Search results for: algorithm techniques

Authors: M. Maier, I. Dedigama, J. Majasan, Y. Wu, Q. Meyer, L. Castanheira, G. Hinds, P. R. Shearing, D. J. L. Brett

Abstract:

Increasing the efficiency of electrolyser technology is commonly seen as one of the main challenges on the way to the Hydrogen Economy. There is a significant lack of understanding of the different states of operation of polymer electrolyte membrane water electrolysers (PEMWE) and how these influence the overall efficiency. This in particular means the two-phase flow through the membrane, gas diffusion layers (GDL) and flow channels. In order to increase the efficiency of PEMWE and facilitate their spread as commercial hydrogen production technology, new analytic approaches have to be found. Acoustic emission (AE) offers the possibility to analyse the processes within a PEMWE in a non-destructive, fast and cheap in-situ way. This work describes the generation and analysis of AE data coming from a PEM water electrolyser, for, to the best of our knowledge, the first time in literature. Different experiments are carried out. Each experiment is designed so that only specific physical processes occur and AE solely related to one process can be measured. Therefore, a range of experimental conditions is used to induce different flow regimes within flow channels and GDL. The resulting AE data is first separated into different events, which are defined by exceeding the noise threshold. Each acoustic event consists of a number of consequent peaks and ends when the wave diminishes under the noise threshold. For all these acoustic events the following key attributes are extracted: maximum peak amplitude, duration, number of peaks, peaks before the maximum, average intensity of a peak and time till the maximum is reached. Each event is then expressed as a vector containing the normalized values for all criteria. Principal Component Analysis is performed on the resulting data, which orders the criteria by the eigenvalues of their covariance matrix. This can be used as an easy way of determining which criteria convey the most information on the acoustic data. In the following, the data is ordered in the two- or three-dimensional space formed by the most relevant criteria axes. By finding spaces in the two- or three-dimensional space only occupied by acoustic events originating from one of the three experiments it is possible to relate physical processes to certain acoustic patterns. Due to the complex nature of the AE data modern machine learning techniques are needed to recognize these patterns in-situ. Using the AE data produced before allows to train a self-learning algorithm and develop an analytical tool to diagnose different operational states in a PEMWE. Combining this technique with the measurement of polarization curves and electrochemical impedance spectroscopy allows for in-situ optimization and recognition of suboptimal states of operation.

Keywords: acoustic emission, gas diffusion layers, in-situ diagnosis, PEM water electrolyser

Procedia PDF Downloads 138

Authors: Fábio A. S. Mota, Mauro A. S. S. Ravagnani, E. P. Carvalho

Abstract:

In the present paper the design of plate heat exchangers is formulated as an optimization problem considering two mathematical modeling. The number of plates is the objective function to be minimized, considering implicitly some parameters configuration. Screening is the optimization method used to solve the problem. Thermal and hydraulic constraints are verified, not viable solutions are discarded and the method searches for the convergence to the optimum, case it exists. A case study is presented to test the applicability of the developed algorithm. Results show coherency with the literature.

Keywords: plate heat exchanger, optimization, modeling, simulation

Procedia PDF Downloads 501

Authors: Syed Jamil H. Kazmi, Faiza Sarwar

Abstract:

The Indus delta of Sindh Province forms the largest creeks zone of Pakistan. The Sindh coast starts from the mouth of Hab River and terminates at Sir Creek area. In this paper, we have considered the major creeks from the site of Bin Qasim Port in Karachi to Jetty of Keti Bunder in Thatta District. A general decline in the mangrove forest has been observed that within a span of last 25 years. The unprecedented human interventions damage the creeks habitat badly which includes haphazard urban development, industrial and sewage disposal, illegal cutting of mangroves forest, reduced and inconsistent fresh water flow mainly from Jhang and Indus rivers. These activities not only harm the creeks habitat but affected the fish stock substantially. Fishing is the main livelihood of coastal people but with the above-mentioned threats, it is also under enormous pressure by fish catches resulted in unchecked overutilization of the fish resources. This pressure is almost unbearable when it joins with deleterious fishing methods, uncontrolled fleet size, increase trash and by-catch of juvenile and illegal mesh size. Along with these anthropogenic interventions study area is under the red zone of tropical cyclones and active seismicity causing floods, sea intrusion, damage mangroves forests and devastation of fish stock. In order to sustain the natural resources of the Indus Creeks, this study was initiated with the support of FAO, WWF and NIO, the main purpose was to develop a Geo-Spatial dataset for fish stock assessment. The study has been spread over a year (2013-14) on monthly basis which mainly includes detailed fish stock survey, water analysis and few other environmental analyses. Environmental analysis also includes the habitat classification of study area which has done through remote sensing techniques for 22 years’ time series (1992-2014). Furthermore, out of 252 species collected, fifteen species from estuarine and marine groups were short-listed to measure the weight, health and growth of fish species at each creek under GIS data through SPSS system. Furthermore, habitat suitability analysis has been conducted by assessing the surface topographic and aspect derivation through different GIS techniques. The output variables then overlaid in GIS system to measure the creeks productivity. Which provided the results in terms of subsequent classes: extremely productive, highly productive, productive, moderately productive and less productive. This study has revealed the Geospatial tools utilization along with the evaluation of the fisheries resources and creeks habitat risk zone mapping. It has also been identified that the geo-spatial technologies are highly beneficial to identify the areas of high environmental risk in Sindh Creeks. This has been clearly discovered from this study that creeks with high rugosity are more productive than the creeks with low levels of rugosity. The study area has the immense potential to boost the economy of Pakistan in terms of fish export, if geo-spatial techniques are implemented instead of conventional techniques.

Keywords: fish stock, geo-spatial, productivity analysis, risk

Procedia PDF Downloads 230

Authors: Yemane Hailu Fissuh, Zhongzhan Zhang

Abstract:

In biomedical researches and randomized clinical trials, the most commonly interested outcomes are time-to-event so-called survival data. The importance of robust models in this context is to compare the effect of randomly controlled experimental groups that have a sense of causality. Causal estimation is the scientific concept of comparing the pragmatic effect of treatments conditional to the given covariates rather than assessing the simple association of response and predictors. Hence, the causal effect based semiparametric transformation model was proposed to estimate the effect of treatment with the presence of possibly time-varying covariates. Due to its high flexibility and robustness, the semiparametric transformation model which shall be applied in this paper has been given much more attention for estimation of a causal effect in modeling left-truncated and right censored survival data. Despite its wide applications and popularity in estimating unknown parameters, the maximum likelihood estimation technique is quite complex and burdensome in estimating unknown parameters and unspecified transformation function in the presence of possibly time-varying covariates. Thus, to ease the complexity we proposed the modified estimating equations. After intuitive estimation procedures, the consistency and asymptotic properties of the estimators were derived and the characteristics of the estimators in the finite sample performance of the proposed model were illustrated via simulation studies and Stanford heart transplant real data example. To sum up the study, the bias of covariates was adjusted via estimating the density function for truncation variable which was also incorporated in the model as a covariate in order to relax the independence assumption of failure time and truncation time. Moreover, the expectation-maximization (EM) algorithm was described for the estimation of iterative unknown parameters and unspecified transformation function. In addition, the causal effect was derived by the ratio of the cumulative hazard function of active and passive experiments after adjusting for bias raised in the model due to the truncation variable.

Keywords: causal estimation, EM algorithm, semiparametric transformation models, time-to-event outcomes, time-varying covariate

Procedia PDF Downloads 109

Authors: Mangana B. Rampheri, Timothy Dube, Farai Dondofema, Tatenda Dalu

Abstract:

Identifying and delineating groundwater-dependent ecosystems (GDEs) is critical to the well understanding of the GDEs spatial distribution as well as groundwater allocation. However, this information is inadequately understood due to limited available data for the most area of concerns. Thus, this study aims to address this gap using remotely sensed, analytical hierarchy process (AHP) and in-situ data to identify and delineate GDEs in Khakea-Bray Transboundary Aquifer. Our study developed GDEs index, which integrates seven explanatory variables, namely, Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Land-use and landcover (LULC), slope, Topographic Wetness Index (TWI), flow accumulation and curvature. The GDEs map was delineated using the weighted overlay tool in ArcGIS environments. The map was spatially classified into two classes, namely, GDEs and Non-GDEs. The results showed that only 1,34 % (721,91 km2) of the area is characterised by GDEs. Finally, groundwater level (GWL) data was used for validation through correlation analysis. Our results indicated that: 1) GDEs are concentrated at the northern, central, and south-western part of our study area, and 2) the validation results showed that GDEs classes do not overlap with GWL located in the 22 boreholes found in the given area. However, the results show a possible delineation of GDEs in the study area using remote sensing and GIS techniques along with AHP. The results of this study further contribute to identifying and delineating priority areas where appropriate water conservation programs, as well as strategies for sustainable groundwater development, can be implemented.

Keywords: analytical hierarchy process (AHP), explanatory variables, groundwater-dependent ecosystems (GDEs), khakea-bray transboundary aquifer, sentinel-2

Procedia PDF Downloads 96

Authors: Eman M. Sarhan, Doaa A. Ghareeb, Gabriella Ortore, Amr A. Amara, Mohamed M. El-Sayed

Abstract:

Drug salting and nanoparticle-based drug delivery formulations are considered to be an effective means for rendering the hydrophobic drugs’ nano-scale dispersion in aqueous media, and thus circumventing the pitfalls of their poor solubility as well as enhancing their membrane permeability. The current study aims to increase the bioavailability of quaternary ammonium berberine through acid salting and biodegradable bovine serum albumin (BSA)-based nanoparticulate drug formulation. Berberine hydroxide (BBR-OH) that was chemically synthesized by alkalization of the commercially available berberine hydrochloride (BBR-HCl) was then acidified to get Di-berberine sulfate (BBR)₂SO₄. The purified crystals were spectrally characterized. The desolvation technique was optimized for the preparation of size-controlled BSA-BBR-HCl, BSA-BBR-OH, and BSA-(BBR)₂SO₄ nanoparticles. Particle size, zeta potential, drug release, encapsulation efficiency, Fourier transform infrared spectroscopy (FTIR), tandem MS-MS spectroscopy, energy-dispersive X-ray spectroscopy (EDX), scanning and transmitting electron microscopic examination (SEM, TEM), in vitro bioactivity, and in silico drug-polymer interaction were determined. BSA (PDB ID; 4OR0) protonation state at different pH values was predicted using Amber12 molecular dynamic simulation. Then blind docking was performed using Lamarkian genetic algorithm (LGA) through AutoDock4.2 software. Results proved the purity and the size-controlled synthesis of berberine-BSA-nanoparticles. The possible binding poses, hydrophobic and hydrophilic interactions of berberine on BSA at different pH values were predicted. Antioxidant, anti-hemolytic, and cell differentiated ability of tested drugs and their nano-formulations were evaluated. Thus, drug salting and the potentially effective albumin berberine nanoparticle formulations can be successfully developed using a well-optimized desolvation technique and exhibiting better in vitro cellular bioavailability.

Keywords: berberine, BSA, BBR-OH, BBR-HCl, BSA-BBR-HCl, BSA-BBR-OH, (BBR)₂SO₄, BSA-(BBR)₂SO₄, FTIR, AutoDock4.2 Software, Lamarkian genetic algorithm, SEM, TEM, EDX

Procedia PDF Downloads 156

Authors: Farid Khosravikia, Patricia Clayton

Abstract:

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

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

Procedia PDF Downloads 112

Authors: Kayla Renteria

Abstract:

This study explored the role animal-assisted psychotherapy (AAP) can play in treating Post-Traumatic Stress Disorder (PTSD) when incorporated into Trauma-informed cognitive behavioral therapy (TF-CBT). A review of the literature was performed to show how incorporating AAP could benefit TF-CBT since this treatment model often presents difficulties, such as client motivation and avoidance of the exposure element of the intervention. In addition, the fluidity of treatment goals during complex trauma cases was explored, as this issue arose in the case study. This study follows the course of treatment of a 12-year-old female presenting with symptoms of PTSD. Treatment consisted of traditional components of the TF-CBT model, with the added elements of AAP to address typical treatment obstacles in TF-CBT. A registered therapy dog worked with the subject in all sessions throughout her treatment. The therapy dog was incorporated into components such as relaxation and coping techniques, narrative therapy techniques, and psychoeducation on the cognitive triangle. Throughout the study, the client’s situation and clinical needs required the therapist to switch goals to focus on current safety and stability. The therapy dog provided support and neurophysiological benefits to the client through AAP during this shift in treatment. The client was assessed quantitatively using the Child PTSD Symptom Scale Self Report for DSM-5 (CPSS-SR-5) before and after therapy and qualitatively through a feedback form given after treatment. The participant showed improvement in CPSS-SR-V scores, and she reported that the incorporation of the therapy animal improved her therapy. The results of this study show how the use of AAP provided the client a solid, consistent relationship with the therapy dog that supported her through processing various types of traumas. Implications of the results of treatment and for future research are discussed.

Keywords: animal-assisted therapy, trauma-focused cognitive behavioral therapy, PTSD in children, trauma treatment

Procedia PDF Downloads 198

Authors: Roy. H. A. Lindelauf

Abstract:

Cyberattacks are complex processes consisting of multiple interwoven tasks conducted by a set of agents. Interdictions and defenses against such attacks often rely on cyber kill chain (CKC) models. A CKC is a framework that tries to capture the actions taken by a cyber attacker. There exists a growing body of literature on CKCs. Most of this work either a) describes the CKC with respect to one or more specific cyberattacks or b) discusses the tools and technologies used by the attacker at each stage of the CKC. Defenders, facing scarce resources, have to decide where to allocate their resources given the CKC and partial knowledge on the tools and techniques attackers use. In this presentation CKCs are analyzed through the lens of covert projects, i.e., interrelated tasks that have to be conducted by agents (human and/or computer) with the aim of going undetected. Various aspects of covert project models have been studied abundantly in the operations research and game theory domain, think of resource-limited interdiction actions that maximally delay completion times of a weapons project for instance. This presentation has investigated both cooperative and non-cooperative game theoretic covert project models and elucidated their relation to CKC modelling. To view a CKC as a covert project each step in the CKC is broken down into tasks and there are players of which each one is capable of executing a subset of the tasks. Additionally, task inter-dependencies are represented by a schedule. Using multi-glove cooperative games it is shown how a defender can optimize the allocation of his scarce resources (what, where and how to monitor) against an attacker scheduling a CKC. This study presents and compares several cooperative game theoretic solution concepts as metrics for assigning resources to the monitoring of agents.

Keywords: cyber defense, cyber kill chain, game theory, information warfare techniques

Procedia PDF Downloads 127

Authors: S. Abdourraziq, M. A. Abdourraziq

Abstract:

One of the most famous and important applications of solar energy systems is water pumping. It is often used for irrigation or to supply water in countryside or private firm. However, the cost and the efficiency are still a concern, especially with a continued variation of solar radiation and temperature throughout the day. Then, the improvement of the efficiency of the system components is one of the different solutions to reducing the cost. In this paper, we will present a detailed definition of each element of a PV pumping system, and we will present the different MPPT algorithm used in the literature. Our system consists of a PV panel, a boost converter, a motor-pump set, and a storage tank.

Keywords: PV cell, converter, MPPT, MPP, PV pumping system

Procedia PDF Downloads 144

Authors: Dorian Audot, Isobel Margaret Thompson, Dominic Hudson, Joseph Banks, Martin Warner

Abstract:

In competitive swimming, after dives and turns, athletes perform underwater undulatory swimming (UUS), copying marine mammals’ method of locomotion. The body, performing this wave-like motion, accelerates the fluid downstream in its vicinity, generating propulsion with minimal resistance. Through this technique, swimmers can maintain greater speeds than surface swimming and take advantage of the overspeed granted by the dive (or push-off). Almost all previous work has considered UUS when performed at maximum effort. Critical parameters to maximize UUS speed are frequently discussed; however, this does not apply to most races. In only 3 out of the 16 individual competitive swimming events are athletes likely to attempt to perform UUS with the greatest speed, without thinking of the cost of locomotion. In the other cases, athletes will want to control the speed of their underwater swimming, attempting to maximise speed whilst considering energy expenditure appropriate to the duration of the event. Hence, there is a need to understand how swimmers adapt their underwater strategies to optimize the speed within the allocated energetic cost. This paper develops a consistent methodology that enables different sets of UUS kinematics to be investigated. These may have different propulsive efficiencies and force generation mechanisms (e.g.: force distribution along with the body and force magnitude). The developed methodology, therefore, needs to: (i) provide an understanding of the UUS propulsive mechanisms at different speeds, (ii) investigate the key performance parameters when UUS is not performed solely for maximizing speed; (iii) consistently determine the propulsive efficiency of a UUS technique. The methodology is separated into two distinct parts: kinematic data acquisition and computational fluid dynamics (CFD) analysis. For the kinematic acquisition, the position of several joints along the body and their sequencing were either obtained by video digitization or by underwater motion capture (Qualisys system). During data acquisition, the swimmers were asked to perform UUS at a constant depth in a prone position (facing the bottom of the pool) at different speeds: maximum effort, 100m pace, 200m pace and 400m pace. The kinematic data were input to a CFD algorithm employing a two-dimensional Large Eddy Simulation (LES). The algorithm adopted was specifically developed in order to perform quick unsteady simulations of deforming bodies and is therefore suitable for swimmers performing UUS. Despite its approximations, the algorithm is applied such that simulations are performed with the inflow velocity updated at every time step. It also enables calculations of the resistive forces (total and applied to each segment) and the power input of the modeled swimmer. Validation of the methodology is achieved by comparing the data obtained from the computations with the original data (e.g.: sustained swimming speed). This method is applied to the different kinematic datasets and provides data on swimmers’ natural responses to pacing instructions. The results show how kinematics affect force generation mechanisms and hence how the propulsive efficiency of UUS varies for different race strategies.

Keywords: CFD, efficiency, human swimming, hydrodynamics, underwater undulatory swimming

Procedia PDF Downloads 204

Authors: John Tarus, Zhendong Niu, Bakhti Khadidja

Abstract:

In recent years, e-learning recommender systems has attracted great attention as a solution towards addressing the problem of information overload in e-learning environments and providing relevant recommendations to online learners. E-learning recommenders continue to play an increasing educational role in aiding learners to find appropriate learning materials to support the achievement of their learning goals. Although general recommender systems have recorded significant success in solving the problem of information overload in e-commerce domains and providing accurate recommendations, e-learning recommender systems on the other hand still face some issues arising from differences in learner characteristics such as learning style, skill level and study level. Conventional recommendation techniques such as collaborative filtering and content-based deal with only two types of entities namely users and items with their ratings. These conventional recommender systems do not take into account the learner characteristics in their recommendation process. Therefore, conventional recommendation techniques cannot make accurate and personalized recommendations in e-learning environment. In this paper, we propose a recommendation technique combining collaborative filtering and ontology to recommend personalized learning materials to online learners. Ontology is used to incorporate the learner characteristics into the recommendation process alongside the ratings while collaborate filtering predicts ratings and generate recommendations. Furthermore, ontological knowledge is used by the recommender system at the initial stages in the absence of ratings to alleviate the cold-start problem. Evaluation results show that our proposed recommendation technique outperforms collaborative filtering on its own in terms of personalization and recommendation accuracy.

Keywords: collaborative filtering, e-learning, ontology, recommender system

Procedia PDF Downloads 354

Authors: Alfonso Ippolito, Cristiana Bartolomei

Abstract:

A growing demand is felt today for realistic 3D models enabling the cognition and popularization of historical-artistic heritage. Evaluation and preservation of Cultural Heritage is inextricably connected with the innovative processes of gaining, managing, and using knowledge. The development and perfecting of techniques for acquiring and elaborating photorealistic 3D models, made them pivotal elements for popularizing information of objects on the scale of architectonic structures.

Keywords: cultural heritage, databases, non-contact survey, 2D-3D models

Procedia PDF Downloads 408

Authors: Sateesh Kumar Pisini, Swetha Priya Darshini, Sanjay Kumar Shukla

Abstract:

This paper seeks to present a research study on sustainability in construction of retaining wall using geosynthetics. Sustainable construction is a way for the building and infrastructure industry to move towards achieving sustainable development, taking into account environmental, socioeconomic and cultural issues. Geotechnical engineering, being very resource intensive, warrants an environmental sustainability study, but a quantitative framework for assessing the sustainability of geotechnical practices, particularly at the planning and design stages, does not exist. In geotechnical projects, major economic issues to be addressed are in the design and construction of stable slopes and retaining structures within space constraints. In this paper, quantitative indicators for assessing the environmental sustainability of retaining wall with geosynthetics are compared with conventional concrete retaining wall through life cycle assessment (LCA). Geosynthetics can make a real difference in sustainable construction techniques and contribute to development in developing countries in particular. Their imaginative application can result in considerable cost savings over the use of conventional designs and materials. The acceptance of geosynthetics in reinforced retaining wall construction has been triggered by a number of factors, including aesthetics, reliability, simple construction techniques, good seismic performance, and the ability to tolerate large deformations without structural distress. Reinforced retaining wall with geosynthetics is the best cost-effective and eco-friendly solution as compared with traditional concrete retaining wall construction. This paper presents an analysis of the theme of sustainability applied to the design and construction of traditional concrete retaining wall and presenting a cost-effective and environmental solution using geosynthetics.

Keywords: sustainability, retaining wall, geosynthetics, life cycle assessment

Procedia PDF Downloads 2043

Authors: Smita Mondal, Dinesh Kumar Pandey, Prakash Biswas

Abstract:

During past two decades, considerable attention has been given to the value addition of biodiesel derived glycerol (~10wt.%) to make the biodiesel industry economically viable. Among the various glycerol value-addition methods, hydrogenolysis of glycerol to 1,2-propanediol is one of the attractive and promising routes. In this study, highly active and selective γ-Al₂O₃ supported bimetallic Cu-Ni catalyst was developed for selective hydrogenolysis of glycerol to 1,2-propanediol in the liquid phase. The catalytic performance was evaluated in a high-pressure autoclave reactor. The formation of mixed oxide indicated the strong interaction of Cu, Ni with the alumina support. Experimental results demonstrated that bimetallic copper-nickel catalyst was more active and selective to 1,2-PDO as compared to monometallic catalysts due to bifunctional behavior. To verify the effect of calcination temperature on the formation of Cu-Ni mixed oxide phase, the calcination temperature of 20wt.% Cu:Ni(1:1)/Al₂O₃ catalyst was varied from 300°C-550°C. The physicochemical properties of the catalysts were characterized by various techniques such as specific surface area (BET), X-ray diffraction study (XRD), temperature programmed reduction (TPR), and temperature programmed desorption (TPD). The BET surface area and pore volume of the catalysts were in the range of 71-78 m²g⁻¹, and 0.12-0.15 cm³g⁻¹, respectively. The peaks at the 2θ range of 43.3°-45.5° and 50.4°-52°, was corresponded to the copper-nickel mixed oxidephase [JCPDS: 78-1602]. The formation of mixed oxide indicated the strong interaction of Cu, Ni with the alumina support. The crystallite size decreased with increasing the calcination temperature up to 450°C. Further, the crystallite size was increased due to agglomeration. Smaller crystallite size of 16.5 nm was obtained for the catalyst calcined at 400°C. Total acidic sites of the catalysts were determined by NH₃-TPD, and the maximum total acidic of 0.609 mmol NH₃ gcat⁻¹ was obtained over the catalyst calcined at 400°C. TPR data suggested the maximum of 75% degree of reduction of catalyst calcined at 400°C among all others. Further, 20wt.%Cu:Ni(1:1)/γ-Al₂O₃ catalyst calcined at 400°C exhibited highest catalytic activity ( > 70%) and 1,2-PDO selectivity ( > 85%) at mild reaction condition due to highest acidity, highest degree of reduction, smallest crystallite size. Further, the modified Power law kinetic model was developed to understand the true kinetic behaviour of hydrogenolysis of glycerol over 20wt.%Cu:Ni(1:1)/γ-Al₂O₃ catalyst. Rate equations obtained from the model was solved by ode23 using MATLAB coupled with Genetic Algorithm. Results demonstrated that the model predicted data were very well fitted with the experimental data. The activation energy of the formation of 1,2-PDO was found to be 45 kJ mol⁻¹.

Keywords: glycerol, 1, 2-PDO, calcination, kinetic

Procedia PDF Downloads 130

Authors: Nedal N. Marei, Nashaat Nassar

Abstract:

Many treatment techniques have been used to remove the soluble pollutants from wastewater as; dyes and metal ions which could be found in rich amount in the used water of the textile and tanneries industry. The effluents from these industries are complex, containing a wide variety of dyes and other contaminants, such as dispersants, acids, bases, salts, detergents, humectants, oxidants, and others. These techniques can be divided into physical, chemical, and biological methods. Adsorption has been developed as an efficient method for the removal of heavy metals from contaminated water and soil. It is now recognized as an effective method for the removal of both organic and inorganic pollutants from wastewaters. Nanosize materials are new functional materials, which offer high surface area and have come up as effective adsorbents. Nano alumina is one of the most important ceramic materials widely used as an electrical insulator, presenting exceptionally high resistance to chemical agents, as well as giving excellent performance as a catalyst for many chemical reactions, in microelectronic, membrane applications, and water and wastewater treatment. In this study, methylene blue (MB) dye has been used as model dye of textile wastewater in order to synthesize a synthetic MB wastewater. NiO nanoparticles were added in small percentage in the sand packed bed adsorption columns to remove the MB from the synthetic textile wastewater. Moreover, different parameters have been evaluated; flow of the synthetic wastewater, pH, height of the bed, percentage of the NiO to the sand in the packed material. Different mathematical models where employed to find the proper model which describe the experimental data and help to analyze the mechanism of the MB adsorption. This study will provide good understanding of the dyes adsorption using metal oxide nanoparticles in the classical sand bed.

Keywords: adsorption, column, nanoparticles, methylene

Procedia PDF Downloads 249

Authors: Shazia Anwer

Abstract:

This paper has discussed the unusual form of resistance in theatre against censorship board in Pakistan. The atypical approach of dramaturgy created massive space for performers and audiences to integrate and communicate. The social and religious absolutes creates suffocation in Pakistani society, strict control over all Fine and Performing Art has made art political, contemporary dramatics has started an amalgamated theatre to avoid censorship. Contemporary Punjabi theatre techniques are directly dependent on human cognition. The idea of indirect thought processing is not unique but dependent on spectators. The paper has provided an account of these techniques and their specific use for conveying specific messages across the audiences. For the Dramaturge of today, theatre space is an expression representing a linguistic formulation that includes qualities of experimental and non-traditional use of classical theatrical space in the context of fulfilling the concept of open theatre. Paper has explained the transformation of the theatrical experience into an event where the actor and the audience are co-existing and co-experiencing the dramatical experience. The denial of the existence of the 4th -Wall made two-way communication possible. This paper has elaborated that the previously marginalized genres such as naach, jugat, miras, are extensively included to counter the censorship board. Figurativeness, visual elements, allegory, scenic imagery are basic support system for contemporary Punjabi theatre. The body of the actor is used as a source for non-verbal communication, and for an escape from traditional theatrical space which by every means has every element that could be controlled and reprimanded by the controlling authority.

Keywords: communication, Punjabi theatre, figurativeness, censorship

Procedia PDF Downloads 123

Authors: A. Bari

Abstract:

Integer programming is a branch of mathematical programming techniques in operations research in which some or all of the variables are required to be integer valued. Various cuts have been used to solve these problems. We have also developed cuts known as NAZ cut & A-T cut to solve the integer programming problems. These cuts are used to reduce the feasible region and then reaching the optimal solution in minimum number of steps.

Keywords: Integer Programming, NAZ cut, A-T cut, Cutting plane method

Procedia PDF Downloads 349

Authors: Cristian Chacha, David Asiain, Jesús Ponce de León, José Ramón Beltrán

Abstract:

This work explores and characterizes the behavior of the AFE AD5941 in impedance measurement using an embedded algorithm with a constant current AC source. The main aim of this research is to improve the exact measurement of impedance values for their application in EDA-focused wearable devices. Through comprehensive study and characterization, it has been observed that employing a measurement sequence with a constant current source produces results with increased dispersion but higher accuracy. As a result, this approach leads to a more accurate system for impedance measurement.

Keywords: EDA, constant current AC source, wearable, precision, accuracy, impedance

Procedia PDF Downloads 83

Authors: Dale Dzemydiene, Aurelija Burinskiene, Arunas Miliauskas, Kristina Ciziuniene

Abstract:

Data flow and the purpose of reporting the data are different and dependent on business needs. Different parameters are reported and transferred regularly during freight delivery. This business practices form the dataset constructed for each time point and contain all required information for freight moving decisions. As a significant amount of these data is used for various purposes, an integrating methodological approach must be developed to respond to the indicated problem. The proposed methodology contains several steps: (1) collecting context data sets and data validation; (2) multi-objective analysis for optimizing freight transfer services. For data validation, the study involves Grubbs outliers analysis, particularly for data cleaning and the identification of statistical significance of data reporting event cases. The Grubbs test is often used as it measures one external value at a time exceeding the boundaries of standard normal distribution. In the study area, the test was not widely applied by authors, except when the Grubbs test for outlier detection was used to identify outsiders in fuel consumption data. In the study, the authors applied the method with a confidence level of 99%. For the multi-objective analysis, the authors would like to select the forms of construction of the genetic algorithms, which have more possibilities to extract the best solution. For freight delivery management, the schemas of genetic algorithms' structure are used as a more effective technique. Due to that, the adaptable genetic algorithm is applied for the description of choosing process of the effective transportation corridor. In this study, the multi-objective genetic algorithm methods are used to optimize the data evaluation and select the appropriate transport corridor. The authors suggest a methodology for the multi-objective analysis, which evaluates collected context data sets and uses this evaluation to determine a delivery corridor for freight transfer service in the multi-modal transportation network. In the multi-objective analysis, authors include safety components, the number of accidents a year, and freight delivery time in the multi-modal transportation network. The proposed methodology has practical value in the management of multi-modal transportation processes.

Keywords: multi-objective, analysis, data flow, freight delivery, methodology

Procedia PDF Downloads 166

Authors: Mohamed Belhorma, Aboubakar S. Bouchikhi, Belkacem Bounab

Abstract:

This paper addresses modeling a Double A-Arm suspension, a three-dimensional nonlinear model has been developed using the multibody systems formalism. Dynamical study of the different components responses was done, particularly for the wheel assembly. To validate those results, the system was constructed and simulated by RecurDyn, a professional multibody dynamics simulation software. The model has been used as the Objectif function in an optimization algorithm for ride quality improvement.

Keywords: double A-Arm suspension, multibody systems, ride quality optimization, dynamic simulation

Procedia PDF Downloads 122

Authors: Jonas Hamann, Siavash Saki, Tobias Hagen

Abstract:

Discretization of urban areas is a crucial aspect in many spatial analyses. The process of discretization of space into subspaces without overlaps and gaps is called tessellation. It helps understanding spatial space and provides a framework for analyzing geospatial data. Tessellation methods can be divided into two groups: regular tessellations and irregular tessellations. While regular tessellation methods, like squares-grids or hexagons-grids, are suitable for addressing pure geometry problems, they cannot take the unique characteristics of different subareas into account. However, irregular tessellation methods allow the border between the subareas to be defined more realistically based on urban features like a road network or Points of Interest (POI). Even though Python is one of the most used programming languages when it comes to spatial analysis, there is currently no library that combines different tessellation methods to enable users and researchers to compare different techniques. To close this gap, we are proposing TessPy, an open-source Python package, which combines all above-mentioned tessellation methods and makes them easily accessible to everyone. The core functions of TessPy represent the five different tessellation methods: squares, hexagons, adaptive squares, Voronoi polygons, and city blocks. By using regular methods, users can set the resolution of the tessellation which defines the finesse of the discretization and the desired number of tiles. Irregular tessellation methods allow users to define which spatial data to consider (e.g., amenity, building, office) and how fine the tessellation should be. The spatial data used is open-source and provided by OpenStreetMap. This data can be easily extracted and used for further analyses. Besides the methodology of the different techniques, the state-of-the-art, including examples and future work, will be discussed. All dependencies can be installed using conda or pip; however, the former is more recommended.

Keywords: geospatial data science, geospatial data analysis, tessellations, urban studies

Procedia PDF Downloads 112

Authors: Tugba Bayoglu

Abstract:

Aerodynamic parameter estimation is very crucial in missile design phase, since accurate high fidelity aerodynamic model is required for designing high performance and robust control system, developing high fidelity flight simulations and verification of computational and wind tunnel test results. However, in literature, there is not enough missile aerodynamic parameter identification study for three main reasons: (1) most air to air missiles cannot fly with constant speed, (2) missile flight test number and flight duration are much less than that of fixed wing aircraft, (3) variation of the missile aerodynamic parameters with respect to Mach number is higher than that of fixed wing aircraft. In addition to these challenges, identification of aerodynamic parameters for high wind angles by using classical estimation techniques brings another difficulty in the estimation process. The reason for this, most of the estimation techniques require employing polynomials or splines to model the behavior of the aerodynamics. However, for the missiles with a large variation of aerodynamic parameters with respect to flight variables, the order of the proposed model increases, which brings computational burden and complexity. Therefore, in this study, it is aimed to solve nonlinear aerodynamic parameter identification problem for a supersonic air to air missile by using Artificial Neural Networks. The method proposed will be tested by using simulated data which will be generated with a six degree of freedom missile model, involving a nonlinear aerodynamic database. The data will be corrupted by adding noise to the measurement model. Then, by using the flight variables and measurements, the parameters will be estimated. Finally, the prediction accuracy will be investigated.

Keywords: air to air missile, artificial neural networks, open loop simulation, parameter identification

Procedia PDF Downloads 258

Authors: Nasim Eslamirad, Mahdi Rasoulinezhad, Francesco De Luca, Sadok Ben Yahia, Kimmo Sakari Lylykangas, Francesco Pilla

Abstract:

With the ongoing urbanization, cities face increasing environmental challenges impacting human well-being. To tackle these issues, data-driven approaches in urban analysis have gained prominence, leveraging urban data to promote sustainability. Integrating Machine Learning techniques enables researchers to analyze and predict complex environmental phenomena like Urban Heat Island occurrences in urban areas. This paper demonstrates the implementation of data-driven approach and interpretable Machine Learning algorithms with interpretability techniques to conduct comprehensive data analyses for sustainable urban design. The developed framework and algorithms are demonstrated for Tallinn, Estonia to develop sustainable urban strategies to mitigate urban heat waves. Geospatial data, preprocessed and labeled with UHI levels, are used to train various ML models, with Logistic Regression emerging as the best-performing model based on evaluation metrics to derive a mathematical equation representing the area with UHI or without UHI effects, providing insights into UHI occurrences based on buildings and urban features. The derived formula highlights the importance of building volume, height, area, and shape length to create an urban environment with UHI impact. The data-driven approach and derived equation inform mitigation strategies and sustainable urban development in Tallinn and offer valuable guidance for other locations with varying climates.

Keywords: data-driven approach, machine learning transparent models, interpretable machine learning models, urban heat island effect

Procedia PDF Downloads 15

Authors: C. E. Nugraheni, L. Abednego

Abstract:

This paper is concerned with minimization of mean tardiness and flow time in a real single machine production scheduling problem. Two variants of genetic algorithm as meta-heuristic are combined with hyper-heuristic approach are proposed to solve this problem. These methods are used to solve instances generated with real world data from a company. Encouraging results are reported.

Keywords: hyper-heuristics, evolutionary algorithms, production scheduling, meta-heuristic

Procedia PDF Downloads 368

Authors: Maria Inês Pinho

Abstract:

Recent literature on issues of Cultural Management (also called Strategic Management for cultural organizations) systematically seeks for models that allow such equipment to adapt to the constant change that occurs in contemporary societies. In the last decade, the world, and in particular Europe has experienced a serious financial problem that has triggered defensive mechanisms, both in the direction of promoting the balance of public accounts and in the sense of the anonymous loss of the democratic and cultural values of each nation. If in the first case emerged the Troika that led to strong cuts in funding for Culture, deeply affecting those organizations; in the second case, the commonplace citizen is seen fighting for the non-closure of cultural equipment. Despite this, the cultural manager argues that there is no single formula capable of solving the need to adapt to change. In another way, it is up to this agent to know the existing scientific models and to adapt them in the best way to the reality of the institution he coordinates. These actions, as a rule, are concerned with the best performance vis-à-vis external audiences or with the financial sustainability of cultural organizations. They forget, therefore, that all this mechanics cannot function without its internal public, without its Human Resources. The employees of the cultural organization must then have an entrepreneurial posture - must be intrapreneurial. This paper intends to break this form of action and lead the cultural manager to understand that his role should be in the sense of creating value for society, through a good organizational performance. This is only possible with a posture of strategic entrepreneurship. In other words, with a link between: Cultural Management, Cultural Entrepreneurship and Cultural Intrapreneurship. In order to prove this assumption, the case study methodology was used with the symbol of the European Capital of Culture (Casa da Música) as well as qualitative and quantitative techniques. The qualitative techniques included the procedure of in-depth interviews to managers, founders and patrons and focus groups to public with and without experience in managing cultural facilities. The quantitative techniques involved the application of a questionnaire to middle management and employees of Casa da Música. After the triangulation of the data, it was proved that contemporary management of cultural organizations must implement among its practices, the concept of Strategic Entrepreneurship and its variables. Also, the topics which characterize the Cultural Intrapreneurship notion (job satisfaction, the quality in organizational performance, the leadership and the employee engagement and autonomy) emerged. The findings show then that to be sustainable, a cultural organization should meet the concerns of both external and internal forum. In other words, it should have an attitude of citizenship to the communities, visible on a social responsibility and a participatory management, only possible with the implementation of the concept of Strategic Entrepreneurship and its variable of Cultural Intrapreneurship.

Keywords: cultural entrepreneurship, cultural intrapreneurship, cultural organizations, strategic management

Procedia PDF Downloads 161

Authors: Dante J. Dorantes-González, Aldrin Balsa-Yepes

Abstract:

Existing learning techniques such as problem-based learning, project-based learning, or case study learning are learning techniques that focus mainly on technical details, but give no specific guidelines on learner’s experience and emotional learning aspects such as arousal salience and valence, being emotional states important factors affecting engagement and retention. Some approaches involving emotion in educational settings, such as social and emotional learning, lack neuroscientific rigorousness and use of specific neurobiological mechanisms. On the other hand, neurobiology approaches lack educational applicability. And educational approaches mainly focus on cognitive aspects and disregard conditioning learning. First, authors start explaining the reasons why it is hard to learn thoughtfully, then they use the method of neurobiological mapping to track the main limbic system functions, such as the reward circuit, and its relations with perception, memories, motivations, sympathetic and parasympathetic reactions, and sensations, as well as the brain cortex. The authors conclude explaining the major finding: The mechanisms of nonconscious learning and the triggers that guarantee long-term memory potentiation. Afterward, the educational framework for practical application and the instructors’ guidelines are established. An implementation example in engineering education is given, namely, the study of tuned-mass dampers for earthquake oscillations attenuation in skyscrapers. This work represents an original learning technique based on nonconscious learning mechanisms to enhance long-term memories that complement existing cognitive learning methods.

Keywords: emotion, emotion-enhanced memory, learning technique, STEM

Procedia PDF Downloads 77

Authors: Sakshi V. Tantak, Vishap K. Malik, Neelanjney Pilarisetty

Abstract:

A patent is an exclusive right granted for an invention. It can be a product or a process that provides an innovative method of doing something, or offers a new technical perspective or solution to a problem. A patent can be obtained by making the technical information and details about the invention publicly available. The patent owner has exclusive rights to prevent or stop anyone from using the patented invention for commercial uses. Any commercial usage, distribution, import or export of a patented invention or product requires the patent owner’s consent. It has been observed that the central and important parts of patents are scripted in idiosyncratic and complex linguistic structures that can be difficult to read, comprehend or interpret for the masses. The abstracts of these patents tend to obfuscate the precise nature of the patent instead of clarifying it via direct and simple linguistic constructs. This makes it necessary to have an efficient access to this knowledge via concise and transparent summaries. However, as mentioned above, due to complex and repetitive linguistic constructs and extremely long sentences, common extraction-oriented automatic text summarization methods should not be expected to show a remarkable performance when applied to patent documents. Other, more content-oriented or abstractive summarization techniques are able to perform much better and generate more concise summaries. This paper proposes an efficient summarization system for patents using artificial intelligence, natural language processing and deep learning techniques to condense the knowledge and essential information from a patent document into a single summary that is easier to understand without any redundant formatting and difficult jargon.

Keywords: abstractive summarization, deep learning, natural language Processing, patent document

Procedia PDF Downloads 110

Authors: Yashas Bedre Raghavendra, Pim Vullers

Abstract:

This study presents the design and implementation of an abstract cryptographic coprocessor, leveraging AMBA(Advanced Microcontroller Bus Architecture) protocols - APB (Advanced Peripheral Bus) and AHB (Advanced High-performance Bus), to enable seamless integration with the main CPU(Central processing unit) and enhance the coprocessor’s algorithm flexibility. The primary objective is to create a versatile coprocessor that can execute various cryptographic algorithms, including ECC(Elliptic-curve cryptography), RSA(Rivest–Shamir–Adleman), and AES (Advanced Encryption Standard) while providing a robust and secure solution for modern secure embedded systems. To achieve this goal, the coprocessor is equipped with a tightly coupled memory (TCM) for rapid data access during cryptographic operations. The TCM is placed within the coprocessor, ensuring quick retrieval of critical data and optimizing overall performance. Additionally, the program memory is positioned outside the coprocessor, allowing for easy updates and reconfiguration, which enhances adaptability to future algorithm implementations. Direct links are employed instead of DMA(Direct memory access) for data transfer, ensuring faster communication and reducing complexity. The AMBA-based communication architecture facilitates seamless interaction between the coprocessor and the main CPU, streamlining data flow and ensuring efficient utilization of system resources. The abstract nature of the coprocessor allows for easy integration of new cryptographic algorithms in the future. As the security landscape continues to evolve, the coprocessor can adapt and incorporate emerging algorithms, making it a future-proof solution for cryptographic processing. Furthermore, this study explores the addition of custom instructions into RISC-V ISE (Instruction Set Extension) to enhance cryptographic operations. By incorporating custom instructions specifically tailored for cryptographic algorithms, the coprocessor achieves higher efficiency and reduced cycles per instruction (CPI) compared to traditional instruction sets. The adoption of RISC-V 128-bit architecture significantly reduces the total number of instructions required for complex cryptographic tasks, leading to faster execution times and improved overall performance. Comparisons are made with 32-bit and 64-bit architectures, highlighting the advantages of the 128-bit architecture in terms of reduced instruction count and CPI. In conclusion, the abstract cryptographic coprocessor presented in this study offers significant advantages in terms of algorithm flexibility, security, and integration with the main CPU. By leveraging AMBA protocols and employing direct links for data transfer, the coprocessor achieves high-performance cryptographic operations without compromising system efficiency. With its TCM and external program memory, the coprocessor is capable of securely executing a wide range of cryptographic algorithms. This versatility and adaptability, coupled with the benefits of custom instructions and the 128-bit architecture, make it an invaluable asset for secure embedded systems, meeting the demands of modern cryptographic applications.

Keywords: abstract cryptographic coprocessor, AMBA protocols, ECC, RSA, AES, tightly coupled memory, secure embedded systems, RISC-V ISE, custom instructions, instruction count, cycles per instruction

Procedia PDF Downloads 55

Authors: Olupemi E. Oludare

Abstract:

This paper discusses the thematic structure of Yoruba popular music of Southwest Nigeria. It examines the use of themes and variations in early and contemporary Juju music. The work is an outcome of a research developed by the author in his doctoral studies at the University of Lagos, Nigeria, with the aim of analyzing the thematic and motivic developments in Yoruba popular genres. Observations, interviews, live recordings and CDs were used as methods for eliciting information. Field recordings and CDs of selected musical samples were also transcribed and notated. The research established the prevalent use of string of themes by Juju musicians as a compositional technique in moving from one musical section to another, as they communicate the verbal messages in their song. These themes consisting of the popular ‘call and response’ form found in most African music, analogous to the western ‘subject and answer’ style of the fugue or sonata form, although without the tonic–dominant relations. Due to the short and repetitive form of African melodies and rhythms, a theme is restated as a variation, where its rhythmic and melodic motifs are stylistically developed and repeated, but still retaining its recognizable core musical structure. The findings of this study showed that Juju musicians generally often employ a thematic plan where new themes are used to arrange the songs into sections, and each theme is developed into variations in order to further expand the music, eliminate monotony, and create musical aesthetics, serving as hallmark of its musical identity. The study established the musical and extra-musical attributes of the genre, while recommending further research towards analyzing the various compositional techniques employed in African popular genres.

Keywords: compositional techniques, popular music, theme and variation, thematic development

Procedia PDF Downloads 401