Search results for: artificial cell

Authors: Kitty Kioskli, Theofanis Fotis, Nineta Polemi

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The European Union (EU) directive Artificial Intelligence (AI) Act in Article 9 requires that risk management of AI systems includes both technical and human oversight, while according to NIST_AI_RFM (Appendix C) and ENISA AI Framework recommendations, claim that further research is needed to understand the current limitations of social threats and human-AI interaction. AI threats within social contexts significantly affect the security and trustworthiness of the AI systems; they are interrelated and trigger technical threats as well. For example, lack of explainability (e.g. the complexity of models can be challenging for stakeholders to grasp) leads to misunderstandings, biases, and erroneous decisions. Which in turn impact the privacy, security, accountability of the AI systems. Based on the NIST four fundamental criteria for explainability it can also classify the explainability threats into four (4) sub-categories: a) Lack of supporting evidence: AI systems must provide supporting evidence or reasons for all their outputs. b) Lack of Understandability: Explanations offered by systems should be comprehensible to individual users. c) Lack of Accuracy: The provided explanation should accurately represent the system's process of generating outputs. d) Out of scope: The system should only function within its designated conditions or when it possesses sufficient confidence in its outputs. Biases may also stem from historical data reflecting undesired behaviors. When present in the data, biases can permeate the models trained on them, thereby influencing the security and trustworthiness of the of AI systems. Social related AI threats are recognized by various initiatives (e.g., EU Ethics Guidelines for Trustworthy AI), standards (e.g. ISO/IEC TR 24368:2022 on AI ethical concerns, ISO/IEC AWI 42105 on guidance for human oversight of AI systems) and EU legislation (e.g. the General Data Protection Regulation 2016/679, the NIS 2 Directive 2022/2555, the Directive on the Resilience of Critical Entities 2022/2557, the EU AI Act, the Cyber Resilience Act). Measuring social threats, estimating the risks to AI systems associated to these threats and mitigating them is a research challenge. In this paper it will present the efforts of two European Commission Projects (FAITH and THEMIS) from the HorizonEurope programme that analyse the social threats by building cyber-social exercises in order to study human behaviour, traits, cognitive ability, personality, attitudes, interests, and other socio-technical profile characteristics. The research in these projects also include the development of measurements and scales (psychometrics) for human-related vulnerabilities that can be used in estimating more realistically the vulnerability severity, enhancing the CVSS4.0 measurement.

Keywords: social threats, artificial Intelligence, mitigation, social experiment

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Authors: Mohammad Rahmani Rezaiyeh, Mojtaba Rahmani Rezaiyeh, Mehrdad Rahmani Rezaiyeh

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Smart speed bump is a new invention and I am invented it. Smart speed bump is a system that can change the position of speed bumps either active or passive in necessary situations. The basic system of smart speed bumps is based on a robotic system which includes mechanic, electronic and artificial intelligence. The smart speed bump is capable of smart decision making and can change its position by anticipating the peak of terrific hours. It can be noted to the advantages of this system such as preventing the waste of petrol while crossing speed bumps, traffic management, accelerating, flowing and securing traffic, reducing accidents and judicial records.

Keywords: invention, smart, robotic system, speed bump, traffic, management

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Authors: Béla Kovács, Éva Bódi, Farzaneh Garousi, Szilvia Várallyay, Dávid Andrási

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For analysis of elements in different food, feed and food raw material samples generally a flame atomic absorption spectrometer (FAAS), a graphite furnace atomic absorption spectrometer (GF-AAS), an inductively coupled plasma optical emission spectrometer (ICP-OES) and an inductively coupled plasma mass spectrometer (ICP-MS) are applied. All the analytical instruments have different physical and chemical interfering effects analysing food and food raw material samples. The smaller the concentration of an analyte and the larger the concentration of the matrix the larger the interfering effects. Nowadays, it is very important to analyse growingly smaller concentrations of elements. From the above analytical instruments generally the inductively coupled plasma mass spectrometer is capable of analysing the smallest concentration of elements. The applied ICP-MS instrument has Collision Cell Technology (CCT) also. Using CCT mode certain elements have better detection limits with 1-3 magnitudes comparing to a normal ICP-MS analytical method. The CCT mode has better detection limits mainly for analysis of selenium (arsenic, germanium, vanadium, and chromium). To elaborate an analytical method for selenium with an inductively coupled plasma mass spectrometer the most important interfering effects (problems) were evaluated: 1) isobaric elemental, 2) isobaric molecular, and 3) physical interferences. Analysing food and food raw material samples an other (new) interfering effect emerged in ICP-MS, namely the effect of various matrixes having different evaporation and nebulization effectiveness, moreover having different quantity of carbon content of food, feed and food raw material samples. In our research work the effect of different water-soluble compounds furthermore the effect of various quantity of carbon content (as sample matrix) were examined on changes of intensity of selenium. So finally we could find “opportunities” to decrease the error of selenium analysis. To analyse selenium in food, feed and food raw material samples, the most appropriate inductively coupled plasma mass spectrometer is a quadrupole instrument applying a collision cell technique (CCT). The extent of interfering effect of carbon content depends on the type of compounds. The carbon content significantly affects the measured concentration (intensities) of Se, which can be corrected using internal standard (arsenic or tellurium).

Keywords: selenium, ICP-MS, food, food raw material

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Authors: Yosra M. Badiei, Evelyn Ortiz, Marisa Portenti, David Szalda

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Water-oxidation half-reaction is a critical reaction that can be driven by a sustainable energy source (e.g., solar or wind) and be coupled with a chemical fuel making reaction which stores the released electrons and protons from water (e.g., H₂ or methanol). The use of molecular water-oxidation catalysts (WOC) allow the rationale design of redox active metal centers and provides a better understanding of their structure-activity-relationship. Herein, the structure of a Ru(III) complex bearing a doubly deprotonated N,N'-bis(aryl)pyridine-2,6-dicarboxamide ligand which contains a water molecule in its primary coordination sphere was elucidated by single-crystal X-ray diffraction. Further spectroscopic experimental data and pH-dependent electrochemical studies reveal its water-oxidation reactivity. Emphasis on mechanistic details for O₂ formation of this complex will be addressed.

Keywords: water-oxidation, catalysis, ruthenium, artificial photosynthesis

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Authors: Rojith K. Balakrishnan

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Spontaneous tumour lysis syndrome is a constellation of electrolyte abnormalities and an acute renal failure which occurs in the setting of rapid cell turnover prior to the administration of cytotoxic chemotherapy. While spontaneous tumour lysis well-described in patients with Burkitt lymphoma, it is thought to occur less commonly in patients with other hematological malignancies. We present a case of forty-year-old female who presented with features of acute renal failure, on further evaluation turned out to be a newly diagnosed acute myeloid leukemia with spontaneous tumour lysis best of our knowledge only three cases of AML with spontaneous tumour lysis has reported world wide.

Keywords: AML, tumour lysis, renal failure, myeloid leukemia

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Authors: Qiaoyue Kuang, Yang Li, Mizuki Endo, Takeaki Ozawa

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G protein-coupled receptors (GPCR) are the largest family of receptor proteins that detect molecules outside the cell and activate cellular responses. Of the GPCRs, dopamine receptors, which recognize extracellular dopamine, are essential to mammals due to their roles in numerous physiological events, including autonomic movement, hormonal regulation, emotions, and the reward system in the brain. To precisely understand the physiological roles of dopamine receptors, it is important to spatiotemporally control the signaling mediated by dopamine receptors, which is strongly dependent on their surface expression. Conventionally, chemical-induced interactions were applied to trigger the endocytosis of cell surface receptors. However, these methods were subjected to diffusion and therefore lacked temporal and special precision. To further understand the receptor-mediated signaling and to control the plasma membrane expression of receptors, an optogenetic tool called E-fragment was developed. The C-terminus of a light-sensitive photosensory protein cyptochrome2 (CRY2) was attached to β-Arrestin, and the E-fragment was generated by fusing the C-terminal peptide of vasopressin receptor (V2R) to CRY2’s binding partner protein CIB. The CRY2-CIB heterodimerization triggered by blue light stimulation brings β-Arrestin to the vicinity of membrane receptors and results in receptor endocytosis. In this study, the E-fragment system was applied to dopamine receptors 1 and 2 (DRD1 and DRD2) to control dopamine signaling. First, confocal fluorescence microscope observation qualitatively confirmed the light-induced endocytosis of E-fragment fused receptors. Second, NanoBiT bioluminescence assay verified quantitatively that the surface amount of E-fragment labeled receptors decreased after light treatment. Finally, GloSensor bioluminescence assay results suggested that the E-fragment-dependent receptor light-induced endocytosis decreased cAMP production in DRD1 signaling and attenuated the inhibition effect of DRD2 on cAMP production. The developed optogenetic tool was able to induce receptor endocytosis by external light, providing opportunities to further understand numerous physiological activities by controlling receptor-mediated signaling spatiotemporally.

Keywords: dopamine receptors, endocytosis, G protein-coupled receptors, optogenetics

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Authors: Justin Woulfe

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Siloed logistics and supply chain management systems throughout the Department of Defense (DOD) has led to disparate approaches to modeling and simulation (M&S), a lack of understanding of how one system impacts the whole, and issues with “optimal” solutions that are good for one organization but have dramatic negative impacts on another. Many different systems have evolved to try to understand and account for uncertainty and try to reduce the consequences of the unknown. As the DoD undertakes expansive digital transformation initiatives, there is an opportunity to fuse and leverage traditionally disparate data into a centrally hosted source of truth. With a streamlined process incorporating machine learning (ML) and artificial intelligence (AI), advanced M&S will enable informed decisions guiding program success via optimized operational readiness and improved mission success. One of the current challenges is to leverage the terabytes of data generated by monitored systems to provide actionable information for all levels of users. The implementation of a cloud-based application analyzing data transactions, learning and predicting future states from current and past states in real-time, and communicating those anticipated states is an appropriate solution for the purposes of reduced latency and improved confidence in decisions. Decisions made from an ML and AI application combined with advanced optimization algorithms will improve the mission success and performance of systems, which will improve the overall cost and effectiveness of any program. The Systecon team constructs and employs model-based simulations, cutting across traditional silos of data, aggregating maintenance, and supply data, incorporating sensor information, and applying optimization and simulation methods to an as-maintained digital twin with the ability to aggregate results across a system’s lifecycle and across logical and operational groupings of systems. This coupling of data throughout the enterprise enables tactical, operational, and strategic decision support, detachable and deployable logistics services, and configuration-based automated distribution of digital technical and product data to enhance supply and logistics operations. As a complete solution, this approach significantly reduces program risk by allowing flexible configuration of data, data relationships, business process workflows, and early test and evaluation, especially budget trade-off analyses. A true capability to tie resources (dollars) to weapon system readiness in alignment with the real-world scenarios a warfighter may experience has been an objective yet to be realized to date. By developing and solidifying an organic capability to directly relate dollars to readiness and to inform the digital twin, the decision-maker is now empowered through valuable insight and traceability. This type of educated decision-making provides an advantage over the adversaries who struggle with maintaining system readiness at an affordable cost. The M&S capability developed allows program managers to independently evaluate system design and support decisions by quantifying their impact on operational availability and operations and support cost resulting in the ability to simultaneously optimize readiness and cost. This will allow the stakeholders to make data-driven decisions when trading cost and readiness throughout the life of the program. Finally, sponsors are available to validate product deliverables with efficiency and much higher accuracy than in previous years.

Keywords: artificial intelligence, digital transformation, machine learning, predictive analytics

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Authors: Farnaz Farhangi

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Many people in New Zealand believe that the access to water is inexhaustible, and it comes from a history of virtually unrestricted access to it. For the region like Hamilton which is one of New Zealand’s fastest growing cities, it is crucial for policy makers to know about the future water consumption and implementation of rules and regulation such as universal water metering. Hamilton residents use water freely and they do not have any idea about how much water they use. Hence, one of proposed objectives of this research is focusing on forecasting water consumption using different methods. Residential water consumption time series exhibits seasonal and trend variations. Seasonality is the pattern caused by repeating events such as weather conditions in summer and winter, public holidays, etc. The problem with this seasonal fluctuation is that, it dominates other time series components and makes difficulties in determining other variations (such as educational campaign’s effect, regulation, etc.) in time series. Apart from seasonality, a stochastic trend is also combined with seasonality and makes different effects on results of forecasting. According to the forecasting literature, preprocessing (de-trending and de-seasonalization) is essential to have more performed forecasting results, while some other researchers mention that seasonally non-adjusted data should be used. Hence, I answer the question that is pre-processing essential? A wide range of forecasting methods exists with different pros and cons. In this research, I apply double seasonal ARIMA and Artificial Neural Network (ANN), considering diverse elements such as seasonality and calendar effects (public and school holidays) and combine their results to find the best predicted values. My hypothesis is the examination the results of combined method (hybrid model) and individual methods and comparing the accuracy and robustness. In order to use ARIMA, the data should be stationary. Also, ANN has successful forecasting applications in terms of forecasting seasonal and trend time series. Using a hybrid model is a way to improve the accuracy of the methods. Due to the fact that water demand is dominated by different seasonality, in order to find their sensitivity to weather conditions or calendar effects or other seasonal patterns, I combine different methods. The advantage of this combination is reduction of errors by averaging of each individual model. It is also useful when we are not sure about the accuracy of each forecasting model and it can ease the problem of model selection. Using daily residential water consumption data from January 2000 to July 2015 in Hamilton, I indicate how prediction by different methods varies. ANN has more accurate forecasting results than other method and preprocessing is essential when we use seasonal time series. Using hybrid model reduces forecasting average errors and increases the performance.

Keywords: artificial neural network (ANN), double seasonal ARIMA, forecasting, hybrid model

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Authors: Regiane A. Oliveira, Carlos E. Vaz Rossell, Rubens Maciel Filho

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Lactic acid, besides a valuable chemical may be considered a platform for other chemicals. In fact, the feasibility of hemicellulosic sugars as feedstock for lactic acid production process, may represent the drop of some of the barriers for the second generation bioproducts, especially bearing in mind the 5-carbon sugars from the pre-treatment of sugarcane bagasse. Bearing this in mind, the purpose of this study was to use the hemicellulosic liquor from sugarcane bagasse as a substrate to produce lactic acid by fermentation. To release of sugars from hemicellulose it was made a pre-treatment with a diluted sulfuric acid in order to obtain a xylose's rich liquor with low concentration of inhibiting compounds for fermentation (≈ 67% of xylose, ≈ 21% of glucose, ≈ 10% of cellobiose and arabinose, and around 1% of inhibiting compounds as furfural, hydroxymethilfurfural and acetic acid). The hemicellulosic sugars associated with 20 g/L of yeast extract were used in a fermentation process with Lactobacillus plantarum to produce lactic acid. The fermentation process pH was controlled with automatic injection of Ca(OH)2 to keep pH at 6.00. The lactic acid concentration remained stable from the time when the glucose was depleted (48 hours of fermentation), with no further production. While lactic acid is produced occurs the concomitant consumption of xylose and glucose. The yield of fermentation was 0.933 g lactic acid /g sugars. Besides, it was not detected the presence of by-products, what allows considering that the microorganism uses a homolactic fermentation to produce its own energy using pentose-phosphate pathway. Through facultative heterofermentative metabolism the bacteria consume pentose, as is the case of L. plantarum, but the energy efficiency for the cell is lower than during the hexose consumption. This implies both in a slower cell growth, as in a reduction in lactic acid productivity compared with the use of hexose. Also, L. plantarum had shown to have a capacity for lactic acid production from hemicellulosic hydrolysate without detoxification, which is very attractive in terms of robustness for an industrial process. Xylose from hydrolyzed bagasse and without detoxification is consumed, although the hydrolyzed bagasse inhibitors (especially aromatic inhibitors) affect productivity and yield of lactic acid. The use of sugars and the lack of need for detoxification of the C5 liquor from sugarcane bagasse hydrolyzed is a crucial factor for the economic viability of second generation processes. Taking this information into account, the production of second generation lactic acid using sugars from hemicellulose appears to be a good alternative to the complete utilization of sugarcane plant, directing molasses and cellulosic carbohydrates to produce 2G-ethanol, and hemicellulosic carbohydrates to produce 2G-lactic acid.

Keywords: fermentation, lactic acid, hemicellulosic sugars, sugarcane

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Authors: Henrry Vicente Rojas Asuero, Holger Manuel Benavides Muñoz

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Approximately 80% of the energy consumed worldwide is generated from fossil sources, which are responsible for the emission of a large volume of greenhouse gases. For this reason, the global trend, at present, is the widespread use of energy produced from renewable sources. This seeks safety and diversification of energy supply, based on social cohesion, economic feasibility and environmental protection. In this scenario, small hydropower systems (P ≤ 10MW) stand out due to their high efficiency, economic competitiveness and low environmental impact. Small hydropower systems, along with wind and solar energy, are expected to represent a significant percentage of the world's energy matrix in the near term. Among the various technologies present in the state of the art, relating to small hydropower systems, is the Gravitational Water Vortex Power Plant, a recent technology that excels because of its versatility of operation, since it can operate with jumps in the range of 0.70 m-2.00 m and flow rates from 1 m3/s to 20 m3/s. Its operating system is based on the utilization of the energy of rotation contained within a large water vortex artificially induced. This paper presents the study and experimental design of an optimal hydraulic structure with the capacity to induce the artificial formation of a gravitational water vortex trough a system of easy application and high efficiency, able to operate in conditions of very low head and minimum flow. The proposed structure consists of a channel, with variable base, vortex inductor, tangential flow generator, coupled to a circular tank with a conical transition bottom hole. In the laboratory test, the angular velocity of the water vortex was related to the geometric characteristics of the inductor channel, as well as the influence of the conical transition bottom hole on the physical characteristics of the water vortex. The results show angular velocity values of greater magnitude as a function of depth, in addition the presence of the conical transition in the bottom hole of the circular tank improves the water vortex formation conditions while increasing the angular velocity values. Thus, the proposed system is a sustainable solution for the energy supply of rural areas near to watercourses.

Keywords: experimental model, gravitational water vortex power plant, renewable energy, small hydropower

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Authors: Melby Chacko, Jagannath Nayak

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The aging behaviour of 6061 Al-15 vol% SiC composite was investigated using Rockwell B hardness measurement. The composite was solutionized at 350°C and quenched in water. The composite was aged at room temperature (T4 treatment) and also at 140°C, 160°C, 180°C and 200°C (T6 treatment). The natural and artificial aging behaviour of composite was studied using aging curves determined at different temperatures. The aging period for peak aging for different temperatures was identified. The time required for attaining peak aging decreased with increase in the aging temperature. The peak hardness was found to increase with increase with aging temperature and the highest peak hardness was observed at 180ºC. Beyond 180ºC the peak hardness was found to be decreasing.

Keywords: 6061 Al-SiC composite, aging curve, Rockwell B hardness, T4, T6 treatments

Procedia PDF Downloads 271

Authors: Angga S. Fajar, A. Aminullah, J. Kiyono, R. A. Safitri

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This paper discusses about the application of ANN for optimizing of bridge structure design. ANN has been applied in various field of science concerning prediction and optimization. The structural optimization has several benefit including accelerate structural design process, saving the structural material, and minimize self-weight and mass of structure. In this paper, there are three types of bridge structure that being optimized including PSC I-girder superstructure, composite steel-concrete girder superstructure, and RC bridge pier. The different optimization strategy on each bridge structure implement back propagation method of ANN is conducted in this research. The optimal weight and easier design process of bridge structure with satisfied error are achieved.

Keywords: bridge structures, ANN, optimization, back propagation

Procedia PDF Downloads 377

Authors: S. Besnaci, S. Bensoltane, H. Locif, S. Saadi

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To validate an ecotoxicological approach to assessing toxicological effects caused by the oxide powder of nano-iron Fe2O3, we searched in the ecotoxicology laboratory cell bodies bio accumulators and bio-indicators of soil pollution the snail Helix aspersa. In this study, we evaluated the toxicity of nano Fe2O3 during a very sensitive phase of development H.aspersa (embryonic stage). During embryonic development, we observed in treated with various concentrations of nano Fe2O3 (1.25 g/l, 1.5 g/l, and 2 g/l) compared to control, the deformation of the membrane of the egg and accumulation of this molecule at the rear of the egg proven by the photographs, as with the influence on the hatching percentage.

Keywords: eggs, embryotoxicity, Fe2O3, Helix aspersa, nanoparticles

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Authors: Rohit Mittal, Bright Keswani, Amit Mithal

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This paper provides a sense about the application of computational intelligence techniques used to develop computer games, especially car racing. For the deep sense and knowledge of artificial intelligence, this paper is divided into various sections that is optimization, imitation, innovation and combining approach of optimization and imitation. This paper is mainly concerned with combining approach which tells different aspects of using fitness measures and supervised learning techniques used to imitate aspects of behavior. The main achievement of this paper is based on modelling player behaviour and evolving new game content such as racing tracks as single car racing on single track.

Keywords: evolution algorithm, genetic, optimization, imitation, racing, innovation, gaming

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Authors: Zoe Boon Suan Loy

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An exploratory research undertaken in 2000 at the beginning of the COVID-19 pandemic examined the changing roles of Singapore school leaders as they lead teachers in developing future-ready learners. While it is evident that ‘LEADER’ qualities epitomize the knowledge, competencies, and skills required, recent events in an increasing VUCA and BANI world characterized by massively disruptive Ukraine -Russian war, unabating tense US-Sino relations, issues related to sustainability, and rapid ageing will have an impact on school leadership. As an increasingly complex endeavour, this requires a relook as they lead teachers in nurturing holistically-developed future-ready students. Digitalisation, new technology, and the push for a green economy will be the key driving forces that will have an impact on job availability. Similarly, the rapid growth of artificial intelligence (AI) capabilities, including ChatGPT, will aggravate and add tremendous stress to the work of school leaders. This paper seeks to explore the key school leadership shifts required beyond the ‘LEADER’ qualities as school leaders respond to the changes, challenges, and opportunities in the 21st C new normal. The research findings for this paper are based on an exploratory qualitative study on the perceptions of 26 school leaders (vice-principals) who were attending a milestone educational leadership course at the National Institute of Education, Nanyang Technological University, Singapore. A structured questionnaire is designed to collect the data, which is then analysed using coding methodology. Broad themes on key competencies and skills of future-ready leaders in the Singapore education system are then identified. Key Findings: In undertaking their leadership roles as leaders of future-ready learners, school leaders need to demonstrate the ‘LEADER’ qualities. They need to have a long-term view, understand the educational imperatives, have a good awareness of self and the dispositions of a leader, be effective in optimizing external leverages and are clear about their role expectations. These ‘LEADER’ qualities are necessary and relevant in the post-Covid era. Beyond this, school leaders with ‘LEADER’ qualities are well supported by the Ministry of Education, which takes cognizance of emerging trends and continually review education policies to address related issues. Concluding Statement: Discussions within the education ecosystem and among other stakeholders on the implications of the use of artificial intelligence and ChatGPT on the school curriculum, including content knowledge, pedagogy, and assessment, are ongoing. This augurs well for school leaders as they undertake their responsibilities as leaders of future-ready learners.

Keywords: Singapore education system, ‘LEADER’ qualities, school leadership, future-ready leaders, future-ready learners

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Authors: Nendouvhada Livhuwani Portia, Nicole Sibuyi, Kwazikwakhe Gabuza, Adewale Fadaka

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Metabolic dysfunction-associated liver disease (MASLD) is a chronic condition characterized by excessive fat accumulation in the liver, distinct from conditions caused by alcohol, viral hepatitis, or medications. MASLD is often linked with metabolic syndrome, including obesity, diabetes, hyperlipidemia, and hypertriglyceridemia. This disease can progress to metabolic dysfunction-associated steatohepatitis (MASH), marked by liver inflammation and scarring, potentially leading to cirrhosis. However, only 43-44% of patients with steatosis develop MASH, and 7-30% of those with MASH progress to cirrhosis. The exact mechanisms underlying MASLD and its progression remain unclear, and there are currently no specific therapeutic strategies for MASLD/MASH. While anti-obesity and anti-diabetic medications can reduce progression, they do not fully treat or reverse the disease. As an alternative, green-synthesized metal nanoparticles (MNPs) are emerging as potential treatments for liver diseases due to their anti-diabetic, anti-inflammatory, and anti-obesity properties with minimal side effects. MNPs like gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs) have been shown to improve metabolic processes by lowering blood glucose, body fat, and inflammation. The study aimed to explore the effects of green-synthesized MNPs on gene expression in an in vitro model of MASH using C3A/HepG2 liver cells. The MASH model was created by exposing these cells to free fatty acids (FFAs) followed by lipopolysaccharide (LPS) to induce inflammation. Cell viability was assessed with the Water-Soluble Tetrazolium (WST)-1 assay, and lipid accumulation was measured using the Oil Red O (ORO) assay. Additionally, mitochondrial membrane potential was assessed by the tetramethyl rhodamine, methyl ester (TMRE) assay, and inflammation was measured with an Enzyme-Linked Immunosorbent Assay (ELISA). The study synthesized AuNPs from Carpobrotus edulis fruit (CeF) and avocado seed (AvoSE) and AgNPs from Salvia africana-lutea (SAL) using optimized conditions. The MNPs were characterized by UV-Vis spectrophotometry and Dynamic Light Scattering (DLS). The nanoparticles were tested at various concentrations for their impact on the C3A/HepG2-induced MASH model. Among the MNPs tested, AvoSE-AuNPs showed the most promise. They reduced cell proliferation and intracellular lipid content more effectively than CeFE-AuNPs and SAL-AgNPs. Molecular analysis using real-time polymerase chain reaction revealed that AvoSE-AuNPs could potentially reverse MASH effects by reducing the expression of key pro-inflammatory and metabolic genes, including tumor necrosis factor-alpha (TNF-α), Fas cell surface death receptor (FAS), Peroxisome proliferator-activated receptor (PPAR)-α, PPAR-γ, and Sterol regulatory element-binding protein (SREBPF)-1. Further research is needed to confirm the molecular mechanisms behind the effects of these MNPs and to identify the specific phytochemicals responsible for their synthesis and bioactivities.

Keywords: gold nanoparticles, green nanotechnology, metal nanoparticles, obesity

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Authors: K. Ghosh, S. N. Tripathi, Manish Joshi, Y. S. Mayya, Arshad Khan, B. K. Sapra

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We have developed a CFD coupled aerosol microphysics model in the context of aerosol generation from a glowing wire. The governing equations can be solved implicitly for mass, momentum, energy transfer along with aerosol dynamics. The computationally efficient framework can simulate temporal behavior of total number concentration and number size distribution. This formulation uniquely couples standard K-Epsilon scheme with boundary layer model with detailed aerosol dynamics through residence time. This model uses measured temperatures (wire surface and axial/radial surroundings) and wire compositional data apart from other usual inputs for simulations. The model predictions show that bulk fluid motion and local heat distribution can significantly affect the aerosol behavior when the buoyancy effect in momentum transfer is considered. Buoyancy generated turbulence was found to be affecting parameters related to aerosol dynamics and transport as well. The model was validated by comparing simulated predictions with results obtained from six controlled experiments performed with a laboratory-made hot wire nanoparticle generator. Condensation particle counter (CPC) and scanning mobility particle sizer (SMPS) were used for measurement of total number concentration and number size distribution at the outlet of reactor cell during these experiments. Our model-predicted results were found to be in reasonable agreement with observed values. The developed model is fast (fully implicit) and numerically stable. It can be used specifically for applications in the context of the behavior of aerosol particles generated from glowing wire technique and in general for other similar large scale domains. Incorporation of CFD in aerosol microphysics framework provides a realistic platform to study natural convection driven systems/ applications. Aerosol dynamics sub-modules (nucleation, coagulation, wall deposition) have been coupled with Navier Stokes equations modified to include buoyancy coupled K-Epsilon turbulence model. Coupled flow-aerosol dynamics equation was solved numerically and in the implicit scheme. Wire composition and temperature (wire surface and cell domain) were obtained/measured, to be used as input for the model simulations. Model simulations showed a significant effect of fluid properties on the dynamics of aerosol particles. The role of buoyancy was highlighted by observation and interpretation of nucleation zones in the planes above the wire axis. The model was validated against measured temporal evolution, total number concentration and size distribution at the outlet of hot wire generator cell. Experimentally averaged and simulated total number concentrations were found to match closely, barring values at initial times. Steady-state number size distribution matched very well for sub 10 nm particle diameters while reasonable differences were noticed for higher size ranges. Although tuned specifically for the present context (i.e., aerosol generation from hotwire generator), the model can also be used for diverse applications, e.g., emission of particles from hot zones (chimneys, exhaust), fires and atmospheric cloud dynamics.

Keywords: nanoparticles, k-epsilon model, buoyancy, CFD, hot wire generator, aerosol dynamics

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Authors: Nizar Sakli, Chayma Bahar, Chokri Baccouch, Hedi Sakli

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If connected objects multiply, they are becoming a challenge in more than one way. In particular by their consumption and their supply of electricity. A large part of the new generations of connected objects will only be able to develop if it is possible to make them entirely autonomous in terms of energy. Some manufacturers are therefore developing products capable of recovering energy from their environment. Vital solutions in certain contexts, such as the medical industry. Energy recovery from the environment is a reliable solution to solve the problem of powering wireless connected objects. This paper presents and study a optically transparent solar patch antenna in frequency band of 2.4 GHz for connected objects in the future standard 5G for energy harvesting and RF transmission.

Keywords: antenna, IoT, solar cell, wireless communications

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Authors: R. D. Esposito, V. M. Barberá, P. García Morales, P. Dorado Rodríguez, J. Sanz, M. Fuentes, D. Planes Meseguer, M. Saceda, L. Fernández Fornos, M. P. Ventero

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Results obtained by our group on glioblastoma multiforme (GBM) primary cultures , show a dramatic potentiation of radiation effects when 2 units/ml of D-amino acid oxidase (DAO) enzyme are added, free or immobilized in magnetic nanoparticles, to irradiated samples just after the irradiation. Cell cultures were exposed to radiation doses of 7Gy and 15Gy of 6 MV photons from a clinical linear accelerator. At both doses, we observed a clear enhancing effect of radiation-induced damages due to the addition of DAO.

Keywords: D-amino Acid Oxidase (DAO) enzyme, magnetic particles, nanotechnology, radiation therapy enhancement

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Authors: Nilda Tri Putri, Muhammad Taufik

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Tools and agricultural machinery (Alsintan) is a tool used in agribusiness activities. Alsintan used to change the traditional farming systems generally use manual equipment into modern agriculture with mechanization. CV Nugraha Chakti Consultant make an action plan for industrial development Alsintan West Sumatra in 2012 to develop medium industries of Alsintan become a major industry of Alsintan, one of efforts made is increase the production capacity of the industry Alsintan. Production capacity for superior products as hydrotiller and threshers set each for 2.000 units per year. CV Citra Dragon as one of the medium industry alsintan in West Sumatra has a plan to relocate the existing plant to meet growing consumer demand each year. Increased production capacity and plant relocation plan has led to a change in the layout; therefore need to design the layout of the plant facility CV Citra Dragon. First step the to design of plant layout is design the layout of the production floor. The design of the production floor layout is done by applying group technology layout. The initial step is to do a machine grouping and part family using the Average Linkage Clustering (ALC) and Rank Order Clustering (ROC). Furthermore done independent work station design and layout design using the Modified Spanning Tree (MST). Alternative selection layout is done to select the best production floor layout between ALC and ROC cell grouping. Furthermore, to design the layout of warehouses, offices and other production support facilities. Activity Relationship Chart methods used to organize the placement of factory facilities has been designed. After structuring plan facilities, calculated cost manufacturing facility plant establishment. Type of layout is used on the production floor layout technology group. The production floor is composed of four cell machinery, assembly area and painting area. The total distance of the displacement of material in a single production amounted to 1120.16 m which means need 18,7minutes of transportation time for one time production. Alsintan Factory has designed a circular flow pattern with 11 facilities. The facilities were designed consisting of 10 rooms and 1 parking space. The measure of factory building is 84 m x 52 m.

Keywords: Average Linkage Clustering (ALC), Rank Order Clustering (ROC), Modified Spanning Tree (MST), Activity Relationship Chart (ARC)

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Authors: A. Majidian

Abstract:

The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.

Keywords: life prediction, condenser tube, neural network, fuzzy logic

Procedia PDF Downloads 356

Authors: Yeliz Ekinci, Melis Koyuncu

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Successful planning of solid waste management systems requires successful prediction of the amount of solid waste generated in an area. Waste management planning can protect the environment and human health, hence it is tremendously important for countries. The lack of information in waste generation can cause many environmental and health problems. Turkey is a country that plans to join European Union, hence, solid waste management is one of the most significant criteria that should be handled in order to be a part of this community. Solid waste management system requires a good forecast of solid waste generation. Thus, this study aims to forecast solid waste generation in Turkey. Artificial Neural Network and Linear Regression models will be used for this aim. Many models will be run and the best one will be selected based on some predetermined performance measures.

Keywords: forecast, solid waste generation, solid waste management, Turkey

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Authors: Luvisa Kusuma, Panji Yudha Prakasa

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Cloud storage, as a subservice of infrastructure as a service (IaaS) in Cloud Computing, is the model of nerworked storage where data can be stored in server. In this paper, we propose a secure cloud storage system consisting of two main components; client as a user who uses the cloud storage service and server who provides the cloud storage service. In this system, we propose the protocol schemes to guarantee against security attacks in the data transmission. The protocols are login protocol, upload data protocol, download protocol, and push data protocol, which implement hybrid cryptographic mechanism based on data encryption before it is sent to the cloud, so cloud storage provider does not know the user's data and cannot analysis user’s data, because there is no correspondence between data and user.

Keywords: cloud storage, security, cryptographic protocol, artificial intelligence

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Authors: Andrew Kurochkin, Kostiantyn Bokhan

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In this study, a new meme dataset of ~650K meme instances was created, a technology of meme generation based on the state of the art deep learning technique - GPT-2 model was researched, a comparative analysis of machine-generated memes and human-created was conducted. We justified that Amazon Mechanical Turk workers can be used for the approximate estimating of users' behavior in a social network, more precisely to measure engagement. It was shown that generated memes cause the same engagement as human memes that produced low engagement in the social network (historically). Thus, generated memes are less engaging than random memes created by humans.

Keywords: content generation, computational social science, memes generation, Reddit, social networks, social media interaction

Procedia PDF Downloads 143

Authors: Z. H. Safari, M. Anbia, G. H. Kouzegari, R. Amirkhani

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Toluene and Nitro derivatives are widely used in industry particularly in various defense applications. Tri-nitro-toluene derivative is a powerful basic explosive material that is a basis upon which to compare equivalent explosive power of similar materials. The aim of this paper is to measure the explosive power of these hazardous substances in fuels having different shelf-life and therefore optimizing their storage and maintenance. The methodology involves measuring the amounts of di- nitro- toluene and tri-nitro-toluene in the aged samples at 90 ° C by gas chromatography. Results show no significant difference in the concentration of the TNT compound over a given time while there was a significant difference in DNT compound over the same period. The underlying reason is attributed to the simultaneous production of the material with destruction of stabilizer.

Keywords: dinitrotoluene, trinitrotoluene, double-base solid propellants, artificial aging

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Authors: C. P. Chukwuka, E. V. Chukwuka, F. Ukwadi

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This paper focuses on the advancement and societal impact of artificial intelligence (AI) systems. It explores the need for a theoretical framework in corporate governance, specifically in the context of 'hybrid' companies that have a mix of private and government ownership. The paper emphasizes the potential of AI to address challenges faced by these companies and highlights the importance of the less-explored state model in corporate governance. The aim of this research is to enhance AI systems for global impact and positive societal outcomes. It aims to explore the role of AI in refining corporate governance in hybrid companies and uncover nuanced insights into complex ownership structures. The methodology involves leveraging the capabilities of AI to address the challenges faced by hybrid companies in corporate governance. The researchers will analyze existing theoretical frameworks in corporate governance and integrate AI systems to improve problem-solving and understanding of intricate systems. The paper suggests that improved AI systems have the potential to shape a more informed and responsible corporate landscape. AI can uncover nuanced insights and navigate complex ownership structures in hybrid companies, leading to greater efficacy and positive societal outcomes. The theoretical importance of this research lies in the exploration of the role of AI in corporate governance, particularly in the context of hybrid companies. By integrating AI systems, the paper highlights the potential for improved problem-solving and understanding of intricate systems, contributing to a more informed and responsible corporate landscape. The data for this research will be collected from existing literature on corporate governance, specifically focusing on hybrid companies. Additionally, data on AI capabilities and their application in corporate governance will be collected. The collected data will be analyzed through a systematic review of existing theoretical frameworks in corporate governance. The researchers will also analyze the capabilities of AI systems and their potential application in addressing the challenges faced by hybrid companies. The findings will be synthesized and compared to identify patterns and potential improvements. The research concludes that AI systems have the potential to enhance corporate governance in hybrid companies, leading to greater efficacy and positive societal outcomes. By leveraging AI capabilities, nuanced insights can be uncovered, and complex ownership structures can be navigated, shaping a more informed and responsible corporate landscape. The findings highlight the importance of integrating AI in refining problem-solving and understanding intricate systems for global impact.

Keywords: advancement, artificial intelligence, challenges, societal impact

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Authors: Zulfiqar Ali

Abstract:

The current survey reports and data demonstrate a declining trend of admissions in instructor-led synchronous diploma programs in Canadian public higher education institutes. A significant impact can also be seen on various Information Technology (IT) related diploma programs in prominent Canadian higher education institutes across the country. The significant external factors that impact the students’ interests in admission in instructor-led synchronous Information Technology related diploma programs include but not limited to easy access to online learning materials provided by external competitors. The high involvement of the IT giants like Microsoft, Cisco, Google, AWS, Linux in training and certification programs through their Learning Management Systems (LMS) came with their academy’s establishment. They offer and provide very scientific advanced kind of learning and teaching resources embedded with cloud and artificial Intelligence (AI) tools, techniques and design. The other external factor is the best fit of rate of change of technology (velocity) in business vis-à-vis the rate of change of adoption and transformation of could-based Artificial Intelligence (AI) in Canadian public higher education institutes for diploma programs. The significant internal factors may include but are not limited to the legacy type of curriculum design, tools, techniques, style, and delivery. The other major contribution in declining admission trend in Canadian public higher education institute’s IT related programs.is the diversity of learning and teaching styles comes from existing hiring and immigration processes. The proposed research addresses the major contribution of both internal and external factors in declining admission trend in instructor-led synchronous diploma programs in Canadian public higher education institutes. The research approaches to be adopted for the proposed work include collecting data, filtering data, quantitative analysis, qualitative analysis and mixed approach. The focal point of this research is the contribution of major internal factors in declining admission trend including curriculum design, delivery methods, academic integrity, velocity, cloud-based AI tools, techniques and integration with existing learning management system. Finally, the research results come up with analysis-based recommendations and design to cope with challenge of declining admission trend in Canadian public higher education institutes diploma programs vis-à-vis internal and external factors.

Keywords: advanced curriculum design, analysis of internal educational factors, analysis of external educational factors, educational technology

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Authors: Hooman Torabifard

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In the last few years, with the progress of technology and computers and artificial intelligence entry into all kinds of scientific and industrial fields, the lifestyles of human life have changed and in general, the way of humans live on earth has many changes and development. Until now, some of the changes has occurred in the context of digital images and image processing and still continues. However, besides all the benefits, there have been disadvantages. One of these disadvantages is the multiplicity of images with high volume and data; the focus of this paper is on improving and developing a method for summarizing and enhancing the productivity of these images. The general method used for this purpose in this paper consists of a set of methods based on data obtained from image processing and using the PSO (Particle swarm optimization) algorithm. In the remainder of this paper, the method used is elaborated in detail.

Keywords: image summarization, particle swarm optimization, image threshold, image processing

Procedia PDF Downloads 139

Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

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Authors: Brahim Berbaoui

Abstract:

In this paper, a new particle swarm optimization (PSO) based method is proposed for the implantation of optimal harmonic power flow in power systems. In this algorithm approach, proportional integral controller for reference compensating currents of active power filter is performed in order to minimize the total harmonic distortion (THD). The simulation results show that the new control method using PSO approach is not only easy to be implanted, but also very effective in reducing the unwanted harmonics and compensating reactive power. The studies carried out have been accomplished using the MATLAB Simulink Power System Toolbox.

Keywords: shunt active power filter, power quality, current control, proportional integral controller, particle swarm optimization

Procedia PDF Downloads 619