Miracle-dz/UniFAR-FTUs huggingface dataset:
Grass–Legume Mixture with Rhizobium Inoculation Enhanced the Restoration Effects of Organic Fertilizer

The establishment of artificial grassland is crucial in restoring degraded grassland and resolving the forage–livestock conflict, and the application of organic fertilizer and complementary seeding of grass–legume mixture are effective methods to enhance grass growth in practice. However, its mechanism behind the underground is largely unclear. Here, by utilizing organic fertilizer in the alpine region of the Qinghai–Tibet Plateau, this study assessed the potential of grass–legume mixtures with and without the inoculation of Rhizobium for the restoration of degraded grassland. The results demonstrated that the application of organic fertilizer can increase the forage yield and soil nutrient contents of degraded grassland, and they were 0.59 times and 0.28 times higher than that of the control check (CK), respectively. The community composition and structure of soil bacteria and fungi were also changed by applying organic fertilizer. Based on this, the grass–legume mixture inoculated with Rhizobium can further increase the contribution of organic fertilizer to soil nutrients and thus enhance the restoration effects for degraded artificial grassland. Moreover, the application of organic fertilizer significantly increased the colonization of gramineous plant by native mycorrhizal fungi, which was ~1.5–2.0 times higher than CK. This study offers a basis for the application of organic fertilizer and grass–legume mixture in the ecological restoration of degraded grassland.
A Robust CCS Predictive Current Control for Photovoltaic Energy Storage System Based on a Nonlinear Disturbance Observer

This article introduces a new control strategy for a bidirectional DC/DC converter used in photovoltaic energy storage systems (PV-ESSs), aimed to address the DC bus voltage deviation problem. The purpose of this paper is to design and practically implement a robust continuous control set predictive current control (CCS-PCC) method based on a nonlinear disturbance observer (NDO) to tightly regulate the common DC bus voltage in islanded DC microgrids. The CCS-PCC controller is used to replace the current loop PI controller, which effectively overcomes the shortcomings of the traditional linear lag control and improves the dynamic performance of the system. At the same time, the CCS-PCC method solves the inherent defect of finite control set predictive current control (FCS-PCC), where the switching frequency is not fixed. It effectively reduces the current ripple and realizes constant frequency control. In order to effectively suppress the DC bus voltage fluctuation during transients, the nonlinear disturbance observer is designed and combined with CCS-PCC. The feedforward method based on the NDO was utilized to enhance the disturbance rejection capability of the system. The simulation and experimental results show the feasibility and effectiveness of the proposed CCS-PCC+NDO approach, both during transient and steady-state operating conditions.
Electric-Field-Induced Quantum Phase Transition in Doped Silicene

We study the pairing symmetry in doped silicene subject to a perpendicular external electric field. We find that the electric field induces an interesting quantum phase transition from the singlet chiral $d+id'$-wave superconducting phase to the triplet $f$-wave one. The emergence of the $f$-wave pairing results from the sublattice-symmetry-breaking brought about by the electric field. Furthermore, due to the enhanced density of state, the superconducting critical temperature of the $f$-wave pairing is enhanced by the electric field, which provides possibility to realize this intriguing high angular momentum pairing phase at attainable temperature.
A novel method for generating random fiber distributions for fiber-reinforced materials

A new algorithm to generate Representative Volume Elements (RVEs) of random fiber distributions is presented in this study. The Random Sequence Expansion (RSE)_stirring algorithm generates the initial fiber random distribution. Subsequently, by stirring the generated fibers and highlighting the influence of fiber short-range spacing interactions, the method facilitates the capture of the real fiber distribution. To verify the soundness of the proposed algorithm, the RVE model, generated by the RSE_stirring algorithm, was analyzed using four statistical functions. The model was then statistically compared with the experimentally obtained real fiber distributions in a Completely Spatial Random (CSR) pattern. In this study, the equivalent modulus of the composite was also predicted using RVE and compared with the results predicted by other algorithms. The experimental and the predicted results showed a strong similarity.
On the Dual of the Linearly Constrained Multifacility Location Problem with Arbitrary Norms

In this article the dual of the multifacility location problem with arbitrary norms is developed and its uses indicated. The formulation allows any number of linear constraints in the primal on the individual new facility locations. It is shown that the multipliers associated with the linear equations of the dual are the optimal facility locations of the primal.
Measuring in B ! K Decays

We re-examine the question of measuring the weak phase in B → K�� decays. To this end, we express all B → K�� amplitudes in terms of diagrams. We show that, as in B → K�, there exist relations between certain tree and electroweakpenguin diagrams. The imposition of these relations allows the extraction of from measurements of the B → K�� observables. We estimate the theoretical error in this method to be O(5%).
Prevention of Post-Operative Pain after Elective Brain Surgery: A Meta-Analysis of Randomized Controlled Trials

Background and Objective: To analyze the effects of several drug for pain prevention in adults undergoing craniotomy for elective brain surgery. Material and Methods: A systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. The inclusion criteria were limited to randomized controlled trials (RCTs) that evaluated the effectiveness of pharmacological treatments for preventing post-operative pain in adults (aged 18 years or older) undergoing craniotomies. The main outcome measures were represented by the mean differences in validated pain intensity scales administered at 6 h, 12 h, 24 h and 48 h post-operatively. The pooled estimates were calculated using random forest models. The risk of bias was evaluated using the RoB2 revised tool, and the certainty of evidence was assessed according to the GRADE guidelines. Results: In total, 3359 records were identified through databases and registers’ searching. After study selection, 29 studies and 2376 patients were included in the meta-analysis. The overall risk of bias was low in 78.5% of the studies included. The pooled estimates of the following drug classes were provided: NSAIDs, acetaminophen, local anesthetics and steroids for scalp infiltration and scalp block, gabapentinoids and agonists of adrenal receptors. Conclusions: High-certainty evidence suggests that NSAIDs and acetaminophen may have a moderate effect on reducing post-craniotomy pain 24 h after surgery compared to control and that ropivacaine scalp block may have a bigger impact on reducing post-craniotomy pain 6 h after surgery compared to control. Moderate-certainty evidence indicates that NSAIDs may have a more remarkable effect on reducing post-craniotomy pain 12 h after surgery compared to control. No moderate-to-high-certainty evidence indicates effective treatments for post-craniotomy pain prevention 48 h after surgery.
Motion Planning Based on RRT for Autonomous Road Vehicles: A Review.

The Rapidly-exploring Random Tree is an algorithm which is used in motion planning for autonomous vehicles which have a defined starting point and a defined ending point. The positive point in this algorithm is that it is probabilistically complete that is even in the constrained areas we get a random tree and may find a path through that area. It is very fast as compared to other algorithms such as Genetic Algorithm. There are different kinds of RRT for different application and optimization purposes. This paper compares the simple RRT algorithm with the more optimized RRT* algorithm for autonomous cars.
Linear Operators And Linear Systems An Analytical Approach To Control Theory

Thank you very much for downloading linear operators and linear systems an analytical approach to control theory. As you may know, people have search hundreds times for their chosen readings like this linear operators and linear systems an analytical approach to control theory, but end up in harmful downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some malicious virus inside their laptop.
Data-Driven, Short-Term Prediction of Charging Station Occupation

Enhancing electric vehicle infrastructure by forecasting the availability of charging stations can boost the attractiveness of electric vehicles. The transportation sector plays a crucial role in battling climate change. The majority of available prediction algorithms either achieve poor accuracy or predict the availability at certain points in time in the future. Both of these situations are not ideal and may potentially hinder the model’s applicability to real-world situations. This paper provides a new model for estimating the charging duration of charging events in real time, which may be used to estimate the waiting time of users at fully occupied charging stations. First, the prediction is made using the random forest regressor (RF), and then the prediction is enhanced utilizing the findings of the RF model and real-time information of the currently occurring charging events. We compare the proposed method with the RF model, which is the approach’s foundational model, and the best-performing prediction model of the light gradient boosting machine (LightGBM). Here, we make use of historical information of charging events gathered from 2079 charging stations across Germany’s 4602 fast-charging connectors. To reduce data bias, we specifically simulate prediction requests for 30% of the charging events with various characteristics that were not trained with the model. Overall, the suggested method performs better than both the RF and the LightGBM. In addition, the model’s structure is adaptable and can incorporate real-time information on charging events.
Effects of a High-Protein Diet on Kidney Injury under Conditions of Non-CKD or CKD in Mice

Considering the prevalence of obesity and global aging, the consumption of a high-protein diet (HPD) may be advantageous. However, an HPD aggravates kidney dysfunction in patients with chronic kidney disease (CKD). Moreover, the effects of an HPD on kidney function in healthy individuals are controversial. In this study, we employed a remnant kidney mouse model as a CKD model and aimed to evaluate the effects of an HPD on kidney injury under conditions of non-CKD and CKD. Mice were divided into four groups: a sham surgery (sham) + normal diet (ND) group, a sham + HPD group, a 5/6 nephrectomy (Nx) + ND group and a 5/6 Nx + HPD group. Blood pressure, kidney function and kidney tissue injury were compared after 12 weeks of diet loading among the four groups. The 5/6 Nx groups displayed blood pressure elevation, kidney function decline, glomerular injury and tubular injury compared with the sham groups. Furthermore, an HPD exacerbated glomerular injury only in the 5/6 Nx group; however, an HPD did not cause kidney injury in the sham group. Clinical application of these results suggests that patients with CKD should follow a protein-restricted diet to prevent the exacerbation of kidney injury, while healthy individuals can maintain an HPD without worrying about the adverse effects.
Least Squares Cross-validation for Counting Process Intensities

This paper presents a procedure for choosing the bandwidth when estimating, in the Aalen multiplicative model, the intensity of a counting process by a kernel method. The cross-validation procedure we propose is based, as for density estimation, on the estimation of the integrated square error by use of a leave-one-out principle. In order to state the validity of the proposed method, the precise form of the asymptotic expansion of the mean integrated square error is given and rates of convergence of some error criteria are investigated. These technical results are used for proving n -1-consistency of the estimator of the integrated square error. The particular case of censored survival data is considered and the performance of our procedure is checked via simulations.
Comparing the Importance of Iodine and Isoprene on Tropospheric Photochemistry

Isoprene, arguably the most studied biogenically emitted gas, is thought to have a large impact on tropospheric composition. Other naturally emitted species have been considered to play a less important role. Here the GEOS‐Chem model is used to compare the impacts of isoprene and iodine emissions on present‐day tropospheric composition. Removing isoprene emissions leads to a 3.4% decrease in tropospheric O3 burden, a smaller absolute change than the 5.9% increase from removing iodine emissions. Iodine has a negligible impact on global mean OH concentrations and methane lifetime (+0.6% and +0.05%) whereas isoprene has a substantial impact on both (−4.3% and −4.2%). Isoprene emissions and chemistry are seen as essential for tropospheric chemistry models, but iodine is often not. We suggest that iodine should receive greater attention in model development and experimental research to allow improved predictions of past, present, and future tropospheric O3.
Asymmetric Aluminum Antennas for Self-Calibrating Surface-Enhanced Infrared Absorption Spectroscopy

While there has been a tremendous increase of recent interest in noble metal-based antennas as substrates for surface-enhanced infrared absorption spectroscopy, more abundant and manufacturable metals may offer similar or additional opportunities for this mid-infrared sensing modality. Here we examine the feasibility of aluminum antennas for SEIRA, by designing and fabricating asymmetric aluminum cross antennas with nanometer-scale gaps. The asymmetric cross design enables the simultaneous detection of multiple infrared vibrational resonances over a broad region of the mid-infrared spectrum. The presence of the Al2O3 amorphous surface oxide layer not only passivates the metal antenna structures but also enables a very straightforward covalent binding chemistry for analyte molecules to the antenna through multiple approaches, in this case by the use of carboxylic acid functional groups. The aluminum–oxygen stretching mode of the oxide can be used as a self-calibration standard to quantify the number of ana.
Generating Real-Time Explanations for GNNs via Multiple Specialty Learners and Online Knowledge Distillation

Graph Neural Networks have become increasingly ubiquitous in numerous applications, necessitating explanations of their predictions. However, explaining GNNs is challenging due to the complexity of graph data and model execution. Post-hoc explanation approaches have gained popularity due to their versatility, despite their additional computational costs. Although intrinsically interpretable models can provide instant explanations, they are usually model-specific and can only explain particular GNNs. To address these challenges, we propose a novel, general, and fast GNN explanation framework named SCALE. SCALE trains multiple specialty learners to explain GNNs, as creating a single powerful explainer for examining the attributions of interactions in input graphs is complicated. In training, a black-box GNN model guides learners based on an online knowledge distillation paradigm. During the explanation phase, explanations of predictions are generated by multiple explainers corresponding to trained learners. Edge masking and random walk with restart procedures are implemented to provide structural explanations for graph-level and node-level predictions. A feature attribution module provides overall summaries and instance-level feature contributions. We compare SCALE with state-of-the-art baselines through extensive experiments to demonstrate its explanation correctness and execution performance. Furthermore, we conduct a user study and a series of ablation studies to understand its strengths and weaknesses.
Do We Need a New Foundation to Use Deep Learning to Monitor Weld Penetration?

Deep learning has been successfully used to automate the modeling process that trains a network/model from a given experimental dataset to calculate the output directly using high-dimensional complex raw data. However, the trained network is an inverse of the welding process (forward process) that produces the welding phenomena/measured raw data as the output with the penetration as the input of the forward process. Now the question is in addition to the current state of the weld penetration to be estimated if the forward process also has other inputs to determine its output. If it has, then the inverse model has to be constructed accordingly. This will call for a new foundation for deep learning-based monitoring of penetration. This letter proposed a novel innovative generative adversarial network (GAN) with GRU (Gated Recurrent Unit) in the generator, i.e., GRU-GAN, to model the extremely complex forward process to generate the observed topside welding image (output of the forward process) from the backside images (as comprehensive quantification of weld penetration). It is found that the produced topside welding image is not only determined by the current backside image but also by its history. A new foundation thus must be established to guide deep learning-based monitoring of weld penetration. The prediction model/network as an inverse model must be in compliance with the forward process that includes the history of the state of the weld penetration as its input.
Data processing and preliminary results of the Chang'e-3 VIS/NIR Imaging Spectrometer in-situ analysis

The Chang'e-3 Visible and Near-infrared Imaging Spectrometer (VNIS) is one of the four payloads on the Yutu rover. After traversing the landing site during the first two lunar days, four different areas are detected, and Level 2A and 2B radiance data have been released to the scientific community. The released data have been processed by dark current subtraction, correction for the effect of temperature, radiometric calibration and geometric calibration. We emphasize approaches for reflectance analysis and mineral identification for in-situ analysis with VNIS. Then the preliminary spectral and mineralogical results from the landing site are derived. After comparing spectral data from VNIS with data collected by the M3 instrument and samples of mare that were returned from the Apollo program, all the reflectance data have been found to have similar absorption features near 1000 nm except lunar sample 71061. In addition, there is also a weak absorption feature between 1750∼2400 nm on VNIS, but the slopes of VNIS and M3 reflectance at longer wavelengths are lower than data taken from samples of lunar mare. Spectral parameters such as Band Centers and Integrated Band Depth Ratios are used to analyze mineralogical features. The results show that detection points E and N205 are mixtures of high-Ca pyroxene and olivine, and the composition of olivineat point N205 is higher than that at point E, but the compositions of detection points S3 and N203 are mainly olivine-rich. Since there are no obvious absorption features near 1250 nm, plagioclase is not directly identified at the landing site.
Numerical Simulation of Crack Propagation in Flexible Asphalt Pavements Based on Cohesive Zone Model Developed from Asphalt Mixtures

To give engineers involved in planning and designing of asphalt pavements a more accurate prediction of crack initiation and propagation, theory-based models need to be developed to connect the loading conditions and fracture mechanisms present in laboratory tests and under traffic loading. The aim of this study is to develop a technical basis for the simulation of fracture behavior of asphalt pavements. The cohesive zone model (CZM) approach was applied in the commercial FE software ABAQUS to analyze crack propagation in asphalt layers. The CZM developed from the asphalt mixtures in this study can be used to simulate the fracture behavior of pavements and further optimize both the structure and the materials. The investigations demonstrated that the remaining service life of asphalt pavements under cyclic load after the initial onset of macro-cracks can be predicted. The developed CZM can, therefore, usefully supplement conventional design methods by improving the accuracy of the predicted stress states and by increasing the quality, efficiency, and safety of mechanical design methods by using this more realistic modeling approach.
Dimensionality Reduction of Deep Learning for Earth Observation: Smaller, Faster, Simpler

As deep learning attracts earth observation (EO) community's interest, the challenge to derive explainable, actionable information creates a bottleneck in EO models' development. Computer vision proved that effective results of the DL algorithms imply a considerable amount of training datasets. This is not the case in EO, where images are characterized by a broad variety of sensor data, ranging from multispectral to Synthetic Aperture Radar (SAR) with variable number of spectral bands, polarization, or spatial resolution. In this article, we present effective methodologies for fast training with reduced datasets of simple deep neural networks, while preserving the similar performance of state-of-the-art methods. The hybrid solutions we provide imply the reduction of input data dimension in a convolutional neural network. We replaced dataset's patches with histograms of pixel intensity, Bag-of-Words, or downsampled images. Following the proposed approaches, the training time and the dataset size are significantly reduced, while the performance of classification is preserved. These optimized implementations enable the deployment of lightweight deep learning models for real-time processing tasks able to exhibit accurate results, for instance, a disaster management scenario. We demonstrated the computational efficiency of these approaches on various, complex data, both multispectral and SAR, with different resolutions.
Multi‐strategy‐based leader election mechanism for the Raft algorithm

Raft is a consensus algorithm that implements highly available replicas. As raft requires a leader to drive the protocol execution, it usually employs a round‐robin leader election mechanism to select a random leader among all nodes. However, when the elected leader suffers from performance issues, the protocol will temporarily lose aliveness. To address this issue, we propose a multi‐strategy leader election mechanism that allows nodes to proactively trigger a new round of election when they believe the leader suffers from performance issues, and replace this leader with a new one in a designated priority queue. The formal analysis and experimental results show that the mechanism can improve the system's throughput/latency without additional election time overhead.
Theory of ultrashort laser pulse interaction with a metal

The interaction of subpicosecond laser pulses with metals is studied theoretically using phenomenological two-temperature model. Wide-range approximations for electron thermal conductivity and electron-ion energy exchange rate are proposed. Effects of temperature dependence of the thermophysical characteristics on lattice heating dynamics are discussed. Melting and evaporation kinetics are incorporated into the model to describe the metal ablation. Damage threshold and ablated layer thickness are calculated.