Miracle-dz/UniFAR-FTUs huggingface dataset:
An Enhanced Power Regulation and Seamless Operation Mode Transfer Control Through Cooperative Dual-Interfacing Converters

Both voltage control method (VCM) and current control method (CCM) have been well-developed to control DG units in a microgrid. Although conventional VCM has seamless operation mode transfer capability, it has slow power control dynamics in grid-tied operation. On the other hand, CCM features rapid power control but it can hardly provide voltage support during standalone operation of microgrids. To address these issues, a single DG unit is interfaced to the microgrid with two low power parallel converters. One converter is controlled by VCM while the other one is regulated by CCM. Through a new collaborative power control algorithm for these two converters in a single DG unit, the merits of both VCM and CCM are preserved. First, the power control dynamics of this DG unit can be significantly improved in grid-tied operation. Second, the load demand can be accurately shared by these two interfacing converters during islanding operation. Finally, a true seamless operation mode transfer is obtained at any time instant without preparations. Evaluation results from a three-phase prototype show that the proposed interfacing converter configuration and corresponding control algorithm are more suitable for microgrid applications.
Biomarkers for Barrett’s esophagus – a contemporary review

ABSTRACT Introduction: Esophageal adenocarcinoma (EAC) has a poor 5-year survival rate (10%–18%), and incidence has increased dramatically in the past three decades. Barrett’s esophagus (BE) is the precursor lesion to EAC and is the replacement of the normally squamous lined esophagus with columnar cells that develop an intestinal phenotype characterized by the presence of goblet cells. Given the known precursor state, EAC is amenable to screening and surveillance strategies (analogous to colon cancer). However, unlike from colon cancer screening, BE poses challenges that make effective screening difficult. Robust and concerted effort is under way to find biomarkers of BE. Areas covered: This review summarizes current known biomarkers for BE. These include dysplasia, genomic markers, and gene expression alterations that occur early in the dysplasia/carcinoma sequence. Expert commentary: Despite the tremendous breadth of work in studying molecular advances, the ideal biomarker for BE has not yet been discerned. This review comments on innovations in the field of BE research that combine state-of-the-art molecular advances with simple technologies.
MTTool: A Tool for Software Modeling and Test Generation

MTTool is a novel model-based test tool. It is developed for modeling complex software behavior and generating test cases from the model. Different from existing model-based test tools, the proposed tool realizes the ERE-based testing, where the model is an extended regular expression (ERE). In ERE-based testing, test cases are generated from some decomposed subexpressions, thus alleviating the state-space-explosion problem in model-based testing. This paper introduces the modeling theory of the tool and two modeling ways: constructing regular finite state machine and writing R language. Additionally, three key algorithms in the tool are presented to construct the ERE model, decompose the ERE model, and generate test cases from submodels on the basis of test coverage criteria. Through an example, we demonstrate the application of the three algorithms. MTTool is found to well support the ERE-based test method and can be helpful in popularizing the application of this test method.
Unrolled Optimization with Deep Priors for Intrinsic Image Decomposition

Intrinsic image decomposition is a challenging task, which aims at separating an image into reflectance and shading layers. Traditionally, strong hand-crafted priors such as reflectance sparsity, shading smoothness and depth information, have been used to solve this long-standing ill-posed problem including two variables. Recent researches lay emphasis on the deep neural networks which need to be specific design. To overcome these limitations, we develop a novel unrolled optimization model for intrinsic image decomposition, which incorporate deep priors from the optimization perspective in a more skillful way, rather than directly design the specific network or introduce hand-crafted and human annotation priors. Extensive experimental results illustrate the excellent performance of our method compared with other state-of-the-art methods and we successfully carry out the proposed algorithm for the application based on image decomposition (e.g. low-light image enhancement).
Enabling Grasp Action: Generalized Evaluation of Grasp Stability via Contact Stiffness from Contact Mechanics Insight

Performing a grasp is a pivotal capability for a robotic gripper. We propose a new evaluation approach of grasping stability via constructing a model of grasping stiffness based on the theory of contact mechanics. First, the mathematical models are built to explore soft contact and the general grasp stiffness between a finger and an object. Next, the grasping stiffness matrix is constructed to reflect the normal, tangential and torsion stiffness coefficients. Finally, we design two grasping cases to verify the proposed measurement criterion of grasping stability by comparing different grasping configurations. Specifically, a standard grasping index is used and compared with the minimum eigenvalue index of the constructed grasping stiffness we built. The comparison result reveals a similar tendency between them for measuring the grasping stability and thus, validates the proposed approach.
Nucleation‐Controlled Solution Deposition of Silver Nanoplate Architectures for Facile Derivatization and Catalytic Applications

Due to their distinctive electronic, optical, and chemical properties, metal nanoplates represent important building blocks for creating functional superstructures. Here, a general deposition method for synthesizing Ag nanoplate architectures, which is compatible with a wide substrate range (flexible, curved, or recessed; consisting of carbon, silicon, metals, oxides, or polymers) is reported. By adjusting the reaction conditions, nucleation can be triggered in the bulk solution, on seeds and by electrodeposition, allowing the production of nanoplate suspensions as well as direct surface modification with open‐porous nanoplate films. The latter are fully percolated, possess a large, easily accessible surface, a defined nanostructure with {111} basal planes, and expose defect‐rich, particularly reactive edges in high density, making them compelling platforms for heterogeneous catalysis, and electro‐ and flow chemistry. This potential is showcased by exploring the catalytic performance of the nanoplates in the reduction of carbon dioxide, 4‐nitrophenol, and hydrogen peroxide, devising two types of microreactors, and by tuning the nanoplate functionality with derivatization reactions.
Phase Synchronization Dynamics of Neural Network during Seizures

Epilepsy has been considered as a network-level disorder characterized by recurrent seizures, which result from network reorganization with evolution of synchronization. In this study, the brain networks were established by calculating phase synchronization based on electrocorticogram (ECoG) signals from eleven refractory epilepsy patients. Results showed that there was a significant increase of synchronization prior to seizure termination and no significant difference of the transitions of network states among the preseizure, seizure, and postseizure periods. Those results indicated that synchronization might participate in termination of seizures, and the network states transitions might not dominate the seizure evolution.
Assessing soil properties and nutrient availability under conservation agriculture practices in a reclaimed sodic soil in cereal-based systems of North-West India

ABSTRACT Soil quality degradation associated with resources scarcity is the major concern for the sustainability of conventional rice-wheat system in South Asia. Replacement of conventional management practices with conservation agriculture (CA) is required to improve soil quality. A field experiment was conducted to assess the effect of CA on soil physical (bulk density, penetration resistance, infiltration) and chemical (N, P, K, S, micronutrients) properties after 4 years in North-West India. There were four scenarios (Sc) namely conventional rice-wheat cropping system (Sc1); partial CA-based rice-wheat-mungbean system (RWMS) (Sc2); CA-based RWMS (Sc3); and CA-based maize-wheat-mungbean (Sc4) system. Sc2 (1.52 Mg m−3) showed significantly lower soil bulk density (BD). In Sc3 and Sc4, soil penetration resistance (SPR) was reduced and infiltration was improved compared to Sc1. Soil organic C was significantly higher in Sc4 than Sc1. Available N was 33% and 68% higher at 0–15 cm depth in Sc3 and Sc4, respectively, than Sc1. DTPA extractable Zn and Mn were significantly higher under Sc3 and Sc4 compared to Sc1. Omission study showed 30% saving in N and 50% in K in wheat after four years. Therefore, CA improved soil properties and nutrient availability and have potential to reduce external fertilizer inputs in long run.
Detecting cities in aerial night-time images by learning structural invariants using single reference augmentation

This paper examines, if it is possible to learn structural invariants of city images by using only a single reference picture when producing transformations along the variants in the dataset. Previous work explored the problem of learning from only a few examples and showed that data augmentation techniques benefit performance and generalization for machine learning approaches. First a principal component analysis in conjunction with a Fourier transform is trained on a single reference augmentation training dataset using the city images. Secondly a convolutional neural network is trained on a similar dataset with more samples. The findings are that the convolutional neural network is capable of finding images of the same category whereas the applied principal component analysis in conjunction with a Fourier transform failed to solve this task.
Modular Manifold Ranking for Image Recommendation

With the rapid development of social networking, user requirement suffers more and more from intention gap of user interest and semantic gap of multimedia. It becomes urgent to investigate personalized recommendation. In this paper, we propose modular manifold ranking (MMR) for image recommendation. MMR attempts to construct global image manifold over CNN based features extraction to involve content relations in recommendation. Specifically, manifold modularity is introduced to perform a flexible manifold learning for large scale database in a manner of manifold decomposition. MMR employed manifold ranking to propagate users' interests to the whole image manifold and estimate user-image correlation for image recommendation. The experimental analysis illustrates that the proposed method successfully extends the scalability of manifold ranking and achieves good performance in social image recommendation compared with its competitors.
No Association Between Ljungan Virus Seropositivity and the Beta-cell Damaging Process in the Finnish Type 1 Diabetes Prediction and Prevention Study Cohort

Background: Ljungan virus (LV) has not confirmed to associate with any human disease, but a possible connection with type 1 diabetes has been suggested. LV is a rodent-borne picornavirus that induces a diabetes-like condition in rodents. Approximately 30% of adults and 60% of children are seropositive in Finland. The Finnish Type 1 Diabetes Prediction and Prevention study enabled the use of very well characterized sample panels from children seroconverted to positivity for multiple islet autoantibodies during their prospective observation from birth; in addition, samples from age, sex, human leukocyte antigen (HLA), and residence area matched control children. Methods: We analyzed LV IgG seroprevalence in 102 case children (65 had also developed type 1 diabetes), in addition to nondiabetic control children. LV and human parechovirus (HPeV) immunofluorescence assays were used to analyze LV and HPeV-specific IgG from 102 plasma samples taken at the time of islet autoantibody appearance and from 204 samples from the matched control children. Results: Altogether 46.1% of the case and 50.7% of the control children were positive for LV IgG (odds ratio 0.8; 95% confidence interval, 0.47–1.36; P = 0.416) and 67.6% versus 79.8% were positive for HPeV IgG, respectively (odds ratio 0.49, 0.27–0.9, P = 0.023). Conclusions: Thus, no risk associations between LV or HPeV-specific IgG and islet autoimmunity were observed. However, a trend for significantly higher prevalence of HPeV antibodies in control children (P = 0.023) suggests a possible protective association of this virus with islet autoimmunity.
Joint Beamforming and Jamming Design for mmWave Information Surveillance Systems

This paper addresses the design of joint beamforming and jamming for a millimeter wave (mmWave) information surveillance system where a suspicious transmitter in the network sends messages to a suspicious receiver under the supervision of a surveillant controller (SC), which not only carries out the duty of a base station or other access point, but also legitimately monitor the suspicious link. Specifically, we seek to maximize the effective monitoring rate for information surveillance by jointly optimizing the analog transmit and receive beamforming vectors of the suspicious link, the analog jamming and monitoring beamforming vectors at the SC and the jamming signal’s power level under transmit power, successful monitoring, and self-interference power constraints at the SC, along with unit modulus constraint on the elements of the radio frequency analog beamforming vectors. The resulting optimization problem is quite challenging due to the tight coupling of the design variables in the objective function and constraints. To solve it, we develop a novel algorithm based on the penalty dual decomposition (PDD) technique, where the exacting constraints are penalized and dualized into the objective function as augmented Lagrangian components. The proposed PDD-based algorithm performs double-loop iterations, i.e., the inner loop resorts to the concave-convex procedure to update the optimization variables; while the outer loop adjusts the Lagrange multipliers and penalty parameter of the augmented Lagrangian cost function. We show that the proposed PDD-based joint beamforming and jamming algorithm converges to a stationary solution of the original problem. Based on our simulation results, the proposed algorithm achieves significantly better performance than the conventional beamforming and jamming algorithms.
Robotic Arm Rehabilitation in Chronic Stroke Patients With Aphasia May Promote Speech and Language Recovery (but Effect Is Not Enhanced by Supplementary tDCS)

Objective: This study aimed to determine the extent to which robotic arm rehabilitation for chronic stroke may promote recovery of speech and language function in individuals with aphasia. Methods: We prospectively enrolled 17 individuals from a hemiparesis rehabilitation study pairing intensive robot assisted therapy with sham or active tDCS and evaluated their speech (N = 17) and language (N = 9) performance before and after a 12-week (36 session) treatment regimen. Performance changes were evaluated with paired t-tests comparing pre- and post-test measures. There was no speech therapy included in the treatment protocol. Results: Overall, the individuals significantly improved on measures of motor speech production from pre-test to post-test. Of the subset who performed language testing (N = 9), overall aphasia severity on a standardized aphasia battery improved from pre-test baseline to post-test. Active tDCS was not associated with greater gains than sham tDCS. Conclusions: This work indicates the importance of considering approaches to stroke rehabilitation across different domains of impairment, and warrants additional exploration of the possibility that robotic arm motor treatment may enhance rehabilitation for speech and language outcomes. Further investigation into the role of tDCS in the relationship of limb and speech/language rehabilitation is required, as active tDCS did not increase improvements over sham tDCS.
Multifilar Resistors from DC up to 30 MHz

The function of the frequency dependence of resistance for bifilar resistor remains unchanged in the frequency range from DC to 30 MHz. The parameters of this function measured at one frequency make it possible to calculate the resistance at any other frequency of the range. These parameters are determined by the residual parameters of the resistor - its inductance and capacitance. Reducing the physical dimensions of the resistor leads to a decrease in its residual parameters and reduces of its frequency dependence.
Parameterization Models for Traceable Characterization of Planar CPW SOL Calibration Standards

Methods for traceable characterization and uncertainty evaluation of planar I-port CPW short-open-Ioad (SOL) devices are developed. The agreement of modelling and verification measurement results greatly supports the application of the proposed parameterization models and used FEM-based EM modelling of CPW structures for traceable characterization of planar CPW-based SOL devices in the frequency range between a few kHz up to 50 GHz.
Security-Reliability Tradeoff for Distributed Antenna Systems in Heterogeneous Cellular Networks

In this paper, we investigate physical-layer security for a spectrum-sharing heterogeneous cellular network comprised of a macro cell and a small cell, where a passive eavesdropper is assumed to tap the transmissions of both the macro and small cells. In the macro cell, a macro base station (MBS) equipped with multiple distributed antennas sends its confidential information to a macro user (MU) through an opportunistic transmit antenna. Meanwhile, in the small cell, a small base station (SBS) transmits to a small user (SU) over the same spectrum used by MBS. We propose an interference-canceled opportunistic antenna selection (IC-OAS) scheme to enhance the physical-layer security for the heterogeneous network. To be specific, when MBS sends its confidential message to MU through an opportunistic distributed antenna, a special signal is artificially designed and emitted at the MBS to ensure that the received interference at MU from SBS is canceled out. For comparison, the conventional interference-limited opportunistic antenna selection (IL-OAS) is considered as a benchmark. We characterize the security-reliability tradeoff (SRT) for the proposed IC-OAS and conventional IL-OAS schemes in terms of deriving their closed-form expressions of intercept probability and outage probability. Numerical results show that compared with the conventional IL-OAS, the proposed IC-OAS scheme not only brings SRT benefits to the macro cell but also has the potential of improving the SRT of small cell by increasing the number of distributed antennas. Additionally, by jointly taking into account the macro cell and small cell, an overall SRT of the proposed IC-OAS scheme is shown to be significantly better than that of the conventional IL-OAS approach in terms of a sum intercept probability versus sum outage probability.
Effect of ankle–foot orthoses on motor performance in cerebral palsy

vation than non-disabled peers. These longer muscle phasic activities during each cycle may also contribute to increase the energy cost of walking and thus the fatigability in individuals with CP. In addition, children diagnosed with spastic CP could exhibit a tissue(coactivation) related ankle hyperresistance leading to a reduced passive ankle dorsiflexion during the stance phase of gait. Such phenomenon could contribute toward abnormal locomotion, wasted mechanical energy, and thus high levels of fatigue. No information on the physical activity level of children with CP was provided in the study by Eken et al. Yet, such information is required since those with high biomechanical walking economy were found to be more physically active and less fatigable. In our opinion, the level of habitual physical activity should be systematically quantified using objective or subjective methods such as accelerometry, diary, or questionnaires to compare children with CP to non-disabled peers more equitably. Further research is needed to determine whether exercise programs used to improve walking economy increase the level of physical activity and whether any improvement in habitual physical activity translates into a reduced energy cost of gait and lower levels of fatigue in CP.
Investigating the relationship between psychological hardiness and resilience with depression in women with breast cancer.

INTRODUCTION Considering the fact that depression in patients with breast cancer increases the severity and complications of medical treatments such as chemotherapy (including fatigue, nausea, and cognitive problems), the identification of its related factors can be one of the important steps in reducing the complications of this disease. Therefore, the purpose of this study was to determine the relationship between psychological hardiness and resilience with depression in women with breast cancer. MATERIALS AND METHODS The present study was a descriptive-analytic study, the population of which included all women with breast cancer under chemotherapy referred to Oncology Center of Imam Hassan Mojtaba Hospital in Dezful city from July 2015 to December 2016. A sample size of 114 people was selected. Data collection instruments included demographic information form, Ahvaz Psychological Hardiness Scale, Connor-Davidson Resilience Scale (CD-RISC), and Beck Depression Inventory. Data were analyzed using SPSS ver.21. FINDINGS Findings showed that 61.4% of respondents had depressive symptoms. Mild, moderate and severe depression symptoms were prevalent among 37.7%, 20.2% and 3.5% of the participants, respectively. Also, there is a significant relationship between resiliency and hardiness with depression in women with breast cancer (P<0.05). DISCUSSION AND CONCLUSION The depression rate in women with breast cancer decreased if hardiness and resiliency against the disease has been increased. This means that there is a need for training courses and counseling services for women with breast cancer in order to improve their mental health status.
Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment From Hand Radiograph

Skeletal bone age assessment is a common clinical practice to investigate endocrinology, and genetic and growth disorders of children. However, clinical interpretation and bone age analyses are time-consuming, labor intensive, and often subject to inter-observer variability. This advocates the need of a fully automated method for bone age assessment. We propose a regression convolutional neural network (CNN) to automatically assess the pediatric bone age from hand radiograph. Our network is specifically trained to place more attention to those bone age related regions in the X-ray images. Specifically, we first adopt the attention module to process all images and generate the coarse/fine attention maps as inputs for the regression network. Then, the regression CNN follows the supervision of the dynamic attention loss during training; thus, it can estimate the bone age of the hard (or “outlier”) images more accurately. The experimental results show that our method achieves an average discrepancy of 5.2–5.3 months between clinical and automatic bone age evaluations on two large datasets. In conclusion, we propose a fully automated deep learning solution to process X-ray images of the hand for bone age assessment, with the accuracy comparable to human experts but with much better efficiency.