Comprehensive Advantages Research Articles

Research on Low Power BIST Based on LFSR Reseeding

With the increasing scale and complexity of the internal circuit, the function of the chip is more powerful, but it will bring serious problems to the test of the chip. The internal power consumption of the chip in the test mode is much higher than that in the normal working mode, especially in the process of built-in self-test, the excessive power consumption will damage the circuit under test and lead to the failure of the chip. The low power test vector generation technology reduces the test power by preprocessing the test vector set. However, the modification of the test vector set results in the low failure coverage in the test process. LFSR replaying technology is a common method of generating test vectors in built-in self-test. It can improve the coverage of test faults by loading test vector seeds into linear feedback shift register. However, while improving the fault coverage, the technology will generate high test power consumption in the circuit under test. In design for testability (DFT), it is a hot topic to generate low-power test vectors by combining LFSR reseeding technology with low-power test vector generation technology. Aiming at the problem of high power consumption caused by test vectors in built-in self-test, this paper proposes a low power test vector generation method based on LFSR reseeding. On the basis of studying the influence of test vector on dynamic test power consumption, the linear correlation between test vector seed and test vector is analyzed deeply. A model of dynamic test power consumption optimization based on Hamming distance sorting test vector seed is proposed to realize the design of low-power test vector seed generation algorithm. Combined with LFSR reseeding technology, a low-power test vector generator based on test vector seed sorting is designed. The simulation design of test vector generator is based on ISCAS85 and ISCAS89. The experimental results show that the total number of test vector seed storage bits is reduced by 64.39%, the average fault coverage is 97.42%, the average area overhead is 4.32%, and the dynamic test power consumption is reduced by 44.21%. Compared with other schemes, the proposed low-power test vector generation technology based on LFSR reseeding has some comprehensive advantages in reducing the number of seed storage bits, improving fault coverage, reducing circuit area overhead and reducing power consumption.

A Multi-Agent Optimal Bidding Strategy in Multi-Operator VPPs Based on SGHSA

As an individual plant participating in the power market, the virtual power plant (VPP) is regarded as the ultimate configuration of the energy Internet, and effective dispatching is a challenge. This paper proposes a multi-agent optimal bidding strategy based on a self-adaptive global optimal harmony search algorithm (SGHSA) to solve the problem of multi-operator participation in virtual power station scheduling. The method takes multiple agents to simulate the bidding process in the VPPs and distributes the profits for the operators based on the market mechanism to optimize the distributed energy resources (DERs). Case studies are provided and show that the proposed method realizes the optimal distribution of power generation and demand level, which improves the comprehensive advantage of the VPP in electricity market transactions.

Predicting Conserved Water Molecules in Binding Sites of Proteins Using Machine Learning Methods and Combining Features.

Water molecules play an important role in many biological processes in terms of stabilizing protein structures, assisting protein folding, and improving binding affinity. It is well known that, due to the impacts of various environmental factors, it is difficult to identify the conserved water molecules (CWMs) from free water molecules (FWMs) directly as CWMs are normally deeply embedded in proteins and form strong hydrogen bonds with surrounding polar groups. To circumvent this difficulty, in this work, the abundance of spatial structure information and physicochemical properties of water molecules in proteins inspires us to adopt machine learning methods for identifying the CWMs. Therefore, in this study, a machine learning framework to identify the CWMs in the binding sites of the proteins was presented. First, by analyzing water molecules' physicochemical properties and spatial structure information, six features (i.e., atom density, hydrophilicity, hydrophobicity, solvent-accessible surface area, temperature B-factors, and mobility) were extracted. Those features were further analyzed and combined to reach a higher CWM identification rate. As a result, an optimal feature combination was determined. Based on this optimal combination, seven different machine learning models (including support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), discriminant analysis (DA), naïve Bayes (NB), and ensemble learning (EL)) were evaluated for their abilities in identifying two categories of water molecules, i.e., CWMs and FWMs. It showed that the EL model was the desired prediction model due to its comprehensive advantages. Furthermore, the presented methodology was validated through a case study of crystal 3skh and extensively compared with Dowser++. The prediction performance showed that the optimal feature combination and the desired EL model in our method could achieve satisfactory prediction accuracy in identifying CWMs from FWMs in the proteins' binding sites.

Employment Status: The Pandemic Situation in India

For the last two years, the global pandemic of Covid 19 caused by SARS CoV-2 has emerged as an immense burden on the health and economic system worldwide affecting several countries. Over 30 million people have been infected by the corona virus in India. Covid 19 can infect people of all genders and Ages. More than 45 percentages of Indian households lost their income due to the Covid 19 Pandemic then the previous year. The Indian Economy was expected to loss around Rs. 32,000 crores in the initial stage of pandemic itself. Almost all sectors of the economy has been adversely affected. Exports are sharply declined. Food and Agriculture, Aviation and Tourism are worse affected. Telecommunication and Pharmaceutical companies are having comprehensive advantages and continuously developed in the pandemic situation. Global level, the impact of Covid 19 is negative and creates high level of inequality, pain and strain, gender equality and gender strategy management are adversely affected. The study based on secondary information collected from the Centre for Monitoring of Indian Economy from the various quarter and World Bank Report during the pandemic period. The paper focused on unemployment rate in different sectors of the Indian economy and the violence against for Women in India during the covid 19 periods. As per International Labour Organization (2021), about 400 million workers in India are at the risk of being pushed into poverty due to cause by lockdown and Covid 19. The unemployment rate in India was recorded at 8 percent. Hence, detailed discussions are essential through this paper.

Architected Materials for Additive Manufacturing: A Comprehensive Review.

One of the main advantages of Additive Manufacturing (AM) is the ability to produce topologically optimized parts with high geometric complexity. In this context, a plethora of architected materials was investigated and utilized in order to optimize the 3D design of existing parts, reducing their mass, topology-controlling their mechanical response, and adding remarkable physical properties, such as high porosity and high surface area to volume ratio. Thus, the current re-view has been focused on providing the definition of architected materials and explaining their main physical properties. Furthermore, an up-to-date classification of cellular materials is presented containing all types of lattice structures. In addition, this research summarized the developed methods that enhance the mechanical performance of architected materials. Then, the effective mechanical behavior of the architected materials was investigated and compared through the existing literature. Moreover, commercial applications and potential uses of the architected materials are presented in various industries, such as the aeronautical, automotive, biomechanical, etc. The objectives of this comprehensive review are to provide a detailed map of the existing architected materials and their mechanical behavior, explore innovative techniques for improving them and highlight the comprehensive advantages of topology optimization in industrial applications utilizing additive manufacturing and novel architected materials.

Clinical evaluation of acute necrotizing encephalopathy in children

ObjectiveAcute necrotizing encephalopathy (ANE) is a rare but severe encephalopathy and is associated with a high morbidity and mortality. We aimed to analyze and compare the clinical features and predictive indicators of pediatric ANE.Materials and methodsThis retrospective study included children with ANE diagnosed at Guangzhou Women and Children’s Medical Center between November 2018 and January 2020. Pediatric patients’ information, including clinical characteristics, laboratory tests, neuroelectrophysiology and brain magnetic resonance imaging (MRI) findings, MRI score, brainstem auditory evoked potential (BAEP) grades, ANE severity scores (ANE-SS), and modified Rankin scale (mRS), were collected.ResultsTwelve ANE patients were included. Among them, one patient (8.3%) died from brainstem dysfunction, one (8.3%) recovered and 10 (83.3%) experienced neurological sequelae. All patients had an initial viral infection and neurological symptoms such as acute disturbance of consciousness (ADOC) or seizure, and the interval from onset of the disease to neurological manifestations was 3 (1.25–3) days. MRI score-I ranged from 1 to 3 (1.8 ± 0.7), MRI score-II ranged from 1 to 4 (2.5 ± 1.1). ANE-SS varied from 1 to 6 (3.9 ± 1.3). The scores of mRS were from 0 to 6 (2.9 ± 1.7). Higher MRI score were associated with worse outcomes, while the BAEP grade and ANE-SS score were not significantly associated with mRS.ConclusionANE is a severe encephalopathy syndrome with rapid progression, resulting in serious neurological sequelae. Compared with BAEP grade and ANE-SS, brain MRI shows more comprehensive advantages in predicting the prognosis of ANE patients. More in-depth research and better indicators are still needed to support the evaluation and treatment of ANE.

Mechanism for One‐Pot Synthesis of 0D‐2D Carbon Materials in the Bubbles Inside Molten Salts

AbstractThe molten salts can be used as media for hydrocarbon pyrolysis owing to their excellent thermal properties, fluidity, and ease of products separation. The bubbling of hydrocarbon gases through the molten salts is an effective approach for exploiting the comprehensive advantages of the molten salts. However, the catalytic capability of the molten salts to crack CH bonds is lower than that of molten metals/alloys. Although the addition of transition metal salts into molten salts improves the catalytic performance, the catalytic mechanism is far from well understood. Herein, bubbling chemical vapor deposition (B‐CVD) for one‐pot synthesis of 0D (carbon black, CB)/2D (graphene, Gr) carbon materials in molten salt at 850 °C is developed. Spectroscopic results suggest that the ionic complexes of the transition metal (Cr, Fe, Ru, Ni, Pd, Cu) chloride are the active species to realize the efficient pyrolysis of methane (CH4). The confined space introduced by the bubble acts as a mini‐reactor to define the product morphology and accelerate the growth. Beneficial to the synergistic effect between ionic complex promotion and bubble confined space, the mass production of CB/Gr simultaneously with high efficiency and low cost at relatively low temperature is achieved.

Bubble Templated Flexible Ceramic Nanofiber Aerogels with Cascaded Resonant Cavities for High-Temperature Noise Absorption.

Aviation noise pollution has become a significant public health problem, especially with the endless improvement of flight speed and loading capacity. Existing aviation noise absorbers have fatal defects of large weight, weak high-temperature stability, and difficulty to achieve both good low-frequency (<1000 Hz) and high-frequency (up to 6000 Hz) noise absorption simultaneously. Herein, we report a robust strategy to create flexible ceramic nanofiber aerogels with cascaded resonant cavities by the air bubbles-assisted freeze-casting technology. The stable hinged resonance cavity structures coassembled by flexible ceramic nanofibers, soft montmorillonite nanosheets, and silica sol glue endow the aerogels with temperature-invariant compressibility (from -196 to 1100 °C) and bendability. Moreover, the comprehensive advantages of cascaded resonance cavities and interconnected fibrous networks enable flexible ceramic nanofiber aerogels to have temperature-invariant full-frequency noise absorption performance (noise reduction coefficient up to 0.66 in 63-6300 Hz). The synthesis of this flexible ceramic nanofiber aerogel provides a versatile platform for the design of high-efficiency noise-absorbing material for various fields.

Sloshing dynamics of liquid tank with built-in buoys for wave energy harvesting

This paper proposes a novel design of liquid tank with built-in buoys for wave energy harvesting, named the ’sloshing wave energy converter (S-WEC)’. When the tank is oscillated by external loads (such as ocean waves), internal liquid sloshing is activated, and the mechanical energy of sloshing waves can be absorbed by the power take-off (PTO) system attached to these buoys. A fully-nonlinear numerical model is established based on the boundary element method for a systematic investigation on dynamic properties of the proposed S-WEC. A motion decoupling algorithm based on auxiliary functions is developed to solve the nonlinear interaction of sloshing waves and floating buoys in the tank. An artificial damping model is introduced to reflect viscous effects of the sloshing liquid. Physical experiments are carried out on a scaled S-WEC model to validate the mathematical and numerical methodologies. Natural frequencies of the S-WEC system are first investigated through spectrum analyses on motion histories of the buoy and sloshing liquid. The viscous damping strength is identified through comparisons with experimental measurements. Effects of the PTO damping on power generation characteristics of S-WEC is further explored. An optimal PTO damping can be found for each excitation frequency, leading to the maximisation of both the power generation and conversion efficiency of the buoy. To determine a constant PTO damping for engineering design, a practical approach based on diagram analyses is proposed, where the averaged conversion efficiency can reach 70%. Effects of the buoy’s geometry on power generation characteristics of the S-WEC are also investigated. The geometry factors including the draught-to-width ratio (DWR) and inclination bottom angle of the buoys are investigated. For cases under consideration, the conversion efficiency of the S-WEC can even reach over 90%. In engineering practice, the present design of S-WEC can be a promising technical solution of ocean wave energy harvesting, based on its comprehensive advantages on survivability enhancement, metal corrosion or fouling organism inhibition, power generation stability and efficiency, and so on.

Tailored MoS2 bilayer grafted onto N/S-doped carbon for ultra-stable potassium-ion capacitor

Few-layers MoS2-based materials have a comprehensive advantage in accommodating potassium-ions due to the rich active S-Mo-S sites and rapid ionic dynamics. Unfortunately, the serious self-stacking of MoS2 molecular layer resulted in insufficient space to buffer the stress/strain caused by K-ions intercalation, which makes MoS2-based electrode exhibits low capacitance and poor cyclic stability in practical studies. To solve this, an in situ induced deposition strategy is proposed to construct a MoS2/C hybrid (labelled as NS-C@b-MoS2). In this NS-C@b-MoS2 hybrid, the MoS2 molecular with bilayer structure is grafted onto a 3D N/S dual-doping carbon skeleton in one spatial-chemical doubly confined mode. This unique hybrid mode not only expose more S-Mo-S bonds to adsorb K-ions, but also avoid the structural collapse of electrode by spatial-chemical doubly confinement effect of carbon skeleton. Consequently, the resultant NS-C@b-MoS2 anode delivers a high specific capacity of 451.2 mAh g−1 at 0.1 A g−1 and an ultra-long cycle stability up to 20,000 cycles with only 0.0013 % fading per cycle. Moreover, the assembled NS-C@b-MoS2//NSAC potassium-ion capacitor delivers a high energy density of 126.5 Wh kg−1 and retains 55.2 Wh kg−1 after 65,000 cycles at 3800 W kg−1, which over the state-of-the-art reported performance.

Comparative study on the high frequency performances of the easy-plane FeNi@SiO2 powder soft magnetic composite

In this work, an easy-plane FeNi@SiO2 powder soft magnetic composite (SMC) was imitated and fabricated and its high frequency magnetic properties were comparatively investigated with a non-easy-plane composite. Due to the planar distribution of easy magnetization axes, the easy-plane composite exhibits a constant permeability of 38 up to 100 MHz. Moreover, the easy-plane SMC exhibits a lower core loss at higher frequencies. Loss separated results show that the hysteresis loss plays a dominant loss component in the composite, rather than dominant excess loss in the non-easy-plane powder composite. These results indicate that, compared with non-easy-plane powder composite, the easy-plane powder composites exhibit comprehensive advantages of higher permeability, wider effective operating frequencies and lower loss, which suggest that the use of easy-plane ferromagnetic powder is a promising and efficient measure to develop a new generation of soft magnetic composites for higher frequency application.

Elucidation of the Density of States for Polycrystalline Silicon Vertical Thin-Film Transistors

The polycrystalline silicon vertical thin-film transistors (TFTs) with different active layer thicknesses of 150 and 300 nm were fabricated by a five-mask process and electrically characterized. The vertical TFT with 150-nm active layer thickness shows comprehensive advantages over its counterpart with 300-nm active layer, especially with a higher ON/OFF current ratio <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I_{\mathrm{ON}}/I_{\mathrm{OFF}}$ </tex-math></inline-formula> of more than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> and higher field-effect mobility, excluding the access resistance effect. The electrical parameters were analyzed by the density of states (DOS) calculation, and smaller DOS is deduced for the device with 150-nm active layer for the same energy level. The detailed elucidation of the DOS was analyzed by introducing the intrinsic mobility and the grain boundary barrier height at the flat-band state, which gives the detailed expressions for the DOS. Polycrystalline silicon lateral TFT was also introduced to verify this evaluation method.

Formability, surface quality and compressive fracture behavior of AlMgScZr alloy lattice structure fabricated by selective laser melting

Due to the comprehensive advantages of lightweight and integration of structure and function, aluminum alloy lattice structures are widely applied in aerospace, automotive industry and biomedical fields. Selective laser melting (SLM) additive manufacturing technology can significantly improve the overall performance of the structure, reduce the production cycle and manufacturing cost by integrated manufacturing. In this paper, five lattice structures (BCC, BCCZ, FCC, FCCZ, Cubic) of AlMgScZr alloy were fabricated by SLM, and the formability, surface quality and compressive properties were systematically studied. It shows that printing defects are easily formed at high power and fast scanning speed. Comparatively, the lattice structures fabricated at low power and slow scanning speed (100 W-250 mm/s) have better overall forming performance at the same energy density. The Cubic type structure has the highest compressive strength of the five lattice structures, which has a compressive strength of 95.2 MPa under the 200 W-500 mm/s condition.

Self-healing action of Bi in high-performance Sb–Bi–Sn positive electrodes for liquid metal batteries

Li||Sb–Bi–Sn liquid metal batteries (LMBs) have comprehensive advantages of a high energy density, excellent rate capability, low material costs, and high cycle performance. However, the corrosion of the positive electrode to the current collector at high temperatures cannot be neglected over the long term. Here, we investigate the corrosion behavior of SS304 in two types of Sb–Bi–Sn alloys under static and cell cycling conditions. Interestingly, we find that the cell performance of Li||Sb–Bi–Sn LMBs are not affected by corrosion between the positive electrode and stainless-steel current collector. The corrosion products and their participation in the cell reaction process are determined using ex situ analyses and electrochemical measurements. Bi not only inhibits the dissolution of SS304 in Sb–Bi–Sn alloy but also spontaneously promotes the decomposition of Fe–Sb and Fe–Cr–Sb corrosion products during lithiation, releasing the active component Sb, and Sb–Bi–Sn alloy is recovered. Encouragingly, even in the event of corrosion, the cell capacity, efficiency, and cycle stability remain almost unaffected due to this self-healing action of Bi. This study represents a new direction toward inhibiting the corrosion of positive electrode and stabilizing cell performance for practical application and commercialization by alloying.

Analogical Interference in Constructionalization: The Emergence of the Hebrew Desiderative ba le-X Y

Abstract This paper investigates the constructionalization of the Hebrew desiderative ba le-X Y (‘X feels like Y’; lit. ‘come.prs.m.sg to-X Y’), which exemplifies the less frequent pathway from motion to desire. Drawing on Diachronic Construction Grammar framework, we provide an account that considers both the construction’s ancestor and similar desiderative constructions existing at the time of emergence. Based on qualitative and quantitative analyses, we suggest ba le-X Y evolved via partial realization of a metaphoric construction conceptualizing experiencers as the goals of emotional forces, e.g. desires and urges. We further argue that this deviation in realization was modeled after a semantically similar, superficially resembling, desiderative construction which is more syntactically compacted. The motivation for this analogical interference is explained by the production and comprehension advantages of the resulting target construction. This paper then provides support for analogy-based interference effects in the formation of form-meaning pairings.

Construction of a Virtual Reality Model of Smart Tourism Scenic Spot Based on Cloud Computing

This paper studies the technology and cloud platform of “cloud computing” in detail. Technically, this paper studies Map-Reduce, HDFS, and BigTable in cloud platform technology. Using the theoretical thinking of comprehensive advantages, the design ideas and principles of the smart tourism service model based on the network platform are proposed, and the overall architecture of the smart tourism service model based on the network platform is constructed according to the three dimensions of the tourist demand level, network platform development stage, and service resource aggregation and integration. The virtual network interaction platform is realized through Unity3D combined with Web3D technology, and the system interaction function is realized according to the communication principle of the front and back of the web page, including the fast switching of scenic spots, the selection of roaming methods, and the realization of navigation maps. In the user evaluation stage, usability and user satisfaction of the system were effectively measured by the SUS system usability scale and the heuristic experiment combined with Likert scale. The virtual reality system of smart tourist attractions based on somatosensory interaction integrates immersion, interactivity, gameplay, education, and dissemination. It can break the time and space barriers for the creation and experience of smart tourist attractions, improve the extremely cold experience environment of smart tourist attractions, increase the display and publicity dimensions of smart tourist attractions, change the situation of one-way passive reception of traditional smart tourist attractions, and improve the interaction and interaction of tourists. It also provides new teaching and design methods for creators of smart tourism scenic spots, which is conducive to the promotion of teaching in smart tourism scenic spots. It enhances the importance of ice and snow culture in the ice and snow industry, changes the traditional business model of ice and snow scenic spots, and uses modern technology to make up for the cultural development defects of smart tourism scenic spots. The digital sustainable development construction of smart tourism scenic spots provides new ideas.

Microstructure and Corrosion Behavior of Laser Cladding FeCoNiCrBSi Based High-Entropy Alloy Coatings

High-entropy amorphous alloys designed based on the concept of multi-principal components have the comprehensive advantages of high passivation element content and amorphous structure, and are considered as one of the promising alternative protective materials in extreme marine environments. However, based on the composition of traditional amorphous alloys, the multi-principal design significantly reduces its glass forming ability. In order to improve the glass formation ability of high-entropy amorphous alloys, this study attempts to design Fe19.6Co19.6Ni19.6Cr19.6(B13.72Si5.88)19.6Y2 alloy by microalloying on the basis of traditional FeCoNiCrBSi high-entropy amorphous alloy. The traditional Fe43.6Co6Ni17.4Cr9B17.5Si1.5Nb5 iron-based amorphous alloy was selected as the comparison material. Then, spherical alloy powders were prepared by gas atomization. The amorphous nanocrystalline composite coatings were deposited on the 304 stainless steel by laser cladding technology. The microstructure of the coatings was characterized by scanning electron microscopy and X-ray diffractometer. The corrosion behavior of laser cladding coatings in 3.5 wt.% NaCl solution were investigated in detail. The results show that the Fe43.6Co6Ni17.4Cr9B17.5Si1.5Nb5 powder is composed of FCC, Laves and boride phases. Whereas the Fe19.6Co19.6Ni19.6Cr19.6(B13.72Si5.88)19.6Y2 high-entropy amorphous alloy powder is composed of FCC and boride phases. Due to the remelting and multiple heat treatments during the preparation of the laser cladding coatings, borides were precipitated in both coatings. The microstructure of the two coatings from the bonding area with the substrate to the top layer are plane grains, dendrite, equiaxed grains and amorphous phase, respectively. Fe19.6Co19.6Ni19.6Cr19.6(B13.72Si5.88)19.6Y2 high-entropy amorphous alloy coating exhibits high corrosion potential, passivation film resistance and low corrosion current density in 3.5 wt.% NaCl solution. In addition, the passivation film formed on the coating has higher Cr content and lower defect concentration, showing more excellent corrosion resistance.

Spherical Fuzzy Soft Topology and Its Application in Group Decision-Making Problems

The spherical fuzzy soft set is a generalized soft set model, which is more realistic, practical, and accurate. It is an extended version of existing fuzzy soft set models that can be used to describe imprecise data in real-world scenarios. The paper seeks to introduce the new concept of spherical fuzzy soft topology defined on spherical fuzzy soft sets. In this work, we define some basic concepts including spherical fuzzy soft basis, spherical fuzzy soft subspace, spherical fuzzy soft interior, spherical fuzzy soft closure, and spherical fuzzy soft boundary. The properties of these defined set are also discussed and explained with an appropriate examples. Also, we establish certain important results and introduce spherical fuzzy soft separation axioms, spherical fuzzy soft regular space, and spherical fuzzy soft normal space. Furthermore, as an application, a group decision-making algorithm is presented based on the TOPSIS (Technique of Order Preference by Similarity to an Ideal Solution) method for solving the decision-making problems. The applicability of the proposed method is demonstrated through a numerical example. The comprehensive advantages of the proposed work have been stated over the existing methods.

First Demonstration of Recognition of Manganese Crust by Deep-Learning Networks with a Parametric Acoustic Probe

The quantitative evaluations of mineral resources and delineation of promising areas in survey regions for future mining have attracted many researchers’ interest. Cobalt-Rich manganese crusts (Mn-crusts), as one of the three significant strategic submarine mineral resources, lack effective and low-cost detection devices for surveying since the challenging distribution requires a high vertical and horizontal resolution. To solve this problem, we have built an engineering prototype parametric acoustic probe named PPPAAP19. With the echo data acquired by the probe, the interpretation of the accurate thickness information and the seabed classification using the deep learning network-based method are realized. We introduce the acoustic dataset of the minerals collected from two sea trials. Firstly, the preprocessing method and data augment strategy used to form the dataset are described. Afterward, the performances of several baseline approaches are assessed on the dataset, and the experimental results show that they all achieve high accuracy for binary classification. We find that the end-to-end approach for binary classification based on a 1D Convolution Neural Network has a comprehensive advantage. Such a demonstration validates the possibility of binary classification for recognizing the ferromanganese crust only in an acoustic manner, which may significantly contribute to the efficiency of the survey.

Heterotic prediction of hybrid performance based on genome-wide SNP markers and the phenotype of parental inbred lines in heading Chinese cabbage (Brassica rapa L. ssp. pekinensis)

Chinese cabbage (Brassica rapa L. ssp. Pekinensis) is the most widely consumed leafy vegetable in Asia. Most commercial cultivars are F1 hybrids with high yield and good quality. To increase the breeding efficiency, heterotic prediction of hybrid performance was studied. Fourteen parental lines of Chinese cabbage were studied in a half-diallel cross to produce 91 hybrids. The average mid-parent heterosis (MPH) for 14 agronomic traits ranged from −5.07% to 106.76%, while the average high-parent heterosis (HPH) ranged from −109.90% to 80.56%. Significant HPH was observed in the growth period (−109.90%), biomass (80.56%), and plant weight (15.20%), which exhibited comprehensive advantages in breeding. Two methods were used to calculate the genetic distance (GD) between the parents based on 2,444,676 single nucleotide polymorphisms (SNPs): total SNPs (GDtotal) and homozygous differential SNPs (GDhomo). Parental clustering based on total SNPs divided the parents into three major types, namely “flat-topped”, “ovate”, and “cylindrical”. These types were more suitable for their traditional pedigree relationships. The correlation between the GD, mean parental phenotype, and heterosis indicated that the phenotype of the parental inbred lines may be useful for predicting heterosis because it is rapid and simple, whereas GD calculated using SNP markers led to a moderate improvement in heterotic prediction. GDhomo was a better predictor of heterosis and F1 performance than GDtotal. These results confirmed that whole-genome SNP markers are an effective tool for evaluating GD in Chinese cabbage and are useful for parent selection in breeding.