Due to their simplicity, cheapness, and ease of maintenance, induction motors (IMs) are the most widely used motors in the industry. However, if they are not properly controlled, the load torque and motor speed will fluctuate in an unsatisfactory fashion. To effectively control the load torque and speed of these IMs, it is necessary to use specialized drives. The entire system (IMs + Drives) will experience uncertainty, nonlinearities, and disruptions, which calls for an outstanding performance control structure. The sensorless sliding mode predictive torque control (SSM-PTC) for both AC-DC converter and DC-AC inverter, which are utilized for feeding the IM, is investigated in this work. The AC-DC converter is controlled using the SSM-PTC method in order to follow the DC-link reference voltage throughout any changes in the operating point of the IM. While the DC-AC inverter is controlled using a sensorless predictive power control (SPPC). Within a unity power factor, this SPPC regulates the reactive power flow between the motor and the supply to account for the undesirable harmonic components of the grid current. In addition, an experimental performance improvement of SSM-PTC of IM supplied by a 5-leg AC-DC-AC power converter using extended Kalman filter (EKF) without weighting factor (WF) is also studied in this work. Design and implantation of the suggested control systems are performed using a dSPACE 1104 card. The experimental results of the proposed converter control demonstrate that the suggested approach effectively regulated the DC link, reducing load torque and speed fluctuations. In the context of inverter control, a prompt active power response yields a motor current waveform that resembles a sinusoidal pattern, exhibiting minimal levels of harmonic distortion.
Concerns have recently been raised about the validity of scales used in the L2 motivational self system tradition, particularly in relation to sufficient discriminant validity among some of its scales. These concerns highlight the need to systematically examine the validity of scales used in this tradition. In this study, we therefore compiled a list of 18 scales in widespread use and administered them to Korean learners of English ( N = 384). Testing the factorial structure of these scales using multiple exploratory and confirmatory factor-analytic criteria revealed severe discriminant validity issues. For example, the ideal L2 self was not discriminant from linguistic self-confidence, suggesting that participant responses to such ideal L2 self items is not driven by actual–ideal discrepancies as previously presumed but more likely by self-efficacy beliefs. We discuss these results in the context of the need to encourage systematic psychometric validation research in the language motivation field.
Shell and tube heat exchangers are widely used in the oil and gas, petrochemical and nuclear power sectors. The most important aspect of the turnaround is the routine testing and inspection of the in-service heat exchanger. Heat exchangers in use are the primary cause of tube flaws such as pitting, corrosion, erosion, fretting corrosion, crevice corrosion, stress corrosion cracking, wear, galvanic corrosion, fatigue cracking, microbiologically influenced corrosion, and so forth. For research, a seamless nickel–iron–chromium alloy-tubed SB 163 (UNS 8810) heat exchanger was taken. The total number of tubes that need to be inspected is 286 tubes with a specification of 19.05 mm OD × 2.11 mm thickness × 5000 mm length. From the different advanced NDT techniques, the eddy current testing (ECT) was chosen for inspection since it has become one of the most popular NDT methods for locating discontinuities in heat exchanger tubes. A standard calibration tube was used to calibrate the system's response and set the sensitivity. During the inspection, the ECT probe was inserted from the below side up to the other end and collected while pulling the probe from the tube. Inspection data were recorded for the length of each tube. All indications were assessed, and defects were categorized as different extents of wall thickness loss after inspection. The inspection report shows how many tubes were found defective and need to be plugged or removed (retubing). From the inspection data (the result of statistics), defects like corrosion, pitting, wall loss, wear, etc., that resulted in notable material loss were identified. Inspection data also summarized where the defects were located (like between the first baffle and the second baffle) and are helpful for further research. After analysis using ECT reports, the defective tubes were categorized according to their defect percentage and location, which helps to do retubing effectively by avoiding the 100% retubing concept.
This paper explores the epistemological affordances of Islamic ethics as alternative knowledge within intercultural education. Despite the calls for epistemological plurality in intercultural education that centre epis-temologies of the South, educators may find it hard to reaffirm their situated knowledges and practices because they may have been overwhelmed by the wide endorsements of the mainstream literature. Drawing on in-depth interviews with 25 EFL teachers, this study aims to (a) unpack educators' perspectives around the adoption of alternative knowledges anchored in local epistemologies and sensibilities, (b) foreground educators' epistemic positioning around alternative knowledges and how they are perceived as sites for cognitive and pedagogical renewal to account for local particularities and conditions and (c) examine inter-epistemic tensions within educators' reasoning in terms of how they navigate (in)congruencies between the mainstream and Islamic philosophy at the conceptual, pedagogical and practical levels. Findings reveal that educators acknowledge the legitimacy of Islamic ethics and their epistemological/pedagogical significance in intercultural education. However, some factors may problematize educators' attempts at making use of Islamic ethics including the additional burden of reflecting alternative knowl-edges while attending to contextual factors (class size, the course's orientation, exams, time constraints, etc.) and the lack of sufficient training in intercultural education.
Flipped learning has become a popular approach in various educational fields, including second language teaching. In this approach, the conventional educational process is reversed so that learners do their homework and prepare the material before going to class. Class time is then devoted to practice, discussion, and higher-order thinking tasks in order to consolidate learning. In this article, we meta-analysed 56 language learning reports involving 61 unique samples and 4,220 participants. Our results showed that flipped classrooms outperformed traditional classrooms, g = 0.99, 95% CI (0.81, 1.17), z = 10.90, p < .001. However, this effect had high heterogeneity (about 86%), while applying the Trim and Fill method for publication bias made it shrink to g = 0.58, 95% CI (0.37, 0.78). Moderator analysis also showed that reports published in non-SSCI-indexed journals tended to find larger effects compared to indexed ones, conference proceedings, and university theses. The effect of flipped learning did not seem to vary by age, but it did vary by proficiency level in that the higher proficiency the higher the effects. Flipped learning also had a clear and substantial effect on most language outcomes. In contrast, whether the intervention used videos and whether the platform was interactive did not turn out to be significant moderators. Meta-regression showed that longer interventions resulted in only a slight reduction in the effectiveness of this approach. We discuss the implications of these findings and recommend that future research moves beyond asking whether flipped learning is effective to when and how its effectiveness is maximized.
In recent years, the scientific community has called for improvements in the credibility, robustness and reproducibility of research, characterized by increased interest and promotion of open and transparent research practices. While progress has been positive, there is a lack of consideration about how this approach can be embedded into undergraduate and postgraduate research training. Specifically, a critical overview of the literature which investigates how integrating open and reproducible science may influence student outcomes is needed. In this paper, we provide the first critical review of literature surrounding the integration of open and reproducible scholarship into teaching and learning and its associated outcomes in students. Our review highlighted how embedding open and reproducible scholarship appears to be associated with (i) students' scientific literacies (i.e. students’ understanding of open research, consumption of science and the development of transferable skills); (ii) student engagement (i.e. motivation and engagement with learning, collaboration and engagement in open research) and (iii) students' attitudes towards science (i.e. trust in science and confidence in research findings). However, our review also identified a need for more robust and rigorous methods within pedagogical research, including more interventional and experimental evaluations of teaching practice. We discuss implications for teaching and learning scholarship.
Multisystem inflammatory syndrome in children (MIS-C) is a relatively new syndrome associated with coronavirus disease 2019 (COVID-19) that is characterized by a severe clinical course compared to pediatric
The need for energy has significantly increased in the world in recent years. Various research works were presented to develop Renewable Energy Sources (RESs) as green energy Distributed Generations (DGs) to satisfy this demand. In addition, alleviating environmental problems caused by utilizing conventional power plants is diminished by these renewable sources. The optimal location and size of the DG-RESs significantly affect the performance of Radial Distribution Systems (RDSs) through the fine bus voltage profile, senior power quality, low power losses, and high efficiency. This paper investigates the use of PV (photovoltaic) and (Wind Turbine) WT systems as a DG source in RDSs. This investigation is presented via the optimal location and size of the PV and WT systems, which are the most used DG sources. This optimization problem aims to maximize system efficiency by minimizing power losses and improving both voltage profile and power quality using White Shark Optimization (WSO). This algorithm emulates the attitude of great white sharks when foraging using their senses of hearing and smell. It confirms the balance between exploration and exploitation to discover optimization that is considered as the main advantage of this approach in attaining the global minimum. To assess the suggested approach, three common RDSs are utilized, namely, IEEE 33, 69, and 85 node systems. The results prove that the applied WSO approach can find the best location and size of the RESs to reduce power loss, ameliorate the voltage profile, and outlast other recent strategies. Adding more units provides a high percentage of reducing losses by at least 93.52% in case of WTs, rather than 52.267% in the case of PVs. Additionally, the annual saving increased to USD 74,371.97, USD 82,127.257, and USD 86,731.16 with PV penetration, while it reached USD 104,872.96, USD 116,136.57, and USD 155,184.893 with WT penetration for the 33, 69, and 85 nodes, respectively. In addition, a considerable enhancement in the voltage profiles with the growth of PV and WT units was confirmed. The ability of the suggested WSO for feasible implementation was validated and inspected by preserving the restrictions and working constraints.
Residential buildings are the major sector in the construction industry that consume energy and affect the whole environment. Controlling these effects has always been the foremost concern, especially the existing ones. This paper addresses a case study of existing residential buildings at Jubail Industrial City in the Kingdom of Saudi Arabia, where the units are reproduced all over the districts.With proper design and maintenance, green roofs can provide cost-effective and sustainable solutions for existing residential buildings. The best set of strategies through the Climate Consultant software have been studied where the strategy of adding the green roof comes as a priority for the selected city. Green roofs, also known as vegetated roofs or rooftop gardens, offer a wide range of benefits for existing residential buildings. These benefits include improved insulation, reduction of stormwater runoff, increased biodiversity, and improved air quality. In addition, green roofs can also provide aesthetic and recreational benefits for residents. Green roofs can reduce a building’s energy consumption, lower urban heat island effects, and extend the roof's life.By using the green roofs suitable type for the existing buildings and simulating the two scenarios for the existing and the proposed cases using the Design Builder software, while using Climate Consultant software recommends strategies, results revealed that the green roof option could reduce the total energy consumption by at least 8.8 %. Besides benefiting the building's users, this approach will provide an economical solution to protect the environment in terms of reducing power consumption and environmental pollution.
WATCH THE VIDEO: https://www.bilibili.com/video/BV1FS4y1j7ed - In contemporary methodological thinking, replication holds a central place. However, relatively little attention has been paid to replication in the context of complex dynamic systems theory (CDST), perhaps due to uncertainty regarding the epistemology-methodology match between these domains. In this paper, we explore the place of replication in relation to open systems and argue that three conditions must be in place for replication research to be effective: results interpretability, theoretical maturity, and termino-logical precision. We consider whether these conditions are part of the applied linguistics body of work, and then propose a more comprehensive framework centering on what we call SUBSTANTIATION RESEARCH, only one aspect of which is replication. Using this framework, we discuss three approaches to dealing with replication from a CDST perspective theory. These approaches are moving from a representing to an intervening mindset, from a comprehensive theory to a mini-theory mindset, and from individual findings to a cumulative mindset.
Survival in the early life stages is a major factor determining the growth and stability of wildlife populations. For sea turtles, nest location must provide favorable conditions to support embryonic development. Hatching success and incubation environment of green turtle eggs were examined in July 2019 at Karan Island, a major nesting site for the species in the Arabian Gulf. Mean hatching success averaged at 38.8 % (range = 2.5-75.0 %, n = 14). Eggs that suffered early embryonic death (EED) and late embryonic death (LED) represented 19.8 % (range: 3.3-64.2 %) and 41.4 % (range: 4.8-92.6 %) of the clutch on average, respectively. Nest sand was either coarse (0.5-1 mm: mean 44.8 %, range = 30.4-56.9 % by dry weight, n = 14) or medium (0.25-0.5 mm: mean 33.6 %, range = 12.0-45.5 % by dry weight, n = 14). Mean sand moisture (4.0 %, range = 3.2-4.9 %, n = 14) was at the lower margin for successful development. Hatching success was significantly higher in clutches with sand salinity <1500 EC.uS/cm (n = 5) than those above 2500 EC.uS/cm (n = 5). Mean clutch temperatures at 1200 h increased by an average of 5.4 °C during the 50-d post-oviposition from 31.2 °C to 36.6 °C. Embryos experienced lethally high temperatures in addition to impacts of other environmental factors (salinity, moisture, sand grain size), which was related to reduced hatching success. Conservation initiatives must consider the synergistic influence of the above parameters in formulating strategies to improve the overall resilience of the green turtle population in the Arabian Gulf to anthropogenic and climate change-related stressors.
Fog computing, which is an extension of cloud computing is one of the cornerstone for Internet of Things, that witnessed rapid growth because of its ability to enhance several difficult problems such as network congestion, latency, and the lack of regional autonomy. However, privacy concerns and the resulting inefficiency are causing the performance of fog computing to suffer. While suffering from poisoning attacks, the vast majority of current works do not take into consideration of proper balance between them. Specifically, we present blockchain‐enabled secure federated learning in vehicle network (BSFLVN) system model for traffic flow prediction in urban computing to overcome the aforementioned difficulties and narrow the gap. When vehicle devices trade local learning updates, BSFLVN enables them to be exchanged with a blockchain‐based global learning model, which is confirmed by miners. To improve accuracy of classification we proposed federated learning‐based gated recurrent unit for local model update using FL‐GRNN algorithm and global model update using FL‐AGG algorithm. The BSFLVN, which is built on top of this, allows autonomous deep learning‐based GRU to take place without the need for a centralized authority to maintain the global model and coordinate by using the PoW consensus mechanism of blockchain. For preserving privacy and security of local and global model updates we employ the LDP mechanism. For the analysis of the latency performance of BSFLVN, as well as the derivation of the best block production rate we consider communication, consensus delays, and computing cost. The results of a thorough examination demonstrate that BSFLVN outperforms its competitors in terms of privacy protection, efficiency, and resistance to poisoning attacks, among other areas. Various deadline time iteration process is simulated for result evaluation using Fashion MINIST data in which over 93% of accuracy is obtained.
Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions.
In this study, we investigated two core understandings related to the nature of L2 student engagement from a self-determination theory framework: (1) that there is a dual process of development that follows from the initial conditions of the L2 classroom environment (specifically teacher motivational practice: autonomy-supportive vs. controlling), which leads to two distinct motivational experiences (need satisfaction vs. need frustration), which in turn results in qualitatively different types of student classroom functioning (engagement vs. disengagement); and (2) that there are reciprocal effects between the L2 classroom environment and student classroom functioning. We collected data from 1,742 students enrolled in general-purpose postsecondary English courses in mainland China at three waves in a 17-week semester, and tested a longitudinal dual-process, reciprocal-effects model. Our analyses showed that student perceptions of teacher motivational practice at the beginning of the semester predicted psychological need satisfaction at later time points. Psychological need satisfaction in turn predicted later classroom engagement. Student engagement also had feedback effects and predicted subsequent perceptions of teacher motivational practice. We discuss the implications of these results for L2 learning and teaching and consider ways that future research might build on our design and extend these findings.
Recent technological advances in artificial intelligence (AI) have paved the way for improved and in many cases the creation of entirely new and innovative, electronic writing tools. These writing support systems assist during and after the writing process making them indispensable to many writers in general and to students in particular who can get human-like sentence completion suggestions and text generation. Although the wide adoption of these tools by students has been faced with a steady growth of scientific publications in the field, the results of these studies are often contradictory and their validity may be questioned. To gain a deeper understanding of the validity of AI-powered writing assistance tools, we conducted a systematic review of the recent empirical AI-powered writing assistance studies. The purpose of this review is twofold. First, we wanted to explore the recent scholarly publications that evaluated the use of AI-powered writing assistance tools in the classroom in terms of their types, uses, limits, and potential for improving students’ writing skills. Second, the review also sought to explore the perceptions of educators and researchers about learners’ use of AI-powered writing tools and review their recommendations on how to best ingrate these tools into the contemporary and future classroom. Using the Scopus research database, a total of 104 peer-reviewed papers were identified and analyzed. The findings indicate that students are increasingly using a variety of AI-powered writing assistance tools for improving their writing. The tools they are using can be categorized into four main groups: (1) automated writing evaluation tools, (2) tools that provide automated writing corrective feedback, (3) AI-powered machine translators, and (4) GPT-3 automatic text generators. The analysis also highlighted the scholars’ recommendations regarding dealing with learners’ use of AI-powered writing assistance tools and grouped the recommendations into two groups for researchers and educators.
Photovoltaic (PV) systems are one of the promising renewable energy sources that have many industrial applications; one of them is water pumping systems. This paper proposes a new application of a PV system for water pumping using a three-phase induction motor while maximizing the daily quantity of water pumped while considering maximizing both the efficiency of the three-phase induction motor and the harvested power from the PV system. This harvesting is performed through maximum power point tracking (MPPT) of the PV system. The proposed technique is applied to a PV-powered 3 phase induction motor water pumping system (PV-IMWPS) at any operating point. Firstly, an analytical approach is offered to find the optimal firing pattern of the inverter (V-F) for the motor through optimal flux control. This flux control is presented for maximizing the pump flow rate while achieving MPPT for the PV system and maximum efficiency of the motor at any irradiance and temperature. The provided analytical optimal flux control is compared to a fixed flux one to ascertain its effectiveness. The obtained feature of the suggested optimal flux control validates a significant improvement in the system performances, including the daily pumped quantity, motor power factor, and system efficiency. Then converting the data from the first analytical step into an intelligent approach using an adaptive neuro-fuzzy inference system (ANFIS). This ANFIS is trained offline with the input (irradiance and temperature) while the output is the inverter pattern to enhance the performance of the proposed pumping system, PV-IMWPS.
Background : Happiness at work is an important factor in employee satisfaction, productivity and retention. This study aimed to investigate the relationship between perceived manager’s emotional intelligence and happiness at work, and whether job satisfaction and affective organizational commitment mediate this relationship. Method: A questionnaire was distributed online to a random sample of 350 schoolteachers in Saudi public schools teaching different majors. Results: Structural equation modelling results showed that satisfaction and affective organizational commitment fully mediated the relationship between perceived manager’s emotional intelligence and happiness at work. Conclusion: Our results support the hypothesis that perceived manager’s emotional intelligence influences employee happiness through its influence on increasing or decreasing job satisfaction and affective organizational commitment. These findings therefore provide insight into employee’s wellbeing and potentially how to promote it.
Social networking websites are now considered to be the best platforms for the dissemination of news articles. However, information sharing in social media platforms leads to explosion of fake news. Traditional detection methods were focusing on content analysis, while the current researchers examining social features of the news. In this work, we proposed a novel artificial intelligence (AI)-assisted fake news detection with deep natural language processing (NLP) model. The proposed work is characterized in four layers: publisher layer, social media networking layer, enabled edge layer, and cloud layer. In this work, four steps were carried out: 1) data acquisition; 2) information retrieval (IR); 3) NLP-based data processing and feature extraction; and 4) deep learning-based classification model that classifies news articles as fake or real using credibility score of publishers, users, messages, headlines, and so on. Three datasets, such as Buzzface, FakeNewsNet, and Twitter, were used for evaluation of the proposed model, and simulation results were computed. This proposed model obtained an average accuracy of 99.72% and an $F1$ score of 98.33%, which outperforms other existing methods.
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