Search

Through formation flying, the distributed spaceborne SAR(synthetic aperture radar) system can increase the number of spatial degree of freedoms (DOFs) and provide flexible multi-baselines for SAR-GMTI (ground moving target indication), which improves the system performance. This paper proposes an a priori knowledge-based adaptive clutter cancellation and moving target detection technique applied to the distributed spaceborne SAR-GMTI systems. Firstly, the adaptive clutter cancellation technique is exploited to suppress the ground clutter. A priori knowledge, such as road network information, is integrated to the adaptive clutter cancellation processor to reduce any moving target steering vector mismatch. Secondly, adaptive matched filter (AMF) and adaptive beamformer orthogonal rejection test (ABORT) are exploited as adaptive detection techniques for moving target detection. Due to the dense road network, the moving target steering vector estimation may be ambiguous for the different position and orientation of the roads. The multiple hypothesis testing (MHT) technique is proposed to detect the moving targets and resolve the potential ambiguities. A scheme is exploited to detect, classify, and relocate the moving targets. Finally, simulation experiments and performance analysis have demonstrated the effectiveness and robustness of the proposed technique. [ABSTRACT FROM AUTHOR]

Steel-fiber-reinforced scoria aggregate concrete (SFSAC), which contains scoria aggregate and steel fiber, was developed to reduce the environmental impacts and improve the energy efficiency of buildings. Experimental studies were performed. The test variables included steel fiber volume contents (0%, 0.5%, 1.0%, and 1.5%), freeze-thaw cycles (0 and 25 times), and temperature (20 °C, 200 °C, 400 °C, 600 °C, and 800 °C). Mass loss, relative dynamic elastic modulus, mechanical properties, and the variation pattern of the complete stress–strain curves were analyzed through rapid freeze-thaw, high-temperature, and mechanical tests. The test results showed that after 25 freeze-thaw cycles and then exposure to high temperatures, the surfaces of SFSAC specimens showed aggregate spalling accompanied by dense cracks. Moreover, the residual mechanical properties of steel-fiber-reinforced natural aggregate concrete (SFNAC) were better than those of natural aggregate concrete (NAC). Although the incorporation of steel fiber cannot significantly improve the anti-freezing performance of SFSAC, it can improve the residual mechanical properties of SFSAC, and the optimal amount of incorporation is 1%, considering the economic cost factors. The stress–strain curves of both SFSAC and SFNAC showed the same trend after freeze-thaw cycles and then high temperatures, i.e., the peak stress decreased, the peak strain increased, and the descending section tended to level off. Finally, based on the concrete damage mechanics theory, considering the role of steel fibers in the uniaxial compression process of scoria aggregate concrete (SAC) and the effect of freeze-thaw and high-temperature tests on the SFSAC, the mechanical damage model and the uniaxial compression stress–strain constitutive model were proposed as being able to highly accurately reflect the overall process damage characteristics of SFSAC after freeze-thaw and then high-temperature tests, and also provided a theoretical basis for the high-temperature resistance assessment of SFSAC structures in cold regions. [ABSTRACT FROM AUTHOR]

Simple Summary: Radiotherapy, a crucial cancer treatment, has evolved significantly over the years. Traditionally, treatment plans were based on initial scans used throughout the treatment course, accounting for changes in the patient's anatomy by additional margins to targets. However, the field has moved towards decreasing margins with the advancement of delivery and targeting accuracy in order to decrease toxicity, and the increasing use of image guidance has illuminated patient anatomical changes such as organ deformation, weight loss, tumor shrinkage, and even biological changes that are unaccounted for by the conventional approach. Adaptive radiotherapy (ART) addresses this by adjusting treatment plans according to these changes. ART can be conducted in two ways: online (adjustments made during treatment sessions) and offline (adjustments made between treatment sessions). Advances in technology, especially in medical imaging (CT, MRI, and PET scans) and artificial intelligence, have made ART more feasible and efficient. ART offers more precise cancer treatment by adapting to changes in the patient's body, leading to better outcomes with fewer side effects. Radiotherapy, a crucial technique in cancer therapy, has traditionally relied on the premise of largely unchanging patient anatomy during the treatment course and encompassing uncertainties by target margins. This review introduces adaptive radiotherapy (ART), a notable innovation that addresses anatomy changes and optimizes the therapeutic ratio. ART utilizes advanced imaging techniques such as CT, MRI, and PET to modify the treatment plan based on observed anatomical changes and even biological changes during the course of treatment. The narrative review provides a comprehensive guide on ART for healthcare professionals and trainees in radiation oncology and anyone else interested in the topic. The incorporation of artificial intelligence in ART has played a crucial role in improving effectiveness, particularly in contour segmentation, treatment planning, and quality assurance. This has expedited the process to render online ART feasible, lowered the burden for radiation oncology practitioners, and enhanced the precision of dynamically personalized treatment. Current technical and clinical progress on ART is discussed in this review, highlighting the ongoing development of imaging technologies and AI and emphasizing their contribution to enhancing the applicability and effectiveness of ART. [ABSTRACT FROM AUTHOR]

Negative thermal expansion (NTE) ceramics Sm0.85Zn0.15MnO3 (SZMO) and ZrMgMo3O12 (ZMMO) were selected to prepare Sm0.85Zn0.15MnO3-ZrMgMo3O12/Al-20Si (SZMO-ZMMO/Al-20Si) composites using ball milling and vacuum heating-press sintering processes in this study. The synergistic effect of the SZMO and ZMMO NTE ceramic reinforcements on the microstructure, mechanical properties, and coefficient of thermal expansion (CTE) of the composites was investigated. The results show that the processes of ball milling and sintering did not induce the decomposition of SZMO or ZMMO NTE ceramic reinforcements, nor did they promote a reaction between the Al-20Si matrix and SZMO or ZMMO NTE ceramic reinforcements. However, the excessive addition of SZMO and ZMMO NTE ceramics led to their aggregation within the composite. Adding a small amount of SZMO in combination with ZMMO effectively increased hardness and yield strength while reducing CTE in the Al-20Si alloy. The improvement in strength was primarily provided by SZMO, while the inhibition effect on CTE was primarily provided by ZMMO. An evaluation parameter denoted as α was proposed to evaluate the synergy effects of SZMO and ZMMO NTE ceramic reinforcements on the mechanical properties and CTE of the composites. Based on this parameter, among all composites fabricated, adding 2.5 vol% SZMO NTE ceramic and 10 vol% ZMMO NTE ceramic resulted in an optimal balance between CTE and strength for these composites with a compressive yield strength of 349.72 MPa and a CTE of 12.55 × 10−6/K, representing a significant increase in yield strength by 79.20% compared to that of Al-20Si alloy along with a notable reduction in CTE by 26.44%. [ABSTRACT FROM AUTHOR]

Transition metal selenides have garnered considerable attention in the field of electrocatalytic oxygen evolution reaction (OER). However, their OER performances still lag behind those of Ir-based materials due to limited exposed active sites, inefficient electron transfer and inadequate stability. In this study, we have successfully synthesized nitrogen-doped NiSe2 nanosheets, which exhibit high efficiency and long-term stability for the OER, requiring only 320 mV to reach a current density of 10 mA cm−2. The nitrogen doping plays a crucial role in effectively regulating the work function and semiconductor characteristics of NiSe2, which facilitates the electron transport and optimizes the catalytic sites. Furthermore, the NiSe2 nanosheets present a larger surface area with more exposed active sites, thus resulting in exceptional OER catalytic activity. The nitrogen-doped NiSe2 nanosheets also display superior stability, maintaining a sustained current density throughout an 8-h OER operation. [ABSTRACT FROM AUTHOR]

Nickel-based alloys have demonstrated significant promise as structural materials for Gen-IV nuclear reactors. However, the understanding of the interaction mechanism between the defects resulting from displacement cascades and solute hydrogen during irradiation remains limited. This study aims to investigate the interaction between irradiation-induced point defects and solute hydrogen on nickel under diverse conditions using molecular dynamics simulations. In particular, the effects of solute hydrogen concentrations, cascade energies, and temperatures are explored. The results show a pronounced correlation between these defects and hydrogen atoms, which form clusters with varying hydrogen concentrations. With increasing the energy of a primary knock-on atom (PKA), the number of surviving self-interstitial atoms (SIAs) also increases. Notably, at low PKA energies, solute hydrogen atoms impede the clustering and formation of SIAs, while at high energies, they promote such clustering. The impact of low simulation temperatures on defects and hydrogen clustering is relatively minor. High temperature has a more obvious effect on the formation of clusters. This atomistic investigation offers valuable insights into the interaction between hydrogen and defects in irradiated environments, thereby informing material design considerations for next-generation nuclear reactors. [ABSTRACT FROM AUTHOR]

Simple Summary: The gastrointestinal tract (GIT) is the largest and most complex endocrine organ; there are numerous endocrine cells in the mucosal layer of gastrointestinal tissue, which can secrete a variety of gastrointestinal hormones that regulate gastrointestinal digestion and absorption. The small alpine mammals, plateau zokor (Eospalax baileyi) and plateau pika (Ochotona curzoniae), live in the unique ecotope with cold, hypoxic environments and short plant-growing seasons, resulting in their differential adaptive digestive strategies for foods. In this study, we compared the distribution pattern of argyrophilic cells and the expression of 5-HT cells, Gas cells, and Glu cells in the GIT of the plateau zokor and plateau pika by using histochemical and immunohistochemical methods. Our results showed that the distribution pattern of endocrine cells in the GIT of plateau zokor and plateau pika not only have specific interspecies differences, but also exhibit parallel adaptation to the alpine environments. In addition, we conclude that the distribution pattern of endocrine cells in the GIT is consistent with the respective animals' diets. Endocrine cells can secrete a variety of gastrointestinal hormones that regulate gastrointestinal digestion and absorption, which, in turn, play an important role in animal growth, metabolism, and acclimation. The small alpine mammals, plateau zokor (Eospalax baileyi) and plateau pika (Ochotona curzoniae), live in a unique ecotope with cold, hypoxic environments and short plant-growing seasons, resulting in differential adaptive digestive strategies for foods. Studying the distribution pattern of endocrine cells in the gastrointestinal tract (GIT) of these two animals can lead to a better understanding of the survival strategies of animals in an alpine environment. In this study, we used histochemical and immunohistochemical methods to compare the distribution pattern of argyrophilic cells and the expression of 5-HT cells, Gas cells, and Glu cells in the GIT of the plateau zokor with those of the plateau pika. The results showed that these endocrine cells we studied were widely distributed in the gastrointestinal organs of both these small mammals, and their morphology and distribution location in the GIT were almost the same. However, there were significant differences in the distribution density of argyrophilic cells between different organs in the GIT. The distribution density of argyrophilic cells in the duodenum, jejunum, ileum, and rectum of plateau zokor was significantly lower than that of plateau pika (p < 0.05) and, in the cecum of plateau zokor, was significantly higher than that of plateau pika (p < 0.001). The positive expression of 5-HT cells in the corpus I, corpus II, and pylorus of the stomach, duodenum, ileum, and rectum of plateau zokor was significantly higher than that of plateau pika (p < 0.01). In addition, the positive expression of Glu cells in the cecum was significantly higher (p < 0.01) and in the duodenum and colon was significantly lower (p < 0.05) in the plateau zokor than in the plateau pika. We conclude that the distribution pattern of endocrine cells in the GIT is consistent with the respective animals' diets, with the plateau zokor feeding on high-fiber roots and plateau pika preferring to intake the aboveground parts of plants with lower fibers. [ABSTRACT FROM AUTHOR]

Nowadays, great efforts have been spent on addressing concerns over energy and environmental crises. Among these efforts, electrocatalysis is widely recognized and studied for its high efficiency and easy processability. As a class of emerging electrocatalysts, metal aerogels (MAs) stand out in the last decade. In virtue of their three-dimensional conductive pathways, their library of catalytically/optically active sites, and their robust network structures, MAs have unique advantages in electrocatalysis. However, due to the short history of MAs, there is insufficient research on them, leaving significant room for material design and performance optimization. This perspective will mainly focus on electrocatalysis with MAs, aiming to summarize the state-of-the-art progress and to guide the on-target design of efficient MAs-based electrocatalysts towards energy- and environment-related applications. [ABSTRACT FROM AUTHOR]

The lack of an efficient and stable electrocatalyst for oxygen evolution reaction (OER) greatly hinders the development of various electrochemical energy conversion and storage techniques. In this study, we report a facile synthesis of FeCoNiCuIr high-entropy alloy nanoparticles (HEA NPs) by a one-step heat-up method. The involvement of glucose made the NPs grow uniformly and increased the valence of Ir. The resulting FeCoNiCuIr NPs exhibit excellent OER performance in alkaline solution, with a low overpotential of 360 mV to achieve a current density of 10 mA cm−2 at a Tafel slope of as low as 70.1 mV dec−1. In addition, high stability has also been observed, which remained at 94.2% of the current density after 10 h constant electrolysis, with a constant current of 10 mA cm−2. The high electrocatalytic activity and stability are ascribed to the cocktail effect and synergistic effect between the constituent elements. Our work holds the potential to be extended to the design and synthesis of high-performance electrocatalysts. [ABSTRACT FROM AUTHOR]

Simple Summary: Raphanus is an important genus of Brassicaceae and has undergone a lengthy evolutionary process. However, the inter- and intraspecific phylogenetic relationships and genetic diversity are not well understood. To elucidate these issues, we SSR-sequenced 939 wild, semi-wild and cultivated accessions, and discovered that Europe was the origin center of radishes with diverse European wild radishes, and Europe, South Asia and East Asia might be three independent domestication centers. There was considerable genetic differentiation within European cultivated radishes. European primitive cultivated radish exhibited gene flow with black radish/oil radish and rat-tail radish. Among Asian cultivated radishes, rat-tail radish was a sister to the clade of Chines big radish (including Japanese wild radish), suggesting that they may share the most recent common ancestry. Japanese wild radish had strong gene exchange with Japanese/Korea big radish, oil radish and rat-tail radish. American wild radish developed from natural hybridization between European wild radish and European small radish. All these demonstrated that European primitive cultivated type, American wild radish and Japanese wild radish might have played indispensable roles in radish evolution. Our study provides new perspectives into the origin, evolution and genetic diversity of Raphanus and facilitates the conservation and exploitation of radish germplasm resources. Raphanus has undergone a lengthy evolutionary process and has rich diversity. However, the inter- and intraspecific phylogenetic relationships and genetic diversity of this genus are not well understood. Through SSR-sequencing and multi-analysis of 939 wild, semi-wild and cultivated accessions, we discovered that the European wild radish (EWR) population is separated from cultivated radishes and has a higher genetic diversity. Frequent intraspecific genetic exchanges occurred in the whole cultivated radish (WCR) population; there was considerable genetic differentiation within the European cultivated radish (ECR) population, which could drive radish diversity formation. Among the ECR subpopulations, European primitive cultivated radishes (EPCRs) with higher genetic diversity are most closely related to the EWR population and exhibit a gene flow with rat-tail radishes (RTRs) and black radishes (BRs)/oil radishes (ORs). Among Asian cultivated radishes (ACRs), Chinese big radishes (CBRs) with a relatively high diversity are furthest from the EWR population, and most Japanese/Korean big radishes (JKBRs) are close to CBR accessions, except for a few old Japanese landraces that are closer to the EPCR. The CBR and JKBR accessions are independent of RTR accessions; however, phylogenetic analysis indicates that the RTR is sister to the clade of CBR (including JWR), which suggests that the RTR may share the most recent common ancestry with CBRs and JWRs. In addition, Japanese wild radishes (JWRs), (namely, R. sativus forma raphanistroides) are mainly scattered between CBRs and EPCRs in PCoA analysis. Moreover, JWRs have a strong gene exchange with the JKBR, OR and RTR subpopulations. American wild radishes (AWRs) are closely related to European wild and cultivated radishes, and have a gene flow with European small radishes (ESRs), suggesting that the AWR developed from natural hybridization between the EWR and the ESR. Overall, this demonstrates that Europe was the origin center of the radish, and that Europe, South Asia and East Asia appear to have been three independent domestication centers. The EPCR, AWR and JWR, as semi-wild populations, might have played indispensable transitional roles in radish evolution. Our study provides new perspectives into the origin, evolution and genetic diversity of Raphanus and facilitates the conservation and exploitation of radish germplasm resources. [ABSTRACT FROM AUTHOR]

Tobacco (Nicotiana tabacum), is a world's major non-food agricultural crop widely cultivated for its economic value. Among several color change associated biological processes, plastid pigment metabolism is of trivial importance in postharvest plant organs during curing and storage. However, the molecular mechanisms involved in carotenoid and chlorophyll metabolism, as well as color change in tobacco leaves during curing, need further elaboration. Here, proteomic analysis at different curing stages (0 h, 48 h, 72 h) was performed in tobacco cv. Bi'na1 with an aim to investigate the molecular mechanisms of pigment metabolism in tobacco leaves as revealed by the iTRAQ proteomic approach. Our results displayed significant differences in leaf color parameters and ultrastructural fingerprints that indicate an acceleration of chloroplast disintegration and promotion of pigment degradation in tobacco leaves due to curing. In total, 5931 proteins were identified, of which 923 (450 up-regulated, 452 down-regulated, and 21 common) differentially expressed proteins (DEPs) were obtained from tobacco leaves. To elucidate the molecular mechanisms of pigment metabolism and color change, 19 DEPs involved in carotenoid metabolism and 12 DEPs related to chlorophyll metabolism were screened. The results exhibited the complex regulation of DEPs in carotenoid metabolism, a negative regulation in chlorophyll biosynthesis, and a positive regulation in chlorophyll breakdown, which delayed the degradation of xanthophylls and accelerated the breakdown of chlorophylls, promoting the formation of yellow color during curing. Particularly, the up-regulation of the chlorophyllase-1-like isoform X2 was the key protein regulatory mechanism responsible for chlorophyll metabolism and color change. The expression pattern of 8 genes was consistent with the iTRAQ data. These results not only provide new insights into pigment metabolism and color change underlying the postharvest physiological regulatory networks in plants, but also a broader perspective, which prompts us to pay attention to further screen key proteins in tobacco leaves during curing. [ABSTRACT FROM AUTHOR]

Near-infrared (NIR) spectral sensors can deliver the spectral response of light absorbed by materials. Data analysis technology based on NIR sensors has been a useful tool for quality identification. In this paper, an improved deep convolutional neural network (CNN) with batch normalization and MSRA (Microsoft Research Asia) initialization is proposed to discriminate the tobacco cultivation regions using data collected from NIR sensors. The network structure is created with six convolutional layers and three full connection layers, and the learning rate is controlled by exponential attenuation method. One-dimensional kernel is applied as the convolution kernel to extract features. Meanwhile, the methods of L2 regularization and dropout are used to avoid the overfitting problem, which improve the generalization ability of the network. Experimental results show that the proposed deep network structure can effectively extract the complex characteristics inside the spectrum, which proves that it has excellent recognition performance on tobacco cultivation region discrimination, and it also demonstrates that the deep CNN is more suitable for information mining and analysis of big data. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of lung cancer is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (X-ray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Nodule detections were performed through faster R-CNN on efficiently-learned features from CMixNet and U-Net like encoder–decoder architecture. Classification of the nodules was performed through a gradient boosting machine (GBM) on the learned features from the designed 3D CMixNet structure. To reduce false positives and misdiagnosis results due to different types of errors, the final decision was performed in connection with physiological symptoms and clinical biomarkers. With the advent of the internet of things (IoT) and electro-medical technology, wireless body area networks (WBANs) provide continuous monitoring of patients, which helps in diagnosis of chronic diseases—especially metastatic cancers. The deep learning model for nodules' detection and classification, combined with clinical factors, helps in the reduction of misdiagnosis and false positive (FP) results in early-stage lung cancer diagnosis. The proposed system was evaluated on LIDC-IDRI datasets in the form of sensitivity (94%) and specificity (91%), and better results were obatined compared to the existing methods. [ABSTRACT FROM AUTHOR]

Vehicle detection is a challenging task in computer vision. In recent years, numerous vehicle detection methods have been proposed. Since the vehicles may have varying sizes in a scene, while the vehicles and the background in a scene may be with imbalanced sizes, the performance of vehicle detection is influenced. To obtain better performance on vehicle detection, a multi-scale vehicle detection method was proposed in this paper by improving YOLOv2. The main contributions of this paper include: (1) a new anchor box generation method Rk-means++ was proposed to enhance the adaptation of varying sizes of vehicles and achieve multi-scale detection; (2) Focal Loss was introduced into YOLOv2 for vehicle detection to reduce the negative influence on training resulting from imbalance between vehicles and background. The experimental results upon the Beijing Institute of Technology (BIT)-Vehicle public dataset demonstrated that the proposed method can obtain better performance on vehicle localization and recognition than that of other existing methods. [ABSTRACT FROM AUTHOR]

Vehicle detection is one of the important applications of object detection in intelligent transportation systems. It aims to extract specific vehicle-type information from pictures or videos containing vehicles. To solve the problems of existing vehicle detection, such as the lack of vehicle-type recognition, low detection accuracy, and slow speed, a new vehicle detection model YOLOv2_Vehicle based on YOLOv2 is proposed in this paper. The k-means++ clustering algorithm was used to cluster the vehicle bounding boxes on the training dataset, and six anchor boxes with different sizes were selected. Considering that the different scales of the vehicles may influence the vehicle detection model, normalization was applied to improve the loss calculation method for length and width of bounding boxes. To improve the feature extraction ability of the network, the multi-layer feature fusion strategy was adopted, and the repeated convolution layers in high layers were removed. The experimental results on the Beijing Institute of Technology (BIT)-Vehicle validation dataset demonstrated that the mean Average Precision (mAP) could reach 94.78%. The proposed model also showed excellent generalization ability on the CompCars test dataset, where the "vehicle face" is quite different from the training dataset. With the comparison experiments, it was proven that the proposed method is effective for vehicle detection. In addition, with network visualization, the proposed model showed excellent feature extraction ability. [ABSTRACT FROM AUTHOR]