Search results for: named entity recognition natural

Authors: M. S. Ahmed, F. A. Mohammad

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The aim of this paper is to perform experimental modal analysis (EMA) of reinforced concrete (RC) square slabs. EMA is the process of determining the modal parameters (Natural Frequencies, damping factors, modal vectors) of a structure from a set of frequency response functions FRFs (curve fitting). Although experimental modal analysis (or modal testing) has grown steadily in popularity since the advent of the digital FFT spectrum analyzer in the early 1970’s, studying all members and materials using such method have not yet been well documented. Therefore, in this work, experimental tests were conducted on RC square specimens (0.6m x 0.6m with 40 mm). Experimental analysis is based on freely supported boundary condition. Moreover, impact testing as a fast and economical means of finding the modes of vibration of a structure was used during the experiments. In addition, Pico Scope 6 device and MATLAB software were used to acquire data, analyze and plot Frequency Response Function (FRF). The experimental natural frequencies which were extracted from measurements exhibit good agreement with analytical predictions. It is showed that EMA method can be usefully employed to perform the dynamic behavior of RC slabs.

Keywords: natural frequencies, mode shapes, modal analysis, RC slabs

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Authors: Evgeny Pyshkin, Maxim Mozgovoy, Vladislav Volkov

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The paper follows a discourse on computer-assisted language learning. We examine problems of foreign language teaching and learning and introduce a metamodel that can be used to define learning models of language grammar structures in order to support teacher/student interaction. Special attention is paid to the concept of a virtual language lab. Our approach to language education assumes to encourage learners to experiment with a language and to learn by discovering patterns of grammatically correct structures created and managed by a language expert.

Keywords: computer-assisted instruction, language learning, natural language grammar models, HCI

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Authors: Hossein Keshtkar, Ali Nasiri Toosi

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With the growing concerns about gasoline environmental pollution and also the need for a more widely available fuel source, natural gas is finding its way to the automotive engines. But before this could happen industrially, simulations of natural gas direct injection need to take place to maximize and optimize power output. KIVA is one of the most powerful tools when it comes to engine simulation. Widely accepted by both researchers and the industry, KIVA an open-source code, offers great in-depth simulation and analyzation. KIVA can compute complex phenomena’s which can occur inside the chamber before, whilst and after ignition. One downside to KIVA, is its in-capability of simulating gaseous injections, making it useful for only liquidized fuel. In this study, we developed a numerical code, to enable the simulation of gaseous injection within the KIVA code. By introducing our code as a subroutine, we modified the original KIVA program. To ensure the correct application of gaseous fuel injection using our modified KIVA code, we simulated two different cases and compared them with their experimental data. We concluded our modified version of KIVA’s simulation results came in very close to those measured experimentally.

Keywords: gaseous injections, KIVA, natural gas direct injection, numerical code, simulation

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Authors: Z. Gharaee

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Recognizing actions performed by others is important in our daily lives since it is necessary for communicating with others in a proper way. We perceive an action by observing the kinematics of motions involved in the performance. We use our experience and concepts to make a correct recognition of the actions. Although building the action concepts is a life-long process, which is repeated throughout life, we are very efficient in applying our learned concepts in analyzing motions and recognizing actions. Experiments on the subjects observing the actions performed by an actor show that an action is recognized after only about two hundred milliseconds of observation. In this study, hierarchical action recognition architecture is proposed by using growing grid layers. The first-layer growing grid receives the pre-processed data of consecutive 3D postures of joint positions and applies some heuristics during the growth phase to allocate areas of the map by inserting new neurons. As a result of training the first-layer growing grid, action pattern vectors are generated by connecting the elicited activations of the learned map. The ordered vector representation layer receives action pattern vectors to create time-invariant vectors of key elicited activations. Time-invariant vectors are sent to second-layer growing grid for categorization. This grid creates the clusters representing the actions. Finally, one-layer neural network developed by a delta rule labels the action categories in the last layer. System performance has been evaluated in an experiment with the publicly available MSR-Action3D dataset. There are actions performed by using different parts of human body: Hand Clap, Two Hands Wave, Side Boxing, Bend, Forward Kick, Side Kick, Jogging, Tennis Serve, Golf Swing, Pick Up and Throw. The growing grid architecture was trained by applying several random selections of generalization test data fed to the system during on average 100 epochs for each training of the first-layer growing grid and around 75 epochs for each training of the second-layer growing grid. The average generalization test accuracy is 92.6%. A comparison analysis between the performance of growing grid architecture and self-organizing map (SOM) architecture in terms of accuracy and learning speed show that the growing grid architecture is superior to the SOM architecture in action recognition task. The SOM architecture completes learning the same dataset of actions in around 150 epochs for each training of the first-layer SOM while it takes 1200 epochs for each training of the second-layer SOM and it achieves the average recognition accuracy of 90% for generalization test data. In summary, using the growing grid network preserves the fundamental features of SOMs, such as topographic organization of neurons, lateral interactions, the abilities of unsupervised learning and representing high dimensional input space in the lower dimensional maps. The architecture also benefits from an automatic size setting mechanism resulting in higher flexibility and robustness. Moreover, by utilizing growing grids the system automatically obtains a prior knowledge of input space during the growth phase and applies this information to expand the map by inserting new neurons wherever there is high representational demand.

Keywords: action recognition, growing grid, hierarchical architecture, neural networks, system performance

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Authors: Frazer Kirk, Matthew Yong, Peter Williams, Andrie Strobel

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Pulmonary valve papillary fibroelastoma is an exceedingly rare pathology. The experience and literature regarding them are largely anecdotal and based on sporadic, single case reports. Throughout their known history, two features remain salient that they are classically asymptomatic and found incidentally. The demographic profile of those affected is unclear, as reports regarding those affected are mixed, and there is no clear gender or age predominance, although there is some suggestion of a predisposition to affect females. Nor has there been a well-structured epidemiological study of the entity. Interestingly they are becoming more common on peri-mortum examination. Here-after we describe our experience with a symptomatic presentation of pulmonary papillary fibroelastoma masquerading as a pulmonary embolism and its subsequent assessment and management, with intraoperative photography and echocardiography for reference.

Keywords: cardiac tumor, pulmonary valve, fibroelastoma, cardiac surgery

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Authors: E. Kam, G. Karahan, H. Aslıyuksek, A. Bozkurt

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This study aims to determine the natural radioactivity levels in some foodstuffs produced in Turkey. For this purpose, 48 different foods samples were collected from different land parcels throughout the country. All samples were analyzed to designate both gross alpha and gross beta radioactivities and the radionuclides’ concentrations. The gross alpha radioactivities were measured as below 1 Bq kg^-1 in most of the samples, some of them being due to the detection limit of the counting system. The gross beta radioactivity levels ranged from 1.8 Bq kg^-1 to 453 Bq kg^-1, larger levels being observed in leguminous seeds while the highest level being in haricot bean. The concentrations of natural radionuclides in the foodstuffs were investigated by the method of gamma spectroscopy. High levels of ⁴⁰K were measured in all the samples, the highest activities being again in leguminous seeds. Low concentrations of ²³⁸U and ²²⁶Ra were found in some of the samples, which are comparable to the reported results in the literature. Based on the activity concentrations obtained in this study, average annual effective dose equivalents for the radionuclides ²²⁶Ra, ²³⁸U, and ⁴⁰K were calculated as 77.416 µSv y^-1, 0.978 µSv y^-1, and 140.55 µSv y^-1, respectively.

Keywords: foods, radioactivity, gross alpha, gross beta, annual equivalent dose, Turkey

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Authors: A. W. J. Wong, I. H. Ibrahim

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Increasing urbanisation has driven building designers to incorporate natural ventilation in the designs of sustainable buildings. This project utilises Computational Fluid Dynamics (CFD) to investigate the natural ventilation of an academic building, SIT@SP, using an assessment criterion based on daily mean temperature and mean velocity. The areas of interest are the pedestrian level of first and fourth levels of the building. A reference case recommended by the Architectural Institute of Japan was used to validate the simulation model. The validated simulation model was then used for coupled simulations on SIT@SP and neighbouring geometries, under two wind speeds. Both steady and transient simulations were used to identify differences in results. Steady and transient results are agreeable with the transient simulation identifying peak velocities during flow development. Under a lower wind speed, the first level was sufficiently ventilated while the fourth level was not. The first level has excessive wind velocities in the higher wind speed and the fourth level was adequately ventilated. Fourth level flow velocity was consistently lower than those of the first level. This is attributed to either simulation model error or poor building design. SIT@SP is concluded to have a sufficiently ventilated first level and insufficiently ventilated fourth level. Future works for this project extend to modifying the urban geometry, simulation model improvements, evaluation using other assessment metrics and extending the area of interest to the entire building.

Keywords: buildings, CFD Simulations, natural ventilation, urban airflow

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Authors: James Jr. Mashiyane, Risuna Nkolele, Stephanie J. Müller, Gciniwe S. Dlamini, Rebone L. Meraba, Darlington S. Mapiye

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When performing language tasks in natural language processing (NLP), the dimensionality of word embeddings is chosen either ad-hoc or is calculated by optimizing the Pairwise Inner Product (PIP) loss. The PIP loss is a metric that measures the dissimilarity between word embeddings, and it is obtained through matrix perturbation theory by utilizing the unitary invariance of word embeddings. Unlike in natural language, in genomics, especially in genome sequence processing, unlike in natural language processing, there is no notion of a “word,” but rather, there are sequence substrings of length k called k-mers. K-mers sizes matter, and they vary depending on the goal of the task at hand. The dimensionality of word embeddings in NLP has been studied using the matrix perturbation theory and the PIP loss. In this paper, the sufficiency and reliability of applying word-embedding algorithms to various genomic sequence datasets are investigated to understand the relationship between the k-mer size and their embedding dimension. This is completed by studying the scaling capability of three embedding algorithms, namely Latent Semantic analysis (LSA), Word2Vec, and Global Vectors (GloVe), with respect to the k-mer size. Utilising the PIP loss as a metric to train embeddings on different datasets, we also show that Word2Vec outperforms LSA and GloVe in accurate computing embeddings as both the k-mer size and vocabulary increase. Finally, the shortcomings of natural language processing embedding algorithms in performing genomic tasks are discussed.

Keywords: word embeddings, k-mer embedding, dimensionality reduction

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Authors: Gou-Moon Choi, Woo-Young Jung, Chan-Young Yune

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Recently, the natural disaster has increased worldwide. In Korea, the damage to life and property caused by a typhoon, heavy rain, heavy snow, and an extreme wind also increases every year. Among natural disasters, the frequency and the strength of wind have increased because sea surface temperature has risen due to the increase of the average temperature of the Earth. In the case of extreme wind disaster, it is impossible to control or reduce the occurrence, and the recovery cost always exceeds the damage cost. Therefore, quantitative estimation of the damage cost for extreme wind needs to be established beforehand to install proactive countermeasures. In this study, the damage cost for private properties was analyzed based on the data for the past 10 years in Korea. The damage cost curve was also suggested for the metropolitan cities and provinces. The result shows the possibility for the regional application of the damage cost curve because the damage cost of the regional area is estimated based on the cost of cities and provinces.

Keywords: damage cost, extreme wind, natural disaster, private property

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Authors: Raymond Dominic Uzoh

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Starch fillers were extracted from three plant sources namely amora tuber (a wild variety of Irish potato), sweet potato and yam starch and their particle size, pH, amylose, and amylopectin percentage decomposition determined accordingly by high performance liquid chromatography (HPLC). The starch was introduced into natural rubber in liquid phase (through gelatinization) by the latex compounding method and compounded according to standard method. The prepared starch/natural rubber composites was characterized by Instron Universal testing machine (UTM) for tensile mechanical properties. The composites was further characterized by x-ray diffraction and crosslink density analysis. The particle size determination showed that amora starch granules have the highest particle size (156 × 47 μm) followed by yam starch (155× 40 μm) and then the sweet potato starch (153 × 46 μm). The pH test also revealed that amora starch has a near neutral pH of 6.9, yam 6.8, and sweet potato 5.2 respectively. Amylose and amylopectin determination showed that yam starch has a higher percentage of amylose (29.68), followed by potato (22.34) and then amora starch with the lowest value (14.86) respectively. The tensile mechanical properties testing revealed that yam starch produced the best tensile mechanical properties followed by amora starch and then sweet potato starch. The structure, crystallinity/amorphous nature of the product composite was confirmed by x-ray diffraction, while the nature of crosslinking was confirmed by swelling test in toluene solvent using the Flory-Rehner approach. This research study has rendered a workable strategy for enhancing interfacial interaction between a hydrophilic filler (starch) and hydrophobic polymeric matrix (natural rubber) yielding moderately good tensile mechanical properties for further exploitation development and application in the rubber processing industry.

Keywords: natural rubber, fillers, starch, amylose, amylopectin, crosslink density

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Authors: Ahmad K. Samaila, Basant K. Jha

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This study is devoted to investigate the effect of transpiration on transient as well as steady-state natural convection flow of a reactive viscous fluid in a vertical channel formed by two infinite vertical parallel porous plates. The Boussinesq assumption is applied and the nonlinear governing equations of energy and momentum are developed. The problem is solved numerically using implicit finite difference method and analytically for steady-state case using perturbation method. Solutions are presented in graphical form for fluid temperature, velocity, and skin-friction and wall heat transfer rate for various parametric values. It is found that velocity, temperature, rate of heat transfer as well as skin-friction are strongly affected by mass leakage through the porous plates.

Keywords: transpiration, reactive viscous fluid, porous plates, natural convection, suction/injection

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Authors: Prathiksha Kamath, Pratibha Jamkhandi, Prateek Ghanti, Priyanshu Gupta, M. Lakshmi Neelima

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Combining Image Captioning and Visual Question Answering (VQA) tasks have emerged as a new and exciting research area. The image captioning task involves generating a textual description that summarizes the content of the image. VQA aims to answer a natural language question about the image. Both these tasks include computer vision and natural language processing (NLP) and require a deep understanding of the content of the image and semantic relationship within the image and the ability to generate a response in natural language. There has been remarkable growth in both these tasks with rapid advancement in deep learning. In this paper, we present a comprehensive review of recent progress in combining image captioning and visual question-answering (VQA) tasks. We first discuss both image captioning and VQA tasks individually and then the various ways in which both these tasks can be integrated. We also analyze the challenges associated with these tasks and ways to overcome them. We finally discuss the various datasets and evaluation metrics used in these tasks. This paper concludes with the need for generating captions based on the context and captions that are able to answer the most likely asked questions about the image so as to aid the VQA task. Overall, this review highlights the significant progress made in combining image captioning and VQA, as well as the ongoing challenges and opportunities for further research in this exciting and rapidly evolving field, which has the potential to improve the performance of real-world applications such as autonomous vehicles, robotics, and image search.

Keywords: image captioning, visual question answering, deep learning, natural language processing

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Authors: Zerrin Erginkaya, Gözde Konuray

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Microbial degradation of foods is defined as a decrease of food safety due to microorganism activity. Organic acids, sulfur dioxide, sulfide, nitrate, nitrite, dimethyl dicarbonate and several preservative gases have been used as chemical preservatives in foods as well as natural preservatives which are indigenous in foods. It is determined that usage of herbal preservatives such as blueberry, dried grape, prune, garlic, mustard, spices inhibited several microorganisms. Moreover, it is determined that animal origin preservatives such as whey, honey, lysosomes of duck egg and chicken egg, chitosan have antimicrobial effect. Other than indigenous antimicrobials in foods, antimicrobial agents produced by microorganisms could be used as natural preservatives. The antimicrobial feature of preservatives depends on the antimicrobial spectrum, chemical and physical features of material, concentration, mode of action, components of food, process conditions, and pH and storage temperature. In this review, studies about antimicrobial components which are indigenous in food (such as herbal and animal origin antimicrobial agents), antimicrobial materials synthesized by microorganisms, and their usage as an antimicrobial agent to preserve foods are discussed.

Keywords: animal origin preservatives, antimicrobial, chemical preservatives, herbal preservatives

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Authors: S. Ahmed John

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Marine forms-bacteria, actinobacteria, cynobacteria, fungi, microalgae, seaweeds mangroves and other halophytes an extremely important oceanic resources and constituting over 90% of the oceanic biomass. The marine natural products have lead to the discovery of many compounds considered worthy for clinical applications. The marine sources have the highest probability of yielding natural products. Natural derivatives play an important role to prevent the cancer incidences as synthetic drug transformation in mangrove. 28.12% of anticancer compound extracted from the mangroves. Exchocaria agollocha has the anti cancer compounds. The present investigation reveals the potential of the Exchocaria agollocha with biotechnological applications for anti cancer, antimicrobial drug discovery, environmental remediation, and developing new resources for the industrial process. The anti-cancer activity of Exchocaria agollocha was screened from 3.906 to 1000 µg/ml of concentration with the dilution leads to 1:1 to 1:128 following methanol and chloroform extracts. The cell viability in the Exchocaria agollocha was maximum at the lower concentration where as low at the higher concentration of methanol and chloroform extracts when compare to control. At 3.906 concentration, 85.32 and 81.96 of cell viability was found at 1:128 dilution of methanol and chloroform extracts respectively. At the concentration of 31.25 following 1:16 dilution, the cell viability was 65.55 in methanol and 45.55 in chloroform extracts. However, at the higher concentration, the cell viability 22.35 and 8.12 was recorded in the extracts of methanol and chloroform. The cell viability was more in methanol when compare to chloroform extracts at lower concentration. The present findings gives current trends in screening and the activity analysis of metabolites from mangrove resources and to expose the models to bring a new sustain for tackling cancer. Bioactive compounds of Exchocaria agollocha have extensive use in treatment of many diseases and serve as a compound and templates for synthetic modification.

Keywords: bio-active product, compounds, natural products and microalgae

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Authors: Yanin Hosakun, Sujitra Wongkasemjit, Thanyalak Chaisuwan

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Carbon dioxide removal from natural gas is an important process because the existence of carbon dioxide in natural gas contributes to pipeline corrosion, reduces the heating value, and takes up volume in the pipeline. In this study, bacterial cellulose was chosen for the CO2/CH4 gas separation membrane due to its unique structure and prominent properties. Additionally, it can simply be obtained by culturing the bacteria so called “Acetobacter xylinum” through fermentation of coconut juice. Bacterial cellulose membranes with and without silver ions were prepared and studied for the separation performance of CO2 and CH4.

Keywords: bacterial cellulose, CO2, CH4 separation, membrane, nata de coco

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Authors: Ruchika Sharma

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In the past decade, there has been an explosion of research that has examined children’s understanding of TV advertisements and its persuasive intent, socialization of child consumer and child psychology. However, it is evident from the literature review that no studies in this area have covered advertising messages and its impact on children’s brand recall and ad recognition. Copywriters use various creative devices to lure the consumers and very impressionable consumers such as children face far more drastic effects of these creative ways of persuasion. On the basis of Piaget’s theory of cognitive development as a theoretical basis for predicting/understanding children’s response and understanding, a quasi-experiment was carried out for the study, that manipulated measurement timing and advertising messages (familiar vs. unfamiliar) keeping gender and age group as two prominent factors. This study also examines children’s understanding of Advertisements and its elements, predominantly - Language, keeping in view Fishbein’s model. Study revealed significant associations between above mentioned factors and children’s brand recall and ad identification. Further, to test the reliability of the findings on larger sample, bootstrap simulation technique was used. The simulation results are in accordance with the findings of experiment, suggesting that the conclusions obtained from the study can be generalized for entire children’s (as consumers) market in India.

Keywords: advertising, brand recall, cognitive development, preferences

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Authors: Nouf A. Aldarmahi, Nesrin I. Tarbiah, Nuha A. Alkhattabi, Huda F. Alshaibi

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Nigella sativa which is known as dark cumin is a well-known example for a widely applicable herbal medicine. Nigella sativa can be effective in a variety of diseases such as hypertension, diabetes, bronchitis, gastrointestinal upset, and cancer. The anticancer effect of Nigella sativa appeared to be mediated by immune-modulatory effect through stimulating human natural killer (NK) cells. This is a type of lymphocytes which is part of the innate immunity, also known as the first line of defense in the body against pathogens. This study investigated the effect of thymoquinone as a major component of Nigella sativa on the molecular cytotoxic pathway of NK cell and the role of thymoquinone therapeutic effect on NK cells. NK cells were cultured with breast tumor cells in different ways and cultured media was collected and the concentration of perforin, granzyme B and interferon-α were measured by ELISA. The cytotoxic effect of NK cells on breast tumor cells was enhanced in the presence of thymoquinone, with increased activity of perforin in NK cells. This improved anticancer effect of thymoquinone on breast cancer cells.

Keywords: breast cancer, cancer cells, natural killer cells, thymoquinone

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Authors: Uroš Durlević, Ivan Novković, Nina Čegar, Stefanija Stojković

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Snow avalanches represent one of the most dangerous natural phenomena in mountain regions worldwide. Material and human casualties caused by snow avalanches can be very significant. In this study, using geographic information systems and remote sensing, the natural conditions of the Šar Mountains were analyzed for geospatial modeling of dry slab avalanches. For this purpose, the Fuzzy Analytic Hierarchy Process (FAHP) multi-criteria analysis method was used, within which fifteen environmental criteria were analyzed and evaluated. Based on the existing analyzes and results, it was determined that a significant area of the Šar Mountains is very highly susceptible to the occurrence of dry slab avalanches. The obtained data can be of significant use to local governments, emergency services, and other institutions that deal with natural disasters at the local level. To our best knowledge, this is one of the first research in the Republic of Serbia that uses the FAHP method for geospatial modeling of dry slab avalanches.

Keywords: GIS, FAHP, Šar Mountains, snow avalanches, environmental protection

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Authors: Mingbao Chen

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The blue economy is a new comprehensive marine economy integrated with resources, industries, and regions, and is an upgraded version of the marine economy. The blue economy attaches great importance to the coordinated development of the ecological environment and the economy, which is an emerging economic form advocated by all countries in the world. This paper constructs the model including four variables：natural capital, economic capital, intellectual capital, cultural capital. Theoretically, this paper deduces the function mechanism of variables on economic growth, and empirically calculates the driving force and influence of the blue economy on the national economy by using data of China's 2006-2015 year. The results show that natural capital and economic capital remain the main factors of blue growth in the blue economy. And with the development of economic society and technological progress, the role of intellectual capital and cultural capital is bigger and bigger. Therefore, promoting the development of marine science and technology and culture is the focus of the future blue economic development.

Keywords: blue growth, natural capital, intellectual capital, cultural capital

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Authors: Rizqi W. Romadhon, Nur Arifan

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Halal Tourism is a part of tourism industry which is based on Islamic Principle and addressed to the Muslim tourist. The potency of halal tourism is very broad to be developed, because the growth of Muslim populations is rapidly increasing. Indonesia is one of the biggest countries with Majority of its population is Muslim, therefore human resources and natural resources have very good potential to be part of the Halal tourism industry. But the fact is Indonesia can not optimize the potential of human resources and natural resources as well as neighboring countries carried out. This paper will discuss the reasons of the importance of developing Halal tourism, and the factors influencing the success of developing halal tourism in Indonesia, and also the optimization strategies which can be adopted by the government so that the Halal tourism industry in Indonesia has a sustainable competitive advantage. The existence of this research is expected to government, tourism agents and others can optimize the potency of Indonesia’s Human resources and natural resources for developing Halal tourism industry in Indonesia.

Keywords: halal tourism, Islamic principle, optimization, sustainable competitive advantage

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Authors: Emin Z. Mahmud

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This paper presents the observations from a series of shaking-table tests done on a 1:1 scaled confined masonry wall model, with opening for a door – specimens CMDuS (confined masonry wall with opening for a door before strengthening) and CMDS (confined masonry wall with opening for a door after strengthening). Frequency and stiffness changes before and after GFRP (Glass Fiber Reinforced Plastic) wall strengthening are analyzed. Definition of dynamic properties of the models was the first step of the experimental testing, which enabled acquiring important information about the achieved stiffness (natural frequencies) of the model. The natural frequency was defined in the Y direction of the model by applying resonant frequency search tests. It is important to mention that both specimens CMDuS and CMDS are subjected to the same effects. The tests are realized in the laboratory of the Institute of Earthquake Engineering and Engineering Seismology (IZIIS), Skopje. The specimens were examined separately on the shaking table, with uniaxial, in-plane excitation. After testing, samples were strengthened with GFRP and re-tested. The initial frequency of the undamaged model CMDuS is 13.55 Hz, while at the end of the testing, the frequency decreased to 6.38 Hz. This emphasizes the reduction of the initial stiffness of the model due to damage, especially in the masonry and tie-beam to tie-column connection. After strengthening of the damaged wall, the natural frequency increases to 10.89 Hz. This highlights the beneficial effect of the strengthening. After completion of dynamic testing at CMDS, the natural frequency is reduced to 6.66 Hz.

Keywords: behaviour of masonry structures, Eurocode, frequency, masonry, shaking table test, strengthening

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Authors: Abdelkarim Boua, Abdelhakim Chillali

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The study of non-associative structures in algebraic structures has become a separate entity; for, in the case of groups, their corresponding non-associative structure i.e. loops is dealt with separately. Similarly there is vast amount of research on the nonassociative structures of semigroups i.e. groupoids and that of rings i.e. nonassociative rings. However it is unfortunate that we do not have a parallel notions or study of non-associative near-rings. In this work we shall attempt to generalize a few known results and study the commutativity of Jordan ideal in 3-prime near-rings satisfying certain identities involving the Jordan ideal. We study the derivations satisfying certain differential identities on Jordan ideals of 3-prime near-rings. Moreover, we provide examples to show that hypothesis of our results are necessary. We give some new results and examples concerning the existence of Jordan ideal and derivations in near-rings. These near-rings can be used to build a new codes.

Keywords: 3-prime near-rings, near-rings, Jordan ideal, derivations

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Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: multiclass classification, convolution neural network, OpenCV

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Authors: Jessy Rugeyo, William Donner

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This research delves into the resilience of Rwanda's public health infrastructure amidst natural disasters, a critical issue given that the Northern Province alone has witnessed no fewer than 1500 cases of disaster ranging from floods and landslides in the last five years, with more than 200 people killed and thousands of homes destroyed, according to MINEMA. In an era where climate change escalates the frequency and intensity of such disasters, fortifying the resilience of public health systems is paramount. This study offers a comprehensive analysis of the existing state of Rwanda's public health infrastructure and its ability to manage such crises. Employing a mix of literature review, case studies, and policy analysis, the study discerns key vulnerabilities and brings to light the intricacies of disaster management in Rwanda. Case studies centered around past natural disasters in Rwanda provide critical insights into the strengths and weaknesses of the existing disaster response mechanisms. A thorough critique of related disaster management and public health infrastructure policies reveals areas of commendable practice, along with gaps calling for policy enhancements. Findings guide the proposition of targeted strategies to bolster the resilience of Rwanda's public health infrastructure. This research serves as a significant contribution to the domains of disaster studies and public health, offering valuable insights for policymakers, public health and disaster management professionals in Rwanda and similar contexts. It presents actionable recommendations for improvement, underscoring the potential for enhancing Rwanda's disaster management capacity. By advocating for the strengthening of public health infrastructure resilience, the research highlights the potential for improved public health outcomes following natural disasters, thereby showcasing significant implications for public health and disaster management in the country, particularly in the face of a changing climate.

Keywords: public health infrastructure, disaster resilience, natural disaster, disaster management, emergency preparedness, health policy

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Authors: Zhen Cheng, Xinyu Dai, Shujian Huang, Jiajun Chen

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Recently, explanatory natural language inference has attracted much attention for the interpretability of logic relationship prediction, which is also known as explanation generation for Natural Language Inference (NLI). Existing explanation generators based on discriminative Encoder-Decoder architecture have achieved noticeable results. However, we find that these discriminative generators usually generate explanations with correct evidence but incorrect logic semantic. It is due to that logic information is implicitly encoded in the premise-hypothesis pairs and difficult to model. Actually, logic information identically exists between premise-hypothesis pair and explanation. And it is easy to extract logic information that is explicitly contained in the target explanation. Hence we assume that there exists a latent space of logic information while generating explanations. Specifically, we propose a generative model called Variational Explanation Generator (VariationalEG) with a latent variable to model this space. Training with the guide of explicit logic information in target explanations, latent variable in VariationalEG could capture the implicit logic information in premise-hypothesis pairs effectively. Additionally, to tackle the problem of posterior collapse while training VariaztionalEG, we propose a simple yet effective approach called Logic Supervision on the latent variable to force it to encode logic information. Experiments on explanation generation benchmark—explanation-Stanford Natural Language Inference (e-SNLI) demonstrate that the proposed VariationalEG achieves significant improvement compared to previous studies and yields a state-of-the-art result. Furthermore, we perform the analysis of generated explanations to demonstrate the effect of the latent variable.

Keywords: natural language inference, explanation generation, variational auto-encoder, generative model

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Authors: Ahmad Abdul-Razzak Aboudi Al-Issa

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Thermal is the main important aspect in any hydraulic system since it is affected on the hydraulic system performance. Therefore must be simulated the hydraulic system -that was designed- in this aspect before constructing it. In this study, an existed expert system was using to simulate the thermal aspect of a designed hydraulic system that will be used in an industrial field. The expert system which is used in this study is (Hydraulic System Calculations), and its symbol (HSC). HSC had been designed and coded in an interactive program userfriendly named (Microsoft Visual Basic 2010).

Keywords: fluid power, hydraulic system, thermal and hydrodynamic, expert system

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Authors: Jiří F. Urbánek, Jiří Barta

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Societal security, continuity scenarios, and methodological cycling approach understands in this article. Namely, societal security organizational challenges ask implementation of international standards BS 25999-2 and global ISO 22300 which is a family of standards for business continuity management system. Efficient global organization system is distinguished of high entity´s complexity, connectivity, and interoperability, having not only cooperative relations in a fact. Competing business have numerous participating ´enemies´, which are in apparent or hidden opponent and antagonistic roles with prosperous organization systems, resulting to a crisis scene or even to a battle theater. Organization business continuity scenarios are necessary for such ´a play´ preparedness, planning, management, and overmastering in real environments.

Keywords: business continuity, societal security, crisis scenarios cycles, interoperability

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Authors: Jinwon Kang, Seonghak Jo, Joohee Ahn, Junghye Choi, Sun-Young Lee

Abstract:

A morphological priming has proved its importance by showing that segmentation occurs in morphemes when visual words are recognized within a noticeably short time. Regarding Sino-Korean prefixes, this study conducted an experiment on visual masked priming tasks with 57 ms stimulus-onset asynchrony (SOA) to see how individual differences in the amount of morphological knowledge affect morphological priming. The relationship between the prime and target words were classified as morphological (e.g., 미개척 migaecheog [unexplored] – 미해결 mihaegyel [unresolved]), semantical (e.g., 친환경 chinhwangyeong [eco-friendly]) – 무공해 mugonghae [no-pollution]), and orthographical (e.g., 미용실 miyongsil [beauty shop] – 미확보 mihwagbo [uncertainty]) conditions. We then compared the priming by configuring irrelevant paired stimuli for each condition’s control group. As a result, in the behavioral data, we observed facilitatory priming from a group with high morphological knowledge only under the morphological condition. In contrast, a group with low morphological knowledge showed the priming only under the orthographic condition. In the event-related potential (ERP) data, the group with high morphological knowledge presented the N250 only under the morphological condition. The findings of this study imply that individual differences in morphological knowledge in Korean may have a significant influence on the segmental processing of Korean word recognition.

Keywords: ERP, individual differences, morphological priming, sino-Korean prefixes

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Authors: Jain Jyoti, Jain Shorab, Sinha Shishir

Abstract:

In the field of material engineering, composites are in great concern for their nonbiodegradability and their cost. In order to reduce its cost and weight, plant derived fibers witnessed miraculous triumph. Plant fibers can be of different types like seed fibers, blast fibers, leaf fibers, etc. Composites can be reinforced with exclusively one type of natural fiber or also can be combined with two or more different types of natural or synthetic fibers to boost up their specific properties. Among all natural fibers, wheat straw, bagasse, kenaf, pineapple leaf, banana, coir, ramie, flax, etc. pineapple leaf fibers have very good mechanical properties. Being hydrophilic in nature, pineapple leaf fibers have very less affinity towards all types of polymer matrixes like HDPE, LDPE, PET, epoxy, etc. Surface treatments like alkaline treatment in different concentrations were conducted to improve its adhesion and compatibility towards hydrophobic polymer matrix i.e. epoxy resin. Pineapple leaf fiber epoxy composites have been prepared using hand layup method. Effect of fiber loading and surface treatments have been studied for different mechanical properties i.e. tensile strength, flexural strength and impact properties of pineapple leaf fiber composites. Analysis of fiber morphology has also been studied using FTIR, XRD. Scanning electron microscopy has also been used to study and compare the morphology of untreated and treated fibers. Also, the fracture surface has been reviewed comparing the reported literature of other eminent researchers of this field.

Keywords: composite, mechanical, natural fiber, pineapple leaf fiber

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Authors: M. Masroor Akhtar Khan, Moin Uddin, Lalit Varshney

Abstract:

Recently, there has been a rapidly expanding interest in finding applications of natural polymers in view of value addition to agriculture. It is now being realized that radiation processing of natural polysaccharides can be beneficially utilized either to improve the existing methodologies used for processing the natural polymers or to impart value addition to agriculture by converting them into more useful form. Gamma-ray irradiation is employed to degrade and lower the molecular weight of some of the natural polysaccharides like alginates, chitosan and carrageenan into small sized oligomers. When these oligomers are applied to plants as foliar sprays, they elicit various kinds of biological and physiological activities, including promotion of plant growth, seed germination, shoot elongation, root growth, flower production, suppression of heavy metal stress, etc. Furthermore, application of these oligomers can shorten the harvesting period of various crops and help in reducing the use of insecticides and chemical fertilizers. In recent years, the oligomers of sodium alginate obtained by irradiating the latter with gamma-rays at 520 kGy dose are being employed. It was noticed that the oligomers derived from the natural polysaccharides could induce growth, photosynthetic efficiency, enzyme activities and most importantly the production of secondary metabolite in the plants like Artemisia annua, Beta vulgaris, Catharanthus roseus, Chrysopogon zizanioides, Cymbopogon flexuosus, Eucalyptus citriodora, Foeniculum vulgare, Geranium sp., Mentha arvensis, Mentha citrata, Mentha piperita, Mentha virdis, Papaver somniferum and Trigonella foenum-graecum. As a result of the application of these oligomers, the yield and/or contents of the active constituents of the aforesaid plants were significantly enhanced. The productivity, as well as quality of medicinal and aromatic plants, may be ameliorated by this novel technique in an economical way as a very little quantity of these irradiated (depolymerised) polysaccharides is needed. Further, this is a very safe technique, as we did not expose the plants directly to radiation. The radiation was used to depolymerize the polysaccharides into oligomers.

Keywords: essential oil, medicinal and aromatic plants, plant production, radiation processed polysaccharides, active constituents

Procedia PDF Downloads 432