Search results for: Honeycomb network

Authors: Ajay Kumar Singh, Arvind Kumar, S. P. Singh

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Cellulose nanofibrils (CNF) have shown potential of their extensive use in various fields, including papermaking, due to their unique characteristics. In this study, CNF’s were prepared by fibrillating the pulp obtained from raw materials e.g. bagasse, hardwood and softwood using enzymatic pretreatment followed by mechanical refining. These nanofibrils, when examined under FE-SEM, show that partial fibrillation on fiber surface has resulted in production of nanofibers. Mixing these nanofibers with the unrefined and normally refined fibers show their reinforcing effect. This effect is manifested in observing the improvement in the physical and mechanical properties e.g. tensile index and burst index of paper. Tear index, however, was observed to decrease on blending with nanofibers. The optical properties of paper sheets made from blended fibers showed no significant change in comparison to those made from only mechanically refined pulp. Mixing of normal pulp fibers with nanofibers show increase in ºSR and consequent decrease in drainage rate. These changes observed in mechanical, optical and other physical properties of the paper sheets made from nanofibrils blended pulp have been tried to explain considering the distribution of the nanofibrils alongside microfibrils in the fibrous network. Since usually, paper/boards with higher strength are observed to have diminished optical properties which is a drawback in their quality, the present work has the potential for developing paper/boards having improved strength alongwith undiminished optical properties utilising the concepts of nanoscience and nanotechnology.

Keywords: enzymatic pretreatment, mechanical refining, nanofibrils, paper properties

Procedia PDF Downloads 353

Authors: Md. Mahadi Hasan Sany, Mumenunnessa Keya, Sharun Khushbu, Sheikh Abujar

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Since the beginning of China's economic reform, trade between the U.S. and China has grown rapidly, and has increased since China's accession to the World Trade Organization in 2001. The US imports more than it exports from China, reducing the trade war between China and the U.S. for the 2019 trade deficit, but in 2020, the opposite happens. In international and U.S. trade, Washington launched a full-scale trade war against China in March 2016, which occurred a catastrophic epidemic. The main goal of our study is to measure and predict trade relations between China and the U.S., before and after the arrival of the COVID epidemic. The ML model uses different data as input but has no time dimension that is present in the time series models and is only able to predict the future from previously observed data. The LSTM (a well-known Recurrent Neural Network) model is applied as the best time series model for trading forecasting. We have been able to create a sustainable forecasting system in trade between China and the US by closely monitoring a dataset published by the State Website NZ Tatauranga Aotearoa from January 1, 2015, to April 30, 2021. Throughout the survey, we provided a 180-day forecast that outlined what would happen to trade between China and the US during COVID-19. In addition, we have illustrated that the LSTM model provides outstanding outcome in time series data analysis rather than RFR and SVR (e.g., both ML models). The study looks at how the current Covid outbreak affects China-US trade. As a comparative study, RMSE transmission rate is calculated for LSTM, RFR and SVR. From our time series analysis, it can be said that the LSTM model has given very favorable thoughts in terms of China-US trade on the future export situation.

Keywords: RFR, China-U.S. trade war, SVR, LSTM, deep learning, Covid-19, export value, forecasting, time series analysis

Procedia PDF Downloads 198

Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: Qinglin Liu, Zhiyong Lin, Zongheng Wei, Jianfeng Wen, Congming Yi, Hai Liu

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The multi-channel blind rendezvous problem in distributed cognitive radio networks (DCRNs) refers to how users in the network can hop to the same channel at the same time slot without any prior knowledge (i.e., each user is unaware of other users' information). The channel hopping (CH) technique is a typical solution to this blind rendezvous problem. In this paper, we propose a quinary coding and matrix structure-based CH algorithm called QCMS-CH. The QCMS-CH algorithm can guarantee the rendezvous of users using only one cognitive radio in the scenario of the asynchronous clock (i.e., arbitrary time drift between the users), heterogeneous channels (i.e., the available channel sets of users are distinct), and symmetric role (i.e., all users play a same role). The QCMS-CH algorithm first represents a randomly selected channel (denoted by R) as a fixed-length quaternary number. Then it encodes the quaternary number into a quinary bootstrapping sequence according to a carefully designed quaternary-quinary coding table with the prefix "R00". Finally, it builds a CH matrix column by column according to the bootstrapping sequence and six different types of elaborately generated subsequences. The user can access the CH matrix row by row and accordingly perform its channel, hoping to attempt rendezvous with other users. We prove the correctness of QCMS-CH and derive an upper bound on its Maximum Time-to-Rendezvous (MTTR). Simulation results show that the QCMS-CH algorithm outperforms the state-of-the-art in terms of the MTTR and the Expected Time-to-Rendezvous (ETTR).

Keywords: channel hopping, blind rendezvous, cognitive radio networks, quaternary-quinary coding

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Authors: Haidy S. Ghali, Engy Serag, A. Samer Ezeldin

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Copper is an essential raw material used in the construction industry. During the year 2021 and the first half of 2022, the global market suffered from a significant fluctuation in copper raw material prices due to the aftermath of both the COVID-19 pandemic and the Russia-Ukraine war, which exposed its consumers to an unexpected financial risk. Thereto, this paper aims to develop two ANN-LSTM price prediction models, using Python, that can forecast the average monthly copper prices traded in the London Metal Exchange; the first model is a multivariate model that forecasts the copper price of the next 1-month and the second is a univariate model that predicts the copper prices of the upcoming three months. Historical data of average monthly London Metal Exchange copper prices are collected from January 2009 till July 2022, and potential external factors are identified and employed in the multivariate model. These factors lie under three main categories: energy prices and economic indicators of the three major exporting countries of copper, depending on the data availability. Before developing the LSTM models, the collected external parameters are analyzed with respect to the copper prices using correlation and multicollinearity tests in R software; then, the parameters are further screened to select the parameters that influence the copper prices. Then, the two LSTM models are developed, and the dataset is divided into training, validation, and testing sets. The results show that the performance of the 3-Month prediction model is better than the 1-Month prediction model, but still, both models can act as predicting tools for diverse economic situations.

Keywords: copper prices, prediction model, neural network, time series forecasting

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Authors: Daud Mustafa Minhas, Raja Rehan Khalid, Georg Frey

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The next generation power systems are equipped with abundantly available free renewable energy resources (RES). During their low-cost operations, the price of electricity significantly reduces to a lower value, and sometimes it becomes negative. Therefore, it is recommended not to operate the traditional power plants (e.g. coal power plants) and to reduce the losses. In fact, it is not a cost-effective solution, because these power plants exhibit some shutdown and startup costs. Moreover, they require certain time for shutdown and also need enough pause before starting up again, increasing inefficiency in the whole power network. Hence, there is always a trade-off between avoiding negative electricity prices, and the startup costs of power plants. To exploit this trade-off and to increase the profit of a power plant, two main contributions are made: 1) introducing retrofit technology for state of art coal power plant; 2) proposing optimal control strategy for a power plant by exploiting different flexibility features. These flexibility features include: improving ramp rate of power plant, reducing startup time and lowering minimum load. While, the control strategy is solved as mixed integer linear programming (MILP), ensuring optimal solution for the profit maximization problem. Extensive comparisons are made considering pre and post-retrofit coal power plant having the same efficiencies under different electricity price scenarios. It concludes that if the power plant must remain in the market (providing services), more flexibility reflects direct economic advantage to the plant operator.

Keywords: discrete optimization, power plant flexibility, profit maximization, unit commitment model

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Authors: Hedi Dghim, Ahmed El-Naggar, Istvan Erlich

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Power electronic converters are being introduced in low voltage (LV) grids at an increasingly rapid rate due to the growing adoption of power electronic-based home appliances in residential grid. Photovoltaic (PV) systems are considered one of the potential installed renewable energy sources in distribution power systems. This trend has led to high distortion in the supply voltage which consequently produces harmonic currents in the network and causes an inherent voltage unbalance. In order to investigate the effect of harmonic distortions, a case study of a typical LV grid configuration with high penetration of 3-phase and 1-phase rooftop mounted PV from southern Germany was first considered. Electromagnetic transient (EMT) simulations were then carried out under the MATLAB/Simulink environment which contain detailed models for power electronic-based loads, ohmic-based loads as well as 1- and 3-phase PV. Note that, the switching patterns of the power electronic circuits were considered in this study. Measurements were eventually performed to analyze the distortion levels when PV operating under different solar irradiance. The characteristics of the load-side harmonic impedances were analyzed, and their harmonic contributions were evaluated for different distortion levels. The effect of the high penetration of PV on the harmonic distortion of both positive and negative sequences was also investigated. The simulation results are presented based on case studies. The current distortion levels are in agreement with relevant standards, otherwise the Total Harmonic Distortion (THD) increases under low PV power generation due to its inverse relation with the fundamental current.

Keywords: harmonic distortion analysis, power quality, PV systems, residential distribution system

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Authors: Mayank, Navneet Kaur, Narinder Singh

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The V599 mutations in the BRAF protein are extremely oncogenic, responsible for countless of malignant conditions. Along with wild type, V599E, V599D, and V599R are the important mutated variants of the BRAF proteins. The BRAF inhibitory anticancer agents are continuously developing, and sorafenib is a BRAF inhibitor that is under clinical use. The crystal structure of sorafenib bounded to wild type, and V599 is known, showing a similar interaction pattern in both the case. The mutated 599th residue, in both the case, is also found not interacting directly with the co-crystallized sorafenib molecule. However, the IC50 value of sorafenib was found extremely different in both the case, i.e., 22 nmol/L for wild and 38 nmol/L for V599E protein. Molecular docking study and MMGBSA binding energy results also revealed a significant difference in the binding pattern of sorafenib in both the case. Therefore, to explore the role of distinctively situated 599th residue, we have further conducted comprehensive computational studies. The molecular dynamics simulation, residue interaction network (RIN) analysis, and residue correlation study results revealed the importance of the 599th residue on the therapeutic outcome and overall dynamic of the BRAF protein. Therefore, although the position of 599th residue is very much distinctive from the ligand-binding cavity of BRAF, still it has exceptional control over the overall functional outcome of the protein. The insight obtained here may seem extremely important and guide us while designing ideal BRAF inhibitory anticancer molecules.

Keywords: BRAF, oncogenic, sorafenib, computational studies

Procedia PDF Downloads 115

Authors: Sophia Shi

Abstract:

Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

Procedia PDF Downloads 131

Authors: Peng Chun-Yi, Chen Wei-Hsuan, Ueng Shyh-Kuang

Abstract:

This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

Procedia PDF Downloads 59

Authors: Aina Barcelona, Carolyn Oldham, Jordi Colomer, Teresa Serra

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The loss of seagrass meadows worldwide is being tackled by formulating coastal restoration strategies. Seagrass loss results in a network of vegetated patches which are barely interconnected, and consequently, the ecological services they provide may be highly compromised. Hence, there is a need to optimize coastal management efforts in order to implement successful restoration strategies, not only through modifying the architecture of the canopies but also by gathering together information on the hydrodynamic conditions of the seabeds. To obtain information on the hydrodynamics within the patches of vegetation, this study deals with the scale analysis of the minimum lengths of patch management strategies that can be effectively used on. To this aim, a set of laboratory experiments were conducted in a laboratory flume where the plant densities, patch lengths, and hydrodynamic conditions were varied to discern the vegetated patch lengths that can provide optimal ecosystem services for canopy development. Two possible patch behaviours based on the turbulent kinetic energy (TKE) production were determined: one where plants do not interact with the flow and the other where plants interact with waves and produce TKE. Furthermore, this study determines the minimum patch lengths that can provide successful management restoration. A canopy will produce TKE, depending on its density, the length of the vegetated patch, and the wave velocities. Therefore, a vegetated patch will produce plant-wave interaction under high wave velocities when it presents large lengths and high canopy densities.

Keywords: seagrass, minimum patch size, turbulent kinetic energy, oscillatory flow

Procedia PDF Downloads 197

Authors: Elham Taghizadeh, Mostafa Abedzadeh, Mostafa Setak

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Logistics network is expected that opened facilities work continuously for a long time horizon without any failure; but in real world problems, facilities may face disruptions. This paper studies a reliable joint inventory location problem to optimize cost of facility locations, customers’ assignment, and inventory management decisions when facilities face failure risks and doesn’t work. In our model we assume when a facility is out of work, its customers may be reassigned to other operational facilities otherwise they must endure high penalty costs associated with losing service. For defining the model closer to real world problems, the model is proposed based on p-median problem and the facilities are considered to have limited capacities. We define a new binary variable (Z_is) for showing that customers are not assigned to any facilities. Our problem involve a bi-objective model; the first one minimizes the sum of facility construction costs and expected inventory holding costs, the second one function that mention for the first one is minimizes maximum expected customer costs under normal and failure scenarios. For solving this model we use NSGAII and MOSS algorithms have been applied to find the pareto- archive solution. Also Response Surface Methodology (RSM) is applied for optimizing the NSGAII Algorithm Parameters. We compare performance of two algorithms with three metrics and the results show NSGAII is more suitable for our model.

Keywords: joint inventory-location problem, facility location, NSGAII, MOSS

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Authors: Prashansa Singh, Rohit Bajaj, Manjot Kaur

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In the modern agricultural landscape, precision farming has emerged as a pivotal strategy for enhancing crop yield and optimizing resource utilization. This paper introduces an innovative Crop Recommendation System (CRS) that leverages the Internet of Things (IoT) technology and the Message Queuing Telemetry Transport (MQTT) protocol to collect critical environmental and soil data via sensors deployed across agricultural fields. The system is designed to address the challenges of real-time data acquisition, efficient data transmission, and dynamic crop recommendation through the application of advanced Artificial Intelligence (AI) and Machine Learning (ML) models. The CRS architecture encompasses a network of sensors that continuously monitor environmental parameters such as temperature, humidity, soil moisture, and nutrient levels. This sensor data is then transmitted to a central MQTT server, ensuring reliable and low-latency communication even in bandwidth-constrained scenarios typical of rural agricultural settings. Upon reaching the server, the data is processed and analyzed by AI/ML models trained to correlate specific environmental conditions with optimal crop choices and cultivation practices. These models consider historical crop performance data, current agricultural research, and real-time field conditions to generate tailored crop recommendations. This implementation gets 99% accuracy.

Keywords: Iot, MQTT protocol, machine learning, sensor, publish, subscriber, agriculture, humidity

Procedia PDF Downloads 68

Authors: K. Hamdi, Z. Aboura, W. Harizi, K. Khellil

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Improving the electrical properties of organic matrix composites has been investigated in several studies. Thus, to extend the use of composites in more varied application, one of the actual barrier is their poor electrical conductivities. In the case of carbon fiber composites, organic matrix are in charge of the insulating properties of the resulting composite. However, studying the properties of continuous carbon fiber nano-filled composites is less investigated. This work tends to characterize the effect of carbon black nano-fillers on the properties of the woven carbon fiber composites. First of all, SEM observations were performed to localize the nano-particles. It showed that particles penetrated on the fiber zone (figure1). In fact, by reaching the fiber zone, the carbon black nano-fillers created network connectivity between fibers which means an easy pathway for the current. It explains the noticed improvement of the electrical conductivity of the composites by adding carbon black. This test was performed with the four points electrical circuit. It shows that electrical conductivity of 'neat' matrix composite passed from 80S/cm to 150S/cm by adding 9wt% of carbon black and to 250S/cm by adding 17wt% of the same nano-filler. Thanks to these results, the use of this composite as a strain gauge might be possible. By the way, the study of the influence of a mechanical excitation (flexion, tensile) on the electrical properties of the composite by recording the variance of an electrical current passing through the material during the mechanical testing is possible. Three different configuration were performed depending on the rate of carbon black used as nano-filler. These investigation could lead to develop an auto-instrumented material.

Keywords: carbon fibers composites, nano-fillers, strain-sensors, auto-instrumented

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Authors: Parthiban Srinivasan

Abstract:

With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

Procedia PDF Downloads 356

Authors: Shabadini Sampath, Jinglang Feng

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With huge kinetic energy, space debris poses a major threat to astronauts’ space activities and spacecraft in orbit if a collision happens. The active removal of space debris is required in order to avoid frequent collisions that would occur. In addition, the amount of space debris will increase uncontrollably, posing a threat to the safety of the entire space system. But the safe and reliable removal of large-scale space debris has been a huge challenge to date. While capturing and deorbiting space debris, the space manipulator has to achieve high control precision. However, due to uncertainties and unknown disturbances, there is difficulty in coordinating the control of the space manipulator. To address this challenge, this paper focuses on developing a robust and intelligent control algorithm that controls joint movement and restricts it on the sliding manifold by reducing uncertainties. A neural network adaptive sliding mode controller (NNASMC) is applied with the objective of finding the control law such that the joint motions of the space manipulator follow the given trajectory. A computed torque control (CTC) is an effective motion control strategy that is used in this paper for computing space manipulator arm torque to generate the required motion. Based on the Lyapunov stability theorem, the proposed intelligent controller NNASMC and CTC guarantees the robustness and global asymptotic stability of the closed-loop control system. Finally, the controllers used in the paper are modeled and simulated using MATLAB Simulink. The results are presented to prove the effectiveness of the proposed controller approach.

Keywords: GNC, active removal of space debris, AI controllers, MatLabSimulink

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Authors: Rodrigo T. Sepulveda-Hirose, Ana B. Carrera-Aguilar, Magnolia G. Martinez-Rivera, Pablo de J. Angeles-Salto, Carlos Herrera-Ventosa

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In order to diminish health risks, it is of major importance to monitor air quality. However, this process is accompanied by the high costs of physical and human resources. In this context, this research is carried out with the main objective of developing a predictive model for concentrations of inhalable particles (PM10-2.5) using remote sensing. To develop the model, satellite images, mainly from Landsat 8, of the Mexico City’s Metropolitan Area were used. Using historical PM10 and PM2.5 measurements of the RAMA (Automatic Environmental Monitoring Network of Mexico City) and through the processing of the available satellite images, a preliminary model was generated in which it was possible to observe critical opportunity areas that will allow the generation of a robust model. Through the preliminary model applied to the scenes of Mexico City, three areas were identified that cause great interest due to the presumed high concentration of PM; the zones are those that present high plant density, bodies of water and soil without constructions or vegetation. To date, work continues on this line to improve the preliminary model that has been proposed. In addition, a brief analysis was made of six models, presented in articles developed in different parts of the world, this in order to visualize the optimal bands for the generation of a suitable model for Mexico City. It was found that infrared bands have helped to model in other cities, but the effectiveness that these bands could provide for the geographic and climatic conditions of Mexico City is still being evaluated.

Keywords: air quality, modeling pollution, particulate matter, remote sensing

Procedia PDF Downloads 155

Authors: Fayza M. Obied-Allah

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Customer’ needs, quality, and value creation while reducing costs through supply chain management provides challenges and opportunities for companies and researchers. In the light of these challenges, modern ideas must contribute to counter these challenges and exploit opportunities. Perhaps this paper will be one of these contributions. This paper discusses the impact of the quality cost on revenue sharing as a most important incentive to configure business networks. No doubt that the costs directly affect the size of income generated by a business network, so this paper investigates the impact of quality costs on business networks revenue, and their impact on the decision to participate the revenue among the companies in the supply chain. This paper develops the quality cost approach to align with the modern era, the developed model includes five categories besides the well-known four categories (namely prevention costs, appraisal costs, internal failure costs, and external failure costs), a new category has been developed in this research as a new vision of the relationship between quality costs and innovations of industry. This new category is Recycle Cost. This paper is organized into six sections, Section I shows quality costs overview in the supply chain. Section II discusses revenue sharing between the parties in supply chain. Section III investigates the impact of quality costs in revenue sharing decision between partners in supply chain. The fourth section includes survey study and presents statistical results. Section V discusses the results and shows future opportunities for research. Finally, Section VI summarizes the theoretical and practical results of this paper.

Keywords: quality cost, recycle cost, revenue sharing, supply chain management

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Authors: Manee Arpanantikul, Pennapa Unsanit, Dolrat Rujiwatthanakorn, Aporacha Lumdubwong

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In order to continuously perform self-care based on the sufficiency economy philosophy for the length of women’s lives is not easy. However, there are different ways that women can use to carry out self-care activities regularly. Some women individually perform self-care while others perform self-care in groups. Little is known about ways to sustaining self-care of women based on the fundamental principle of Thai culture. The purpose of this study was to investigate ways to sustaining self-care based on the sufficiency economy philosophy of Thai middle-aged women living in the community in order to achieve future healthy aging. This study employed a qualitative research design. Twenty women who were willing to participate in this study were recruited. Data collection were conducted through in-depth interviews with tape recording, doing field notes, and observation. All interviews were transcribed verbatim, and data were analyzed by using content analysis. The findings showed ways to sustaining self-care of Thai community women to achieve future healthy aging consisting of 7 themes: 1) having determination, 2) having a model, 3) developing a leader, 4) carrying on performing activities, 5) setting up rules, 6) building self-care culture, and 7) developing a self-care group/network. The findings of this study suggested that in order to achieve self-care sustainability women should get to know themselves, have intention and belief, together with having the power of community and support. Therefore, having self-care constantly will prevent disease and promote healthy in women’s lives.

Keywords: qualitative research, sufficiency economy philosophy, Thai middle-aged women, ways to sustaining self-care

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Authors: Taekyong Lee, Joon-Young Kim, Jae-Min Cha

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Piping and instrumentation diagram (P&ID) is an essential design drawing describing the interconnection of process equipment and the instrumentation installed to control the process. P&IDs are modified and managed throughout a whole life cycle of a process plant. For the ease of data transfer, P&IDs are generally handed over from a design company to an engineering company as portable document format (PDF) which is hard to be modified. Therefore, engineering companies have to deploy a great deal of time and human resources only for manually converting P&ID images into a computer aided design (CAD) file format. To reduce the inefficiency of the P&ID conversion, various symbols and texts in P&ID images should be automatically recognized. However, recognizing information in P&ID images is not an easy task. A P&ID image usually contains hundreds of symbol and text objects. Most objects are pretty small compared to the size of a whole image and are densely packed together. Traditional recognition methods based on geometrical features are not capable enough to recognize every elements of a P&ID image. To overcome these difficulties, state-of-the-art deep learning models, RetinaNet and connectionist text proposal network (CTPN) were used to build a system for recognizing symbols and texts in a P&ID image. Using the RetinaNet and the CTPN model carefully modified and tuned for P&ID image dataset, the developed system recognizes texts, equipment symbols, piping symbols and instrumentation symbols from an input P&ID image and save the recognition results as the pre-defined extensible markup language format. In the test using a commercial P&ID image, the P&ID information recognition system correctly recognized 97% of the symbols and 81.4% of the texts.

Keywords: object recognition system, P&ID, symbol recognition, text recognition

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Authors: Claire Biasco, Thaier Hayajneh

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A smart city is the integration of digital technologies in urban environments to enhance the quality of life. Smart cities capture real-time information from devices, sensors, and network data to analyze and improve city functions such as traffic analysis, public safety, and environmental impacts. Current smart cities face controversy due to their reliance on real-time data tracking and surveillance. Internet of Things (IoT) devices and blockchain technology are converging to reshape smart city infrastructure away from its centralized model. Connecting IoT data to blockchain applications would create a peer-to-peer, decentralized model. Furthermore, blockchain technology powers the ability for IoT device data to shift from the ownership and control of centralized entities to individuals or communities with Decentralized Autonomous Organizations (DAOs). In the context of smart cities, DAOs can govern cyber-physical systems to have a greater influence over how urban services are being provided. This paper will explore how the core components of a smart city now apply to DAOs. We will also analyze different definitions of DAOs to determine their most important aspects in relation to smart cities. Both categorizations will provide a solid foundation to conduct a cybersecurity assessment of DAOs in smart cities. It will identify the benefits and risks of adopting DAOs as they currently operate. The paper will then provide several mitigation methods to combat cybersecurity risks of DAO integrations. Finally, we will give several insights into what challenges will be faced by DAO and blockchain spaces in the coming years before achieving a higher level of maturity.

Keywords: blockchain, IoT, smart city, DAO

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Authors: Raouf Alizadeh, Kadijeh Hemmati

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The aim of this study is to synthesize several series of hydrogels from combination of a natural based polymer (Quince seed mucilage QSD), a synthetic copolymer contained methoxy poly ethylene glycol -polycaprolactone (mPEG-PCL) in the presence of different amount of multi-walled carbon nanotube (f-MWNT). Mono epoxide functionalized mPEG (mP EG-EP) was synthesized and reacted with sodium azide in the presence of NH4Cl to afford mPEG- N3(-OH). Then ring opening polymerization (ROP) of ε–caprolactone (CL) in the presence of mPEG- N3(-OH) as initiator and Sn(Oct)2 as catalyst led to preparation of mPEG-PCL- N3(-OH ) which was grafted onto propagylated f-MWNT by the click reaction to obtain mPEG-PCL- f-MWNT (-OH ). In the presence of mPEG- N3(-Br) and mixture of NHS/DCC/ QSD, hybrid hydrogels were successfully synthesized. The copolymers and hydrogels were characterized using different techniques such as, scanning electron microscope (SEM) and thermogravimetric analysis (TGA). The gel content of hydrogels showed dependence on the weight ratio of QSD:mPEG-PCL:f-MWNT. The swelling behavior of the prepared hydrogels was also studied under variation of pH, immersion time, and temperature. According to the results, the swelling behavior of the prepared hydrogels showed significant dependence in the gel content, pH, immersion time and temperature. The highest swelling was observed at room temperature, in 60 min and at pH 8. The loading and in-vitro release of quercetin as a model drug were investigated at pH of 2.2 and 7.4, and the results showed that release rate at pH 7.4 was faster than that at pH 2.2. The total loading and release showed dependence on the network structure of hydrogels and were in the range of 65- 91%. In addition, the cytotoxicity and release kinetics of the prepared hydrogels were also investigated.

Keywords: antioxidant, drug delivery, Quince Seed Mucilage(QSD), swelling behavior

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Authors: Shutchapol Chopvitayakun

Abstract:

Nowadays, especially for the last 5 years, mobile devices, mobile applications and mobile users, through the deployment of wireless communication and mobile phone cellular network, all these components are growing significantly bigger and stronger. They are being integrated into each other to create multiple purposes and pervasive deployments into every business and non-business sector such as education, medicine, traveling, finance, real estate and many more. Objective of this study was to develop a mobile application for seniors or last-year students who enroll the internship program at each tertiary school (undergraduate school) and do onsite practice at real field sties, real organizations and real workspaces. During the internship session, all students as the interns are required to exercise, drilling and training onsite with specific locations and specific tasks or may be some assignments from their supervisor. Their work spaces are both private and government corporates and enterprises. This mobile application is developed under schema of a transactional processing system that enables users to keep daily work or practice log, monitor true working locations and ability to follow daily tasks of each trainee. Moreover, it provides useful guidance from each intern’s advisor, in case of emergency. Finally, it can summarize all transactional data then calculate each internship cumulated hours from the field practice session for each individual intern.

Keywords: internship, mobile application, Android OS, smart phone devices, mobile transactional processing system, guidance and monitoring, tertiary education, senior students, model view controller (MVC)

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Authors: Rakesh Kumar, A. K. Ghosh

Abstract:

The paper presents the modeling of linear and nonlinear longitudinal aerodynamics using real flight data of Hansa-3 aircraft gathered at low and high angles of attack. The Neural-Gauss-Newton (NGN) method has been applied to model the linear and nonlinear longitudinal dynamics and estimate parameters from flight data. Unsteady aerodynamics due to flow separation at high angles of attack near stall has been included in the aerodynamic model using Kirchhoff’s quasi-steady stall model. NGN method is an algorithm that utilizes Feed Forward Neural Network (FFNN) and Gauss-Newton optimization to estimate the parameters and it does not require any a priori postulation of mathematical model or solving of equations of motion. NGN method was validated on real flight data generated at moderate angles of attack before application to the data at high angles of attack. The estimates obtained from compatible flight data using NGN method were validated by comparing with wind tunnel values and the maximum likelihood estimates. Validation was also carried out by comparing the response of measured motion variables with the response generated by using estimates a different control input. Next, NGN method was applied to real flight data generated by executing a well-designed quasi-steady stall maneuver. The results obtained in terms of stall characteristics and aerodynamic parameters were encouraging and reasonably accurate to establish NGN as a method for modeling nonlinear aerodynamics from real flight data at high angles of attack.

Keywords: parameter estimation, NGN method, linear and nonlinear, aerodynamic modeling

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Authors: Aida Mammadova, Juan Pastor Ivars

Abstract:

Japanese gardens can be considered as the unique hubs to preserve urban biodiversity, as they provide the habitat for the diverse network of living organisms, facilitating to the movement of the rare species around the urban landscape, became the refuge for the moss and many endangered species. For the centuries, Japanese gardens were considered as ecologically sustainable and well-organized ecosystems, due to the skilled maintenances and management. However, unfortunately, due to the depopulations and ageing in Japanese societies, gardens are becoming more abandoned, and there is an urgent need to increase the awareness about the importance of the Japanese gardens to preserve the urban biodiversity. In this study, we have conducted the participatory educational field trips for 12 students into the to the five gardens protected by Kanazawa City and learned about the preservation activities conducted at the governmental, municipal, and local levels. After the courses, students have found a strong linkage between the gardens with the traditional culture. Kanazawa City, for more than 400 years is famous with traditional craft makings and tea ceremonies, and it was noticed that the cultural diversity of the city was strongly supported by the biodiversity of the gardens, and loss of the gardens would bring to the loss of the traditional culture. Using the experiential approach during the fieldworks, it was observed by the students that the linkage between the bio-cultural diversity strongly depends on humans’ activities. The continuous management and maintenance of the gardens are the contributing factor for the preservation of urban diversity. However, garden management is very time and capital consuming process, and it was also noticed that there is a big need to attract all levels of the society to preserve the urban biodiversity through the participatory urbanism.

Keywords: biodiversity, conservation, educational fieldwork, Japanese gardens

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Authors: Chakraborty Sudipta, A. R. Kambekar, Sarma Arnab

Abstract:

Sea-level rise being one of the most important impacts of anthropogenic induced climate change resulting from global warming and melting of icebergs at Arctic and Antarctic, the investigations done by various researchers both on Indian Coast and elsewhere during the last decade has been reviewed in this paper. The paper aims to ascertain the propensity of consistency of different suggested methods to predict the near-accurate future sea level rise along the coast of Mumbai. Case studies at East Coast, Southern Tip and West and South West coast of India have been reviewed. Coastal Vulnerability Index of several important international places has been compared, which matched with Intergovernmental Panel on Climate Change forecasts. The application of Geographic Information System mapping, use of remote sensing technology, both Multi Spectral Scanner and Thematic Mapping data from Landsat classified through Iterative Self-Organizing Data Analysis Technique for arriving at high, moderate and low Coastal Vulnerability Index at various important coastal cities have been observed. Instead of data driven, hindcast based forecast for Significant Wave Height, additional impact of sea level rise has been suggested. Efficacy and limitations of numerical methods vis-à-vis Artificial Neural Network has been assessed, importance of Root Mean Square error on numerical results is mentioned. Comparing between various computerized methods on forecast results obtained from MIKE 21 has been opined to be more reliable than Delft 3D model.

Keywords: climate change, Coastal Vulnerability Index, global warming, sea level rise

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Authors: Claire Norman-Maillet

Abstract:

Insider fraud, otherwise known as occupational, employee, or internal fraud, is a financial crime threat. Perpetrated by defrauding (or attempting to defraud) one’s current, prospective, or past employer, an ‘employee’ covers anyone employed by the company, including board members and contractors. The Coronavirus pandemic has forced insider fraud into the spotlight, and it isn’t dimming. As the focus of most academics and practitioners has historically been on that of ‘external fraud’, insider fraud is often overlooked or not considered to be a real threat. However, since COVID-19 changed the working world, pushing most of us into remote or hybrid working, employers cannot easily keep an eye on what their staff are doing, which has led to reliance on trust and transparency. This, therefore, brings about an increased risk of insider fraud perpetration. The objective of this paper is to explore why insider fraud is, therefore, now the biggest threat to a business. To achieve the research objective, participating individuals within the financial crime sector (either as a practitioner or consultants) attended semi-structured interviews with the researcher. The principal recruitment strategy for these individuals was via the researcher’s LinkedIn network. The main findings in the research suggest that insider fraud has been ignored and rejected as a threat to a business, owing to a reluctance to admit that a colleague may perpetrate. A positive of the Coronavirus pandemic is that it has forced insider fraud into a more prominent position and giving it more importance on a business’ agenda and risk register. Despite insider fraud always having been a possibility (and therefore a risk) within any business, it is very rare that a business has given it the attention it requires until now, if at all. The research concludes that insider fraud needs to prioritised by all businesses, and even ahead of external fraud. The research also provides advice on how a business can add new or enhance existing controls to mitigate the risk.

Keywords: insider fraud, occupational fraud, COVID-19, COVID, coronavirus, pandemic, internal fraud, financial crime, economic crime

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Authors: Shibinu S, Mohamed Haseeb N

Abstract:

In times of crisis, the diaspora's role and the help it offers are seen to be vital in determining how many countries, particularly low- and middle-income nations that significantly rely on remittances, recover. Twenty-one lakh twenty thousand Keralites have emigrated abroad, with 81.2 percent of these outflows occurring in the Gulf Cooperative Council (GCC). Most of them are semi-skilled or low-skilled laborers employed in GCC nations. Additionally, a sizeable portion of migrants are employed in industrialized nations like the UK and the US. These nations have seen the development of a highly robust Indian Diaspora. India's development is largely dependent on the generosity of its diaspora, and the nation has benefited greatly from the substantial contributions made by several emigrant generations. Its strength was noticeable during the COVID-19 and Kerala floods. Millions of people were displaced, millions of properties were damaged, and many people died as a result of the 2018 Kerala floods. The Malayalee diaspora played a crucial role in the reconstruction of Kerala by providing support for the rescue efforts underway on the ground through their extensive worldwide network. During COVID-19, an analogous outreach was also noted, in which the diaspora assisted stranded migrants across the globe. Together with the work the diaspora has done for the state's development and recovery, there has also been a recent outpouring of assistance during the COVID-19 pandemic. The study focuses on the subtleties of diaspora philanthropic scholarship and how Kerala was able to recover from the COVID-19 pandemic and floods thanks to it. Semi-structured in-depth interviews with migrants, migrant organizations, and beneficiaries from the diaspora through snowball sampling to better understand the role that diaspora philanthropy plays in times of crisis.

Keywords: crises, diaspora, remittances, COVID-19, flood, economic development of Kerala

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Authors: Junzhang Wu, Yifeng Zou, Alessandro Manzardo, Antonio Scipioni

Abstract:

Dynamic expiration dates (DEDs) play an essential role in reducing food waste in the context of the sustainable cold chain and food system. However, it is unknown for the trades-off in retail benefits when setting an expiration date on fresh food products. This study aims to develop a multi-dimensional decision-making model that integrates DEDs with food waste based on wireless sensor network technology. The model considers the initial quality of fresh food and the change rate of food quality with the storage temperature as cross-independent variables to identify the potential impacts of food waste in retail by applying s DEDs system. The results show that retail benefits from the DEDs system depend on each scenario despite its advanced technology. In the DEDs, the storage temperature of the retail shelf leads to the food waste rate, followed by the change rate of food quality and the initial quality of food products. We found that the DEDs system could reduce food waste when food products are stored at lower temperature areas. Besides, the potential of food savings in an extended replenishment cycle is significantly more advantageous than the fixed expiration dates (FEDs). On the other hand, the information-sharing approach of the DEDs system is relatively limited in improving sustainable assessment performance of food waste in retail and even misleads consumers’ choices. The research provides a comprehensive understanding to support the techno-economic choice of the DEDs associated with food waste in retail.

Keywords: dynamic expiry dates (DEDs), food waste, retail benefits, fixed expiration dates (FEDs)

Procedia PDF Downloads 114

Authors: Laleh Boroumand, Muhammad Shiraz, Abdullah Gani, Rashid Hafeez Khokhar

Abstract:

Cloud computing being the use of hardware and software that are delivered as a service over a network has its application in the area of health care. Due to the emergency cases reported in most of the medical centers, prompt for an efficient scheme to make health data available with less response time. To this end, we propose a mobile global healthcare village (MGHV) model that combines the components of three deployment model which include country, continent and global health cloud to help in solving the problem mentioned above. In the creation of continent model, two (2) data centers are created of which one is local and the other is global. The local replay the request of residence within the continent, whereas the global replay the requirements of others. With the methods adopted, there is an assurance of the availability of relevant medical data to patients, specialists, and emergency staffs regardless of locations and time. From our intensive experiment using the simulation approach, it was observed that, broker policy scheme with respect to optimized response time, yields a very good performance in terms of reduction in response time. Though, our results are comparable to others when there is an increase in the number of virtual machines (80-640 virtual machines). The proportionality in increase of response time is within 9%. The results gotten from our simulation experiments shows that utilizing MGHV leads to the reduction of health care expenditures and helps in solving the problems of unqualified medical staffs faced by both developed and developing countries.

Keywords: cloud computing (MCC), e-healthcare, availability, response time, service broker policy

Procedia PDF Downloads 377