Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Magnetically Geared Permanent Magnet Brushless Motor for Hybrid Electric Vehicles

The magnetic field modulated variable speed permanent magnet brushless motor (MFMVS-PMBM) is a new type of power shunting device for hybrid electric vehicles (HEVs), which is composed of magnetic gear and permanent magnet brushless motor. Compared with the conventional double rotor motor, the MFMVS-PMBM shows a more complex electromechanical energy conversion relationship due to its special magnetic field modulation principle. In order to obtain better electromagnetic characteristics of the motor, the key parameters of magnetic gear and permanent magnet brushless motor are optimized in this study. The effects of gear ratio, the number of blocks per pole of Halbach array and the angle of connecting bridge and magnetic isolation bridge on the torque of magnetic gear are analyzed. The electromagnetic characteristics of the motor, such as pole slot matching, permanent magnet thickness and slot opening width of permanent magnet brushless motor, are also analyzed. Finally, the prototype is made and the experimental platform is built. The results show that MFMVS-PMBM has good sinusoidal back electromotive force (EMF), high transmission efficiency and power factor.
An Efficient Method for the Mass Unbalance Analysis of a Rotor System Using FFT and Lissajous Diagram

Unbalance analysis is essential in the rotor system. However, some problems still remain in the aspects of computational efficiency and accuracy. In the present paper a new method is proposed for estimating the mass unbalance of a rotating shaft by using the vibration signals. This is an advanced new method for the detection of a mass unbalance and its phase position. Based on the signal processing with FFT, an estimator is designed to detect the mass of unbalance. And an improved Lissajous diagram is also introduced with statistical analysis, which make it possible to compute the phase position of the mass unbalance efficiently and arranged at a certain location of the shaft. The proposed method is demonstrated and validated through several test examples.
Public investment and export diversification in low skilled labor force economies. Evidence from sub Saharan Africa

Abstract Export diversification is a means for sustainable economic growth in low-income countries. Consequently, public investment is made in various sectors to attain diversification. In this study, we assessed the long-run elasticity of export diversification to various forms of public expenditure in economies with a dominant unskilled labor force. Public investments in agriculture, education, manufacturing and mining, and transport and ICT were found to promote diversification in the long term, but all were inconsequential in the short term except for education. We conclude that for countries where a larger proportion of the labor force is unskilled public investment in public expenditures in economic and supporting sectors can encourage export diversification. However, outcomes are as dependent on the quantity of investment as they do on quality. Deliberate strategies to promote diversification should be encouraged.
Domain Adaptation with Representation Learning and Nonlinear Relation for Time Series

In many real-world scenarios, machine learning models fall short in prediction performance due to data characteristics changing from training on one source domain to testing on a target domain. There has been extensive research to address this problem with Domain Adaptation (DA) for learning domain invariant features. However, when considering advances for time series, those methods remain limited to the use of hard parameter sharing (HPS) between source and target models, and the use of domain adaptation objective function. To address these challenges, we propose a soft parameter sharing (SPS) DA architecture with representation learning while modeling the relation as non-linear between parameters of source and target models and modeling the adaptation loss function as the squared Maximum Mean Discrepancy (MMD). The proposed architecture advances the state-of-the-art for time series in the context of activity recognition and in fields with other modalities, where SPS has been limited to a linear relation. An additional contribution of our work is to provide a study that demonstrates the strengths and limitations of HPS versus SPS. Experiment results showed the success of the method in three domain adaptation cases of multivariate time series activity recognition with different users and sensors.
A Detailed Full-Order Discrete-Time Modeling and Stability Prediction of the Single-Phase Dual Active Bridge DC-DC Converter

The standard methodology to obtain the model of a power electronic converter is achieved by averaging the state-space dynamics of the converter’s state variables. But the average of the transformer current is null over a switching cycle in the resonant dc-dc converter. Therefore, the conventional method is not suitable for resonant converters, including the phase-shifted bidirectional dual active bridge (PSBDAB) converter. The two-time scale discrete-type models can resolve the problem associated with the standard state-space averaging methodology. The time-scale segregates the dynamics of the PSBDAB converter into fast and slow state variables, which can be modeled separately and eases the analysis of the PSBDAB converter. The effect of the core-loss of the inductor, dead-time of the semiconductor devices, output filter capacitor’s equivalent series resistance, semiconductor on-resistance, and the transformer copper loss components are included in the model to improve its steady-state and dynamics characteristics. Moreover, the stability analysis using a bifurcation diagram is carried out for the digitally controlled closed-loop of the system. Furthermore, the critical gain for the stable region with variations in the circuit parameters like load resistance, circuit equivalent inductance, and voltage demand is extensively studied. The modeling and stability analysis is validated in the simulation and experimental setup. The results verify that the proposed method accurately predicts the stable region with variations in the system circuit parameters. Thus this study provides a guide to select and tune the controller parameter to ensure the converter operates within the boundaries of the stable region.
Exploring Fine-Grained Cluster Structure Knowledge for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to leverage knowledge from a labeled source domain to learn an accurate model in an unlabeled target domain. However, many previous approaches propose to learn domain agnostic feature representations using a global distribution alignment objective, which does not consider the fine-grained cluster structures in the source and target domains. As such, the goal of this paper is to address two challenging problems: 1) how to thoroughly explore fine-grained cluster structure knowledge in the source and target domains, 2) how to effectively incorporate these structure knowledge for adaptation. Regarding the first point, we are motivated by structural domain similarity assumption and propose structural representation learning, which is achieved by enforcing structural consistency between the source and target domains while retaining their individual discriminative properties. Regarding the second point, we firstly devise a novel structural centroid-based label prediction method, which explicitly models structural representations to form discriminative source and target cluster centroids, and estimates the label distribution of each target sample through the cosine similarity between its corresponding target cluster centroid and all the other source cluster centroids. Then, we adopt clustering learning to incorporate these discriminative structure knowledge for adaptation by minimizing the KL divergence between the predictive target label distribution and an introduced auxiliary one. Comprehensive experiments and analyses on four benchmark datasets demonstrate the superiority of the proposed discriminative clustering framework.
Students’ Attitudes and Competences in Modeling Using 3D Cartoon Toy Design Maker

With the rapid development of education and information technology, there has been an increasing focus on maker education, which emphasizes cultivating students’ creative thinking and problem-solving ability. This paper presents our efforts to create a 3D cartoon toy design curriculum system that offers a playful and collaborative way to engage students with creation-based learning. It consists of a 3D modeling software and a series of 3D cartoon toy design courses. The former was developed according to students’ cognitive characteristics and hands-on operation habits, and the toy design courses include activities such as hand-painting, computer modeling, pattern design, handcraft, and creative display. This 3D cartoon toy design course was preliminarily implemented in one kindergarten and two primary schools in Hangzhou, an eastern city in mainland China. A learning assessment of participating students was conducted upon completion of the course. Results show that (1) the initial attitudes of junior students towards learning 3D cartoon toy design were mainly influenced by their interests, whereas senior students paid more attention to turning their ideas into reality with the help of technical tools. (2) All students were highly successful at mastering foundational modeling concepts, but the junior students were less successful at mastering abstract modeling concepts. (3) The course was most successful in developing students’ collaborative ability and content creation. (4) Additionally, teachers were successful at promoting a collaborative and communicative environment. Findings are discussed.
Avian botulism is a primary, year-round threat to adult survival in the endangered Hawaiian Duck on Kaua‘i, Hawai‘i, USA

ABSTRACT Adult survival is the most important demographic parameter influencing population dynamics for many bird taxa. Thus, understanding how survival probabilities and causes of mortality vary throughout the annual cycle is critical for developing informed and effective management strategies. In this study, we used radio-telemetry data to evaluate the effects of biotic (e.g., sex, peak [September–April] vs. off-peak [May–August] nesting seasons) and abiotic factors (e.g., rainfall, year, bi-monthly interval) on adult survival, estimate annual survival probabilities, and identify primary sources of mortality for Hawaiian Ducks (Anas wyvilliana), an endangered, non-migratory dabbling duck, on the island of Kaua‘i, Hawai‘i, USA over 2013 and 2014. Additionally, we used contemporaneous Hawaiian Duck carcass recovery and surveillance data to examine temporal and climatic associations with avian botulism outbreaks. Our results suggested bi-monthly survival decreased with total rainfall during the preceding 2-month interval. Survival did not vary with sex, between peak and off-peak nesting seasons, or between the two years of this study. Annual survival probabilities (62–80%) were relatively low compared to the closely related Laysan Duck (Anas laysanensis) on Laysan Island. Primary causes of mortality included avian botulism and presumed predation by cats (Felis catus). The botulism surveillance dataset revealed support for the effect of rainfall on the number of sick and dead birds recovered (n = 216), with generally a greater number of recoveries during months with middle-range total rainfall during the concurrent and preceding months. Our study provides critical baseline demographic data for population monitoring and highlights the importance of managing botulism risk and non-native mammalian predators for the recovery of the endangered Hawaiian Duck. LAY SUMMARY Understanding how adult survival probabilities and causes of mortality vary throughout the annual cycle is instrumental for develo in informed and effective conservation strate ies We used radio-telemetry data to evaluate factors influencing adult survival and identify sources of mortality for the endangered Hawaiian Duck on Kaua‘i, and we used botulism surveillance data to assess factors associated with avian botulism outbreaks. Avian botulism was the primary cause of adult mortality and posed a risk year-round. Increased rainfall was associated with lower survival and higher numbers of sick and dead birds attributed to botulism except during wettest periods when botulism recoveries decreased Survival did not differ between sexes or peak and off-peak nesting seasons, but the rate of predation was higher for females, and all female predation events (n = 4) occurred during the peak-nesting season. Our study provides critical demographic data for population monitoring and highlights the importance of managing botulism risk and non-native mammalian predators (e.g., feral cats) for Hawaiian Duck recovery.
Strategies for Biocontrol of Listeria monocytogenes Using Lactic Acid Bacteria and Their Metabolites in Ready-to-Eat Meat- and Dairy-Ripened Products

Listeria monocytogenes is one of the most important foodborne pathogens. This microorganism is a serious concern in the ready-to-eat (RTE) meat and dairy-ripened products industries. The use of lactic acid bacteria (LAB)-producing anti-L. monocytogenes peptides (bacteriocins) and/or lactic acid and/or other antimicrobial system could be a promising tool to control this pathogen in RTE meat and dairy products. This review provides an up to date about the strategies of use of LAB and their metabolites in RTE meat products and dairy foods by selecting the most appropriate strains, by analysing the mechanism by which they inhibit L. monocytogenes and methods of effective application of LAB, and their metabolites in these kinds of products to control this pathogen throughout the processing and storage. The selection of LAB with anti-L. monocytogenes activity allows to dispose of effective strains in meat and dairy-ripened products, achieving reductions form 2–5 logarithmic cycles of this pathogen throughout the ripening process. The combination of selected LAB strains with antimicrobial compounds, such as acid/sodium lactate and other strategies, as the active packaging could be the next future innovation for eliminating risk of L. monocytogenes in meat and dairy-ripened products.
An LSTM-based aerodynamic parameter recognition method for skip gliding targets

High-speed skip gliding targets have become a new threat to national defense due to its high speed and large maneuverability. The trajectories of these targets have skip gliding characteristics, which cause serious model mismatch and reduce the trajectory estimation accuracy. Considering the trajectory estimation of skip gliding targets, an aerodynamic parameter recognition method is proposed based on LSTM to solve the model mismatch problem in trajectory estimation. First, the dynamic model of the targets is given, and the aerodynamic labels are defined by analyzing the recognition requirements. Then, considering the skip gliding characteristics of the targets, LSTM-based aerodynamic recognition networks are designed and trained by the available trajectory data. Finally, the aerodynamic recognition results and effect on trajectory estimation are analyzed in typical scenarios. Simulation results show that the proposed method can accurately recognize aerodynamic parameters and suppress the model mismatch, which improve the accuracy and dynamic performance of trajectory estimation.
Measurement of Humidity by Using Temperature and Humidity Sensor SHTxx

Traditional measurement of humidity needs conversion circuit,which should be calibrated and tested within accuracy,so its application is very inconvenient.The new generation digital temperature and humidity sensor SHTxx is produced by Swiss Sensirion Inc based on unique CMOSens technology.A relative humidity sensor,a temperature sensor,an analog to digital converter and a serial interface circuit are integrated on the same chip SHTxx.The sensor has the characteristics of tiny size,low power,high reliability,exemption of peripheral circuit and full-interchange.Microprocessor can communicate with SHTxx by the serial interface of two wires which is not compatible with I2C interfaces.Using SHTxx and MCU,the design of the measuring equipment is simply given.
Three-dimensional imaging of ground penetrating radar with total variation minimization

Most of the traditional compressed sensing ground penetrating radar imaging is performed in a two-dimensional form, and it is impossible to accurately determine the specific location of the underground target. In order to solve these problems, a three-dimensional imaging method is proposed for ground penetrating radar. In this method, the data is observed from a planar MIMO array, and a dictionary matrix is constructed into a three-dimensional model. Finally, the underground scene in three dimensions is reconstructed with the total variation minimization. The experimental results show that in the layered media scene, the proposed method can accurately image the target by using only a small amount of data.
Comparison of Immune Checkpoint Molecules PD-1 and PD-L1 in Paired Primary and Recurrent Glioma: Increasing Trend When Recurrence

Purpose: This study aims to investigate PD-1/PD-L1 expression patterns in paired primary and recurrent gliomas. Methods: From January 2008 to December 2014, 42 patients who underwent surgical resections of primary and recurrent gliomas were retrospectively included. PD-1/PD-L1 protein expression in tumors was evaluated through immunohistochemistry. Results: In primary gliomas, PD-1 and PD-L1 expression was evident in 9 (22.0%) and 14 (33.3%) patients. In the paired recurrent glioma, PD-1 and PD-L1 expression was evident in 25 (61.0%) and 31 (74.0%) lesions. Both PD-1 and PD-L1 showed significantly enhanced expression after recurrence (p < 0.005; p < 0.005). For PD-L1 expression in recurrent gliomas, the adjuvant therapy group showed significantly increased expression compared to primary gliomas (p < 0.005). For PD-1- primary gliomas, if the matched recurrent gliomas showed PD-1+, the PFS became worse than the remaining recurrent gliomas PD-1- (12.7 vs. 25.9 months, p = 0.032). Interestingly, for PD-L1- primary gliomas, if the matched recurrent gliomas showed PD-L1+, the OS became better than the remaining recurrent gliomas PD-L1- (33.8 vs. 17.5 months, p < 0.001). Conclusions: In the study, we found the expression of PD-1/PD-L1 increased significantly in recurrent gliomas and the elevated level of PD-L1 was tightly associated with adjuvant treatment, suggesting the potential therapeutic and predictive value of PD-1 and PD-L1 in the treatment of recurrent gliomas.
Meeting the 24-Hour Movement Guidelines and Outcomes in Adolescents with ADHD: A Cross-Sectional Observational Study

According to the 24-Hour Movement Guidelines, meeting daily recommendations for physical activity, sleep, and screen time is important for obtaining optimal health benefits. This cross-sectional observational study aimed to examine (a) the prevalence of meeting the movement guidelines; and (b) the associations between meeting the guidelines and selected outcomes in adolescents with attention-deficit/hyperactivity disorder (ADHD). Data from the 2018–2019 National Survey for Children’s Health dataset was used. Participants were adolescents (10–17 years) with ADHD and without other chronic conditions. Outcomes were flourishing, school engagement, and body weight status. Exposures of interest were adherence to the movement guidelines. The frequency of the participants’ adherence to the guidelines was estimated, and regression analyses were conducted to examine the associations between adherence to the guidelines and outcomes, adjusting for potential confounders. Complete observations were available for 634 adolescents with ADHD. Overall, 46.8% of the participants met at least one movement guideline, but only 6.5% met all three. The number of guidelines met had a significant and positive association with flourishing and school engagement (β = 0.21/0.17, ptrend < 0.001). Compared with meeting all three guidelines, significant associations with lower flourishing levels were found in participants who met none, sleep only, and sedentary time only (β = −0.38–−0.13, p < 0.05). Similar findings were identified in the school engagement outcome. Adherence to the guidelines was, however, not significantly associated with the odds of being overweight or obese. Collectively, the findings suggest the movement guidelines may be appropriate for extending to adolescents with ADHD and there is a need to increase adherence to the guidelines in this group.
Development and Performance Analysis of Pneumatic Soft‐Bodied Bionic Flipper

The sea lion fore flipper has superior underwater movement performance. Its structural characteristics and propulsion mechanism are drawn, combined with the large deformation and hyperelastic properties of silicone, a pneumatic soft‐bodied bionic flipper with strong underwater mobility, good environmental adaptability, and flexible attitude transformation. Based on Yeoh's second‐order constitutive model of silicone, the nonlinear dynamic analysis of the main limb of the bionic flipper is carried out, and the deformation analysis theoretical model is established. Then, the force analysis of infinitesimal and integral of the wing fin of the bionic flipper is analyzed by the blade element theory, to obtain the theoretical model of underwater propulsion dynamic performance. The superiority of the hydrodynamic performance of the bionic flipper is analyzed and confirmed using the bidirectional fluid–structure coupling numerical simulation algorithm. An experimental test platform is built to test the physical model of the bionic flipper. The motion and dynamic characteristics curves of the bionic flipper are obtained and compared with the corresponding numerical simulation results. The results show that the theoretical model and numerical simulation are accurate, and the structure is feasible, which can provide methods and references for the research and implementation of the underwater propeller.