Search results for: generative composition

Authors: Mawarni Mohamed, Sharifah Shahira A. Hamid

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Leisure time is an important component to offset the sedentary lifestyle of the people. Women tend to benefit from leisure activities not only to reduce stress but also to provide opportunities for well-being and self-satisfaction. This study was conducted to investigate body composition and leisure time use among women in Selangor from the influences of community settlement. A total of 419 women aged 18-65 years were selected to participate in this study. Descriptive statistics, t-test and ANOVA were used to analyze the level of physical activity and the relationship between leisure-time use and body composition were made to analyze the physical lifestyles. The results showed that women with normal body composition seem to be involved in more passive activities than women with less weight gain and obesity. Thus, the study recommended that the government and other health and recreational agencies should develop more places and activities suitable for leisure preference for women in their community settlement so they become more interested to engage in more active recreational and physical activities.

Keywords: body composition, community settlement, leisure time, physical lifestyles

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Authors: M. Priscila Ramos, Omar O. Chisari, Juan Pablo Vila Martínez

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Current literature about trade liberalization of environmental goods and services (EGS) raises doubts about the extent of the triple win-win situation for trade, development and the environment. However, much of this literature does not consider the possibility that this agreement carries technological transmissions, either through trade or foreign direct investment. This paper presents a computable general equilibrium model calibrated for Argentina, where there are alternative technologies (one dirty and one clean according to carbon emissions) to produce the same goods. In this context, the trade liberalization of EGS allows to increase GDP, trade, reduce unemployment and improve the households welfare. However, the capital mobility appears as the key assumption to jointly reach the environmental target, when the positive scale effect generated by the increase in trade is offset by the change in the composition of production (composition and technical effects by the use of the clean alternative technology) and of consumption (composition effect by substitution of relatively lesspolluting imported goods).

Keywords: CGE modeling, CO2 emissions, composition effect, scale effect, technique effect, trade liberalization of EGS

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Authors: Tanju Teker, Yener Tekeli̇, Esra Karpuz

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Salvia species are known as medicinal plant and often used in public. The antimicrobial effects and essential oil composition of Salvia verticillata L. subsp. amasiaca were determined. The antimicrobial activity is determined by using disk diffusion method against two Gram-positive bacteria, two Gram-negative bacteria and one kind of yeast and essential oil composition was determined by GC - MS. As a result of antimicrobial analysis while sample has shown very strong antimicrobial activity against Staphylococcus aureus, moderately effective against Pseudomonas aeruginosa and low effective against Enterococcus faecalis, it has not shown antimicrobial activity against Escherichia coli and C. albicans. Trans-caryophyllene (% 35.07), germacrene-d (% 10.98) and caryopyllene oxide (% 5.81) are the main components of essential oil composition.

Keywords: salvia, medicinal plant, antimicrobial activity, essential oil

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Authors: Samia Sadouki Chibani, Abdelkamel Tari

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Web service composition combines available services to provide new functionality. Given the number of available services with similar functionalities and different non functional aspects (QoS), the problem of finding a QoS-optimal web service composition is considered as an optimization problem belonging to NP-hard class. Thus, an optimal solution cannot be found by exact algorithms within a reasonable time. In this paper, a meta-heuristic bio-inspired is presented to address the QoS aware web service composition; it is based on Elephant Herding Optimization (EHO) algorithm, which is inspired by the herding behavior of elephant group. EHO is characterized by a process of dividing and combining the population to sub populations (clan); this process allows the exchange of information between local searches to move toward a global optimum. However, with Applying others evolutionary algorithms the problem of early stagnancy in a local optimum cannot be avoided. Compared with PSO, the results of experimental evaluation show that our proposition significantly outperforms the existing algorithm with better performance of the fitness value and a fast convergence.

Keywords: bio-inspired algorithms, elephant herding optimization, QoS optimization, web service composition

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Authors: V. Hakimzadeh, M. Noori, M. maleki

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The aim of this study was to determine the composition and antimicrobial effect of Mentha piperita essential oil in "in-vitro" condition. The chemical composition of the essential oil obtained by hydro-distillation was examined by GC/MS and the antimicrobial effect was studied on the growth of seven microbial species including Bacillus cereus, Pseudomonas aeruginosa and Proteus vulgaris using micro-dilution method. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were determined. Chemical composition analysis identified a total of 28 compounds in which the main components were menthol (32%), mentone (13.4), menthyl acetate (12%), 1,8-cineole (8.2%) and neomenthol (4%) representing 69.6 % of the total oil. Other separated components accounted for less than 30.4% of the oil. Results of antimicrobial analysis showed that the MIC values for Bacillus cereus, Pseudomonas aeruginosa and Proteus vulgaris was respectively 50, 200 and 100 µg/ml and the MBC was determined at 200, 400 and 200 µg/ml respectively. The results of the present study indicated that Mentha piperita essential oil had significant antimicrobial activity.

Keywords: antimicrobial activity, essential oil composition, Mentha piperita

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Authors: Ehsan Vaziri Goodarzi, Mahmood Houshmand, Omid Fatahi Valilai, Vahidreza Ghezavati, Shahrooz Bamdad

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One important paradigm of industry 4.0 is Cloud Manufacturing (CM). In CM everything is considered as a service, therefore, the CM platform should consider all service provider's capabilities and tries to integrate services in an equilibrium state. This research develops a framework for implementing manufacturing cloud service composition in the equilibrium state. The developed framework using well-known tools called axiomatic design (AD) and game theory. The research has investigated the factors for forming equilibrium for measures of the manufacturing cloud service composition. Functional requirements (FRs) represent the measures of manufacturing cloud service composition in the equilibrium state. These FRs satisfied by related Design Parameters (DPs). The FRs and DPs are defined by considering the game theory, QoS, consumer needs, parallel and cooperative services. Ultimately, four FRs and DPs represent the framework. To insure the validity of the framework, the authors have used the first AD’s independent axiom.

Keywords: axiomatic design, manufacturing cloud service composition, cloud manufacturing, industry 4.0

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Authors: Vlad Andrei Raducanu, Mariana Lucia Angelescu, Ion Cinca, Vasile Danut Cojocaru, Doina Raducanu

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By using smart digital tools, such as generative design (GD) and digital fabrication (DF), problems of high actuality concerning resources optimization (materials, energy, time) can be solved and applications or products of free-form type can be created. In the new digital technology materials are active, designed in response to a set of performance requirements, which impose a total rethinking of old material practices. The article presents the design procedure key steps of a free-form architectural object - a column type one with connections to get an adaptive 3D surface, by using the parametric design methodology and by exploiting the properties of conventional metallic materials. In parametric design the form of the created object or space is shaped by varying the parameters values and relationships between the forms are described by mathematical equations. Digital parametric design is based on specific procedures, as shape grammars, Lindenmayer - systems, cellular automata, genetic algorithms or swarm intelligence, each of these procedures having limitations which make them applicable only in certain cases. In the paper the design process stages and the shape grammar type algorithm are presented. The generative design process relies on two basic principles: the modeling principle and the generative principle. The generative method is based on a form finding process, by creating many 3D spatial forms, using an algorithm conceived in order to apply its generating logic onto different input geometry. Once the algorithm is realized, it can be applied repeatedly to generate the geometry for a number of different input surfaces. The generated configurations are then analyzed through a technical or aesthetic selection criterion and finally the optimal solution is selected. Endless range of generative capacity of codes and algorithms used in digital design offers various conceptual possibilities and optimal solutions for both technical and environmental increasing demands of building industry and architecture. Constructions or spaces generated by parametric design can be specifically tuned, in order to meet certain technical or aesthetical requirements. The proposed approach has direct applicability in sustainable architecture, offering important potential economic advantages, a flexible design (which can be changed until the end of the design process) and unique geometric models of high performance.

Keywords: parametric design, algorithmic procedures, free-form architectural object, sustainable architecture

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Authors: Snehalata Tembhurne

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Introduction: A healthy menstrual cycle is a sign of women’s sound health.Various variables may influence the length and regularity of menstrual cycle.Studies have revealed that menstrual cycle abnormalities may be associated with psychological stress,lack of physical exercise, alteration in body composition,endocrine disturbances,higher estrogen levels as seen in obese females.Hence there is an urgent need to find out the relationship between variations in body mass composition(BMI & body fat%) with menstrual abnormalities like primary dysmenorrhoea. Aim: To find out the relationship between body mass composition and primary dysmenorrhea. Objectives: 1.To check whether there is any association between body mass index and primary dysmenorrhoea.2.To check whether there is any association between body fat percentage and primary dysmenorrhoea. NULL HYPOTHESES-There is no relationship between body mass composition and primary dysmenorrhea. Hypothesis: There exists a relationship between body mass composition and primary dysmenorrhea. Materials and Methods: The study was conducted over a period of 6 months with 90 samples selected on random basis. The procedure was explained to the participant and a written consent was taken thereafter. The participant was made to stand on the BODY COMPOSITION SCANNING MONITOR, which scanned the physical profile of the participant (height, weight, BMI, body fat percentage and visceral fat).Thereafter, the candidate was asked about her menstrual irregularities and was asked to grade her level of dysmenorrhoea (if present) using the Verbal Dimensional Dysmenorrhea Scale. Results: Chi square test of association was used to find out the association between body mass composition(body mass index,body fat percentage) and primary dysmenorrhea.The chi-square value for association between body mass index and primary dysmenorrhea was 38.63 p<0.001 which was statistically significant.The chi-square value for the association of body fat % & primary dysmenorrhea was 30.09,p<0.001which was statistically significant. Conclusion: Study shows that there exists a significant relationship between body mass composition and primary dysmenorrhea and as the value of Body mass index and body fat percentages goes on increasing in females, the severity of primary dysmenorrhea also increases.

Keywords: body mass index, body composition screening monitor, primary dysmenorrhea, verbal dimensional dysmenorrhea scale

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Authors: Wesley Teskey, Vedran Glavas, Julian Wegener

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Generating battery cathode microstructures is an important area of research, given the proliferation of the use of automotive batteries. Currently, finite element analysis (FEA) is often used for simulations of battery cathode microstructures before physical batteries can be manufactured and tested to verify the simulation results. Unfortunately, a key drawback of using FEA is that this method of simulation is very slow in terms of computational runtime. Generative AI offers the key advantage of speed when compared to FEA, and because of this, generative AI is capable of evaluating very large numbers of candidate microstructures. Given AI generated candidate microstructures, a subset of the promising microstructures can be selected for further validation using FEA. Leveraging the speed advantage of AI allows for a better final microstructural selection because high speed allows for the evaluation of many more candidate microstructures. For the approach presented, battery cathode 3D candidate microstructures are generated using Gaussian Mixture Models (GMMs) and pix2pix. This approach first uses GMMs to generate a population of spheres (representing the “active material” of the cathode). Once spheres have been sampled from the GMM, they are placed within a microstructure. Subsequently, the pix2pix sweeps over the 3D microstructure (iteratively) slice by slice and adds details to the microstructure to determine what portions of the microstructure will become electrolyte and what part of the microstructure will become binder. In this manner, each subsequent slice of the microstructure is evaluated using pix2pix, where the inputs into pix2pix are the previously processed layers of the microstructure. By feeding into pix2pix previously fully processed layers of the microstructure, pix2pix can be used to ensure candidate microstructures represent a realistic physical reality. More specifically, in order for the microstructure to represent a realistic physical reality, the locations of electrolyte and binder in each layer of the microstructure must reasonably match the locations of electrolyte and binder in previous layers to ensure geometric continuity. Using the above outlined approach, a 10x to 100x speed increase was possible when generating candidate microstructures using AI when compared to using a FEA only approach for this task. A key metric for evaluating microstructures was the battery specific power value that the microstructures would be able to produce. The best generative AI result obtained was a 12% increase in specific power for a candidate microstructure when compared to what a FEA only approach was capable of producing. This 12% increase in specific power was verified by FEA simulation.

Keywords: finite element analysis, gaussian mixture models, generative design, Pix2Pix, structural design

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Authors: Howard Gomwe, Eunice Seekoe

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There is arguably dearth of literature regarding body physical fitness and body composition amongst primary schools in South Africa. For this reason, the study is aimed at investigating and accessing how body composition relates to physical fitness amongst learners between 9 – 14 years of age in the Eastern Cape Province of South Africa. In order to achieve this, a school-based cross-sectional survey was carried out among 876 primary school learners aged 9 to14 years. Body composition indicators were measured and/or calculated, whilst physical fitness was evaluated by a 20 m shuttle run, push-ups, sit and reach as well as sit-ups, according to the EUROFIT fitness standards. Out of 876 participants, a total of 870 were retained. Of these, 351 (40.34%) were boys and 519 (59.66%) were girls. The average age of learners was 11.04 ± 1.50 years, with boys having a importantly (p = 0.002) higher average age (M = 11.24; SD = 1.51 years) as compared to that of girls (M = 10.91; SD = 1.48 years). The non-parametric Spearman Rho correlation coefficients revealed several significant and negative relationships between body composition measurements with physical fitness characteristics, which were stronger in girls than in boys. The findings advocate for policy makers and responsible authorities to initiate the development of policies and interventions targeted at encouraging physical activity and healthy promotion among primary school learners in South Africa, especially in girls.

Keywords: BMI, body composition, physical fitness, children

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Authors: M. Mohemmed Sha Mohamed Kunju, Abdalla A. Al-Ameen Abdurahman, T. Manesh Thankappan, A. Mohamed Mustaq Ahmed Hameed

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Web service composition is an effective approach to complete the web based tasks with desired quality. A single web service with limited functionality is inadequate to execute a specific task with series of action. So, it is very much required to combine multiple web services with different functionalities to reach the target. Also, it will become more and more challenging, when these services are from different providers with identical functionalities and varying QoS, so while composing the web services, the overall QoS is considered to be the major factor. Also, it is not true that the expected QoS is always attained when the task is completed. A single web service in the composed chain may affect the overall performance of the task. So care should be taken in different aspects such as functionality of the service, while composition. Dynamic and automatic service composition is one of the main option available. But to achieve the actual functionality of the task, quality of the individual web services are also important. Normally the QoS of the individual service can be evaluated by using the non-functional parameters such as response time, throughput, reliability, availability, etc. At the same time, the QoS is not needed to be at the same level for all the composed services. So this paper proposes a framework that allows composing the services in terms of QoS by setting the appropriate weight to the non-functional parameters of each individual web service involved in the task. Experimental results show that the importance given to the non-functional parameter while composition will definitely improve the performance of the web services.

Keywords: composition, non-functional parameters, quality of service, web service

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Authors: Hussain Sajid, Jung-Hun Shin, Kum-Won Cho

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Image super-resolution is the most common computer vision problem with many important applications. Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory requirements of GAN-based SR (mainly generators) lead to performance degradation and increased energy consumption, making it difficult to implement it onto resource-constricted devices. To relieve such a problem, In this paper, we introduce an optimized and highly efficient architecture for SR-GAN (generator) model by utilizing model compression techniques such as Knowledge Distillation and pruning, which work together to reduce the storage requirement of the model also increase in their performance. Our method begins with distilling the knowledge from a large pre-trained model to a lightweight model using different loss functions. Then, iterative weight pruning is applied to the distilled model to remove less significant weights based on their magnitude, resulting in a sparser network. Knowledge Distillation reduces the model size by 40%; pruning then reduces it further by 18%. To accelerate the learning process, we employ the Horovod framework for distributed training on a cluster of 2 nodes, each with 8 GPUs, resulting in improved training performance and faster convergence. Experimental results on various benchmarks demonstrate that the proposed compressed model significantly outperforms state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and image quality for x4 super-resolution tasks.

Keywords: single-image super-resolution, generative adversarial networks, knowledge distillation, pruning

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Authors: Po-Chun Wang, Yan-Ru Lai, Sophia I. C. Lin, Alvin W. Y. Su

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The Generative Theory of Tonal Music (GTTM) provides systematic approaches to recognizing local boundaries of music. The rules have been implemented in some automated melody segmentation algorithms. Besides, there are also deep learning methods with GTTM features applied to boundary detection tasks. However, these studies might face constraints such as a lack of or inconsistent label data. The GTTM database is currently the most widely used GTTM database, which includes manually labeled GTTM rules and local boundaries. Even so, we found some problems with these labels. They are sometimes discrepancies with GTTM rules. In addition, since it is labeled at different times by multiple musicians, they are not within the same scope in some cases. Therefore, in this paper, we examine this database with musicians from the aspect of classical music and relabel the scores. The relabeled database - GTTM Database v2.0 - will be released for academic research usage. Despite the experimental and statistical results showing that the relabeled database is more consistent, the improvement in boundary detection is not substantial. It seems that we need more clues than GTTM rules for boundary detection in the future.

Keywords: dataset, GTTM, local boundary, neural network

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Authors: Ferruh Taspinar, Gulce K. Seyyar, Gamze Kurt, Eda O. Okur, Emrah Afsar, Ismail Saracoglu, Betul Taspinar

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Alterations of physical activity can effect body composition (especially body fat ratio); however body mass index may not sufficient to indicate these minimal differences. The aim of this study was to evaluate the relationship between body composition and physical activity in university students. In this study, 132 university students (mean age; 21.21±1.51) were included. Tanita BC-418 and International Physical Activity Questionnaire (IPAQ) were used to evaluate participants. The correlation between the parameters was analysed via Spearman correlation analysis. Significance level in statistical analyses was accepted is 0.05. The results showed that there was no correlation between body mass index and physical activity (p>0.05). There was a positive correlation between body muscle ratio and physical activity, whereas a negative correlation between body fat ratio and physical activity (p<0.05). This study showed that body fat and muscle ratio affects the level of physical activity in healthy university students. Therefore, we thought that physical activity might reduce effects of the diseases caused by disturbed body composition. Further studies are required to support this idea.

Keywords: body composition, body mass index, physical activity, university student

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Authors: Howard Gomwe, Eunice Seekoe, Philemon Lyoka, Chioneso Show Marange, Dennyford Mafa

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There is arguably a lack of literature regarding body physical fitness and body composition amongst primary school learners in South Africa. For this reason, the study is aimed at investigating and accessing how body composition relates to physical fitness amongst primary school learners in the Eastern Cape Province of South Africa. In order to achieve this, a school-based cross-sectional survey was carried out among 876 primary school learners aged 9 to 14 years. Body composition indicators were measured and/or calculated, whilst physical fitness was evaluated according to the EUROFIT fitness standards by a 20 m shuttle run, push-ups, sit and reach as well as sit-ups. Out of 876 participants, a total of 870 were retained. Of these, 351 (40.34%) were boys, and 519 (59.66%) were girls. The average age of learners was 11.04 ± 1.50 years, with boys having a significantly (p = 0.002) higher mean age (M = 11.24; SD = 1.51 years) as compared to that of girls (M = 10.91; SD = 1.48 years). The non-parametric Spearman Rho correlation coefficients revealed several significant and negative relationships between body composition measurements with physical fitness characteristics, which were stronger in girls than in boys. The findings advocate for policymakers and responsible authorities to initiate the development of policies and interventions targeted at encouraging physical activity and health promotion among primary school learners in South Africa, especially in girls.

Keywords: BMI, body composition, body fat, children, physical fitness, primary school

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Authors: P. Karthick, K. Mahesh

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Video has become an increasingly significant component of our digital everyday contact. With the advancement of greater contents and shows of the resolution, its significant volume poses serious obstacles to the objective of receiving, distributing, compressing, and revealing video content of high quality. In this paper, we propose the primary beginning to complete a deep video compression model that jointly upgrades all video compression components. The video compression method involves splitting the video into frames, comparing the images using convolutional neural networks (CNN) to remove duplicates, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using generative adversarial network (GAN) and recorded with long short-term memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps in frame level compression. Pixel wise comparison is performed using K-nearest neighbours (KNN) over the frame, clustered with K-means, and singular value decomposition (SVD) is applied for each and every frame in the video for all three color channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format, and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, frames per second (FPS), and quality results demonstrate a significant resampling rate. On average, the result produced had approximately a 10% deviation in quality and more than 50% in size when compared with the original video.

Keywords: video compression, K-means clustering, convolutional neural network, generative adversarial network, singular value decomposition, pixel visualization, stochastic gradient descent, frame per second extraction, RGB channel extraction, self-detection and deciding system

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Authors: Abdelaziz Hussien

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This article reports investigations of a study into the effectiveness of a traits cooperative learning (TCL) on teacher candidates’ writing achievement, composition, and attitudes towards traits of writing approach and small group learning. Mixed methodologies were used with the participants in a repeated measures quasi-experimental design. Forty-two class teacher candidates, enrolled in the Bahrain Teachers College, completed the pre and post author-developed measures. The results suggest that TCL has a positive effect on the participants’ writing achievement, composition, and attitudes towards traits of writing approach, but not on the attitudes towards small group learning. Further implications to teacher education are presented.

Keywords: trait-based language education, cooperative learning, writing achievement, writing composition, traits of writing, teacher education

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Authors: Arya Prakash Padhi, Anupam Chakrabarti, Rajib Chowdhury

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Topology or layout optimization often gives better performing economic structures and is very helpful in the conceptual design phase. But traditionally it is being done in finite element-based optimization schemes which, although gives a good result, is very time-consuming especially in 3D structures. Among other alternatives machine learning, especially deep learning-based methods, have a very good potential in resolving this computational issue. Here convolutional neural network (3D-CNN) based variational auto encoder (VAE) is trained using a dataset generated from commercially available topology optimization code ABAQUS Tosca using solid isotropic material with penalization (SIMP) method for compliance minimization. The encoded data in latent space is then fed to a 3D generative adversarial network (3D-GAN) to generate the outcome in 64x64x64 size. Here the network consists of 3D volumetric CNN with rectified linear unit (ReLU) activation in between and sigmoid activation in the end. The proposed network is seen to provide almost optimal results with significantly reduced computational time, as there is no iteration involved.

Keywords: 3D generative adversarial network, deep learning, structural topology optimization, variational auto encoder

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Authors: Hamed Alqahtani, Manolya Kavakli-Thorne

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The large pose discrepancy is one of the critical challenges in face recognition during video surveillance. Due to the entanglement of pose attributes with identity information, the conventional approaches for pose-independent representation lack in providing quality results in recognizing largely posed faces. In this paper, we propose a practical approach to disentangle the pose attribute from the identity information followed by synthesis of a face using a classifier network in latent space. The proposed approach employs a modified generative adversarial network framework consisting of an encoder-decoder structure embedded with a classifier in manifold space for carrying out factorization on the latent encoding. It can be further generalized to other face and non-face attributes for real-life video frames containing faces with significant attribute variations. Experimental results and comparison with state of the art in the field prove that the learned representation of the proposed approach synthesizes more compelling perceptual images through a combination of adversarial and classification losses.

Keywords: disentanglement, face detection, generative adversarial networks, video surveillance

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Authors: Cassandra Pratt Romero, Andres Gomez de Silva Garza

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This paper describes a computational system designed to imitate human inspiration during musical composition. The system is called MIS (Musical Inspiration Simulator). The MIS system is inspired by media to which human beings are exposed daily (visual, textual, or auditory) to create new musical compositions based on the emotions detected in said media. After building the system we carried out a series of evaluations with volunteer users who used MIS to compose music based on images, texts, and audio files. The volunteers were asked to judge the harmoniousness and innovation in the system's compositions. An analysis of the results points to the difficulty of computational analysis of the characteristics of the media to which we are exposed daily, as human emotions have a subjective character. This observation will direct future improvements in the system.

Keywords: human inspiration, musical composition, musical composition by computer, theory of sensation and human perception

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Authors: Sinarwati Mohamad Suhaili, Naomie Salim, Mohamad Nazim Jambli

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Sequence-to-sequence (seq2seq) models augmented with attention mechanisms are playing an increasingly important role in automated customer service. These models, which are able to recognize complex relationships between input and output sequences, are crucial for optimizing chatbot responses. Central to these mechanisms are neural attention weights that determine the focus of the model during sequence generation. Despite their widespread use, there remains a gap in the comparative analysis of different attention weighting functions within seq2seq models, particularly in the domain of chatbots using the Customer Support Twitter (CST) dataset. This study addresses this gap by evaluating four distinct attention-scoring functions—dot, multiplicative/general, additive, and an extended multiplicative function with a tanh activation parameter — in neural generative seq2seq models. Utilizing the CST dataset, these models were trained and evaluated over 10 epochs with the AdamW optimizer. Evaluation criteria included validation loss and BLEU scores implemented under both greedy and beam search strategies with a beam size of k=3. Results indicate that the model with the tanh-augmented multiplicative function significantly outperforms its counterparts, achieving the lowest validation loss (1.136484) and the highest BLEU scores (0.438926 under greedy search, 0.443000 under beam search, k=3). These results emphasize the crucial influence of selecting an appropriate attention-scoring function in improving the performance of seq2seq models for chatbots. Particularly, the model that integrates tanh activation proves to be a promising approach to improve the quality of chatbots in the customer support context.

Keywords: attention weight, chatbot, encoder-decoder, neural generative attention, score function, sequence-to-sequence

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Authors: Martin Georgiev, Milena Nanova, Damyan Damov

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Architects, engineers, and developers need to analyse and implement a wide spectrum of data in different formats, if they want to produce viable residential developments. Usually, this data comes from a number of different sources and is not well structured. The main objective of this research project is to provide parametric tools working with real geodesic data that can generate residential solutions. Various codes, regulations and design constraints are described by variables and prioritized. In this way, we establish a common workflow for architects, geodesists, and other professionals involved in the building and investment process. This collaborative medium ensures that the generated design variants conform to various requirements, contributing to a more streamlined and informed decision-making process. The quantification of distinctive characteristics inherent to typical residential structures allows a systematic evaluation of the generated variants, focusing on factors crucial to designers, such as daylight simulation, circulation analysis, space utilization, view orientation, etc. Integrating real geodesic data offers a holistic view of the built environment, enhancing the accuracy and relevance of the design solutions. The use of generative algorithms and parametric models offers high productivity and flexibility of the design variants. It can be implemented in more conventional CAD and BIM workflow. Experts from different specialties can join their efforts, sharing a common digital workspace. In conclusion, our research demonstrates that a generative parametric approach based on real geodesic data and collaborative decision-making could be introduced in the early phases of the design process. This gives the designers powerful tools to explore diverse design possibilities, significantly improving the qualities of the building investment during its entire lifecycle.

Keywords: architectural design, residential buildings, urban development, geodesic data, generative design, parametric models, workflow optimization

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Authors: Esther Ekundayo

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The nature of AI-generated works makes it difficult to identify an author. Although, some scholars have suggested that all the players involved in its creation should be allocated authorship according to their respective contribution. From the programmer who creates and designs the AI to the investor who finances the AI and to the user of the AI who most likely ends up creating the work in question. While others suggested that this issue may be resolved by the UK computer-generated works (CGW) provision under Section 9(3) of the Copyright Designs and Patents Act 1988. However, under the UK and Nigerian copyright law, only human-created works are recognised. This is usually assessed based on their originality. This simply means that the work must have been created as a result of its author’s creative and intellectual abilities and not copied. Such works are literary, dramatic, musical and artistic works and are those that have recently been a topic of discussion with regards to generative artificial intelligence (Generative AI). Unlike Nigeria, the UK CDPA recognises computer-generated works and vests its authorship with the human who made the necessary arrangement for its creation . However, making necessary arrangement in the case of Nova Productions Ltd v Mazooma Games Ltd was interpreted similarly to the traditional authorship principle, which requires the skills of the creator to prove originality. Although, some recommend that computer-generated works complicates this issue, and AI-generated works should enter the public domain as authorship cannot be allocated to AI itself. Additionally, the UKIPO recognising these issues in line with the growing AI trend in a public consultation launched in the year 2022, considered whether computer-generated works should be protected at all and why. If not, whether a new right with a different scope and term of protection should be introduced. However, it concluded that the issue of computer-generated works would be revisited as AI was still in its early stages. Conversely, due to the recent developments in this area with regards to Generative AI systems such as ChatGPT, Midjourney, DALL-E and AIVA, amongst others, which can produce human-like copyright creations, it is therefore important to examine the relevant issues which have the possibility of altering traditional copyright principles as we know it. Considering that the UK and Nigeria are both common law jurisdictions but with slightly differing approaches to this area, this research, therefore, seeks to answer the following questions by comparative analysis: 1)Who is the author of an AI-generated work? 2)Is the UK’s CGW provision worthy of emulation by the Nigerian law? 3) Would a sui generis law be capable of protecting AI-generated works and its author under both jurisdictions? This research further examines the possible barriers to the implementation of the new law in Nigeria, such as limited technical expertise and lack of awareness by the policymakers, amongst others.

Keywords: authorship, artificial intelligence (AI), generative ai, computer-generated works, copyright, technology

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Authors: Valerija Dunkić, Nada Bezić

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Salvia is a genus of the well-known medicinal plant of Lamiaceae family and growing wild throughout the world. This abstract reports the comparation of the essential oils from leaves and flowers composition of Salvia pratensis L. from mountain Velebit, Croatia. Water distilled essential oils from aerial parts of investigation plant have been analysed by GC and GC/MS using VF-5ms capillary column. Fifty-three constituents, representing 99.4% of the leaf oil composition; 51 constituents, representing 86.8% of the flower oil composition. Essential oil yield varied from 0.9% to 1.3% in the leaf and flower parts of the plant. The flower essential oil was characterized by a high concentration of E-caryophyllene (21.9%) and germacrene D (10.2%). Major constituents of the leaf oil were linalool (17.7%), linalool acetate (15.3%) and limonene (9.8%). The comparative results clearly indicated that the leaf and flower oil compositions of S. pratensis were quite different in terms of major components content. The present study gives additional knowledge about secondary metabolites contents on the genus Salvia.

Keywords: essential oil, leaf, flower, Salvia pratensis L.

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Authors: V. Lokesh, M. Manjunathan, S. Sairam, K. Saithsh Kumar, R. Anantharaj

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The densities of pure and its binary mixtures of 1-Butyl-3-methyl imidazolium bis (trifluoro methyl sulfonyl) imide and 1–Ethyl-3-methyl imidazolium ethyl sulphate at different temperature, over the entire composition range were measured at 293.15, 298.15, 303.15, 308.15, 313.15, 318.15, 323.15, 328.15, 33.15, 338.15, 343.15 K. In this study, the liquid-liquid extraction procedure was used. From this experimental data, the excess molar volumes, apparent molar volume, partial molar volumes and the excess partial molar volumes have been calculated for over the whole composition range. Hence, the effect of temperature and composition on all derived thermodynamic properties of this binary mixture will be discussed in terms of intermolecular interactions.

Keywords: ionic liquid, interaction energy, effect of temperature, effect of composition

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Authors: Semuel Unwakoly, Reinner Puppela, Maresthy Rumalean, Healthy Kainama

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Fish is a kind of food that contains many nutritions, one of those is the long chain of unsaturated fatty acids as omega-3 and omega-6 fatty acids and essential amino acid in enough amount for the necessity of our body. Like pelagic fish that found in the sea of Maluku. This research was done to identify fatty acids and amino acids composition in Moonfish (M. maculata) using transesterification reaction steps and Gas Chromatograph-Mass Spectrophotometer (GC-MS) and High-Performance Liquid Chromatography (HPLC). The result showed that fatty acids composition in Moonfish (M. maculata) contained tridecanoic acid (2.84%); palmitoleic acid (2.65%); palmitic acid (35.24%); oleic acid (6.2%); stearic acid (14.20%); and 5,8,11,14-eicosatetraenoic acid (1.29%) and 12 amino acids composition that consist of 7 essential amino acids, were leucine, isoleucine, valine, phenylalanine, methionine, lysine, and histidine, and also 5 non-essential amino acid, were tyrosine, glycine, alanine, glutamic acid, and arginine.Thus, these fishes can be used by the people to complete the necessity of essential fatty acid and amino acid.

Keywords: Moonfish (M. maculata), fatty acid, amino acid, GC-MS, HPLC

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Authors: Murtaza Adil, Sen Yang, Zhou Chao, Song Xiaoping

Abstract:

The morphotropic phase boundary (MPB) recently has attracted constant interest in ferromagnetic systems for obtaining enhanced large magnetoelastic response. In the present study, structural and magnetoelastic properties of MPB involved ferromagnetic Tb_1-xGd_xFe₂ (0≤x≤1) system has been investigated. The change of easy magnetic direction from <111> to <100> with increasing x up MPB composition of x=0.9 is detected by step-scanned [440] synchrotron X-ray diffraction reflections. The Gd substitution for Tb changes the composition for the anisotropy compensation near MPB composition of x=0.9, which was confirmed by the analysis of detailed scanned XRD, magnetization curves and the calculation of the first anisotropy constant K₁. The spin configuration diagram accompanied with different crystal structures for Tb_1-xGd_xFe₂ was designed. The calculated first anisotropy constant K₁ shows a minimum value at MPB composition of x=0.9. In addition, the large ratio between magnetostriction, and the absolute values of the first anisotropy constant │λ_S∕K₁│ appears at MPB composition, which makes it a potential material for magnetostrictive application. Based on experimental results, a theoretically approach was also proposed to signify that the facilitated magnetization rotation and enhanced magnetoelastic effect near MPB composition are a consequence of the anisotropic flattening of free energy of ferromagnetic crystal. Our work specifies the universal existence of MPB in ferromagnetic materials which is important for substantial improvement of magnetic and magnetostrictive properties and may provide a new route to develop advanced functional materials.

Keywords: free energy, magnetic anisotropy, magnetostriction, morphotropic phase boundary (MPB)

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Authors: Jacqueline Rose T. Alipo-on, Francesca Isabelle F. Escobar, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar Al Dahoul

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Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases, which are considered one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis of ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heartbeat types. The dataset used in this work is the synthetic MIT-BIH Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%.

Keywords: heartbeat classification, convolutional neural network, electrocardiogram signals, generative adversarial networks, long short-term memory, ResNet-50

Procedia PDF Downloads 87

Authors: Ferruh Taspinar, Gulce K. Seyyar, Gamze Kurt, Eda O. Okur, Emrah Afsar, Ismail Saracoglu, Betul Taspinar

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Overweight and obesity has been associated with postural balance. The aim of this study was to investigate the relationship between body composition and balance. One hundred and thirty two young adults (58 male, 74 female) were included in the study. Mean age of participants were found as 21.21±1.51 years. Body composition (body mass index, total body fat ratio, total body muscle ratio) and balance (right anterior, right postero-medial, right postero-lateral, left anterior, left postero-medial, left postero-lateral) were evaluated by Tanita BC-418 and Y balance test, respectively. Pearson correlation analysis was used to evaluate the correlation between the parameters. Significance level in statistical analysis was accepted as 0.05. According to results, no correlation was found between body mass index and balance parameters. There was negative correlation between total body fat ratio and balance parameters (r=0.419-0.509, p˂0.05). On the other hand, positive correlation was found between total body muscle ratio and balance parameters (r=0.390-0.494, p˂0.05). This study demonstrated that body fat and muscle ratio affects the balance. Body composition should be considered in rehabilitation programs including postural balance training.

Keywords: balance, body composition, body mass, young adults

Procedia PDF Downloads 346

Authors: Rohit Thirumdas, Annapure U. S.

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Low-pressure air plasma is used to investigate the antioxidant properties and fatty acid composition of rice bran at different power levels (40 W and 60 W). We observed partial hydrogenation of rice bran oil after the treatment. The fatty acid composition analysis by gas chromatography showed an increase of 28.2% in palmitic acid and a 29.4% decrease in linoleic acid. FTIR spectrum shows no new peak formation, which confirms negligible amounts of trans-fatty acids. There is a decrease in peroxide value and iodine value, which can be correlated to an increase in saturated fatty acids. The total polyphenolic content was observed to be increased by 20.1% after the treatment. There is an increase in reducing power and DPPH % inhibition of rice bran due to plasma treatment. This study shows cold plasma treatment can be considered an alternative technology for the hydrogenation of oils, replacing traditional toxic processes.

Keywords: cold plasma, rice bran, fatty acid composition, hydrogenation of oils, antioxidant properties

Procedia PDF Downloads 104