Search results for: laying performance

Authors: Krzysztof T. Piotrowski

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The main goal of the research was to investigate differences in attention in two kinds of musical performance - recall and improvisation. Musical recall is a sample of convergent production that requires intensively focused attention. Inversely, musical improvisation is a divergent task and probably requires a different way of attentional control. The study was designed in dual task paradigm. Participants were to remember a simple melody and then recall or improvise, simultaneously performing the spatial attentional test on computer screen. The result shows that improvising participants find spatial goals in more disperse way. The conclusion is that musical improvisation requires extensification of attention to occur.

Keywords: attention, creativity, divergent task, musical improvisation

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Authors: Motonori Tamura

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Ceramics coatings consisting of fine crystal grains, with diameters of about 100 nm or less, provided superior hydrogen-permeation barriers. Applying TiN, TiC or Al₂O₃ coatings on a stainless steel substrate reduced the hydrogen permeation by a factor of about 100 to 5,000 compared with uncoated substrates. Effect of the microstructure of coatings on hydrogen-permeation behavior is studied. The test specimens coated with coatings, with columnar crystals grown vertically on the substrate, tended to exhibit higher hydrogen permeability. The grain boundaries of the coatings became trap sites for hydrogen, and microcrystalline structures with many grain boundaries are expected to provide effective hydrogen-barrier performance.

Keywords: hydrogen permeation, tin coating, microstructure, crystal grain, stainless steel

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Authors: P. Tej, P. Kněž, M. Blank

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The paper presents design and production of thin-walled U-profile footbridge made of UHPFRC. The main structure of the bridge is one prefabricated shell structure made of UHPFRC with dispersed steel fibers without any conventional reinforcement. The span of the bridge structure is 10 m and the clear width of 1.5 m. The thickness of the UHPFRC shell structure oscillated in an interval of 30-45 mm. Several calculations were made during the bridge design and compared with the experiments. For the purpose of verifying the calculations, a segment of 1.5 m was first produced, followed by the whole footbridge for testing. After the load tests were done, the design was optimized to cast the final footbridge.

Keywords: footbridge, non-linear analysis, shell structure, UHPFRC, Ultra-High Performance Fibre Reinforced Concrete

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Authors: Satien Janpla, Kunyanuth Kularbphettong

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This paper aims to present the development of a web‑based system to serve the need of selling OTOP products in Samut Songkhram, Thailand. This system was designed to promote and sell OTOP products on website. We describe the design approaches and functional components of this system. The system was developed by PHP and JavaScript and MySQL database System. To evaluate the system performance, questionnaires were used to measure user satisfaction with system usability by specialists and users. The results were satisfactory as followed: Means for specialists and users were 4.05 and 3.97, and standard deviation for specialists and users were 0.563 and 0.644 respectively. Further analysis showed that the quality of One Tambon One Product (OTOP) Website was also at a good level as well.

Keywords: web-based system, OTOP, product, website

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Authors: Chen Guo, Heng Tang, Ben Niu

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Clustering splits objects into different groups based on similarity, making the objects have higher similarity in the same group and lower similarity in different groups. Thus, clustering can be treated as an optimization problem to maximize the intra-cluster similarity or inter-cluster dissimilarity. In real-world applications, the datasets often have some complex characteristics: sparse, overlap, high dimensionality, etc. When facing these datasets, simultaneously optimizing two or more objectives can obtain better clustering results than optimizing one objective. However, except for the objectives weighting methods, traditional clustering approaches have difficulty in solving multi-objective data clustering problems. Due to this, evolutionary multi-objective optimization algorithms are investigated by researchers to optimize multiple clustering objectives. In this paper, the Data Clustering algorithm based on Multi-objective Periodic Bacterial Foraging Optimization with two Learning Archives (DC-MPBFOLA) is proposed. Specifically, first, to reduce the high computing complexity of the original BFO, periodic BFO is employed as the basic algorithmic framework. Then transfer the periodic BFO into a multi-objective type. Second, two learning strategies are proposed based on the two learning archives to guide the bacterial swarm to move in a better direction. On the one hand, the global best is selected from the global learning archive according to the convergence index and diversity index. On the other hand, the personal best is selected from the personal learning archive according to the sum of weighted objectives. According to the aforementioned learning strategies, a chemotaxis operation is designed. Third, an elite learning strategy is designed to provide fresh power to the objects in two learning archives. When the objects in these two archives do not change for two consecutive times, randomly initializing one dimension of objects can prevent the proposed algorithm from falling into local optima. Fourth, to validate the performance of the proposed algorithm, DC-MPBFOLA is compared with four state-of-art evolutionary multi-objective optimization algorithms and one classical clustering algorithm on evaluation indexes of datasets. To further verify the effectiveness and feasibility of designed strategies in DC-MPBFOLA, variants of DC-MPBFOLA are also proposed. Experimental results demonstrate that DC-MPBFOLA outperforms its competitors regarding all evaluation indexes and clustering partitions. These results also indicate that the designed strategies positively influence the performance improvement of the original BFO.

Keywords: data clustering, multi-objective optimization, bacterial foraging optimization, learning archives

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Authors: Furkan Ozkan, M. Selcuk Arslan, Hatice Mercan

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Electric vehicles are becoming increasingly popular asa sustainable alternative to traditional combustion engine vehicles. However, to fully realize the potential of EVs in reducing environmental impact and energy consumption, efficient control strategies are essential. This study explores the application of green wave control using model predictive control for electric vehicles, coupled with energy consumption modeling using neural networks. The use of MPC allows for real-time optimization of the vehicles’ energy consumption while considering dynamic traffic conditions. By leveraging neural networks for energy consumption modeling, the EV's performance can be further enhanced through accurate predictions and adaptive control. The integration of these advanced control and modeling techniques aims to maximize energy efficiency and range while navigating urban traffic scenarios. The findings of this research offer valuable insights into the potential of green wave control for electric vehicles and demonstrate the significance of integrating MPC and neural network modeling for optimizing energy consumption. This work contributes to the advancement of sustainable transportation systems and the widespread adoption of electric vehicles. To evaluate the effectiveness of the green wave control strategy in real-world urban environments, extensive simulations were conducted using a high-fidelity vehicle model and realistic traffic scenarios. The results indicate that the integration of model predictive control and energy consumption modeling with neural networks had a significant impact on the energy efficiency and range of electric vehicles. Through the use of MPC, the electric vehicle was able to adapt its speed and acceleration profile in realtime to optimize energy consumption while maintaining travel time objectives. The neural network-based energy consumption modeling provided accurate predictions, enabling the vehicle to anticipate and respond to variations in traffic flow, further enhancing energy efficiency and range. Furthermore, the study revealed that the green wave control strategy not only reduced energy consumption but also improved the overall driving experience by minimizing abrupt acceleration and deceleration, leading to a smoother and more comfortable ride for passengers. These results demonstrate the potential for green wave control to revolutionize urban transportation by enhancing the performance of electric vehicles and contributing to a more sustainable and efficient mobility ecosystem.

Keywords: electric vehicles, energy efficiency, green wave control, model predictive control, neural networks

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Authors: Jacob Wood, David Wilson, Stephen Hughes

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The Ausmelt Top Submerged Lance (TSL) Process has been widely applied for the processing of both primary and secondary copper, nickel, lead, tin, and zinc-bearing feed materials. Continual development and evolution of the technology over more than 30 years has resulted in a more intense smelting process with higher energy efficiency, improved metal recoveries, lower operating costs, and reduced fossil fuel consumption. This paper covers a number of recent advances to the technology, highlighting their positive impacts on smelter operating costs, environmental performance, and contribution towards increased circularity in metals production.

Keywords: ausmelt TSL, smelting, circular economy, energy efficiency

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Authors: Majid Pourahmadi

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The performance of time-reversal MUSIC algorithm will be dramatically degrades in presence of strong noise and multiple scattering (i.e. when scatterers are close to each other). This is due to error in determining the number of scatterers. The present paper provides a new approach to alleviate such a problem using an information theoretic criterion referred as minimum description length (MDL). The merits of the novel approach are confirmed by the numerical examples. The results indicate the time-reversal MUSIC yields accurate estimate of the target locations with considerable noise and multiple scattering in the received signals.

Keywords: microwave imaging, time reversal, MUSIC algorithm, minimum description length (MDL)

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Authors: Al Omari Moahmmed Ahmed

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In the Bayesian, we developed an approach by using non-informative prior with covariate and obtained by using Gauss quadrature method to estimate the parameters of the covariate and reliability function of the Weibull regression distribution with Type-I censored data. The maximum likelihood seen that the estimators obtained are not available in closed forms, although they can be solved it by using Newton-Raphson methods. The comparison criteria are the MSE and the performance of these estimates are assessed using simulation considering various sample size, several specific values of shape parameter. The results show that Bayesian with non-informative prior is better than Maximum Likelihood Estimator.

Keywords: non-informative prior, Bayesian method, type-I censoring, Gauss quardature

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Authors: Messaouda Belaid, Salima Kebbouche-Gana

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The negative effect of the Varroa bee colony is very important. They cause morphological and physiological changes, causing a decrease in performance of individuals and long-term death of the colony. Indirectly, they weaken the bees become much more sensitive to the different pathogenic organisms naturally present in the colony. This work aims to research secondary infections of microbial origin occurred in the worker bee nurse due to parasitism by Varroa destructor. The feeding behaviour of Varroa may causes damaging host integument. The results show that the microbial contamination enable to be transmitted into honeybee heamocoel are Bacillus sp, Pseudomonas sp, Enterobacter, Aspergillus.

Keywords: honeybee, Apis mellifera intermissa, microbial contamination, Varroa destructor

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Authors: Hsiu-Tsu Cho

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Collective efficacy refers to a group’s sense of its capacity to complete a task successfully or to reach objectives. Little effort has been expended on investigating the relationship between the emotion expression of a leader and collective efficacy. In this study, we examined the impact of the different emotions and emotion expression of a group leader on collective efficacy and explored whether the emotion–expressive effects differed under conditions of negative and positive emotions. A total of 240 undergraduate and graduate students recruited using Facebook and posters at a university participated in this research. The participants were separated randomly into 80 groups of four persons consisting of three participants and a confederate. They were randomly assigned to one of five conditions in a 2 (pride vs. guilt) × 2 (emotion expression of group leader vs. no emotion expression of group leader) factorial design and a control condition. Each four-person group was instructed to get the reward in a group competition of solving the five-disk Tower of Hanoi puzzle and making decisions on an investment case. We surveyed the participants by employing the emotional measure revised from previous researchers and collective efficacy questionnaire on a 5-point scale. To induce an emotion of pride (or guilt), the experimenter announced whether the group performance was good enough to have a chance of getting the reward (ranking the top or bottom 20% among all groups) after group task. The leader (confederate) could either express or not express a feeling of pride (or guilt) following the instruction according to the assigned condition. To check manipulation of emotion, we added a control condition under which the experimenter revealed no results regarding group performance in maintaining a neutral emotion. One-way ANOVAs and post hoc pairwise comparisons among the three emotion conditions (pride, guilt, and control condition) involved assigning pride and guilt scores (pride: F(1,75) = 32.41, p < .001; guilt: F(1,75) = 6.75, p < .05). The results indicated that manipulations of emotion were successful. A two-way between-measures ANOVA was conducted to examine the predictions of the main effects of emotion types and emotion expression as well as the interaction effect of these two variables on collective efficacy. The experimental findings suggest that pride did not affect collective efficacy (F(1,60) = 1.90, ns.) more than guilt did and that the group leader did not motivate collective efficacy regardless of whether he or she expressed emotion (F(1,60) = .89, ns.). However, the interaction effect of emotion types and emotion expression was statistically significant (F(1,60) = 4.27, p < .05, ω2 = .066); the effects accounted for 6.6% of the variance. Additional results revealed that, under the pride condition, the leader enhanced group efficacy when expressing emotion, whereas, under the guilt condition, an expression of emotion could reduce collective efficacy. Overall, these findings challenge the assumption that the effect of expression emotion are the same on all emotions and suggest that a leader should be cautious when expressing negative emotions toward a group to avoid reducing group effectiveness.

Keywords: collective efficacy, group leader, emotion expression, pride, guilty

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Authors: Fatima Zohra Rahou, A. Guen Bouazza, B. Bouazza

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Invention of transistor is the foundation of electronics industry. Metal Oxide Semiconductor Field Effect Transistor (MOSFET) has been the key for the development of nanoelectronics technology. In the first part of this manuscript, we present a new generation of MOSFET transistors based on SOI (Silicon-On-Insulator) technology. It is a partially depleted Silicon-On-Insulator (PD SOI MOSFET) transistor simulated by using SILVACO software. This work was completed by the presentation of some results concerning the influence of parameters variation (channel length L and gate oxide thickness Tox) on our PDSOI n-MOSFET structure on its drain current and kink effect.

Keywords: SOI technology, PDSOI MOSFET, FDSOI MOSFET, kink effect

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Authors: Tesnim Charrad, Kaouther Nouira, Ahmed Ferchichi

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In order to reduce the number of deaths due to heart problems, we propose the use of Hierarchical Temporal Memory Algorithm (HTM) which is a real time anomaly detection algorithm. HTM is a cortical learning algorithm based on neocortex used for anomaly detection. In other words, it is based on a conceptual theory of how the human brain can work. It is powerful in predicting unusual patterns, anomaly detection and classification. In this paper, HTM have been implemented and tested on ECG datasets in order to detect cardiac anomalies. Experiments showed good performance in terms of specificity, sensitivity and execution time.

Keywords: cardiac anomalies, ECG, HTM, real time anomaly detection

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Authors: Ye. Shapovalov, V. Shapovalov, O. Stryzhak, A. Salyuk

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Over the past decades, amount of scientific information has been growing exponentially. It became more complicated to process and systemize this amount of data. The approach to systematization of scientific information on the production of biogas based on the ontological IT platform “T.O.D.O.S.” has been developed. It has been proposed to select semantic characteristics of each work for their further introduction into the IT platform “T.O.D.O.S.”. An ontological graph with a ranking function for previous scientific research and for a system of selection of microorganisms has been worked out. These systems provide high performance of information management of scientific information.

Keywords: ontology-based analysis, analysis of scientific data, methanogenesis, microorganism hierarchy, 'T.O.D.O.S.'

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Authors: M. Derradji, W. B. Liu

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The current work discusses the effects of adding various types of ceramic fillers on the curing behavior, thermal, mechanical, anticorrosion, and UV shielding properties of the bisphenol-A based phthalonitrile resins. The effects of different ceramic filler contents and sizes as well as their surface treatments are also discussed in terms of their impact on the morphology and mechanisms of enhancement. The synergistic effect obtained by these combinations extends the use of the phthalonitrile resins to more exigent applications such as aerospace and military. The presented results reveal the significant advantages that can be obtained from the preparation of hybrid materials based on phthalonitrile resins and open the way for further research in the field.

Keywords: mechanical properties, particle reinforced composites, polymer matrix composites (PMCs), thermal properties

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Authors: S. Boukais, A. Nekmouche, N. Khelil, A. Kezmane

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The first intent of this study is to develop a finite element model that can predict correctly the behavior of the reinforced concrete column. Second aim is to use the finite element model to investigate and evaluate the effect of the strengthening method by jacketing of the reinforced concrete column, by considering different interface contact between the old and the new concrete. Four models were evaluated, one by considering perfect contact, the other three models by using friction coefficient of 0.1, 0.3 and 0.5. The simulation was carried out by using Abaqus software. The obtained results show that the jacketing reinforcement led to significant increase of the global performance of the behavior of the simulated reinforced concrete column.

Keywords: strengthening, jacketing, rienforced concrete column, Abaqus, simulation

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Authors: Branislav Kovačević, Andrej Pilipović, Zoran Novčić, Marina Milović, Lazar Kesić, Milan Drekić, Saša Pekeč, Leopold Poljaković Pajnik, Saša Orlović

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Poplars present one of the most important tree species used for phytoremediation in the northern hemisphere. They can be used either as direct “cleaners” of the contaminated soils or as buffer zones preventing the contaminant plume to the surrounding environment. In order to produce appropriate planting material for this purpose, there is a long process of the breeding of the most favorable candidates. Although the development of the poplar propagation technology has been evolving for decades, white poplar nursery production, as well as the establishment of short-rotation coppice plantations, still considerably depends on the success of hardwood cuttings’ survival. This is why easy rooting is among the most desirable properties in white poplar breeding. On the other hand, there are many opportunities for the optimization of the technological procedures in order to meet the demands of particular genotype (clonal technology). In this study the effect of the term of hardwood cuttings’ preparation of four white poplar clones on their survival and further growth of rooted cuttings in nursery conditions were tested. There were three terms of cuttings’ preparation: the beginning of February (2nd Feb 2023), the beginning of March (3rd Mar 2023) and the end of March (21nd Mar 2023), which is regarded as the standard term. The cuttings were stored in cool chamber at 2±2°C. All cuttings were planted on the same date (11th Apr 2023), in soil prepared with rotary tillage, and then cultivated by usual nursey procedures. According to the results obtained after the bud set (29th Sept 2023) there were significant differences in the survival and growth of rooted cuttings between examined terms of cutting preparation. Also, there were significant differences in the reaction of examined clones on terms of cutting preparation. In total, the best results provided cuttings prepared at the first term (2nd Feb 2023) (survival rate of 39.4%), while performance after two later preparation terms was significantly poorer (20.5% after second and 16.5% after third term). These results stress the significance of dormancy preservation in cuttings of examined white poplar clones for their survival, which could be especially important in context of climate change. Differences in clones’ reaction to term of cutting preparation suggest necessity of adjustment of the technology to the needs of particular clone i.e. design of clone specific technology.

Keywords: rooting, Populus alba, nursery, clonal technology

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Authors: Mahesh Kothari, K. D. Gharde

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The ANN and fuzzy logic (FL) models were developed to predict the runoff and sediment yield for catchment of Kal river, India using 21 years (1991 to 2011) rainfall and other hydrological data (evaporation, temperature and streamflow lag by one and two day) and 7 years data for sediment yield modelling. The ANN model performance improved with increasing the input vectors. The fuzzy logic model was performing with R value more than 0.95 during developmental stage and validation stage. The comparatively FL model found to be performing well to ANN in prediction of runoff and sediment yield for Kal river.

Keywords: transferred function, sigmoid, backpropagation, membership function, defuzzification

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Authors: Benjawan Rangsikamol, Chutimet Srinilta

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This research paper deals with extraction of Thai character features using the proposed histogram projection so as to improve the recognition performance. The process starts with transformation of image files into binary files before thinning. After character thinning, the skeletons are entered into the proposed extraction using histogram projection (horizontal and vertical) to extract unique features which are inputs of the subsequent recognition step. The recognition rate with the proposed extraction technique is as high as 97 percent since the technique works very well with the idiosyncrasies of Thai characters.

Keywords: character recognition, histogram projection, multilayer perceptron, Thai character features extraction

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Authors: Abdallah A. Hlayel

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Operations research science (OR) deals with good success in developing and applying scientific methods for problem solving and decision-making. However, by using OR techniques, we can enhance the use of computer decision support systems to achieve optimal management for institutions. OR applies comprehensive analysis including all factors that affect on it and builds mathematical modeling to solve business or organizational problems. In addition, it improves decision-making and uses available resources efficiently. The adoption of OR by universities would definitely contributes to the development and enhancement of the performance of OR techniques. This paper provides an understanding of the structures, approaches and models of OR in problem solving and decision-making.

Keywords: best candidates' method, decision making, decision support system, operations research

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Authors: R. Vishnu, K. S. Reddy, Amrendra Kumar

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The present guideline for the construction of flexible pavement in India, IRC 37: 2012 recommends to use viscous grade VG 40 bitumen in both wearing and binder bituminous layers. However, most of the bitumen production plants in India are unable to produce the air-blown VG40 grade bitumen. This requires plant’s air-blowing technique modification, and often the manufactures finds it as uneconomical. In this context, stiffer grade bitumen can be produced if bitumen is modified. Gilsonite, which is naturally occurring asphalt have been found to be used for increasing the stiffness of binders. The present study evaluates the physical, rheological characteristics of Gilsonite modified binders and the performance characteristics of these binders when used in the mix.

Keywords: bitumen, gilsonite, stiffness, laboratory evaluation

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Authors: Amy M. Kwon

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Support vector machine (SVM) is a well-known algorithm in machine learning due to its superior performance, and it also functions well in multiple-instance (MI) problems. Our study proposes a schematic algorithm to select instances based on Hausdorff distance, which can be adapted to SVMs as input vectors under the MI setting. Based on experiments on five benchmark datasets, our strategy for adapting representation outperformed in comparison with original approach. In addition, task execution times (TETs) were reduced by more than 80% based on MissSVM. Hence, it is noteworthy to consider this representation adaptation to SVMs under MI-setting.

Keywords: support vector machine, Margin, Hausdorff distance, representation selection, multiple-instance learning, machine learning

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Authors: Şeyda Akçalı

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This study explores the concept of social sustainability and its characteristics in terms of neighborhood (mahalle) which is a social phenomenon in Turkish urban life. As social sustainability indicators that moving away traditional themes toward multi-dimensional measures, the solutions for urban strategies may be achieved through learning lessons from historical precedents. It considers the inherent values of traditional urban forms contribute to the evolution of the city as well as the social functions of it. The study aims to measure non-tangible issues in order to evaluate social sustainability in historic urban environments and how they could contribute to the current urban planning strategies. The concept of neighborhood (mahalle) refers to a way of living that represents the organization of Turkish social and communal life rather than defining an administrative unit for the city. The distinctive physical and social features of neighborhood illustrate the link between social sustainability and historic urban environment. Instead of having a nostalgic view of past, it identifies both the failures and successes and extract lessons of traditional urban environments and adopt them to modern context. First, the study determines the aspects of social sustainability which are issued as the key themes in the literature. Then, it develops a model by describing the social features of mahalle which show consistency within the social sustainability agenda. The model is used to analyze the performance of traditional housing area in the historical city center of Bursa, Turkey whether it meets the residents’ social needs and contribute collective functioning of the community. Through a questionnaire survey exercised in the historic neighborhoods, the residents are evaluated according to social sustainability criteria of neighborhood. The results derived from the factor analysis indicate that social aspects of neighborhood are social infrastructure, identity, attachment, neighborliness, safety and wellbeing. Qualitative evaluation shows the relationship between key aspects of social sustainability and demographic and socio-economic factors. The outcomes support that inherent values of neighborhood retain its importance for the sustainability of community although there must be some local arrangements for few factors with great attention not to compromise the others. The concept of neighborhood should be considered as a potential tool to support social sustainability in national political agenda and urban policies. The performance of underlying factors in historic urban environment proposes a basis for both examining and improving traditional urban areas and how it may contribute to the overall city.

Keywords: historical city center, mahalle, neighborhood, social sustainability, traditional urban environment, Turkey

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Authors: M. Bharath, Vasudevan Raghavan, B. V. S. S. S. Prasad, S. R. Chakravarthy

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In this experimental study, effects of addition of rice husk on coal gasification in a bubbling fluidized bed gasifier, operating at atmospheric pressure with air as gasifying agent, are reported. Rice husks comprising of 6.5% and 13% by mass are added to coal. Results show that, when rice husk is added the methane yield increases from volumetric percentage of 0.56% (with no rice husk) to 2.77% (with 13% rice husk). CO and H₂ remain almost unchanged and CO₂ decreases with addition of rice husk. The calorific value of the synthetic gas is around 2.73 MJ/Nm³. All performance indices, such as cold gas efficiency and carbon conversion, increase with addition of rice husk.

Keywords: bubbling fluidized bed reactor, calorific value, coal gasification, rice husk

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Authors: Diogo Silva, Fadul Rodor, Carlos Moraes

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This work compares the results of multidimensional function approximation using two algorithms: the classical Particle Swarm Optimization (PSO) and the Quantum Particle Swarm Optimization (QPSO). These algorithms were both tested on three functions - The Rosenbrock, the Rastrigin, and the sphere functions - with different characteristics by increasing their number of dimensions. As a result, this study shows that the higher the function space, i.e. the larger the function dimension, the more evident the advantages of using the QPSO method compared to the PSO method in terms of performance and number of necessary iterations to reach the stop criterion.

Keywords: PSO, QPSO, function approximation, AI, optimization, multidimensional functions

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Authors: Dezie L. Warganegara, Dewi Tamara

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The objectives of this study are to investigate the existence of the sticky cost behaviour of firms listed in the Indonesia Stock Exchange (IDX) and to find an evidence on the effects of sticky operating expenses (SG&A expenses) on profitability of firms. For the first objective, this study found that the sticky cost behaviour does exist. For the second objective, this study finds that the stickier the operating expenses the less future profitability of the firms. This study concludes that sticky cost affects negatively to the performance and, therefore, firms should include flexibility in designing the cost structure of their firms.

Keywords: sticky costs, Indonesia Stock Exchange (IDX), profitability, operating expenses, SG&A

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Authors: M. Amanipour, J. Towfighi, E. Ganji Babakhani, M. Heidari

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Cylindrical alumina microfiltration membrane (GMITM Corporation, inside diameter=9 mm, outside diameter=13 mm, length= 50 mm) with an average pore size of 0.5 micrometer and porosity of about 0.35 was used as the support for membrane reactor. This support was soaked in boehmite sols, and the mean particle size was adjusted in the range of 50 to 500 nm by carefully controlling hydrolysis time, and calcined at 650 °C for two hours. This process was repeated with different boehmite solutions in order to achieve an intermediate layer with an average pore size of about 50 nm. The resulting substrate was then coated with a thin and dense layer of silica by counter current chemical vapour deposition (CVD) method. A boehmite sol with 10 wt.% of nickel which was prepared by a standard procedure was used to make the catalytic layer. BET, SEM, and XRD analysis were used to characterize this layer. The catalytic membrane reactor was placed in an experimental setup to evaluate the permeation and hydrogen separation performance for a steam reforming reaction. The setup consisted of a tubular module in which the membrane was fixed, and the reforming reaction occurred at the inner side of the membrane. Methane stream, diluted with nitrogen, and deionized water with a steam to carbon (S/C) ratio of 3.0 entered the reactor after the reactor was heated up to 500 °C with a specified rate of 2 °C/ min and the catalytic layer was reduced at presence of hydrogen for 2.5 hours. Nitrogen flow was used as sweep gas through the outer side of the reactor. Any liquid produced was trapped and separated at reactor exit by a cold trap, and the produced gases were analyzed by an on-line gas chromatograph (Agilent 7890A) to measure total CH₄ conversion and H₂ permeation. BET analysis indicated uniform size distribution for catalyst with average pore size of 280 nm and average surface area of 275 m².g^-1. Single-component permeation tests were carried out for hydrogen, methane, and carbon dioxide at temperature range of 500-800 °C, and the results showed almost the same permeance and hydrogen selectivity values for hydrogen as the composite membrane without catalytic layer. Performance of the catalytic membrane was evaluated by applying membranes as a membrane reactor for methane steam reforming reaction at gas hourly space velocity (GHSV) of 10,000 h⁻¹ and 2 bar. CH₄ conversion increased from 50% to 85% with increasing reaction temperature from 600 °C to 750 °C, which is sufficiently above equilibrium curve at reaction conditions, but slightly lower than membrane reactor with packed nickel catalytic bed because of its higher surface area compared to the catalytic layer.

Keywords: catalytic membrane, hydrogen, methane steam reforming, permeance

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Authors: Garry A. Einicke, Langford B. White

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This paper describes an optimum filter and smoother for recovering a Markov process message from noisy measurements. The developments follow from an equivalence between a state space model and a hidden Markov chain. The ensuing filter and smoother employ transition probability matrices and approximate probability distribution vectors. The properties of the optimum solutions are retained, namely, the estimates are unbiased and minimize the variance of the output estimation error, provided that the assumed parameter set are correct. Methods for estimating unknown parameters from noisy measurements are discussed. Signal recovery examples are described in which performance benefits are demonstrated at an increased calculation cost.

Keywords: optimal filtering, smoothing, Markov chains

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Authors: Glenn Auld Knierim

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Automated robotic winding of high-temperature superconductors (HTS) addresses precision, efficiency, and reliability critical to the commercialization of products. Today’s HTS materials are mature and commercially promising but require manufacturing attention. In particular to the exaggerated rectangular cross-section (very thin by very wide), winding precision is critical to address the stress that can crack the fragile ceramic superconductor (SC) layer and destroy the SC properties. Damage potential is highest during peak operations, where winding stress magnifies operational stress. Another challenge is operational parameters such as magnetic field alignment affecting design performance. Winding process performance, including precision, capability for geometric complexity, and efficient repeatability, are required for commercial production of current HTS. Due to winding limitations, current HTS magnets focus on simple pancake configurations. HTS motors, generators, MRI/NMR, fusion, and other projects are awaiting robotic wound solenoid, planar, and spherical magnet configurations. As with conventional power cables, full transposition winding is required for long length alternating current (AC) and pulsed power cables. Robotic production is required for transposition, periodic swapping of cable conductors, and placing into precise positions, which allows power utility required minimized reactance. A full transposition SC cable, in theory, has no transmission length limits for AC and variable transient operation due to no resistance (a problem with conventional cables), negligible reactance (a problem for helical wound HTS cables), and no long length manufacturing issues (a problem with both stamped and twisted stacked HTS cables). The Infinity Physics team is solving manufacturing problems by developing automated manufacturing to produce the first-ever reliable and utility-grade commercial SC cables and magnets. Robotic winding machines combine mechanical and process design, specialized sense and observer, and state-of-the-art optimization and control sequencing to carefully manipulate individual fragile SCs, especially HTS, to shape previously unattainable, complex geometries with electrical geometry equivalent to commercially available conventional conductor devices.

Keywords: automated winding manufacturing, high temperature superconductor, magnet, power cable

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Authors: Hala M. Abdel Mageed, Ahmed I. Omar, Shady H. E. Abdel Aleem

Abstract:

This paper describes in details a commercial green building that has been designed and constructed in Marsa Matrouh, Egypt. The balance between homebuilding and the sustainable environment has been taken into consideration in the design and construction of this building. The building consists of one floor with 3 m height and 2810 m² area while the envelope area is 1400 m². The building construction fulfills the natural ventilation requirements. The glass curtain walls are about 50% of the building and the windows area is 300 m². 6 mm greenish gray tinted temper glass as outer board lite, 6 mm safety glass as inner board lite and 16 mm thick dehydrated air spaces are used in the building. Visible light with 50% transmission, 0.26 solar factor, 0.67 shading coefficient and 1.3 W/m².K thermal insulation U-value are implemented to realize the performance requirements. Optimum electrical distribution for lighting system, air conditions and other electrical loads has been carried out. Power and quantity of each type of the lighting system lamps and the energy consumption of the lighting system are investigated. The design of the air conditions system is based on summer and winter outdoor conditions. Ventilated, air conditioned spaces and fresh air rates are determined. Variable Refrigerant Flow (VRF) is the air conditioning system used in this building. The VRF outdoor units are located on the roof of the building and connected to indoor units through refrigerant piping. Indoor units are distributed in all building zones through ducts and air outlets to ensure efficient air distribution. The green building energy consumption is evaluated monthly all over one year and compared with the consumed energy in the non-green conditions using the Hourly Analysis Program (HAP) model. The comparison results show that the total energy consumed per year in the green building is about 1,103,221 kWh while the non-green energy consumption is about 1,692,057 kWh. In other words, the green building total annual energy cost is reduced from 136,581 $ to 89,051 $. This means that, the energy saving and consequently the money-saving of this green construction is about 35%. In addition, 13 points are awarded by applying one of the most popular worldwide green energy certification programs (Leadership in Energy and Environmental Design “LEED”) as a rating system for the green construction. It is concluded that this green building ensures sustainability, saves energy and offers an optimum energy performance with minimum cost.

Keywords: energy consumption, energy saving, green building, leadership in energy and environmental design, sustainability

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