Search results for: next generation networks

Authors: Hameed Rukayat Opeyemi, Pericles Pilidis, Pagone Emanuele

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An infinitesimal increase in performance can have immense reduction in operating and capital expenses in a power generation system. Therefore, constant studies are being carried out to improve both conventional and novel power cycles. Globally, power producers are constantly researching on ways to minimize emission and to collectively downsize the total cost rate of power plants. A substantial spurt of developmental technologies of low carbon cycles have been suggested and studied, however they all have their limitations and financial implication. In the area of carbon abatement in power plants, three major objectives conflict: The cost rate of the plant, Power output and Environmental impact. Since, an increase in one of this parameter directly affects the other. This poses a multi-objective problem. It is paramount to be able to discern the point where improving one objective affects the other. Hence, the need for a Pareto-based optimization algorithm. Pareto-based optimization algorithm helps to find those points where improving one objective influences another objective negatively and stops there. The application of Pareto-based optimization algorithm helps the user/operator/designer make an informed decision. This paper sheds more light on areas that multi-objective optimization has been applied in carbon abatement technologies in the last five years, developments and prospects.

Keywords: gas turbine, low carbon technology, pareto optimal, multi-objective optimization

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Authors: Rupamoni Thakur, Madhusmita Dehingia, Narayan C. Talukdar, Mojibur R. Khan

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The human gut is an extremely active fermentation site and is inhabited by diverse bacterial species. Consumption of alcoholic beverages has been shown to substantially modulate the human gut bacterial profile (GBP) of an individual. Assam, a major north-eastern state of India, is home to a number of tribal populations of which the tea-tribes form a major community. These tea-tribal communities are known to prepare and consume a locally-prepared alcoholic beverage from fermented jaggery, whose chemical composition is unknown. In this study, we demonstrate the effect of daily intake of the locally-prepared alcoholic beverage on the GBP of the tea-tribal communities and correlate it with the changes in the biochemical biomarkers of the population. The fecal bacterial diversity of 40 drinkers and 35 non-drinking healthy individuals were analyzed by polymerase chain reaction (PCR)–denaturing gradient gel electrophoresis (DGGE). The results suggested that the GBP was significantly modulated in the fermented-beverage consuming subjects. Significant difference was also observed in the serum biochemical parameters such as triglyceride, total cholesterol and the liver marker enzymes (ASAT/ALAT and GGT). Further studies to identify the GBP of drinkers vs non-drinkers through Next-generation Sequencing (NGS) analysis and to correlate the changes with the biochemical biomarkers of the population is underway.

Keywords: alcoholic beverage, gut bacterial profile, PCR-DGGE analysis, tea-tribes of India

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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: Tim Nikolsky

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This paper seeks to explore the different ways in which music producers choose to embrace various levels of technology based on musical values, objectives, affordability, access and workflow benefits. Current digital audio production workflow is questioned. Engineers and music producers of today are increasingly divorced from the tangibility of music production. Making music no longer requires you to reach over and turn a knob. Ideas of authenticity in music production are being redefined. Calculations from the mathematical algorithm with the pretty pictures are increasingly being chosen over hardware containing transformers and tubes. Are mouse clicks and movements equivalent or inferior to the master brush strokes we are seeking to conjure? We are making audio production decisions visually by constantly looking at a screen rather than listening. Have we compromised our music objectives and values by removing the ‘hands-on’ nature of music making? DAW interfaces are making our musical decisions for us not necessarily in our best interests. Technological innovation has presented opportunities as well as challenges for education. What do music production students actually need to learn in a formalised education environment, and to what extent do they need to know it? In this brave new world of omnipresent music creation tools, do we still need tangibility in music production? Interviews with prominent Australian music producers that work in a variety of fields will be featured in this paper, and will provide insight in answering these questions and move towards developing an understanding how tangibility can be rediscovered in the next generation of music production.

Keywords: analogue, digital, digital audio workstation, music production, plugins, tangibility, technology, workflow

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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: Anika Pallapothu

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Periodontitis, a severe gum disease, poses a significant threat to the integrity of bone and soft tissues supporting teeth, primarily initiated by bacterial accumulation around the gum line. Conventional treatments like scaling/root planning and plaque removal are widely employed, but integrating modern technologies such as nanotechnology holds promise for innovative therapeutic approaches. This study explores the utilization of silver nanoparticles encapsulated within cellulose nanofiber (CNF) and mesoporous bioactive glass hydrogel matrices for periodontitis management. Silver nanoparticles exhibit potent antimicrobial properties by disrupting microbial cell membranes, inducing reactive oxygen species (ROS) generation, and interfering with vital cellular processes like ATP production and nucleic acid synthesis. Mesoporous bioactive glass, renowned for its high surface area, osteoconductive, and bioactivity, presents a favorable platform for pharmaceutical applications. Incorporating CNF enhances the properties of the hydrogel due to its biocompatibility, biodegradability, and water absorption capacity. The proposed composite material is anticipated to exert beneficial effects in periodontitis treatment by demonstrating antibacterial and anti-inflammatory activities, offering a promising avenue for future therapeutic interventions.

Keywords: periodontitis, cellulose nanofibers, silver nanoparticles, mesoporous bioactive glass, antibacterial activity, anti-inflammatory activity

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Authors: Arati Inamdar, Siddharth Bhattacharyya, Kester Haye

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Collision tumors are rare entities characterized by neoplasms of two different cell populations with distinct separating boundaries. Such tumors could be benign, malignant, or a combination of both. The exact mechanism of origin for collision tumors is predicted to be tumor heterogeneity or concurrent occurrence of neoplasm in the same organ. We present two cases of plasmacytoma presenting as a collision tumor, one with a tumor of hematological origin and another with a non-hematological origin, namely Chronic Lymphocytic Leukemia and Adenocarcinoma of the colon, respectively. The immunohistochemical stains and flowcytometry analysis performed on the specimens aided incorrect diagnosis. Interestingly, neoplastic cells of plasmacytoma in the first case demonstrated strong cytokeratin along with weak Epithelial Specific Antigen/ Epithelial cell adhesion molecule Monoclonal Antibody (MOC31) positivity, indicating that the tumor may influence the microenvironment of the tumor in the vicinity. Furthermore, the next-generation sequencing studies performed on the specimen with plasmacytoma and chronic lymphocytic lymphoma demonstrated BReast CAncer gene (BRCA2) and Tumor Necrosis Factor Alpha Induced Protein 3 (TNFAIP3) as a disease associated variants suggestive of risk for multiple tumors including collision tumors. Our reports highlight the unique collision tumors involving plasmacytoma, which have never been reported previously, as well as provide necessary insights about the underline genetic aberrations and tumor heterogeneity through sequencing studies and allow clonality assessment for subsequent tumors.

Keywords: BRCA2, collision tumor, chronic lymphocytic leukemia, plasmacytoma

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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: Yuhanis Ibrahim, Sung-Pil Lee

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This study attempts to address factors that contribute to outline public art factors assessment, memorial monument specifically. Memorial monuments hold significant and rich message whether the intention of the art is to mark and commemorate important event or to inform younger generation about the past. Public monument should relate to the public and raise awareness about the significant issue. Therefore, by investigating the impact of the existing public memorial art will hopefully shed some lights to the upcoming public art projects’ stakeholders to ensure the lucid memorial message is delivered to the public directly. Public is the main actor as public is the fundamental purpose that the art was created. Perception is framed as one of the reliable evaluation tools to assess the public art impact factors. The Malaysia National Monument was selected to be the case study for the investigation. The public’s perceptions were gathered using a questionnaire that involved (n-115) participants to attain keywords, and next Semantical Differential Methodology (SDM) was adopted to evaluate the perceptions about the memorial monument. These perceptions were then measured with Reliability Factor and then were factorised using Factor Analysis of Principal Component Analysis (PCA) method to acquire concise factors for the monument assessment. The result revealed that there are four factors that influence public’s perception on the monument which are aesthetic, audience, topology, and public reception. The study concludes by proposing the factors for public memorial art assessment for the next future public memorial projects especially in Malaysia.

Keywords: factor analysis, public art, public perception, semantical differential methodology

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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: Taehyun Kim, Hyunjoo Park, Taehyun Kim

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Public deliberation committee on Shin-Gori Nuclear Reactors No. 5 & 6 in South Korea recently suggested policy recommendation in July 2017 including complementary measures for resumption of construction: 1) nuclear power generation reduction, 2) expansion of investment to increase proportion of renewable energy, 3) repositories of spent nuclear fuel. Even when constructing eco-friendly renewable energy facilities such as solar and wind power plants, local residents are opposed to construction of these facilities due to environmental pollution and health impacts. In order to transform eco-friendly energy, it is necessary to convert nuclear energy into renewable energy and to take measures to increase the acceptance of residents through the participation of citizens. Therefore, this study aims to compare the factors of local acceptance of renewable energy facilities and spent nuclear fuel repositories through literature review and in-depth interview. The results show that environmental and economic concerns, risk perceptions, sociality, demographic characteristics and subjective recognition types affect the local acceptance for spent nuclear fuel repository. The factors of local acceptance for renewable energy facilities are partially coincide with those for spent nuclear fuel repository. The results of this study will contribute to improving residents' acceptance and reducing conflicts when determining the location of facilities in the future.

Keywords: local acceptance, renewable energy facility, spent nuclear fuel repository, interview

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Authors: Ranjith Kumar Kankala, Wei-Zhi Lin, Chia-Hung Lee

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Antibiotic resistance in bacteria has become an emergency situation clinically. To improve the efficacy of antibiotics in resistant strains, advancement of nanoparticles is inevitable than ever. Herewith, we demonstrate a design by immobilizing tetracycline (TET) in copper substituted mesoporous silica nanoparticles (Cu-MSNs) through a pH-sensitive coordination link, enabling its release in the acidic environment. Subsequently, MSNs are coated with silver nanoparticles stabilized polyethyleneimine (PEI-SNP) to act against drug-resistant (MDR) bacterial strains. Silver ions released from SNP are capable of sensitizing the resistant strains and facilitate the generation of free radicals capable of damaging the cell components. In addition, copper ions in the framework are also capable of generating free radicals through Fenton-like reaction. Furthermore, the nanoparticles are well-characterized physically, and various antibacterial efficacious tests against isolated multidrug resistant bacterial strain were highly commendable. However, this formulation has no significant toxic effect on normal mammalian fibroblast cells accounting its high biocompatibility. These MSN trio-hybrids, i.e., SNP, tetracycline, and copper ions result in synergistic effects, and their advancement could bypass resistance and allow synergism for effective treatment of antibiotic clinically.

Keywords: antibiotic resistance, copper, mesoporous silica nanoparticles, Ph-sensitive release, polyethyleneimine, silver, tetracycline

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Authors: Chau Nguyen Pham Minh

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The aim of this study is to answer the following research question: What do we know about virtual customer integration in innovation development based on existing empirical research? The paper is based on a systematic review of 136 articles which were published in the past 16 years. The analysis focuses on three areas: what forms of virtual customer integration (e.g. netnography, online co-creation, virtual experience) have been applied in innovation development; how have virtual customer integration methods effectively been utilized by firms; and what are the influences of virtual customer integration on innovation development activities? Through the detailed analysis, the study provides researchers with broad understanding about virtual customer integration in innovation development. The study shows that practitioners and researchers increasingly pay attention on using virtual customer integration methods in developing innovation since those methods have dominant advantages in interact with customers in order to generate the best ideas for innovation development. Additionally, the findings indicate that netnography has been the most common method in integrating with customers for idea generation; while virtual product experience has been mainly used in product testing. Moreover, the analysis also reveals the positive and negative influences of virtual customer integration in innovation development from both process and strategic perspectives. Most of the review studies examined the phenomenon from company’s perspectives to understand the process of applying virtual customer integration methods and their impacts; however, the customers’ perspective on participating in the virtual interaction has been inadequately studied; therefore, it creates many potential interesting research paths for future studies.

Keywords: innovation, virtual customer integration, co-creation, netnography, new product development

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Authors: Imran Ullah Khana, Mohd Hafiz Dzarfan Othmanb, Haslenda Hashima, Takeshi Matsuurad, A. F. Ismailb, M. Rezaei-DashtArzhandib, I. Wan Azelee

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Biogas upgrading is a widely studied and discussed topic, and its utilization as a natural gas substitute has gained significant attention in recent years. The production of biomethane provides a versatile application in both heat and power generation and as a vehicular fuel. This paper systematically reviews the state of the art of biogas upgrading technologies with upgrading efficiency, methane (CH4) loss, environmental effect, development and commercialization, and challenges in terms of energy consumption and economic assessment. The market situation for biogas upgrading has changed rapidly in recent years, giving membrane separation a significant market share with traditional biogas upgrading technologies. In addition, the potential utilization of biogas, efficient conversion into bio-compressed natural gas (bio-CNG), and storage systems are investigated in depth. Two storing systems for bio-CNG at filling stations, namely buffer and cascade storage systems are used. The best storage system should be selected on the basis of the advantages of both systems. Also, the fuel economy and mass emissions for bio-CNG and CNG-filled vehicles are studied. There is the same fuel economy and less carbon dioxide (CO2) emission for bio-CNG. Based on the results of comparisons between the technical features of upgrading technologies, various specific requirements for biogas utilization and the relevant investment, and operating and maintenance costs, future recommendations are made for biogas upgrading.

Keywords: biogas upgrading, cost, utilization, bio-CNG, storage, energy

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Authors: Chaya B. M., C. Dhanush, Inti Sai Srikar, Akula Pavan Parvatalu, Chirag Gowda R

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Recently, there has been a lot of interest in µ-LED technology because of its exceptional qualities, including auto emission, high visibility, low consumption of power, rapid response and longevity. Light-emitting diodes (LED) using III-nitride, such as lighting sources, visible light communication (VLC) devices, and high-power devices, are finding increasing use as miniaturization technology advances. The use of micro-LED displays in place of traditional display technologies like liquid crystal displays (LCDs) and organic light-emitting diodes (OLEDs) is one of the most prominent recent advances, which may even represent the next generation of displays. The development of fully integrated, multifunctional devices and the incorporation of extra capabilities into micro-LED displays, such as sensing, light detection, and solar cells, are the pillars of advanced technology. Due to the wide range of applications for micro-LED technology, the effectiveness and dependability of these devices in numerous harsh conditions are becoming increasingly important. Enough research has been conducted to overcome the under-effectiveness of micro-LED devices. In this paper, different Micro LED design structures are proposed in order to achieve optimized optical properties. In order to attain improved external quantum efficiency (EQE), devices' light extraction efficiency (LEE) has also been boosted.

Keywords: finite difference time domain, light out coupling efficiency, far field intensity, power density, quantum efficiency, flat panel displays

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Authors: Suhas Pednekar, Prashant Chavan, Ramesh Chaughule, Deepak Patkar

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Iron oxide (Fe3O4) magnetic nanoparticles (MNPs) are one of the most attractive nanomaterials for various biomedical applications. An important potential medical application of polymer-coated iron oxide nanoparticles (NPs) is as imaging agents. Composition, size, morphology and surface chemistry of these nanoparticles can now be tailored by various processes to not only improve magnetic properties but also affect the behavior of nanoparticles in vivo. MNPs are being actively investigated as the next generation of magnetic resonance imaging (MRI) contrast agents. Also, there is considerable interest in developing magnetic nanoparticles and their surface modifications with therapeutic agents. Our study involves the synthesis of biocompatible cancer drug coated with iron oxide nanoparticles and to evaluate their efficacy as MRI contrast agents. A simple and rapid microwave method to prepare Fe3O4 nanoparticles has been developed. The drug was successfully conjugated to the Fe3O4 nanoparticles which can be used for various applications. The relaxivity R2 (reciprocal of the spin-spin relaxation time T2) is an important factor to determine the efficacy of Fe nanoparticles as contrast agents for MRI experiments. R2 values of the coated magnetic nanoparticles were also measured using MRI technique and the results showed that R2 of the Fe complex consisting of Fe3O4, polymer and drug was higher than that of bare Fe nanoparticles and polymer coated nanoparticles. This is due to the increase in hydrodynamic sizes of Fe NPs. The results with various amounts of iron molar concentrations are also discussed. Using MRI, it is seen that the R2 relaxivity increases linearly with increase in concentration of Fe NPs in water.

Keywords: cancer drug, hydrodynamic size, magnetic nanoparticles, MRI

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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: Yan-Lin Liu, Cheng-Ting Shih, Jay Wu

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Tablet devices have been introduced into the medical environment in recent years. The performance of display can be varied based on the use of different hardware specifications and types of display technologies. Therefore, the differences between tablet devices and medical diagnostic LCDs have to be verified to ensure that image quality is not jeopardized for clinical diagnosis in a picture archiving and communication system (PACS). In this study, a set of randomized object test patterns (ROTPs) were developed, which included randomly located spheres in abdominal CT images. Five radiologists were asked to independently review the CT images on different generations of iPads and a diagnostic monochrome medical LCD monitor. Receiver operating characteristic (ROC) analysis was performed by using a five-point rating scale, and the average area under curve (AUC) and average reading time (ART) were calculated. The AUC values for the second generation iPad, iPad mini, iPad Air, and monochrome medical monitor were 0.712, 0.717, 0.725, and 0.740, respectively. The differences between iPads were not significant. The ARTs were 177 min and 127 min for iPad mini and medical LCD monitor, respectively. A significant difference appeared (p = 0.04). The results show that the iPads were slightly inferior to the monochrome medical LCD monitor. However, tablet devices possess advantages in portability and versatility, which can improve the convenience of rapid diagnosis and teleradiology. With advances in display technology, the applicability of tablet devices and mobile devices may be more diversified in PACS.

Keywords: tablet devices, PACS, receiver operating characteristic, LCD monitor

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Authors: Debabrata Das, Preeti Mishra

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A two-phase anaerobic process combining biohydrogen followed by biomethane (biohythane technology) serves as an environment-friendly and economically sustainable approach for the improved valorization of organic wastes. Suitability of the pure cultures like Klebsiela pneumonia, C. freundii, B. coagulan, etc. and mixed acidogenic cultures for the biohydrogen production was already studied. The characteristics of organic wastes play a critical role in biohydrogen production. The choice of an appropriate combination of complementary organic wastes can vastly improve the bioenergy generation besides achieving the significant cost reduction. Suitability and economic viability of using the groundnut deoiled cake (GDOC), mustard deoiled cake (MDOC), distillers’ dried grain with soluble (DDGS) and algal biomass (AB) as a co-substrate were studied for a biohythane production. Results show that maximum gaseous energy of 20.7, 9.3, 16.7 and 15.6 % was recovered using GDOC, MDOC, DDGS and AB in the two stage biohythane production, respectively. Both GDOC and DDGS were found to be better co-substrates as compared to MDOC and AB in terms of hythane production, respectively. The maximum cumulative hydrogen and methane production of 150 and 64 mmol/L were achieved using GDOC. Further, 98 % reduction in substrate input cost (SIC) was achieved using the co-supplementation procedure.

Keywords: Biohythane, algal biomass, distillers’ dried grain with soluble (DDGS), groundnut deoiled cake (GDOC), mustard deoiled cake (MDOC)

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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: Pello Alfonso-Muniozguren, Madeleine Bussemaker, Ralph Chadeesingh, Caryn Jones, David Oakley, Judy Lee, Devendra Saroj

Abstract:

Current industrialized livestock agriculture is growing every year leading to an increase in the generation of wastewater that varies considerably in terms of organic content and microbial population. Therefore, suitable wastewater treatment methods are required to ensure the wastewater quality meet regulations before discharge. In the present study, a combined lab scale activated sludge-filtration-ozonation system was used to treat a pre-treated abattoir wastewater. A hydraulic retention time of 24 hours and a solid retention time of 13 days were used for the activated sludge process, followed by a filtration step (4-7 µm) and using ozone as tertiary treatment. An average reduction of 93% and 98% was achieved for Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD), respectively, obtaining final values of 128 mg/L COD and 12 mg/L BOD. For the Total Suspended Solids (TSS), the average reduction increased to 99% in the same system, reducing the final value down to 3 mg/L. Additionally, 98% reduction in Phosphorus (P) and a complete inactivation of Total Coliforms (TC) was obtained after 17 min ozonation time. For Total Viable Counts (TVC), a drastic reduction was observed with 30 min ozonation time (6 log inactivation) at an ozone dose of 71 mg O3/L. Overall, the combined process was sufficient to meet discharge requirements without further treatment for the measured parameters (COD, BOD, TSS, P, TC, and TVC).

Keywords: abattoir waste water, activated sludge, ozone, waste water treatment

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

Abstract:

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: Abdulrahman Nami Almutairi

Abstract:

In the face of escalating climate change concerns, the concept of smart cities emerges as a promising approach to mitigate carbon emissions and move towards carbon neutrality. This paper provides a comprehensive review of the role of Natural Gas in achieving carbon neutrality. Natural gas has often been seen as a transitional fuel in the context of reducing carbon emissions. Its main role stems from being cleaner than coal and oil when burned for electricity generation and industrial processes. The urgent need to address this global issue has prompted a global shift towards cleaner energy sources and sustainable practices. In this endeavor, natural gas has emerged as a pivotal player, hailed for its potential to mitigate carbon emissions, and facilitate the transition to a low-carbon economy. With its lower carbon intensity compared to conventional fossil fuels, natural gas presents itself as a promising alternative for meeting energy demands while reducing environmental impact. As the world stands at a critical juncture in the fight against climate change, exploring the potential of natural gas as a transitional fuel offers insights into pathways towards a more sustainable and resilient future. By critically evaluating its opportunities and challenges, we can harness the potential of natural gas as a transitional fuel while advancing towards a cleaner, more resilient energy system. Through collaborative efforts and informed decision-making, we can pave the way for a future where energy is not only abundant but also environmentally sustainable and socially equitable.

Keywords: natural gas, clean fuel, carbon emissions, global warming, environmental protection

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Authors: Anuradha Chug, Sunali Gandhi

Abstract:

Regression Testing is a very expensive and time-consuming process carried out to ensure the validity of modified software. Due to the availability of insufficient resources to re-execute all the test cases in time constrained environment, efforts are going on to generate test data automatically without human efforts. Many search based techniques have been proposed to generate efficient, effective as well as optimized test data, so that the overall cost of the software testing can be minimized. The generated test data should be able to uncover all potential lapses that exist in the software or product. Inspired from the natural behavior of bat for searching her food sources, current study employed a meta-heuristic, search-based bat algorithm for optimizing the test data on the basis certain parameters without compromising their effectiveness. Mathematical functions are also applied that can effectively filter out the redundant test data. As many as 50 Java programs are used to check the effectiveness of proposed test data generation and it has been found that 86% saving in testing efforts can be achieved using bat algorithm while covering 100% of the software code for testing. Bat algorithm was found to be more efficient in terms of simplicity and flexibility when the results were compared with another nature inspired algorithms such as Firefly Algorithm (FA), Hill Climbing Algorithm (HC) and Ant Colony Optimization (ACO). The output of this study would be useful to testers as they can achieve 100% path coverage for testing with minimum number of test cases.

Keywords: regression testing, test case selection, test case prioritization, genetic algorithm, bat algorithm

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Authors: Maryam Hasheminasab, Ahmed Cheldavi, Ahmed Kishk

Abstract:

Frequency-diverse arrays (FDAs) have garnered significant attention from researchers due to their ability to combine frequency diversity with the inherent spatial diversity of an array. The introduction of frequency diversity in FDAs enables the generation of auto-scanning patterns that are range-dependent, which can have advantageous applications in communication and radar systems. However, the main challenge in implementing FDAs lies in determining the technique for distributing frequencies among the array elements. One approach to address this challenge is by utilizing coupled oscillators, which are a technique commonly employed in active microwave theory. Nevertheless, the limited stability range of coupled oscillators poses another obstacle to effectively utilizing this technique. In this paper, we explore the possibility of employing a coupled oscillator array in the mode lock state (MLS) for implementing frequency distribution in FDAs. Additionally, we propose and simulate the use of a digital phase-locked loop (DPLL) as a backup technique to stabilize the oscillators. Through simulations, we validate the functionality of this technique. This technique holds great promise for advancing the implementation of phased arrays and overcoming current scan rate and phase shifter limitations, especially in millimeter wave frequencies.

Keywords: angle-changing rate, auto scanning beam, pull-in range, hold-in range, locking range, mode locked state, frequency locked state

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