Search results for: algorithm symbol recognition

Authors: Mohamed Abdalla Khalil Mahmoud, Omnia Haidar Suliman

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Information and Communication technologies have revolutionized the way services are developed and offered to customers and have achieved evident success in a variety of vital sectors and widely contributed to the growth and resilience of the economy worldwide. Consequently, governments, especially of developing countries, have turned their attention to examine possible ways to utilize contemporary technology advances to offer essential governmental services to citizens, especially in areas where government agencies are not present. This paper investigates the challenges that impede governments of developing countries to provide basic services to its constituents nationwide. Sudan, as a case study, has taken major steps to provide essential governmental services via electronic channels. However, these services are still not widely used by the citizens, resulting in waste of financial and human resources and efforts that could have been invested more appropriately. This paper examines the challenges that hinder the Sudan’s government in their pursuit of availing its services via electronic channels. Different categories of e-government challenges, such as organizational, technological, social and, demographic, and financial and economic, have been explored in order to pinpoint the major challenges. A structured questionnaire is used to survey the target population of e-government professionals and executives who have direct involvement in the implementation of this nationwide endeavor in Sudan. The survey has successfully identified the main challenges that have high impact on the government’s effort to offer its services via electronic channels, such as Lack of coordination between public and private sectors and Lack of the benefits recognition of the e-government program. The findings of this paper can be used as a solid foundation for improving the way governmental services are offered to citizens in Sudan, resulting in a successful investment of financial and human resources and benefiting the targeted customers of all types.

Keywords: citizen, digital, e-channels, public sector, Sudan, technology

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Authors: Hassan Nour, Nouh Mounadi, Oussama Abchir, Belaidi Salah, Samir Chtita

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Cholinesterase enzymes are biological catalysts essential for the transformation of acetylcholine, which is a neurotransmitter implicated in memory and learning, into acetic acid and choline, altering the neurotransmission process in Alzheimer’s disease patients. Therefore, inhibition of cholinesterase enzymes is a relevant strategy for the symptomatic treatment of Alzheimer’s disease. The current investigation aims to explore potential Cholinesterase (ChE) inhibitors through a comprehensive computational approach. Forty-nine phytoconstituents extracted from Cannabis sativa L were in-silico screened using molecular docking, pharmacokinetic and toxicological analysis to evaluate their possible inhibitory effect towards the cholinesterase enzymes. Two phytoconstituents belonging to cannabinoid derivatives were revealed to be promising candidates for Alzheimer therapy by acting as cholinesterase inhibitors. They have exhibited high binding affinities towards the cholinesterase enzymes and showed their ability to interact with key residues involved in cholinesterase enzymatic activity. In addition, they presented good ADMET profiles allowing them to be promising oral drug candidates. Furthermore, molecular dynamics (MD) simulations were executed to explore their interactions stability under mimetic biological conditions and thus support our findings. To corroborate the docking results, the binding free energy corresponding to the more stable ligand-ChE complexes was re-estimated by applying the MM-PBSA method. MD and MM-PBSA studies affirmed that the ligand-ChE recognition is spontaneous reaction leading to stable complexes. The conducted investigations have led to great findings that would strongly guide the pharmaceutical industries towards the rational development of potent anti-Alzheimer agents.

Keywords: alzheimer’s disease, molecular docking, cannabis sativa l, cholinesterase inhibitors

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Authors: Adeline Meriaux, Claire Gaiani, Jennifer Burgain, Frantz Fournier, Lionel Muniglia, Jérémy Petit

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Spray-drying process is widely used for the production of dairy powders for food and pharmaceuticals industries. It involves the atomization of a liquid feed into fine droplets, which are subsequently dried through contact with a hot air flow. The resulting powders permit transportation cost reduction and shelf life increase but can also exhibit various interesting functionalities (flowability, solubility, protein modification or acid gelation), depending on operating conditions and milk composition. Indeed, particles porosity, surface composition, lactose crystallization, protein denaturation, protein association or crust formation may change. Links between spray-drying conditions and physicochemical and functional properties of powders were investigated by a design of experiment methodology and analyzed by principal component analysis. Quadratic models were developed, and multicriteria optimization was carried out by the use of genetic algorithm. At the time of abstract submission, verification spray-drying trials are ongoing. To perform experiments, milk from dairy farm was collected, skimmed, froze and spray-dried at different air pressure (between 1 and 3 bars) and outlet temperature (between 75 and 95 °C). Dry matter, minerals content and proteins content were determined by standard method. Solubility index, absorption index and hygroscopicity were determined by method found in literature. Particle size distribution were obtained by laser diffraction granulometry. Location of the powder color in the Cielab color space and water activity were characterized by a colorimeter and an aw-value meter, respectively. Flow properties were characterized with FT4 powder rheometer; in particular, compressibility and shearing test were performed. Air pressure and outlet temperature are key factors that directly impact the drying kinetics and powder characteristics during spray-drying process. It was shown that the air pressure affects the particle size distribution by impacting the size of droplet exiting the nozzle. Moreover, small particles lead to more cohesive powder and less saturated color of powders. Higher outlet temperature results in lower moisture level particles which are less sticky and can explain a spray-drying yield increase and the higher cohesiveness; it also leads to particle with low water activity because of the intense evaporation rate. However, it induces a high hygroscopicity, thus, powders tend to get wet rapidly if they are not well stored. On the other hand, high temperature provokes a decrease of native serum proteins, which is positively correlated to gelation properties (gel point and firmness). Partial denaturation of serum proteins can improve functional properties of powder. The control of air pressure and outlet temperature during the spray-drying process significantly affects the physicochemical and functional properties of powder. This study permitted to better understand the links between physicochemical and functional properties of powder to identify correlations between air pressure and outlet temperature. Therefore, mathematical models have been developed, and the use of genetic algorithm will allow the optimization of powder functionalities.

Keywords: dairy powders, spray-drying, powders functionalities, design of experiment

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Authors: Insaf Bellamine, Hamid Tairi

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Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.

Keywords: Color Space-Time Interest Points (CSTIP), Color Structure-Texture Image Decomposition, Motion Detection, clustering

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Authors: Ayaz Ahmad

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In this work, we present the effect of noise on the solution of a partial differential equation (PDE) in three different setting. We shall first consider random initial condition for two nonlinear dispersive PDE the non linear Schrodinger equation and the Kortteweg –de vries equation and analyse their effect on some special solution , the soliton solutions.The second case considered a linear partial differential equation , the wave equation with random initial conditions allow to substantially decrease the computational and data storage costs of an algorithm to solve the inverse problem based on the boundary measurements of the solution of this equation. Finally, the third example considered is that of the linear transport equation with a singular drift term, when we shall show that the addition of a multiplicative noise term forbids the blow up of solutions under a very weak hypothesis for which we have finite time blow up of a solution in the deterministic case. Here we consider the problem of wave propagation, which is modelled by a nonlinear dispersive equation with noisy initial condition .As observed noise can also be introduced directly in the equations.

Keywords: drift term, finite time blow up, inverse problem, soliton solution

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Authors: Shadab Shishegar, Sophie Duchesne, Genevieve Pelletier

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Sustainability as one of the key elements of Smart cities, can be realized by employing Real Time Control Strategies for city’s infrastructures. Nowadays Stormwater management systems play an important role in mitigating the impacts of urbanization on natural hydrological cycle. These systems can be managed in such a way that they meet the smart cities standards. In fact, there is a huge potential for sustainable management of urban stormwater and also its adaptability to global challenges like climate change. Hence, a dynamically managed system that can adapt itself to instability of the environmental conditions is desirable. A Global Predictive Real Time Control approach is proposed in this paper to optimize the performance of stormwater management basins in terms of flooding prevention. To do so, a mathematical optimization model is developed then solved using Genetic Algorithm (GA). Results show an improved performance at system-level for the stormwater basins in comparison to static strategy.

Keywords: environmental sustainability, optimization, real time control, storm water management

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Authors: Aimen Saleh

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The Silurian Lower Acacus sandstone is one of the main reservoirs in North West Libya. Our aim in this study is to grasp a robust understanding of the hydrocarbon potential and distribution in the area. To date, the depositional environment of the Lower Acacus reservoir still open to discussion and contradiction. Henceforth, building three dimensional (3D) property modelling is one way to support the analysis and description of the reservoir, its properties and characterizations, so this will be of great value in this project. The 3D model integrates different data set, these incorporates well logs data, petrophysical reservoir properties and seismic data as well. The finalized depositional environment model of the Lower Acacus concludes that the area is located in a deltaic transitional depositional setting, which ranges from a wave dominated delta into tide dominated delta type. This interpretation carried out through a series of steps of model generation, core description and Formation Microresistivity Image tool (FMI) interpretation. After the analysis of the core data, the Lower Acacus layers shows a strong effect of tidal energy. Whereas these traces found imprinted in different types of sedimentary structures, for examples; presence of some crossbedding, such as herringbones structures, wavy and flaser cross beddings. In spite of recognition of some minor marine transgression events in the area, on the contrary, the coarsening upward cycles of sand and shale layers in the Lower Acacus demonstrate presence of a major regressive phase of the sea level. However, consequently, we produced a final package of this model in a complemented set of facies distribution, porosity and oil presence. And also it shows the record of the petroleum system, and the procedure of Hydrocarbon migration and accumulation. Finally, this model suggests that the area can be outlined into three main segments of hydrocarbon potential, which can be a textbook guide for future exploration and production strategies in the area.

Keywords: Acacus, Ghadames , Libya, Silurian

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Authors: M. Wigwe, M. Y Soloman, E. Pirayesh, R. Eghorieta, N. Stegent

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The moving reference point (MRP) technique has been used in the analyses of the first three stages of two fracturing jobs. The results obtained verify the proposition that a hydraulic fracture in shale grows in spurts rather than in a continuous pattern as originally interpreted by Nolte-Smith technique. Rather than a continuous Mode I fracture that is followed by Mode II, III or IV fractures, these fracture modes could alternate throughout the pumping period. It is also shown that the Nolte-Smith time parameter plot can be very helpful in identifying the presence of natural fractures that have been intersected by the hydraulic fracture. In addition, with the aid of a fracture length-time plot generated from any fracture simulation that matches the data, the distance from the wellbore to the natural fractures, which also translates to the actual fracture length for the stage, can be determined. An algorithm for this technique is developed. This procedure was used for the first 9 minutes of the simulated frac job data. It was observed that after 7mins, the actual fracture length is about 150ft, instead of 250ft predicted by the simulator output. This difference gets larger as the analysis proceeds.

Keywords: shale, fracturing, reservoir, simulation, frac-length, moving-reference-point

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Authors: Joon-Hoon Park

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The fundamental concept of observability is important in both theoretical and practical points of modern control systems. In modern control theory, a control system has criteria for determining the design solution exists for the system parameters and design objectives. The idea of observability relates to the condition of observing or estimating the state variables from the output variables that is generally measurable. To design closed-loop control system, the practical problems of implementing the feedback of the state variables must be considered and implementing state feedback control problem has been existed in this case. All the state variables are not available, so it is requisite to design and implement an observer that will estimate the state variables form the output parameters. However sometimes unknown inputs are presented in control systems as practical cases. This paper presents a design method and algorithm for observer of control system with unknown input parameters based on Rationalized Haar transform. The proposed method is more advantageous than the other numerical method.

Keywords: orthogonal functions, rationalized Haar transforms, control system observer, algebraic method

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Authors: Chao Yu, Qing-Guo Wang, Dan Zhang

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The outlier detection problem for dynamic systems is formulated as a matrix decomposition problem with low-rank, sparse matrices and further recast as a semidefinite programming (SDP) problem. A fast algorithm is presented to solve the resulting problem while keeping the solution matrix structure and it can greatly reduce the computational cost over the standard interior-point method. The computational burden is further reduced by proper construction of subsets of the raw data without violating low rank property of the involved matrix. The proposed method can make exact detection of outliers in case of no or little noise in output observations. In case of significant noise, a novel approach based on under-sampling with averaging is developed to denoise while retaining the saliency of outliers and so-filtered data enables successful outlier detection with the proposed method while the existing filtering methods fail. Use of recovered “clean” data from the proposed method can give much better parameter estimation compared with that based on the raw data.

Keywords: outlier detection, system identification, matrix decomposition, low-rank matrix, sparsity, semidefinite programming, interior-point methods, denoising

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Authors: Omer Oral, Y. Emre Yilmaz

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Topology optimization is an approach that optimizes material distribution within a given design space for a certain load and boundary conditions by providing performance goals. It uses various restrictions such as boundary conditions, set of loads, and constraints to maximize the performance of the system. It is different than size and shape optimization methods, but it reserves some features of both methods. In this study, interior structures of the parts were optimized by using SIMP (Solid Isotropic Material with Penalization) method. The volume of the part was preassigned parameter and minimum deflection was the objective function. The basic idea behind the theory was considered, and different methods were discussed. Rhinoceros 3D design tool was used with Grasshopper and TopOpt plugins to create and optimize parts. A Grasshopper algorithm was designed and tested for different beams, set of arbitrary located forces and support types such as pinned, fixed, etc. Finally, 2.5D shapes were obtained and verified by observing the changes in density function.

Keywords: Grasshopper, lattice structure, microstructures, Rhinoceros, solid isotropic material with penalization method, TopOpt, topology optimization

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Authors: Mahdieh Khalilinezhad, Silvana Dellepiane, Gianni Vernazza

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The aim of the present work is to build a model based on tissue characterization that is able to discriminate pathological and non-pathological regions from three-phasic CT images. Based on feature selection in different phases, in this research, we design a neural network system that has optimal neuron number in a hidden layer. Our approach consists of three steps: feature selection, feature reduction, and classification. For each ROI, 6 distinct set of texture features are extracted such as first order histogram parameters, absolute gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet, for a total of 270 texture features. We show that with the injection of liquid and the analysis of more phases the high relevant features in each region changed. Our results show that for detecting HCC tumor phase3 is the best one in most of the features that we apply to the classification algorithm. The percentage of detection between these two classes according to our method, relates to first order histogram parameters with the accuracy of 85% in phase 1, 95% phase 2, and 95% in phase 3.

Keywords: multi-phasic liver images, texture analysis, neural network, hidden layer

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Authors: Tianyi Wang, Hieyong Jeong, An Guo, Yuko Ohno

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Healthcare robot for assisting sit-to-stand (STS) performance had aroused numerous research interests. To author’s best knowledge, knowledge about how evaluating healthcare robot is still unknown. Robot should be labeled as fault if users feel demanding during STS when they are assisted by robot. In this research, we aim to propose a method to evaluate sit-to-stand assist robot through center of pressure (CoP), then classify different STS performance. Experiments were executed five times with ten healthy subjects under four conditions: two self-performed STSs with chair heights of 62 cm and 43 cm, and two robot-assisted STSs with chair heights of 43 cm and robot end-effect speed of 2 s and 5 s. CoP was measured using a Wii Balance Board (WBB). Bayesian classification was utilized to classify STS performance. The results showed that faults occurred when decreased the chair height and slowed robot assist speed. Proposed method for fault classification showed high probability of classifying fault classes form others. It was concluded that faults for STS assist robot could be detected by inspecting center of pressure and be classified through proposed classification algorithm.

Keywords: center of pressure, fault classification, healthcare robot, sit-to-stand movement

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Authors: Al Omari Moahmmed Ahmed

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These papers describe the Bayesian Estimator using Markov Chain Monte Carlo and Lindley’s approximation and the maximum likelihood estimation of the Weibull distribution with Type-I censored data. The maximum likelihood method can’t estimate the shape parameter in closed forms, although it can be solved by numerical methods. Moreover, the Bayesian estimates of the parameters, the survival and hazard functions cannot be solved analytically. Hence Markov Chain Monte Carlo method and Lindley’s approximation are used, where the full conditional distribution for the parameters of Weibull distribution are obtained via Gibbs sampling and Metropolis-Hastings algorithm (HM) followed by estimate the survival and hazard functions. The methods are compared to Maximum Likelihood counterparts and the comparisons are made with respect to the Mean Square Error (MSE) and absolute bias to determine the better method in scale and shape parameters, the survival and hazard functions.

Keywords: weibull distribution, bayesian method, markov chain mote carlo, survival and hazard functions

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Authors: Sangeeta Palekar, Neeraj Rangwani, Akash Poddar, Jayu Kalambe

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The bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia, which can cause severe medical problems like nerve damage or kidney diseases. This paper presents a method for predicting the glucose concentration in blood samples using image processing and machine learning algorithms. The glucose solution is prepared by the glucose oxidase (GOD) and peroxidase (POD) method. An experimental database is generated based on the colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using image processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like multiple linear regression, decision tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. The multiple linear regression algorithm predicts the results with 97% accuracy. The image processing and machine learning-based approach reduce the hardware complexities of existing platforms.

Keywords: artificial intelligence glucose detection, glucose oxidase, peroxidase, image processing, machine learning

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Authors: Guru Prakash, Revanth Dugalam

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A road’s condition often deteriorates due to harsh loading such as overload due to trucks, and severe environmental conditions such as heavy rain, snow load, and cyclic loading. In absence of proper maintenance planning, this results in potholes, wide cracks, bumps, and increased roughness of roads. In this paper, a data-driven model will be developed to detect these damages using vibration and image signals. The key idea of the proposed methodology is that the road anomaly manifests in these signals, which can be detected by training a machine learning algorithm. The use of various machine learning techniques such as the support vector machine and Radom Forest method will be investigated. The proposed model will first be trained and tested with artificially simulated data, and the model architecture will be finalized by comparing the accuracies of various models. Once a model is fixed, the field study will be performed, and data will be collected. The field data will be used to validate the proposed model and to predict the future road’s health condition. The proposed will help to automate the road condition monitoring process, repair cost estimation, and maintenance planning process.

Keywords: SVM, data-driven, road health monitoring, pot-hole

Procedia PDF Downloads 86

Authors: Fiona Bei Na Tan, Glen Wen Kiat Ho, Ee Leen Liow, Li Yin Tan, Sean Wei Loong Ho

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Background: Shoulder arthroscopy is an increasingly common procedure. Pneumothorax post-shoulder arthroscopy is a rare complication. Objectives: Our aim is to highlight a case report of pneumothorax post shoulder arthroscopy and to conduct a literature review to evaluate the possible risk factors associated with developing a pneumothorax during or after shoulder arthroscopy. Case Report: We report the case of a 75-year-old male non-smoker who underwent left shoulder arthroscopy without regional anaesthesia and in the left lateral position. The general anaesthesia and surgery were uncomplicated. The patient was desaturated postoperatively and was found to have a pneumothorax on examination and chest X-ray. A chest tube drain was inserted promptly into the right chest. He had an uncomplicated postoperative course. Methods: PubMed Medline and Cochrane database search was carried out using the terms shoulder arthroplasty, pneumothorax, pneumomediastinum, and subcutaneous emphysema. We selected full-text articles written in English. Results: Thirty-two articles were identified and thoroughly reviewed. Based on our inclusion and exclusion criteria, 14 articles, which included 20 cases of pneumothorax during or after shoulder arthroscopy, were included. Eighty percent (16/20) of pneumothoraxes occurred postoperatively. In the articles that specify the side of pneumothorax, 91% (10/11) occur on the ipsilateral side of the arthroscopy. Eighty-eight percent (7/8) of pneumothoraxes occurred when subacromial decompression was performed. Fifty-six percent (9/16) occurred in patients placed in the lateral decubitus position. Only 30% (6/20) occurred in current or ex-smokers, and only 25% (5/20) had a pre-existing lung condition. Overall, of the articles that posit a mechanism, 75% (9/12) deem the pathogenesis to be multifactorial. Conclusion: The exact mechanism of pneumothorax is currently unknown. Awareness of this complication and timely recognition are important to prevent life-threatening sequelae. Surgeons should have a low threshold to obtain diagnostic plain radiographs in the event of clinical suspicion.

Keywords: rotator cuff repair, decompression, pressure, complication

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Authors: Ruslan Safarov, Zhanat Shomanova, Yuri Nossenko, Zhandos Mussayev, Ayana Baltabek

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Geochemical maps of distribution of polluting elements V, Cr, Mn, Co, Ni, Cu, Zn, Mo, Cd, Pb on the territory of the Pavlodar city (Kazakhstan), which is an industrial hub were designed. The samples of soil were taken from 100 locations. Elemental analysis has been performed using XRF. The obtained data was used for training of the computational model with gradient boosting algorithm. The optimal parameters of model as well as the loss function were selected. The computational model was used for prediction of polluting elements concentration for 1000 evenly distributed points. Based on predicted data geochemical maps were created. Additionally, the total pollution index Zc was calculated for every from 1000 point. The spatial distribution of the Zc index was visualized using GIS (QGIS). It was calculated that the maximum coverage area of the territory of the Pavlodar city belongs to the moderately hazardous category (89.7%). The visualization of the obtained data allowed us to conclude that the main source of contamination goes from the industrial zones where the strategic metallurgical and refining plants are placed.

Keywords: Pavlodar, geochemical map, gradient boosting, CatBoost, QGIS, spatial distribution, heavy metals

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Authors: Mahera Abdulrahman, Satish Chandrasekhar Nair

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Growing demand for human health research in the United Arab Emirates (UAE) has prompted the need to develop a robust research ethics oversight, particularly given the large unskilled-worker immigrant population and the elderly citizens utilizing health services. Examination of the structure, function, practices and outcomes of the human research ethics committees (HREC) was conducted using two survey instruments, reliable and validated. Results indicate that in the absence of a national ethics regulatory body, the individual emirate’s governed 21 HRECs covering health facilities and academic institutions in the UAE. Among the HRECs, 86% followed International Council for Harmonization-Good Clinical Practice guidelines, 57% have been in operation for more than five years, 81% reviewed proposals within eight weeks, 48% reviewed for clinical and scientific merit apart from ethics, and 43% handled more than 50 research proposals per year. However, researcher recognition, funding transparency, adverse event reporting systems were widespread in less than one-third of all HRECs. Surprisingly, intellectual property right was not included as a research output. Research was incorporated into the vision and mission statements of many (62%) organizations and, mechanisms such as research publications, collaborations, and recognitions were employed as key performance indicators to measure research output. In spite, resources to generate research output such as dedicated budget (19%), support staff (19%) and continuous training and mentoring program for medical residents and HREC members were somehow lacking. HREC structure and operations in the UAE are similar to other regions of the world, resources allocation for efficient, quality monitoring, continuous training, and the creation of a clinical research network are needed to strengthen the clinical research enterprise to scale up for the future. It is anticipated that the results of this study will benefit investigators, regulators, pharmaceutical sponsors and the policy makers in the region.

Keywords: institutional review board, ethics committee, human research ethics, United Arab Emirates (UAE)

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Authors: Walid Hassairi, Moncef Bousselmi, Mohamed Abid

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Functional verification is a major part of today’s system design task. Several approaches are available for verification on a high abstraction level, where designs are often modeled using MATLAB/Simulink. However, different approaches are a barrier to a unified verification flow. In this paper, we propose a co-simulation interface between SystemC and MATLAB and Simulink to enable functional verification of multi-abstraction levels designs. The resulting verification flow is tested on JPEG compression algorithm. The required synchronization of both simulation environments, as well as data type conversion is solved using the proposed co-simulation flow. We divided into two encoder jpeg parts. First implemented in SystemC which is the DCT is representing the HW part. Second, consisted of quantization and entropy encoding which is implemented in Matlab is the SW part. For communication and synchronization between these two parts we use S-Function and engine in Simulink matlab. With this research premise, this study introduces a new implementation of a Hardware SystemC of DCT. We compare the result of our simulation compared to SW / SW. We observe a reduction in simulation time you have 88.15% in JPEG and the design efficiency of the supply design is 90% in SDR.

Keywords: hardware/software, co-design, co-simulation, systemc, matlab, s-function, communication, synchronization

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Authors: Kashi Raj Pandey

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Transformative learning is a form of learning rooted in learners' life experiences and their inherent love for learning. It emphasizes the importance of incorporating students' everyday work through the use of learning diaries and reflective journals. It encourages learners to take a proactive role in their own improvement, fostering creativity and promoting informed discussions about the learning process. Reflecting on the personal experience with English language learning in a rural village in Nepal where rote memorization was the prevailing teaching method, this traditional approach hindered a deeper understanding of the language, prompting the author to recognize the need for more effective pedagogy. In this study, the author delved into the cultural contextualization of English language learning, taking into account learners' backgrounds. The study’s findings highlighted the importance of equity, inclusion, mutuality, and social justice in the classroom, emphasizing the significance of integrating students' lived experiences into the pedagogical approach. This, in turn, can encourage students to engage in profound and collaborative learning practices within the realm of English language education. Upon successfully implementing the research findings, including the eight key conditions of transformative learning, in multiple classrooms, the author collaborated with international educationists and government stakeholders in Nepal. The purpose was to disseminate the research findings, conduct teacher training workshops, and systematically enhance Nepali students’ English language learning. These methods have already demonstrated a significant improvement in student engagement within the same school where the author once learned English as a child. This study aims to explore teachers’ decision-making process regarding the transition from traditional teaching methods to interactive ones, which have gained national recognition within the ESL/EFL teaching community in Nepal. By sharing these experiences, it is expected that other teachers will also contemplate adopting transformative learning pedagogy in their own classrooms.

Keywords: reflection, student engagement, pedagogy, transformative learning

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Authors: Mohammad Atiqur Rahman

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The study aimed at inventorying the threatened biodiversity of Bangladesh and assessing the rate of loss of biodiversity caused due to environmental degradation for conservation management. The impact assessment of environmental depletion and rate of biodiversity loss determination have been made by a long term field investigation, examination of preserved herbarium specimens and survey of relevant floristic literature following the IUCN’s threatened criteria of assessing Red List Plants under the Flora Bangladesh Project. Biodiversity of Bangladesh, as evaluated, has been affected to a large extent during the last four and half decades due to spontaneous environmental degradation caused by frequent occurrence of cyclonic storms and tidal bores since 1970 and flooding, draught, unilateral diversion of trans-boundary waters by operating Farakka Barrage since 1975, indiscriminate destruction and over exploitation of natural resources, unplanned development and industrialization, overpopulation etc. Depletion of world’s largest mangrove biodiversity in Sundarbans, coastal and island biodiversity in southern part, agro-biodiversity and agro-fisheries all over the country, Haor and wetland biodiversity of plain lands, terrestrial and forest biodiversity in central and eastern hilly part of Bangladesh, as assessed, have greatly been occurred at a higher rate due to environmental degradation which in turn affect directly or indirectly the economy, food security and environmental health of the country. Complete inventory of 30 plant families resulted in the recognition of 45.18% species of Bangladesh as threatened environmentally and 13.23% species as possibly extinct from the flora since these have neither been reported or could be traced in the field for more than 100 years. The rate of extinction is determined to be 2.65% per 20 years. Hence the study indicates that the loss of biodiversity and environmental degradation in Bangladesh occurring at an alarming rate. The study focuses on the issues of environment, the extent of loss of different plant biodiversities in Bangladesh, prioritizing and implementing national conservation strategies for sustainable management of the environment.

Keywords: Bangladesh, biodiversity, conservation, environmental management

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Authors: Pac Ordu

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The paper was conceived on the premise that employment of tertiary education graduates has become an endemic problem in Nigeria. Recognizing the objective of current education as schooling for paid employment, the paper identified that the basic objective of present-day education should be schooling to become self-employed. While schooling to become a successful employee was identified as the focus for the older generation, schooling to become self-employed was defined as the focus for 21st-century teaching and learning. Hence, the paper condemned the inability of curriculum implementers to teach creative trends to enable students to acquire practical skills and small business operation-oriented competencies. A review of some disciplines was made to show the new trend of education that would empower Nigerian students with small business enterprise operation skills for self-employment on graduation. This was further made to draw the attention of institutions and curriculum designers to the need for our curriculum to be functional in line with demands of the innovative economic environment. The paper also noted that at periods of recession with its attendant effects, was the best period for students of entrepreneurship to dream and create their small business enterprises. It highlighted the role of Federal College of Education (Technical) Omoku, Rivers State, Nigeria and the national recognition it has received for developing an innovative, practical model of teaching entrepreneurship education in Nigeria Colleges of Education system. In order to equip students for economic survival on graduation, the introduction of innovative teaching can only be successful if lecturers shift their focus away from the conventional emphasis on theory to students’ energy quotients. While the paper obviously recommended that lecturers should be creative and teach outside the curriculum box, it further recommended that students should use this period of their studentship to dream, create and operate their own small business enterprises.

Keywords: 21st century students, curriculum, entrepreneurship, hands-on-training, innovative

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Authors: Sergio Celaschi, Henrique Canavarro de Alencar, Claaudecir Biazoli

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Effluent quality is of the highest priority for compliance with the permit limits of environmental protection agencies and ensures the protection of their local water system. Of the pollutants monitored, the biochemical oxygen demand (BOD) posed one of the greatest challenges. This work presents a solution for wastewater treatment plants - WWTP’s ability to react to different situations and meet treatment goals. Delayed BOD5 results from the lab take 7 to 8 analysis days, hindered the WWTP’s ability to react to different situations and meet treatment goals. Reducing BOD turnaround time from days to hours is our quest. Such a solution is based on a system of two BOD bioreactors associated with Digital Twin (DT) and Machine Learning (ML) methodologies via an Internet of Things (IoT) platform to monitor and control a WWTP to support decision making. DT is a virtual and dynamic replica of a production process. DT requires the ability to collect and store real-time sensor data related to the operating environment. Furthermore, it integrates and organizes the data on a digital platform and applies analytical models allowing a deeper understanding of the real process to catch sooner anomalies. In our system of continuous time monitoring of the BOD suppressed by the effluent treatment process, the DT algorithm for analyzing the data uses ML on a chemical kinetic parameterized model. The continuous BOD monitoring system, capable of providing results in a fraction of the time required by BOD5 analysis, is composed of two thermally isolated batch bioreactors. Each bioreactor contains input/output access to wastewater sample (influent and effluent), hydraulic conduction tubes, pumps, and valves for batch sample and dilution water, air supply for dissolved oxygen (DO) saturation, cooler/heater for sample thermal stability, optical ODO sensor based on fluorescence quenching, pH, ORP, temperature, and atmospheric pressure sensors, local PLC/CPU for TCP/IP data transmission interface. The dynamic BOD system monitoring range covers 2 mg/L < BOD < 2,000 mg/L. In addition to the BOD monitoring system, there are many other operational WWTP sensors. The CPU data is transmitted/received to/from the digital platform, which in turn performs analyses at periodic intervals, aiming to feed the learning process. BOD bulletins and their credibility intervals are made available in 12-hour intervals to web users. The chemical kinetics ML algorithm is composed of a coupled system of four first-order ordinary differential equations for the molar masses of DO, organic material present in the sample, biomass, and products (CO₂ and H₂O) of the reaction. This system is solved numerically linked to its initial conditions: DO (saturated) and initial products of the kinetic oxidation process; CO₂ = H₂0 = 0. The initial values for organic matter and biomass are estimated by the method of minimization of the mean square deviations. A real case of continuous monitoring of BOD wastewater effluent quality is being conducted by deploying an IoT application on a large wastewater purification system located in S. Paulo, Brazil.

Keywords: effluent treatment, biochemical oxygen demand, continuous monitoring, IoT, machine learning

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Authors: Sami Baraketi, Jean Marie Garcia, Olivier Brun

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Wavelength Division Multiplexing (WDM) is the dominant transport technology used in numerous high capacity backbone networks, based on optical infrastructures. Given the importance of costs (CapEx and OpEx) associated to these networks, resource management is becoming increasingly important, especially how the optical circuits, called “lightpaths”, are routed throughout the network. This requires the use of efficient algorithms which provide routing strategies with the lowest cost. We focus on the lightpath routing and wavelength assignment problem, known as the RWA problem, while optimizing wavelength fragmentation over the network. Wavelength fragmentation poses a serious challenge for network operators since it leads to the misuse of the wavelength spectrum, and then to the refusal of new lightpath requests. In this paper, we first establish a new Integer Linear Program (ILP) for the problem based on a node-link formulation. This formulation is based on a multilayer approach where the original network is decomposed into several network layers, each corresponding to a wavelength. Furthermore, we propose an efficient heuristic for the problem based on a greedy algorithm followed by a post-treatment procedure. The obtained results show that the optimal solution is often reached. We also compare our results with those of other RWA heuristic methods.

Keywords: WDM, lightpath, RWA, wavelength fragmentation, optimization, linear programming, heuristic

Procedia PDF Downloads 528

Authors: O. Rahli, N. Mimouni, R. Bennacer, K. Bouhadef

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This numerical study investigates the travelling wave’s appearance and the behavior of Poiseuille-Rayleigh-Benard (PRB) flow induced in 3D thermosolutale mixed convection (TSMC) in horizontal rectangular channels. The governing equations are discretized by using a control volume method with third order Quick scheme in approximating the advection terms. Simpler algorithm is used to handle coupling between the momentum and continuity equations. To avoid the excessively high computer time, full approximation storage (FAS) with full multigrid (FMG) method is used to solve the problem. For a broad range of dimensionless controlling parameters, the contribution of this work is to analyzing the flow regimes of the steady longitudinal thermoconvective rolls (noted R//) for both thermal and mass transfer (TSMC). The transition from the opposed volume forces to cooperating ones, considerably affects the birth and the development of the longitudinal rolls. The heat and mass transfers distribution are also examined.

Keywords: heat and mass transfer, mixed convection, poiseuille-rayleigh-benard flow, rectangular duct

Procedia PDF Downloads 299

Authors: Cheng Xu

Abstract:

As the metaverse emerges as a dynamic virtual simulacrum of reality, its implications on user privacy have become a focal point of interest. While previous discussions have ventured into metaverse privacy dynamics, a glaring empirical gap persists, especially concerning the effects of personalization in the context of news recommendation services. This study stands at the forefront of addressing this void, meticulously examining how users' privacy concerns shift within the metaverse's personalization context. Through a pre-registered randomized controlled experiment, participants engaged in a personalization task across both the metaverse and traditional online platforms. Upon completion of this task, a comprehensive news recommendation service provider offers personalized news recommendations to the users. Our empirical findings reveal that the metaverse inherently amplifies privacy concerns compared to traditional settings. However, these concerns are notably mitigated when users have a say in shaping the algorithms that drive these recommendations. This pioneering research not only fills a significant knowledge gap but also offers crucial insights for metaverse developers and policymakers, emphasizing the nuanced role of user input in shaping algorithm-driven privacy perceptions.

Keywords: metaverse, privacy concerns, personalization, digital interaction, algorithmic recommendations

Procedia PDF Downloads 117

Authors: Yingjie Zhang, Ming Li, Ying Zhang, Jing Zhang, Zuolei Hu

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In the actual start-up process of DC motors, the DC drive system often faces a conflict between energy consumption and acceleration performance. To resolve the conflict, this paper proposes a comprehensive performance index that energy consumption index is added on the basis of classical control performance index in the DC motor starting process. Taking the comprehensive performance index as the cost function, particle swarm optimization algorithm is designed to optimize the comprehensive performance. Then it conducts simulations on the optimization of the comprehensive performance of the DC motor on condition that the weight coefficient of the energy consumption index should be properly designed. The simulation results show that as the weight of energy consumption increased, the energy efficiency was significantly improved at the expense of a slight sacrifice of fastness indicators with the comprehensive performance index method. The energy efficiency was increased from 63.18% to 68.48% and the response time reduced from 0.2875s to 0.1736s simultaneously compared with traditional proportion integrals differential controller in energy saving.

Keywords: comprehensive performance index, energy consumption, acceleration performance, particle swarm optimal control

Procedia PDF Downloads 165

Authors: Si Mon Kueh, Tom J. Kazmierski

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There are about 1% of the world population suffering from the hidden disability known as epilepsy and major developing countries are not fully equipped to counter this problem. In order to reduce the inconvenience and danger of epilepsy, different methods have been researched by using a artificial neural network (ANN) classification to distinguish epileptic waveforms from normal brain waveforms. This paper outlines the aim of achieving massive ANN parallelization through a dedicated hardware using bit-serial processing. The design of this bit-serial Neural Processing Element (NPE) is presented which implements the functionality of a complete neuron using variable accuracy. The proposed design has been tested taking into consideration non-idealities of a hardware ANN. The NPE consists of a bit-serial multiplier which uses only 16 logic elements on an Altera Cyclone IV FPGA and a bit-serial ALU as well as a look-up table. Arrays of NPEs can be driven by a single controller which executes the neural processing algorithm. In conclusion, the proposed compact NPE design allows the construction of complex hardware ANNs that can be implemented in a portable equipment that suits the needs of a single epileptic patient in his or her daily activities to predict the occurrences of impending tonic conic seizures.

Keywords: Artificial Neural Networks (ANN), bit-serial neural processor, FPGA, Neural Processing Element (NPE)

Procedia PDF Downloads 324

Authors: Mohamed Hafid, Marcel Lacroix

Abstract:

This study aimed at developing an inverse heat transfer approach for predicting the time-varying freezing front and the temperature distribution of tumors during cryosurgery. Using a temperature probe pressed against the layer of tumor, the inverse approach is able to predict simultaneously the metabolic heat generation and the blood perfusion rate of the tumor. Once these parameters are predicted, the temperature-field and time-varying freezing fronts are determined with the direct model. The direct model rests on one-dimensional Pennes bioheat equation. The phase change problem is handled with the enthalpy method. The Levenberg-Marquardt Method (LMM) combined to the Broyden Method (BM) is used to solve the inverse model. The effect (a) of the thermal properties of the diseased tissues; (b) of the initial guesses for the unknown thermal properties; (c) of the data capture frequency; and (d) of the noise on the recorded temperatures is examined. It is shown that the proposed inverse approach remains accurate for all the cases investigated.

Keywords: cryosurgery, inverse heat transfer, Levenberg-Marquardt method, thermal properties, Pennes model, enthalpy method

Procedia PDF Downloads 201