Search results for: fibre networks

Authors: Zouari Ezzeddine, Ghraieb Ikram

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From the results of the academic literature which evokes the limitations of previous studies, this article shows the reasons for multidimensionality Prediction of financial bubbles. A new framework for modeling study predicting financial bubbles by linking a set of variable presented on several dimensions dictating its multidimensional character. It takes into account the preferences of financial actors. A multicriteria anticipation of the appearance of bubbles in international financial markets helps to fight against a possible crisis.

Keywords: classical measures, predictions, financial bubbles, multidimensional, artificial neural networks

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Authors: Simon J. Wilde, David M. Herold, Michael J. Bryant

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Social media networks have become the hotbed for advertising activities due mainly to their increasing consumer/user base and, secondly, owing to the ability of marketers to accurately measure ad exposure and consumer-based insights on such networks. More than half of the world’s population (4.8 billion) now uses social media (60%), with 150 million new users having come online within the last 12 months (to June 2022). As the use of social media networks by users grows, key business strategies used for interacting with these potential customers have matured, especially social media advertising. Unlike other traditional media outlets, social media advertising is highly interactive and digital channel specific. Social media advertisements are clearly targetable, providing marketers with an extremely powerful marketing tool. Yet despite the measurable benefits afforded to businesses engaged in social media advertising, recent controversies (such as the relationship between Facebook and Cambridge Analytica in 2018) have only heightened the role trust and privacy play within these social media networks. Using a web-based quantitative survey instrument, survey participants were recruited via a reputable online panel survey site. Respondents to the survey represented social media users from all states and territories within Australia. Completed responses were received from a total of 258 social media users. Survey respondents represented all core age demographic groupings, including Gen Z/Millennials (18-45 years = 60.5% of respondents) and Gen X/Boomers (46-66+ years = 39.5% of respondents). An adapted ADTRUST scale, using a 20 item 7-point Likert scale, measured trust in social media advertising. The ADTRUST scale has been shown to be a valid measure of trust in advertising within traditional media, such as broadcast media and print media, and, more recently, the Internet (as a broader platform). The adapted scale was validated through exploratory factor analysis (EFA), resulting in a three-factor solution. These three factors were named reliability, usefulness and affect, and the willingness to rely on. Factor scores (weighted measures) were then calculated for these factors. Factor scores are estimates of the scores survey participants would have received on each of the factors had they been measured directly, with the following results recorded (Reliability = 4.68/7; Usefulness and Affect = 4.53/7; and Willingness to Rely On = 3.94/7). Further statistical analysis (independent samples t-test) determined the difference in factor scores between the factors when age (Gen Z/Millennials vs. Gen X/Boomers) was utilized as the independent, categorical variable. The results showed the difference in mean scores across all three factors to be statistically significant (p<0.05) for these two core age groupings: (1) Gen Z/Millennials Reliability = 4.90/7 vs. Gen X/Boomers Reliability = 4.34/7; (2) Gen Z/Millennials Usefulness and Affect = 4.85/7 vs Gen X/Boomers Usefulness and Affect = 4.05/7; and (3) Gen Z/Millennials Willingness to Rely On = 4.53/7 vs Gen X/Boomers Willingness to Rely On = 3.03/7. The results clearly indicate that older social media users lack trust in the quality of information conveyed in social media ads when compared to younger, more social media-savvy consumers. This is especially evident with respect to Factor 3 (Willingness to Rely On), whose underlying variables reflect one’s behavioral intent to act based on the information conveyed in advertising. These findings can be useful to marketers, advertisers, and brand managers in that the results highlight a critical need to design ‘authentic’ advertisements on social media sites to better connect with these older users in an attempt to foster positive behavioral responses from within this large demographic group – whose engagement with social media sites continues to increase year on year.

Keywords: social media advertising, trust, older consumers, internet studies

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Authors: Soukaina Ounss, Hamid Mounir, Abdellatif El Marjani

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Sandwich materials with composite reinforced skins are mostly required in advanced construction applications with a view to ensure resistant structures. Their lightweight, their high flexural stiffness and their optimal thermal insulation make them a suitable solution to obtain efficient structures with performing rigidity and optimal energy safety. In this paper, the mechanical behavior of a sandwich beam with composite skins reinforced by unidirectional carbon fibers is investigated numerically through analyzing the impact of reinforcements specifications on the longitudinal elastic modulus in order to select the adequate sandwich configuration that has an interesting rigidity and an accurate convergence to the analytical approach which is proposed to verify performed numerical simulations. Therefore, concerned study starts by testing flexion performances of skins with various fibers orientations and volume fractions to determine those to use in sandwich beam. For that, the combination of a reinforcement inclination of 30° and a volume ratio of 60% is selected with the one with 60° of fibers orientation and 40% of volume fraction, this last guarantees to chosen skins an important rigidity with an optimal fibers concentration and a great enhance in convergence to analytical results in the sandwich model for the reason of the crucial core role as transverse shear absorber. Thus, a resistant sandwich beam is elaborated from a face-sheet constituted from two layers of previous skins with fibers oriented in 60° and an epoxy core; concerned beam has a longitudinal elastic modulus of 54 Gpa (gigapascal) that equals to the analytical value by a negligible error of 2%.

Keywords: fibers orientation, fibers volume ratio, longitudinal elastic modulus, sandwich beam

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Authors: Sipho Mbatha, Anne Mastament-Mason

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The Multi-Fibre Arrangement (MFA) which regulated all trade in the Apparel Manufacturing Industries (AMI) for four decades was dissolved in 2005. Since 2005, the Apparel Manufacturing Industry of South Africa (AMISA) has been battling to adjust to an environment of liberalised trade, mainly due to strategic, infrastructural and skills factors. In developing competitive advantage strategy for the AMISA, the study aimed to do the following (1) to apply Porter’s diamond model’s determinant “Factor Condition” as framework to develop competitive advantage strategies. (2) Examine the effectiveness of government policy Industrial Policy Action Plan (IPAP 2007) in supporting AMISA. (3) Examine chance events that could be used as bases for competitive advantage strategies for the AMISA. This study found that the lack of advanced skills and poor infrastructure are affecting the competitive advantage of AMISA. The then Clothing, Textiles, Leather and Footwear Sector Education and Training Authority (CTLF-SETA) has also fallen short of addressing the skills gap within the apparel manufacturing industries. The only time that AMISA have shown signs of competitive advantage was when they made use of government grants and incentives available to only compliant AMISA. The findings have shown that the apparel retail groups have shown support for the AMISA by shouldering raw material costs, making it easier to manufacture the required apparel at acceptable lead times. AMISA can compete in low end apparel, provided quick response is intensified, the development of local textiles and raw materials is expedited.

Keywords: compliance rule, apparel manufacturing idustry, factor conditions, advance skills, industrial policy action plan of South Africa

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Authors: Linghui Meng, James Atlas, Deborah Munro

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There is an increasing demand for prosthetics capable of mimicking natural limb movements and hand gestures, but precise movement control of prosthetics using only electrode signals continues to be challenging. This study considers the implementation of machine learning as a means of improving accuracy and presents an initial investigation into hand gesture recognition using models based on electromyographic (EMG) signals. EMG signals, which capture muscle activity, are used as inputs to machine learning algorithms to improve prosthetic control accuracy, functionality and adaptivity. Using logistic regression, a machine learning classifier, this study evaluates the accuracy of classifying two hand gestures from the publicly available Ninapro dataset using two-time series feature extraction algorithms: Time Series Feature Extraction (TSFE) and Convolutional Neural Networks (CNNs). Trials were conducted using varying numbers of EMG channels from one to eight to determine the impact of channel quantity on classification accuracy. The results suggest that although both algorithms can successfully distinguish between hand gesture EMG signals, CNNs outperform TSFE in extracting useful information for both accuracy and computational efficiency. In addition, although more channels of EMG signals provide more useful information, they also require more complex and computationally intensive feature extractors and consequently do not perform as well as lower numbers of channels. The findings also underscore the potential of machine learning techniques in developing more effective and adaptive prosthetic control systems.

Keywords: EMG, machine learning, prosthetic control, electromyographic prosthetics, hand gesture classification, CNN, computational neural networks, TSFE, time series feature extraction, channel count, logistic regression, ninapro, classifiers

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Authors: Kubra Sultan Yuzuncuyil, Aytekin İsman, Berkay Bulus

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With the developments of information and communication technologies, the forms of molding public opinion have changed. In the presence of Internet, the notion of activism has been endowed with digital codes. Activists have engaged the use of Internet into their campaigns and the process of creating collective identity. Activist movements have been incorporating the relevance of new communication technologies for their goals and opposition. Creating and managing activism through Internet is called Online Activism. In this main, Slow Food Movement which was emerged within the philosophy of defending regional, fair and sustainable food has been engaging Internet into their activist campaign. This movement supports the idea that a new food system which allows strong connections between plate and planet is possible. In order to make their voices heard, it has utilized social networks and develop particular skills in the framework online activism. This study analyzes online activist skills of Slow Food Movement (SFM) develop and attempts to measure its effectiveness. To achieve this aim, it adopts the model proposed by Sivitandies and Shah and conduct both qualitiative and quantiative content analysis on social network use of Slow Food Movement. In this regard, the sample is chosen as the official profile and analyzed between in a three month period respectively March-May 2017. It was found that SFM develops particular techniques that appeal to the model of Sivitandies and Shah. The prominent skill in this regard was found as hyperlink abbreviation and use of multimedia elements. On the other hand, there are inadequacies in hashtag and interactivity use. The importance of this study is that it highlights and discusses how online activism can be engaged into a social movement. It also reveals current online activism skills of SFM and their effectiveness. Furthermore, it makes suggestions to enhance the related abilities and strengthen its voice on social networks.

Keywords: slow food movement, Twitter, internet, online activism

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Authors: Hien Thi Dieu Truong, Mahmoud Al-Sarayreh, Pullanagari Reddy, Marlon M. Reis, Richard Archer

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New Zealand mānuka honey is a honeybee product derived mainly from Leptospermum scoparium nectar. The potent antibacterial activity of mānuka honey derives principally from methylglyoxal (MGO), in addition to the hydrogen peroxide and other lesser activities present in all honey. MGO is formed from dihydroxyacetone (DHA) unique to L. scoparium nectar. Mānuka honey also has an idiosyncratic phenolic profile that is useful as a chemical maker. Authentic mānuka honey is highly valuable, but almost all honey is formed from natural mixtures of nectars harvested by a hive over a time period. Once diluted by other nectars, mānuka honey irrevocably loses value. We aimed to apply hyperspectral imaging to honey frames before bulk extraction to minimise the dilution of genuine mānuka by other honey and ensure authenticity at the source. This technology is non-destructive and suitable for an industrial setting. Chemometrics using linear Partial Least Squares (PLS) and Support Vector Machine (SVM) showed limited efficacy in interpreting chemical footprints due to large non-linear relationships between predictor and predictand in a large sample set, likely due to honey quality variability across geographic regions. Therefore, an advanced modelling approach, one-dimensional convolutional neural networks (1D-CNN), was investigated for analysing hyperspectral data for extraction of biochemical information from honey. The 1D-CNN model showed superior prediction of honey quality (R² = 0.73, RMSE = 2.346, RPD= 2.56) to PLS (R² = 0.66, RMSE = 2.607, RPD= 1.91) and SVM (R² = 0.67, RMSE = 2.559, RPD=1.98). Classification of mono-floral manuka honey from multi-floral and non-manuka honey exceeded 90% accuracy for all models tried. Overall, this study reveals the potential of HSI and deep learning modelling for automating the evaluation of honey quality in frames.

Keywords: mānuka honey, quality, purity, potency, deep learning, 1D-CNN, chemometrics

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Authors: Chin-Chia Jane

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In a transportation network, travel time refers to the transmission time from source node to destination node, whereas reliability refers to the probability of a successful connection from source node to destination node. With an increasing emphasis on quality of service (QoS), both performance indexes are significant in the design and analysis of transportation systems. In this work, we extend the well-known flow network model for transportation networks so that travel time and reliability are integrated into the QoS measurement simultaneously. In the extended model, in addition to the general arc capacities, each intermediate node has a time weight which is the travel time for per unit of commodity going through the node. Meanwhile, arcs and nodes are treated as binary random variables that switch between operation and failure with associated probabilities. For pre-specified travel time limitation and demand requirement, the QoS of a transportation network is the probability that source can successfully transport the demand requirement to destination while the total transmission time is under the travel time limitation. This work is pioneering, since existing literatures that evaluate travel time reliability via a single optimization path, the proposed QoS focuses the performance of the whole network system. To compute the QoS of transportation networks, we first transfer the extended network model into an equivalent min-cost max-flow network model. In the transferred network, each arc has a new travel time weight which takes value 0. Each intermediate node is replaced by two nodes u and v, and an arc directed from u to v. The newly generated nodes u and v are perfect nodes. The new direct arc has three weights: travel time, capacity, and operation probability. Then the universal set of state vectors is recursively decomposed into disjoint subsets of reliable, unreliable, and stochastic vectors until no stochastic vector is left. The decomposition is made possible by applying existing efficient min-cost max-flow algorithm. Because the reliable subsets are disjoint, QoS can be obtained directly by summing the probabilities of these reliable subsets. Computational experiments are conducted on a benchmark network which has 11 nodes and 21 arcs. Five travel time limitations and five demand requirements are set to compute the QoS value. To make a comparison, we test the exhaustive complete enumeration method. Computational results reveal the proposed algorithm is much more efficient than the complete enumeration method. In this work, a transportation network is analyzed by an extended flow network model where each arc has a fixed capacity, each intermediate node has a time weight, and both arcs and nodes are independent binary random variables. The quality of service of the transportation network is an integration of customer demands, travel time, and the probability of connection. We present a decomposition algorithm to compute the QoS efficiently. Computational experiments conducted on a prototype network show that the proposed algorithm is superior to existing complete enumeration methods.

Keywords: quality of service, reliability, transportation network, travel time

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Authors: Băilă Diana Irinel, Păcurar Răzvan, Păcurar Ancuța

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Although the volumes of fibre reinforced polymer composites (FRPs) used for aircraft applications is a relatively small percentage of total use, the materials often find their most sophisticated applications in this industry. In aerospace, the performance criteria placed upon materials can be far greater than in other areas – key aspects are light-weight, high-strength, high-stiffness, and good fatigue resistance. Composites were first used by the military before the technology was applied to commercial planes. Nowadays, composites are widely used, and this has been the result of a gradual direct substitution of metal components followed by the development of integrated composite designs as confidence in FRPs has increased. The airplane uses a range of components made from composites, including the fin and tailplane. In the last years, composite materials are increasingly used in automotive applications due to the improvement of material properties. In the aerospace and automotive sector, the fuel consumption is proportional to the weight of the body of the vehicle. A minimum of 20% of the cost can be saved if it used polymer composites in place of the metal structures and the operating and maintenance costs are alco very low. Glass fiber-epoxy composites are widely used in the making of aircraft and automobile body parts and are not only limited to these fields but also used in ship building, structural applications in civil engineering, pipes for the transport of liquids, electrical insulators in reactors. This article was establish the high-performance of composite material, a type glass-epoxy used in automotive and aeronautic domains, concerning the tensile and flexural tests and SEM analyzes.

Keywords: glass-epoxy composite, traction and flexion tests, SEM analysis, acoustic emission (AE) signals

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Authors: Akim Borbuev, Francisco de León

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Economic and population growth in densely-populated urban areas introduce major challenges to distribution system operators, planers, and designers. To supply added loads, utilities are frequently forced to invest in new distribution feeders. However, this is becoming increasingly more challenging due to space limitations and rising installation costs in urban settings. This paper proposes the conversion of critical alternating current (ac) distribution feeders into direct current (dc) feeders to increase the power transfer capacity by a factor as high as four. Current trends suggest that the return of dc transmission, distribution, and utilization are inevitable. Since a total system-level transformation to dc operation is not possible in a short period of time due to the needed huge investments and utility unreadiness, this paper recommends that feeders that are expected to exceed their limits in near future are converted to dc. The increase in power transfer capacity is achieved through several key differences between ac and dc power transmission systems. First, it is shown that underground cables can be operated at higher dc voltage than the ac voltage for the same dielectric stress in the insulation. Second, cable sheath losses, due to induced voltages yielding circulation currents, that can be as high as phase conductor losses under ac operation, are not present under dc. Finally, skin and proximity effects in conductors and sheaths do not exist in dc cables. The paper demonstrates that in addition to the increased power transfer capacity utilities substituting ac feeders by dc feeders could benefit from significant lower costs and reduced losses. Installing dc feeders is less expensive than installing new ac feeders even when new trenches are not needed. Case studies using the IEEE 342-Node Low Voltage Networked Test System quantify the technical and economic benefits of dc feeders.

Keywords: DC power systems, distribution feeders, distribution networks, power transfer capacity

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Authors: Laxmi R. Kasaraneni

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The Internet connects all the networks, including the nation’s critical infrastructure that are used extensively by not only a nation’s government and military to protect sensitive information and execute missions, but also the primary infrastructure that provides services that enable modern conveniences such as education, potable water, electricity, natural gas, and financial transactions. It has become the central nervous system for the government, the citizens, and the industries. When it is attacked, the effects can ripple far and wide impacts not only to citizens’ well-being but nation’s economy, civil infrastructure, and national security. As such, these critical services may be targeted by malicious hackers during cyber warfare, it is imperative to not only protect them and mitigate any immediate or potential threats, but to also understand the current or potential impacts beyond the IT networks or the organization. The Nation’s IT infrastructure which is now vital for communication, commerce, and control of our physical infrastructure, is highly vulnerable to attack. While existing technologies can address some vulnerabilities, fundamentally new architectures and technologies are needed to address the larger structural insecurities of an infrastructure developed in a more trusting time when mass cyber attacks were not foreseen. This research is intended to improve the core functions of the Internet and critical-sector information systems by providing a clear path to create a safe, secure, and resilient cyber environment that help stakeholders at all levels of government, and the private sector work together to develop the cybersecurity capabilities that are key to our economy, national security, and public health and safety. This research paper also emphasizes the present and future cyber security threats, the capabilities and goals of cyber attackers, a strategic concept and steps to implement cybersecurity for maximum effectiveness, enabling technologies, some strategic assumptions and critical challenges, and the future of cyberspace.

Keywords: critical challenges, critical infrastructure, cyber security, enabling technologies, national security

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Authors: Caroline Rocco, Feyza Karasu, Céline Croutxé-Barghorn, Xavier Allonas, Maxime Lecompère, Gérard Riess, Yujing Zhang, Catarina Esteves, Leendert van der Ven, Rolf van Benthem Gijsbertus de With

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Acrylates are widely used in UV-curing technology. Their high reactivity can, however, limit their conversion due to early vitrification. In addition, the free radical photopolymerization is known to be sensitive to oxygen inhibition leading to tacky surfaces. Although epoxides can lead to full polymerization, they are sensitive to humidity and exhibit low polymerization rate. To overcome the intrinsic limitations of both classes of monomers, Interpenetrating Polymer Networks (IPNs) can be synthesized. They consist of at least two cross linked polymers which are permanently entangled. They can be achieved under thermal and/or light induced polymerization in one or two steps approach. IPNs can display homogeneous to heterogeneous morphologies with various degrees of phase separation strongly linked to the monomer miscibility and also synthesis parameters. In this presentation, we synthesize UV-cured methacrylate - epoxide based IPNs with different chemical compositions in order to get a better understanding of their formation and phase separation. Miscibility before and during the photopolymerization, reaction kinetics, as well as mechanical properties and morphology have been investigated. The key parameters controlling the morphology and the phase separation, namely monomer miscibility and synthesis parameters have been identified. By monitoring the stiffness changes on the film surface, atomic force acoustic microscopy (AFAM) gave, in conjunction with polymerization kinetic profiles and thermomechanical properties, explanations and corroborated the miscibility predictions. When varying the methacrylate / epoxide ratio, it was possible to move from a miscible and highly-interpenetrated IPN to a totally immiscible and phase-separated one.

Keywords: investigation of properties and morphology, kinetics, phase separation, UV-cured IPNs

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Authors: Muneeb Ullah, Daishihan, Xiadong Young

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Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively.

Keywords: classification, deep learning, medical images, CXR, GAN.

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Authors: S. Hussain, N. Ahmad, S. Alam, M. Bezabhi, W. H. Hendriks, P. Yu, J. W. Cone

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The high content of lignin in cell walls is the major limiting factor in the digestion and utilisation of cereal crop residues by ruminants. The aim of this study was to evaluate the effectiveness of the white rot fungus, Pleurotus ostreatus (P. ostreatus), to degrade lignin and to enhance the rumen degradability of maize stover, rice straw, wheat straw and their mixture in equal proportion on a dry-matter (DM) basis. Four samples of each substrate were incubated aerobically in triplicate with P. ostreatus for 0 (Control), 21, 28 and 35 days under solid-state conditions (temperature, 24 ͦ C; humidity, 70± 5%). The changes in chemical composition, DM and nutrient losses, and rumen fermentation characteristics using in vitro DM digestibility (DMD) and the in vitro gas production (GP) technique were measured. The results showed that incubation with P. ostreatus decreased (P < 0.001) the contents of neutral detergent fibre and lignin with a concomitant increase (P < 0.001) in the contents of ash and crude protein. The losses of nutrients differed (P < 0.001) among the straw types, with rice straw and maize stover showing the largest (P < 0.05) lignin degradation compared to wheat and mixed straws. The DMD and 72-h cumulative GP increased (P < 0.001) consistently with increasing fungal incubation period and for all substrates the highest values of DMD and GP were measured after 35 days of incubation with P. ostreatus. The lignin degradation was strongly associated with hemicellulose degradation (r = 0.71) across the various straws. Results of the present study demonstrated that incubation of low-quality crop residues with P. ostreatus under solid-state conditions upgrades their feeding value by reducing the content of lignin and increasing the content of crude protein and ruminal degradation.

Keywords: crop residues, lignin degradation, maize stovers, wheat straws, white rot fungi

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Authors: Anika Chebrolu

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Mono-targeted drugs can be of limited efficacy against complex diseases. Recently, multi-target drug design has been approached as a promising tool to fight against these challenging diseases. However, the scope of current computational approaches for multi-target drug design is limited. DeepLig presents a de-novo drug discovery platform that uses reinforcement learning to generate and optimize novel, potent, and multitargeted drug candidates against protein targets. DeepLig’s model consists of two networks in interplay: a generative network and a predictive network. The generative network, a Stack- Augmented Recurrent Neural Network, utilizes a stack memory unit to remember and recognize molecular patterns when generating novel ligands from scratch. The generative network passes each newly created ligand to the predictive network, which then uses multiple Graph Attention Networks simultaneously to forecast the average binding affinity of the generated ligand towards multiple target proteins. With each iteration, given feedback from the predictive network, the generative network learns to optimize itself to create molecules with a higher average binding affinity towards multiple proteins. DeepLig was evaluated based on its ability to generate multi-target ligands against two distinct proteins, multi-target ligands against three distinct proteins, and multi-target ligands against two distinct binding pockets on the same protein. With each test case, DeepLig was able to create a library of valid, synthetically accessible, and novel molecules with optimal and equipotent binding energies. We propose that DeepLig provides an effective approach to design multi-targeted drug therapies that can potentially show higher success rates during in-vitro trials.

Keywords: drug design, multitargeticity, de-novo, reinforcement learning

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Authors: Seppo Sirkemaa

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Information systems infrastructure is the basis of business systems and processes in the company. It should be a reliable platform for business processes and activities but also have the flexibility to change business needs. The development of an infrastructure that is robust, reliable, and flexible is a challenge. Understanding technological capabilities and business needs is a key element in the development of successful information systems infrastructure.

Keywords: development, information technology, networks, technology

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Authors: Al-Amin, Huanjun Jiang, Anayat Ali

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Reinforced concrete frames (RC), especially Ordinary RC frames, are prone to structural failures/collapse during seismic events, leading to a large proportion of debris from the structures, which obstructs adjacent areas, including streets. These blocked areas severely impede post-earthquake resilience. This study uses computational simulation (FEM) to investigate the amount of debris generated by the seismic collapse of an ordinary reinforced concrete moment frame building and its effects on the adjacent pedestrian and road network. A three-story ordinary reinforced concrete frame building, primarily designed for gravity load and earthquake resistance, was selected for analysis. Sixteen different ground motions were applied and scaled up until the total collapse of the tested building to evaluate the failure mode under various seismic events. Four types of collapse direction were identified through the analysis, namely aligned (positive and negative) and skewed (positive and negative), with aligned collapse being more predominant than skewed cases. The amount and distribution of debris around the collapsed building were assessed to investigate the interaction between collapsed buildings and adjacent street networks. An interaction was established between a building that collapsed in an aligned direction and the adjacent pedestrian walkway and narrow street located in an unplanned old city. The FEM model was validated against an existing shaking table test. The presented results can be utilized to simulate the interdependency between the debris generated from the collapse of seismic-prone buildings and the resilience of street networks. These findings provide insights for better disaster planning and resilient infrastructure development in earthquake-prone regions.

Keywords: building collapse, earthquake-induced debris, ORC moment resisting frame, street network

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Authors: Mathew Wakefield, Matthew Mitchell, Lisa Wise, Christopher McCarthy

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The properties of memory representations in artificial neural networks have cognitive implications. Distributed representations that encode instances as a pattern of activity across layers of nodes afford memory compression and enforce the selection of a single point in instance space. These encoding schemes also appear to distort the representational space, as well as trading off the ability to validate that input information is within the bounds of past experience. In contrast, a localist representation which encodes some meaningful information into individual nodes in a network layer affords less memory compression while retaining the integrity of the representational space. This allows the validity of an input to be determined. The validity (or familiarity) of input along with the capacity of localist representation for multiple instance selections affords a memory sampling approach that dynamically balances the bias-variance trade-off. When the input is familiar, bias may be high by referring only to the most similar instances in memory. When the input is less familiar, variance can be increased by referring to more instances that capture a broader range of features. Using this approach in a localist instance memory network, an experiment demonstrates a relationship between representational conflict, generalization performance, and memorization demand. Relatively small sampling ranges produce the best performance on a classic machine learning dataset of visual objects. Combining memory validity with conflict detection produces a reliable confidence judgement that can separate responses with high and low error rates. Confidence can also be used to signal the need for supervisory input. Using this judgement, the need for supervised learning as well as memory encoding can be substantially reduced with only a trivial detriment to classification performance.

Keywords: artificial neural networks, representation, memory, conflict monitoring, confidence

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Authors: Houman Tamaddon, Mehrdad Behnia, Masud Behnia

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An accurate study of blood flow is associated with an accurate vascular pattern and geometrical properties of the organ of interest. Due to the complexity of vascular networks and poor accessibility in vivo, it is challenging to reconstruct the entire vasculature of any organ experimentally. The objective of this study is to introduce an innovative approach for the reconstruction of a full vascular tree from available morphometric data. Our method consists of implementing morphometric data on those parts of the vascular tree that are smaller than the resolution of medical imaging methods. This technique reconstructs the entire arterial tree down to the capillaries. Vessels greater than 2 mm are obtained from direct volume and surface analysis using contrast enhanced computed tomography (CT). Vessels smaller than 2mm are reconstructed from available morphometric and distensibility data and rearranged by applying Murray’s Laws. Implementation of morphometric data to reconstruct the branching pattern and applying Murray’s Laws to every vessel bifurcation simultaneously, lead to an accurate vascular tree reconstruction. The reconstruction algorithm generates full arterial tree topography down to the first capillary bifurcation. Geometry of each order of the vascular tree is generated separately to minimize the construction and simulation time. The node-to-node connectivity along with the diameter and length of every vessel segment is established and order numbers, according to the diameter-defined Strahler system, are assigned. During the simulation, we used the averaged flow rate for each order to predict the pressure drop and once the pressure drop is predicted, the flow rate is corrected to match the computed pressure drop for each vessel. The final results for 3 cardiac cycles is presented and compared to the clinical data.

Keywords: blood flow, morphometric data, vascular tree, Strahler ordering system

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Authors: Seyed-Ali Sadegh-Zadeh, Mahboobe Bahrami, Sahar Ahmadi, Seyed-Yaser Mousavi, Hamed Atashbar, Amir M. Hajiyavand

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This paper presents an investigation into the concept of neural reshaping, which is crucial for achieving strong artificial intelligence through the development of AI algorithms with very high plasticity. By examining the plasticity of both human and artificial neural networks, the study uncovers groundbreaking insights into how these systems adapt to new experiences and situations, ultimately highlighting the potential for creating advanced AI systems that closely mimic human intelligence. The uniqueness of this paper lies in its comprehensive analysis of the neural reshaping process in both human and artificial intelligence systems. This comparative approach enables a deeper understanding of the fundamental principles of neural plasticity, thus shedding light on the limitations and untapped potential of both human and AI learning capabilities. By emphasizing the importance of neural reshaping in the quest for strong AI, the study underscores the need for developing AI algorithms with exceptional adaptability and plasticity. The paper's findings have significant implications for the future of AI research and development. By identifying the core principles of neural reshaping, this research can guide the design of next-generation AI technologies that can enhance human and artificial intelligence alike. These advancements will be instrumental in creating a new era of AI systems with unparalleled capabilities, paving the way for improved decision-making, problem-solving, and overall cognitive performance. In conclusion, this paper makes a substantial contribution by investigating the concept of neural reshaping and its importance for achieving strong AI. Through its in-depth exploration of neural plasticity in both human and artificial neural networks, the study unveils vital insights that can inform the development of innovative AI technologies with high adaptability and potential for enhancing human and AI capabilities alike.

Keywords: neural plasticity, brain adaptation, artificial intelligence, learning, cognitive reshaping

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Authors: Stephen L. Green, Alexander N. Gorban, Ivan Y. Tyukin

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Within the past decade, using Convolutional Neural Networks (CNN)’s to create Deep Learning systems capable of translating Sign Language into text has been a breakthrough in breaking the communication barrier for deaf-mute people. Conventional research on this subject has been concerned with training the network to recognize the fingerspelling gestures of a given language and produce their corresponding alphanumerics. One of the problems with the current developing technology is that images are scarce, with little variations in the gestures being presented to the recognition program, often skewed towards single skin tones and hand sizes that makes a percentage of the population’s fingerspelling harder to detect. Along with this, current gesture detection programs are only trained on one finger spelling language despite there being one hundred and forty-two known variants so far. All of this presents a limitation for traditional exploitation for the state of current technologies such as CNN’s, due to their large number of required parameters. This work aims to present a technology that aims to resolve this issue by combining a pretrained legacy AI system for a generic object recognition task with a corrector method to uptrain the legacy network. This is a computationally efficient procedure that does not require large volumes of data even when covering a broad range of sign languages such as American Sign Language, British Sign Language and Chinese Sign Language (Pinyin). Implementing recent results on method concentration, namely the stochastic separation theorem, an AI system is supposed as an operate mapping an input present in the set of images u ∈ U to an output that exists in a set of predicted class labels q ∈ Q of the alphanumeric that q represents and the language it comes from. These inputs and outputs, along with the interval variables z ∈ Z represent the system’s current state which implies a mapping that assigns an element x ∈ ℝⁿ to the triple (u, z, q). As all xi are i.i.d vectors drawn from a product mean distribution, over a period of time the AI generates a large set of measurements xi called S that are grouped into two categories: the correct predictions M and the incorrect predictions Y. Once the network has made its predictions, a corrector can then be applied through centering S and Y by subtracting their means. The data is then regularized by applying the Kaiser rule to the resulting eigenmatrix and then whitened before being split into pairwise, positively correlated clusters. Each of these clusters produces a unique hyperplane and if any element x falls outside the region bounded by these lines then it is reported as an error. As a result of this methodology, a self-correcting recognition process is created that can identify fingerspelling from a variety of sign language and successfully identify the corresponding alphanumeric and what language the gesture originates from which no other neural network has been able to replicate.

Keywords: convolutional neural networks, deep learning, shallow correctors, sign language

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Authors: N. Attari, S. Amziane, M. Chemrouk

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A large number of old buildings have been identified as having potentially critical detailing to resist earthquakes. The main reinforcement of lap-spliced columns just above the joint region, discontinuous bottom beam reinforcement, and little or no joint transverse reinforcement are the most critical details of interior beam column joints in such buildings. This structural type constitutes a large share of the building stock, both in developed and developing countries, and hence it represents a substantial exposure. Direct observation of damaged structures, following the Algiers 2003 earthquake, has shown that damage occurs usually at the beam-column joints, with failure in bending or shear, depending on geometry and reinforcement distribution and type. While substantial literature exists for the design of concrete frame joints to withstand this type of failure, after the earthquake many structures were classified as slightly damaged and, being uneconomic to replace them, at least in the short term, suitable means of repairs of the beam column joint area are being studied. Furthermore; there exists a large number of buildings that need retrofitting of the joints before the next earthquake. The paper reports the results of the experimental programme, constituted of three beam-column reinforced concrete joints at a scale of one to three (1/3) tested under the effect of a pre-stressing axial load acting over the column. The beams were subjected at their ends to an alternate cyclic loading under displacement control to simulate a seismic action. Strain and cracking fields were monitored with the help a digital recording camera. Following the analysis of the results, a comparison can be made between the performances in terms of ductility, strength and mode of failure of the different strengthening solution considered.

Keywords: fibre reinforced polymers, joints, reinforced concrete, beam columns

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Authors: Pradeep Kumar

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The present work is based on application of cement mortar with natural sand and discontinuous steel fiber through which bending behavior of skinny beam was evaluated. This research is to study the effects of combining reinforcing steel meshes (continuous steel reinforcement) with discontinuous fibers as reinforcement in skinny walled Portland cement based cement mortar with crushed stone dust as a fine aggregate. The term ‘skinny’ means thickness of the beams is less than 25 mm. The main idea behind this combination is to satisfy the ultimate strength limit state through the steel mesh reinforcement (as a main reinforcement) and to control the cracking under service loads through fiber (Recron 3s) reinforcement (as secondary reinforcement). The main object of this study is to carry out the bending behavior of mortar reinforced thin beam with only one layer of steel mesh (with various transfer wire spacing) and with a recron 3s (Reliance) fifers. The wide experimental program with bending tests is undertaken. The following variables are investigated: (a) the reference mesh size - 25.4 x 25.4 mm and 50.8 x 50.8 mm; (b) the transverse wire spacing - 25.4 mm, 50.8 mm, and no transverse wires; (c) the type of fibers – Reliance (Recron 3s, 6mm length); and (d) the fiber volume fraction – 0.1% and 0.25%. Some of the main conclusions are: (a) the use of recron 3s fibers leads to a little better overall performance than that with no fiber; (b) an increase in equivalent stress is observed when 0.1% RF,0.25% R Fibers are used; (c) when 25.4 x 50.8 size steel mesh is used, no noticeable change in behavior is observed in comparison to specimens without fibers; and (d) for no fibers 0.1% and o.1% RF the transverse wire spacing has some little effect on the equivalent stress for RF fibers, the transverse wire has no influence but the equivalent stress are increased.

Keywords: cement mortar, crushed stone dust, fibre, steel mesh

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Authors: Juan Alfredo Guevara Carrio, Swamy Toolahalli Thipperudra, Riddhi Naik Dharmeshbhai, Sergio Graniero Echeverrigaray, Jose Vitorio Emiliano, Antonio Helio Castro

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In order to advance the hydrogen economy, several industrial sectors can potentially benefit from the trillions of stimulus spending for post-coronavirus. Blending hydrogen into natural gas pipeline networks has been proposed as a means of delivering it during the early market development phase, using separation and purification technologies downstream to extract the pure H₂ close to the point of end-use. This first step has been mentioned around the world as an opportunity to use existing infrastructures for immediate decarbonisation pathways. Among current technologies used to extract hydrogen from mixtures in pipelines or liquid carriers, membrane separation can achieve the highest selectivity. The most efficient approach for the separation of H₂ from other substances by membranes is offered from the research of 2D layered materials due to their exceptional physical and chemical properties. Graphene-based membranes, with their distribution of pore sizes in nanometers and angstrom range, have shown fundamental and economic advantages over other materials. Their combination with the structure of ceramic and geopolymeric materials enabled the synthesis of nanocomposites and the fabrication of membranes with long-term stability and robustness in a relevant range of physical and chemical conditions. Versatile separation modules have been developed for hydrogen separation, which adaptability allows their integration in industrial prototypes for applications in heavy transport, steel, and cement production, as well as small installations at end-user stations of pipeline networks. The developed membranes and prototypes are a practical contribution to the technological challenge of supply pure H₂ for the mentioned industries as well as hydrogen energy-based fuel cells.

Keywords: graphene nano-composite membranes, hydrogen separation and purification, separation modules, indsutrial prototype

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Authors: Christine F. Boos, Fernando M. Azevedo

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Electroencephalogram (EEG) is a record of the electrical activity of the brain that has many applications, such as monitoring alertness, coma and brain death; locating damaged areas of the brain after head injury, stroke and tumor; monitoring anesthesia depth; researching physiology and sleep disorders; researching epilepsy and localizing the seizure focus. Epilepsy is a chronic condition, or a group of diseases of high prevalence, still poorly explained by science and whose diagnosis is still predominantly clinical. The EEG recording is considered an important test for epilepsy investigation and its visual analysis is very often applied for clinical confirmation of epilepsy diagnosis. Moreover, this EEG analysis can also be used to help define the types of epileptic syndrome, determine epileptiform zone, assist in the planning of drug treatment and provide additional information about the feasibility of surgical intervention. In the context of diagnosis confirmation the analysis is made using long term EEG recordings with at least 24 hours long and acquired by a minimum of 24 electrodes in which the neurophysiologists perform a thorough visual evaluation of EEG screens in search of specific electrographic patterns called epileptiform discharges. Considering that the EEG screens usually display 10 seconds of the recording, the neurophysiologist has to evaluate 360 screens per hour of EEG or a minimum of 8,640 screens per long term EEG recording. Analyzing thousands of EEG screens in search patterns that have a maximum duration of 200 ms is a very time consuming, complex and exhaustive task. Because of this, over the years several studies have proposed automated methodologies that could facilitate the neurophysiologists’ task of identifying epileptiform discharges and a large number of methodologies used neural networks for the pattern classification. One of the differences between all of these methodologies is the type of input stimuli presented to the networks, i.e., how the EEG signal is introduced in the network. Five types of input stimuli have been commonly found in literature: raw EEG signal, morphological descriptors (i.e. parameters related to the signal’s morphology), Fast Fourier Transform (FFT) spectrum, Short-Time Fourier Transform (STFT) spectrograms and Wavelet Transform features. This study evaluates the application of these five types of input stimuli and compares the classification results of neural networks that were implemented using each of these inputs. The performance of using raw signal varied between 43 and 84% efficiency. The results of FFT spectrum and STFT spectrograms were quite similar with average efficiency being 73 and 77%, respectively. The efficiency of Wavelet Transform features varied between 57 and 81% while the descriptors presented efficiency values between 62 and 93%. After simulations we could observe that the best results were achieved when either morphological descriptors or Wavelet features were used as input stimuli.

Keywords: Artificial neural network, electroencephalogram signal, pattern recognition, signal processing

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Authors: Erica Lima

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The objective of this paper is to discuss relevant points about teaching translation in Brazilian universities and the possible impacts of blogs and social networks to translator education today. It is intended to analyze the curricula of Brazilian translation courses, contrasting them to information obtained from two social networking groups of great visibility in the area concerning essential characteristics to become a successful profession. Therefore, research has, as its main corpus, a few undergraduate translation programs’ syllabuses, as well as a few postings on social networks groups that specifically share professional opinions regarding the necessity for a translator to obtain a degree in translation to practice the profession. To a certain extent, such comments and their corresponding responses lead to the propagation of discourses which influence the ideas that aspiring translators and recent graduates end up having towards themselves and their undergraduate courses. The postings also show that many professionals do not have a clear position regarding the translator education; while refuting it, they also encourage “free” courses. It is thus observed that cyberspace constitutes, on the one hand, a place of mobilization of people in defense of similar ideas. However, on the other hand, it embodies a place of tension and conflict, in view of the fact that there are many participants and, as in any other situation of interlocution, disagreements may arise. From the postings, aspects related to professionalism were analyzed (including discussions about regulation), as well as questions about the classic dichotomies: theory/practice; art/technique; self-education/academic training. As partial result, the common interest regarding the valorization of the profession could be mentioned, although there is no consensus on the essential characteristics to be a good translator. It was also possible to observe that the set of socially constructed representations in the group reflects characteristics of the world situation of the translation courses (especially in some European countries and in the United States), which, in the first instance, does not accurately reflect the Brazilian idiosyncrasies of the area.

Keywords: cyberspace, teaching translation, translator education, university

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Authors: Mohammed Alruwaili‬

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Nowadays, renewable energy resources are playing an important role in replacing traditional energy resources such as fossil fuels by integrating solar energy with conventional energy. Concerns about the environment led to an intensive search for a renewable energy source. The Rapid growth of distributed energy resources will have prompted increasing interest in the integrated distributing network in the Kingdom of Saudi Arabia next few years, especially after the adoption of new laws and regulations in this regard. Photovoltaic energy is one of the promising renewable energy sources that has grown rapidly worldwide in the past few years and can be used to produce electrical energy through the photovoltaic process. The main objective of the research is to study the impact of PV in distribution networks based on real data and details. In this research, site survey and computer simulation will be dealt with using the well-known computer program software ETAB to simulate the input of electrical distribution lines with other variable inputs such as the levels of solar radiation and the field study that represent the prevailing conditions and conditions in Diriah, Riyadh region, Saudi Arabia. In addition, the impact of adding distributed generation units (DGs) to the distribution network, including solar photovoltaic (PV), will be studied and assessed for the impact of adding different power capacities. The result has been achieved with less power loss in the loop distribution network from the current condition by more than 69% increase in network power loss. However, the studied network contains 78 buses. It is hoped from this research that the efficiency, performance, quality and reliability by having an enhancement in power loss and voltage profile of the distribution networks in Riyadh City. Simulation results prove that the applied method can illustrate the positive impact of PV in loop distribution generation.

Keywords: renewable energy, smart grid, efficiency, distribution network

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Authors: Ying-Lun Tseng Chiu-Ying Chen

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Taiwan's Mental Health Act was amended at the end of 2022; they added regulations regarding refusing compulsory treatment by patients with mental illnesses. In addition, not only by an examination committee, the judge must also assess the patient's need for compulsory treatment. Additionally, the maximum of compulsory hospitalization has been reduced from an unlimited period to a maximum of 60 days. They aim to promote the healthcare autonomy of individuals with mental illnesses in Taiwan and prevent their silenced voice in medical decision-making while they still possess rationality. Furthermore, they plan to use community support and social care networks to replace the current practice of compulsory treatment in Taiwan. This study uses qualitative research methodology, utilizing interview guidelines to inquire about the experiences of Taiwanese who have undergone compulsory hospitalization, compulsory community treatment, and compulsory medical care. The interviews aimed to explore their feelings when they were subjected to compulsory medical intervention, the inside of their illness, their opinions after treatments, and whether alternative medical interventions proposed by them were considered. Additionally, participants also asked about their personal life history and their support networks in their lives. We collected 12 Taiwanese who had experienced compulsory medical interventions and were interviewed 14 times. The findings indicated that participants still possessed rationality during the onset of their illness. However, when they have other treatments to replace compulsory medical, they sometimes diverge from those of the doctors and their families. Finally, doctors prefer their professional judgment and patients' families' option. Therefore, Taiwanese mental health patients' power of decision-making still needs to improve. Because this research uses qualitative research, so difficult to find participants, and the sample size rate was smaller than Taiwan's population, it may have biases in the analysis. So, Taiwan still has significant progress in enhancing the decision-making rights of participants in the study.

Keywords: medical decision making, compulsory treatment, medical ethics, mental health act

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Authors: Saleh Alshehri

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Human skin detection is a vital step in many applications. Some of the applications are critical especially those related to security. This leverages the importance of a high-performance detection algorithm. To validate the accuracy of the algorithm, image databases are usually used. However, the suitability of these image databases is still questionable. It is suggested that the suitability can be measured mainly by the span the database covers of the color space. This research investigates the validity of three famous image databases.

Keywords: image databases, image processing, pattern recognition, neural networks

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Authors: Netanel Stern

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Artificial intelligence (AI) algorithms have emerged as powerful tools in the field of psychiatry, offering new possibilities for enhancing diagnosis and treatment outcomes. This article explores the utilization of AI algorithms in psychiatry, highlighting their potential to revolutionize patient care. Various AI algorithms, including machine learning, natural language processing (NLP), reinforcement learning, clustering, and Bayesian networks, are discussed in detail. Moreover, ethical considerations and future directions for research and implementation are addressed.

Keywords: AI, software engineering, psychiatry, neuroimaging

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