Search results for: green infrastructure network

Authors: Youssef R. Hassan, Mohamed S. Hasanin, Reda M. Abdelhameed

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Recently, innovations in packaging security features become more important to see the originality of packaging in industrial application. Luminescent 3d printing materials have been a promising property which can provides a unique opportunity for the design and application of 3D printing. Lack emission of terbium ions, as a source of green emission, in salt form prevent its uses in industrial applications, so searching about stable and highly emitter material become essential. Nowadays, metal organic frameworks (MOFs) play an important role in designing light emitter material. In this work, fluorescent high density polyethylene (FHDPE) composite filament with Tb-benzene 1,3,5-tricarboxylate (Tb-BTC) MOFs for 3D printing have been successfully developed.HDPE pellets were mixed with Tb-BTC and melting extrustion with single screw extruders. It was found that Tb-BTCuniformly dispersed in the HDPE matrix and significantly increased the crystallinity of PE, which not only maintained the good thermal property but also improved the mechanical properties of Tb-BTC@HDPE composites. Notably, the composite filaments emitted ultra-bright green light under UV lamp, and the fluorescence intensity increased as the content of Tb-BTC increased. Finally, several brightly luminescent exquisite articles could be manufactured by fused deposition modeling (FDM) 3D printer with these new fluorescent filaments. In this context, the development of novel fluorescent Tb-BTC@HDPE composites was combined with 3D printing technology to amplified the packaging Security Features.

Keywords: 3D printing, fluorescent, packaging, security

Procedia PDF Downloads 105

Authors: Myungjin Lee, Daegun Han, Jongsung Kim, Soojun Kim, Hung Soo Kim

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Recently, the localized heavy rainfall and typhoons are frequently occurred due to the climate change and the damage is becoming bigger. Therefore, we may need a more accurate prediction of the rainfall and runoff. However, the gauge rainfall has the limited accuracy in space. Radar rainfall is better than gauge rainfall for the explanation of the spatial variability of rainfall but it is mostly underestimated with the uncertainty involved. Therefore, the ensemble of radar rainfall was simulated using error structure to overcome the uncertainty and gauge rainfall. The simulated ensemble was used as the input data of the rainfall-runoff models for obtaining the ensemble of runoff hydrographs. The previous studies discussed about the accuracy of the rainfall-runoff model. Even if the same input data such as rainfall is used for the runoff analysis using the models in the same basin, the models can have different results because of the uncertainty involved in the models. Therefore, we used two models of the SSARR model which is the lumped model, and the Vflo model which is a distributed model and tried to simulate the optimum runoff considering the uncertainty of each rainfall-runoff model. The study basin is located in Han river basin and we obtained one integrated runoff hydrograph which is an optimum runoff hydrograph using the blending methods such as Multi-Model Super Ensemble (MMSE), Simple Model Average (SMA), Mean Square Error (MSE). From this study, we could confirm the accuracy of rainfall and rainfall-runoff model using ensemble scenario and various rainfall-runoff model and we can use this result to study flood control measure due to climate change. Acknowledgements: This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 18AWMP-B083066-05).

Keywords: radar rainfall ensemble, rainfall-runoff models, blending method, optimum runoff hydrograph

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Authors: Aditya Purohit, Neha Bansal

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Unplanned urbanization and multidirectional sprawl of the cities have resulted in increased motorization and deteriorating transport conditions like traffic congestion, longer commuting, pollution, increased carbon footprint, and above all increased fatalities. In order to overcome these problems, various practices have been adopted including– promoting and implementing mass transport; traffic junction channelization; smart transport etc. However, these methods are found to be primarily focusing on vehicular mobility rather than people accessibility. With this research gap, this paper tries to resolve the mobility issues for Ahmedabad city in India, which being the economic capital Gujarat state has a huge commuter and visitor inflow. This research aims to resolve the traffic congestion and urban mobility issues focusing on Gujarat State Regional Transport Corporation (GSRTC) for the city of Ahmadabad by analyzing the existing operations and network structure of GSRTC followed by finding possibilities of integrating it with other modes of urban transport. The network restructuring (NR) methodology is used with appropriate variations, based on commuter demand and growth pattern of the city. To do these ‘scenarios’ based on priority issues (using 12 parameters) and their best possible solution, are established after route network analysis for 2700 population sample of 20 traffic junctions/nodes across the city. Approximately 5% sample (of passenger inflow) at each node is considered using random stratified sampling technique two scenarios are – Scenario 1: Resolving mobility issues by use of Special Purpose Vehicle (SPV) in joint venture to GSRTC and Private Operators for establishing feeder service, which shall provide a transfer service for passenger for movement from inner city area to identified peripheral terminals; and Scenario 2: Augmenting existing mass transport services such as BRTS and AMTS for using them as feeder service to the identified peripheral terminals. Each of these has now been analyzed for the best suitability/feasibility in network restructuring. A desire-line diagram is constructed using this analysis which indicated that on an average 62% of designated GSRTC routes are overlapping with mass transportation service routes of BRTS and AMTS in the city. This has resulted in duplication of bus services causing traffic congestion especially in the Central Bus Station (CBS). Terminating GSRTC services on the periphery of the city is found to be the best restructuring network proposal. This limits the GSRTC buses at city fringe area and prevents them from entering into the city core areas. These end-terminals of GSRTC are integrated with BRTS and AMTS services which help in segregating intra-state and inter-state bus services. The research concludes that absence of integrated multimodal transport network resulted in complexity of transport access to the commuters. As a further scope of research comparing and understanding of value of access time in total travel time and its implication on generalized cost on trip and how it varies city wise may be taken up.

Keywords: mass transportation, multi-modal integration, network restructuring, travel behavior, urban transport

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Authors: Muhammad Saeed, Sheeba Khalid

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Aqueous pollution due to the textile industry is an important issue. Photocatalysis using metal oxides as catalysts is one of the methods used for eradication of dyes from textile industrial effluents. In this study, the synthesis, characterization, and evaluation of photocatalytic activity of Ag-TiO₂ are reported. TiO₂ catalysts with 2, 4, 6 and 8% loading of Ag were prepared by green methods using Azadrachea indica leaves' extract as reducing agent and titanium dioxide and silver nitrate as precursor materials. The 4% Ag-TiO₂ exhibited the best catalytic activity for degradation of dyes. Prepared catalyst was characterized by advanced techniques. Catalytic degradation of methylene blue and rhodamine B were carried out in Pyrex glass batch reactor. Deposition of Ag greatly enhanced the catalytic efficiency of TiO₂ towards degradation of dyes. Irradiation of catalyst excites electrons from conduction band of catalyst to valence band yielding an electron-hole pair. These photoexcited electrons and positive hole undergo secondary reaction and produce OH radicals. These active radicals take part in the degradation of dyes. More than 90% of dyes were degraded in 120 minutes. It was found that there was no loss catalytic efficiency of prepared Ag-TiO₂ after recycling it for two times. Photocatalytic degradation of methylene blue and rhodamine B followed Eley-Rideal mechanism which states that dye reacts in fluid phase with adsorbed oxygen. 27 kJ/mol and 20 kJ/mol were found as activation energy for photodegradation of methylene blue and rhodamine B dye respectively.

Keywords: TiO₂, Ag-TiO₂, methylene blue, Rhodamine B., photo degradation

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Authors: Ahmad Saadiq, Neeraj Sahu

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Sediment, which comprises of solid particles of mineral and organic material are transported by water. In river systems, the amount of sediment transported is controlled by both the transport capacity of the flow and the supply of sediment. The transport of sediment in rivers is important with respect to pollution, channel navigability, reservoir ageing, hydroelectric equipment longevity, fish habitat, river aesthetics and scientific interests. The sediment load transported in a river is a very complex hydrological phenomenon. Hence, sediment transport has attracted the attention of engineers from various aspects, and different methods have been used for its estimation. So, several experimental equations have been submitted by experts. Though the results of these methods have considerable differences with each other and with experimental observations, because the sediment measures have some limits, these equations can be used in estimating sediment load. In this present study, two black box models namely, an SRC (Sediment Rating Curve) and ANN (Artificial Neural Network) are used in the simulation of the suspended sediment load. The study is carried out for Seonath subbasin. Seonath is the biggest tributary of Mahanadi river, and it carries a vast amount of sediment. The data is collected for Jondhra hydrological observation station from India-WRIS (Water Resources Information System) and IMD (Indian Meteorological Department). These data include the discharge, sediment concentration and rainfall for 10 years. In this study, sediment load is estimated from the input parameters (discharge, rainfall, and past sediment) in various combination of simulations. A sediment rating curve used the water discharge to estimate the sediment concentration. This estimated sediment concentration is converted to sediment load. Likewise, for the application of these data in ANN, they are normalised first and then fed in various combinations to yield the sediment load. RMSE (root mean square error) and R² (coefficient of determination) between the observed load and the estimated load are used as evaluating criteria. For an ideal model, RMSE is zero and R² is 1. However, as the models used in this study are black box models, they don’t carry the exact representation of the factors which causes sedimentation. Hence, a model which gives the lowest RMSE and highest R² is the best model in this study. The lowest values of RMSE (based on normalised data) for sediment rating curve, feed forward back propagation, cascade forward back propagation and neural network fitting are 0.043425, 0.00679781, 0.0050089 and 0.0043727 respectively. The corresponding values of R² are 0.8258, 0.9941, 0.9968 and 0.9976. This implies that a neural network fitting model is superior to the other models used in this study. However, a drawback of neural network fitting is that it produces few negative estimates, which is not at all tolerable in the field of estimation of sediment load, and hence this model can’t be crowned as the best model among others, based on this study. A cascade forward back propagation produces results much closer to a neural network model and hence this model is the best model based on the present study.

Keywords: artificial neural network, Root mean squared error, sediment, sediment rating curve

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Authors: Habib Gorine, Rabia Saleh

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Mobile Ad-Hoc Networks are the special type of wireless networks which share common security requirements with other networks such as confidentiality, integrity, authentication, and availability, which need to be addressed in order to secure data transfer through the network. Their routing protocols are vulnerable to various malicious attacks which could have a devastating consequence on data security. In this paper, three types of attacks such as selfish, gray hole, and black hole attacks have been applied to the two most important routing protocols in MANET named dynamic source routing and ad-hoc on demand distance vector in order to analyse and compare the impact of these attacks on the Network performance in terms of throughput, average delay, packet loss, and consumption of energy using NS2 simulator.

Keywords: MANET, wireless networks, routing protocols, malicious attacks, wireless networks simulation

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Authors: Yogesh Mahor, Subhash Nigam, Abhai Khare

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Road safety education is a composite subject which should be viewed holistically if taken into accoubehavior change communication, safe road infrastructure and low enforcement. Specific and customized road safety education is crucial for each type of road user and learners in the formal and informal teaching and various kind of training programs directly sponsored by state and center government, as they are active contributors to shaping a community and responsible citizens. The aim of this discussion article is to explore a strategy to integrate road safety education into the formal curriculum of schools, higher education institutions, driving schools, skill development centers, various government funded urban and rural development training institutions and their work plans as standing agenda. By applying the desktop research method, the article conceptualizes what the possible focus of road safety education and training should be. The article then explores international common practices in road safety education and training, and considers the necessary synergy between education, road engineering and low enforcement. The article uses secondary data collected from documents which are then analysed in a sectoral way. A well-designed road safety strategy for mainstreaming education and government-sponsored training is urgently needed, facilitating partnerships in various sectors to implement such education in the students and learners in multidisciplinary ways.

Keywords: road safety education, curriculum-based road safety education, behavior change communication, low enforcement, road engineering, safe system approach, infrastructure development consultants

Procedia PDF Downloads 133

Authors: Sathapath Kilaso

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Thailand has a large range of geographic diversity. Thailand can be divided into 5 regions which are North Region, East Region, West Region, South Region and North-East Region which each region has a different geographic and climate. Especially in North Region, the geographic is mountain and intermontane plateau which will be a reason that the roads in the North Region have a lot of bends. So the driver in the North Region road will have to have a very high skill of driving. If the accident is occurred, the emergency rescue will have a hard time to reach the accident area and rescue the victim of the accident as the long distance and steep road. This article will apply the concept of the wireless sensor network with the micro-controller to alert the driver when the driver reaches the very dangerous bend.

Keywords: wireless sensor network, motion sensor, smart alert, dangerous bend

Procedia PDF Downloads 279

Authors: A. B. Benshak, M. G. Yilkangnha, V. Y. Nanle

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Social crises and violent conflict can inflict direct (short-term) impact on the environment like poisoning water bodies, climate change, deforestation, destroying the chemical component of the soil due to the chemical and biological weapons used. It can also impact the environment indirectly (long-term), e.g., the destruction of political and economic infrastructure to manage the environmental resources and breaking down traditional conservation practices, population displacement and refugee flows which puts pressure on the already inadequate resources, infrastructure, facilities, amenities, services etc. This study therefore examines the impact of social crises on the environment in Jos Plateau State with emphasis on the long-term impact, analyze the relationship between crises and the environment and assess the perception of people on social crises because much work have concentrated on other repercussions such as the economy, health etc that are more politically expedient. The data for this research were collected mostly through interviews, questionnaire, dailies and reports on the subject matter. The data and findings were presented in tables and results showed that the environment is directly and indirectly impacted by crises and that these impacts can in turn result to a continuous cycle of violent activities if not addressed because of the inadequacies in the supply of infrastructural facilities, resources and so on caused by the inflow of displaced population. Recommendations were made on providing security to minimize conflict occurrences in Jos and its environs, minimizing the impact of social crises on the environment, provision of adequate infrastructural facilities to carter for population rise, renewal and regeneration schemes, etc. which will go a long way in mitigating the impact of crises on the environment.

Keywords: environment, impact, long-term, social crises

Procedia PDF Downloads 348

Authors: Charline Monnier, Rudolf Andrys, Irene Castellino, Lucie Zemanova

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Due to their inherent sustainability and compatibility with green chemistry principles, enzymes are attracting increasing attention for various applications like bioremediation or biocatalysis. These natural catalysts boast remarkable substrate specificity and operate under mild biological conditions. However, their intrinsic limitations, such as instability at high temperatures or in organic solvents, impede their wider applicability. Enzyme immobilization on supportive matrices emerges as a promising strategy to address these challenges. This approach not only facilitates enzyme reusability but also offers the potential to modulate their stability, activity, and selectivity. The present study investigates the immobilization and application of two distinct groups of hydrolases on supportive matrices: PETases, naturally capable of PolyEthylene Terephthalate (PET) degradation, and cholinesterases (ChEs), key enzymes in neurotransmitter regulation. All tested enzymes will be immobilized on porous and non-porous particles using both covalent and non-covalent methods. Additionally, the stability of PETases and cholinesterases will be explored, followed by exposure to denaturing conditions to assess their resilience under harsh conditions. Furthermore, due to the exceptional catalytic efficiency and selectivity, their biocatalytic efficiency will be tested using xenobiotic substrates, aiming to establish them as replacements for conventional chemical catalysts in environmentally friendly processes. By exploiting the power of enzyme immobilization, this research strives to unlock the full potential of these biocatalysts for sustainable and efficient technological advancements.

Keywords: biocatalysis, bioremediation, enzyme efficiency, enzyme immobilization, green chemistry

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Authors: Lina Dagilienė

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The article explores the impact of normative institutional factors on the development of sustainability reporting. The vast majority of research in the scientific literature focuses on mandatory institutional factors, i.e. how public institutions and market regulators affect sustainability reporting. Meanwhile, there is lack of empirical data for the impact of normative institutional factors. The effect of normative factors in this paper is based on the role of non-governmental organizations (NGO) and institutional theory. The case of Global Compact Local Network in the developing country was examined. The research results revealed that in the absence of regulated factors, companies were not active with regard to social disclosures; they presented non-systemized social information of a descriptive nature. Only 10% of sustainability reports were prepared using the GRI methodology. None of the reports were assured by third parties.

Keywords: institutional theory, normative, sustainability reporting, Global Compact Local Network

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Authors: Durga Prasad Sapkota, Madhu Sudan Kayastha, Koichi Wakita

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In this paper, we have compared and analyzed the electron absorption properties between with and without excitonic effect bulk in high purity GaAs spatial light modulator for an optical fiber communication network. The electroabsorption properties such as absorption spectra, change in absorption spectra, change in refractive index and extinction ratio have been calculated. We have also compared the result of absorption spectra and change in absorption spectra with the experimental results and found close agreement with experimental results.

Keywords: exciton, refractive index change, extinction ratio, GaAs

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Authors: Jaiden X. Schraut

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Medical Imaging Segmentation of Chest X-rays is used for the purpose of identification and differentiation of lung cancer, pneumonia, COVID-19, and similar respiratory diseases. Widespread application of computer-supported perception methods into the diagnostic pipeline has been demonstrated to increase prognostic accuracy and aid doctors in efficiently treating patients. Modern models attempt the task of segmentation and classification separately and improve diagnostic efficiency; however, to further enhance this process, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional CNN module for auxiliary classification output. The proposed model achieves a final Jaccard Index of .9634 for image segmentation and a final accuracy of .9600 for classification on the COVID-19 radiography database.

Keywords: chest X-ray, deep learning, image segmentation, image classification

Procedia PDF Downloads 148

Authors: Weikang Gong, Chunhua Li

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Dynamics plays an essential role in function exertion of proteins. Elastic network model (ENM), a harmonic potential-based and cost-effective computational method, is a valuable and efficient tool for characterizing the intrinsic dynamical properties encoded in biomacromolecule structures and has been widely used to detect the large-amplitude collective motions of proteins. Gaussian network model (GNM) and anisotropic network model (ANM) are the two often-used ENM models. In recent years, many ENM variants have been proposed. Here, we propose a small but effective modification (denoted as modified mENM) to the multiscale ENM (mENM) where fitting weights of Kirchhoff/Hessian matrixes with the least square method (LSM) is modified since it neglects the details of pairwise interactions. Then we perform its comparisons with the original mENM, traditional ENM, and parameter-free ENM (pfENM) on reproducing dynamical properties for the six representative proteins whose molecular dynamics (MD) trajectories are available in http://mmb.pcb.ub.es/MoDEL/. In the results, for B-factor prediction, mENM achieves the best performance among the four ENM models. Additionally, it is noted that with the weights of the multiscale Kirchhoff/Hessian matrixes modified, interestingly, the modified mGNM/mANM still has a much better performance than the corresponding traditional ENM and pfENM models. As to dynamical cross-correlation map (DCCM) calculation, taking the data obtained from MD trajectories as the standard, mENM performs the worst while the results produced by the modified mENM and pfENM models are close to those from MD trajectories with the latter a little better than the former. Generally, ANMs perform better than the corresponding GNMs except for the mENM. Thus, pfANM and the modified mANM, especially the former, have an excellent performance in dynamical cross-correlation calculation. Compared with GNMs (except for mGNM), the corresponding ANMs can capture quite a number of positive correlations for the residue pairs nearly largest distances apart, which is maybe due to the anisotropy consideration in ANMs. Furtherly, encouragingly the modified mANM displays the best performance in capturing the functional motional modes, followed by pfANM and traditional ANM models, while mANM fails in all the cases. This suggests that the consideration of long-range interactions is critical for ANM models to produce protein functional motions. Based on the analyses, the modified mENM is a promising method in capturing multiple dynamical characteristics encoded in protein structures. This work is helpful for strengthening the understanding of the elastic network model and provides a valuable guide for researchers to utilize the model to explore protein dynamics.

Keywords: elastic network model, ENM, multiscale ENM, molecular dynamics, parameter-free ENM, protein structure

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Authors: Giselle Maggie-Fer Castañeda Lozano

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The main objective of this research was to explore the role of neural network architectures in simulating behavioral phenomena as a potential explanation for selective associations, specifically related to biological constraints on learning. Biological constraints on learning refer to the limitations observed in conditioning procedures, where learning is expected to occur. The study involved simulations of five different experiments exploring various phenomena and sources of biological constraints in learning. These simulations included the interaction between response and reinforcer, stimulus and reinforcer, specificity of stimulus-reinforcer associations, species differences, neuroanatomical constraints, and learning in uncontrolled conditions. The overall results demonstrated that by manipulating neural network architectures, conditions can be created to model and explain diverse biological constraints frequently reported in comparative psychology literature as learning typicities. Additionally, the simulations offer predictive content worthy of experimental testing in the pursuit of new discoveries regarding the specificity of learning. The implications and limitations of these findings are discussed. Finally, it is suggested that this research could inaugurate a line of inquiry involving the use of neural networks to study biological factors in behavior, fostering the development of more ethical and precise research practices.

Keywords: comparative psychology, connectionism, conditioning, experimental analysis of behavior, neural networks

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Authors: Chimmoe Fomo Michelle Larissa

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The challenges of cybersecurity in Africa are increasingly significant as the continent undergoes rapid digital transformation. With the rise of internet connectivity, mobile phone usage, and digital financial services, Africa faces unique cybersecurity threats. The significance of this study lies in understanding these threats and the multifaceted challenges that hinder effective cybersecurity measures across the continent. The methodologies employed in this study include a comprehensive analysis of existing cybersecurity frameworks in various African countries, surveys of key stakeholders in the digital ecosystem, and case studies of cybersecurity incidents. These methodologies aim to provide a detailed understanding of the current cybersecurity landscape, identify gaps in existing policies, and evaluate the effectiveness of implemented security measures. Major findings of the study indicate that Africa faces numerous cybersecurity challenges, including inadequate regulatory frameworks, insufficient cybersecurity awareness, and a shortage of skilled professionals. Additionally, the prevalence of cybercrime, such as financial fraud, data breaches, and ransomware attacks, exacerbates the situation. The study also highlights the role of international cooperation and regional collaboration in addressing these challenges and improving overall cybersecurity resilience. In conclusion, addressing cybersecurity challenges in Africa requires a multifaceted approach that involves strengthening regulatory frameworks, enhancing public awareness, and investing in cybersecurity education and training. The study underscores the importance of regional and international collaboration in building a robust cybersecurity infrastructure capable of mitigating the risks associated with the continent's digital growth.

Keywords: Africa, cybersecurity, challenges, digital infrastructure, cybercrime

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Authors: Viacheslav Shkuratskyy, Aminu Bello Usman, Michael O’Dea, Saifur Rahman Sabuj

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This paper examines relationships between solar activity and earthquakes; it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to affect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth.

Keywords: k-nearest neighbour, support vector regression, random forest regression, long short-term memory network, earthquakes, solar activity, sunspot number, solar wind, solar flares

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Authors: Pengchao Wei, Xiangzhong Fang

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Noise is ubiquitous in this world. Denoising is an essential technology, especially in image semantic segmentation, where noises are generally categorized into two main types i.e. feature noise and label noise. The main focus of this paper is aiming at modeling label noise, investigating the behaviors of different types of label noise on image semantic segmentation tasks using K-Nearest-Neighbor and Convolutional Neural Network classifier. The performance without label noise and with is evaluated and illustrated in this paper. In addition to that, the influence of feature noise on the image semantic segmentation task is researched as well and a feature noise reduction method is applied to mitigate its influence in the learning procedure.

Keywords: convolutional neural network, denoising, feature noise, image semantic segmentation, k-nearest-neighbor, label noise

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Authors: E. Kayitesi, S. Moyo, V. Mavumengwana

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Cleome gynandra (spider plant) is an African green leafy vegetable categorized as an indigenous, underutilized and has been reported to contain essential phenolic compounds. Phenolic compounds play a significant role in human diets due to their proposed health benefits. These compounds however may be affected by different processing methods such as cooking and drying. Cleome gynandra was subjected to boiling, steam blanching, and drying processes and analysed for Total Phenolic Content (TPC), Total Flavonoid Content (TFC), antioxidant activity and flavonoid composition. Cooking and drying significantly (p < 0.05) increased the levels of phenolic compounds and antioxidant activity of the vegetable. The boiled sample filtrate exhibited the lowest TPC followed by the raw sample while the steamed sample depicted the highest TPC levels. Antioxidant activity results showed that steamed sample showed the highest DPPH, FRAP and ABTS with mean values of 499.38 ± 2.44, 578.68 ± 5.19, and 214.39 ± 12.33 μM Trolox Equivalent/g respectively. An increase in quercetin-3-rutinoside, quercetin-rhamnoside and kaempferol-3-rutinoside occurred after all the cooking and drying methods employed. Cooking and drying exerted positive effects on the vegetable’s phenolic content, antioxidant activity as a whole, but with varied effects on the individual flavonoid molecules. The results obtained help in defining the importance of African green leafy vegetable and resultant processed products as functional foods and their potential to exert health promoting properties.

Keywords: Cleome gynandra, phenolic compounds, cooking, drying, health promoting properties

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Authors: Zina Saheb, Ezz El-Masry, Jean-François Bousquet

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This paper presents an enhanced efficiency simultaneous dual band energy harvesting system for wireless body area network. A bulk biasing is used to enhance the efficiency of the adapted rectifier design to reduce V_th of MOSFET. The presented circuit harvests the radio frequency (RF) energy from two frequency bands: 1 GHz and 2.4 GHz. It is designed with TSMC 65-nm CMOS technology and high quality factor dual matching network to boost the input voltage. Full circuit analysis and modeling is demonstrated. The simulation results demonstrate a harvester with an efficiency of 23% at 1 GHz and 46% at 2.4 GHz at an input power as low as -30 dBm.

Keywords: energy harvester, simultaneous, dual band, CMOS, differential rectifier, voltage boosting, TSMC 65nm

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Authors: Eileen Y. Wong, Katherine Jin, Anat Talmon

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Background: Help seeking behaviors are highly contingent on socio-cultural factors such as ethnicity. Both Latino and Asian Americans underutilize mental health services compared to their White American counterparts. This difference may be related to the composite of one’s social support system, which includes family cohesion and social networks. Previous studies have found that Latino families are characterized by higher levels of family cohesion and social support, and Asian American families with greater family cohesion exhibit lower levels of help seeking behaviors. While both are broadly considered collectivist communities, within-culture variability is also significant. Therefore, this study aims to investigate the relationship between help seeking behaviors in the two cultures with levels of family cohesion and strength of social network. We also consider such relationships in light of previous traumatic events and diagnoses, particularly post-traumatic stress disorder (PTSD), to understand whether clinically diagnosed individuals differ in their strength of network and help seeking behaviors. Method: An adult sample (N = 2,990) from the National Latino and Asian American Study (NLAAS) provided data on participants’ social network, family cohesion, likelihood of seeking professional help, and DSM-IV diagnoses. T-tests compared Latino American (n = 1,576) and Asian American respondents (n = 1,414) in strength of social network, level of family cohesion, and likelihood of seeking professional help. Linear regression models were used to identify the probability of help-seeking behavior based on ethnicity, PTSD diagnosis, and strength of social network. Results: Help-seeking behavior was significantly associated with family cohesion and strength of social network. It was found that higher frequency of expressing one’s feelings with family significantly predicted lower levels of help-seeking behaviors (β = [-.072], p = .017), while higher frequency of spending free time with family significantly predicted higher levels of help-seeking behaviors (β = [.129], p = .002) in the Asian American sample. Subjective importance of family relations compared to that of one’s peers also significantly predict higher levels of help-seeking behaviors (β = [.095], p = .011) in the Asian American sample. Frequency of sharing one’s problems with relatives significantly predicted higher levels of help-seeking behaviors (β = [.113], p < .01) in the Latino American sample. A PTSD diagnosis did not have any significant moderating effect. Conclusion: Considering the underutilization of mental health services in Latino and Asian American minority groups, it is crucial to understand ways in which help seeking behavior can be encouraged. Our findings suggest that different dimensions within family cohesion and social networks have differential impacts on help-seeking behavior. Given the multifaceted nature of family cohesion and cultural relevance, the implications of our findings for theory and practice will be discussed.

Keywords: family cohesion, social networks, Asian American, Latino American, help-seeking behavior

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Authors: Marina Yurievna Aleksandrova

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Research of the Holocaust retains value not only for history but also for sociology and psychology. One of the most important fields of study is how people were coping during and after this traumatic event. The aim of this paper is to identify the main contexts of the topic of love and to determine which contexts are more characteristic for different groups of victims of the Holocaust (gender, nationality, age). In this research, transcripts of interviews with Holocaust victims that were collected during 1946 for the "Voices of the Holocaust" project were used as data. Main contexts were analyzed with methods of network analysis and latent semantic analysis and classified by gender, age, and nationality with random forest. The results show that love is articulated and described significantly differently for male and female informants, nationality is shown results with lower values of quality metrics, as well as the age.

Keywords: Holocaust, latent semantic analysis, network analysis, text-mining, random forest

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Authors: Hamed Azad, Azadeh M. Ardakani

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Visual content, as an integral part of social media marketing, is growing dramatically. They are, in different technological usage categories (i.e., photos, graphics, animation IGTV, Stories, Livestreams, and Reels), associated with improving customer engagement behaviours (CEBs) in social commerce (SC). However, few researchers have explored the impact of specific and occasional contents that respect green products, gender equality, religious freedom, and LGBTs' rights. This study aims to compare and analyse how the ten best global brands (Interbrand's) in different categories communicate with customers on Instagram. Netnography approach and method used to conduct the data collection and data analysis of 1072 Instagram posts and 10494 comments. The results show that brands in fashion, sport, and homeware categories (H&M, Nike, and Ikea) emerge to use more effective content with the above global values elements than other brand categories. Findings also indicate that some different themes such as celebrities, models, pets, kids, aged and disabled people are part of visual management strategies on Instagram brands' pages. This research aims to inform researchers to consider all aspects of visual elements in content quality and marketing managers to increase brand optimisation, awareness, and authenticity by promoting contemporary world values on Instagram.

Keywords: green products, gender equality, religious freedom, LGBTs, Instagram, netnography

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Authors: Sarah Atalla, Shubham Gupta, Kim Alipio, Tanya Maric

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Background: Chelsea and Westminster Hospital have introduced the Birmingham Symptom Specific Obstetric Triage System (BSOTS) for patients who present acutely to the Maternity Assessment Suite (MAS) to prioritise care by urgency. The primary objective was to evaluate whether BSOTS was used appropriately to assess patients (defined as a 90% threshold). The secondary objective was to assess whether patients were seen within their designated triaged timeframe (defined as a 90% threshold). Methodology: MAS records were retrospectively reviewed for a randomly selected one-week period of data from 2020 (21/09/2020 - 27/09/2020). 189 patients presented to MAS during this time. Data were collected on the presenting complaint, time of attendance (divided into four time categories), and triage colour code for the urgency of a review by a doctor (red: immediately, orange: within 15 minutes, yellow: within 1 hour, green: within 4 hours). The number of triage waiting times that were breached and the outcome of the attendance was noted. Results: 49% of patients presenting to MAS during this time period were triaged, which therefore did not meet the 90% target. 67% of patients who were triaged were seen within their allocated timeframe as designated by their triage colour code, which therefore did not meet the 90% target. The most frequent reason for patient attendance was reduced fetal movements (30.5% of attendances). The busiest time of day (when most patients presented) was between 06:01-12:00, and this was also when the highest number of patients were not triaged (26 patients or 54% of patients presenting in this time category). The most used triage category (59%) was the green colour code (to be seen by a doctor within 4 hours), followed by orange (24%), yellow (14%), and red (3%). 45% of triaged patients were admitted, whilst 55% were discharged. 62% of patients allocated to the green triage category were discharged, as compared to 56% of yellow category patients, 27% of orange category patients, and 50% of red category patients. The time of patient presentation to the hospital was also associated with the level of urgency and outcome. Patients presenting from 12:01 to 18:00 were more likely to be discharged (72% discharged) compared to 00:01-06:00 where only 12.5% of patients were discharged. Conclusion: The triage system for assessing the urgency of acutely presenting obstetric patients is only being effectively utilised for 49% of patients. There is potential for enhancing the employment of the triage system to enable further efficiency and boost the promotion of patient safety. It is noted that MAS was busiest at 06:01 - 12:00 when there was also the highest number of non-triaged patients – this highlights some areas where we can improve, including higher levels of staffing, better use of BSOTS to triage patients, and patient education.

Keywords: birmingham, BSOTS, maternal, obstetric, pregnancy, specific, symptom, triage

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Authors: Petr Šimek

Abstract:

This work deals with the problem of synchronization of bus frames during transmission using USB (Universal Serial Bus). The principles for synchronization between USB and the non-deterministic CAN (Controller Area Network) bus will be described here. Furthermore, the work deals with ensuring the time sequence of communication frames when receiving from multiple communication bus channels. The structure of a general object for storing frames from different types of communication buses, such as CAN and LIN (Local Interconnect Network), will be described here. Finally, an evaluation of the communication throughput of bus frames for USB High speed will be performed. The creation of this architecture was based on the analysis of the communication of control units with a large number of communication buses. For the design of the architecture, a test HW with a USB-HS interface was used, which received previously known messages, which were compared with the received result. The result of this investigation is the block architecture of the control program for test HW ensuring correct data transmission via the USB bus.

Keywords: analysis, CAN, interface, LIN, synchronization, USB

Procedia PDF Downloads 69

Authors: Rajinder Kumar Gupta, Rajni Kant Agrawal, Jaganniwas

Abstract:

One of the most frightening phenomena of nature is the occurrence of earthquake as it has terrible and disastrous effects. Many earthquakes occur every day worldwide. There is need to have knowledge regarding the trends in earthquake occurrence worldwide. The recoding and interpretation of data obtained from the establishment of the worldwide system of seismological stations made this possible. From the analysis of recorded earthquake data, the earthquake parameters and source parameters can be computed and the earthquake catalogues can be prepared. These catalogues provide information on origin, time, epicenter locations (in term of latitude and longitudes) focal depths, magnitude and other related details of the recorded earthquakes. Theses catalogues are used for seismic hazard estimation. Manual interpretation and analysis of these data is tedious and time consuming. A geographical information system is a computer based system designed to store, analyzes and display geographic information. The implementation of integrated GIS technology provides an approach which permits rapid evaluation of complex inventor database under a variety of earthquake scenario and allows the user to interactively view results almost immediately. GIS technology provides a powerful tool for displaying outputs and permit to users to see graphical distribution of impacts of different earthquake scenarios and assumptions. An endeavor has been made in present study to compile the earthquake data for the whole world in visual Basic on ARC GIS Plate form so that it can be used easily for further analysis to be carried out by earthquake engineers. The basic data on time of occurrence, location and size of earthquake has been compiled for further querying based on various parameters. A preliminary analysis tool is also provided in the user interface to interpret the earthquake recurrence in region. The user interface also includes the seismic hazard information already worked out under GHSAP program. The seismic hazard in terms of probability of exceedance in definite return periods is provided for the world. The seismic zones of the Indian region are included in the user interface from IS 1893-2002 code on earthquake resistant design of buildings. The City wise satellite images has been inserted in Map and based on actual data the following information could be extracted in real time: • Analysis of soil parameters and its effect • Microzonation information • Seismic hazard and strong ground motion • Soil liquefaction and its effect in surrounding area • Impacts of liquefaction on buildings and infrastructure • Occurrence of earthquake in future and effect on existing soil • Propagation of earth vibration due of occurrence of Earthquake GIS based earthquake information system has been prepared for whole world in Visual Basic on ARC GIS Plate form and further extended micro level based on actual soil parameters. Individual tools has been developed for liquefaction, earthquake frequency etc. All information could be used for development of infrastructure i.e. multi story structure, Irrigation Dam & Its components, Hydro-power etc in real time for present and future.

Keywords: GIS based earthquake information system, microzonation, analysis and real time information about liquefaction, infrastructure development

Procedia PDF Downloads 322

Authors: Srinivas Peri, Siva Abhishek Sirivella, Tejaswini Kallakuri, Uzair Ahmad

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Smart cities and intelligent transportation systems rely heavily on effective traffic management and infrastructure planning. This research tackles the data scarcity challenge by generating realistically synthetic traffic data from the PeMS-Bay dataset, enhancing predictive modeling accuracy and reliability. Advanced techniques like TimeGAN and GaussianCopula are utilized to create synthetic data that mimics the statistical and structural characteristics of real-world traffic. The future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is anticipated to capture both spatial and temporal correlations, further improving data quality and realism. Each synthetic data generation model's performance is evaluated against real-world data to identify the most effective models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are employed to model and predict complex temporal dependencies within traffic patterns. This holistic approach aims to identify areas with low vehicle counts, reveal underlying traffic issues, and guide targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study facilitates data-driven decision-making that improves urban mobility, safety, and the overall efficiency of city planning initiatives.

Keywords: GAN, long short-term memory (LSTM), synthetic data generation, traffic management

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Authors: Frank Kuebler, Rolf Steinhilper

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Artificial neural networks (ANN) as well as Design of Experiments (DOE) based regression analysis (RA) are mainly used for modeling of complex systems. Both methodologies are commonly applied in process and quality control of manufacturing processes. Due to the fact that resource efficiency has become a critical concern for manufacturing companies, these models needs to be extended to predict resource-consumption of manufacturing processes. This paper describes an approach to use neural networks as well as DOE based regression analysis for predicting resource consumption of manufacturing processes and gives a comparison of the achievable results based on an industrial case study of a turning process.

Keywords: artificial neural network, design of experiments, regression analysis, resource efficiency, manufacturing process

Procedia PDF Downloads 530

Authors: Seyed Sadegh Naseralavi, Najmeh Bemani

Abstract:

In this paper, different concrete codes including America, New Zealand, Mexico, Italy, India, Canada, Hong Kong, Euro Code and Britain are compared with the Iranian concrete design code. First, by using Adaptive Neuro Fuzzy Inference System (ANFIS), the codes having the most correlation with the Iranian ninth issue of the national regulation are determined. Consequently, two anticipated methods are used for comparing the codes: Artificial Neural Network (ANN) and Multi-variable regression. The results show that ANN performs better. Predicting is done by using only tensile steel ratio and with ignoring the compression steel ratio.

Keywords: adaptive neuro fuzzy inference system, anticipate method, artificial neural network, concrete design code, multi-variable regression

Procedia PDF Downloads 288

Authors: M. Saiful Islam, M. Mohandes, S. Rehman, S. Badran

Abstract:

Increasing necessity of wind power is directing us to have precise knowledge on wind resources. Methodical investigation of potential locations is required for wind power deployment. High penetration of wind energy to the grid is leading multi megawatt installations with huge investment cost. This fact appeals to determine appropriate places for wind farm operation. For accurate assessment, detailed examination of wind speed profile, relative humidity, temperature and other geological or atmospheric parameters are required. Among all of these uncertainty factors influencing wind power estimation, vertical extrapolation of wind speed is perhaps the most difficult and critical one. Different approaches have been used for the extrapolation of wind speed to hub height which are mainly based on Log law, Power law and various modifications of the two. This paper proposes a Artificial Neural Network (ANN) and Genetic Algorithm (GA) based hybrid model, namely GA-NN for vertical extrapolation of wind speed. This model is very simple in a sense that it does not require any parametric estimations like wind shear coefficient, roughness length or atmospheric stability and also reliable compared to other methods. This model uses available measured wind speeds at 10m, 20m and 30m heights to estimate wind speeds up to 100m. A good comparison is found between measured and estimated wind speeds at 30m and 40m with approximately 3% mean absolute percentage error. Comparisons with ANN and power law, further prove the feasibility of the proposed method.

Keywords: wind profile, vertical extrapolation of wind, genetic algorithm, artificial neural network, hybrid machine learning

Procedia PDF Downloads 494