Miracle-dz/UniFAR-FTUs huggingface dataset:
A Potential Solution for Intelligent Energy Management - Knowledge Graph

Energy Internet is a complex network formed on the basis of the Internet and other cutting-edge information technologies. It takes distributed renewable energy as main energy source and tightly coupled with natural gas networks, transportation networks and other systems. This complex structure poses severe challenges for energy management, including complex context modeling, heterogeneous data analysis, distributed nodes integration and dynamic controlling. The great achievement of semantic technologies obtained in the area of energy management verified the advantages of knowledge engineering in complex system scheduling and controlling. As an improved form of knowledge representation, knowledge graph has great potential in complicated energy management, which consists of a large number of interconnected concepts, entities and events. By now the knowledge graph has been widely used in various areas, such as semantic searching, automatic question answering and intelligent decision-making. However it is hardly seen in the filed of energy. In this paper, after a brief introduction of the background of Energy Internet, we illustrate some existing works related to semantic energy management. Then we introduce the meaning of knowledge graph and the key technologies which are critical to the construction of a knowledge graph. It is expected that more successful use cases of knowledge graph in the field of Energy Internet would come out in the near future.
Protection of phototrophic iron(II)-oxidizing bacteria from UV irradiation by biogenic iron(III) minerals: Implications for early Archean banded iron formation

On the Archean Earth (4.0–2.5 Ga) the lack of a protective ozone layer meant that harmful ultraviolet radiation (UVR) reached the surface almost unattenuated. For survival, primitive photosynthetic bacteria would have required strategies preventing UV-induced damage to their DNA. At that time, a fraction of the planktonic cells were likely anoxygenic photosynthesizers that oxidized dissolved Fe(II) to Fe(III) during their metabolism. The result of their metabolism was most likely the ferric mineral precursors for the deposition of banded iron formations. Although Fe(III) (oxyhydr)oxide minerals absorb UV radiation while still transmitting higher wavelengths, it is unknown whether minerals produced by Fe(II) oxidizers could have acted as an in situ UVR sunscreen. Here we demonstrate that the anoxygenic phototrophic Fe(II) oxidizers Rhodopseudomonas palustris strain TIE-1 and Rhodobacter ferrooxidans strain SW2 form nanometer-sized grains of ferrihydrite that are loosely attached to the cell surfaces. These biogenic Fe(III) minerals were shown to protect the bacteria from UV-C irradiation, while cells grown in the absence of Fe(II) displayed diminished cell viability as a consequence of damage to their DNA. Importantly, this study implies that primitive Fe(II)-oxidizing bacteria would have been able to produce their own UV screen, enabling them to live in the shallow photic zone of ancient oceans.
ffects of eye-closure on confidence-accuracy relations in eyewitness estimony

Confidence judgments about the quality of memory can have serious implications in eyewitness settings. Three experiments investigated the effect of eye-closure during eyewitness interviews on confidenceaccuracy relations in event recall. In all experiments, participants viewed video-taped events and were subsequently questioned about the event, while they had their eyes open or closed. Participants provided confidence ratings for each response. We found that participants were generally able to monitor the accuracy of their responses, although they displayed underconfidence for imprecise responses. Importantly, across all experiments, eye-closure increased accuracy without significantly inflating confidence or impairing confidence-accuracy relations. Moreover, in Experiment 3, reducing distraction (e.g., etacognitive monitoring
Full-Duplex MAC Protocol for Wi-Fi/LTE-U Coexistence Networks

Long-term evolution in unlicensed band (LTE-U) has been an increasing interest for exploiting more spectrum resources. However, the spectrum sharing poses a significant challenge especially regarding the coexistence between LTE-U and Wi-Fi systems. In case of LTE-U acquires the unlicensed spectrum by sharing with the Wi-Fi system, the performance of the Wi-Fi system will be degraded. How to protect Wi-Fi communications and to achieve harmonious coexistence need to be addressed. In this paper, a listen-and-talk access mechanism featuring carrier sensing medium access/collision detection (CSMA/CD) for Wi-Fi is introduced into the LTE-U/Wi-Fi coexistence networks. Specifically, Wi-Fi user can transmit and sense simultaneously by leveraging full-duplex (FD) techniques to avoid long collision and improve spectrum utilization on unlicensed band. In addition, we analyze the system throughput with regard to false alarm and miss detection which are existence due to the residual self-interference (RSI). We further investigate the fairness between Wi-Fi and LTE-U, and for Wi-Fi traffic protection we derive the constriction of false alarm and miss detection. Based on the fairness-throughput trade off, we propose a FD/HD mode switching scheme. Finally, simulation results are conducted using ns-2 simulator, which verify the analysis of throughput in LTE-U/Wi-Fi coexistence networks.
Peak Regulation Strategy of Regional Power Systems for Large-Scale Renewable Generation Accommodation

The integration of large-scale renewable energy has brought great challenges for the control and operation of power systems. In order to accommodate the renewable power as much as possible, the system operators should properly coordinate the conventional generators such hydro and thermal units in the system. In this paper, a peak regulation strategy is proposed for the control of multiple heterogeneous generators to maximize the renewable energy usage ratio in a regional power system. To this end, first the hydro generators is utilized to alleviate the stress for peak regulation due to dramatic fluctuation of the renewable power. Then an optimization model is established with objective function being maximizing the peak regulation capability of the system in a weekly period. The obtained optimal solution of the model can ensure the peak regulation requirement of the system during the whole planning-period while avoiding the frequent switch of on-off operation for the thermal units within the system.
Evidence of enrichment by individual SN from elemental abundance ratios in the very metal-poor dSph galaxy Boötes I

Aims. We establish the mean metallicity from high-resolution spectroscopy for the recently found dwarf spheroidal galaxy Bootes I and test whether it is a common feature for ultra-faint dwarf spheroidal galaxies to show signs of inhomogeneous chemical evolution (e. g. as found in the Hercules dwarf spheroidal galaxy). Methods. We analyse high-resolution, moderate signal-to-noise spectra for seven red giant stars in the Bootes I dSph galaxy using standard abundance analysis techniques. In particular, we assume local thermodynamic equilibrium and employ spherical model atmospheres and codes that take the sphericity of the star into account when calculating the elemental abundances. Results. We confirm previous determinations of the mean metallicity of the Bootes I dwarf spheroidal galaxy to be -2.3 dex. Whilst five stars are clustered around this metallicity, one is significantly more metal-poor, at -2.9 dex, and one is more metal-rich at, -1.9 dex. Additionally, we find that one of the stars, Boo-127, shows an atypically high [Mg/Ca] ratio, indicative of stochastic enrichment processes within the dSph galaxy. Similar results have previously only been found in the Hercules and Draco dSph galaxies and appear, so far, to be unique to this type of galaxy.
In-vivo EEG Changes During A Panic Attack in A Patient With Specific Phobia

Since early adolescence a 47-year-old male suffers from specific phobia with panic disorder (PD) which is similar to agoraphobia. The panic starts when he crosses the borders of his domicile, regardless of the type of transport. On request of client and his GP, we recorded his EEG during a car trip from his house to a distant village. We aimed to explore underlying EEG changes that may accompany the initiation and the progression of his panic attacks. The panic attack took place during the car-ride where we detected an increase of frontal beta (β) activity (13-26 Hz) and a relatively sudden decrease of frontal-central theta (θ) activity (3-6 Hz). These findings suggest that EEG recordings can be useful to monitor the beginning and the end of a panic attack and may determine the exact EEG changes that initiate a panic attack.
Motion Switching With Sensory and Instruction Signals by Designing Dynamical Systems Using Deep Neural Network

To ensure that a robot is able to accomplish an extensive range of tasks, it is necessary to achieve a flexible combination of multiple behaviors. This is because the design of task motions suited to each situation would become increasingly difficult as the number of situations and the types of tasks performed by them increase. To handle the switching and combination of multiple behaviors, we propose a method to design dynamical systems based on point attractors that accept (i) “instruction signals” for instruction-driven switching. We incorporate the (ii) “instruction phase” to form a point attractor and divide the target task into multiple subtasks. By forming an instruction phase that consists of point attractors, the model embeds a subtask in the form of trajectory dynamics that can be manipulated using sensory and instruction signals. Our model comprises two deep neural networks: A convolutional autoencoder and a multiple time-scale recurrent neural network. In this study, we apply the proposed method to manipulate soft materials. To evaluate our model, we design a cloth-folding task that consists of four subtasks and three patterns of instruction signals, which indicate the direction of motion. The results depict that the robot can perform the required task by combining subtasks based on sensory and instruction signals. And, our model determined the relations among these signals using its internal dynamics.
Assessing the Impact of Spatial Resolution on the Estimation of Leaf Nitrogen Concentration Over the Full Season of Paddy Rice Using Near-Surface Imaging Spectroscopy Data

Timely monitoring nitrogen status of rice crops with remote sensing can help us optimize nitrogen fertilizer management and reduce environmental pollution. Recently, the use of near-surface imaging spectroscopy is emerging as a promising technology that can collect hyperspectral images with spatial resolutions ranging from millimeters to decimeters. The spatial resolution is crucial for the efficiency in the image sampling across rice plants and the separation of leaf signals from the background. However, the optimal spatial resolution of such images for monitoring the leaf nitrogen concentration (LNC) in rice crops remains unclear. To assess the impact of spatial resolution on the estimation of rice LNC, we collected ground-based hyperspectral images throughout the entire growing season over 2 consecutive years and generated ten sets of images with spatial resolutions ranging from 1.3 to 450 mm. These images were used to determine the sensitivity of LNC prediction to spatial resolution with three groups of vegetation indices (VIs) and two multivariate methods Gaussian Process regression (GPR) and Partial least squares regression (PLSR). The reflectance spectra of sunlit-, shaded-, and all-leaf leaf pixels separated from background pixels at each spatial resolution were used to predict LNC with VIs, GPR and PLSR, respectively. The results demonstrated all-leaf pixels generally exhibited more stable performance than sunlit- and shaded-leaf pixels regardless of estimation approaches. The predictions of LNC required stage-specific LNC~VI models for each vegetative stage but could be performed with a single model for all the reproductive stages. Specifically, most VIs achieved stable performances from all the resolutions finer than 14 mm for the early tillering stage but from all the resolutions finer than 56 mm for the other stages. In contrast, the global models for the prediction of LNC across the entire growing season were successfully established with the approaches of GPR or PLSR. In particular, GPR generally exhibited the best prediction of LNC with the optimal spatial resolution being found at 28 mm. These findings represent significant advances in the application of ground-based imaging spectroscopy as a promising approach to crop monitoring and understanding the effects of spatial resolution on the estimation of rice LNC.
ECCN: Orchestration of Edge-Centric Computing and Content-Centric Networking in the 5G Radio Access Network

Edge-centric computing (ECC) and content- centric networking (CCN) will be the most important technologies in future 5G networks. However, due to different architectures and protocols, it is still a challenge to fuse ECC and CCN together and provide manageable and flexible services. In this article, we present ECCN, an orchestrating scheme that integrates ECC and CCN into a hierarchical structure with software defined networking (SDN). We introduce the SDN technology into the hierarchical structure to decouple data and control planes of ECC and CCN, and then design an SDN protocol to control the data forwarding. We also implement two demonstration applications in our testbed to evaluate the ECCN scheme. The experimental results from the testbed applications, and extensive simulations show ECCN outperforms original structures.
A novel PKP2 mutation and intrafamilial phenotypic variability in ARVC/D

Background: Arrhythmogenic ventricular cardiomyopathy (AVC) is an inherited cardiac disorder affecting 1 in 1000 individuals worldwide. The mean diagnosed age of disease is 31 years. In this article, an Iranian family reported that they were affected by ARVC due to a novel PKP2 mutation. Methods: Clinical evaluations, 12-lead ECG, CMR, and signal-averaged ECG were performed. After DNA extraction, genetic testing was done, and PCR-sequencing was applied to find causal mutations. Segregation analysis was also performed for the family. Results: ARVC criteria were documented in the patients. Genetic testing revealed a novel chain termination mutation (p.Tyr168Ter) in PKP2 gene; this mutation was transmitted from the mother to her 23-year-old son, but only the son was affected with ARVC. Conclusion: Modifier genes were indicated using interactome analysis of Plakophilin 2 protein (PKP2); they might have led to phenotypic variability through cellular mechanisms, such as nonsense-mediated mRNA decay. At least, 9 proteins were identified that might have affected Plakophilin 2 protein function, and consequently, rationalizing this intrafamilial phenotypic variability. This study highlighted the role of modifier genes involved in ARVC as well as the major role of PKP2 mutation in developing the disease in our population.
Sulphur abundances in halo giants from the [S ı] line at 1082 nm and the [S ı] triplet around 1045 nm

Context. It is still debated whether or not the Galactic chemical evolution of sulphur in the halo follows the flat trend with [Fe/H] that is ascribed to the result of explosive nucleosynthesis in type II SNe. It has been suggested that the disagreement between different investigations of sulphur abundances in halo stars might be owing to problems with the diagnostics used, that a new production source of sulphur might be needed in the early Universe, like hypernovae, or that the deposition of supernova ejecta into the interstellar medium is time-delayed. Aims. The aim of this study is to try to clarify this situation by measuring the sulphur abundance in a sample of halo giants using two diagnostics: the S I triplet around 1045 nm and the [S I] line at 1082 nm. The latter of the two is not believed to be sensitive to non-LTE effects. We can thereby minimize the uncertainties in the diagnostic used and estimate the usefulness of the triplet for the sulphur determination in halo K giants. We will also be able to compare our sulphur abundance differences from the two diagnostics with the expected non-LTE effects in the 1045 nm triplet previously calculated by others. Methods. High-resolution near-infrared spectra of ten K giants were recorded using the spectrometer CRIRES mounted at VLT. Two standard settings were used, one covering the S I triplet and one covering the [S I] line. The sulphur abundances were individually determined with equivalent widths and synthetic spectra for the two diagnostics using tailored 1D model atmospheres and relying on non-LTE corrections from the litterature. Effects of convective inhomogeneities in the stellar atmospheres are investigated. Results. The sulphur abundances derived from both the [S I] line and the non-LTE corrected 1045 nm triplet favor a flat trend for the evolution of sulphur. In contrast to some previous studies, we saw no "high" values of [S/Fe] in our sample. Conclusions. We corroborate the flat trend in the [S/Fe] vs. [Fe/H] plot for halo stars found in some previous studies but do not find a scatter or a rise in [S/Fe] as obtained in other works. We find the sulphur abundances deduced from the non-LTE corrected triplet to be somewhat lower than the abundances from the [S I] line, possibly indicating too large non-LTE corrections. Considering 3D modeling, however, they might instead be too small. Moreover, we show that the [S I] line can be used as a sulphur diagnostic down to [Fe/H] similar to - 2.3 in giants.
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks

Deep neural networks have shown promising results for various clinical prediction tasks such as diagnosis, mortality prediction, predicting duration of stay in hospital, etc. However, training deep networks -- such as those based on Recurrent Neural Networks (RNNs) -- requires large labeled data, high computational resources, and significant hyperparameter tuning effort. In this work, we investigate as to what extent can transfer learning address these issues when using deep RNNs to model multivariate clinical time series. We consider transferring the knowledge captured in an RNN trained on several source tasks simultaneously using a large labeled dataset to build the model for a target task with limited labeled data. An RNN pre-trained on several tasks provides generic features, which are then used to build simpler linear models for new target tasks without training task-specific RNNs. For evaluation, we train a deep RNN to identify several patient phenotypes on time series from MIMIC-III database, and then use the features extracted using that RNN to build classifiers for identifying previously unseen phenotypes, and also for a seemingly unrelated task of in-hospital mortality. We demonstrate that (i) models trained on features extracted using pre-trained RNN outperform or, in the worst case, perform as well as task-specific RNNs; (ii) the models using features from pre-trained models are more robust to the size of labeled data than task-specific RNNs; and (iii) features extracted using pre-trained RNN are generic enough and perform better than typical statistical hand-crafted features.
Phase retrieval for 1D and 2D crystals

The extreme intensity and extremely short x-ray pulses produced by x-ray free electron lasers (XFELs), so-called 4th generation x-ray sources, are opening new horizons for x-ray crystallography (XRC). This new source of x-rays is not limited by the same rules that govern and plague synchrotron-based x-ray crystallography, which utilises 3rd generation x-ray sources. Small crystals, such as 1D and 2D crystals or even nano-crystals can now be used with X-ray free electron lasers allowing studies of essential proteins, such as membrane proteins, close to their natural environment. Studies on fibrous proteins can also potentially take advantage of what X-ray free electron lasers have to offer, allowing data to be collected using a single 1D crystal rather than a fiber. The crystallographic phase problem with such finite crystals is eased as the lack of periodicity allows for additional sampling of the Fourier amplitude in addition to the common Bragg sampling. Unfortunately, the phases are still not measurable and thus direct Fourier inversion is still not available. In this paper, we characterise uniqueness of the phase problem for a selection of crystalline specimens. Ab initio phase retrieval using the difference map algorithm for 1D and 2D crystals is also presented.
Energy-Efficient QoS-Based Congestion Control for Reliable Communications in Wireless Multimedia Sensor Networks

Congestion is a major cause of energy wastage and Quality of Service (QoS) degradation in wireless communication systems. Saving energy and maintaining high QoS levels are especially important in Wireless Multimedia Sensor Networks (WMSN), due to the limited sensor node energy resources and QoS-related application requirements. This paper proposes an energy efficient and QoS-aware congestion control scheme for reliable communications over WMSNs (eqCC). The proposed solution makes use of QoS feedback and current battery energy levels of sensor nodes in order to adapt sending data rate. We employ reinforcement learning by formulating the problem in terms of a Markov Decision Process (MDP) and solve it using the Q-Learning tech- nique. The proposed eqCC is validated using simulations and is compared with classic TCP and Flush, another congestion control algorithm for Wireless Sensor Networks (WSN). The results show how eqCC outperforms the other solutions under high and low network load.
A Novel Dielectric Strip Resonator Filter

In this letter, a novel half-wavelength dielectric strip resonator that consists of a thin dielectric strip with high permittivity and a low-permittivity substrate with the bottom ground plane is proposed and theoretically investigated. The dielectric strip is used to replace the metal strip of the traditional microstrip line. Since there is no induced surface current on the dielectric strip, the unloaded quality factor ( $Q_{u}$ ) of the proposed resonator can be improved significantly, especially at high frequency. Meanwhile, the proposed resonator can be easily excited by the tapped microstrip line for constructing the bandpass filter (BPF), because its E-field distribution is similar to the microstrip counterpart. For demonstration, a third-order dielectric strip resonator BPF centered at 10.5 GHz is designed, implemented, and measured. Good agreement between the simulated and measured results can be observed. The minimum in-band loss is only 0.6 dB.