Search results for: computer networks

Authors: Shaode Yu, Jiajian Meng, Bing Zhu, Hang Yu, Qiurui Sun

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Speech emotion recognition (SER) is for recognizing human subjective emotions through audio data in-depth analysis. From speech audios, how to comprehensively extract emotional information and how to effectively fuse extracted features remain challenging. This paper presents a dual-stream SER framework that embraces both full training and transfer learning of different networks for thorough feature encoding. Besides, a plug-and-play cross-attention fusion (CAF) module is implemented for the valid integration of the dual-stream encoder output. The effectiveness of the proposed CAF module is compared to the other three fusion modules (feature summation, feature concatenation, and feature-wise linear modulation) on two databases (RAVDESS and IEMO-CAP) using different dual-stream encoders (full training network, DPCNN or TextRCNN; transfer learning network, HuBERT or Wav2Vec2). Experimental results suggest that the CAF module can effectively reconcile conflicts between features from different encoders and outperform the other three feature fusion modules on the SER task. In the future, the plug-and-play CAF module can be extended for multi-branch feature fusion, and the dual-stream SER framework can be widened for multi-stream data representation to improve the recognition performance and generalization capacity.

Keywords: speech emotion recognition, cross-attention fusion, dual-stream, pre-trained

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Authors: Daneal Rorke, Gueguim Kana

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The shift towards renewable energy sources for biofuel production has received increasing attention. However, the use and pre-treatment of lignocellulosic material are inundated with the generation of fermentation inhibitors which severely impact the feasibility of bioprocesses. This study reports the profiling of all volatile compounds generated during microwave assisted chemical pre-treatment of sorghum leaves. Furthermore, the optimization of reducing sugar (RS) from microwave assisted acid pre-treatment of sorghum leaves was assessed and gave a coefficient of determination (R2) of 0.76, producing an optimal RS yield of 2.74 g FS/g substrate. The development of an intelligent model to predict volatile compound fractions gave R2 values of up to 0.93 for 21 volatile compounds. Sensitivity analysis revealed that furfural and phenol exhibited high sensitivity to acid concentration, alkali concentration and S:L ratio, while phenol showed high sensitivity to microwave duration and intensity as well. These findings illustrate the potential of using an intelligent model to predict the volatile compound fraction profile of compounds generated during pre-treatment of sorghum leaves in order to establish a more robust and efficient pre-treatment regime for biofuel production.

Keywords: artificial neural networks, fermentation inhibitors, lignocellulosic pre-treatment, sorghum leaves

Procedia PDF Downloads 243

Authors: Naghmeh Moradpoor Sheykhkanloo

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Structured Query Language Injection (SQLI) attack is a code injection technique in which malicious SQL statements are inserted into a given SQL database by simply using a web browser. Losing data, disclosing confidential information or even changing the value of data are the severe damages that SQLI attack can cause on a given database. SQLI attack has also been rated as the number-one attack among top ten web application threats on Open Web Application Security Project (OWASP). OWASP is an open community dedicated to enabling organisations to consider, develop, obtain, function, and preserve applications that can be trusted. In this paper, we propose an effective pattern recognition neural network model for detection and classification of SQLI attacks. The proposed model is built from three main elements of: a Uniform Resource Locator (URL) generator in order to generate thousands of malicious and benign URLs, a URL classifier in order to: 1) classify each generated URL to either a benign URL or a malicious URL and 2) classify the malicious URLs into different SQLI attack categories, and an NN model in order to: 1) detect either a given URL is a malicious URL or a benign URL and 2) identify the type of SQLI attack for each malicious URL. The model is first trained and then evaluated by employing thousands of benign and malicious URLs. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed approach.

Keywords: neural networks, pattern recognition, SQL injection attacks, SQL injection attack classification, SQL injection attack detection

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Authors: Baghdasaryan Marinka, Ulikyan Azatuhi

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The efficient usage of the compensation abilities of the electrical drive synchronous motors used in production processes can essentially improve the technical and economic indices of the process.  Reducing the flows of the reactive electrical energy due to the compensation of reactive power allows to significantly reduce the load losses of power in the electrical networks. As a result of analyzing the scientific works devoted to the issues of regulating the excitation of the synchronous motors, the need for comprehensive investigation and estimation of the excitation mode has been substantiated. By means of the obtained transmission functions, in the Simulink environment of the software package MATLAB, the transition processes of the excitation mode have been studied. As a result of obtaining and estimating the graph of the Nyquist plot and the transient process, the necessity of developing the Proportional-Integral-Derivative (PID) regulator has been justified. The transient processes of the system of the PID regulator have been investigated, and the amplitude–phase characteristics of the system have been estimated. The analysis of the obtained results has shown that the regulation indices of the developed system have been improved. The developed system can be successfully applied for regulating the excitation voltage of different-power synchronous motors, operating with a changing load, ensuring a value of the power coefficient close to 1.

Keywords: transition process, synchronous motor, excitation mode, regulator, reactive power

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Authors: Eduardo Álvarez, Gerardo Quiroga, Jorge Orozco, Gabriel Chavira

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AmI proposes a new way of thinking about computers, which follows the ideas of the Ubiquitous Computing vision of Mark Weiser. In these, there is what is known as a Disappearing Computer Initiative, with users immersed in intelligent environments. Hence, technologies need to be adapted so that they are capable of replacing the traditional inputs to the system by embedding these in every-day artifacts. In this work, we present an approach, which uses Radiofrequency Identification (RFID) and Near Field Communication (NFC) technologies. In the latter, a new form of interaction appears by contact. We compare both technologies by analyzing their requirements and advantages. In addition, we propose using a combination of RFID and NFC.

Keywords: touching interaction, ambient intelligence, ubiquitous computing, interaction, NFC and RFID

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Authors: Abdelkarim M. Ertiame, D. W. Yu, D. L. Yu, J. B. Gomm

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In this paper, a new robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics and using the weighted sum-squared prediction error as the residual. The recursive orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. The several actuator and sensor faults are simulated in a nonlinear simulation of the reactor in Simulink. The scheme is used to detect and isolate the faults on-line. The simulation results show the effectiveness of the scheme even the process is subjected to disturbances and uncertainties including significant changes in the monomer feed rate, fouling factor, impurity factor, ambient temperature and measurement noise. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.

Keywords: Robust fault detection, cascade control, independent RBF model, RBF neural networks, Chylla-Haase reactor, FDI under closed-loop control

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Authors: Tanveer Hussain, Khan Muhammad, Amin Ullah, Mi Young Lee, Sung Wook Baik

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The Big Data generated from distributed vision sensors installed on large scale in smart cities create hurdles in its efficient and beneficial exploration for browsing, retrieval, and indexing. This paper presents a three-folded framework for effective video summarization of such data and provide a compact and representative format of Big Video Data. In the first fold, the paper acquires input video data from the installed cameras and collect clues such as type and count of objects and clarity of the view from a chunk of pre-defined number of frames of each view. The decision of representative view selection for a particular interval is based on fuzzy inference system, acquiring a precise and human resembling decision, reinforced by the known clues as a part of the second fold. In the third fold, the paper forwards the selected view frames to the summary generation mechanism that is supported by a saliency-aware convolution neural network (CNN) model. The new trend of fuzzy rules for view selection followed by CNN architecture for saliency computation makes the multi-view video summarization (MVS) framework a suitable candidate for real-world practice in smart cities.

Keywords: big video data analysis, fuzzy logic, multi-view video summarization, saliency detection

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Authors: Tesfaw Belayneh Abebe

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Integrating low-altitude earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) within a non-terrestrial network (NTN) with the assistance of reconfigurable intelligent surfaces (RIS), we investigate the problem of how to enhance throughput through integrated LEO satellites and UAVs with the assistance of RIS. We propose a method to jointly optimize the associations with the LEO satellite, the 3D trajectory of the UAV, and the phase shifts of the RIS to maximize communication throughput for RIS-assisted integrated LEO satellite and UAV-enabled wireless communications, which is challenging due to the time-varying changes in the position of the LEO satellite, the high mobility of UAVs, an enormous number of possible control actions, and also the large number of RIS elements. Utilizing a multi-agent double deep Q-network (MADDQN), our approach dynamically adjusts LEO satellite association, UAV positioning, and RIS phase shifts. Simulation results demonstrate that our method significantly outperforms baseline strategies in maximizing throughput. Lastly, thanks to the integrated network and the RIS, the proposed scheme achieves up to 65.66x higher peak throughput and 25.09x higher worst-case throughput.

Keywords: integrating low-altitude earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs) within a non-terrestrial network (NTN), reconfigurable intelligent surfaces (RIS), multi-agent double deep Q-network (MADDQN)

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Authors: Mostafa Mjahed

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Tilt Rotor UAVs (Unmanned Aerial Vehicles) are naturally unstable and difficult to maneuver. The purpose of this paper is to design controllers for the stabilization and trajectory tracking of this type of UAV. To this end, artificial intelligence methods have been exploited. First, the dynamics of this UAV was modeled using the Lagrange-Euler method. The conventional method based on Proportional, Integral and Derivative (PID) control was applied by decoupling the different flight modes. To improve stability and trajectory tracking of the Tilt Rotor, the fuzzy approach and the technique of multilayer neural networks (NN) has been used. Thus, Fuzzy Proportional Integral and Derivative (FPID) and Neural Network-based Proportional Integral and Derivative controllers (NNPID) have been developed. The meta-heuristic approach based on Particle Swarm Optimization (PSO) method allowed adjusting the setting parameters of NNPID controller, giving us an improved NNPID-PSO controller. Simulation results under the Matlab environment show the efficiency of the approaches adopted. Besides, the Tilt Rotor UAV has become stable and follows different types of trajectories with acceptable precision. The Fuzzy, NN and NN-PSO-based approaches demonstrated their robustness because the presence of the disturbances did not alter the stability or the trajectory tracking of the Tilt Rotor UAV.

Keywords: neural network, fuzzy logic, PSO, PID, trajectory tracking, tilt-rotor UAV

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Authors: Chien-kuo Chang, Bo-wei Wu, Yi-yun Tang, Min-chiu Wu

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This paper proposes a power equipment diagnosis system based on partial discharge (PD), which is characterized by increasing the readability of experimental data and the convenience of operation. This system integrates a variety of analysis programs of different data formats and different programming languages and then establishes a set of interfaces that can follow and expand the structure, which is also helpful for subsequent maintenance and innovation. This study shows a case of using the developed Convolutional Neural Networks (CNN) to integrate with this system, using the designed model architecture to simplify the complex training process. It is expected that the simplified training process can be used to establish an adaptive deep learning experimental structure. By selecting different test data for repeated training, the accuracy of the identification system can be enhanced. On this platform, the measurement status and partial discharge pattern of each equipment can be checked in real time, and the function of real-time identification can be set, and various training models can be used to carry out real-time partial discharge insulation defect identification and insulation state diagnosis. When the electric power equipment entering the dangerous period, replace equipment early to avoid unexpected electrical accidents.

Keywords: partial discharge, convolutional neural network, partial discharge analysis platform, adaptive deep learning

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Authors: Azadeh Mohajer Milani

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Urbanization constitutes an irreversible global trend, presenting myriad challenges such as heightened energy consumption, pollution, congestion, and the depletion of natural resources. Today's urban landscapes have emerged as focal points for economic, social, and environmental challenges, underscoring the pressing need for sustainable development. This article delves into the realm of sustainable urban development, concentrating on the pivotal role played by integrated green spaces as an optimal solution to address environmental concerns within cities. The study utilizes Tehran as a case study. Our findings underscore the imperative of preserving and expanding green spaces in urban areas, coupled with the establishment of well-designed ecological networks, to enhance environmental quality and elevate the sustainability of cities. Notably, Tehran's urban green spaces exhibit a disjointed design, lacking a cohesive network to connect various patches and corridors, resulting in significant environmental impacts. The results emphasize the necessity of a balanced and proportional distribution of urban green spaces and the creation of a cohesive patch-corridor-matrix network tailored to the ecological and social needs of residents. This approach is crucial for fostering a more sustainable and livable urban environment for all species, with a specific focus on humans.

Keywords: ecology, sustainable urban development, sustainable landscape, urban green space network

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Authors: M. Kosacka, I. Kudelska

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Vehicle is one of the most influential and complex product worldwide, which affects people’s life, state of the environment and condition of the economy (all aspects of sustainable development concept) during each stage of lifecycle. With the increase of vehicles’ number, there is growing potential for management of End of Life Vehicle (ELV), which is hazardous waste. From one point of view, the ELV should be managed to ensure risk elimination, but from another point, it should be treated as a source of valuable materials and spare parts. In order to obtain materials and spare parts, there are established recycling networks, which are an example of sustainable policy realization at the national level. The basic object in the polish recycling network is dismantling facility. The output material streams in dismantling stations include waste, which very often generate costs and spare parts, that have the biggest potential for revenues creation. Both outputs are stored into warehouses, according to the law. In accordance to the revenue creation and sustainability potential, it has been placed a strong emphasis on storage process. We present the concept of storage method, which takes into account the specific of the dismantling facility in order to support decision-making process with regard to the principles of sustainable development. The method was developed on the basis of case study of one of the greatest dismantling facility in Poland.

Keywords: dismantling, end of life vehicles, sustainability, storage

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Authors: Moustafa Ahmed, Fumio Koyama

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Directly-modulated semiconductor lasers, including edge-emitting and vertical-cavity surface-emitting lasers, have received considerable interest recently for use in data transmitters in cost-effective high-speed data centers, metro, and access networks. Optical feedback has been proved as an efficient technique to boost the modulation bandwidth and enhance the speed of the semiconductor laser. However, both the laser linewidth and frequency chirping in directly-modulated lasers are sensitive to both intensity modulation and optical feedback. These effects along width fiber dispersion affect the transmission bit rate and distance in single-mode fiber links. In this work, we continue our recent research on directly-modulated semiconductor lasers with modulation bandwidth in the millimeter-wave band by introducing simultaneous modeling and simulations on both the frequency chirping and lineshape broadening. The lasers are operating under strong optical feedback. The model takes into account the multiple reflections of laser reflections of laser radiation in the external cavity. The analyses are given in terms of the chirp-to-modulated power ratio, and the results are shown for the possible dynamic states of continuous wave, period-1 oscillation, and chaos.

Keywords: chirp, linewidth, optical feedback, semiconductor laser

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Authors: Hashem Dadashpoor, Sadegh Saeidi Shirvan

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While with the evolution of the economy towards a knowledge-based economy, innovation is a requirement for metropolitan regions, the adoption of an open innovation strategy is an option and a requirement for many industrial firms in these regions. Studies show that investing in research and development units cannot alone increase innovation. Within the framework of the theory of learning regions, this gap, which scholars call it the ‘innovation gap’, is filled with regional features of firms. This paper attempts to investigate the factors affecting the open innovation of firms in metropolitan regions, and it searches for these in territorial innovation models and, in particular, the theory of learning regions. In the next step, the effect of identified factors which is considered as regional learning factors in this research is analyzed on the innovation of sample firms by SPSS software using multiple linear regression. The case study of this research is constituted of industrial enterprises from two groups of food industry and auto parts in Toos industrial town in Mashhad metropolitan region. For data gathering of this research, interviews were conducted with managers of industrial firms using structured questionnaires. Based on this study, the effect of factors such as size of firms, inter-firm competition, the use of local labor force and institutional infrastructures were significant in the innovation of the firms studied, and 44% of the changes in the firms’ innovation occurred as a result of the change in these factors.

Keywords: regional knowledge networks, learning regions, interactive learning, innovation

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Authors: Gurmail Singh

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Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS).

Keywords: bullwhip effect, hybrid techniques, net stock amplification, supply chain flexibility

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Authors: Irfan Anjum Manarvi, Fawzi Aljassir

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Human bones have been a keen area of research over a long time in the field of biomechanical engineering. Medical professionals, as well as engineering academics and researchers, have investigated various bones by using medical, mechanical, and materials approaches to discover the available body of knowledge. Their major focus has been to establish properties of these and ultimately develop processes and tools either to prevent fracture or recover its damage. Literature shows that mechanical professionals conducted a variety of tests for hardness, deformation, and strain field measurement to arrive at their findings. However, they considered these results accuracy to be insufficient due to various limitations of tools, test equipment, difficulties in the availability of human bones. They proposed the need for further studies to first overcome inaccuracies in measurement methods, testing machines, and experimental errors and then carry out experimental or theoretical studies. Finite Element analysis is a technique which was developed for the aerospace industry due to the complexity of design and materials. But over a period of time, it has found its applications in many other industries due to accuracy and flexibility in selection of materials and types of loading that could be theoretically applied to an object under study. In the past few decades, the field of biomechanical engineering has also started to see its applicability. However, the work done in the area of Tarsals, metatarsals and phalanges using this technique is very limited. Therefore, present research has been focused on using this technique for analysis of these critical bones of the human body. This technique requires a 3-dimensional geometric computer model of the object to be analyzed. In the present research, a 3d laser scanner was used for accurate geometric scans of individual tarsals, metatarsals, and phalanges from a typical human foot to make these computer geometric models. These were then imported into a Finite Element Analysis software and a length refining process was carried out prior to analysis to ensure the computer models were true representatives of actual bone. This was followed by analysis of each bone individually. A number of constraints and load conditions were applied to observe the stress and strain distributions in these bones under the conditions of compression and tensile loads or their combination. Results were collected for deformations in various axis, and stress and strain distributions were observed to identify critical locations where fracture could occur. A comparative analysis of failure properties of all the three types of bones was carried out to establish which of these could fail earlier which is presented in this research. Results of this investigation could be used for further experimental studies by the academics and researchers, as well as industrial engineers, for development of various foot protection devices or tools for surgical operations and recovery treatment of these bones. Researchers could build up on these models to carryout analysis of a complete human foot through Finite Element analysis under various loading conditions such as walking, marching, running, and landing after a jump etc.

Keywords: tarsals, metatarsals, phalanges, 3D scanning, finite element analysis

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Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

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Authors: Valentin P. Velikov

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Automatic program generation saves time, human resources, and allows receiving syntactically clear and logically correct modules. The 4-th generation programming languages are related to drawing the data and the processes of the subject area, as well as, to obtain a frame of the respective information system. The application can be separated in interface and business logic. That means, for an interactive generation of the needed system to be used an already existing toolkit or to be created a new one.

Keywords: computer science, graphical user interface, user dialog interface, dialog frames, data modeling, subject area modeling

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Authors: Marina Vicario, Amadeo Argüelles, Pilar Gómez, Carlos Hernández

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In this paper, the Computer Research Network of the National Polytechnic Institute of Mexico proposes a paradigmatic model for the inference of cognitive patterns in digital learning systems. This model leads to metadata architecture useful for analysis and prediction in online learning systems; especially on MOOc's architectures. The model is in the design phase and expects to be tested through an institutional of courses project which is going to develop for the MOOc.

Keywords: cognitive footprints, learning analytics, predictive learning, digital learning, educational computing, educational informatics

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Authors: Robert Joseph M. Licup

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The deployment of the fifth-generation (5G) mobile system through mmWave frequencies is the new solution in the requirement to provide higher bandwidth readily available for all users. The usage pattern of the mobile users has moved towards either the work from home or online classes set-up because of the pandemic. Previous mobile technologies can no longer meet the high speed, and bandwidth requirement needed, given the drastic shift of transactions to the home. The millimeter-wave (mmWave) underutilized frequency is utilized by the fifth-generation (5G) cellular networks that support multi-gigabit-per-second (Gbps) transmission. However, due to its short wavelengths, high path loss, directivity, blockage sensitivity, and narrow beamwidth are some of the technical challenges that need to be addressed. Different tools, technologies, and scenarios are explored to support network design, accurate channel modeling, implementation, and deployment effectively. However, there is a big challenge on how the consumer will adopt this solution and maximize the benefits offered by the 5G Technology. This research proposes to study the intricacies of technology diffusion, individual attitude, behaviors, and how technology adoption will be attained. The agent based simulation model shaped by the actual applications, technology solution, and related literature was used to arrive at a computational model. The research examines the different attributes, factors, and intricacies that can affect each identified agent towards technology adoption.

Keywords: agent-based model, AnyLogic, 5G O21, 5G mmWave solutions, technology adoption

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Authors: Mahmoud Ahmed Farrag, Ali Zain Elabdeen Heikal, Mohamed Shawky Ahmed, Ahmed Osama Amer

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Crowd-sourced data refers to data that is collected and shared by a large number of individuals or organizations, often through the use of digital technologies such as mobile devices and social media. The shortage in crash data collection in developing countries makes it difficult to fully understand and address road safety issues in these regions. In developing countries, crowd-sourced data can be a valuable tool for improving road safety, particularly in urban areas where the majority of road crashes occur. This study is -to our best knowledge- the first to develop safety performance functions using crowd-sourced data by adopting a negative binomial structure model and the Full Bayes model to investigate traffic safety for urban road networks and provide insights into the impact of roadway characteristics. Furthermore, as a part of the safety management process, network screening has been undergone through applying two different methods to rank the most hazardous road segments: PCR method (adopted in the Highway Capacity Manual HCM) as well as a graphical method using GIS tools to compare and validate. Lastly, recommendations were suggested for policymakers to ensure safer roads.

Keywords: crowdsourced data, road crashes, safety performance functions, Full Bayes models, network screening

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Authors: Benmoussa Dennai, H. Benslimane, A. Helmaoui

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Photovoltaic conversion is the direct conversion of electromagnetic energy into electrical energy continuously. This electromagnetic energy is the most solar radiation. In this work we performed a computer modelling using AMPS 1D optimization of hetero-junction solar cells GaInP / GaAs configuration for p / n. We studied the influence of the thickness the base layer in the cell offers on the open circuit voltage, the short circuit current and efficiency.

Keywords: optimization, photovoltaic cell, GaInP / GaAs AMPS-1D, hetetro-junction

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Authors: Giuffrida Graziella, Pennisi Stefania, Coppa Federica, Iannello Giulia, Cartelli Simone, Lo Faro Riccardo, Ferruggia Greta, Brundo Maria Violetta

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Cladode chemical composition may vary according to soil factors, cultivation season, and plant age. The primary metabolites of cladodes are water, carbohydrates, and proteins. The carbohydrates in cladodes are divided into two types: structural and storage. Polysaccharides from Opuntia ficus‐indica (L.) Mill plants build molecular networks with the capacity to retain water; thus, they act as mucoprotective agents. Mucilage is the main polysaccharide of cladodes; it contains polymers of β‐d‐galacturonic acid bound in positions (1–4) and traces of R‐linked l‐rhamnose (1-2). Mucilage regulates both the cell water content during prolonged drought and the calcium flux in the plant cells. The in vitro analysis of keratinocytes in monolayer, through the scratch-wound-healing assay, provided promising results. After 48 hours of exposure, the wound scratch was almost completely closed in cells treated with cladode extract. After 72 hours, the treated cells reached complete confluence, while in the untreated cells (negative control) the confluence was reached after 96 hours. We also added a positive control group of cells treated with colchicine, which inhibited wound closure for a more comprehensive analysis.

Keywords: cladodes, metabolites, polysaccharide, scratch-wound-healing assay

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Authors: Gregory Sullivan

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The evolution of global warming has escalated the number and complexity of forest fires around the world. As an example, the United States and Brazil combined generated more than 30,000 forest fires last year. The impact to our environment, structures and individuals is incalculable. The world has learned to try to take this in stride, trying multiple ways to contain fires. Some countries are trying to use cameras in limited areas. There are discussions of using hundreds of low earth orbit satellites and linking them together, and, interfacing them through ground networks. These are all truly noble attempts to defeat the forest fire phenomenon. But there is a better, simpler answer. A bigger piece of the solutions puzzle is to see the fires while they are small, soon after initiation. The approach is to see the fires while they are very small and report their location (latitude and longitude) to local first responders. This is done by placing a sensor at geostationary orbit (GEO: 26,000 miles above the earth). By placing this small satellite in GEO, we can “stare” at the earth, and sense temperature changes. We do not “see” fires, but “measure” temperature changes. This has already been demonstrated on an experimental scale. Fires were seen at close to initiation, and info forwarded to first responders. it were the first to identify the fires 7 out of 8 times. The goal is to have a small independent satellite at GEO orbit focused only on forest fire initiation. Thus, with one small satellite, focused only on forest fire initiation, we hope to greatly decrease the impact to persons, property and the environment.

Keywords: space detection, wildfire early warning, demonstration wildfire detection and action from space, space detection to first responders

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Authors: Sonika Raj, Amarjeet Singh, Vijay Lakshmi Sharma

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Introduction: In 21st century, due to revolution in Information and Communication Technologies (ICTs), there has been phenomenal development in quality and quantity of knowledge in the field of medical science. So, the access to relevant information to physicians is critical to the delivery of effective healthcare services to patients. The study was conducted to assess the information needs and attitudes of the medical professionals; to determine the sources and channels of information used by them; to ascertain the current usage of ICTs and the barriers faced by them in utilization of ICTs in health information access. Methodology: This descriptive cross-sectional study was carried in 2015 on hundred medical professionals working in public and private sectors of Chandigarh. The study used both quantitative and qualitative method for data collection. A semi structured questionnaire and interview schedule was used to collect data on information seeking needs, access to ICTs and barriers to healthcare information access. Five Data analysis was done using SPSS-16 and qualitative data was analyzed using thematic approach. Results: The most preferred sources to access healthcare information were internet (85%), trainings (61%) and communication with colleagues (57%). They wanted information on new drug therapy and latest developments in respective fields. All had access to computer with but almost half assessed their computer knowledge as average and only 3% had received training regarding usage. Educational status (p=0.004), place of work (p=0.004), number of years in job (p=0.004) and sector of job (p=0.04) of doctors were found to be significantly associated with their active search for information. The major themes that emerged from in-views were need; types and sources of healthcare information; exchange of information among different levels of healthcare providers; usage of ICTs to obtain and share information; barriers to access of healthcare information and quality of health information materials and involvement in their development process Conclusion and Recommendations: The medical professionals need information in their in their due course of work. However, information needs of medical professionals were not being adequately met. There should be training of professional regarding internet skills and the course on bioinformatics should be incorporated in the curricula of medical students. The policy framework must be formulated that will encourage and promote the use of ICTs as tools for health information access and dissemination.

Keywords: health information, ICTs, medical professionals, qualitative

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Authors: Colin Smith, Linsey S Passarella

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Due to the proliferation and decentralized nature of academic publication, no widely accepted scheme exists for organizing papers by their scientific field of study (FoS) to the author’s best knowledge. While many academic journals require author provided keywords for papers, these keywords range wildly in scope and are not consistent across papers, journals, or field domains, necessitating alternative approaches to paper classification. Past attempts to perform field-of-study (FoS) classification on scientific texts have largely used a-hierarchical FoS schemas or ignored the schema’s inherently hierarchical structure, e.g. by compressing the structure into a single layer for multi-label classification. In this paper, we introduce an application of a Layered Neural Network (LNN) to the problem of performing supervised hierarchical classification of scientific fields of study (FoS) on research papers. In this approach, paper embeddings from a pretrained language model are fed into a top-down LNN. Beginning with a single neural network (NN) for the highest layer of the class hierarchy, each node uses a separate local NN to classify the subsequent subfield child node(s) for an input embedding of concatenated paper titles and abstracts. We compare our LNN-FOS method to other recent machine learning methods using the Microsoft Academic Graph (MAG) FoS hierarchy and find that the LNN-FOS offers increased classification accuracy at each FoS hierarchical level.

Keywords: hierarchical classification, layer neural network, scientific field of study, scientific taxonomy

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Authors: Benzegaou Ali, Brani Benabderrahmane

Abstract:

This work describes a numerical study of the TOX–clinching process using diffuse elements. A computer code baptized SEMA "Static Explicit Method Analysis" is developed to simulate the clinch joining process. The FE code is based on an Updated Lagrangian scheme. The used resolution method is based on an explicit static approach. The integration of the elasto-plastic behavior law is realized with an algorithm of Simo and Taylor. The tools are represented by plane facets.

Keywords: diffuse elements, numerical simulation, clinching, contact, large deformation

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Authors: Md. Ataur Rahman Bhuiyan, Quazi Mahabubul Hasan, Md. Rifat Ullah

Abstract:

In this research, we did explore the effectiveness of the gaming environment in education and compared it with the traditional education system. We take several workshops in both learning environments. We measured student’s performance by providing a grading score (by professional academics) on their attitude in different criteria. We also collect data from survey questionnaires to understand student’s experiences towards education and study. Finally, we examine the impact of the different learning environments by applying statistical hypothesis tests, the T-test, and the ANOVA test.

Keywords: gamification, game-based learning, education, statistical analysis, human-computer interaction

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Authors: Lorenza Abbracciavento, Valerio De Biagi

Abstract:

Infrastructures are key elements to ensure the operational functionality of the transport system. The collapse of a single bridge or, equivalently, a tunnel can leads an entire motorway to be considered completely inaccessible. As a consequence, the paralysis of the communications network determines several important drawbacks for the community. Recent chronicle events have demonstrated that ensuring the functional continuity of the strategic infrastructures during and after a catastrophic event makes a significant difference in terms of life and economical losses. Moreover, it has been observed that RC structures located in mountain environments show a worst state of conservation compared to the same typology and aging structures located in temperate climates. Because of its morphology, in fact, the mountain environment is particularly exposed to severe collapse and deterioration phenomena, generally: natural hazards, e.g. rock falls, and meteorological hazards, e.g. freeze-thaw cycles or heavy snows. For these reasons, deep investigation on the characteristics of these processes becomes of fundamental importance to provide smart and sustainable solutions and make the infrastructure system more resilient. In this paper, the design of a monitoring system in mountainous environments is presented and analyzed in its parts. The method not only takes into account the peculiar climatic conditions, but it is integrated and interacts with the environment surrounding.

Keywords: structural health monitoring, resilience of bridges, mountain infrastructures, infrastructural network, maintenance

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Authors: Bashayer Almalki, Mayar Bajrai, Dana Mirah, Kholood Alghamdi, Hala Sanyour

Abstract:

This research aims to investigate the features of an AI chess app that are most preferred by users. A questionnaire was used as the methodology to gather responses from a varied group of participants. The questionnaire consisted of several questions related to the features of the AI chess app. The responses were analyzed using descriptive statistics and factor analysis. The findings indicate that the most preferred features of an AI chess app are the ability to play against the computer, the option to adjust the difficulty level, and the availability of tutorials and puzzles. The results of this research could be useful for developers of AI chess apps to enhance the user experience and satisfaction.

Keywords: chess, game, application, computics

Procedia PDF Downloads 66