Search results for: algorithm integration

Authors: Αlexios Kotsakis, Stefanos Katsavounis, Dimitra Alexiou

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Networks can be utilized to represent project planning problems, using nodes for activities and arcs to indicate precedence relationship between them. For fixed activity duration, a simple algorithm calculates the amount of time required to complete a project, followed by the activities that comprise the critical path. Program Evaluation and Review Technique (PERT) generalizes the above model by incorporating uncertainty, allowing activity durations to be random variables, producing nevertheless a relatively crude solution in planning problems. In this paper, based on the findings of the relevant literature, which strongly suggests that a Beta distribution can be employed to model earthmoving activities, we utilize Monte Carlo simulation, to estimate the project completion time distribution and measure the influence of skewness, an element inherent in activities of modern technical projects. We also extract the activity criticality index, with an ultimate goal to produce more accurate planning estimations.

Keywords: beta distribution, PERT, Monte Carlo simulation, skewness, project completion time distribution

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Authors: A. Ikhlef

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Image noise in computed tomography, is mainly caused by the statistical noise, system noise reconstruction algorithm filters. Since last few years, low dose x-ray imaging became more and more desired and looked as a technical differentiating technology among CT manufacturers. In order to achieve this goal, several technologies and techniques are being investigated, including both hardware (integrated electronics and photon counting) and software (artificial intelligence and machine learning) based solutions. From a hardware point of view, electronic noise could indeed be a potential driver for low and ultra-low dose imaging. We demonstrated that the reduction or elimination of this term could lead to a reduction of dose without affecting image quality. Also, in this study, we will show that we can achieve this goal using conventional electronics (low cost and affordable technology), designed carefully and optimized for maximum detective quantum efficiency. We have conducted the tests using large imaging objects such as 30 cm water and 43 cm polyethylene phantoms. We compared the image quality with conventional imaging protocols with radiation as low as 10 mAs (<< 1 mGy). Clinical validation of such results has been performed as well.

Keywords: computed tomography, electronic noise, scintillation detector, x-ray detector

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Authors: J. Chamorro-Servent, S. Tassani, M. A. Gonzalez-Ballester, L. J. Roca, J. Romeu, O. Camara

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Electromagnetic and microwave imaging (MWI) have been used in medical imaging in the last years, being the most common applications of breast cancer and stroke detection or monitoring. In those applications, the subject or zone to observe is surrounded by a number of antennas, and the Nyquist criterium can be satisfied. Additionally, the space between the antennas (transmitting and receiving the electromagnetic fields) and the zone to study can be prepared in a homogeneous scenario. However, this may differ in other cases as could be intracardiac catheters, stomach monitoring devices, pelvic organ systems, liver ablation monitoring devices, or uterine fibroids’ ablation systems. In this work, we analyzed different MWI algorithms to find the most suitable method for dealing with an intrabody scenario. Due to the space limitations usually confronted on those applications, the device would have a cylindrical configuration of a maximum of eight transmitters and eight receiver antennas. This together with the positioning of the supposed device inside a body tract impose additional constraints in order to choose a reconstruction method; for instance, it inhabitants the use of well-known algorithms such as filtered backpropagation for diffraction tomography (due to the unusual configuration with probes enclosed by the imaging region). Finally, the difficulty of simulating a realistic non-homogeneous background inside the body (due to the incomplete knowledge of the dielectric properties of other tissues between the antennas’ position and the zone to observe), also prevents the use of Born and Rytov algorithms due to their limitations with a heterogeneous background. Instead, we decided to use a time-reversed algorithm (mostly used in geophysics) due to its characteristics of ignoring heterogeneities in the background medium, and of focusing its generated field onto the scatters. Therefore, a 2D time-reversed finite difference time domain was developed based on the time-reversed approach for microwave breast cancer detection. Simultaneously an in-silico testbed was also developed to compare ground-truth dielectric properties with corresponding microwave imaging reconstruction. Forward and inverse problems were computed varying: the frequency used related to a small zone to observe (7, 7.5 and 8 GHz); a small polyp diameter (5, 7 and 10 mm); two polyp positions with respect to the closest antenna (aligned or disaligned); and the (transmitters-to-receivers) antenna combination used for the reconstruction (1-1, 8-1, 8-8 or 8-3). Results indicate that when using the existent time-reversed method for breast cancer here for the different combinations of transmitters and receivers, we found false positives due to the high degrees of freedom and unusual configuration (and the possible violation of Nyquist criterium). Those false positives founded in 8-1 and 8-8 combinations, highly reduced with the 1-1 and 8-3 combination, being the 8-3 configuration de most suitable (three neighboring receivers at each time). The 8-3 configuration creates a region-of-interest reduced problem, decreasing the ill-posedness of the inverse problem. To conclude, the proposed algorithm solves the main limitations of the described intrabody application, successfully detecting the angular position of targets inside the body tract.

Keywords: FDTD, time-reversed, medical imaging, microwave imaging

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Authors: Mohammed Nasser Al-Suqri

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Former information-seeking models are proposed more than two decades ago. These already existed models were given prior to the evolution of digital information era and Artificial Intelligence (AI) technologies. Lack of current information seeking models within Library and Information Studies resulted in fewer advancements for teaching students about information-seeking behaviors, design of library tools and services. In order to better facilitate the aforementioned concerns, this study aims to propose state-of-the-art model while focusing on the information seeking behavior of library users in the Sultanate of Oman. This study aims for the development, designing and contextualizing the real-time user-centric information seeking model capable of enhancing information needs and information usage along with incorporating critical insights for the digital library practices. Another aim is to establish far-sighted and state-of-the-art frame of reference covering Artificial Intelligence (AI) while synthesizing digital resources and information for optimizing information-seeking behavior. The proposed study is empirically designed based on a mix-method process flow, technical surveys, in-depth interviews, focus groups evaluations and stakeholder investigations. The study data pool is consist of users and specialist LIS staff at 4 public libraries and 26 academic libraries in Oman. The designed research model is expected to facilitate LIS by assisting multi-dimensional insights with AI integration for redefining the information-seeking process, and developing a technology rich model.

Keywords: artificial intelligence, information seeking, information behavior, information seeking models, libraries, Sultanate of Oman

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Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Tajinder Kaur, Anu Bala

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An image retrieval system is a computer system for browsing, searching, and retrieving images from a large database of digital images a new algorithm meant for content-based image retrieval (CBIR) is presented in this paper. The proposed method combines the color and texture features which are extracted the global and local information of the image. The local texture feature is extracted by using local binary patterns (LBP), which are evaluated by taking into consideration of local difference between the center pixel and its neighbors. For the global color feature, the color histogram (CH) is used which is calculated by RGB (red, green, and blue) spaces separately. In this paper, the combination of color and texture features are proposed for content-based image retrieval. The performance of the proposed method is tested on Corel 1000 database which is the natural database. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP and CH.

Keywords: color, texture, feature extraction, local binary patterns, image retrieval

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Authors: Adam Patterson

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This study analyzes the self-reported experiences of virtual reality users to develop insight into an optimal learning path for education within virtual reality. This research uses a sample of 1000 observations to statistically define factors influencing (i) immersion level and (ii) motion sickness rating for virtual reality experience respondents of college age. This paper recommends an efficient duration for each virtual reality session, to minimize sickness and maximize engagement, utilizing modern machine learning methods such as association rules. The goal of this research, in augmentation with previous literature, is to inform logistical decisions relating to implementation of pilot instruction for virtual reality at the collegiate level. Future research will include a Randomized Control Trial (RCT) to quantify the effect of virtual reality education on student learning outcomes and engagement measures. Current research aims to maximize the treatment effect within the RCT by optimizing the learning benefits of virtual reality. Results suggest significant gender heterogeneity amongst likelihood of reporting motion sickness. Females are 1.7 times more likely, than males, to report high levels of motion sickness resulting from a virtual reality experience. Regarding duration, respondents were 1.29 times more likely to select the lowest level of motion sickness after an engagement lasting between 24.3 and 42 minutes. Conversely, respondents between 42 to 60 minutes were 1.2 times more likely to select the higher levels of motion sickness.

Keywords: applications and integration of e-education, practices and cases in e-education, systems and technologies in e-education, technology adoption and diffusion of e-learning

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Authors: Osama A. Omer, Amal Hamed

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Successful blind image Super-Resolution algorithms require the exact estimation of the Point Spread Function (PSF). In the absence of any prior information about the imagery system and the true image; this estimation is normally done by trial and error experimentation until an acceptable restored image quality is obtained. Multi-frame blind Super-Resolution algorithms often have disadvantages of slow convergence and sensitiveness to complex noises. This paper presents a Super-Resolution image reconstruction algorithm based on estimation of the PSF that yields the optimum restored image quality. The estimation of PSF is performed by the knife-edge method and it is implemented by measuring spreading of the edges in the reproduced HR image itself during the reconstruction process. The proposed image reconstruction approach is using L1 norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. A series of experiment results show that the proposed method can outperform other previous work robustly and efficiently.

Keywords: blind, PSF, super-resolution, knife-edge, blurring, bilateral, L1 norm

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Authors: Carlos Alberto Ríos-Reyes, Efraín Casadiego-Quintero

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We propose a generalized workflow for mineralogy investigation of unconventional reservoirs using multi-scale imaging and pore-scale analyses. This workflow can be used for the integral evaluation of these resources. The Cretaceous La Luna Formation´s mudstones in the Middle Magdalena Valley Basin (Colombia) inherently show a heterogeneous pore system with organic and inorganic pores. For this reason, it is necessary to carry out the integration of high resolution 2D images of mapping by conventional petrography, scanning electron microscopy and quantitative evaluation of minerals by scanning electron microscopy to describe their organic and inorganic porosity to understand the transport mechanism through pores. The analyzed rocks show several pore types, including interparticle pores, organoporosity, intraparticle pores, intraparticle pores, and microchannels and/or microfractures. The existence of interconnected pores in pore system of these rocks promotes effective pathways for primary gas migration and storage space for residual hydrocarbons in mudstones, which is very useful in this type of gas reservoirs. It is crucial to understand not only the porous system of these rocks and their mineralogy but also to project the gas flow in order to design the appropriate strategies for the stimulation of unconventional reservoirs. Keywords: mudstones; La Luna Formation; gas storage; migration; hydrocarbon.

Keywords: mudstones, La luna formation, gas storage, migration, hydrocarbon

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Authors: Bikshalu Kalagadda, Srikanth Rangu

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The important object of images segmentation is to extract objects with respect to some input features. One of the important methods for image segmentation is Level set method. Generally medical images and synthetic images with low contrast of pixel profile, for such images difficult to locate interested features in images. In conventional level set function, develops irregularity during its process of evaluation of contour of objects, this destroy the stability of evolution process. For this problem a remedy is proposed, a new hybrid algorithm is Clustering Level Set Evolution. Kernel fuzzy particles swarm optimization clustering with the Distance Regularized Level Set (DRLS) and Selective Binary, and Gaussian Filtering Regularized Level Set (SBGFRLS) methods are used. The ability of identifying different regions becomes easy with improved speed. Efficiency of the modified method can be evaluated by comparing with the previous method for similar specifications. Comparison can be carried out by considering medical and synthetic images.

Keywords: segmentation, clustering, level set function, re-initialization, Kernel fuzzy, swarm optimization

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Authors: Tyng-Rong Roan, Fuji Foo, Wenwey Hseush

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Recent development of AI and edge computing plays a critical role to capture meaningful events such as detection of an unattended bag. One of the core problems is re-identification across multiple CCTVs. Immediately following the detection of a meaningful event is to track and trace the objects related to the event. In an extensive environment, the challenge becomes severe when the number of CCTVs increases substantially, imposing difficulties in achieving high accuracy while maintaining real-time performance. The algorithm that re-identifies cross-boundary objects for extensive tracking is referred to Extensive Re-Identification, which emphasizes the issues related to the complexity behind a great number of CCTVs. The Spatial-Temporal Awareness approach challenges the conventional thinking and concept of operations which is labor intensive and time consuming. The ability to perform Extensive Re-Identification through a multi-sensory network provides the next-level insights – creating value beyond traditional risk management.

Keywords: long-short-term memory, re-identification, security critical application, spatial-temporal awareness

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Authors: V. Makis, L. Jafari

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In this paper, we develop an optimal Bayesian chart to control the expected number of defects per inspection unit in production processes with long production runs. We formulate this control problem in the optimal stopping framework. The objective is to determine the optimal stopping rule minimizing the long-run expected average cost per unit time considering partial information obtained from the process sampling at regular epochs. We prove the optimality of the control limit policy, i.e., the process is stopped and the search for assignable causes is initiated when the posterior probability that the process is out of control exceeds a control limit. An algorithm in the semi-Markov decision process framework is developed to calculate the optimal control limit and the corresponding average cost. Numerical examples are presented to illustrate the developed optimal control chart and to compare it with the traditional u-chart.

Keywords: Bayesian u-chart, economic design, optimal stopping, semi-Markov decision process, statistical process control

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Authors: I. Alhajri, G. Zahedi, R. Alazmi, A. Akbari

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In this paper, an Artificial Neural Network (ANN) was developed to predict Asphaltene Precipitation (AP) during the injection of carbon dioxide into crude oil reservoirs. In this study, the experimental data from six different oil fields were collected. Seventy percent of the data was used to develop the ANN model, and different ANN architectures were examined. A network with the Trainlm training algorithm was found to be the best network to estimate the AP. To check the validity of the proposed model, the model was used to predict the AP for the thirty percent of the data that was unevaluated. The Mean Square Error (MSE) of the prediction was 0.0018, which confirms the excellent prediction capability of the proposed model. In the second part of this study, the ANN model predictions were compared with modified Hirschberg model predictions. The ANN was found to provide more accurate estimates compared to the modified Hirschberg model. Finally, the proposed model was employed to examine the effect of different operating parameters during gas injection on the AP. It was found that the AP is mostly sensitive to the reservoir temperature. Furthermore, the carbon dioxide concentration in liquid phase increases the AP.

Keywords: artificial neural network, asphaltene, CO2 injection, Hirschberg model, oil reservoirs

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Authors: Yihao Kuang, Bowen Ding

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With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graphs and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improved strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain a better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference

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Authors: Abdulrahman Sumayli, Saad M. AlShahrani

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For the hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of the process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models are optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with an RMSE of 1.38 × 10− 2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions is estimated in different ranges of temperatures and pressures of 150 ◦C–350 ◦C and 1.2 MPa–10.8 MPa, respectively

Keywords: temperature, pressure variations, machine learning, oil treatment

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Authors: Kyllie Cripps

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In Australia, not dissimilar to other jurisdictions with indigenous populations, indigenous women are more likely to experience violence than any other section of society. In recent years in response to horrific examples of Indigenous women’s deaths, Australian Coronial courts have investigated, wanting to know more about the circumstances that led to the deaths. This paper critically examined 12 Coronial Court investigations from around Australia, analyzing them thematically. The analysis highlighted the differential vulnerability of indigenous women to intimate partner homicides. In all the cases reviewed, it was evident that the women’s deaths, in most instances were entirely preventable. Evidence was also presented demonstrating that services were aware of the women’s heightened risks but were unable to sufficiently coordinate themselves to provide wrap around support to minimise the risk of violence and to maximise the women’s safety. Consequently, putting the women in environments where their deaths were both predictable and inevitable. The profound system failings at the intersections of law, policy, and practice have ultimately cost indigenous women their lives. This paper firstly explores the nuances of the Coronial Court findings – demonstrating the similarities and differences present within the cases. Part two interrogates the reported system failings, and part three considers potential improvements in system integration to prevent future deaths. The paper concludes recognizing that Indigenous women play important valued roles in indigenous communities, their loss has profound costs and consequences, and to honor their memory, we must learn from their deaths and improve responses to intimate partner violence.

Keywords: homicide, intimate partner violence, indigenous women

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Authors: Jung Park, Anca Mazare, Klaus Von Der Mark, Patrik Schmuki

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In clinical application of TiO2 implants on tooth and hip replacement, migration, adhesion and differentiation of neighboring mesenchymal stem cells onto implant surfaces are critical steps for successful bone regeneration. In a recent decade, accumulated attention has been paid on nanoscale electrochemical surface modifications on TiO2 layer for improving bone-TiO2 surface integration. We generated, on titanium surfaces, self-assembled layers of vertically oriented TiO2 nanotubes with defined diameters between 15 and 100 nm and here we show that mesenchymal stem cells finely sense TiO2 nanotubular geometry and quickly decide their cell fate either to differentiation into osteoblasts or to programmed cell death (apoptosis) on TiO2 nanotube layers. These cell fate decisions are critically dependent on nanotube size differences (15-100nm in diameters) of TiO2 nanotubes sensing by integrin clustering. We further demonstrate that nanoscale topography-sensing is feasible not only in mesenchymal stem cells but rather seems as generalized nanoscale microenvironment-cell interaction mechanism in several cell types composing bone tissue network including osteoblasts, osteoclast, endothelial cells and hematopoietic stem cells. Additionally we discuss the synergistic effect of simultaneous stimulation by nanotube-bound growth factor and nanoscale topographic cues on enhanced bone regeneration.

Keywords: TiO2 nanotube, stem cell fate decision, nano-scale microenvironment, bone regeneration

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Authors: Sergio Paez Moncaleano, Alvaro Mauricio Montenegro

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This paper presents a novel statistical methodology for measuring and founding constructs in Latent Regression Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations on Item Response Theory (IRT). In addition, based on the fundamentals of submodel theory and with a convergence of many ideas of IRT, we propose an algorithm not just to solve the dimensionality problem (nowadays an open discussion) but a new research field that promises more fear and realistic qualifications for examiners and a revolution on IRT and educational research. In the end, the methodology is applied to a set of real data set presenting impressive results for the coherence, speed and precision. Acknowledgments: This research was financed by Colciencias through the project: 'Multidimensional Item Response Theory Models for Practical Application in Large Test Designed to Measure Multiple Constructs' and both authors belong to SICS Research Group from Universidad Nacional de Colombia.

Keywords: item response theory, dimensionality, submodel theory, factorial analysis

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Authors: Ali Mashayekh, Mahdiye Khorasani, Thomas Weyh

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The tendency to use the Battery-Electric vehicle (BEV) for the low and medium driving range or even high driving range has been growing more and more. As a result, higher safety, reliability, and durability of the battery pack as a component of electric vehicles, which has a great share of cost and weight of the final product, are the topics to be considered and investigated. Battery aging can be considered as the predominant factor regarding the reliability and durability of BEV. To better understand the aging process, offline battery characterization has been widely used, which is time-consuming and needs very expensive infrastructures. This paper presents the substitute method for the conventional battery characterization methods, which is based on battery Modular Multilevel Management (BM3). According to this Topology, the battery cells can be drained and charged concerning their capacity, which allows varying battery pack structures. Due to the integration of the power electronics, the output voltage of the battery pack is no longer fixed but can be dynamically adjusted in small steps. In other words, each cell can have three different states, namely series, parallel, and bypass in connection with the neighbor cells. With the help of MATLAB/Simulink and by using the BM3 modules, the battery string model is created. This model allows us to switch two cells with the different SoC as parallel, which results in the internal balancing of the cells. But if the parallel switching lasts just for a couple of ms, we can have a perturbation pulse which can stimulate the cells out of the relaxation phase. With the help of modeling the voltage response pulse of the battery, it would be possible to characterize the cell. The Online EIS method, which is discussed in this paper, can be a robust substitute for the conventional battery characterization methods.

Keywords: battery characterization, SoH estimation, RLS, BEV

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Authors: Marine Kobalava, Eter Narimanishvili, Nino Grigolaia

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The paper analyzes the contemporary problems of public relations in Georgia. The approaches to public attitudes towards the relationship with the population of the country are studied on a global scale, the importance of forming the concept of public relations in Georgia in terms of globalization is justified. The basic components of public relations are characterized by the RACE system, namely analyzing research, action, communication, evaluation. The main challenges of public relations are identified in the research process; taking into consideration the scope of globalization, the influence of social, economic, and political changes in Georgia on PR development are identified. The article discusses the public relations as the strategic management function that facilitates communication with the society, recognition of public interests, and their prediction. In addition, the feminization of the sector is considered to be the most important achievement of public relations in the modern world. The conclusion is that the feminization indicator of the field is an unconditional increase in the employment rates of women. In the paper, the problems of globalization and public relations in the industrial countries are studied, the directions of improvement of public relations with the background of peculiarities of different countries and globalization process are proposed. Public relations under globalization are assessed in accordance with the theory of benefits and requirements, and the requirements are classified according to informational, self-identification, integration, social interaction, and other types of signs. In the article, conclusions on the current challenges of public relations in Georgia are made, and the recommendations for their solution, taking into consideration globalization processes in the world, are proposed.

Keywords: public relations, globalization, RACE system, public relationship concept, feminization

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Authors: Jose A. Aznar-Moreno, Xi Jiang, Stefan Burén, Luis M. Rubio

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Integration of prokaryotic nitrogen fixation (nif) genes into the plastid genome for expression of functional nitrogenase components could render plants capable of assimilating atmospheric N2 making their crops less dependent of nitrogen fertilizers. The nitrogenase Fe protein component (NifH) has been used as proxy for expression and targeting of Nif proteins within plant and yeast cells. Here we use tobacco plants with the Azotobacter vinelandii nifH and nifM genes integrated into the plastid genome. NifH and its maturase NifM were constitutively produced in leaves, but not roots, during light and dark periods. Nif protein expression in transplastomic plants was stable throughout development. Chloroplast NifH was soluble, but it only showed in vitro activity when isolated from leaves collected at the end of the dark period. Exposing the plant extracts to elevated temperatures precipitated NifM and apo-NifH protein devoid of [Fe4S4] clusters, dramatically increasing the specific activity of remaining NifH protein. Our data indicate that the chloroplast endogenous [Fe-S] cluster biosynthesis was insufficient for complete NifH maturation, albeit a negative effect on NifH maturation due to excess NifM in the chloroplast cannot be excluded. NifH and NifM constitutive expression in transplastomic plants did not affect any of the following traits: seed size, germination time, germination ratio, seedling growth, emergence of the cotyledon and first leaves, chlorophyll content and plant height throughout development.

Keywords: NifH, chloroplast, nitrogen fixation, crop improvement, transplastomic plants, fertilizer, biotechnology

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Authors: Sylvanus N. Wosu

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Research shows that factors, such as low motivation, preparation, resources, emotional and social integration, and fears of risk-taking, are the most common barriers to access, matriculation, and retention into science, technology, engineering, and mathematics (STEM) disciplines for underrepresented (URM) students. These factors have been shown to impact students’ attraction and success in STEM fields. Standardized tests such as the SAT and ACT often used as predictor of success, are not always true predictors of success for African and Hispanic American students. Without an adequate academic support environment, even a high SAT score does not guarantee academic success in science and engineering. This paper proposes a model for Academic Coaching for building success and inclusive excellence in STEM education. Academic coaching is framed as a process of motivating students to be independent learners through relational mentorship, facilitating learning supports inside and outside of the classroom or school environment, and developing problem-solving skills and success attitudes that lead to higher performance in the specific subjects. The model is formulated based on best strategies and practices for enriching Academic Performance Impact skills and motivating students’ interests in STEM. A scaled model for measuring the Academic Performance Impact (API) index and STEM is discussed. The study correlates API with state standardized test and shows that the average impact of those skills can be predicted by the Academic Performance Impact (API) index or Academic Preparedness Index.

Keywords: diversity, equity, graduate education, inclusion, inclusive excellence, model

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Authors: Victor Milanes, Martha Hubertz

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In the wake of COVID-19, all aspects of life have been estranged, and humanity has been forced to shift toward a more technologically integrated mode of operation. Essential work such as Healthcare, business, and public policy are a few notable industries that were initially dependent upon face-to-face modality but have completely reimagined their operation style. Unique to these fields, education was particularly strained because academics, teachers, and professors alike were obligated to shift their curriculums online over the course of a few weeks while also maintaining the expectation that they were educating their students to a similar level accomplished pre-pandemic. This was notable as research indicates two key concepts: Students prefer face-to-face modality, and due to the disruption in academic continuity/style, there was a negative impact on student's overall education and performance. With these two principles in mind, this study aims to inquire what online strategies could be best employed by teachers to educate their students, as well as what strategies could be adopted in a multimodal setting if deemed necessary by the instructor or outside convoluting factors (Such as the case of COVID-19, or a personal matter that demands the teacher's attention away from the classroom). Strategies and methods will be cross-analyzed via a ranking system derived from various recognized teaching assessments, in which engagement, retention, flexibility, interest, and performance are specifically accounted for. We expect to see an emphasis on positive social pressure as a dominant factor in the improved propensity for education, as well as a preference for visual aids across platforms, as research indicates most individuals are visual learners.

Keywords: technological integration, multimodal teaching, education, student engagement

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Authors: Benjamin Bobbia, Matthias Picard

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In recent years active learning methodologies based on the representativity of the data seems more promising to limit overfitting. The presented query methodology for regression using the Wasserstein distance measuring the representativity of our labelled dataset compared to the global distribution. In this work a crucial use of GroupSort Neural Networks is made therewith to draw a double advantage. The Wasserstein distance can be exactly expressed in terms of such neural networks. Moreover, one can provide explicit bounds for their size and depth together with rates of convergence. However, heterogeneity of the dataset is also considered by weighting the Wasserstein distance with the error of approximation at the previous step of active learning. Such an approach leads to a reduction of overfitting and high prediction performance after few steps of query. After having detailed the methodology and algorithm, an empirical study is presented in order to investigate the range of our hyperparameters. The performances of this method are compared, in terms of numbers of query needed, with other classical and recent query methods on several UCI datasets.

Keywords: active learning, Lipschitz regularization, neural networks, optimal transport, regression

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Authors: Francesco Pinci

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This study focuses on sustainability and consumer behavior in relation to packaging aesthetics. It investigates the significance of product packaging as a potent marketing tool with a specific emphasis on commercially available pasta as a case study. The research delves into the visual components of packaging, encompassing aspects such as color, shape, packaging material, and logo design. The findings of this study hold particular relevance for food and beverage companies as they seek to gain a comprehensive understanding of the factors influencing consumer purchasing decisions. Furthermore, the study places a significant emphasis on the sustainability aspects of packaging, exploring how eco-friendly and environmentally conscious packaging choices can impact consumer preferences and behaviors. The insights generated from this research contribute to a more sustainable approach to packaging practices and inform marketers on the effective integration of sustainability principles in their branding strategies. Overall, this study provides valuable insights into the dynamic interplay between aesthetics, sustainability, and consumer behavior, offering practical implications for businesses seeking to align their packaging practices with sustainable and consumer-centric approaches. In this study, packaging designs and images from the website of Eataly US.Eataly is one of the leading distributors of authentic Italian pasta worldwide, and its website serves as a rich source of packaging visuals and product representations. By analyzing the packaging and images showcased on the Eataly website, the study gained valuable insights into consumer behavior and preferences regarding pasta packaging in the context of sustainability and aesthetics.

Keywords: consumer behaviour, sustainability, food marketing, neuromarketing

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Authors: Adine Barett, Jermel Watson, Anteneh Girma, Kacem Thabet

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In information security, secure communication of data across networks has always been a problem at the forefront. Transfer of information across networks is susceptible to being exploited by attackers engaging in malicious activity. In this paper, we leverage steganography and cryptography to create a layered security solution to protect the information being transmitted. The first layer of security leverages crypto- graphic techniques to scramble the information so that it cannot be deciphered even if the steganography-based layer is compromised. The second layer of security relies on steganography to disguise the encrypted in- formation so that it cannot be seen. We consider three cryptographic cipher methods in the cryptography layer, namely, Playfair cipher, Blowfish cipher, and Hills cipher. Then, the encrypted message is passed through the least significant bit (LSB) to the steganography algorithm for further encryption. Both encryption approaches are combined efficiently to help secure information in transit over a network. This multi-layered encryption is a solution that will benefit cloud platforms, social media platforms and networks that regularly transfer private information such as banks and insurance companies.

Keywords: cryptography, steganography, layered security, Cipher, encryption

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Authors: Sajid Abas, Jon Pyo Hong, Jung-Ryun Le, Seungryong Cho

Abstract:

Computed tomography and laminography are heavily investigated in a compressive sensing based image reconstruction framework to reduce the dose to the patients as well as to the radiosensitive devices such as multilayer microelectronic circuit boards. Nowadays researchers are actively working on optimizing the compressive sensing based iterative image reconstruction algorithm to obtain better quality images. However, the effects of the sampled data’s properties on reconstructed the image’s quality, particularly in an insufficient sampled data conditions have not been explored in computed laminography. In this paper, we investigated the effects of two data properties i.e. sampling density and data incoherence on the reconstructed image obtained by conventional computed laminography and a recently proposed method called spherical sinusoidal scanning scheme. We have found that in a compressive sensing based image reconstruction framework, the image quality mainly depends upon the data incoherence when the data is uniformly sampled.

Keywords: computed tomography, computed laminography, compressive sending, low-dose

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Authors: Iqbaldeep Kaur, Amarjeet Kaur

Abstract:

Software Reusability is important part of software development. So component based software development in case of software testing has gained a lot of practical importance in the field of software engineering from academic researcher and also from software development industry perspective. Finding test cases for efficient reuse of test cases is one of the important problems aimed by researcher. Clustering reduce the search space, reuse test cases by grouping similar entities according to requirements ensuring reduced time complexity as it reduce the search time for retrieval the test cases. In this research paper we proposed approach for re-usability of test cases by unsupervised approach. In unsupervised learning we proposed k-mean and Support Vector Machine. We have designed the algorithm for requirement and test case document clustering according to its tf-idf vector space and the output is set of highly cohesive pattern groups.

Keywords: software testing, reusability, clustering, k-mean, SVM

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Authors: Tingting Lin, Manfred von Willich, Dafu Lou, Phil Eisen

Abstract:

A white-box implementation of a cryptographic algorithm is a software implementation intended to resist extraction of the secret key by an adversary. To date, most of the white-box techniques are used to protect block cipher implementations. However, a large proportion of the white-box implementations are proven to be vulnerable to affine equivalence attacks and other algebraic attacks, as well as differential computation analysis (DCA). In this paper, we identify a class of block ciphers for which we propose a method of constructing white-box implementations. Our method is based on threshold implementations and operations in composite fields. The resulting implementations consist of lookup tables and few exclusive OR operations. All intermediate values (inputs and outputs of the lookup tables) are masked. The threshold implementation makes the distribution of the masked values uniform and independent of the original inputs, and the operations in composite fields reduce the size of the lookup tables. The white-box implementations can provide resistance against algebraic attacks and DCA-like attacks.

Keywords: white-box, block cipher, composite field, threshold implementation

Procedia PDF Downloads 155

Authors: Junjie Wu, Hongjuan Hou, Eric Hu, Yongping Yang

Abstract:

Solar aided power generation (SAPG) technology has been proven as an efficient way to make use of solar energy for power generation purpose. In an SAPG plant, a solar field consisting of parabolic solar collectors is normally used to supply the solar heat in order to displace the high pressure/temperature extraction steam. To understand the performance of such a SAPG plant, a new simulation model was developed by the authors recently, in which the boiler was treated, as a series of heat exchangers unlike other previous models. Through the simulations using the new model, it was found the outlet properties of reheated steam, e.g. temperature, would decrease due to the introduction of the solar heat. The changes make the (lower stage) turbines work under off-design condition. As a result, the whole plant’s performance may not be optimal. In this paper, the second solar filed was proposed to increase the inlet temperature of steam to be reheated, in order to bring the outlet temperature of reheated steam back to the designed condition. A 600MW SAPG plant was simulated as a case study using the new model to understand the impact of the second solar field on the plant performance. It was found in the study, the 2nd solar field would improve the plant’s performance in terms of cycle efficiency and solar-to-electricity efficiency by 1.91% and 6.01%. The solar-generated electricity produced by per aperture area under the design condition was 187.96W/m2, which was 26.14% higher than the previous design.

Keywords: solar-aided power generation system, off-design performance, coal-saving performance, boiler modelling, integration schemes

Procedia PDF Downloads 276