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Air conditioning contributes a high percentage of energy consumption over the world. The efficient prediction of energy consumption can help to reduce energy consumption. Traditionally, multidimensional air conditioning energy consumption data could only be processed sequentially for each dimension, thus resulting in inefficient feature extraction. Furthermore, due to reasons such as implicit correlations between hyperparameters, automatic hyperparameter optimization (HPO) approaches can not be easily achieved. In this paper, we propose an auto-optimization parallel energy consumption prediction approach based on reinforcement learning. It can parallel process multidimensional time series data and achieve the automatic optimization of model hyperparameters, thus yielding an accurate prediction of air conditioning energy consumption. Extensive experiments on real air conditioning datasets from five factories have demonstrated that the proposed approach outperforms existing prediction solutions, with an increase in average accuracy by 11.48% and an average performance improvement of 32.48%.

The high-efficiency preparation of large-area microstructures of optical materials and precision graphic etching technology is one of the most important application directions in the atomic and near-atomic-scale manufacturing industry. Traditional focused ion beam (FIB) and reactive ion etching (RIE) methods have limitations in precision and efficiency, hindering their application in automated mass production. The pulsed ion beam (PIB) method addresses these issues by enhancing ion beam deflection to achieve high-resolution material removal on a macro scale, which can reach the equivalent removal resolution of 6.4 × 10−4 nm. Experiments were conducted on a quartz sample (10 × 10 × 1 mm) with a specific pattern mask using the custom PIB processing device. The surface morphology, etching depth, and roughness were measured post-process. The results demonstrated that precise control over cumulative sputtering time yielded well-defined patterns with expected average etching depths and surface roughness. This confirms the PIB technique’s potential for precise atomic depth image transfer and its suitability for industrial automation, offering a significant advancement in microfabrication technology.

The performance of corrosion-induced cracking of reinforced concrete members under transverse constraints was studied. Based on the theory of elastic-plastic mechanics and the hypothesis of uniform corrosion of a steel bar, a three-layer hollow cylinder model was established to predict the critical corrosion of the steel bar at the time of the cracking of the concrete cover. Taking the constraint of stirrups on surrounding concrete into consideration, it can be used to predict the corrosion rate of members with stirrups at the time of the cracking of the concrete cover, which further expands the application range of the corrosion-induced cracking models of concrete. On this basis, the critical corrosion rate of concrete under different stirrup ratios at the time of cracking was measured. The calculated results of the model are in accordance with experimental data. For corner steel bars, when the stirrup spacing is less than 100 mm, the existence of stirrups can effectively delay the occurrence of rust expansion cracks and enhance the durability of the structure. On the basis of this study, the problem of corrosion expansion and cracking of the concrete cover caused by non-uniform corrosion of steel bars along longitudinal and radial directions needs to be further studied in the future.

Solid-contact ion-selective electrodes (SC-ISEs) have the advantages of easy miniaturization, even chip integration, easy carrying, strong stability, and more favorable detection in complex environments. They have been widely used in conjunction with portable, wearable, and intelligent detection devices, as well as in on-site analysis and timely monitoring in the fields of environment, industry, and medicine. This article provides a comprehensive review of the composition of sensors based on redox capacitive and double-layer capacitive SC-ISEs, as well as the ion–electron transduction mechanisms in the solid-contact (SC) layer, particularly focusing on strategies proposed in the past three years (since 2021) for optimizing the performance of SC-ISEs. These strategies include the construction of ion-selective membranes, SC layer, and conductive substrates. Finally, the future research direction and possibilities in this field are discussed and prospected.

Curved grid stiffeners, compared to straight stiffeners, offer greater flexibility in adjusting the force transmission paths and give better structural performance. In this paper, a level-set-based density method is employed to generate layouts of curved grid stiffeners so that the critical buckling load factor (BLF) of the stiffened structures is improved. During the optimization process, volume constraint is incorporated to control material utilization, and gradient constraints are employed to maintain uniformity in the width of the stiffeners. Finally, the proposed method is demonstrated through several numerical examples.

The integration of Synthetic Aperture Radar (SAR) imaging technology with deep neural networks has experienced significant advancements in recent years. Yet, the scarcity of high-quality samples and the difficulty of extracting prior information from SAR data have experienced limited progress in this domain. This study introduces an innovative sparse SAR imaging approach using a self-supervised non-local asymmetric pixel-shuffle blind spot network. This strategy enables the network to be trained without labeled samples, thus solving the problem of the scarcity of high-quality samples. Through asymmetric pixel-shuffle downsampling (AP) operation, the spatial correlation between pixels is broken so that the blind spot network can adapt to the actual scene. The network also incorporates a non-local module (NLM) into its blind spot architecture, enhancing its capability to analyze a broader range of information and extract more comprehensive prior knowledge from SAR data. Subsequently, Plug and Play (PnP) technology is used to integrate the trained network into the sparse SAR imaging model to solve the regularization term problem. The optimization of the inverse problem is achieved through the Alternating Direction Method of Multipliers (ADMM) algorithm. The experimental results of the unlabeled samples demonstrate that our method significantly outperforms traditional techniques in reconstructing images across various regions.

In order to solve the problems of methods that use a single form of sensing, the ease of causing deformation damage to the targets with a low hardness during grasping, and the slow sliding inhibition of a prosthetic hand when the grasping target slides, which are problems that exist in most current intelligent prosthetic hands, this study introduces an adaptive control strategy for prosthetic hands based on multi-sensor sensing. Using a force-sensing resistor (FSR) to collect changes in signals generated after contact with a target, a prosthetic hand can classify the target’s hardness level and adaptively provide the desired grasping force so as to reduce the deformation of and damage to the target in the process of grasping. A fiber-optic sensor collects the light reflected by the object to identify its surface roughness, so that the prosthetic hand adaptively adjusts the sliding inhibition method according to the surface roughness information to improve the grasping efficiency. By integrating information on the hardness and surface roughness of the target, an adaptive control strategy for a prosthetic hand is proposed. The experimental results showed that the adaptive control strategy was able to reduce the damage to the target by enabling the prosthetic hand to achieve stable grasping; after grasping the target with an initial force and generating sliding, the efficiency of slippage inhibition was improved, the target could be stably grasped in a shorter time, and the hardness, roughness and weight ranges of targets that could be grasped by the prosthetic hand were enlarged, thus improving the success rate of stable grasping under extreme conditions.

To investigate the effects of nano-SiO2 (NS) and polyvinyl alcohol (PVA) fibers on the durability and mechanical properties of cementitious composites subjected to saline freeze–thaw cycling, a series of PVA fiber-reinforced cementitious composite (PFRCC) specimens were prepared using various fiber contents, and a series of NS and PVA fiber-reinforced cementitious composite (NPFRCC) specimens were prepared using various combinations of NS and fiber contents. Durability and fracture toughness tests were subsequently conducted on the specimens after different numbers of saline freeze–thaw cycles. The results indicate that the degradation of material properties can be divided into slow and accelerated damage stages before/after 50 freeze–thaw cycles. The durability and fracture toughness of the specimen series tended to increase, then decrease with increasing NS and PVA contents, suggesting optimum levels. When the PVA fiber content was 0.5%, PFRCC specimens had the best durability after saline freeze–thaw cycles; when the NS and PVA fiber contents were 1.0% and 0.5%, respectively, NPFRCC specimens had the best durability and fracture properties, and the initiation toughness, destabilization toughness, and fracture energy after 100 saline freeze–thaw cycles were 120.69%, 160.02%, and 451.31%, respectively. The results of this study may guide future exploration of the durability and mechanical properties of concrete subjected to freeze–thaw action.

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Peony seed oil, known for its high nutritional value and low production yield, has become a crucial component in high-quality health products. Consequently, enhancing the extraction efficiency of peony seed oil has become an industry objective. Pulse electric field (PEF) technology, as a non-thermal extraction method, has shown promising advancements in improving plant oil yield by enhancing cell permeability. In this study, we designed a static parallel plate PEF treatment unit to process peony seed particles. By manipulating pulse voltage parameters, we investigated the effects of particle size and PEF strength on the oil yield. We also analyzed and evaluated tocopherol in the oil before and after treatment. The results demonstrated that PEF significantly increased the oil yield. Both treated and control groups exhibited gradually increased oil yields with decreasing particle size until reaching saturation at a certain particle size. Increasing voltage frequency did not have a significant impact on the oil yield; however, increasing voltage amplitude resulted in an optimal point for maximum oil yield. Analysis of oil composition indicated that PEF appropriately increased tocopherol content. These findings provide a foundation for further optimization of PEF parameters to assist in extracting peony seed oil and facilitate its industrial application.

Nano-doped hollow fiber is currently receiving extensive attention due to its multifunctionality and booming development. However, the microfluidic fabrication of nano-doped hollow fiber in a simple, smooth, stable, continuous, well-controlled manner without system blockage remains challenging. In this study, we employ a microfluidic method to fabricate nano-doped hollow fiber, which not only makes the preparation process continuous, controllable, and efficient, but also improves the dispersion uniformity of nanoparticles. Hydrogel hollow fiber doped with carbon nanotubes is fabricated and exhibits superior electrical conductivity (15.8 S m−1), strong flexibility (342.9%), and versatility as wearable sensors for monitoring human motions and collecting physiological electrical signals. Furthermore, we incorporate iron tetroxide nanoparticles into fibers to create magnetic-driven micromotors, which provide trajectory-controlled motion and the ability to move through narrow channels due to their small size. In addition, manganese dioxide nanoparticles are embedded into the fiber walls to create self-propelled micromotors. When placed in a hydrogen peroxide environment, the micromotors can reach a top speed of 615 μm s−1 and navigate hard-to-reach areas. Our nano-doped hollow fiber offers a broad range of applications in wearable electronics and self-propelled machines and creates promising opportunities for sensors and actuators.

Forest fires ignited by lightning accounted for 68.28% of all forest fires in the Greater Khingan Mountains (GKM) region of northeast China. Forecasting the incidence of lightning-triggered forest fires in the region is imperative for mitigating deforestation, preserving biodiversity, and safeguarding distinctive natural habitats and resources. Lightning monitoring data and vegetation moisture content have emerged as pivotal factors among the various influences on lightning-induced fires. This study employed innovative satellite remote sensing technology to swiftly acquire vegetation moisture content data across extensive forested regions. Firstly, the most suitable method to identify the lightning strikes that resulted in fires and two crucial lightning parameters correlated with fire occurrence are confirmed. Secondly, a logistic regression method is proposed for predicting the likelihood of fires triggered by lightning strikes. Finally, the method underwent verification using five years of fire data from the GKM area, resulting in an AUC value of 0.849 and identifying the primary factors contributing to lightning-induced fires in the region.

The drastic increase of forest fire occurrence, which in recent years has posed severe threat and damage worldwide to the natural environment and human society, necessitates smoke detection of the early forest fire. First, a semantic segmentation method based on multiple color spaces feature fusion is put forward for forest fire smoke detection. Considering that smoke images in different color spaces may contain varied and distinctive smoke features which are beneficial for improving the detection ability of a model, the proposed model integrates the function of multi-scale and multi-type self-adaptive weighted feature fusion with attention augmentation to extract the enriched and complementary fused features of smoke, utilizing smoke images from multi-color spaces as inputs. Second, the model is trained and evaluated on part of the FIgLib dataset containing high-quality smoke images from watchtowers in the forests, incorporating various smoke types and complex background conditions, with a satisfactory smoke segmentation result for forest fire detection. Finally, the optimal color space combination and the fusion strategy for the model is determined through elaborate and extensive experiments with a superior segmentation result of 86.14 IoU of smoke obtained.

Forest fires, as severe natural disasters, pose significant threats to ecosystems and human societies, and their spread is characterized by constant evolution over time and space. This complexity presents an immense challenge in predicting the course of forest fire spread. Traditional methods of forest fire spread prediction are constrained by their ability to process multidimensional fire-related data, particularly in the integration of spatiotemporal information. To address these limitations and enhance the accuracy of forest fire spread prediction, we proposed the AutoST-Net model. This innovative encoder–decoder architecture combines a three-dimensional Convolutional Neural Network (3DCNN) with a transformer to effectively capture the dynamic local and global spatiotemporal features of forest fire spread. The model also features a specially designed attention mechanism that works to increase predictive precision. Additionally, to effectively guide the firefighting work in the southwestern forest regions of China, we constructed a forest fire spread dataset, including forest fire status, weather conditions, terrain features, and vegetation status based on Google Earth Engine (GEE) and Himawari-8 satellite. On this dataset, compared to the CNN-LSTM combined model, AutoST-Net exhibits performance improvements of 5.06% in MIou and 6.29% in F1-score. These results demonstrate the superior performance of AutoST-Net in the task of forest fire spread prediction from remote sensing images.

Partially impaired sensor arrays pose a significant challenge in accurately estimating signal parameters. The occurrence of bad data is highly probable, resulting in random loss of source information and substantial performance degradation in parameter estimation. In this paper, a tensor variational sparse Bayesian learning (TVSBL) method is proposed for the estimate of direction of arrival (DOA) and polarization parameters jointly based on a conformal polarization sensitive array (CPSA), taking into account scenarios with the partially impaired sensor array. First, a sparse tensor-based received data model is developed for CPSAs that incorporates bad data. Then, a column vector detection method is proposed to diagnose the positions of the impaired sensors. In scenarios involving partially impaired sensor arrays, a low-rank matrix completion method is employed to recover the random loss of signal information. Finally, variational sparse Bayesian learning (VSBL) and minimum eigenvector methods are utilized sequentially to obtain the DOA and polarization parameters estimation, successively. Furthermore, the Cramér-Rao bound is given for the proposed method. Simulation results validated the effectiveness of the proposed method.

In clinical practice, colon cancer is a prevalent malignant tumor of the digestive system, characterized by a complex and progressive process involving multiple genes and molecular pathways. Historically, research efforts have primarily focused on investigating individual genes; however, our current study aims to explore the collective impact of multiple genes on colon cancer and to identify potential therapeutic targets associated with these genes. For this research, we acquired the gene expression profiles and RNA sequencing data of colon cancer from TCGA. Subsequently, we conducted differential gene expression analysis using R, followed by GO and KEGG pathway enrichment analyses. To construct a protein–protein interaction (PPI) network, we selected survival-related genes using the log-rank test and single-factor Cox regression analysis. Additionally, we performed LASSO regression analysis, immune infiltration analysis, mutation analysis, and cMAP analysis, as well as an investigation into ferroptosis. Our differential expression and survival analyses identified 47 hub genes, and subsequent LASSO regression analysis refined the focus to 23 key genes. These genes are closely linked to cancer metastasis, proliferation, apoptosis, cell cycle regulation, signal transduction, cancer microenvironment, immunotherapy, and neurodevelopment. Overall, the hub genes discovered in our study are pivotal in colon cancer and are anticipated to serve as important biological markers for the diagnosis and treatment of the disease.

Low-dimensional metal halides with efficient luminescence properties have received widespread attention recently. However, nontoxic and stable low-dimensional metal halides with efficient blue emission are rarely reported. We used a solvothermal synthesis method to synthesize tetravalent zirconium ion-doped all-inorganic zero-dimensional Cs2ZnCl4 for the first time. Bright blue emission in the range of 370 nm–700 nm with a emission maximum at 456 nm was observed in Zr4+:Cs2ZnCl4 accompanied by a large Stokes shift, which was due to self-trapped excitons (STEs) caused by the lattice vibrations of the twisted structure. Simultaneously, the PLQY of Zr4+:Cs2ZnCl4 achieve an impressive 89.67%, positioning it as a compelling contender for future applications in blue-light technology.

Oceanic targets, including ripples, islands, vessels, and coastlines, display distinct sparse characteristics, rendering the ocean a significant arena for sparse Synthetic Aperture Radar (SAR) imaging rooted in sparse signal processing. Deep neural networks (DNNs), a current research emphasis, have, when integrated with sparse SAR, attracted notable attention for their exceptional imaging capabilities and high computational efficiency. Yet, the efficiency of traditional unfolding techniques is impeded by their architecturally inefficient design, which curtails their information transmission capacity and consequently detracts from the quality of reconstruction. This paper unveils a novel Memory-Augmented Deep Unfolding Network (MADUN) for SAR imaging in marine environments. Our methodology harnesses the synergies between deep learning and algorithmic unfolding, enhanced with a memory component, to elevate SAR imaging’s computational precision. At the heart of our investigation is the incorporation of High-Throughput Short-Term Memory (HSM) and Cross-Stage Long-Term Memory (CLM) within the MADUN framework, ensuring robust information flow across unfolding stages and solidifying the foundation for deep, long-term informational correlations. Our experimental results demonstrate that our strategy significantly surpasses existing methods in enhancing the reconstruction of sparse marine scenes.

Martian rocks contain crucial information about the genesis of Mars and the historical evolution of Martian climate change. Consequently, extracting and examining Martian rocks are pivotal in advancing our comprehensive understanding of the red planet. However, the current core drill string is prone to wear and tear, and the samples are susceptible to thermal denaturation. To address these challenges, we introduce two novel types of drill bits, the conical straight junk slot and the conical spiral junk slot, both employing impregnated diamond as the primary material. Comparative experiments were meticulously conducted to evaluate the influence of different junk configurations on drilling parameters, including speed, abrasion resistance, drilling force loading, and sample temperature rise. The findings unequivocally demonstrate the superior performance of the spiral junk slot. Furthermore, simulations were performed to examine the drilling process on basalt using a fixed configuration drill bit, validating the occurrence of the sample temperature rise. The research presented in this paper offers valuable programmatic references and essential data support for future Martian rock coring drilling missions.

The use of automation technology in agriculture has become particularly important as global agriculture is challenged by labor shortages and efficiency gains. The automated process for harvesting apples, an important agricultural product, relies on efficient and accurate detection and localization technology to ensure the quality and quantity of production. Adverse lighting conditions can significantly reduce the accuracy of fruit detection and localization in automated apple harvesting. Based on deep-learning techniques, this study aims to develop an accurate fruit detection and localization method under adverse light conditions. This paper explores the LE-YOLO model for accurate and robust apple detection and localization. The traditional YOLOv5 network was enhanced by adding an image enhancement module and an attention mechanism. Additionally, the loss function was improved to enhance detection performance. Secondly, the enhanced network was integrated with a binocular camera to achieve precise apple localization even under adverse lighting conditions. This was accomplished by calculating the 3D coordinates of feature points using the binocular localization principle. Finally, detection and localization experiments were conducted on the established dataset of apples under adverse lighting conditions. The experimental results indicate that LE-YOLO achieves higher accuracy in detection and localization compared to other target detection models. This demonstrates that LE-YOLO is more competitive in apple detection and localization under adverse light conditions. Compared to traditional manual and general automated harvesting, our method enables automated work under various adverse light conditions, significantly improving harvesting efficiency, reducing labor costs, and providing a feasible solution for automation in the field of apple harvesting.

Logistics services are integral to urban economic activity, and delving into the spatial distribution traits and evolutionary pathways of various kinds of logistics service node facilities (LSNF) is markedly valuable for understanding a city’s functional spatial makeup and refining the spatial layout of logistics services. This study quantitatively and qualitatively analyzes the spatial congregation and spreading characteristics of diverse LSNFs in Wuhan in 2011, 2014, 2017, and 2020, employing kernel density analysis, average nearest neighbor index, mean center, and distance distribution frequency, seeking to characterize the spatial evolution characteristics of LSNF, alongside examining the trends in distances to city cores, principal adjoining roads, and production and consumption sites. The following conclusions were made: (1) Between 2011 and 2020, various types of LSNFs in Wuhan experienced a pattern characterized by the noticeable coexistence of spatial expansion and agglomeration, particularly visible after 2014. The degree of agglomeration is classified in a descending order as follows: CWC, STN, PSN, and PDN. (2) An “absolute diffusion” phenomenon characterizes the distribution of distances between various kinds of LSNFs and city cores or neighboring roads, with the lion’s share of high-frequency distribution zones spreading beyond city cores by 5–10 km, and a majority of the LSNFs being situated within 1 km from adjacent roads. (3) While the LSNF collective exhibits a stronger tendency towards the consumption facet, it reflects a surrounding of industrial production sites on the production facet and locations of manufactured goods consumption on the consumption facet, followed by locations of agricultural product consumption and comprehensive consumption sites.

Colorectal cancer (CRC), a prevalent malignant tumor of the digestive system, ranks as the third and second in global incidence and mortality, respectively, in 2020, with 1.93 million new cases (≈10% of all cancers). There are 940,000 deaths (≈9.4% of all cancers), and the incidence of CRC in younger patients (under 50 years of age) has become a new trend. The pathogenesis of CRC is primarily attributed to a series of genetic and epigenetic abnormalities within normal colonic epithelial cells, coupled with the reshaping of the tumor microenvironment in the surrounding stroma. This process leads to the transformation of colorectal adenomas into invasive adenocarcinomas. Although genetic changes are known to be the primary driving force in the occurrence and progression of CRC, recent research indicates that epigenetic regulation serves as a crucial molecular marker in cancer, playing a significant role in the pathological and physiological control of interactions between genetics and the environment. This review discusses the current global epidemiology of CRC, its risk factors, and preventive treatment strategies. The current study explores the latest advancements in the epigenetic regulation of CRC, including DNA methylation, histone modifications, and non-coding RNAs (ncRNAs). These developments hold potential as screening tools, prognostic biomarkers, and therapeutic targets for CRC.

Efforts to enhance the speed and reduce the energy consumption of underwater vehicles have led to the proposal of a novel mucus release structure inspired by the secretion of mucus cells on fish skin. This structure features interconnected microgrooves with excellent flexibility for adjusting to different states, effectively reducing drag through mucus release. Numerical analysis of the drag reduction performance of the mucous-releasing micro-pore structure was conducted using ANSYS Fluent 19.2 software. This structure is capable of reducing the velocity gradient near the wall and, owing to the presence of micro-pore structures, decreasing the overall compressed area, thereby achieving drag reduction effects. The experimental results revealed a drag reduction effect of 20.56% when the structure was bent at an angle of 120°. The drag reduction varied under different attitudes such as tension and compression. This mucus release structure achieves reusability through a direct mucous injection process. This research provides valuable insights for the drag reduction study of underwater vehicles, such as ships and submarines, laying a foundation for advancing the development and applications of this field in the future.

This study combined the characteristics of rock mass structure, channel characteristics of main and tributary rivers, and flow conditions in the Three Gorges Reservoir area of the Yangtze River and designed and constructed a three-dimensional granular rock shallow-water landslide surge model test system under dynamic water flow conditions. The main influencing variables, including the volume of the landslide body, the inclination of the sliding surface, the water depth of the channel, and the flow velocity, were determined. A four-factor four-level orthogonal test design and a single-factor test design were combined to determine 28 sets of test conditions. Based on existing field surveys and physical model test simulation results, the motion process of the landslide body was divided into four stages: starting, accelerating, slowly accelerating or decelerating, and rapidly decelerating. The variations in the motion and velocity of the landslide body over time were determined by analyzing the image sequence extracted using a high-speed camera. The morphological characteristics of the landslide accumulation body were analyzed, and the submerged rate of the landslide body was statistically calculated. The relationship between the maximum bottom width of the landslide underwater accumulation body and the volume of the underwater accumulation body was established.

The present study focuses on investigating the influence of oil and solid body temperatures on elastohydrodynamic lubrication (EHL) film formation. Experimental and numerical simulation methods are employed to examine three heating methods: oil and ball heating, disc heating, and entire system heating. A preliminary comparison between the measured results and numerical simulations confirms the impact of heating methods on film formation while validating the availability of the numerical models. Further numerical analysis reveals that in the case of oil and ball heating, the temperature gradient induced by differences in solid body temperatures plays a more significant role in film formation compared to the conventional thermal-viscosity wedge effect caused by EHL film shear. This effect is further amplified at large sliding–rolling ratios and in steel–steel contacts. The overall film formation is primarily governed by the oil inlet temperature, whereas local film formation characterized by a dimple shape is influenced by both thermal gradient effects and thermal-viscosity wedge effects. This study provides valuable insights for selecting appropriate heating methods in experiments as well as understanding how temperature differences affect film formation in practical engineering.

As derivatives of metal halide perovskite materials, low-dimensional metal halide materials have become important materials that have attracted much attention in recent years. As one branch, zinc-based metal halides have the potential for practical applications due to their lead-free, low-toxicity and high-stability characteristics. However, pure zinc-based metal halide materials are still limited by their poor optical properties and cannot achieve large-scale practical applications. Therefore, in this work, we report an organic–inorganic hybrid zero-dimensional zinc bromide, (TDMP)ZnBr4, using transition metal Mn2+ ions as dopants and incorporating them into the (TDMP)ZnBr4 lattice. The original non-emissive (TDMP)ZnBr4 exhibits bright green emission under the excitation of external UV light after the introduction of Mn2+ ions with a PL peak position located at 538 nm and a PLQY of up to 91.2%. Through the characterization of relevant photophysical properties and the results of theoretical calculations, we confirm that this green emission in Mn2+:(TDMP)ZnBr4 originates from the 4T1 → 6A1 optical transition process of Mn2+ ions in the lattice structure, and the near-unity PLQY benefits from highly localized electrons generated by the unique zero-dimensional structure of the host material (TDMP)ZnBr4. This work provides theoretical guidance and reference for expanding the family of zinc-based metal halide materials and improving and controlling their optical properties through ion doping.

Lead halide perovskites have been widely used in optoelectronic devices due to their excellent properties; however, the toxicity of lead and the poor stability of these perovskites hinder their further application. Herein, we report a zero-dimensional (0D) lead-free organic manganese (II) bromide hybrid compound of (TBA)2MnBr4 (TBA+ = tetrabutylammonium cation) single crystals (SCs) with great environmental stability. The (TBA)2MnBr4 SCs show a strong green emission peak at 518 nm with a high photoluminescence quantum yield (PLQY) of 84.98% at room temperature, which is attributed to the d-d transition of single Mn2+ ions, as also confirmed through density functional calculation. A green light-emitting diode was produced based on (TBA)2MnBr4 SCs, which exhibited CIE coordinates (0.17, 0.69) close to those of standard green. A photodetector fabricated by the (TBA)2MnBr4 SCs shows an obvious photo response with a rapid millisecond rise/decay response time (at 365 nm). Our findings promote the research of Mn(II)-based organic–inorganic hybrid materials and pave the way by using these materials for future high-performance optoelectronic devices.

Rapid construction of versatile perfusable vascular networks in vitro with cylindrical channels still remains challenging. Here, a microfiber-patterned method is developed to precisely fabricate versatile well-controlled perfusable vascular networks with cylindrical channels. This method uses tensile microfibers as an easy-removable template to rapidly generate cylindrical-channel chips with one-dimensional, two-dimensional, three-dimensional and multilayered structures, enabling the independent and precise control over the vascular geometry. These perfusable and cytocompatible chips have great potential to mimic vascular networks. The inner surfaces of a three-dimensional vascular network are lined with the human umbilical vein endothelial cells (HUVECs) to imitate the endothelialization of a human blood vessel. The results show that HUVECs attach well on the inner surface of channels and form endothelial tubular lumens with great cell viability. The simple, rapid and low-cost technique for versatile perfusable vascular networks offers plenty of promising opportunities for microfluidics, tissue engineering, clinical medicine and drug development.

The assemblage of oceanic islands and seamounts, arising from the widespread presence of mature oceans, plays a crucial role in reconstructing the evolutionary history of the paleoocean. Oceanic islands or seamounts within the Longmuco-Shuanghu metamorphic complex, a remnant of the Paleo-Tethys Ocean in the central Tibetan Plateau, have seldom been reported due to their remoteness. This study has identified an oceanic island-seamount in the Maoershan area, situated to the west of the Longmuco-Shuanghu metamorphic complex, composed of basalt, diabase, limestone, and siliceous rocks. Based on field observations, petrology, zircon U-Pb dating, whole-rock geochemistry, and Sr-Nd isotopes analyses, we have identified a suite of mafic rocks with OIB affinity. The youngest zircon U-Pb age cluster was concentrated at ~243–241 Ma. The geochemical characteristics of the siliceous rocks indicate a mixture of terrigenous material, suggesting that they formed in a continental margin. In combination with regional geological data, we conclude that the Longmuco-Shuanghu Paleo-Tethys Ocean remained open during the Middle Triassic. Furthermore, a fraction of the oceanic island-seamounts underwent scraping and transformed into a metamorphic complex, while other segments experienced deep subduction, resulting in the formation of high-pressure metamorphic rocks. Collectively, these processes gave rise to the distinctive high-pressure metamorphic complex within the central Qiangtang terrane.

Single-crystal silicon carbide has excellent electrical, mechanical, and chemical properties. However, due to its high hardness material properties, achieving high-precision manufacturing of single-crystal silicon carbide with an ultra-smooth surface is difficult. In this work, quantum dots were introduced as a sacrificial layer in polishing for pulsed-ion-beam sputtering of single-crystal SiC. The surface of single-crystal silicon carbide with a quantum-dot sacrificial layer was sputtered using a pulsed-ion beam and compared with the surface of single-crystal silicon carbide sputtered directly. The surface roughness evolution of single-crystal silicon carbide etched using a pulsed ion beam was studied, and the mechanism of sacrificial layer sputtering was analyzed theoretically. The results show that direct sputtering of single-crystal silicon carbide will deteriorate the surface quality. On the contrary, the surface roughness of single-crystal silicon carbide with a quantum-dot sacrificial layer added using pulsed-ion-beam sputtering was effectively suppressed, the surface shape accuracy of the Ø120 mm sample was converged to 7.63 nm RMS, and the roughness was reduced to 0.21 nm RMS. Therefore, the single-crystal silicon carbide with the quantum-dot sacrificial layer added via pulsed-ion-beam sputtering can effectively reduce the micro-morphology roughness phenomenon caused by ion-beam sputtering, and it is expected to realize the manufacture of a high-precision ultra-smooth surface of single-crystal silicon carbide.

The one-time application of blended urea (BU), combining controlled-release urea (CRU) and uncoated urea, has proven to be a promising nitrogen (N) management strategy. However, the long-term sustainability of blending urea remains largely unexplored. To assess whether a single application of blended urea could effectively replace split uncoated urea applications, a long-term field experiment was conducted in the North China Plain (NCP). The results indicated that, when compared to common urea (CU) at the optimal N rate (180 kg N ha−1), BU achieved comparable grain yields, N uptake and NUE (61% vs. 62). BU exhibited a 12% higher 0–20 cm soil organic nitrogen stock and a 9% higher soil organic carbon (C) stock. Additionally, BU reduced life–cycle reactive N (Nr) losses and the N footprint by 10%, and lowered greenhouse gas (GHG) emissions and the C footprint by 7%. From an economic analysis perspective, BU demonstrated comparable private profitability and a 3% greater ecosystem economic benefit. Therefore, BU under the optimal N rate has the potential to substitute split applications of common urea in the long–term and can be regarded as a sustainable N management strategy for wheat and maize production in the NCP.

The higher proportion of distributed photovoltaic and lower fossil energy integrated into the power network brings huge challenges in power supply reliability and planning. The distributed photovoltaic planning method based on big data is proposed. According to the impact of stochastic photovoltaics and loads on reliability planning, the probability model of distributed photovoltaic and load is analyzed, and the dynamic capacity–load ratios are presented based on big data. The multi-scenario generation and reduction algorithm of stochastic distributed photovoltaic and load planning is studied, and a source–load scenario matching model is proposed based on big data. According to the big data scenario of source–load, the reliability indexes and dynamic capacity–load ratio may be obtained. Finally, the IEEE 33-bus system is used as an example, and the results show that distributed photovoltaic planning methods based on big data can improve photovoltaic utilization and power supply reliability.

Based on the Quick Access Recorder (QAR) data covering over 9000 routes in China, the monthly and intra-day distribution characteristics of aircraft bumpiness at different levels were analyzed, and the relationships between the eddy dissipation rate (EDR) and other aircraft flight status elements during bumpiness occurrence were also analyzed. Afterward, aircraft bumpiness routes were constructed using 19 machine learning models. The analyses show that (1) aircraft bumpiness was mainly concentrated between 0:00 a.m. and 17:00 p.m. Severe aircraft bumpiness occurred more frequently in the early morning in January, especially between 5:00 a.m. and 6:00 a.m., and moderate bumpiness always occurred from 3:00 a.m. to 11:00 a.m. (2) The relationship between the left and right attack angles and aircraft bumpiness on the routes was more symmetrical, with a center at 0 degrees, unlike in the approach area where the hotspots were mainly concentrated in the range of −5 to 0 degrees. In the approach area, the larger the Mach number, the more severe the bumpiness. (3) The performances of the Automatic Relevance Determination Regression (ARD), Partial Least Squares Regression (PLS), Elastic-Net Regression (ENR), Classification and Regression Tree (CART), Passive Aggressive Regression (PAR), Random Forest (RF), Stochastic Gradient Descent Regression (SGD), and Tweedie Regression (TWD) based models were relatively good, while the performances of the Huber Regression (HUB), Least Angle Regression (LAR), Polynomial Regression (PLN), and Ridge Regressor (RR) based models were very poor. The aircraft bumpiness prediction models performed best over the approach area of ZBDT (airport in Datong), ZULS (airport in Lhasa), ZPPP (airport in Kunming), and ZLQY (airport in Qingyang). The model performed best in predicting the ZLLL-ZBDT air route (flight routes for Lanzhou to Datong) with different prediction times.

As a driving force for regional development, innovation holds an increasing position in regional competitiveness, and a reasonable and coordinated innovation network structure can promote high-quality regional development. Utilizing the modified gravity model and social network analysis method, an innovation network composed of 27 cities in the Yangtze River Delta urban agglomeration from 2010 to 2021 was studied. The following conclusions were founded: (1) The innovation development level in the Yangtze River Delta urban agglomeration was constantly improving, and the innovation development level generally showed a spatial pattern of high in the southeast and low in the northwest. (2) The intensity and density of innovation network correlations in urban agglomerations were increasing, and the centrality of network nodes had an obvious hierarchical characteristic. The innovation network had a significant core–periphery spatial structure, with core cities that had higher centrality, such as Shanghai, Nanjing, and Hangzhou, playing the role of “intermediaries” and “bridges”, while cities with lower centrality, such as Anhui and cities in northern Jiangsu, generally played the role of “periphery actors” in the network. (3) The spatial correlation network of innovation of the Yangtze River Delta urban agglomeration could be divided into four blocks, namely, main benefit, broker, two-way spillover, and net spillover, and the spillover effect among them had obvious gradient characteristics of hierarchy.

In a reservoir area, landslides can generate waves that pose a significant threat to bridge piers, endangering both property and human safety. This study utilized three-dimensional water tank experiments to simulate the generation of landslide-induced waves and their impact on bridge piers located on both riverbanks. The analysis focused on the types and distribution patterns of wave pressures on bridge piers. The results reveal the following key findings: (1) The results show that the wave pressures on the piers can be classified into two types: Pulsating pressure (Ppu) and Resonance pressure (Pre). Ppu represents the pressure generated during the wave action process. Ppu closely corresponds to the wave height-time process, with frequencies ranging from 0.2 to 0.5 Hz. Pre occurs prior to Ppu. Pre represents high-frequency vibrational waves generated when bridge piers resonate during wave action. Pre is observed primarily in deep water conditions and on the opposite riverbank, with frequencies ranging from 300 to 900 Hz. (2) On the bridge piers of the opposite riverbank, Ppu exhibits a nearly vertical distribution along the water depth, while on the same side, Ppu exhibits a sawtooth-like decrease along the water depth. Ppu increases with greater landslide volume and steeper landslide angles, and the maximum wave pressure distribution occurs near the water surface. (3) The distribution of Pre/Ppu along the water depth exhibits three forms: multiple-peak, single-peak, and no-peak, with the maximum positions for all conditions of Pre/Ppu occurring at approximately one-third of the water depth from the surface (Y/h=0.3). Finally, predictive formulas for the maximum wave pressures are provided.

Fused LEO navigation systems have been proposed as a low-cost means of supplementing and backing up global navigation satellite system (GNSS) navigation services based on low-earth orbit (LEO) constellations, which means broadcasting navigation signals based on the spectrum and hardware of the currently planned communication constellation. In this paper, we introduce Doppler-aided positioning to fused LEO navigation systems, which can improve the positioning performance and availability of fused LEO navigation systems by addressing insufficient pseudorange measures caused by insufficient navigation resources or the early stages of system construction. Theoretical analysis and simulation results show that Doppler-aided positioning based on the weighted least squares (WLS) method can improve the positioning accuracy of pseudorange positioning and achieve 95% three-dimensional errors within 21 m, even if the number of pseudorange measurements is less than four. Therefore, Doppler-aided positioning can expand the application scenarios of independent navigation services for fused LEO navigation systems.

Diabetic nephropathy (DN) refers to kidney damage caused by diabetes and is one of the major microvascular complications of diabetes. This disease has a certain degree of concealment in the early stage, with clinical symptoms appearing later and a higher mortality rate. Therefore, the detection of early biomarkers for DN is of great importance in reducing kidney function damage. The common biomarkers for DN mainly include glomerular and tubular lesion markers. At present, clinical diagnosis often uses a combination of multiple indicators and symptoms, and the development of a simple, efficient, and sensitive multi-marker detection platform is particularly important for the early diagnosis of DN. In recent years, with the vigorous development of various biomimetic molecular recognition technologies, biomimetic recognition biosensors (BRBS) have many advantages, such as easy preparation, low cost, high stability, and repeatability under harsh environmental conditions, and have great application potential in the analysis of DN biomarkers. This article reviews the research progress of molecularly imprinted polymers (MIPs) construction technology and aptamers assembly technology developed in the field of biomimetic sensor research in recent years, as well as the detection of DN biomarkers based on BRBS, and prospects for their development.

The effect of flume width (b) on overland flow dynamics was investigated in this study. Experiments were conducted with five different flow discharges and five flume widths (0.05–0.30 m, with an interval of 0.05 m). The findings revealed that a narrow flume had a noticeable impact on flow acceleration as the slope length increased. Relative average deviation (RAD) was calculated to evaluate the influence of sidewall effects on flow velocity. The coefficient of variation in the RAD ranged from 1.90% to 3.65%. The RAD has extremely significant differences between different widths when the flow is 0.02–0.08 m2/min. The significant differences in the RAD at different widths decrease as the flow rate increases. The flow regime was evaluated using the ratio of the thickness of the viscous sublayer to the water depth (δ/h), which proved to be a better indicator than the Reynolds number for studying flow regimes in different flume widths. Furthermore, it was observed that the energy variation was smaller in narrow flumes (b = 0.5–0.10 m) compared to wider flumes (b = 0.25–0.30 m). When the flume width ranged from 0.15 to 0.30 m, the specific energy change increased. These results contribute to further understanding of the hydraulic characteristics of overland flow and provide theoretical references for optimizing experimental design.

Researchers have previously investigated the role of Trichopria drosophilae as a pupal parasitoid in the biological control of Drosophila suzukii in China. Here, we investigated the ability of T. drosophilae to parasitize D. suzukii pupae at different temperatures. To do this, we evaluated the functional response of T. drosophilae to D. suzukii pupae at different temperatures and investigated the specific effects of density on parasitism. The results show that the parasitic functional response of T. drosophilae under different high-temperature stimuli is Holling type II. After processing at 29 °C, the instantaneous search rate was 1.1611; the theoretical maximum parasitic value was 20.88 at 31 °C. The parasitic efficiency decreased with increasing stimulation temperature, as the host pupa density increased from 5 to 25, and the strongest search effect occurred at 0.87 at 27 °C. The searching effect of T. drosophilae at each temperature fell gradually with an increase in prey density from 5 to 25. At 31 °C, the theoretical parasitic maximum of T. drosophilae reached a maximum of 20.88 pupae. At this temperature, when a pair of T. drosophilae was placed in a pupa density of 50, its actual total number of parasites was 18.60.

In previous works, inverse design methods have primarily focused on manipulating the optical power to achieve specific design targets. In this paper, we use the inverse design method to enable the precise engineering of the optical phase. As a proof of concept, we present a series of phase shifters (PSs) with varying phase shifts, which are inversely designed and theoretically validated on the silicon-on-insulator (SOI) platform. The designed PSs exhibit remarkable accuracy, with deviations of below 1°. These proposed PSs feature an ultra-compact footprint of 3 µm in length and offer superior fabrication tolerances compared to conventional structures. Over the 1535–1565 nm bandwidth, the discrepancy between the actual and target phase shifts remains below ±1° for all phase shifters, while the insertion loss is consistently below 0.035 dB. Moreover, the feasibility of the designed five PSs is verified using 2 × 2 multimode interference couplers (MMI).

Forest fires are one of the most devastating natural disasters, and technologies based on remote sensing satellite data for fire prevention and control have developed rapidly in recent years. Early forest fire smoke in remote sensing images, on the other hand, is thin and tiny in area, making it difficult to detect. Satellites with high spatial resolution sensors can collect high-resolution photographs of smoke, however the impact of the satellite’s repeat access time to the same area means that forest fire smoke cannot be detected in time. Because of their low spatial resolution, photos taken by satellites with shorter return durations cannot capture small regions of smoke. This paper presents an early smoke detection method for forest fires that combines a super-resolution reconstruction network and a smoke segmentation network to address these issues. First, a high-resolution remote sensing multispectral picture dataset of forest fire smoke was created, which included diverse years, seasons, areas, and land coverings. The rebuilt high-resolution images were then obtained using a super-resolution reconstruction network. To eliminate data redundancy and enhance recognition accuracy, it was determined experimentally that the M11 band (2225–2275 nm) is more sensitive to perform smoke segmentation in VIIRS images. Furthermore, it has been demonstrated experimentally that improving the accuracy of reconstructed images is more effective than improving perceptual quality for smoke recognition. The final results of the super-resolution image segmentation experiment conducted in this paper show that the smoke segmentation results have a similarity coefficient of 0.742 to the segmentation results obtained using high-resolution satellite images, indicating that our method can effectively segment smoke pixels in low-resolution remote sensing images and provide early warning of forest fires.

Tomographic synthetic aperture radar (TomoSAR) obtains elevation resolution by adding multiple baselines successively in the direction perpendicular to the line of sight, thereby realizing three-dimensional (3D) reconstruction of complex scenes and significantly promoting the development of the 3D application field. However, a large data redundancy and long mapping time in traditional 3D imaging lead to a large data transmission burden, low efficiency, and high costs. To solve the above problems, this paper proposes a distributed SAR high-resolution and wide-swath (HRWS) 3D imaging technology scheme. The proposed scheme overcomes the size limitation of traditional single-satellite antennas through the multi-channel arrangement of multiple satellites in the elevation direction to achieve HRWS imaging; meanwhile, the distributed SAR system is integrated with tomographic processing technology to realize 3D imaging of difficult areas by using the elevation directional resolution of TomoSAR systems. HRWS 3D SAR increases the baseline length and channel number by transmission in turn, which leads to excessive pulse repetition frequency and causes echoes of different pulse signals to overlap in the same receiving cycle, resulting in range ambiguity and thus seriously affecting the quality of the 3D reconstruction. To solve this problem, this paper proposes a range ambiguity resolution algorithm based on multi-beam forming and verifies it on the measured data from airborne array SAR. Compared with the traditional TomoSAR, the distributed HRWS 3D SAR scheme proposed in this paper can obtain a greater mapping bandwidth with the same resolution in a single flight, thereby enhancing the time correlation, reducing data redundancy, and greatly improving mapping efficiency.

Reasonable welding speeds are a prerequisite for obtaining high-quality joints by friction stir welding (FSW). In this paper, 2195-T8 Al-Li alloy FSW joints were successfully fabricated at different welding speeds (100–600 mm/min) with a constant rotation speed. The effect of welding speed on the microstructure and mechanical properties was analyzed under different experimental methods. Microstructural characterization was conducted using optical microscopy (OM), scanning electron microscopy (SEM), electron backscattered diffraction (EBSD) and transmission electron microscopy (TEM). Mechanical properties were measured by a hardness test and a tensile test. The results showed that the original T1 precipitates disappeared in the nugget zone (NZ), generating many dislocations. With welding speeds increasing, joints obtained at lower welding speeds developed coarser T1 precipitates in the heat-affected zone. Also, the equiaxed grains with a bigger size and a higher fraction of high angle boundaries (HABs) were detected in the NZ of these joints. As the welding speed increased, the area of hardness value changes gradually shrunk, which was consistent with the trend of the cross-section morphology. A kissing bond and macroscopic cracking were observed in the joints that were fabricated at the higher welding speeds. The appearance of those defects significantly reduced the ultimate tensile strength and elongation of the joints at high welding speeds. Fracture morphologies of the different joints were all characterized in quasi-cleavage fractures.

(1) Ferroptosis is a type of cellular death caused by lipid-dependent iron peroxide, which plays a major role in cancer. Long noncoding RNAs (lncRNAs) are increasingly recognized as key regulating substances in ferroptosis; (2) RNA sequencing expressions and clinical data of 519 patients with colon adenocarcinoma (COAD) were downloaded from The Cancer Genome Atlas (TCGA) database. The expression levels of lncRNAs related to ferroptosis were screened with Pearson correlation analysis. Differential genes were enriched with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. LncRNAs related to ferroptosis were determined with univariate Cox regression and multivariate Cox regression analyses, and patients with COAD were classified into high- and low-risk subgroups according to their median risk score. The prognostic value was further examined, and the association between ferroptosis-related lncRNAs (frlncRNAs) and survival in patients with high and low risks of COAD was validated. A TCGA–COAD data set was used for receiver operating characteristic (ROC) analysis and detrended correspondence analysis (DCA) to assess prediction accuracy. Finally, a nomogram was constructed to predict survival probability; (3) We obtained a model consisting of a five-frlncRNAs signature comprising AP003555.1, AP001469.3, ITGB1-DT, AC129492.1, and AC010973.2 for determining the overall survival (OS) of patients with COAD. The survival analysis and ROC curves showed that the model had good robustness and predictive performance on the TCGA training set; (4) We found that a five-frlncRNAs signature may play a potential role in anti-COAD immunity. Risk characteristics based on frlncRNAs can accurately predict the prognosis and immunotherapy response of patients with COAD.

Breast cancer continuously poses serious clinical challenges to human health due to its intrinsic heterogenicity and evolving drug resistance. Recently, increasing evidence has shown that pyroptosis, known as a programmed and inflammatory form of cell death, participates in tumorigenesis, progression, and remodeling of the tumor immune microenvironment (TIME). However, a comprehensive insight into pyroptosis-related signatures for breast cancer remains elusive. The current study established a pyroptosis-related lncRNA signature using transcriptome data and corresponding clinical information from The Cancer Genome Atlas (TCGA). Pyroptosis-related gene clusters, the associated differential expression in breast cancer patients’ subtypes, and the potential mechanisms were all discussed. This integrative analysis revealed a unique signature underpinning the dichotomy of breast cancer progression and survival outcomes. Interestingly, the pyroptosis-related lncRNA signature was revealed as closely intertwined with the TIME. A correlation was established between the pyroptosis-related LncRNA signature and the TIME, underlying the mutual effect between pyroptosis and the immune responses implicated in breast cancer. The findings in this work underline the critical role exerted by pyroptosis in breast cancer, providing new insights into disease progression, prognosis, and therapeutic potential. This work has been poised to provide new avenues for personalized, immune-based cancer therapeutics by enhancing our understanding of pyroptosis in breast cancer.

To provide reliable input information for the load design and extraction of lunar soil water ice samples, it is necessary to study the water content distribution and water migration of simulated lunar soil water ice samples. On this basis, the temperature field model and the hydrothermal coupling relationship are proposed. The temperature field model was constructed by combining energy conservation and Fourier’s heat transfer law. The coupling relationship was established, and the hydrothermal coupling model was obtained by testing the unfrozen water content using the nuclear magnetic resonance method. Finite element software was used to solve the model numerically, and the water migration rule of the soil water ice samples at different ambient temperatures were analyzed. Thin-wall drilling tests were carried out on the simulated lunar soil water ice samples to obtain water content data for different locations, and the simulation results were verified. Due to the migration effect of the cold end of the water, the closer we tested to the edge of the sample, the higher the water content was. The higher the ambient temperature was, the more pronounced the water migration phenomenon of the whole sample was. These research results provide a basis for sampling scheme design.

As typical environmental endocrine disruptors and nonsteroidal anti-inflammatory drugs, bisphenol A and ibuprofen in water supplies can cause great harm to the ecological environment and human health. In this study, magnetic covalent organic framework composites Fe3O4@COF-300 were synthesized by the hydrothermal method and used to remove bisphenol A and ibuprofen from water. Fe3O4@COF-300 could be rapidly separated from the matrix by external magnetic fields, and could selectively adsorb bisphenol A and ibuprofen in the presence of coexisting compounds such as phenol, Congo red, and amino black 10B. The removal efficiency of ibuprofen was 96.12–98.52% at pH in the range of 2–4 and that of bisphenol A was 92.18–95.62% at pH in the range of 2–10. The adsorption of bisphenol A and ibuprofen followed a pseudo-second-order kinetic and Langmuir model, and was a spontaneous endothermic process with the maximum adsorption amounts of 173.31 and 303.03 mg∙g−1, respectively. The material presented favorable stability and reusability, and the removal efficiency of bisphenol A and ibuprofen after 5 cycles was still over 92.15% and 89.29%, respectively. Therefore, the prepared composite Fe3O4@COF-300 exhibited good performance in the adsorption of bisphenol A and ibuprofen in water.

Fused silica has become the preferred optical material in the field of inertial confinement fusion (ICF) due to its excellent performance; however, these costly optical elements are vulnerable, and their manufacture is time-consuming. Therefore, the restoration of laser-induced damage for these optical elements is of great value. To restrain the post-restoration raised rim problem in the CO2 laser repair process to improve the restoration quality, the separate influences of key parameters of laser power, irradiation duration, and laser beam diameter on post-restoration pit morphology are compared in combined simulation and experimental studies. An optimized, patterned CO2 laser strategy is proposed and verified; the results indicate that, with the strategy, the rim height decreases from 2.6 μm to 1.52 μm, and maximal photo thermal absorption is decreased from 784.2 PPM to 209.43 PPM.

At present, the packing method of “plastic bag–buffer packing–packing paper box” is adopted for bearing packaging. However, the common packing method has a poor packing effect and poor versatility. In this study, a new biomimetic cobweb cushion is proposed to solve the problem of insufficient cushioning capacity of high-precision bearing cushion packaging pads. First, according to the nature of cobweb form, the cobweb cushion structure configuration is determined. Next, based on the structure of the cushion and the relationship between the parameters of radial thread and spiral thread, a mechanical and target optimization model is established. The stress nephogram of bearing and the cobweb cushion are analyzed under three drop heights of 381, 610, and 700 mm, in the finite element simulation software to ensure that the maximum bearings stress is not beyond the material yield strength. Via the 3D printing technology, a cobweb cushion shell cast is made. Drop tests of the bearing were performed, and the results were verified with the finite element simulation analysis. This research can provide technical support for the protection of high-precision bearings from accidental drops during transportation.

While numerous studies have suggested that pyrolysis of the forestry residues to produce biochar and return it back for forestry use is an effective way for the utilization of forestry wastes, there have been few reports addressing the effect of pyrolysis temperature on its agronomic functions, especially the secondary metabolism of the medicinal tree. In this study, two kinds of biochar were pyrolyzed using poplar sawdust at 350 °C (C350) and 600 °C (C600), respectively, then applied as auxiliary substrate material for the cultivation of Cyclocarya paliurus seedlings in a greenhouse to study their effects on soil properties, seedling growth and the accumulation of main secondary metabolites in C. paliurus leaves. The results showed that biochar application definitely ameliorated most of the soil properties, and this improvement was further enhanced as the pyrolysis temperature of the biochar increased. Compared with the basic substrate (CK), the growth of C. paliurus seedlings increased under C600 and decreased under C350, but the application of two biochars had no significant effect on the content of the main secondary metabolites in the leaves. In contrast, the seedling growth with the direct application of poplar sawdust (C0) was significantly inhibited, while the content of secondary metabolites in the leaves increased significantly. In terms of the yield of leaf secondary metabolites, the application of biochar with high pyrolysis temperature (C600) had the highest yield, while the direct application of poplar sawdust had the lowest yield. In addition, the contents of several metabolite monomers were higher under C0 than these of the other three treatments. The results indicated a certain trade-off between the primary growth and the synthesis of leaf secondary metabolites in C. paliurus affected by different biochars and poplar sawdust application, while the accumulation of leaf secondary metabolites was generally dependent on leaf biomass.