The complete chloroplast genome of common walnut (Juglans regia)

Common walnut (Juglans regia L.) is cultivated in temperate regions worldwide for its wood and nuts. The complete chloroplast genome of J. regia was sequenced using the Illumina MiSeq platform. This is the first complete chloroplast sequence for the Juglandaceae, a family that includes numerous species of economic importance. The chloroplast genome of J. regia was 160 367 bp in length, with 36.11% GC content. It contains a pair of inverted repeats (26 035 bp) which were separated by a large single copy (89 872 bp) and a small single copy region (18 425 bp). A total of 137 genes were annotated, which included 86 protein-coding genes, three pseudogenes (two ycf15 and one infA), 40 tRNA genes and eight rRNA genes. The neighbour-joining phylogenetic analysis with the reported chloroplast genomes showed that common walnut chloroplasts are most closely related to those of the Fagaceae family

Remote Sensing Image Detection Based on YOLOv4 Improvements

Remote sensing image target object detection and recognition are widely used both in military and civil fields. There are many models proposed for this purpose, but their effectiveness on target object detection in remote sensing images is not ideal due to the influence of climate conditions, obstacles and confusing objects presented in images, image clarity, and associated problems with small-target and multi-target detection and recognition. Therefore, how to accurately detect target objects in images is an urgent problem to be solved. To this end, a novel model, called YOLOv4_CE, is proposed in this paper, based on the classical YOLOv4 model with added improvements, resulting from replacing the backbone feature-extraction CSPDarknet53 network with a ConvNeXt-S network, replacing the Complete Intersection over Union (CIoU) loss with the Efficient Intersection over Union (EIoU) loss, and adding a coordinate attention mechanism to YOLOv4, as to improve its remote sensing image detection capabilities. The results, obtained through experiments conducted on two open data sets, demonstrate that the proposed YOLOv4_CE model outperforms, in this regard, both the original YOLOv4 model and four other state-of-the-art models, namely Faster R-CNN, Gliding Vertex, Oriented R-CNN, and EfficientDet, in terms of the mean average precision (mAP) and F1 score, by achieving respective values of 95.03% and 0.933 on the NWPU VHR-10 data set, and 95.89% and 0.937 on the RSOD data set.National Key Research and Development Program of China under Grant 2017YFE0135700; MES under Grant No. D01-168/28.07.2022 for NCDSC part of the Bulgarian National Roadmap on RIs; Telecommunications Research Centre (TRC), University of Limerick, Ireland

Climatic and Soil Factors Shape the Demographical History and Genetic Diversity of a Deciduous Oak (Quercus liaotungensis) in Northern China

Past and current climatic changes have affected the demography, patterns of genetic diversity, and genetic structure of extant species. The study of these processes provides valuable information to forecast evolutionary changes and to identify conservation priorities. Here, we sequenced two functional nuclear genes and four chloroplast DNA regions for 105 samples from 21 populations of Quercus liaotungensis across its distribution range. Coalescent-based Bayesian analysis, approximate Bayesian computation (ABC), and ecological niche modeling (ENM) were integrated to investigate the genetic patterns and demographical history of this species. Association estimates including Mantel tests and multiple linear regressions were used to infer the effects of geographical and ecological factors on temporal genetic variation and diversity of this oak species. Based on multiple loci, Q. liaotungensis populations clustered into two phylogenetic groups; this grouping pattern could be the result of adaptation to habitats with different temperature and precipitation seasonality conditions. Demographical reconstructions and ENMs suggest an expansion decline trend of this species during the Quaternary climatic oscillations. Association analyses based on nuclear data indicated that intraspecific genetic differentiation of Q. liaotungensis was clearly correlated with ecological distance; specifically, the genetic diversity of this species was significantly correlated with temperature seasonality and soil pH, but negatively correlated with precipitation. Our study highlights the impact of Pleistocene climate oscillations on the demographic history of a tree species in Northern China, and suggests that climatic and soil conditions are the major factors shaping the genetic diversity and population structure of Q. liaotungensis

Complex Knowledge Graph Embeddings Based on Convolution and Translation

Link prediction involves the use of entities and relations that already exist in a knowledge graph to reason about missing entities or relations. Different approaches have been proposed to date for performing this task. This paper proposes a combined use of the translation-based approach with the Convolutional Neural Network (CNN)-based approach, resulting in a novel model, called ConCMH. In the proposed model, first, entities and relations are embedded into the complex space, followed by a vector multiplication of entity embeddings and relational embeddings and taking the real part of the results to generate a feature matrix of their interaction. Next, a 2D convolution is used to extract features from this matrix and generate feature maps. Finally, the feature vectors are transformed into predicted entity embeddings by obtaining the inner product of the feature mapping and the entity embedding matrix. The proposed ConCMH model is compared against state-of-the-art models on the four most commonly used benchmark datasets and the obtained experimental results confirm its superiority in the majority of cases

Detection of River Floating Garbage Based on Improved YOLOv5

The random dumping of garbage in rivers has led to the continuous deterioration of water quality and affected people’s living environment. The accuracy of detection of garbage floating in rivers is greatly affected by factors such as floating speed, night/daytime natural light, viewing angle and position, etc. This paper proposes a novel detection model, called YOLOv5_CBS, for the detection of garbage objects floating in rivers, based on improvements of the YOLOv5 model. Firstly, a coordinate attention (CA) mechanism is added to the original C3 module (without compressing the number of channels in the bottleneck), forming a new C3-CA-Uncompress Bottleneck (CCUB) module for improving the size of the receptive field and allowing the model to pay more attention to important parts of the processed images. Then, the Path Aggregation Network (PAN) in YOLOv5 is replaced with a Bidirectional Feature Pyramid Network (BiFPN), as proposed by other researchers, to enhance the depth of information mining and improve the feature extraction capability and detection performance of the model. In addition, the Complete Intersection over Union (CIoU) loss function, which was originally used in YOLOv5 for the calculation of location score of the compound loss, is replaced with the SCYLLA-IoU (SIoU) loss function, so as to speed up the model convergence and improve its regression precision. The results, obtained through experiments conducted on two datasets, demonstrate that the proposed YOLOv5_CBS model outperforms the original YOLOv5 model, along with three other state-of-the-art models (Faster R-CNN, YOLOv3, and YOLOv4), when used for river floating garbage objects detection, in terms of the recall, average precision, and F1 score achieved by reaching respective values of 0.885, 90.85%, and 0.8669 on the private dataset, and 0.865, 92.18%, and 0.9006 on the Flow-Img public dataset

Effects of SMILE Surgery on Intraocular Pressure, Central Corneal Thickness, Axial Length, Peripapillary Retinal Nerve Fiber Layer, and Macular Ganglion Cell Complex Thickness

Purpose. To evaluate the change in intraocular pressure (IOP), central corneal thickness (CCT), axial length, peripapillary retinal nerve fiber layer (RNFL) thickness, and macular ganglion cell complex (GCC) thickness after small incision lenticule extraction (SMILE) surgery. Methods. This prospective observational study was conducted in Espace Nouvelle Vision, Ophthalmological Clinic, Paris, France. Fifty eyes of 25 patients were enrolled in this study and underwent SMILE surgeries. IOP, central corneal thickness (CCT), axial length (AL), peripapillary RNFL thickness, and macular GCC thickness were measured before and at 3 months after SMILE. Results. The mean preoperative spherical equivalent was −3.15 ± 1.50 diopters (D), and the mean postoperative value was 0.15 ± 0.28 D. After SMILE surgery, IOP decreased from 15.03 ± 2.79 mmHg to 11.02 ± 2.73 mmHg and 10.02 ± 2.21 mmHg at 1 and 3 months, respectively (P<0.01 for both comparisons). The mean decrease in measured IOP as a function of ablation depth was 0.065 ± 0.031 mmHg/μm. CCT decreased from 545.98 ± 26.61 μm to 478.40 ± 30.26 μm after SMILE surgery (P<0.01). AL decreased from 24.80 ± 0.84 mm to 24.70 ± 0.83 mm (P<0.01). There was no statistically significant change in mean peripapillary RNFL or mean GCC thickness after SMILE surgery. Conclusions. SMILE surgery modified IOP measurement, CCT, and AL but did not change peripapillary RNFL and macular GCC thicknesses. The postoperative drop in measured IOP might be explained by the decreased CCT. An accurate re-evaluation of AL should be performed before cataract surgery among post-SMILE patients

Two Novel Models for Traffic Sign Detection Based on YOLOv5s

Object detection and image recognition are some of the most significant and challenging branches in the field of computer vision. The prosperous development of unmanned driving technology has made the detection and recognition of traffic signs crucial. Affected by diverse factors such as light, the presence of small objects, and complicated backgrounds, the results of traditional traffic sign detection technology are not satisfactory. To solve this problem, this paper proposes two novel traffic sign detection models, called YOLOv5-DH and YOLOv5-TDHSA, based on the YOLOv5s model with the following improvements (YOLOv5-DH uses only the second improvement): (1) replacing the last layer of the &lsquo;Conv + Batch Normalization + SiLU&rsquo; (CBS) structure in the YOLOv5s backbone with a transformer self-attention module (T in the YOLOv5-TDHSA&rsquo;s name), and also adding a similar module to the last layer of its neck, so that the image information can be used more comprehensively, (2) replacing the YOLOv5s coupled head with a decoupled head (DH in both models&rsquo; names) so as to increase the detection accuracy and speed up the convergence, and (3) adding a small-object detection layer (S in the YOLOv5-TDHSA&rsquo;s name) and an adaptive anchor (A in the YOLOv5-TDHSA&rsquo;s name) to the YOLOv5s neck to improve the detection of small objects. Based on experiments conducted on two public datasets, it is demonstrated that both proposed models perform better than the original YOLOv5s model and three other state-of-the-art models (Faster R-CNN, YOLOv4-Tiny, and YOLOv5n) in terms of the mean accuracy (mAP) and F1 score, achieving mAP values of 77.9% and 83.4% and F1 score values of 0.767 and 0.811 on the TT100K dataset, and mAP values of 68.1% and 69.8% and F1 score values of 0.71 and 0.72 on the CCTSDB2021 dataset, respectively, for YOLOv5-DH and YOLOv5-TDHSA. This was achieved, however, at the expense of both proposed models having a bigger size, greater number of parameters, and slower processing speed than YOLOv5s, YOLOv4-Tiny and YOLOv5n, surpassing only Faster R-CNN in this regard. The results also confirmed that the incorporation of the T and SA improvements into YOLOv5s leads to further enhancement, represented by the YOLOv5-TDHSA model, which is superior to the other proposed model, YOLOv5-DH, which avails of only one YOLOv5s improvement (i.e., DH)

Detection of River Floating Garbage Based on Improved YOLOv5

The random dumping of garbage in rivers has led to the continuous deterioration of water quality and affected people’s living environment. The accuracy of detection of garbage floating in rivers is greatly affected by factors such as floating speed, night/daytime natural light, viewing angle and position, etc. This paper proposes a novel detection model, called YOLOv5_CBS, for the detection of garbage objects floating in rivers, based on improvements of the YOLOv5 model. Firstly, a coordinate attention (CA) mechanism is added to the original C3 module (without compressing the number of channels in the bottleneck), forming a new C3-CA-Uncompress Bottleneck (CCUB) module for improving the size of the receptive field and allowing the model to pay more attention to important parts of the processed images. Then, the Path Aggregation Network (PAN) in YOLOv5 is replaced with a Bidirectional Feature Pyramid Network (BiFPN), as proposed by other researchers, to enhance the depth of information mining and improve the feature extraction capability and detection performance of the model. In addition, the Complete Intersection over Union (CIoU) loss function, which was originally used in YOLOv5 for the calculation of location score of the compound loss, is replaced with the SCYLLA-IoU (SIoU) loss function, so as to speed up the model convergence and improve its regression precision. The results, obtained through experiments conducted on two datasets, demonstrate that the proposed YOLOv5_CBS model outperforms the original YOLOv5 model, along with three other state-of-the-art models (Faster R-CNN, YOLOv3, and YOLOv4), when used for river floating garbage objects detection, in terms of the recall, average precision, and F1 score achieved by reaching respective values of 0.885, 90.85%, and 0.8669 on the private dataset, and 0.865, 92.18%, and 0.9006 on the Flow-Img public dataset

Mechanistic Study on the Corrosion of (La,Sr)(Co,Fe)O_3-δ Cathodes Induced by CO₂

Solid Oxide Fuel Cell (SOFC) cathodes operating in ambient atmospheric conditions inevitably encounter CO2 contamination, leading to sustained performance deterioration. In this investigation, we examined the impact of CO2 on three variants of (La,Sr)(Co,Fe)O3-δ cathodes and employed the distribution of relaxation times method to distinguish distinct electrochemical processes based on impedance spectra analysis. We meticulously analyzed and discussed the corrosion resistance of these (La,Sr)(Co,Fe)O3-δ cathodes under high CO2 concentrations, relying on the experimental data. Electrochemical impedance spectroscopy results revealed that La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF−6428), La0.4Sr0.6Co0.2Fe0.8O3-δ (LSCF−4628), and La0.4Sr0.6Co0.2Fe0.7Nb0.1O3-δ (LSCFN−46271) cathodes exhibited persistent degradation when exposed to CO2 at temperatures of 650 °C or 800 °C during the durability-testing period. An increase in electrode polarization resistance was observed upon CO2 introduction to the electrode, but electrode performance recovered upon returning to a pure air environment. Furthermore, X-ray diffraction and scanning electron microscopy analyses confirmed that CO2 did not cause permanent damage to the (La,Sr)(Co,Fe)O3-δ cathodes. These findings indicate that the (La,Sr)(Co,Fe)O3-δ cathodes exhibit excellent resistance to CO2-induced corrosion

Complex knowledge graph embeddings based on convolution and translation

Link prediction involves the use of entities and relations that already exist in a knowledge graph to reason about missing entities or relations. Different approaches have been proposed to date for performing this task. This paper proposes a combined use of the translation-based approach with the Convolutional Neural Network (CNN)-based approach, resulting in a novel model, called ConCMH. In the proposed model, first, entities and relations are embedded into the complex space, followed by a vector multiplication of entity embeddings and relational embeddings and taking the real part of the results to generate a feature matrix of their interaction. Next, a 2D convolution is used to extract features from this matrix and generate feature maps. Finally, the feature vectors are transformed into predicted entity embeddings by obtaining the inner product of the feature mapping and the entity embedding matrix. The proposed ConCMH model is compared against state-of-the-art models on the four most commonly used benchmark datasets and the obtained experimental results confirm its superiority in the majority of cases. </p