Miracle-dz/UniFAR-FTUs huggingface dataset:
Operating Point Selected Flux-Weakening Control of Induction Motor for Torque-Improved High-Speed Operation Under Multiple Working Conditions

Due to the requirements of multiple working conditions for induction motor in high-speed flux-weakening operation, conventional “maximum torque oriented” flux-weakening controller reveals its own limitations. This paper focuses on holistic torque performance optimization for multi-condition operation. Torque ripple and dynamic response under two kinds of specific working conditions (high-speed braking and load step change) are explicitly analyzed. The theoretically precise maximum torque point is derived to extract the possible output torque for deep flux-weakening operation. Further, an operating point selected flux-weakening controller (OPS-FC) is proposed. It retains the maximum output torque capability for step acceleration and full-load conditions, as well as holding other novel features for the changing working conditions. First, the selected operating point is automatically manipulated for torque ripple alleviation under non-maximum torque condition. Second, since the voltage reference in proposed OPS-FC is directly constructed, the system robustness is enhanced. Finally, speed range is extended to the deep flux-weakening operation, and the complex vector decoupling part of d-axis current regulator is first applied and shared as a flux-weakening path for closed-loop control. Hence, the complicated PI regulator tuning is averted. The proposed scheme is experimentally verified on a 3.7 kW commercial platform.
Computer simulation of the torque distribution system of a hybrid heavy truck in conditions of slippage of one of the wheels

Mathematical and computer models for the motion of a hybrid heavy truck in conditions of wheel slip have been developed. Algorithms for the redistribution of the torque between the wheels to provide controllability and stability of motion along the guided path have been proposed. The efficiency of the developed algorithms is confirmed by the results of computer simulation in the MATLAB / Simulink system.
EM‐driven constrained miniaturization of antennas using adaptive in‐band reflection acceptance threshold

Numerical optimization of geometry parameters is a critical stage of the design process of compact antennas. It is also challenging because size reduction is constrained by the necessity of fulfilling imposed electrical performance requirements. Furthermore, full‐wave electromagnetic (EM) analysis needs to be used for reliable performance evaluation of the antenna structure, which is computationally expensive. In this paper, an improved procedure for optimization‐driven design miniaturization of antenna structures is considered. We investigate the properties of explicit formulation with the footprint area minimization being a primary design objective and a penalty function introduced to control reflection characteristic of the antenna at hand. Deficiencies of this approach are pointed out, in particular, difficulties in exploring the boundary of the feasible part of the design space (otherwise necessary due to the fact that the reflection constraint is normally active at the minimum size antenna design). A novel formulation with adaptive adjustment of the maximum acceptable in‐band reflection level threshold is proposed and demonstrated to be superior over the conventional (fixed threshold) setup, ie, leading to smaller footprints without degrading electrical performance parameters. Our considerations are illustrated using 2 broadband antenna structures.
Mechanical simulation and installation position optimisation of a lifting cylinder of a scissors aerial work platform

The force of a lifting cylinder is the main factor of the alternating stress of scissors arms in a scissors aerial work platform, which seriously affects the fatigue life of structures. Aiming at this problem, the installation position parameters that affect the force of the lifting cylinder was simulated and optimised. First, the mechanical model of the lifting system was established based on the principle of virtual work, and the position parameters that affect the force of lifting cylinder were determined. Then, the advanced modeling environment for performing simulation of engineering systems (AMESim) simulation model was used to simulate the lifting process of the lifting system, and the reliability of the simulation model was verified by the pressure test of the hydraulic system. Finally, based on the simulation model, the design of experiment method was used to optimise the installation position of the lifting cylinder in the design and development environment of AMESim, and the optimisation effect was verified through the test on a real vehicle. The result shows that the peak load of the lifting cylinder is reduced by ∼12%, and the average of the steady-state value decreases by ∼20% after the installation position is optimised.
Identify Patterns in Online Bin Packing Problem: An Adaptive Pattern-Based Algorithm

Bin packing is a typical optimization problem with many real-world application scenarios. In the online bin packing problem, a sequence of items is revealed one at a time, and each item must be packed into a bin immediately after its arrival. Inspired by duality in optimization, we proposed pattern-based adaptive heuristics for the online bin packing problem. The idea is to predict the distribution of items based on packed items, and to apply this information in packing future arrival items in order to handle uncertainty in online bin packing. A pattern in bin packing is a combination of items that can be packed into a single bin. Patterns selected according to past items are adopted and periodically updated in scheduling future items in the algorithm. Symmetry in patterns and the stability of patterns in the online bin packing problem are discussed. We have implemented the algorithm and compared it with the Best-Fit in a series of experiments with various distribution of items to show its effectiveness.
Promoting Long-lasting Learning Through Instructional Design

Passively listening to a lecture (deWinstanley & Bjork, 2002), skimming a textbook chapter, or googling for an answer to a homework problem is not conducive to deep and lasting high-order learning. At the same time, presenting complex concepts in problem-based classes might overload students’ working memory capacity. Effective student learning necessitates students to process information in their working memories, as well as storing information, facts and skills, in their long-term memories. Students must then be able to retrieve this information into their working memory in the future, in order to apply the information to new situations. That is, students must be able to recognize the characteristics of a future situation or problem and correctly retrieve the appropriate information stored in their long-term memory (Kirschner, Sweller, & Clark, 2006) to tackle the issue. Using the framework of Cognitive Load Theory, this article proposes an instructional model that promotes five strategies for learning and teaching; i.e. spacing, retrieval practice, elaboration, interleaving, and concrete examples, to effectively help students store and retrieve information from their long-term memory.
Ovarian cancer in primary care

Ovarian cancer continues to be the leading cause of gynaecological cancer death in the UK. It has previously been described as the ‘silent killer’, as symptoms are typically absent or non-specific early in the disease leading to a later stage at diagnosis where fewer treatment options are available. Stage at presentation influences patient survival, and therefore, early detection within primary care is vital. Currently, there is no screening programme for ovarian cancer, and so diagnosis relies on investigation of symptomatic women presenting to healthcare professionals and an awareness of vague symptoms that could indicate ovarian cancer pathology. This article aims to provide an overview of ovarian cancer, including epidemiology, risk factors, signs and symptoms, and diagnosis within primary care. We will also briefly discuss current treatment strategies for ovarian cancer.
Business analysts' contributions to the dynamic capabilities of agile software development teams

PurposeLiterature reveals ongoing debates around the role of business analysts in agile software development (ASD) teams. This can be attributed, in part, to a knowledge gap concerning how business analysts contribute to overall team capabilities, particularly those which are essential in enabling teams to respond to fast-paced environmental changes. The purpose of this study was to address this gap by investigating how business analysts (BAs) contribute to the dynamic capabilities of ASD teams.Design/methodology/approachThrough a deductive approach, this study adapted and applied a research model based on the team dynamic capabilities (DC) theory to explore the contributions of BAs in agile teams. The study was executed using a qualitative, single case study research strategy directed at an ASD team in the financial services industry. Moreover, data were collected through face-to-face, semi-structured interviews; a focus group; non-participant observation and physical artefacts review. The thematic analysis technique was used to analyse the data.FindingsThe study contributes to teams DC theory through four theoretical propositions centred on the role of BAs. The proposition highlights how BAs relationship management, tacit knowledge sharing, task mental models and transactive memory are key contributors of ASD teams' DC. The study also found that BAs contribute to ASD teams' ability to embrace agile principles 2, 4, 6 and 12. This study can inform the design of capacity development programmes for individual team members and BAs and thus help managers curate teams that will best promote DC.Practical implicationsThis study can inform the design of capacity development programmes for individual team members and BAs and thus help managers curate teams that will best promote DC.Originality/valueThis study builds on the relatively few studies which focus on DC within software development (SD) teams and ASD project teams. Moreover, the study explores how an individual (i.e. a BA) can contribute to the DC of a team.
Integrating the Gut Microbiome and Stress-Diathesis to Explore Post-Trauma Recovery: An Updated Model

Musculoskeletal conditions of traumatic and non-traumatic origin represent an ongoing health challenge. While the last three decades have seen significant advancement in our understanding of musculoskeletal conditions, the mechanisms of a delayed or lack of recovery are still a mystery. Here, we present an expansion of the integrated stress-diathesis model through the inclusion of the gut microbiome. Connecting the microbiome with known adverse neurobiologic, microbiologic and pathophysiologic sequelae following an injury, trauma or stressful event may help improve our knowledge of the pathogenesis of poor recovery. Such knowledge could provide a foundation for the exploration and development of more effective interventions to prevent the transition from acute to chronic pain.
Predicting China’s carbon intensity through 2050: an integrated analysis method

ABSTRACT Carbon intensity is one of the key indexes in the process of decarbonizing development. Therefore, our paper investigates possible pathways of China’s carbon intensity to 2050 depending on China’s development stage and trends. We first investigate the major influence factors of carbon intensity based on the Kaya identity, and then forecast China’s future carbon intensity with a generalized regression neural network model coupled with a scenario analysis with official data from 1978 to 2020. Our results show that: (1) China’s carbon intensity will decrease under the benchmark growth, high growth, and low growth scenarios but vary by amounts and rates of decrease due to the settings of the influences factors; (2) China is able to achieve the reduction targets of 2025 under the benchmark growth and low growth scenario, but cannot achieve the target under the high grow scenario. However, China can meet its reduction target by 2030 in all scenarios. (3) The Chinese government should aim to reduce carbon intensity by 23–25%, 40–44% and 64–70% by 2035, 2040 and 2050 as compared to the level in 2030. Therefore, China should keep promoting the deep low-carbon transformation of economic development model, and realize high-quality economic development in order to achieve these objectives.
A high efficiency deep learning method for the x-ray image defect detection of casting parts

In the manufacturing industry, digital radiography (DR) images are often used to detect internal defects in casting parts. With the development of computer technology, increasingly more researchers use computer algorithms instead of manual inspection. However, traditional computer vision methods are generally not efficient and robust. In this study, we propose a DR image defect detection methodology based on deep learning technology. In order to train and evaluate the deep learning model, we create a casting defect DR image dataset, which includes 18 311 DR images labelled for two types of objects—defects and inclusions. In the methodology, an object detection method baseline named YOLOv3_EfficientNet, which replaces the backbone of YOLOv3_darknet53 with EfficientNet, is used. This operation leads to a significant improvement in the mean average precision value on YOLOv3 and greatly reduces the inference time and storage space. Then, a data enhancement method based on DR image features is used, which can increase the diversity of the clarity and the shapes of defects randomly. To further facilitate the deployment of models on embedded devices with an acceptable accuracy loss range, a depth separable convolution operation is adopted. Regarding the bounding box regression, we perform some relevant research in the training and inference stages of the model, and the accuracy of the model was improved in both stages of them according to the experiments. The experiments proved that every type we adopted could benefit the model’s performance.
The level of community behaviour towards hearing loss in Indonesia

Background: Hearing loss is the most common disability in the world, with a prevalence of 5% of the world's population of 466 million people. Knowledge about noise exposure and hearing protection equipment is related to hearing loss. Health is associated with a person's healthy lifestyle behaviour. This study aimed to determine the level of community behaviour towards hearing loss. Methods: This study used a descriptive analytical approach with a cross-sectional study design. The population in this study was Indonesian people who were not healthcare workers aged 17 years and over. The sample inclusion criteria included individuals who were not healthcare workers (such as doctors, nurses and midwives etc.), aged 17 years and over, and willing to participate in the study. The sampling method in this study was consecutive sampling. Results: Based on the distribution of patients, there were more female participants (1484, 61.6%) than male participants. Diploma-3 (D3) was the most common education type, with as many as 1095 people (45.4%), while the least common education type was not in school (eight, 0.3%). Most participants were in the ‘employee’ profession, namely 509 people (21.1%), while the lowest number of participants was in the Police profession (20, 0.8%). There were significant relationships between the respondent's knowledge and action about hearing loss, and the respondent’s knowledge and attitudes regarding hearing loss (p=0.000). Conclusions: There were significant relationships between the respondent's knowledge and actions about hearing loss and the respondent's knowledge and attitude, while there was no significant relationship between the respondent's attitude and actions regarding hearing loss.
Design of a Smart Conducting Nanocomposite with an Extended Strain Sensing Range by Conjugating Hybrid Structures

In recent years, flexible and wearable strain sensors, consisting of a polymer matrix and a conducting filler, have received extensive attention owing to their physical advantages, such as being lightweight, stretchable, and having the potential for application to complex forms. However, achieving a low hysteresis of the relative change in resistance, wide sensing range, and reduced plastic deformation is still challenging. To address these issues, in this study, we developed hybrid conducting composites with a wide range of sensing abilities and low hysteresis. The bi-layer composites, comprising a carbon nanotube (CNT) composite layer with reinforced/conducting properties, and a natural rubber-based layer with extreme strain properties, could effectively circumvent their limitations. Compared to single-layer CNT composites, the bi-layer structure could increase the tensile strain with reduced plastic deformation, resulting in the prevention of surface cracks on the CNT composite. In addition, it has the benefit of measuring a wider sensing range, which cannot be measured in a single-CNT composite system. A cyclic stretching/releasing test was performed to demonstrate that the strain sensor exhibited excellent reproducibility. Our results can function as a useful design guide for stretchable sensor applications.
Extra Proximal-Gradient Network with Learned Regularization for Image Compressive Sensing Reconstruction

Learned optimization algorithms are promising approaches to inverse problems by leveraging advanced numerical optimization schemes and deep neural network techniques in machine learning. In this paper, we propose a novel deep neural network architecture imitating an extra proximal gradient algorithm to solve a general class of inverse problems with a focus on applications in image reconstruction. The proposed network features learned regularization that incorporates adaptive sparsification mappings, robust shrinkage selections, and nonlocal operators to improve solution quality. Numerical results demonstrate the improved efficiency and accuracy of the proposed network over several state-of-the-art methods on a variety of test problems.
The Effectiveness of Self-Guided Digital Interventions to Improve Physical Activity and Exercise Outcomes for People With Chronic Conditions: A Systematic Review and Meta-Analysis

Objective The aim of this systematic review was to determine the effectiveness of self-guided digital physical activity (PA) and exercise interventions to improve physical activity and exercise (PA&E) outcomes for people living with chronic health conditions. Digital health interventions, especially those with minimal human contact, may offer a sustainable solution to accessing ongoing services and support for this population. Methods A comprehensive and systematic search was conducted up to December 2021, through seven databases, for randomized trials that evaluated the effect of self-guided web- or internet-based PA interventions on physical activity or exercise outcomes. Included studies had to have interventions with minimal human contact and interaction with participants needed to be automatically generated. All studies were screened for eligibility and relevant data were extracted. Two independent reviewers assessed the risk of bias using the Cochrane risk of bias tool. Standardized mean differences and 95% confidence intervals (CI) were calculated. PA data were pooled, and forest plots were generated. Results Sixteen studies met the eligibility criteria and included a total of 2,439 participants. There was wide variation in health conditions and intervention characteristics in mode and parameters of delivery, and in the application of theory and behavioral strategies. Self-reported PA in the intervention group was greater than controls at the end of the intervention [standardized mean difference (SMD) 0.2, 95% CI = 0.1, 0.3] and at follow up (SMD 0.3, 95% CI 0.2–0.5). The difference in objectively measured PA was small and non-significant (SMD 0.3, 95% CI −0.2 to 0.9). All interventions included behavioral strategies and ten of the sixteen were underpinned by theory. Conclusions Self-guided digital PA&E interventions provided a positive effect on PA immediately after the intervention. An unexpected and positive finding was a sustained increase in PA at follow-up, particularly for interventions where the behavioral strategies were underpinned by a theoretical framework. Interventions with minimal contact have the potential to support sustained PA engagement at least as well as interventions with supervision. Systematic Review Registration https://www.crd. york.ac. uk/prospero/, identifier: CRD42019132464.
Context-Aware Intelligent Traffic Light Control through Secure Messaging

In this paper, we propose STCM, a context-aware secure traffic control model to manage competing traffic flows at a given intersection by using secure messages with real-time traffic information. The vehicle is modeled as a virtual sensor which reports the traffic state, such as its speed and location, to a traffic light controller through a secure and computationally lightweight protocol. During the reporting process, a vehicle’s identity and location are kept anonymous to any other vehicle in the system. At an intersection, the traffic light controller receives the messages with traffic information, verifies the identities of the vehicles, and dynamically implements and optimizes the traffic light phases in real-time. Moreover, the system is able to detect the presence of emergency vehicles (such as ambulances and fire fighting trucks) in the communication range and prioritize the intersection crossing of such vehicles to in order to minimize their waiting times. The simulation results demonstrate that the system significantly reduces the waiting time of the vehicles in both light and heavy traffic flows compared to the pretimed signal control and the adaptive Webster’s method. Simulation results also yield effective robustness against impersonating attacks from malicious vehicles.
Management of Severe Traumatic Brain Injury in Pediatric Patients

The optimal management of severe traumatic brain injury (TBI) in the pediatric population has not been well studied. There are a limited number of research articles studying the management of TBI in children. Given the prevalence of severe TBI in the pediatric population, it is crucial to develop a reference TBI management plan for this vulnerable population. In this review, we seek to delineate the differences between severe TBI management in adults and children. Additionally, we also discuss the known molecular pathogenesis of TBI. A better understanding of the pathophysiology of TBI will inform clinical management and development of therapeutics. Finally, we propose a clinical algorithm for the management and treatment of severe TBI in children using published data.
An Effective EMI Source Reconstruction Method Based on Phaseless Near-Field and Dynamic Differential Evolution

An effective electromagnetic interference (EMI) source reconstruction method based on dynamic differential evolution (DDE) is proposed in this article. In this method, an unknown EMI source is represented by an equivalent dipole array by using the scanned phaseless magnetic near fields. Different from the conventional differential evolution (CDE) method, DDE individually updates every equivalent dipole instead of the whole dipole array for each iteration. It converges faster and takes less computing time than CDE. The equivalent dipole model reconstructed by DDE can thereafter predict the near field and 3-m far field efficiently. The results of numerical and measurement examples have verified the effectiveness and robustness of the proposed method.