Search results for: computable general equilibrium model

Authors: Aisulu Tileukulova, Zhexebay Dauren

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Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

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Authors: Foad Saadi

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Aim of this work was to determine the approximate Epoxy forming composition range of Cr-Zn system for the composites produced by forming compositing. It was predicted by MI edema semi-empirical model that the composition had to be in the range of 30-60 wt. % tin, while Cr-32Zn had the most susceptibility to produce amorphous composite. In the next stage, some different compositions of Cr-Zn were foamingly composited, where one of them had the proper predicted composition. Products were characterized by SDM analysis. There was a good agreement between calculation and experiments, in which Cr-32Zn composite had the most amorphization degree.

Keywords: Cr-Zn system, forming compositing, amorphous composite, MI edema model

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Authors: Nandini Nalika, Suhel Parvez

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Nanotechnology has emerged to play a vital role in developing all through the industrial world with an immense production of nanomaterials including nanoparticles (NPs). Many toxicological studies have confirmed that due to unique small size and physico-chemical properties of NPs (1-100nm), they can be potentially hazardous. Metallic NPs of small size have been shown to induce higher levels of cellular oxidative stress and can easily pass through the Blood Brain Barrier (BBB) and significantly accumulate in brain. With the wide applications of titanium dioxide nanoparticles (TNPs) in day-to-day life in form of cosmetics, paints, sterilisation and so on, there is growing concern regarding the deleterious effects of TNPs on central nervous system and mitochondria appear to be important cellular organelles targeted to the pro-oxidative effects of NPs and an important source that contribute significantly for the production of reactive oxygen species after some toxicity or an injury. The aim of our study was to elucidate the effect of TNPs in anatase form with different concentrations (5-50 µg/ml) following with various oxidative stress markers in isolated brain mitochondria as an in vitro model. Oxidative stress was determined by measuring the different oxidative stress markers like lipid peroxidation as well as the protein carbonyl content which was found to be significantly increased. Reduced glutathione content and major glutathione metabolizing enzymes were also modulated signifying the role of glutathione redox cycle in the pathophysiology of TNPs. The study also includes the mitochondrial enzymes (Complex 1, Complex II, complex IV, Complex V ) and the enzymes showed toxicity in a relatively short time due to the effect of TNPs. The study provide a range of concentration that were toxic to the neuronal cells and data pointing to a general toxicity in brain mitochondria by TNPs, therefore, it is in need to consider the proper utilization of NPs in the environment.

Keywords: mitochondria, nanoparticles, brain, in vitro

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Authors: Miroslav Dumbrovsky

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The paper deals with positive impacts of land consolidation on decreasing soil degradation with the main emphasis on soil and water conservation in the landscape. The importance of land degradation is very high because of its impact on crop productivity and many other adverse effects. Soil degradation through soil erosion is causing losses in crop productivity and quality of the environment, through decreasing quality of soil and water (especially water resources). Negative effects of conventional farming practices are increased water erosion, as well as crusting and compaction of the topsoil and subsoil. Soil erosion caused by water destructs the soil’s structure, reduces crop productivity due to deterioration in soil physical and chemical properties such as infiltration rate, water holding capacity, loss of nutrients needed for crop production, and loss of soil carbon. Recently, a new process of complex land consolidation in the Czech Republic has provided a unique opportunity for improving the quality of the environment and sustainability of the crop production by means a better soil and water conservation. The present process of the complex land consolidation is not only a reallocation of plots, but this system consists of a new layout of plots within a certain territory, aimed at establishing the integrated land-use economic units, based on the needs of individual landowners and land users. On the other hand, the interests of the general public and the environmental protection have to be solved, too. From the general point of view, a large part of the Czech landscape shall be reconstructed in the course of complex land consolidation projects. These projects will be based on new integrated soil-economic units, spatially arranged in a designed multifunctional system of soil and water conservation measures, such as path network and a territorial system of ecological stability, according to structural changes in agriculture. This new approach will be the basis of a rational economic utilization of the region which will comply with the present ecological and aesthetic demands at present.

Keywords: soil degradation, land consolidation, soil erosion, soil conservation

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Authors: Hussein Altartouri

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The purpose of this paper is to present a modeling and control of the active suspension system using robust state feedback controller implemented for a half car model. This system represents a mechatronic system which contains all the essential components to be considered a complete mechatronic system. This system must adapt different conditions which are difficult to compromise, such as disturbances, slippage, and motion on rough road (that contains rocks, stones, and other miscellanies). Some current automobile suspension systems use passive components only by utilizing spring and damping coefficient with fixed rates. Vehicle suspensions systems are used to provide good road handling and improve passenger comfort. Passive suspensions only offer compromise between these two conflicting criteria. Active suspension poses the ability to reduce the traditional design as a compromise between handling and comfort by directly controlling the suspensions force actuators. In this study, the robust state feedback controller implemented to the active suspensions system for half car model.

Keywords: half-car model, active suspension system, state feedback, road profile

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Authors: Donatella Giuliani

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In this research, we propose an unsupervised grayscale image segmentation method based on a combination of the Firefly Algorithm and the Gaussian Mixture Model. Firstly, the Firefly Algorithm has been applied in a histogram-based research of cluster means. The Firefly Algorithm is a stochastic global optimization technique, centered on the flashing characteristics of fireflies. In this context it has been performed to determine the number of clusters and the related cluster means in a histogram-based segmentation approach. Successively these means are used in the initialization step for the parameter estimation of a Gaussian Mixture Model. The parametric probability density function of a Gaussian Mixture Model is represented as a weighted sum of Gaussian component densities, whose parameters are evaluated applying the iterative Expectation-Maximization technique. The coefficients of the linear super-position of Gaussians can be thought as prior probabilities of each component. Applying the Bayes rule, the posterior probabilities of the grayscale intensities have been evaluated, therefore their maxima are used to assign each pixel to the clusters, according to their gray-level values. The proposed approach appears fairly solid and reliable when applied even to complex grayscale images. The validation has been performed by using different standard measures, more precisely: the Root Mean Square Error (RMSE), the Structural Content (SC), the Normalized Correlation Coefficient (NK) and the Davies-Bouldin (DB) index. The achieved results have strongly confirmed the robustness of this gray scale segmentation method based on a metaheuristic algorithm. Another noteworthy advantage of this methodology is due to the use of maxima of responsibilities for the pixel assignment that implies a consistent reduction of the computational costs.

Keywords: clustering images, firefly algorithm, Gaussian mixture model, meta heuristic algorithm, image segmentation

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Authors: Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty, Charles A. Kamhoua

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Malicious users exploit vulnerabilities in computer systems, significantly disrupting their performance and revealing the inadequacies of existing protective solutions. Even machine learning-based approaches, designed to ensure reliability, can be compromised by adversarial attacks that undermine their robustness. This paper addresses two critical aspects of enhancing model reliability. First, we focus on improving model performance and robustness against adversarial threats. To achieve this, we propose a strategy by harnessing deep reinforcement learning. Second, we introduce an approach leveraging generative adversarial networks to counter adversarial attacks effectively. Our results demonstrate substantial improvements over previous works in the literature, with classifiers exhibiting enhanced accuracy in classification tasks, even in the presence of adversarial perturbations. These findings underscore the efficacy of the proposed model in mitigating intrusions and adversarial attacks within the machine-learning landscape.

Keywords: machine learning, reliability, adversarial attacks, deep-reinforcement learning, robustness

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Authors: Arun Kumar Yadav, Badam Singh Kushvah

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In the present work, we investigated displaced periodic orbits in the linear order in the circular restricted three-body Sun-Jupiter system, where the third mass-less body utilizes solar electric sail. The electric solar sail is a new space propulsion concept which uses the solar wind momentum for producing thrust, and it is somewhat like to the more well-known solar radiation pressure sail which is often called simply the solar sail. Moreover, we implement the feedback linearization control scheme to perform the stabilization and trajectory tracking for the nonlinear system. Further, we derived periodic orbits analytically in linear order by introducing a first order approximation. These approximate analytic solutions are utilized in a numerical search to determine displaced periodic orbit in the full nonlinear model. We found the displaced periodic orbit for the defined non-linear model and stabilized the model.

Keywords: solar electric sail, circular restricted three-body problem (CRTBP), displaced orbit, feedback linearization control

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Authors: M. E. Kolpakova, A. A. Jakovleva, L. S. Poliakova, H. El Amghari, S. Soliman, D. R. Faizullina, V. V. Sharoyko

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Brain neuroplasticity is associated with blood-brain barrier vascular endothelial proteoglycans and post-stroke microglial activation. The study of the mechanisms of reperfusion injury limitation by remote ischemic postconditioning (RC) is of interest due to the effects on functional recovery after cerebral ischemia. The goal of the study is the assessment of the role of syndecan-1 (SDC-1) in restriction of ischemic-reperfusion injury on middle cerebral artery model in rats using RC protocol. Randomized controlled trials were conducted. Ischemia was performed by middle cerebral artery occlusion by Belayev L. (1996) on the Wistar rat-males (n= 87) weighting 250 ± 50 g. under general anesthesia (Zoletil 100 и Xylazine 2%). Syndecan-1 (SDC-1) concentration difference in plasma samples of false operated animals and animals with brain ischemia was 30% (30 min. МСАо: 41.4 * ± 1.3 ng/ml). SDC-1 concentration in animal plasma samples with ischemia + RC protocol was 112% (30 min МСАо+ RC): 67.8**± 5.8 ng/ml). Calculation of infarction volume in the ischemia group revealed brain injury in 31.97 ± 2.5%; the volume of infarction was 13.6 ± 1.3% in 30 min. МCАо + RC group. Swelling of tissue in the group 30 min. МCАо + RC was 16 ± 2.1%; it was 47 ± 3.3%. in 30 min. МCАо group. Correlation analysis showed a high direct correlation relationship between infarct area and muscle strength in the right forelimb (КК=0.72) in the 30 min. МCАо + RC group. Correlation analysis showed very high inverse correlation between infarct area and capillary blood flow in the 30 min. МCАо + RC group (p <0.01; r = -0.98). We believe the SDC-1 molecule in blood plasma may play role of potential messenger of ischemic-reperfusion injury restriction mechanisms. This leads to infarct-limiting effect of remote ischemic postconditioning and early functioning recovery.

Keywords: ischemia, МСАо, remote ischemic postconditioning, syndecan-1

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Authors: Sami Meddeb, M. L Giorgi, J. L. Courouau

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This work presents the progress of studies aiming to guarantee the lifetime of 316L(N) steel in a sodium-cooled fast reactor by determining the elementary corrosion mechanism, which is akin to an accelerated dissolution by dissolved oxygen. The mechanism involving iron, the main element of steel, is particularly studied in detail, from the viewpoint of the data available in the literature, the modeling of the various mechanisms hypothesized. Experiments performed in the CORRONa facility at controlled temperature and dissolved oxygen content are used to test both literature data and hypotheses. Current tests, performed at various temperatures and oxygen content, focus on specifying the chemical reaction at play, determining its free enthalpy, as well as kinetics rate constants. Specific test configuration allows measuring the reaction kinetics and the chemical equilibrium state in the same test. In the current state of progress of these tests, the dissolution of iron accelerated by dissolved oxygen appears as directly related to a chemical complexation reaction of mixed iron-sodium oxide (Na-Fe-O), a compound that is soluble in the liquid sodium solution. Results obtained demonstrate the presence in the solution of this corrosion product, whose kinetics is the limiting step under the conditions of the test. This compound, the object of hypotheses dating back more than 50 years, is predominant in solution compared to atomic iron, presumably even for the low oxygen concentration, and cannot be neglected for the long-term corrosion modeling of any heat transfer system.

Keywords: corrosion, sodium fast reactors, iron, oxygen

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Authors: T. H. Bitebe, T. M. Lima, F. Charrua-Santos, C. J. Matias Oliveira

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This study aims to present a proposal for an economic integration model for the development of the manufacturing industry in Cabinda, Angola. It seeks to analyze the degree of economic integration of Cabinda and the dynamics of the manufacturing industry. Therefore, in the same way, to gather information to support the decision-making for public financing programs that will aim at the disengagement of the manufacturing industry in Angola and Cabinda in particular. The Cabinda Province is the 18^th of Angola, the enclave is located in a privileged area of the African and arable land.

Keywords: economic integration, industrial development, Cabinda industry, Angola

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Authors: Akhtar Nawaz, Sahar Noor, Iftikhar Hussain

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This paper considers advanced hybrid manufacturing planning both push and pull system in which each customer order has a due date by demand forecast and customer orders. We present a tool for model for tool development that requires an absolute due dates and customer orders in a manufacturing supply chain. It is vital for the manufacturing companies to face the problem of variations in demands, increase in varieties by maintaining safety stock and to minimize components obsolescence and uselessness. High inventory cost and low delivery lead time is expected in push type of system and on contrary high delivery lead time and low inventory cost is predicted in the pull type. For this tool for model we need an MRP system for the push and pull environment and control of inventories in push parts and lead time in the pull part. To retain process data quickly, completely and to improve responsiveness and minimize inventory cost, a tool is required to deal with the high product variance and short cycle parts. In practice, planning and scheduling are interrelated and should be solved simultaneously with supply chain to ensure that the due dates of customer orders are met. The proposed tool for model considers alternative process plans for job types, with precedence constraints for job operations. Such a tool for model has not been treated in the literature. To solve the model, tool was developed, so a new technique was required to deal with the issue of high product variance and short life cycles in assemble to order.

Keywords: hybrid manufacturing system, supply chain system, make to order, make to stock, assemble to order

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Authors: Benjamin Morse, Michaela Zint, Jennifer Carman

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This study proposes a behavioral model in which ecotourists’ level of observational learning shapes their subsequent environmentally responsible behavioral intentions through ecotourism participation. Unlike past studies that have focused on individual attributes such as attitudes, locus of control, personal responsibility, knowledge, skills or effect, this present study explores select social attributes as potential antecedents to environmentally responsible behaviors. A total of 207 completed questionnaires were obtained from ecotourists in Korea and path analyses were conducted to explore the degree in which the hypothesized model directly and indirectly explained ecotourists’ environmentally responsible behavioral intentions. Results suggest that observational learning and its associated predictors (i.e., engagement, observation, reproduction and reinforcement) are key determinants of ecotourists environmentally responsible behavioral intentions. The application of observational learning proved to be informative, and has a number of implications for improving ecotourism programs. Our model also lays out a theoretical framework for future research.

Keywords: ecotourism, observational learning, environmentally responsible behavior, social learning theory

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Authors: Munaza Mubeen

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The colonial Urdu literature has played a significant role in uplifting the overall status of the Indians. Taking example of the Bhopal State, the contribution of the Royal ladies holds no match in the contemporary age. Similarly, the efforts of the Begamat-e-Bhopal in the field of education had been acknowledged across the Sub-continent. It had been noticed that with the downfall of the Mughal Empire at the hands of the British Raj, the local culture in the Indian sub-continent in general and in Bhopal State in proper got deteriorated. This led to uncertainty, chaos and an anti-British sentiment which created hatred against them in the entire sub-continent. Likely, in Bhopal State, the role of women in field of education also vanished. However, this change was felt by the ladies in the Royal Household where they struggled for restoration of the status quo with their personal efforts. The prominent contributors from the Royal household included Qudseya Begum, Nawab Sikander Begum, Nawab Shah Jahan Begum and Nawab Sultan Johan-they are known as Begamat-i-Bhopal. They established schools, colleges and universities to impart formal and non-formal education respectively. Their systematic approach and continued efforts constituted a dynamic aspect of women’ empowerment. Focused on the female education, however, one of the by-products of their efforts was an introduction of socio-cultural and politico-religious awareness among the middle class womenfolk. Unfortunately, the role of these Bagamat-i-Bhopal has been left untouched or ignored by the academic scholars. This study critically analyzed the role of Beghamat-i-Bhopal regarding female education in Hindustan in general and in Bhopal in particular. Similarly, the contribution of the contemporary writers in development of female education in Bhopal has also been discussed in this paper. This paper is based on the 'historico-textual' approach following structural reason which has established a justifiable argument. The researcher, therefore, has taken into consideration all the chronological nuances in order to accord greater visibility to the contents. The data has been described, analyzed and interpreted inclusively and in generalized manner.

Keywords: colonialism, Begamaat e Bhopal, female education, State of Bhopal

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Authors: J. DeBoard, R. Dietrich, J. Hughes, K. Yurko, G. Harms

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Alzheimer’s disease (AD) is a predominant type of dementia and is likely a major cause of neural network impairment. The pathogenesis of this neurodegenerative disorder has yet to be fully elucidated. There are currently no known cures for the disease, and the best hope is to be able to detect it early enough to impede its progress. Beyond age and genetics, another prevalent risk factor for AD might be traumatic brain injury (TBI), which has similar neurodegenerative hallmarks. Our research focuses on obtaining information and methods to be able to predict when neurodegenerative effects might occur at a clinical level by observation of events at a cellular and molecular level in model mice. First, we wish to introduce our evidence that brain damage can be observed via brain imaging prior to the noticeable loss of neuromuscular control in model mice of AD. We then show our evidence that some blood biomarkers might be able to be early predictors of AD in the same model mice. Thus, we were interested to see if we might be able to predict which mice might show long-term neurodegenerative effects due to differing degrees of TBI and what level of TBI causes further damage and earlier death to the AD model mice. Upon application of TBIs via an apparatus to effectively induce extremely mild to mild TBIs, wild-type (WT) mice and AD mouse models were tested for cognition, neuromuscular control, olfactory ability, blood biomarkers, and brain imaging. Experiments are currently still in process, and more results are therefore forthcoming. Preliminary data suggest that neuromotor control diminishes as well as olfactory function for both AD and WT mice after the administration of five consecutive mild TBIs. Also, seizure activity increases significantly for both AD and WT after the administration of the five TBI treatment. If future data supports these findings, important implications about the effect of TBI on those at risk for AD might be possible.

Keywords: Alzheimer's disease, blood biomarker, neurodegeneration, neuromuscular control, olfaction, traumatic brain injury

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Authors: Maurice Bilioniere, Katie Lanneau

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Suddenly appeared in 2011, invasions of sargasso seaweeds Fluitans and Natans are a climatic hazard which causes many problems in the Caribbean. Faced with the growth and frequency of the phenomenon of massive sargasso stranding on their coasts, the French West Indies are moving towards the path of industrial recovery. In this context of innovative projects, we will analyze the necessary requirements for the management and performance of the supply chain, taking into account the observed volatility of the sargasso input. Our prospective approach will consist in studying the theoretical framework of modeling a hybrid supply chain by coupling the discreet event simulation (DES) with a valuation of the process costs according to the "activity-based costing" method (ABC). The PREVA approach (PRocess EVAluation) chosen for our modeling has the advantage of evaluating the financial flows of the logistic process using an analytical model chained with an action model for the evaluation or optimization of physical flows.

Keywords: sargasso, PREVA modeling, supply chain, ABC method, discreet event simulation (DES)

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Authors: M. Bilal Ishfaq, Adnan N. Qureshi

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COVID-19 is the most infectious disease these days, it was first reported in Wuhan, the capital city of Hubei in China then it spread rapidly throughout the whole world. Later on 11 March 2020, the World Health Organisation (WHO) declared it a pandemic. Since COVID-19 is highly contagious, it has affected approximately 219M people worldwide and caused 4.55M deaths. It has brought the importance of accurate diagnosis of respiratory diseases such as pneumonia and COVID-19 to the forefront. In this paper, we propose a hybrid approach for the automated detection of COVID-19 using medical imaging. We have presented the hybridization of manually extracted and convolutional features. Our approach combines Haralick texture features and convolutional features extracted from chest X-rays and CT scans. We also employ a minimum redundancy maximum relevance (MRMR) feature selection algorithm to reduce computational complexity and enhance classification performance. The proposed model is evaluated on four publicly available datasets, including Chest X-ray Pneumonia, COVID-19 Pneumonia, COVID-19 CTMaster, and VinBig data. The results demonstrate high accuracy and effectiveness, with 0.9925 on the Chest X-ray pneumonia dataset, 0.9895 on the COVID-19, Pneumonia and Normal Chest X-ray dataset, 0.9806 on the Covid CTMaster dataset, and 0.9398 on the VinBig dataset. We further evaluate the effectiveness of the proposed model using ROC curves, where the AUC for the best-performing model reaches 0.96. Our proposed model provides a promising tool for the early detection and accurate diagnosis of COVID-19, which can assist healthcare professionals in making informed treatment decisions and improving patient outcomes. The results of the proposed model are quite plausible and the system can be deployed in a clinical or research setting to assist in the diagnosis of COVID-19.

Keywords: COVID-19, feature engineering, artificial neural networks, radiology images

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Authors: Yael Neumann

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Attention deficits are common in people with aphasia (PWA). Two treatment approaches address these deficits: domain-general methods like Play Attention, which focus on cognitive functioning, and domain-specific methods like Language-Specific Attention Treatment (L-SAT), which use linguistically based tasks. Research indicates that L-SAT can improve both attentional deficits and functional language skills, while Play Attention has shown success in enhancing attentional capabilities among school-aged children with attention issues compared to standard cognitive training. This study employed a randomized controlled cross-over single-subject design to evaluate the effectiveness of these two attention treatments over 25 weeks. Four PWA participated, undergoing a battery of eight standardized tests measuring language and cognitive skills. The treatments were counterbalanced. Play Attention used EEG sensors to detect brainwaves, enabling participants to manipulate items in a computer game while learning to suppress theta activity and increase beta activity. An algorithm tracked changes in the theta-to-beta ratio, allowing points to be earned during the games. L-SAT, on the other hand, involved hierarchical language tasks that increased in complexity, requiring greater attention from participants. Results showed that for language tests, Participant 1 (moderate aphasia) aligned with existing literature, showing L-SAT was more effective than Play Attention. However, Participants 2 (very severe) and 3 and 4 (mild) did not conform to this pattern; both treatments yielded similar outcomes. This may be due to the extremes of aphasia severity: the very severe participant faced significant overall deficits, making both approaches equally challenging, while the mild participant performed well initially, leaving limited room for improvement. In attention tests, Participants 1 and 4 exhibited results consistent with prior research, indicating Play Attention was superior to L-SAT. Participant 2, however, showed no significant improvement with either program, although L-SAT had a slight edge on the Visual Elevator task, measuring switching and mental flexibility. This advantage was not sustained at the one-month follow-up, likely due to the participant’s struggles with complex attention tasks. Participant 3's results similarly did not align with prior studies, revealing no difference between the two treatments, possibly due to the challenging nature of the attention measures used. Regarding participation and ecological tests, all participants showed similar mild improvements with both treatments. This limited progress could stem from the short study duration, with only five weeks allocated for each treatment, which may not have been enough time to achieve meaningful changes affecting life participation. In conclusion, the performance of participants appeared influenced by their level of aphasia severity. The moderate PWA’s results were most aligned with existing literature, indicating better attention improvement from the domain-general approach (Play Attention) and better language improvement from the domain-specific approach (L-SAT).

Keywords: attention, language, cognitive rehabilitation, neurofeedback

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Authors: A. W. J. Wong, I. H. Ibrahim

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Increasing urbanisation has driven building designers to incorporate natural ventilation in the designs of sustainable buildings. This project utilises Computational Fluid Dynamics (CFD) to investigate the natural ventilation of an academic building, SIT@SP, using an assessment criterion based on daily mean temperature and mean velocity. The areas of interest are the pedestrian level of first and fourth levels of the building. A reference case recommended by the Architectural Institute of Japan was used to validate the simulation model. The validated simulation model was then used for coupled simulations on SIT@SP and neighbouring geometries, under two wind speeds. Both steady and transient simulations were used to identify differences in results. Steady and transient results are agreeable with the transient simulation identifying peak velocities during flow development. Under a lower wind speed, the first level was sufficiently ventilated while the fourth level was not. The first level has excessive wind velocities in the higher wind speed and the fourth level was adequately ventilated. Fourth level flow velocity was consistently lower than those of the first level. This is attributed to either simulation model error or poor building design. SIT@SP is concluded to have a sufficiently ventilated first level and insufficiently ventilated fourth level. Future works for this project extend to modifying the urban geometry, simulation model improvements, evaluation using other assessment metrics and extending the area of interest to the entire building.

Keywords: buildings, CFD Simulations, natural ventilation, urban airflow

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Authors: Ozcan Yavas, Berrak Erol Nalbur, Sermin Gunarslan

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A materiality matrix is a tool that organizations use to prioritize their activities and adapt to the increasing sustainability requirements in recent years. For the materiality index to move from business models to the sustainability business model stage, it must be done with all partners in the raw material, supply, production, product, and end-of-life product stages. Within the scope of this study, the Materiality Matrix was used to transform the business model into a sustainability business model and to create a sustainability roadmap in a factory producing electric buses. This matrix determines the necessary roadmap for all stakeholders to participate in the process, especially in sectors that produce sustainable products, such as the electric vehicle sector, and to act together with the cradle-to-cradle approach of sustainability roadmaps. Global Reporting Initiative analysis was used in the study conducted with 1150 stakeholders within the scope of the study, and 43 questions were asked to the stakeholders under the main headings of 'Legal Compliance Level,' 'Environmental Strategies,' 'Risk Management Activities,' 'Impact of Sustainability Activities on Products and Services,' 'Corporate Culture,' 'Responsible and Profitable Business Model Practices' and 'Achievements in Leading the Sector' and Economic, Governance, Environment, Social and Other. The results of the study aimed to include five 1st priority issues and four 2nd priority issues in the sustainability strategies of the organization in the short and medium term. When the studies carried out in the short term are evaluated in terms of Sustainability and Environmental Risk Management, it is seen that the studies are still limited to the level of legal legislation (60%) and individual studies in line with the strategies (20%). At the same time, the stakeholders expect the company to integrate sustainability activities into its business model within five years (35%) and to carry out projects to become the first company that comes to mind with its success leading the sector (20%). Another result obtained within the study's scope is identifying barriers to implementation. It is seen that the most critical obstacles identified by stakeholders with climate change and environmental impacts are financial deficiency and lack of infrastructure in the dissemination of sustainable products. These studies are critical for transitioning to sustainable business models for the electric vehicle sector to achieve the EU Green Deal and CBAM targets.

Keywords: sustainability business model, materiality matrix, electricity bus, carbon neutrality, sustainability management

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Authors: Hesam Khajehsaeid, Naeimeh Alagheband

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Magnetorheological fluids (MRF) are a category of smart materials. They exhibit a reversible change from a Newtonian-like fluid to a semi-solid state upon application of an external magnetic field. In contrast to ordinary fluids, MRFs can tolerate shear stresses up to a threshold value called yield stress which strongly depends on the strength of the magnetic field, magnetic particles volume fraction and temperature. Even beyond the yield, a magnetic field can increase MR fluid viscosity up to several orders. As yield stress is an important parameter in the design of MR devices, in this work, the effects of magnetic field intensity and magnetic particle concentration on the yield stress of MRFs are investigated. Four MRF samples with different particle concentrations are developed and tested through flow-ramp analysis to obtain the flow curves at a range of magnetic field intensity as well as shear rate. The viscosity of the fluids is determined by means of the flow curves. The results are then used to determine the yield stresses by means of the steady stress sweep method. The yield stresses are then determined by means of a modified form of the dipole model as well as empirical models. The exponential distribution function is used to describe the orientation of particle chains in the dipole model under the action of the external magnetic field. Moreover, the modified dipole model results in a reasonable distribution of chains compared to previous similar models.

Keywords: magnetorheological fluids, yield stress, particles concentration, dipole model

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Authors: Ayoub Moufid, Najib Bennis, Soumia El Hani

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The objective of greenhouse climate control is to improve the culture development and to minimize the production costs. A greenhouse is an open system to external environment and the challenge is to regulate the internal climate despite the strong meteorological disturbances. The internal state of greenhouse considered in this work is defined by too relevant and coupled variables, namely inside temperature and hygrometry. These two variables are chosen to describe the internal state of greenhouses due to their importance in the development of plants and their sensitivity to external climatic conditions, sources of weather disturbances. A multivariable model is proposed and validated by considering a greenhouse as black-box system and the least square method is applied to parameters identification basing on collected experimental measures. To regulate the internal climate, we propose a Model Predictive Control (MPC) scheme. This one considers the measured meteorological disturbances and the physical and operational constraints on the control and state variables. A successful feasibility study of the proposed controller is presented, and simulation results show good performances despite the high interaction between internal and external variables and the strong external meteorological disturbances. The inside temperature and hygrometry are tracking nearly the desired trajectories. A comparison study with an On/Off control applied to the same greenhouse confirms the efficiency of the MPC approach to inside climate control.

Keywords: climate control, constraints, identification, greenhouse, model predictive control, optimization

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Authors: Castells Pau, Poetsch Christophe

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The effect of gust and turbulence encounters on aircraft is a wide field of study which allows different approaches, from high-fidelity multidisciplinary simulations to more simplified models adapted to industrial applications. The typical main goal is to predict the gust loads on the aircraft in order to ensure a safe design and achieve certification. Another topic widely studied is the gust loads reduction through an active control law. The impact of gusts on aircraft handling qualities is of interest as well in the analysis of in-service events so as to evaluate the aircraft response and the performance of the flight control laws. Traditionally, gust loads and handling qualities are addressed separately with different models adapted to the specific needs of each discipline. In this paper, an assessment of the differences between both models is presented and a strategy to better account for the physics of gust encounters in a typical flight dynamics model is proposed based on the model used for gust loads analysis. The applied corrections aim to capture the gust unsteady aerodynamics and propagation as well as the effect of dynamic flexibility at low frequencies. Results from the gust loads model at different flight conditions and measures from real events are used for validation. An assessment of a possible extension of steady aerodynamic nonlinearities to low frequency range is also addressed. The proposed corrections provide meaningful means to evaluate the performance and possible adjustments of the flight control laws.

Keywords: flight dynamics, gust loads, handling qualities, unsteady aerodynamics

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Authors: Theena Thayalan, K. N. Ramesh Babu

Abstract:

Machining induced residual stress (RS) is one of the most important surface integrity parameters that characterize the near surface layer of a mechanical component, which plays a crucial role in controlling the performance, especially its fatigue life. Since experimental determination of RS is expensive and time consuming, it would be of great benefit if they could be predicted. In such case, it would be possible to select the cutting parameters required to produce a favorable RS profile. In the present study, an effort has been made to develop a 'two dimensional finite element model (FEM)' to simulate orthogonal cutting process and to predict surface and sub-surface RS using the commercial FEA software DEFORM-2D. The developed finite element model has been validated through experimental investigation of RS. In the experimentation, the orthogonal cutting tests were carried out on AISI 4340 by varying the cutting speed (VC) and uncut chip thickness (f) at three levels and the surface & sub-surface RS has been measured using XRD and Electro polishing techniques. The comparison showed that the RS obtained using developed numerical model is in reasonable agreement with that of experimental data.

Keywords: FEM, machining, residual stress, XRF

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Authors: Yusri Yusof, Kamran Latif

Abstract:

Computer Numeric Control (CNC) machine has been widely used in the industries since its inception. Currently, in CNC technology has been used for various operations like milling, drilling, packing and welding etc. with the rapid growth in the manufacturing world the demand of flexibility in the CNC machines has rapidly increased. Previously, the commercial CNC failed to provide flexibility because its structure was of closed nature that does not provide access to the inner features of CNC. Also CNC’s operating ISO data interface model was found to be limited. Therefore, to overcome that problem, Open Architecture Control (OAC) technology and STEP-NC data interface model are introduced. At present the Personal Computer (PC) has been the best platform for the development of open-CNC systems. In this paper, both ISO data interface model interpretation, its verification and execution has been highlighted with the introduction of the new techniques. The proposed is composed of ISO data interpretation, 3D simulation and machine motion control modules. The system is tested on an old 3 axis CNC milling machine. The results are found to be satisfactory in performance. This implementation has successfully enabled sustainable manufacturing environment.

Keywords: CNC, ISO 6983, ISO 14649, LabVIEW, open architecture control, reconfigurable manufacturing systems, sustainable manufacturing, Soft-CNC

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Authors: John Lamb, Robert Epstein, Vasundhara L. Bhupathi, Sanjeev Kumar Marimekala

Abstract:

In this paper, we focus on the importance of scalable architecture for data centers and buildings in general to help an enterprise achieve environmental sustainability. The scalable architecture helps in many ways, such as adaptability to the business and user requirements, promotes high availability and disaster recovery solutions that are cost effective and low maintenance. The scalable architecture also plays a vital role in three core areas of sustainability: economy, environment, and social, which are also known as the 3 pillars of a sustainability model. If the architecture is scalable, it has many advantages. A few examples are that scalable architecture helps businesses and industries to adapt to changing technology, drive innovation, promote platform independence, and build resilience against natural disasters. Most importantly, having a scalable architecture helps industries bring in cost-effective measures for energy consumption, reduce wastage, increase productivity, and enable a robust environment. It also helps in the reduction of carbon emissions with advanced monitoring and metering capabilities. Scalable architectures help in reducing waste by optimizing the designs to utilize materials efficiently, minimize resources, decrease carbon footprints by using low-impact materials that are environmentally friendly. In this paper we also emphasize the importance of cultural shift towards the reuse and recycling of natural resources for a balanced ecosystem and maintain a circular economy. Also, since all of us are involved in the use of computers, much of the scalable architecture we have studied is related to data centers.

Keywords: scalable architectures, sustainability, application design, disruptive technology, machine learning and natural language processing, AI, social media platform, cloud computing, advanced networking and storage devices, advanced monitoring and metering infrastructure, climate change

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Authors: Jihène Khalifa

Abstract:

This study analyzed the relationships between Tunisia’s economic growth, food security, agricultural productivity, and climate change using the ARDL model for the period from 1990 to 2022. The ARDL model reveals a positive correlation between economic growth and lagged agricultural productivity. Additionally, the vector autoregressive (VAR) model highlights the beneficial impact of lagged agricultural productivity on economic growth and the negative effect of rainfall on economic growth. Granger causality analysis identifies unidirectional relationships from economic growth to agricultural productivity, crop production, food security, and temperature variations, as well as from temperature variations to crop production. Furthermore, a bidirectional causality is established between crop production and food security. The study underscores the impact of climate change on crop production and suggests the need for adaptive strategies to mitigate these climate effects.

Keywords: economic growth, climate change, agriculture, ARDL, Granger causality, VAR

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Authors: Animish Jain

Abstract:

This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

Procedia PDF Downloads 79

Authors: Minoo Ghasemzadeh, Rouzbeh Riazi, Shidvash Vakilipour, Alireza Ramezani

Abstract:

In this study, thermo acoustic oscillations of a lean premixed combustor has been investigated, and a mono-dimensional code was developed in this regard. The linearized equations of motion are solved for perturbations with time dependence〖 e〗^iwt. Two flame models were considered in this paper and the effect of mean flow and boundary conditions were also investigated. After manipulation of flame heat release equation together with the equations of flow perturbation within the main components of the combustor model (i.e., plenum/ premixed duct/ and combustion chamber) and by considering proper boundary conditions between the components of model, a system of eight homogeneous equations can be obtained. This simplification, for the main components of the combustor model, is convenient since low frequency acoustic waves are not affected by bends. Moreover, some elements in the combustor are smaller than the wavelength of propagated acoustic perturbations. A convection time is also assumed to characterize the required time for the acoustic velocity fluctuations to travel from the point of injection to the location of flame front in the combustion chamber. The influence of an extended flame model on the acoustic frequencies of combustor was also investigated, assuming the effect of flame speed as a function of equivalence ratio perturbation, on the rate of flame heat release. The abovementioned system of equations has a related eigenvalue equation which has complex roots. The sign of imaginary part of these roots determines whether the disturbances grow or decay and the real part of these roots would give the frequency of the modes. The results show a reasonable agreement between the predicted values of dominant frequencies in the present model and those calculated in previous related studies.

Keywords: combustion instability, dominant frequencies, flame speed, premixed combustor

Procedia PDF Downloads 379

Authors: Abdelmadjid Mammeri, Fatima Zohra Mahi, Luca Varani, H. Marinchoi

Abstract:

We propose an analytical model for the admittance and the noise calculations of the InGaAs transistor and diode. The development of the small-signal admittance takes into account the longitudinal and transverse electric fields through a pseudo two-dimensional approximation of the Poisson equation. The frequency-dependent of the small-signal admittance response is determined by the total currents and the potentials matrix relation between the gate and the drain terminals. The noise is evaluated by using the real part of the transistor/diode admittance under a small-signal perturbation. The analytical results show that the admittance spectrum exhibits a series of resonant peaks corresponding to the excitation of plasma waves. The appearance of the resonance is discussed and analyzed as functions of the channel length and the temperature. The model can be used, on one hand; to control the appearance of the plasma resonances, and on other hand; can give significant information about the noise frequency dependence in the InGaAs transistor and diode.

Keywords: InGaAs transistors, InGaAs diode, admittance, resonant peaks, plasma waves, analytical model

Procedia PDF Downloads 316