Abstract: The paper presents a multiple criteria decision making analysis process to determine the most suitable regional aircraft type according to a set of evaluation criteria. The main purpose of this study is to use different decision making methods to determine the most suitable regional aircraft for aviation operators. In this context, the nine regional aircraft types were analyzed using multiple criteria decision making analysis methods. Preference analysis for reference ideal solution (PARIS) was used in regional aircraft selection process. The findings of the proposed model show that the ranking results of the multiple criteria decision making models are consistent with each other, and the proposed method is efficient, and the results are valid. Finally, the Embraer E195-E2 model regional aircraft is chosen as the most suitable aircraft type.
Abstract: This paper presents preference programming technique based multiple criteria decision making analysis for selecting a facility location for a new organization or expansion of an existing facility which is of vital importance for a decision support system and strategic planning process. The implementation of decision support systems is considered crucial to sustain competitive advantage and profitability persistence in turbulent environment. As an effective strategic management and decision making is necessary, multiple criteria decision making analysis supports the decision makers to formulate and implement the right strategy. The investment cost associated with acquiring the property and facility construction makes the facility location selection problem a long-term strategic investment decision, which rationalize the best location selection which results in higher economic benefits through increased productivity and optimal distribution network. Selecting the proper facility location from a given set of alternatives is a difficult task, as many potential qualitative and quantitative multiple conflicting criteria are to be considered. This paper solves a facility location selection problem using preference programming, which is an effective multiple criteria decision making analysis tool applied to deal with complex decision problems in the operational research environment. The ranking results of preference programming are compared with WSM, TOPSIS and VIKOR methods.
Abstract: This paper presents an original application of multiple criteria decision making analysis theory to the evaluation of aircraft selection problem. The selection of an optimal, efficient and reliable fleet, network and operations planning policy is one of the most important factors in aircraft selection problem. Given that decision making in aircraft selection involves the consideration of a number of opposite criteria and possible solutions, such a selection can be considered as a multiple criteria decision making analysis problem. This study presents a new integrated approach to decision making by considering the multiple criteria utility theory and the maximal regret minimization theory methods as well as aircraft technical, economical, and environmental aspects. Multiple criteria decision making analysis method uses different normalization techniques to allow criteria to be aggregated with qualitative and quantitative data of the decision problem. Therefore, selecting a suitable normalization technique for the model is also a challenge to provide data aggregation for the aircraft selection problem. To compare the impact of different normalization techniques on the decision problem, the vector, linear (sum), linear (max), and linear (max-min) data normalization techniques were identified to evaluate aircraft selection problem. As a logical implication of the proposed approach, it enhances the decision making process through enabling the decision maker to: (i) use higher level knowledge regarding the selection of criteria weights and the proposed technique, (ii) estimate the ranking of an alternative, under different data normalization techniques and integrated criteria weights after a posteriori analysis of the final rankings of alternatives. A set of commercial passenger aircraft were considered in order to illustrate the proposed approach. The obtained results of the proposed approach were compared using Spearman's rho tests. An analysis of the final rank stability with respect to the changes in criteria weights was also performed so as to assess the sensitivity of the alternative rankings obtained by the application of different data normalization techniques and the proposed approach.
Abstract: In this paper, multiple criteria decision making analysis technique, is presented for ranking and selection of a set of determined alternatives - fighter aircraft - which are associated with a set of decision factors. In fighter aircraft design, conflicting decision criteria, disciplines, and technologies are always involved in the design process. Multiple criteria decision making analysis techniques can be helpful to effectively deal with such situations and make wise design decisions. Multiple criteria decision making analysis theory is a systematic mathematical approach for dealing with problems which contain uncertainties in decision making. The feasibility and contributions of applying the multiple criteria decision making analysis technique in fighter aircraft selection analysis is explored. In this study, an integrated framework incorporating multiple criteria decision making analysis technique in fighter aircraft analysis is established using entropy objective weighting method. An improved integrated multiple criteria decision making analysis method is utilized to aggregate the multiple decision criteria into one composite figure of merit, which serves as an objective function in the decision process. Therefore, it is demonstrated that the suitable multiple criteria decision making analysis method with decision solution provides an effective objective function for the decision making analysis. Considering that the inherent uncertainties and the weighting factors have crucial decision impacts on the fighter aircraft evaluation, seven fighter aircraft models for the multiple design criteria in terms of the weighting factors are constructed. The proposed multiple criteria decision making analysis model is based on integrated entropy index procedure, and additive multiple criteria decision making analysis theory. Hence, the applicability of proposed technique for fighter aircraft selection problem is considered. The constructed multiple criteria decision making analysis model can provide efficient decision analysis approach for uncertainty assessment of the decision problem. Consequently, the fighter aircraft alternatives are ranked based their final evaluation scores, and sensitivity analysis is conducted.
Abstract: There has been an increased number of immigrants arriving in Canada and a concurrent rise in the number of immigrant youth suffering from drug abuse. Immigrant youths’ drug abuse has become a significant social and public health concern for researchers. This paper explores the nature of immigrant youths’ drug abuse by examining the factors influencing the onset of substance misuse, the barriers that discourage youth to seek out treatment, and how to resolve addictions amidst immigrant youth. Findings demonstrate that diminished parental supervision, acculturation challenges, peer conformity, discrimination, and ethnic marginalization are all significant factors influencing youth to use drugs as an outlet for their pain, while culturally incompetent care and fear of family and culture-based addiction stigma act as barriers discouraging youth from seeking out addiction support. To resolve addiction challenges amidst immigrant youth, future research should focus on promoting and implementing culturally sensitive practices and psychoeducational initiatives into immigrant communities and within public health policies.
Abstract: This paper aims to present an objective quantitative methodology on how to evaluate individual’s scholarly research output using multiple criteria decision analysis. A multiple criteria decision making analysis (MCDMA) methodological process is adopted to build a multiple criteria evaluation model. With the introduction of the scholar index, which gives significant information about a researcher's productivity and the scholarly impact of his or her publications in a single number (s is the number of publications with at least s citations); cumulative research citation index; the scholar index is included in the citation databases to cover the multidimensional complexity of scholarly research performance and to undertake objective evaluations with scholar index. The scholar index, one of publication activity indexes, is analyzed by considering it to be the most appropriate sciencemetric indicator which allows to smooth over many drawbacks of scholarly output assessment by mere calculation of the number of publications (quantity) and citations (quality). Hence, this study includes a set of indicators-based scholar index to be used for evaluating scholarly researchers. Google Scholar open science database was used to assess and discuss scholarly productivity and impact of researchers. Based on the experiment of computing the scholar index, and its derivative indexes for a set of researchers on open research database platform, quantitative methods of assessing scholarly research output were successfully considered to rank researchers. The proposed methodology considers the ranking, and the selection of data on which a scholarly research performance evaluation was based, the analysis of the data, and the presentation of the multiple criteria analysis results.
Abstract: The acquisition of the correct pronunciation in Chinese is closely linked to the initial phase of the study. Based on the contrastive analysis, we determine the differences in the pronunciation of stop consonants in Chinese and Slovak taking into consideration the place and manner of articulation to gain a better understanding of the students' main difficulties in the process of acquiring correct pronunciation of Chinese stop consonants. We employ the software Praat for the analysis of the recorded samples with an emphasis on the pronunciation of the students with a varying command of Chinese. The comparison of the voice onset time (VOT) length for the individual consonants in the students' pronunciation and the pronunciation of the native speaker exposes the differences between the correct pronunciation and the deviant pronunciation of the students.
Abstract: A multi-beam BTS (Base Transceiver Station) antenna has been developed using dual parabolic cylindrical reflectors. The ±45° polarization feeds are used in spatial diversity MIMO (Multi-Input Multi-Output). They can be replaced by single-port orthogonally polarized feeds. Then, with two sets of beams generated above each other, the ± 45° polarization ports of any conventional transceiver can be connected to two of these beam sets. Thus, with two-port transceivers, the system will be equivalent to 4x4 MIMO, instead of 2x2. Radio Frequency (RF) power combiners/splitters can also be used to combine the multiple beams into a single beam or any arbitrary number of beams/ports. The gain of the combined-beam will be more than 20-24 dBi instead of 17-18 dBi of conventional wide-beam antennas. Furthermore, the gain of the combined beam will be high over the whole beam angle. Moreover, the users will always be close to the peak gain value of the combined beam regardless of their location within the combined beam angle. The frequency bands of all the combined beams are adjusted such that they all have the same frequency band. Different configurations of RF power splitter/combiners can be used to provide any arbitrary number of beams/ports according to the requirements of any existing base station configuration.
Abstract: The notion of Threshold Concepts has offered a fertile new perspective on the transformative effects of mastery of particular concepts on student understanding of subject matter and their developing identities as inductees into disciplinary discourse communities. Only by successfully traversing essential knowledge thresholds can neophytes achieve the more sophisticated understandings of subject matter possessed by mature members of a discipline. This paper uses thematic analysis of disciplinary guiding principles to identify nine candidate Threshold Concepts that appear to underpin effective TESOL practice. The relationship between these candidate TESOL Threshold Concepts, TESOL principles, and TESOL instructional techniques appears to be amenable to a schematic representation based on superordinate categories of TESOL practitioner concern and, as such, offers an alternative to the view of Threshold Concepts as a privileged subset of disciplinary core concepts. The paper concludes by exploring the potential of a Threshold Concepts framework to productively inform TESOL initial teacher education (ITE) and in-service education and training (INSET).
Abstract: t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embedding. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic, and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n2) to O(n2/k), and the memory requirement from n2 to 2(n/k)2 which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.
Abstract: Atmospheric carbon dioxide (CO2) has dominated the discussion around the causes of climate change. This is a reflection of a 100-year time horizon for all greenhouse gases that became a norm. The 100-year time horizon is much too long – and yet, almost all mitigation efforts, including those set in the near-term frame of within 30 years, are still geared toward it. In this paper, we show that for a 30-year time horizon, methane (CH4) is the greenhouse gas whose radiative forcing exceeds that of CO2. In our analysis, we use the radiative forcing of greenhouse gases in the atmosphere, because they directly affect the rise in temperature on Earth. We found that in 2019, the radiative forcing (RF) of methane was ~2.5 W/m2 and that of carbon dioxide was ~2.1 W/m2. Under a business-as-usual (BAU) scenario until 2050, such forcing would be ~2.8 W/m2 and ~3.1 W/m2 respectively. There is a substantial spread in the data for anthropogenic and natural methane (CH4) emissions, along with natural gas, (which is primarily CH4), leakages from industrial production to consumption. For this reason, we estimate the minimum and maximum effects of a reduction of these leakages, and assume an effective immediate reduction by 80%. Such action may serve to reduce the annual radiative forcing of all CH4 emissions by ~15% to ~30%. This translates into a reduction of RF by 2050 from ~2.8 W/m2 to ~2.5 W/m2 in the case of the minimum effect that can be expected, and to ~2.15 W/m2 in the case of the maximum effort to reduce methane leakages. Under the BAU, we find that the RF of CO2 will increase from ~2.1 W/m2 now to ~3.1 W/m2 by 2050. We assume a linear reduction of 50% in anthropogenic emission over the course of the next 30 years, which would reduce the radiative forcing of CO2 from ~3.1 W/m2 to ~2.9 W/m2. In the case of "net zero," the other 50% of only anthropogenic CO2 emissions reduction would be limited to being either from sources of emissions or directly from the atmosphere. In this instance, the total reduction would be from ~3.1 W/m2 to ~2.7 W/m2, or ~0.4 W/m2. To achieve the same radiative forcing as in the scenario of maximum reduction of methane leakages of ~2.15 W/m2, an additional reduction of radiative forcing of CO2 would be approximately 2.7 -2.15 = 0.55 W/m2. In total, one would need to remove ~660 GT of CO2 from the atmosphere in order to match the maximum reduction of current methane leakages, and ~270 GT of CO2 from emitting sources, to reach "negative emissions". This amounts to over 900 GT of CO2.
Abstract: Understanding and using the functional paradigm is a challenge for many programmers. Looking for logical errors in code may take a lot of a developer’s time when a program grows in size. In order to facilitate both processes, this paper presents HaskellFL, a tool that uses fault localization techniques to locate a logical error in Haskell code. The Haskell subset used in this work is sufficiently expressive for those studying Functional Programming to get immediate help debugging their code and to answer questions about key concepts associated with the functional paradigm. HaskellFL was tested against Functional Programming assignments submitted by students enrolled at the Functional Programming class at the Federal University of Minas Gerais and against exercises from the Exercism Haskell track that are publicly available in GitHub. This work also evaluated the effectiveness of two fault localization techniques, Tarantula and Ochiai, in the Haskell context. Furthermore, the EXAM score was chosen to evaluate the tool’s effectiveness, and results showed that HaskellFL reduced the effort needed to locate an error for all tested scenarios. The results also showed that the Ochiai method was more effective than Tarantula.
Abstract: The most important process of the water treatment plant process is coagulation, which uses alum and poly aluminum chloride (PACL). Therefore, determining the dosage of alum and PACL is the most important factor to be prescribed. This research applies an artificial neural network (ANN), which uses the Levenberg–Marquardt algorithm to create a mathematical model (Soft Jar Test) for chemical dose prediction, as used for coagulation, such as alum and PACL, with input data consisting of turbidity, pH, alkalinity, conductivity, and, oxygen consumption (OC) of the Bangkhen Water Treatment Plant (BKWTP), under the authority of the Metropolitan Waterworks Authority of Thailand. The data were collected from 1 January 2019 to 31 December 2019 in order to cover the changing seasons of Thailand. The input data of ANN are divided into three groups: training set, test set, and validation set. The coefficient of determination and the mean absolute errors of the alum model are 0.73, 3.18 and the PACL model are 0.59, 3.21, respectively.
Abstract: Due to the concerns about the depletion of fossil fuel sources and the deteriorating environment, the attempt to investigate the production of renewable energy will play a crucial role as a potential to alleviate the dependency on mineral fuels. One particular area of interest is generation of bio-oil through sewage sludge (SS) pyrolysis. SS can be a potential candidate in contrast to other types of biomasses due to its availability and low cost. However, the presence of high molecular weight hydrocarbons and oxygenated compounds in the SS bio-oil hinders some of its fuel applications. In this context, catalytic pyrolysis is another attainable route to upgrade bio-oil quality. Among different catalysts (i.e., zeolites) studied for SS pyrolysis, activated chars (AC) are eco-friendly alternatives. The beneficial features of AC derived from SS comprise the comparatively large surface area, porosity, enriched surface functional groups and presence of a high amount of metal species that can improve the catalytic activity. Hence, a sludge-based AC catalyst was fabricated in a single-step pyrolysis reaction with NaOH as the activation agent and was compared with HZSM5 zeolite in this study. The thermal decomposition and kinetics were invested via thermogravimetric analysis (TGA) for guidance and control of pyrolysis and catalytic pyrolysis and the design of the pyrolysis setup. The results indicated that the pyrolysis and catalytic pyrolysis contain four obvious stages and the main decomposition reaction occurred in the range of 200-600 °C. Coats-Redfern method was applied in the 2nd and 3rd devolatilization stages to estimate the reaction order and activation energy (E) from the mass loss data. The average activation energy (Em) values for the reaction orders n = 1, 2 and 3 were in the range of 6.67-20.37 kJ/mol for SS; 1.51-6.87 kJ/mol for HZSM5; and 2.29-9.17 kJ/mol for AC, respectively. According to the results, AC and HZSM5 both were able to improve the reaction rate of SS pyrolysis by abridging the Em value. Moreover, to generate and examine the effect of the catalysts on the quality of bio-oil, a fixed-bed pyrolysis system was designed and implemented. The composition analysis of the produced bio-oil was carried out via gas chromatography/mass spectrometry (GC/MS). The selected SS to catalyst ratios were 1:1, 2:1 and 4:1. The optimum ratio in terms of cracking the long-chain hydrocarbons and removing oxygen-containing compounds was 1:1 for both catalysts. The upgraded bio-oils with HZSM5 and AC were in the total range of C4-C17 with around 72% in the range of C4-C9. The bio-oil from pyrolysis of SS contained 49.27% oxygenated compounds while the presence of HZSM5 and AC dropped to 7.3% and 13.02%, respectively. Meanwhile, generation of value-added chemicals such as light aromatic compounds were significantly improved in the catalytic process. Furthermore, the fabricated AC catalyst was characterized by BET, SEM-EDX, FT-IR and TGA techniques. Overall, this research demonstrated that AC is an efficient catalyst in the pyrolysis of SS and can be used as a cost-competitive catalyst in contrast to HZSM5.
Abstract: Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., entropy, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one-class classification (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, principal component analysis (PCA), kernel principal component analysis (KPCA), and autoassociative neural network (ANN) are presented and their performance are compared. It is also shown that, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 95%.
Abstract: Since the beginning of the 21st century, we have been observing in some seaside resorts aging demography, combined with an increase in second homes. These seaside resorts are said to have become places undergoing profound changes, leading to hybridization of functions (personal services, health, residential, etc.) and practices. All of these issues are part of the challenges of silver tourism, which stems from the silver economy. The Hauts-de-France region is made up of numerous seaside resorts that have a significant proportion of second homes in their real estate stock. The seaside resorts have tourist offers based on sports and leisure activities. They also offer a suitable environment for the installation of this category of the population. This set of attractive criteria in the choice of installation in seaside resorts is likely to be replaced by personal and health services due to the advanced age of the population. The resorts of Le Touquet Paris-Plage, Bray-Dunes, Neufchâtel-Hardelot and Le Crotoy seem to be evolving towards other functions of residential resorts, as opposed to seaside resorts This paper will be an opportunity to present the results of the surveys we conducted in 4 seaside resorts in the Hauts-de-France region, where more than 420 retired secondary residents were questioned. The results show that nearly 90% of retirees spend their time in their second home at any time of the year. The criteria that lead them there are school vacations and the weather. More than 40% of them have been living there for more than 20 years. The reasons for the installations are the living environment (83%) and the quality of life (79%). Their activities are walking and strolling, as well as sports. More than 99% of the respondents do not take into account the health service offers. Personal services are also little taken into account - around 60% of respondents say they do not know whether personal services exist in the resort. 80% of respondents answer that their grandchildren benefit from activities organized by the commune and the tourist offices during their stay. To conclude, the influx of retired secondary residents will not lead to a change in the functions of the seaside resorts. Their classic tourist offers - leisure and sports activities, the environment - will remain the attractive criteria of the seaside resorts. The results of the study prove that personal services and health services are not the first choice criteria in the installation of retired secondary residents, quite the contrary. We can even complete that retirees in secondary residences are demanding and concerned about living in a calm, safe and clean environment and quality of life.
Abstract: This paper presents the application of fuzzy set theory to implement of mobile advertisement anti-fraud systems. Mobile anti-fraud is a method aiming to identify mobile advertisement fraudsters. One of the main problems of mobile anti-fraud is the lack of evidence to prove a user to be a fraudster. In this paper, we implement an application by using fuzzy set theory to demonstrate how to detect cheaters. The advantage of our method is that the hardship in detecting fraudsters in small data samples has been avoided. We achieved this by giving each user a suspicious degree showing how likely the user is cheating and decide whether a group of users (like all users of a certain APP) together to be fraudsters according to the average suspicious degree. This makes the process more accurate as the data of a single user is too small to be predictable.
Abstract: This study used the methodology of a retrospective review to assess Sino-US relations and foreign relations strategies of Trump and Biden and found that while the Trump administration has ignited a trade war and a technology war with China, the stage is set for the Biden administration as to how it will handle Sino-US relations. We conclude that Biden is apparently tough on China and may counter the influence of China but will seek to maintain strategic cooperation with China on issues of mutual interest and there might be a renegotiation of the trade deal.
Abstract: This paper highlights skills that a leader needs to acquire to lead a team successfully. With an appropriate vision and strategy, a team can be inspired, influenced and easily led. The importance of setting codes of conduct and establishing mutual agreements between the team members can help in minimizing issues and improving overall productivity. Leadership skills include the power of questioning (PoQ), effective communication, identification of team member responsibilities, and assessment of self and the team. This paper will highlight the impact of good leadership on work progress and overall team performance. The paper explains how leaders make correct decisions by avoiding hasty actions that could generate new errors, mistakes, and issues. The importance of positive expectations for the team is addressed in this paper that could result in efficient control of the work with better outcomes.
Abstract: This article outlines the ballistics of main seat ejection cartridges for aircraft application. The ballistics of main seat ejection cartridges plays a vital role during the ejection of the pilot in an emergency. The ballistic parameters such as maximum pressure, time to reach the maximum pressure, and time required to reach half the maximum pressure that responsible to the spinal injury of the pilot are assessed. Therefore, the evaluations of these parameters are very critical during various stages of development. Elaborate testing is carried out for main seat ejection cartridges on seat ejection tower (SET) at different operating temperatures considering physiological limits. As these trials are cumbersome in nature, a vented vessel (VV) testing facility is devised to lay down the performance parameters at hot and cold temperature conditions. Single base (SB) propellant having hepta-tubular configuration is selected as the main filling. Gun powder plays the role of a booster based on ballistic requirements. The evaluation methodology of various performance parameters of main seat ejection cartridges is explained in this paper. Physiological parameters such as maximum seat ejection velocity, acceleration, and rate of rising of acceleration are also experimentally determined on SET. All the parameters are observed well within physiological limits. This paper addresses the internal ballistic of main seat ejection cartridges, propellant selection, its calculation, and evaluation of various performance parameters for aircraft application.