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.

Multi-dimensional multiplexing optical secret sharing framework with cascaded liquid crystal holograms

Secret sharing is a promising technology for information encryption by splitting the secret information into different shares.However,the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited.Herein,we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal(LC)holograms.The LC holograms are used as spatially separated shares to carry secret images.The polarization of the incident light and the distance between different shares are served as secret keys,which can significantly improve the information security and capacity.Besides,the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency,which further increases the information security.In implementation,an artificial neural network(ANN)model is developed to carefully design the phase distribution of each LC hologram.With the advantage of high security,high capacity and simple configuration,our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display.

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.

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.

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.

Proximity Effect of Fe-Zn Bimetallic Catalysts on CO_(2) Hydrogenation Performance

The interaction between a promoter and an active metal crucially impacts catalytic performance.Nowadays,the influence of promoter contents and species has been intensively considered.In this study,we investigate the effect of the iron(Fe)-zinc(Zn)proximity of Fe-Zn bimetallic catalysts on CO_(2)hydrogenation performance.To eliminate the size effect,Fe_(2)O_(3)and ZnO nanoparticles with uniform size are first prepared by the thermal decomposition method.By changing the loading sequence or mixing method,a series of Fe-Zn bimetallic catalysts with different Fe-Zn distances are obtained.Combined with a series of characterization techniques and catalytic performances,Fe-Zn bimetallic proximity for compositions of Fe species is discussed.Furthermore,we observe that a smaller Fe-Zn distance inhibits the reduction and carburization of the Fe species and facilitates the oxidation of carbides.Appropriate proximity of Fe and Zn(i.e.,Fe_1Zn_(1)-imp and Fe_(1)Zn_(1)-mix samples)results in a suitable ratio of the Fe_5C_(2)and Fe_(3)O_(4)phases,simultaneously promoting the reverse water-gas shift and Fischer-Tropsch synthesis reactions.This study provides insight into the proximity effect of bimetallic catalysts on CO_(2)hydrogenation performance.

TurboID Proximity Labeling of a Protocadherin Protein to Characterize Interacting Protein Complex

The study of the neuron has always been a fundamental aspect when it came to studying mental illnesses such as autism and depression. The protein protocadherin-9 (PCDH9) is an important transmembrane protein in the development of the neuron synapse. Hence, research on its protein interactome is key to understanding its functionality and specific properties. A newly discovered biotin ligase, TurboID, is a proximity labeler that is designed to be able to label and observe transmembrane proteins, something that previous methods struggled with. The TurboID method is verified in HEK293T cells and primary cultured mouse cortical neurons. Results have proven the validity of the TurboID method in observing PCDH9-interacting proteins.

Envelope Method and More General New Global Structures of Solutions for Multi-dimensional Conservation Law

For the two-dimensional(2D)scalar conservation law,when the initial data contain two different constant states and the initial discontinuous curve is a general curve,then complex structures of wave interactions will be generated.In this paper,by proposing and investigating the plus envelope,the minus envelope,and the mixed envelope of 2D non-selfsimilar rarefaction wave surfaces,we obtain and the prove the new structures and classifications of interactions between the 2D non-selfsimilar shock wave and the rarefaction wave.For the cases of the plus envelope and the minus envelope,we get and prove the necessary and sufficient criterion to judge these two envelopes and correspondingly get more general new structures of 2D solutions.

Research and Practice on the Multi-dimensional Course Goal Achievement Evaluation of Software Engineering

According to the standards of engineering education accreditation,the achievement paths and evaluation criteria of course goals are presented,aimed at the objectives of software engineering courses and the characteristics of hybrid teaching in Kunming University of Science and Technology.Then a multi-dimensional evaluation system for course goal achievement of software engineering is proposed.The practice’s results show that the multi-dimensional course goal achievement evaluation is helpful to the continuous improvement of course teaching,which can effectively support the evaluation of graduation outcomes.

An Ordinal Multi-Dimensional Classification(OMDC)for Predictive Maintenance

Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.

Data inversion of multi-dimensional magnetic resonance in porous media

Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension allows the study of the correlation,exchange processes,and separation of overlapping spectral information.The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media.Apart from Fourier transform,methods have been developed for processing the multi-dimensional time-domain data,identifying the fluid components,and estimating pore surface permeability via joint relaxation and diffusion spectra.Through the resolution of spectroscopic signals with spatial encoding gradients,multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases.Signals in each voxel are usually expressed as multi-exponential decay,representing microstructures or environments along multiple pore scales.The separation of contributions from different environments is a common ill-posed problem,which can be resolved numerically.Moreover,the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra.This paper reviews the algorithms that have been proposed to process multidimensional MR datasets in different scenarios.Detailed information at the microscopic level,such as tissue components,fluid types and food structures in multi-disciplinary sciences,could be revealed through multi-dimensional MR.

Intelligent 6G Wireless Network with Multi-Dimensional Information Perception

Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.

Air target recognition method against ISRJ for radio frequency proximity sensors using chaotic stream encryption

The interrupted-sampling repeater jamming(ISRJ)can cause false targets to the radio-frequency proximity sensors(RFPSs),resulting in a serious decline in the target detection capability of the RFPS.This article proposes a recognition method for RFPSs to identify the false targets caused by ISRJ.The proposed method is realized by assigning a unique identity(ID)to each RFPS,and each ID is a periodically and chaotically encrypted in every pulse period.The processing technique of the received signal is divided into ranging and ID decryption.In the ranging part,a high-resolution range profile(HRRP)can be obtained by performing pulse compression with the binary chaotic sequences.To suppress the noise,the singular value decomposition(SVD)is applied in the preprocessing.Regarding ID decryption,targets and ISRJ can be recognized through the encryption and decryption processes,which are controlled by random keys.An adaptability analysis conducted in terms of the peak-to-side lobe ratio(PSLR)and bit error rate(BER)indicates that the proposed method performs well within a 70-k Hz Doppler shift.A simulation and experimental results show that the proposed method achieves extremely stable target and ISRJ recognition accuracies at different signal-to-noise ratios(SNRs)and jamming-to-signal ratios(JSRs).

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.

Watson-Jones Anatomical Approach for Open Reduction and Internal Fixation of Proximal Femoral Fractures without Image Intensifier in a Low-Resource Setting

Introduction: Standard procedures for surgical fixation of proximal femoral fractures (PFF) require an image intensifier which in developing countries remains a luxury. We hypothesized that, with a well-codified technique, the Watson Jones approach (WJA) without image intensifier nor traction table, can allow open reduction and internal fixation (ORIF) of PFF using Dynamic hip screw (DHS), with satisfactory outcome. Patients and methods: Forty one consecutive patients (mean age 59.5 ± 21.6 years, 61% males) who were followed in a Teaching Hospital for PFF treated by ORIF using the WJA and DHS from January 2016 to December 2020 were reassessed. The outcome measures were the quality of the reduction, the positioning of the implants, the tip-apex distance (TAD), the rate and delay of consolidation, the functional results using Postel Merle d’Aubigné (PMA) score, the rate of surgical site infection (SSI) and the overall mortality. Logistic regression was used to determine factors associated with mechanical failure. Results: The mean follow-up period was 33.8 ± 15.0 months. Fracture reduction was good in 31 (75.6%) cases and acceptable in 8(19.5%) cases. Implant position was fair to good in 37 (90.2%) patients. The mean TAD was 26.1 ± 3.9 mm. Three patients developed SSI. Consolidation was achieved in 38 (92.6%) patients. The functional results were good to excellent in 80.5% of patients. The overall mortality rate was 7.3%. There were an association between mechanical failure and osteoporosis (p = 0.04), fracture reduction (p = 0.003), and TAD (p = 0.025). In multivariate logistic regression, no independent factors were predictive of mechanical failure. Conclusion: This study shows that ORIF using DHS for PFF via the Watson-Jones approach without an image intensifier can give satisfactory anatomical and functional outcomes in low-resource settings. It provides and validates a reliable and reproducible technique that deserves to be diffused to surgeons in austere areas over the world.

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.

Almost Sure Convergence of Proximal Stochastic Accelerated Gradient Methods

Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of multiple functions, the stochastic proximal gradient method performs well. However, research on its accelerated version remains unclear. This paper proposes a proximal stochastic accelerated gradient (PSAG) method to address problems involving a combination of smooth and non-smooth components, where the smooth part corresponds to the average of multiple block sums. Simultaneously, most of convergence analyses hold in expectation. To this end, under some mind conditions, we present an almost sure convergence of unbiased gradient estimation in the non-smooth setting. Moreover, we establish that the minimum of the squared gradient mapping norm arbitrarily converges to zero with probability one.

仅根据Proximity数据构建向量空间模型的方法

在实际应用中，许多研究对象都是抽象的，难以用某种特征向量的形式表示，这使得许多成熟的数据挖掘和机器学习方法难以被采用。不过，通常可将其转化成一个Proximity数据矩阵，使得矩阵中的元素表示两个对象间某种“比较”关系。针对该问题，本文提出仅根据Proximity数据矩阵利用多维尺度分析法（MDS）将研究对象进行向量化表示，即构建了一种向量空间模型。最后，对汉语科技词系统中的词语进行了聚类分析，结果表明，向量空间模型构建后再聚类的结果明显优于直接针对Proximity数据进行聚类分析的结果，从而验证了该方法的可行性和有效性。

一种CCSDS Proximity-1协议下全双工/半双工及单工的融合实现方法

针对深空探测任务中对产品小型化及低功耗的需求,各功能间的融合及模块的复用成为深空探测类产品设计中不可或缺的一环.本文提出了一种CCSDS Proximity-1协议下全双工/半双工及单工的融合实现方法,该方法通过将全双工模式、半双工模式及单工模式中的流式处理部分及特定功能部分从主框架中剥离,并进行融合设计,形成通用的VHDL结构;将其余调度控制部分集成在FPGA中的MicroBlaze结构中.通用的VHDL结构意味着资源配置最优,而MicroBlaze结构因其灵活性,可实现对不同模式的灵活切换.本方法将应用到下一代火星探测临近空间链路收发信机中,通过采用仅有1片V5的开发板,实现结果表明,CCSDS Proximity-1协议下的全双工、半双工及单工功能性能正常,且资源的消耗量仅为5%ALMs,6%DSP和8%RAM.