Design and analysis of an advanced thermal management system for the solar close observations and proximity experiments spacecraft

In this paper,the mission and the thermal environment of the Solar Close Observations and Proximity Experiments(SCOPE)spacecraft are analyzed,and an advanced thermal management system(ATMS)is designed for it.The relationship and functions of the integrated database,the intelligent thermal control system and the efficient liquid cooling system in the ATMS are elaborated upon.For the complex thermal field regulation system and extreme space thermal environment,a modular simulation and thermal field planning method are proposed,and the feasibility of the planning algorithm is verified by numerical simulation.A solar array liquid cooling system is developed,and the system simulation results indicate that the temperatures of the solar arrays meet the requirements as the spacecraft flies by perihelion and aphelion.The advanced thermal management study supports the development of the SCOPE program and provides a reference for the thermal management in other deep-space exploration programs.

d-跟踪与遍历性及proximality的关系

主要证明如果动力系统具有■跟踪性质或者■跟踪性质并且具有弱一致几乎周期性,则它是拓扑遍历的.同时得到了渐近平均跟踪性质与proximality的一些关系.

Progress of Proximity Sensors for Potential Applications in Electronic Skins

Recently,electronic skins and fl exible wearable devices have been developed for widespread applications in medical monitoring,artifi cial intelligence,human–machine interaction,and artifi cial prosthetics.Flexible proximity sensors can accurately perceive external objects without contact,introducing a new way to achieve an ultrasensitive perception of objects.This article reviews the progress of fl exible capacitive proximity sensors,fl exible triboelectric proximity sensors,and fl exible gate-enhanced proximity sensors,focusing on their applications in the electronic skin fi eld.Herein,their working mechanism,materials,preparation methods,and research progress are discussed in detail.Finally,we summarize the future challenges in developing fl exible proximity sensors.

Strong Anisotropic Order Parameters at All-Nitride Ferromagnet/Superconductor Interfaces

Proximity effects between superconductors and ferromagnets(SC/FM)hold paramount importance in comprehending the spin competition transpiring at their interfaces.This competition arises from the interplay between Cooper pairs and ferromagnetic exchange interactions.The proximity effects between transition metal nitrides(TMNs)are scarcely investigated due to the formidable challenges of fabricating high-quality SC/FM interfaces.We fabricated heterostructures comprising SC titanium nitride(TiN)and FM iron nitride(Fe_(3)N)with precise chemical compositions and atomically well-defined interfaces.The magnetoresistance of Fe_(3)N/TiN heterostructures shows a distinct magnetic anisotropy and strongly depends on the external perturbations.Moreover,the superconducting transition temperatureT_(C) and critical field of TiN experience notable suppression when proximity to Fe_(3)N.We observe the intriguing competition of interfacial spin orientations near𝑇T_(C)(∼1.25 K).These findings not only add a new materials system for investigating the interplay between superconductor and ferromagnets,but also potentially provide a building block for future research endeavors and applications in the realms of superconducting spintronic devices.

Molecular Mechanisms of Intracellular Delivery of Nanoparticles Monitored by an Enzyme‑Induced Proximity Labeling

Achieving increasingly finely targeted drug delivery to organs,tissues,cells,and even to intracellular biomacromolecules is one of the core goals of nanomedicines.As the delivery destination is refined to cellular and subcellular targets,it is essential to explore the delivery of nanomedicines at the molecular level.However,due to the lack of technical methods,the molecular mechanism of the intracellular delivery of nanomedicines remains unclear to date.Here,we develop an enzyme-induced proximity labeling technology in nanoparticles(nano-EPL)for the real-time monitoring of proteins that interact with intracellular nanomedicines.Poly(lactic-co-glycolic acid)nanoparticles coupled with horseradish peroxidase(HRP)were fabricated as a model(HRP(+)-PNPs)to evaluate the molecular mechanism of nano delivery in macrophages.By adding the labeling probe biotin-phenol and the catalytic substrate H_(2)O_(2)at different time points in cellular delivery,nano-EPL technology was validated for the real-time in situ labeling of proteins interacting with nanoparticles.Nano-EPL achieves the dynamic molecular profiling of 740 proteins to map the intracellular delivery of HRP(+)-PNPs in macrophages over time.Based on dynamic clustering analysis of these proteins,we further discovered that different organelles,including endosomes,lysosomes,the endoplasmic reticulum,and the Golgi apparatus,are involved in delivery with distinct participation timelines.More importantly,the engagement of these organelles differentially affects the drug delivery efficiency,reflecting the spatial–temporal heterogeneity of nano delivery in cells.In summary,these findings highlight a significant methodological advance toward understanding the molecular mechanisms involved in the intracellular delivery of nanomedicines.

Global Solutions to Nonconvex Problems by Evolution of Hamilton-Jacobi PDEs

Computing tasks may often be posed as optimization problems.The objective functions for real-world scenarios are often nonconvex and/or nondifferentiable.State-of-the-art methods for solving these problems typically only guarantee convergence to local minima.This work presents Hamilton-Jacobi-based Moreau adaptive descent(HJ-MAD),a zero-order algorithm with guaranteed convergence to global minima,assuming continuity of the objective function.The core idea is to compute gradients of the Moreau envelope of the objective(which is"piece-wise convex")with adaptive smoothing parameters.Gradients of the Moreau envelope(i.e.,proximal operators)are approximated via the Hopf-Lax formula for the viscous Hamilton-Jacobi equation.Our numerical examples illustrate global convergence.

Magnetic proximity effect in the two-dimensional ε-Fe_(2)O_(3)/NbSe_(2)heterojunction

Two-dimensional(2D)magnet/superconductor heterostructures can promote the design of artificial materials for exploring 2D physics and device applications by exotic proximity effects.However,plagued by the low Curie temperature and instability in air,it is hard to realize practical applications for the reported layered magnetic materials at present.In this paper,we developed a space-confined chemical vapor deposition method to synthesize ultrathin air-stable ε-Fe_(2)O_(3) nanosheets with Curie temperature above 350 K.The ε-Fe_(2)O_(3)/NbSe_(2) heterojunction was constructed to study the magnetic proximity effect on the superconductivity of the NbSe_(2) multilayer.The electrical transport results show that the subtle proximity effect can modulate the interfacial spin–orbit interaction while undegrading the superconducting critical parameters.Our work paves the way to construct 2D heterojunctions with ultrathin nonlayered materials and layered van der Waals(vdW)materials for exploring new physical phenomena.

Two-Stage Client Selection Scheme for Blockchain-Enabled Federated Learning in IoT

Federated learning enables data owners in the Internet of Things(IoT)to collaborate in training models without sharing private data,creating new business opportunities for building a data market.However,in practical operation,there are still some problems with federated learning applications.Blockchain has the characteristics of decentralization,distribution,and security.The blockchain-enabled federated learning further improve the security and performance of model training,while also expanding the application scope of federated learning.Blockchain has natural financial attributes that help establish a federated learning data market.However,the data of federated learning tasks may be distributed across a large number of resource-constrained IoT devices,which have different computing,communication,and storage resources,and the data quality of each device may also vary.Therefore,how to effectively select the clients with the data required for federated learning task is a research hotspot.In this paper,a two-stage client selection scheme for blockchain-enabled federated learning is proposed,which first selects clients that satisfy federated learning task through attribute-based encryption,protecting the attribute privacy of clients.Then blockchain nodes select some clients for local model aggregation by proximal policy optimization algorithm.Experiments show that the model performance of our two-stage client selection scheme is higher than that of other client selection algorithms when some clients are offline and the data quality is poor.

Proximal Femur Bionic Nail (PFBN): A Panacea for Unstable Intertrochanteric Femur Fracture

With the aging population,intertrochanteric femur fracture in the elderly has become one of the most serious public health issues and a hot topic of research in trauma orthopedics.Due to the limitations of internal fixation techniques and the insufficient mechanical design of nails,the occurrence of complications delays patient recovery after surgical treatment.Design of a proximal femur bionic nail(PFBN)based on Zhang’s N triangle theory provides triangular supporting fixation,which dramatically decreases the occurrence of complications and has been widely used for clinical treatment of unstable intertrochanteric femur fracture worldwide.In this work,we developed an equivalent biomechanical model to analyze improvement in bone remodeling of unstable intertrochanteric femur fracture through PFBN use.The results show that compared with proximal femoral nail antirotation(PFNA)and InterTan,PFBN can dramatically decrease the maximum strain in the proximal femur.Based on Frost’s mechanostat theory,the local mechanical environment in the proximal femur can be regulated into the medium overload region by using a PFBN,which may render the proximal femur in a state of physiological overload,favoring post-operative recovery of intertrochanteric femur fracture in the elderly.This work shows that PFBN may constitute a panacea for unstable intertrochanteric femur fracture and provides insights into improving methods of internal fixation.

Impact Force Localization and Reconstruction via ADMM-based Sparse Regularization Method

In practice,simultaneous impact localization and time history reconstruction can hardly be achieved,due to the illposed and under-determined problems induced by the constrained and harsh measuring conditions.Although l_(1) regularization can be used to obtain sparse solutions,it tends to underestimate solution amplitudes as a biased estimator.To address this issue,a novel impact force identification method with l_(p) regularization is proposed in this paper,using the alternating direction method of multipliers(ADMM).By decomposing the complex primal problem into sub-problems solvable in parallel via proximal operators,ADMM can address the challenge effectively.To mitigate the sensitivity to regularization parameters,an adaptive regularization parameter is derived based on the K-sparsity strategy.Then,an ADMM-based sparse regularization method is developed,which is capable of handling l_(p) regularization with arbitrary p values using adaptively-updated parameters.The effectiveness and performance of the proposed method are validated on an aircraft skin-like composite structure.Additionally,an investigation into the optimal p value for achieving high-accuracy solutions via l_(p) regularization is conducted.It turns out that l_(0.6)regularization consistently yields sparser and more accurate solutions for impact force identification compared to the classic l_(1) regularization method.The impact force identification method proposed in this paper can simultaneously reconstruct impact time history with high accuracy and accurately localize the impact using an under-determined sensor configuration.

一种基于迭代近端投影的被动声纳探测离网格DOA估计方法

Traditional direction of arrival(DOA)estimation methods based on sparse reconstruction commonly use convex or smooth functions to approximate non-convex and non-smooth sparse representation problems.This approach often introduces errors into the sparse representation model,necessitating the development of improved DOA estimation algorithms.Moreover,conventional DOA estimation methods typically assume that the signal coincides with a predetermined grid.However,in reality,this assumption often does not hold true.The likelihood of a signal not aligning precisely with the predefined grid is high,resulting in potential grid mismatch issues for the algorithm.To address the challenges associated with grid mismatch and errors in sparse representation models,this article proposes a novel high-performance off-grid DOA estimation approach based on iterative proximal projection(IPP).In the proposed method,we employ an alternating optimization strategy to jointly estimate sparse signals and grid offset parameters.A proximal function optimization model is utilized to address non-convex and non-smooth sparse representation problems in DOA estimation.Subsequently,we leverage the smoothly clipped absolute deviation penalty(SCAD)function to compute the proximal operator for solving the model.Simulation and sea trial experiments have validated the superiority of the proposed method in terms of higher resolution and more accurate DOA estimation performance when compared to both traditional sparse reconstruction methods and advanced off-grid techniques.

Evolving patterns of agricultural production space in China:A network-based approach

The agricultural production space,as where and how much each agricultural product grows,plays a vital role in meeting the increasing and diverse food demands.Previous studies on agricultural production patterns have predominantly centered on individual or specific crop types,using methods such as remote sensing or statistical metrological analysis.In this study,we characterize the agricultural production space(APS)by bipartite network connecting agricultural products and provinces,to reveal the relatedness between diverse agricultural products and the spatiotemporal characteristic of provincial production capabilities in China.The results show that core products are cereal,pork,melon,and pome fruit;meanwhile the milk,grape,and fiber crop show an upward trend in centrality,which is in line with diet structure changes in China over the past decades.The little changes in community components and structures of agricultural products and provinces reveal that agricultural production patterns in China are relatively stable.Additionally,identified provincial communities closely resemble China's agricultural natural zones.Furthermore,the observed growth in production capabilities in North and Northeast China implies their potential focus areas for future agricultural production.Despite the superior production capa-bilities of southern provinces,recent years have witnessed a notable decline,warranting special attentions.The findings provide a comprehensive perspective for understanding the complex relationship of agricultural prod-ucts'relatedness,production capabilities and production patterns,which serve as a reference for the agricultural spatial optimization and agricultural sustainable development.

一种基于稀疏优化和Nesterov动量策略的模型剪枝算法

随着深度学习快速发展,模型的参数量和计算复杂度爆炸式增长,在移动终端上部署面临挑战,模型剪枝成为深度学习模型落地应用的关键。目前,基于正则化的剪枝方法通常采用L2正则化并结合基于数量级的重要性标准,是一种经验性的方法,缺乏理论依据,精度难以保证。受Proximal梯度方法求解稀疏优化问题的启发,本文提出一种能够在深度神经网络上直接产生稀疏解的Prox⁃NAG优化方法,并设计了与之配套的迭代剪枝算法。该方法基于L1正则化,利用Nesterov动量求解优化问题,克服了原有正则化剪枝方法对L2正则化和数量级标准的依赖,是稀疏优化从传统机器学习向深度学习的自然推广。在CIFAR10数据集上对ResNet系列模型进行剪枝实验,实验结果证明Prox⁃NAG剪枝算法较原有剪枝算法性能有所提升。

L_(1)-Smooth SVM with Distributed Adaptive Proximal Stochastic Gradient Descent with Momentum for Fast Brain Tumor Detection

Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes.Machine learning models have become key players in automating brain tumor detection.Gradient descent methods are the mainstream algorithms for solving machine learning models.In this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor detection.Firstly,the smooth hinge loss is introduced to be used as the loss function of SVM.It avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent optimization.Secondly,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the model.Finally,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth SVM.Distributed computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of data.The DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core resources.Due to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence speed.From the perspective of loss reduction,DPGD converges faster than PGD.The experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor detection.Frompre-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.

Percutaneous antegrade management of large proximal ureteral stones using non-papillary puncture

Objective:To evaluate the feasibility and the safety of medial non-papillary percutaneous nephrolithotomy(npPCNL)for the management of large proximal ureteral stones.Methods:We evaluated prospectively collected data of 37 patients with large proximal ureteral stones more than 1.5 cm in diameter treated by prone npPCNL.Depending on stone size,in-toto stone removal or lithotripsy using the Lithoclast®Trilogy(EMS Medical,Nyon,Switzerland)was performed.Perioperative parameters including operative time(from start of puncture to the skin suturing),stone extraction time(from the first insertion of the nephroscope to the extraction of all stone fragments),and the stone-free rate were evaluated.Results:Twenty-one males and 16 females underwent npPCNL for the management of large upper ureteral calculi.The median age and stone size of treated patients were 58(interquartile range[IQR]:51-69)years and 19.3(IQR:18.0-22.0)mm,respectively.The median operative time and stone extraction time were 25(IQR:21-29)min and 8(IQR:7-10)min,respectively.One case(2.7%)of postoperative bleeding and two cases(5.4%)of prolonged fever were managed conservatively.The stone-free rate at a 1-month follow-up was 94.6%.Conclusion:The npPCNL provides a straight route to the ureteropelvic junction and proximal ureter.Approaching from a dilated portion of the ureter under low irrigation pressure with larger diameter instruments results in effective and safe stone extraction within a few minutes.

An Efficient Smoothing and Thresholding Image Segmentation Framework with Weighted Anisotropic-Isotropic Total Variation

In this paper,we design an efficient,multi-stage image segmentation framework that incorporates a weighted difference of anisotropic and isotropic total variation(AITV).The segmentation framework generally consists of two stages:smoothing and thresholding,thus referred to as smoothing-and-thresholding(SaT).In the first stage,a smoothed image is obtained by an AITV-regularized Mumford-Shah(MS)model,which can be solved efficiently by the alternating direction method of multipliers(ADMMs)with a closed-form solution of a proximal operator of the l_(1)-αl_(2) regularizer.The convergence of the ADMM algorithm is analyzed.In the second stage,we threshold the smoothed image by K-means clustering to obtain the final segmentation result.Numerical experiments demonstrate that the proposed segmentation framework is versatile for both grayscale and color images,effcient in producing high-quality segmentation results within a few seconds,and robust to input images that are corrupted with noise,blur,or both.We compare the AITV method with its original convex TV and nonconvex TVP(O<p<1)counterparts,showcasing the qualitative and quantitative advantages of our proposed method.

Mechanisms of shoulder trauma: Current concepts

Acute traumatic injuries to the glenohumeral articulation are common.The types of injuries depend on age,muscle strength,bone density,and biomechanics of the traumatic event.Understanding the different mechanisms of trauma and how they affect the functional anatomical structures of the shoulder joint is crucial for the treatment of these lesions.Therefore,when clinicians have knowledge of these mechanisms they can accurately diagnose and treat shoulder pathology and predict distinct injury patterns.Here,we have described the fundamentals of the mechanisms of injury of the glenohumeral dislocation,dislocation with fracture of the humeral head,and the proximal humerus fracture.We have focused on common injury mechanisms and the correlation with radiological diagnostics.Radiological and laboratory findings of distinct types of injury were also discussed.

Clinical efficacy of modified Kamikawa anastomosis in patients with laparoscopic proximal gastrectomy

BACKGROUND With the increasing incidence of proximal gastric cancer,laparoscopic proximal gastrectomy has been applied.However,reflux esophagitis often occurs after traditional esophagogastric anastomosis.In order to solve this problem,several methods of digestive tract reconstruction have emerged,but the most satisfying method remains to be discussed.Therefore,we modified traditional Kamikawa anastomosis to investigate the appropriate digestive tract reconstruction in laparo-scopic proximal gastrectomy.All the patients were successfully operated on without conversion to laparotomy.The duration of operation and digestive tract reconstruction were 203.500(150-224)min and 87.500(73-111)min,respectively.The intraoperative amount of bleeding was 20.500 mL±0.696 mL.The time of postoperative first flatus,the first postoperative fluid intake,and the postoperative length of stay were 2(1-3)d,4(3-5)d,and 9(8-10)d,respectively.All the patients were followed up for 12-23 months.The body mass index at 6 and 12 months after surgery were 22.577 kg/m2±3.098 kg/m2 and 22.594 kg/m2±3.207 kg/m2,respectively.The nutrition risk screening 2002 score,the patient-generated subjective global assessment score,and the gastroesophageal reflux disease scale score were good at 6 and 12 months after surgery.Reflux esophagitis and anastomotic stenosis were not observed in any of the patients during their 12-month postoperative gastroscopy or upper gastrointestinal tract visits.All the patients exhibited no tumor recurrence or metastasis.CONCLUSION The modified Kamikawa anastomosis is safe and feasible for laparoscopic proximal gastrectomy and has good antireflux effects and nutritional status.

Correction method for moderate and severe degrees of hallux valgus associated with transfer metatarsalgia

BACKGROUND Hallux valgus(HV)is a common foot deformity that manifests with increasing age,especially in women.The associated foot pain causes impaired gait and decreases quality of life.Moderate and severe HV is a deformity that is charac-terized by the involvement of lesser rays and requires complex surgical treatment.In this study,we attempted to develop a procedure for this condition.AIM To analyse the treatment results of patients who underwent simultaneous surgical correction of all parts of a static forefoot deformity.METHODS We conducted a prospective clinical trial between 2016 and 2021 in which 30 feet with moderate or severe HV associated with Tailor’s bunion and metatarsalgia were surgically treated via a new method involving surgical correction of all associated problems.This method included a modified Lapidus procedure,M2M3 tarsometatarsal arthrodesis,intermetatarsal fusion of the M4 and M5 bases,and the use of an original external fixation apparatus to enhance correction power.Preoperative,postoperative,and final follow-up radiographic data and American Orthopaedic Foot and Ankle Society(AOFAS)scores were compared,and P values<0.05 were considered to indicate statistical significance.RESULTS The study included 28 females(93.3%)and 2 males feet(6.7%),20(66.7%)of whom had a moderate degree of HV and 10(33.3%)of whom had severe deformity.M2 and M3 metatarsalgia was observed in 21 feet,and 9 feet experienced pain only at M2.The mean follow-up duration was 11 months.All patients had good correction of the HV angle[preoperative median,36.5 degrees,interquartile range(IQR):30-45;postoperative median,10 degrees,IQR:8.8-10;follow-up median,11.5 degrees,IQR:10-14;P<0.01].At follow-up,metatarsalgia was resolved in most patients(30 vs 5).There was a clinically negligible decrease in the corrected angles at the final follow-up,and the overall AOFAS score was significantly better(median,65 points,IQR:53.8-70;vs 80 points,IQR:75-85;P<0.01).CONCLUSION The developed method showed good sustainability of correction power in a small sample of patients at the one-year follow-up.Randomized clinical trials with larger samples,as well as long-term outcome assessments,are needed in the future.

Comparative efficacy of proximal femoral nail vs dynamic condylar screw in treating unstable intertrochanteric fractures

BACKGROUND Among the most frequent hip fractures are trochanteric fractures,which usually occur from low-energy trauma like minor falls,especially in older people with osteoporotic bones.AIM To evaluate the treatment efficacy of dynamic condylar screws(DCS)and proximal femoral nails(PFN)for unstable intertrochanteric fractures.METHODS To find pertinent randomized controlled trials and retrospective observational studies comparing PFN with DCS for the management of unstable femoral intertrochanteric fractures,a thorough search was carried out.For research studies published between January 1996 and April 2024,PubMed,EMBASE,Scopus,Web of Science,Cochrane Library,and Google Scholar were all searched.The complete texts of the papers were retrieved,vetted,and independently examined by two investigators.Disputes were settled by consensus,and any disagreements that persisted were arbitrated by a third author.RESULTS This study included six articles,comprising a total of 173 patients.Compared to the DCS,the PFN had a shorter operation time[mean difference(MD):-41.7 min,95%confidence interval(95%CI):-63.04 to-20.35,P=0.0001],higher success rates with closed reduction techniques[risk ratio(RR):34.05,95%CI:11.12-104.31,P<0.00001],and required less intraoperative blood transfusion(MD:-1.4 units,95%CI:-1.80 to-1.00,P<0.00001).Additionally,the PFN showed shorter fracture union time(MD:-6.92 wk,95%CI:-10.27 to-3.57,P<0.0001)and a lower incidence of reoperation(RR:0.37,95%CI:0.17-0.82,P=0.01).However,there was no discernible variation regarding hospital stay,implant-related complications,and infections.CONCLUSION Compared to DCS,PFN offers shorter operative times,reduces the blood transfusions requirements,achieves higher closed reduction success,enables faster fracture healing,and lowers reoperation incidence.