Search results for: thermal performance

Authors: Jun Ming Moey, Zhiyaun Chen, David Nicholson

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Bad actors are often hard to detect in data that imprints their behaviour patterns because they are comparatively rare events embedded in non-bad actor data. An unsupervised machine learning framework is applied here to detect bad actors in financial crime datasets that record millions of transactions undertaken by hundreds of actors (<0.01% bad). Specifically, the framework combines ‘shallow’ (PCA, Isolation Forest) and ‘deep’ (Autoencoder) methods to detect outlier patterns. Detection performance analysis for both the individual methods and their combination is reported.

Keywords: detection, machine learning, deep learning, unsupervised, outlier analysis, data science, fraud, financial crime

Procedia PDF Downloads 76

Authors: Vikram Saini, Lillie Dewan

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This paper presents the iterative schemes based on Least square, Hierarchical Least Square and Stochastic Approximation Gradient method for the Identification of Wiener model with parametric structure. A gradient method is presented for the parameter estimation of wiener model with noise conditions based on the stochastic approximation. Simulation results are presented for the Wiener model structure with different static non-linear elements in the presence of colored noise to show the comparative analysis of the iterative methods. The stochastic gradient method shows improvement in the estimation performance and provides fast convergence of the parameters estimates.

Keywords: hard non-linearity, least square, parameter estimation, stochastic approximation gradient, Wiener model

Procedia PDF Downloads 381

Authors: Rachel S. E. Lim, Winnie L. C. Tan

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Visual literacy is about making meaning through the interaction of images, words, and sounds. Graphic communication students typically develop visual literacy through critique and production of studio-based projects for their portfolios. However, the abrupt switch to online learning during the COVID-19 pandemic has made it necessary to consider new strategies of visualization and planning to scaffold teaching and learning. This study, therefore, investigated how MIRO, a cloud-based visual collaboration platform, could be used to develop the visual literacy confidence of 30 diploma in graphic communication students attending a graphic design course at a Singapore arts institution. Due to COVID-19, the course was taught fully online throughout a 16-week semester. Guided by Kolb’s Experiential Learning Cycle, the two lecturers developed students’ engagement with visual literacy concepts through different activities that facilitated concrete experiences, reflective observation, abstract conceptualization, and active experimentation. Throughout the semester, students create, collaborate, and centralize communication in MIRO with infinite canvas, smart frameworks, a robust set of widgets (i.e., sticky notes, freeform pen, shapes, arrows, smart drawing, emoticons, etc.), and powerful platform capabilities that enable asynchronous and synchronous feedback and interaction. Students then drew upon these multimodal experiences to brainstorm, research, and develop their motion design project. A survey was used to examine students’ perceptions of engagement (E), confidence (C), learning strategies (LS). Using multiple regression, it¬ was found that the use of MIRO helped students develop confidence (C) with visual literacy, which predicted performance score (PS) that was measured against their application of visual literacy to the creation of their motion design project. While students’ learning strategies (LS) with MIRO did not directly predict confidence (C) or performance score (PS), it fostered positive perceptions of engagement (E) which in turn predicted confidence (C). Content analysis of students’ open-ended survey responses about their learning strategies (LS) showed that MIRO provides organization and structure in documenting learning progress, in tandem with establishing standards and expectations as a preparatory ground for generating feedback. With the clarity and sequence of the mentioned conditions set in place, these prerequisites then lead to the next level of personal action for self-reflection, self-directed learning, and time management. The study results show that the affordances of MIRO can develop visual literacy and make up for the potential pitfalls of student isolation, communication, and engagement during online learning. The context of how MIRO could be used by lecturers to orientate students for learning in visual literacy and studio-based projects for future development are discussed.

Keywords: design education, graphic communication, online learning, visual literacy

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Authors: Khalid Salhi, El Miloud Jaara, Mohammed Talibi Alaoui

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In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB color space. These features are then used to train the network Kohonen, which will be represented by the underlying probability density function, the segmentation of this map is made by morphological watershed transformation. The performance of our color-texture segmentation approach is compared first, to color-based methods or texture-based methods only, and then to k-means method.

Keywords: segmentation, color-texture, neural networks, fractal, watershed

Procedia PDF Downloads 326

Authors: Mesias Alfeus, Kirsty Fitzhenry, Alessia Lederer

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The present paper provides empirical studies to estimate defaultable bonds in the South African financial market. The main goal is to estimate the unobservable factors affecting bond yields for South African major banks. The maximum likelihood approach is adopted for the estimation methodology. Extended Kalman filtering techniques are employed in order to tackle the situation that the factors cannot be observed directly. Multi-dimensional Cox-Ingersoll-Ross (CIR)-type factor models are considered. Results show that default risk increased sharply in the South African financial market during COVID-19 and the CIR model with jumps exhibits a better performance.

Keywords: default intensity, unobservable state variables, CIR, α-CIR, extended kalman filtering

Procedia PDF Downloads 92

Authors: Anyia Nduka, Aslan Bin Amad Senin, Ayu Azrin Bte Abdul Aziz

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A management practice characterised by a value chain as its relatively flexible culture is replacing the old bureaucratic model of organisational practice that was built on dominance. Using a management practice fruition paradigm, the study delves into the implications of organisational culture and leadership. Developing a theory of leadership called the “cultural model” of organisational leadership by explaining how the shift from bureaucracy to management practises altered the roles and interactions of leaders. This model is well-grounded in leadership theory, considering the concept's adaptability to different leadership ideologies. In organisations where operational procedures and borders are not clearly defined, hierarchies are flattened, and work collaborations are sometimes based on contracts rather than employment. This cultural model of organizational leadership is intended to be a useful tool for predicting how effectively a leader will perform.

Keywords: leadership, organizational culture, management practices, efficiency

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Authors: Cyril Mae B. Mationg, Alexa T. Belizar, Vethany B. Bellen

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This study aims to determine the use of coconut husks and sugarcane bagasse as natural components in making water-resistant tote bags. The study consists of three concentrations: 70% Coconut Husk - 30% Sugarcane Bagasse, 70% cellulose, and 30% cellulose. The results of these tests revealed that, out of the three concentration concentrations, the one consisting of 70% Coconut Husk and 30% sugarcane bagasse exhibited superior performance in breaking capacity and water penetration. During tensile strength testing, the coconut husk and sugarcane bagasse withstood a force of 207.7 Newtons (N) in the machine direction and 216.5 N in the cross-machine direction.

Keywords: coconut husk, sugarcane bagasse, tote bags, water resistance

Procedia PDF Downloads 51

Authors: Diego S. Caetano, Doreen E. Kalz, Louise L. B. Lomardo, Luiz P. Rosa

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The energy consumption in non-residential buildings in Brazil has a great impact on the national infrastructure. The growth of the energy consumption has a special role over the building cooling systems, supported by the increased people's requirements on hygrothermal comfort. This paper presents how the occupants of office buildings notice and evaluate the hygrothermic comfort regarding temperature, humidity, and air movement, considering the cooling systems presented at the buildings studied, analyzed by real occupants in areas of hot and humid climate. The paper presents results collected over a long time from 3 office buildings in the cities of Rio de Janeiro and Niteroi (Brazil) in 2015 and 2016, from daily questionnaires with eight questions answered by 114 people between 3 to 5 weeks per building, twice a day (10 a.m. and 3 p.m.). The paper analyses 6 out of 8 questions, emphasizing on the perception of temperature, humidity, and air movement. Statistics analyses were made crossing participant answers and humidity and temperature data related to time high time resolution time. Analyses were made from regressions comparing: internal and external temperature, and then compared with the answers of the participants. The results were put in graphics combining statistic graphics related to temperature and air humidity with the answers of the real occupants. Analysis related to the perception of the participants to humidity and air movements were also analyzed. The hygrothermal comfort statistic model of the European standard DIN EN 15251 and that from the Brazilian standard NBR 16401 were compared taking into account the perceptions of the hygrothermal comfort of the participants, with emphasis on air humidity, taking basis on prior studies published on this same research. The studies point out a relative tolerance for higher temperatures than the ones determined by the standards, besides a variation on the participants' perception concerning air humidity. The paper presents a group of detailed information that permits to improve the quality of the buildings based on the perception of occupants of the office buildings, contributing to the energy reduction without health damages and demands of necessary hygrothermal comfort, reducing the consumption of electricity on cooling.

Keywords: thermal comfort, energy consumption, energy standards, comfort models

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Authors: Yamileth P. Herrera, Ronald R. Gutierrez, Carlos, Pacheco-Bustos

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This research aims at the numerical modeling of a rectangular contact tank in order to improve the hydrodynamic behavior and the retention time of the water to be treated with the disinfecting agent. The methodology to be followed includes a hydraulic analysis of the tank to observe the fluid velocities, which will allow evidence of low-speed areas that may generate pathogenic agent incubation or high-velocity areas, which may decrease the optimal contact time between the disinfecting agent and the microorganisms to be eliminated. Based on the results of the numerical model, the efficiency of the tank under the geometric and hydraulic conditions considered will be analyzed. This would allow the performance of the tank to be improved before starting a construction process, thus avoiding unnecessary costs.

Keywords: contact tank, numerical models, hydrodynamic modeling, residence time

Procedia PDF Downloads 153

Authors: Hamad Almohamadi, Nabeel H. Alharthi, Abdulrahman Aljabri

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In this study, two-dimensional CuFeOx is deposited on nickel foam for the fabrication of electrocatalyst for oxygen evolution reaction (OER). The in-situ hydrothermal synthesis of CuFeOx in presence of aloe vera extract was found to yield unique nano-rose-like morphology which aided to improve the electrochemical surface area of the electrode. The phytochemical assisted synthesis of CuFeOx using 75% aloe vera extract resulted in improved OER electrocatalytic performance by attaining the overpotential of 310 mV for 50 mA cm−2 and 410 mV for 100 mA cm−2. The electrode also sustained robust stability throughout the 50 h of chronopotentiometry studies under alkaline electrolyte conditions, thus proving to be prospective electrode material for efficient OER in electrochemical water splitting.

Keywords: water splitting, phytochemicals, oxygen evaluation reaction, Tafel's slope, stability

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Authors: Teera Intararuang

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The purpose of this research is to study on the challenge and opportunity for waterway route management for promoting cultural tourism in Angsila District, Chonburi Province. To accomplish the goals and objectives, qualitative research will be applied. The research instruments used are observation, basic interviews, in-depth interviews, and interview key local performance. The study also uses both primary data and secondary data. From research result, it is revealed that all respondents had appreciated and strongly agree to promote their waterway route tourism as an intend for further increase for their income. However, it has some challenges to success this project due to natural obstacles such as water level, seasons and high temperature. Moreover, they lack financial support from government sectors also.

Keywords: Angsila community, waterway tourism route, cultural tourism, way of life

Procedia PDF Downloads 233

Authors: Galal Elkobrosy, Amr M. Abdelrazek, Bassuny M. Elsouhily, Mohamed E. Khidr

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Crank shaft length, connecting rod length, crank angle, engine rpm, cylinder bore, mass of piston and compression ratio are the inputs that can control the performance of the slider crank mechanism and then its efficiency. Several combinations of these seven inputs are used and compared. The throughput engine torque predicted by the simulation is analyzed through two different regression models, with and without interaction terms, developed according to multi-linear regression using LU decomposition to solve system of algebraic equations. These models are validated. A regression model in seven inputs including their interaction terms lowered the polynomial degree from 3^rd degree to 1^st degree and suggested valid predictions and stable explanations.

Keywords: design of experiments, regression analysis, SI engine, statistical modeling

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Authors: M. A. Ben Messaoud, A. Bouzid, N. Ellouze

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Speech Segmentation is the measure of the change point detection for partitioning an input speech signal into regions each of which accords to only one speaker. In this paper, we apply two features based on multi-scale product (MP) of the clean speech, namely the spectral centroid of MP, and the zero crossings rate of MP. We focus on multi-scale product analysis as an important tool for segmentation extraction. The multi-scale product is based on making the product of the speech wavelet transform coefficients at three successive dyadic scales. We have evaluated our method on the Keele database. Experimental results show the effectiveness of our method presenting a good performance. It shows that the two simple features can find word boundaries, and extracted the segments of the clean speech.

Keywords: multiscale product, spectral centroid, speech segmentation, zero crossings rate

Procedia PDF Downloads 483

Authors: Mamadou I. Wade, Henry C. Ogworonjo, Madiha Gul, Mandoye Ndoye, Mohamed Chouikha, Wayne Patterson

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In this paper, we present a novel application of the Paillier cryptographic system to the encryption of RGB (Red Green Blue) images. In this method, an RGB image is first separated into its constituent channel images, and the Paillier encryption function is applied to each of the channels pixel intensity values. Next, the encrypted image is combined and compressed if necessary before being transmitted through an unsecured communication channel. The transmitted image is subsequently recovered by a decryption process. We performed a series of security and performance analyses to the recovered images in order to verify their robustness to security attack. The results show that the proposed image encryption scheme produces highly secured encrypted images.

Keywords: image encryption, Paillier cryptographic system, RBG image encryption, Paillier

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Authors: Tim Farrelly

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In recent years, AI-powered object detection systems have opened the doors for innovative new applications and products, especially those operating in the real world or ‘on edge’ – namely, in sport. This paper investigates the viability of an ultra-low parameter convolutional neural network specially designed for the detection of footballs on ‘on the edge’ devices. The main contribution of this paper is the exploration of integrating new design features (depth-wise separable convolutional blocks and squeezed and excitation modules) into an ultra-low parameter network and demonstrating subsequent improvements in performance. The results show that tracking the ball from Full HD images with negligibly high accu-racy is possible in real-time.

Keywords: deep learning, object detection, machine vision applications, sport, network design

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Authors: Rishabh Shukla, Achin Agrawal, Anupam Saxena, S. Mandal

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The existence of a crack, affects the mechanical behaviour and various properties of a structure to a great degree. This paper focuses on recognizing the parameters that gets changed due to the formation of cracks and have a great impact on the performance of the structure. Spalling is a major concern as it leaves the reinforcement bars more susceptible to environmental attacks. Beams of cross section 300 mm × 500 mm are designed and for a calculated area of steel, two different arrangements of reinforced bars are analysed. Results are prepared for different stages of cracking for each arrangement of rebars. The parameters for both arrangements are then compared. The Finite Element Analysis (FEA) is carried out and changes in the properties like flexural strength, Elasticity and modal frequency are reported. The conclusions have been drawn by comparing the results.

Keywords: cracks, elasticity, spalling, FEA

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Authors: Dumont Maxime, Correia Carlos, Sauvage Jean-François, Schwartz Noah, Gray Morgan

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Context: The concept of the segmented telescope is unavoidable to build extremely large telescopes (ELT) in the quest for spatial resolution, but it also allows one to fit a large telescope within a reduced volume of space (JWST) or into an even smaller volume (Standard Cubesat). Cubesats have tight constraints on the computational burden available and the small payload volume allowed. At the same time, they undergo thermal gradients leading to large and evolving optical aberrations. The pupil segmentation comes nevertheless with an obvious difficulty: to co-phase the different segments. The CubeSat constraints prevent the use of a dedicated wavefront sensor (WFS), making the focal-plane images acquired by the science detector the most practical alternative. Yet, one of the challenges for the wavefront sensing is the non-linearity between the image intensity and the phase aberrations. Plus, for Earth observation, the object is unknown and unrepeatable. Recently, several studies have suggested Neural Networks (NN) for wavefront sensing; especially convolutional NN, which are well known for being non-linear and image-friendly problem solvers. Aims: We study in this paper the prospect of using NN to measure the phasing aberrations of a segmented pupil from the focal-plane image directly without a dedicated wavefront sensing. Methods: In our application, we take the case of a deployable telescope fitting in a CubeSat for Earth observations which triples the aperture size (compared to the 10cm CubeSat standard) and therefore triples the angular resolution capacity. In order to reach the diffraction-limited regime in the visible wavelength, typically, a wavefront error below lambda/50 is required. The telescope focal-plane detector, used for imaging, will be used as a wavefront-sensor. In this work, we study a point source, i.e. the Point Spread Function [PSF] of the optical system as an input of a VGG-net neural network, an architecture designed for image regression/classification. Results: This approach shows some promising results (about 2nm RMS, which is sub lambda/50 of residual WFE with 40-100nm RMS of input WFE) using a relatively fast computational time less than 30 ms which translates a small computation burder. These results allow one further study for higher aberrations and noise.

Keywords: wavefront sensing, deep learning, deployable telescope, space telescope

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Authors: Moung Young Lee, Chul Gyu Song

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Photoacoustic imaging is the imaging technology that combines the optical imaging and ultrasound. This provides the high contrast and resolution due to optical imaging and ultrasound imaging, respectively. We developed the real-time photoacoustic tomography (PAT) system using linear-ultrasound transducer and digital acquisition (DAQ) board. There are two types of algorithm for reconstructing the photoacoustic signal. One is back-projection algorithm, the other is FFT algorithm. Especially, we used the non-uniform FFT algorithm. To evaluate the performance of our system and algorithms, we monitored two wires that stands at interval of 2.89 mm and 0.87 mm. Then, we compared the images reconstructed by algorithms. Finally, we monitored the two hairs crossed and compared between these algorithms.

Keywords: back-projection, image comparison, non-uniform FFT, photoacoustic tomography

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Authors: Silvia Franco, Marcos Sarasola

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The Faculty of Business Administration at the Catholic University of Uruguay is performing an important educational innovation, unique in the country. In preparing future professionals in companies, teamwork competence is very important. However, there is no often a systematic and specific training in the acquisition of this competence in undergraduate students. For this reason, we have designed and implemented an educational innovation through an intervention method to improve teamwork competence for undergraduate students of business studies. Students’ teams are integrated according to the complementary roles of Belbin; changes in teamwork competence during training period are measured with CCSAC tool; classroom methodology in the prio-border teamwork by Team-Based Learning. Methodology also integrates coaching and support team performance during the first two semesters.

Keywords: business students, teamwork, learning, competences

Procedia PDF Downloads 351

Authors: Farzad Pargar

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We introduce the preventive maintenance and renewal scheduling problem for a multi-unit system over a finite and discretized time horizon. Given the latest possible time for carrying out the next maintenance and renewal projects after the previous ones and considering several common set-up costs, the introduced scheduling model tries to minimize the cost of projects by grouping them and simultaneously finding the optimal balance between doing maintenance and renewal. We present a 0-1 pure integer linear programming that determines which projects should be performed together on which location and in which period (e.g., week or month). We consider railway track as a case for our study and test the performance of the proposed model on a set of test problems. The experimental results show that the proposed approach performs well.

Keywords: maintenance, renewal, scheduling, mathematical programming model

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Authors: Varun Agarwal

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Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: Onder Yakut, Oguzhan Timus, Emine Dogru Bolat

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Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats.

Keywords: arrhythmic beat detection, ECG, HRV, kNN classifier

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Authors: Hadi Adibi-Asl, Alireza Doosthosseini, Amir Taghavipour

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This paper studies the design of an adaptive control strategy to tune an active steering system for better drivability and maneuverability. In the first step, adaptive control strategy is applied to estimate the uncertain parameters on-line (e.g. cornering stiffness), then the estimated parameters are fed into the pole placement controller to generate corrective feedback gain to improve the steering system dynamic’s characteristics. The simulations are evaluated for three types of road conditions (dry, wet, and icy), and the performance of the adaptive pole placement control (APPC) are compared with pole placement control (PPC) and a passive system. The results show that the APPC strategy significantly improves the yaw rate and side slip angle of a bicycle plant model.

Keywords: adaptive control, active steering, pole placement, vehicle dynamics

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Authors: Sidharth Talan, Sanjay Kumar Sharma, Eoin Joseph Wallace, Nikita Agrawal

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Underground mining is at the core of rapidly evolving metals and minerals sector due to the increasing mineral consumption globally. Even though the surface mines are still more abundant on earth, the scales of industry are slowly tipping towards underground mining due to rising depth and complexities of orebodies. Thus, the efficient and productive functioning of underground operations depends significantly on the synchronized performance of key elements such as operating site, mining equipment, manpower and mine services. Production drilling is the process of conducting long hole drilling for the purpose of charging and blasting these holes for the production of ore in underground metal mines. Thus, production drilling is the crucial segment in the underground metal mining value chain. This paper presents the process mapping tool to evaluate the production drilling process in the underground metal mining operation by dividing the given process into three segments namely Input, Process and Output. The three segments are further segregated into factors and sub-factors. As per the study, the major input factors crucial for the efficient functioning of production drilling process are power, drilling water, geotechnical support of the drilling site, skilled drilling operators, services installation crew, oils and drill accessories for drilling machine, survey markings at drill site, proper housekeeping, regular maintenance of drill machine, suitable transportation for reaching the drilling site and finally proper ventilation. The major outputs for the production drilling process are ore, waste as a result of dilution, timely reporting and investigation of unsafe practices, optimized process time and finally well fragmented blasted material within specifications set by the mining company. The paper also exhibits the drilling loss matrix, which is utilized to appraise the loss in planned production meters per day in a mine on account of availability loss in the machine due to breakdowns, underutilization of the machine and productivity loss in the machine measured in drilling meters per unit of percussion hour with respect to its planned productivity for the day. The given three losses would be essential to detect the bottlenecks in the process map of production drilling operation so as to instigate the action plan to suppress or prevent the causes leading to the operational performance deficiency. The given tool is beneficial to mine management to focus on the critical factors negatively impacting the production drilling operation and design necessary operational and maintenance strategies to mitigate them. 

Keywords: process map, drilling loss matrix, SIPOC, productivity, percussion rate

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Authors: Harshada Lohokare

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Biofouling is major issue in Reverse Osmosis (RO) membranes operation. To address the biofouling issue in raw water as well as wastewater recycle / reuse application requires effective biofouling control program. Current biocides (2,2-dibromo-3-nitrilopropionamide, isothiazolinone) are costly and hence often under-dosed. The membrane compatibility, as well as the microbio efficiency of the RO membrane biocide was studied. Based on the biofouling potential, the biocide product and it’s dosage was studied. It was found that these products need to be dosed continuous as well as intermittent dosage based on the microbio load. This study shows that depending on the application and microbio fouling potential, products can be chosen to mitigate the biofouling issues and improve the RO membrane performance.

Keywords: reverse osmosis membrane, biofouling, biocide, stabilized halogen

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Authors: Majid Farsadrouh Rashti, Amir Shafiee Kisomi, Parisa Jahani

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The direct ethanol fuel cells (DEFCs) are contemplated as a promising energy source because, In addition to being used in portable electronic devices, it is also used for electric vehicles. The synthesis of bimetallic nanostructures due to their novel optical, catalytic and electronic characteristic which is precisely in contrast to their monometallic counterparts is attracting extensive attention. Galvanic replacement (sometimes is named to as cementation or immersion plating) is an uncomplicated and effective technique for making nanostructures (such as core-shell) of different metals, semiconductors, and their application in DEFCs. The replacement of galvanic does not need any external power supply compared to electrodeposition. In addition, it is different from electroless deposition because there is no need for a reducing agent to replace galvanizing. In this paper, a fast method for the palladium (Pd) wire nanostructures synthesis with the great surface area through galvanic replacement reaction utilizing copper nanowires (CuNWS) as a template by the assistance of ultrasound under room temperature condition is proposed. To evaluate the morphology and composition of Pd@ Copper nanowires/MultiWalled Carbon nanotubes-Chitosan, emission scanning electron microscopy, energy dispersive X-ray spectroscopy were applied. In order to measure the phase structure of the electrocatalysts were performed via room temperature X-ray powder diffraction (XRD) applying an X-ray diffractometer. Various electrochemical techniques including chronoamperometry and cyclic voltammetry were utilized for the electrocatalytic activity of ethanol electrooxidation and durability in basic solution. Pd@ Copper nanowires/MultiWalled Carbon nanotubes-Chitosan catalyst demonstrated substantially enhanced performance and long-term stability for ethanol electrooxidation in the basic solution in comparison to commercial Pd/C that demonstrated the potential in utilizing Pd@ Copper nanowires/MultiWalled Carbon nanotubes-Chitosan as efficient catalysts towards ethanol oxidation. Noticeably, the Pd@ Copper nanowires/MultiWalled Carbon nanotubes-Chitosan presented excellent catalytic activities with a peak current density of 320.73 mAcm² which was 9.5 times more than in comparison to Pd/C (34.2133 mAcm²). Additionally, activation energy thermodynamic and kinetic evaluations revealed that the Pd@ Copper nanowires/MultiWalled Carbon nanotubes-Chitosan catalyst has lower compared to Pd/C which leads to a lower energy barrier and an excellent charge transfer rate towards ethanol oxidation.

Keywords: core-shell structure, electrocatalyst, ethanol oxidation, galvanic replacement reaction

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Authors: Sofia Ayouche, Rachid Ellaia, Rajae Aboulaich

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Today, insurers may use the yield curve as an indicator evaluation of the profit or the performance of their portfolios; therefore, they modeled it by one class of model that has the ability to fit and forecast the future term structure of interest rates. This class of model is the Nelson-Siegel-Svensson model. Unfortunately, many authors have reported a lot of difficulties when they want to calibrate the model because the optimization problem is not convex and has multiple local optima. In this context, we implement a hybrid Particle Swarm optimization and Nelder Mead algorithm in order to minimize by least squares method, the difference between the zero-coupon curve and the NSS curve.

Keywords: optimization, zero-coupon curve, Nelson-Siegel-Svensson, particle swarm optimization, Nelder-Mead algorithm

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Authors: Ahmed Fathi Mohamed, Nasir Shafiq, Muhd Fadhil Nuruddin, Ali Elheber Ahmed

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Steel fiber adds to Self-Compacting Concrete (SCC) to enhance it is properties and achieves the requirement. This research work focus on the using of different percentage of steel fiber in SCC mixture contains fly ash and microwave incinerator rice husk ash (MIRHA) as supplementary material. Fibers affect several characteristics of SCC in the fresh and the hardened state. To optimize fiber-reinforced self-compacting concrete (FSCC), The possible fiber content of a given mix composition is an essential input parameter. The aim of the research is to study the properties of fiber reinforced self–compacting (FRSCC) and to develop the expert system/computer program of mix proportion for calculating the steel fiber content and pozzolanic replacement that can be applied to investigate the compressive strength of FSCC mix.

Keywords: self-compacting concrete, silica fume, steel fiber, fresh taste

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Authors: Heidi von Korff

Abstract:

The electricity grid is changing in terms of flexibility. Distributed generation (DG) policy is being discussed worldwide and implemented. Developers and utilities are seeking a pathway towards Zero Net Energy (ZNE) communities and the interconnection to the distribution grid. Using the VISDOM platform for establishing a method for managing and monitoring energy consumption loads of ZNE communities as a capacity resource for the grid. Reductions in greenhouse gas emissions and energy security are primary policy drivers for incorporating high-performance energy standards and sustainability practices in residential households, such as a market transformation of ZNE and nearly ZNE (nZNE) communities. This research investigates how load data impacts ZNE, to see if there is a correlation to the daily load variations in a single ZNE home. Case studies will include a ZNE community in California and a nearly ZNE community (All – Electric) in the Netherlands, which both are in measurement and verification (M&V) phases and connected to the grid for simulations of methods.

Keywords: zero net energy, distributed generation, renewable energy, zero net energy community

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Authors: Amrane Meriem, Oukid Salyha

Abstract:

The presence of defects or bugs in software can lead to costly setbacks, operational inefficiencies, and compromised user experiences. The integration of Machine Learning(ML) techniques has emerged to predict and preemptively address software defects. ML represents a proactive strategy aimed at identifying potential anomalies, errors, or vulnerabilities within code before they manifest as operational issues. By analyzing historical data, such as code changes, feature im- plementations, and defect occurrences. This en- ables development teams to anticipate and mitigate these issues, thus enhancing software quality, reducing maintenance costs, and ensuring smoother user interactions. In this work, we used a recommendation system to improve the performance of ML models in terms of predicting the code severity and effort estimation.

Keywords: software engineering, machine learning, bugs detection, effort estimation

Procedia PDF Downloads 67