Miracle-dz/UniFAR-FTUs huggingface dataset:
Pluripotent Pericycle Cells Trigger Different Growth Outputs by Integrating Developmental Cues into Distinct Regulatory Modules

During post-embryonic development, the pericycle specifies the stem cells that give rise to both lateral roots (LRs) and the periderm, a suberized barrier that protects the plant against biotic and abiotic stresses. Comparable auxin-mediated signaling hubs regulate meristem establishment in many developmental contexts; however, it is unknown how specific outputs are achieved. Using the Arabidopsis root as a model, we show that while LR formation is the main auxin-induced program after de-etiolation, plants with age become competent to form a periderm in response to auxin. The establishment of the vascular cambium acts as the developmental switch required to trigger auxin-mediated periderm initiation. Moreover, distinct auxin signaling components and targets control LR versus periderm formation. Among the periderm-specific-promoting transcription factors, WUSCHEL-RELATED HOMEOBOX 4 (WOX4) and KNAT1/BREVIPEDICELLUS (BP) stand out as their specific overexpression in the periderm results in an increased number of periderm layers, a trait of agronomical importance in breeding programs targeting stress tolerance. These findings reveal that specificity in pericycle stem cell fate is achieved by the integration of developmental cues into distinct regulatory modules.
Drivers of Extinction Risk in Terrestrial Vertebrates

Rising rates of extinction create an urgent need to identify the mechanisms and drivers of endangerment. One critical question is whether major phylogenetic lineages are equally at risk to the same threats. We used the IUCN Red List classification to explore the effect of four major threatening processes (habitat alteration, invasive species, climate change and overexploitation) on 7,441 species in four terrestrial vertebrate classes. As expected, species rated as vulnerable to a higher number of threats were also at greater risk of extinction. However, this pattern differed strongly among classes. Notably, invasive species and climate change were strongly associated with increased risk of extinction in birds but not mammals. These large‐scale differences might be artifacts of differing methodologies used by class specialists to classify species vulnerability; or might reflect biological differences. That ambiguity needs to be resolved, because it has strong implications for the assessment and amelioration of threatening processes.
Output Tracking of Boolean Control Networks With Impulsive Effects

In this article, the output tracking of Boolean control networks with impulsive effects (BCNs-IE) is discussed. Based on structure matrices of BCNs-IE and controllability matrices with respect to state subsets, stable complex attractors are studied. By constructing an auxiliary BCN-IE and using stable complex attractors, a necessary and sufficient condition for the output tracking problem is proposed. In addition, an algorithm to design state feedback controllers for the output tracking problem is provided. Moreover, an example is given to show the validity of the obtained results.
Precision Tracking Control of Piezoelectric-Driven Motion System Based on Enhanced ADRC

Due to the fine micro/nanometer resolution, fast response, and large mechanical force, piezoelectric-driven stage has been employed in high-precision positioning system. However, piezoelectric actuator induces the problems of nonlinear hysteresis, creep, and time-varying uncertainties. It is hard to achieve satisfactory performance using traditional linear control method. Thus, a generalized extended state observer (GESO) based enhanced active disturbance reject control (ADRC) is developed to improve the system performance in this paper. The proposed GESO observer could estimate total time varying disturbance and compensate it in the feed-forward channel. Experimental results are given to show the effectiveness of the proposed control scheme.
Semi-MCNN: A Semisupervised Multi-CNN Ensemble Learning Method for Urban Land Cover Classification Using Submeter HRRS Images

Submeter high-resolution remote sensing image land cover classification could provide significant help for urban monitoring, management, and planning. Deep learning (DL)-based models have achieved remarkable performance in many land cover classification tasks through end-to-end supervised learning. However, the excellent performance of DL-based models relies heavily on a large number of well-annotated samples, which is impossible in practical land cover classification scenarios. Additionally, the training set could contain all of the different land cover types. To overcome these problems, in this article, a semisupervised multiple-CNN ensemble learning method, namely semi-MCNN, is proposed to solve the land cover classification problem. Considering the lack of labeled samples, a semisupervised learning strategy was adopted to leverage large amounts of unlabeled data. In the proposed approach, an automatic sample selection method called an ensembled teacher model dataset generation was adopted to select samples and generate a dataset from large amounts of unlabeled data automatically. To tackle the error propagation problem, an important strategy was adopted to correct the errors by pretraining on the selected unlabeled data, and fine-tuning on the labeled data. Moreover, the semisupervised idea together with the multi-CNN ensemble framework was integrated into an end-to-end architecture. This could significantly improve the generalization ability of the semisupervised model, as well as the classification accuracy. Experiments were conducted on Shenzhen's land cover data and two other public remote sensing datasets. These experiments confirmed the superior performance of the proposed semi-MCNN compared to the state-of-the-art land cover classification models.
Bayesian Neural Network Based Method of Remaining Useful Life Prediction and Uncertainty Quantification for Aircraft Engine

Remaining useful life (RUL) prediction is a key component of reliability evaluation and conditional-basedmaintenance (CBM). In the existing prediction methods, neural networks (NNs) are widely used because of the high accuracy. However, most of the traditional NNs prediction methods only focus on accuracy without the ability in handling the problem of uncertainty, where the generalization of the method is limited and their application to practical application are challenging. In this paper, an efficient prediction method based on the Bayesian Neural Network (BNN) is proposed. Network weights are assumed to follow the Gaussian distribution, based on which they can be updated by Bayes’ theorem and the confidence interval (CI) is consequently derived. The method is verified on the C-MAPSS data set published by NASA and the degradation starting point is determined via change point detection method. The experimental results demonstrate that the method performs well in prediction accuracy with the capability of the uncertainty quantification, which is critical for the condition monitoring of complex systems.
Low-Dose CT Image Reconstruction With a Deep Learning Prior

In low-dose computed tomography (LDCT), a penalized weighted least squares (PWLS) approach that incorporates the Poisson statistics of X-ray photons can significantly reduce excessive quantum noise. To improve the quality of LDCT images, prior information such as the total variation, Markov random field, and nonlocal mean, can be imposed onto the target image. However, this information may be limited to reflect the characteristics of the target images, thereby resulting in unexpected side effects (e.g. blurry images). In this paper, we propose a PWLS method combined with a deep learning prior, which is learned from standard dose CT (SDCT) images. The proposed model learns a noise reduction function that maps an LDCT image to its corresponding SDCT images and estimates the prior distribution using a Pearson $\chi ^{2}$ divergence. The model can be converted to the least squares generative adversarial network with an added PWLS objective, where the optimal generator acts as the noise reduction function. We reformulate the proposed model as a constrained optimization problem and solve it using the alternating optimization (AO) algorithm. Clinical SDCT and simulated LDCT scans of ten patients were used to show the validity of the proposed method. Results show that the proposed method outperforms other PWLS methods, by imposing priors such as the total variation and the nonlocal mean.
Distributed Resilient Platoon Control of Vehicular Cyber Physical Systems Subject to DoS Attacks

This paper investigates the platoon control problem for vehicular cyber physical systems (VCPSs) subject to Denial-of-Service (DoS) attacks which often cause significant transmitted data packet delay or dropout by jamming the communication channels among vehicles. As a result, the VCPSs may contain isolated vehicles which cannot receive information from others. This in truth can lead to performance degradation, or even collisions of vehicles. To mitigate such adverse effects of DoS attacks on VCPSs, this paper proposes a distributed control protocol for each vehicle to track the leading vehicle. In order to guarantee asymptotic tracking performances under both DoS attacks and the existing recovery mechanism, this paper characterizes the upper bounds of the time duration rate and the frequency of the communication network being in the state of containing no spanning tree, respectively, by using a state transformation method and multiple Lyapunov functions. The controller gains are also jointly designed in association with the parameters of the upper bounds. Numerical results show the effectiveness of the derived theoretical results.
Depth Sensor Selection for Specific Application

To find the solutions to various technical issues in image processing we often want to be able to obtain not only color but also depth information. Information about the depth helps with a better understanding and description of the scene. As a practical matter, we aim to convert the geometrical information about the object to the digital format with the highest possible accuracy. For such a conversion there are many existing methods of creating a 3D model, e.g. photogrammetry, laser scanners. These methods provide high-quality 3D information; however, they are high-priced and often limited by the size of both scanner and object and the scanning time. As an alternative, we present an optical depth sensor that can be used excluding other emerging disadvantages with lower quality of an output model though. In the future, we consider the usage of the selected sensor for specific biomedical application: diagnostic support of obstructive sleep apnea.
A Kernel Unfolding Approach to Trade Data Movement with Computation Power for CNN Acceleration

Convolutional neural networks (CNN) achieves human-level accuracy on the image classification applications. However, its complicated structure brings the large requirement on the MAC operations and result in huge cost on the data movement. In addition, this situation becomes worse when the asymmetric growth of the computing power and memory speed happens on the von Neumann-based architecture. Recently, processing in memory (PIM) design is adopted to reduce the data communication cost by storing parameters into memory. However, significant cost on feeding input feature map is a big challenge, especially for high bandwidth but long access latency PIM devices. Thus, we explore an idea that how to trade the space in PIM to eliminate such cost. A kernel unfolding technique is proposed to eliminate the duplicated feeding on input feature map, and meanwhile, memory cells in PIM are highly utilized to achieve peak computing throughput. Thus, the memory bandwidth could be utilized efficiently and the corresponding execution time could be reduced significantly. The results show that the proposed design could achieve up to 16.2× cycle improvement compared to traditional PIM designs.
Hybrid-Precoding for mmWave Multi-User Communications in the Presence of Beam-Misalignment

In this paper, we propose the hybrid-precoding design that alleviates the performance loss caused by beam-misalignment in the mmWave multi-user communication systems. To this end, we firstly design the beam-misalignment aware fully-digital precoders for two distinct scenarios. First, for a base-station (BS) with full estimated channel-state-information (CSI), the minimum-mean-squared-error metric incorporating the ‘error-statistics’ of the beam-misalignment error is used to analytically derive a closed-form expression for the fully-digital precoder, which maximizes the array gain while suppressing the inter-user interference for each user-equipment (UE). Second, for a BS which can only acquire partial estimated CSI, a min-max non-convex optimization is considered to obtain the fully-digital precoder, which minimizes the maximum loss in the array gains of the expected beam-misalignment ‘error-range’ over the UEs while cancelling the inter-user interference. Subsequently, we propose the hybrid-precoding design that approximates the fully-digital designs based on the gradient-projection method, which is mathematically proven to converge to an approximate local solution with further reduced complexity compared to the state-of-the-art algorithms. Finally, the proposed hybrid-precoding design is further extended to the wideband mmWave communication systems. Numerical results show that the proposed hybrid-precoding design can effectively alleviate the performance degradation incurred by the beam-misalignment.
Anti-synchronization of a class of chaotic systems with uncertainty and disturbance using the UDE-based control method

This paper investigates the anti-synchronization of a class of chaotic systems with both model uncertainty and external disturbance. Firstly, a new uncertainty and disturbance estimation (UDE)-based adaptive control method is proposed, which combines the adaptive control method and the UDE-based control method. Secondly, a 4D hyper-chaotic system is taken as an example, and the anti-synchronization of this system is realized. Finally, the effectiveness and correctness of the proposed method is verified by numerical simulation.
Biology and Population Dynamics of Trissolcus halyomorphae

Trissolcus halyomorphae is an important parasitoid of eggs of Halyomorpha halys. Male emergence is much earlier than female, and one host egg only comes out of a parasitoid. Adults mate and oviposit immediately after emergence. Single female can lay 40.6 eggs averagely. It prefers to oviposit eggs inside fresh host eggs. Developmental period and longevity of the wasp are closely related to temperature. The shortest developmental period occurred at 30 ℃, and the shortest longevity at higher temperature. The thermal threshold and effective accumulated temperature are 12.2 ℃ and 132.5 d·℃ respectively. Supplementary nutrition can remarkably prolong adult longevity. The mid and last ten days of June, the last ten days of July and the first ten days of August are the peak periods of wasp abundance.
Literature Review - Electric Vehicles Life Cycle Assessment

These days it is notable that the responsibilities regarding the environment are involving issues as climate change, changes in ecosystems, depletion of stratospheric ozone layer, even changes in hydrological systems, soil degradation, etc. Hence, Life Cycle Assessment (LCA) was created to evaluatethe consequences of the product's behavior on the environment. Through this analysis it will be possible to follow a final product from the extraction and processing of the necessary raw material, considering all its phases of production, transport, use, cost-effectiveness, maintenance and recycling or until its reintegration into the environment. LCA encompasses risk estimation and environmental impact and, based on International Standards ISO 14040 and ISO 14044, this methodology can be applied at every phase of the product’s life cycle.This scientific paper synthesizes the studies on the following subjects: the increasing importance of the electrical vehicle’s manufactory, the current situation of implementing sustainable development of electric vehicle (EV) components, along with a discussion of neglecting LCA applications, as a premise for balancing the negative impact of a product during its long-life cycle for the environment. Thus, the purpose of this scientific article is a review of the most relevant scientific articles written based on a life cycle impact assessment approach of EVs or its components (battery, electric motor, etc.). It will be discussed in depth the importance of an LCA analysis in each step of its implementation, the related main topics in the previous analyses, as well as the interpretation of the results obtained.
Distributed Optimization for Multi-agent Systems with Unknown High-frequency-gain Signs

In this paper, we consider a distributed optimization problem for a group of single-integrator agents without a prior knowledge of their high-frequency-gain signs. By an embedded design, we first propose an optimal signal generator to generate the global optimal point, and then solve the distributed optimization problem by designing some tracking controllers for these agents. A numerical example is given to verify the effectiveness of our algorithms.
A Composite Selective Harmonic Elimination Model Predictive Control for Seven-Level Hybrid-Clamped Inverters With Optimal Switching Patterns

A composite strategy that combines selective harmonic elimination pulsewidth modulation (SHE-PWM) and model predictive control (MPC) for seven-level hybrid-clamped (7L-HC) inverters is presented in this article. By introducing the unified SHE formulation, all seven-level switching patterns and corresponding switching angles can be obtained simultaneously. Therefore, the optimal switching pattern with the designed optimization goal of each modulation index can be evaluated, and the best expected output performance is achieved. For the voltage balancing issue of 7L-HC, MPC is adopted to control the dc-link and flying capacitors. After receiving the output voltage level signal from the SHE-PWM modulator, the optimal switching state that belongs to the received output voltage level that minimizes the cost function is selected by the MPC module, where the cost function is designed to simultaneously balance capacitor voltages and reduce the switching frequency. Dynamic weighting factors with variable band limits are also proposed to further improve the system performance. The potential industrial application of high-power motor drive is used as an example in designing the key parameters for both SHE and MPC parts. Simulation and experimental results confirmed the validity of this composite SHE-MPC strategy in reducing the switching frequency and improving harmonic performances while keeping capacitor voltages well balanced.
H∞ Control for Markovian Jump Systems with Partly Known Transition Probabilities in Discrete-time Domain

In this paper, the H∞ controller design problem is investigated for the discrete-time Markovian jump linear system in the presence of incomplete transition descriptions. A less conservative criterion is presented such that the considered system is stochastically stable with a guaranteed H∞ performance index by using the properties of the transition probabilities. In addition, a H∞ state-feedback controller is proposed for the discrete-time Markovian jump system with incomplete transition description via LMI formulation. Finally, some simulation results are given to verify the effectiveness of the presented methods.
Fine root biomass and space-time dynamics of 4 types main plantation community,in Hunan Provine

The fine root biomass and space-time dynamics in Cunninghamia lanceolata,Pinus massoniana,Liquidambar formosana and Cinnamomum camphora main plantation communities in Tianjiling National Forestry Park of Changsha,were evaluated by using auger / soil core method from January to December 2007.The results show that the differences of fine root biomass(diameter ≤2 mm) among 4 types forest communities varied greatly(P0.05). Annual mean alive fine root biomasses in Cunninghamia lanceolata,Pinus massoniana,Liquidambar formosana and Cinnamomum camphora plantation communities were 0.893,1.123,0.826 and 1.958 t/hm2,and dead fine root biomasses were 0.086,0.119,0.093 and 0.184 t/hm2,respectively.Total fine root biomasses in 0～60 cm layer were Cinnamomum camphora Pinus massonianaCunninghamia lanceolataLiquidambar formosana forest community.Significant seasonal variation of alive fine root biomasses and dead fine root biomasses were found between 4 types community,alive fine root biomasses were single peak,and dead fine root biomasses were irregular curve.Alive fine root biomasses and dead fine root biomasses of 4 types community were found decreasing with soil depth,distributing in the soil layer of 0～30 cm,about 66.01%～76.15% and 67.83%～76.53%,respectively.Significant exponential relationships were found between alive fine root biomasses of Cunninghamia lanceolata,Liquidambar formosana and Cinnamomum camphora communities with soil temperatures,and between dead fine root biomass of Pinus massoniana community with soil temperature.Significant line relationships were found between dead fine root biomasses of Cunninghamia lanceolata,Liquidambar formosana and Cinnamomum camphora communities with soil moistures,however,not significant relationships were found between fine root biomasses of 4 types community with soil moistures.Multiple regression analysis showed that variations of fine root biomass were greater with soil temperature than moisture,but variations of fine root necromass were greater with soil moisture than temperature.
ZyConChain: A Scalable Blockchain for General Applications

Blockchain’s popularity has seen a historic rise over the last decade. However, existing blockchain systems have a major issue with scalability, which has become one of the main obstacles in technology’s adoption in mainstream. There have been several attempts to address this limitation by identifying Blockchain’s scalability/performance bottlenecks (e.g. those mainly related to consensus algorithms), and thus proposed different solutions (e.g., new consensus protocols) to address such limitations. Other works applied sharding to tackle the issue. All solutions however have mainly focused on Cryptocurrency applications, and thus addressing the scalability of blockchain systems for general applications remains a concern. This work proposes a scalable blockchain protocol for general applications (i.e., not restricted to Cryptocurrencies). To improve the two major factors affecting transaction scalability, namely throughput and latency, we needed to modify both the blockchain structure as well as the block generation process. ZyConChain, the proposed Blockchain system, introduces three types of blocks that form three separate chains: parentBlock, sideBlock and state block. These blocks are generated based on different consensus algorithms, as each algorithm has specific properties that make it suitable for each type of block. To improve the overall performance, ZyConChain generates sideBlocks (that carry transactions) at a high rate and keep them in a pool. To generate parentBlock, miners, instead of packing transactions into a block as they do in conventional blockchains, pack sideBlocks into a parentBlock. SideBlocks are generated based on an adapted Zyzzyva consensus protocol, with $O(\log n)$ complexity. This has reduced the final consensus complexity per transaction, in comparison to previous work. To enable the protocol to scale out with the increase in the number of nodes, ZyConChain applied sharding technique. Parallel state chains have also been introduced to address cross-shard transactions.