Search results for: drying models

Authors: Zebider Asire Munyelet, Yibeltal Chanie Manie

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In the modern digital landscape, the increase of social media platforms has become identical to the evolution of online consumer behavior. This study investigates the complicated relationship between social media and the purchasing decisions of online buyers. Through an extensive review of existing literature and empirical research, the aim is to comprehensively analyze the multidimensional impact that social media exerts on the various stages of the online buyer's journey. The investigation encompasses the exploration of how social media platforms serve as influential channels for information dissemination, product discovery, and consumer engagement. Additionally, the study investigates the psychological aspects underlying the role of social media in shaping buyer preferences, perceptions, and trust in online transactions. The methodologies employed include both quantitative and qualitative analyses, incorporating surveys, interviews, and data analytics to derive meaningful insights. Statistical models are applied to distinguish patterns in online buyer behavior concerning product awareness, brand loyalty, and decision-making processes. The expected outcomes of this research contribute not only to the academic understanding of the dynamic interplay between social media and online buyer behavior but also offer practical implications for marketers, e-commerce platforms, and policymakers.

Keywords: social platforms, buyer behavior, consumer behavior, digital era

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Authors: Danish Farooq, Janos Juhasz

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Car-motorcycle accidents have been observed higher in recent years, which caused mainly riders’ fatalities and serious injuries. In-depth crash investigation methods aim to investigate the main factors which are likely involved in fatal road accidents and injury outcomes. The main objective of this study is to investigate the involved factors in car-motorcycle accidents in Budapest city. The procedure included statistical analysis and data sampling to identify car-motorcycle accidents by dominant accident types based on collision configurations. The police report was used as a data source for specified accidents, and simulation models were plotted according to scale (M 1:200). Car-motorcycle accidents were simulated in Virtual Crash software for 5 seconds before the collision. The simulation results showed that the main involved factors to car-motorcycle accidents were human behavior and view obstructions. The comprehensive, in-depth analysis also found that most of the car drivers and riders were unable to perform collision avoidance manoeuvres before the collision. This study can help the traffic safety authorities to focus on simulated involved factors to solve road safety issues in car-motorcycle accidents. The study also proposes safety measures to improve safe movements among road users.

Keywords: car motorcycle accidents, in-depth analysis, microscopic simulation, safety measures

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Authors: Marouen Hamdi, Johannes A. Poulis

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This study investigates the impact of UV/ozone treatment on the adhesion of ethylene propylene diene methylene (EPDM) rubber, polyvinyl chloride (PVC), and acrylonitrile butadiene styrene (ABS) materials. The experimental tests consist of contact angle measurements, standardized adhesion tests, and spectroscopic and microscopic observations. Also, commonly-used surface free energy models were applied to characterize the wettability of the materials. Preliminary results show that the treatment enhances the wettability of the examined polymers. Also, it considerably improved the adhesion strength of PVC and ABS and shifted their failure modes from adhesive to cohesive, without a significant effect on EPDM. Spectroscopic characterization showed significant oxidation-induced changes in the chemical structures of treated PVC and ABS surfaces. Also, new morphological changes (microcracks, micro-holes, and wrinkles) were observed on these two materials using the SEM. These chemical and morphological changes on treated PVC and ABS promote more reactivity and mechanical interlocking with the adhesive, which explains the improvement in their adhesion strength. After characterizing the adhesion strength of the systems, accelerated ageing tests in controlled environment chambers will be conducted to determine the effect of temperature, moisture, and UV radiation on the performance of the polymeric bonded joints.

Keywords: accelerated tests, adhesion strength, ageing of polymers, UV/ozone treatment

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Authors: A. Alao Olumuyiwa

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Health hazards reported to be associated with exposure to electromagnetic radiations which include brain tumors, genotoxic effects, neurological effects, immune system deregulation, allergic responses and some cardiovascular effects are discussed under a closed tabular model in this study. This review however showed that there is strong and robust evidence that chronic exposures to electromagnetic frequency across the spectrum, through strength, consistency, biological plausibility and many dose-response relationships, may result in brain cancer and other carcinogenic disease symptoms. There is therefore no safe threshold because of the genotoxic nature of the mechanism that may however be involved. The discussed study explains that the cell phone has induced effects upon the blood –brain barrier permeability and the cerebellum exposure to continuous long hours RF radiation may result in significant increase in albumin extravasations. A physical Biomodeling approach is however employed to review this health effects using Specific Absorption Rate (SAR) of different GSM machines to critically examine the symptoms such as a decreased loco motor activity, increased grooming and reduced memory functions in a variety of animal spices in classified grouped and sub grouped models.

Keywords: brain cancer, electromagnetic radiations, physical biomodeling, specific absorption rate (SAR)

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Authors: Hui-Qin Xu, Xing Lv, Yu-Han Tao

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The depth study aimed on the mechanism of morroniside in protecting diabetic nephropathy. The diabetic mice models with blood glucose above 15mmol/L were divided into model, aminoguanidine, metformin, captopril, morroniside low-dose, and morroniside high-dose groups. And normal group was set simultaneously. All groups were fed with high AGEs food except normal group. Each group was intragastric administration of the corresponding medicine except model and normal groups. After 12 weeks, all the indictors were measured. It showed that the morroniside could reduce blood glucose significantly, urinary protein, serum urea nitrogen, creatine, pathological changes, AGEs levels, renal cortex RAGE mRNA and RAGE protein expression levels; increase food consumption, water intake, urine volume, insulin secretion. As a conclusion, morroniside from cornus officinalis can protect renal in diabetic mice, its mechanism may be related to the proliferation of islet cells, rectify glycometabolism, reduce serum and kidney AGEs content, and descend renal RAGEmRNA and RAGE protein expression levels.

Keywords: cornus officinalis, diabetic nephropathy, morroniside, RAGE protein

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Authors: Thuan Nguyen

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In the present time, data to a data-driven knowledge-based economy is the same as oil to the industrial age hundreds of years ago. Data is everywhere in vast volumes! Big data analytics is expected to help firms not only efficiently improve performance but also completely transform how they should run their business. However, employing the emergent technology successfully is not easy, and assessing the roles of big data in improving firm performance is even much harder. There was a lack of studies that have examined the impacts of big data analytics on organizational performance. This study aimed to fill the gap. The present study suggested using firms’ intellectual capital as a proxy for big data in evaluating its impact on organizational performance. The present study employed the Value Added Intellectual Coefficient method to measure firm intellectual capital, via its three main components: human capital efficiency, structural capital efficiency, and capital employed efficiency, and then used the structural equation modeling technique to model the data and test the models. The financial fundamental and market data of 100 randomly selected publicly listed firms were collected. The results of the tests showed that only human capital efficiency had a significant positive impact on firm profitability, which highlighted the prominent human role in the impact of big data technology.

Keywords: big data, big data analytics, intellectual capital, organizational performance, value added intellectual coefficient

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Authors: Munoz A. Tatiana, Solano R. Brandon, Montes C. Juan, Cierco G. Javier

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The hydrodynamic cavitation is a process in which the energy that the fluids have in the phase changes is used. From this energy, local temperatures greater than 5000 °C are obtained where thermal cracking of the fluid molecules takes place. The process applied to heavy oil affects variables such as viscosity, density, and composition, which constitutes an important improvement in the quality of crude oil. In this study, the need to design a software through mathematical integration models of mixing, cavitation, kinetics, and reactor, allows modeling changes in density, viscosity, and composition of a heavy oil crude, when the fluid passes through a hydrodynamic cavitation reactor. In order to evaluate the viability of this technique in the industry, a heavy oil of 18° API gravity, was simulated using naphtha as a hydrogen donor at concentrations of 1, 2 and 5% vol, where the simulation results showed an API gravity increase to 0.77, 1.21 and 1.93° respectively and a reduction viscosity by 9.9, 12.9 and 15.8%. The obtained results allow to have a favorable panorama on this technological development, an appropriate visualization on the generation of innovative knowledge of this technique and the technical-economic opportunity that benefits the development of the hydrocarbon sector related to heavy crude oil that includes the largest world oil production.

Keywords: hydrodynamic cavitation, thermal cracking, hydrogen donor, heavy oil upgrading, simulator

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Authors: Maria Krambia-Kapardis, Andreas Kapardis

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The relevant literature has been inundated with arguments for ethics, moral values, honesty, resilience, trust in leadership as well as responsible leadership. In many countries around the globe, and as shown by some recent reports, many political leaders are not role models and do not show best practices by being ethical, responsible, compassionate, and resilient. Journalists, whistleblowers, WikiLeaks, Al Jazeera, and the International Consortium of Investigative Journalists (ICIJ) have been brought out from the shadow of political leaders who lack the virtues/attributes outlined above by the UN Global Compact. A number of political leaders who lack ethical and responsible leadership skills will continue to find loopholes to enrich themselves and their close friends and relatives. Some researchers use the Millon Inventory of Diagnostic; however, this test, while it provides helpful and useful insights into the personality of a person who leads or inspire his/her people but does not show if that person is ethical, motivating, and empowers his people with trust and honesty. Thus, it is recommended that political leaders ought to undergo training that encompasses Aristotelian Ethics by embedding the appropriate values and behaviours in their strategies, policies, and decisions, enhancing the change factors that will help in the implementation of a more sustainable development model. Finally, there is a need to develop a pedagogy and a curriculum which enables the development of responsible political leaders.

Keywords: political leaders, corruption, anti-corruption, political corruption

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Authors: Nur Sarma, Paul M. Tuohy, Siniša Djurović

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This paper presents an in-depth investigation of the effects of several grid supply harmonic voltages on the stator currents of an example wound rotor induction machine. The observed effects of higher order grid supply harmonics are identified using a finite element time stepping transient model, as well as a time-stepping electromagnetic model. In addition, a number of analytical equations to calculate the spectral content of the stator currents are presented in the paper. The presented equations are validated through comparison with the obtained spectra predicted using the finite element and electromagnetic models. The presented study provides a better understanding of the origin of supply harmonic effects identified in the stator currents of the example wound rotor induction machine. Furthermore, the study helps to understand the effects of higher order supply harmonics on the harmonic emissions of the wound rotor induction machine.  

Keywords: wound rotor induction machine, supply harmonics, current spectrum, power spectrum, power quality, harmonic emmisions, finite element analysis

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Authors: Ebru Turgal, Beyza Doganay Erdogan

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Machine learning aims to model the relationship between the response and features. Medical decision-making researchers would like to make decisions about patients’ course and treatment, by examining the repeated measurements over time. Boosting approach is now being used in machine learning area for these aims as an influential tool. The aim of this study is to show the usage of multivariate tree boosting in this field. The main reason for utilizing this approach in the field of decision-making is the ease solutions of complex relationships. To show how multivariate tree boosting method can be used to identify important features and feature-time interaction, we used the data, which was collected retrospectively from Ankara University Chest Diseases Department records. Dataset includes repeated PF ratio measurements. The follow-up time is planned for 120 hours. A set of different models is tested. In conclusion, main idea of classification with weighed combination of classifiers is a reliable method which was shown with simulations several times. Furthermore, time varying variables will be taken into consideration within this concept and it could be possible to make accurate decisions about regression and survival problems.

Keywords: boosted multivariate trees, longitudinal data, multivariate regression tree, panel data

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Authors: Kritiyaporn Kunsook

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Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.

Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

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Floods have huge environmental and economic impact. Therefore, flood prediction is given a lot of attention due to its importance. This study analysed the annual maximum streamflow (discharge) (AMS or AMD) of Karkheh River in Karkheh River Basin for flood predicting using ARIMA model. For this purpose, we use the Box-Jenkins approach, which contains four-stage method model identification, parameter estimation, diagnostic checking and forecasting (predicting). The main tool used in ARIMA modelling was the SAS and SPSS software. Model identification was done by visual inspection on the ACF and PACF. SAS software computed the model parameters using the ML, CLS and ULS methods. The diagnostic checking tests, AIC criterion, RACF graph and RPACF graphs, were used for selected model verification. In this study, the best ARIMA models for Annual Maximum Discharge (AMD) time series was (4,1,1) with their AIC value of 88.87. The RACF and RPACF showed residuals’ independence. To forecast AMD for 10 future years, this model showed the ability of the model to predict floods of the river under study in the Karkheh River Basin. Model accuracy was checked by comparing the predicted and observation series by using coefficient of determination (R²).

Keywords: time series modelling, stochastic processes, ARIMA model, Karkheh river

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Authors: Amany A. Ragheb, Ghada Ragheb, Abd ElRahman A.

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Egypt has a countless heritage of mansions, castles, cities, towns, villages, industrial and manufacturing sites. This richness of heritage provides endless and matchless prospects for culture. Despite being famous worldwide, Egypt’s heritage still is in constant need of protection. Political conflicts and religious revolutions form a direct threat to buildings in various areas, historic, archaeological sites, and religious monuments. Egypt has witnessed two revolutions in less than 60 years; both had an impact on its architectural heritage. In this paper, the authors aim to review legal and policy framework to protect the cultural heritage and present the risk management strategy for cultural heritage in conflict. Through a review of selected international models of devastated architectural heritage in conflict zones and highlighting some of their changes, we can learn from the experiences of other countries to assist towards the development of a methodology to halt the plundering of architectural heritage. Finally, the paper makes an effort to enhance the formulation of a risk management strategy for protection and conservation of cultural heritage, through which to end the plundering of Egypt’s architectural legacy in the Egyptian community (revolutions, 1952 and 2011); and by presenting to its surrounding community the benefits derived from maintaining it.

Keywords: cultural heritage, legal regulation, risk management, preservation

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Authors: Fahad Al Beyahi, Justin Thomas, Md Mamunur Rashid

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Various definitions of digital well-being have emerged in recent years, most of which center on the impacts -beneficial and detrimental- of digital technology on health and well-being (psychological, social, and financial). Other definitions go further, emphasizing the attainment of balance, viewing digital well-being as wholly subjective, the individual’s perception of optimal balance between the benefits and ills associated with online connectivity. Based on this broad conceptualization of digital well-being, we undertook a global survey measuring various dimensions of this emerging construct. The survey was administered across 35 nations and 7 world regions, with 1000 participants within each territory (N= 35000). Along with attitudinal, behavioral, and sociodemographic variables, the survey included measures of depression, anxiety, problematic social media use, gaming disorder, and other relevant metrics. Coupled with nation-level policy audits, these data were used to create a multinational (global) digital well-being index. Nations are ranked based on various dimensions of digital well-being, and predictive models are used to identify resilience and risk factors for problem technology use. In this paper, we will discuss key findings from the survey and the index. This work can inform public policy and shape our responses to the emerging implications of lives increasingly lived online and interconnected with digital technology.

Keywords: technology, health, behavioral addiction, digital wellbeing

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Authors: Saima Nasreen, Uzaira Rafique, Shery Ehrman, Muhammad Aqeel Ashraf

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The release of heavy metals into the environment is a potential threat to water and soil quality as well as to plant, animal and human health. In current research work, organically functionalized mesoporous silicates (MSU-H) were prepared by the co-condensation between sodium silicate and oregano alkoxysilanes in the presence of the nonionic surfactant triblock copolymer P104. The surfactant was used as a template for improving the porosity of the hybrid gels. Synthesized materials were characterized by TEM, FT-IR, SEM/EDX, TG, surface area analysis. The surface morphology and textural properties of such materials varied with various kinds of groups in the channels. In this study, removal of some heavy metals ions from aqueous solution by adsorption process was investigated. Batch adsorption studies show that the adsorption capacity of metal ions on the functionalized silicates is more than that on pure MSU-H. Data shows adsorption on synthesized materials is a time efficient process, suggesting adsorption on external surface as well as the mesoporous process. Adsorption models of Langmuir, Freundlich, and Temkin depicted equal goodness for all adsorbents, whereas pseudo 2nd order kinetics is in best agreement with experimental data.

Keywords: heavy metals, mesoporous silica, hybrid, adsorption, freundlich, langmuir, temkin

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Authors: Arias Fernando, Juan Bojórquez, Edén Bojórquez, Alfredo Reyes-Salazar, Fernando de J. Velarde, Robespierre Chávez, J. Martin Leal, Victor Baca

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The present study focuses on the optimization of different structural systems employed in tall steel buildings, with a specific focus on the city of Acapulco, Guerrero, a region known for its high seismic activity. Using the spectral modal method, analyses were conducted to assess the ability of these buildings to withstand seismic forces and other external loads. After performing a detailed analysis of various models, the results were compared based on various engineering parameters, including maximum interstory drift, base shear, displacements, and the total weight of the structures, the latter being considered as an estimate of the cost of the proposed systems. The findings of this study indicate that steel frames stand out as a viable option for tall buildings in question. However, areas of potential improvement were identified, suggesting opportunities for further optimization of the design and seismic resistance of these structures. This study provides a deep and insightful perspective on the optimization of structural systems in tall steel buildings, offering valuable information for engineers and professionals in the field involved in similar projects.

Keywords: high seismic zone, irregular buildings, optimization design, steel buildings

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Authors: S. Kamil Akin, Turgut Acikara

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In recent years the world and our country has experienced several explosion events occurred due to terrorist attacks and accidents. In these explosion events many people have lost their lives and many buildings have been damaged. If structures were designed taking the blast loads into account, these results may not have happened or the casualties would have been less. In this thesis analysis of the protection walls have been conducted to prevent the building damage from blast loads. These analyzes was carried out for two different types of wall, concrete and reinforced concrete. Analyses were carried out on four different thicknesses of each wall element. In each wall element the stresses and displacements of the exposed surface due to the detonation charge has been calculated. The limit shear stress and displacement of the wall element according to their material properties has been taken into account. As the result of the analyses the standoff distances and TNT equivalent amount has been determined. According to equivalent TNT amounts and standoff distances the structural response of the protective wall elements has been observed. These structural responses have been observed by ABAQUS finite element package. Explosion loads were brought into effect to the protective wall element models by using the ABAQUS / CONWEP.

Keywords: blast loading, blast wave, TNT equivalent method, CONWEP, finite element analysis, detonation

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Authors: A. D. Susanti, W. B. Sediawan, S. K. Wirawan, Budhijanto, Ritmaleni

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Rice bran oil contains significant amounts of oryzanol, a natural antioxidant that considered has higher antioxidant activity than vitamin E (tocopherol). Oryzanol reviewed has several health properties and interested in pharmacy, nutrition, and cosmetics. For practical usage, isolation and purification would be necessary due to the low concentration of oryzanol in crude rice bran oil (0.9-2.9%). Batch chromatography has proved as a promising process for the oryzanol recovery, but productivity was still low and scale-up processes of industrial interest have not yet been described. In order to improve productivity of batch chromatography, a continuous chromatography design namely Simulated Moving Bed (SMB) concept have been proposed. The SMB concept has interested for continuous commercial scale separation of binary system (oryzanol and rice bran oil), and rice bran oil still obtained as side product. Design of SMB chromatography for oryzanol separation requires quantification of its equilibrium. In this study, equilibrium of oryzanol separation conducted in batch adsorption using silica as the adsorbent and n-hexane/acetone (9:1) as the eluent. Three isotherm models, namely the Henry, Langmuir, and Freundlich equations, have been applied and modified for the experimental data to establish appropriate correlation for each sample. It turned out that the model quantitatively describe the equilibrium experimental data and will directed for design of SMB chromatography.

Keywords: adsorption, equilibrium, oryzanol, rice bran oil, simulated moving bed

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Authors: H. Ahmed, A. Schlenkhoff, R. Rousta, R. Abdelaziz

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Vertical slotted walls can be used as permeable breakwaters to provide economical and environmental protection from undesirable waves and currents inside the port. The permeable breakwaters are partially protection and have been suggested to overcome the environmental disadvantages of fully protection breakwaters. For regular waves a semi-analytical model is based on an eigenfunction expansion method and utilizes a boundary condition at the surface of each wall are developed to detect the energy dissipation through the slots. Extensive laboratory tests are carried out to validate the semi-analytic models. The structure of the physical model contains two walls and it consists of impermeable upper and lower part, where the draft is based a decimal multiple of the total depth. The middle part is permeable with a porosity of 50%. The second barrier is located at a distant of 0.5, 1, 1.5 and 2 times of the water depth from the first one. A comparison of the theoretical results with previous studies and experimental measurements of the present study show a good agreement and that, the semi-analytical model is able to adequately reproduce most the important features of the experiment.

Keywords: permeable breakwater, double vertical slotted walls, semi-analytical model, transmission coefficient, reflection coefficient, energy dissipation coefficient

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Authors: Chalakorn Chitsaart, Suchada Rianmora, Mann Rattana-Areeyagon, Wutichai Namjaiprasert

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G-code is the main factor in computer numerical control (CNC) machine for controlling the tool-paths and generating the profile of the object’s features. For obtaining high surface accuracy of the surface finish, non-stop operation is required for CNC machine. Recently, to design a new product, the strategy that concerns about a change that has low impact on business and does not consume lot of resources has been introduced. Cost and time for designing minor changes can be reduced since the traditional geometric details of the existing models are applied. In order to support this strategy as the alternative channel for machining operation, this research proposes the automatic generating codes for CNC milling operation. Using this technique can assist the manufacturer to easily change the size and the geometric shape of the product during the operation where the time spent for setting up or processing the machine are reduced. The algorithm implemented on MATLAB platform is developed by analyzing and evaluating the geometric information of the part. Codes are created rapidly to control the operations of the machine. Comparing to the codes obtained from CAM, this developed algorithm can shortly generate and simulate the cutting profile of the part.

Keywords: geometric shapes, milling operation, minor changes, CNC Machine, G-code, cutting parameters

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Authors: Maryam Maleki, Esther Mead, Mohammad Arani, Nitin Agarwal

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The proliferation of social media platforms like Twitter has heightened the consequences of the spread of misinformation. To understand and model the spread of misinformation, in this paper, we leveraged the SEIZ (Susceptible, Exposed, Infected, Skeptics) epidemiological model to describe the underlying process that delineates the spread of misinformation on Twitter. Compared to the other epidemiological models, this model produces broader results because it includes the additional Skeptics (Z) compartment, wherein a user may be Exposed to an item of misinformation but not engage in any reaction to it, and the additional Exposed (E) compartment, wherein the user may need some time before deciding to spread a misinformation item. We analyzed misinformation regarding the unrest in Washington, D.C. in the month of March 2020, which was propagated by the use of the #DCblackout hashtag by different users across the U.S. on Twitter. Our analysis shows that misinformation can be modeled using the concept of epidemiology. To the best of our knowledge, this research is the first to attempt to apply the SEIZ epidemiological model to the spread of a specific item of misinformation, which is a category distinct from that of rumor and hoax on online social media platforms. Applying a mathematical model can help to understand the trends and dynamics of the spread of misinformation on Twitter and ultimately help to develop techniques to quickly identify and control it.

Keywords: Black Lives Matter, epidemiological model, mathematical modeling, misinformation, SEIZ model, Twitter

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Authors: Xinyuan Chen, Mohd Nizam Husen, Zhongmei Zhou, Gongde Guo, Wei Gao

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Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention for its enormous value; however, existing models lack capabilities to capture both local interactions and global dependencies simultaneously with evolutionary dynamics, while the latest achievements in convolutions and Transformers haven't been employed in this area. What’s more, periodic patterns in TKGs haven’t been fully explored either. To this end, a multi-stage hybrid architecture with convolution-backed Transformers is introduced in TKGC tasks for the first time combining the Hawkes process to model evolving event sequences in a continuous-time domain. In addition, the seasonal-trend decomposition is adopted to identify periodic patterns. Experiments on six public datasets are conducted to verify model effectiveness against state-of-the-art (SOTA) methods. An extensive ablation study is carried out accordingly to evaluate architecture variants as well as the contributions of independent components in addition, paving the way for further potential exploitation. Besides complexity analysis, input sensitivity and safety challenges are also thoroughly discussed for comprehensiveness with novel methods.

Keywords: temporal knowledge graph completion, convolution, transformer, Hawkes process, periodicity

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Authors: Zebider Asire Munyelet, Yibeltal Chanie Manie

Abstract:

In the modern digital landscape, the increase of social media platforms has become identical to the evolution of online consumer behavior. This study investigates the complicated relationship between social media and the purchasing decisions of online buyers. Through an extensive review of existing literature and empirical research, the aim is to comprehensively analyze the multidimensional impact that social media exerts on the various stages of the online buyer's journey. The investigation encompasses the exploration of how social media platforms serve as influential channels for information dissemination, product discovery, and consumer engagement. Additionally, the study investigates into the psychological aspects underlying the role of social media in shaping buyer preferences, perceptions, and trust in online transactions. The methodologies employed include both quantitative and qualitative analyses, incorporating surveys, interviews, and data analytics to derive meaningful insights. Statistical models are applied to distinguish patterns in online buyer behavior concerning product awareness, brand loyalty, and decision-making processes. The expected outcomes of this research contribute not only to the academic understanding of the dynamic interplay between social media and online buyer behavior but also offer practical implications for marketers, e-commerce platforms, and policymakers.

Keywords: consumer Behavior, social media, online purchasing, online transaction

Procedia PDF Downloads 76

Authors: Malarvizhi Selvaraj, Paul Fergus, Andy Shaw

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Uterine contractions produced during labour have the potential to damage the foetus by diminishing the maternal blood flow to the placenta. In order to observe this phenomenon labour and delivery are routinely monitored using cardiotocography monitors. An obstetrician usually makes the diagnosis of foetus hypoxia by interpreting cardiotocography recordings. However, cardiotocography capture and interpretation is time-consuming and subjective, often lead to misclassification that causes damage to the foetus and unnecessary caesarean section. Both of these have a high impact on the foetus and the cost to the national healthcare services. Automatic detection of foetal heart rate may be an objective solution to help to reduce unnecessary medical interventions, as reported in several studies. This paper aim is to provide a system for better identification and interpretation of abnormalities of the fetal heart rate using RStudio. An open dataset of 552 Intrapartum recordings has been filtered with 0.034 Hz filters in an attempt to remove noise while keeping as much of the discriminative data as possible. Features were chosen following an extensive literature review, which concluded with FIGO features such as acceleration, deceleration, mean, variance and standard derivation. The five features were extracted from 552 recordings. Using these features, recordings will be classified either normal or abnormal. If the recording is abnormal, it has got more chances of hypoxia.

Keywords: cardiotocography, foetus, intrapartum, hypoxia

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Authors: Asadullah I. Qazi, Mansoor Ahsan, Zahir Ashraf, Uzair Ahmad

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System identification of an Unmanned Aerial Vehicle (UAV), to acquire its mathematical model, is a significant step in the process of aircraft flight automation. The need for reliable mathematical model is an established requirement for autopilot design, flight simulator development, aircraft performance appraisal, analysis of aircraft modifications, preflight testing of prototype aircraft and investigation of fatigue life and stress distribution etc.  This research is aimed at system identification of a fixed wing UAV by means of specifically designed flight experiment. The purposely designed flight maneuvers were performed on the UAV and aircraft states were recorded during these flights. Acquired data were preprocessed for noise filtering and bias removal followed by parameter estimation of longitudinal dynamics transfer functions using MATLAB system identification toolbox. Black box identification based transfer function models, in response to elevator and throttle inputs, were estimated using least square error   technique. The identification results show a high confidence level and goodness of fit between the estimated model and actual aircraft response.

Keywords: fixed wing UAV, system identification, black box modeling, longitudinal dynamics, least square error

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Authors: O. A. Ajigini, E. N. Mwim

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Cybersecurity is the protection of computers, programs, networks, and data from attack, damage, unauthorised, unintended access, change, or destruction. Organisations collect, process and store their confidential and sensitive information on computers and transmit this data across networks to other computers. Moreover, the advent of internet technologies has led to various cyberattacks resulting in dangerous consequences for organisations. Therefore, with the increase in the volume and sophistication of cyberattacks, there is a need to develop models and make recommendations for the management of cybersecurity threats in organisations. This paper reports on various threats that cause malicious damage to organisations in cyberspace and provides measures on how these threats can be eliminated or reduced. The paper explores various aspects of protection measures against cybersecurity threats such as handling of sensitive data, network security, protection of information assets and cybersecurity awareness. The paper posits a model and recommendations on how to manage cybersecurity threats in organisations effectively. The model and the recommendations can then be utilised by organisations to manage the threats affecting their cyberspace. The paper provides valuable information to assist organisations in managing their cybersecurity threats and hence protect their computers, programs, networks and data in cyberspace. The paper aims to assist organisations to protect their information assets and data from cyberthreats as part of the contributions toward community engagement.

Keywords: confidential information, cyberattacks, cybersecurity, cyberspace, sensitive information

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Authors: Amin Nezarat, Naeime Seifadini

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Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic and air pollution. The majority of existing trip mode inference models operate based on human selected features and traditional machine learning algorithms. However, human selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and Bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

Keywords: predicting, deep learning, neural network, urban trip

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Authors: Yaw Akoto

Abstract:

The social construction of disability in a Ghanaian society has created a restriction on the development of the academic potentials of persons with disabilities. Ghanaian societal perceptions position persons with disabilities as needy, evil, feeble and 'abnormal' that a person with disability cannot contribute anything meaningful to their own development, society, and the nation as well. Almost all Ghanaian cultures believe the Gods visit evil people with disability as such they erect barriers that limit them to select and enroll in education. The few people with disabilities who gain admission to schools drop out due to these barriers erected by the society and institutions. However, there are very few of these students who are able to pursue their education at the higher education level despite these challenges. This qualitative study explores the motivation of students with disabilities to select and enroll in a Ghanaian university. The study used semi-structured interview to solicit information from students with disabilities in a Ghanaian university. Although the quality of students with disabilities experience was affected by culture, discrimination, marginalisation, and lack of support, the prospect of using themselves as role models, employment opportunities and family impingement were among others that pushed them to embark on their educational journey. The findings of this study have implications for societal and institutional levels for restructuring and refining societal perception and institutional policies on disabilities.

Keywords: beliefs, Ghanaian university, social construction, students with disabilities

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Authors: Emanuel Koseos

Abstract:

Data-Driven Decision Making (DDDM) refers to making decisions that are based on historical data in order to inform practice, develop strategies and implement policies that benefit organizational settings. In educational technology, DDDM facilitates the implementation of differential educational learning approaches such as Educational Data Mining (EDM) and Competency-Based Education (CBE), which commonly target university classrooms. There is a current need for DDDM models applied to middle and secondary schools from a concern for assessing the needs, progress and performance of students and educators with respect to regional standards, policies and evolution of curriculums. To address these concerns, we propose a DDDM reference model developed using educational key process initiatives as inputs to a machine learning framework implemented with statistical software (SAS, R) to provide a best-practices, complex-free and automated approach for educators at their regional level. We assessed the efficiency of the model over a six-year period using data from 45 schools and grades K-12 in the Langley, BC, Canada regional school district. We concluded that the model has wider appeal, such as business learning systems.

Keywords: competency-based learning, data-driven decision making, machine learning, secondary schools

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Authors: Nazim Habibov, Alena Auchynnikava, Lida Fan

Abstract:

The exploration of the relationship between social trust and the support of the welfare system in transitional countries has attracted growing interests in recent decades. This study estimates the effects of interpersonal and institutional trust on the support of the welfare system in 27 countries in Eastern Europe the former Soviet Union. We estimate the data sets from the Life-in-Transition Survey 2010 and 2016 with binomial regression models. The results indicate that both interpersonal and institutional trust have positive effects on the support for the welfare system in all the three areas under investigation: helping the needy, public healthcare and public education, both in the less developed countries of the former Soviet Union and in the more developed Eastern European countries. Furthermore, the positive effects of interpersonal and institutional trust on support for helping the needy, public healthcare and public education were found to grow over time. In conclusion, this study confirms that interpersonal and institutional trusts have positive effects for the public support of the welfare system in these transitional countries under investigation, regardless of their level of development.

Keywords: central and eastern Europe, former Soviet union, international social welfare policy, comparative social welfare policy

Procedia PDF Downloads 130