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Objective: This study aim to quantify the differences in knee biomechanics during gait between knee osteoarthritis (KOA) patients and healthy individuals. Methods: Twenty KOA patients (4 males and 16 females, 66.2 ± 7.7 years) and twenty controls (16 males and 4 females, 64.8 ± 5.4 years) were recruited for gait test using the motion capture system and force-platform system. The spatiotemporal parameters, knee kinematics and kinetics, and tibiofemoral contact force (TFCF) were calculated using an improved musculoskeletal model. Results: KOA patients walked with reduced speed (48.6 %), stride length (32.9 %), stride height (33.0 %), time proportions of single-support phases (19.2 %), increased gait cycle time (31.0 %), time proportions of stance (8.5 %) and double-support phases (57.7–75.9 %). KOA patients had significant smaller peak flexion angle (29.1 %), flexion ROM (50.6 %) and peak flexion moment (90.2 %), greater peak adduction moment (KAM) (40.7 %), peak rotation moments (KRM) (50.0 %), KAM impulse (106.2 %) and KRM impulse (126.0 %). In proximodistal direction, greater medial TFCF impulse (238 %), total and medial first-peak TFCF (9.6 % and 15.2 %), and smaller lateral peak TFCF (33.3 %) and TFCF impulse (38.4 %) were found in KOA patients. Besides, significant differences were found in the total, medial and lateral peak TFCFs and TFCF impulses in mediolateral direction, and the medial and lateral TFCFs and TFCF impulses in anteroposterior direction. Conclusions: Significant differences were found in the spatiotemporal parameters, knee kinematics and kinetics, and TFCF between the two groups. The results of this study have important implication for clinicians and rehabilitation physicians. These quantified biomechanical differences can provide data support for the personalized and quantified rehabilitation strategies, give suggestions for the exercises of KOA patients, help monitor disease, evaluate surgical treatment, and develop more effective preoperative planning and postoperative rehabilitation strategies.

Abstract Objective To investigate the genetic underpinnings of the association between type 2 diabetes (T2D), glycemic indicators such as fasting glucose (FG), fasting insulin (FI), and glycated hemoglobin (GH), and venous thromboembolism (VTE), encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE), thereby contributing novel insights to the scholarly discourse within this domain. Methods Genome-wide association study (GWAS) summary data pertaining to exposures (T2D, FG, FI, GH) and outcomes (VTE, DVT, PE) were acquired from the IEU Open GWAS database, encompassing participants of European descent, including both male and female individuals. Two-sample Mendelian randomization (MR) analyses were conducted utilizing the TwoSampleMR and MRPRESSO packages within the R programming environment. The primary analytical approach employed was the random-effects inverse variance weighted (IVW) method. Heterogeneity was assessed via Cochran’s Q statistic for MR-IVW and Rucker’s Q statistic for MR-Egger. Horizontal pleiotropy was evaluated using the intercept test of MR Egger and MR pleiotropy residual sum and outlier (MR-PRESSO) analysis, with the latter also employed for outlier detection. Additionally, a “Leave one out” analysis was conducted to ascertain the influence of individual single nucleotide polymorphisms (SNPs) on MR results. Results The random-effects IVW analysis revealed a negative genetic causal association between T2D) and VTE (P = 0.008, Odds Ratio [OR] 95% confidence interval [CI] = 0.896 [0.827–0.972]), as well as between FG and VTE (P = 0.002, OR 95% CI = 0.655 [0.503–0.853]), GH and VTE (P = 0.010, OR 95% CI = 0.604 [0.412–0.884]), and GH and DVT (P = 0.002, OR 95% CI = 0.413 [0.235–0.725]). Conversely, the random-effects IVW analysis did not detect a genetic causal relationship between FI and VTE (P > 0.05), nor between T2D, FG, or FI and DVT (P > 0.05), or between T2D, FG, FI, or GH and PE (P > 0.05). Both the Cochran’s Q statistic for MR-IVW and Rucker’s Q statistic for MR-Egger indicated no significant heterogeneity (P > 0.05). Moreover, the intercept tests of MR Egger and MR-PRESSO suggested the absence of horizontal pleiotropy (P > 0.05). MR-PRESSO analysis identified no outliers, while the “Leave one out” analysis underscored that the MR analysis was not influenced by any single SNP. Conclusion Our investigation revealed that T2D, FG, and GH exhibit negative genetic causal relationships with VTE at the genetic level, while GH demonstrates a negative genetic causal relationship with DVT at the genetic level. These findings furnish genetic-level evidence warranting further examination of VTE, DVT, and PE, thereby making a contribution to the advancement of related research domains.

This study aimed to investigate whether there is a causal relationship between educational attainment and delirium at the genetic level using the Mendelian randomization method, and provide new evidence for studies in this field. We found a causal relationship between educational attainment and delirium at the genetic level after excluding confounders using Mendelian randomization. The inverse variance weighting method of random effects was the main analysis method. The weighted median and Mendelian Randomization-Egger methods, as well as simple, and weighted modes were used as supplementary analysis methods. Additionally, horizontal pleiotropy tests were conducted, including the Mendelian Randomization-Egger intercept test and Mendelian Randomization Pleiotropy RESidual Sum and Outlier. Cochran's Q statistic was used to assess the size of heterogeneity. We retrieved all second single nucleotide polymorphism features and performed multivariate Mendelian randomization to adjust for the effect of potential confounders on our results. The inverse variance weighting suggested a negative correlation between genetically predicted educational attainment and delirium (0.67[0.49–0.92], p = 0.013); Mendelian Randomization Pleiotropy RESidual Sum and Outlier (0.67[0.49–0.92], p = 0.013) and multivariate Mendelian randomization (0.52[0.33–0.82], p = 0.005) results were generally consistent with the inverse variance weighting method. The Mendelian Randomization-Egger, simple, and weighted mode results were consistent with the inverse variance weighting results. Our results were not affected by pleiotropy or heterogeneity (p > 0.05, for both pleiotropy and heterogeneity). In addition, the “leave-one-out” analysis showed that the results of our Mendelian randomization analysis were not influenced by individual single nucleotide polymorphisms. Studies have found a causal relationship between educational attainment and delirium at the genetic level; higher educational attainment may be a protective factor against delirium. Clinically, more attention should be paid to patients at a high risk of delirium with low educational attainment.

Osteoarthritis (OA) is mainly caused by ageing, strain, trauma, and congenital joint abnormalities, resulting in articular cartilage degeneration. During the pathogenesis of OA, the changes in subchondral bone (SB) are not only secondary manifestations of OA, but also an active part of the disease, and are closely associated with the severity of OA. In different stages of OA, there were microstructural changes in SB. Osteocytes, osteoblasts, and osteoclasts in SB are important in the pathogenesis of OA. The signal transduction mechanism in SB is necessary to maintain the balance of a stable phenotype, extracellular matrix (ECM) synthesis, and bone remodelling between articular cartilage and SB. An imbalance in signal transduction can lead to reduced cartilage quality and SB thickening, which leads to the progression of OA. By understanding changes in SB in OA, researchers are exploring drugs that can regulate these changes, which will help to provide new ideas for the treatment of OA. Cite this article: Bone Joint Res 2023;12(9):536–545.

Abstract In view of the current debate about the relationship between lipids and deep venous thrombosis (DVT) in clinical studies, a two-sample Mendelian randomization (MR) study was conducted to clarify the effects of five circulating lipids (apolipoprotein A1, apolipoprotein B, low-density lipoprotein, high-density lipoprotein and triglycerides) on DVT from the perspective of genetic inheritance. Five lipids (exposure) were analysed by MR with DVT (outcome) from two different data sources. For the analysis, we used inverse variance weighting and a weighted mode, weighted median, simple mode and MR–Egger regression to analyse the effect of circulating lipids on DVT. In addition, we used the MR–Egger intercept test, Cochran’s Q test and “leave-one-out” sensitivity analysis to evaluate horizontal multiplicity, heterogeneity and stability, respectively, in the analysis. In the analysis, the two-sample Mendelian randomization analysis of five common circulating lipids and DVT showed that common circulating lipids had no causal effect on DVT, which is somewhat inconsistent with the findings of many published observational studies. Based on our results, our two-sample MR analysis failed to detect a statistically significant causal relationship between five common circulating lipids and DVT.

Introduction: Certain growth factors (GFs) are associated with constipation, but few studies has analyzed the causal associations between the two. Therefore, this study used two-sample Mendelian randomization (MR) to systematically analyze the causal associations between GF levels and constipation based on data from genome-wide association studies (GWAS).Methods: Both GF and constipation data were obtained from European populations. GFs, as an exposure variable, were obtained from a genetic map of the human plasma proteome containing 3,301 samples, another GWAS dataset on 90 circulating proteins containing 30,931 samples, and a GWAS dataset containing 3,788 samples. Constipation, as an outcome variable, was obtained from the FinnGen project containing 26,919 cases and 282,235 controls and another UK Biobank dataset containing 3,328 cases and 459,682 controls. Single-nucleotide polymorphisms strongly associated with GFs were regarded as instrumental variables. Inverse-variance weighting, MR–Egger regression, weight median, simple mode, and weight mode methods were used to determine genetic associations. Cochran’s Q test, Egger intercept, and Mendelian Randomization Pleiotropy RESidual Sum and Outlier tests were used to analyze sensitivity.Results: The IVW analysis based on FinnGen showed that NGFI-A-binding protein 2 and vascular endothelial growth factor receptor 2 were inversely associated with constipation, and that fibroblast growth factor 7 and transforming growth factor beta receptor II levels were positively associated with constipation. The IVW analysis based on UK Biobank showed that proheparin-binding epidermal growth factor, platelet-derived growth factor AA, and vascular endothelial growth factor121 were inversely associated with constipation.Conclusion: This study showed that some GFs are genetically associated with the risk of constipation.

Abstract For the first time, a configurable NbOx memristor is achieved that can be configured as an artificial synapse or neuron after fabrication by controlling the forming compliance current (FCC). When the FCC ≤ 2 mA, the memristors exhibit the resistive‐switching (RS) property, enabling multiple types of synaptic plasticity, including short‐term potentiation, paired‐pulse facilitation, short‐term memory, and long‐term memory. When the FCC ≥ 3 mA, the memristors can be electroformed and exhibit the threshold switching (TS) property with excellent endurance (>1012), thus achieving various biological neuron characteristics, such as threshold‐triggering, strength‐modulation of spike frequency, and leaky integrate‐and‐fire. This enables the successful implementation of a spiking Pavlov's dog that employs the spikes as information carrier by connecting an RS NbOx memristor as artificial synapse and a TS memristor as artificial neuron in series. Furthermore, a fully NbOx memristors‐based single‐layer spiking neural network is simulated. It is first found that, due to the forgetting property of synapse, the recognition accuracy for the Modified National Institute of Standards and Technology handwritten digits is increased from 85.49% to 91.45%. This study provides a solid foundation for the development of neuromorphic machines based on the principles of the human brain.

Background: Rheumatoid arthritis (RA) increases the risk of depression. However, studies on the effects of RA on the dose of depression medications are limited. Therefore, in this study, we used two-sample Mendelian randomization (MR) to explore whether RA increases the dose of depression medications and gain a more comprehensive understanding of the relationship between RA and depression. Methods: Two-sample MR was used to evaluate the causal effect of RA on the dose of depression medications. The aggregated data on RA originated from extensive genome-wide association studies (GWASs) of European descent (14,361 cases and 42,923 controls). The summary GWAS data for the dose of depression medications were derived from the FinnGen consortium (58,842 cases and 59,827 controls). Random effects inverse-variance weighted (IVW), MR-Egger regression, weighted median, and fixed effects IVW methods were used for the MR analysis. Random effects IVW was the primary method. The heterogeneity of the MR results was detected using the IVW Cochran’s Q test. The pleiotropy of the MR results was detected using MR-Egger regression and the MR pleiotropy residual sum and outlier (MR-PRESSO) test. Finally, a leave-one-out analysis was performed to determine whether the MR results were affected by a specific single-nucleotide polymorphism (SNP). Results: The primary method, random effects IVW, revealed that genetically predicted RA had a positive causal association with the dose of depression medications (Beta, 0.035; 95% confidence interval (CI), 0.007–0.064; p = 0.015). The IVW Cochran’s Q test results revealed no heterogeneity in the MR analysis (p > 0.05). The MR-Egger regression and MR-PRESSO tests revealed that there was no pleiotropy in our MR analysis. The leave-one-out analysis confirmed that a single SNP did not affect the MR results, indicating the study’s robustness. Conclusion: Using MR techniques, we discovered that having RA increases the dose of depression medications; however, the exact mechanisms and pathways still need to be further explored.

Objective: Ankylosing spondylitis (AS) is associated with a variety of gut microbiotas. We aim to analyze the causal relationship between the two at the genetic level. Methods: Mendelian randomization (MR) is a type of instrumental variables (IVs) analysis; MR follows the Mendelian genetic rule of “parental alleles are randomly assigned to offspring” and takes genetic variation as IVs to infer the causal association between exposure factors and study outcome in observational studies. Genome-wide association study (GWAS) summary data of AS were from the FinnGen consortium, and the gut microbiota (Bacteroides, Streptococcus, Proteobacteria, Lachnospiraceae) were from the MiBioGen consortium. The TwoSampleMR and MRPRESSO packages of the R were used to perform a two-sample MR study. Random-effects inverse variance weighted (IVW) was the main analysis method, and MR Egger, weighted median, simple mode, and weighted mode were used as supplementary methods. We examined heterogeneity and horizontal pleiotropy, and examined whether the analysis results were influenced by a single SNP. We applied radial variants of the IVW and MR-Egger model for the improved visualization of the causal estimate. We further examined the causal relationship between AS and gut microbiota, and the robustness of the analysis results. Finally, we performed maximum likelihood, penalized weighted median, and IVW (fixed effects) to further identify the potential causal association. Results: The random-effects IVW results showed that Bacteroides (p = 0.965, OR 95% confidence interval [CI] = 0.990 [0.621–1.579]), Streptococcus (p = 0.591, OR 95% CI = 1.120 [0.741–1.692]), Proteobacteria (p = 0.522, OR 95% CI = 1.160 [0.737–1.826]), and Lachnospiraceae (p = 0.717, OR 95% CI = 1.073 [0.732–1.574]) have no genetic causal relationship with AS. There was no heterogeneity, horizontal pleiotropy or outliers, and results were normally distributed. The MR analysis results were not driven by a single SNP. Conclusions: This study showed that Bacteroides, Streptococcus, Proteobacteria and Lachnospiraceae, four common gut microbiotas associated with AS, had no causal relationship with AS at the genetic level. This study makes a positive contribution to the genetics of AS, but the insufficient number of gut microbiota included is a limitation.

Based on previous observational studies, the causal association between circulating adiponectin (CA) levels and ankylosing spondylitis (AS) risk remains unclear. Therefore, this study aims to investigate whether CA levels are related to the risk of AS. We carried out a bidirectional two-sample Mendelian randomization (MR) analysis to examine the causal correlation between CA levels and AS via published genome-wide association study (GWAS) datasets. Single-nucleotide polymorphisms (SNPs) related to CA levels were derived from a large GWAS that included 39,883 individuals of European descent. SNPs related to AS were obtained from the FinnGen consortium (2252 cases and 227,338 controls). The random-effects inverse variance weighted (IVW) method was the primary method utilized in our research. We also used four complementary approaches to improve the dependability of this study (MR–Egger regression, Weighted median, Weighted mode, and Simple mode). Random-effects IVW (odds ratio [OR], 1.00; 95% confidence interval [CI], 0.79–1.27, p = 0.984) and four complementary methods all indicated that genetically predicted CA levels were not causally related to the risk of AS. In reverse MR analysis, there is little evidence to support the genetic causality between the risk of AS and CA levels.

The ubiquity of refractory organic matter in aquatic environments necessitates innovative removal strategies. Sulfate radical-based advanced oxidation has emerged as an attractive solution, offering high selectivity, enduring efficacy, and anti-interference ability. Among many technologies, sulfite activation, leveraging its cost-effectiveness and lower toxicity compared to conventional persulfates, stands out. Yet, the activation process often relies on transition metals, suffering from low atom utilization. Here we introduce a series of single-atom catalysts (SACs) employing transition metals on g-C3N4 substrates, effectively activating sulfite for acetaminophen degradation. We highlight the superior performance of Fe/CN, which demonstrates a degradation rate constant significantly surpassing those of Ni/CN and Cu/CN. Our investigation into the electronic and spin polarization characteristics of these catalysts reveals their critical role in catalytic efficiency, with oxysulfur radical-mediated reactions predominating. Notably, under visible light, the catalytic activity is enhanced, attributed to an increased generation of oxysulfur radicals and a strengthened electron donation-back donation dynamic. The proximity of Fe/CN's d-band center to the Fermi level, alongside its high spin polarization, is shown to improve sulfite adsorption and reduce the HOMO-LUMO gap, thereby accelerating photo-assisted sulfite activation. This work advances the understanding of SACs in environmental applications and lays the groundwork for future water treatment technologies.

The development of proton exchange membrane water electrolyzers (PEMWEs) is primarily challenged by the slow and unstable oxygen evolution reaction (OER) at the anode. Thus, the pursuit of low‐cost, highly efficient, and durable electrocatalysts is the foundation for the widespread adoption of PEMWEs. In the past few decades, various types of electrocatalysts are proposed to serve as anode materials for acidic OER, but only a few demonstrate catalytic activity and stability comparable to iridium‐ and ruthenium‐based electrocatalysts. In recent years, some transition‐metal oxides are studied as potential candidates for acidic OER. For instance, spinel‐type oxides, e.g., Co3O4, and corresponding compounds demonstrate promise due to the rich coordination structure of metallic ions and moderate adoption energy for intermedia. In this review, recent advances in spinel‐type oxides for acidic OER are summarized. First, a fundamental understanding of reaction mechanisms for OER in acidic media is introduced. Thereafter, recent progress in rational design principles and optimization strategies for spinel oxides is systematically summarized. Finally, challenges and perspectives for the development of spinel‐based acidic OER electrocatalysts are discussed.

Abstract Herein, we report bifunctional molybdenum‐doped nickel sulfide on nickel foam (Mo‐NiSx/NF) for magnetic field‐enhanced overall water splitting under alkaline conditions. Proper doping of Mo can lead to optimization of the electronic structure of NiSx, which accelerates the dissociation of H2O and the adsorption of OH− in the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) processes, respectively. In addition, the magnetically active Mo‐NiSx/NF can further enhance the HER and OER activity under an applied magnetic field due to the magnetoresistance effect and the ferromagnetic (FM) exchange‐field penetration effect. As a result, Mo‐NiSx/NF requires low overpotentials of 307 mV at 50 mA cm−2 (for OER) and 136 mV at 10 mA cm−2 (for HER) under a magnetic field of 10000 G. Furthermore, the electrolytic cell constructed by the bifunctional Mo‐NiSx/NFs as both the cathode and the anode shows a low cell voltage of 1.594 V at 10 mA cm−2 with optimal stability over 60 h under the magnetic field. Simultaneous enhancement of the HER and OER processes by an external magnetic field through rational design of electrocatalysts might be promising for overall water splitting applications.

With the gradual increase in spacecraft in orbit, space debris monitoring has become the key to the sustainable development of space missions. A staring debris detection method is proposed for high-density stars with long tails. In order to solve the problem that the gray level of a long trailing image is not stable and continuous, rectangular fitting is used to complete the aggregation of the trailing image and reduce the influence of noise on the trailing information. The occluded state of the target was analyzed, the feature calculation method was improved, the semi-occluded scene was statistically classified, the fully connected network (FCN) based finite point feature was accurately classified, and the semi-occluded image was extracted. Based on the extracted semi-occluded image, the inter-frame association can improve the success probability of target association and realize the effective detection and tracking of debris. The detection accuracy was tested for the changing inter-frame interval and signal-to-noise ratio (SNR), and the relationship between the index parameters and key parameters was given. Compared with previous literature, this design can detect and track the occluded target with a detection rate of more than 90% and a false alarm rate of less than 10%.

Contamination of phenolic compounds has devastating effects on the environment. Therefore, its harmless treatment and recycling have received extensive attention. Herein, a novel method for preparing N-S doped phenolic resin (NSPR) from phenols, N and S groups in semi-coking wastewater, and formaldehyde are developed. The KOH is consequently incorporated into the NSPR through simultaneous carbonization and activation in a single step to produce porous carbon material (NSPC). The as-obtained NSPC exhibits a high specific capacitance of 182 F g−1 at 0.5 A g−1, a high energy density of 9.1 Wh kg−1 at a power density of 0.15 kW kg−1, and remarkable cycling stability in aqueous KOH electrolyte. This outstanding electrochemical performance is attributed to its ultrahigh specific surface area (SSA, 2,523 m2 g−1), enormous total pore volume (Vt, 1.30 cm3 g−1), rational pore structure, and N-S heteroatom self-doping (0.76 at% N and 0.914 at% S), which ensures adequate charge storage, rapid electrolyte ion diffusion, and contributed pseudo-capacitance. This work not only provides a facile method for transforming phenolic wastewater into high-value products but also offers a cost-effective and high-performance porous carbon material for supercapacitors.

Developing efficient and stable earth-abundant electrocatalysts for acidic oxygen evolution reaction is challenging. Here, the authors modify the local bonding environment of Co3O4 by CeO2 nanocrystallites to regulate the redox properties, thus enhance the catalytic activity.

Development of high capacity, high‐rate performance, long cycling life, and low‐cost electrode materials is highly desirable for sodium‐ion batteries (SIBs) and potassium‐ion batteries (PIBs) to pave the way for their rapid commercialization. Herein, NiP2 nanoparticles loaded in multi‐shelled hollow N‐doped carbon microspheres (NiP2@MHNC) are successfully prepared. The built‐in heterogeneous interfaces between NiP2 and N‐doped carbon promote Na+/K+ diffusion; the robust C–N, N–Ni bonds stabilize the structure of electrodes and accelerate the electrons transfer; and the multi‐shelled hollow structure with large specific surface area effectively buffers the volume expansion to ensure cycling stability. Endowed with the aforementioned synergistic effect, the NiP2@MHNC exhibits remarkable enhancement in electrochemical performance with 346.6 mA h g−1 after 300 cycles for SIB and 142 mA h g−1 after 200 cycles for PIBs at 0.1 A g−1. Moreover, the synergistic effect on electrochemical reaction kinetics is systematically analyzed. Further, the mechanism of sodium storage for NiP2@MHNC is also investigated. The research experience and conclusions in this study based on synergistic effect of heterogeneous interfaces, N‐doped carbon, and multi‐shelled hollow structure open up a meaningful route to design other similar advanced composite electrode materials in energy storage and conversion field.

Satellite remote sensing plays an important role in wildfire detection. Methods using the brightness and temperature difference of remote sensing images to determine if a wildfire has occurred are one of the main research directions of forest fire monitoring. However, common wildfire detection algorithms are mainly based on a fixed brightness temperature threshold to distinguish wildfire pixels and non-wildfire pixels, which reduces the applicability of the algorithm in different space–time regions. This paper presents an adaptive wildfire detection algorithm, DBTDW, based on a dynamic brightness temperature threshold. First, a regression dataset, MODIS_DT_Fire, was constructed based on moderate resolution imaging spectroradiometry (MODIS) to determine the wildfire brightness temperature threshold. Then, based on the meteorological information, normalized difference vegetation index (NDVI) information, and elevation information provided by the dataset, the DBTDW algorithm was used to calculate and obtain the minimum brightness temperature threshold of the burning area by using the Planck algorithm and Otsu algorithm. Finally, six regression models were trained to establish the correlation between factors and the dynamic brightness temperature threshold of wildfire. The root-mean-square error (RMSE) and mean absolute error (MAE) were used to evaluate the regression performance. The results show that under the XGBoost model, the DBTDW algorithm has the best prediction effect on the dynamic brightness temperature threshold of wildfire (leave-one-out method: RMSE/MAE = 0.0730). Compared with the method based on a fixed brightness temperature threshold, the method proposed in this paper to adaptively determine the brightness temperature threshold of wildfire has higher universality, which will help improve the effectiveness of satellite remote fire detection.

Optical remote sensing ship target detection has become an essential means of ocean supervision, coastal defense, and frontier defense. Accurate, effective, fast, and real-time remote sensing data processing is the critical technology in this field. This paper proposes a real-time detection algorithm for moving targets in low-resolution wide-area remote sensing images, which includes four steps: pre-screening, simplified HOG feature identification, sequence correlation identification, and facilitated Yolo identification. It can effectively detect and track targets in low-resolution sequence data. Firstly, iterative morphological processing was used to improve the contrast of low-resolution ship target profile edge features compared with the sea surface background. Next, the target area after adaptive segmentation was used to eliminate false alarms. As a result, the invalid background information of extensive comprehensive data was quickly eliminated. Then, support vector machine classification of S-HOG feature was carried out for suspected targets, and interference such as islands and reefs, broken clouds, and waves were eliminated according to the shape characteristics of ship targets. The method of multi-frame data association and searching for adjacent target information between frames was adopted to eliminate the interference of static targets and broken clouds with similar contours. Finally, the sequential marks were further trained and learned, and further false alarm elimination was completed based on the clipped Yolo network. Compared with the traditional Yolo Tiny V2/V3 series network, this method had higher computational speed and better detection performance. The F1 number of detection results was increased by 3%, and the calculation time was reduced by 66%.

Intelligence development has put forward increasing requirements of real-time planning and dynamic feedback in controlling robotic arms. It has become essential in engineering applications to complete the kinematics calculation of complex manipulators in real time. This paper proposes a matrix cascading multiplication equivalent reduced-order parallel computing method for the multiplication of homogeneous matrices in the process of forward and inverse kinematics solutions, which reduces the order of the matrix according to the distribution of zero vectors in the homogeneous matrix calculations. The method removes the unnecessary multiplication in joint homogeneous matrixes containing zero vectors. It obtains the optimal calculation order of the cascade matrix multiplication through dynamic planning searches, improving the efficiency of effective dot product calculation by parallel computation. Calculation processes and specific examples are presented in this paper. Compared with the previous algorithms, the proposed algorithm reduces the calculation cycle by 90%, effectively improving the real-time calculation efficiency in the complex control process.

Water electrolysis offers a zero-carbon route to generate renewable energy conversion systems. Herein, a self-supported nickel phosphosulfide nanosheet (NS) electrocatalyst was fabricated at a low temperature on carbon cloth, which was then subjected to Ar etching to enhance its catalytic activity. Etching resulted in better hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) performance than other samples, with overpotentials of 103.1 mV (at 10 mA cm−2) and 278.9 mV (at 50 mA cm−2), respectively. The characterization results confirmed that Ar etching created a thin amorphous layer around the NiPS3 NSs, which increased the number of active sites and modulated their electronic structures. These 3D-structured NiPS3 NSs and their subsequent Ar etching process show promise for applications in overall water splitting in alkaline media.

At present, a large number of scholars have conducted related research on topology optimization for additive manufacturing (AM). However, there are few relevant research reports on the impact of different directions of additive manufacturing on the optimal design and manufacturing results. In this paper, using the bidirectional evolutionary optimization (BESO) method, anisotropic optimization analysis was carried out on space nodes that are currently popular in the field of additive manufacturing and topology optimization. The elastic constants in different directions were used as anisotropic material properties for optimization research in this paper through tensile testing, which was carried out on 316L stainless-steel specimens fabricated using Selective Laser Melting (SLM) technology. In addition, SEM analyses were performed to explore the microscopic appearance of the material. The study found that additive manufacturing is affected by the printing direction in terms of both macroscopic mechanical properties and microscopic material structure; the deformation obtained by anisotropic optimization was about 1.1–2.3% smaller than that obtained by isotropic optimization.

Designing a high-strength node is significant for space structures. Topological optimization can optimally allocate the material distribution of components to meet performance requirements. Although the material distribution after topology optimization is optimum, the structure becomes complicated to manufacture. By using additive manufacturing technology, this problem can be well solved. At present, both topology optimization technology and additive manufacturing technology are quite mature, but their application in the design of spatial nodes is very recent and less researched. This paper involves the study and improvement of the node optimization design–manufacturing integrated method. This study used the BESO optimization algorithm as the research algorithm. Through a reasonable improvement of the material interpolation method, the algorithm’s dependence on the experience of selecting the material penalty index P was reduced. On this basis, the secondary development was carried out, and a multisoftware integration was carried out for optimization and manufacturing. The spatial node was taken as the research object, and the calculation results of the commercial finite element software were compared. The comparison showed that the algorithm used in this paper was better. Not only was it not trapped in a local optimum, but the maximum stress was also lower. In addition, this paper proposed a practical finite element geometric model extraction method and smoothing of the optimized nodes, completing the experiment of the additive manufacturing forming of the nodes. It provides ideas for processing jagged edges brought by the BESO algorithm. This paper verified the feasibility of the multisoftware integration method of optimized manufacturing.

Abstract Rhenium dichalcogenides (ReX2, X = S or Se) are catalysts that have great promise for the photoenhanced hydrogen evolution reaction (PE‐HER) because of their unique physiochemical properties. However, the catalytic performance is still restricted by their low concentration of electrocatalytic activity sites and poor injection of hot electrons. Herein, dual‐enhancement in ReSe2 nanosheets (NSs) with high concentration of active sites and efficient use of hot electrons is simultaneously achieved with moderate Mo doping. Contributions from exposed catalytically active sites, improved electrical conductivity, and enhanced solar spectral response are systematically investigated. Superior PE‐HER catalytical performance is obtained in Re0.94Mo0.06Se2, which has more catalytically active sites and optimized band structure than other Re1−xMoxSe2 samples. Here, it is demonstrated that only doping can reduce the overpotential (η10) from 239 to 174 mV at −10 mA cm−2 (Δ1η10 = 65 mV). Then, η10 is further improved to 137 mV under simulated AM 1.5 sun illumination (Δ2η10 = 37 mV). The total improvement (Δη10) toward PE‐HER is 102 mV (Δ1η10 + Δ2η10 = 102 mV) in optimal Re0.94Mo0.06Se2. This work presents a new perspective for researching high‐efficiency photoenhanced HER ReSe2‐based electrocatalysts and other layered transition metal dichalcogenides.

Abstract This paper presents a study of edaphic characteristics and their relationship with plant distribution in the Qaidam Basin, Tibetan Plateau, and establishes a distribution model for plants in sandy gravel Gobi to dry salt lake areas. All of the communities in the study area were dominated by plants with strong saline-alkaline tolerance. In this area, salts appeared to migrate to the surface; the surface soil was striped, and the salt distribution varied from sandy gravel Gobi to dry salt lake areas. The salt composition mainly consisted of NaCl in the surface crust. In the subsurface layers, the salt composition was dominated by Ca2+, Cl− and SO42−. The type of vegetation at the study site can be divided into two categories: salt-tolerant vegetation and weakly salt-tolerant vegetation. The salt-tolerant vegetation is influenced by Na+, Cl−, and the salinity. The soil of these vegetation communities had a higher salt and Na+ concentration and a lower Ca2+ and K+ concentration. The weakly salt-tolerant vegetation is mainly affected by the Ca2+/Na+ and K+/Na+ ratios. Based on the above results, a vegetation distribution model for saline lakes on the inland plateau was established.

If a number of literature studies point at the positive role of coupling materials with non-thermal plasma, particularly for Volatile Organic Compounds (VOC) removal, most of them focus on the direct plasma-material interaction to understand the coupling. However, a key contribution relies in the VOC–material interaction. Therefore, this study focuses on the adsorption step of targeted VOCs to provide a new insight on plasma–material coupling. The adsorption of acetone, used as probe VOC, is explored on two widespread coupling materials: TiO2 and CeO2. First, their behaviors are compared regarding acetone uptake. This process is reactive and creates other organic species than acetone on both surfaces. Second, the metal oxide behaviors are compared regarding ozone uptake. Interestingly, under typical VOC treatment configuration, i.e., with organics on their surfaces, ozone uptake is driven by the adsorbed organics, not directly by the metal oxides anymore. Finally, the ozonation of both materials, preliminary exposed to acetone, is explored through the evolution of the adsorbed organics and the corresponding mineralization, i.e., CO and CO2 formation. It evidences that the reactive adsorption of VOCs plays a key role in making the surface organics ready for an efficient oxidation and mineralization under post-plasma exposure.

Electrochemical water splitting in alkaline media plays an important role in mass production of hydrogen. Ruthenium (Ru), as the cheapest member of platinum-group metals, has attracted much attention, and the incorporation of trace amount of Ru with cobalt phosphide could significantly improve the hydrogen evolution reaction (HER) catalytic activity. In this work, ruthenium-incorporated cobalt phosphide nanocubes are synthesized via a reaction between Co–Co Prussian blue analog (Co-PBA) and ruthenium chloride (RuCl3) followed by the phosphidation. The sample with a Ru content of ~2.04 wt.% exhibits the best HER catalytic activity with a low overpotential of 51 and 155 mV, to achieve the current densities of −10 and −100 mA cm−2, respectively, and the Tafel slope of 53.8 mV dec−1, which is comparable to the commercial Pt/C. This study provides a new perspective to the design and construction of high performance electrocatalysts for HER and other catalytic applications in a relatively low price.

We present a plasmonic nanostructure design, integrated both of the metallic triangular grating and dielectric antireflection coatings, for ultrathin crystalline silicon solar cells. Considering both polarizations, the current improvement of the optimized structure can reach 76.8% under air mass 1.5 global illuminations. The mode analysis confirms that the enhanced optical absorption is attributed to improved coupling to both guided modes and localized modes. The absorption enhancement of such architecture is a little sensitive to the angle of incidence. The enhancement is still as high as 50% at 60 degrees of incidence.

In the context of coupling nonthermal plasmas with catalytic materials, CeO2 is used as adsorbent for toluene and combined with plasma for toluene oxidation. Two configurations are addressed for the regeneration of toluene saturated CeO2: (i) in plasma-catalysis (IPC); and (ii) post plasma-catalysis (PPC). As an advanced oxidation technique, the performances of toluene mineralization by the plasma-catalytic systems are evaluated and compared through the formation of CO2. First, the adsorption of 100 ppm of toluene onto CeO2 is characterized in detail. Total, reversible and irreversible adsorbed fractions are quantified. Specific attention is paid to the influence of relative humidity (RH): (i) on the adsorption of toluene on CeO2; and (ii) on the formation of ozone in IPC and PPC reactors. Then, the mineralization yield and the mineralization efficiency of adsorbed toluene are defined and investigated as a function of the specific input energy (SIE). Under these conditions, IPC and PPC reactors are compared. Interestingly, the highest mineralization yield and efficiency are achieved using the in-situ configuration operated with the lowest SIE, that is, lean conditions of ozone. Based on these results, the specific impact of RH on the IPC treatment of toluene adsorbed on CeO2 is addressed. Taking into account the impact of RH on toluene adsorption and ozone production, it is evidenced that the mineralization of toluene adsorbed on CeO2 is directly controlled by the amount of ozone produced by the discharge and decomposed on the surface of the coupling material. Results highlight the key role of ozone in the mineralization process and the possible detrimental effect of moisture.

Beyond tailored and synthetic catalysts sought out for ozone decomposition, mineral dusts provide naturally mixed metal oxide materials. The steady-state uptake of O3 evidenced across a wide concentration range signifies the catalytic decomposition of O3. The geocatalytic properties of such natural mineral dust open up new perspectives in atmospheric chemistry and catalytic processes.