Search results for: embedded network

Authors: Nasim Nosoudi, Amir Zadeh, Hunter White, Joshua Conrad, Joon W. Shim

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De novo Malignancy has become one of the major causes of death after transplantation, so early cancer diagnosis and detection can drastically improve survival rates post-transplantation. Most previous work focuses on using artificial intelligence (AI) to predict transplant success or failure outcomes. In this work, we focused on predicting de novo malignancy after liver transplantation using AI. We chose the patients that had malignancy after liver transplantation with no history of malignancy pre-transplant. Their donors were cancer-free as well. We analyzed 254,200 patient profiles with post-transplant malignancy from the US Organ Procurement and Transplantation Network (OPTN). Several popular data mining methods were applied to the resultant dataset to build predictive models to characterize de novo malignancy after liver transplantation. Recipient's bilirubin, creatinine, weight, gender, number of days recipient was on the transplant waiting list, Epstein Barr Virus (EBV), International normalized ratio (INR), and ascites are among the most important factors affecting de novo malignancy after liver transplantation

Keywords: De novo malignancy, bilirubin, data mining, transplantation

Procedia PDF Downloads 105

Authors: Mohsen Mazidi, Ali Abbaspour, Mahmud Fotuhi-Firuzabad, Mohamamd Rastegar

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To tackle the air pollution issues, Plug-in Hybrid Electric Vehicles (PHEVs) are proposed as an appropriate solution. Charging a large amount of PHEV batteries, if not controlled, would have negative impacts on the distribution system. The control process of charging of these vehicles can be centralized in parking lots that may provide a chance for better coordination than the individual charging in houses. In this paper, an optimization-based approach is proposed to determine the optimum PHEV parking capacities in candidate nodes of the distribution system. In so doing, a profile for charging and discharging of PHEVs is developed in order to flatten the network load profile. Then, this profile is used in solving an optimization problem to minimize the distribution system losses. The outputs of the proposed method are the proper place for PHEV parking lots and optimum capacity for each parking. The application of the proposed method on the IEEE-34 node test feeder verifies the effectiveness of the method.

Keywords: loss, plug-in hybrid electric vehicle (PHEV), PHEV parking lot, V2G

Procedia PDF Downloads 543

Authors: Mikkel Snorre Wilms Boysen

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From a theoretical perspective, expertise is generally considered a precondition for creativity. The assumption is that an individual needs to master the common and accepted rules and techniques within a certain knowledge-domain in order to create something new and valuable. However, real life cases, and a limited amount of empirical studies, demonstrate that this assumption may be overly simple. In this article, this question is explored through a number of semi-experimental case studies conducted within the fields of music, technology, and youth culture. The studies indicate that, in various ways, expertise plays an important part in creative processes. However, the case studies also indicate that expertise sometimes leads to an entrenched perspective, in the sense that knowledge and experience may work as a path into the well-known rather than into the unknown. In this article, these issues are explored with reference to different theoretical approaches to creativity and learning, including actor-network theory, the theory of blind variation and selective retention, and Csikszentmihalyi’s system model. Finally, some educational aspects and implications of this are discussed.

Keywords: creativity, expertise , education, technology

Procedia PDF Downloads 322

Authors: Davi Marinho de Araujo Falcão, Ronaldo Moreira Salles, Paulo Henrique Maranhão

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Disruption Tolerant Networks (DTN) are an evolution of Mobile Adhoc Networks (MANET) and work good in scenarioswhere nodes are sparsely distributed, with low density, intermittent connections and an end-to-end infrastructure is not possible to guarantee. Therefore, DTNs are recommended for high latency applications that can last from hours to days. The maritime scenario has mobility characteristics that contribute to a DTN network approach, but the concern with data security is also a relevant aspect in such scenarios. Continuing the previous work, which evaluated the performance of some DTN protocols (Epidemic, Spray and Wait, and Direct Delivery) in three warship scenarios and proposed the application of discriminant analysis, as a classification technique for secure connections, in the Epidemic protocol, thus, the current article proposes a new analysis of the directional discriminant function with opening angles smaller than 90 degrees, demonstrating that the increase in directivity influences the selection of a greater number of secure connections by the directional discriminant Epidemic protocol.

Keywords: DTN, discriminant function, epidemic protocol, security, tactical messages, warship scenario

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Authors: Zhaoxin Luo, Michael Zhu

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In natural languages, there are always complex semantic hierarchies. Obtaining the feature representation based on these complex semantic hierarchies becomes the key to the success of the model. Several RNN models have recently been proposed to use latent indicators to obtain the hierarchical structure of documents. However, the model that only uses a single-layer latent indicator cannot achieve the true hierarchical structure of the language, especially a complex language like Chinese. In this paper, we propose a deep layered model that stacks arbitrarily many RNN layers equipped with latent indicators. After using EM and training it hierarchically, our model solves the computational problem of stacking RNN layers and makes it possible to stack arbitrarily many RNN layers. Our deep hierarchical model not only achieves comparable results to large pre-trained models on the Chinese short text classification problem but also achieves state of art results on the Chinese long text classification problem.

Keywords: nature language processing, recurrent neural network, hierarchical structure, document classification, Chinese

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Authors: B. Philip, J. Littlewood, R. Radford, N. Evans, T. Whyman, D. P. Jones

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UK buildings and energy infrastructures are heavily dependent on natural gas, a large proportion of which is used for domestic space heating. However, approximately half of the gas consumed in the UK is imported. Improving energy security and reducing carbon emissions are major government drivers for reducing gas dependency. In order to do so there needs to be a wholesale shift in the energy provision to householders without impacting on thermal comfort levels, convenience or cost of supply to the end user. Heat pumps are seen as a potential alternative in modern well insulated homes, however, can the same be said of older homes? A large proportion of housing stock in Britain was built prior to 1919. The age of the buildings bears testimony to the quality of construction; however, their thermal performance falls far below the minimum currently set by UK building standards. In recent years significant sums of money have been invested to improve energy efficiency and combat fuel poverty in some of the most deprived areas of Wales. Increasing energy efficiency of older properties remains a significant challenge, which cannot be achieved through insulation and air-tightness interventions alone, particularly when alterations to historically important architectural features of the building are not permitted. This paper investigates the energy demand of pre-1919 dwellings in a former Welsh mining village, the feasibility of meeting that demand using water from the disused mine workings to supply a district heat network and potential barriers to success of the scheme. The use of renewable solar energy generation and storage technologies, both thermal and electrical, to reduce the load and offset increased electricity demand, are considered. A wholistic surveying approach to provide a more accurate assessment of total household heat demand is proposed. Several surveying techniques, including condition surveys, air permeability, heat loss calculations, and thermography were employed to provide a clear picture of energy demand. Additional insulation can bring unforeseen consequences which are detrimental to the fabric of the building, potentially leading to accelerated dilapidation of the asset being ‘protected’. Increasing ventilation should be considered in parallel, to compensate for the associated reduction in uncontrolled infiltration. The effectiveness of thermal performance improvements are demonstrated and the detrimental effects of incorrect material choice and poor installation are highlighted. The findings show estimated heat demand to be in close correlation to household energy bills. Major areas of heat loss were identified such that improvements to building thermal performance could be targeted. The findings demonstrate that the use of heat pumps in older buildings is viable, provided sufficient improvement to thermal performance is possible. Addition of passive solar thermal and photovoltaic generation can help reduce the load and running cost for the householder. The results were used to predict future heat demand following energy efficiency improvements, thereby informing the size of heat pumps required.

Keywords: heat demand, heat pump, renewable energy, retrofit

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Authors: Qian Yi Ooi

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At present, airfoil parameters are still designed and optimized according to the scale of conventional aircraft, and there are still some slight deviations in terms of scale differences. However, insufficient parameters or poor surface mesh quality is likely to occur if these small deviations are embedded in a future civil aircraft with a size that is quite different from conventional aircraft, such as a blended-wing-body (BWB) aircraft with future potential, resulting in large deviations in geometric similarity in computational fluid dynamics (CFD) simulations. To avoid this situation, the study on the CFD calculation on the geometric similarity of airfoil parameters and the quality of the surface mesh is conducted to obtain the ability of different parameterization methods applied on different airfoil scales. The research objects are three airfoil scales, including the wing root and wingtip of conventional civil aircraft and the wing root of the giant hybrid wing, used by three parameterization methods to compare the calculation differences between different sizes of airfoils. In this study, the constants including NACA 0012, a Reynolds number of 10 million, an angle of attack of zero, a C-grid for meshing, and the k-epsilon (k-ε) turbulence model are used. The experimental variables include three airfoil parameterization methods: point cloud method, B-spline curve method, and class function/shape function transformation (CST) method. The airfoil dimensions are set to 3.98 meters, 17.67 meters, and 48 meters, respectively. In addition, this study also uses different numbers of edge meshing and the same bias factor in the CFD simulation. Studies have shown that with the change of airfoil scales, different parameterization methods, the number of control points, and the meshing number of divisions should be used to improve the accuracy of the aerodynamic performance of the wing. When the airfoil ratio increases, the most basic point cloud parameterization method will require more and larger data to support the accuracy of the airfoil’s aerodynamic performance, which will face the severe test of insufficient computer capacity. On the other hand, when using the B-spline curve method, average number of control points and meshing number of divisions should be set appropriately to obtain higher accuracy; however, the quantitative balance cannot be directly defined, but the decisions should be made repeatedly by adding and subtracting. Lastly, when using the CST method, it is found that limited control points are enough to accurately parameterize the larger-sized wing; a higher degree of accuracy and stability can be obtained by using a lower-performance computer.

Keywords: airfoil, computational fluid dynamics, geometric similarity, surface mesh quality

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Authors: Shanxiong Chen, Xu Han, Xiaolong Wang, Hui Ma

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Yi is an ethnic group mainly living in mainland China, with its own spoken and written language systems, after development of thousands of years. Ancient Yi is one of the six ancient languages in the world, which keeps a record of the history of the Yi people and offers documents valuable for research into human civilization. Recognition of the characters in ancient Yi helps to transform the documents into an electronic form, making their storage and spreading convenient. Due to historical and regional limitations, research on recognition of ancient characters is still inadequate. Thus, deep learning technology was applied to the recognition of such characters. Five models were developed on the basis of the four-layer convolutional neural network (CNN). Alpha-Beta divergence was taken as a penalty term to re-encode output neurons of the five models. Two fully connected layers fulfilled the compression of the features. Finally, at the softmax layer, the orthographic features of ancient Yi characters were re-evaluated, their probability distributions were obtained, and characters with features of the highest probability were recognized. Tests conducted show that the method has achieved higher precision compared with the traditional CNN model for handwriting recognition of the ancient Yi.

Keywords: recognition, CNN, Yi character, divergence

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Authors: Uswah Khairuddin, Rubiyah Yusof, Nenny Ruthfalydia Rosli

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This paper presents a comparative study between k-Nearest Neighbour (k-NN) and Multi-Layer Perceptron Neural Network (MLP-NN) classifier using Genetic Algorithm (GA) as feature selector for wood recognition system. The features have been extracted from the images using Grey Level Co-Occurrence Matrix (GLCM). The use of GA based feature selection is mainly to ensure that the database used for training the features for the wood species pattern classifier consists of only optimized features. The feature selection process is aimed at selecting only the most discriminating features of the wood species to reduce the confusion for the pattern classifier. This feature selection approach maintains the ‘good’ features that minimizes the inter-class distance and maximizes the intra-class distance. Wrapper GA is used with k-NN classifier as fitness evaluator (GA-kNN). The results shows that k-NN is the best choice of classifier because it uses a very simple distance calculation algorithm and classification tasks can be done in a short time with good classification accuracy.

Keywords: feature selection, genetic algorithm, optimization, wood recognition system

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Authors: Muhammad Umar Khan, Mahesh Kumar, Faraz Neakakhtar

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Although industrialization marks to the economy of a country yet it increases the pollution and temperature of the environment. The world is now shifting towards green energy because the utilization of fossil fuels is resulting in global warming. So we need to develop systems that can operate on renewable energy resources and have low heat losses. The combined solar and geothermal multigeneration system can solve this issue. Rather than making rankine cycle purely a solar-driven, heat from solar is used to drive vapour absorption cycle and reheated to generate power using geothermal reservoir. The results are displayed by using Engineering Equation Solver software, where inputs are varied to optimize the energy and exergy efficiencies of the system. The cooling effect is 348.2 KW, while the network output is 23.8 MW and reducing resultant emission of 105553 tons of CO₂ per year. This eco-friendly multigeneration system is capable of eliminating the use of fossil fuels and increasing the geothermal energy efficiency.

Keywords: cooling effect, eco-friendly, green energy, heat loses, multigeneration system, renewable energy, work output

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Authors: Helen S. Joyner (Melito), Mohammad Anvari

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Concentrated emulsions stabilized by proteins are systems of great importance in food, pharmaceutical and cosmetic products. Controlling emulsion rheology is critical for ensuring desired properties during formation, storage, and consumption of emulsion-based products. Studies on concentrated emulsions have focused on rheology of monodispersed systems. However, emulsions used for industrial applications are polydispersed in nature, and this polydispersity is regarded as an important parameter that also governs the rheology of the concentrated emulsions. Therefore, the objective of this study was to characterize rheological (small and large deformation behaviors) and microstructural properties of concentrated emulsions which were not truly monodispersed as usually encountered in food products such as margarines, mayonnaise, creams, spreads, and etc. The concentrated emulsions were prepared at different concentrations of fish gelatin (0.2, 0.4, 0.8% w/v in the whole emulsion system), oil-water ratio 80-20 (w/w), homogenization speed 10000 rpm, and 25oC. Confocal laser scanning microscopy (CLSM) was used to determine the microstructure of the emulsions. To prepare samples for CLSM analysis, FG solutions were stained by Fluorescein isothiocyanate dye. Emulsion viscosity profiles were determined using shear rate sweeps (0.01 to 100 1/s). The linear viscoelastic regions (LVRs) of the emulsions were determined using strain sweeps (0.01 to 100% strain) for each sample. Frequency sweeps were performed in the LVR (0.1% strain) from 0.6 to 100 rad/s. Large amplitude oscillatory shear (LAOS) testing was conducted by collecting raw waveform data at 0.05, 1, 10, and 100% strain at 4 different frequencies (0.5, 1, 10, and 100 rad/s). All measurements were performed in triplicate at 25oC. The CLSM results revealed that increased fish gelatin concentration resulted in more stable oil-in-water emulsions with homogeneous, finely dispersed oil droplets. Furthermore, the protein concentration had a significant effect on emulsion rheological properties. Apparent viscosity and dynamic moduli at small deformations increased with increasing fish gelatin concentration. These results were related to increased inter-droplet network connections caused by increased fish gelatin adsorption at the surface of oil droplets. Nevertheless, all samples showed shear-thinning and weak gel behaviors over shear rate and frequency sweeps, respectively. Lissajous plots, or plots of stress versus strain, and phase lag values were used to determine nonlinear behavior of the emulsions in LAOS testing. Greater distortion in the elliptical shape of the plots followed by higher phase lag values was observed at large strains and frequencies in all samples, indicating increased nonlinear behavior. Shifts from elastic-dominated to viscous dominated behavior were also observed. These shifts were attributed to damage to the sample microstructure (e.g. gel network disruption), which would lead to viscous-type behaviors such as permanent deformation and flow. Unlike the small deformation results, the LAOS behavior of the concentrated emulsions was not dependent on fish gelatin concentration. Systems with different microstructures showed similar nonlinear viscoelastic behaviors. The results of this study provided valuable information that can be used to incorporate concentrated emulsions in emulsion-based food formulations.

Keywords: concentrated emulsion, fish gelatin, microstructure, rheology

Procedia PDF Downloads 275

Authors: Gerhard D. Muller, Nadine C. Sonnenberg, Suné Donoghue

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This study delves into the intricacies of male and female consumers’ perceptions of Corporate Social Responsibility (CSR) in the South African clothing retail sector, a sector experiencing increasing consumption, yet facing significant environmental and social challenges. The aim is to discern between male and female consumers’ perceptions of clothing retailers’ CSR initiatives based on the Triple Bottom Line (TBL) framework, which evaluates organizational sustainability across social, environmental, and economic domains. Methodologically, the study is embedded in a quantitative research paradigm adopting a cross-sectional survey design. A purposive sampling strategy was used to recruit male and female respondents from a diverse South African demographic background. A structured questionnaire was developed and included established consumer CSR perception scales that were adapted for the purposes of this study. The questionnaire was distributed via online platforms. The data collected from the online survey, were split by gender to allow for comparison between male and female consumers’ perceptions of clothing retailers’ CSR initiatives. Exploratory Factor Analysis (EFA) was conducted on each of the datasets. The EFA for females revealed a five-factor solution, whereas the male EFA presented a six-factor solution, with the notable addition of an Economic Performance dimension. Results indicate subtle differences in the gender groups’ CSR perceptions. While both genders seem to value clothing retailers’ focus on quality services, females seem to have more pronounced perceptions surrounding clothing retailers’ contributions to social and environmental causes. Males, on the other hand, seem to be more discerning in their perceptions surrounding clothing retailers’ support of social and environmental causes. Ethical stakeholder relationships emerged as a shared concern across genders. Still, males presented a distinct factor, Economic Performance, highlighting a gendered divergence in the weighting of economic success and financial performance in CSR evaluation. The implications of these results are multifaceted. Theoretically, the study enriches the discourse on CSR by integrating gender insights into the TBL framework, offering a greater understanding of consumers’ CSR perceptions in the South African clothing retail context. Practically, it provides actionable insights for clothing retailers, suggesting that CSR initiatives should be gender-sensitive and communicate the TBL's elements effectively to resonate with the pertinent concerns of each segment. Additionally, the findings advocate for a contextualized approach to CSR in emerging markets that aligns with local cultural and social differences.

Keywords: consumer perceptions, corporate Social responsibility, gender differentiation, triple bottom line

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Authors: Juan Bojórquez, Henry E. Reyes, Edén Bojórquez, Alfredo Reyes-Salazar

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This paper addressed the use of Artificial Intelligence to obtain the structural reliability of reinforced concrete buildings. For this purpose, artificial neuronal networks (ANN) are developed to predict seismic demand hazard curves. In order to have enough input-output data to train the ANN, a set of reinforced concrete buildings (low, mid, and high rise) are designed, then a probabilistic seismic hazard analysis is made to obtain the seismic demand hazard curves. The results are then used as input-output data to train the ANN in a feedforward backpropagation model. The predicted values of the seismic demand hazard curves found by the ANN are then compared. Finally, it is concluded that the computer time analysis is significantly lower and the predictions obtained from the ANN were accurate in comparison to the values obtained from the conventional methods.

Keywords: structural reliability, seismic design, machine learning, artificial neural network, probabilistic seismic hazard analysis, seismic demand hazard curves

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Authors: Kaustubh Chakradeo, Michael Delves, Sofya Titarenko

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Malaria is a serious disease which affects hundreds of millions of people around the world, each year. If not treated in time, it can be fatal. Despite recent developments in malaria diagnostics, the microscopy method to detect malaria remains the most common. Unfortunately, the accuracy of microscopic diagnostics is dependent on the skill of the microscopist and limits the throughput of malaria diagnosis. With the development of Artificial Intelligence tools and Deep Learning techniques in particular, it is possible to lower the cost, while achieving an overall higher accuracy. In this paper, we present a VGG-based model and compare it with previously developed models for identifying infected cells. Our model surpasses most previously developed models in a range of the accuracy metrics. The model has an advantage of being constructed from a relatively small number of layers. This reduces the computer resources and computational time. Moreover, we test our model on two types of datasets and argue that the currently developed deep-learning-based methods cannot efficiently distinguish between infected and contaminated cells. A more precise study of suspicious regions is required.

Keywords: convolution neural network, deep learning, malaria, thin blood smears

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Authors: Rajagopal Kavitha, Van Lun Low, Mohd Sofian-Azirun, Chee Dhang Chen, Mohd Yusof Farida Zuraina, Mohd Salleh Ahmad Firdaus, Navaratnam Shanti, Abdul Haiyee Zaibunnisa

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This study investigated the hidden genetic lineages in the oriental latrine fly Chrysomya megacephala (Fabricius) across four states (i.e., Johore, Pahang, Perak and Selangor) and a federal territory (i.e., Kuala Lumpur) in Malaysia using Cytochrome b (Cyt b) genetic marker. The Cyt b phylogenetic tree and haplotype network revealed three distinct genetic lineages of Ch. megacephala. Lineage A, the basal clade was restricted to flies that originated from Kuala Lumpur and Selangor, while Lineages B and C, comprised of flies from all studied populations. An overlap of the three genetically divergent groups of Ch. megacephala was observed. However, the flies from both Kuala Lumpur and Selangor populations consisted of three different lineages, indicating that they are genetically diverse compared to those from Pahang, Perak and Johore.

Keywords: forensic entomology, calliphoridae, mitochondrial DNA, cryptic lineage

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Authors: Ronald Mraz, Gary C. Kessler, Ethan Gold, John G. Cline

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The International Ice Patrol (IIP) continually monitors iceberg activity in the North Atlantic by direct observation using ships, aircraft, and satellite imagery. Daily reports detailing navigational boundaries of icebergs have significantly reduced the risk of iceberg contact. What is currently lacking is formatting this data for automatic transmission and display of iceberg navigational boundaries in commercial navigation equipment. This paper describes the methodology and implementation of a system to format iceberg limit information for dissemination through existing radio network communications. This information will then automatically display on commercial navigation equipment. Additionally, this information is reformatted for Google Earth rendering of iceberg track line limits. Having iceberg limit information automatically available in standard navigation equipment will help support full autonomous operation of sailing vessels.

Keywords: iceberg, iceberg risk, iceberg track lines, AIS messaging, international ice patrol, North American ice service, google earth, autonomous surface vessels

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Authors: James Rigor Camacho, Wansu Lim

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Over the last few years, the development of new wearable and processing technologies has accelerated in order to harness physiological data such as electroencephalograms (EEGs) for EEG-based applications. EEG has been demonstrated to be a source of emotion recognition signals with the highest classification accuracy among physiological signals. However, when emotion recognition systems are used for real-time classification, the training unit is frequently left to run offline or in the cloud rather than working locally on the edge. That strategy has hampered research, and the full potential of using an edge AI device has yet to be realized. Edge AI devices are computers with high performance that can process complex algorithms. It is capable of collecting, processing, and storing data on its own. It can also analyze and apply complicated algorithms like localization, detection, and recognition on a real-time application, making it a powerful embedded device. The NVIDIA Jetson series, specifically the Jetson Nano device, was used in the implementation. The cEEGrid, which is integrated to the open-source brain computer-interface platform (OpenBCI), is used to collect EEG signals. An EEG-based real-time emotion recognition system on Edge AI is proposed in this paper. To perform graphical spectrogram categorization of EEG signals and to predict emotional states based on input data properties, machine learning-based classifiers were used. Until the emotional state was identified, the EEG signals were analyzed using the K-Nearest Neighbor (KNN) technique, which is a supervised learning system. In EEG signal processing, after each EEG signal has been received in real-time and translated from time to frequency domain, the Fast Fourier Transform (FFT) technique is utilized to observe the frequency bands in each EEG signal. To appropriately show the variance of each EEG frequency band, power density, standard deviation, and mean are calculated and employed. The next stage is to identify the features that have been chosen to predict emotion in EEG data using the K-Nearest Neighbors (KNN) technique. Arousal and valence datasets are used to train the parameters defined by the KNN technique.Because classification and recognition of specific classes, as well as emotion prediction, are conducted both online and locally on the edge, the KNN technique increased the performance of the emotion recognition system on the NVIDIA Jetson Nano. Finally, this implementation aims to bridge the research gap on cost-effective and efficient real-time emotion recognition using a resource constrained hardware device, like the NVIDIA Jetson Nano. On the cutting edge of AI, EEG-based emotion identification can be employed in applications that can rapidly expand the research and implementation industry's use.

Keywords: edge AI device, EEG, emotion recognition system, supervised learning algorithm, sensors

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Authors: Tim Bolle, Franziska Kreimendahl, Thomas Gries, Stefan Jockenhoevel

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The therapeutic treatment of extensive, deep wounds is limited. Autologous split-skin grafts are used as a so-called ‘gold standard’. Most common deficits are the defects at the donor site, the risk of scarring as well as the limited availability and quality of the autologous grafts. The aim of this project is a tissue engineered dermal-epidermal skin replacement to overcome the limitations of the gold standard. A key requirement for the development of such a three-dimensional implant is the formation of a functional capillary-like network inside the implant to ensure a sufficient nutrient and gas supply. Tailored three-dimensional warp knitted spacer fabrics are used to reinforce the mechanically week fibrin gel-based scaffold and further to create a directed in vitro pre-vascularization along the parallel-oriented pile yarns within a co-culture. In this study various three-dimensional warp knitted spacer fabrics were developed in a factorial design to analyze the influence of the machine parameters such as the stitch density and the pattern of the fabric on the scaffold performance and further to determine suitable parameters for a successful fibrin gel-incorporation and a physiological performance of the scaffold. The fabrics were manufactured on a Karl Mayer double-bar raschel machine DR 16 EEC/EAC. A fine machine gauge of E30 was used to ensure a high pile yarn density for sufficient nutrient, gas and waste exchange. In order to ensure a high mechanical stability of the graft, the fabrics were made of biocompatible PVDF yarns. Key parameters such as the pore size, porosity and stress/strain behavior were investigated under standardized, controlled climate conditions. The influence of the input parameters on the mechanical and morphological properties as well as the ability of fibrin gel incorporation into the spacer fabric was analyzed. Subsequently, the pile yarns of the spacer fabrics were colonized with Human Umbilical Vein Endothelial Cells (HUVEC) to analyze the ability of the fabric to further function as a guiding structure for a directed vascularization. The cells were stained with DAPI and investigated using fluorescence microscopy. The analysis revealed that the stitch density and the binding pattern have a strong influence on both the mechanical and morphological properties of the fabric. As expected, the incorporation of the fibrin gel was significantly improved with higher pore sizes and porosities, whereas the mechanical strength decreases. Furthermore, the colonization trials revealed a high cell distribution and density on the pile yarns of the spacer fabrics. For a tailored reinforcing structure, the minimum porosity and pore size needs to be evaluated which still ensures a complete incorporation of the reinforcing structure into the fibrin gel matrix. That will enable a mechanically stable dermal graft with a dense vascular network for a sufficient nutrient and oxygen supply of the cells. The results are promising for subsequent research in the field of reinforcing mechanically weak biological scaffolds and develop functional three-dimensional scaffolds with an oriented pre-vascularization.

Keywords: fibrin-gel, skin replacement, spacer fabric, pre-vascularization

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Authors: Natalia Khvorostianov, Larissa Remennick

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Recent immigrants seldom join the ranks of volunteers for various social causes. This gap reflects both material reasons (immigrants’ lower income and lack of free time) and cultural differences (value systems, religiosity, language barrier, attitudes towards host society, etc.). Immigrants from the former socialist countries are particularly averse to organized forms of volunteering for a host of reasons rooted in their past, including the memories of false or forced forms of collectivism imposed by the state. In this qualitative study, based on 21 semi-structured interviews, we explored the perceptions and practices of volunteer work among FSU immigrants - participants in one volunteering project run by an Israeli NGO for the benefit of elderly ex-Soviet immigrants. Our goal was to understand the motivations of immigrant volunteers and the role of volunteering in the processes of their own social and economic integration in their adopted country – Israel. The results indicate that most volunteers chose causes targeting fellow immigrants, their resettlement and well-being, and were motivated by the wish to build co-ethnic support network and overcome marginalization in the Israeli society. Other volunteers were driven by the need for self-actualization in the context of underemployment and occupational downgrading.

Keywords: FSU immigrants, integration, volunteering, participation, social capital

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Authors: Benjamin D Reese Jr

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Implicit bias is understood as unconscious attitudes, stereotypes, or associations that can influence the cognitions, actions, decisions, and interactions of an individual without intentional control. These unconscious attitudes or stereotypes are often targeted toward specific groups of people based on their gender, race, age, perceived sexual orientation or other social categories. Since the late 1980s, there has been a proliferation of research that hypothesizes that the operation of implicit bias is the result of the brain needing to process millions of bits of information every second. Hence, one’s prior individual learning history provides ‘shortcuts’. As soon as one see someone of a certain race, one have immediate associations based on their past learning, and one might make assumptions about their competence, skill, or danger. These assumptions are outside of conscious awareness. In recent years, an alternative conceptualization has been proposed. The ‘bias of crowds’ theory hypothesizes that a given context or situation influences the degree of accessibility of particular biases. For example, in certain geographic communities in the United States, there is a long-standing and deeply ingrained history of structures, policies, and practices that contribute to racial inequities and bias toward African Americans. Hence, negative biases among groups of people towards African Americans are more accessible in such contexts or communities. This theory does not focus on individual brain functioning or cognitive ‘shortcuts.’ Therefore, attempts to modify individual perceptions or learning might have negligible impact on those embedded environmental systems or policies that are within certain contexts or communities. From the ‘bias of crowds’ perspective, high levels of racial bias in a community can be reduced by making fundamental changes in structures, policies, and practices to create a more equitable context or community rather than focusing on training or education aimed at reducing an individual’s biases. The current paper acknowledges and supports the foundational role of long-standing structures, policies, and practices that maintain racial inequities, as well as inequities related to other social categories, and highlights the critical need to continue organizational, community, and national efforts to eliminate those inequities. It also makes a case for providing individual leaders with a deep understanding of the dynamics of how implicit biases impact cognitions, actions, decisions, and interactions so that those leaders might more effectively develop structural changes in the processes and systems under their purview. This approach incorporates both the importance of an individual’s learning history as well as the important variables within the ‘bias of crowds’ theory. The paper also offers a model for leadership education, as well as examples of structural changes leaders might consider.

Keywords: implicit bias, unconscious bias, bias, inequities

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Authors: Liana Khandolishvili, Giorgi Dvalashvili

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Karst waters in the world play an important role in the water supply. Among them, the Vaucluse in Chiatura municipality (Georgia) is used as drinking water and is irreplaceable for the local population. Accordingly, it is important to assess their geo-ecological conditions and take care to maintain sustainability. The aim of the paper is to identify the hazards of pollution of underground waters in the karst environment and to develop a scheme for their protection, which will take into consideration both the hydrogeological characteristics and the role of humans. To achieve this goal, the EPIK method was selected using which an epikarst zone of the study area was studied in detail, as well as the protective cover, infiltration conditions and frequency of karst network development, after which the conditions of karst waters in Chiatura municipality was assessed, their main pollutants were identified and the recommendations were prepared for their protection. The results of the study showed that the karst water pollution rate in Chiatura municipality is highest, where karst-fissured layers are represented and intensive extraction works are underway. The EPIK method is innovative in Georgia and was first introduced on the example of karst waters of Chiatura municipality.

Keywords: cave, EPIK method, pollution, Karst waters, geology, geography, ecology

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Authors: L. Chetibi, D. Hamana, T. O. Busko, M. P. Kulish, S. Achour

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TiO2 nanostructures have attracted much attention due to their optical, dielectric and photocatalytic properties as well as applications including optical coating, photocatalysis and photoelectrochemical solar cells. This work aims to prepare TiO2 nanofibers (NFs) on titanium substrate (Ti) by in situ oxidation of Ti foils in a mixture solution of concentrated H2O2 and NaOH followed by proton exchange and calcinations. Scanning Electron microscopy (SEM) revealed an obvious network of TiO2 nanofibers. The photoluminescence (PL) spectra of these nanostructures revealed a broad intense band in the visible light range with a reduced near edge band emission. The PL bands in the visible region, mainly, results from surface oxygen vacancies and others defects. After irradiation with Ti+ ions (the irradiation energy was E = 140 keV with doses of 1013 ions/cm2), the intensity of the PL spectrum decreased as a consequence of the radiation treatment. The irradiation with Ti+ leads to a reduction of defects and generation of non irradiative defects near to the level of the conduction band as evidenced by the PL results. On the other hand, reducing the surface defects on TiO2 nanostructures may improve photocatalytic and optoelectronic properties of this nanostructure.

Keywords: TiO2, nanofibers, photoluminescence, irradiation

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Authors: Katherine O'Grady-Bray

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For some deaf children, reading books can be a challenge. Frank Barnes School (FBS) provides guided reading time with Teachers of the Deaf, in which they read books with deaf children using a bilingual approach. The vocabulary and context of the story is explained to deaf children in BSL so they develop skills bridging English and BSL languages. However, the success of this practice is only achieved if the person is fluent in both languages. FBS piloted a scheme to convert an Oxford Reading Tree (ORT) book into an e-book that can be read using tablets. Deaf readers at FBS have access to both languages (BSL and English) during lessons and outside the classroom. The pupils receive guided reading sessions with a Teacher of the Deaf every morning, these one to one sessions give pupils the opportunity to learn how to bridge both languages e.g. how to translate English to BSL and vice versa. Generally, due to our pupils’ lack of access to incidental learning, gaining new information about the world around them is limited. This highlights the importance of quality time to scaffold their language development. In some cases, there is a shortfall of parental support at home due to poor communication skills or an unawareness of how to interact with deaf children. Some families have a limited knowledge of sign language or simply don’t have the required learning environment and strategies needed for language development with deaf children. As the majority of our pupils’ preferred language is BSL we use that to teach reading and writing English. If this is not mirrored at home, there is limited opportunity for joint reading sessions. Development of the e-Book required planning and technical development. The overall production took time as video footage needed to be shot and then edited individually for each page. There were various technical considerations such as having an appropriate background colour so not to draw attention away from the signer. Appointing a signer with the required high level of BSL was essential. The language and pace of the sign language was an important consideration as it was required to match the age and reading level of the book. When translating English text to BSL, careful consideration was given to the nonlinear nature of BSL and the differences in language structure and syntax. The e-book was produced using Apple’s ‘iBook Author’ software which allowed video footage of the signer to be embedded on pages opposite the text and illustration. This enabled BSL translation of the content of the text and inferences of the story. An interpreter was used to directly ‘voice over’ the signer rather than the actual text. The aim behind the structure and layout of the e-book is to allow parents to ‘read’ with their deaf child which helps to develop both languages. From observations, the use of e-books has given pupils confidence and motivation with their reading, developing skills bridging both BSL and English languages and more effective reading time with parents.

Keywords: bilingual book, e-book, BSL and English, bilingual e-book

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Authors: Ahmed Emad Ahmed, Zeyad Ahmed Hussein, Mohamed Salama Afifi, Ahmed Mohammed Eid

Abstract:

Water has a great importance in life. In order to deliver water from resources to the users, many procedures should be taken by the water engineers. One of the main procedures to deliver water to the community is by designing pressurizer pipe networks for water. The main aim of this work is to calculate the water demand of a small town and then design a simple water network to distribute water resources among the town with the smallest losses. Literature has been mentioned to cover the main point related to water distribution. Moreover, the methodology has introduced two approaches to solve the research problem, one by the iterative method of Hardy-cross and the other by water software Pipe Flow. The results have introduced two main designs to satisfy the same research requirements. Finally, the researchers have concluded that the use of water software provides more abilities and options for water engineers.

Keywords: looping pipe networks, hardy cross networks accuracy, relative error of hardy cross method

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Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

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Authors: Monica Arnaudo, Monika Topel, Bjorn Laumert

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Within the Swedish context, the current trend of relatively low electricity prices promotes the electrification of the energy infrastructure. The residential heating sector takes part in this transition by proposing a switch from a centralized district heating system towards a distributed heat pumps-based setting. When it comes to urban environments, two issues arise. The first, seen from an electricity-sector perspective, is related to the fact that existing networks are limited with regards to their installed capacities. Additional electric loads, such as heat pumps, can cause severe overloads on crucial network elements. The second, seen from a heating-sector perspective, has to do with the fact that the indoor comfort conditions can become difficult to handle when the operation of the heat pumps is limited by a risk of overloading on the distribution grid. Furthermore, the uncertainty of the electricity market prices in the future introduces an additional variable. This study aims at assessing the extent to which distributed heat pumps can penetrate an existing heat energy network while respecting the technical limitations of the electricity grid and the thermal comfort levels in the buildings. In order to account for the multi-disciplinary nature of this research question, a cosimulation modeling approach was adopted. In this way, each energy technology is modeled in its customized simulation environment. As part of the cosimulation methodology: a steady-state power flow analysis in pandapower was used for modeling the electrical distribution grid, a thermal balance model of a reference building was implemented in EnergyPlus to account for space heating and a fluid-cycle model of a heat pump was implemented in JModelica to account for the actual heating technology. With the models set in place, different scenarios based on forecasted electricity market prices were developed both for present and future conditions of Hammarby Sjöstad, a neighborhood located in the south-east of Stockholm (Sweden). For each scenario, the technical and the comfort conditions were assessed. Additionally, the average cost of heat generation was estimated in terms of levelized cost of heat. This indicator enables a techno-economic comparison study among the different scenarios. In order to evaluate the levelized cost of heat, a yearly performance simulation of the energy infrastructure was implemented. The scenarios related to the current electricity prices show that distributed heat pumps can replace the district heating system by covering up to 30% of the heating demand. By lowering of 2°C, the minimum accepted indoor temperature of the apartments, this level of penetration can increase up to 40%. Within the future scenarios, if the electricity prices will increase, as most likely expected within the next decade, the penetration of distributed heat pumps can be limited to 15%. In terms of levelized cost of heat, a residential heat pump technology becomes competitive only within a scenario of decreasing electricity prices. In this case, a district heating system is characterized by an average cost of heat generation 7% higher compared to a distributed heat pumps option.

Keywords: cosimulation, distributed heat pumps, district heating, electrical distribution grid, integrated energy systems

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Authors: Francis Padi, Solomon Nunoo, John Kojo Annan

Abstract:

The paper "Survey on Fiber Optic Deployment for Telecommunications Operators in Ghana: Coverage Gap, Recommendations and Research Directions" presents a comprehensive survey on the deployment of fiber optic networks for telecommunications operators in Ghana. It addresses the challenges encountered by operators using microwave transmission systems for backhauling traffic and emphasizes the advantages of deploying fiber optic networks. The study delves into the coverage gap, provides recommendations, and outlines research directions to enhance the telecommunications infrastructure in Ghana. Additionally, it evaluates next-generation optical access technologies and architectures tailored to operators' needs. The paper also investigates current technological solutions and regulatory, technical, and economical dimensions related to sharing mobile telecommunication networks in emerging countries. Overall, this paper offers valuable insights into fiber optic network deployment for telecommunications operators in Ghana and suggests strategies to meet the increasing demand for data and mobile applications.

Keywords: survey on fiber optic deployment, coverage gap, recommendations, research directions

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Authors: Luther Ollier, Sylvie Thiria, Anastase Charantonis, Carlos E. Mejia, Michel Crépon

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Sea Surface Height Anomaly (SLA) is a signature of the sub-mesoscale dynamics of the upper ocean. Sea Surface Temperature (SST) is driven by these dynamics and can be used to improve the spatial interpolation of SLA fields. In this study, we focused on the temporal evolution of SLA fields. We explored the capacity of deep learning (DL) methods to predict short-term SLA fields using SST fields. We used simulated daily SLA and SST data from the Mercator Global Analysis and Forecasting System, with a resolution of (1/12)◦ in the North Atlantic Ocean (26.5-44.42◦N, -64.25–41.83◦E), covering the period from 1993 to 2019. Using a slightly modified image-to-image convolutional DL architecture, we demonstrated that SST is a relevant variable for controlling the SLA prediction. With a learning process inspired by the teaching-forcing method, we managed to improve the SLA forecast at five days by using the SST fields as additional information. We obtained predictions of a 12 cm (20 cm) error of SLA evolution for scales smaller than mesoscales and at time scales of 5 days (20 days), respectively. Moreover, the information provided by the SST allows us to limit the SLA error to 16 cm at 20 days when learning the trajectory.

Keywords: deep-learning, altimetry, sea surface temperature, forecast

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights

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Authors: N. Drir, L. Barazane, M. Loudini

Abstract:

It is well known that photovoltaic (PV) cells are an attractive source of energy. Abundant and ubiquitous, this source is one of the important renewable energy sources that have been increasing worldwide year by year. However, in the V-P characteristic curve of GPV, there is a maximum point called the maximum power point (MPP) which depends closely on the variation of atmospheric conditions and the rotation of the earth. In fact, such characteristics outputs are nonlinear and change with variations of temperature and irradiation, so we need a controller named maximum power point tracker MPPT to extract the maximum power at the terminals of photovoltaic generator. In this context, the authors propose here to study the modeling of a photovoltaic system and to find an appropriate method for optimizing the operation of the PV generator using two intelligent controllers respectively to track this point. The first one is based on artificial neural networks and the second on fuzzy logic. After the conception and the integration of each controller in the global process, the performances are examined and compared through a series of simulation. These two controller have prove by their results good tracking of the MPPT compare with the other method which are proposed up to now.

Keywords: maximum power point tracking, neural networks, photovoltaic, P&O

Procedia PDF Downloads 339