Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Verification of LV Network Topologies

The increasing penetration of low carbon technologies and electric vehicles is expected to place a considerable burden to Low Voltage (LV) power distribution networks. The previously passive management of LV networks will no longer be sustainable to cope with this growth. A more proactive approach is required to manage LV networks, usually achieved by using Power System State Estimation (PSSE) tools. The effective use of PSSE tools however, requires accurate reporting of each LV network's topology and configuration of assets (cables) that compose it. This paper presents a method for ongoing accuracy improvement of the LV networks topologies, by automating the process of approximating missing cable information and validating the network architectures. The method proposed was derived based on the assumptions that a specific network infrastructure was selected to meet a certain distributed customer loading requirement. The results obtained agrees with the assumptions, providing also a mechanism that scores the confidence level for the choices made to approximate any missing asset information.
Object Recognition on Long Range Thermal Image Using State of the Art DNN

This paper presents a method for detecting objects on long range thermal images. This problem is extremely complicated due to the low resolution of these images, and due to the fact that the objects that we want to detect have areas as small as 50 pixels. In order to solve this problem we have developed, trained and tested a large number (2640) of variations of the state of the art algorithm for visible camera object detection, called YOLO Darknet - You Only Look Once Darknet. The paper succeeds in showing that the deep neural network YOLO Darknet can be adapted to work on thermal images and, furthermore, it proves that the network is capable of detecting and classifying objects that are hard to detect with the human eye.
The Effect of Surface Treatments on the Degradation of Biomedical Mg Alloys—A Review Paper

This report reviews the effects of chemical, physical, and mechanical surface treatments on the degradation behavior of Mg alloys via their influence on the roughness and surface morphology. Many studies have been focused on technically-used AZ alloys and a few investigations regarding the surface treatment of biodegradable and Al-free Mg alloys, especially under physiological conditions. These treatments tailor the surface roughness, homogenize the morphology, and decrease the degradation rate of the alloys. Conversely, there have also been reports which showed that rough surfaces lead to less pitting and good cell adherence. Besides roughness, there are many other parameters which are much more important than roughness when regarding the degradation behavior of an alloy. These studies, which indicate the relationship between surface treatments, roughness and degradation, require further elaboration, particularly for biomedical Mg alloy applications.
Research on the Acoustic-Structure Coupling Characteristics of a Piezoelectric Micro-Jet Used for Lubricating

The injection performance of the piezoelectric micro-jet is mainly determined by the acoustic characteristics created in the cavity by the vibration of the piezoelectric vibrator. As sensors are difficult to be placed into the cavity of the piezoelectric micro-jet, the inside acoustic characteristics are hard to be tested by experiments. Thus, an acoustic-structure coupling model is given, and simulation analyses are carried out in this paper to study a piezoelectric micro-jet used for lubricating. The suitable working frequency for getting better lubricating performance is obtained by the frequency response characteristics analyses. The driving mechanism is revealed by comparing with the injection results. The influences of excitations on the acoustic pressure at the nozzle part are analyzed, and the methods for adjusting the working performance are obtained. The acoustic structure coupling model and results are verified by comparing with the experiment results.
In Search of a Field-Based Relationship Between Benthic Macrofauna and Biogeochemistry in a Modern Brackish Coastal Sea

During several cruises in the southern Baltic Sea conducted in different seasons from 2014 to 2016, sediment cores were collected for the investigation of pore-water biogeochemistry and associated nutrient fluxes across the sediment-water interface. Six stations were positioned along a salinity gradient (ranging from 22 to 8) and covered various sedimentary habitats ranging from mud to sand. Integrated fluxes of nutrients in the supernatant water and sediment oxygen consumption were additionally derived from incubations of intact sediment cores. Subsequently, sediment from the pore-water and incubation cores was sieved for taxonomic identification and estimation of benthic macrofauna density. This combined dataset was used to determine the dominant factors influencing the vertical distribution of geochemical parameters in the pore-waters of the studied habitats and to find similarities and patterns explaining significant variations of solute fluxes across the sediment-water interface. A statistical relationship between the thickness of sulfide-free surface sediments, solute fluxes of sulfide, ammonium and phosphate as well as oxygen consumption and taxonomic and functional characteristics of macrobenthic communities were tested. Our data and modeling results indicate that bioturbation and bioirrigation alter near-surface pore-water nutrient concentrations towards bottom water values. Besides sediment properties and microbial activity, the biogeochemical fluxes can further be explained by the functional structure of benthic macrofauna. Community bioturbation potential, species richness and biomass of biodiffusers were the best proxies among the tested set of biotic and abiotic parameters and could explain 63% of multivariate total benthic flux variations. The effects of macrobenthos on ecosystem functioning differ between sediment types, specific locations and seasons. Both, species distribution and nutrient fluxes are temporally dynamic. Those natural patterns, as well as potential anthropogenic and natural disturbances (e.g. fishery, storm events), may cause impacts on field data in a way beyond our present capability of quantitative prediction, and require more detailed seasonal studies. The data presented here adds to our understanding of the complexity of natural ecosystem functioning under anthropogenic pressure.
Viral MHCI inhibition evades tissue-resident memory T cell formation and responses

&NA; Tissue‐resident memory CD8+ T cells (TRMs) confer rapid protection and immunity against viral infections. Many viruses have evolved mechanisms to inhibit MHCI presentation in order to evade CD8+ T cells, suggesting that these mechanisms may also apply to TRM‐mediated protection. However, the effects of viral MHCI inhibition on the function and generation of TRMs is unclear. Herein, we demonstrate that viral MHCI inhibition reduces the abundance of CD4+ and CD8+ TRMs, but its effects on the local microenvironment compensate to promote antigen‐specific CD8+ TRM formation. Unexpectedly, local cognate antigen enhances CD8+ TRM development even in the context of viral MHCI inhibition and CD8+ T cell evasion, strongly suggesting a role for in situ cross‐presentation in local antigen‐driven TRM differentiation. However, local cognate antigen is not required for CD8+ TRM maintenance. We also show that viral MHCI inhibition efficiently evades CD8+ TRM effector functions. These findings indicate that viral evasion of MHCI antigen presentation has consequences on the development and response of antiviral TRMs. &NA; Lauron et al. demonstrate that viral MHCI inhibition within infected cells reduces local antigen‐driven generation of resident memory CD8+ T cells. Additionally, resident memory CD8+ T cells are insufficient in controlling peripheral infection in the context of viral MHCI evasion.
A Dual-Band Focal Plane Array of Kinetic Inductance Bolometers Based on Frequency-Selective Absorbers

Passive imaging cameras at submillimeter wavelengths with large format focal plane arrays (FPAs) are being developed as the next generation of security screening systems. In this contribution, a dual-band FPA for security imagers at submillimeter wave frequencies is presented. The detectors are based on bolometric superconducting kinetic inductance resonators, which allow the development of large FPAs at medium-cooled temperatures. Two frequency-selective absorber (FSA) sets coupled to superconductive resonator lines are designed to implement a dual-color security imager. The performance of the dual-band imager is evaluated using spectral analysis approach that combines Fourier optics with a Floquet-mode field representation. The geometry of the unit cells is based on a Jerusalem cross configuration and the designed FSAs show a stable angular response and a rejection 1 to 3 of the undesired bandwidth. The detectors in the dual-band FPA are distributed over a hexagonal grid to maximize their physical size and then improve their sensitivity. The effective point spread function of the imager coupled to a black-body point source over a wide frequency band (1:6) was demonstrated experimentally with excellent agreement to the one estimated by using the proposed spectral technique.
Feature Analysis for Discrimination of Motor Unit Action Potentials

In electrophysiological signal processing for intramuscular electromyography data (nEMG), single motor unit activity is of great interest. The changes of action potential (MUAP) morphology, motor unit (MU) activation, and recruitment provide the most informative part to study the nature causality in neuromuscular disorders. In practice, for a single nEMG recording, more than one motor unit activities (in the surrounding area of a needle electrode) are usually collected. Such a fact makes the MUAP discrimination that separates single unit activities a crucial task. Most neurology laboratories worldwide still recruit specialists who spend hours to manually or semi-automatically sort MUAPs. From a machine learning perspective, this task is analogous to the clustering-based classification problem in which the number of classes and other class information are unfortunately missing. In this paper, we present a feature analysis strategy to help better utilize unsupervised (i.e., totally automated) methods for MUAP discrimination. To that end, we extract a large pool of features from each MUAP. Then we select the top ranked candidates using clusterability scores as selection criteria. We found spectrograms of wavelet decomposition as a top-ranking feature, highly correlated to the motor unit reference and was more separable than existing features. Using a correlation-based clustering technique, we demonstrate the sorting performance with this feature set. Compared with the reference produced by human experts, our method obtained a comparable result (e.g., equivalent number of classes was found, identical MUAP morphology in each pair of corresponding MU class, and similar histograms of MUs). Taking the manual labels as references, our method got a much higher sensitivity and accuracy than the compared unsupervised sorting method. We obtained a similar result in MUAP classification to the reference.
Muscle ciliary neurotrophic factor receptor α contributes to motor neuron STAT3 activation following peripheral nerve lesion

Expression of the ciliary neurotrophic factor (CNTF) receptor essential ligand binding subunit, CNTF receptor α (CNTFRα), is induced in motor neurons and skeletal muscle following peripheral nerve lesion. We previously found muscle CNTFRα promotes motor neuron axon regeneration post‐lesion. Both nerve lesion and CNTF administration activate motor neuron signal transducer and activator of transcription 3 (STAT3), a transcription factor implicated in axon growth, suggesting CNTF receptors may contribute to the lesion‐induced STAT3 activation. However, many receptor types signal through STAT3, and if CNTF receptors contribute, motor neuron receptors seemed most likely to regulate motor neuron STAT3. To determine the role played by muscle CNTFRα, we used in vivo, muscle‐specific CNTFRα depletion in mice and report here that this selectively impairs the second phase, sustained motor neuron STAT3 activation post‐lesion. Thus, muscle CNTFRα makes an essential contribution to motor neuron STAT3 activation during axon regeneration and may thereby promote axon regeneration through such signaling. We also report CNTFRα quantitative PCR suggesting involvement of many denervated muscle types, as well as muscle damaged at the lesion site. The present data add to the evidence suggesting that enhancing muscle CNTFRα expression may promote motor neuron regeneration in trauma and disease.
Online Coordination of Plugged-In Electric Vehicles and Optimal Rescheduling of Switched Shunt Capacitors in Smart Grid Considering Battery Charger Harmonics

This paper presents a practical solution to improve the performance and power quality of the smart grid (SG) with a high penetration of plugged-in electric vehicles (PEVs). The random, mobile, and time-variant PEV demands can overload lines, transformers, and distributed generations while the collective impacts of their battery charger harmonics may increase the overall total harmonic distortion (THD) of SG. To overcome these issues, this paper incorporates the optimal rescheduling of switched shunt capacitors (SSCs) and their day-ahead schedules in a recently developed online maximum sensitivity selection-based PEV coordination algorithm with the inclusion of harmonics. This will not only improve customer satisfaction by fully charging all vehicles before 8 A.M. for the next day travel but also reduce node voltage fluctuations, overall THD, and system losses. To check the performance of the proposed approach, detailed simulations are performed on the modified IEEE 23-kV medium voltage distribution network with seven SSCs and 22 low-voltage residential networks that are populated with PEVs with nonlinear battery chargers and industry nonlinear loads.
Recruiting potent membrane penetrability in tumor cell-targeted protein-only nanoparticles

The membrane pore-forming activities of the antimicrobial peptide GWH1 have been evaluated in combination with the CXCR4-binding properties of the peptide T22, in self-assembling protein nanoparticles with high clinical potential. The resulting materials, of 25 nm in size and with regular morphologies, show a dramatically improved cell penetrability into CXCR4+ cells (more than 10-fold) and enhanced endosomal escape (the lysosomal degradation dropping from 90% to 50%), when compared with equivalent protein nanoparticles lacking GWH1. These data reveal that GWH1 retains its potent membrane activity in form of nanostructured protein complexes. On the other hand, the specificity of T22 in the CXCR4 receptor binding is subsequently minimized but, unexpectedly, not abolished by the presence of the antimicrobial peptide. The functional combination T22-GWH1 results in 30% of the nanoparticles entering cells via CXCR4 while also exploiting pore-based uptake. Such functional materials are capable to selectively deliver highly potent cytotoxic drugs upon chemical conjugation, promoting CXCR4-dependent cell death. These data support the further development of GWH1-empowered cell-targeted proteins as nanoscale drug carriers for precision medicines. This is a very promising approach to overcome lysosomal degradation of protein nanostructured materials with therapeutic value.
Interleukin-37 (IL-37) Suppresses Pertussis Toxin-Induced Inflammatory Myopathy in a Rat Model

Background Recent data have demonstrated the potential immunosuppressive roles of interleukin-37 (IL-37) in several diseases, but whether it is involved in the pathogenesis of inflammatory myopathy has not been elucidated. Material/Methods An experimental autoimmune myositis (EAM) model was built by subcutaneous injections of pertussis toxin (PTX) and purified rabbit myosin (10mg/kg) emulsified with an equal volume of conventional complete Freund’s adjuvant (CFA) in a Lewis model. Autoimmune myositis Lewis model rats were divided into 3 groups: group A rats (control group) were injected with CFA in saline weekly; group B (IL-37 group) rats were injected with saline with IL-37 and CFA in saline weekly; and group C (IL-37 + SIS3 group) rats were injected with IL-37, CFA, and SIS3. ELISA was also used to assess the expressions of TNF-α, IL-6, IL-1β, TGF-β1, and CK. HE staining was performed to assess pathological changes in lung and muscle tissues. Results The expressions of TNF-α, IL-6, IL-1β, TGF-β1, and CK significantly increased in autoimmune myositis Lewis model rats. After IL-37 treatment, the expression of TNF-α, IL-6, IL-1β, TGF-β1, and CK was significantly reduced, as were the inflammatory responses of lung and muscle. However, SIS3 reduced the effects of IL-37 on the autoimmune myositis Lewis model rats. Conclusioans These findings indicate that IL-37 protects against inflammatory response via regulating Smad3 in autoimmune myositis Lewis model rats.
Economic carbon cycle feedbacks may offset additional warming from natural feedbacks

Significance The response of different economic sectors and energy infrastructure to climate warming is complex and difficult to compare with land and ocean carbon cycle feedbacks. Our analysis provides a framework for assessing such economic responses and comparing climate feedbacks in integrated assessment and earth system models. A better understanding of the potential effect of an economically driven feedback may improve our ability to estimate limits on cumulative emissions necessary to meet specific climate stabilization targets. We find that a net negative feedback from economic damages on fossil fuels may be strong enough to offset the positive feedback from terrestrial and marine ecosystems; however, these economic losses may disproportionately affect vulnerable populations and make climate mitigation more difficult. As the Earth warms, carbon sinks on land and in the ocean will weaken, thereby increasing the rate of warming. Although natural mechanisms contributing to this positive climate–carbon feedback have been evaluated using Earth system models, analogous feedbacks involving human activities have not been systematically quantified. Here we conceptualize and estimate the magnitude of several economic mechanisms that generate a carbon–climate feedback, using the Kaya identity to separate a net economic feedback into components associated with population, GDP, heating and cooling, and the carbon intensity of energy production and transportation. We find that climate-driven decreases in economic activity (GDP) may in turn decrease human energy use and thus fossil fuel CO2 emissions. In a high radiative forcing scenario, such decreases in economic activity reduce fossil fuel emissions by 13% this century, lowering atmospheric CO2 by over 100 ppm in 2100. The natural carbon–climate feedback, in contrast, increases atmospheric CO2 over this period by a similar amount, and thus, the net effect including both feedbacks is nearly zero. Our work highlights the importance of improving the representation of climate–economic feedbacks in scenarios of future change. Although the effects of climate warming on the economy may offset weakening land and ocean carbon sinks, a loss of economic productivity will have high societal costs, potentially increasing wealth inequity and limiting resources available for effective adaptation.
Low-Power Regulator for Micro Energy Harvesting Applications

In micro energy harvesting applications one of the most essential blocks of the power management unit is the regulator. In this paper we present a low-power regulator consisting of a comparator, a voltage reference and an output voltage monitor. The output voltage of the regulation is set at 1.1V. The average current consumption of the regulator at 10kHz is 1.6nA. The energy/conversion ratio of the proposed comparator is 20fJ at $\mathrm{V}_{dd}\,=0.8\mathrm{V}$. The current consumption of the voltage reference and the output voltage monitor is 19pA and 22pA, respectively. These numbers make our solution very suitable for energy harvesters that have very limited energy levels available, as is the case of applications like implantable devices.
Study on Renewable Penetration Limits in a Typical Indonesian Islanded Microgrid Considering the Impact of Variable Renewables Integration and the Empowering Flexibility on Grid Stability

This study assesses the impacts of variable renewabIes and the use of flexibility on frequency, voltage, and angle stability in a typical isolated microgrid in Indonesia. The simulations in Power Factory have illustrated the flexibility options and the stability behavior evolution, considering a parametric study on the shares of inverter-based variable renewable energy resources. The 10% limit of variable renewabIes in Indonesian microgrids is also discussed and proven to be rather pessimistic, as this research reaches the instantaneous penetration rate varying from 0% to 31.3% in the considered scenarios.
A Robust Neural Index of High Face Familiarity

Humans are remarkably accurate at recognizing familiar faces, whereas their ability to recognize, or even match, unfamiliar faces is much poorer. However, previous research has failed to identify neural correlates of this striking behavioral difference. Here, we found a clear difference in brain potentials elicited by highly familiar faces versus unfamiliar faces. This effect starts 200 ms after stimulus onset and reaches its maximum at 400 to 600 ms. This sustained-familiarity effect was substantially larger than previous candidates for a neural familiarity marker and was detected in almost all participants, representing a reliable index of high familiarity. Whereas its scalp distribution was consistent with a generator in the ventral visual pathway, its modulation by repetition and degree of familiarity suggests an integration of affective and visual information.
Steady and unsteady aerodynamic flow studies over a 1303 UCAV configuration

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Spectral Smoothing by Multiple Radar Pattern Multiplication for Improved Accuracy

This paper presents a novel technique for improving radar accuracy. In particular, by studying the product of multiple radar beam pattern spectrums from different radar sub-module views, a refined and improved total radar response can be obtained. We examine this radar signal processing approach using a 24GHz frequency-modulated continuous wave (FMCW) radar system equipped with broadband 1.5GHz substrate integrated waveguide (SIW) antenna arrays to ensure high range resolution. The transmitter is defined by 4 sawtooth signals generated by separated radar sub-modules, each having a multiple input multiple output (MIMO) configuration with 4 receivers at each radar submodule. The measured spectral multiplication of the individual radar sub-modules results in a signal quality improvement of more than 10dB when compared to a MIMO radar configuration with the same architecture.