Effects of Nitrogen Addition on the Mixed Litter Decomposition in Stipa baicalensis Steppe in Inner Mongolia

During the past two centuries, global changes (i.e., enhanced nitrogen deposition) have exerted profound effects on ecological processes of steppe ecosystems. We used litterbag method and mixed litters of three different plant species tissues (Stipa baicalensis: Sb, Leymus chinensis: Lc and Artemisia frigid: Af), endemic to Stipa baicalensis Steppe, and measured the mass loss of mixtures over 417 days under the N addition treatment. We studied the effect of N addition (N0: no N addition;N15: 1.5 g N/m2·a;N30: 3.0 g N/m2·a;N50: 5.0 g N/m2·a;N100: 10.0 g N/m2·a;N150: 15.0 g N/m2·a) on the rate of mixed litter decomposition and nutrient dynamics change. The decomposition constant (k) of leaf mixtures was higher than that of root mixtures. The k values of leaf mixed combinations were 0.880 (Sb + Lc), 1.231 (Lc + Af), 1.027 (Sb + Lc + Af), respectively. The k value of stem was 0.806 (Lc + Af) and the root mixed combinations were 0.665 (Sb + Lc), 0.979 (Lc + Af) and 1.164 (Sb + Lc + Af), respectively. The results indicated that N addition had significantly effect on the mixed litter decomposition and nutrient releasing. The rate of plant tissues litter decomposition had different response to N addition. In the context of N addition, litter decomposition rate and nutrient dynamics were changed by synthetic effect of decaying time, specie types and N addition dose. Our findings suggested that prairie plants may adapt to environmental change by adjusting litter quality, thus retaining the stability of the steppe ecosystem.

Investigations of the mechanical response of dummy HTPB propellant grain under ultrahigh acceleration overload conditions using onboard flight-test measurements

In this paper,to study the mechanical responses of a solid propellant subjected to ultrahigh acceleration overload during the gun-launch process,specifically designed projectile flight tests with an onboard measurement system were performed.Two projectiles containing dummy HTPB propellant grains were successfully recovered after the flight tests with an ultrahigh acceleration overload value of 8100 g.The onboard-measured time-resolved axial displacement,contact stress and overload values were successfully obtained and analysed.Uniaxial compression tests of the dummy HTPB propellant used in the gunlaunched tests were carried out at low and intermediate strain rates to characterize the propellant's dynamic properties.A linear viscoelastic constitutive model was employed and applied in finite-element simulations of the projectile-launching process.During the launch process,the dummy propellant grain exhibited large deformation due to the high acceleration overload,possibly leading to friction between the motor case and propellant grain.The calculated contact stress showed good agreement with the experimental results,though discrepancies in the overall displacement of the dummy propellant grain were observed.The dynamic mechanical response process of the dummy propellant grain was analysed in detail.The results can be used to estimate the structural integrity of the analysed dummy propellant grain during the gun-launch process.

Prospective Cohort Investigation on Physical Activity of Osteoporosis Outcomes(PAOPO)in Jidong:Objectives,Study Design,and Baseline Characteristics

Objective The aim of this study was to investigate the prospective association between physical activity(PA),independently or in conjunction with other contributing factors,and osteoporosis(OP)outcomes.Methods The Physical Activity in Osteoporosis Outcomes(PAOPO)study was a community-based cohort investigation.A structured questionnaire was used to gather the participants’sociodemographic characteristics.Bone mineral density(BMD)measurements were performed to assess OP outcomes,and the relationship between BMD and OP was evaluated within this cohort.Results From 2013 to 2014,8,471 participants aged 18 years and older were recruited from Tangshan,China’s Jidong community.Based on their PA level,participants were categorized as inactive,moderately active,or very active.Men showed higher physical exercise levels than women across the activity groups.BMD was significantly higher in the very active group than in the moderately active and inactive groups.Individuals aged>50 years are at a higher risk of developing OP and osteopenia.Conclusion The PAOPO study offers promising insights into the relationship between PA and OP outcomes,encouraging the implementation of PA in preventing and managing OP.

A DNA tetrahedron-based ferroptosis-suppressing nanoparticle: superior delivery of curcumin and alleviation of diabetic osteoporosis

Diabetic osteoporosis(DOP)is a significant complication that poses continuous threat to the bone health of patients with diabetes;however,currently,there are no effective treatment strategies.In patients with diabetes,the increased levels of ferroptosis affect the osteogenic commitment and differentiation of bone mesenchymal stem cells(BMSCs),leading to significant skeletal changes.To address this issue,we aimed to target ferroptosis and propose a novel therapeutic approach for the treatment of DOP.We synthesized ferroptosis-suppressing nanoparticles,which could deliver curcumin,a natural compound,to the bone marrow using tetrahedral framework nucleic acid(tFNA).This delivery system demonstrated excellent curcumin bioavailability and stability,as well as synergistic properties with tFNA.Both in vitro and in vivo experiments revealed that nanoparticles could enhance mitochondrial function by activating the nuclear factor E2-related factor 2(NRF2)/glutathione peroxidase 4(GPX4)pathway,inhibiting ferroptosis,promoting the osteogenic differentiation of BMSCs in the diabetic microenvironment,reducing trabecular loss,and increasing bone formation.These findings suggest that curcumin-containing DNA tetrahedron-based ferroptosissuppressing nanoparticles have a promising potential for the treatment of DOP and other ferroptosis-related diseases.

Dynamic modeling of spacecraft solar panels deployment with Lie group variational integrator

The spacecraft with multistage solar panels have nonlinear coupling between attitudes of central body and solar panels, especially the rotation of central body is considered in space. The dynamics model is based for dynamics analysis and control, and the multistage solar panels means the dynamics modeling will be very complex. In this research, the Lie group variational integrator method is introduced, and the dynamics model of spacecraft with solar panels that connects together by flexible joints is built. The most obvious character of this method is that the attitudes of central body and solar panels are all described by three-dimensional attitude matrix. The dynamics models of spacecraft with one and three solar panels are established and simulated. The study shows Lie group variational integrator method avoids parameters coupling and effectively reduces difficulty of modeling. The obtained continuous dynamics model based on Lie group is a set of ordinary differential equations and equivalent with traditional dynamics model that offers a basis for the geometry control.

Analysis of the Chemical Safety Facility Investment Performance in China

This paper adopts the accident incidence, the gross industry output value, the investment in safety facilities, and per capita wage of employment as the indexes to empirically analyze the investment performance of chemical safety facilities using time series data by VECM in China. The empirical results indicate that for China’s chemical industry, increasing investment fails to improve the short-term safety level significantly because of the offsetting behavior of workers. Over the long term, the offsetting behavior tends to disappear, and the chemical accident incidence can be decreased through increasing investment. Poor safety awareness among workers is one of the causes of accident incidences. The conclusions provide theoretical support for China to perfect chemical industry safety management.

Design and Control of an Autonomous Bat-like Perching UAV

Perching allows small Unmanned Aerial Vehicles(UAVs)to maintain their altitude while significantly extending their flight duration and reducing noise.However,current research on flying habitats is poorly adapted to unstructured environments,and lacks autonomous capabilities,requiring ideal experimental environments and remote control by personnel.To solve these problems,in this paper,we propose a bat-like UAV perching mechanism by investigating the bat upside-down perching method,which realizes double self-locking in the perching state using the ratchet and four-link dead point mechanisms.Based on this perching mechanism,this study proposes a control strategy for UAVs to track targets and accomplish flight perching autonomously by combining a binocular camera,single-point LiDAR,and pressure sensors.Autonomous perching experiments were conducted for crossbar-type objects outdoors.The experimental results show that a multirotor UAV equipped with the perching mechanism and sensors can reliably achieve autonomous flight perching and re-flying off the target outdoors.The power consumption is reduced to 2.9%of the hovering state when perched on the target object.

Generation artificial intelligence(GenAI)and Biomaterials Translational:steering innovation without misdirection

In the dynamic landscape of generative artificial intelligence(GenAI),recent developments,such as the artificial research organisation OpenAI’s introduction of the text-to-video generation tool Sora,have again catapulted GenAI into the limelight.Thus reigniting discussions on the swift march towards an era of future controlled usage of artificial general intelligence(AGI).Concurrently,in the realm of scientific research,the use of GenAI tools like DALL·E to generate inaccurate scientific illustrations for publication and the skepticism surrounding findings published in Nature from the AI-driven automated laboratory,A-Lab,have sparked widespread scientific controversy1.

高性能壳聚糖基准固态离子热原电池构建及其热电性能研究

利用高分子基准固态离子热原电池将低品质热能转化为电能是提升能源综合利用的有效途径.以壳聚糖为基体,通过接枝双氰胺和胍盐离子,制备了兼具高离子电导率和高热功率的双胍盐壳聚糖-FeCl_(2/3)基离子型热电材料(CGH-G),并使材料的柔性和尺寸稳定性均有所增强.通过引入2种带正电荷的氨基离子,显著增强了离子热电材料的热扩散效应,使其热功率由2.16 mV/K提升至4.84 mV/K,同时显著降低了热电材料的阻抗.将所制备的壳聚糖基离子热电材料组装成准固态离子热原电池,在温差为25 K、外加5Ω负载的环境条件下,其功率密度达1.33 W/m^(2)的同时可实现25.43 kJ/m^(2)的高能量密度输出.此外,多个柔性离子热原电池串联后展现出较高的输出稳定性,显示出壳聚糖基离子热电材料在废弃能源利用方面的广阔应用前景.

Bioactive elements manipulate bone regeneration

While bone tissue is known for its inherent regenerative abilities,various pathological conditions and trauma can disrupt its meticulously regulated processes of bone formation and resorption.Bone tissue engineering aims to replicate the extracellular matrix of bone tissue as well as the sophisticated biochemical mechanisms crucial for effective regeneration.Traditionally,the field has relied on external agents like growth factors and pharmaceuticals to modulate these processes.Although efficacious in certain scenarios,this strategy is compromised by limitations such as safety issues and the transient nature of the compound release and half-life.Conversely,bioactive elements such as zinc(Zn),magnesium(Mg)and silicon(Si),have garnered increasing interest for their therapeutic benefits,superior stability,and reduced biotic risks.Moreover,these elements are often incorporated into biomaterials that function as multifaceted bioactive components,facilitating bone regeneration via release on-demand.By elucidating the mechanistic roles and therapeutic efficacy of the bioactive elements,this review aims to establish bioactive elements as a robust and clinically viable strategy for advanced bone regeneration.

Vehicle color recognition based on smooth modulation neural network with multi-scale feature fusion

Vehicle Color Recognition(VCR)plays a vital role in intelligent traffic management and criminal investigation assistance.However,the existing vehicle color datasets only cover 13 classes,which can not meet the current actual demand.Besides,although lots of efforts are devoted to VCR,they suffer from the problem of class imbalance in datasets.To address these challenges,in this paper,we propose a novel VCR method based on Smooth Modulation Neural Network with Multi-Scale Feature Fusion(SMNN-MSFF).Specifically,to construct the benchmark of model training and evaluation,we first present a new VCR dataset with 24 vehicle classes,Vehicle Color-24,consisting of 10091 vehicle images from a 100-hour urban road surveillance video.Then,to tackle the problem of long-tail distribution and improve the recognition performance,we propose the SMNN-MSFF model with multiscale feature fusion and smooth modulation.The former aims to extract feature information from local to global,and the latter could increase the loss of the images of tail class instances for training with class-imbalance.Finally,comprehensive experimental evaluation on Vehicle Color-24 and previously three representative datasets demonstrate that our proposed SMNN-MSFF outperformed state-of-the-art VCR methods.And extensive ablation studies also demonstrate that each module of our method is effective,especially,the smooth modulation efficiently help feature learning of the minority or tail classes.Vehicle Color-24 and the code of SMNN-MSFF are publicly available and can contact the author to obtain.

Optimal Variable Stiffness Control and Its Applications in Bionic Robotic Joints:A Review

Variable Stiffness Actuation(VSA)is an efficient,safe,and robust actuation technology for bionic robotic joints that have emerged in recent decades.By introducing a variable stiffness elastomer in the actuation system,the mechanical-electric energy conversion between the motor and the load could be adjusted on-demand,thereby improving the performance of the actuator,such as the peak power reduction,energy saving,bionic actuation,etc.At present,the VSA technology has achieved fruitful research results in designing the actuator mechanism and the stiffness adjustment servo,which has been widely applied in articulated robots,exoskeletons,prostheses,etc.However,how to optimally control the stiffness of VSAs in different application scenarios for better actuator performance is still challenging,where there is still a lack of unified cognition and viewpoints.Therefore,from the perspective of optimal VSA performance,this paper first introduces some typical structural design and servo control techniques of common VSAs and then explains the methods and applications of the Optimal Variable Stiffness Control(OVSC)approaches by theoretically introducing different types of OVSC mathematical models and summarizing OVSC methods with varying optimization goals and application scenarios or cases.In addition,the current research challenges of OVSC methods and possible innovative insights are also presented and discussed in-depth to facilitate the future development of VSA control.

Converging technologies in biomaterial translational research

In the realm of scientific innovation,the study of biomaterials emerges as a field of profound significance,bridging the gap between theoretical exploration and translational application.1 The essence of biomaterial research lies not only in understanding the intricate relationships between biological systems and materials but more importantly,in the translational potential these materials hold.2 The true value of this research unfolds in its application-from regenerative medicine to bioengineered solutions,where these materials become pivotal in addressing some of the most pressing clinical challenges.Meanwhile,the necessity for translating laboratory research into real-world applications has become increasingly urgent,as global ageing intensifies and public attention to health concerns grows.

电商商品嵌入表示分类方法

借鉴近些年来在自然语言处理领域卓有成效的一种词嵌入模型word2vec,提出两种商品嵌入表示模型item2vec和w-item2vec。提出的两种模型通过对用户在每次购买时对商品的比较和选择行为进行建模,将商品表示为一个低维空间的向量,该向量可以有效地对不同商品之间的关系和性质进行度量。应用这一性质,使用item2vec和w-item2vec得到的向量对商品进行分类,试验结果表明:在仅使用10%数据训练的基础上,w-item2vec对商品分类的准确率可以接近50%。两种模型分类准确性均显著优于其他模型。

Endothelium-Mimicking Multifunctional Coating Modified Cardiovascular Stents via a Stepwise Metal-Catechol-(Amine)Surface Engineering Strategy

Stenting is currently the major therapeutic treatment for cardiovascular diseases.However,the nonbiogenic metal stents are inclined to trigger a cascade of cellular and molecular events including inflammatory response,thrombogenic reactions,smooth muscle cell hyperproliferation accompanied by the delayed arterial healing,and poor reendothelialization,thus leading to restenosis along with late stent thrombosis.To address prevalence critical problems,we present an endothelium-mimicking coating capable of rapid regeneration of a competently functioning new endothelial layer on stents through a stepwise metal(copper)-catechol-(amine)(MCA)surface chemistry strategy,leading to combinatorial endothelium-like functions with glutathione peroxidase-like catalytic activity and surface heparinization.Apart from the stable nitric oxide(NO)generating rate at the physiological level(2:2×10^(-10) mol/cm^(2)/min lasting for 60 days),this proposed strategy could also generate abundant amine groups for allowing a high heparin conjugation efficacy up to∼1μg/cm^(2),which is considerably higher than most of the conventional heparinized surfaces.The resultant coating could create an ideal microenvironment for bringing in enhanced antithrombogenicity,anti-inflammation,anti-proliferation of smooth muscle cells,re-endothelialization by regulating relevant gene expressions,hence preventing restenosis in vivo.We envision that the stepwise MCA coating strategy would facilitate the surface endothelium-mimicking engineering of vascular stents and be therefore helpful in the clinic to reduce complications associated with stenosis.

Review of Recent Progress in Robotic Knee Prosthesis Related Techniques:Structure,Actuation and Control

As the essential technology of human-robotics interactive wearable devices,the robotic knee prosthesis can provide above-knee amputations with functional knee compensations to realize their physical and psychological social regression.With the development of mechanical and mechatronic science and technology,the fully active knee prosthesis that can provide subjects with actuating torques has demonstrated a better wearing performance in slope walking and stair ascent when compared with the passive and the semi-active ones.Additionally,with intelligent human-robotics control strategies and algorithms,the wearing effect of the knee prosthesis has been greatly enhanced in terms of stance stability and swing mobility.Therefore,to help readers to obtain an overview of recent progress in robotic knee prosthesis,this paper systematically categorized knee prostheses according to their integrated functions and introduced related research in the past ten years(2010−2020)regarding(1)mechanical design,including uniaxial,four-bar,and multi-bar knee structures,(2)actuating technology,including rigid and elastic actuation,and(3)control method,including mode identification,motion prediction,and automatic control.Quantitative and qualitative analysis and comparison of robotic knee prosthesis-related techniques are conducted.The development trends are concluded as follows:(1)bionic and lightweight structures with better mechanical performance,(2)bionic elastic actuation with energy-saving effect,(3)artificial intelligence-based bionic prosthetic control.Besides,challenges and innovative insights of customized lightweight bionic knee joint structure,highly efficient compact bionic actuation,and personalized daily multi-mode gait adaptation are also discussed in-depth to facilitate the future development of the robotic knee prosthesis.

Design and Experiment of a Deformable Bird-inspired UAV Perching Mechanism

Energy consumption and acoustic noise can be significantly reduced through perching in the sustained flights of small Unmanned Aerial Vehicles(UAVs).However,the existing flying perching robots lack good adaptability or loading capacity in unstructured environments.Aiming at solving these problems,a deformable UAV perching mechanism with strong adaptability and high loading capacity,which is inspired by the structure and movements of birds'feet,is presented in this paper.Three elastic toes,an inverted crank slider mechanism used to realize the opening and closing movements,and a gear mechanism used to deform between two configurations are included in this mechanism.With experiments on its performance towards different objects,Results show that it can perch on various objects reliably,and its payload is more than 15 times its weight.By integrating it with a quadcopter,it can perch on different types of targets in outdoor environments,such as tree branches,cables,eaves,and spherical lamps.In addition,the energy consumption of the UAV perching system when perching on objects can be reduced to 0.015 times that of hovering.

Extremes of threshold-dependent Gaussian processes

In this paper,we are concerned with the asymptotic behavior,as u→∞,of P{sup_t∈|0,T|X_u(t)>u},where X_u(t),t∈|0,T|,u>0 is a family of centered Gaussian processes with continuous trajectories.A key application of our findings concerns P{sup_t∈|0,T|(X(t)+g(t))>u},as u→∞,for X a centered Gaussian process and g some measurable trend function.Further applications include the approximation of both the ruin time and the ruin probability of the Brownian motion risk model with constant force of interest.

Bone Fracture Reduction Surgery-aimed Bone Connection Robotic Hand

Bone connection with robot is an important topic in the research of robot assisted fracture reduction surgery.With the method to achieve bone-robot connection in current robots,requirements on reliability and low trauma can not be satisfied at the same time.In this paper,the design,manufacturing,and experiments of a novel Bone Connection Robotic Hand(BCRH)with variable stiffness capability are carried out through the bionics research on human hand and the principle of particle jamming.BCRH’s variable stiffness characteristic is a special connection between“hard connection”and“soft connection”,which is different from the existing researches.It maximizes the reliability of bone-robot connection while minimizes trauma,meets the axial load requirement in clinical practice,and effectively shortens the operating time to less than 40 s(for mode 1)or 2 min(for mode 2).Meanwhile,a theoretical analysis of bone-robot connection failure based on particle jamming is carried out to provide references for the research in this paper and other related studies.

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of thin-walled structural components

The use of artificial intelligence to process sensor data and predict the dimensional accuracy of machined parts is of great interest to the manufacturing community and can facilitate the intelligent production of many key engineering components.In this study,we develop a predictive model of the dimensional accuracy for precision milling of thin-walled structural components.The aim is to classify three typical features of a structural component—squares,slots,and holes—into various categories based on their dimensional errors(i.e.,“high precision,”“pass,”and“unqualified”).Two different types of classification schemes have been considered in this study:those that perform feature extraction by using the convolutional neural networks and those based on an explicit feature extraction procedure.The classification accuracy of the popular machine learning methods has been evaluated in comparison with the proposed deep learning model.Based on the experimental data collected during the milling experiments,the proposed model proved to be capable of predicting dimensional accuracy using cutting parameters(i.e.,“static features”)and cutting-force data(i.e.,“dynamic features”).The average classification accuracy obtained using the proposed deep learning model was 9.55%higher than the best machine learning algorithm considered in this paper.Moreover,the robustness of the hybrid model has been studied by considering the white Gaussian and coherent noises.Hence,the proposed hybrid model provides an efficient way of fusing different sources of process data and can be adopted for prediction of the machining quality in noisy environments.