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In synthetic aperture radar (SAR) images, ships are often arbitrary-oriented and densely arranged in complex backgrounds, posing enormous challenges for ship detection. However, most existing methods detect ships with horizontal bounding boxes, which leads to the redundancy of detected regions. Furthermore, the high Intersection-over-Union (IoU) between two horizontal bounding boxes of densely arranged ships can cause missing detection. In this paper, a multi-stage rotational region based network (MSR2N) is proposed to solve the above problems. In MSR2N, the rotated bounding boxes, which can reduce background noise and prevent missing detection caused by high IoUs, are utilized to represent ship regions. MSR2N consists of three modules: feature pyramid network (FPN), rotational region proposal network (RRPN), and multi-stage rotational detection network (MSRDN). First of all, the FPN is applied to combine high-resolution features with semantically strong features. Second, in RRPN, a rotation-angle-dependent strategy is employed to generate multi-angle anchors which can represent arbitrary-oriented ship regions more felicitously than horizontal anchors. Finally, the MSRDN with three sub-networks is proposed to regress proposals of ship regions stage by stage. Meanwhile, the incrementally increasing IoU thresholds are selected for resampling positive and negative proposals in sequential stages of MSRDN, which eliminates close false positive proposals successively. With the above characteristics, MSR2N is more suitable and robust for ship detection in SAR images. The experimental results on SAR ship detection dataset (SSDD) show that the MSR2N has achieved state-of-the-art performance.

In this paper, the density of the time to ruin is studied in the context of the classical compound Poisson risk model. Both one-dimensional and two-dimensional Fourier-cosine series expansions are used to approximate the density of the time to ruin, and the approximation errors are also obtained. Some numerical examples are also presented to show that the proposed method is very efficient.

The assessment and prediction of regional water quality are fundamental inputs to environmental planning and watershed ecological management. This paper explored spatiotemporal changes in the correlation of water quality parameters (WQPs) and land-use types (LUTs) in a reticular river network area. Water samples of 44 sampling sites were collected every quarter from 2016 to 2018 and evaluated for dissolved oxygen (DO), total phosphorus (TP), ammonia nitrogen (NH 3 -N), and permanganate index (COD Mn ). A redundancy analysis (RDA) and stepwise multiple linear regression (SMLR) were applied to analyze the land-use type impacts on seasonal WQPs at five buffer scales (100, 200, 500, 800, and 1000 m). The Kruskal–Wallis test results revealed significant seasonal differences in NH 3 -N, TP, COD Mn , and DO. The area percentages of farmland, water area and built-up land in the study area were 38.96%, 22.75% and16.20%, respectively, for a combined total area percentage of nearly 80%. Our study showed that orchard land had an especially favorable influence on WQPs. Land-use type impacts on WQPs were more significant during the dry season than the wet season. The total variation explained by LUTs regarding WQPs at the 1 km buffer scale was slightly stronger than at smaller buffer scales. Built-up land had a negative effect on WQPs, but orchard and forest-grassland had a positive effect on WQPs. The effects of water area and farmland on WQPs were complex on different buffer scales. These findings are helpful for improving regional water resource management and environmental planning.

Smart Grids (SGs) are governed by advanced computing, control technologies, and networking infrastructure. However, compromised cybersecurity of the smart grid not only affects the security of existing energy systems but also directly impacts national security. The increasing number of cyberattacks against the smart grid urgently necessitates more robust security protection technologies to maintain the security of the grid system and its operations. The purpose of this review paper is to provide a thorough understanding of the incumbent cyberattacks’ influence on the entire smart grid ecosystem. In this paper, we review the various threats in the smart grid, which have two core domains: the intrinsic vulnerability of the system and the external cyberattacks. Similarly, we analyze the vulnerabilities of all components of the smart grid (hardware, software, and data communication), data management, services and applications, running environment, and evolving and complex smart grids. A structured smart grid architecture and global smart grid cyberattacks with their impact from 2010 to July 2022 are presented. Then, we investigated the the thematic taxonomy of cyberattacks on smart grids to highlight the attack strategies, consequences, and related studies analyzed. In addition, potential cybersecurity solutions to smart grids are explained in the context of the implementation of blockchain and Artificial Intelligence (AI) techniques. Finally, technical future directions based on the analysis are provided against cyberattacks on SGs.

Spectrum matching is the most common method for compound identification in mass spectrometry (MS). However, some challenges limit its efficiency, including the coverage of spectral libraries, the accuracy, and the speed of matching. In this study, a million-scale in-silico EI-MS library is established. Furthermore, an ultra-fast and accurate spectrum matching (FastEI) method is proposed to substantially improve accuracy using Word2vec spectral embedding and boost the speed using the hierarchical navigable small-world graph (HNSW). It achieves 80.4% recall@10 accuracy (88.3% with 5 Da mass filter) with a speedup of two orders of magnitude compared with the weighted cosine similarity method (WCS). When FastEI is applied to identify the molecules beyond NIST 2017 library, it achieves 50% recall@1 accuracy. FastEI is packaged as a standalone and user-friendly software for common users with limited computational backgrounds. Overall, FastEI combined with a million-scale in-silico library facilitates compound identification as an accurate and ultra-fast tool. Accuracy loss and slow speed affect the identification of compounds through matching of mass spectra using a large-scale spectral library. Here the authors use Word2vec spectral embedding and hierarchical navigable small-world graph to improve accuracy and speed of spectral matching on their own million-scale in-silico library.

The pesticide residues in agri-foods are threatening people’s health. This study aims to establish a fast and low-cost surface-enhanced Raman scattering (SERS) method for the on-site detection of flumetsulam in wheat. The two-step modified concentrated gold nanoparticles (AuNPs) acted as the SERS substrate with the aid of NaCl and MgSO4. NaCl is served as the activator to modify AuNPs, while MgSO4 is served as the aggregating agent to form high-density hot spots. The activation and aggregation are two essential collaborative procedures to generate remarkable SERS enhancement and achieve the trace-level detection of flumetsulam. This method exhibits good enhancement effect with an enhancement factor of 106 and wide linear range (5–1000 μg/L). With simple pretreatment, the flumetsulam residue in real wheat samples can be successfully detected with the limit of detection (LOD) down to 0.01 μg/g, which is below the maximum residue limit of flumetsulam in wheat (0.05 μg/g) set in China. The recovery of flumetsulam residue in wheat ranges from 88.3% to 95.6%. These results demonstrate that the proposed SERS method is a powerful technique for the detection of flumetsulam in wheat, which implies the great application potential in the rapid detection of other pesticide residues in various agri-foods.

Benzene is a crucial industrial hydrocarbon, posing significant health risks due to its toxic metabolites like hydroquinone (HQ). This study investigates the role of clusterin (CLU) in benzene toxicity by analyzing its protein and mRNA levels, as well as the expression of Bcl-2 and Bax , to evaluate the feasibility of CLU as a biomarker for chronic benzene poisoning. HL-60 cells were induced to differentiate into neutrophil-like cells using 1% Dimethyl Sulfoxide (DMSO). Enzyme-linked immunosorbent assay (ELISA) and RT-PCR were used to analyze CLU protein and mRNA levels. ELISA was employed to detect sCLU protein content in cell culture supernatants, and western blot was used to assess Bcl-2 and Bax expression. The optimal time for 1% DMSO to induce HL-60 cells into neutrophil-like cells was 48 h. As HQ concentration increased, HL-60 cell viability decreased, CLU protein and sCLU protein levels in the supernatant decreased, CLU mRNA levels decreased, Bcl-2 protein expression decreased, and Bax expression increased. HQ exposure reduces CLU protein concentration and mRNA levels in neutrophil-like cells induced from HL-60 cells, indicating that CLU could be a potential biomarker for chronic benzene poisoning.

Background Adoption of thermal processing of the diet drives human evolution and gut microbiota diversity changes in a dietary habit-dependent manner. However, whether thermal processing of food triggers gut microbial variation remains unknown. Herein, we compared the microbiota of non-thermally processed and thermally processed food (NF and TF) and investigated gut microbiota associated with NF and TF in catfish Silurus meridionalis and C57BL/6 mice to assess effects of thermal processing of food on gut microbiota and to further identify the differences in host responses. Results We found no differences in overall microbial composition and structure in the pairwise NF and TF, but identified differential microbial communities between food and gut. Both fish and mice fed TF had significantly lower gut microbial diversity than those fed NF. Moreover, thermal processing of food triggered the changes in their microbial communities. Comparative host studies further indicated host species determined gut microbial assemblies, even if fed with the same food. Fusobacteria was the most abundant phylum in the fish, and Bacteroidetes and Firmicutes dominated in the mice. Besides the consistent reduction of Bacteroidetes and the balanced Protebacteria , the response of other dominated gut microbiota in the fish and mice to TF was taxonomically opposite at the phylum level, and those further found at the genus level. Conclusions Our results reveal that thermal processing of food strongly contributes to the reduction of gut microbial diversity and differentially drives microbial alterations in a host-dependent manner, suggesting specific adaptations of host-gut microbiota in vertebrates responding to thermal processing of food. These findings open a window of opportunity to understand the decline in gut microbial diversity and the community variation in human evolution and provide new insights into the host-specific microbial assemblages associated with the use of processing techniques in food preparation in humans and domesticated animals.

Highlights Fe single-atom catalysts (h 3 -FNCs) with high loading, high catalytic activity and high stability were synthesized via a method capable of increasing both the metal loading and mass-specific activity by exchanging zinc with iron. The “density effect,” derived from the sufficiently high density of active sites, has been discovered for the first time, leading to a significant alteration in the intrinsic activity of single-atom metal sites. The superior oxidase-like catalytic performance of h 3 -FNCs ensures highly effective bacterial eradication. The current single-atom catalysts (SACs) for medicine still suffer from the limited active site density. Here, we develop a synthetic method capable of increasing both the metal loading and mass-specific activity of SACs by exchanging zinc with iron. The constructed iron SACs (h 3 -FNC) with a high metal loading of 6.27 wt% and an optimized adjacent Fe distance of ~ 4 Å exhibit excellent oxidase-like catalytic performance without significant activity decay after being stored for six months and promising antibacterial effects. Attractively, a “density effect” has been found at a high-enough metal doping amount, at which individual active sites become close enough to interact with each other and alter the electronic structure, resulting in significantly boosted intrinsic activity of single-atomic iron sites in h 3 -FNCs by 2.3 times compared to low- and medium-loading SACs. Consequently, the overall catalytic activity of h 3 -FNC is highly improved, with mass activity and metal mass-specific activity that are, respectively, 66 and 315 times higher than those of commercial Pt/C. In addition, h 3 -FNCs demonstrate efficiently enhanced capability in catalyzing oxygen reduction into superoxide anion (O 2 · − ) and glutathione (GSH) depletion. Both in vitro and in vivo assays demonstrate the superior antibacterial efficacy of h 3 -FNCs in promoting wound healing. This work presents an intriguing activity-enhancement effect in catalysts and exhibits impressive therapeutic efficacy in combating bacterial infections.

Restoration of endodontically treated premolar is in high risk for biomechanical failure, and often presents with subgingival margins. Proximal box elevation (PBE) has been used to relocate subgingival cavity outlines. To evaluate the influence of PBE on fracture resistance and gingival microleakage of premolars with endodontic access cavities following ceramic endocrown. Eighty sound maxillary premolars with standardized Class II cavities on mesial surfaces were randomly assigned to four groups (n = 20 in each group). Groups E1, E2 and E3, with proximal margins located in dentin/cementum, 2 mm below the cemento-enamel junction (CEJ), simulated subgingival location. Group E4 (supragingival group), with proximal margins located in enamel, 1 mm above the CEJ, was used as the positive control. For margin elevation of the proximal cavities, bulk-fill Smart Dentin Replacement (SDR), a visible light cured resin composite, was applied in group E1, and conventional resin composite (3M Z350 XT, a light-activated composite) was placed in group E2. Group E3 was only treated with a ceramic crown and served as the negative control. In all groups, computer-aided design (CAD) ceramic endocrowns were adhesively inserted, and fracture resistance, failure mode and microleakage were evaluated. A higher fracture resistance value was observed in PBE groups E1 and E2, regardless of the materials used (P = 0.038, and 0.010, respectively, vs E3), and fracture resistance in group E1 was higher than that in group E2. In teeth without PBE, the percentage of catastrophic failures reached 70%. Compared to group E3, a lower frequency distribution of microleakage was detected in supragingival group E4 (P = 0.031). No increased percentage of microleakage was observed in groups treated with PBE. For endodontically treated maxillary premolars restored with ceramic endocrowns, PBE increases fracture resistance but not microleakage.