Miracle-dz/UniFAR-FTUs huggingface dataset:
Switching-Cycle Control and Sensing Techniques for High-Density SiC-Based Modular Converters

Nowadays high power density has become an emerging need for the medium-voltage (MV) high-power converters in applications of power distribution systems in urban areas and transportation carriers like ship, airplane, and so forth. The limited footprint or space resource cost such immensely high price that introducing expensive advanced equipment to save space becomes a cost-effective option. To this end, replacing conventional Si IGBT with the superior SiC MOSFET to elevate the power density of MV modular converters has been defined as the concentration of this research work. As the modular multilevel converter (MMC) is the most typical modular converter for high power applications, the research topic is narrowed down to study the SiC MOSFET-based MMC. Fundamentals of the MMC is firstly investigated by introducing a proposed state-space switching model, followed by unveiling all possible operation scenarios of the MMC. The lower-frequency energy fluctuation on passive components of the MMC is interpreted and prior-art approaches to overcome it are presented. By scrutinizing the converter’s switching states, a new switching-cycle control (SCC) approach is proposed to balance the capacitor energy within one switching cycle is explored. An open-loop model-predictive method is leveraged to study the behavior of the SCC, and then a hybrid-current-mode (HCM) approach to realize the closed-loop SCC on hardware is proposed and verified in simulation. In order to achieve the hybrid-current-mode SCC (HCM-SCC), a highperformance Rogowski switch-current sensor (RSCS) is proposed and developed. As sensing the switching current is a critical necessity for HCM-SCC, the RSCS is designed to meet all the requirement for the control purposes. A PCB-embedded shielding design is proposed to improve the sensor accuracy under high dv/dt noises caused by the rapid switching transients of SiC MOSFET. The overall system and control validations have been conducted on a highpower MMC prototype. The basic unit of the MMC prototype is a SiC Power Electronics Building Block (PEBB) rated at 1 kV DC bus voltage. Owing to the proposed SCC, the PEBB development has achieved high power density with considerable reduction of passive component size. Finally, experimental results exhibit the excellent performance of the RSCS and the HCM-SCC. Switching-Cycle Control and Sensing Techniques for High-Density SiC-Based Modular Converters
The magnitude of a product recall: offshore outsourcing vs. captive offshoring effects

The escalation in product recalls in recent years is attributed to the rise of globalisation and associated challenges of offshoring. Extant SCM research suggests that product recalls have a significant negative impact on financial performance, but gaps exist relative to the managerial actions to minimise their impact. Recall response strategies have become more important in the press, given that a single recall may result in the mandatory withdrawal of millions of products from the market, with firms incurring enormous logistics costs and brand damage. In this study, we address this gap in the research, and using a measure of product recall defined as the volume of products withdrawn from the market due to product quality failure. We explore the scale of the recall in the context of pharmaceutical sector global sourcing strategies, exploring whether the variation in global sourcing decisions not only increases the likelihood of a recall, but also influences the capability to minimise the total cost of recall. Our results suggest that offshore outsourcing and captive offshoring have opposite effects in terms of their influence on the magnitude of product recall. We summarise the implications through a compelling set of insights for future global sourcing strategy research themes.
Multi-objective transport network design with a reversible simulated annealing algorithm

In order to rationally coordinate inconsistent objectives in transport network design, this research newly develops a multi-objective network layout optimisation model solved by an improved Simulated Annealing Algorithm (SAA). Two temperature control variables and one cost difference control variable are defined in the proposed SAA. They work in cooperation to restart the optimum search from the latest temporary optimal solution if the search is made excessively in any searching direction as well as expand the searching area for the globally optimal network layout with the minimum operation cost. The genetic algorithm is embedded into the reversible SAA to iteratively provide a network configuration with the minimum total time expense of all the transports for the minimisation of the network operation cost. It is confirmed that the new optimisation model solved by the reversible SAA integrating the genetic algorithm is able to effectively minimise both the total transport time expense and the network operation cost with searching for the best fits between these two basically inconsistent objectives from different perspectives. The proposed approach can be utilised to optimise configurations of not only urban transit lines for passenger mobility organisation but also logistics transportation routes for manufacturing production management. © 2018 CPE, University of Maribor. All rights reserved.
Evaluation of the measles case-based surveillance system in Kwekwe city, 2017-2020: a descriptive cross-sectional study

Introduction in 2011, WHO African region set a target for elimination of measles by 2020. During period 2017-2020, Kwekwe city, with an estimated population of 117,116, detected one case of suspected measles. This was against a target of 2 cases per year. We evaluated the system to establish why it was failing to detect at least 2 cases per year. Methods we conducted a descriptive cross-sectional study using the Centre for Disease Control (CDC) Updated Guidelines. Nineteen health facilities were selected and fifty-seven health workers were randomly recruited. An interviewer-administered questionnaire and checklists were used to collect data. We generated frequencies, proportions, and means. Results the mean years in service was 22.8 years (SD=12.6). Thirty (52.6%) respondents had fair knowledge. Fourteen (73.7%) of the nineteen respondents who had ever completed case investigation forms took between 10-20 minutes to complete. Only two (10.5%) of the nineteen facilities had case investigation forms. The majority of the respondents 54 (93%) were willing to continue participating in the measles Community Base Surveillance System (CBSS). None of the health facilities had used the system to inform decision-making. Reasons highlighted for poor suspected measles case detection included lack of health worker training 28/57 (49.1%). Conclusion despite the high age in service, knowledge of the surveillance system was mostly fair. The system was found to be simple, not stable and not useful. The main reason for the system failure was lack of health worker training. We recommend retraining on Integrated Disease Surveillance and Response (IDSR) and case investigation forms distribution.
Additive Manufacturing for the Manufacture of Gas Turbine Engine Components: Literature Review and Future Perspectives

Additive manufacturing (AM) is an emerging rapid manufacturing technique that builds parts by tracing their cross section, layer upon layer. This technology has many unique capabilities that are not found in conventional manufacturing techniques. One of these is its ability to produce very complex part geometries without the need for any tooling. This unique potential makes it the future manufacturing technique for very complex and intricate geometries such as gas turbine components. The current review investigates the available metal additive manufacturing techniques and materials, in respect of their applicability for gas turbine engine components. From the investigation, it is clear that AM is in a promising progress for the manufacture of aircraft gas turbine components. The current limitations of AM techniques for the production of gas turbine engine components are also covered. The future perspective of this technology in this regard has also been discussed.
A Conceptual Framework for Increasing Innovation through Improved Selection of Specialized Professionals

Abstract Engineering managers involved in product development and manufacturing are responsible for proposing and developing ideas and concepts that will benefit new or existing products and processes. Improved methods are required to identify, prioritize, and integrate the opinions, ideas, and concepts proposed by specialized professionals (SP) or subject matter experts into their existing product development and process improvement. Identifying the optimal SPs and utilizing these for concept generation efforts can lead engineering management working in the new product/technology development space toward an improved idea/concept generation, increased innovation, and ultimately, superior products and processes. Methods for locating, assessing, and ranking SPs vary with every organization, but in general, systematic methods and processes are not considered, which results in the reliance on internal or limited external experts. This study aims to directly impact the engineering managers by bridging the gap between the traditional business management and core engineering management for providing guidance and improving the way in which the practicing engineering managers deal with new product development and management of technology. This experimental study is derived using a real-world engineering problem, which is faced in the field of engineering management, and the results support the implementation of a conceptual SP-focused framework that provides an increased quality of the concept generation outputs, improved preparation for future product development stages, and reduced costs.
Loss of Continuity of Care in Pediatric Neurology Services during COVID-19 Lockdown: An Additional Stressor for Parents

Background. This study aimed to investigate the consequence of the COVID 19-related lockdown on the well-being of children with neurological and neurodevelopmental disorders and the repercussion on parental stress during the period 9 March 2020–3 May 2020. Methods. A web-based survey was shared via mail with the parents of children affected by chronic neurologic disorders and neurodevelopmental disorders in the continuity of care in two Italian tertiary centers, independently by the severity of the diseases and the required frequency of controls. For each patient, they were asked to identify a single main caregiver, among the two parents, to fill in the questionnaire. Parental stress was measured via the Perceived Stress Scale (PSS). Statistical analysis was performed with IBM SPSS Statistics version 25. The differences between the clinical groups were performed with one way ANOVA. The dimensional effect of the clinical variables on outcome was evaluated by multiple linear regression analysis. Results. The survey was completed by 250 parents (response rate = 48.9 %). Sars-Cov2 infection was reported in two patients only. A total of 44.2% of the patients had completely interrupted school activities while 70% of parents underwent changes in their job modalities. Health care services were disrupted in 77% of patients. Higher PSS scores were detected in the parents of children with neurodevelopmental disorders (p = 0.035). Conclusions. The loss of continuity of care during the lockdown must be considered as a risk factor for parents caring for children with chronic neurologic diseases and neurodevelopmental disorders in further phases of the current pandemic.
Assessment of the Contribution of Local and Regional Biomass Burning on PM2.5 in New York/New Jersey Metropolitan Area

The sources of fine particulate matter (PM 2.5 , particles with diameter < 2.5 µ m) in four monitoring sites in the New York/New Jersey metropolitan statistical area from 2007 to 2017 were apportioned by positive matrix factorization (PMF) of chemical speciation data. Biomass burning, secondary inorganic (i
Optimizing the Scale of Observation for Intertidal Habitat Classification through Multiscale Analysis

Monitoring intertidal habitats, such as oyster reefs, salt marshes, and mudflats, is logistically challenging and often cost- and time-intensive. Remote sensing platforms, such as unoccupied aircraft systems (UASs), present an alternative to traditional approaches that can quickly and inexpensively monitor coastal areas. Despite the advantages offered by remote sensing systems, challenges remain concerning the best practices to collect imagery to study these ecosystems. One such challenge is the range of spatial resolutions for imagery that is best suited for intertidal habitat monitoring. Very fine imagery requires more collection and processing times. However, coarser imagery may not capture the fine-scale patterns necessary to understand relevant ecological processes. This study took UAS imagery captured along the Gulf of Mexico coastline in Florida, USA, and resampled the derived orthomosaic and digital surface model to resolutions ranging from 3 to 31 cm, which correspond to the spatial resolutions achievable by other means (e.g., aerial photography and certain commercial satellites). A geographic object-based image analysis (GEOBIA) workflow was then applied to datasets at each resolution to classify mudflats, salt marshes, oyster reefs, and water. The GEOBIA process was conducted within R, making the workflow open-source. Classification accuracies were largely consistent across the resolutions, with overall accuracies ranging from 78% to 82%. The results indicate that for habitat mapping applications, very fine resolutions may not provide information that increases the discriminative power of the classification algorithm. Multiscale classifications were also conducted and produced higher accuracies than single-scale workflows, as well as a measure of uncertainty between classifications.
A Model of the On-State Voltage across IGBT Modules Based on Physical Structure and Conduction Mechanisms

The on-state voltage is an important electrical parameter of insulated gate bipolar transistor (IGBT) modules. Due to limits in instrumentation and methods, it is difficult to ensure accurate measurements of the on-state voltage in practical working conditions. Based on the physical structure and conduction mechanism of the IGBT module, this paper models the on-state voltage and gives a detailed method for extracting the on-state voltage. Experiments not only demonstrate the feasibility of the on-state voltage separation method but also suggest a method for measuring and extracting the model parameters. Furthermore, on-state voltage measurements and simulation results certified the accuracy of this method.
Transcriptome Analysis Reveals the Regulatory Networks of Cytokinin in Promoting Floral Feminization in Castanea henryi

Castanea henryi is a monoecious plant with a low female-to-male ratio, which limits its yield. The phytohormone cytokinin (CK) plays a crucial role in flower development, especially gynoecium development. Here, the feminizing effect of CK on the development of C. henryi was confirmed by the exogenous spraying of N-(2-chloro-4-pyridyl)-N’-phenylurea (CPPU). Spraying CPPU at 125 mg·L−1 thrice changed the male catkin into a pure female catkin, whereas at 5 mg·L−1 and 25 mg·L−1, only a part of the male catkin was transformed into a female catkin. A comparative transcriptome analysis of male catkins subjected to CPPU was performed to study the mechanism of the role of CKs in sex differentiation. Using Pearson’s correlation analysis between hormone content and hormone synthesis gene expression, four key genes, LOG1, LOG3, LOG7 and KO, were identified in the CK and GA synthesis pathways. Moreover, a hub gene in the crosstalk between JA and the other hormone signaling pathways, MYC2, was identified, and 15 flowering-related genes were significantly differentially expressed after CPPU treatment. These results suggest that CK interacts with other phytohormones to determine the sex of C. henryi, and CK may directly target floral organ recognition genes to control flower sex.
Source Apportionment of Fine Particulate Matter during the Day and Night in Lanzhou, NW China

Source apportionment of PM2.5 in Lanzhou, China, was carried out using positive matrix factorization (PMF). Seventeen elements (Ca, Fe, K, Ti, Ba, Mn, Sr, Cd, Se, Pb, Cu, Zn, As, Ni, Co, Cr, V), water-soluble ions (Na+, NH4+, K+, Mg2+, Ca2, Cl−, NO3−, SO42−), and organic carbon (OC) and elemental carbon (EC) were analyzed. The results indicated that the mean concentration of PM2.5 was 178.63 ± 96.99 μg/m3. In winter, the PM2.5 concentration was higher during the day than at night, and the opposite was the case in summer, and the nighttime PM2.5 concentration was 1.3 times higher than during the day. Water-soluble ions were the dominant component of PM2.5 during the study. PMF source analysis revealed six sources in winter, during the day and night: salt lakes, coal combustion, vehicle emissions, secondary aerosols, soil dust, and industrial emissions. In summer, eight sources during the day and night were identified: soil dust, coal combustion, industrial emissions, vehicle emissions, secondary sulfate, salt lakes, secondary aerosols, and biomass burning. Secondary aerosols, coal combustion, and vehicle emissions were the dominant sources of PM2.5. In winter, the proportions of secondary aerosols and soil dust sources were greater during the day than at night, and the opposite was the case in summer. The coal source, industrial emissions source, and motor vehicle emissions source were greater at night than during the day in winter. This work can serve as a case study for further in-depth research on PM2.5 pollution and source apportionment in Lanzhou, China.
Adaptive Local Discriminant Analysis and Distribution Matching for Domain Adaptation in Hyperspectral Image Classification

Multimodally distributed data is very common in remote sensing images, such as hyperspectral images (HSIs). It is important to capture the local manifold structure while preserving the global discriminant information in the multimodal data. In this article, an adaptive local discriminant analysis and distribution matching (ALDADM) method is designed for the domain adaptation (DA) in HSI classification. ALDADM uses adaptive local discriminant analysis to extract features that are discriminative and robust to multimodally distributed data. Meanwhile, considering the spectral properties of HSI, the domain shift is reduced by distribution matching and subspace alignment. During this learning process, ALDADM only selects density peak samples to reduce the influence of interfering samples. Four DA tasks are designing using University of Pavia, Center of Pavia, Yancheng, and Botswana HSI datasets. Compared with existing DA methods, the experimental results demonstrate that our ALDADM offers a superior performance.
Stock Ranking Prediction Based on an Adversarial Game Neural Network

With the globalization of the economy and financial markets today, predicting stock rankings and constructing appropriate portfolio strategies have become hot research topics for many scholars. However, because the stock market has different styles in different periods, market-style switching will seriously affect the prediction performance of the model. To eliminate the influence of style exposure and make the stock selection performance of the deep learning model more balanced, we propose an adversarial game neural network model based on LSTM and an attention mechanism for stock ranking prediction. We also combined trading tasks to construct an MS-WRSE loss function that considers stock rankings to optimize the network. Compared with classic time series prediction models, the adversarial game neural network can eliminate the influence of market-style factors on stock ranking predictions through the mutual game between the main neural network and the auxiliary neural network. This can strengthen the stock selection performance of the model.
Perceived Epidemic Risk and Depression Symptoms during the COVID-19 Pandemic: The Mediating Role of Security and the Moderation Role of Perceived Discrimination

The 2019 coronavirus disease pandemic has resulted in a significant increase in the incidence and prevalence of mental health problems such as anxiety and depression, posing a threat to peoples’ lives and health safety all over the world. Research suggests some potential relationships among perceived risk, discrimination, security, and depression symptoms. However, little attention has been paid to the complex mechanisms of the associations between these variables. This study aimed to examine the mediating role of security and moderation role of perceived discrimination in the prediction of perceived epidemic risk on depression symptoms. Thus, we aimed to identify if perceived epidemic risk is a positive predictor of depression. A cross-sectional study was conducted through an anonymous online survey in China during the COVID-19 pandemic which measured perceived epidemic risk, discrimination, security, and depression symptoms. A total of 3443 valid questionnaires were obtained. The results indicated that depression symptoms were predicted by perceived epidemic risk through the mediating role of security, and this mediating role of security was moderated by perceived discrimination. Specifically, high levels of perceived discrimination may lead to a significant decrease in personal security, thus clustering depressive symptoms. These findings shed light on the influence of the perceived risk of the epidemic on depression symptoms in the context of the epidemic situation, which may help to develop targeted interventions.
Bile Acid Regulates Mononuclear Phagocytes and T Helper 17 Cells to Control Candida albicans in the Intestine

Invasive Candida albicans (CA) infections often arise from the intestine and cause life-threatening infections in immunocompromised individuals. The role of gut commensal microbiota, metabolites, and host factors in the regulation of CA colonization in the intestine is poorly understood. Previous findings from our lab indicate that taurocholic acid (TCA), a major bile acid present in the intestine, promotes CA colonization and dissemination. Here, we report that oral administration of TCA to CA-infected mice significantly decreased the number of mononuclear phagocytes and CD4+ IL17A+ T helper 17 cells that play a critical role in controlling CA in the intestine. Collectively, our results indicate that TCA modulates mucosal innate and adaptive immune responses to promote CA colonization in the intestine.
Exploring Boarding Strategies for High-Speed Railway

In light of the increasing demand for passenger transportation on high-speed railway (HSR), the pedestrian flow at HSR stations has become quite crowded in many countries, which has attracted researchers to study the HSR boarding behavior. In this paper, we propose three boarding strategies based on the features of the boarding behavior at an origin HSR station; we then use a cellular automaton (CA) model to study the impacts of boarding strategies on each passenger’s motion during the boarding process at HSR station. The simulation results indicate that some of the three strategies can optimize some passengers’ boarding time and relieve the congestion degree, and the positive impacts on the boarding process are the most prominent when the three strategies are used simultaneously. The results can help administrators to effectively organize the boarding process at the origin HSR station.