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The ability to use actuator and load models online in combination with experimental data becomes increasingly important in future electrical drives. Among the applications of such online model in torque and flux controllers, model predictive control (MPC) has gained increasing interest, allowing to deal in real time and in a suboptimal way with drive variations captured by the model used. For such ability, sufficient processing power is required to handle the model in real time, which appears to be less of a problem in recent drives. This article contributes to the current prediction in MPC, applied to an interior permanent-magnet synchronous machine (IPMSM) drive. The study aims at improving the prediction, through an online estimation of the current time gradient coefficients, and this by considering consecutive space vector step responses. Through measuring the resulting current responses, an online correction of the current gradients is realized. A two-step method, which consists of an estimation step and a prediction step, will be described in order to improve the current prediction. Experimental results obtained from a 3.7-kW IPMSM drive will verify that the proposed geometrical approach effectively improves the conventional short window current predictions.

Finite control set model predictive control (FCS-MPC), especially the reference-voltage-vector-based model predictive control (MPC) (RVV-MPC), considers a limited set of input vectors. However, the control performance is not satisfactory enough due to a severe lack of voltage input variants. In particular, additional restraints, such as limiting the currents within setting boundaries, could not be supported with RVV-MPC. In this article, to truly comply with the boundary settings, a reference variant FCS-MPC approach is introduced. In the proposed strategy, reference currents are considered under the maximum torque per current (MTPC) control. As boundary settings could not be supported in RVV-MPC, it is better to search for the optimal current reference that is feasible within the imposed boundaries. Therefore, rather than fixing the current reference, a well-considered neighborhood of the MTPC reference is introduced, and these variants are processed, resulting in a set of optimal candidate inputs. As multiple, but still finite, optimal solutions become possible, additional efforts can be made within the objective function to restrain the current and reduce the torque ripple. The size of the current reference space can be automatically adapted to generate current references that correspond to truly bounded current vectors. A trade-off is then made between control performance and meeting the boundary settings.

In this article, a novel torque boundary-based model predictive torque control (MPTC) is proposed to improve the torque and flux performance in permanent magnet synchronous motor (PMSM) drives by straightforwardly reducing the corresponding ripples. For this purpose, an optimal sequential list of two voltage vectors is considered as candidate input and the time durations of the two vectors to be applied are determined based on a predefined torque ripple tolerance band. Considering the torque outcomes both at the switching instant and at the end of the control period, the torque can be limited within the upper and lower boundaries of the preset tolerance band during the whole control period. Meanwhile, by monitoring the number of defined valid vector sequences, the torque boundaries can be optimized online. As a result, the torque ripple can be restrained to a fairly small range, and the torque boundary design work is avoided. In addition, according to the predicted torque outcomes of the candidate inputs, a group of vector sequences can be excluded from the control set before optimization, reducing the computation cost. Moreover, due to the preset torque ripple tolerance, the weighting factor in the cost function is eliminated, thereby avoiding the corresponding tuning work. Experimental results are presented to reveal the effectiveness of the proposed strategy.

Cobalt carbides nanoparticles have great potential as novel materials in magnetic fields. However, it is difficult to synthesize single phase cobalt carbides, especially Co3C. In this paper, we propose a one‐pot wet‐chemistry approach to synthesize single phase ferromagnetic Co3C without using any alkali as nucleation solvent. The particle size of Co3C nanoparticle is about 3 nm. The magnetic properties of Co3C nanoparticles are measured at 300 K from –30 to 30 kOe. Compared with Co2C, Co3C exhibits a large saturation magnetization of 46.1 emu g–1 and a high coercivity of 1.5 kOe. Small amount of carbon and small particle size of the Co3C nanoparticle are believed to contribute to large magnetization and high coercivity. Magnetic measurements also reveal the spin glass behavior caused by the small Co3C nanoparticle at the spin freezing temperature of 43 K. This work provides a facile method to synthesize single phase Co3C nanoparticle which is applicable in magnetic field.

Introduction: Information has the power to protect against unexpected events and control any crisis such as the COVID-19 pandemic Since COVID-19 has already rapidly spread all over the world, only technology-driven data management can provide accurate information to manage the crisis This study aims to explore the potential of big data technologies for controlling COVID-19 transmission and managing it effectively Methods: A systematic review guided by PRISMA guidelines has been performed to obtain the key elements Results: This study identified the thirty-two most relevant documents for qualitative analysis This study also reveals 10 possible sources and 8 key applications of big data for analyzing the virus infection trend, transmission pattern, virus association, and differences of genetic modifications It also explores several limitations of big data usage including unethical use, privacy, and exploitative use of data Conclusions: The findings of the study will provide new insight and help policymakers and administrators to develop data-driven initiatives to tackle and manage the COVID-19 crisis [ABSTRACT FROM AUTHOR] Copyright of Archives of Medical Science is the property of Termedia Publishing House and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )

Cobalt is an important catalyst for the Fischer–Tropsch synthesis (FTS) reaction. We present a pretreatment process to enhance the activity of cobalt catalyst by carburizing it with an intermediate time. Cobalt catalyst carburized for 0.5 h showed higher activity than the cobalt catalyst without carburization and cobalt catalyst carburized for 2 h. TEM analysis revealed the existence of different phases of Co3C, Co, and Co2C for these catalysts, with Co3C showing high activity for the FTS reaction. With the help of DFT calculations, we assessed the structure–performance relationships among the Co3C, Co, and Co2C, and found that Co3C was more active for CO activation. Our study provides a new idea to rationally design Co-based FTS catalysts with high activity.

Environmental cost refers to the cost paid by enterprises to reduce environmental pollution and resource depletion in production and operation. To help enterprises reduce environmental costs, a manufacturing environmental cost control algorithm based on machine learning is proposed. The probabilistic neural network is used to classify the current environmental cost control level of different manufacturing enterprises. Then, the particle swarm optimization (PSO) algorithm is improved to build a multi-objective backbone PSO algorithm for multi-objective decision-making, which is used in the selection of environmental cost control methods. The experimental results show that there is a strong correlation between the original data classification and the proposed probabilistic neural network, and the correlation reaches 96.1%. PSO performance test results show that the algorithm has the best performance, the best stability, and the shortest time needed to find the optimal solution set when the initial particle number is 140 and the number of iterations is 60. Based on the comprehensive experimental results, the following conclusions are drawn. Enterprises should strengthen collaboration and cooperation with customers, suppliers, and waste-profiting enterprises, so as to well control environmental costs. To sum up, the proposed model provides some references for the adoption of machine learning in environmental cost control of manufacturing enterprises.

Hall sensors provide significant commutation signals for brushless DC (BLDC) motors. However, installation errors of Hall sensors are almost inevitable for BLDC motors, which can lead to commutation errors, further current ripples and torque ripples. Meanwhile, abnormal diode conducting in inactive phase can be aroused by large commutation errors at high speed. Therefore, the commutation errors make a negative influence on motor efficiency and degrade the performance of BLDC motors. Here, a simple and fast method to estimate the commutation error is proposed. Based on the analysis of the commutation process under commutation error, only the turn-off phase current at the commutation instant is required for the estimation of delayed commutation error, while only the time interval of the turn-off phase current decreasing to zero after commutation instant is needed when advanced commutation occurs. In order not to affect the dynamic performance of a BLDC motor, this study utilises a straightforward compensating method of commutation error. The experimental results represent that the proposed method is effective at different speeds, load torques, and under load-variation with good transient performance.

The field of educational leadership tends to focus on formal schooling policy and process. Today, however, this emphasis seems incomplete, given people’s growing capacity to learn, educate, and lea...

PurposeThe purpose of this paper is, first, to examine student perspectives of their university experience in terms of the soft employability skills they develop; second, how prepared those students feel for the future employment market and finally investigate whether there are differences in perceptions between Chinese and Malaysian students given their different educational experience.Design/methodology/approachIn this study, 361 predominantly Chinese undergraduate students at two universities, one in China and the other in Malaysia completed the 15-item Goldsmiths soft skills inventory using an online survey.FindingsThe results, analysed using factor analysis and confirmatory factor analysis, indicated that the university curriculum develops student soft skills, particularly in the Malaysian university and supports the relationship between soft skill and student preparedness for employment. The results also indicate that compared with the respondents from the Chinese university, the Malaysian university respondents were more likely to be positive to statements concerning their respective university’s ability to develop their soft skills.Research limitations/implicationsSuch findings have implications for education providers and business in that it is important for universities to embed soft skills into the curriculum in order to develop graduate work readiness.Originality/valueWhat this research contributes is not only consolidation of existing research in the contemporary context of a disruptive jobs market, it takes research forward through analysing student perceptions from two universities, one in Malaysia and the other in China, of the skills they develop at university and the importance of soft skills to them and their perceptions of future employment and employability. Such research will provide insight, in particular, into the role of education providers, the phenomena of underemployment among graduates in China, and be of practical significance to employers and their perception that graduates lack the necessary soft skills for the workplace (Anonymous, 2017a; Stapleton, 2017; British Council, 2015; Chan, 2015).

Model-based predictive control that highly depends on system parameters is widely investigated. In contrast, model-free predictive current control (MFPCC) can be performed based on input or output measured data, rather than on any system model information, hence eliminating the influence of parameter uncertainties. In such a strategy, the current gradients due to each of the possible voltage vectors are stored and used to predict future currents. Current gradient knowledge therefore provides a significant foundation for MFPCC. In conventional MFPCC, however, the stagnation existing in the current gradient update always impacts the reliability of current gradients and further worsens the control performance. In this article, an improved MFPCC with an advanced current gradient updating mechanism is proposed. In the proposed strategy, to guarantee the reliability of the current gradients, two contiguous measured current gradients due to the applied voltage vectors are used to estimate the current gradients for all the possible voltage vectors. This simple method takes advantage of the mathematical relationships between the voltage vectors. In this way, all the current gradients can be obtained within one control period, effectively reducing the stagnation effect in conventional MFPCC. The proposed MFPCC scheme is evaluated on a permanent magnet synchronous machine drive setup to demonstrate its effectiveness.

This paper proposes a method to minimize rotor position estimation error caused by cross-saturation effect when the permanent magnet synchronous machine rotates under a high load. A sensorless method for finite control set model predictive control (FCS-MPC) based on current responses is analyzed, the location deviation of the current response point caused by the high loading distortion is studied and the location deviation is used to evaluate the rotor position estimation error. Then, the estimation error can be compensated to decrease the high loading distortion effect on rotor position estimation. The effectiveness of the proposed method is verified by the experimental results.

As a global concern, disaster resilience is considering as the priority sector of almost all countries in the world. This study intends to explore the potential of big data for disaster management through increasing resilience against socio-ecological vulnerability. A qualitative approach focusing desk review and secondary data have been used to substantiate the arguments. This study argues that disaster resilience is an integrated function of the adaptive, absorptive and transformative capacity of an individual or society to withstand and cope with the adverse effects of the disaster. Big data technologies create an opportunity to supply huge information to enhance these capacities so that a social system can face natural disasters properly. This study also emphasizes the major principles of big data for effective use for disaster management like open source tools, strong infrastructure, developing local skills, context-specific data sources, data sharing with ethics, awareness about the right of data and learning from experience. This study also argues that big data is a potential tool for policymakers, administrators, and related stakeholders to take necessary actions during and after disasters like an early warning system, weather forecasting, emergency evacuation, immediate responses, relief distribution, training needs assessment and increasing trained individuals.

The oligomerization of olefins is important to the production of the high hydrocarbons. In the oligomerization process, it is always accompanied by the reaction of hydrogenation. In the present article, we report that surface carbon affects the ethylene (C2H4) hydrogenation on the supported cobalt catalysts. Co/SiO2 catalyst is carburized with CO and Co3C structure as well as Co structure is revealed with Transmission Electron Microscopy (TEM). We investigate ethylene hydrogenation on the Co/SiO2 catalyst and the carburized Co3C-Co/SiO2 catalyst. It exhibits a high selectivity of C4 products on the carburized Co3C-Co/SiO2 catalyst. We further apply Density Functional Theory (DFT) calculation to study the effect of surface carbon on the dimerization of ethylene. Co(0001) and Co3C(2 2 1) are chosen to represent the Co/SiO2 and Co3C-Co/SiO2 catalysts, respectively. The dimerization of ethylene occurs by CCH3* species coupling instead of CH2CH2* coupling. The effective barrier of CCH3* coupling on Co(0001) is higher than that on Co3C(2 2 1). This explains the high selectivity to C4 products on the carburized Co3C-Co/SiO2 catalyst. Our study provides experimental and theoretical insight for the effect of surface carbon to the reaction of ethylene with hydrogen. It may guide us to reasonable design of the catalyst for olefin oligomerization.

In this paper, a current boundary based model predictive torque control is proposed to improve torque and flux control performance of permanent magnet synchronous motor (PMSM). To reduce torque and flux ripple, two voltage vectors are applied in one control period. Based on the current variations under the two vectors, torque is forced to reach a preset boundary at the end of a control period and can be limited to a band during the whole period. In addition, according to the predictive switching instants and the predictive current, some vector combinations can be excluded from the control set, which significantly reduces the computational burden of cost function evaluation. Simulation results reveal the effectiveness of the proposed strategy.

Resilience development is viewed as a practical and effective approach to overcome the dynamic and uncertain conditions of an administrative system. This article explores factors of public administration that can enhance the adaptive capacity of administrative systems to achieve resilience. A conceptual model is developed, based on an extensive literature review, to interpret administrative resilience across several indicators. In addition, the case of Bangladesh riverine island (char) areas served as a case study validating the conceptual model. The study reveals that administrative resilience is the ability of the administrative system to provide appropriate measures to uncertainties and bounce back to previous conditions after facing risks, shocks, and disasters, and other threats to organizational stability. It also argues that administrative resilience is far better than the conventional administrative approaches in terms of organizational flexibility, quick response to uncertain and rapidly changing conditions, and strengthening the socio-ecological system for tackling vulnerability. Case study analysis confirms the conceptual model and reveals a great need to improve administrative resilience practices in riverine islands.

To reduce the computational burden of a conventional model predictive torque controller (MPTC), a reference voltage vector based MPTC strategy is proposed. The reference voltage vector is obtained from the reference stator flux vector and the reference torque. According to the location of the reference voltage vector, a first optimal vector can be determined in a quite straightforward way, improving the system dynamic performance. Furthermore, in order to decrease the torque and flux ripple, a root mean square (RMS) based solution is employed to generate the reference voltage vector and calculate the duty ratio. This method aims at minimizing the RMS error of flux and torque during the whole control period. Then, the steady state performance is improved. Besides, since the new cost function contains only the reference voltage vector, the weighting factor in conventional MPTC is eliminated. In addition, to keep a balance between the steady state performance and switching frequency, the candidates for the second optimal vector are restricted to a certain scope. Simulations were carried out and the results verified the validation of the proposed MPTC strategy.

This paper proposes an improved two-vector model-predictive torque control (MPTC) strategy to reduce the average torque ripple and improve the flux tracking performance. When determining the duty ratio of vector combination, this method aims at restricting the root mean square (RMS) error of both torque and flux during the whole control period. Every vector combination and corresponding time duration are evaluated in the cost function, which leads to global restriction of torque ripple and flux ripple. In order to avoid increasing switching frequency and computational burden, a restriction is added on the second vector. The three candidates of the second vector are the two adjacent vectors of the first one and zero vector. Simulation results are provided to show the effectiveness of the proposed strategy.

With the rapid development of Chengdu’s economy, the problem of air pollution has gradually drawn attention by scholars. Based on the time series data covers 2014 through 2019 of Chengdu, this research investigates the seasonal difference in air pollution and the possible influence of six pollutants on air pollution. The results indicated that there is a significant seasonal difference of air pollution in Chengdu and air pollutants are mainly fine particulate matter and ozone. Based on the findings, the government might consider achieve precise emission reductions for the pollution sources of PM2.5 and O3.

Cobalt carbide nanoparticles including Co2C and Co3C attract great interest as magnetic materials due to their large coercivities and high saturation magnetization. However, it’s hard to obtain the pure phase Co2C with good magnetic properties. A facile method to synthesize the pure phase ferromagnetic Co2C via a one-step polyol reduction process is developed. The crystal structure, morphology and composition of Co2C nanoparticles are characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS). Saturation magnetization and coercivity of the Co2C measured at 300 K are 13.6 emu g−1 and 0.58 kOe, respectively. The carbon contents, particle size and morphology of the Co2C are responsible for the large magnetization and high coercivity. This work will provide a strategy to synthesize pure metal carbides for applications in magnetic fields or energy materials.

This paper aims to explore the challenges and opportunities for information resource management (IRM) and suitability of big data for ensuring e-governance in Bangladesh. A systematic review of literature and meta-analysis method is employed with various levels of scales and indicators. Literature survey shows that a number of models have been developed to explain e-governance but systematic research on the challenges and opportunities of IRM and suitability of big data in the process of e-governance is still lacking. This article argues that information management in the public sector can improve transparency which leads to reducing corruption, speeding the service delivery, reducing public hassle, providing easy access to public services, reducing error and reducing poverty. This paper further argues that implementation of big data for IRM has a significant role in cost-effective service delivery to citizens with the utilization of resources which controls corruption and ensure sustainable development. The findings suggest that every public sector should be brought under information resource management like public service information system (PSIS) for land management, e-police system, human resource information system (HRIS), SMS as an alternative delivery channel for meeting citizens easy access, transparency and measurement.

In an organizational context, school reform may be thought of as resting upon a conceptual tripod of leadership, creativity, and innovation. Our chapter, however, highlights how East Asian school reform efforts are hindered by normative and semantic restrictions on the meaning of these concepts. Specifically, it discusses how leadership and creativity tend to be privileged within East Asian culture, generally applied only to individuals or groups with formal rank or socially ascribed status. In turn, we argue, such privileging limits the potential for educational innovation. Stripped of their privileged meanings, we further suggest that leadership and creativity be viewed as (1) natural human phenomena; (2) fundamentally similar expressions of human experience; and (3) valuable resources for school organizational change. To highlight the powerful potential of such understandings, we describe how creativity and leadership can be conceptualized as vectors of force within an organizational field. Using this framework, we then examine how formal and informal characteristics of typical East Asian school contexts restrict or allow the flow and progress of school reform efforts.

Over the past two decades, the concepts of educational creativity and leadership have attracted tremendous attention throughout East Asia. Driven in large part by isomorphic tendencies within a global organizational environment, the two ideas have also acquired the trappings of institutional myth. Often overlooked in the schools literature, however, are the strong conceptual similarity and interconnection between creativity and leadership. Of particular note in East Asian schools is the intrinsic tension they share between being understood as properties of formal or ascribed status and as natural human characteristics. Based on the prior literature, examples, and analysis, this conceptual paper: (1) examines the “twin concept” nature of creativity and leadership within schools, proposing that they be understood less in terms of formal or ascribed status and more as vectors of natural activity within school organizations; (2) suggests ways in which East Asian school reform movements can take advantage of such activity; and (3) raises questions regarding the meaning of “reform” in East Asian schools.