Search results for: heatmap visualization techniques

Authors: Nor Diana Ahmad, Eric Atwell, Brandon Bennett

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Text processing techniques for English have been developed for several decades. But for the Malay language, text processing methods are still far behind. Moreover, there are limited resources, tools for computational linguistic analysis available for the Malay language. Therefore, this research presents the use of natural language processing (NLP) in processing Malay translated Qur’an text. As the result, a new language resource for Malay translated Qur’an was created. This resource will help other researchers to build the necessary processing tools for the Malay language. This research also develops a simple question-answer prototype to demonstrate the use of the Malay Qur’an resource for text processing. This prototype has been developed using Python. The prototype pre-processes the Malay Qur’an and an input query using a stemming algorithm and then searches for occurrences of the query word stem. The result produced shows improved matching likelihood between user query and its answer. A POS-tagging algorithm has also been produced. The stemming and tagging algorithms can be used as tools for research related to other Malay texts and can be used to support applications such as information retrieval, question answering systems, ontology-based search and other text analysis tasks.

Keywords: language resource, Malay translated Qur'an, natural language processing (NLP), text processing

Procedia PDF Downloads 320

Authors: Hakim Aguedal, Abdelkader Iddou, Abdallah Aziz, Djillali Reda Merouani, Ferhat Bensaleh, Saleh Bensadek

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The textile industry produces high amount of colored effluent each year. The management or treatment of these discharges depends on the applied techniques. Adsorption is one of wastewater treatment techniques destined to treat this kind of pollution, and the performance and efficiency predominantly depend on the nature of the adsorbent used. Therefore, scientific research is directed towards the development of new materials using different physical and chemical treatments to improve their adsorption capacities. In the same perspective, we looked at the effect of the heat treatment on the effectiveness of diatomite, which is found in abundance in Algeria. The textile dye Orange Bezaktiv (SRL-150) which is used as organic pollutants in this study is provided by the textile company SOITEXHAM in Oran city (west Algeria). The effect of different physicochemical parameters on the adsorption of SRL-150 on natural and modified diatomite is studied, and the results of the kinetics and adsorption isotherms were modeled.

Keywords: wastewater treatment, diatomite, adsorption, dye pollution, kinetic, isotherm

Procedia PDF Downloads 280

Authors: Priyadarshini Kiran, Gulshan Kumar

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: Social media are becoming the most important tools for interaction among learners, pedagogues and parents where everybody can share, exchange, comment, discuss and create information and knowledge in a collaborative way. The present case study attempts to highlight the role of social media (WhatsApp) in nurturing learning habits in toddlers with the help of parents in primary education. The Case study is based on primary data collected from a primary school situated in a small town in the northern state of Uttar Pradesh, India. In research methodology, survey and structured interviews have been used as a tool collected from parents and pedagogues. The findings Suggest: - To nurture learning habits in toddlers, parents and pedagogues use social media site (WhatsApp) in real-time and that too is convenient and handy; - Skill enhancement on the part of Pedagogues as a result of employing innovative teaching-learning techniques; - Social media sites serve as a social connectivity tool to ward off negativity and monotony on the part of parents and pedagogues in the wake of COVID- 19

Keywords: innovative teaching-learning techniques, pedagogues, social media, nurture, toddlers

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Authors: Usman Ghani

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Feedback is a vital element for improving student learning in a simulation-based training as it guides and refines learning through scaffolding. A number of studies in literature have shown that students’ learning is enhanced when feedback is provided with personalized tutoring that offers specific guidance and adapts feedback to the learner in a one-to-one environment. Thus, emulating these adaptive aspects of human tutoring in simulation provides an effective methodology to train individuals. This paper presents the results of a study that investigated the effectiveness of automating different types of feedback techniques such as Knowledge-of-Correct-Response (KCR) and Answer-Until- Correct (AUC) in software simulation for learning basic information technology concepts. For the purpose of comparison, techniques like simulation with zero or no-feedback (NFB) and traditional hands-on (HON) learning environments are also examined. The paper presents the summary of findings based on quantitative analyses which reveal that the simulation based instructional strategies are at least as effective as hands-on teaching methodologies for the purpose of learning of IT concepts. The paper also compares the results of the study with the earlier studies and recommends strategies for using feedback mechanism to improve students’ learning in designing and simulation-based IT training.

Keywords: simulation, feedback, training, hands-on, labs

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Authors: Abdulkader Helwan

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Rheumatoid joint inflammation is characterized as a perpetual incendiary issue which influences the joints by hurting body tissues Therefore, there is an urgent need for an effective intelligent identification system of knee Rheumatoid arthritis especially in its early stages. This paper is to develop a new intelligent system for the identification of Rheumatoid arthritis of the knee utilizing image processing techniques and neural classifier. The system involves two principle stages. The first one is the image processing stage in which the images are processed using some techniques such as RGB to gryascale conversion, rescaling, median filtering, background extracting, images subtracting, segmentation using canny edge detection, and features extraction using pattern averaging. The extracted features are used then as inputs for the neural network which classifies the X-ray knee images as normal or abnormal (arthritic) based on a backpropagation learning algorithm which involves training of the network on 400 X-ray normal and abnormal knee images. The system was tested on 400 x-ray images and the network shows good performance during that phase, resulting in a good identification rate 97%.

Keywords: rheumatoid arthritis, intelligent identification, neural classifier, segmentation, backpropoagation

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Authors: Pratima Kumari, Sukha Ranjan Samadder

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This research aims to contribute to the sustainable production of graphene materials by exploring the extraction of graphite from spent primary and secondary batteries. The increasing demand for graphene materials, a versatile and high-performance material, necessitates environmentally friendly methods for its synthesis. The process involves a well-planned methodology, beginning with the gathering and categorization of batteries, followed by the disassembly and careful removal of graphite from anode structures. The use of environmentally friendly solvents and mechanical techniques ensures an efficient and eco-friendly extraction of graphite. Advanced approaches such as the modified Hummers' method and chemical reduction process are utilized for the synthesis of graphene materials, with a focus on optimizing parameters. Various analytical techniques such as Fourier-transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy, thermogravimetric analysis, and Raman spectroscopy were employed to validate the quality and structure of the produced graphene materials. The major findings of this study reveal the successful implementation of the methodology, leading to the production of high-quality graphene materials suitable for advanced material applications. Thorough characterization using various advanced techniques validates the structural integrity and purity of the graphene. The economic viability of the process is demonstrated through a comprehensive economic analysis, highlighting the potential for large-scale production. This research contributes to the field of sustainable production of graphene materials by offering a systematic methodology that efficiently transforms spent batteries into valuable graphene resources. Furthermore, the findings not only showcase the potential for upcycling electronic waste but also address the pressing need for environmentally conscious processes in advanced material synthesis.

Keywords: spent primary batteries, spent secondary batteries, graphite extraction, advanced material synthesis, circular economy approach

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Authors: O. Kahraman, M. S. Alatas, O. B. Citil

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In determination of values of feeds which, are used in ruminant nutrition, different methods are used like in vivo, in vitro, in situ or in sacco. Generally, the most reliable results are taken from the in vivo studies. But because of the disadvantages like being hard, laborious and expensive, time consuming, being hard to keep the experiment conditions under control and too much samples are needed, the in vitro techniques are more preferred. The most widely used in vitro techniques are two-staged digestion technique and gas production technique. In vitro gas production technique is based on the measurement of the CO2 which is released as a result of microbial fermentation of the feeds. In this review, the factors affecting the results obtained from in vitro gas production technique (Hohenheim Feed Test) were discussed. Some factors must be taken into consideration when interpreting the findings obtained in these studies and also comparing the findings reported by different researchers for the same feeds. These factors were discussed in 3 groups: factors related to animal, factors related to feeds and factors related with differences in the application of method. These factors and their effects on the results were explained. Also it can be concluded that the use of in vitro gas production technique in feed evaluation routinely can be contributed to the comprehensive feed evaluation, but standardization is needed in this technique to attain more reliable results.

Keywords: In vitro, gas production technique, Hohenheim feed test, standardization

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Authors: Abdelhadi Adam Salih Denei

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Silver nanoparticles (AgNPs) have been used in cancer therapy, and the area of nanomedicine has made unheard-of strides in recent years. A thorough summary of the development and assessment of AgNPs for their possible use in the fight against cancer is the goal of this review. Targeted delivery methods have been designed to optimise therapeutic efficacy by using AgNPs' distinct physicochemical features, such as their size, shape, and surface chemistry. Firstly, the study provides an overview of the several synthesis routes—both chemical and green—that are used to create AgNPs. Natural extracts and biomolecules are used in green synthesis techniques, which are becoming more and more popular since they are biocompatible and environmentally benign. It is next described how synthesis factors affect the physicochemical properties of AgNPs, emphasising how crucial it is to modify these parameters for particular therapeutic uses. An extensive analysis is conducted on the anticancer potential of AgNPs, emphasising their capacity to trigger apoptosis, impede angiogenesis, and alter cellular signalling pathways. The analysis also investigates the potential benefits of combining AgNPs with currently used cancer treatment techniques, including radiation and chemotherapy. AgNPs' safety profile for use in clinical settings is clarified by a comprehensive evaluation of their cytotoxicity and biocompatibility.

Keywords: silver nanoparticles, cancer, nanocarrier system, targeted delivery

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

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Authors: Aleksandra Zysk, Pawel Badura

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Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: classification, singing, spectral analysis, vocal emission, vocal register

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Authors: Rubén Valcarce-Álvarez, Francisco Jáñez-Martino, Rocío Alaiz-Rodríguez

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Cybersecurity challenges, including scams, drug sales, the distribution of child sexual abuse material, fake news, and hate speech on both the surface and deep web, have significantly increased over the past decade. Users who post such content often employ strategies to evade detection by automated filters. Among these tactics, text obfuscation plays an essential role in deceiving detection systems. This approach involves modifying words to make them more difficult for automated systems to interpret while remaining sufficiently readable for human users. In this work, we aim at spotting obfuscated words and the employed techniques, such as leetspeak, word inversion, punctuation changes, and mixed techniques. We benchmark Named Entity Recognition (NER) using models from the BERT family as well as two large language models (LLMs), Llama and Mistral, on XX_NER_WordCamouflage dataset. Our experiments evaluate these models by comparing their precision, recall, F1 scores, and accuracy, both overall and for each individual class.

Keywords: natural language processing (NLP), text obfuscation, named entity recognition (NER), deep learning

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Authors: Surajo Isa

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In as much as mathematics is important to national development, it is regrettable to note that in Nigeria Students academic achievement especially in public examinations remains poor. Among the several factors responsible for such a poor performance is the lack of bringing cultural elements into the conventional school mathematics. The design for this study is triangulation in nature which is set to examined 800 students From 20 School (40 each from male and female schools). Ten (10) male and ten (10) female schools consisting of 400 male and 400 female students to formed the experiment and control groups with a further sub-groping of samples to represent urban and rural settings for both male and female groups. While the experimental groups were taught using ethnomathematics techniques, the control groups were taught using conventional techniques, the results of a t-test for independent samples at p =0.05 level of significance with tcritical = 1.968 showed that (a) boys performed significantly better than girls (b) there is no significantly difference in performance between urban and rural girls (c) significant difference in academic performance was obtained between urban and rural boys. Generally, it was observed that teaching mathematics with ethnomathematics technique would help in great achievement in mathematics.

Keywords: ethnomathematics, achievement, gender, settlement

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Authors: A. Achour L. Chekour, A. Mekroud

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Tribology, the science of lubrication, friction and wear, plays an important role in science "crossroads" initiated by the recent developments in the industry. Its multidisciplinary nature reinforces its scientific interest. It covers all the sciences that deal with the contact between two solids loaded and relative motion. It is thus one of the many intersections more clearly established disciplines such as solid mechanics and the fluids, rheological, thermal, materials science and chemistry. As for his experimental approach, it is based on the physical and processing signals and images. The optimization of operating conditions by cutting tool must contribute significantly to the development and productivity of advanced automation of machining techniques because their implementation requires sufficient knowledge of how the process and in particular the evolution of tool wear. In addition, technological advances have developed the use of very hard materials, refractory difficult machinability, requiring highly resistant materials tools. In this study, we present the behavior wear a machining tool during the roughing operation according to the cutting parameters. The interpretation of the experimental results is based mainly on observations and analyzes of sharp edges e tool using the latest techniques: scanning electron microscopy (SEM) and optical rugosimetry laser beam.

Keywords: friction, wear, tool, cutting

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Authors: Derssie Mebratu

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As a large variety of micro services, web services, social graphic applications, and media applications are continuously developed, it is substantially vital to design and build a reliable, efficient, and faster memory tiering system. Despite limited design, implementation, and deployment in the last few years, several techniques are currently developed to improve a memory tiering system in a cloud. Some of these techniques are to develop an optimal scanning frequency; improve and track pages movement; identify pages that recently accessed; store pages across each tiering, and then identify pages as a hot, warm, and cold so that hot pages can store in the first tiering Dynamic Random Access Memory (DRAM) and warm pages store in the second tiering Compute Express Link(CXL) and cold pages store in the third tiering Non-Volatile Memory (NVM). Apart from the current proposal and implementation, we also develop a new technique based on a machine learning algorithm in that the throughput produced 25% improved performance compared to the performance produced by the baseline as well as the latency produced 95% improved performance compared to the performance produced by the baseline.

Keywords: machine learning, bayesian optimization, memory tiering, CXL, DRAM

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Authors: Layaly Ayoub, Mariana Ribeiro

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Introduction: Breast cancer is currently considered the leading cause of cancer-related deaths among women in Brazil. Mastectomy, essential in this treatment, often necessitates subsequent breast reconstruction to restore physical appearance and aid in the emotional and psychological recovery of patients. The choice between immediate or delayed reconstruction is influenced by factors such as the type and stage of cancer, as well as the patient's overall health. The decision between autologous breast reconstruction or implant-based reconstruction requires a detailed analysis of individual conditions and needs. Objectives: This study analyzes the techniques and indications used in post-mastectomy breast reconstruction. Methodology: Literature review conducted in the PubMed and SciELO databases, focusing on articles that met the inclusion and exclusion criteria and descriptors. Results: After mastectomy, breast reconstruction is commonly performed. It is necessary to determine the type of technique to be used in each case depending on the specific characteristics of each patient. The tissue expander technique is indicated for patients with sufficient skin and tissue post-mastectomy, who do not require additional radiotherapy, and who opt for a less complex surgery with a shorter recovery time. This procedure promotes the gradual expansion of soft tissues where the definitive implant will be placed. Both temporary and permanent expanders offer flexibility, allowing for adjustment in the expander size until the desired volume is reached, enabling the skin and tissues to adapt to the breast implant area. Conversely, autologous reconstruction is indicated for patients who will undergo radiotherapy, have insufficient tissue, and prefer a more natural solution. This technique uses the transverse rectus abdominis muscle (TRAM) flap, the latissimus dorsi muscle flap, the gluteal flap, and local muscle flaps to shape a new breast, potentially combined with a breast implant. Conclusion: In this context, it is essential to conduct a thorough evaluation regarding the technique to be applied, as both have their benefits and challenges.

Keywords: indications, post-mastectomy, breast reconstruction, techniques

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Authors: Mohammed Hasan Salahat, Stave Wade

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This paper presents an application of a “Systematic Soft Domain Driven Design Framework” as a soft systems approach to domain-driven design of information systems development. The framework combining techniques from Soft Systems Methodology (SSM), the Unified Modeling Language (UML), and an implementation pattern knows as ‘Naked Objects’. This framework have been used in action research projects that have involved the investigation and modeling of business processes using object-oriented domain models and the implementation of software systems based on those domain models. Within this framework, Soft Systems Methodology (SSM) is used as a guiding methodology to explore the problem situation and to develop the domain model using UML for the given business domain. The framework is proposed and evaluated in our previous works, and a real case study ‘Information Retrieval System for Academic Research’ is used, in this paper, to show further practice and evaluation of the framework in different business domain. We argue that there are advantages from combining and using techniques from different methodologies in this way for business domain modeling. The framework is overviewed and justified as multi-methodology using Mingers Multi-Methodology ideas.

Keywords: SSM, UML, domain-driven design, soft domain-driven design, naked objects, soft language, information retrieval, multimethodology

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: Ramoshweu Melvin Sethobya, Emmanuel Chirenje, Mihlali Hobo, Simon Sebothoma

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The recent surge in residential infrastructure development around the metropolitan areas of South Africa has necessitated conditions for thorough geotechnical assessments to be conducted prior to site developments to ensure human and infrastructure safety. This paper appraises the success in the application of multi-method geophysical techniques for the delineation of sinkhole vulnerability in a residential landscape. Geophysical techniques ERT, MASW, VES, Magnetics and gravity surveys were conducted to assist in mapping sinkhole vulnerability, using an existing sinkhole as a constraint at Venterspost town, West of Johannesburg city. A combination of different geophysical techniques and results integration from those proved to be useful in the delineation of the lithologic succession around sinkhole locality, and determining the geotechnical characteristics of each layer for its contribution to the development of sinkholes, subsidence and cavities at the vicinity of the site. Study results have also assisted in the determination of the possible depth extension of the currently existing sinkhole and the location of sites where other similar karstic features and sinkholes could form. Results of the ERT, VES and MASW surveys have uncovered dolomitic bedrock at varying depths around the sites, which exhibits high resistivity values in the range 2500-8000ohm.m and corresponding high velocities in the range 1000-2400 m/s. The dolomite layer was found to be overlain by a weathered chert-poor dolomite layer, which has resistivities between the range 250-2400ohm.m, and velocities ranging from 500-600m/s, from which the large sinkhole has been found to collapse/ cave in. A compiled 2.5D high resolution Shear Wave Velocity (Vs) map of the study area was created using 2D profiles of MASW data, offering insights into the prevailing lithological setup conducive for formation various types of karstic features around the site. 3D magnetic models of the site highlighted the regions of possible subsurface interconnections between the currently existing large sinkhole and the other subsidence feature at the site. A number of depth slices were used to detail the conditions near the sinkhole as depth increases. Gravity surveys results mapped the possible formational pathways for development of new karstic features around the site. Combination and correlation of different geophysical techniques proved useful in delineation of the site geotechnical characteristics and mapping the possible depth extend of the currently existing sinkhole.

Keywords: resistivity, magnetics, sinkhole, gravity, karst, delineation, VES

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Authors: Ling Liyun

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In the field of contemporary architecture, complex forms of architectural works continue to emerge in the world, along with some new terminology emerged: digital architecture, parametric design, algorithm generation, building information modeling, CNC construction and so on. Architects gradually mastered the new skills of mathematical logic in the form of exploration, virtual simulation, and the entire design and coordination in the construction process. Digital construction technology has a greater degree in controlling construction, and ensure its accuracy, creating a series of new construction techniques. As a result, the use of digital technology is an improvement and expansion of the practice of digital architecture design revolution. We worked by reading and analyzing information about the digital architecture development process, a large number of cases, as well as architectural design and construction as a whole process. Thus current developments were introduced and discussed in our paper, such as architectural discourse, design theory, digital design models and techniques, material selecting, as well as artificial intelligence space design. Our paper also pays attention to the representative three cases of digital design and construction experiment at great length in detail to expound high-informatization, high-reliability intelligence, and high-technique in constructing a humane space to cope with the rapid development of urbanization. We concluded that the opportunities and challenges of the shift existed in architectural paradigms, such as the cooperation methods, theories, models, technologies and techniques which were currently employed in digital design research and digital praxis. We also find out that the innovative use of space can gradually change the way people learn, talk, and control information. The past two decades, digital technology radically breaks the technology constraints of industrial technical products, digests the publicity on a particular architectural style (era doctrine). People should not adapt to the machine, but in turn, it’s better to make the machine work for users.

Keywords: artificial intelligence, collaboration, digital architecture, digital design theory, material selection, space construction

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Authors: Sertac Arslan, Sezer Kefeli

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In this paper, firstly supercavitating phenomenon and supercavity shape design parameters are explained and then drag force calculation methods of high speed supercavitating torpedoes are investigated with numerical techniques and verified with empirical studies. In order to reach huge speeds such as 200, 300 knots for underwater vehicles, hydrodynamic hull drag force which is proportional to density of water (ρ) and square of speed should be reduced. Conventional heavy weight torpedoes could reach up to ~50 knots by classic underwater hydrodynamic techniques. However, to exceed 50 knots and reach about 200 knots speeds, hydrodynamic viscous forces must be reduced or eliminated completely. This requirement revives supercavitation phenomena that could be implemented to conventional torpedoes. Supercavitation is the use of cavitation effects to create a gas bubble, allowing the torpedo to move at huge speed through the water by being fully developed cavitation bubble. When the torpedo moves in a cavitation envelope due to cavitator in nose section and solid fuel rocket engine in rear section, this kind of torpedoes could be entitled as Supercavitating Torpedoes. There are two types of cavitation; first one is natural cavitation, and second one is ventilated cavitation. In this study, disk cavitator is modeled with natural cavitation and supercavitation phenomenon parameters are studied. Moreover, drag force calculation is performed for disk shape cavitator with numerical techniques and compared via empirical studies. Drag forces are calculated with computational fluid dynamics methods and different empirical methods. Numerical calculation method is developed by comparing with empirical results. In verification study cavitation number (σ), drag coefficient (CD) and drag force (D), cavity wall velocity (U

Keywords: cavity envelope, CFD, high speed underwater vehicles, supercavitation, supercavity flows

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Authors: Zhang Xingnan, Huang Jingjia, Feng Yue, Burra Venkata Durga Kumar

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This paper proposes a comprehensive approach to mitigate ROP (Return-Oriented Programming) attacks by combining internal operating system protection mechanisms and hardware-assisted techniques. Through extensive literature review, we identify the effectiveness of ASLR (Address Space Layout Randomization) and LBR (Last Branch Record) in preventing ROP attacks. We present a process involving buffer overflow detection, hardware-assisted ROP attack detection, and the use of Turing detection technology to monitor control flow behavior. We envision a specialized tool that views and analyzes the last branch record, compares control flow with a baseline, and outputs differences in natural language. This tool offers a graphical interface, facilitating the prevention and detection of ROP attacks. The proposed approach and tool provide practical solutions for enhancing software security.

Keywords: operating system, ROP attacks, returning-oriented programming attacks, ASLR, LBR, CFI, DEP, code randomization, hardware-assisted CFI

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Authors: Wided Bakari, Patrice Bellot, Omar Trigui, Mahmoud Neji

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This article constitutes an opening to think of the modeling and analysis of Arabic texts in the context of a question-answer system. It is a question of exceeding the traditional approaches focused on morphosyntactic approaches. Furthermore, we present a new approach that analyze a text in order to extract correct answers then transform it to logical predicates. In addition, we would like to represent different levels of information within a text to answer a question and choose an answer among several proposed. To do so, we transform both the question and the text into logical forms. Then, we try to recognize all entailment between them. The results of recognizing the entailment are a set of text sentences that can implicate the user’s question. Our work is now concentrated on an implementation step in order to develop a system of question-answering in Arabic using techniques to recognize textual implications. In this context, the extraction of text features (keywords, named entities, and relationships that link them) is actually considered the first step in our process of text modeling. The second one is the use of techniques of textual implication that relies on the notion of inference and logic representation to extract candidate answers. The last step is the extraction and selection of the desired answer.

Keywords: NLP, Arabic language, question-answering, recognition text entailment, logic forms

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Authors: Sang Wook Park, Se Woon Choi, Yousok Kim, Byung Kwan Oh, Hyo Seon Park

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Various retrofit techniques for reinforced concrete frame with infill wall have been steadily developed. Among those techniques, strengthening methodology based on diagonal FRP strips (FRP bracings) has numerous advantages such as feasibility of implementing without interrupting the building under operation, reduction of cost and time, and easy application. Considering the safety of structure and retrofit cost, the most appropriate retrofit solution is needed. Thus, the objective of this study is to suggest pareto-optimal solution for existing building using FRP bracings. To find pareto-optimal solution analysis, NSGA-II is applied. Moreover, the seismic performance of retrofit building is evaluated. The example building is 5-storey, 3-bay RC frames with infill wall. Nonlinear static pushover analyses are performed with FEMA 356. The criterion of performance evaluation is inter-story drift ratio at the performance level IO, LS, CP. Optimal retrofit solutions is obtained for 32 individuals and 200 generations. Through the proposed optimal solutions, we confirm the improvement of seismic performance of the example building.

Keywords: retrofit, FRP bracings, reinforced concrete frame with infill wall, seismic performance evaluation, NSGA-II

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Authors: Georgia H. Mega

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Art street, a category of artwork displayed in public spaces, has been recognized as a potential tool for promoting reflective thinking in both adult and primary education. Educational techniques that encourage critical and creative thinking, as well as deeper reflection, have been developed and applied in educational curricula. This paper aims to explore the potential of art street in cultivating learners' reflective awareness toward multiculturalism. The main objective of this case study is to investigate the possibilities that art street offers in terms of developing learners' critical reflection, regardless of their age. The study compares two art street works from Greece and Norway, focusing on their common theme of multiculturalism. The study adopts a qualitative methodology, specifically a case study approach. This approach allows for an in-depth analysis of the two selected art street works and their impact on learners' reflective thinking. The study demonstrates that art street can effectively cultivate learners' reflective awareness of multiculturalism. The selected works of art, despite being created by different artists and displayed in different cities, share similar content and convey messages that facilitate reflective dialogue on cultural osmosis. Both adult and primary education approaches utilize the same art street works to achieve reflective awareness. This paper contributes to the existing literature on reflective learning processes by highlighting the potential of art street as a means for encouraging reflective thinking. It builds upon the theoretical frameworks of adult education theorists such as Freire and Mezirow, as well as those of primary education theorists such as Perkins and Project Zero. Data for this study were collected through observation and analysis of two art street works, one from Greece and one from Norway. These works were selected based on their common theme of multiculturalism. Analysis Procedures: The collected data were analyzed using qualitative analysis techniques. The researchers examined the content and messages conveyed by the selected art street works and explored their impact on learners' reflective thinking. The central question addressed in this study is whether art street can develop learners' critical reflection toward multiculturalism, regardless of their age. The findings of this study support the notion that art street can effectively cultivate learners' reflective awareness toward multiculturalism. The selected art street works, despite their differences in origin and location, share common themes that encourage reflective dialogue. The use of art street in both adult and primary education approaches showcases its potential as a tool for promoting reflective learning processes. Overall, this paper contributes to the understanding of art street as a means for reflective thinking in the field of adult and primary education.

Keywords: art street, educational techniques, multiculturalism, observation of artworks, reflective awareness

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Authors: Manvar Sagar, Nikul Virpariya

Abstract:

The advancement in data mining techniques plays an important role in many applications. In context of privacy and security issues, the problems caused by association rule mining technique are investigated by many research scholars. It is proved that the misuse of this technique may reveal the database owner’s sensitive and private information to others. Many researchers have put their effort to preserve privacy in Association Rule Mining. Amongst the two basic approaches for privacy preserving data mining, viz. Randomization based and Cryptography based, the later provides high level of privacy but incurs higher computational as well as communication overhead. Hence, it is necessary to explore alternative techniques that improve the over-heads. In this work, we propose an efficient, collusion-resistant cryptography based approach for distributed Association Rule mining using Shamir’s secret sharing scheme. As we show from theoretical and practical analysis, our approach is provably secure and require only one time a trusted third party. We use secret sharing for privately sharing the information and code based identification scheme to add support against malicious adversaries.

Keywords: Privacy, Privacy Preservation in Data Mining (PPDM), horizontally partitioned database, EMHS, MFI, shamir secret sharing

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Authors: Wen Li, Zhengyu Bai, Qi Zhang

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The demand for smart scheduling platform in agriculture, particularly in the scheduling process of machinery equipment, is high. With the continuous development of agricultural machinery equipment technology, a large number of agricultural machinery equipment and agricultural machinery cooperative service organizations continue to appear in China. The large area of cultivated land and a large number of agricultural activities in the central and western regions of China have made the demand for smart and efficient agricultural machinery equipment scheduling platforms more intense. In this study, we design and implement a platform for agricultural machinery equipment scheduling to allocate agricultural machinery equipment resources reasonably. With agricultural machinery equipment scheduling platform taken as the research object, we discuss its research significance and value, use the service blueprint technology to analyze and characterize the agricultural machinery equipment schedule workflow, the network analytic method to obtain the demand platform function requirements, and divide the platform functions through the platform function division diagram. Simultaneously, based on the case-based reasoning (CBR) algorithm, the equipment scheduling module of the agricultural machinery equipment scheduling platform is realized; finally, a design scheme of the agricultural machinery equipment scheduling platform architecture is provided, and the visualization interface of the platform is established via VB programming language. It provides design ideas and theoretical support for the construction of a modern agricultural equipment information scheduling platform.

Keywords: case-based reasoning, service blueprint, system design, ANP, VB programming language

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Authors: C. Labate, M. P. De Santo, G. Lombardo, R. Barberi, M. Lombardo, N. M. Ziebarth

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In the past decades, the importance of corneal biomechanics in the normal and pathological functions of the eye has gained its credibility. In fact, the mechanical properties of biological tissues are essential to their physiological function. We are convinced that an improved understanding of the nanomechanics of corneal tissue is important to understand the basic molecular interactions between collagen fibrils. Ultimately, this information will help in the development of new techniques to cure ocular diseases and in the development of biomimetic materials. Therefore, nanotechnology techniques are powerful tools and, in particular, Atomic Force Microscopy has demonstrated its ability to reliably characterize the biomechanics of biological tissues either at the micro- or nano-level. In the last years, we have investigated the mechanical anisotropy of the human corneal stroma at both the tissue and molecular levels. In particular, we have focused on corneal cross-linking, an established procedure aimed at slowing down or halting the progression of the disease known as keratoconus. We have obtained the first evidence that riboflavin/UV-A corneal cross-linking induces both an increase of the elastic response and a decrease of the viscous response of the most anterior stroma at the scale of stromal molecular interactions.

Keywords: atomic force spectroscopy, corneal stroma, cross-linking, viscoelasticity

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Authors: Lo Kar Yin, Law Ka Mei

Abstract:

Architectural, engineering, construction and operations (AECO) industry practitioners have been well adapting to the dynamic construction market from the fundamental training of its discipline. As further triggered by the pandemic since 2019, great steps are taken in virtual environment and the best collaboration is strived with project teams without boundaries. With adoption of Building Information Modeling-based approach and qualitative analysis, this paper is to review quantity take-off and cost estimation process through modeling techniques in liaison with suppliers, fabricators, subcontractors, contractors, designers, consultants and services providers in the construction industry value chain for automatic project cost budgeting, project cost control and cost evaluation on design options of in-situ reinforced-concrete construction and Modular Integrated Construction (MiC) at design stage, variation of works and cash flow/spending analysis at construction stage as far as practicable, with a view to sharing the findings for enhancing mutual trust and co-operation among AECO industry practitioners. It is to foster development through a common prototype of design and build project delivery method in NEC Engineering and Construction Contract (ECC) Options A and C.

Keywords: building information modeling, cost estimation, quantity take-off, modeling techniques

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Authors: Dulanjali Ranasinghe, Isuru Supasan, Kaushalya Premachandra, Ranjan Dissanayake, Ajith Rajapaksha, Eustace Fernando

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Proteomics studies of organisms are considered to be significantly information-rich compared to their genomic counterparts because proteomes of organisms represent the expressed state of all proteins of an organism at a given time. In modern top-down and bottom-up proteomics workflows, the primary analysis methods employed are gel–based methods such as two-dimensional (2D) electrophoresis and mass spectrometry based methods. Machine learning (ML) and artificial intelligence (AI) have been used increasingly in modern biological data analyses. In particular, the fields of genomics, DNA sequencing, and bioinformatics have seen an incremental trend in the usage of ML and AI techniques in recent years. The use of aforesaid techniques in the field of proteomics studies is only beginning to be materialised now. Although there is a wealth of information available in the scientific literature pertaining to proteomics workflows, no comprehensive review addresses various aspects of the combined use of proteomics and machine learning. The objective of this review is to provide a comprehensive outlook on the application of machine learning into the known proteomics workflows in order to extract more meaningful information that could be useful in a plethora of applications such as medicine, agriculture, and biotechnology.

Keywords: proteomics, machine learning, gel-based proteomics, mass spectrometry

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Authors: Sachin Saxena, Manju Srivastava

Abstract:

The present study describes a stable, highly sensitive and selective analytical sensor. Fe-BTC was synthesized at room temperature using the noble Iron-trimesate system. The high surface area of as synthesized Fe-BTC proved MOFs as ideal modifiers for glassy carbon electrode. The characterization techniques such as TGA, XRD, FT-IR, BET (BET surface area= 1125 m2/gm) analysis explained the electrocatalytic behaviour of Fe-BTC towards these two drugs. The material formed is cost effective and exhibit higher catalytic behaviour towards analyte systems. The synergism between synthesized Fe-BTC and electroanalytical techniques helped in developing a highly sensitive analytical method for studying the redox fate of ARP and RZ, respectively. Cyclic voltammetry of ferricyanide system proved Fe-BTC/GCE with an increase in 132% enhancement in peak current value as compared to that of GCE. The response characteristics of cyclic voltammetry (CV) and square wave voltammetry (SWV) revealed that the ARP and RZ could be effectively accumulated at Fe-BTC/GCE. On the basis of the electrochemical measurements, electrode dynamics parameters have been evaluated. Present study opens up new field of applications of MOFs modified GCE for drug sensing.

Keywords: MOFs, anti-psychotic, electrochemical sensor, anti-migraine drugs

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