Search results for: dual graph

Authors: Sherine A. El Baradei

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Global warming and climatic change are very important topics that are being studied and investigated nowadays as they have lots of diverse impacts on mankind, water quality, aquatic life, wildlife,…etc. Also, many water and hydraulics structures like dams and barrages are being built every day to satisfy water consumption needs, irrigation purposes and power generating purposes. Each of global warming and water structures alone has diversity of impacts on water quality and aquatic life in rivers. This research is investigating the dual combined effect of both water structures and global warming on the water quality and aquatic life through mathematical modeling. A case study of the Esna Barrage on the Nile River in Egypt is being studied. This research study is taking into account the effects of both seasons; namely, winter and summer and their effects on air and hence water temperature of the Nile reach under study. To do so, the study is conducted on the last 23 years to investigate the effect of global warming and climatic change on the studied river water. The mathematical model is then combining the dual effect of the Esna barrage and the global warming on the water quality; as well as, on aquatic life of the Nile reach under study. From the results of the mathematical model, it could be concluded that the dual effect of water structures and global warming is very negative on the water quality and the aquatic life in rivers upstream those structures.

Keywords: aquatic life, barrages, climatic change, dissolved oxygen, global warming, river, water quality, water structures

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Authors: Rafikoddin Kazi

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Almost all the major religions of the world condemn the transactions based on interest which promotes self-centered and materialistic thinking. Still, it is amazing to note that it has become the tradition of transaction at world level hence it is called traditional financial system. The main feature of this system is that it considers economic aspects of the transaction only. This system supports the economic development and not the welfare of humankind. However, it is worth mentioning the fact that, except Islamic financial system no other financial system stood in front of it as a viable alternative system. Although many countries have tried to create financial infrastructure and system, still the Malaysian Islamic financial system has got its own peculiarity. It has made tremendous progress in creating sound Islamic Financial system. However, the historical aspect of this country which has passed through Islamic and traditional financial system has got its own advantages and disadvantages. The advantageous factor is that, despite having mix and heterogeneous culture, it has succeeded in creating Islamic Financial System based on the dual legal system to satisfy the needs of multi-cultural factors. This fact has proved that Islamic Financial System does not need purely Muslim population. However, due to adoption of the dual legal system, several legal issues have been taken place. According to this system, the application of Islamic Law has been limited only up to some family and religious matters. The rest of the matters are being dealt with under the traditional laws, the principles and practices of which are different from that of the Islamic Legal System. The matter becomes all the more complicated when the cases are partially or simultaneously concerned with traditional vis-à-vis Islamic Laws as it requires expertise in both the legal systems. However, the educational principles and systems are different in respect of both the systems. To face this problem, Shariah Advisory Council has been established. But the Multiplicity of Shariah authorities without judicial power has created confusion at various levels. Therefore, some experts have stressed the need for improving, empowering the Islamic financial, legal system to make it more integrated and holistic. In view of the above, an endeavor has been made in this paper to throw some light on the matters related to the adoption of the dual legal system. The paper is conceptual in nature and the method adopted is the intensive survey of literature thereby all the information has been gathered from the secondary sources.

Keywords: Islamic financial system, Islamic legal system, Islamic capital market (ICM) , traditional financial system

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Authors: Abiodun Ayodele Sanyaolu

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The research was carried out to investigate the time series analysis on quarterly production of fruit juice at the National Horticultural Research Institute Ibadan from 2010 to 2018. Documentary method of data collection was used, and the method of least square and moving average were used in the analysis. From the calculation and the graph, it was glaring that there was increase, decrease, and uniform movements in both the graph of the original data and the tabulated quarter values of the original data. Time series analysis was used to detect the trend in the highest number of fruit juice and it appears to be good over a period of time and the methods used to forecast are additive and multiplicative models. Since it was observed that the production of fruit juice is usually high in January of every year, it is strongly advised that National Horticultural Research Institute should make more provision for fruit juice storage outside this period of the year.

Keywords: fruit juice, least square, multiplicative models, time series

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Authors: Yi-Jung Chen, Yunshi Liu

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To explain how followers can perceive whether or not transformational leaders are authentic on the basis of their leadership behaviors based on value-based leadership theory, this study adopts the dual-focus model of transformational leadership and evaluates leaders’ corporate social responsibility values along with followers’ perceptions of leaders’ values. Using dyadic questionnaires, the final study sample consisted of 252 followers and 43 leaders at a private firm in Taiwan. Results show that followers perceive corporate social responsibility values of transformational leaders through their group-focused leadership behaviors because such group-focused leadership is in line with these values.

Keywords: authentic transformational leadership, corporate social responsibility value, value-based leadership theory, dual-focus leadership

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Authors: Tesfaye Fenta Boka, Niu Zhendong

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Recommender systems play a crucial role in connecting individuals with the items they require, as is particularly evident in the rapid growth of online grocery shopping platforms. These systems predominantly rely on user-centered recommendations, where items are suggested based on individual preferences, garnering considerable attention and adoption. However, our focus lies on the item-centered recommendation task within the grocery shopping context. In the reverse next item period recommendation (RNPR) task, we are presented with a specific item and challenged to identify potential users who are likely to consume it in the upcoming period. Despite the ever-expanding inventory of products on online grocery platforms, the cold start item problem persists, posing a substantial hurdle in delivering personalized and accurate recommendations for new or niche grocery items. To address this challenge, we propose a Graph Neural Network (GNN)-based approach. By capitalizing on the inherent relationships among grocery items and leveraging users' historical interactions, our model aims to provide reliable and context-aware recommendations for cold-start items. This integration of GNN technology holds the promise of enhancing recommendation accuracy and catering to users' individual preferences. This research contributes to the advancement of personalized recommendations in the online grocery shopping domain. By harnessing the potential of GNNs and exploring item-centered recommendation strategies, we aim to improve the overall shopping experience and satisfaction of users on these platforms.

Keywords: recommender systems, cold start item recommendations, online grocery shopping platforms, graph neural networks

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Authors: So Hyun Ahn, Geu Rim Seo, Byungsoo Shin, Kwang-Won Yu, Hyung Joo Suh, Sung Hee Han

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We investigated the effects of GOS, lactulose and DuOligo in loperamide-induced constipation rat model. Dual-Oligo saccharide (DuOligo) was composed with galacto-oligosaccharide (GOS) and lactulose. GOS is an important dietary prebiotics that affects intestinal microbiota and barrier functions to have beneficial effect and lactulose is used as a laxative to treat constipation. To study the DuOligo, after adaptation period of four weeks of Spraque-Dawley rats, loperamide (5 mg/kg, twice per day) was injected to induce constipation for 1 week. DuOligo increased the fecal pellet amount, fecal weight, and water content of feces in rats with loperamide-induced constipation. DuOligo groups tended to produce more total fatty acid than that of observed for the control group. Gastrointestinal transit ratio and length and area of intestinal mucosa increased after treatment with DuOligo in loperamide-induced rats. These results showed that oral administration of DuOligo significantly modulated intestinal peristalsis.

Keywords: constipation, DuOligo, GOS, lactulose

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Authors: Jens Ehm

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Manufacturing in supply chains requires an efficient organisation of production and transport processes in order to guarantee the supply of all partners within the chain with the material that is needed for the reliable fulfilment of tasks. If one partner is not able to supply products for a certain period, these products might be missing as the working material for the customer to perform the next manufacturing step, potentially as supply for further manufacturing steps. This way, local disruptions can influence the whole supply chain. In order to avoid material shortages, an efficient scheduling of tasks is necessary. However, the occurrence of unexpected disruptions cannot be eliminated, so that a modification of the schedule should be arranged as fast as possible. This paper discusses the challenges for the implementation of real-time scheduling and control methods and presents a graph-based approach that enables the integrated scheduling of production and transport processes for multiple supply chain partners and offers the potential for quick adaptations to parts of the initial schedule.

Keywords: production, logistics, integrated scheduling, real-time scheduling

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Authors: Jing Li, Li Jin, Fulin Wang, Jie Dong, Wenjiang Ding

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Mg-Li dual-phase alloy exhibits better combination of yield strength and elongation than the Mg single-phase alloy. To exploit its deformation behavior, the deformation mechanisms of Mg-2Gd alloy with or without Li addition, i.e., Mg-6Li-2Gd and Mg-2Gd alloy, have been studied at both microscale and mesoscale. EBSD-assisted slip trace, twin trace, and texture evolution analysis show that the α-Mg phase of Mg-6Li-2Gd alloy exhibits different microscopic deformation mechanisms with the Mg-2Gd alloy, i.e., mainly prismatic slip in the former one, while basal slip, prismatic slip and extension twin in the latter one. Further Schmid factor analysis results attribute this different intra-phase deformation mechanisms to the higher critical resolved shear stress (CRSS) value of extension twin and lower ratio of CRSSprismatic /CRSSbasal in the α-Mg phase of Mg-6Li-2Gd alloy. Additionally, Li addition can induce dual-phase microstructure in the Mg-6Li-2Gd alloy, leading to the formation of hetero-deformation induced (HDI) stress at the mesoscale. This can be evidenced by the hysteresis loops appearing during the loading-unloading-reloading (LUR) tensile tests and the activation of multiple slip activity in the α-Mg phase neighboring β-Li phase. The Mg-6Li-2Gd alloy shows higher yield strength is due to the harder α-Mg phase arising from solid solution hardening of Li addition, as well asthe strengthening of soft β-Li phase by the HDI stress during yield stage. Since the strain hardening rate of Mg-6Li-2Gd alloy is lower than that of Mg-2Gd alloy after ~2% strain, which is partly due to the weak contribution of HDI stress, Mg-6Li-2Gd alloy shows no obvious increase of uniform elongation than the Mg-2Gd alloy.But since the β-Li phase is effective in blunting the crack tips, the Mg-6Li-2Gd alloy shows ununiform elongation, which, thus, leads to the higher total elongation than the Mg-2Gd alloy.

Keywords: Mg-Li-Gd dual-phase alloy, phase boundary, HDI stress, dislocation slip activity, mechanical properties

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Authors: Hugo Daneluzzo, Christelle Rabbat, Alan Jean-Marie

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Water electrolysis is one of the most advanced technologies for producing hydrogen and can be easily combined with electricity from different sources. Under the influence of electric current, water molecules can be split into oxygen and hydrogen. The production of hydrogen by water electrolysis favors the integration of renewable energy sources into the energy mix by compensating for their intermittence through the storage of the energy produced when production exceeds demand and its release during off-peak production periods. Among the various electrolysis technologies, anion exchange membrane (AEM) electrolyser cells are emerging as a reliable technology for water electrolysis. Modeling and simulation are effective tools to save time, money, and effort during the optimization of operating conditions and the investigation of the design. The modeling and simulation become even more important when dealing with multiphysics dynamic systems. One of those systems is the AEM electrolysis cell involving complex physico-chemical reactions. Once developed, models may be utilized to comprehend the mechanisms to control and detect flaws in the systems. Several modeling methods have been initiated by scientists. These methods can be separated into two main approaches, namely equation-based modeling and graph-based modeling. The former approach is less user-friendly and difficult to update as it is based on ordinary or partial differential equations to represent the systems. However, the latter approach is more user-friendly and allows a clear representation of physical phenomena. In this case, the system is depicted by connecting subsystems, so-called blocks, through ports based on their physical interactions, hence being suitable for multiphysics systems. Among the graphical modelling methods, the bond graph is receiving increasing attention as being domain-independent and relying on the energy exchange between the components of the system. At present, few studies have investigated the modelling of AEM systems. A mathematical model and a bond graph model were used in previous studies to model the electrolysis cell performance. In this study, experimental data from literature were simulated using OpenModelica using bond graphs and mathematical approaches. The polarization curves at different operating conditions obtained by both approaches were compared with experimental ones. It was stated that both models predicted satisfactorily the polarization curves with error margins lower than 2% for equation-based models and lower than 5% for the bond graph model. The activation polarization of hydrogen evolution reactions (HER) and oxygen evolution reactions (OER) were behind the voltage loss in the AEM electrolyzer, whereas ion conduction through the membrane resulted in the ohmic loss. Therefore, highly active electro-catalysts are required for both HER and OER while high-conductivity AEMs are needed for effectively lowering the ohmic losses. The bond graph simulation of the polarisation curve for operating conditions at various temperatures has illustrated that voltage increases with temperature owing to the technology of the membrane. Simulation of the polarisation curve can be tested virtually, hence resulting in reduced cost and time involved due to experimental testing and improved design optimization. Further improvements can be made by implementing the bond graph model in a real power-to-gas-to-power scenario.

Keywords: hydrogen production, anion-exchange membrane, electrolyzer, mathematical modeling, multiphysics modeling

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Authors: Pimploi Tirastittam, Phutthiwat Waiyawuththanapoom

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Nowadays the promotion of the public transportation system in the Bangkok Metropolitan Area is increased such as the “Free Bus for Thai Citizen” Campaign and the prospect of the several MRT routes to increase the convenient and comfortable to the Bangkok Metropolitan area citizens. But citizens do not make full use of them it because the citizens are lack of the data and information and also the confident to the public transportation system of Thailand especially in the time and safety aspects. This research is the Public Transport Planning System by Dijkstra Algorithm: Case Study Bangkok Metropolitan Area by focusing on buses, BTS and MRT schedules/routes to give the most information to passengers. They can choose the way and the routes easily by using Dijkstra STAR Algorithm of Graph Theory which also shows the fare of the trip. This Application was evaluated by 30 normal users to find the mean and standard deviation of the developed system. Results of the evaluation showed that system is at a good level of satisfaction (4.20 and 0.40). From these results we can conclude that the system can be used properly and effectively according to the objective.

Keywords: Dijkstra algorithm, graph theory, public transport, Bangkok metropolitan area

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Authors: Zhijun Peng, Xiang Li, Dayou Li, Raouf Mobasheri, Abdel Aitouche

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To implement carbon capture and storage (CCS) for eliminating carbon dioxide (CO₂) emissions, this paper describes a study on oxy-fuel combustion (OFC) with an ethanol-gasoline dual-fuel spark ignition (DFSI) engine under economical oxygen consumption at low and mid-high loads which was performed by 1D simulation. It is demonstrated that under OFC mode without other optimisation, brake mean effective pressure (BMEP) can meet the requirement at mid-high load, but it has a considerable decline at low load compared to conventional air combustion (CAC) mode. Moreover, there is a considerable deterioration in brake specific fuel consumption (BSFC) compared to that of CAC mode. A practical method is proposed to optimise the DFSI engine performance under OFC mode by changing intake charge components and utilising appropriate water injection (WI) strategies.

Keywords: oxy-fuel combustion, dual-fuel spark ignition engine, ethanol, gasoline, computer simulation

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Authors: Po-Jen Chen, Jian-Jiun Ding, Hung-Wei Hsu, Chien-Yao Wang, Jia-Ching Wang

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A more accurate scene classification algorithm using label relations and the hierarchy neural network was developed in this work. In many classification algorithms, it is assumed that the labels are mutually exclusive. This assumption is true in some specific problems, however, for scene classification, the assumption is not reasonable. Because there are a variety of objects with a photo image, it is more practical to assign multiple labels for an image. In this paper, two label relations, which are exclusive relation and hierarchical relation, were adopted in the classification process to achieve more accurate multiple label classification results. Moreover, the hierarchy neural network (hierarchy NN) is applied to classify the image and the directed acyclic graph structure is used for predicting a more reasonable result which obey exclusive and hierarchical relations. Simulations show that, with these techniques, a much more accurate scene classification result can be achieved.

Keywords: convolutional neural network, label relation, hierarchy neural network, scene classification

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Authors: Shashank Patidar, Sumit Kumar, Atul Srivastava, Suneet Singh

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Present work is concerned with the numerical investigation of thermal response of biological tissues during laser-based photo-thermal therapy for destroying cancerous/abnormal cells with minimal damage to the surrounding normal cells. Light propagation through the biological sample is mathematically modelled by transient radiative transfer equation. In the present work, application of the Lattice Boltzmann Method is extended to analyze transport of short-pulse radiation in a participating medium.In order to determine the two-dimensional temperature distribution inside the tissue medium, the RTE has been coupled with Penne’s bio-heat transfer equation based on Fourier’s law by several researchers in last few years.

Keywords: lattice Boltzmann method, transient radiation transfer equation, dual phase lag model

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Authors: Grigoria Athanasaki, Veerarajan Vimala, A. M. Kannan, Louis Cindrella

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Energy usage has been increased throughout the years, leading to severe environmental impacts. Since the majority of the energy is currently produced from fossil fuels, there is a global need for clean energy solutions. Proton Exchange Membrane Fuel Cells (PEMFCs) offer a very promising solution for transportation applications because of their solid configuration and low temperature operations, which allows them to start quickly. One of the main components of PEMFCs is the Gas Diffusion Layer (GDL), which manages water and gas transport and shows direct influence on the fuel cell performance. In this work, a novel dual-layer GDL with gradient porosity was prepared, using polyethylene glycol (PEG) as pore former, to improve the gas diffusion and water management in the system. The microporous layer (MPL) of the fabricated GDL consists of carbon powder PUREBLACK, sodium dodecyl sulfate as a surfactant, 34% wt. PTFE and the gradient porosity was created by applying one layer using 30% wt. PEG on the carbon substrate, followed by a second layer without using any pore former. The total carbon loading of the microporous layer is ~ 3 mg.cm-2. For the assembly of the catalyst layer, Nafion membrane (Ion Power, Nafion Membrane NR211) and Pt/C electrocatalyst (46.1% wt.) were used. The catalyst ink was deposited on the membrane via microspraying technique. The Pt loading is ~ 0.4 mg.cm-2, and the active area is 5 cm2. The sample was ex-situ characterized via wetting angle measurement, Scanning Electron Microscopy (SEM), and Pore Size Distribution (PSD) to evaluate its characteristics. Furthermore, for the performance evaluation in-situ characterization via Fuel Cell Testing using H2/O2 and H2/air as reactants, under 50, 60, 80, and 100% relative humidity (RH), took place. The results were compared to a single layer GDL, fabricated with the same carbon powder and loading as the dual layer GDL, and a commercially available GDL with MPL (AvCarb2120). The findings reveal high hydrophobic properties of the microporous layer of the GDL for both PUREBLACK based samples, while the commercial GDL demonstrates hydrophilic behavior. The dual layer GDL shows high and stable fuel cell performance under all the RH conditions, whereas the single layer manifests a drop in performance at high RH in both oxygen and air, caused by catalyst flooding. The commercial GDL shows very low and unstable performance, possibly because of its hydrophilic character and thinner microporous layer. In conclusion, the dual layer GDL with PEG appears to have improved gas diffusion and water management in the fuel cell system. Due to its increasing porosity from the catalyst layer to the carbon substrate, it allows easier access of the reactant gases from the flow channels to the catalyst layer, and more efficient water removal from the catalyst layer, leading to higher performance and stability.

Keywords: gas diffusion layer, microporous layer, proton exchange membrane fuel cells, relative humidity

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Authors: Stefan K. Behfar

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The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

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Authors: Amit Sarkar, Jayanta K. Mahato, Tushar Bhattacharya, Amrita Kundu, P. C. Chakraborti

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With increasing the demand for safety and fuel efficiency of automobiles, automotive manufacturers are looking for light weight, high strength steel with excellent formability and corrosion resistance. Dual-phase steel is finding applications in automotive sectors, because of its high strength, good formability, and high corrosion resistance. During service automotive components suffer from environmental attack and thereby gradual degradation of the components occurs reducing the service life of the components. The objective of the present investigation is to assess the effect of deformation on corrosion behaviour of DP590 grade dual phase steel which is used in automotive industries. The material was received from TATA Steel Jamshedpur, India in the form of 1 mm thick sheet. Tensile properties of the steel at strain rate of 10-3 sec-1: 0.2 % Yield Stress is 382 MPa, Ultimate Tensile Strength is 629 MPa, Uniform Strain is 16.30% and Ductility is 29%. Rectangular strips of 100x10x1 mm were machined keeping the long axis of the strips parallel to rolling direction of the sheet. These strips were longitudinally deformed at a strain rate at 10-3 sec-1 to a different percentage of strain, e.g. 2.5, 5, 7.5,10 and 12.5%, and then slowly unloaded. Small specimens were extracted from the mid region of the unclamped portion of these deformed strips. These small specimens were metallographic polished, and corrosion behaviour has been studied by potentiodynamic polarization, electrochemical impedance spectra, and cyclic polarization and potentiostatic tests. Present results show that among three different environments, the 3.5 pct NaCl solution is most aggressive in case of DP 590 dual-phase steel. It is observed that with the increase in the amount of deformation, corrosion rate increases. With deformation, the stored energy increases and leads to enhanced corrosion rate. Cyclic polarization results revealed highly deformed specimen are more prone to pitting corrosion as compared to the condition when amount of deformation is less. It is also observed that stability of the passive layer decreases with the amount of deformation. With the increase of deformation, current density increases in a passive zone and passive zone is also decreased. From Electrochemical impedance spectroscopy study it is found that with increasing amount of deformation polarization resistance (Rp) decreases. EBSD results showed that average geometrically necessary dislocation density increases with increasing strain which in term increased galvanic corrosion as dislocation areas act as the less noble metal.

Keywords: dual phase 590 steel, prestrain, potentiodynamic polarization, cyclic polarization, electrochemical impedance spectra

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Authors: Anisha Mandal, Swambabu Varanasi

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Lead is one of the most prevalent toxic heavy metal ions, and its pollution poses a significant threat to the environment and human health. On the other hand, Ethylenediaminetetraacetic acid is a widely used metal chelating agent that, due to its poor biodegradability, is an incessant pollutant to the environment. For the first time, a green, simple, and cost-effective approach is used to hydrothermally synthesise photoluminescent carbon dots using Moringa Oleifera Gum in a single step. Then, using Moringa Oleifera Gum-derived carbon dots, a photoluminescent "ON-OFF-ON" mechanism for dual mode detection of trace Pb2+ and EDTA was proposed. MOG-CDs detect Pb2+ selectively and sensitively using a photoluminescence quenching mechanism, with a detection limit (LOD) of 0.000472 ppm. (1.24 nM). The quenched photoluminescence can be restored by adding EDTA to the MOG-CD+Pb2+ system; this strategy is used to quantify EDTA at a level of detection of 0.0026 ppm. (8.9 nM). The quantification of Pb2+ and EDTA in actual samples encapsulated the applicability and dependability of the proposed photoluminescent probe.

Keywords: carbon dots, photoluminescence, sensor, moringa oleifera gum

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Authors: Da Tao, Mingfu Qin, Wenkai Li, Tieyan Wang

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While the use of electronic personal health portals has gained increasing popularity in the healthcare industry, users usually experience difficulty in comprehending and correctly responding to personal health information, partly due to inappropriate or poor presentation of the information. The way personal health information is visualized may affect how users perceive and assess their personal health information. This study was conducted to examine the effects of information visualization format and visualization mode on the comprehension and perceptions of personal health information among personal health information users with eye tracking techniques. A two-factor within-subjects experimental design was employed, where participants were instructed to complete a series of personal health information comprehension tasks under varied types of visualization mode (i.e., whether the information visualization is static or dynamic) and three visualization formats (i.e., bar graph, instrument-like graph, and text-only format). Data on a set of measures, including comprehension performance, perceptions, and eye movement indicators, were collected during the task completion in the experiment. Repeated measure analysis of variance analyses (RM-ANOVAs) was used for data analysis. The results showed that while the visualization format yielded no effects on comprehension performance, it significantly affected users’ perceptions (such as perceived ease of use and satisfaction). The two graphic visualizations yielded significantly higher favorable scores on subjective evaluations than that of the text format. While visualization mode showed no effects on users’ perception measures, it significantly affected users' comprehension performance in that dynamic visualization significantly reduced users' information search time. Both visualization format and visualization mode had significant main effects on eye movement behaviors, and their interaction effects were also significant. While the bar graph format and text format had similar time to first fixation across dynamic and static visualizations, instrument-like graph format had a larger time to first fixation for dynamic visualization than for static visualization. The two graphic visualization formats yielded shorter total fixation duration compared with the text-only format, indicating their ability to improve information comprehension efficiency. The results suggest that dynamic visualization can improve efficiency in comprehending important health information, and graphic visualization formats were favored more by users. The findings are helpful in the underlying comprehension mechanism of visualized personal health information and provide important implications for optimal design and visualization of personal health information.

Keywords: eye tracking, information comprehension, personal health information, visualization

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Authors: R. R. Galiev, I. I. Lykov, A. E. Shitikov, I. A. Bilenko

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Dual-comb interferometry is based on the mixing of two optical frequency combs with slightly different lines spacing which results in the mapping of the optical spectrum into the radio-frequency domain for future digitizing and numerical processing. The dual-comb approach enables diverse applications, including metrology, fast high-precision spectroscopy, and distance range. Ordinary frequency-modulated continuous-wave (FMCW) laser-based Light Identification Detection and Ranging systems (LIDARs) suffer from two main disadvantages: slow and unreliable mechanical, spatial scan and a rather wide linewidth of conventional lasers, which limits speed measurement resolution. Dual-comb distance measurements with Allan deviations down to 12 nanometers at averaging times of 13 microseconds, along with ultrafast ranging at acquisition rates of 100 megahertz, allowing for an in-flight sampling of gun projectiles moving at 150 meters per second, was previously demonstrated. Nevertheless, pump lasers with EDFA amplifiers made the device bulky and expensive. An alternative approach is a direct coupling of the laser to a reference microring cavity. Backscattering can tune the laser to the eigenfrequency of the cavity via the so-called self-injection locked (SIL) effect. Moreover, the nonlinearity of the cavity allows a solitonic frequency comb generation in the very same cavity. In this work, we developed a fully integrated, power-efficient, electrically driven dual-micro comb source based on the semiconductor lasers SIL to high-quality integrated Si3N4 microresonators. We managed to obtain robust 1400-1700 nm combs generation with a 150 GHz or 1 THz lines spacing and measure less than a 1 kHz Lorentzian withs of stable, MHz spaced beat notes in a GHz band using two separated chips, each pumped by its own, self-injection locked laser. A deep investigation of the SIL dynamic allows us to find out the turn-key operation regime even for affordable Fabry-Perot multifrequency lasers used as a pump. It is important that such lasers are usually more powerful than DFB ones, which were also tested in our experiments. In order to test the advantages of the proposed techniques, we experimentally measured a minimum detectable speed of a reflective object. It has been shown that the narrow line of the laser locked to the microresonator provides markedly better velocity accuracy, showing velocity resolution down to 16 nm/s, while the no-SIL diode laser only allowed 160 nm/s with good accuracy. The results obtained are in agreement with the estimations and open up ways to develop LIDARs based on compact and cheap lasers. Our implementation uses affordable components, including semiconductor laser diodes and commercially available silicon nitride photonic circuits with microresonators.

Keywords: dual-comb spectroscopy, LIDAR, optical microresonator, self-injection locking

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Authors: M. Taouzari, A. Sardi, J. El Aoufi, Ahmed Mouhsen

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The purpose of this work is to design a reader antenna fed by 90° hybrid coupler that would ensure a tag which is detected regardless of its orientation for the radio frequency identification system covering the UHF and ISM bands frequencies. Based on this idea, the proposed work is dividing in two parts, first part is about study and design hybrid coupler using the resonators planar called T-and Pi networks operating in commercial bands. In the second part, the proposed antenna fed by the hybrid coupler is designed on FR4 substrate with dielectric permittivity 4.4, thickness dielectric 1.6mm and loss tangent 0.025. The T-slot is inserted in patch of the proposed antenna fed by the hybrid coupler is first designed, optimized and simulated using electromagnetic simulator ADS and then simulated in a full wave simulation software CST Microwave Studio. The simulated antenna by the both softwares achieves the expected performances in terms of matching, pattern radiation, phase shifting, gain and size.

Keywords: dual band antenna, RFID, hybrid coupler, polarization, radiation pattern

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Authors: Mouslem Damkhi

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This paper focuses on possible states of a football tournament final table according to the number of participating teams. Each team holds a position in the table with which it is possible to determine the highest and lowest points for that team. This paper proposes an optimized search space based on the minimum and maximum number of points which can be gained by each team to produce and enumerate the possible states for a football tournament final table. The proposed search space minimizes producing the invalid states which cannot occur during a football tournament. The generated states are filtered by a validity checking algorithm which seeks to reach a tournament graph based on a generated state. Thus, the algorithm provides a way to determine which team’s wins, draws and loses values guarantee a particular table position. The paper also presents and discusses the experimental results of the approach on the tournaments with up to eight teams. Comparing with a blind search algorithm, our proposed approach reduces generating the invalid states up to 99.99%, which results in a considerable optimization in term of the execution time.

Keywords: combinatorics, enumeration, graph, tournament

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Authors: M. Ndeley

Abstract:

The performance of global manufacturing supply chains depends on the interaction of production and transport processes. Currently, the scheduling of these processes is done separately without considering mutual requirements, which leads to no optimal solutions. An integrated scheduling of both processes enables the improvement of supply chain performance. The integrated production and transport scheduling problem (PTSP) is NP-hard, so that heuristic methods are necessary to efficiently solve large problem instances as in the case of global manufacturing supply chains. This paper presents a heuristic scheduling approach which handles the integration of flexible production processes with intermodal transport, incorporating flexible land transport. The method is based on a graph that allows a reformulation of the PTSP as a shortest path problem for each job, which can be solved in polynomial time. The proposed method is applied to a supply chain scenario with a manufacturing facility in South Africa and shipments of finished product to customers within the Country. The obtained results show that the approach is suitable for the scheduling of large-scale problems and can be flexibly adapted to different scenarios.

Keywords: production and transport scheduling problem, graph based scheduling, integrated scheduling

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Authors: Amira ben Hassine, A. Valverde, V. Serafín, C. Muñoz-San Martín, M. Garranzo-Asensio, M. Gamella, R. Barderas, M. Pedrero, N. Raouafi, S. Campuzano, P. Yáñez-Sedeño, J. M. Pingarrón

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This work describes the development of a dual electrochemical immunosensing platform for accurate determination of two target proteins, IL-13 Receptor α2 (IL-13Rα2) and E-cadherin (E-cad). The proposed methodology is based on the use of sandwich immunosensing approaches (involving horseradish peroxidase-labeled detector antibodies) implemented onto magnetic microbeads (MBs) and amperometric transduction at screen-printed dual carbon electrodes (SPdCEs). The magnetic bioconjugates were captured onto SPdCEs and the amperometric transduction was performed using the H2O2/hydroquinone (HQ) system. Under optimal experimental conditions, the developed bio platform demonstrates linear concentration ranges of 1.0–25 and 5.0-100 ng mL-1, detection limits of 0.28 and 1.04 ng mL-1 for E-cad and IL-13Rα2, respectively, and excellent selectivity against other non-target proteins. The developed immuno-platform also offers a good reproducibility among amperometric responses provided by nine different sensors constructed in the same manner (Relative Standard Deviation values of 3.1% for E-cad and 4.3% for IL-13Rα2). Moreover, obtained results confirm the practical applicability of this bio-platform for the accurate determination of the endogenous levels of both extracellular receptors in colon cancer cells (both intact and lysed) with different metastatic potential and serum and tissues from patients diagnosed with colorectal cancer at different grades. Interesting features in terms of, simplicity, speed, portability and sample amount required to provide quantitative results, make this immuno-platform more compatible than conventional methodologies with the clinical diagnosis and prognosis at the point of care.

Keywords: electrochemistry, mmunosensors, biosensors, E-cadherin, IL-13 receptor α2, cancer colorectal

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Authors: Gabriel Montague, Max Vilgalys, Catherine H. Crawford, Jorge Ortiz, Dava Newman

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There has never been so much publicly accessible atmospheric and environmental data. The possibilities of these data are exciting, but the sheer volume of available datasets represents a new challenge for researchers. The task of identifying and working with a new dataset has become more difficult with the amount and variety of available data. Datasets are often documented in ways that differ substantially from the common English used to describe the same topics. This presents a barrier not only for new scientists, but for researchers looking to find comparisons across multiple datasets or specialists from other disciplines hoping to collaborate. This paper proposes a method for addressing this obstacle: creating a knowledge graph to bridge the gap between everyday English language and the technical language surrounding these datasets. Knowledge graph generation is already a well-established field, although there are some unique challenges posed by working with Earth data. One is the sheer size of the databases – it would be infeasible to replicate or analyze all the data stored by an organization like The National Aeronautics and Space Administration (NASA) or the European Space Agency. Instead, this approach identifies topics from metadata available for datasets in NASA’s Earthdata database, which can then be used to directly request and access the raw data from NASA. By starting with a single metadata standard, this paper establishes an approach that can be generalized to different databases, but leaves the challenge of metadata harmonization for future work. Topics generated from the metadata are then linked to topics from a collection of English queries through a variety of standard and custom natural language processing (NLP) methods. The results from this method are then compared to a baseline of elastic search applied to the metadata. This comparison shows the benefits of the proposed knowledge graph system over existing methods, particularly in interpreting natural language queries and interpreting topics in metadata. For the research community, this work introduces an application of NLP to the ecological and environmental sciences, expanding the possibilities of how machine learning can be applied in this discipline. But perhaps more importantly, it establishes the foundation for a platform that can enable common English to access knowledge that previously required considerable effort and experience. By making this public data accessible to the full public, this work has the potential to transform environmental understanding, engagement, and action.

Keywords: earth metadata, knowledge graphs, natural language processing, question-answer systems

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Authors: Catelyn Coyle, Helena Kwakwa

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Objective: As novel and better tolerated therapies become available, effective HCV testing and care models become increasingly necessary to not only identify individuals with active infection but also link them to HCV providers for medical evaluation and treatment. Our aim is to describe an effective HCV testing and linkage to care model piloted in a network of five community health centers located in Philadelphia, PA. Methods: In October 2012, National Nursing Centers Consortium piloted a routine opt-out HCV testing model in a network of community health centers, one of which treats HCV, HIV, and co-infected patients. Key aspects of the model were medical assistant initiated testing, the use of laboratory-based reflex test technology, and electronic medical record modifications to prompt, track, report and facilitate payment of test costs. Universal testing on all adult patients was implemented at health centers serving patients at high-risk for HCV. The other sites integrated high-risk based testing, where patients meeting one or more of the CDC testing recommendation risk factors or had a history of homelessness were eligible for HCV testing. Mid-course adjustments included the integration of dual HIV testing, development of a linkage to care coordinator position to facilitate the transition of HIV and/or HCV-positive patients from primary to specialist care, and the transition to universal HCV testing across all testing sites. Results: From October 2012 to June 2015, the health centers performed 7,730 HCV tests and identified 886 (11.5%) patients with a positive HCV-antibody test. Of those with positive HCV-antibody tests, 838 (94.6%) had an HCV-RNA confirmatory test and 590 (70.4%) progressed to current HCV infection (overall prevalence=7.6%); 524 (88.8%) received their RNA-positive test result; 429 (72.7%) were referred to an HCV care specialist and 271 (45.9%) were seen by the HCV care specialist. The best linkage to care results were seen at the test and treat the site, where of the 333 patients were current HCV infection, 175 (52.6%) were seen by an HCV care specialist. Of the patients with active HCV infection, 349 (59.2%) were unaware of their HCV-positive status at the time of diagnosis. Since the integration of dual HCV/HIV testing in September 2013, 9,506 HIV tests were performed, 85 (0.9%) patients had positive HIV tests, 81 (95.3%) received their confirmed HIV test result and 77 (90.6%) were linked to HIV care. Dual HCV/HIV testing increased the number of HCV tests performed by 362 between the 9 months preceding dual testing and first 9 months after dual testing integration, representing a 23.7% increment. Conclusion: Our HCV testing model shows that integrated routine testing and linkage to care is feasible and improved detection and linkage to care in a primary care setting. We found that prevalence of current HCV infection was higher than that seen in locally in Philadelphia and nationwide. Intensive linkage services can increase the number of patients who successfully navigate the HCV treatment cascade. The linkage to care coordinator position is an important position that acts as a trusted intermediary for patients being linked to care.

Keywords: HCV, routine testing, linkage to care, community health centers

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Authors: Haonan Hu, Shuge Lei, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Jijun Tang

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This paper focuses on the classification task of breast ultrasound images and conducts research on the reliability measurement of classification results. A dual-channel evaluation framework was developed based on the proposed inference reliability and predictive reliability scores. For the inference reliability evaluation, human-aligned and doctor-agreed inference rationals based on the improved feature attribution algorithm SP-RISA are gracefully applied. Uncertainty quantification is used to evaluate the predictive reliability via the test time enhancement. The effectiveness of this reliability evaluation framework has been verified on the breast ultrasound clinical dataset YBUS, and its robustness is verified on the public dataset BUSI. The expected calibration errors on both datasets are significantly lower than traditional evaluation methods, which proves the effectiveness of the proposed reliability measurement.

Keywords: medical imaging, ultrasound imaging, XAI, uncertainty measurement, trustworthy AI

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: Ionut Vintu, Stefan Laible, Ruth Schulz

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Agricultural robotics has been developing steadily over recent years, with the goal of reducing and even eliminating pesticides used in crops and to increase productivity by taking over human labor. The majority of crops are arranged in rows. The first step towards autonomous robots, capable of driving in fields and performing crop-handling tasks, is for robots to robustly detect the rows of plants. Recent work done towards autonomous driving between plant rows offers big robotic platforms equipped with various expensive sensors as a solution to this problem. These platforms need to be driven over the rows of plants. This approach lacks flexibility and scalability when it comes to the height of plants or distance between rows. This paper proposes instead an algorithm that makes use of cheaper sensors and has a higher variability. The main application is in tree nurseries. Here, plant height can range from a few centimeters to a few meters. Moreover, trees are often removed, leading to gaps within the plant rows. The core idea is to combine row detection algorithms with graph-based localization methods as they are used in SLAM. Nodes in the graph represent the estimated pose of the robot, and the edges embed constraints between these poses or between the robot and certain landmarks. This setup aims to improve individual plant detection and deal with exception handling, like row gaps, which are falsely detected as an end of rows. Four methods were developed for detecting row structures in the fields, all using a point cloud acquired with a 3D LiDAR as an input. Comparing the field coverage and number of damaged plants, the method that uses a local map around the robot proved to perform the best, with 68% covered rows and 25% damaged plants. This method is further used and combined with a graph-based localization algorithm, which uses the local map features to estimate the robot’s position inside the greater field. Testing the upgraded algorithm in a variety of simulated fields shows that the additional information obtained from localization provides a boost in performance over methods that rely purely on perception to navigate. The final algorithm achieved a row coverage of 80% and an accuracy of 27% damaged plants. Future work would focus on achieving a perfect score of 100% covered rows and 0% damaged plants. The main challenges that the algorithm needs to overcome are fields where the height of the plants is too small for the plants to be detected and fields where it is hard to distinguish between individual plants when they are overlapping. The method was also tested on a real robot in a small field with artificial plants. The tests were performed using a small robot platform equipped with wheel encoders, an IMU and an FX10 3D LiDAR. Over ten runs, the system achieved 100% coverage and 0% damaged plants. The framework built within the scope of this work can be further used to integrate data from additional sensors, with the goal of achieving even better results.

Keywords: 3D LiDAR, agricultural robots, graph-based localization, row detection

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Authors: Javier Sedano-Cibrián, Julio Manuel de Luis-Ruiz, Rubén Pérez-Álvarez, Raúl Pereda-García, Beatriz Malagón-Picón

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Structural inspection activities are necessary to ensure the correct functioning of infrastructures. Unmanned Aerial Vehicle (UAV) techniques have become more popular than traditional techniques. Specifically, UAV Photogrammetry allows time and cost savings. The development of this technology has permitted the use of low-cost thermal sensors in UAVs. The representation of 3D thermal models with this type of equipment is in continuous evolution. The direct processing of thermal images usually leads to errors and inaccurate results. A methodology is proposed for the generation of 3D thermal models using dual sensors, which involves the application of visible Red-Blue-Green (RGB) and thermal images in parallel. Hence, the RGB images are used as the basis for the generation of the model geometry, and the thermal images are the source of the surface temperature information that is projected onto the model. Mining/industrial facilities representations that are obtained can be used for inspection activities.

Keywords: aerial thermography, data processing, drone, low-cost, point cloud

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Authors: Spoorthi Sripad

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Rapid advancements in LiDAR (Light Detection and Ranging) technology, coupled with the synergy of remote sensing, have revolutionized Earth observation methodologies. This paper delves into the transformative impact of integrated LiDAR and remote sensing systems. Focusing on miniaturization, cost reduction, and improved resolution, the study explores the evolving landscape of terrestrial and aquatic environmental monitoring. The integration of multi-wavelength and dual-mode LiDAR systems, alongside collaborative efforts with other remote sensing technologies, presents a comprehensive approach. The paper highlights the pivotal role of LiDAR in environmental assessment, urban planning, and infrastructure development. As the amalgamation of LiDAR and remote sensing reshapes Earth observation, this research anticipates a paradigm shift in our understanding of dynamic planetary processes.

Keywords: LiDAR, remote sensing, earth observation, advancements, integration, environmental monitoring, multi-wavelength, dual-mode, technology, urban planning, infrastructure, resolution, miniaturization

Procedia PDF Downloads 61