Explore the vast range of titles available.

Background The population of plants is a crucial indicator in plant phenotyping and agricultural production, such as growth status monitoring, yield estimation, and grain depot management. To enhance the production efficiency and liberate labor force, many automated counting methods have been proposed, in which computer vision-based approaches show great potentials due to the feasibility of high-throughput processing and low cost. In particular, with the success of deep learning, more and more deeper learning-based approaches are introduced to deal with agriculture automation. Since different detection- and regression-based counting models have distinct characteristics, how to choose an appropriate model given the target task at hand remains unexplored and is important for practitioners. Results Targeting in-field maize tassels as a representative case study, the goal of this work is to present a comprehensive benchmark of state-of-the-art object detection and object counting methods, including Faster R-CNN, YOLOv3, FaceBoxes, RetinaNet, and the leading counting model of maize tassels—TasselNet. We create a Maize Tassel Detection Counting (MTDC) dataset by supplementing bounding box annotations to the Maize Tassels Counting (MTC) dataset to allow the training of detection models. We investigate key factors effecting the practical applications of the models, such as convergence behavior, scale robustness, speed-accuracy trade-off, as well as parameter sensitivity. Based on our benchmark, we summarise the advantages and limitations of each method and suggest several possible directions to improve current detection- and regression-based counting approaches to benefit next-generation intelligent agriculture. Conclusions Current state-of-the-art detection- and regression-based counting approaches can all achieve a relatively high degree of accuracy when dealing with in-field maize tassels, with at least 0.85 R 2 values and 28.2% rRMSE error. While detection-based methods are more robust than regression-based methods in scale variations and can infer extra information (e.g., object positions and sizes), the latter ones have significantly faster convergence behaviors and inference speed. To choose an appropriate in-filed plant counting method, accuracy, robustness, speed and some other algorithm-specific factors should be taken into account with the same priority. This work sheds light on different aspects of existing detection and counting approaches and provides guidance on how to tackle in-field plant counting. The MTDC dataset is made available at https://git.io/MTDC

Al–Bi–Sn–Cu composite powders for hydrogen generation were designed from the calculated phase diagram and prepared by the gas atomization process. The morphologies and structures of the composite powders were investigated using X-ray diffraction (XRD) and a scanning electron microscope (SEM) equipped with energy-dispersive X-ray (EDX) spectroscopy, and the results indicate that the Cu additive enhanced the phase separation between the Al-rich phase and the (Bi, Sn)-rich phase. The hydrogen generation performances were investigated by reacting the materials with distilled water. The Al–Bi–Sn–Cu powders reveal a stable hydrogen generation rate, and the Al–10Bi–7Sn–3Cu (wt%) powder exhibits the best hydrogen generation performance in 50 °C distilled water which reaches 856 mL/g in 800 min. In addition, the antioxidation properties of the powders were also studied. The Al–10Bi–7Sn–3Cu (wt%) powder has a good resistance to oxidation and moisture, which shows great potential for being the hydrogen source for fuel cell applications.

3D printing technology has the advantages of constructing ceramic-based catalyst supports with a complex three-dimensional(3D) flow field, outstanding thermostability and resistance to corrosion, which is suitable for methanol steam reforming (MSR). In order to master the chemical characteristics of ceramic-based 3D printed catalyst supports used in MSR, the mathematical models of reaction rate based on dynamic reaction partial pressure were established, and the accuracy of the model was verified by the experimental methods. The comparison between reaction rate models from the literature and the models derived from experiments was performed in detail. With the introduction of contact time and specific area of active component particles, a lower deviation of the mathematical models can be reached with a minimum of 10%. The model generated by 3D printing technology can be utilized for the high-precision design of ceramic-based 3D printed catalyst supports used for MSR.

Abstract Background Individuals whose copies of the survival motor neuron 1 (SMN1) gene exist on the same chromosome are considered silent carriers for spinal muscular atrophy (SMA). Conventional screening for SMA only determines SMN1 copy number without any information regarding how those copies are arranged. A single nucleotide variant (SNV) rs143838139 is highly linked with the silent carrier genotype, so testing for this SNV can more accurately assess risk to a patient of having an affected child. Methods Using a custom-designed SNV-specific Taqman genotyping assay, we determined and validated a model for silent-carrier detection in the laboratory. Results An initial cohort of 21 pilot specimens demonstrated results that were 100% concordant with a reference laboratory method; this cohort was utilized to define the reportable range. An additional 177 specimens were utilized for a broader evaluation of clinical validity and reproducibility. Allelic-discrimination analysis demonstrated tight clustering of genotype groupings and excellent reproducibility, with a coefficient of variation for all genotypes ranging from 1% to 4%. Conclusion The custom-developed Taqman SNV genotyping assay we tested provides a rapid, accurate, and cost-effective method for routine SMA silent-carrier screening and considerably improves detection rates of residual risk for SMA carriers.

Under the dual-carbon target, CO 2 mineralization through solid wastes presents a mutually beneficial approach for permanent carbon emission reduction at a low material cost, while also enabling the resource utilization of these wastes. However, despite its potential, a comprehensive understanding about the effect of industrial solid waste properties and operating parameters on the carbonation process, and the mechanism of direct aqueous carbonation is still lacking. A series of experiments were conducted to compare the carbonation performance of fly ash, steel slag, and carbide slag. Subsequently, CO 2 mineralization by carbide slag was systematically studied under various operating parameters due to its high CO 2 sequestration capacity. Results showed the reactivity of CaO and Ca(OH) 2 was higher than that of CaO·SiO 2 and 2CaO·SiO 2 . Carbide slag demonstrated a sequestration capacity of 610.8 g CO 2 /kg and carbonation efficiency ζ Ca of 62.04% under the conditions of 65 °C, 1.5 MPa initial CO 2 pressure, 15 mL/g liquid-to-solid ratio, and 200 r/min stirring speed. Moreover, the formation of carbonates was confirmed through XRD, SEM-EDS, TG, and FTIR. A mechanism analysis revealed that initially, the rate of the carbonation process was primarily controlled by the mass transfer of CO 2 in the gas–liquid interface. However, the rate-determining step gradually shifted to the mass transfer of Ca 2+ in the solid–liquid interface as the reaction time increased. This study lays the foundation for the large-scale implementation of CO 2 sequestration through carbide slag carbonation.

Hollow nanostructures have attracted considerable attention owing to their largesurface area, tunable cavity, and low density. In this study, a unique flower-likeC@SnOx@C hollow nanostructure （denoted as C@SnOx@C-1） was synthesizedthrough a novel one-pot approach. The C@SnOx@C-1 had a hollow carbon coreand interlaced petals on the shell. Each petal was a SnO2 nanosheet coatedwith an ultrathin carbon layer -2 nm thick. The generation of the hollow carboncore, the growth of the SnO2 nanosheets, and the coating of the carbon layerswere simultaneously completed via a hydrothermal process using resorcinol-formaldehyde resin-coated SiO2 nanospheres, tin chloride, urea, and glucose asprecursors. The resultant architecture with a large surface area exhibitedexcellent lithium-storage performance, delivering a high reversible capacity of756.9 mA.h.g-1 at a current density of 100 mA.g-1 after 100 cycles.

The successes of intelligent systems have quite relied on the artificial learning of information, which lead to the broad applications of neural learning solutions. As a common sense, the training of neural networks can be largely improved by specifically defined initialization, neuron layers as well as the activation functions. Though there are sequential layer based initialization available, the generalized solution to initial stages is still desired. In this work, an improved approach to initialization of neural learning is presented, which adopts the shrinkage approach to initialize the transformation of each layer of networks. It can be universally adapted for the structures of any networks with random layers, while stable performance can be attained. Furthermore, the smooth learning of networks is adopted in this work, due to the diverse influence on neural learning. Experimental results on several artificial data sets demonstrate that, the proposed method is able to present robust results with the shrinkage initialization, and competent for smooth learning of neural networks.

Embryonic stem （ES） cells are powerful tools for generating genetically modified animals that can assist in advancing our knowledge of mammalian physiology and disease. Pigs provide outstanding models of human genetic diseases due to the striking similarities to human anatomy, physiology and genetics, but progress with porcine genetic engineering has been hampered by the lack of germline-competent pig ES ceils.

In recent years, pattern analysis plays an important role in data mining and recognition, and many variants have been proposed to handle complicated scenarios. In the literature, it has been quite familiar with high dimensionality of data samples, but either such characteristics or large data have become usual sense in real-world applications. In this work, an improved maximum margin criterion (MMC) method is introduced firstly. With the new definition of MMC, several variants of MMC, including random MMC, layered MMC, 2D^2 MMC, are designed to make adaptive learning applicable. Particularly, the MMC network is developed to learn deep features of images in light of simple deep networks. Experimental results on a diversity of data sets demonstrate the discriminant ability of proposed MMC methods are compenent to be adopted in complicated application scenarios.