Search results for: pre-processing image algorithms

Authors: Monica Dantas

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At what socio-economic costs can the food industry offer products at low prices? This research seeks to understand and to explore how we attribute competence and meaning, what enables the outcomes of agriculture and what institutions provides validation regarding food production. This study objective is to explain and interpret conditions of the present state of agriculture in Brazil centring on two distinct segments, agribusiness and family farming, as the Brazilian, rapidly changing political environment unfolds. The approach is grounded in multidisciplinary literature drawing from the politics of development, the sociology of food, the sustainability framework and the conceptual differences between agribusiness and family farming regarding the innate purpose of the two segments. In addition, a quantitative portion of the research includes secondary data analysis from statistical measurements, economic indicators, federal budget information, and census data to compare the two segments, conveying a general snapshot of the conditions investigated. The results raised questions about the perceived image of the success of agribusiness, against some contradicting economic checks and balances. Analyzing how public funds are invested in agriculture shed light on what can enable or undermine the development of food systems in Brazil. It also revealed how politics, ideology, and corporations might influence the Brazilian Federal. In the 2000-2018 observed timeline of annual federal spending on agriculture in Brazil, there is variation in the amount invested in family farming that seems to 'coincide' with the ideological direction of the federal government in power. It was also observed that significant changes in the institutional framework and financial support either promoted or purposely undermined family farming importance using public institutions to validate support for agribusiness.

Keywords: food politics, sustainability, family farming, food system, public budget

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Authors: Sa'ad M. Ismail, Hung-Hsun Yen, Christina M. Murray, Helen M. S. Davies

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In the dog, the median sacral crest is formed by the fusion of three sacral spinous processes. In greyhounds with standard sacrums, this fusion in the median sacral crest consists of the fusion of three sacral spinous processes while it consists of four in greyhounds with sacrocaudal fusion. In the present study, variations in the angulation of the first sacral spinous process in association with different types of sacrocaudal fusion in the greyhound were investigated. Sacrums were collected from 207 greyhounds (102 sacrums; type A (unfused) and 105 with different types of sacrocaudal fusion; types: B, C and D). Sacrums were cleaned by boiling and dried and then were placed on their ventral surface on a flat surface and photographed from the left side using a digital camera at a fixed distance. The first sacral spinous process angle (1st SPA) was defined as the angle formed between the cranial border of the cranial ridge of the first sacral spinous process and the line extending across the most dorsal surface points of the spinous processes of the S1, S2, and S3. Image-Pro Express Version 5.0 imaging software was used to draw and measure the angles. Two photographs were taken for each sacrum and two repeat measurements were also taken of each angle. The mean value of the 1st SPA in greyhounds with sacrocaudal fusion was less (98.99°, SD ± 11, n = 105) than those in greyhounds with standard sacrums (99.77°, SD ± 9.18, n = 102) but was not significantly different (P < 0.05). Among greyhounds with different types of sacrocaudal fusion the mean value of the 1st SPA was as follows: type B; 97.73°, SD ± 10.94, n = 39, type C: 101.42°, SD ± 10.51, n = 52, and type D: 94.22°, SD ± 11.30, n = 12. For all types of fusion these angles were significantly different from each other (P < 0.05). Comparing the mean value of the1st SPA in standard sacrums (Type A) with that for each type of fusion separately showed that the only significantly different angulation (P < 0.05) was between standard sacrums and sacrums with sacrocaudal fusion sacrum type D (only body fusion between the S1 and Ca1). Different types of sacrocaudal fusion were associated with variations in the angle of the first sacral spinous process. These variations may affect the alignment and biomechanics of the sacral area and the pattern of movement and/or the force produced by both hind limbs to the cranial parts of the body and may alter the loading of other parts of the body. We concluded that any variations in the sacrum anatomical features might change the function of the sacrum or surrounding anatomical structures during movement.

Keywords: angulation of first sacral spinous process, biomechanics, greyhound, locomotion, sacrocaudal fusion

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Authors: M. S. Jouini, A. Al-Sumaiti, M. Tembely, K. Rahimov

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Characterizing rock properties of carbonate reservoirs is highly challenging because of rock heterogeneities revealed at several length scales. In the last two decades, the Digital Rock Physics (DRP) approach was implemented successfully in sandstone rocks reservoirs in order to understand rock properties behaviour at the pore scale. This approach uses 3D X-ray Microtomography images to characterize pore network and also simulate rock properties from these images. Even though, DRP is able to predict realistic rock properties results in sandstone reservoirs it is still suffering from a lack of clear workflow in carbonate rocks. The main challenge is the integration of properties simulated at different scales in order to obtain the effective rock property of core plugs. In this paper, we propose several approaches to characterize absolute permeability in some carbonate core plugs samples using multi-scale numerical simulation workflow. In this study, we propose a procedure to simulate porosity and absolute permeability of a carbonate rock sample using textures of Micro-Computed Tomography images. First, we discretize X-Ray Micro-CT image into a regular grid. Then, we use a textural parametric model to classify each cell of the grid using supervised classification. The main parameters are first and second order statistics such as mean, variance, range and autocorrelations computed from sub-bands obtained after wavelet decomposition. Furthermore, we fill permeability property in each cell using two strategies based on numerical simulation values obtained locally on subsets. Finally, we simulate numerically the effective permeability using Darcy’s law simulator. Results obtained for studied carbonate sample shows good agreement with the experimental property.

Keywords: multiscale modeling, permeability, texture, micro-tomography images

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Authors: Hind Hassan Arruwaite

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Conceptual metaphor is a cognitive semantic tool that provides access to people's conceptual systems. The correlation in the human conceptual system surpasses limited time and specific cultures. The universal associations provide universal schemas that organize people's conceptualization of the world. The study aims to explore how the cultural associations used in conceptual metaphors create commonalities and harmony between people of the world. In the research methodology, the researcher implemented Critical Metaphor Analysis, Metaphor Candidate Identification and Metaphor Identification Procedure models to deliver qualitative and descriptive findings. The semantic tension was the key criterion in identifying metaphorically used words in the headlines. The research materials are the oil trade conceptual metaphors used in the headlines of Arab News and Saudi Gazette Newspapers. The data will be uploaded to the self-constructed corpus to examine electronic lists for identifying conceptual metaphors. The study investigates the types of conceptual metaphors used in the headlines of the newspapers, the cultural associations identified in the conceptual metaphors, and whether the identified cultural associations in conceptual metaphors create universal conceptual schemas. The study aligned with previous seminal works on conceptual metaphor theory in emphasizing the distinctive power of conceptual metaphors in exposing the cultural associations that unify people's perceptions. The correlation of people conceptualization provides universal schemas that involve elements of human sensorimotor experiences. The study contributes to exposing the shared cultural associations that ensure the commonality of all humankind's thinking mechanism.

Keywords: critical discourse analysis, critical metaphor analysis, conceptual metaphor theory, primary and specific metaphors, corpus-driven approach, universal associations, image schema, sensorimotor experience, oil trade

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Authors: Jaydip M. Desai, Antonio Valdevit, Arthur Ritter

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Robotic surgery is used to enhance minimally invasive surgical procedure. It provides greater degree of freedom for surgical tools but lacks of haptic feedback system to provide sense of touch to the surgeon. Surgical robots work on master-slave operation, where user is a master and robotic arms are the slaves. Current, surgical robots provide precise control of the surgical tools, but heavily rely on visual feedback, which sometimes cause damage to the inner organs. The goal of this research was to design and develop a real-time simulink based robotic system to study force feedback mechanism during instrument-object interaction. Setup includes three Velmex XSlide assembly (XYZ Stage) for three dimensional movement, an end effector assembly for forceps, electronic circuit for four strain gages, two Novint Falcon 3D gaming controllers, microcontroller board with linear actuators, MATLAB and Simulink toolboxes. Strain gages were calibrated using Imada Digital Force Gauge device and tested with a hard-core wire to measure instrument-object interaction in the range of 0-35N. Designed simulink model successfully acquires 3D coordinates from two Novint Falcon controllers and transfer coordinates to the XYZ stage and forceps. Simulink model also reads strain gages signal through 10-bit analog to digital converter resolution of a microcontroller assembly in real time, converts voltage into force and feedback the output signals to the Novint Falcon controller for force feedback mechanism. Experimental setup allows user to change forward kinematics algorithms to achieve the best-desired movement of the XYZ stage and forceps. This project combines haptic technology with surgical robot to provide sense of touch to the user controlling forceps through machine-computer interface.

Keywords: surgical robot, haptic feedback, MATLAB, strain gage, simulink

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Authors: Pranav Gulati, Isha Sharma

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Hypertension and hypotension are known to have repercussions on the health of an individual, with hypertension contributing to an increased probability of risk to cardiovascular diseases and hypotension resulting in syncope. This prompts the development of a non-invasive, non-intrusive, continuous and cuff-less blood pressure monitoring system to detect blood pressure variations and to identify individuals with acute and chronic heart ailments, but due to the unavailability of such devices for practical daily use, it becomes difficult to screen and subsequently regulate blood pressure. The complexities which hamper the steady monitoring of blood pressure comprises of the variations in physical characteristics from individual to individual and the postural differences at the site of monitoring. We propose to develop a continuous, comprehensive cardio-analysis tool, based on reflective photoplethysmography (PPG). The proposed device, in the form of an eyewear captures the PPG signal and estimates the systolic and diastolic blood pressure using a sensor positioned near the temporal artery. This system relies on regression models which are based on extraction of key points from a pair of PPG wavelets. The proposed system provides an edge over the existing wearables considering that it allows for uniform contact and pressure with the temporal site, in addition to minimal disturbance by movement. Additionally, the feature extraction algorithms enhance the integrity and quality of the extracted features by reducing unreliable data sets. We tested the system with 12 subjects of which 6 served as the training dataset. For this, we measured the blood pressure using a cuff based BP monitor (Omron HEM-8712) and at the same time recorded the PPG signal from our cardio-analysis tool. The complete test was conducted by using the cuff based blood pressure monitor on the left arm while the PPG signal was acquired from the temporal site on the left side of the head. This acquisition served as the training input for the regression model on the selected features. The other 6 subjects were used to validate the model by conducting the same test on them. Results show that the developed prototype can robustly acquire the PPG signal and can therefore be used to reliably predict blood pressure levels.

Keywords: blood pressure, photoplethysmograph, eyewear, physiological monitoring

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Authors: S. Zhang, L. Zhang, X. Chen

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Due to the large uncertainty on the mechanical properties of FRP (fibre reinforced plastic), the reliability evaluation of FRP structures are currently receiving much attention in industry. However, possible statistical correlation between ply mechanical properties has been so far overlooked, and they are mostly assumed to be independent random variables. In this study, the statistical correlation between ply mechanical properties of uni-directional and plain weave composite is firstly analyzed by a combination of Monte-Carlo simulation and finite element modeling of the FRP unit cell. Large linear correlation coefficients between the in-plane mechanical properties are observed, and the correlation coefficients are heavily dependent on the uncertainty of the fibre volume ratio. It is also observed that the correlation coefficients related to Poisson’s ratio are negative while others are positive. To experimentally achieve the statistical correlation coefficients between in-plane mechanical properties of FRP, all concerned in-plane mechanical properties of the same specimen needs to be known. In-plane shear modulus of FRP is experimentally derived by the approach suggested in the ASTM standard D5379M. Tensile tests are conducted using the same specimens used for the shear test, and due to non-uniform tensile deformation a modification factor is derived by a finite element modeling. Digital image correlation is adopted to characterize the specimen non-uniform deformation. The preliminary experimental results show a good agreement with the numerical analysis on the statistical correlation. Then, failure probability of laminate plates is calculated in cases considering and not considering the statistical correlation, using the Monte-Carlo and Markov Chain Monte-Carlo methods, respectively. The results highlight the importance of accounting for the statistical correlation between ply mechanical properties to achieve accurate failure probability of laminate plates. Furthermore, it is found that for the multi-layer laminate plate, the statistical correlation between the ply elastic properties significantly affects the laminate reliability while the effect of statistical correlation between the ply strength is minimal.

Keywords: failure probability, FRP, reliability, statistical correlation

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Authors: Carla Palencia-Aguilar

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Calculations of Net Carbon Balance can be obtained by means of Net Biome Productivity (NBP), Net Ecosystem Productivity (NEP), and Net Primary Production (NPP). The latter is an important component of the biosphere carbon cycle and is easily obtained data from MODIS MOD17A3HGF; however, the results are only available yearly. To overcome data availability, bands 33 to 36 from MODIS MYD021KM (obtained on a daily basis) were analyzed and compared with NPP data from the years 2000 to 2021 in 7 sites where surface mining takes place in the Colombian territory. Coal, Gold, Iron, and Limestone were the minerals of interest. Scales and Units as well as thermal anomalies, were considered for net carbon balance per location. The NPP time series from the satellite images were filtered by using two Matlab filters: First order and Discrete Transfer. After filtering the NPP time series, comparing the graph results from the satellite’s image value, and running a linear regression, the results showed R2 from 0,72 to 0,85. To establish comparable units among NPP and bands 33 to 36, the Greenhouse Gas Equivalencies Calculator by EPA was used. The comparison was established in two ways: one by the sum of all the data per point per year and the other by the average of 46 weeks and finding the percentage that the value represented with respect to NPP. The former underestimated the total CO2 emissions. The results also showed that coal and gold mining in the last 22 years had less CO2 emissions than limestone, with an average per year of 143 kton CO2 eq for gold, 152 kton CO2 eq for coal, and 287 kton CO2 eq for iron. Limestone emissions varied from 206 to 441 kton CO2 eq. The maximum emission values from unfiltered data correspond to 165 kton CO2 eq. for gold, 188 kton CO2 eq. for coal, and 310 kton CO2 eq. for iron and limestone, varying from 231 to 490 kton CO2 eq. If the most pollutant limestone site improves its production technology, limestone could count with a maximum of 318 kton CO2 eq emissions per year, a value very similar respect to iron. The importance of gathering data is to establish benchmarks in order to attain 2050’s zero emissions goal.

Keywords: carbon dioxide, NPP, MODIS, MINING

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Authors: Angelis P. Barlampas

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The purpose of this study is to inform the reader, about the various applications of artificial intelligence (AI), in cardiac imaging. AI grows fast and its role is crucial in medical specialties, which use large amounts of digital data, that are very difficult or even impossible to be managed by human beings and especially doctors.Artificial intelligence (AI) refers to the ability of computers to mimic human cognitive function, performing tasks such as learning, problem-solving, and autonomous decision making based on digital data. Whereas AI describes the concept of using computers to mimic human cognitive tasks, machine learning (ML) describes the category of algorithms that enable most current applications described as AI. Some of the current applications of AI in cardiac imaging are the follows: Ultrasound: Automated segmentation of cardiac chambers across five common views and consequently quantify chamber volumes/mass, ascertain ejection fraction and determine longitudinal strain through speckle tracking. Determine the severity of mitral regurgitation (accuracy > 99% for every degree of severity). Identify myocardial infarction. Distinguish between Athlete’s heart and hypertrophic cardiomyopathy, as well as restrictive cardiomyopathy and constrictive pericarditis. Predict all-cause mortality. CT Reduce radiation doses. Calculate the calcium score. Diagnose coronary artery disease (CAD). Predict all-cause 5-year mortality. Predict major cardiovascular events in patients with suspected CAD. MRI Segment of cardiac structures and infarct tissue. Calculate cardiac mass and function parameters. Distinguish between patients with myocardial infarction and control subjects. It could potentially reduce costs since it would preclude the need for gadolinium-enhanced CMR. Predict 4-year survival in patients with pulmonary hypertension. Nuclear Imaging Classify normal and abnormal myocardium in CAD. Detect locations with abnormal myocardium. Predict cardiac death. ML was comparable to or better than two experienced readers in predicting the need for revascularization. AI emerge as a helpful tool in cardiac imaging and for the doctors who can not manage the overall increasing demand, in examinations such as ultrasound, computed tomography, MRI, or nuclear imaging studies.

Keywords: artificial intelligence, cardiac imaging, ultrasound, MRI, CT, nuclear medicine

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Authors: Adel Khorramrouz, Monireh Ahmadi Bani, Ehsan Norouzi, Morvarid Lalenoor

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Background: Plantar pressure distribution measurement has been used for a long time to assess foot disorders. Plantar pressure is an important component affecting the foot and ankle function and Changes in plantar pressure distribution could indicate various foot and ankle disorders. Morphologic and mechanical properties of the foot may be important factors affecting the plantar pressure distribution. Accurate and early measurement may help to reduce the prevalence of pes planus. With recent developments in technology, new techniques such as machine learning have been used to assist clinicians in predicting patients with foot disorders. Significance of the study: This study proposes a neural network learning-based flat foot classification methodology using static foot pressure distribution. Methodologies: Data were collected from 895 patients who were referred to a foot clinic due to foot disorders. Patients with pes planus were labeled by an experienced physician based on clinical examination. Then all subjects (with and without pes planus) were evaluated for static plantar pressures distribution. Patients who were diagnosed with the flat foot in both feet were included in the study. In the next step, the leg length was normalized and the network was trained for plantar pressure mapping images. Findings: From a total of 895 image data, 581 were labeled as pes planus. A computational neural network (CNN) ran to evaluate the performance of the proposed model. The prediction accuracy of the basic CNN-based model was performed and the prediction model was derived through the proposed methodology. In the basic CNN model, the training accuracy was 79.14%, and the test accuracy was 72.09%. Conclusion: This model can be easily and simply used by patients with pes planus and doctors to predict the classification of pes planus and prescreen for possible musculoskeletal disorders related to this condition. However, more models need to be considered and compared for higher accuracy.

Keywords: foot disorder, machine learning, neural network, pes planus

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Authors: Zahid Ullah, Atlas Khan

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The rapid growth of data in diverse domains has created an urgent need for effective utilization of mathematical and statistical sciences in data analytics. This abstract explores the journey from theory to practice, emphasizing the importance of harnessing mathematical and statistical innovations to unlock the full potential of data analytics. Drawing on a comprehensive review of existing literature and research, this study investigates the fundamental theories and principles underpinning mathematical and statistical sciences in the context of data analytics. It delves into key mathematical concepts such as optimization, probability theory, statistical modeling, and machine learning algorithms, highlighting their significance in analyzing and extracting insights from complex datasets. Moreover, this abstract sheds light on the practical applications of mathematical and statistical sciences in real-world data analytics scenarios. Through case studies and examples, it showcases how mathematical and statistical innovations are being applied to tackle challenges in various fields such as finance, healthcare, marketing, and social sciences. These applications demonstrate the transformative power of mathematical and statistical sciences in data-driven decision-making. The abstract also emphasizes the importance of interdisciplinary collaboration, as it recognizes the synergy between mathematical and statistical sciences and other domains such as computer science, information technology, and domain-specific knowledge. Collaborative efforts enable the development of innovative methodologies and tools that bridge the gap between theory and practice, ultimately enhancing the effectiveness of data analytics. Furthermore, ethical considerations surrounding data analytics, including privacy, bias, and fairness, are addressed within the abstract. It underscores the need for responsible and transparent practices in data analytics, and highlights the role of mathematical and statistical sciences in ensuring ethical data handling and analysis. In conclusion, this abstract highlights the journey from theory to practice in harnessing mathematical and statistical sciences in data analytics. It showcases the practical applications of these sciences, the importance of interdisciplinary collaboration, and the need for ethical considerations. By bridging the gap between theory and practice, mathematical and statistical sciences contribute to unlocking the full potential of data analytics, empowering organizations and decision-makers with valuable insights for informed decision-making.

Keywords: data analytics, mathematical sciences, optimization, machine learning, interdisciplinary collaboration, practical applications

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Authors: John Acord, Ankita Batla, Kiran Khepar, Maude Schmidt, Charlotte Allen, Russ Bradford

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Objectives: Despite the availability oftreatment options, Parkinson’s disease (PD) continues to impact heavily on a patient’s quality of life (QoL), as many symptoms that bother the patient remain unexplored and untreated in clinical settings. The aims of this research were to understand the burden of PDsymptoms from a patient perspective, particularly those which are the most persistent and debilitating, and to determine if current treatments and treatment algorithms adequately focus on their resolution. Methods: A13-question, online, patient-reported survey was created based on the MDS-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)and symptoms listed on Parkinson’s Disease Patient Advocacy Groups websites, and then validated by 10 Parkinson’s patients. In the survey, patients were asked to choose both their most common and their most bothersome symptoms, whether they had received treatment for those and, if so, had it been effective in resolving those symptoms. Results: The most bothersome symptoms reported by the 111 participants who completed the survey were sleep problems (61%), feeling tired (56%), slowness of movements (54%), and pain in some parts of the body (49%). However, while 86% of patients reported receiving dopamine or dopamine like drugs to treat their PD, far fewer reported receiving targeted therapies for additional symptoms. For example, of the patients who reported having sleep problems, only 33% received some form of treatment for this symptom. This was also true for feeling tired (30% received treatment for this symptom), slowness of movements (62% received treatment for this symptom), and pain in some parts of the body (61% received treatment for this symptom). Additionally, 65% of patients reported that the symptoms they experienced were not adequately controlled by the treatments they received, and 9% reported that their current treatments had no effect on their symptoms whatsoever. Conclusion: The survey outcomes highlight that the majority of patients involved in the study received treatment focused on their disease, however, symptom-based treatments were less well represented. Consequently, patient-reported symptoms such as sleep problems and feeling tired tended to receive more fragmented intervention than ‘classical’ PD symptoms, such as slowness of movement, even though they were reported as being amongst the most bothersome symptoms for patients. This research highlights the need to explore symptom burden from the patient’s perspective and offer Customised treatment/support for both motor and non-motor symptoms maximize patients’ quality of life.

Keywords: survey, patient reported symptom burden, unmet needs, parkinson's disease

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Authors: Gihan Thilakarathna, Fernando Ishara, Rathnayake Yasith, Bandara A. M. R. Y.

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E-learning is a crucial component of intelligent education. Even in the midst of a pandemic, E-learning is becoming increasingly important in the educational system. Several e-learning programs are accessible for students. Here, we decided to create an e-learning framework for children. We've found a few issues that teachers are having with their online classes. When there are numerous students in an online classroom, how does a teacher recognize a student's focus on academics and below-the-surface behaviors? Some kids are not paying attention in class, and others are napping. The teacher is unable to keep track of each and every student. Key challenge in e-learning is online exams. Because students can cheat easily during online exams. Hence there is need of exam proctoring is occurred. In here we propose an automated online exam cheating detection method using a web camera. The purpose of this project is to present an E-learning platform for math education and include games for kids as an alternative teaching method for math students. The game will be accessible via a web browser. The imagery in the game is drawn in a cartoonish style. This will help students learn math through games. Everything in this day and age is moving towards automation. However, automatic answer evaluation is only available for MCQ-based questions. As a result, the checker has a difficult time evaluating the theory solution. The current system requires more manpower and takes a long time to evaluate responses. It's also possible to mark two identical responses differently and receive two different grades. As a result, this application employs machine learning techniques to provide an automatic evaluation of subjective responses based on the keyword provided to the computer as student input, resulting in a fair distribution of marks. In addition, it will save time and manpower. We used deep learning, machine learning, image processing and natural language technologies to develop these research components.

Keywords: math, education games, e-learning platform, artificial intelligence

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Authors: Samuel O. Alamu, Seong W. Lee, Blaise Kalmia, Marc J. Louise Caballes, Xuejun Qian

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The application of combustion technologies for thermal conversion of biomass and solid wastes to energy has been a major solution to the effective handling of wastes over a long period of time. Lab-scale biomass combustion systems have been observed to be economically viable and socially acceptable, but major concerns are the environmental impacts of the process and deviation of temperature distribution within the combustion chamber. Both high and low combustion chamber temperature may affect the overall combustion efficiency and gaseous emissions. Therefore, there is an urgent need to develop a control system which measures the deviations of chamber temperature from set target values, sends these deviations (which generates disturbances in the system) in the form of feedback signal (as input), and control operating conditions for correcting the errors. In this research study, major components of the feedback control system were determined, assembled, and tested. In addition, control algorithms were developed to actuate operating conditions (e.g., air velocity, fuel feeding rate) using ladder logic functions embedded in the Programmable Logic Controller (PLC). The developed control algorithm having chamber temperature as a feedback signal is integrated into the lab-scale swirling fluidized bed combustor (SFBC) to investigate the temperature distribution at different heights of the combustion chamber based on various operating conditions. The air blower rates and the fuel feeding rates obtained from automatic control operations were correlated with manual inputs. There was no observable difference in the correlated results, thus indicating that the written PLC program functions were adequate in designing the experimental study of the lab-scale SFBC. The experimental results were analyzed to study the effect of air velocity operating at 222-273 ft/min and fuel feeding rate of 60-90 rpm on the chamber temperature. The developed temperature-based feedback control system was shown to be adequate in controlling the airflow and the fuel feeding rate for the overall biomass combustion process as it helps to minimize the steady-state error.

Keywords: air flow, biomass combustion, feedback control signal, fuel feeding, ladder logic, programmable logic controller, temperature

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Authors: Ram Moorat Singh, Arun Kumar Sharma, Ravi Chaurey

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Assessment of characteristics of Himalayan glaciers with or without glacier lakes was carried out for 1937glaciers of Kosi sub-basin, Ganga basin by using remote sensing and GIS techniques. Analysis of IRS-P6 AWiFS Data of 2004-07 periods, SRTM DEM and MODIS Land Surface Temperature (LST) data (15year mean) using image processing and GIS tools has provided significant information on various glacier parameters. The glacier area, length, width, ice exposed area, debris cover area, glacier slope, orientation, elevation and temperature data was analysed. The 119 supra glacier lakes and 62 moraine dam/peri-glacier lakes (area > 0.02 km2) in the study were studied to discern the suitable glacier conditions for glacier lake formation. On analysis it is observed that the glacial lakes are preferably formed in association with large dimension glaciers (area, length and width), glaciers with higher percent ice exposed area, lower percent debris cover area and in general mean elevation value greater than 5300 m amsl. On analysis of lake type shows that the moraine dam lakes are formed associated with glaciers located at relatively higher altitude as compared to altitude of glaciers with supra glacier lakes. Analysis of frequency of occurrence of lakes vis a vis glacier orientation shows that more number of glacier lakes are formed associated with glaciers having orientation south, south east, south west, east and west directions. The supra glacial lakes are formed in association with glaciers having higher mean temperature as compared to moraine dam lakes as verified using LST data of 15 years (2000-2014).

Keywords: remote sensing, supra glacial lake, Himalaya, Kosi sub-basin, glaciers, moraine-dammed lake

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Authors: Loes Nijskens

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The city of The Hague is coping with several social issues: high unemployment rates, segregation and environmental pollution. The amount of social enterprises in The Hague that want to tackle these issues is increasing, but no clear image exists of the stimulants and obstacles social entrepreneurs encounter. In this qualitative study 20 starting and established social entrepreneurs, investors and stimulators of social entrepreneurship have been interviewed. The findings indicate that the majority of entrepreneurs situated in The Hague focuses on creating jobs (the so called social nurturers) and diminishing food waste. Moreover, the study found smaller groups of social connectors, (who focus on stimulating the social cohesion in the city) and social traders (who create a market for products from developing countries). For the social nurturers, working together with local government to find people with a distance to the labour market is a challenge. The entrepreneurs are missing a governance approach within the local government, wherein space is provided to develop suitable legislation and projects in cooperation with several stakeholders in order to diminish social problems. All entrepreneurs in the sample face(d) the challenge of having a clear purpose of their business in the beginning. Starting social entrepreneurs tend to be idealistic without having defined a business model. Without a defined business model it is difficult to find proper funding for their business. The more advanced enterprises cope with the challenge of measuring social impact. The larger they grow, the more they have to ‘defend’ themselves towards the local government and their customers, of mainly being social. Hence, the more experienced social nurturers still find it difficult to work together with the local government. They tend to settle their business in other municipalities, where they find more effective public-private partnerships. Al this said, the eco-system for social enterprises in The Hague is on the rise. To stimulate the amount and growth of social enterprises the cooperation between entrepreneurs and local government, the developing of social business models and measuring of impact needs more attention.

Keywords: obstacles, social enterprises, stimulants, the Hague

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Authors: Hsin Hua He

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The purpose of this study is to strengthen the social cohesion of the local community by injecting the cultural and creative concept into the industrial heritage transformation. The paradigms of industrial heritage research tend to explore from the perspective of space analysis, which concerned less about the cultural regeneration and the development of local culture. The paradigms of cultural quarter research use to from the perspective of creative economy and urban planning, concerned less about the social impacts and the interaction between residents and industrial sites. This research combines these two research areas of industrial heritage and cultural quarter, and focus on the social and cultural aspects. The transformation from the industrial heritage into a cultural park not only enhances the cultural capital and the quality of residents’ lives, but also preserves the unique local values. Internally it shapes the local identity, while externally establishes the image of the city. This paper uses Westergasfabriek Cultural Park in Amsterdam as the case study, through literature analysis, field work, and depth interview to explore how the cultural regeneration transforms industrial heritage. In terms of the planners’ and residents’ point of view adopt the theory of community participation, social capital, and sense of place to analyze the social impact of the industrial heritage transformation. The research finding is through cultural regeneration policies like holding cultural activities, building up public space, social network and public-private partnership, and adopting adaptive reuse to fulfil the people’s need and desire and reach the social cohesion. Finally, the study will examine the transformation of Taiwan's industrial heritage into cultural and creative quarters. The results are expected to use the operating experience of the Amsterdam cases and provide directions for Taiwan’s industrial heritage management to meet the cultural, social, economic symbiosis.

Keywords: cultural regeneration, community participation, social capital, sense of place, industrial heritage transformation

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Authors: Amaladhithyan Krishnamoorthy, Ruvaitha Banu

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Vision is considered as the most important sense in humans, without which leading a normal can be often difficult. There are many existing smart canes for visually impaired with obstacle detection using ultrasonic transducer to help them navigate. Though the basic smart cane increases the safety of the users, it does not help in filling the void of visual loss. This paper introduces the concept of Digi-Buddy which is an evolved smart cane for visually impaired. The cane consists for several modules, apart from the basic obstacle detection features; the Digi-Buddy assists the user by capturing video/images and streams them to the server using a wide-angled camera, which then detects the objects using Deep Convolutional Neural Network. In addition to determining what the particular image/object is, the distance of the object is assessed by the ultrasonic transducer. The sound generation application, modelled with the help of Natural Language Processing is used to convert the processed images/object into audio. The object detected is signified by its name which is transmitted to the user with the help of Bluetooth hear phones. The object detection is extended to facial recognition which maps the faces of the person the user meets in the database of face images and alerts the user about the person. One of other crucial function consists of an automatic-intimation-alarm which is triggered when the user is in an emergency. If the user recovers within a set time, a button is provisioned in the cane to stop the alarm. Else an automatic intimation is sent to friends and family about the whereabouts of the user using GPS. In addition to safety and security by the existing smart canes, the proposed concept devices to be implemented as a prototype helping visually-impaired visualize their surroundings through audio more in an amicable way.

Keywords: artificial intelligence, facial recognition, natural language processing, internet of things

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Authors: Frank Fan

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PharmD (Doctor of Pharmacy) education has confronted an increasing challenge — curricular overload, a phenomenon resulting from the expansion of curricular requirements, as PharmD education strives to produce graduates who are practice-ready. The aftermath of the global pandemic has amplified the need for healthcare professionals, leading to a growing trend of assigning more responsibilities to them to address the global healthcare shortage. For instance, the pharmacist’s role has expanded to include not only compounding and distributing medication but also providing clinical services, including minor ailments management, patient counselling and vaccination. Consequently, PharmD programs have responded by continually expanding their curricula adding more requirements. While these changes aim to enhance the education and training of future professionals, they have also led to unintended consequences, including curricular overload, student burnout, and a potential decrease in program quality. To address the issue and ensure program quality, there is a growing need for evidence-based curriculum reforms. My research seeks to integrate Cognitive Load Theory, emerging machine learning algorithms within artificial intelligence (AI), and statistical approaches to develop a quantitative framework for optimizing curriculum design within the PharmD program at the University of Toronto, the largest PharmD program within Canada, to provide quantification and measurement of issues that currently are only discussed in terms of anecdote rather than data. This research will serve as a guide for curriculum planners, administrators, and educators, aiding in the comprehension of how the pharmacy degree program compares to others within and beyond the field of pharmacy. It will also shed light on opportunities to reduce the curricular load while maintaining its quality and rigor. Given that pharmacists constitute the third-largest healthcare workforce, their education shares similarities and challenges with other health education programs. Therefore, my evidence-based, data-driven curriculum analysis framework holds significant potential for training programs in other healthcare professions, including medicine, nursing, and physiotherapy.

Keywords: curriculum, curriculum analysis, health professions education, reflective writing, machine learning

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Authors: Prarabdh Tiwari, M. Vidya Sagar, K. Bhima Raju, Joy Choudhury, Subash Chandra, E. Nagaiah, Shakeel Ahmed

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Passive seismic, a wavefield interferometric imaging, low cost and rapid tool for subsurface investigation is used for various geotechnical purposes such as hydrocarbon exploration, seismic microzonation, etc. With the recent advancement, its application has also been extended to groundwater exploration by means of finding the bedrock depth. Council of Scientific & Industrial Research (CSIR)-National Geophysical Research Institute (NGRI) has experimented passive seismic studies along with electrical resistivity tomography for groundwater in hard rock (Choutuppal, Hyderabad). Passive Seismic with Electrical Resistivity (ERT) can give more clear 2-D subsurface image for Groundwater Exploration in Hard Rock area. Passive seismic data were collected using a Tromino, a three-component broadband seismometer, to measure background ambient noise and processed using GRILLA software. The passive seismic results are found corroborating with ERT (Electrical Resistivity Tomography) results. For data acquisition purpose, Tromino was kept over 30 locations consist recording of 20 minutes at each station. These location shows strong resonance frequency peak, suggesting good impedance contrast between different subsurface layers (ex. Mica rich Laminated layer, Weathered layer, granite, etc.) This paper presents signature of passive seismic for hard rock terrain. It has been found that passive seismic has potential application for formation characterization and can be used as an alternative tool for delineating litho-stratification in an urban condition where electrical and electromagnetic tools cannot be applied due to high cultural noise. In addition to its general application in combination with electrical and electromagnetic methods can improve the interpreted subsurface model.

Keywords: passive seismic, resonant frequency, Tromino, GRILLA

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Authors: Desplanches Maxime

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Accurate estimation of the remaining useful life of lithium-ion batteries for electronic devices is crucial. Data-driven methodologies encounter challenges related to data volume and acquisition protocols, particularly in capturing a comprehensive range of aging indicators. To address these limitations, we propose a hybrid approach that integrates an electrochemical model with state-of-the-art data analysis techniques, yielding a comprehensive database. Our methodology involves infusing an aging phenomenon into a Newman model, leading to the creation of an extensive database capturing various aging states based on non-destructive parameters. This database serves as a robust foundation for subsequent analysis. Leveraging advanced data analysis techniques, notably principal component analysis and t-Distributed Stochastic Neighbor Embedding, we extract pivotal information from the data. This information is harnessed to construct a regression function using either random forest or support vector machine algorithms. The resulting predictor demonstrates a 5% error margin in estimating remaining battery life, providing actionable insights for optimizing usage. Furthermore, the database was built from the Newman model calibrated for aging and performance using data from a European project called Teesmat. The model was then initialized numerous times with different aging values, for instance, with varying thicknesses of SEI (Solid Electrolyte Interphase). This comprehensive approach ensures a thorough exploration of battery aging dynamics, enhancing the accuracy and reliability of our predictive model. Of particular importance is our reliance on the database generated through the integration of the electrochemical model. This database serves as a crucial asset in advancing our understanding of aging states. Beyond its capability for precise remaining life predictions, this database-driven approach offers valuable insights for optimizing battery usage and adapting the predictor to various scenarios. This underscores the practical significance of our method in facilitating better decision-making regarding lithium-ion battery management.

Keywords: Li-ion battery, aging, diagnostics, data analysis, prediction, machine learning, electrochemical model, regression

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Authors: Milena Nanova, Radul Shishkov, Damyan Damov, Martin Georgiev

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This research paper presents an in-depth exploration of the use of generative design in urban residential architecture, with a dual focus on aligning aesthetic values with legal and environmental constraints. The study aims to demonstrate how generative design methodologies can innovate residential building designs that are not only legally compliant and environmentally conscious but also aesthetically compelling. At the core of our research is a specially developed generative design framework tailored for urban residential settings. This framework employs computational algorithms to produce diverse design solutions, meticulously balancing aesthetic appeal with practical considerations. By integrating site-specific features, urban legal restrictions, and environmental factors, our approach generates designs that resonate with the unique character of urban landscapes while adhering to regulatory frameworks. The paper places emphasis on algorithmic implementation of the logical constraint and intricacies in residential architecture by exploring the potential of generative design to create visually engaging and contextually harmonious structures. This exploration also contains an analysis of how these designs align with legal building parameters, showcasing the potential for creative solutions within the confines of urban building regulations. Concurrently, our methodology integrates functional, economic, and environmental factors. We investigate how generative design can be utilized to optimize buildings' performance, considering them, aiming to achieve a symbiotic relationship between the built environment and its natural surroundings. Through a blend of theoretical research and practical case studies, this research highlights the multifaceted capabilities of generative design and demonstrates practical applications of our framework. Our findings illustrate the rich possibilities that arise from an algorithmic design approach in the context of a vibrant urban landscape. This study contributes an alternative perspective to residential architecture, suggesting that the future of urban development lies in embracing the complex interplay between computational design innovation, regulatory adherence, and environmental responsibility.

Keywords: generative design, computational design, parametric design, algorithmic modeling

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Authors: Ka Ho Yau, Jan Frederick Engels, Kwok Kei Lai, Reinhard Renneberg

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Lateral flow test is a prevalent technology in various sectors such as food, pharmacology and biomedical sciences. Colloidal gold (CG) is widely used as the signalling molecule because of the ease of synthesis, bimolecular conjugation and its red colour due to intrinsic SPRE. However, the production of colloidal gold is costly and requires vigorous conditions. The stability of colloidal gold are easily affected by environmental factors such as pH, high salt content etc. Silica nanoparticles are well known for its ease of production and stability over a wide range of solvents. Using reverse micro-emulsion (w/o), silica nanoparticles with different sizes can be produced precisely by controlling the amount of water. By incorporating different water-soluble dyes, a rainbow colour of the silica nanoparticles could be produced. Conjugation with biomolecules such as antibodies can be achieved after surface modification of the silica nanoparticles with organosilane. The optimum amount of the antibodies to be labelled was determined by Bradford Assay. In this work, we have demonstrated the ability of the dye-doped silica nanoparticles as a signalling molecule in lateral flow test, which showed a semi-quantitative measurement of the analyte. The image was further analysed for the LOD=10 ng of the analyte. The working range and the linear range of the test were from 0 to 2.15μg/mL and from 0 to 1.07 μg/mL (R2=0.988) respectively. The performance of the tests was comparable to those using colloidal gold with the advantages of lower cost, enhanced stability and having a wide spectrum of colours. The positives lines can be imaged by naked eye or by using a mobile phone camera for a better quantification. Further research has been carried out in multicolour detection of different biomarkers simultaneously. The preliminary results were promising as there was little cross-reactivity being observed for an optimized system. This approach provides a platform for multicolour detection for a set of biomarkers that enhances the accuracy of diseases diagnostics.

Keywords: colorimetric detection, immunosensor, paper-based biosensor, silica

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Authors: Komal Ghafoor, Tauqir Ahmad, Murtaza Hanif, Hira Zaheer

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Assistive technology has been developed to support visually impaired people in their social interactions. Conversation assistive technology is designed to enhance communication skills, facilitate social interaction, and improve the quality of life of visually impaired individuals. This technology includes speech recognition, text-to-speech features, and other communication devices that enable users to communicate with others in real time. The technology uses natural language processing and machine learning algorithms to analyze spoken language and provide appropriate responses. It also includes features such as voice commands and audio feedback to provide users with a more immersive experience. These technologies have been shown to increase the confidence and independence of visually impaired individuals in social situations and have the potential to improve their social skills and relationships with others. Overall, conversation-assistive technology is a promising tool for empowering visually impaired people and improving their social interactions. One of the key benefits of conversation-assistive technology is that it allows visually impaired individuals to overcome communication barriers that they may face in social situations. It can help them to communicate more effectively with friends, family, and colleagues, as well as strangers in public spaces. By providing a more seamless and natural way to communicate, this technology can help to reduce feelings of isolation and improve overall quality of life. The main objective of this research is to give blind users the capability to move around in unfamiliar environments through a user-friendly device by face, object, and activity recognition system. This model evaluates the accuracy of activity recognition. This device captures the front view of the blind, detects the objects, recognizes the activities, and answers the blind query. It is implemented using the front view of the camera. The local dataset is collected that includes different 1st-person human activities. The results obtained are the identification of the activities that the VGG-16 model was trained on, where Hugging, Shaking Hands, Talking, Walking, Waving video, etc.

Keywords: dataset, visually impaired person, natural language process, human activity recognition

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Authors: Zbigniew Czyż, Adam Majczak, Lukasz Grabowski

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The paper presents a description of geometric models, computational algorithms, and results of numerical analyses of charge exchange in a two-stroke opposed-piston engine. The research engine was a newly designed internal Diesel engine. The unit is characterized by three cylinders in which three pairs of opposed-pistons operate. The engine will generate a power output equal to 100 kW at a crankshaft rotation speed of 3800-4000 rpm. The numerical investigations were carried out using ANSYS FLUENT solver. Numerical research, in contrast to experimental research, allows us to validate project assumptions and avoid costly prototype preparation for experimental tests. This makes it possible to optimize the geometrical model in countless variants with no production costs. The geometrical model includes an intake manifold, a cylinder, and an outlet manifold. The study was conducted for a series of modifications of manifolds and intake and exhaust ports to optimize the charge exchange process in the engine. The calculations specified a swirl coefficient obtained under stationary conditions for a full opening of intake and exhaust ports as well as a CA value of 280° for all cylinders. In addition, mass flow rates were identified separately in all of the intake and exhaust ports to achieve the best possible uniformity of flow in the individual cylinders. For the models under consideration, velocity, pressure and streamline contours were generated in important cross sections. The developed models are designed primarily to minimize the flow drag through the intake and exhaust ports while the mass flow rate increases. Firstly, in order to calculate the swirl ratio [-], tangential velocity v [m/s] and then angular velocity ω [rad / s] with respect to the charge as the mean of each element were calculated. The paper contains comparative analyses of all the intake and exhaust manifolds of the designed engine. Acknowledgement: This work has been realized in the cooperation with The Construction Office of WSK "PZL-KALISZ" S.A." and is part of Grant Agreement No. POIR.01.02.00-00-0002/15 financed by the Polish National Centre for Research and Development.

Keywords: computational fluid dynamics, engine swirl, fluid mechanics, mass flow rates, numerical analysis, opposed-piston engine

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Authors: Zainab S. Ahmed, Mohammed S. Ali, Nadia A. Elshiekh, Sami Adam Ibrahim, Ghada M. El-Tayeb, Ahmed H. Elsadig, Rihab A. Omer, Sofia B. Mohamed

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Maple syrup urine (MSUD) is an autosomal recessive disease that causes a deficiency in the enzyme branched-chain alpha-keto acid (BCKA) dehydrogenase. The development of disease has been associated with SNPs in the DBT gene. Despite that, the computational analysis of SNPs in coding and noncoding and their functional impacts on protein level still remains unknown. Hence, in this study, we carried out a comprehensive in silico analysis of missense that was predicted to have a harmful influence on DBT structure and function. In this study, eight different in silico prediction algorithms; SIFT, PROVEAN, MutPred, SNP&GO, PhD-SNP, PANTHER, I-Mutant 2.0 and MUpo were used for screening nsSNPs in DBT including. Additionally, to understand the effect of mutations in the strength of the interactions that bind protein together the ELASPIC servers were used. Finally, the 3D structure of DBT was formed using Mutation3D and Chimera servers respectively. Our result showed that a total of 15 nsSNPs confirmed by 4 software (R301C, R376H, W84R, S268F, W84C, F276C, H452R, R178H, I355T, V191G, M444T, T174A, I200T, R113H, and R178C) were found damaging and can lead to a shift in DBT gene structure. Moreover, we found 7 nsSNPs located on the 2-oxoacid_dh catalytic domain, 5 nsSNPs on the E_3 binding domain and 3 nsSNPs on the Biotin Domain. So these nsSNPs may alter the putative structure of DBT’s domain. Furthermore, we detected all these nsSNPs are on the core residues of the protein and have the ability to change the stability of the protein. Additionally, we found W84R, S268F, and M444T have high significance, and they affected Leucine, Isoleucine, and Valine, which reduces or disrupt the function of BCKD complex, E2-subunit which the DBT gene encodes. In conclusion, based on our extensive in-silico analysis, we report 15 nsSNPs that have possible association with protein deteriorating and disease-causing abilities. These candidate SNPs can aid in future studies on Maple Syrup Urine Disease type II base in the genetic level.

Keywords: DBT gene, ELASPIC, in silico analysis, UCSF chimer

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Authors: Smruti Mahapatra, Dipankar Biswas, Richard Um, Michael Meggyesy, Riccardo Serra, Noah Gorelick, Steven Marra, Amir Manbachi, Mark G. Luciano

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Intracranial pressure (ICP) is profoundly regulated by the effects of cardiac pulsation and the volume of the incoming blood. Furthermore, these effects on ICP are incremented by the presence of a rigid skull that does not allow for changes in total volume during the cardiac cycle. These factors play a pivotal role in cerebrospinal fluid (CSF) dynamics and distribution, with consequences that are not well understood to this date and that may have a deep effect on the Central Nervous System (CNS) functioning. We designed this study with two specific aims: (a) To study how pulsatility influences local CSF flow, and (b) To study how modulating intracranial pressure affects drug distribution throughout the SAS globally. In order to achieve these aims, we built an elaborate in-vitro model of the SAS closely mimicking the dimensions and flow rates of physiological systems. To modulate intracranial pressure, we used an intracranially implanted, cardiac-gated, volume-oscillating balloon (CADENCE device). Commercially available dye was used to visualize changes in CSF flow. We first implemented two control cases, seeing how the tracer behaves in the presence of pulsations from the brain phantom and the balloon individually. After establishing the controls, we tested 2 cases, having the brain and the balloon pulsate together in sync and out of sync. We then analyzed the distribution area using image processing software. The in-sync case produced a significant increase, 5x times, in the tracer distribution area relative to the out-of-sync case. Assuming that the tracer fluid would mimic blood flow movement, a drug introduced in the SAS with such a system in place would enhance drug distribution and increase the bioavailability of therapeutic drugs to a wider spectrum of brain tissue.

Keywords: blood-brain barrier, cardiac-gated, cerebrospinal fluid, drug delivery, neurosurgery

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Authors: Yichen Lin, Hsiao-Han Chen, Chi-Chen Jan

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Since 1990, the research area of Integrated Marketing Communications (IMC) has been presented from a different perspective. With advances in information technology and the rise of consumer consciousness, businesses are in a competitive environment. There is an urgent need to adopt more profitable and effective integrated marketing strategies to increase core competitiveness. The goal of the company's sustainable management is to increase consumers' willingness to purchase and to maximize profits. This research uses six aspects of IMC, which includes awareness integration, unified image, database integration, customer-based integration, stakeholders-based integration, and evaluation integration to examine the role of marketing strategies in the strengths and weaknesses of the six components of integrated marketing communications, their effectiveness, the most important components and the most important components that need improvement. At the same time, social media such as FaceBook, Instagram, Youtube, Line, or even TikTok have become marketing tools which firms adopt them more and more frequently in the marketing strategy. In the end of 2019, the outbreak of COVID-19 did really affect the global industries. Lockdown policies also accelerated closure of brick-mentor stores worldwide. Online purchases rose dramatically. Hence, the effectiveness of online marketing will be essential to maintain the business. This study uses multiple-case studies to extend the effects of social media and IMC. Moreover, the study would also explore the differences of social media and IMC during COVID-19. Through literature review and multiple-case studies, it is found that using social media combined with IMC did really help companies expand their business and make good connections with stakeholders. One of previous studies also used system theory to explore the interrelationship among Integrated Marketing Communication, collaborative marketing, and global brand building. Even during pandemic, firms could still maintain the operation and connect with their customers more tightly.

Keywords: integration marketing communications, multiple-case studies, social media, system theory

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Authors: John K. Avor, Choong-Koo Chang

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The transfer of electrical loads at power generation stations from Standby Auxiliary Transformer (SAT) to Unit Auxiliary Transformer (UAT) and vice versa is through a fast bus transfer scheme. Fast bus transfer is a time-critical application where the transfer process depends on various parameters, thus transfer schemes apply advance algorithms to ensure power supply reliability and continuity. In a nuclear power generation station, supply continuity is essential, especially for critical class 1E electrical loads. Bus transfers must, therefore, be executed accurately within 4 to 10 cycles in order to achieve safety system requirements. However, the main problem is that there are instances where transfer schemes scrambled due to inaccurate interpretation of key parameters; and consequently, have failed to transfer several critical loads from UAT to the SAT during main generator trip event. Although several techniques have been adopted to develop robust transfer schemes, a combination of Artificial Neural Network and Fuzzy Systems (Neuro-Fuzzy) has not been extensively used. In this paper, we apply the concept of Neuro-Fuzzy to determine plant operating mode and dynamic prediction of the appropriate bus transfer algorithm to be selected based on the first cycle of voltage information. The performance of Sequential Fast Transfer and Residual Bus Transfer schemes was evaluated through simulation and integration of the Neuro-Fuzzy system. The objective for adopting Neuro-Fuzzy approach in the bus transfer scheme is to utilize the signal validation capabilities of artificial neural network, specifically the back-propagation algorithm which is very accurate in learning completely new systems. This research presents a combined effect of artificial neural network and fuzzy systems to accurately interpret key bus transfer parameters such as magnitude of the residual voltage, decay time, and the associated phase angle of the residual voltage in order to determine the possibility of high speed bus transfer for a particular bus and the corresponding transfer algorithm. This demonstrates potential for general applicability to improve reliability of the auxiliary power distribution system. The performance of the scheme is implemented on APR1400 nuclear power plant auxiliary system.

Keywords: auxiliary power system, bus transfer scheme, fuzzy logic, neural networks, reliability

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Authors: Rachel P. Vasconcellos

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The Russian constructivism is usually enshrined in history as another ''modernist ism'', that is, as an artistic phenomenon related to the early twentieth century‘s zeitgeist. What we aim in this essay is to analyze the constructivist movement not over the Art History field neither through the aesthetic debate, but through a geographical critical theory, taking the main idea of construction in the concrete sense of production of space. Seen from the perspective of the critique of space, the constructivist production is presented as a plan of totality, designed as socialist society‘s spatiality, contemplating and articulating all its scalar levels: the objects of everyday life, the building, the city and the territory. The constructivist avant-garde manifests a geographical ideology, launching the foundation‘s basis of modern planning ideology. Taken in its political sense, the artistic avant-garde of the Russian Revolution intended to anticipate the forms of a social future already put in progress: their plastic research pointed to new formal expressions to revolutionary contents. With the foundation of new institutions under a new State, it was given to the specialized labor of artists, architects, and planners the task of designing the socialist society, based on the thesis of scientific socialism. Their projects were developed under the politico-economics imperatives to the Soviet modernization – that is: the structural needs of industrialization and inclusion of all people in the productive work universe. This context shapes the creative atmosphere of the constructivist avant-garde, which uses the methods of engineering to the transform everyday life. Architecture, urban planning, and state planning integrated must then operate as spatial arrangement morphologically able to produce socialist life. But due to the intrinsic contradictions of the process, the rational and geometric aesthetic of the City-Machine appears, finally, as an image of a scientific socialist utopia.

Keywords: city-machine, critique of space, production of space, soviet territorialization

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