Search results for: complex network platform

Authors: Nasrullah khan

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Today business environment is very dynamic and most of organizations are in tough competition for their added values and sustainable hold in market. To achieve such objectives, organizations must have dynamic and creative people as optimized process. To get these people, there should strong human resource management system in organizations. There are multiple approaches have been devised in literature to hire more job relevant and more suitable people. This study proposed an ANP (Analytic Network Process) approach to hire faculty members for a university system. This study consists of two parts. In fist part, a through literature survey and universities interview are conducted in order to find the common criteria for the selection of academic staff. In second part the available candidates are prioritized on the basis of the relative values of these criteria. According to results the GRE & foreign language, GPA and research paper writing were most important factors for the selection of academic staff.

Keywords: creative people, ANP, academic staff, business environment

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Authors: Reuben Kinyuru Njuguna, Martin Mabuya Njuguna

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Digital platforms, such as The Internet, and the advent of digital marketing strategies, have led to many changes in the marketing of goods and services. These have resulted in improved service quality, enhanced customer relations, productivity gains, marketing transaction cost reductions, improved customer service and flexibility in fulfilling customers’ changing needs and lifestyles. Consequently, the purpose of this study was to determine the effect of digital marketing practices on the financial performance of mobile network operators in the telecommunications industry in Kenya. The objectives of the study were to establish how digital customer relationship management strategies on performance of mobile network operators in Kenya. The study used an explanatory cross-sectional survey research design, while the target population was made up of from the 4 major mobile network operators in Kenya, namely Safaricom Limited, Airtel Networks Kenya Limited, Finserve Africa Limited and Telkom Kenya Limited. Sampling strategy was stratified sampling with a sample size of 97 respondents. Digital customer relationship strategies were seen to influence firm performance, through enhancing convenience, building trust, encouraging growth in market share through creating sustainable relationships, building commitment with customers, enhancing customer retention and customer satisfaction. Digital customer relationship management were seen to maximize gross profits by increasing customer satisfaction, loyalty and retention. The study recommended upscaling the use of digital customer relationship management strategies to further enhance firm performance, given their great potential in this regard.

Keywords: customer relationship management, customer service delivery, performance, customer satisfaction

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Authors: Morsy Ahmed Morsy Ismail

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In this paper, the structure of a coherent homodyne receiver for the Binary Phase Shift Keying (BPSK) Optical Code Division Multiple Access (OCDMA) network is introduced based on the Multi-Length Weighted Modified Prime Code (ML-WMPC) for multimedia applications. The Bit Error Rate (BER) of this homodyne detection is evaluated as a function of the number of active users and the signal to noise ratio for different code lengths according to the multimedia application such as audio, voice, and video. Besides, the Mach-Zehnder interferometer is used as an external phase modulator in homodyne detection. Furthermore, the Multiple Access Interference (MAI) and the receiver noise in a shot-noise limited regime are taken into consideration in the BER calculations.

Keywords: OCDMA networks, bit error rate, multiple access interference, binary phase-shift keying, multimedia

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Authors: Fei Xu, Guofan Zhang, Xiao Liu

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Many major marine invertebrate phyla are characterized by indirect development. These animals transit from planktonic larvae to benthic adults via settlement and metamorphosis, which has many advantages for organisms to adapt marine environment. Studying the biological process of metamorphosis is thus a key to understand the origin and evolution of indirect development. Although the mechanism of metamorphosis has been largely studied on their relationships with the marine environment, microorganisms, as well as the neurohormones, little is known on the gene regulation network (GRN) during metamorphosis. We treated competent oyster pediveligers with epinephrine, which was known to be able to effectively induce oyster metamorphosis, and analyzed the dynamics of gene and proteins with transcriptomics and proteomics methods. The result indicated significant upregulation of protein synthesis system, as well as some transcription factors including Homeobox, basic helix-loop-helix, and nuclear receptors. The result suggested the GRN complexity of the transition stage during oyster metamorphosis.

Keywords: indirect development, gene regulation network, protein synthesis, transcription factors

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Authors: Naziema Begum Jappie

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In higher education, particularly within South African universities engaged in regional and global collaborations, the integration of intercultural competencies into teaching, learning, and assessment is essential for student success. Intercultural competencies and the digital platform are intwined in the fabric of teaching, learning, and assessments for student success in higher education. These are integral to virtual learning and exchange within higher education, which are expected to develop these competencies. However, this is not always the case because these are not always explicitly integrated into the academic agenda. Despite the prevalence of international students and exchange programmes, there is often a lack of deliberate integration of these competencies into academic agendas, even for South African students from different cultural, ethnic and language groups. This research addresses this gap by examining the impact of infusing intercultural activities into both face-to-face and digital learning platforms. Adopting an intersectional perspective, the study recognizes how social identities interact to shape individuals' self-perceptions and experiences in a university. Methodologically, this study employs a mixed-methods approach, combining quantitative surveys and qualitative interviews to assess the effectiveness of integrating intercultural competencies into digital platforms. Surveys administered to students and faculty measure changes in intercultural skills and attitudes before and after the implementation of targeted interventions. In-depth interviews with participants will provide further insights into the qualitative aspects of these changes, including their experiences and perceptions of the integration process. The research evaluates whether the strategic integration of intercultural competencies into digital platforms enhances students' intercultural skills and social justice awareness. The findings provide valuable insights for higher education academics and internationalization practitioners seeking to develop effective strategies for cultivating intercultural competencies among students.

Keywords: digital platform, higher education, intercultural competencies, interventions

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Authors: Abhinay Puvvala, Veerendra Kumar Rai

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A bottleneck situation arises when the outflow is lesser than the inflow in a pipe-like setup. A more practical interpretation of bottlenecks emphasizes on the realization of Service Level Objectives (SLOs) at given workloads. Our approach detects two key aspects of bottlenecks – when and where. To identify ‘when’ we continuously poll on certain key metrics such as resource utilization, processing time, request backlog and throughput at a system level. Further, when the slope of the expected sojourn time at a workload is greater than ‘K’ times the slope of expected sojourn time at the previous step of the workload while the workload is being gradually increased in discrete steps, a bottleneck situation arises. ‘K’ defines the threshold condition and is computed based on the system’s service level objectives. The second aspect of our approach is to identify the location of the bottleneck. In multi-tier systems with a complex network of layers, it is a challenging problem to locate bottleneck that affects the overall system performance. We stage the system by varying workload incrementally to draw a correlation between load increase and system performance to the point where Service Level Objectives are violated. During the staging process, multiple metrics are monitored at hardware and application levels. The correlations are drawn between metrics and the overall system performance. These correlations along with the Service Level Objectives are used to arrive at the threshold conditions for each of these metrics. Subsequently, the same method used to identify when a bottleneck occurs is used on metrics data with threshold conditions to locate bottlenecks.

Keywords: bottleneck, workload, service level objectives (SLOs), throughput, system performance

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Authors: Rapelang E. Simon, Thebeetsile A. Olebetse, Joseph R. Maritinkole, Ruth O. Moleleke

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The P and S wave seismic velocity ratios (Vp/Vs) of some aftershocks are investigated using the method ofWadati diagrams. These aftershocks occurred after the 3rdApril 2017 Botswana’s Mw 6.5 earthquake and were recorded by the Network of Autonomously Recording Seismographs (NARS)-Botswana temporary network deployed from 2013 to 2018. In this paper, P and S wave data with good signal-to-noise ratiofrom twenty events of local magnitude greater or equal to 4.0are analysed with the Seisan software and used to infer properties of the upper crust in Botswana. The Vp/Vsratiosare determined from the travel-times of body waves and then converted to Poisson’s ratio, which is useful in determining the physical state of the subsurface materials. The Vp/Vs ratios of the upper crust in Botswana show regional variations from 1.70 to 1.77, with an average of 1.73. The Poisson’s ratios range from 0.24to 0.27 with an average of 0.25 and correlate well with the geological structures in Botswana.

Keywords: Botswana, earthquake, poisson's ratio, seismic velocity, Vp/Vs ratio

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Authors: Lokendra Kumar Devangan, Ajay Mishra

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This paper describes the implementation of a large-scale SAS/OR model with significant pre-processing, scenario analysis, and post-processing work done using SAS. A large cement manufacturer with ten geographically distributed manufacturing plants for two variants of cement, around 400 warehouses serving as transshipment points, and several thousand distributor locations generating demand needed to optimize this multi-echelon, multi-modal transport supply chain separately for planning and allocation purposes. For monthly planning as well as daily allocation, the demand is deterministic. Rail and road networks connect any two points in this supply chain, creating tens of thousands of such connections. Constraints include the plant’s production capacity, transportation capacity, and rail wagon batch size constraints. Each demand point has a minimum and maximum for shipments received. Price varies at demand locations due to local factors. A large mixed integer programming model built using proc OPTMODEL decides production at plants, demand fulfilled at each location, and the shipment route to demand locations to maximize the profit contribution. Using base SAS, we did significant pre-processing of data and created inputs for the optimization. Using outputs generated by OPTMODEL and other processing completed using base SAS, we generated several reports that went into their enterprise system and created tables for easy consumption of the optimization results by operations.

Keywords: production planning, mixed integer optimization, network model, network optimization

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Authors: Dachuan Shi, M. Hecht, Y. Ye

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With the trend of digitalization in railway freight transport, a large number of freight wagons in Germany have been equipped with telematics devices, commonly placed on the wagon body. A telematics device contains a GPS module for tracking and a 3-axis accelerometer for shock detection. Besides these basic functions, it is desired to use the integrated accelerometer for condition monitoring without any additional sensors. Wheel flats as a common type of failure on wheel tread cause large impacts on wagons and infrastructure as well as impulsive noise. A large wheel flat may even cause safety issues such as derailments. In this sense, this paper proposes a machine learning approach for wheel flat detection by using car body accelerations. Due to suspension systems, impulsive signals caused by wheel flats are damped significantly and thus could be buried in signal noise and disturbances. Therefore, it is very challenging to detect wheel flats using car body accelerations. The proposed algorithm considers the envelope spectrum of car body accelerations to eliminate the effect of noise and disturbances. Subsequently, a 1-D convolutional neural network (CNN), which is well known as a deep learning method, is constructed to automatically extract features in the envelope-frequency domain and conduct classification. The constructed CNN is trained and tested on field test data, which are measured on the underframe of a tank wagon with a wheel flat of 20 mm length in the operational condition. The test results demonstrate the good performance of the proposed algorithm for real-time fault detection.

Keywords: fault detection, wheel flat, convolutional neural network, machine learning

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Authors: R. Boudjelthia

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The aim of this study is to determine the influence of mix composition of concrete as the content of water and cement, water–binder ratio, and the replacement of fly ash on the durability of self compacting concrete (SCC) by using artificial neural networks (ANNs). To achieve this, an ANNs model is developed to predict the durability of self compacting concrete which is expressed in terms of chloride ions permeability in accordance with ASTM C1202-97 or AASHTO T277. Database gathered from the literature for the training and testing the model. A sensitivity analysis was also conducted using the trained and tested ANN model to investigate the effect of fly ash on the durability of SCC. The results indicate that the developed model is reliable and accurate. the durability of SCC expressed in terms of total charge passed over a 6-h period can be significantly improved by using at least 25% fly ash as replacement of cement. This study show that artificial neural network have strong potentialas a feasible tool for predicting accurately the durability of SCC containing fly ash.

Keywords: artificial neural networks, durability, chloride ions permeability, self compacting concrete

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Authors: Rachelle McFarlane

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The Second Decade of Action Road Safety has begun with increased focus on countries who are disproportionately affected by road fatalities. Researchers highlight the low effectiveness of road safety campaigns in Latin America and the Caribbean (LAC) still reporting approximately 130,000 deaths and six million injuries annually. The regional fatality rate 19.2 per 100,000 with heightened concern for persons 15 to 44 years. In 2021, 483 Jamaicans died in 435 crashes, with 33% of these fatalities occurring during Covid-19 curfew hours. The study objective is to conduct a systemic safety review of Jamaican road crashes and provide a framework for its use in complementing traditional methods. The methodology involves the use of the FHWA Systemic Safety Project Selection Tool for analysis. This tool reviews systemwide data in order to identify risk factors across the network associated with severe and fatal crashes, rather that only hotspots. A total of 10,379 crashes with 745 fatalities and serious injuries were reviewed. Of the focus crash types listed, 50% of ‘Pedestrian Accidents’ resulted in fatalities and serious injuries, followed by 32% ‘Bicycle’, 24% ‘Single’ and 12% of ‘Head-on’. This study seeks to understand the associated risk factors with these priority crash types across the network and recommend cost-effective countermeasures across common sites. As we press towards Vision Zero, the inclusion of the systemic safety review method, complementing traditional methods, may create a wider impact in reducing road fatalities and serious injury by targeting issues across network with similarities; focus crash types and contributing factors.

Keywords: systemic safety review, risk factors, road crashes, crash types

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Authors: Mingxin Li, Liya Ni

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Autonomous parking is a valuable feature applicable to many robotics applications such as tour guide robots, UV sanitizing robots, food delivery robots, and warehouse robots. With auto-parking, the robot will be able to park at the charging zone and charge itself without human intervention. As compared to self-driving vehicles, auto-parking is more challenging for a small-scale mobile robot only equipped with a front camera due to the camera view limited by the robot’s height and the narrow Field of View (FOV) of the inexpensive camera. In this research, auto-parking of a small-scale mobile robot with a front camera only was achieved in a four-step process: Firstly, transfer learning was performed on the AlexNet, a popular pre-trained convolutional neural network (CNN). It was trained with 150 pictures of empty parking slots and 150 pictures of occupied parking slots from the view angle of a small-scale robot. The dataset of images was divided into a group of 70% images for training and the remaining 30% images for validation. An average success rate of 95% was achieved. Secondly, the image of detected empty parking space was processed with edge detection followed by the computation of parametric representations of the boundary lines using the Hough Transform algorithm. Thirdly, the positions of the entrance point and center of available parking space were predicted based on the robot kinematic model as the robot was driving closer to the parking space because the boundary lines disappeared partially or completely from its camera view due to the height and FOV limitations. The robot used its wheel speeds to compute the positions of the parking space with respect to its changing local frame as it moved along, based on its kinematic model. Lastly, the predicted entrance point of the parking space was used as the reference for the motion control of the robot until it was replaced by the actual center when it became visible again by the robot. The linear and angular velocities of the robot chassis center were computed based on the error between the current chassis center and the reference point. Then the left and right wheel speeds were obtained using inverse kinematics and sent to the motor driver. The above-mentioned four subtasks were all successfully accomplished, with the transformed learning, image processing, and target prediction performed in MATLAB, while the motion control and image capture conducted on a self-built small scale differential drive mobile robot. The small-scale robot employs a Raspberry Pi board, a Pi camera, an L298N dual H-bridge motor driver, a USB power module, a power bank, four wheels, and a chassis. Future research includes three areas: the integration of all four subsystems into one hardware/software platform with the upgrade to an Nvidia Jetson Nano board that provides superior performance for deep learning and image processing; more testing and validation on the identification of available parking space and its boundary lines; improvement of performance after the hardware/software integration is completed.

Keywords: autonomous parking, convolutional neural network, image processing, kinematics-based prediction, transfer learning

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Authors: Nitish Suvarna, Anjali Awasthi

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In an era marked by rapid technological advancements, the integration of Autonomous Vehicles into supply chain networks represents a transformative shift, promising to redefine the paradigms of logistics and transportation. This thesis delves into a comprehensive assessment of the impact of autonomous vehicles on supply chain performance, with a particular focus on network design, operational efficiency, and environmental sustainability. Employing the advanced simulation capabilities of anyLogistix (ALX), the study constructs a digital twin of a conventional supply chain network, encompassing suppliers, production facilities, distribution centers, and customer endpoints. The research methodically integrates Autonomous Vehicles into this intricate network, aiming to unravel the multifaceted effects on transportation logistics including transit times, cost-efficiency, and sustainability. Through simulations and scenarios analysis, the study scrutinizes the operational resilience and adaptability of supply chains in the face of dynamic market conditions and disruptive technologies like Autonomous Vehicles. Furthermore, the thesis undertakes carbon footprint analysis, quantifying the environmental benefits and challenges associated with the adoption of Autonomous Vehicles in supply chain operations. The insights from this research are anticipated to offer a strategic framework for industry stakeholders, guiding the adoption of Autonomous Vehicles to foster a more efficient, responsive, and sustainable supply chain ecosystem. The findings aim to serve as a cornerstone for future research and practical implementations in the realm of intelligent transportation and supply chain management.

Keywords: autonomous vehicle, agri-food supply chain, ALX simulation, anyLogistix

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Authors: Mohammad Zafar-ul Islam

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The breeding programme of Houbara bustard was started in Saudi Arabia in 1986 to undertake the restoration of native species such as Houbara through a programme of re-introduction, involving the release of captive-bred birds in the wild. Two sites were selected for houbara re-introduction, i.e., Mahazat as-Sayd and Saja Umm Ar-Rimth protected areas in 1988 and 1998 respectively. Both the areas are fenced fairly level, sandy plain with a few rock outcrops. Captive bred houbara have been released in Mahazat since 1992 by NWRC and those birds have been successfully breeding since then. The nesting season of the houbara at Mahazat recorded from February to May and on an average 20-25 nests are located each year but no nesting recorded in Saja. Houbara are monitored using radio transmitters through aerial tracking technique and also a vehicle for terrestrial tracking. Total population of houbara in Mahazat is roughly estimated around 300-400 birds, using the following: N = n1+n2+n3+n4+n5 (n1 = released or wild-born, radio, regularly monitored/checked; n2 = radio tagged missing; n3 = wild born chicks not recorded; n4 = wild born chicks, recorded but not tagged; n5 = immigrants). However, in Saja only 4-7 individuals of houbara have been survived since 2001 because most of the birds are predated immediately after the release. The mean annual home was also calculated using Kernel and Convex polygons methods with Range VII software. The minimum density of houbara was also calculated. In order to know the houbara movement or their migration to other regions, two captive-reared male houbara that were released into the wild and one wild born female were fitted with Platform Transmitter Terminals (PTT). The home range shows that wild-born female has larger movement than two males. More areas need to be selected for reintroduction programme to establish the network of sites to provide easy access to move these birds and mingle with the wild houbara. Some potential sites have been proposed which require more surveys to check the habitat suitability.

Keywords: re-introduction, survival rate, home range, Saudi Arabia

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Authors: Zahrasadat Hosseini, Jie Yuan

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Point-of-care (POC) diagnostic devices based on lab-on-a-chip (LOC) technology have the potential to revolutionize medical diagnostics. However, the development of an ideal microfluidic system based on LOC technology for diagnostics purposes requires overcoming several obstacles, such as improving sensitivity, selectivity, portability, cost-effectiveness, and prototyping methods. While numerous studies have introduced technologies and systems that advance these criteria, existing systems still have limitations. Electrochemical enzyme-linked immunosorbent assay (e-ELISA) in a LOC device offers numerous advantages, including enhanced sensitivity, decreased turnaround time, minimized sample and analyte consumption, reduced cost, disposability, and suitability for miniaturization, integration, and multiplexing. In this study, we present a novel design and fabrication method for a microfluidic diagnostic platform that integrates screen-printed electrochemical carbon/silver chloride electrodes on flexible printed circuit boards with flexible, multilayer, polydimethylsiloxane (PDMS) microfluidic networks to accurately manipulate and pre-immobilize analytes for performing electrochemical enzyme-linked immunosorbent assay (e-ELISA) for multiplexed quantification of blood serum biomarkers. We further demonstrate fast, cost-effective prototyping, as well as accurate and reliable detection performance of this device for quantification of interleukin-6-spiked samples through electrochemical analytics methods. We anticipate that our invention represents a significant step towards the development of user-friendly, portable, medical-grade POC diagnostic devices.

Keywords: lab-on-a-chip, point-of-care diagnostics, electrochemical ELISA, biomarker quantification, fast prototyping

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Authors: Praveena Nair Sivasankaran, Thomas Arthur Ward, Rubentheren Viyapuri

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Paper describes a methodology to fabricate a simplified dragonfly wing structure using balsa wood and red prepreg fibre glass. These simplified wing structures were created for use in Biomimetic Micro Air Vehicles (BMAV). Dragonfly wings are highly corrugated and possess complex vein structures. In order to mimic the wings function and retain its properties, a simplified version of the wing was designed. The simplified dragonfly wing structure was created using a method called spatial network analysis which utilizes Canny edge detection method. The vein structure of the wings were carved out in balsa wood and red prepreg fibre glass. Balsa wood and red prepreg fibre glass was chosen due to its ultra- lightweight property and hence, highly suitable to be used in our application. The fabricated structure was then immersed in a nanocomposite solution containing chitosan as a film matrix, reinforced with chitin nanowhiskers and tannic acid as a crosslinking agent. These materials closely mimic the membrane of a dragonfly wing. Finally, the wings were subjected to a bending test and comparisons were made with previous research for verification. The results had a margin of difference of about 3% and thus the structure was validated.

Keywords: dragonfly wings, simplified, Canny edge detection, balsa wood, red prepreg, chitin, chitosan, tannic acid

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Authors: Juraj Camaj, Anna Dolinayova, Jana Lalinska, Miroslav Bariak

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Planning of infrastructure and processes in logistic center within the frame of various kinds of logistic hubs and technological activities in them represent quite complex problem. The main goal is to design appropriate layout, which enables to realize expected operation on the desired levels. The simulation software represents progressive contemporary experimental technique, which can support complex processes of infrastructure planning and all of activities on it. It means that simulation experiments, reflecting various planned infrastructure variants, investigate and verify their eligibilities in relation with corresponding expected operation. The inducted approach enables to make qualified decisions about infrastructure investments or measures, which derive benefit from simulation-based verifications. The paper represents simulation software for simulation infrastructural layout and technological activities in marshalling yard, intermodal terminal, warehouse and combination between them as the parts of logistic center.

Keywords: marshalling yard, intermodal terminal, warehouse, transport technology, simulation

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Authors: Weiyao Zhu, Qian Qi, Ming Yue, Dongxu Ma

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Due to the nano-micro pore structures and the massive multi-stage multi-cluster hydraulic fracturing in shale gas reservoirs, the multi-scale seepage flows are much more complicated than in most other conventional reservoirs, and are crucial for the economic development of shale gas. In this study, a new multi-scale non-linear flow model was established and simplified, based on different diffusion and slip correction coefficients. Due to the fact that different flow laws existed between the fracture network and matrix zone, a three-zone composite model was proposed. Then, according to the conformal transformation combined with the law of equivalent percolation resistance, the productivity equation of a horizontal fractured well, with consideration given to diffusion, slip, desorption, and absorption, was built. Also, an analytic solution was derived, and the interference of the multi-cluster fractures was analyzed. The results indicated that the diffusion of the shale gas was mainly in the transition and Fick diffusion regions. The matrix permeability was found to be influenced by slippage and diffusion, which was determined by the pore pressure and diameter according to the Knudsen number. It was determined that, with the increased half-lengths of the fracture clusters, flow conductivity of the fractures, and permeability of the fracture network, the productivity of the fractured well also increased. Meanwhile, with the increased number of fractures, the distance between the fractures decreased, and the productivity slowly increased due to the mutual interference of the fractures. In regard to the fractured horizontal wells, the free gas was found to majorly contribute to the productivity, while the contribution of the desorption increased with the increased pressure differences.

Keywords: multi-scale, fracture network, composite model, productivity

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Authors: Zhaoyang Liu

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It has been highly desired to develop a high-performance membrane for separating oil/water emulsions with the combined features of high water flux, high oil separation efficiency, and high mechanical stability. Here, we demonstrated a design for high-performance membranes constructed with ultra-long titanate nanofibers (over 30 µm in length)/cellulose microfibers. An integrated network membrane was achieved with these ultra-long nano/microfibers, contrast to the non-integrated membrane constructed with carbon nanotubes (5 µm in length)/cellulose microfibers. The morphological properties of the prepared membranes were characterized by A FEI Quanta 400 (Hillsboro, OR, United States) environmental scanning electron microscope (ESEM). The hydrophilicity, underwater oleophobicity and oil adhesion property of the membranes were examined using an advanced goniometer (Rame-hart model 500, Succasunna, NJ, USA). More specifically, the hydrophilicity of membranes was investigated by analyzing the spreading process of water into membranes. A filtration device (Nalgene 300-4050, Rochester, NY, USA) with an effective membrane area of 11.3 cm² was used for evaluating the separation properties of the fabricated membranes. The prepared oil-in-water emulsions were poured into the filtration device. The separation process was driven under vacuum with a constant pressure of 5 kPa. The filtrate was collected, and the oil content in water was detected by a Shimadzu total organic carbon (TOC) analyzer (Nakagyo-ku, Kyoto, Japan) to examine the separation efficiency. Water flux (J) of the membrane was calculated by measuring the time needed to collect some volume of permeate. This network membrane demonstrated good mechanical flexibility and robustness, which are critical for practical applications. This network membrane also showed high separation efficiency (99.9%) for oil/water emulsions with oil droplet size down to 3 µm, and meanwhile, has high water permeation flux (6.8 × 10³ L m⁻² h⁻¹ bar⁻¹) at low operation pressure. The high water flux is attributed to the interconnected scaffold-like structure throughout the whole membrane, while the high oil separation efficiency is attributed to the nanofiber-made nanoporous selective layer. Moreover, the economic materials and low-cost fabrication process of this membrane indicate its great potential for large-scale industrial applications.

Keywords: membrane, inorganic nanofibers, oil/water separation, emulsions

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Authors: Mohamed Ghorab

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Local generated and distributed system for thermal and electrical energy is sighted in the near future to reduce transmission losses instead of the centralized system. Distributed Energy Resources (DER) is designed at different sizes (small and medium) and it is incorporated in energy distribution between the hubs. The energy generated from each technology at each hub should meet the local energy demands. Economic and environmental enhancement can be achieved when there are interaction and energy exchange between the hubs. Network energy system and CO₂ optimization between different six hubs presented Canadian community level are investigated in this study. Three different scenarios of technology systems are studied to meet both thermal and electrical demand loads for the six hubs. The conventional system is used as the first technology system and a reference case study. The conventional system includes boiler to provide the thermal energy, but the electrical energy is imported from the utility grid. The second technology system includes combined heat and power (CHP) system to meet the thermal demand loads and part of the electrical demand load. The third scenario has integration systems of CHP and Organic Rankine Cycle (ORC) where the thermal waste energy from the CHP system is used by ORC to generate electricity. General Algebraic Modeling System (GAMS) is used to model DER system optimization based on energy economics and CO₂ emission analyses. The results are compared with the conventional energy system. The results show that scenarios 2 and 3 provide an annual total cost saving of 21.3% and 32.3 %, respectively compared to the conventional system (scenario 1). Additionally, Scenario 3 (CHP & ORC systems) provides 32.5% saving in CO₂ emission compared to conventional system subsequent case 2 (CHP system) with a value of 9.3%.  

Keywords: distributed energy resources, network energy system, optimization, microgeneration system

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Authors: Adnan Baig, Ishteeaq Naeem, Saad Mansoor

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The Internet of Things (IoT) gadgets have advanced quickly in recent years, and their use is steadily rising daily. However, cyber-attackers can target these gadgets due to their distributed nature. Additionally, many IoT devices have significant security flaws in their implementation and design, making them vulnerable to security threats. Hence, these threats can cause important data security and privacy loss from a single attack on network devices or systems. Botnets are a significant security risk that can harm the IoT network; hence, sophisticated techniques are required to mitigate the risk. This work uses a machine learning-based method to identify IoT orchestrated by botnets. The proposed technique identifies the net attack by distinguishing between legitimate and malicious traffic. This article proposes a hyperparameter tuning model to improvise the method to improve the accuracy of existing processes. The results demonstrated an improved and more accurate indication of botnet-based cyber-attacks.

Keywords: Internet of Things, Botnet, BoT-IoT dataset, ML techniques

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Authors: Chengming Li, Yong Yin, Peipei Guo, Xiaoli Liu

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Without taking account of the attribute richness of POI (point of interest) data and spatial distribution limited by roads, a POI generalization method considering both attribute information and spatial distribution has been proposed against the existing point generalization algorithm merely focusing on overall information of point groups. Hierarchical characteristic of urban POI information expression has been firstly analyzed to point out the measurement feature of the corresponding hierarchy. On this basis, an urban POI generalizing strategy has been put forward: POIs urban road network have been divided into three distribution pattern; corresponding generalization methods have been proposed according to the characteristic of POI data in different distribution patterns. Experimental results showed that the method taking into account both attribute information and spatial distribution characteristics of POI can better implement urban POI generalization in the mapping presentation.

Keywords: POI, road network, selection method, spatial information expression, distribution pattern

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Authors: Justin Durham, Chanda Rooney, Robert Mather, Mickie Vanhoy

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The psychological construct of empathy is to understand a person’s cognitive perspective and experience the other person’s emotional state. Deciphering emotional states is conducive for interpreting vicarious pain. Observing others' physical pain activates neural networks related to the actual experience of pain itself. The study addresses empathy as a nonlinear dynamic process of simulation for individuals to understand the mental states of others and experience vicarious pain, exhibiting self-organized criticality. Such criticality follows from a combination of neural networks with an excitatory feedback loop generating bistability to resonate permutated empathy. Cortical networks exhibit diverse patterns of activity, including oscillations, synchrony and waves, however, the temporal dynamics of neurophysiological activities underlying empathic processes remain poorly understood. Mu rhythms are EEG oscillations with dominant frequencies of 8-13 Hz becoming synchronized when the body is relaxed with eyes open and when the sensorimotor system is in idle, thus, mu rhythm synchrony is expected to be highest in baseline conditions. When the sensorimotor system is activated either by performing or simulating action, mu rhythms become suppressed or desynchronize, thus, should be suppressed while observing video clips of painful injuries if previous research on mirror system activation holds. Twelve undergraduates contributed EEG data and survey responses to empathy and psychopathy scales in addition to watching consecutive video clips of sports injuries. Participants watched a blank, black image on a computer monitor before and after observing a video of consecutive sports injuries incidents. Each video condition lasted five-minutes long. A BIOPAC MP150 recorded EEG signals from sensorimotor and thalamocortical regions related to a complex neural network called the ‘pain matrix’. Physical and social pain are activated in this network to resonate vicarious pain responses to processing empathy. Five EEG single electrode locations were applied to regions measuring sensorimotor electrical activity in microvolts (μV) to monitor mu rhythms. EEG signals were sampled at a rate of 200 Hz. Mu rhythm desynchronization was measured via 8-13 Hz at electrode sites (F3 & F4). Data for each participant’s mu rhythms were analyzed via Fast Fourier Transformation (FFT) and multifractal time series analysis.

Keywords: desynchronization, dynamical systems theory, electroencephalography (EEG), empathy, multifractal time series analysis, mu waveform, neurophysiology, pain simulation, social cognition

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Authors: Sergey Ermolin, Olga Ermolin

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A Deep Convolution Neural Network with Triplet Loss is used to identify duplicate images in real-estate advertisements in the presence of image artifacts such as watermarking, cropping, hue/brightness adjustment, and others. The effects of batch normalization, spatial dropout, and various convergence methodologies on the resulting detection accuracy are discussed. For comparative Return-on-Investment study (per industry request), end-2-end performance is benchmarked on both Nvidia Titan GPUs and Intel’s Xeon CPUs. A new real-estate dataset from San Francisco Bay Area is used for this work. Sufficient duplicate detection accuracy is achieved to supplement other database-grounded methods of duplicate removal. The implemented method is used in a Proof-of-Concept project in the real-estate industry.

Keywords: visual recognition, convolutional neural networks, triplet loss, spatial batch normalization with dropout, duplicate removal, advertisement technologies, performance benchmarking

Procedia PDF Downloads 343

Authors: Jang kyun Cho, Jeong-dong Lee

Abstract:

The advent of the Internet, mobile communications, and social network services has stimulated social interactions among consumers, allowing people to affect one another’s innovation adoptions by exchanging information more frequently and more quickly. Previous diffusion models, such as the Bass model, however, face limitations in reflecting such recent phenomena in society. These models are weak in their ability to model interactions between agents; they model aggregated-level behaviors only. The agent based model, which is an alternative to the aggregate model, is good for individual modeling, but it is still not based on an economic perspective of social interactions so far. This study assumes the presence of social utility from other consumers in the adoption of innovation and investigates the effect of individual interactions on innovation diffusion by developing a new model called the interaction-based diffusion model. By comparing this model with previous diffusion models, the study also examines how the proposed model explains innovation diffusion from the perspective of economics. In addition, the study recommends the use of a small-world network topology instead of cellular automata to describe innovation diffusion. This study develops a model based on individual preference and heterogeneous social interactions using utility specification, which is expandable and, thus, able to encompass various issues in diffusion research, such as reservation price. Furthermore, the study proposes a new framework to forecast aggregated-level market demand from individual level modeling. The model also exhibits a good fit to real market data. It is expected that the study will contribute to our understanding of the innovation diffusion process through its microeconomic theoretical approach.

Keywords: innovation diffusion, agent based model, small-world network, demand forecasting

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Authors: Shahul Hamid Khan, S. Santhosh, Abhinav Kumar Sharma

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Environmental performance along with social performance is becoming vital factors for industries to achieve global standards. With a good environmental policy global industries are differentiating them from their competitors. This paper concentrates on multi stage, multi product and multi period manufacturing network. Bi-objective mathematical models for total cost and total emission for the entire forward supply chain are considered. Here five different problems are considered by varying the number of suppliers, manufacturers, and environmental levels, for illustrating the taken mathematical model. GA, and Random search are used for finding the optimal solution. The input parameters of the optimal solution are used to find the tradeoff between the initial investment by the industry and the long term benefit of the environment.

Keywords: closed loop supply chain, genetic algorithm, random search, green supply chain

Procedia PDF Downloads 550

Authors: África Vicario, Fernando J. Álvarez, Felipe Parralejo, Fernando Aranda

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The irrigation systems of traditional agriculture, such as gravity-fed irrigation, produce a great waste of water because, generally, there is no control over the amount of water supplied in relation to the water needed. The AIRON Project tries to solve this problem by implementing an IoT-based system to sensor the irrigation plots so that the state of the crops and the amount of water used for irrigation can be known remotely. The IoT system consists of a sensor network that measures the humidity of the soil, the weather conditions (temperature, relative humidity, wind and solar radiation) and the irrigation water flow. The communication between this network and a central gateway is conducted by means of long-range wireless communication that depends on the characteristics of the irrigation plot. The main objective of the AIRON project is to deploy an IoT sensor network in two different plots of the irrigation community of Aranjuez in the Spanish region of Madrid. The first plot is 2 km away from the central gateway, so LoRa has been used as the base communication technology. The problem with this plot is the absence of mains electric power, so devices with energy-saving modes have had to be used to maximize the external batteries' use time. An ESP32 SOC board with a LoRa module is employed in this case to gather data from the sensor network and send them to a gateway consisting of a Raspberry Pi with a LoRa hat. The second plot is located 18 km away from the gateway, a range that hampers the use of LoRa technology. In order to establish reliable communication in this case, the long-term evolution (LTE) standard is used, which makes it possible to reach much greater distances by using the cellular network. As mains electric power is available in this plot, a Raspberry Pi has been used instead of the ESP32 board to collect sensor data. All data received from the two plots are stored on a proprietary server located at the irrigation management company's headquarters. The analysis of these data by means of machine learning algorithms that are currently under development should allow a short-term prediction of the irrigation water demand that would significantly reduce the waste of this increasingly valuable natural resource. The major finding of this work is the real possibility of deploying a remote sensing system for irrigated plots by using Commercial-Off-The-Shelf (COTS) devices, easily scalable and adaptable to design requirements such as the distance to the control center or the availability of mains electrical power at the site.

Keywords: internet of things, irrigation water control, LoRa, LTE, smart farming

Procedia PDF Downloads 92

Authors: Mohammad Reza Taherkhani‎

Abstract:

Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make a connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: underwater sensor networks, clustering, learning automata, energy consumption

Procedia PDF Downloads 367

Authors: Sergey N. Ermoliev, Margarita A. Belousova, Aida D. Goncharenko

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Application of the diagnostic complex of highly informative functional methods (electromyography, reodentography, laser Doppler flowmetry, reoperiodontography, vital computer capillaroscopy, optical tissue oximetry, laser fluorescence diagnosis) allows to perform a multifactorial analysis of the dental status and to prescribe complex etiopathogenetic treatment. Introduction. It is necessary to create a complex of innovative highly informative and safe functional diagnostic methods for improvement of the quality of patient treatment by the early detection of stomatologic diseases. The purpose of the present study was to investigate the etiology and pathogenesis of functional disorders identified in the pathology of hard tissue, dental pulp, periodontal, oral mucosa and chewing function, and the creation of new approaches to the diagnosis of dental diseases. Material and methods. 172 patients were examined. Density of hard tissues of the teeth and jaw bone was studied by intraoral ultrasonic densitometry (USD). Electromyographic activity of masticatory muscles was assessed by electromyography (EMG). Functional state of dental pulp vessels assessed by reodentography (RDG) and laser Doppler flowmetry (LDF). Reoperiodontography method (RPG) studied regional blood flow in the periodontal tissues. Microcirculatory vascular periodontal studied by vital computer capillaroscopy (VCC) and laser Doppler flowmetry (LDF). The metabolic level of the mucous membrane was determined by optical tissue oximetry (OTO) and laser fluorescence diagnosis (LFD). Results and discussion. The results obtained revealed changes in mineral density of hard tissues of the teeth and jaw bone, the bioelectric activity of masticatory muscles, regional blood flow and microcirculation in the dental pulp and periodontal tissues. LDF and OTO methods estimated fluctuations of saturation level and oxygen transport in microvasculature of periodontal tissues. With LFD identified changes in the concentration of enzymes (nicotinamide, flavins, lipofuscin, porphyrins) involved in metabolic processes Conclusion. Our preliminary results confirmed feasibility and safety the of intraoral ultrasound densitometry technique in the density of bone tissue of periodontium. Conclusion. Application of the diagnostic complex of above mentioned highly informative functional methods allows to perform a multifactorial analysis of the dental status and to prescribe complex etiopathogenetic treatment.

Keywords: electromyography (EMG), reodentography (RDG), laser Doppler flowmetry (LDF), reoperiodontography method (RPG), vital computer capillaroscopy (VCC), optical tissue oximetry (OTO), laser fluorescence diagnosis (LFD)

Procedia PDF Downloads 283

Authors: Hosuk Shin, Young-Hyun Seo, Eunhak Lee, Seung-Young Kho

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Currently, in Seoul, users have the privilege to avoid riding crowded buses with the installation of Bus Information System (BIS). BIS has three levels of on-board bus ridership level information (spacious, normal, and crowded). However, there are flaws in the system due to it being real time which could provide incomplete information to the user. For example, a bus comes to the station, and on the BIS it shows that the bus is crowded, but on the stop that the user is waiting many people get off, which would mean that this station the information should show as normal or spacious. To fix this problem, this study predicts the bus ridership level using smart card data to provide more accurate information about the passenger ridership level on the bus. An Artificial Neural Network (ANN) is an interconnected group of nodes, that was created based on the human brain. Forecasting has been one of the major applications of ANN due to the data-driven self-adaptive methods of the algorithm itself. According to the results, the ANN algorithm was stable and robust with somewhat small error ratio, so the results were rational and reasonable.

Keywords: smartcard data, ANN, bus, ridership

Procedia PDF Downloads 170