Search results for: image retrieval

Authors: S. M. Rifat, André L. Marchildon, Mark F. Tachie

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The effect of upstream surface roughness over a smooth forward facing step in an open channel was investigated using a particle image velocimetry technique. Three different upstream surface topographies consisting of hydraulically smooth wall, sandpaper 36 grit and sand grains were examined. Besides the wall roughness conditions, all other upstream flow characteristics were kept constant. It was also observed that upstream roughness decreased the approach velocity by 2% and 10% but increased the turbulence intensity by 14% and 35% at the wall-normal distance corresponding to the top plane of the step compared to smooth upstream. The results showed that roughness decreased the reattachment lengths by 14% and 30% compared to smooth upstream. Although the magnitudes of maximum positive and negative Reynolds shear stress in separated and reattached region were 0.02Ue for all the cases, the physical size of both the maximum and minimum contour levels were decreased by increasing upstream roughness.

Keywords: forward facing step, open channel, separated and reattached turbulent flows, wall roughness

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Authors: Sezin Ekşioğlu

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Multilabel classification has a huge importance for several applications, it is also a challenging research topic. It is a kind of supervised learning that contains binary targets. The distance between multilabel and binary classification is having more than one class in multilabel classification problems. Features can belong to one class or many classes. There exists a wide range of applications for multi label prediction such as image labeling, text categorization, gene functionality. Even though features are classified in many classes, they may not always be properly classified. There are many ensemble methods for the classification. However, most of the researchers have been concerned about better multilabel methods. Especially little ones focus on both efficiency of classifiers and pairwise relationships at the same time in order to implement better multilabel classification. In this paper, we worked on modified ensemble methods by getting benefit from k-Nearest Neighbors and neural network structure to address issues within a beneficial way and to get better impacts from the multilabel classification. Publicly available datasets (yeast, emotion, scene and birds) are performed to demonstrate the developed algorithm efficiency and the technique is measured by accuracy, F1 score and hamming loss metrics. Our algorithm boosts benchmarks for each datasets with different metrics.

Keywords: multilabel, classification, neural network, KNN

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Authors: Nizar Souiden, Riadh Ladhari

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Like tobacco and alcohol, recreational marijuana falls under the umbrella of ‘sin’ industries’. Notwithstanding this general negative image surrounding marijuana use, some scholars argue that most of the widely believed claims made about recreational marijuana users are irrelevant and that marijuana use can even improve individuals’ decision-making. This study intends to shed light on this particular product category (i.e., marijuana) often overlooked or portrayed as taboo from a business view. More specifically, it investigates whether legalizing the consumption of recreational marijuana would be perceived as ethical and whether companies/organizations involved in the commercialization of this particular product would be held socially responsible. Based on primary data collected in Canada, this study aims to answer the following questions: 1) What moral thoughts do individuals hold with regard to the consumption of recreational marijuana? 2) How do these moral thoughts determine consumers’ attitude toward the consumption of recreational marijuana? Regardless of the legalization of recreational marijuana in some countries such as Canada, probing people’s opinions, and investigating their attitudes toward the consumption of recreational marijuana is of important interest to different stakeholders such as consumers, public organizations, private businesses, and trade associations.

Keywords: recreational marijuana, moral thoughts, ethics, attitude

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Authors: S. M. Kulkarni, D. S. Bormane, S. L. Nalbalwar

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The rapid development in the technology have dramatic impact on the medical health care field. Medical data base obtained with latest machines like CT Machine, MRI scanner requires large amount of memory storage and also it requires large bandwidth for transmission of data in telemedicine applications. Thus, there is need for video compression. As the database of medical images contain number of frames (slices), hence while coding of these images there is need of motion estimation. Motion estimation finds out movement of objects in an image sequence and gets motion vectors which represents estimated motion of object in the frame. In order to reduce temporal redundancy between successive frames of video sequence, motion compensation is preformed. In this paper three step search (TSS) block matching algorithm is implemented on different types of video sequences. It is shown that three step search algorithm produces better quality performance and less computational time compared with exhaustive full search algorithm.

Keywords: block matching, exhaustive search motion estimation, three step search, video compression

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Authors: Kelemewerk Destalem, Joongjae Cho, Jaeseong Lee, Ju H. Park, Joonhyuk Yoo

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Background subtraction and temporal difference are often used for moving object detection in video. Both approaches are computationally simple and easy to be deployed in real-time image processing. However, while the background subtraction is highly sensitive to dynamic background and illumination changes, the temporal difference approach is poor at extracting relevant pixels of the moving object and at detecting the stopped or slowly moving objects in the scene. In this paper, we propose a moving object detection scheme based on adaptive background subtraction and temporal difference exploiting dynamic background updates. The proposed technique consists of a histogram equalization, a linear combination of background and temporal difference, followed by the novel frame-based and pixel-based background updating techniques. Finally, morphological operations are applied to the output images. Experimental results show that the proposed algorithm can solve the drawbacks of both background subtraction and temporal difference methods and can provide better performance than that of each method.

Keywords: background subtraction, background updating, real time, light weight algorithm, temporal difference

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Authors: Paulo Cesar Pereira Junior, Alexandre Monteiro, Rafael da Luz Ribeiro, Antonio Carlos Sobieranski, Aldo von Wangenheim

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In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models.

Keywords: convolutional neural networks, deep learning, digital image processing, precision agriculture, semantic segmentation, unmanned aerial vehicles

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Authors: Thanakrit Taweesoontorn, Sarucha Yanyong, Poom Konghuayrob

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Robotic arms have gained popularity in various industries due to their accuracy and efficiency. This research proposes a method for bin-picking tasks using the Cobot, combining the YOLOv5 CNNs model for object detection and pose estimation with traditional feature detection (FAST), feature description (BRISK), and matching algorithms. By integrating these algorithms and utilizing a small-scale depth sensor camera for capturing depth and color images, the system achieves real-time object detection and accurate pose estimation, enabling the robotic arm to pick objects correctly in both position and orientation. Furthermore, the proposed method is implemented within the ROS framework to provide a seamless platform for robotic control and integration. This integration of robotics, cameras, and AI technology contributes to the development of industrial robotics, opening up new possibilities for automating challenging tasks and improving overall operational efficiency.

Keywords: robotic vision, image processing, applications of robotics, artificial intelligent

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Authors: Bikshalu Kalagadda, Srikanth Rangu

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The important object of images segmentation is to extract objects with respect to some input features. One of the important methods for image segmentation is Level set method. Generally medical images and synthetic images with low contrast of pixel profile, for such images difficult to locate interested features in images. In conventional level set function, develops irregularity during its process of evaluation of contour of objects, this destroy the stability of evolution process. For this problem a remedy is proposed, a new hybrid algorithm is Clustering Level Set Evolution. Kernel fuzzy particles swarm optimization clustering with the Distance Regularized Level Set (DRLS) and Selective Binary, and Gaussian Filtering Regularized Level Set (SBGFRLS) methods are used. The ability of identifying different regions becomes easy with improved speed. Efficiency of the modified method can be evaluated by comparing with the previous method for similar specifications. Comparison can be carried out by considering medical and synthetic images.

Keywords: segmentation, clustering, level set function, re-initialization, Kernel fuzzy, swarm optimization

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Authors: Julia A. Ermakova

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As a member of a multicultural society, it is imperative that individuals demonstrate the highest level of decorum in order to exemplify the beauty of their culture. Adab, the practice of praiseworthy words and deeds, as well as possessing good manners and pursuing that which is considered good, is a fundamental concept that guards against all types of mistakes. In Islam, etiquette for every situation in life is taught, and it constitutes the way of life for a Muslim. In light of this, the personality of an elegant Muslim woman can be described as one who embodies the following qualities: Firstly, cultural speech and erudition are essential components. Improving one's intellect, learning new things, reading diverse literature, expanding one's vocabulary, working on articulation, and avoiding obscene speech and verbosity are crucial. Additionally, listening more than speaking and being willing to discuss one's culture when asked are commendable qualities. Conversely, it is important to avoid discussing foolish matters with foolish people and to be able to respond appropriately and change the subject if someone attempts to hurt or manipulate. Secondly, the style of speech is also of paramount importance. It is recommended to speak in a measured tone with a quiet voice and deep breathing. Avoiding rushing and shortness of breath is also recommended. Thirdly, awareness of how to greet others is essential. Combining Shariah and small talk etiquette, such as making a gesture of respect by putting one's hand to the chest and smiling slightly when a man offers a handshake, is recommended. Understanding the rules of small talk, taboo topics, and self-presentation is also important. Fourthly, knowing how to give and receive compliments without devaluing them is imperative. Knowledge of the rules of good manners and etiquette, both secular and Shariah, is also essential. Fifthly, avoiding arguments and responding elegantly to rudeness and tactlessness is a sign of an elegant Muslim woman. Treating everyone with respect and avoiding prejudices, taboo topics, inappropriate questions, and bad habits are all aspects of politeness. Sixthly, a neat appearance appropriate to Shariah and the local community, as well as a well-put-together outfit with a touch of elegance and style, are crucial. Posture, graceful movement, and a pleasant gaze are also important. Finally, good spirits and inner calm are key to projecting a harmonious image, which encourages people to listen attentively. Giving thanks to Allah in every situation in life is the key to maintaining good spirits. In conclusion, an elegant Muslim woman in a multicultural society is characterized by her high moral qualities and adherence to Islamic etiquette. These qualities, such as cultural speech and erudition, style of speech, awareness of how to greet, knowledge of good manners and etiquette, avoiding arguments, politeness, a neat appearance, and good spirits, all contribute to projecting an image of elegance and respectability. By exemplifying these qualities, Muslim women can serve as positive ambassadors for their culture and religion in diverse societies.

Keywords: adab, elegance, muslim woman, multicultural societies, good manners, etiquette

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Authors: Mobarok Hossain Bhuyain

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Video surveillance processing has attracted various security fields transforming it into one of the leading research fields. Today's demand for detection and tracking of human mobility for security is very useful for human security, such as in crowded areas. Accordingly, video surveillance technology has seen a rapid advancement in recent years, with algorithms analyzing the behavior of people under surveillance automatically. The main motivation of this research focuses on the detection and tracking of the elderly and socially vulnerable people in crowded areas. Degenerate people are a major health concern, especially for elderly people and socially vulnerable people. One major disadvantage of video surveillance is the need for continuous monitoring, especially in crowded areas. To assist the security monitoring live surveillance video, image processing, and artificial intelligence methods can be used to automatically send warning signals to the monitoring officers about elderly people and socially vulnerable people.

Keywords: human detection, target tracking, neural network, particle filter

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Authors: Alfonsina Buoniconto

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The aim of this study is to investigate the degree of cross-linguistic and intra-linguistic variation in the encoding of motion events (MEs) in English and Italian, these being typologically different languages both showing signs of disobedience to their respective types. As a matter of fact, the traditional typological classification of MEs encoding distributes languages into two macro-types, based on the preferred locus for the expression of Path, the main ME component (other components being Figure, Ground and Manner) characterized by conceptual and structural prominence. According to this model, Satellite-framed (SF) languages typically express Path information in verb-dependent items called satellites (e.g. preverbs and verb particles) with main verbs encoding Manner of motion; whereas Verb-framed languages (VF) tend to include Path information within the verbal locus, leaving Manner to adjuncts. Although this dichotomy is valid altogether, languages do not always behave according to their typical classification patterns. English, for example, is usually ascribed to the SF type due to the rich inventory of postverbal particles and phrasal verbs used to express spatial relations (i.e. the cat climbed down the tree); nevertheless, it is not uncommon to find constructions such as the fog descended slowly, which is typical of the VF type. Conversely, Italian is usually described as being VF (cf. Paolo uscì di corsa ‘Paolo went out running’), yet SF constructions like corse via in lacrime ‘She ran away in tears’ are also frequent. This paper will try to demonstrate that such a typological overlapping is due to the fact that the semantic units making up MEs are distributed within several loci of the sentence –not only verbs and satellites– thus determining a number of different constructions stemming from convergent factors. Indeed, the linguistic expression of motion events depends not only on the typological nature of languages in a traditional sense, but also on a series morphological, lexical, and syntactic resources, as well as on inferential, discursive, usage-related, and cultural factors that make semantic information more or less accessible, frequent, and easy to process. Hence, rather than describe English and Italian in dichotomic terms, this study focuses on the investigation of cross-linguistic and intra-linguistic variation in the use of all the strategies made available by each linguistic system to express motion. Evidence for these assumptions is provided by parallel corpora analysis. The sample texts are taken from two contemporary Italian novels and their respective English translations. The 400 motion occurrences selected (200 in English and 200 in Italian) were scanned according to the MODEG (an acronym for Motion Decoding Grid) methodology, which grants data comparability through the indexation and retrieval of combined morphosyntactic and semantic information at different levels of detail.

Keywords: construction typology, motion event encoding, parallel corpora, satellite-framed vs. verb-framed type

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Authors: Mohammed M. Siddeq, Mohammed H. Rasheed, Omar M. Salih, Marcos A. Rodrigues

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This research proposes a data encoding and decoding method based on the Matrix Minimization algorithm. This algorithm is applied to high-frequency coefficients for compression/encoding. The algorithm starts by converting every three coefficients to a single value; this is accomplished based on three different keys. The decoding/decompression uses a search method called QSS (Quick Sequential Search) Decoding Algorithm presented in this research based on the sequential search to recover the exact coefficients. In the next step, the decoded data are saved in an auxiliary array. The basic idea behind the auxiliary array is to save all possible decoded coefficients; this is because another algorithm, such as conventional sequential search, could retrieve encoded/compressed data independently from the proposed algorithm. The experimental results showed that our proposed decoding algorithm retrieves original data faster than conventional sequential search algorithms.

Keywords: matrix minimization algorithm, decoding sequential search algorithm, image compression, DCT, DWT

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Authors: Emily Wood, James K. Westphal, Itamar Lerner

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Background: Polysomnography (PSG) recordings are regularly used in research and clinical settings to study sleep and sleep-related disorders. Typical PSG studies are conducted in professional laboratories and performed by qualified researchers. However, the number of sleep labs worldwide is disproportionate to the increasing number of individuals with sleep disorders like sleep apnea and insomnia. Consequently, there is a growing need to supply cheaper yet reliable means to measure sleep, preferably autonomously by subjects in their own home. Over the last decade, a variety of devices for self-monitoring of sleep became available in the market; however, very few have been directly validated against PSG to demonstrate their ability to perform reliable automatic sleep scoring. Two popular mobile EEG-based systems that have published validation results, the DREEM 3 headband and the Z-Machine, have never been directly compared one to the other by independent researchers. The current study aimed to compare the performance of DREEM 3 and the Z-Machine to help investigators and clinicians decide which of these devices may be more suitable for their studies. Methods: 26 participants have completed the study for credit or monetary compensation. Exclusion criteria included any history of sleep, neurological or psychiatric disorders. Eligible participants arrived at the lab in the afternoon and received the two devices. They then spent two consecutive nights monitoring their sleep at home. Participants were also asked to keep a sleep log, indicating the time they fell asleep, woke up, and the number of awakenings occurring during the night. Data from both devices, including detailed sleep hypnograms in 30-second epochs (differentiating Wake, combined N1/N2, N3; and Rapid Eye Movement sleep), were extracted and aligned upon retrieval. For analysis, the number of awakenings each night was defined as four or more consecutive wake epochs between sleep onset and termination. Total sleep time (TST) and the number of awakenings were compared to subjects’ sleep logs to measure consistency with the subjective reports. In addition, the sleep scores from each device were compared epoch-by-epoch to calculate the agreement between the two devices using Cohen’s Kappa. All analysis was performed using Matlab 2021b and SPSS 27. Results/Conclusion: Subjects consistently reported longer times spent asleep than the time reported by each device (M= 448 minutes for sleep logs compared to M= 406 and M= 345 minutes for the DREEM and Z-Machine, respectively; both ps<0.05). Linear correlations between the sleep log and each device were higher for the DREEM than the Z-Machine for both TST and the number of awakenings, and, likewise, the mean absolute bias between the sleep logs and each device was higher for the Z-Machine for both TST (p<0.001) and awakenings (p<0.04). There was some indication that these effects were stronger for the second night compared to the first night. Epoch-by-epoch comparisons showed that the main discrepancies between the devices were for detecting N2 and REM sleep, while N3 had a high agreement. Overall, the DREEM headband seems superior for reliably scoring sleep at home.

Keywords: DREEM, EEG, seep monitoring, Z-machine

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Authors: Lavanya Madhuri Bollipo, K. V. Kadambari

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Objective: To discuss key issues in the diagnosis of Parkinson disease (PD), To discuss features influencing PD progression, To discuss importance of brain SPECT data in PD diagnosis, and To discuss the essentiality of machine learning techniques in early diagnosis of PD. An accurate and early diagnosis of PD is nowadays a challenge as clinical symptoms in PD arise only when there is more than 60% loss of dopaminergic neurons. So far there are no laboratory tests for the diagnosis of PD, causing a high rate of misdiagnosis especially when the disease is in the early stages. Recent neuroimaging studies with brain SPECT using 123I-Ioflupane (DaTSCAN) as radiotracer shown to be widely used to assist the diagnosis of PD even in its early stages. Machine learning techniques can be used in combination with image analysis procedures to develop computer-aided diagnosis (CAD) systems for PD. This paper addressed recent studies involving diagnosis of PD in its early stages using brain SPECT data with Machine Learning Techniques.

Keywords: Parkinson disease (PD), dopamine transporter, single-photon emission computed tomography (SPECT), support vector machine (SVM)

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Authors: Charles Masulani Mwale

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Background: It is argued that 85% of children with the disability live in resource-poor countries where there are few available disability services. A majority of these children, including their parents, suffer a lot as a result of the disability and its associated stigmatization, leading to a marginalized life. These parents also experience more stress and mental health problems such as depression, compared with families of normal developing children. There is little research from Africa addressing these issues especially among parents of intellectually disabled children. WHO encourages research on the impact that child with a disability have on their family and appropriate training and support to the families so that they can promote the child’s development and well-being. This study investigated the parenting experiences, mechanisms of coping with these challenges and psychosocial needs while caring for children with intellectual disabilities in both rural and urban settings of Lilongwe and Mzuzu. Methods: This is part of a larger Mixed-methods study aimed at developing a contextualized psychosocial intervention for parents of intellectually disabled children. 16 focus group discussions and four in-depth interviews were conducted with parents in catchments areas for St John of God and Children of Blessings in Mzuzu and Lilongwe cities respectively. Ethical clearance was obtained from COMREC. Data were stored in NVivo software for easy retrieval and management. All interviews were tape-recorded, transcribed and translated into English. Note-taking was performed during all the observations. Data triangulation from the interviews, note taking and the observations were done for validation and reliability. Results: Caring for intellectually disabled children comes with a number of challenges. Parents experience stigma and discrimination; fear for the child’s future; have self-blame and guilt; get coerced by neighbors to kill the disabled child; and fear violence by and to the child. Their needs include respite relief, improved access to disability services, education on disability management and financial support. For their emotional stability, parents cope by sharing with others and turning to God while other use poor coping mechanisms like alcohol use. Discussion and Recommendation: Apart from neighbors’ coercion to eliminate the child life, the findings of this study are similar to those done in other countries like Kenya and Pakistan. It is recommended that parents get educated on disability, its causes, and management to array fears of unknown. Community education is also crucial to promote community inclusiveness and correct prevailing myths associated with disability. Disability institutions ought to intensify individual as well as group counseling services to these parents. Further studies need to be done to design culturally appropriate and specific psychosocial interventions for the parents to promote their psychological resilience.

Keywords: psychological distress, intellectual disability, psychosocial interventions, mental health, psychological resilience, children

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Authors: Bipasa Dey, Varsha Panwar, Tanmay Dutta

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Laccase, a multicopper oxidase (EC 1.10.3.2) have been at the forefront as a superior industrial biocatalyst. They are versatile in terms of bestowing sustainable and ecological catalytic reactions such as polymerisation, xenobiotic degradation and bioremediation of phenolic and non-phenolic compounds. Regardless of the wide biotechnological applications, the critical limiting factors viz. reusability, retrieval, and storage stability still prevail. This can cause an impediment in their applicability. Crosslinked enzyme aggregates (CLEAs) have emerged as a promising technique that rehabilitates these essential facets, albeit at the expense of their enzymatic activity. The carrier free crosslinking method prevails over the carrier-bound immobilisation in conferring high productivity, low production cost owing to the absence of additional carrier and circumvent any non-catalytic ballast which could dilute the volumetric activity. To the best of our knowledge, the ε-amino group of lysyl residue is speculated as the best choice for forming Schiff’s base with glutaraldehyde. Despite being most preferrable, excess glutaraldehyde can bring about disproportionate and undesirable crosslinking within the catalytic site and hence could deliver undesirable catalytic losses. Moreover, the surface distribution of lysine residues in Trametes versicolor laccase is significantly less. Thus, to mitigate the adverse effect of glutaraldehyde in conjunction with scaling down the degradation or catalytic loss of the enzyme, crosslinking with inert substances like gelatine, collagen, Bovine serum albumin (BSA) or excess lysine is practiced. Analogous to these molecules, sugars have been well known as a protein stabiliser. It helps to retain the structural integrity, specifically secondary structure of the protein during aggregation by changing the solvent properties. They are comprehended to avert protein denaturation or enzyme deactivation during precipitation. We prepared crosslinked enzyme aggregates (CLEAs) of laccase from T. versicolor with the aid of sugars. The sugar CLEAs were compared with the classic BSA and glutaraldehyde laccase CLEAs concerning physico-chemical properties. The activity recovery for the fructose CLEAs were found to be ~20% higher than the non-sugar CLEA. Moreover, the 𝐾𝑐𝑎𝑡𝐾𝑚⁄ values of the CLEAs were two and three-fold higher than BSA-CLEA and GACLEA, respectively. The half-life (t1/2) deciphered by sugar-CLEA was higher than the t1/2 of GA-CLEAs and free enzyme, portraying more thermal stability. Besides, it demonstrated extraordinarily high pH stability, which was analogous to BSA-CLEA. The promising attributes of increased storage stability and recyclability (>80%) gives more edge to the sugar-CLEAs over conventional CLEAs of their corresponding free enzyme. Thus, sugar-CLEA prevails in furnishing the rudimentary properties required for a biocatalyst and holds many prospects.

Keywords: cross-linked enzyme aggregates, laccase immobilization, enzyme reusability, enzyme stability

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Authors: Varsha Singh, Shraddha Prajapati, M. B. Kiran

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Texture identification is useful in predicting the functionality of a component. Many of the existing texture identification methods are of contact in nature, which limits its measuring speed. These contact measurement techniques use a diamond stylus and the diamond stylus being sharp going to damage the surface under inspection and hence these techniques can be used in statistical sampling. Though these contact methods are very accurate, they do not give complete information for full characterization of surface. In this context, the presented method assumes special significance. The method uses a relatively low cost vision system for image acquisition. Software is developed based on wavelet transform, for analyzing texture images. Specimens are made using different manufacturing process (shaping, grinding, milling etc.) During experimentation, the specimens are illuminated using proper lighting and texture images a capture using CCD camera connected to the vision system. The software installed in the vision system processes these images and subsequently identify the texture of manufacturing processes.

Keywords: diamond stylus, manufacturing process, texture identification, vision system

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Authors: Andrea J. Kirk-Jenkins

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Women in late adolescence and young adulthood are particularly vulnerable to disordered eating, and prior research indicates that those within the college and sorority communities may be especially susceptible. Research has primarily involved comparing eating disorder symptoms between sorority women and non-sorority members using formal eating disorder assessments. This phenomenological study examined sorority members’ (N = 10) perceptions of and lived experiences with various disordered eating behaviors within the sorority culture. Data from individual interviews and photographs indicated two structural themes and 11 textural themes related to factors associated with disordered eating behaviors. These findings point to the existence of both positive and negative aspects of sorority culture, normalization of disordered eating behaviors, and pressure to attain or maintain an ideal body image. Implications for university stakeholders, including college counselors, health center staff, and extracurricular program leaders, are discussed. Further research on the identified textural themes as well as a longitudinal study exploring how perceptions change from rush to alumnae status is suggested.

Keywords: eating disorders, disorder eating behaviors, sorority women, sorority culture, college women

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Authors: Alexander Hudson, Shaogang Gong

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Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate (≥80%) predictions of protein class and architecture from structures determined at low (>3A) resolution, using a deep convolutional neural network trained on high-resolution (≤3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution.

Keywords: transfer learning, protein distance maps, protein structure classification, neural networks

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Authors: Richard, Iyan Subiyanto, Chairul Hudaya

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Activated carbon has been playing a significant role for many applications, especially in energy storage devices. However, commercially activated carbons generally require complicated processes and high production costs. Therefore, in this study, an activated carbon originating from green coke waste, that is economically affordable will be used as a carbon source. To synthesize activated carbon, KOH as an activator was employed with variation of C:KOH in ratio of 1:2, 1:3, 1:4, and 1:5, respectively, with an activation temperature of 700°C. The characterizations of activated carbon are obtained from Scanning Electron Microscopy, Energy Dispersive X-Ray, Raman Spectroscopy, and Brunauer-Emmett-Teller. The optimal activated carbon sample with specific surface area of 2,024 m²/g with high carbon content ( > 80%) supported by the high porosity carbon image obtained by SEM was prepared at C:KOH ratio of 1:4. The result shows that the synthesized activated carbon would be an ideal choice for energy storage device applications. Therefore, this study is expected to reduce the costs of activated carbon production by expanding the utilization of petroleum waste.

Keywords: activated carbon, energy storage material, green coke, specific surface area

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Authors: Rania Ahmed Kadry Abdel Gawad Birry, Mohamed El-Habrouk

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This paper presents a brief survey of the techniques used for sign language recognition along with the types of sensors used to perform the task. It presents a modified method for identification of an isolated sign language gesture using Microsoft Kinect with the OpenNI framework. It presents the way of extracting robust features from the depth image provided by Microsoft Kinect and the OpenNI interface and to use them in creating a robust and accurate gesture recognition system, for the purpose of ASL translation. The Prime Sense’s Natural Interaction Technology for End-user - NITE™ - was also used in the C++ implementation of the system. The algorithm presents a simple finger counting algorithm for static signs as well as directional Finite State Machine (FSM) description of the hand motion in order to help in translating a sign language gesture. This includes both letters and numbers performed by a user, which in-turn may be used as an input for voice pronunciation systems.

Keywords: American sign language, finger counting, hand tracking, Microsoft Kinect

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Authors: Manuel Alejandro Taborda Ceballos, Martin Sommerfeld

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Transport phenomena and dispersion of non-spherical particle in turbulent flows are found everywhere in industrial application and processes. Powder handling, pollution control, pneumatic transport, particle separation are just some examples where the particle encountered are not only spherical. These types of multiphase flows are wall bounded and mostly highly turbulent. The particles found in these processes are rarely spherical but may have various shapes (e.g., fibers, and rods). Although research related to the behavior of regular non-spherical particles in turbulent flows has been carried out for many years, it is still necessary to refine models, especially near walls where the interaction fiber-wall changes completely its behavior. Imaging-based experimental studies on dispersed particle-laden flows have been applied for many decades for a detailed experimental analysis. These techniques have the advantages that they provide field information in two or three dimensions, but have a lower temporal resolution compared to point-wise techniques such as PDA (phase-Doppler anemometry) and derivations therefrom. The applied imaging techniques in dispersed two-phase flows are extensions from classical PIV (particle image velocimetry) and PTV (particle tracking velocimetry) and the main emphasis was simultaneous measurement of the velocity fields of both phases. In a similar way, such data should also provide adequate information for validating the proposed models. Available experimental studies on the behavior of non-spherical particles are uncommon and mostly based on planar light-sheet measurements. Especially for elongated non-spherical particles, however, three-dimensional measurements are needed to fully describe their motion and to provide sufficient information for validation of numerical computations. For further providing detailed experimental results allowing a validation of numerical calculations of non-spherical particle dispersion in turbulent flows, a water channel test facility was built around a horizontal closed water channel. Into this horizontal main flow, a small cross-jet laden with fiber-like particles was injected, which was also solely driven by gravity. The dispersion of the fibers was measured by applying imaging techniques based on a LED array for backlighting and high-speed cameras. For obtaining the fluid velocity fields, almost neutrally buoyant tracer was used. The discrimination between tracer and fibers was done based on image size which was also the basis to determine fiber orientation with respect to the inertial coordinate system. The synchronous measurement of fluid velocity and fiber properties also allow the collection of statistics of fiber orientation, velocity fields of tracer and fibers, the angular velocity of the fibers and the orientation between fiber and instantaneous relative velocity. Consequently, an experimental study the behavior of elongated non-spherical particles in wall bounded turbulent flows was achieved. The development of a comprehensive analysis was succeeded, especially near the wall region, where exists hydrodynamic wall interaction effects (e.g., collision or lubrication) and abrupt changes of particle rotational velocity. This allowed us to predict numerically afterwards the behavior of non-spherical particles within the frame of the Euler/Lagrange approach, where the particles are therein treated as “point-particles”.

Keywords: crossflow, non-spherical particles, particle tracking velocimetry, PIV

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Authors: Aarushi Agarwal, Tara Singh, Anju Lata Singh, Trayambak Tiwari, Indramani Lal Singh

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The existing interdependent relationship between attention and consciousness has been put to debate since long. To testify the nature, dual-task paradigm has been used to simultaneously manipulate awareness and attention. With central discrimination task which is attentional demanding, participants also perform simple discrimination task in the periphery in near absence of attention. Individual-based analysis of performance accuracy in single and dual condition showed and above chance level performance i.e. more than 80%. In order to widen the understanding of extent of discrimination carried in near absence of attention, natural image and its geometric equivalent shape were presented in the periphery; synthetic objects accounted to lower level of performance than natural objects in dual condition. The gaze plot and heatmap indicate that peripheral performance do not necessarily involve saccade every time, verifying the discrimination in the periphery was in near absence of attention. Thus our studies show an interdependent nature of attention and awareness.

Keywords: attention, awareness, dual task paradigm, natural and geometric images

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Authors: Rohit T. P., Sahil Athrij, Sasi Gopalan

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Currently, the content entertainment industry is dominated by mobile devices. As the trends slowly shift towards Augmented/Virtual Reality applications the computational demands on these devices are increasing exponentially and we are already reaching the limits of hardware optimizations. This paper proposes a software solution to this problem. By leveraging the capabilities of cloud computing we can offload the work from mobile devices to dedicated rendering servers that are way more powerful. But this introduces the problem of latency. This paper introduces a protocol that can achieve high-performance low latency Augmented/Virtual Reality experience. There are two parts to the protocol, 1) In-flight compression The main cause of latency in the system is the time required to transmit the camera frame from client to server. The round trip time is directly proportional to the amount of data transmitted. This can therefore be reduced by compressing the frames before sending. Using some standard compression algorithms like JPEG can result in minor size reduction only. Since the images to be compressed are consecutive camera frames there won't be a lot of changes between two consecutive images. So inter-frame compression is preferred. Inter-frame compression can be implemented efficiently using WebGL but the implementation of WebGL limits the precision of floating point numbers to 16bit in most devices. This can introduce noise to the image due to rounding errors, which will add up eventually. This can be solved using an improved interframe compression algorithm. The algorithm detects changes between frames and reuses unchanged pixels from the previous frame. This eliminates the need for floating point subtraction thereby cutting down on noise. The change detection is also improved drastically by taking the weighted average difference of pixels instead of the absolute difference. The kernel weights for this comparison can be fine-tuned to match the type of image to be compressed. 2) Dynamic Load distribution Conventional cloud computing architectures work by offloading as much work as possible to the servers, but this approach can cause a hit on bandwidth and server costs. The most optimal solution is obtained when the device utilizes 100% of its resources and the rest is done by the server. The protocol balances the load between the server and the client by doing a fraction of the computing on the device depending on the power of the device and network conditions. The protocol will be responsible for dynamically partitioning the tasks. Special flags will be used to communicate the workload fraction between the client and the server and will be updated in a constant interval of time ( or frames ). The whole of the protocol is designed so that it can be client agnostic. Flags are available to the client for resetting the frame, indicating latency, switching mode, etc. The server can react to client-side changes on the fly and adapt accordingly by switching to different pipelines. The server is designed to effectively spread the load and thereby scale horizontally. This is achieved by isolating client connections into different processes.

Keywords: 2D kernelling, augmented reality, cloud computing, dynamic load distribution, immersive experience, mobile computing, motion tracking, protocols, real-time systems, web-based augmented reality application

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Authors: Viktorija Voidogaitė

Abstract:

Catholic education is based on the belief that every human being is created in the image and likeness of God. It is also influenced by the idea that the Kingdom of Heaven belongs to the humble and vulnerable. These principles emphasize the importance of serving the most vulnerable members of the Church community and promoting inclusivity without discrimination. This perspective emphasizes the need to protect the weakest members with compassion. However, realizing such an ideal in practice proves challenging, as the shortcomings and errors prevalent in any society often stem from the actions of Christians within that society. The evolution of these connections is observed throughout the historical development of Catholic education. In some European countries, Catholic education has become elitist, with limited room for inclusivity. This creates a conspicuous gap between the principles of the Evangelical community and elite Catholic schools and gymnasiums. Some schools appear to be most inclined to educate only those students who best align with their profile, leaving those needing assistance on the margins. As we advance into the third decade of the 21st century, there emerges a fundamental consideration: whether individuals who can assist the underprivileged and the infirm are being emphasized. Yet, it remains an open question whether these individuals will also possess the willingness and capability to construct a community or society that is inclusive and accessible to all.

Keywords: inclusion, Catholic education, inclusive education, becoming

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Authors: Yalong Jiang, Zheru Chi

Abstract:

In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.

Keywords: CNN, convolutional neural network, capsule network, capacity optimization, character recognition, data augmentation, semantic segmentation

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Authors: Saghar Karimi, Mohammad Saeed Ahrari Khafi, Amin Abolhasani Foroughi

Abstract:

In recent decades, keeping exotic species as pet animals has become widespread. Turtles are exotic species from chelonians, which are interested by many people. Caspian pond and European pond turtles from Emydidea family are commonly kept as pets in Iran. Presence of the shell in turtles makes achievement to a comprehensive clinical examination impossible. Respiratory system is one of the most important structures to be examined completely. Presence of the air in the respiratory system makes radiography the first modality to think of; however, image quality would be affected by the shell. Computed tomography (CT) as a radiography-based and non-invasive technique provides cross-sectional scans with little superimposition. The aim of this study was to depict normal computed tomographic anatomy of the respiratory system in Caspian Pond Turtle. Five adult Caspian pond turtle were scanned using a 16-detector CT machine. Our results showed that computed tomography is able to well illustrated different parts of respiratory system in turtle and can be used for detecting abnormalities and disorders.

Keywords: anatomy, computed tomography, respiratory system, turtle

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Authors: Haonan Hu, Shuge Lei, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Jijun Tang

Abstract:

This paper focuses on the classification task of breast ultrasound images and conducts research on the reliability measurement of classification results. A dual-channel evaluation framework was developed based on the proposed inference reliability and predictive reliability scores. For the inference reliability evaluation, human-aligned and doctor-agreed inference rationals based on the improved feature attribution algorithm SP-RISA are gracefully applied. Uncertainty quantification is used to evaluate the predictive reliability via the test time enhancement. The effectiveness of this reliability evaluation framework has been verified on the breast ultrasound clinical dataset YBUS, and its robustness is verified on the public dataset BUSI. The expected calibration errors on both datasets are significantly lower than traditional evaluation methods, which proves the effectiveness of the proposed reliability measurement.

Keywords: medical imaging, ultrasound imaging, XAI, uncertainty measurement, trustworthy AI

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Authors: Azza Taha Zaki

Abstract:

The death of tragedy is probably the most distinctive literary controversy of the twentieth century. There is common critical consent that tragedy in the classical sense of the word is no longer possible. Thinkers, philosophers, and critics such as Nietzsche, Durrenmatt, and George Steiner have all agreed that the decline of the genre in the modern age is due to the total lack of a unified world image and the absence of a shared vision in a fragmented and ideologically diversified world. The production of Rosencrantz and Guildenstern are Dead in 1967 marked the rise of the genre of tragi-comedy as a more appropriate reflection of the spirit of the age. At the hands of such great dramatists as Tom Stoppard (1937- ), the revived genre was not used as an extra comic element to give some comic relief to an otherwise tragic text, but it was given a postmodernist touch to serve the interpretation of the dilemma of man in the postmodernist world. This paper will study features of postmodernist tragi-comedy in Rosencrantz and Guildenstern are Dead as one of the most important plays in modern British theatre and investigate Stoppard’s vision of man and life as influenced by postmodernist thought and philosophy.

Keywords: British, drama, postmodernist, Stoppard, tragi-comedy

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

Abstract:

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

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

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