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Prolactin (PRL) plays an important role in animal follicle development and ovulation. However, its regulatory effects on the different stages of the estrus cycle in ewes are unclear. In this study, bromocriptine (BCR, PRL inhibitor) was used to study the effect of PRL on the secretion of reproductive hormones and gene expressions in order to explore its regulatory effects on the sexual cycle of ewes. Eighty healthy ewes with the same parity and similar weights were randomly assigned to the control group (C, n = 40) and the treatment group (T, n = 40, fed bromocriptine). After estrus synchronization, thirty-one ewes with overt signs of estrus were selected from each group. Six blood samples were randomly obtained by jugular venipuncture to measure the concentration of PRL, estrogen (E2), progesterone (P4), luteinizing hormone (LH), follicle-stimulating hormone (FSH), and gonadotropin-releasing hormone (GnRH) in the proestrus, estrus, metestrus, and diestrus. At the same time, we collected the ovaries of the six ewes in vivo after anesthesia in order to detect follicle and corpus luteum (CL) counts and measure the expression of hormone-receptor and apoptosis-related genes. The results show that PRL inhibition had no significant effects on the length of the estrus cycle (p > 0.05). In proestrus, the number of large and small follicles, the levels of E2, FSH, and GnRH, and the expressions of ER, FSHR, and the apoptotic gene Caspase-3 were increased (p < 0.05); and the number of middle follicles and the expression of anti-apoptotic gene Bcl-2 were decreased (p < 0.05) in the T group. In estrus, the number of large follicles, the levels of E2 and GnRH, and the expressions of the StAR, CYP19A1, and Bcl-2 genes were increased (p < 0.05), and the number of middle follicles was decreased (p < 0.05) in the T group. In metestrus, the number of small follicles and the expression of LHR (p < 0.05) and the pro-apoptotic gene Bax were increased (p < 0.05); the number of middle follicles was decreased (p < 0.05) in the T group. In diestrus, the number of large follicles, middle follicles, and CL, the level of P4, and the expressions of PR, 3β-HSD, StAR, Caspase-3, and Bax were increased (p < 0.05); the number of small follicles and the expression of Bcl-2 were decreased (p < 0.05) in the T group. In summary, PRL inhibition can affect the secretion of reproductive hormones, the follicle count, and the gene expression during the estrus cycle. These results provide a basis for understanding the mechanisms underlying the regulation of the ewe estrus cycle by PRL.

Aims: This study aimed to analyze the related research on the influence of dietary patterns on IBD carried out over the past 30 years to obtain the context of the research field and to provide a scientific basis and guidance for the prevention and treatment of IBD. Methods: The literature on the effects of dietary patterns on inflammatory bowel disease published over the past three decades was retrieved from the Web of Science Core Collection (WoSCC) database. CiteSpace, VOSviewer, the R software (version 4.3.0) bibliometrix package, the OALM platform, and other tools were used for the analyses. Results: The growth of scientific papers related to this topic can be divided into two stages: before and after 2006. Overall, the growth of the relevant literature was in line with Price’s literature growth curve. Subrata Ghosh and Antonio Gasbarrini are the authors with the highest academic influence in the field, and Lee D.’s research results are widely recognized by researchers in this field. Among the 72 countries involved in the study, the United States contributed the most, while China developed rapidly with regard to research being carried out in this area. From a regional perspective, countries and institutions in North America, Europe, and East Asia have made the most significant contributions to this field and have the closest cooperation. Among the 1074 articles included in the study, the most influential ones tended to consider the mechanism of the effect of dietary patterns on IBD from the perspective of the microbiome. Multiple tools were used for keyword analysis and mutual verification. The results showed that NF-κB, the Mediterranean diet, fatty acids, fecal microbiota, etc., are the focus and trends of current research. Conclusions: A Mediterranean-like dietary pattern may be a good dietary habit for IBD patients. Carbohydrates, fatty acids, and inulin-type fructans are closely related to IBD. Fatty acid, gut microbiota, NF-κB, oxidative stress, and endoplasmic reticulum stress are the hot topics in the study of the effects of dietary patterns on IBD and will be emerging research trends.

The mercury that is released from the centralized treatment of municipal solid waste is an important source of atmospheric mercury. We chose the main urban area of Changchun as a representative area. Environmental factors such as total mercury content, temperature, wind speed, and other factors were measured in samples from the trash cans of two types of collection points (trash cans and garbage stations), the topsoil under the selected trash cans, and the ambient air above the selected trash cans. The potential ecological risks of mercury pollution were evaluated. The results showed that the mercury content levels of all sample types in the refuse transfer station were higher than the garbage cans and there were no significant differences observed between soil surface mercury and garbage cans. The mercury content levels in the atmosphere and the surface soil at the garbage collection points were found to increase along the cascade relationship of the garbage collection. However, there were no correlations observed between the atmospheric mercury content levels and the surface soil mercury content levels with the attachments and the sum of the former two. There were no correlations observed between surface soil and the attachments, or among the attachments, surface soil, and the atmospheric mercury content levels. The mercury content levels in the attachments, surface soil, and atmosphere of the garbage collection points in the study area were negatively correlated with the loop lines. Meanwhile, the potential ecological risk indexes of the garbage cans and garbage stations were found to be high. The chronic non-carcinogenic risks of mercury to children and adults were determined to be very low. The risks of mercury to children were higher when compared with adults. The highest non-carcinogenic risks of mercury pollution were determined to be within the central area of Changchun.

Mercury (Hg) is a global pollutant that may potentially have serious impacts on human health and ecologies. The gaseous elemental mercury (GEM) exchanges between terrestrial surfaces and the atmosphere play important roles in the global Hg cycle. This study investigated GEM exchange fluxes over two land cover types (including Artemisia anethifolia coverage and removal and bare soil) using a dynamic flux chamber attached to the LumexR RA915+ Hg analyzer during the growing season from May to September of 2018, in which the interactive effects of plant coverage and meteorological conditions were highlighted. The daily mean ambient levels of GEM and the total mercury concentrations of the soil (TSM) were determined to be 12.4 ± 3.6 to 16.4 ± 5.6 ng·m−3 and 32.8 to 36.2 ng·g−1, respectively, for all the measurements from May to September. The GEM exchange fluxes (ng·m−2·h−1) during the five-month period for the three treatments included the net emissions from the soil to the atmosphere (mean 5.4 to 7.1; range of −27.0 to 47.3), which varied diurnally, with releases occurring during the daytime hours and depositions occurring during the nighttime hours. Significant differences were observed in the fluxes between the vegetation coverage and removal during the growing months (p < 0.05). In addition, it was determined that the Hg fluxes were positively correlated with the solar radiation and air/soil temperature levels and negatively correlated with the air relative humidity and soil moisture under all the conditions (p < 0.05). Overall, the results obtained in this study demonstrated that the grassland soil served as both a source and a sink for atmospheric Hg, depending on the season and meteorological factors. Furthermore, the plants played an important inhibiting role in the Hg exchanges between the soil and the atmosphere.

Understanding the effects of precipitation variations on plant biochemical and functional traits is crucial to predict plant adaptation to future climate changes. The dominant species, Stipa glareosa, plays an important role in maintaining the structure and function of plant communities in the desert steppe, Inner Mongolia. However, little is known about how altered precipitation affects biochemical and functional traits of S. glareosa in different communities in the desert steppe. Here, we examined the responses of biochemical and functional traits of S. glareosa in shrub- and grass-dominated communities to experimentally increased precipitation (control, +20%, +40%, and +60%). We found that +40% and +60% increased plant height and leaf dry matter content (LDMC) and decreased specific leaf area (SLA) of S. glareosa in grass community. For biochemical traits in grass community, +60% decreased the contents of protein and chlorophyll b (Cb), while +40% increased the relative electrical conductivity and superoxide dismutase. Additionally, +20% increased LDMC and malondialaenyde, and decreased SLA and protein in shrub community. Chlorophyll a, Cb, carotenoids, protein and superoxide dismutase in the grass community differed with shrub community, while +60% caused differences in SLA, LDMC, leaf carbon content, malondialaenyde and peroxidase between two communities. The positive or negative linear patterns were observed between different functional and biochemical traits in grass- rather than shrub-community. Soil water content explained changes in some biochemical traits in the grass community, but not for functional traits. These results suggest that increased precipitation can affect functional traits of S. glareosa in the grass community by altering biochemical traits caused by soil water content. The biochemical and functional traits of S. glareosa were more sensitive to extreme precipitation in grass- than shrub-community in the desert steppe. Our study highlights the important differences in adaptive strategies of S. glareosa in different plant communities at the same site to precipitation changes.

The use of cheaper and recyclable biomaterials (like eggshells) to synthesize high purity hydroxyapatite (HAp) with better properties (small particle size, large surface area and pore volume) for applications (in environmental remediation, bone augmentation and replacement, and drug delivery systems) is vital since high-purity synthetic calcium sources are expensive. In this work, pure and mesoporous HAp nanopowder with large pore volume (1.4 cm3/g) and surface area (284.1 m2/g) was produced from raw eggshells at room temperature using a simple two-step procedure. The control of precursor droplets could stabilize the pH value of the reaction solution, because of the size of the needle (of the syringe pump used for precursor additions) leading to production of HAp with high surface area and pore size. The as-produced HAp revealed high ibuprofen (as a model drug) loading (1.38 g/g HAp), enhanced dissolution and controllable release of the drug via solute-saturated supercritical carbon dioxide.

A series of organogelators based on L-phenylalanine has been synthesized and their gelation properties in various organic solvents were investigated. The results showed that these organogelators were capable of forming stable thermal and reversible organogels in various organic solvents at low concentrations, and the critical gel concentration (CGC) of certain solvents was less than 1.0 wt%. Afterward, the corresponding enthalpies (ΔHg) were extracted by using the van ’t Hoff equation, as the gel−sol temperature (TGS) was the function of the gelator concentration. The study of gelling behaviors suggested that L-phenylalanine dihydrazide derivatives were excellent gelators in solvents, especially BOC−Phe−OdHz (compound 4). The effects of the solvent on the self-assembly of gelators were analyzed by the Kamlet−Taft model, and the gelation ability of compound 4 in a certain organic solvent was described by Hansen solubility parameters and a Teas plot. Morphological investigation proved that the L-phenylalanine dihydrazide derivatives could assemble themselves into an ordered structure such as a fiber or sheet. Fourier-transform infrared spectroscopy (FTIR) and hydrogen nuclear magnetic resonance (1H NMR) studies indicated that hydrogen bonding, π−π stacking, and van der Waals forces played important roles in the formation of a gel.

This paper presents an evaluation of the bearing capacity of cross-bracing in steel transmission tower structures. Design guidelines (ASCE 10-15, BS EN 50341-1, GB 50017-2017, and DL/T 5486-2020) related to the buckling capacity of the cross-bracing are summarized and compared with the experimental results. The current design provisions obtained the bearing capacity from the equivalent slenderness ratio, and then the stability coefficient and buckling capacity were derived. The calculated bearing capacity based on the design code tends to be overly progressive for smaller slenderness ratios (particularly those below 100), except for EN 50341-1-2012. Conversely, for larger slenderness ratios, ASCE 10-15 and DL/T 5486-2020 Class A design codes lean towards being overly progressive, while GB 50017-2017 and EN 50341-1-2012 codes tend to be more conservative. The design standard appears to exhibit unsafe predictions for Class A and B connections with low slenderness ratios and Class C connections. It needs to be noted that the effects of torsional stiffness and joint connection type are not considered in the current design codes, which are proved to be nonnegligible by the test results. In this paper, the bearing capacity calculation formula is proposed by introducing a modified effective length coefficient (K), and both the torsional stiffness and joint connection type are taken into account. The modified bearing capacity is verified with the test results; the correlation coefficient is 0.997, and the coefficient of variation is 0.04. It can provide a reference for the engineering design of steel lattice transmission tower structures.

This paper discusses the multi-player non-cooperative game of nonlinear stochastic time-varying systems described by Itô-type differential equations in a finite time interval. Multi-player non-cooperative game problems are represented by multi-objective Pareto (MOP) control problems to describe the fact that each player has their own goals. By applying Hamilton–Jacobi inequalities (HJIs), the criterion of upper bounds of the MOP boundary is obtained for nonlinear stochastic systems, and the corresponding strategies are designed for such games, so the MOP problem is transformed into a HJI-constrained MOP problem. In order to overcome the difficulty of solving HJIs, a global linearization method is proposed to approximate the nonlinear systems. By the proposed global linearization method, multi-player non-cooperative game problems are transformed into Riccati equation-constrained MOP problems, and the approximate solutions of HJI-constrained MOP problems are obtained. Finally, a practical example is given to illustrate the effectiveness of the proposed method.

Automated segmentation of abdominal organs and tumors in medical images is a challenging yet essential task in medical image analysis. Deep learning has shown excellent performance in many medical image segmentation tasks, but most prior efforts were fragmented, addressing individual organ and tumor segmentation tasks with specialized networks. To tackle the challenges of abdominal organ and tumor segmentation using partially labeled datasets, we introduce Re-parameterizing Mixture-of-Diverse-Experts (RepMode) to abdominal organ and tumor segmentation. Within the RepMode framework, the Mixture-of-Diverse-Experts (MoDE) block forms the foundation, learning generalized parameters applicable across all tasks. We seamlessly integrate the MoDE block into a U-shaped network with dynamic heads, addressing multi-scale challenges by dynamically combining experts with varying receptive fields for each organ and tumor. Our framework incorporates task encoding in both the encoder–decoder section and the segmentation head, enabling the network to adapt throughout the entire system based on task-related information. We evaluate our approach on the multi-organ and tumor segmentation (MOTS) dataset. Experiments show that DoDRepNet outperforms previous methods, including multi-head networks and single-network approaches, giving a highly competitive performance compared with the original single network with dynamic heads. DoDRepNet offers a promising approach to address the complexities of abdominal organ and tumor segmentation using partially labeled datasets, enhancing segmentation accuracy and robustness.

The goal of cross-view image based geo-localization is to determine the location of a given street-view image by matching it with a collection of geo-tagged aerial images, which has important applications in the fields of remote sensing information utilization and augmented reality. Most current cross-view image based geo-localization methods focus on the image content and ignore the relations between feature nodes, resulting in insufficient mining of effective information. To address this problem, this study proposes feature relation guided cross-view image based geo-localization. This method first processes aerial remote sensing images using a polar transform to achieve the geometric coarse alignment of ground-to-aerial images, and then realizes local contextual feature concern and global feature correlation modeling of the images through the feature relation guided attention generation module designed in this study. Specifically, the module includes two branches of deformable convolution based multiscale contextual feature extraction and global spatial relations mining, which effectively capture global structural information between feature nodes at different locations while correlating contextual features and guiding global feature attention generation. Finally, a novel feature aggregation module, MixVPR, is introduced to aggregate global feature descriptors to accomplish image matching and localization. After experimental validation, the cross-view image based geo-localization algorithm proposed in this study yields results of 92.08%, 97.70%, and 98.66% for the top 1, top 5, and top 10 metrics, respectively, in CVUSA, a popular public cross-view dataset, and exhibits superior performance compared to algorithms of the same type.

This paper is to study finite horizon linear quadratic (LQ) non-zero sum Nash game for discrete-time infinite Markov jump stochastic systems (IMJSSs). Based on the theory of stochastic analysis, a countably infinite set of coupled generalized algebraic Riccati equations are solved and a necessary and sufficient condition for the existence of Nash equilibrium points is obtained. From a new perspective, the finite horizon mixed robust H2/H∞ control is investigated, and summarize the relationship between Nash game and H2/H∞ control problem. Moreover, the feasibility and validity of the proposed method has been proved by applying it to a numerical example.

The fermentation process can be affected when the starter culture enters the viable but nonculturable (VBNC) state. Therefore, it is of interest to investigate how VBNC cells change physiologically. Lacticaseibacillus (L.) paracasei Zhang is both a probiotic and a starter strain. This study aimed to investigate the metabolomic differences between VBNC and recovered L. paracasei Zhang cells. First, L. paracasei Zhang was induced to enter the VBNC state by keeping the cells in a liquid de Man–Rogosa–Sharpe (MRS) medium at 4 °C for 220 days. Flow cytometry was used to sort the induced VBNC cells, and three different types of culture media (MRS medium, skim milk with 1% yeast extract, and skim milk) were used for cell resuscitation. Cell growth responses in the three types of recovery media suggested that the liquid MRS medium was the most effective in reversing the VBNC state in L. paracasei Zhang. Metabolomics analysis revealed 25 differential metabolites from five main metabolite classes (amino acid, carbohydrate, lipid, vitamin, and purine and pyrimidine). The levels of L-cysteine, L-alanine, L-lysine, and L-arginine notably increased in the revived cells, while cellulose, alginose, and guanine significantly decreased. This study confirmed that VBNC cells had an altered physiology.

Object detection in remote sensing images faces the challenges of a complex background, large object size variations, and high inter-class similarity. To address these problems, we propose an adaptive adjacent layer feature fusion (AALFF) method, which is developed on the basis of RTMDet. Specifically, the AALFF method incorporates an adjacent layer feature fusion enhancement (ALFFE) module, designed to capture high-level semantic information and accurately locate object spatial positions. ALFFE also effectively preserves small objects by fusing adjacent layer features and employs involution to aggregate contextual information in a wide spatial range for object essential features extraction in complex backgrounds. Additionally, the adaptive spatial feature fusion (ASFF) module is introduced to guide the network to select and fuse the crucial features to improve the adaptability to objects with different sizes. The proposed method achieves mean average precision (mAP) values of 77.1%, 88.9%, and 95.7% on the DIOR, HRRSD, and NWPU VHR-10 datasets, respectively. Notably, our approach achieves mAP75 values of 60.8% and 79.0% on the DIOR and HRRSD datasets, respectively, surpassing the state-of-the-art performance on the DIOR dataset.

Feature selection is a typical multiobjective problem including two conflicting objectives. In classification, feature selection aims to improve or maintain classification accuracy while reducing the number of selected features. In practical applications, feature selection is one of the most important tasks in remote sensing image classification. In recent years, many metaheuristic algorithms have attempted to explore feature selection, such as the dragonfly algorithm (DA). Dragonfly algorithms have a powerful search capability that achieves good results, but there are still some shortcomings, specifically that the algorithm’s ability to explore will be weakened in the late phase, the diversity of the populations is not sufficient, and the convergence speed is slow. To overcome these shortcomings, we propose an improved dragonfly algorithm combined with a directed differential operator, called BDA-DDO. First, to enhance the exploration capability of DA in the later stages, we present an adaptive step-updating mechanism where the dragonfly step size decreases with iteration. Second, to speed up the convergence of the DA algorithm, we designed a new differential operator. We constructed a directed differential operator that can provide a promising direction for the search, then sped up the convergence. Third, we also designed an adaptive paradigm to update the directed differential operator to improve the diversity of the populations. The proposed method was tested on 14 mainstream public UCI datasets. The experimental results were compared with seven representative feature selection methods, including the DA variant algorithms, and the results show that the proposed algorithm outperformed the other representative and state-of-the-art DA variant algorithms in terms of both convergence speed and solution quality.

Triamine-based HBPI membranes are known for high gas separation selectivity and physical stability, but their permeabilities are still very low. In this study, we utilized a tetramine monomer called TPDA (N,N,N′,N′-tetrakis(4-aminophenyl)-1,4-benzenediamine) as a crosslinking center and incorporated an additional diamine comonomer called DAM (2,4,6-trimethyl-1,3-diaminobenzene) to enhance gas separation performance, especially gas permeability. The findings demonstrated that the resultant 6FDA−DAM/TPDA membranes based on tetramine TPDA exhibited a greater amount of free volume compared to the triamine-based HBPI membranes, resulting in significantly higher gas permeabilities. Furthermore, the higher concentration of DAM component led to the generation of more fractional free volumes (FFV). Consequently, the gas permeabilities of the 6FDA−DAM/TPDA membranes increased with an increase in DAM content, with a minimal compromise on selectivity. The enhanced gas permeabilities of the 6FDA−DAM/TPDA membranes enabled them to minimize the footprint required for membrane installations in real-world applications. Moreover, the 6FDA−DAM/TPDA membranes exhibited remarkable durability against physical aging and plasticization, thanks to the incorporation of a hyperbranched network structure.

The 6FDA-based network PI has attracted significant attention for gas separation. A facile strategy to tailor the micropore structure within the network PI membrane prepared by the in situ crosslinking method is extremely significant for achieving an advanced gas separation performance. In this work, the 4,4′-diamino-2,2′-biphenyldicarboxylic acid (DCB) or 3,5-diaminobenzoic acid (DABA) comonomer was incorporated into the 6FDA-TAPA network polyimide (PI) precursor via copolymerization. The molar content and the type of carboxylic-functionalized diamine were varied in order to easily tune the resulting network PI precursor structure. Then, these network PIs containing carboxyl groups underwent further decarboxylation crosslinking during the following heat treatment. Properties involving thermal stabilities, solubility, d-spacing, microporosity, and mechanical properties were investigated. Due to the decarboxylation crosslinking, the d-spacing and the BET surface areas of the thermally treated membranes were increased. Moreover, the content of DCB (or DABA) played a key role in determining the overall gas separation performance of the thermally treated membranes. For instance, after the heating treatment at 450 °C, 6FDA-DCB:TAPA (3:2) showed a large increment of about ~532% for CO2 gas permeability (~266.6 Barrer) coupled with a decent CO2/N2 selectivity~23.6. This study demonstrates that incorporating the carboxyl-containing functional unit into the PI backbone to induce decarboxylation offers a practical approach with which to tailor the micropore structure and corresponding gas transport properties of 6FDA-based network PIs prepared by the in situ crosslinking method.

Background: Facial paralysis (FP) is a common symptom after stroke, which influences the quality of life and prognosis of patients. Recently, peripheral magnetic stimulation (PMS) shows potential effects on peripheral and central nervous system damage. However, the effect of PMS on FP after stroke is still unclear. Methods: In this study, we applied PMS on the facial nerve of nine stroke patients with FP. At the same time, laser speckle contrast imaging (LSCI) was used to explore the facial skin blood flow (SkBF) in 19 healthy subjects and nine stroke patients with FP before and after the PMS intervention. The whole face was divided into 14 regions to compare the SkBF in different sub-areas. Results: In baseline SkBF, we found that there were no significant differences in the SkBF between the left and right faces in the healthy subjects. However, there was a significant difference in the SkBF between the affected and unaffected faces in Region 7 (Chin area, p = 0.046). In the following five minutes after the PMS intervention (Pre_0–5 min), the SkBF increased in Region 5 (p = 0.014) and Region 7 (p = 0.046) and there was an increasing trend in Region 3 (p = 0.088) and Region 6 (p = 0.069). In the five to ten minutes after the intervention (Post_6–10 min), the SkBF increased in Region 5 (p = 0.009), Region 6 (p = 0.021) and Region 7 (p = 0.023) and there was an increasing trend in Region 3 (p = 0.080) and left and right whole face (p = 0.051). Conclusions: These pilot results indicate that PMS intervention could increase facial skin blood flow in stroke patients with FP. A further randomized controlled trial can be performed to explore its possible clinical efficacy.

Remote sensing for image object detection has numerous important applications. However, complex backgrounds and large object-scale differences pose considerable challenges in the detection task. To overcome these issues, we proposed a one-stage remote sensing image object detection model: a multi-feature information complementary detector (MFICDet). This detector contains a positive and negative feature guidance module (PNFG) and a global feature information complementary module (GFIC). Specifically, the PNFG is used to refine features that are beneficial for object detection and explore the noisy features in a complex background of abstract features. The proportion of beneficial features in the feature information stream is increased by suppressing noisy features. The GFIC uses pooling to compress the deep abstract features and improve the model’s ability to resist feature displacement and rotation. The pooling operation has the disadvantage of losing detailed feature information; thus, dilated convolution is introduced for feature complementation. Dilated convolution increases the receptive field of the model while maintaining an unchanged spatial resolution. This can improve the ability of the model to recognize long-distance dependent information and establish spatial location relationships between features. The detector proposed also improves the detection performance of objects at different scales in the same image using a dual multi-scale feature fusion strategy. Finally, classification and regression tasks are decoupled in space using a decoupled head. We experimented on the DIOR and NWPU VHR-10 datasets to demonstrate that the newly proposed MFICDet achieves competitive performance compared to current state-of-the-art detectors.

Object detection is used widely in remote sensing image interpretation. Although most models used for object detection have achieved high detection accuracy, computational complexity and low detection speeds limit their application in real-time detection tasks. This study developed an adaptive feature-aware method of object detection in remote sensing images based on the single-shot detector architecture called adaptive feature-aware detector (AFADet). Self-attention is used to extract high-level semantic information derived from deep feature maps for spatial localization of objects and the model is improved in localizing objects. The adaptive feature-aware module is used to perform adaptive cross-scale depth fusion of different-scale feature maps to improve the learning ability of the model and reduce the influence of complex backgrounds in remote sensing images. The focal loss is used during training to address the positive and negative sample imbalance problem, reduce the influence of the loss value dominated by easily classified samples, and enhance the stability of model training. Experiments are conducted on three object detection datasets, and the results are compared with those of the classical and recent object detection algorithms. The mean average precision(mAP) values are 66.12%, 95.54%, and 86.44% for three datasets, which suggests that AFADet can detect remote sensing images in real-time with high accuracy and can effectively balance detection accuracy and speed.

In this paper, we propose an end-to-end single-image super-resolution neural network by leveraging hybrid multi-scale features of images. Different from most existing convolutional neural network (CNN) based solutions, our proposed network depends on the observation that image features extracted by CNN contain hybrid multi-scale features: both multi-scale local texture features and global structural features. By effectively exploiting these multi-scale and local-global features, our network involves far fewer parameters, leading to a large decrease in memory usage and computation during inference. Our network benefits from three key modules: (1) an efficient and lightweight feature extraction module (EFblock); (2) a hybrid multi-scale feature enhancement module (HMblock); and (3) a reconstruction–restoration module (DRblock). Experiments on five popular benchmarks demonstrate that our super-resolution approach achieves better performance with fewer parameters and less memory consumption, compared to more than 20 SOTAs. In summary, we propose a novel multi-scale super-resolution neural network (HMSF), which is more lightweight, has fewer parameters, and requires less execution time, but has better performance than the state-of-the-art methods. Compared to SOTAs, this method is more practical and better suited to run on constrained devices, such as PCs and mobile devices, without the need for a high-performance server.

Creating oxygen vacancies and introducing heterostructures are two widely used strategies in Co-based oxides for their efficient electrocatalytic performance, yet both strategies have rarely been used together to design a bifunctional electrocatalyst for an efficient overall water splitting. Herein, we propose a facile strategy to synthesize oxygen-defect-rich Co9S8/CoO hetero-nanoparticles with a nitrogen-doped carbon shell (ODR-Co9S8/CoO/NC) through the in situ conversion of heterojunction along with surface-induced oxygen vacancies, simply via annealing the precursor Co3S4/Co(OH)2/ZIF-67. The as-prepared ODR-Co9S8/CoO/NC shows excellent bifunctional catalytic activities, featuring a low overpotential of 217 mV at 10 mA cm−2 in the oxygen evolution reaction (OER) and 160 mV at 10 mA cm−2 in the hydrogen evolution reaction (HER). This performance excellency is attributed to unique heterostructure and oxygen defects in Co9S8/CoO nanoparticles, the current work is expected to offer new insights to the design of cost-effective, noble-metal-free electrocatalysts.