Miracle-dz/UniFAR-FTUs huggingface dataset:
Asynchronous operation characteristics and soft-starting method for the brushless doubly-fed motor

The brushless doubly-fed motor (BDFM) is versatile and can perform under several modes of operation. Other than the commonly used doubly-fed mode, BDFM can also operate in an asynchronous mode wherein the control winding of the motor is short circuited. The proposed study first analysed the mechanical characteristics of BDFM operation under this mode, whereby it was noted that there exist two induction torque components corresponding to different pole pairs within the motor. The motor, therefore, behaved as a conventional induction machine. Based on the above analysis and by introducing the concept of equivalent resistance of the machine-side inverter, an asynchronous soft-starting method for BDFM is proposed herein. By switching one side of the inverter bridge arms to the off state and adjusting the duty cycle on the other side, a controllable equivalent resistance is obtained and inserted into the control winding of BDFM. Consequently, the starting characteristic of BDFM can be improved to achieve soft start-up of the motor. Finally, experiments were conducted on a 30 kW BDFM prototype, of which, the results verify the accuracy of the torque analysis and improved performance of the proposed soft-starting method.
UX Requirements to Public Systems for All: Formalisation or Innovation

Many countries are developing e-government applications for digitalisation of the interaction between citizens and government administrations. To be successful, such applications must be usable and provide good user experience for all. In Denmark, egovernment applications have traditionally been developed through a contract-based approach; but the experience has been quite negative, in particular in terms of user experience and innovation. To increase the user experience and provide a broader range of innovative solutions, the Danish government and the organization of the municipalities have produced guidelines and material for a more user-centred development process for the ongoing digitalisation of local government services. We present the guidelines and material together with the findings from case studies in four IT companies, where we have interviewed employees and conducted redesign workshops. Our findings indicate that the material and guidelines are a step forward, but they are too general and have failed to ensure a reasonable level of usability and user experience.
Long-Lasting Effects of Spironolactone After its Withdrawal in Patients with Hyperandrogenic Skin Disorders.

OBJECTIVE Hyperandrogenic skin disorders, such as hirsutism, acne and alopecia, affect approximately 10-20% of women of reproductive age, reducing quality of life and causing psychological impairment. Spironolactone is a commonly used antiandrogen, especially in women who are not sexually active or have contraindications to hormonal contraceptives. The aim of this study was to evaluate the effects of spironolactone, especially after its withdrawal, in patients with hyperandrogenic skin disorders. METHODS Retrospective analysis of 63 women with hyperandrogenic skin symptoms due to polycystic ovary syndrome (PCOS), treated with spironolactone for at least 6 months as first-line treatment. RESULTS After a mean time of treatment of 25.7 months, all patients reported a significant improvement in hyperandrogenic skin disorders; only 5 patients were dissatisfied and required the addition of an oral contraceptive. The therapy was well tolerated and the most frequent side-effect was intermestrual bleeding in 68.2% of cases, affecting mainly classic PCOS phenotype. Thirthy-eight patients showed prolonged effects 33.7 months after spironolactone withdrawal, whereas 20 relapsed 17.5 months after discontinuation. No significant difference in clinical and biochemical parameters was found between these two groups both at baseline and after spironolactone treatment. Ovulatory PCOS patients were treated for a shorter time and reported earlier relapse than classic PCOS patients. CONCLUSION Spironolactone is an effective and safe treatment for hyperandrogenic skin disorders, showing long-lasting effects even several months after its discontinuation.
On the Utility of ToxCast-Based Predictive Models to Evaluate Potential Metabolic Disruption by Environmental Chemicals

Background: Research suggests environmental contaminants can impact metabolic health; however, high costs prohibit in vivo screening of putative metabolic disruptors. High-throughput screening programs, such as ToxCast, hold promise to reduce testing gaps and prioritize higher-order (in vivo) testing. Objectives: We sought to a) examine the concordance of in vitro testing in 3T3-L1 cells to a targeted literature review for 38 semivolatile environmental chemicals, and b) assess the predictive utility of various expert models using ToxCast data against the set of 38 reference chemicals. Methods: Using a set of 38 chemicals with previously published results in 3T3-L1 cells, we performed a metabolism-targeted literature review to determine consensus activity determinations. To assess ToxCast predictive utility, we used two published ToxPi models: a) the 8-Slice model published by Janesick et al. (2016) and b) the 5-Slice model published by Auerbach et al. (2016). We examined the performance of the two models against the Janesick in vitro results and our own 38-chemical reference set. We further evaluated the predictive performance of various modifications to these models using cytotoxicity filtering approaches and validated our best-performing model with new chemical testing in 3T3-L1 cells. Results: The literature review revealed relevant publications for 30 out of the 38 chemicals (the remaining 8 chemicals were only examined in our previous 3T3-L1 testing). We observed a balanced accuracy (average of sensitivity and specificity) of 0.86 comparing our previous in vitro results to the literature-derived calls. ToxPi models provided balanced accuracies ranging from 0.55 to 0.88, depending on the model specifications and reference set. Validation chemical testing correctly predicted 29 of 30 chemicals as per 3T3-L1 testing, suggesting good adipogenic prediction performance for our best adapted model. Discussion: Using the most recent ToxCast data and an updated ToxPi model, we found ToxCast performed similarly to that of our own 3T3-L1 testing in predicting consensus calls. Furthermore, we provide the full ranked list of largely untested chemicals with ToxPi scores that predict adipogenic activity and that require further investigation. https://doi.org/10.1289/EHP6779
Young children expect pretend object identities to be known only by their partners in joint pretence

Abstract The present study examined whether three‐year‐old children (age = 42–48 months, n = 57; 31 boys) understand that object identities stipulated during pretend play could only be known by people witnessing the stipulation. Children participated in pretend scenarios that included some objects and two experimenters. Two pretend episodes corresponded to an object: one connected to its conventional function, the other to a pretend identity made‐up on the spot. These episodes happened either in the presence or absence of the other person. In the test phase, this experimenter expressed an intention to do something with an object and asked for a ‘missing’ prop. The prediction was that in case she was present previously, children would be more likely to select the prop corresponding to a pretence stipulation, compared to when she was absent. The results confirmed this pattern: in the absent condition, 68.42% of the participants chose the prop connected to the conventional use of the object, while 31.58% chose the prop corresponding to its identity stipulated in pretend play. It seems that preschool aged children refrain from generalizing their knowledge about the pretend identity of an object, in case their interactive partner could not know of this identity.
A New Method for Cooperative Relay Selection Based on Hybrid Intelligent Algorithm

Aiming at the problem of relay selection in multi-user multi-relay cooperative communication system,a new mothod for cooperative relay selection based on hybrid intelligent algorithm is proposed. The proposed method is different from the current relay selection method which assigns one relay node for each source node. It establishes an optimization model which assigns one or more relay nodes for each source node,and maximizes the minimum received signal-to-noise ratio(SNR) of multi-user multi-relay cooperative system. A hybrid algorithm which combines simulated annealing(SA) and genetic algorithm(GA) is employed to search the optimal solution for relay selection problem. Simulation results show that the proposed method can significantly improve the received SNR at the destination,and the algorithm possesses strong global search capability and fast optimization capability.
Cobimetinib Sensitizes Cervical Cancer to Paclitaxel via Suppressing Paclitaxel-Induced ERK Activation

Introduction: Chemoresistance remains the main cause of treatment failure in cervical cancer and novel therapeutic strategies are required. Cobimetinib, a potent yet selective inhibitor of MEK1 and 2, is currently used to treat melanoma clinically. In this work, we identified cobimetinib as a promising candidate for treating cervical cancer. Methods: The in vitro and in vivo efficacies of cobimetinib were examined using cervical cancer cell cultures and xenograft mouse model. Its combination with paclitaxel was analyzed using the combination index. Immunoblotting was performed on MAPK and ERK pathways. Results: Cobimetinib displays a potent anti-cervical cancer activity in a panel of cell lines regardless of cellular origin and HPV presence, and its combination with paclitaxel is synergistic in inhibiting cervical cancer cells. This is achieved by the growth inhibition and caspase-dependent apoptosis induction, through inhibiting MAPK/ERK activation. In addition, paclitaxel activates ERK in cervical cancer cells, and this can be reversed by cobimetinib. We finally confirm the efficacy of cobimetinib alone and its combination with paclitaxel in the cervical cancer xenograft mouse model. Discussion/Conclusion: Our preclinical findings will accelerate the initialization of clinical trials to use combination of cobimetinib and paclitaxel for treating cervical cancer. Our work also emphasizes the therapeutic value of targeting MAPK/ERK to overcome chemoresistance in cervical cancer.
The forecasting method of selecting-best model based on artificial neural networks and its application

In order to deal with the problem of how to scientifically determine the weights of combined forecasting models(CFM),a new method is presented in this paper.According to the principle of the best selection,the combined forecasting model is transformed into a problem of pattern recognition,it can be resolved by the method of improved BP artificial neural network(ANN) which owns the ability of non-linear mapping.The given example shows that the so called selecting-best forecasting model not only successfully avoids the complex progress of computing the weights of combined forecasting models,but also owns the properties of clear concept,easy operation and good characters of the forecasting models.As a special case of variable-weighting CFM,it has some values in application.
Telomere-mediated lung disease

Parenchymal lung disease is the fourth leading cause of death in the United States; among the top causes, it continues on the rise. Telomeres and telomerase have historically been linked to cellular processes related to aging and cancer, but surprisingly in the recent decade, genetic discoveries have linked the most apparent manifestations of telomere and telomerase dysfunction in humans to the etiology of lung disease: both idiopathic pulmonary fibrosis and emphysema. The short telomere defect is pervasive in a subset of idiopathic pulmonary fibrosis (IPF) patients, and human IPF is the phenotype most intimately tied to germline defects in telomere maintenance. One-third of families with pulmonary fibrosis carries germline mutations in telomerase or other telomere maintenance genes, and one half of patients with apparently sporadic IPF have short telomere length. Beyond explaining genetic susceptibility, short telomere length uncovers clinically relevant syndromic extrapulmonary disease including a T cell immunodeficiency and a propensity to myeloid malignancies. Recognizing this subset of patients who shares a unifying molecular defect has provided a precision medicine paradigm wherein the telomere-mediated lung disease diagnosis provides more prognostic value than histopathology or multi-disciplinary evaluation. Here, we critically evaluate this progress emphasizing how the genetic findings puts forth a new pathogenesis paradigm of age-related lung disease that links telomere abnormalities to alveolar stem senescence, remodeling and defective gas exchange.
MA-GANet: A Multi-Attention Generative Adversarial Network for Defocus Blur Detection

Background clutters pose challenges to defocus blur detection. Existing approaches often produce artifact predictions in background areas with clutter and relatively low confident predictions in boundary areas. In this work, we tackle the above issues from two perspectives. Firstly, inspired by the recent success of self-attention mechanism, we introduce channel-wise and spatial-wise attention modules to attentively aggregate features at different channels and spatial locations to obtain more discriminative features. Secondly, we propose a generative adversarial training strategy to suppress spurious and low reliable predictions. This is achieved by utilizing a discriminator to identify predicted defocus map from ground-truth ones. As such, the defocus network (generator) needs to produce ‘realistic’ defocus map to minimize discriminator loss. We further demonstrate that the generative adversarial training allows exploiting additional unlabeled data to improve performance, a.k. a. semi-supervised learning, and we provide the first benchmark on semi-supervised defocus detection. Finally, we demonstrate that the existing evaluation metrics for defocus detection generally fail to quantify the robustness with respect to thresholding. For a fair and practical evaluation, we introduce an effective yet efficient $AUF_\beta $ metric. Extensive experiments on three public datasets verify the superiority of the proposed methods compared against state-of-the-art approaches.
Meta-prospective memory accuracy in young adults with and without depressive symptoms.

Prospective memory (PM) is the ability to remember an intention in the future. Individuals with depression are candidates for PM failures, resulting in detrimental consequences, such as forgetting to take a medication or implement therapeutic techniques; inaccurate assessments of one's PM abilities can exacerbate these issues. The current study examined if appraisals about one's prospective memory (meta-PM) performance differs between healthy and depressed adults. Data were gathered from 137 adults and included self-reported depression, PM beliefs, objective PM, and assessment of executive functions (EFs). Participants were separated into depressed/healthy categories based on a self-report measure. There was a non-significant correlation between self-reported PM and objective PM for both depressed (r = .06, p = .61) and healthy (r = .08, p = .52) groups, suggesting both groups had inaccurate meta-PM. There were non-significant differences in meta-PM between these groups (Fisher's Z = -0.09, p = .93), but exploratory gender analyses revealed women's meta-PM was significantly less accurate than men's. Women had higher reports of depression and PM complaints compared to men. This study lends evidence that depression is not necessarily related to worse meta-PM accuracy, despite depression's association with memory complaints, and that women are at greater risk for inaccurate meta-PM.
Flexible solar photovoltaic deployments for Singapore: an economic assessment

This article presents an economic and technical analysis of the feasibility of various solar photovoltaic (PV) deployments in Singapore. In this study, the Levelised Costs of Electricity (LCOE) methodology is used to evaluate the cost of electricity for various solar PV systems that include Rooftop PV, Building Integrated PV, Floating PV and Offshore Islets PV deployments. Using actual company data on system and operating costs and reasonable estimates for irradiation and other key parameters, we find that the cost of electricity for rooftop PV deployments is on par with the current residential electricity tariff rate in Singapore. In addition, through projections of LCOE carried out for various PV deployments, we find that while all PV deployments are generally becoming more cost-competitive to match current technologies - such as combined cycle gas turbines (CCGT) - floating and offshore deployments of PV systems also have the potential to become competitive to CCGT by 2025.
Circuit-Breaker Mechanical Characteristics Test Methods and Selection

Introduction was made to several mechanical characteristics test methods of circuit-breaker,together with installation method,test accuracy,parameter input etc noticeable issues in sensor use process. A kind of high accuracy mechanical characteristics test method using dynamic contact in analog of sliding block to connect liner sensor was given. This test method shows that the movement state of circuit-breaker dynamic contacts is more visual,test value more accurate,meeting dynamic contacts actual moving tracks.
Laboratory Testing of Enhancing the Bearing Capacity of Strip Footing with Woven Geotextiles

Laboratory sand box tests were performed in this study to investigate the improvement in bearing capacity of a strip footing reinforced with woven geotextile in dry sand. The test cell is 0.9m wide, 0.9m long and 1.0m high. A hydraulic loading system was used to apply the normal force to a 0.85m × 0.1m × 0.05m (length × width × height) strip footing. The unreinforced bearing capacity was obtained and compared with value calculated using Terzaghi’s equation. Various parameters such as: burial depth, length of reinforcement, number of layers and distance between layers were varied to investigate their effects on the bearing capacity. Results of these tests indicate that the optimum burial depth of reinforcement is about 0.4 times the width of footing, with a bearing capacity ratio (BCR) of about 1.67. The optimum reinforcement length is about 3~4 times the width of footing with a BCR value of 1.81. The improvements in bearing capacity obtained from laboratory testing are in accord with previous numerical simulation. However, the loading behaviors are quite different due to the failure mechanism assumed in the numerical simulation.
Ultrafast Image Categorization in Biology and Neural Models

Humans are able to categorize images very efficiently, in particular to detect the presence of an animal very quickly. Recently, deep learning algorithms based on convolutional neural networks (CNNs) have achieved higher than human accuracy for a wide range of visual categorization tasks. However, the tasks on which these artificial networks are typically trained and evaluated tend to be highly specialized and do not generalize well, e.g., accuracy drops after image rotation. In this respect, biological visual systems are more flexible and efficient than artificial systems for more general tasks, such as recognizing an animal. To further the comparison between biological and artificial neural networks, we re-trained the standard VGG 16 CNN on two independent tasks that are ecologically relevant to humans: detecting the presence of an animal or an artifact. We show that re-training the network achieves a human-like level of performance, comparable to that reported in psychophysical tasks. In addition, we show that the categorization is better when the outputs of the models are combined. Indeed, animals (e.g., lions) tend to be less present in photographs that contain artifacts (e.g., buildings). Furthermore, these re-trained models were able to reproduce some unexpected behavioral observations from human psychophysics, such as robustness to rotation (e.g., an upside-down or tilted image) or to a grayscale transformation. Finally, we quantified the number of CNN layers required to achieve such performance and showed that good accuracy for ultrafast image categorization can be achieved with only a few layers, challenging the belief that image recognition requires deep sequential analysis of visual objects. We hope to extend this framework to biomimetic deep neural architectures designed for ecological tasks, but also to guide future model-based psychophysical experiments that would deepen our understanding of biological vision.
Development of Charging, Discharging Scheduling Algorithm for Economical and Energy Efficient Operation of Multi EV Charging Station

As the number of electric vehicles (EVs) significantly increases, the excessive charging demand of parked EVs in the charging station may incur an instability problem to the electricity network during peak hours. For the charging station to take a microgrid (MG) structure, an economical and energy-efficient power management scheme is required for the power provision of EVs while considering the local load demand of the MG. For these purposes, this study presents the power management scheme of interdependent MG and EV fleets aided by a novel EV charging/discharging scheduling algorithm. In this algorithm, the maximum amount of discharging power from parked EVs is determined based on the difference between local load demand and photovoltaic (PV) power production to alleviate imbalances occurred between them. For the power management of the MG with charging/discharging scheduling of parked EVs in the PV-based charging station, multi-objective optimization is performed to minimize the operating cost and grid dependency. In addition, the proposed scheme maximizes the utilization of EV charging/discharging while satisfying the charging requirements of parked EVs. Moreover, a more economical and energy-efficient PV-based charging station is established using the future trends of local load demand and PV power production predicted by a gated recurrent unit (GRU) network. With the proposed EV charging/discharging scheduling algorithm, the operating cost of PV-based charging station is decreased by 167.71% and 28.85% compared with the EV charging scheduling algorithm and the conventional EV charging/discharging scheduling algorithm, respectively. It is obvious that the economical and energy-efficient operation of PV-based charging station can be accomplished by applying the power management scheme with the proposed EV charging/discharging scheduling strategy.
DULA and DEBA: Differentiable Ergonomic Risk Models for Postural Assessment and Optimization in Ergonomically Intelligent pHRI

Ergonomics and human comfort are essential concerns in physical human-robot interaction applications. Defining an accurate and easy-to-use ergonomic assessment model stands as an important step in providing feedback for postural correction to improve operator health and comfort. Common practical methods in the area suffer from inaccurate ergonomics models in performing postural optimization. In order to retain assessment quality, while improving computational considerations, we propose a novel framework for postural assessment and optimization for ergonomically intelligent physical human-robot interaction. We introduce DULA and DEBA, differentiable and continuous ergonomics models learned to replicate the popular and scientifically validated RULA and REBA assessments with more than 99% accuracy. We show that DULA and DEBA provide assessment comparable to RULA and REBA while providing computational benefits when being used in postural optimization. We evaluate our framework through human and simulation experiments. We highlight DULA and DEBA's strength in a demonstration of postural optimization for a simulated pHRI task.