Search results for: profile-based features

Authors: J. D. Martínez-Vargas, C. Castro-Hoyos, G. Castellanos-Dominguez

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In this work, we propose a novel approach for measuring the stationarity level of a multichannel time-series. This measure is based on a stationarity definition over time-varying spectrum, and it is aimed to quantify the relation between local stationarity (single-channel) and global dynamic behavior (multichannel dynamics). To assess the proposed approach validity, we use a well known EEG-BCI database, that was constructed for separate between motor/imagery tasks. Thus, based on the statement that imagination of movements implies an increase on the EEG dynamics, we use as discriminant features the proposed measure computed over an estimation of the non-stationary components of input time-series. As measure of separability we use a t-student test, and the obtained results evidence that such measure is able to accurately detect the brain areas projected on the scalp where motor tasks are realized.

Keywords: stationary measure, entropy, sub-space projection, multichannel dynamics

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Authors: Ran Yue, Zhejing Li

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Context: The context of this study is the field of Digital Basketball Sports Games (DBSG). The existing DBSGs often prioritize competitiveness and confrontation, neglecting the narrative and progressive expression, especially from a female standpoint. This study aims to address this gap by analyzing existing DBSGs and proposing a comprehensive framework tailored to meet the needs and desires of women in basketball. Research Aim: The aim of this research is to examine the narrative perspectives of women in basketball and understand their desires and expectations within the sport. It also seeks to investigate methods to seamlessly integrate women's basketball stories into gameplay, addressing their specific needs and expectations. Additionally, the study aims to develop a digital basketball sports game framework that combines narrative richness and entertainment, with a focus on the female audience. Methodology: The study utilizes affective-arousal theories as a psychological framework to explore how emotional arousal influences player engagement and responses in the digital basketball sports game. It employs in-depth case studies to examine specific instances and gain insights into the implementation and impact of narrative elements and educational features in existing DBSGs. Comparative studies are conducted to analyze different DBSGs, identifying effective strategies and shortcomings. Findings: The research findings contribute to the development of a digital basketball game framework from a female perspective. This framework enhances the completeness, diversity, and inclusivity of digital basketball sports games. By addressing the specific needs of women in basketball, including fundamental knowledge, sports skills, safety awareness, and rehabilitation training methods, the framework provides a foundational reservoir for a broader range of basketball participation. It enriches the gaming experience by enhancing enjoyment, narrative, and diversity. It also acts as a catalyst to encourage more women to engage with basketball stories, participate in the sport, persevere, and derive greater enjoyment while benefiting their physical fitness and health. Theoretical Importance: The study contributes to the existing literature by incorporating game motivation psychology theories and proposing a comprehensive framework that caters to the specific needs of women in basketball. It emphasizes the importance of considering the narrative and progressive expression in DBSGs, especially from a female perspective. The research explores affective-arousal theories and provides insights into how emotional arousal can influence player engagement and responses in digital basketball sports games. Data Collection and Analysis Procedures: The study collects data through in-depth case studies of existing DBSGs, examining specific instances to uncover insights into the implementation and impact of narrative elements and educational features. Comparative studies are conducted to contrast and analyze various DBSGs, identifying effective strategies and shortcomings. The analysis procedures involve identifying commonalities, differences, strengths, and weaknesses among the DBSGs, guiding the development of a female-centric perspective in the proposed framework. Questions Addressed: The study addresses the following questions: What are the narrative perspectives of women in basketball? How can women's basketball stories be seamlessly integrated into gameplay? What are the specific needs and expectations of women in basketball? What effective strategies and shortcomings exist in current DBSGs? How can a digital basketball game framework be developed to cater to the female audience? Conclusion: In conclusion, this study contributes to the field of DBSGs by proposing a comprehensive digital basketball game framework from a female perspective. The framework enhances the inclusivity, diversity, and enjoyment of DBSGs by addressing the specific needs and desires of women in basketball. It provides a foundation for a broader range of basketball participation, enriching the gaming experience and benefiting women's physical fitness and health. The research, using affective-arousal theories and in-depth case studies, provides valuable insights into the implementation and impact of narrative elements and educational features in existing DBSGs, guiding the development of the proposed female-centric framework.

Keywords: digital basketball game, game framework, female perspective, game narratives

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Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

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Emotions classification of text documents is applied to reveal if the document expresses a determined emotion from its writer. As different supervised methods are previously used for emotion documents’ classification, in this research we present a novel model that supports the classification algorithms for more accurate results by the support of TF-IDF measure. Different experiments have been applied to reveal the applicability of the proposed model, the model succeeds in raising the accuracy percentage according to the determined metrics (precision, recall, and f-measure) based on applying the refinement of the lexicon, integration of lexicons using different perspectives, and applying the TF-IDF weighting measure over the classifying features. The proposed model has also been compared with other research to prove its competence in raising the results’ accuracy.

Keywords: emotion detection, TF-IDF, WEKA tool, classification algorithms

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Authors: Dinh Ha, Tran, Chung-Ruey Yen

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This study explored the morphological characteristics and effects of pollination methods on fruit set and characteristics in four red pitaya (Hylocereus spp.) clones. The distinctive morphological recognition and classification among pitaya clones were confirmed by the stem, flower and fruit features. The fruit production season was indicated from the beginning of May to the end of August, the beginning of September with 6-7 flowering cycles per year. The floral stage took from 15-19 days and fruit duration spent 30–32 days. VN White, fully self-compatible, obtained high fruit set rates (80.0-90.5 %) in all pollination treatments and the maximum fruit weight (402.6 g) in hand self- and (403.4 g) in open-pollination. Chaozhou 5 was partially self-compatible while Orejona and F11 were completely self-incompatible. Hand cross-pollination increased significantly fruit set (95.8; 88.4 and 90.2 %) and fruit weight (374.2; 281.8 and 416.3 g) in Chaozhou 5, Orejona, and F11, respectively. TSS contents were not much influenced by pollination methods.

Keywords: Hylocereus spp., morphology, floral phenology, pollination requirement

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Authors: Igor A. Podgorny

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TurboTax AnswerXchange is a social Q/A system supporting users working on federal and state tax returns. Content quality and popularity in the AnswerXchange can be predicted with propensity models using attributes of the question and answer. Using uplift modeling, we identify features of questions and answers that can be modified during the question-asking and question-answering experience in order to optimize the AnswerXchange content quality. We demonstrate that adding details to the questions always results in increased question popularity that can be used to promote good quality content. Responding to close-ended questions assertively improve content quality in the AnswerXchange in 90% of cases. Answering knowledge questions with web links increases the likelihood of receiving a negative vote from 60% of the askers. Our findings provide a rationale for employing the uplift modeling approach for AnswerXchange operations.

Keywords: customer relationship management, human-machine interaction, text mining, uplift modeling

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Authors: M. N. Akhtar, J. N. Akhtar

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The present study evaluates the properties of class F fly ash as a replacement of natural materials in civil engineering construction industry. The low-lime flash similar to class F is the prime variety generated in India, although it has significantly smaller volumes of high-lime fly ash as compared to class C. The chemical and physical characterization of the sample is carried out with the number of experimental approaches in order to investigate all relevant features present in the samples. For chemical analysis, elementary quantitative results from point analysis and scanning electron microscopy (SEM)/dispersive spectroscopy (EDS) techniques were used to identify the element images of different fractions. The physical properties found very close to the range of common soils. Furthermore, the fly ash-based bricks were prepared by the same sample of class F fly ash and the results of compressive strength similar to that of Standard Clay Brick Grade 1 available in the local market of India.

Keywords: fly ash, class F, class C, chemical, physical, SEM, EDS

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Authors: Nazia Tarannum, M. Ayaz Ahmad

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The main goal of this article is to report some interesting features for the fabrication/design of PEDOT:PSS based humidity sensor. Here first we fabricated humidity sensor and then studied its electro-mechanical characteristics. In general the humidity plays an important role in various private and government sectors all over the world. Monitoring and controlling the humidity is a great task for the reliable operation of various systems. The PEDOT:PSS is very much promising humidity sensor and also is fabricated by performing various analyses. The interdigited electrode (IDE) has channel length 200 microns prepared by lithography. Lithography of IDE was done on PPR coated glass substrate using negative mask and exposing it with UV light for 10 secs via DSA. During the above said fabrication, we have taken account for the following steps: •Plasma ashing of IDE •Spincoating of PEDOT:PSS was done @3000 rpm on IDE substrace •Baked the substrace at 130 °C up to time limit 15 mins. •Resistance measurement using Labtracer 2.9 software via Keithley 2400source meter.

Keywords: fabrication method, PEDOT:PSS material, humidity sensor, electro-mechanical

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Authors: YuLung Wu, YuTien Wu, ShuMey Yu

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Game-based learning can enhance the learning motivation of students and provide a means for them to learn through playing games. This study used augmented reality technology to develop an interactive card game as a game-based teaching aid for delivering elementary school science course content with the aim of enhancing student learning processes and outcomes. Through playing the proposed card game, students can familiarize themselves with appearance, features, and foraging behaviors of insects. The system records the actions of students, enabling teachers to determine their students’ learning progress. In this study, 37 students participated in an assessment experiment and provided feedback through questionnaires. Their responses indicated that they were significantly more motivated to learn after playing the game, and their feedback was mostly positive.

Keywords: game-based learning, learning motivation, teaching aid, augmented reality

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Authors: R. Sharma, J. K. Bhatnagar, Poonam, R. C. Sharma

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Metal–air batteries have been designed and developed as an essential source of electric power to propel automobiles, make electronic equipment functional, and use them as the source of power in remote areas and space. High energy and power density, lightweight, easy recharge capabilities, and low cost are essential features of these batteries. Both primary and rechargeable magnesium air batteries are highly promising. Our focus will be on the basics of electrode reaction kinetics of Mg–air cell in this paper. Design and development of Mg or Mg alloys as anode materials, design and composition of air cathode, and promising electrolytes for Mg–air batteries have been reviewed. A brief note on the possible and proposed improvements in design and functionality is also incorporated. This article may serve as the primary and premier document in the critical research area of Mg-air battery systems.

Keywords: air cathode, battery design, magnesium air battery, magnesium anode, rechargeable magnesium air battery

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Authors: Wesam Mohamed Abdel Khalek Ibrahim

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This paper examines the use of metaphors in Arabic political news discourse, focusing particularly on the headlines of the news articles relating to the Egyptian political situation in the period from June 2012 to October 2013. Metaphors are skilfully manipulated in the headlines to influence the public stance towards several events and entities including Egypt, Muslim Brotherhood (MB), Morsi, the June 30th uprising, Al-Sisi and the Armed Forces. The findings reveal that Arabic political news discourse shares basic features with its English counterpart, namely the use of metaphors as persuasive strategies and the presence of certain target domains. Insights gained from this study feed back into the conceptual metaphor theory by providing further evidence to the universality of metaphors.

Keywords: conceptual metaphor theory, political discourse, news discourse, Egyptian political situation

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Authors: K. Mardaleishvili, G. Loladze, G. Shatirishivili, D. Chakhunashvili, A. Vishnevskaya, Z. Kakabadze

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Benign osteoblastoma is a benign tumor of the bone, usually affecting the vertebrae and long tubular bones. It is a rarely seen tumor of the facial bones. The authors present a case of a 28-year-old male patient with a tumor in mandibular body. The lesion was radically resected and histological analysis of the specimen demonstrated features typical of a benign osteoblastoma. The defect of the jaw was reconstructed with titanium implants and decellularized and lyophilized cattle bone matrix with mesenchymal bone marrow stem cells transplantation. This presentation describes the procedures for rehabilitating a patient with decellularized bone scaffold in the region of the face, recovering the facial contours and esthetics of the patient.

Keywords: facial bones, osteoblastoma, stem cells, transplantation

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Authors: Paulo Fernando Silva Filho, Elcio Hideiti Shiguemori

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The selection of specific landmarks for an Unmanned Aerial Vehicles’ Visual Navigation systems based on Automatic Landmark Recognition has significant influence on the precision of the system’s estimated position. At the same time, manual selection of the landmarks does not guarantee a high recognition rate, which would also result on a poor precision. This work aims to develop an automatic landmark selection that will take the image of the flight area and identify the best landmarks to be recognized by the Visual Navigation Landmark Recognition System. The criterion to select a landmark is based on features detected by ORB or AKAZE and edges information on each possible landmark. Results have shown that disposition of possible landmarks is quite different from the human perception.

Keywords: clustering, edges, feature points, landmark selection, X-means

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Authors: Sai Maniveer Adapa, Sai Guptha Perla, Adithya Reddy P.

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Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection.

Keywords: thyroid tumor diagnosis, ultrasound images, deep learning, machine learning, convolutional auto-encoder, support vector machine

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Authors: João Paulo Pascon

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A constitutive model accounting for large strains, thermoviscoplasticity, and ductile damage evolution is proposed in the present work. To this end, a fully Lagrangian framework is employed, considering plane stress conditions and multiplicative split of the deformation gradient. The full model includes Gurson’s void growth, nucleation and coalescence, plastic work heating, strain and strain-rate hardening, thermal softening, and heat conductivity. The contribution of the work is the combination of all the above-mentioned features within the finite-strain setting. The model is implemented in a computer code using triangular finite elements and nonlinear analysis. Two mechanical examples involving ductile damage and finite strain levels are analyzed: an inhomogeneous tension specimen and the necking problem. Results demonstrate the capabilities of the developed formulation regarding ductile fracture and large deformations.

Keywords: ductile damage model, finite element method, large strains, thermoviscoplasticity

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Authors: Kolade Olawande Owoeye, George R. S. Weir

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The widespread of terrorism and extremism activities on the internet has become a major threat to the government and national securities due to their potential dangers which have necessitated the need for intelligence gathering via web and real-time monitoring of potential websites for extremist activities. However, the manual classification for such contents is practically difficult or time-consuming. In response to this challenge, an automated classification system called composite technique was developed. This is a computational framework that explores the combination of both semantics and syntactic features of textual contents of a web. We implemented the framework on a set of extremist webpages dataset that has been subjected to the manual classification process. Therein, we developed a classification model on the data using J48 decision algorithm, this is to generate a measure of how well each page can be classified into their appropriate classes. The classification result obtained from our method when compared with other states of arts, indicated a 96% success rate in classifying overall webpages when matched against the manual classification.

Keywords: extremist, web pages, classification, semantics, posit

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Authors: Baptiste Bouvier

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While we are constantly flooded with stimuli in daily life, attention allows us to select the ones we specifically process and ignore the others. Some salient stimuli may sometimes pass this filter independently of our will, in a "bottom-up" way. The role of the acoustic properties of the timbre of a sound on its salience, i.e., its ability to capture the attention of a listener, is still not well understood. We implemented a paradigm called the "additional singleton paradigm", in which participants have to discriminate targets according to their duration. This task is perturbed (higher error rates and longer response times) by the presence of an irrelevant additional sound, of which we can manipulate a feature of our choice at equal loudness. This allows us to highlight the influence of the timbre features of a sound stimulus on its salience at equal loudness. We have shown that a stimulus that is brighter than the others but not louder leads to an attentional capture phenomenon in this framework. This work opens the door to the study of the influence of any timbre feature on salience.

Keywords: attention, audition, bottom-up attention, psychoacoustics, salience, timbre

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Authors: Butta Singh

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This paper presents a chaotic map based approach for secured embedding of patient’s confidential data in electrocardiogram (ECG) signal. The chaotic map generates predefined locations through the use of selective control parameters. The sample value difference method effectually hides the confidential data in ECG sample pairs at these predefined locations. Evaluation of proposed method on all 48 records of MIT-BIH arrhythmia ECG database demonstrates that the embedding does not alter the diagnostic features of cover ECG. The secret data imperceptibility in stego-ECG is evident through various statistical and clinical performance measures. Statistical metrics comprise of Percentage Root Mean Square Difference (PRD) and Peak Signal to Noise Ratio (PSNR). Further, a comparative analysis between proposed method and existing approaches was also performed. The results clearly demonstrated the superiority of proposed method.

Keywords: chaotic maps, ECG steganography, data embedding, electrocardiogram

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Authors: Godwin Dennison, Edwin Cooper, George Theobald, Richard Dalton

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We report an unusual case of giant filiform polyposis in a patient with ulcerative colitis, causing a large stricture in the colon. A 62-year-old man was referred to the Bowel Cancer Screening Programme with a positive Faecal Immunochemical Test (FIT). He was known to have UC for 30 years. A CT scan showed a 9 cm stricture in the transverse colon suspicious of malignancy. A colonoscopy was attempted three times, and biopsies confirmed features of ulcerative colitis. A laparoscopic assisted transverse colectomy (Left hemicolectomy) was performed, and the histology revealed giant filiform polyposis. This should be considered in a UC patient presenting with signs of obstruction mimicking a carcinoma. Whilst it is a benign condition, because of the size of the lesion, it often causes obstruction, and surgery is indicated to relieve symptoms.

Keywords: giant inflammatory polyposis, filiform polyposis, ulcerative colitis, inflammatory bowel disease

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Authors: B. Kishore Kumar, Rakesh Pogula, T. Kishore Kumar

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The steepness of an audio signal which is produced by the musical instruments, specifically percussive instruments is the perception of how high tone or low tone which can be considered as a frequency closely related to the fundamental frequency. This paper presents a novel method for silence removal and segmentation of music signals produced by the percussive instruments and the performance of proposed method is studied with the help of MATLAB simulations. This method is based on two simple features, namely the signal energy and the spectral centroid. As long as the feature sequences are extracted, a simple thresholding criterion is applied in order to remove the silence areas in the sound signal. The simulations were carried on various instruments like drum, flute and guitar and results of the proposed method were analyzed.

Keywords: percussive instruments, spectral energy, spectral centroid, silence removal

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Authors: Dechao Meng, Yongqi Dong, Qiyuan Feng, Zhangzhang Cui, Xiang Hu, Haoliang Huang, Genhao Liang, Huanhua Wang, Hua Zhou, Hawoong Hong, Jinghua Guo, Qingyou Lu, Xiaofang Zhai, Yalin Lu

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Great efforts have been taken to reveal the intrinsic origins of emerging ferromagnetism (FM) in strained LaCoO₃ (LCO) films. However, some macro magnetic performances of LCO are still not well understood and even controversial, such as magnetic anisotropy. Determining and understanding magnetic anisotropy might help to find the true causes of FM in turn. Perpendicular magnetic anisotropy (PMA) was the first time to be directly observed in high-quality LCO films with different thickness. The in-plane (IP) and out of plane (OOP) remnant magnetic moment ratio of 30 unit cell (u.c.) films is as large as 20. The easy axis lays in the OOP direction with an IP/OOP coercive field ratio of 10. What's more, the PMA could be simply tuned by changing the thickness. With the thickness increases, the IP/OOP magnetic moment ratio remarkably decrease with magnetic easy axis changing from OOP to IP. Such a huge and tunable PMA performance exhibit strong potentials in fundamental researches or applications. What causes PMA is the first concern. More OOP orbitals occupation may be one of the micro reasons of PMA. A cluster-like magnetic domain pattern was found in 30 u.c. with no obvious color contrasts, similar to that of LaAlO₃/SrTiO₃ films. And the nanosize domains could not be totally switched even at a large OOP magnetic field of 23 T. It indicates strong IP characters or none OOP magnetism of some clusters. The IP magnetic domains might influence the magnetic performance and help to form PMA. Meanwhile some possible nonmagnetic clusters might be the reason why the measured moments of LCO films are smaller than the calculated values 2 μB/Co, one of the biggest confusions in LCO films.What tunes PMA seems much more interesting. Totally different magnetic domain patterns were found in 180 u.c. films with cluster magnetic domains surrounded by < 110 > cross-hatch lines. These lines were regarded as structure domain walls (DWs) determined by 3D reciprocal space mapping (RSM). Two groups of in-plane features with fourfold symmetry were observed near the film diffraction peaks in (002) 3D-RSM. One is along < 110 > directions with a larger intensity, which is well match the lines on the surfaces. The other is much weaker and along < 100 > directions, which is from the normal lattice titling of films deposited on cubic substrates. The < 110 > domain features obtained from (103) and (113) 3D-RSMs exhibit similar evolution of the DWs percentages and magnetic behavior. Structure domains and domain walls are believed to tune PMA performances by transform more IP magnetic moments to OOP. Last but not the least, thick films with lots of structure domains exhibit different electrical transport behaviors. A metal-to-insulator transition (MIT) and an angular dependent negative magnetic resistivity were observed near 150 K, higher than FM transition temperature but similar to that of spin-orbital coupling related 1/4 order diffraction peaks.

Keywords: structure domain, magnetic anisotropy, magnetic domain, domain wall, 3D-RSM, strain

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Authors: Tahraoui Boualem

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Researching the historical framework of international crime means examining the historical facts that have contributed to uncovering this serious crime affecting international interests, and the law by which the study of the subject of international crime is determined is international criminal law, which is a branch of public international law. In this context, the historical study of international crime means recognizing the existence of an international community governed by international law, which makes us acknowledge that ancient societies lacked such stable and recurring international relations. Therefore, an attempt to monitor international crime in those ancient societies is only to demonstrate a historical fact that those societies have known some features of this crime, and have contributed in one way or another to the development of international criminal law without defining its concept or legal nature. The international community has affirmed the principle of establishing peace, achieving security, and respecting human rights. As a basis for friendly relations between the people of the international community and in case of prejudice, such as the aggressors breaching the obligations imposed on them, whether in time of peace or war.

Keywords: historical framework, of international crime, peace or war., international law

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Authors: Sheeraz Memon, Namunu C. Maddage, Margaret Lech, Nicholas Allen

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The effect of a clinical environment on the accuracy of the speaker verification was tested. The speaker verification tests were performed within homogeneous environments containing clinically depressed speakers only, and non-depresses speakers only, as well as within mixed environments containing different mixtures of both climatically depressed and non-depressed speakers. The speaker verification framework included the MFCCs features and the GMM modeling and classification method. The speaker verification experiments within homogeneous environments showed 5.1% increase of the EER within the clinically depressed environment when compared to the non-depressed environment. It indicated that the clinical depression increases the intra-speaker variability and makes the speaker verification task more challenging. Experiments with mixed environments indicated that the increase of the percentage of the depressed individuals within a mixed environment increases the speaker verification equal error rates.

Keywords: speaker verification, GMM, EM, clinical environment, clinical depression

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Authors: Amit Tribedi

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Genuine Quantumness present in Quantum Systems is the resource for implementing Quantum Information and Computation Protocols which can outperform the classical counterparts. These Quantumness measures encompass non-local ones known as quantum entanglement (QE) and quantum information theoretic (QIT) ones, e.g. Quantum Discord (QD). In this paper, some well-known measures of QE and QD in some wheel-like frustrated molecular magnetic systems have been studied. One of the systems has already been synthesized using coordination chemistry, and the other is hypothetical, where the dominant interaction is the spin-spin exchange interaction. Exact analytical methods and exact numerical diagonalization methods have been used. Some counter-intuitive non-trivial features, like non-monotonicity of quantum correlations with temperature, persistence of multipartite entanglement over bipartite ones etc. indicated by the behaviour of the correlations and the QIT measures have been found. The measures, being operational ones, can be used to realize the resource of Quantumness in experiments.

Keywords: 0D Magnets, discord, entanglement, frustration

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Saeedeh Barzegar Khalilsaraei, Manoocheher Kelarestaghi, Farshad Eshghi

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The vehicle color, as a prominent and stable feature, helps to identify a vehicle more accurately. As a result, vehicle color recognition is of great importance in intelligent transportation systems. Unlike conventional methods which use only a single Convolutional Neural Network (CNN) for feature extraction or classification, in this paper, four CNNs, with different architectures well-performing in different classes, are trained to extract various features from the input image. To take advantage of the distinct capability of each network, the multiple outputs are combined using a stack generalization algorithm as an ensemble technique. As a result, the final model performs better than each CNN individually in vehicle color identification. The evaluation results in terms of overall average accuracy and accuracy variance show the proposed method’s outperformance compared to the state-of-the-art rivals.

Keywords: Vehicle Color Recognition, Ensemble Algorithm, Stack Generalization, Convolutional Neural Network

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Authors: Carvalho M. Catia, Pinto R. Isabel, Azevedo F. Luis, Guerreiro, T. Alexandre, Barbosa R. Mariana, Pinto S. Marta

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Due to the loss of territory, foreign terrorist fighters who have joined Islamic State are returning to their home countries. In order to counter this threat to international security, it is important to implement deradicalisation programmes, through strategies and processes that can reverse radicalisation. The objectives of this scoping review - which is underway - are to provide a comprehensive overview of the programmes being implemented, its main characteristics, the main motives and processes leading to deradicalisation, and to identify the key findings and the existing gaps in the literature. The methodology to be implemented in this scoping review follows the guidelines proposed by Arksey and O’Malley and by The Joanna Briggs Institute. The main results will be the development of a synthesis map of the deradicalisation programmes existing in the world, its main features, and recommendations to policy-makers and professionals.

Keywords: deradicalisation strategies, psychosocial processes, radicalisation, terrorism

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Authors: Kashif Laeeq, Zubair A. Shaikh

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The paradigm of education is drastically changing from conventional to e-learning model. Due to ease of learning with various other benefits, several educational institutions are adopting the e-learning models. Some institutions are still willing to transform their educational system on to e-learning, but due to limited resources, they are still compromising on the old traditional system. The cloud computing could be one of the best solutions to overcome this problem by providing hardware, software, and infrastructure resources with cost efficient manner. The adoption of cloud computing in education will bring revolution in this paradigm. This paper introduces various positive features of e-learning and presents a way how cloud computing technology can be provisioned e-learning model. This paper also investigates the numerous challenges and opportunities that would be observed in cloud computing adoption in e-learning domain. The concept and knowledge present in this paper may create a new direction of research in the domain of cloud-based e-learning.

Keywords: cloud-based e-learning, e-learning, cloud computing application, smart learning

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Authors: Kwangmin Joo

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Automatic detection of lumbar vertebrae and classification method is proposed for evaluating the degree of disc narrowing. Prior to classification, deep learning based segmentation is applied to detect individual lumbar vertebra. M-net is applied to segment five lumbar vertebrae and fine-tuning segmentation is employed to improve the accuracy of segmentation. Using the features extracted from previous step, clustering technique, k-means clustering, is applied to estimate the degree of disc space narrowing under four grade scoring system. As preliminary study, techniques proposed in this research could help building an automatic scoring system to diagnose the severity of disc narrowing from X-ray images.

Keywords: Disc space narrowing, Degenerative disc disorders, Deep learning based segmentation, Clustering technique

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Authors: Alexander Babin, Leonid Savin

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In the present paper, increasing of energy efficiency of a thrust hybrid bearing with a central feeding chamber is considered. The mathematical model was developed to determine the pressure distribution and the reaction forces, based on the Reynolds equation and static characteristics’ equations. The boundary problem of pressure distribution calculation was solved using the method of finite differences. For various types of lubricants, geometry and operational characteristics, axial gaps can be determined, where the minimal friction coefficient is provided. The next part of the study considers the application of servovalves in order to maintain the desired position of the rotor. The report features the calculation results and the analysis of the influence of the operational and geometric parameters on the energy efficiency of mechatronic fluid-film bearings.

Keywords: active bearings, energy efficiency, mathematical model, mechatronics, thrust multipad bearing

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Authors: Ayyaz Hussain, Tariq Sadad

Abstract:

Breast cancer (BC) is one of the widespread ailments among females globally. The early prognosis of BC can decrease the mortality rate. Exact findings of benign tumors can avoid unnecessary biopsies and further treatments of patients under investigation. However, due to variations in images, it is a tough job to isolate cancerous cases from normal and benign ones. The machine learning technique is widely employed in the classification of BC pattern and prognosis. In this research, a deep convolution neural network (DCNN) called AlexNet architecture is employed to get more discriminative features from breast tissues. To achieve higher accuracy, K-nearest neighbor (KNN) classifiers are employed as a substitute for the softmax layer in deep learning. The proposed model is tested on a widely used breast image database called MIAS dataset for experimental purposes and achieved 99% accuracy.

Keywords: breast cancer, DCNN, KNN, mammography

Procedia PDF Downloads 135