Green technology innovation and carbon emission efficiency: The moderating role of environmental uncertainty.

Given the significant global warming caused by large-scale carbon emissions, it has become a crucial issue affecting human survival and world development. As a supportive means for relevant policies, the implementation of green technology innovation is essential for effectively achieving dual carbon goals. In order to explore the intrinsic relationship and impact mechanism between green technology innovation and carbon emission efficiency, this study takes data from high-energy-consuming manufacturing companies listed on the A-share market in China from 2010 to 2019 as samples. It uses the Super-SBM model to measure the carbon emission efficiency of sample companies and employs a two-way fixed effects model to verify the impact of green technology innovation on the carbon emission efficiency of high-energy-consuming manufacturing enterprises. Furthermore, this study also explores the external mechanism of green technology innovation related to carbon emission efficiency, focusing on the moderating variable of environmental uncertainty. The study found that both the quantity and quality of green technology innovation can significantly promote the carbon emission efficiency of high-energy-consuming manufacturing enterprises, and the accuracy of the results remains unchanged after conducting robustness tests. Environmental uncertainty plays an important role in moderating the process of green technology innovation, affecting the carbon emission efficiency of high-energy-consuming manufacturing enterprises. Heterogeneity tests show that the impact of green technology innovation on carbon emission efficiency is particularly significant in the central region and in enterprises experiencing decline. The policy implications derived from empirical analysis aim to provide empirical evidence for promoting the high-quality development of China's high-energy-consuming manufacturing enterprises.

Classification Method of ECG Signals Based on RANet.

BACKGROUND: Electrocardiograms (ECG) are an important source of information on human heart health and are widely used to detect different types of arrhythmias. OBJECTIVE: With the advancement of deep learning, end-to-end ECG classification models based on neural networks have been developed. However, deeper network layers lead to gradient vanishing. Moreover, different channels and periods of an ECG signal hold varying significance for identifying different types of ECG abnormalities. METHODS: To solve these two problems, an ECG classification method based on a residual attention neural network is proposed in this paper. The residual network (ResNet) is used to solve the gradient vanishing problem. Moreover, it has fewer model parameters, and its structure is simpler. An attention mechanism is added to focus on key information, integrate channel features, and improve voting methods to alleviate the problem of data imbalance. RESULTS: Experiments and verifications are conducted using the PhysioNet/CinC Challenge 2017 dataset. The average F1 value is 0.817, which is 0.064 higher than that for the ResNet model. Compared with the mainstream methods, the performance is excellent.

Optimization of AlGaN-based deep ultraviolet light emitting diodes with superlattice step doped electron blocking layers.

The superlattice electron blocking layer (EBL) has been proposed to reduce the electron leakage of the deep ultraviolet light emitting diodes (DUV-LEDs). However, the hole transport is hindered by the barriers of EBL and the improvement of hole injection efficiency still suffers enormous challenges. The superlattice step doped (SLSD) EBL is proposed to improve the hole injection efficiency while enhancing the electron confinement capability. The SLSD EBL enhances the electron confinement capability by multi-reflection effects on the electron wave function. And a built-in electric field towards the active region is generated by superlattice step doping, which facilitates the transport of holes into the multiple quantum wells. The Advaced Physical Model of Semiconductor Devices (APSYS) software is used to simulate the DUV-LEDs with conventional EBL, superlattice EBL, superlattice doped EBL, and SLSD EBL. The results indicate that the SLSD EBL contributes to the increased electron concentration in the multiple quantum wells, the reduced electron leakage in the p-type region, the increased hole injection current, and the increased radiative recombination rate. When the current is 60 mA, the external quantum efficiency of DUV-LED with SLSD EBL is increased to 5.27% and the output power is increased to 13.81 mW. The SLSD EBL provides a valuable reference for solving the problems of serious electron leakage and insufficient hole injection of the DUV-LEDs.

Non-heavy doped pnp-AlGaN tunnel junction for an efficient deep-ultraviolet light emitting diode with low conduction voltage.

While traditional tunnel junction (TJ) light-emitting diodes (LEDs) can enhance current diffusion and increase hole injection efficiency, their reliance on highly doped AlGaN layers to improve hole tunneling efficiency results in a higher conduction voltage, adversely impacting LED device performance. This paper proposes a non-heavy doped pnp-AlGaN TJ deep ultraviolet (DUV) LED with a low conduction voltage. By inserting the TJ near the active region, between the electron blocking layer and the hole supply layer, the need for heavily doped AlGaN is circumvented. Furthermore, the LED leverages the polarization charge in the pnp-AlGaN TJ layer to decrease the electric field strength, enhancing hole tunneling effects and reducing conduction voltage. The non-heavy doped pnp-AlGaN TJ LED effectively enhances carrier concentration in the quantum well, achieving a more uniform distribution of electrons and holes, thus improving radiative recombination efficiency. Consequently, at an injection current of 120 A/cm2, compared to the traditional structure LED (without TJ), the proposed LED exhibits a 190.7% increase in optical power, a 142.8% increase in maximum internal quantum efficiency (IQE) to 0.85, and a modest efficiency droop of only 5.8%, with a conduction voltage of just 4.1V. These findings offer valuable insights to address the challenges of high heavy doped TJ and elevated conduction voltage in high-performance TJ DUV LEDs.

Study of ultraviolet light emitting diodes with InGaN quantum dots and lattice matched superlattice electron blocking layers.

Ultraviolet light emitting diodes (UV-LEDs) face the challenges including insufficient hole injection and severe electron leakage. Quantum dots (QDs) have been proven to provide three-dimensionally localized states for carriers, thereby enhancing carrier confinement. Therefore, UV-LEDs employing InGaN QDs are designed and studied in this paper. The APSYs software is used to simulate UV-LEDs. Simulation results indicate that the QDs effectively improve the electron and hole concentration in the active region. However, UV-LEDs with QDs experience efficiency droop due to serious electron leakage. What's more, the lattice mismatch between last quantum barrier (LQB) and electron blocking layer (EBL) leads to the polarization field, which induces the downward band bending at the LQB/EBL interface and reduces effective barrier height of EBL for electrons. The AlInGaN/AlInGaN lattice matched superlattice (LMSL) EBL is designed to suppress electron leakage while mitigating lattice mismatch between LQB and EBL. The results indicate that the utilization of QDs and LMSL EBL contributes to increasing the electron and hole concentration in the active region, reducing electron leakage, enhancing radiative recombination rate, and reducing turn-on voltage. The efficiency droop caused by electron leakage is mitigated. When the injection current is 120 mA, the external quantum efficiency is increased to 9.3% and the output power is increased to 38.3 mW. This paper provides a valuable reference for addressing the challenges of insufficient hole injection and severe electron leakage.

Exploring the structural color of micro-nano composite gratings with FDTD simulation and experimental validation.

The significance of micro-nano composite gratings (MNCGs) resides in their applications, including optical devices, sensors, and diffractive elements, which drive research interest in their diffraction characteristics. This study investigates both the diffraction characteristics of MNCGs and the factors that influence them by employing both Finite-Difference Time-Domain (FDTD) methods and experimental validation. The initial focus lies in deciphering the differences in diffraction characteristics between micro-gratings (MGs) and MNCGs by analyzing the coupling effects, diffraction order, color distribution, and intensity variation. Additionally, this research emphatically investigates five aspects to discover the influencing factors of MNCG's diffraction characteristics, such as the height, groove angle of MGs and the period, blaze angle, and height of nano-gratings (NGs). Results show that the structural coloration and saturation of MNCG surpass that of MG. NG plays the actual spectral role, and a reduction in the period of NG leads to enhanced splitting light capability of the white light. The optical detection tests validated the simulation results. The present study reveals the diffractive properties of MNCGs, providing technical insights for the design and processing of optically variable devices.

A Spatial-Temporal Video Quality Assessment Method via Comprehensive HVS Simulation.

The quality of videos is the primary concern of video service providers. Built upon deep neural networks, video quality assessment (VQA) has rapidly progressed. Although existing works have introduced the knowledge of the human visual system (HVS) into VQA, there are still some limitations that hinder the full exploitation of HVS, including incomplete modeling with few HVS characteristics and insufficient connection among these characteristics. In this article, we present a novel spatial-temporal VQA method termed HVS-5M, wherein we design five modules to simulate five characteristics of HVS and create a bioinspired connection among these modules in a cooperative manner. Specifically, on the side of the spatial domain, the visual saliency module first extracts a saliency map. Then, the content-dependency and the edge masking modules extract the content and edge features, respectively, which are both weighted by the saliency map to highlight those regions that human beings may be interested in. On the other side of the temporal domain, the motion perception module extracts the dynamic temporal features. Besides, the temporal hysteresis module simulates the memory mechanism of human beings and comprehensively evaluates the video quality according to the fusion features from the spatial and temporal domains. Extensive experiments show that our HVS-5M outperforms the state-of-the-art VQA methods. Ablation studies are further conducted to verify the effectiveness of each module toward the proposed method. The source code is available at https://github.com/GZHU-DVL/HVS-5M.

Performance improvement of nitride semiconductor-based deep-ultraviolet laser diodes with superlattice cladding layers.

A deep-ultraviolet (DUV) laser diode (LD) consisting of specifically designed cladding layers involving superlattice nitride alloy has been proposed. Simulation studies of different cladding layers were carried out using Crosslight software. It was found that the proposed structure effectively suppresses the leakage of the optical field from the active region and the optical confinement coefficient is 1.45 times higher than that of the conventional structure. The proposed structure has a significant increase in laser power with a low threshold current. Moreover, the introduction of novel cladding layer suppresses the electron and hole leakage from the multiple quantum well (MQW) region, which provides an attractive solution for increasing the stimulated recombination rate in the MQW region leading to the improvement in the performance of the DUV LD.

Does the green credit policy reduce the carbon emission intensity of heavily polluting industries? -Evidence from China's industrial sectors.

Green credit policy (GCP) is an important practical exploration to guide green economic development by financial means. Empirically, however, little is known about the relationship between GCP and industrial carbon emissions intensity (CEI). This study aims to investigate the impact of GCP on the CEI of heavily polluting industries (HPIs) by treating Green Credit Guidelines as a quasi-natural experiment. Using Chinese industry-level panel data and a difference-in-difference model, we find that after the implementation of GCP, the CEI of HPIs decreased by an average of 0.267 tons/104 yuan per year compared to non-HPIs. Resource allocation effect and green innovation effect are two channels through which GCP reduces CEI of HPIs. Moreover, the GCP has a greater effect on the CEI of HPIs with lower state-owned ratios, higher total factor productivity and higher capital dependence. These findings provide policy insights for promoting industrial carbon emissions reduction.

Reliability assessment of train control and management system based on evidential reasoning rule and covariance matrix adaptation evolution strategy algorithm.

The reliability assessment of train control and management system (TCMS) is essential for the condition monitoring of high-speed train. Different from other general complex systems, the TCMS has the characteristics of multi-system unit, strong coupling and multiple factors. Considering the special system operating environment and high safety requirements of high-speed train. In this paper, for the reliability assessment of TCMS, we propose a new quantitative model based on the evidential reasoning rule and covariance matrix adaptation evolution strategy algorithm, the proposed model offers the following advantages: it has a strong modeling capability for the TCMS reliability, it has an interpretable model assessment process, it can describe the assessment result under probabilistic uncertainty and ignorance uncertainty, and it possesses considerable robustness. To make the model interpretable, an assessment hierarchy is established for the TCMS; to improve model robustness, weights interval is applied to replace the trained weights as the model weights. Several traditional methods are compared with the proposed model to demonstrate its performance, the results show that the proposed model has a better training accuracy. Moreover, a case study is conducted to verify the model's functional feasibility.

Genome-wide identification and characterization of Gretchen Hagen3 (GH3) family genes in Brassica napus.

The hormone auxin is involved in many biological processes throughout a plant's lifecycle. However, genes in the GH3 (Gretchen Hagen3) family, one of the three major auxin-responsive gene families, have not yet been identified in oilseed rape (Brassica napus). In this study, we identified 63 BnaGH3 genes in oilseed rape using homology searches. We analyzed the chromosome locations, gene structures, and phylogenetic relationships of the BnaGH3 genes, as well as the cis-elements in their promoters. Most BnaGH3 genes are located on chromosomes A03, A09, C02, C03, and C09, each with 4-7 members. In addition, we analyzed the expression patterns of BnaGH3 genes in seven tissues by transcriptome sequencing and quantitative RT-PCR analysis of plants under exogenous IAA treatment. The BnaGH3 genes showed different expression patterns in various tissues. BnaA.GH3.2-1 and BnaC.GH3.2-1 were expressed in the seed and seed coat during development and in response to IAA treatment. These results shed light on the possible roles of the GH3 gene family in oilseed rape.

Genome wide identification and comparative analysis of glutathione transferases (GST) family genes in Brassica napus.

Glutathione transferases (GSTs) are multifunctional enzymes that play important roles in plant development and responses to biotic and abiotic stress. However, a systematic analysis of GST family members in Brassica napus has not yet been reported. In this study, we identified 179 full-length GST genes in B. napus, 44.2% of which are clustered on various chromosomes. In addition, we identified 141 duplicated GST gene pairs in B. napus. Molecular evolutionary analysis showed that speciation and whole-genome triplication played important roles in the divergence of the B. napus GST duplicated genes. Transcriptome analysis of 21 tissues at different developmental stages showed that 47.6% of duplicated GST gene pairs have divergent expression patterns, perhaps due to structural divergence. We constructed a GST gene coexpression network with genes encoding various transcription factors (NAC, MYB, WRKY and bZIP) and identified six modules, including genes expressed during late seed development (after 40 days; BnGSTU19, BnGSTU20 and BnGSTZ1) and in the seed coat (BnGSTF6 and BnGSTF12), stamen and anther (BnGSTF8), root and stem (BnGSTU21), leaves and funiculus, as well as during the late stage of pericarp development (after 40 days; BnGSTU12 and BnGSTF2) and in the radicle during seed germination (BnGSTF14, BnGSTU1, BnGSTU28, and BnGSTZ1). These findings lay the foundation for elucidating the roles of GSTs in B. napus.

Joint QTL mapping and transcriptome sequencing analysis reveal candidate flowering time genes in Brassica napus L.

BACKGROUND: Optimum flowering time is a key agronomic trait in Brassica napus. To investigate the genetic architecture and genetic regulation of flowering time in this important crop, we conducted quantitative trait loci (QTL) analysis of flowering time in a recombinant inbred line (RIL) population, including lines with extreme differences in flowering time, in six environments, along with RNA-Seq analysis. RESULTS: We detected 27 QTLs distributed on eight chromosomes among six environments, including one major QTL on chromosome C02 that explained 11-25% of the phenotypic variation and was stably detected in all six environments. RNA-Seq analysis revealed 105 flowering time-related differentially expressed genes (DEGs) that play roles in the circadian clock/photoperiod, autonomous pathway, and hormone and vernalization pathways. We focused on DEGs related to the regulation of flowering time, especially DEGs in QTL regions. CONCLUSIONS: We identified 45 flowering time-related genes in these QTL regions, eight of which are DEGs, including key flowering time genes PSEUDO RESPONSE REGULATOR 7 (PRR7) and FY (located in a major QTL region on C02). These findings provide insights into the genetic architecture of flowering time in B. napus.

Integrated mRNA, sRNA, and degradome sequencing reveal oilseed rape complex responses to Sclerotinia sclerotiorum (Lib.) infection.

Sclerotinia stem rot (SSR), caused by the fungal pathogen Sclerotinia sclerotiorum, is a devastating disease resulting in yield losses and decreases in seed quality in oilseed rape (Brassica napus) worldwide. However, the molecular mechanisms underlying the response of oilseed rape to S. sclerotiorum infection at the transcriptional and post-transcriptional levels are poorly understood. Here, we used an integrated omics approach (transcriptome, sRNAome, and degradome sequencing) on the Illumina platform to compare the RNA expression and post-transcriptional profiles of oilseed rape plants inoculated or not with S. sclerotiorum. In total, 7,065 differentially expressed genes (DEGs) compared with the mock-inoculated control at 48 hours post inoculation were identified. These DEGs were associated with protein kinases, signal transduction, transcription factors, hormones, pathogenesis-related proteins, secondary metabolism, and transport. In the sRNA-Seq analysis, 77 known and 176 novel miRNAs were identified; however, only 10 known and 41 novel miRNAs were differentially expressed between the samples inoculated or not with S. sclerotiorum. Degradome sequencing predicted 80 cleavage sites with 64 miRNAs. Integrated mRNA, sRNA and degradome sequencing analysis reveal oilseed rape complex responses to S. sclerotiorum infection. This study provides a global view of miRNA and mRNA expression profiles in oilseed rape following S. sclerotiorum infection.

Genome-Wide Identification of MicroRNAs in Response to Cadmium Stress in Oilseed Rape (Brassica napus L.) Using High-Throughput Sequencing.

MicroRNAs (miRNAs) have important roles in regulating stress-response genes in plants. However, identification of miRNAs and the corresponding target genes that are induced in response to cadmium (Cd) stress in Brassica napus remains limited. In the current study, we sequenced three small-RNA libraries from B. napus after 0 days, 1 days, and 3 days of Cd treatment. In total, 44 known miRNAs (belonging to 27 families) and 103 novel miRNAs were identified. A comprehensive analysis of miRNA expression profiles found 39 differentially expressed miRNAs between control and Cd-treated plants; 13 differentially expressed miRNAs were confirmed by qRT-PCR. Characterization of the corresponding target genes indicated functions in processes including transcription factor regulation, biotic stress response, ion homeostasis, and secondary metabolism. Furthermore, we propose a hypothetical model of the Cd-response mechanism in B. napus. Combined with qRT-PCR confirmation, our data suggested that miRNAs were involved in the regulations of TFs, biotic stress defense, ion homeostasis and secondary metabolism synthesis to respond Cd stress in B. napus.

Genome-Wide Identification and Structural Analysis of bZIP Transcription Factor Genes in Brassica napus.

The basic region/leucine zipper motif (bZIP) transcription factor family is one of the largest families of transcriptional regulators in plants. bZIP genes have been systematically characterized in some plants, but not in rapeseed (Brassica napus). In this study, we identified 247 BnbZIP genes in the rapeseed genome, which we classified into 10 subfamilies based on phylogenetic analysis of their deduced protein sequences. The BnbZIP genes were grouped into functional clades with Arabidopsis genes with similar putative functions, indicating functional conservation. Genome mapping analysis revealed that the BnbZIPs are distributed unevenly across all 19 chromosomes, and that some of these genes arose through whole-genome duplication and dispersed duplication events. All expression profiles of 247 bZIP genes were extracted from RNA-sequencing data obtained from 17 different B. napus ZS11 tissues with 42 various developmental stages. These genes exhibited different expression patterns in various tissues, revealing that these genes are differentially regulated. Our results provide a valuable foundation for functional dissection of the different BnbZIP homologs in B. napus and its parental lines and for molecular breeding studies of bZIP genes in B. napus.

Genome-wide analysis and expression profiling of the GRF gene family in oilseed rape (Brassica napus L.).

Growth regulating-factors (GRFs) are plant-specific transcription factors that help regulate plant growth and development. Genome-wide identification and evolutionary analyses of GRF gene families have been performed in Arabidopsis thaliana, Zea mays, Oryza sativa, and Brassica rapa, but a comprehensive analysis of the GRF gene family in oilseed rape (Brassica napus) has not yet been reported. In the current study, we identified 35 members of the BnGRF family in B. napus. We analyzed the chromosomal distribution, phylogenetic relationships (Bayesian Inference and Neighbor Joining method), gene structures, and motifs of the BnGRF family members, as well as the cis-acting regulatory elements in their promoters. We also analyzed the expression patterns of 15 randomly selected BnGRF genes in various tissues and in plant varieties with different harvest indices and gibberellic acid (GA) responses. The expression levels of BnGRFs under GA treatment suggested the presence of possible negative feedback regulation. The evolutionary patterns and expression profiles of BnGRFs uncovered in this study increase our understanding of the important roles played by these genes in oilseed rape.

Screening of Candidate Leaf Morphology Genes by Integration of QTL Mapping and RNA Sequencing Technologies in Oilseed Rape (Brassica napus L.).

Leaf size and shape play important roles in agronomic traits, such as yield, quality and stress responses. Wide variations in leaf morphological traits exist in cultivated varieties of many plant species. By now, the genetics of leaf shape and size have not been characterized in Brassica napus. In this study, a population of 172 recombinant inbred lines (RILs) was used for quantitative trait locus (QTL) analysis of leaf morphology traits. Furthermore, fresh young leaves of extreme lines with more leaf lobes (referred to as 'A') and extreme lines with fewer lobes (referred to as 'B') selected from the RIL population and leaves of dissected lines (referred to as 'P') were used for transcriptional analysis. A total of 31 QTLs for the leaf morphological traits tested in this study were identified on 12 chromosomes, explaining 5.32-39.34% of the phenotypic variation. There were 8, 6, 2, 5, 8, and 2 QTLs for PL (petiole length), PN (lobe number), LW (lamina width), LL (Lamina length), LL/LTL (the lamina size ratio) and LTL (leaf total length), respectively. In addition, 74, 1,166 and 1,272 differentially expressed genes (DEGs) were identified in 'A vs B', 'A vs P' and 'B vs P' comparisons, respectively. The Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to predict the functions of these DEGs. Gene regulators of leaf shape and size, such as ASYMMETRIC LEAVES 2, gibberellin 20-oxidase 3, genes encoding gibberellin-regulated family protein, genes encoding growth-regulating factor and KNOTTED1-like homeobox were also detected in DEGs. After integrating the QTL mapping and RNA sequencing data, 33 genes, including a gene encoding auxin-responsive GH3 family protein and a gene encoding sphere organelles protein-related gene, were selected as candidates that may control leaf shape. Our findings should be valuable for studies of the genetic control of leaf morphological trait regulation in B. napus.

Genome-Wide Analysis and Expression Profiling of the SUC and SWEET Gene Families of Sucrose Transporters in Oilseed Rape (Brassica napus L.).

Sucrose is the principal transported product of photosynthesis from source leaves to sink organs. SUTs/SUCs (sucrose transporters or sucrose carriers) and SWEETs (Sugars Will Eventually be Exported Transporters) play significant central roles in phloem loading and unloading. SUTs/SUCs and SWEETs are key players in sucrose translocation and are associated with crop yields. The SUT/SUC and SWEET genes have been characterized in several plant species, but a comprehensive analysis of these two gene families in oilseed rape has not yet been reported. In our study, 22 and 68 members of the SUT/SUCs and SWEET gene families, respectively, were identified in the oilseed rape (Brassica napus) genome through homology searches. An analysis of the chromosomal distribution, phylogenetic relationships, gene structures, motifs and the cis-acting regulatory elements in the promoters of BnSUC and BnSWEET genes were analyzed. Furthermore, we examined the expression of the 18 BnSUC and 16 BnSWEET genes in different tissues of "ZS11" and the expression of 9 BnSUC and 7 BnSWEET genes in "ZS11" under various conditions, including biotic stress (Sclerotinia sclerotiorum), abiotic stresses (drought, salt and heat), and hormone treatments (abscisic acid, auxin, cytokinin, brassinolide, gibberellin, and salicylic acid). In conclusion, our study provides the first comprehensive analysis of the oilseed rape SUC and SWEET gene families. Information regarding the phylogenetic relationships, gene structure and expression profiles of the SUC and SWEET genes in the different tissues of oilseed rape helps to identify candidates with potential roles in specific developmental processes. Our study advances our understanding of the important roles of sucrose transport in oilseed rape.