Search results for: site selection optimization

Authors: Abdelali Laid, Abid Hamza, Zeroukhi Houari, Sayah Naimi

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This work relates to the study of the energy levels and the optimization of the Parameter intrinsic (a number of wells and their widths, width of barrier of potential, index of refraction etc.) and extrinsic (temperature, pressure) in the Structure laser diode containing the structure GaInP. The methods of calculation used; - method of the empirical pseudo potential to determine the electronic structures of bands, - graphic method for optimization. The found results are in concord with those of the experiment and the theory.

Keywords: semi-conductor, GaInP/AlGaInP, pseudopotential, energy, alliages

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Authors: Fatemeh Sadat Miri, Morteza Ehsani, Seyed Farshid Hosseini

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The present research is concerned with the optimization of extrusion parameters of ABS sheets by the Taguchi experimental design method. In this design method, three parameters of % recycling ABS, processing temperature and degassing time on mechanical properties, hardness, HDT, and color matching of ABS sheets were investigated. The variations of this research are the dosage of recycling ABS, processing temperature, and degassing time. According to experimental test data, the highest level of tensile strength and HDT belongs to the sample with 5% recycling ABS, processing temperature of 230°C, and degassing time of 3 hours. Additionally, the minimum level of MFI and color matching belongs to this sample, too. The present results are in good agreement with the Taguchi method. Based on the outcomes of the Taguchi design method, degassing time has the most effect on the mechanical properties of ABS sheets.

Keywords: ABS, process optimization, Taguchi, mechanical properties

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Authors: Ehsan Yadegari

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While there are several metaheuristics and exact approaches to solving the Supply Chain Network Design (SCND) problem, there still remains an unfilled gap in using the Teaching-Learning-Based Optimization (TLBO) algorithm. The algorithm has demonstrated desirable results with problems with complicated combinational optimization. The present study introduces a Discrete Self-Study TLBO (DSS-TLBO) with priority-based solution representation that can solve a supply chain network configuration model to lower the total expenses of establishing facilities and the flow of materials. The network features four layers, namely suppliers, plants, distribution centers (DCs), and customer zones. It is designed to meet the customer’s demand through transporting the material between layers of network and providing facilities in the best economic Potential locations. To have a higher quality of the solution and increase the speed of TLBO, a distinct operator was introduced that ensures self-adaptation (self-study) in the algorithm based on the four types of local search. In addition, while TLBO is used in continuous solution representation and priority-based solution representation is discrete, a few modifications were added to the algorithm to remove the solutions that are infeasible. As shown by the results of experiments, the superiority of DSS-TLBO compared to pure TLBO, genetic algorithm (GA) and firefly Algorithm (FA) was established.

Keywords: supply chain network design, teaching–learning-based optimization, improved metaheuristics, discrete solution representation

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Authors: Eduardo F. Mello, Peter H. Bauer

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The primary focus of this paper is the generation of energy-optimal speed trajectories for heterogeneous electric vehicle platoons in urban driving conditions. Optimal speed trajectories are generated for individual vehicles and for an entire platoon under the assumption that they can be executed without errors, as would be the case for self-driving vehicles. It is then shown that the optimization for the “average vehicle in the platoon” generates similar transportation energy savings to optimizing speed trajectories for each vehicle individually. The introduced approach only requires the lead vehicle to run the optimization software while the remaining vehicles are only required to have adaptive cruise control capability. The achieved energy savings are typically between 30% and 50% for stop-to-stop segments in cities. The prime motivation of urban platooning comes from the fact that urban platoons efficiently utilize the available space and the minimization of transportation energy in cities is important for many reasons, i.e., for environmental, power, and range considerations.

Keywords: electric vehicles, energy efficiency, optimization, platooning, self-driving vehicles, urban traffic

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Authors: Mamidi Ramakrishna Rao, Jagdish Mamidi

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With advancing technology, the market environment for wind power generation systems has become highly competitive. The industry has been moving towards higher wind generator power ratings, in particular, off-shore generator ratings. Current off-shore wind turbine generators are in the power range of 10 to 12 MW. Unlike traditional induction motors, slow-speed permanent magnet surface mounted (PMSM) high-power generators are relatively challenging and designed differently. In this paper, PMSM generator design features have been discussed and analysed. The focus attention is on armature windings, harmonics, and permanent magnet. For the power ratings under consideration, the generator air-gap diameters are in the range of 8 to 10 meters, and active material weigh ~60 tons and above. Therefore, material weight becomes one of the critical parameters. Particle Swarm Optimization (PSO) technique is used for weight reduction and performance improvement. Four independent variables have been considered, which are air gap diameter, stack length, magnet thickness, and winding current density. To account for core and teeth saturation, preventing demagnetization effects due to short circuit armature currents, and maintaining minimum efficiency, suitable penalty functions have been applied. To check for performance satisfaction, a detailed analysis and 2D flux plotting are done for the optimized design.

Keywords: offshore wind generator, PMSM, PSO optimization, design optimization

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Authors: Juan Portoviejo Brito, Daniel Icaza Alvarez, Christian Castro Samaniego

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In this article, we present the modeling, simulations, and energy conversion analysis of the solar-wind system for the Quingeo Heritage Center in Ecuador. A numerical model was constructed based on the 19 equations, it was coded in MATLAB R2017a, and the results were compared with the experimental data of the site. The model is built with the purpose of using it as a computer development for the optimization of resources and designs of hybrid systems in the Parish of Quingeo and its surroundings. The model obtained a fairly similar pattern compared to the data and curves obtained in the field experimentally and detailed in manuscript. It is important to indicate that this analysis has been carried out so that in the near future one or two of these power generation systems can be exploited in a massive way according to the budget assigned by the Parish GAD of Quingeo or other national or international organizations with the purpose of preserving this unique colonial helmet in Ecuador.

Keywords: hybrid system, wind turbine, modeling, simulation, Smart Grid, Quingeo Azuay Ecuador

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Authors: A. Mohammadi, M. Moghimi, S. Mohammadi

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Engineers need computational methods which could provide solutions less sensitive to the environmental effects, so the techniques should be used which take the uncertainty to account to control and minimize the risk associated with design and operation. In order to consider uncertainty in engineering problem, the optimization problem should be solved for a suitable range of the each uncertain input variable instead of just one estimated point. Using deterministic optimization problem, a large computational burden is required to consider every possible and probable combination of uncertain input variables. Several methods have been reported in the literature to deal with problems under uncertainty. In this paper, different methods presented and analyzed.

Keywords: uncertainty, Monte Carlo simulated, stochastic programming, scenario method

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Authors: O. D. Eniowo

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“Rewashing” as it is commonly called, involves the process of scooping out and washing chunks of mud from a closed alluvial gold mine site with the purpose of extracting any leftover gold deposits in the site. It is usually carried out by illegal miners who infiltrate closed mine sites with the goal of scavenging for any leftover gold deposits. Expectedly, the practice gives little or no regard for environmental protection. This paper examines the process of “rewashing” in a mining community in Nigeria. It then discusses the looming danger it portends for health, safety, and the environment. The study draws lessons from these occurrences to examine and discuss fit-for-purpose mine closure plans that could be adopted by gold mines in Nigeria and other sub-Saharan African countries.

Keywords: mine planning, mine closure, illegal mining, artisanal mining, environmental sustainability

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Authors: Ouahab Kadri, Leila Hayet Mouss

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In this paper, we present three diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binary ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems, which are clinkering system and pasteurization system.

Keywords: ant colony algorithms, complex and dynamic systems, diagnosis, classification, optimization

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Authors: Andrey V. Timofeev, Dmitry V. Egorov

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This paper introduces an original method of parametric optimization of the structure for multimodal decision-level fusion scheme which combines the results of the partial solution of the classification task obtained from assembly of the mono-modal classifiers. As a result, a multimodal fusion classifier which has the minimum value of the total error rate has been obtained.

Keywords: classification accuracy, fusion solution, total error rate, multimodal fusion classifier

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Authors: Ahlem Dhahri, Waleed Omri, Audrey Becuwe, Abdelwahed Omri

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In industrial processes, decision-making ranges across different scales, from process control to supply chain management. The purchasing decision-making process in the supply chain is presently gaining more attention as a critical contributor to the company's strategic success. Given the scarcity of thorough summaries in the prior studies, this bibliometric analysis aims to adopt a meticulous approach to achieve quantitative knowledge on the constantly evolving subject of purchasing decision-making in supply chain management. Through bibliometric analysis, we examine a sample of 358 peer-reviewed articles from the Scopus database. VOSviewer and Gephi software were employed to analyze, combine, and visualize the data. Data analytic techniques, including citation network, page-rank analysis, co-citation, and publication trends, have been used to identify influential works and outline the discipline's intellectual structure. The outcomes of this descriptive analysis highlight the most prominent articles, authors, journals, and countries based on their citations and publications. The findings from the research illustrate an increase in the number of publications, exhibiting a slightly growing trend in this field. Co-citation analysis coupled with content analysis of the most cited articles identified five research themes mentioned as follows integrating sustainability into the supplier selection process, supplier selection under disruption risks assessment and mitigation strategies, Fuzzy MCDM approaches for supplier evaluation and selection, purchasing decision in vendor problems, decision-making techniques in supplier selection and order lot sizing problems. With the help of a graphic timeline, this exhaustive map of the field illustrates a visual representation of the evolution of publications that demonstrate a gradual shift from research interest in vendor selection problems to integrating sustainability in the supplier selection process. These clusters offer insights into a wide variety of purchasing methods and conceptual frameworks that have emerged; however, they have not been validated empirically. The findings suggest that future research would emerge with a greater depth of practical and empirical analysis to enrich the theories. These outcomes provide a powerful road map for further study in this area.

Keywords: bibliometric analysis, citation analysis, co-citation, Gephi, network analysis, purchasing, SCM, VOSviewer

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Authors: Ashwini Gowda K. S, Archana M. R, Anjaneyappa V

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This paper presents pavement maintenance and management system (PMMS) to obtain optimum pavement maintenance and rehabilitation strategies and maintenance scheduling for a network using a multi-objective genetic algorithm (MOGA). Optimal pavement maintenance & rehabilitation strategy is to maximize the pavement condition index of the road section in a network with minimum maintenance and rehabilitation cost during the planning period. In this paper, NSGA-II is applied to perform maintenance optimization; this maintenance approach was expected to preserve and improve the existing condition of the highway network in a cost-effective way. The proposed PMMS is applied to a network that assessed pavement based on the pavement condition index (PCI). The minimum and maximum maintenance cost for a planning period of 20 years obtained from the non-dominated solution was found to be 5.190x10¹⁰ ₹ and 4.81x10¹⁰ ₹, respectively.

Keywords: genetic algorithm, maintenance and rehabilitation, optimization technique, pavement condition index

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Authors: Ayodele Oluwole Alejo

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Wall finishes are the term to describe an application over a wall surface to provide a suitable surface. Wall finishes are smelt, touched and seen by building occupiers even colour and design affects the user psychology and the atmosphere of our building. Building users/owners seem not to recognize the function of various wall finishes in building and factors to be considered in selecting them suitable for the type and purpose of proposed buildings. Therefore, defects such as deterioration, dampness, and stain may occur when comparisons of wall finishes are not made before the selection of appropriate materials at the design stage with knowledge of the various factors that may hinder the performance or maintenance culture of proposed building of a particular location. This research work investigates and compares various wall finishes in building. Buildings in Ondo state, Nigeria were used as the target area to conduct the research works. The factors bearing on various wall finishes were analyzed to find out their individual and collective impact using suitable analytical tools. The findings revealed that paint with high percentage score was the most preferred wall finishes, whereas wall paper was ranked the least by the respondent findings, Factors considered most in the selection of wall finishes was durability with the highest ranking percentage and least was the cost. The study recommends that skilled worker should carry out operations, quality product should be used and all of wall finishes and materials should be considered before selection.

Keywords: building, construction, design, finishes, wall

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Authors: Soudabeh Shemehsavar

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In this paper, we consider the situation under a life test, in which the failure time of the test units are not related deterministically to an observable stochastic time varying covariate. In such a case, the joint distribution of failure time and a marker value would be useful for modeling the step stress life test. The problem of accelerating such an experiment is considered as the main aim of this paper. We present a step stress accelerated model based on a bivariate Wiener process with one component as the latent (unobservable) degradation process, which determines the failure times and the other as a marker process, the degradation values of which are recorded at times of failure. Parametric inference based on the proposed model is discussed and the optimization procedure for obtaining the optimal time for changing the stress level is presented. The optimization criterion is to minimize the approximate variance of the maximum likelihood estimator of a percentile of the products’ lifetime distribution.

Keywords: bivariate normal, Fisher information matrix, inverse Gaussian distribution, Wiener process

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Authors: Siwimol Phoomniyom, Pathom Karaipoom, Rossaphorn Kittyaowaman

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Background: C. trachomatis is the most common bacterial sexually transmitted infections. Although infection with C. trachomatis can be treated with antibiotic, it is frequently asymptomatic, especially in extragenital sites. Hence, if screening tests are not performed, undetected and untreated is a crucial problem for C. trachomatis infection, especially in Thailand, which is less well studied. We sought to assess the prevalence of C. trachomatis infection in multiple anatomical sites among patients attending Bangrak STIs Center. Methods: We examined laboratory results of all patients at baseline visit from 3 January 2018 to 27 December 2019. These results were tested by a validated in-house real time PCR specify for the cryptic plasmid gene of C. trachomatis. The prevalence of C. trachomatis was analyzed by anatomical sites, sexes, and ages. Urogenital samples were obtained from urethral swab of men and cervical swab of women. The median ages of the patients were 32 years (range 13-89 years). Chi-square test by IBM SPSS statistic version 20 was used to assess difference in the distribution of variables between groups. Results: Among 3,789 patients, the prevalence for C. trachomatis infection was the highest in rectal (16.1%), followed by urogenital (11.2%) and pharyngeal (3.5%) sites. Rectal and urogenital infection in men was higher than in women, with the highest prevalence of 16.6% in rectal site. Both rectal and urogenital sites also showed statistically significant differences between sexes (P<0.001). Meanwhile, pharyngeal C. trachomatis infection rate was higher in women than men. Interestingly, the chlamydia prevalence was the highest in age 13-19 years of all three sites (18.5%, urogenital; 17.7%, rectal; 6.5%, pharyngeal), with statistically significant difference between age groups (P<0.001). Total of 45 C. trachomatis infections, 20.0%, 51.1%, and 6.7% were isolated from urogenital, rectal, and pharyngeal sites. In total, 75.6%, 26.7%, and 80.0% of chlamydia infections would have been missed, if only urogenital, rectal, or pharyngeal screening was performed. Conclusions: The highest source of C. trachomatis infection was the rectal site. While, the highest prevalence in men was at rectal site, that in women was at urogenital site. The highest chlamydia prevalence was found in adolescent age group, indicating that the pediatric population was a high-risk group. This finding also elucidated that a high proportion of C. trachomatis infection would be missed, if only single anatomical site screening was performed, especially in extragenital sites. Hence, extragenital screening is also required for the extensive C. trachomatis detection.

Keywords: chlamydia trachomatis, anatomical sites, sexes, ages

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Authors: Harish Rajak, Swati Singh

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A series of novel pyrimidine based semicarbazone were designed and synthesized on the basis of semicarbazone based pharmacophoric model to satisfy the structural prerequisite crucial for antiepileptic activity. The semicarbazones based pharmacophoric model consists of following four essential binding sites: (i) An aryl hydrophobic binding site with halo substituent; (ii) A hydrogen bonding domain; (iii) An electron donor group and (iv) Another hydrophobic-hydrophilic site controlling the pharmacokinetic features of the anticonvulsant. The aryl semicarbazones has been recognized as a structurally novel class of compounds with remarkable anticonvulsant activity. In the present study, all the test semicarbazones were subjected to molecular docking using Glide v5.8. Some of the compounds were found to interact with ARG192, GLU270 and THR353 residues of 1OHV protein, present in GABA-AT receptor. The chemical structures of the synthesized molecules were characterized by elemental and spectral (IR, 1H NMR, 13C NMR and MS) analysis. The anticonvulsant activities of the compounds were investigated using maximal electroshock seizure (MES) and subcutaneous pentylenetrtrazole (scPTZ) models. The neurotoxicity was evaluated in mice by the rotorod test. The attempts were also made to establish structure-activity relationships among synthesized compounds. The results of the present study confirmed that the pharmacophore model with four binding sites is essential for antiepileptic activity.

Keywords: pyrimidine, semicarbazones, anticonvulsant activity, neurotoxicity

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Authors: W. Du, X. Wang, Jun Cao, H. F. Wang

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Due to the randomness and uncertainty of wind energy, modern power systems integrating large-scale wind generation will be significantly impacted in terms of system performance and technical challenges. System inertia with high wind penetration is decreasing when conventional thermal generators are gradually replaced by wind turbines, which do not naturally contribute to inertia response. The power imbalance caused by wind power or demand fluctuations leads to the instability of system frequency. Accordingly, the need to attach the supplementary virtual inertia control to wind farms (WFs) strongly arises. When multi-wind farms are connected to the grid simultaneously, the selection of which critical WFs to install the virtual inertia control is greatly important to enhance the stability of system frequency. By building the small signal model of wind power systems considering frequency regulation, the installation locations are identified by the geometric measures of the mode observability of WFs. In addition, this paper takes the impacts of grid topology and selection of feedback control signals into consideration. Finally, simulations are conducted on a multi-wind farms power system and the results demonstrate that the designed virtual inertia control method can effectively assist the frequency regulation.

Keywords: frequency regulation, virtual inertia control, installation locations, observability, wind farms

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Authors: Majid Arvan, Peyman Beygi, Sina Rokhsati

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In-time detection of fire in buildings is of great importance. Employing intelligent methods in data processing in fire detection systems leads to a significant reduction of fire damage at lowest cost. In this paper, the raw data obtained from the fire detection sensor networks in buildings is processed by using intelligent methods based on neural networks and the likelihood of fire happening is predicted. In order to enhance the quality of system, the noise in the sensor data is reduced by analyzing wavelets and applying SVD technique. Meanwhile, the proposed neural network is trained using particle swarm optimization (PSO). In the simulation work, the data is collected from sensor network inside the room and applied to the proposed network. Then the outputs are compared with conventional MLP network. The simulation results represent the superiority of the proposed method over the conventional one.

Keywords: intelligent fire detection, neural network, particle swarm optimization, fire sensor network

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Authors: T. Radu, R. Sreenivas, H. Albuflasa, A. Mustafa Khan, W. Aloqab

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The Kingdom of Bahrain, a small island country in the Gulf region, is experiencing fast economic growth resulting in a sharp increase in population and greater than ever amounts of waste being produced. However, waste management in the country is still very basic, with landfilling being the most popular option. Recycling is still a scarce practice, with small recycling businesses and initiatives emerging in recent years. This scenario is typical for other countries in the region, with similar amounts of per capita waste being produced. In this paper, we are reviewing current waste management practices in Bahrain by collecting data published by the Government and various authors, and by visiting the country’s only landfill site, Askar. In addition, we have performed a survey of the residents to learn more about the awareness and attitudes towards sustainable waste management strategies. A review of the available data on waste management indicates that the Askar landfill site is nearing its capacity. The site uses open tipping as the method of disposal. The highest percentage of disposed waste comes from the building sector (38.4%), followed by domestic (27.5%) and commercial waste (17.9%). Disposal monitoring and recording are often based on estimates of weight and without proper characterization/classification of received waste. Besides, there is a need for assessment of the environmental impact of the site with systematic monitoring of pollutants in the area and their potential spreading to the surrounding land, groundwater, and air. The results of the survey indicate low awareness of what happens with the collected waste in the country. However, the respondents have shown support for future waste reduction and recycling initiatives. This implies that the education of local communities would be very beneficial for such governmental initiatives, securing greater participation. Raising awareness of issues surrounding recycling and waste management and systematic effort to divert waste from landfills are the first steps towards securing sustainable waste management in the Kingdom of Bahrain.

Keywords: landfill, municipal solid waste, survey, waste management

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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Authors: Anshul Gupta, T. Shankar

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In this paper, we had analyzed the call drop to provide a good quality of service to user. By optimizing it we can increase the coverage area and also the reduction of interference and congestion created in a network. Basically handover is the transfer of call from one cell site to another site during a call. Here we have analyzed the whole network by two method-statistic model and analytic model. In statistic model we have collected all the data of a network during busy hour and normal 24 hours and in analytic model we have the equation through which we have to find the call drop probability. By avoiding unnecessary handovers we can increase the number of calls per hour. The most important parameter is co-efficient of variation on which the whole paper discussed.

Keywords: coefficient of variation, mean, standard deviation, call drop probability, handover

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Authors: Jin Ho Kim, Yujeong Shin, Seong-Jin Cho, Dong-Jin Kim, U-Syn Ha

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Due to the development of the information industry and technologies, cellular phones have must not only function to communicate, but also have functions such as the Internet, e-banking, entertainment, etc. These phones are called smartphones. The performance of smartphones has improved, because of the various functions of smartphones, and the capacity of the battery has been increased gradually. Recently, linear generators have been embedded in smartphones in order to recharge the smartphone's battery. In this study, optimization is performed and an array change of permanent magnets is examined in order to increase efficiency. We propose an optimal design using design of experiments (DOE) to maximize the generated induced voltage. The thickness of the poleshoe and permanent magnet (PM), the height of the poleshoe and PM, and the thickness of the coil are determined to be design variables. We made 25 sampling points using an orthogonal array according to four design variables. We performed electromagnetic finite element analysis to predict the generated induced voltage using the commercial electromagnetic analysis software ANSYS Maxwell. Then, we made an approximate model using the Kriging algorithm, and derived optimal values of the design variables using an evolutionary algorithm. The commercial optimization software PIAnO (Process Integration, Automation, and Optimization) was used with these algorithms. The result of the optimization shows that the generated induced voltage is improved.

Keywords: smartphone, linear generator, design of experiment, approximate model, optimal design

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Authors: Ahmed Elrewainy

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Mixing in the hyperspectral imaging occurs due to the low spatial resolutions of the used cameras. The existing pure materials “endmembers” in the scene share the spectra pixels with different amounts called “abundances”. Unmixing of the data cube is an important task to know the present endmembers in the cube for the analysis of these images. Unsupervised unmixing is done with no information about the given data cube. Sparsity is one of the recent approaches used in the source recovery or unmixing techniques. The l₁-norm optimization problem “basis pursuit” could be used as a sparsity-based approach to solve this unmixing problem where the endmembers is assumed to be sparse in an appropriate domain known as dictionary. This optimization problem is solved using proximal method “iterative thresholding”. The l₁-norm basis pursuit optimization problem as a sparsity-based unmixing technique was used to unmix real and synthetic hyperspectral data cubes.

Keywords: basis pursuit, blind source separation, hyperspectral imaging, spectral unmixing, wavelets

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Authors: Polycarp Igbojekwe

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The main objective of this study was to determine if emotional labour has become a criterion in performance appraisal, job description, selection, and training schemes in the hotel industry in Nigeria. Our main assumption was that majority of hotel organizations have not built emotional labour into their human resources management schemes. Data were gathered by the use of structured questionnaires designed in Likert format, and interviews. The focus group was managers of the selected hotels. Analyses revealed that majority of the hotels have not built emotional labour into their human resources schemes particularly in the 1, 2, and 3-star hotels. It was observed that service employees of 1, 2, and 3-star hotels have not been adequately trained to perform emotional labour; a critical factor in quality service delivery. Managers of 1, 2, and 3-star hotels have not given serious thought to emotional labour as a critical factor in quality service delivery. The study revealed that suitability of an individual’s characteristics is not being considered as a criterion for selection and performance appraisal for service employees. The implication of this is that, person-job-fit is not seriously considered. It was observed that there has been a disconnect between required emotional competency, its recognition, evaluation, and training. Based on the findings of this study, it is concluded that selection, training, job description and performance appraisal instruments in use in hotels in Nigeria are inadequate. Human resource implications of the findings in this study are presented. It is recommended that hotel organizations should re-design and plan the emotional content and context of their human resources practices to reflect the emotional demands of front line jobs in the hotel industry and the crucial role emotional labour plays during service encounters.

Keywords: emotional labour, employee selection, job description, performance appraisal, person-job-fit, employee compensation

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Authors: Mohamed Elhakeem, A. N. Thanos Papanicolaou, Christopher Wilson, Yi-Jia Chang

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In this study, a physically-based, modelling framework was developed to predict saturated hydraulic conductivity (KSAT) dynamics in the Clear Creek Watershed (CCW), Iowa. The modelling framework integrated selected pedotransfer functions and watershed models with geospatial tools. A number of pedotransfer functions and agricultural watershed models were examined to select the appropriate models that represent the study site conditions. Models selection was based on statistical measures of the models’ errors compared to the KSAT field measurements conducted in the CCW under different soil, climate and land use conditions. The study has shown that the predictions of the combined pedotransfer function of Rosetta and the Water Erosion Prediction Project (WEPP) provided the best agreement to the measured KSAT values in the CCW compared to the other tested models. Therefore, Rosetta and WEPP were integrated with the Geographic Information System (GIS) tools for visualization of the data in forms of geospatial maps and prediction of KSAT variability in CCW due to the seasonal changes in climate and land use activities.

Keywords: saturated hydraulic conductivity, pedotransfer functions, watershed models, geospatial tools

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Authors: Jaehong Yu, Seoung Bum Kim

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Clustering analysis can facilitate the extraction of intrinsic patterns in a dataset and reveal its natural groupings without requiring class information. For effective clustering analysis in high dimensional datasets, unsupervised dimensionality reduction is an important task. Unsupervised dimensionality reduction can generally be achieved by feature extraction or feature selection. In many situations, feature selection methods are more appropriate than feature extraction methods because of their clear interpretation with respect to the original features. The unsupervised feature selection can be categorized as feature subset selection and feature ranking method, and we focused on unsupervised feature ranking methods which evaluate the features based on their importance scores. Recently, several unsupervised feature ranking methods were developed based on ensemble approaches to achieve their higher accuracy and stability. However, most of the ensemble-based feature ranking methods require the true number of clusters. Furthermore, these algorithms evaluate the feature importance depending on the ensemble clustering solution, and they produce undesirable evaluation results if the clustering solutions are inaccurate. To address these limitations, we proposed an ensemble-based feature ranking method with random subspace and multiple-k ensemble (FRRM). The proposed FRRM algorithm evaluates the importance of each feature with the random subspace ensemble, and all evaluation results are combined with the ensemble importance scores. Moreover, FRRM does not require the determination of the true number of clusters in advance through the use of the multiple-k ensemble idea. Experiments on various benchmark datasets were conducted to examine the properties of the proposed FRRM algorithm and to compare its performance with that of existing feature ranking methods. The experimental results demonstrated that the proposed FRRM outperformed the competitors.

Keywords: clustering analysis, multiple-k ensemble, random subspace-based feature evaluation, unsupervised feature ranking

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Authors: Yihao Kuang, Bowen Ding

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With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graphs and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improved strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain a better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference

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Authors: Huai-Feng Wang, Meng-Lin Lou, Ru-Lin Zhang

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One good analysis method in seismic response analysis is direct time integration, which widely adopts Rayleigh damping. An approach is presented for selection of Rayleigh damping coefficients to be used in seismic analyses to produce a response that is consistent with Modal damping response. In the presented approach, the expression of the error of peak response, acquired through complete quadratic combination method, and Rayleigh damping coefficients was set up and then the coefficients were produced by minimizing the error. Two finite element modes of soil layers, excited by 28 seismic waves, were used to demonstrate the feasibility and validity.

Keywords: Rayleigh damping, modal damping, damping coefficients, seismic response analysis

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Authors: Ali Shirazi, Robert A. Taylor, Stephen D. White, Graham L. Morrison

Abstract:

In this paper, a detailed simulation model of a solar-powered triple-effect LiBr–H₂O absorption chiller is developed to supply both cooling and heating demand of a large-scale building, aiming to reduce the fossil fuel consumption and greenhouse gas emissions in building sector. TRNSYS 17 is used to simulate the performance of the system over a typical year. A combined energetic-economic-environmental analysis is conducted to determine the system annual primary energy consumption and the total cost, which are considered as two conflicting objectives. A multi-objective optimization of the system is performed using a genetic algorithm to minimize these objectives simultaneously. The optimization results show that the final optimal design of the proposed plant has a solar fraction of 72% and leads to an annual primary energy saving of 0.69 GWh and annual CO₂ emissions reduction of ~166 tonnes, as compared to a conventional HVAC system. The economics of this design, however, is not appealing without public funding, which is often the case for many renewable energy systems. The results show that a good funding policy is required in order for these technologies to achieve satisfactory payback periods within the lifetime of the plant.

Keywords: economic, environmental, multi-objective optimization, solar air-conditioning, triple-effect absorption chiller

Procedia PDF Downloads 234

Authors: Junjie Peng, Jinbao Chen, Shuai Kong, Danxu Liu

Abstract:

On the basis of traditional resource allocation strategies, the usage of resources on physical servers in cloud data center is great uncertain. It will cause waste of resources if the assignment of tasks is not enough. On the contrary, it will cause overload if the assignment of tasks is too much. This is especially obvious when the applications are the same type because of its resource preferences. Considering CPU intensive application is one of the most common types of application in the cloud, we studied the optimization strategy for CPU intensive applications on the same server. We used resource preferences to analyze the case that multiple CPU intensive applications run simultaneously, and put forward a model which can predict the execution time for CPU intensive applications which run simultaneously. Based on the prediction model, we proposed the method to select the appropriate number of applications for a machine. Experiments show that the model can predict the execution time accurately for CPU intensive applications. To improve the execution efficiency of applications, we propose a scheduling model based on priority for CPU intensive applications. Extensive experiments verify the validity of the scheduling model.

Keywords: cloud computing, CPU intensive applications, resource optimization, strategy

Procedia PDF Downloads 274