-statistical significant difference was obtained at 3mm, 6mm, and 9mm. For centering ability and remaining dentin thickness Group 1 showed non-statistical significant difference at 3mm and 9mm, while statistical significant difference at 6mm was obtained. When comparison of remaining dentin thickness was done at three levels using two groups WaveOne and ProTaper. There was non-statistical significant difference between two groups. Conclusion: WaveOne single reciprocation file respects original canal anatomy better than ProTaper. WaveOne depicted the best centering ability.

Keywords: ShapeOne, WaveOne, transportation, centering ability, dentin thickness, CBCT (Cone Beam Computed Tomography)

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Authors: Mária Csernoch, Piroska Biró

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The legends about “user-friendly” and “easy-to-use” birotical tools (computer-related office tools) have been spreading and misleading end-users. This approach has led us to the extremely high number of incorrect documents, causing serious financial losses in the creating, modifying, and retrieving processes. Our research proved that there are at least two sources of this underachievement: (1) The lack of the definition of the correctly edited, formatted documents. Consequently, end-users do not know whether their methods and results are correct or not. They are not aware of their ignorance. They are so ignorant that their ignorance does not allow them to realize their lack of knowledge. (2) The end-users’ problem-solving methods. We have found that in non-traditional programming environments end-users apply, almost exclusively, surface approach metacognitive methods to carry out their computer related activities, which are proved less effective than deep approach methods. Based on these findings we have developed deep approach methods which are based on and adapted from traditional programming languages. In this study, we focus on the most popular type of birotical documents, the text-based documents. We have provided the definition of the correctly edited text, and based on this definition, adapted the debugging method known in programming. According to the method, before the realization of text editing, a thorough debugging of already existing texts and the categorization of errors are carried out. With this method in advance to real text editing users learn the requirements of text-based documents and also of the correctly formatted text. The method has been proved much more effective than the previously applied surface approach methods. The advantages of the method are that the real text handling requires much less human and computer sources than clicking aimlessly in the GUI (Graphical User Interface), and the data retrieval is much more effective than from error-prone documents.

Keywords: deep approach metacognitive methods, error-prone birotical documents, financial losses, human and computer resources

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Jennifer Nutton, Gayle Ployer, Ky Scott, Jenny Morgan

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Working in a circle within higher education creates a decolonial space of mutual respect, responsibility, and reciprocity that facilitates collaborative learning and deep connections among learners and instructors. This approach is beyond simply facilitating a group in a circle but opens the door to creating a sacred space connecting each member to the land, to the Indigenous peoples who have taken care of the lands since time immemorial, to one another, and to one’s own positionality. These deep connections not only center human knowledges and relationships but also acknowledges responsibilities to land. Working in a circle as a relational pedagogical praxis also disrupts institutional power dynamics by creating a space of collaborative learning and deep connections in the classroom. Inherent within circle work is to facilitate connections not just academically but emotionally, physically, culturally, and spiritually. Recent literature supports the use of online talking circles, finding that it can offer a more relational and experiential learning environment, which is often absent in the virtual world and has been made more evident and necessary since the pandemic. These deeper experiences of learning and connection, rooted in both knowledge and the land, can then be shared with openness and vulnerability with one another, facilitating growth and change. This process of beginning with the land is critical to ensure we have the grounding to obstruct the ongoing realities of colonialism. The authors, who identify as both Indigenous and non-Indigenous, as both educators and learners, reflect on their teaching and learning experiences in circle. They share a relational pedagogical praxis framework that has been successful in educating future social workers, environmental activists, and leaders in social and human services, health, legal and political fields.

Keywords: circle work, relational pedagogies, decolonization, distance education

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Authors: Jiajia Yao, GuanLin Wu, Fang liu, JunShuai Xue, JinCheng Zhang, Yue Hao

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Due to its outstanding material properties like high thermal conductivity and ultra-wide bandgap, Aluminum nitride (AlN) has the promising potential to provide high breakdown voltage and high output power among III-nitrides for various applications in electronics and optoelectronics. This work presents material growth and characterization of strained channel Aluminum nitride/Gallium nitride (AlN/GaN) heterostructures grown by plasma-assisted molecular beam epitaxy (PA-MBE) on AlN-on-sapphire templates. To improve the crystal quality and manifest the ability of the PA-MBE approach, a thick AlN buffer with a thickness of 180 nm is first grown on AlN template, which acts as a back-barrier to enhance the breakdown characteristic and isolates the leakage path existing in the interface between AlN epilayer and AlN template, as well as improve the heat dissipation. The grown AlN buffer features a root-mean-square roughness of 0.2 nm over a scanned area of 2×2 µm2 measured by atomic force microscopy (AFM), and exhibits full-width at half-maximum of 95 and 407 arcsec for the (002) and (102) plane the X-ray rocking curve, respectively, tested by high resolution x-ray diffraction (HR-XRD). With a thin and strained GaN channel, the electron mobility of 294 cm2 /Vs. with a carrier concentration of 2.82×1013 cm-2 at room temperature is achieved in AlN/GaN double-channel heterostructures, and the depletion capacitance is as low as 14 pF resolved by the capacitance-voltage, which indicates the promising opportunities for future applications in next-generation high temperature, high-frequency and high-power electronics with a further increased electron mobility by optimization of heterointerface quality.

Keywords: AlN/GaN, HEMT, MBE, homoepitaxy

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Authors: Faidon Kyriakou, William Dempster, David Nash

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Abdominal Aortic Aneurysm (AAA) is a major life-threatening pathology for which modern approaches reduce the need for open surgery through the use of stenting. The success of stenting though is sometimes jeopardized by the final position of the stent graft inside the human artery which may result in migration, endoleaks or blood flow occlusion. Herein, a finite element (FE) model of the commercial medical device AnacondaTM (Vascutek, Terumo) has been developed and validated in order to create a numerical tool able to provide useful clinical insight before the surgical procedure takes place. The AnacondaTM device consists of a series of NiTi rings sewn onto woven polyester fabric, a structure that despite its column stiffness is flexible enough to be used in very tortuous geometries. For the purposes of this study, a FE model of the device was built in Abaqus® (version 6.13-2) with the combination of beam, shell and surface elements; the choice of these building blocks was made to keep the computational cost to a minimum. The validation of the numerical model was performed by comparing the deployed position of a full stent graft device inside a constructed AAA with a duplicate set-up in Abaqus®. Specifically, an AAA geometry was built in CAD software and included regions of both high and low tortuosity. Subsequently, the CAD model was 3D printed into a transparent aneurysm, and a stent was deployed in the lab following the steps of the clinical procedure. Images on the frontal and sagittal planes of the experiment allowed the comparison with the results of the numerical model. By overlapping the experimental and computational images, the mean and maximum distances between the rings of the two models were measured in the longitudinal, and the transverse direction and, a 5mm upper bound was set as a limit commonly used by clinicians when working with simulations. The two models showed very good agreement of their spatial positioning, especially in the less tortuous regions. As a result, and despite the inherent uncertainties of a surgical procedure, the FE model allows confidence that the final position of the stent graft, when deployed in vivo, can also be predicted with significant accuracy. Moreover, the numerical model run in just a few hours, an encouraging result for applications in the clinical routine. In conclusion, the efficient modelling of a complicated structure which combines thin scaffolding and fabric has been demonstrated to be feasible. Furthermore, the prediction capabilities of the location of each stent ring, as well as the global shape of the graft, has been shown. This can allow surgeons to better plan their procedures and medical device manufacturers to optimize their designs. The current model can further be used as a starting point for patient specific CFD analysis.

Keywords: AAA, efficiency, finite element analysis, stent deployment

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Authors: Godwin Ojochogu Adejo, Evelyn UnekwuOjo Adejo, Sunday UnenwOjo Adejo

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The mainstream contemporary approach to controlling the impact of diseases among poultry birds rely largely on curative measures through the administration of drugs to infected birds. Most times as observed in the deep liter poultry farming system, entire flocks including uninfected birds receive the treatment they do not need. As such, unguarded use of chemical drugs and antibiotics has led to wastage and accumulation of chemical residues in poultry products with associated health hazards to humans. However, wanton and frequent drug usage in poultry is avoidable if feeding and drinking equipment are designed to curb infection transmission among birds. Using toxicological assays as guide and with efficiency and simplicity in view, two newly field-tested and recently patented equipments called 'healthy liquid drinking trough (HDT)' and 'healthy feeding trough (HFT)' that systematically eliminate contamination of the feeding and drinking channels, thereby, curbing wide-spread infection and transmission of diseases in the (intensive) deep litter poultry farming system were designed. Upon combined usage, they automatically and drastically reduced both the amount and frequency of antibiotics use in poultry by over > 50%. Additionally, they conferred optimization of feed and water utilization/elimination of wastage by > 80%, reduced labour by > 70%, reduced production cost by about 15%, and reduced chemical residues in poultry meat or eggs by > 85%. These new and cheap technologies which require no energy input are likely to elevate safety of poultry products for consumers' health, increase marketability locally and for export, and increase output and profit especially among poultry farmers and poor people who keep poultry or inevitably utilize poultry products in developing countries.

Keywords: healthy, trough, toxicological, assay-guided, poultry

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Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

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Authors: Tatjana Jurić, Bojana Blagojević, Denis Uka, Ružica Ždero Pavlović, Boris M. Popović

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Natural deep eutectic solvents (NADES) represent a class of modern systems that have been developed as a green alternative to toxic organic solvents, which are commonly used as extraction media. It has been considered that hydrogen bonding is the main interaction leading to the formation of NADES. The aim of this study was phytochemical characterization and determination of the antioxidant and antibacterial activity of Mentha piperita leaf extracts obtained by six choline chloride-based NADES. NADES were prepared by mixing choline chloride with different hydrogen bond donors in 1:1 molar ratio following the addition of 30% (w/w) water. The mixtures were then heated (60 °C) and stirred (650 rpm) until the clear homogenous liquids were obtained. The Mentha piperita extracts were prepared by mixing 75 mg of peppermint leaves with 1 mL of NADES following by the heating and stirring (60 °C, 650 rpm) within 30 min. The content of six phenolics in extracts was determined using HPLC-PDA. The dominant compounds presented in peppermint leaves - rosmarinic acid and luteolin 7-O-glucoside, were extracted by NADES at a similar level as 70% ethanol. The microdilution method was applied to test the antibacterial activity of extracts. Compared with 70% ethanol, all NADES systems showed higher antibacterial activity towards Pseudomonas aeruginosa (Gram -), Staphylococcus aureus (Gram +), Escherichia coli (Gram -), and Salmonella enterica (Gram -), especially NADES containing organic acids. The majority of NADES extracts showed a better ability to neutralize DPPH radical than conventional solvent and similar ability to reduce Fe3+ to Fe2+ ions in FRAP assay. The obtained results introduce NADES systems as the novel, sustainable, and low-cost solvents with a variety of applications.

Keywords: antibacterial activity, antioxidant activity, green extraction, natural deep eutectic solvents, polyphenols

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Authors: Selim M. Khan, Aaron A. Goodarzi, Joshua M. Taron, Tryggve Rönnqvist

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Indoor air quality, a prime determinant of health, is strongly influenced by the presence of hazardous radon gas within the built environment. As a health issue, dangerously high indoor radon arose within the 20th century to become the 2nd leading cause of lung cancer. While the 21st century building metrics and human behaviors have captured, contained, and concentrated radon to yet higher and more hazardous levels, the issue is rapidly worsening in Canada. It is established that Canadians in the Prairies are the 2nd highest radon-exposed population in the world, with 1 in 6 residences experiencing 0.2-6.5 millisieverts (mSv) radiation per week, whereas the Canadian Nuclear Safety Commission sets maximum 5-year occupational limits for atomic workplace exposure at only 20 mSv. This situation is also deteriorating over time within newer housing stocks containing higher levels of radon. Deep machine learning (LSTM) algorithms were applied to analyze multiple quantitative and qualitative features, determine the most important contributory factors, and predicted radon levels in the known past (1990-2020) and projected future (2021-2050). The findings showed gradual downwards patterns in Sweden, whereas it would continue to go from high to higher levels in Canada over time. The contributory factors found to be the basement porosity, roof insulation depthness, R-factor, and air dynamics of the indoor environment related to human window opening behaviour. Building codes must consider including these factors to ensure adequate indoor ventilation and healthy living that can prevent lung cancer in non-smokers.

Keywords: radon, building metrics, deep learning, LSTM prediction model, lung cancer, canada, sweden

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Authors: S. Alansary, M. Nagi

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This paper presents an approach of conducting semantic annotation of Arabic corpus using the Universal Networking Language (UNL) framework. UNL is intended to be a promising strategy for providing a large collection of semantically annotated texts with formal, deep semantics rather than shallow. The result would constitute a semantic resource (semantic graphs) that is editable and that integrates various phenomena, including predicate-argument structure, scope, tense, thematic roles and rhetorical relations, into a single semantic formalism for knowledge representation. The paper will also present the Interactive Analysis​ tool for automatic semantic annotation (IAN). In addition, the cornerstone of the proposed methodology which are the disambiguation and transformation rules, will be presented. Semantic annotation using UNL has been applied to a corpus of 20,000 Arabic sentences representing the most frequent structures in the Arabic Wikipedia. The representation, at different linguistic levels was illustrated starting from the morphological level passing through the syntactic level till the semantic representation is reached. The output has been evaluated using the F-measure. It is 90% accurate. This demonstrates how powerful the formal environment is, as it enables intelligent text processing and search.

Keywords: semantic analysis, semantic annotation, Arabic, universal networking language

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Authors: Mehrnaz Mostafavi, Mahtab Shabani, Alireza Azani, Fatemeh Ghafari

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Artificial intelligence (AI) can transform breast cancer diagnosis and therapy by providing sophisticated solutions for screening, imaging interpretation, histopathological analysis, and treatment planning. This literature review digs into the many uses of AI in breast cancer treatment, highlighting the need for collaboration between AI scientists and healthcare practitioners. It emphasizes advances in AI-driven breast imaging interpretation, such as computer-aided detection and diagnosis (CADe/CADx) systems and deep learning algorithms. These have shown significant potential for improving diagnostic accuracy and lowering radiologists' workloads. Furthermore, AI approaches such as deep learning have been used in histopathological research to accurately predict hormone receptor status and categorize tumor-associated stroma from regular H&E stains. These AI-powered approaches simplify diagnostic procedures while providing insights into tumor biology and prognosis. As AI becomes more embedded in breast cancer care, it is crucial to ensure its ethical, efficient, and patient-focused implementation to improve outcomes for breast cancer patients ultimately.

Keywords: breast cancer, artificial intelligence, cancer diagnosis, clinical practice

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Authors: Armin Solemanifar, Arthur Wilkinson, Kinjalkumar Patel

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Carbon fibre (CF) - polymer laminate composites have very low densities (approximately 40% lower than aluminium), high strength and high stiffness but in terms of toughness properties they often require modifications. For example, adding rubbers or thermoplastics toughening agents are common ways of improving the interlaminar fracture toughness of initially brittle thermoset composite matrices. The main aim of this project was to toughen CF-epoxy resin laminate composites using hybrid CF-fabrics incorporating Innegra™ a commercial highly-oriented polypropylene (PP) fibre, in which more than 90% of its crystal orientation is parallel to the fibre axis. In this study, the damage tolerance of hybrid (carbon-Innegra, CI) composites was investigated. Laminate composites were produced by resin-infusion using: pure CF fabric; fabrics with different ratios of commingled CI, and two different types of pure Innegra fabrics (Innegra 1 and Innegra 2). Dynamic mechanical thermal analysis (DMTA) was used to measure the glass transition temperature (Tg) of the composite matrix and values of flexural storage modulus versus temperature. Mechanical testing included drop-weight impact, compression-after-impact (CAI), and interlaminar (short-beam) shear strength (ILSS). Ultrasonic C-Scan imaging was used to determine the impact damage area and scanning electron microscopy (SEM) to observe the fracture mechanisms that occur during failure of the composites. For all composites, 8 layers of fabrics were used with a quasi-isotropic sequence of [-45°, 0°, +45°, 90°]s. DMTA showed the Tg of all composites to be approximately same (123 ±3°C) and that flexural storage modulus (before the onset of Tg) was the highest for the pure CF composite while the lowest were for the Innegra 1 and 2 composites. Short-beam shear strength of the commingled composites was higher than other composites, while for Innegra 1 and 2 composites only inelastic deformation failure was observed during the short-beam test. During impact, the Innegra 1 composite withstood up to 40 J without any perforation while for the CF perforation occurred at 10 J. The rate of reduction in compression strength upon increasing the impact energy was lowest for the Innegra 1 and 2 composites, while CF showed the highest rate. On the other hand, the compressive strength of the CF composite was highest of all the composites at all impacted energy levels. The predominant failure modes for Innegra composites observed in cross-sections of fractured specimens were fibre pull-out, micro-buckling, and fibre plastic deformation; while fibre breakage and matrix delamination were a major failure observed in the commingled composites due to the more brittle behaviour of CF. Thus, Innegra fibres toughened the CF composites but only at the expense of reducing compressive strength.

Keywords: hybrid composite, thermoplastic fibre, compression strength, damage tolerance

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Authors: Daniel Llanes, Alfredo Reyes, Sonia E. Ruiz, Federico Valenzuela Beltran

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Steel frames have been used in building construction for more than one hundred years. Beam-column may be connected to columns using either stiffened or unstiffened angles at the top and bottom beam flanges. Designers often assume that these assemblies acted as “pinned” connections for gravity loads and that the stiffened connections would act as “fixed” connections for lateral loads. Observation of damages sustained by buildings during the 1994 Northridge earthquake indicated that, contrary to the intended behavior, in many cases, brittle fractures initiated within the connections at very low levels of plastic demand, and in some cases, while the structures remained essentially elastic. Due to the damage presented in these buildings other type of alternative connections have been proposed. According to a research funded by the Federal Emergency Management Agency (FEMA), the screwed connections have better performance when they are subjected to cyclic loads, but at the same time, these connections have some degree of flexibility. Due to this situation, some researchers ventured into the study of semi-rigid connections. In the present study three steel buildings, constituted by regular frames are analyzed. Two types of connections are considered: pinned and semi-rigid connections. With the aim to estimate their structural capacity, a number of incremental dynamic analyzes are performed. 3D structural models are used for the analyses. The seismic ground motions were recorded on sites near Los Angeles, California, where the structures are supposed to be located. The vulnerability curves of the building are obtained in terms of maximum inter-story drifts. The vulnerability curves (which correspond to the models with two different types of connections) are compared, and its implications on its structural design and performance is discussed.

Keywords: steel frame Buildings, vulnerability curves, semi-rigid connections, pinned connections

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Authors: Pradeep Kumar

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The present work is based on application of cement mortar with natural sand and discontinuous steel fiber through which bending behavior of skinny beam was evaluated. This research is to study the effects of combining reinforcing steel meshes (continuous steel reinforcement) with discontinuous fibers as reinforcement in skinny walled Portland cement based cement mortar with crushed stone dust as a fine aggregate. The term ‘skinny’ means thickness of the beams is less than 25 mm. The main idea behind this combination is to satisfy the ultimate strength limit state through the steel mesh reinforcement (as a main reinforcement) and to control the cracking under service loads through fiber (Recron 3s) reinforcement (as secondary reinforcement). The main object of this study is to carry out the bending behavior of mortar reinforced thin beam with only one layer of steel mesh (with various transfer wire spacing) and with a recron 3s (Reliance) fifers. The wide experimental program with bending tests is undertaken. The following variables are investigated: (a) the reference mesh size - 25.4 x 25.4 mm and 50.8 x 50.8 mm; (b) the transverse wire spacing - 25.4 mm, 50.8 mm, and no transverse wires; (c) the type of fibers – Reliance (Recron 3s, 6mm length); and (d) the fiber volume fraction – 0.1% and 0.25%. Some of the main conclusions are: (a) the use of recron 3s fibers leads to a little better overall performance than that with no fiber; (b) an increase in equivalent stress is observed when 0.1% RF,0.25% R Fibers are used; (c) when 25.4 x 50.8 size steel mesh is used, no noticeable change in behavior is observed in comparison to specimens without fibers; and (d) for no fibers 0.1% and o.1% RF the transverse wire spacing has some little effect on the equivalent stress for RF fibers, the transverse wire has no influence but the equivalent stress are increased.

Keywords: cement mortar, crushed stone dust, fibre, steel mesh

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Authors: Stefano Iannello, Massimiliano Materazzi

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Fluidized bed reactor technologies are one of the most valuable pathways for thermochemical conversions of biogenic fuels due to their good operating flexibility. Nevertheless, there are still issues related to the mixing and separation of heterogeneous phases during operation with highly volatile feedstocks, including biomass and waste. At high temperatures, the volatile content of the feedstock is released in the form of the so-called endogenous bubbles, which generally exert a “lift” effect on the particle itself by dragging it up to the bed surface. Such phenomenon leads to high release of volatile matter into the freeboard and limited mass and heat transfer with particles of the bed inventory. The aim of this work is to get a better understanding of the behaviour of a single reacting particle in a hot fluidized bed reactor during the devolatilization stage. The analysis has been undertaken at different fluidization regimes and temperatures to closely mirror the operating conditions of waste-to-energy processes. Beechwood and polypropylene particles were used to resemble the biomass and plastic fractions present in waste materials, respectively. The non-invasive X-ray technique was coupled to particle tracking algorithms to characterize the motion of a single feedstock particle during the devolatilization with high resolution. A high-energy X-ray beam passes through the vessel where absorption occurs, depending on the distribution and amount of solids and fluids along the beam path. A high-speed video camera is synchronised to the beam and provides frame-by-frame imaging of the flow patterns of fluids and solids within the fluidized bed up to 72 fps (frames per second). A comprehensive mathematical model has been developed in order to validate the experimental results. Beech wood and polypropylene particles have shown a very different dynamic behaviour during the pyrolysis stage. When the feedstock is fed from the bottom, the plastic material tends to spend more time within the bed than the biomass. This behaviour can be attributed to the presence of the endogenous bubbles, which drag effect is more pronounced during the devolatilization of biomass, resulting in a lower residence time of the particle within the bed. At the typical operating temperatures of thermochemical conversions, the synthetic polymer softens and melts, and the bed particles attach on its outer surface, generating a wet plastic-sand agglomerate. Consequently, this additional layer of sand may hinder the rapid evolution of volatiles in the form of endogenous bubbles, and therefore the establishment of a poor drag effect acting on the feedstock itself. Information about the mixing and segregation of solid feedstock is of prime importance for the design and development of more efficient industrial-scale operations.

Keywords: fluidized bed, pyrolysis, waste feedstock, X-ray

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Authors: Rahul Mohite, Lahcen Ouarbya

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This paper focuses on the interplay between humans and computer systems and the ability of these systems to understand and respond to human emotions, including non-verbal communication. Current emotion recognition systems are based solely on either facial or verbal expressions. The limitation of these systems is that it requires large training data sets. The paper proposes a system for recognizing human emotions that combines both speech and emotion recognition. The system utilizes advanced techniques such as deep learning and image recognition to identify facial expressions and comprehend emotions. The results show that the proposed system, based on the combination of facial expression and speech, outperforms existing ones, which are based solely either on facial or verbal expressions. The proposed system detects human emotion with an accuracy of 86%, whereas the existing systems have an accuracy of 70% using verbal expression only and 76% using facial expression only. In this paper, the increasing significance and demand for facial recognition technology in emotion recognition are also discussed.

Keywords: facial reputation, expression reputation, deep gaining knowledge of, photo reputation, facial technology, sign processing, photo type

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