Search results for: conditional random fields

Authors: Cuitao Zhang, Xiongwen He

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According to the new requirements of the future spacecraft, such as networking, modularization and non-cable, this paper studies the CCSDS wireless communications standards, and focuses on the low data-rate wireless communications for spacecraft monitoring and control. The application fields and advantages of wireless communications are analyzed. Wireless communications technology has significant advantages in reducing the weight of the spacecraft, saving time in spacecraft integration, etc. Based on this technology, a scheme for spacecraft data system is put forward. The corresponding block diagram and key wireless interface design of the spacecraft data system are given. The design proposal of the wireless node and information flow of the spacecraft are also analyzed. The results show that the wireless communications scheme is reasonable and feasible. The wireless communications technology can meet the future spacecraft demands in networking, modularization and non-cable.

Keywords: Consultative Committee for Space Data Systems (CCSDS) standards, information flow, non-cable, spacecraft, wireless communications

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Authors: Vural Erdogan

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We created two-dimensional hexagonal cellular automata to obtain complexity by using simple rules same as Conway’s game of life. Considering the game of life rules, Wolfram's works about life-like structures and John von Neumann's self-replication, self-maintenance, self-reproduction problems, we developed 2-states and 3-states hexagonal growing algorithms that reach large populations through random initial states. Unlike the game of life, we used six neighbourhoods cellular automata instead of eight or four neighbourhoods. First simulations explained that whether we are able to obtain sort of oscillators, blinkers, and gliders. Inspired by Wolfram's 1D cellular automata complexity and life-like structures, we simulated 2D synchronous, discrete, deterministic cellular automata to reach life-like forms with 2-states cells. The life-like formations and the oscillators have been explained how they contribute to initiating self-maintenance together with self-reproduction and self-replication. After comparing simulation results, we decided to develop the algorithm for another step. Appending a new state to the same algorithm, which we used for reaching life-like structures, led us to experiment new branching and fractal forms. All these studies tried to demonstrate that complex life forms might come from uncomplicated rules.

Keywords: hexagonal cellular automata, self-replication, self-reproduction, self- maintenance

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Authors: Matich Lyubov

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Today roadmaps are becoming commonly used tools for detecting and designing a desired future for companies, states and the international community. The growing popularity of this method puts tasks such as identifying basic roadmapping principles, creation of concepts and determination of the characteristics of the use of roadmaps depending on the objectives as well as restrictions and opportunities specific to the study area on the agenda. However, the system approach, e.g. the elements which are recognized to be major for high-quality roadmapping, remains one of the main fields for improving the methodology and practice of their development as limited research was devoted to the detailed analysis of the roadmaps from the view of system approach. Therefore, this article is an attempt to examine roadmaps from the view of the system analysis, to compare areas, where, as a rule, roadmaps and systems analysis are considered the most effective tools. To compare the structure and composition of roadmaps and systems models the identification of common points between construction stages of roadmaps and system modeling and the determination of future directions for research roadmaps from a systems perspective are of special importance.

Keywords: technology roadmap, roadmapping, systems analysis, system modeling, innovation management

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Authors: Oscar Javier Herrera, Manuel Angel Camacho

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This paper addresses a cutting edge method of business demand forecasting, based on an empirical probability function when the historical behavior of the data is random. Additionally, it presents error determination based on the numerical method technique ‘propagation of errors’. The methodology was conducted characterization and process diagnostics demand planning as part of the production management, then new ways to predict its value through techniques of probability and to calculate their mistake investigated, it was tools used numerical methods. All this based on the behavior of the data. This analysis was determined considering the specific business circumstances of a company in the sector of communications, located in the city of Bogota, Colombia. In conclusion, using this application it was possible to obtain the adequate stock of the products required by the company to provide its services, helping the company reduce its service time, increase the client satisfaction rate, reduce stock which has not been in rotation for a long time, code its inventory, and plan reorder points for the replenishment of stock.

Keywords: demand forecasting, empirical distribution, propagation of error, Bogota

Procedia PDF Downloads 632

Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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The most aggressive form of brain tumor is called glioma. Glioma is kind of tumor that arises from glial tissue of the brain and occurs quite often. A fully automatic 2D-CNN model for brain tumor segmentation is presented in this paper. We performed pre-processing steps to remove noise and intensity variances using N4ITK and standard intensity correction, respectively. We used Keras open-source library with Theano as backend for fast implementation of CNN model. In addition, we used BRATS 2015 MRI dataset to evaluate our proposed model. Furthermore, we have used SimpleITK open-source library in our proposed model to analyze images. Moreover, we have extracted random 2D patches for proposed 2D-CNN model for efficient brain segmentation. Extracting 2D patched instead of 3D due to less dimensional information present in 2D which helps us in reducing computational time. Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.77 for complete, 0.76 for core, 0.77 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, LGG

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Authors: Mustapha Abdulkadir Gana, Jibrin Attahiru Alhassan, Adamu Musa Baba

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The exponential growth of information sources, resources and the continued Communication Technology (ICT) sophistication of libraries all over the world call for capable and ICT compliant librarians in Nigeria, this article assesses the digital literacy skills of librarians in tertiary institutions in Niger state. The survey research method was applied in the study using a random sampling technique to draw the sample. Fifty-eight copies of the questionnaire were administered while forty-nine copies were completed, returned, and used in the study, which represents 84% of the response rate. Two research questions were answered, and data were analyzed using Statistical Package for the Social Sciences (SPSS). The finding uncovered that the librarians lack the requisite digital literacy skills to access the wealth of digital information resources available. The study recommends some steps to turn around the situations amongst; librarians must be empowered with all necessary digital literacy skills, embark on rigorous training and retraining programs, workshops, conferences, and seminars, there should also be a coherent training policy for the librarians on a sustainable basis to increase their requisite digital literacy skills.

Keywords: digital, information, literacy, skills

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Authors: Jiawei Dai, Xinrong Li, Yulong Sun, Yunxiao Hao

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Recently, participatory approaches have become popular in a variety of fields, including social work, community, and population health, as important research tools for researchers to understand and immerse communities and conceptualize social phenomena. The participatory visual research methods promote the diversification and depth of the exploration process and communication forms to support the feasibility and practicality of the scheme, which helps to further inspire designers and avoid blind spots caused by the solidification of single thinking. This paper focuses on how to develop visual toolkits for participatory methods to assist and shape crowd participation and trigger idea generation in community issues. This project helps to verify the value of participatory visual tools in shaping participation and arousing expression, which provides support for gaining community diversity insights and community problem-solving. In addition, a visual toolbox was developed based on an actual case in a community for field testing, and further discussion was carried out after the data results were analyzed.

Keywords: participatory design, community service, visual toolbox, visual metaphor

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Authors: Seonaid Hume, Gordon Dobie, Graeme West

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Detecting and localizing radioactive sources is a necessity for safe and secure decommissioning of nuclear facilities. An important aspect for the management of the sort-and-segregation process is establishing the spatial distributions and quantities of the waste radionuclides, their type, corresponding activity, and ultimately classification for disposal. The data received from surveys directly informs decommissioning plans, on-site incident management strategies, the approach needed for a new cell, as well as protecting the workforce and the public. Manual classification of nuclear waste from a nuclear cell is time-consuming, expensive, and requires significant expertise to make the classification judgment call. Also, in-cell decommissioning is still in its relative infancy, and few techniques are well-developed. As with any repetitive and routine tasks, there is the opportunity to improve the task of classifying nuclear waste using autonomous systems. Hence, this paper proposes a new framework for the automatic classification of nuclear waste. This framework consists of five main stages; 3D spatial mapping and object detection, object classification, radiological mapping, source localisation based on gathered evidence and finally, waste classification. The first stage of the framework, 3D visual mapping, involves object detection from point cloud data. A review of related applications in other industries is provided, and recommendations for approaches for waste classification are made. Object detection focusses initially on cylindrical objects since pipework is significant in nuclear cells and indeed any industrial site. The approach can be extended to other commonly occurring primitives such as spheres and cubes. This is in preparation of stage two, characterizing the point cloud data and estimating the dimensions, material, degradation, and mass of the objects detected in order to feature match them to an inventory of possible items found in that nuclear cell. Many items in nuclear cells are one-offs, have limited or poor drawings available, or have been modified since installation, and have complex interiors, which often and inadvertently pose difficulties when accessing certain zones and identifying waste remotely. Hence, this may require expert input to feature match objects. The third stage, radiological mapping, is similar in order to facilitate the characterization of the nuclear cell in terms of radiation fields, including the type of radiation, activity, and location within the nuclear cell. The fourth stage of the framework takes the visual map for stage 1, the object characterization from stage 2, and radiation map from stage 3 and fuses them together, providing a more detailed scene of the nuclear cell by identifying the location of radioactive materials in three dimensions. The last stage involves combining the evidence from the fused data sets to reveal the classification of the waste in Bq/kg, thus enabling better decision making and monitoring for in-cell decommissioning. The presentation of the framework is supported by representative case study data drawn from an application in decommissioning from a UK nuclear facility. This framework utilises recent advancements of the detection and mapping capabilities of complex radiation fields in three dimensions to make the process of classifying nuclear waste faster, more reliable, cost-effective and safer.

Keywords: nuclear decommissioning, radiation detection, object detection, waste classification

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Authors: Michael Koblischka, Anjela Koblischka-Veneva, XianLin Zeng, Essia Hannachi, Yassine Slimani

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Superconducting foams of YBa2Cu3O7 (abbreviated Y-123) were produced using the infiltration growth (IG) technique from Y2BaCuO5 (Y-211) foams. The samples were investigated by SEM (scanning electron microscopy) and electrical resistivity measurements. SEM observations indicated the specific microstructure of the foam struts with numerous tiny Y-211 particles (50-100 nm diameter) embedded in channel-like structures between the Y-123 grains. The investigation of the excess conductivity of different prepared composites was analyzed using Aslamazov-Larkin (AL) model. The investigated samples comprised of five distinct fluctuation regimes, namely short-wave (SWF), one-dimensional (1D), two-dimensional (2D), three-dimensional (3D), and critical (CR) fluctuations regimes. The coherence length along the c-axis at zero-temperature (ξc(0)), lower and upper critical magnetic fields (Bc1 and Bc2), critical current density (Jc) and numerous other superconducting parameters were estimated from the data. The analysis reveals that the presence of the tiny Y-211 particles alters the excess conductivity and the fluctuation behavior observed in standard YBCO samples.

Keywords: Excess conductivity, Foam, Microstructure, Superconductor YBa2Cu3Oy

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Authors: Dominic Kwaku Danso Mensah

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The study examined conflict management strategies among head teachers and teachers in selected basic schools in Okai-Koi sub metro in the greater region of Ghana. In all, 270 participants were engaged in the study, comprising 237 teachers, 32 head teachers, and one officer in charge of the Metropolis. The study employed descriptive survey while using purposive and simple random sampling techniques to sample participants. Interview guides and questionnaires were the main instruments used for gathering primary data. The study found that conflict is inevitable in the schools. Conflicts in schools are usually subtle and hardly noticed by outsiders even though they occur on daily basis. The causes of conflict include among other things, high expectation from head teachers, inability to attain goals set, communication from head teachers and power struggle. The study found out that, in managing and resolving conflicts, issues such as identifying and focusing on the problem, building of trust and cooperation, clarifying goals and objectives were seen to be effective means of managing conflict and recommended that management should design and develop conflict management strategies to quickly resolve conflict.

Keywords: basic education, conflict management, organizational harmony, power

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Authors: Zoran Spasovski

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The paper shortly discusses the significance and the benefits of the historical approach in the research of languages by presenting examples of it in the fields of phonetics and phonology, lexicology, morphology, syntax, and even in the onomastics (toponomy and anthroponomy). The examples from the field of phonetics/phonology include insights into animal speech and its evolution into human speech, the evolution of the sounds of human speech from vocals to glides and consonants and from velar consonants to palatal, etc., on well-known examples of former researchers. Those from the field of lexicology show shortly the formation of the lexemes and their evolution; the morphology and syntax are explained by examples of the development of grammar and syntax forms, and the importance of the historical approach in the research of place-names and personal names is briefly outlined through examples of place-names and personal names and surnames, and the conclusions that come from it, in different languages.

Keywords: animal speech, glotogenesis, grammar forms, lexicology, place-names, personal names, surnames, syntax categories

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Authors: Adaoma Igwedibia, Obetta Emmanuela

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The use of absurdity as a catalyst for reflection has gained attention in various domains, including philosophy, literature, and psychology. Absurdity, characterised by its inherent contradiction and irrationality, has been considered a potent tool for stimulating reflection and generating meaningful insights. However, despite its conceptual appeal, a comprehensive understanding of the effectiveness and potential limitations of absurdity in this context remains insufficiently explored. This paper aims to address this gap in knowledge by critically examining the role of absurdity in stimulating reflection and uncovering its precise mechanisms for generating meaningful insights. By reviewing relevant literature and theories, we seek to shed light on the factors that influence the effectiveness of absurdity as a catalyst for reflection and explore its potential limitations. Furthermore, this study intends to provide practical implications for the utilisation of absurdity in various fields, such as education, creativity, and personal development. Through a thorough investigation of existing research and the identification of areas for further exploration, this paper aims to contribute to a more comprehensive understanding of the role of absurdity in stimulating reflection and generating meaningful insights.

Keywords: absurdity, catalyst, reflection, effectiveness

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Authors: Vishwesh Kulkarni, Nikhil Bellarykar

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Cellular complexity stems from the interactions among thousands of different molecular species. Thanks to the emerging fields of systems and synthetic biology, scientists are beginning to unravel these regulatory, signaling, and metabolic interactions and to understand their coordinated action. Reverse engineering of biological networks has has several benefits but a poor quality of data combined with the difficulty in reproducing it limits the applicability of these methods. A few years back, many of the commonly used predictive algorithms were tested on a network constructed in the yeast Saccharomyces cerevisiae (S. cerevisiae) to resolve this issue. The network was a synthetic network of five genes regulating each other for the so-called in vivo reverse-engineering and modeling assessment (IRMA). The network was constructed in S. cereviase since it is a simple and well characterized organism. The synthetic network included a variety of regulatory interactions, thus capturing the behaviour of larger eukaryotic gene networks on a smaller scale. We derive a new set of algorithms by solving a nonlinear optimization problem and show how these algorithms outperform other algorithms on these datasets.

Keywords: synthetic gene network, network identification, optimization, nonlinear modeling

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Authors: Klaus Peter Scherer, Daniel Knöll, Constantin Rieder

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Problem and Purpose: Intelligent systems are available and helpful to support the human being decision process, especially when complex surgical eye interventions are necessary and must be performed. Normally, such a decision support system consists of a knowledge-based module, which is responsible for the real assistance power, given by an explanation and logical reasoning processes. The interview based acquisition and generation of the complex knowledge itself is very crucial, because there are different correlations between the complex parameters. So, in this project (semi)automated self-learning methods are researched and developed for an enhancement of the quality of such a decision support system. Methods: For ophthalmic data sets of real patients in a hospital, advanced data mining procedures seem to be very helpful. Especially subgroup analysis methods are developed, extended and used to analyze and find out the correlations and conditional dependencies between the structured patient data. After finding causal dependencies, a ranking must be performed for the generation of rule-based representations. For this, anonymous patient data are transformed into a special machine language format. The imported data are used as input for algorithms of conditioned probability methods to calculate the parameter distributions concerning a special given goal parameter. Results: In the field of knowledge discovery advanced methods and applications could be performed to produce operation and patient related correlations. So, new knowledge was generated by finding causal relations between the operational equipment, the medical instances and patient specific history by a dependency ranking process. After transformation in association rules logically based representations were available for the clinical experts to evaluate the new knowledge. The structured data sets take account of about 80 parameters as special characteristic features per patient. For different extended patient groups (100, 300, 500), as well one target value as well multi-target values were set for the subgroup analysis. So the newly generated hypotheses could be interpreted regarding the dependency or independency of patient number. Conclusions: The aim and the advantage of such a semi-automatically self-learning process are the extensions of the knowledge base by finding new parameter correlations. The discovered knowledge is transformed into association rules and serves as rule-based representation of the knowledge in the knowledge base. Even more, than one goal parameter of interest can be considered by the semi-automated learning process. With ranking procedures, the most strong premises and also conjunctive associated conditions can be found to conclude the interested goal parameter. So the knowledge, hidden in structured tables or lists can be extracted as rule-based representation. This is a real assistance power for the communication with the clinical experts.

Keywords: an expert system, knowledge-based support, ophthalmic decision support, self-learning methods

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Authors: H. Paredes Gutiérrez, S. T. Pérez-Merchancano

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Motivated by recent experimental and theoretical developments, we investigate the influence of embedded quantum dot (EQD) of different geometries (lens, ring and pyramidal) in a double barrier heterostructure (DBH). We work with a general theory of quantum transport that accounts the tight-binding model for the spin dependent resonant tunneling in a semiconductor nanostructure, and Rashba spin orbital to study the spin orbit coupling. In this context, we use the second quantization theory for Rashba effect and the standard Green functions method. We calculate the current density as a function of the voltage without and in the presence of quantum dots. In the second case, we considered the size and shape of the quantum dot, and in the two cases, we worked considering the spin polarization affected by external electric fields. We found that the EQD generates significant changes in current when we consider different morphologies of EQD, as those described above. The first thing shown is that the current decreases significantly, such as the geometry of EQD is changed, prevailing the geometrical confinement. Likewise, we see that the current density decreases when the voltage is increased, showing that the quantum system studied here is more efficient when the morphology of the quantum dot changes.

Keywords: quantum semiconductors, nanostructures, quantum dots, spin polarization

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Authors: Shereen Nadira Binti Jasney, Mohd Syuhaidi Bin Abu Bakar, Hafizah Binti Rosli

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This quantitative study explored factors that motivate the public to use YouTube; and the elements of criteria, which the public are looking for to accept new talents on User-Generated Content (UGC). There are mass inputs on the net but the publics are still being very selective in accepting new talents. Thus, it is important to identify determinant factors that contribute to the acceptance of new talents on UGC. A total number of 236 respondents have participated in this study using Simple Random Sampling and they were analyzed with descriptive analysis. The findings of this paper advocate that tremendous expansion; and diversification YouTube music offers are main factors that motivated public viewers in using YouTube on accepting new talents. It is also found that by being relatable and concurrently providing interesting contents, having the artist name and song title in the YouTube talent’s title video and the number of views and likes of the video are some of the criteria that the public are looking for in accepting new talents on the UGC. This paper introduces YouTube as a mean of discovering new talents in the music industry where the public, especially the younger generations, whom are actively engaged with current digital landscape that they’ve been presently silver-plated.

Keywords: motivation, criteria, new talents, UGC, YouTube

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Authors: Harsh Vikram Singh, Ankur Rai, Anand Mohan

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In this paper, we propose secure and robust data hiding algorithm based on DCT by Arnold transform and chaotic sequence. The watermark image is scrambled by Arnold cat map to increases its security and then the chaotic map is used for watermark signal spread in middle band of DCT coefficients of the cover image The chaotic map can be used as pseudo-random generator for digital data hiding, to increase security and robustness .Performance evaluation for robustness and imperceptibility of proposed algorithm has been made using bit error rate (BER), normalized correlation (NC), and peak signal to noise ratio (PSNR) value for different watermark and cover images such as Lena, Girl, Tank images and gain factor .We use a binary logo image and text image as watermark. The experimental results demonstrate that the proposed algorithm achieves higher security and robustness against JPEG compression as well as other attacks such as addition of noise, low pass filtering and cropping attacks compared to other existing algorithm using DCT coefficients. Moreover, to recover watermarks in proposed algorithm, there is no need to original cover image.

Keywords: data hiding, watermarking, DCT, chaotic sequence, arnold transforms

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Authors: Babita Adhikari

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There is a general assumption that affluent people consume a variety of balanced nutritious foods on a regular basis, such as fruits, whole grains, lean meat, nuts, and fresh vegetables, because they have greater affordability and market accessibility. A simple random sampling technique and an open-ended questionnaire were used for this study. Findings showed that high socioeconomic status (SES) people in Kathmandu were more concerned with expensive foods, fruits, and vegetables, regardless of their nutrient content. New millionaire groups in Kathmandu are aware of the importance of nutrition and healthy well-being, but their purchasing and consumption habits differ from general perceptions as they learn about fast-food and restaurant culture. On the home front, they buy, cook, and eat expensive foods but are unaware of their nutrient contents. The study critically examines attributes that influence purchase decisions for nutritious and healthy foods in Kathmandu. Despite the fact that a significant amount of literature helps to comprehend that food has to be good in taste, healthy, and affordable, the major driver of food purchases is still the desire to consume.

Keywords: nutritious food, consumer behavior, nutrition, food behavior

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Authors: Yudhajit Datta

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Will is a phenomenon that has puzzled humanity for a long time. It is a belief that Will Power of an individual affects the success achieved by an individual in life. It is thought that a person endowed with great will power can overcome even the most crippling setbacks of life while a person with a weak will cannot make the most of life even the greatest assets. Behavioral aspects of the human experience such as will are rarely subjected to quantitative study owing to the numerous uncontrollable parameters involved. This work is an attempt to subject the phenomena of will to the test of an artificial neural network. The claim being tested is that will power of an individual largely determines success achieved in life. In the study, an attempt is made to incorporate the behavioral phenomenon of will into a computational model using data pertaining to the success of individuals obtained from an experiment. A neural network is to be trained using data based upon part of the model, and subsequently used to make predictions regarding will corresponding to data points of success. If the prediction is in agreement with the model values, the model is to be retained as a candidate. Ultimately, the best-fit model from among the many different candidates is to be selected, and used for studying the correlation between success and will.

Keywords: will power, will, success, apathy factor, random factor, characteristic function, life story

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Authors: Aminu Mubarak Sadis

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This study appraised the maintenance management practice in Federal University Dutse and Jigawa State Polytechnic Dutse, in Nigeria. The Physical Planning, Works and Maintenance Departments of the two Higher Institutions (Federal University Dutse and Jigawa State Polytechnic) are responsible for production and maintenance management of their physical assets. Over–enrollment problem has been a common feature in the higher institutions in Nigeria, Data were collected by the administered questionnaires and subsequent oral interview to authenticate the completed questionnaires. Random sampling techniques was used in selecting 150 respondents across the various institutions (Federal University Dutse and Jigawa State Polytechnic Dutse). Data collected was analyzed using Statistical Package for Social Science (SPSS) and t-test statistical techniques The conclusion was that maintenance management activities are yet to be given their appropriate attention on functions of the university and polytechnic which are crucial to improving teaching, learning and research. The unit responsible for maintenance and managing facilities should focus on their stated functions and effect changes were possible.

Keywords: appraisal, maintenance management, university, Polytechnic, practices

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Authors: Iniye Irene Wodi, Kennedy B. Gibson

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Students’ poor performance in mathematics in both internal and external examinations has been a source of concern to researchers in Nigeria. The cause of this has been attributed to both teachers and students. To this end, this study sought to find out students’ perceptions of teachers’ attributes as a cause of mathematics phobia among secondary school students in Bayelsa State Nigeria. The population of the study comprised of all students of senior secondary schools in Yenagoa metropolis. A sample of 120 students was drawn from this population using clustering and simple random sampling techniques. The instrument for data collection was a researcher constructed questionnaire titled Mathematics Phobia Questionnaire (MPQ). Data were analysed, and the results revealed that students perceived teachers’ attributes such as methods and styles of teaching, difficulty in communication, etc. as causes of mathematics phobia among students in senior secondary schools in Bayelsa State. Based on the result, it was therefore recommended that mathematics teachers should be retrained periodically in order to learn new and innovative ways of teaching mathematics to prevent its phobia among students.

Keywords: mathematics phobia, teacher attributes, teaching method, teaching style

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Authors: Hexiong Liu

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Powder metallurgy is the optimal method for the consolidation and preparation of W(Mo) alloys, which exhibit excellent application prospects at high temperatures. The properties of W(Mo) alloys are closely related to the sintered density. However, controlling the sintered density and porosity of these alloys is still challenging. In the past, the regulation methods mainly focused on time-consuming and costly trial-and-error experiments. In this study, the sintering data for more than a dozen W(Mo) alloys constituted a small-scale dataset, including both solid and liquid phases of sintering. Furthermore, simple descriptors were used to predict the sintered density of W(Mo) alloys based on the descriptor selection strategy and machine learning method (ML), where the ML algorithm included the least absolute shrinkage and selection operator (Lasso) regression, k-nearest neighbor (k-NN), random forest (RF), and multi-layer perceptron (MLP). The results showed that the interpretable descriptors extracted by our proposed selection strategy and the MLP neural network achieved a high prediction accuracy (R>0.950). By further predicting the sintered density of W(Mo) alloys using different sintering processes, the error between the predicted and experimental values was less than 0.063, confirming the application potential of the model.

Keywords: sintered density, machine learning, interpretable descriptors, W(Mo) alloy

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Authors: Jacqueline Y. Thompson, Sam Watson, Lee Middleton, Karla Hemming

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Introduction: The odds ratio (estimated via logistic regression) is a well-established and common approach for estimating covariate-adjusted binary treatment effects when comparing a treatment and control group with dichotomous outcomes. Its popularity is primarily because of its stability and robustness to model misspecification. However, the situation is different for the relative risk and risk difference, which are arguably easier to interpret and better suited to specific designs such as non-inferiority studies. So far, there is no equivalent, widely acceptable approach to estimate an adjusted relative risk and risk difference when conducting clinical trials. This is partly due to the lack of a comprehensive evaluation of available candidate methods. Methods/Approach: A simulation study is designed to evaluate the performance of relevant candidate methods to estimate relative risks to represent conditional and marginal estimation approaches. We consider the log-binomial, generalised linear models (GLM) with iteratively weighted least-squares (IWLS) and model-based standard errors (SE); log-binomial GLM with convex optimisation and model-based SEs; log-binomial GLM with convex optimisation and permutation tests; modified-Poisson GLM IWLS and robust SEs; log-binomial generalised estimation equations (GEE) and robust SEs; marginal standardisation and delta method SEs; and marginal standardisation and permutation test SEs. Independent and identically distributed datasets are simulated from a randomised controlled trial to evaluate these candidate methods. Simulations are replicated 10000 times for each scenario across all possible combinations of sample sizes (200, 1000, and 5000), outcomes (10%, 50%, and 80%), and covariates (ranging from -0.05 to 0.7) representing weak, moderate or strong relationships. Treatment effects (ranging from 0, -0.5, 1; on the log-scale) will consider null (H0) and alternative (H1) hypotheses to evaluate coverage and power in realistic scenarios. Performance measures (bias, mean square error (MSE), relative efficiency, and convergence rates) are evaluated across scenarios covering a range of sample sizes, event rates, covariate prognostic strength, and model misspecifications. Potential Results, Relevance & Impact: There are several methods for estimating unadjusted and adjusted relative risks. However, it is unclear which method(s) is the most efficient, preserves type-I error rate, is robust to model misspecification, or is the most powerful when adjusting for non-prognostic and prognostic covariates. GEE estimations may be biased when the outcome distributions are not from marginal binary data. Also, it seems that marginal standardisation and convex optimisation may perform better than GLM IWLS log-binomial.

Keywords: binary outcomes, statistical methods, clinical trials, simulation study

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Authors: Jakub Berčík, Elena Horská, Ľudmila Nagyová

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Nowadays, the point of sale remains one of the few channels of communication which is not oversaturated yet and has great potential for the future. The fact that purchasing decisions are significantly affected by emotions, while up to 75 % of them are implemented at the point of sale, only demonstrates its importance. The share of impulsive purchases is about 60-75 %, depending on the particular product category. Nevertheless, habits predetermine the content of the shopping cart above all and hence in this regard the role of in-store communication is to disrupt the routine and compel the customer to try something new. This is the reason why it is essential to know how to work with this relatively young branch of marketing communication as efficiently as possible. New global trend in this discipline is evaluating the effectiveness of particular tools in the in-store communication. To increase the efficiency it is necessary to become familiar with the factors affecting the customer both consciously and unconsciously, and that is a task for neuromarketing and sensory marketing. It is generally known that the customer remembers the negative experience much longer and more intensely than the positive ones, therefore it is essential for marketers to avoid this negative experience. The final effect of POP (Point of Purchase) or POS (Point of Sale) tools is conditional not only on their quality and design, but also on the location at the point of sale which contributes to the overall positive atmosphere in the store. Therefore, in-store advertising is increasingly in the center of attention and companies are willing to spend even a third of their marketing communication budget on it. The paper deals with a comprehensive, interdisciplinary research of the impact of traditional as well as innovative tools of in-store communication on the attention and emotional state (valence and arousal) of consumers on the food market. The research integrates measurements with eye camera (Eye tracker) and electroencephalograph (EEG) in real grocery stores as well as in laboratory conditions with the purpose of recognizing attention and emotional response among respondents under the influence of selected tools of in-store communication. The object of the research includes traditional (e.g. wobblers, stoppers, floor graphics) and innovative (e.g. displays, wobblers with LED elements, interactive floor graphics) tools of in-store communication in the fresh unpackaged food segment. By using a mobile 16-channel electroencephalograph (EEG equipment) from the company EPOC, a mobile eye camera (Eye tracker) from the company Tobii and a stationary eye camera (Eye tracker) from the company Gazepoint, we observe the attention and emotional state (valence and arousal) to reveal true consumer preferences using traditional and new unusual communication tools at the point of sale of the selected foodstuffs. The paper concludes with suggesting possibilities for rational, effective and energy-efficient combination of in-store communication tools, by which the retailer can accomplish not only captivating and attractive presentation of displayed goods, but ultimately also an increase in retail sales of the store.

Keywords: electroencephalograph (EEG), emotion, eye tracker, in-store communication

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Authors: Yakubu Bala Mohammed, Nadire Chavus, Mohammed Bulama

Abstract:

Generative Pre-trained Transformer (ChatGPT) represents an artificial intelligence (AI) tool capable of swiftly generating comprehensive responses to prompts and follow-up inquiries. This emerging AI tool was introduced in November 2022 by OpenAI firm, an American AI research laboratory, utilizing substantial language models. This present study aims to delve into the potential future consequences of ChatGPT usage in education using AI-based algorithms. The paper will bring forth the likely potential risks of ChatGBT utilization, such as academic integrity concerns, unfair learning assessments, excessive reliance on AI, and dissemination of inaccurate information using four machine learning algorithms: eXtreme-Gradient Boosting (XGBoost), Support vector machine (SVM), Emotional artificial neural network (EANN), and Random forest (RF) would be used to analyze the study collected data due to their robustness. Finally, the findings of the study will assist education stakeholders in understanding the future implications of ChatGPT usage in education and propose solutions and directions for upcoming studies.

Keywords: machine learning, ChatGPT, education, learning, implications

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Authors: Mohamad Alkhalidi, S. Neelamani, Noor Alanjari

Abstract:

The main purpose of harbors and marinas is to create a calm and safe docking space for marine vessels. Standard rubble mound breakwaters, although widely used, occupy port space and require large amounts of stones or rocks. Kuwait does not have good quality stone, so they are imported at a very high cost. Therefore, there is a need for a new wave energy dissipating structure where stones and rocks are scarce. While permeable slotted vertical walls have been proved to be a suitable alternative to rubble mound breakwaters, the introduction of sloped slotted walls may be more efficient in dissipating wave energy. For example, two slotted barriers with 60degree inclination may be equivalent to three vertical slotted barriers from wave energy dissipation point of view. A detailed physical model study is carried out to determine the effects of slope angle, porosity, and a number of walls on wave energy dissipation for a wide range of random and regular waves. The results of this study can be used to improve and optimize energy dissipation and reduce construction cost.

Keywords: porosity, slope, wave reflection, wave transmission

Procedia PDF Downloads 291

Authors: Sombol Mokhles

Abstract:

Tackling climate change requires expanding networking opportunities between a diverse range of cities to accelerate climate actions. Existing climate city networks have limitations in actively engaging “ordinary” cities in networking processes between cities, as they encourage a few powerful cities to be followed by the many “ordinary” cities. To reimagine the networking opportunities between cities beyond global cities, this paper incorporates “cosmopolitan comparison” to expand our knowledge of a diverse range of cities using a data-driven approach. Through a cosmopolitan perspective, a framework is presented on how to utilise large data to expand knowledge of cities beyond global cities to reimagine the existing hierarchical networking practices. The contribution of this framework is beyond urban climate governance but inclusive of different fields which strive for a more inclusive and cosmopolitan comparison attentive to the differences across cities.

Keywords: cosmopolitan city comparison, data-driven approach, climate city networking, urban climate governance

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Authors: Jamshaid ul Rahman, Akhter Ali, Adnan Manzoor

Abstract:

Learning-based systems have received increasing interest in recent years; recognition structures, including end-to-end speak recognition, are one of the hot topics in this area. A famous work on end-to-end speaker verification by using Angular Softmax Loss gained significant importance and is considered useful to directly trains a discriminative model instead of the traditional adopted i-vector approach. The margin-based strategy in angular softmax is beneficial to learn discriminative speaker embeddings where the random selection of margin values is a big issue in additive angular margin and multiplicative angular margin. As a better solution in this matter, we present an alternative approach by introducing a bit similar form of an additive parameter that was originally introduced for face recognition, and it has a capacity to adjust automatically with the corresponding margin values and is applicable to learn more discriminative features than the Softmax. Experiments are conducted on the part of Fisher dataset, where it observed that the additive parameter with angular softmax to train the front-end and probabilistic linear discriminant analysis (PLDA) in the back-end boosts the performance of the structure.

Keywords: additive parameter, angular softmax, speaker verification, PLDA

Procedia PDF Downloads 104

Authors: Lucie Hayashi

Abstract:

This paper offers a view on a different approach towards a dancer´s career in two very dissimilar countries: on one hand Japan, an economic predator at the end of last century, but suffering under economic crisis from the beginning of the new century; and the Czech Republic, a post-communist country, caught up in capitalist fever from the 1990s on the other. The government’s approach towards culture and dance in these two countries not only has a different history and nature, but also presents a different take on the ideal future development in its respective dance scenes. The level of support from the state budget echoes in all the fields of a professional dance career, dance art and the education of the public towards dance. The message of the statistic data is clear: the production of an enormous number of well trained and expensively educated dancers with no jobs for them in Japan, and a lack of good dancers ready to fill state supported theatre companies in the Czech Republic (that gladly employs Japanese dancers). The paradigm leaves a big exclamation mark on the huge influence the policy has on dance in society, and a question mark on the ideal situation.

Keywords: culture policy, dance, education, employment, Czech Republic, Japan

Procedia PDF Downloads 166

Authors: Amylyn F. Labasano

Abstract:

A significant number of empirical studies are carried out to investigate factors affecting college students’ performance in the academic examination. With a wide-array of literature-and studies-supported findings, this study is limited only on the students’ probability of passing periodical exams which is associated with students’ gender, absences in the class, use of reference book, and hours of study. Binary logistic regression was the technique used in the analysis. The research is based on the students’ record and data collected through survey. The result reveals that gender, use of reference book and hours of study are significant predictors of passing an examination while students’ absenteeism is an insignificant predictor. Females have 45% likelihood of passing the exam than their male classmates. Students who use and read their reference book are 38 times more likely pass the exam than those who do not use and read their reference book. Those who spent more than 3 hours in studying are four (4) times more likely pass the exam than those who spent only 3 hours or less in studying.

Keywords: absences, binary logistic regression, gender, hours of study prediction-causation method, periodical exams, random sampling, reference book

Procedia PDF Downloads 314