Search results for: common vector approach

Authors: Rohit Gupta, Ravinder Agarwal

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Surface electromyographic (sEMG) signal has the potential to identify the human activities and intention. This potential is further exploited to control the artificial limbs using the sEMG signal from residual limbs of amputees. The paper deals with the development of multichannel cost efficient sEMG signal interface for research application, along with evaluation of proposed class dependent statistical approach of the feature selection method. The sEMG signal acquisition interface was developed using ADS1298 of Texas Instruments, which is a front-end interface integrated circuit for ECG application. Further, the sEMG signal is recorded from two lower limb muscles for three locomotions namely: Plane Walk (PW), Stair Ascending (SA), Stair Descending (SD). A class dependent statistical approach is proposed for feature selection and also its performance is compared with 12 preexisting feature vectors. To make the study more extensive, performance of five different types of classifiers are compared. The outcome of the current piece of work proves the suitability of the proposed feature selection algorithm for locomotion recognition, as compared to other existing feature vectors. The SVM Classifier is found as the outperformed classifier among compared classifiers with an average recognition accuracy of 97.40%. Feature vector selection emerges as the most dominant factor affecting the classification performance as it holds 51.51% of the total variance in classification accuracy. The results demonstrate the potentials of the developed sEMG signal acquisition interface along with the proposed feature selection algorithm.

Keywords: classifiers, feature selection, locomotion, sEMG

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Authors: Wang Xingbo

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Computation of determinant in the form |I-X| is primary and fundamental because it can help to compute many other determinants. This article puts forward a time-reducible approach to compute determinant |I-X|. The approach is derived from the Newton’s identity and its time complexity is no more than that to compute the eigenvalues of the square matrix X. Mathematical deductions and numerical example are presented in detail for the approach. By comparison with classical approaches the new approach is proved to be superior to the classical ones and it can naturally reduce the computational time with the improvement of efficiency to compute eigenvalues of the square matrix.

Keywords: algorithm, determinant, computation, eigenvalue, time complexity

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Authors: Ciro Cursio, Giulia Cursio, Pio Luigi Cursio, Luigi Cursio

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Communication between practitioner and patient is of the utmost importance in aesthetic medicine: as of today, images of previous treatments are the most common tool used by doctors to describe and anticipate future results for their patients. However, using photos of other people often reduces the engagement of the prospective patient and is further limited by the number and quality of pictures available to the practitioner. Pre-existing work solves this issue in two ways: 3D scanning of the area with manual editing of the 3D model by the doctor or automatic prediction of the treatment by warping the image with hand-written parameters. The first approach requires the manual intervention of the doctor, while the second approach always generates results that aren’t always realistic. Thus, in one case, there is significant manual work required by the doctor, and in the other case, the prediction looks artificial. We propose an AI-based algorithm that autonomously generates a realistic prediction of treatment results. For the purpose of this study, we focus on hyaluronic acid treatments in the facial area. Our approach takes into account the individual characteristics of each face, and furthermore, the prediction system allows the patient to decide which area of the face she wants to modify. We show that the predictions generated by our system are realistic: first, the quality of the generated images is on par with real images; second, the prediction matches the actual results obtained after the treatment is completed. In conclusion, the proposed approach provides a valid tool for doctors to show patients what they will look like before deciding on the treatment.

Keywords: prediction, hyaluronic acid, treatment, artificial intelligence

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Authors: Nadia el Atlas, Mohammed el Aroussi, Mohammed Wahbi

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In this paper a novel technique of mass characterization based on robust features-fusion is presented. The proposed method consists of mainly four stages: (a) the first phase involves segmenting the masses using edge information’s. (b) The second phase is to calculate and fuse the most relevant morphological features. (c) The last phase is the classification step which allows us to classify the images into benign and malignant masses. In this step we have implemented Support Vectors Machines (SVM) and Artificial Neural Networks (ANN), which were evaluated with the following performance criteria: confusion matrix, accuracy, sensitivity, specificity, receiver operating characteristic ROC, and error histogram. The effectiveness of this new approach was evaluated by a recently developed database: INBREAST database. The fusion of the most appropriate morphological features provided very good results. The SVM gives accuracy to within 64.3%. Whereas the ANN classifier gives better results with an accuracy of 97.5%.

Keywords: breast cancer, mammography, CAD system, features, fusion

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Authors: Adrian Florin Busu

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The communicative approach, or communicative language teaching, used for enhancing communicative skills in students in automatics is a modern teaching approach based on the concept of learning a language through having to communicate real meaning. In the communicative approach, real communication is both the objective of learning and the means through which it takes place. This approach was initiated during the 1970’s and quickly became prominent, as it proposed an alternative to the previous systems-oriented approaches. In other words, instead of focusing on the acquisition of grammar and vocabulary, the communicative approach aims at developing students’ competence to communicate in the target language with an enhanced focus on real-life situations. To put it in an nutshell, CLT considers using the language to be just as important as actually learning the language.

Keywords: communication, approach, objective, learning

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Authors: Peggy Näser

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Factory planning is a systematic, objective-oriented process for planning a factory, structured into a sequence of phases, each of which is dependent on the preceding phase and makes use of particular methods and tools, and extending from the setting of objectives to the start of production. The digital factory, on the other hand, is the generic term for a comprehensive network of digital models, methods, and tools – including simulation and 3D visualisation – integrated by a continuous data management system. Its aim is the holistic planning, evaluation and ongoing improvement of all the main structures, processes and resources of the real factory in conjunction with the product. Digital factory planning has already become established in factory planning. The application of Building Information Modeling has not yet been established in factory planning but has been used predominantly in the planning of public buildings. Furthermore, this concept is limited to the planning of the buildings and does not include the planning of equipment of the factory (machines, technical equipment) and their interfaces to the building. BIM is a cooperative method of working, in which the information and data relevant to its lifecycle are consistently recorded, managed and exchanged in a transparent communication between the involved parties on the basis of digital models of a building. Both approaches, the planning approach of Building Information Modeling and the methodical approach of the Digital Factory, are based on the use of a comprehensive data model. Therefore it is necessary to examine how the approach of Building Information Modeling can be extended in the context of factory planning in such a way that an integration of the equipment planning, as well as the building planning, can take place in a common digital model. For this, a number of different perspectives have to be investigated: the equipment perspective including the tools used to implement a comprehensive digital planning process, the communication perspective between the planners of different fields, the legal perspective, that the legal certainty in each country and the quality perspective, on which the quality criteria are defined and the planning will be evaluated. The individual perspectives are examined and illustrated in the article. An approach model for the integration of factory planning into the BIM approach, in particular for the integrated planning of equipment and buildings and the continuous digital planning is developed. For this purpose, the individual factory planning phases are detailed in the sense of the integration of the BIM approach. A comprehensive software concept is shown on the tool. In addition, the prerequisites required for this integrated planning are presented. With the help of the newly developed approach, a better coordination between equipment and buildings is to be achieved, the continuity of the digital factory planning is improved, the data quality is improved and expensive implementation errors are avoided in the implementation.

Keywords: building information modeling, digital factory, digital planning, factory planning

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Authors: Degale Desta, Cheng Jian

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The most important factor in identifying a person is their face. Even identical twins have their own distinct faces. As a result, identification and face recognition are needed to tell one person from another. A face recognition system is a verification tool used to establish a person's identity using biometrics. Nowadays, face recognition is a common technique used in a variety of applications, including home security systems, criminal identification, and phone unlock systems. This system is more secure because it only requires a facial image instead of other dependencies like a key or card. Face detection and face identification are the two phases that typically make up a human recognition system.The idea behind designing and creating a face recognition system using deep learning with Azure ML Python's OpenCV is explained in this paper. Face recognition is a task that can be accomplished using deep learning, and given the accuracy of this method, it appears to be a suitable approach. To show how accurate the suggested face recognition system is, experimental results are given in 98.46% accuracy using Fast-RCNN Performance of algorithms under different training conditions.

Keywords: deep learning, face recognition, identification, fast-RCNN

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Authors: Apirak Sombat, Teerapol Saleewong, Poom Kumam, Parin Chaipunya, Wiyada Kumam, Anantachai Padcharoen, Yeol Je Cho, Thana Sutthibutpong

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This research is aimed to study a two-step iteration process defined over a finite family of σ-asymptotically quasi-nonexpansive nonself-mappings. The strong convergence is guaranteed under the framework of Banach spaces with some additional structural properties including strict and uniform convexity, reflexivity, and smoothness assumptions. With similar projection technique for nonself-mapping in Hilbert spaces, we hereby use the generalized projection to construct a point within the corresponding domain. Moreover, we have to introduce the use of duality mapping and its inverse to overcome the unavailability of duality representation that is exploit by Hilbert space theorists. We then apply our results for σ-asymptotically quasi-nonexpansive nonself-mappings to solve for ideal efficiency of vector optimization problems composed of finitely many objective functions. We also showed that the obtained solution from our process is the closest to the origin. Moreover, we also give an illustrative numerical example to support our results.

Keywords: asymptotically quasi-nonexpansive nonself-mapping, strong convergence, fixed point, uniformly convex and uniformly smooth Banach space

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Authors: M. Kalyuga

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Prepositions have long been considered to be one of the major challenges for second language learners, since they have multiple uses that differ greatly from one language to another. The traditional approach to second language teaching supplies students with a list of uses of a preposition that they have to memorise and no explanation is provided. Contrary to the traditional grammar approach, the cognitive linguistic approach offers an explanation for the use of prepositions and provides strategies to comprehend and learn prepositions that would be otherwise seem obscure. The present paper demonstrates the use of the cognitive approach for the explanation of prepositions through the example of the Russian preposition 'за'. The paper demonstrates how various spatial and non-spatial uses of this preposition are linked together through metaphorical and metonymical mapping. The diversity of expressions with за is explained by the range of spatial scenes this preposition is associated with.

Keywords: language teaching, Russian, preposition 'за', cognitive approach

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Authors: Khodr Fakih

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The Ombudsman is a procedural mechanism that provides a different approach of dispute resolution. The ombudsman primarily deals with specific grievances from the public against governmental injustice and misconduct. The ombudsman theory is considered an important instrument to any democratic government. This is true since it improves the transparency of the governmental activities in a world in which executive power are rising. Many countries have adopted the concept of Ombudsman but under different terminologies. This paper will provide the different types of Ombudsman and the common activities/processes of fulfilling their mandates.

Keywords: administration, citizens, government, mediator, ombudsman, presidential mediator

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Authors: Ekaterina A. Redkina

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The necessity and importance of teaching professionally oriented content in English needs no proof nowadays. Consequently, the ability to share personal ESP teaching experience seems of great importance. This paper is based on the 8-year ESP and EFL teaching experience at the Moscow State Linguistic University, Moscow, Russia, and presents theoretical analysis of specifics, possible problems, and perspectives of teaching Psychology in English to Russian psychology-students. The paper concerns different issues that are common for different ESP classrooms, and familiar to different teachers. Among them are: designing ESP curriculum (for psychologists in this case), finding the balance between content and language in the classroom, main teaching principles (the 4 C’s), the choice of assessment techniques and teaching material. The main objective of teaching psychology in English to Russian psychology students is developing knowledge and skills essential for professional psychologists. Belonging to international professional community presupposes high-level content-specific knowledge and skills, high level of linguistic skills and cross-cultural linguistic ability and finally high level of professional etiquette. Thus, teaching psychology in English pursues 3 main outcomes, such as content, language and professional skills. The paper provides explanation of each of the outcomes. Examples are also given. Particular attention is paid to the lesson structure, its objectives and the difference between a typical EFL and ESP lesson. There is also made an attempt to find commonalities between teaching ESP and CLIL. There is an approach that states that CLIL is more common for schools, while ESP is more common for higher education. The paper argues that CLIL methodology can be successfully used in ESP teaching and that many CLIL activities are also well adapted for professional purposes. The research paper provides insights into the process of teaching psychologists in Russia, real teaching experience and teaching techniques that have proved efficient over time.

Keywords: ESP, CLIL, content, language, psychology in English, Russian students

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Authors: Ahmet Kayabasi, Ali Akdagli

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In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results.

Keywords: a-shaped compact microstrip antenna, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM)

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Authors: A. T. Kassem

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Fires areconsidered a common hazardous action that any building may face. Most buildings’ structural elements are designed, taking into consideration precautions for fire safety, using deterministic design approaches. Public and highly important buildings are commonly designed considering standard fire rating and, in many cases, contain large compartments with central domes. Real fire scenarios are not commonly brought into action in structural design of buildings because of complexities in both scenarios and analysis tools. This paper presents a modern approach towards analysis of spherical domes in real fire condition via implementation of building information modelling, and adopting a probabilistic approach. BIMhas been implemented to bridge the gap between various software packages enabling them to function interactively to model both real fire and corresponding structural response. Ventilation controlled fires scenarios have been modeled using both “Revit” and “Pyrosim”. Monte Carlo simulation has been adopted to engage the probabilistic analysis approach in dealing with various parameters. Conclusions regarding failure probability and fire endurance, in addition to the effects of various parameters, have been extracted.

Keywords: concrete, spherical domes, ventilation controlled fires, BIM, monte carlo simulation, pyrosim, revit

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Authors: Ashir Ahmed

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Generally, radicalization happens when a person's thinking and behaviour become significantly different from how most of the members of their society and community view social issues and participate politically. Radicalization often leads to violent extremism that refers to the beliefs and actions of people who support or use violence to achieve ideological, religious or political goals. Studies on radicalization negate the common myths that someone must be in a group to be radicalised or anyone who experiences radical thoughts is a violent extremist. Moreover, it is erroneous to suggest that radicalisation is always linked to religion. Generally, the common motives of radicalization include ideological, issue-based, ethno-nationalist or separatist underpinning. Moreover, there are number of factors that further augments the chances of someone being radicalised and may choose the path of violent extremism and possibly terrorism. Since there are numbers of factors (and sometimes quite different) contributing in radicalization and violent extremism, it is highly unlikely to devise a single solution that could produce effective outcomes to deal with radicalization, violent extremism and terrorism. The pathway to deradicalization, like the pathway to radicalisation, is different for everyone. Considering the need of having customized deradicalization resolution, this study proposes a multi-tier framework, called ‘pyramid of deradicalization’ that first help identifying the stage at which an individual could be on the radicalization pathway and then propose a customize strategy to deal with the respective stage. The first tier (tier 1) addresses broader community and proposes a ‘universal approach’ aiming to offer community-based design and delivery of educational programs to raise awareness and provide general information on possible factors leading to radicalization and their remedies. The second tier focuses on the members of community who are more vulnerable and are disengaged from the rest of the community. This tier proposes a ‘targeted approach’ targeting the vulnerable members of the community through early intervention such as providing anonymous help lines where people feel confident and comfortable in seeking help without fearing the disclosure of their identity. The third tier aims to focus on people having clear evidence of moving toward extremism or getting radicalized. The people falls in this tier are believed to be supported through ‘interventionist approach’. The interventionist approach advocates the community engagement and community-policing, introducing deradicalization programmes to the targeted individuals and looking after their physical and mental health issues. The fourth and the last tier suggests the strategies to deal with people who are actively breaking the law. ‘Enforcement approach’ suggests various approaches such as strong law enforcement, fairness and accuracy in reporting radicalization events, unbiased treatment by law based on gender, race, nationality or religion and strengthen the family connections.It is anticipated that the operationalization of the proposed framework (‘pyramid of deradicalization’) would help in categorising people considering their tendency to become radicalized and then offer an appropriate strategy to make them valuable and peaceful members of the community.

Keywords: deradicalization, framework, terrorism, violent extremism

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Authors: Sultan Mahmud, Qamar Zaman, Travis Esau, Young Chang

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Strawberry powdery mildew (PM) is a serious disease that has a significant impact on strawberry production. Field scouting is still a major way to find PM disease, which is not only labor intensive but also almost impossible to monitor disease severity. To reduce the loss caused by PM disease and achieve faster automatic detection of the disease, this paper proposes an approach for detection of the disease, based on image texture and classified with support vector machines (SVMs) and k-nearest neighbors (kNNs). The methodology of the proposed study is based on image processing which is composed of five main steps including image acquisition, pre-processing, segmentation, features extraction and classification. Two strawberry fields were used in this study. Images of healthy leaves and leaves infected with PM (Sphaerotheca macularis) disease under artificial cloud lighting condition. Colour thresholding was utilized to segment all images before textural analysis. Colour co-occurrence matrix (CCM) was introduced for extraction of textural features. Forty textural features, related to a physiological parameter of leaves were extracted from CCM of National television system committee (NTSC) luminance, hue, saturation and intensity (HSI) images. The normalized feature data were utilized for training and validation, respectively, using developed classifiers. The classifiers have experimented with internal, external and cross-validations. The best classifier was selected based on their performance and accuracy. Experimental results suggested that SVMs classifier showed 98.33%, 85.33%, 87.33%, 93.33% and 95.0% of accuracy on internal, external-I, external-II, 4-fold cross and 5-fold cross-validation, respectively. Whereas, kNNs results represented 90.0%, 72.00%, 74.66%, 89.33% and 90.3% of classification accuracy, respectively. The outcome of this study demonstrated that SVMs classified PM disease with a highest overall accuracy of 91.86% and 1.1211 seconds of processing time. Therefore, overall results concluded that the proposed study can significantly support an accurate and automatic identification and recognition of strawberry PM disease with SVMs classifier.

Keywords: powdery mildew, image processing, textural analysis, color co-occurrence matrix, support vector machines, k-nearest neighbors

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Authors: Rotondwa P. Gunununu, Mustapha Mohammed, Felix D. Dakora

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Common bean is the most importantly high protein grain legume grown in Southern Africa for human consumption and income generation. Although common bean can associate with rhizobia to fix N₂ for bacterial use and plant growth, it is reported to be a poor nitrogen fixer when compared to other legumes. N₂ fixation can vary with legume species, genotype and rhizobial strain. Therefore, screening legume germplasm can reveal rhizobia/genotype combinations with high N₂-fixing efficiency for use by farmers. This study assessed symbiotic performance and N₂ fixation in 63 common bean genotypes under field conditions at Malkerns Station in Eswatini, using the ¹⁵N natural abundance technique. The shoots of common bean genotypes were sampled at a pod-filling stage, oven-dried (65oC for 72h), weighed, ground into a fine powder (0.50 mm sieve), and subjected to ¹⁵N/¹⁴N isotopic analysis using mass spectrometry. At maturity, plants from the inner rows were harvested for the determination of grain yield. The results revealed significantly higher modulation (p≤0.05) in genotypes MCA98 and CIM-RM01-97-8 relative to the other genotypes. Shoot N concentration was highest in genotype MCA 98, followed by KAB 10 F2.8-84, with most genotypes showing shoot N concentrations below 2%. Percent N derived from atmospheric N₂ fixation (%Ndfa) differed markedly among genotypes, with CIM-RM01-92-3 and DAB 174, respectively, recording the highest values of 66.65% and 66.22 % N derived from fixation. There were also significant differences in grain yield, with CIM-RM02-79-1 producing the highest yield (3618.75 kg/ha). These results represent an important contribution in the profiling of symbiotic functioning of common bean germplasm for improved N₂ fixation.

Keywords: nitrogen fixation, %Ndfa, ¹⁵N natural abundance, grain yield

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Authors: Somojit Saha, Rohit K. Chatterjee, Sarit K. Das, Avijit Kar

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A new force field is designed for propagation of the parametric contour into deep narrow cortical fold in the application of knowledge based reconstruction of cerebral cortex from MR image of brain. Designing of this force field is highly inspired by the Generalized Gradient Vector Flow (GGVF) model and markedly differs in manipulation of image information in order to determine the direction of propagation of the contour. While GGVF uses edge map as its main driving force, the newly designed force field uses the map of distance between zero valued pixels and their nearest non-zero valued pixel as its main driving force. Hence, it is called Zero-Non-Zero Distance (ZNZD) force field. The objective of this force field is forceful propagation of the contour beyond spurious convergence due to partial volume effect (PVE) in to narrow sulcal fold. Being function of the corresponding non-zero pixel value, the force field has got an inherent property to determine spuriousness of the edge automatically. It is effectively applied along with some morphological processing in the application of cortical reconstruction to breach the hindrance of PVE in narrow sulci where conventional GGVF fails.

Keywords: deformable model, external force field, partial volume effect, cortical reconstruction, MR image of brain

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Authors: Ndibarafinia Young Tobin, Simon Burnett

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In spite of the benefits of implementing whole life costing technique as a valuable approach for comparing alternative building designs allowing operational cost benefits to be evaluated against any initial cost increases and also as part of procurement in the construction industry, its adoption has been relatively slow due to the lack of tangible evidence, ‘know-how’ skills and knowledge of the practice, i.e. the lack of professionals in many establishments with knowledge and training on the use of whole life costing technique, this situation is compounded by the absence of available data on whole life costing from relevant projects, lack of data collection mechanisms and so on. This has proved to be very challenging to those who showed some willingness to employ the technique in a construction project. The knowledge generated from a project can be considered as best practices learned on how to carry out tasks in a more efficient way, or some negative lessons learned which have led to losses and slowed down the progress of the project and performance. Knowledge management in whole life costing practice can enhance whole life costing analysis execution in a construction project, as lessons learned from one project can be carried on to future projects, resulting in continuous improvement, providing knowledge that can be used in the operation and maintenance phases of an assets life span. Purpose: The purpose of this paper is to report an effective approach which can be utilised in capturing knowledge in whole life costing practice in a construction project. Design/methodology/approach: An extensive literature review was first conducted on the concept of knowledge management and whole life costing. This was followed by a semi-structured interview to explore the existing and good practice knowledge management in whole life costing practice in a construction project. The data gathered from the semi-structured interview was analyzed using content analysis and used to structure an effective knowledge capturing approach. Findings: From the results obtained in the study, it shows that the practice of project review is the common method used in the capturing of knowledge and should be undertaken in an organized and accurate manner, and results should be presented in the form of instructions or in a checklist format, forming short and precise insights. The approach developed advised that irrespective of how effective the approach to knowledge capture, the absence of an environment for sharing knowledge, would render the approach ineffective. Open culture and resources are critical for providing a knowledge sharing setting, and leadership has to sustain whole life costing knowledge capture, giving full support for its implementation. The knowledge capturing approach has been evaluated by practitioners who are experts in the area of whole life costing practice. The results have indicated that the approach to knowledge capture is suitable and efficient.

Keywords: whole life costing, knowledge capture, project review, construction industry, knowledge management

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Authors: Mohammad Besharatloo

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Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the user's access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these sub-populations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context.

Keywords: intrusion detection system, differential evolution, firefly algorithm, support vector machine, decision tree

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Authors: Faheem Shahzad Baloch, Muhammad Azhar Nadeem, Muhammad Amjad Nawaz, Ephrem Habyarimana, Gonul Comertpay, Tolga Karakoy, Rustu Hatipoglu, Mehmet Zahit Yeken, Vahdettin Ciftci

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Turkey is considered a bridge between Europe, Asia, and Africa and possibly played an important role in the distribution of many crops including common bean. Hundreds of common bean landraces can be found in Turkey, particularly in farmers’ fields, and they consistently contribute to the overall production. To investigate the existing genetic diversity and hybridization events between the Andean and Mesoamerican gene pools in the Turkish common bean, 188 common bean accessions (182 landraces and 6 modern cultivars as controls) were collected from 19 different Turkish geographic regions. These accessions were characterized using phenotypic data (growth habit and seed weight), geographic provenance, 12557 high-quality whole-genome DArTseq markers, and 3767 novel DArTseq loci were also identified. The clustering algorithms resolved the Turkish common bean landrace germplasm into the two recognized gene pools, the Mesoamerican and Andean gene pools. Hybridization events were observed in both gene pools (14.36% of the accessions) but mostly in the Mesoamerican (7.97% of the accessions), and was low relative to previous European studies. The lower level of hybridization witnessed the existence of Turkish common bean germplasm in its original form as compared to Europe. Mesoamerican gene pool reflected a higher level of diversity, while the Andean gene pool was predominant (56.91% of the accessions), but genetically less diverse and phenotypically more pure, reflecting farmers greater preference for the Andean gene pool. We also found some genetically distinct landraces and overall, a meaningful level of genetic variability which can be used by the scientific community in breeding efforts to develop superior common bean strains.

Keywords: bean germplasm, DArTseq markers, genotyping by sequencing, Turkey, whole genome diversity

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Authors: E. Botchway, C. Godfrey, V. Anderson, C. Catroppa

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Introduction: Sleep disturbances are common after childhood traumatic brain injury (TBI). This systematic review aimed to assess the prevalence, types, and predictors of sleep disturbances in childhood TBI. Methods: Medline, Pubmed, PsychInfo, Web of Science, and EMBASE databases were searched. Out of the 547 articles assessed, 15 met selection criteria for this review. Results: Sleep disturbances were common in children and adolescents with TBI, irrespective of injury severity. Excessive daytime sleepiness and insomnia were the most common sleep disturbances reported. Sleep disturbance was predicted by sex, injury severity, pre-existing sleep disturbances, younger age, pain, and high body mass index. Conclusions: Sleep disturbances are highly prevalent in childhood TBI, regardless of the injury severity. Routine assessment of sleep in survivors of childhood TBI is recommended.

Keywords: traumatic brain injury, sleep diatiurbances, childhood, systematic review

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Authors: Edward Holupka, John Rossman, Tye Morancy, Joseph Aronovitz, Irving Kaplan

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A common modality for the treatment of early stage prostate cancer is the implantation of radioactive seeds directly into the prostate. The radioactive seeds are positioned inside the prostate to achieve optimal radiation dose coverage to the prostate. These radioactive seeds are positioned inside the prostate using Transrectal ultrasound imaging. Once all of the planned seeds have been implanted, two dimensional transaxial transrectal ultrasound images separated by 2 mm are obtained through out the prostate, beginning at the base of the prostate up to and including the apex. A common deep neural network, called DetectNet was trained to automatically determine the position of the implanted radioactive seeds within the prostate under ultrasound imaging. The results of the training using 950 training ultrasound images and 90 validation ultrasound images. The commonly used metrics for successful training were used to evaluate the efficacy and accuracy of the trained deep neural network and resulted in an loss_bbox (train) = 0.00, loss_coverage (train) = 1.89e-8, loss_bbox (validation) = 11.84, loss_coverage (validation) = 9.70, mAP (validation) = 66.87%, precision (validation) = 81.07%, and a recall (validation) = 82.29%, where train and validation refers to the training image set and validation refers to the validation training set. On the hardware platform used, the training expended 12.8 seconds per epoch. The network was trained for over 10,000 epochs. In addition, the seed locations as determined by the Deep Neural Network were compared to the seed locations as determined by a commercial software based on a one to three months after implant CT. The Deep Learning approach was within \strikeout off\uuline off\uwave off2.29\uuline default\uwave default mm of the seed locations determined by the commercial software. The Deep Learning approach to the determination of radioactive seed locations is robust, accurate, and fast and well within spatial agreement with the gold standard of CT determined seed coordinates.

Keywords: prostate, deep neural network, seed implant, ultrasound

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Authors: Steffy Maria Joseph, Abdu Rahiman V, Abdul Hameed K. M.

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Online handwritten character recognition is a challenging field in Artificial Intelligence. The classification success rate of current techniques decreases when the dataset involves similarity and complexity in stroke styles, number of strokes and stroke characteristics variations. Malayalam is a complex south indian language spoken by about 35 million people especially in Kerala and Lakshadweep islands. In this paper, we consider the significant feature extraction for the similar stroke styles of Malayalam. This extracted feature set are suitable for the recognition of other handwritten south indian languages like Tamil, Telugu and Kannada. A classification scheme based on support vector machines (SVM) is proposed to improve the accuracy in classification and recognition of online malayalam handwritten characters. SVM Classifiers are the best for real world applications. The contribution of various features towards the accuracy in recognition is analysed. Performance for different kernels of SVM are also studied. A graphical user interface has developed for reading and displaying the character. Different writing styles are taken for each of the 44 alphabets. Various features are extracted and used for classification after the preprocessing of input data samples. Highest recognition accuracy of 97% is obtained experimentally at the best feature combination with polynomial kernel in SVM.

Keywords: SVM, matlab, malayalam, South Indian scripts, onlinehandwritten character recognition

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Authors: Kong Ngai Pei

Abstract:

The argument scheme approach to argumentation has two components. One is to identify the recurrent patterns of inferences used in everyday discourse. The second is to devise critical questions to evaluate the inferences in these patterns. Although this approach is intuitive and contains many insightful ideas, it has been noted to be not free of problems. One is that due to its disavowing the probability calculus, it cannot give the exact strength of an inference. In order to tackle this problem, thereby paving the way to a more complete normative account of argument strength, it has been proposed, the most promising way is to combine the scheme-based approach with Bayesian networks (BNs). This paper pursues this line of thought, attempting to combine three common schemes, Appeal to Ignorance, Composition, and Division, with BNs. In the first part, it is argued that most (if not all) formulations of the critical questions corresponding to these schemes in the current argumentation literature are incomplete and not very informative. To remedy these flaws, more thorough and precise formulations of these questions are provided. In the second part, how to use graphical idioms (e.g. measurement and synthesis idioms) to translate the schemes as well as their corresponding critical questions to graphical structure of BNs, and how to define probability tables of the nodes using functions of various sorts are shown. In the final part, it is argued that many misuses of these schemes, traditionally called fallacies with the same names as the schemes, can indeed be adequately accounted for by the BN models proposed in this paper.

Keywords: appeal to ignorance, argument schemes, Bayesian networks, composition, division

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Authors: Rohini Hariharan, Yazhini R., Blessy Maria Mathew

Abstract:

The task of detecting email spam is a very important one in the era of digital technology that needs effective ways of curbing unwanted messages. This paper presents an approach aimed at making email spam categorization algorithms transparent, reliable and more trustworthy by incorporating Local Interpretable Model-agnostic Explanations (LIME). Our technique assists in providing interpretable explanations for specific classifications of emails to help users understand the decision-making process by the model. In this study, we developed a complete pipeline that incorporates LIME into the spam classification framework and allows creating simplified, interpretable models tailored to individual emails. LIME identifies influential terms, pointing out key elements that drive classification results, thus reducing opacity inherent in conventional machine learning models. Additionally, we suggest a visualization scheme for displaying keywords that will improve understanding of categorization decisions by users. We test our method on a diverse email dataset and compare its performance with various baseline models, such as Gaussian Naive Bayes, Multinomial Naive Bayes, Bernoulli Naive Bayes, Support Vector Classifier, K-Nearest Neighbors, Decision Tree, and Logistic Regression. Our testing results show that our model surpasses all other models, achieving an accuracy of 96.59% and a precision of 99.12%.

Keywords: text classification, LIME (local interpretable model-agnostic explanations), stemming, tokenization, logistic regression.

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Authors: M. Fayez Al Homsi, Reem Al Homsi

Abstract:

Renal disease is a major cause of morbidity and mortality. Glomerular diseases make a small portion of the renal disease. Lupus glomerulonephritis (GN) is the commonest among the GN of systemic diseases. More than a hundred and eighty-eight consecutive renal biopsies are performed and evaluated for clinically suspected glomerular diseases over a period of two years. As in a standard practice after receiving the ultrasound-guided renal biopsies, the fresh biopsy is divided to three parts, one part is frozen for immunofluorescence evaluation, the second part is placed in 4% glutaraldehyde for electron microscopic evaluation, and the third part is placed in 10% buffered formalin for light microscopic evaluation. Primary glomerular diseases are detected in 83 biopsies; glomerulonephritis (GN) of systemic diseases are identified in 88, glomerular lesions in vascular diseases in 3, glomerular lesions in metabolic diseases in 7, hereditary nephropathies in 2, end-stage kidney in 2, and glomerular lesions in transplantation in 3 biopsies. Among the primary lesions, focal segmental glomerulosclerosis (28) and mesangial proliferative GN (26) were the most common. Lupus GN (67) and Ig A nephropathy (20) were the most common of the GN of systemic diseases. Lupus nephritis biopsies included one biopsy diagnosed as class 1 (normal), 17 biopsies class 2 (mesangial proliferation), 5 biopsies class 3 (focal proliferative GN), 39 biopsies class 4 diffuse proliferative GN), 3 biopsies class 5 (membranous GN), and 2 biopsies class 6 (crescentic GN). Lupus GN is the most common among GN of systemic diseases. While diabetes is very common here, diabetic GN (3 biopsies) is not as common as might one expects. Most likely this is due to sampling and reluctance on part of nephrologists and patients in sampling the kidney in diabetes mellitus.

Keywords: diabetes, glomerulonephritis, lupus, mesangial proliferation, nephropathy

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Authors: Petra Palic, Maruska Vizek

Abstract:

We provide an in-depth analysis of interdependence of asset returns and volatilities in developed and developing countries. The analysis is split into three parts. In the first part, we use multivariate GARCH model in order to provide stylized facts on cross-market volatility spillovers. In the second part, we use a generalized vector autoregressive methodology developed by Diebold and Yilmaz (2009) in order to estimate separate measures of return spillovers and volatility spillovers among sovereign bond, equity, foreign exchange and commodity markets. In particular, our analysis is focused on cross-market return, and volatility spillovers in 19 developed and developing countries. In order to estimate named spillovers, we use daily data from 2008 to 2017. In the third part of the analysis, we use a generalized vector autoregressive framework in order to estimate total and directional volatility spillovers. We use the same daily data span for one developed and one developing country in order to characterize daily volatility spillovers across stock, bond, foreign exchange and commodities markets.

Keywords: cross-market spillovers, sovereign bond markets, equity markets, value at risk (VAR)

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Authors: Ashwini Mohan, Haris Batley

Abstract:

An orthodontic index is used to rate or categorise an individual’s occlusion using a numeric or alphanumeric score. Indexing of malocclusions and their correction is important in epidemiology, diagnosis, communication between clinicians as well as their patients and assessing treatment outcomes. Many useful indices have been put forward, but to the author’s best knowledge, no one method to this day appears to be equally suitable for the use of epidemiologists, public health program planners and clinicians. This article describes the common clinical orthodontic indices and classifications used in United Kingdom.

Keywords: classification, indices, orthodontics, validity

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Authors: Koon Weng Lau, Chee Dhang Chen, Abdul Aziz Azidah, Mohd Sofian-Azirun

Abstract:

Recently, Sarawak state of Malaysia encounter an outbreak of dengue fever. Aedes albopictus has incriminated as one of the important vectors of dengue transmission. Without an effective vaccine, approaches to control or prevent dengue will be a focus on the vectors. The control of Aedes mosquitoes is still dependent on the use of chemical insecticides and insecticide resistance represents a threat to the effectiveness of vector control. This study was conducted to determine the resistance status of 11 active ingredients representing four major insecticide classes: DDT, dieldrin, malathion, fenitrothion, bendiocarb, propoxur, etofenprox, deltamethrin, lambda-cyhalothrin, cyfluthrin, and permethrin. Standard WHO test procedures were conducted to determine the insecticide susceptibility. Aedes albopictus collected from Kapit (resistance ratio, RR = 1.04–3.02), Kuching (RR = 1.17–4.61), and Sibu (RR = 1.06–3.59) exhibited low resistance toward all insecticides except dieldrin. This study reveled that dieldrin is still effective against Ae. albopictus, followed by fenitrothion, cyfluthrin, and deltamethrin. In conclusion, Ae. albopictus in Sarawak exhibited different resistance levels toward various insecticides and alternative solutions should be implemented to prevent further deterioration of the condition.

Keywords: Aedes albopictus, dengue, insecticide resistance, Malaysia

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Authors: Tammy Shel (Aboody)

Abstract:

The integration of philosophy with ethnography, i.e., ethno-philosophy, or any qualitative method, is multi-dimensional. It is, thus, vital to the discourse on caring in the philosophy of education, and in therapy. These two significant dimensions are focal in this proposal’s discussion. The integration of grounded data with philosophy can shed light on cultural, gender, socio-economic and political diversities in the relationships and interactions between and among individuals and societies. This approach can explain miscommunication and, eventually, violent conflicts. The ethno-philosophy study in this proposal focuses on the term caring, through case studies of 5 non-white male and female elementary school teachers in Los Angeles County. The study examined the teachers’ views on caring and, consequently, the implications on their pedagogy. Subsequently, this method turned out to also be a caring approach in therapy. Ethnographic data was juxtaposed with western philosophy. Research discussion unraveled transformable gaps between western patriarchal and feminist philosophy on caring, and that of the teachers. Multiple interpretations and practices of caring were found due to cultural, gender, and socio-economic-political differences. Likewise, two dominant categories emerged. The first is inclusive caring, which is perceived as an ideal, as the compass of humanity that aims towards emancipation from the shackles of inner and external violence. The second is tribal caring, which illuminates the inherently dialectical substantial diversity in the interpretations and praxes of caring. Such angles are absent or minor in traditional western literature. Both categories teach of the incessant dynamic definition of caring, and its subliminal and repressed mechanisms. The multi-cultural aspects can teach us, however, that despite the inclusive common ground we share on caring, and despite personal and social awareness of cultural and gender differences, the hegemonic ruling-class governs the standardized conventional interpretation of caring. Second is the dimension of therapy in ethno-philosophy. Each patient is like a case study per se, and is a self-ethnographer. Thus, the patient is the self-observer and data collector, and the therapist is the philosopher who helps deconstruct into fragments the consciousness that comprises our well-being and self-esteem and acceptance. Together, they both identify and confront hurdles that hinder the pursuit of a more composed attitude towards ourselves and others. Together, they study and re-organize these fragments into a more comprehensible and composed self-acceptance. Therefore, the ethno-philosophy method, which stems from a caring approach, confronts the internal and external conflicts that govern our relationships with others. It sheds light on the dark and subliminal spots in our minds and hearts that operate us. Unveiling the hidden spots helps identify a shared ground that can supersede miscommunication and conflicts among and between people. The juxtaposition of ethnography with philosophy, as a caring approach in education and therapy, emphasizes that planet earth is like a web. Hence, despite the common mechanism that stimulates a caring approach towards the other, ethno-philosophy can help undermine the ruling patriarchal oppressive forces that define and standardize caring relationships, and to subsequently bridge gaps between people.

Keywords: caring, philosophy of education, ethnography, therapy, research

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