The soft cancellation decoding of polar codes achieves a better performance than the belief propagation decoding with lower computational time and space complexities.However,because the soft cancellation decoding is b...The soft cancellation decoding of polar codes achieves a better performance than the belief propagation decoding with lower computational time and space complexities.However,because the soft cancellation decoding is based on the successive cancellation decoding,the decoding efficiency and performance with finite-length blocks can be further improved.Exploiting the idea of the successive cancellation list decoding,the soft cancellation decoding can be improved in two aspects:one is by adding branch decoding to the error-prone information bits to increase the accuracy of the soft information,and the other is through using partial iterative decoding to reduce the time and computational complexities.Compared with the original method,the improved soft cancellation decoding makes progress in the error correction performance,increasing the decoding efficiency and reducing the computational complexity,at the cost of a small increase of space complexity.展开更多
The ＂soft canning＂ heat preservation technique is invented by Baoshan Iron ＆ Steel Co., Ltd. （Hereafter referred to Baosteel）, using the flexible insulation material which can be stuck on the surface of the heate...The ＂soft canning＂ heat preservation technique is invented by Baoshan Iron ＆ Steel Co., Ltd. （Hereafter referred to Baosteel）, using the flexible insulation material which can be stuck on the surface of the heated ingot or billet in the superalloy thermal process. This adhesive insulation material can reduce the temperature drop of the ingot or billet during the transferring or hot working process, and can deform with the billet without dropping. The surface temperature drop can be effectively reduced, so the products can be obtained a good surface quality and the structural uniformity can be improved. The technique is applied to hard-wrought superalloy forging of cogging, superalloy rods finished forging fire and superalloy hot die forging processing,and good results have been achieved.展开更多
Local precise drug delivery is conducive to improving therapeutic efficacy and minimizing off-target toxicity.Current local delivery approaches are focused mostly on superficial or postoperative tumor lesions,due to t...Local precise drug delivery is conducive to improving therapeutic efficacy and minimizing off-target toxicity.Current local delivery approaches are focused mostly on superficial or postoperative tumor lesions,due to the challenges posed by the inaccessibility of deep-seated tumors.Herein,we report a magnetic continuum soft robot capable of non-invasive and site-specific delivery of prodrug nanoassemblies-loaded hydrogel.The nanoassemblies are co-assembled from redox-responsive docetaxel prodrug and oxaliplatin prodrug,and subsequently embedded into a hydrogel matrix.The hydrogel precursor and crosslinker are synchronously delivered using the soft robot under magnetic guidance and in situ crosslinked at the gastric cancer lesions,forming a drug depot for sustained release and long-lasting treatment.As the hydrogel gradually degrades,the nanoassemblies are internalized by tumor cells.The redox response ability enables them to be selectively activatedwithin tumor cells to trigger the release of docetaxel and oxaliplatin,exerting a synergistic anti-tumor effect.We find that the combination effectively induces immunogenic cell death of gastric tumor,enhancing antitumor immune responses.This strategy offers an intelligent and controllable integration platform for precise drug delivery and combined chemo-immunotherapy.展开更多
Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine lea...Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine learning(ML)methods struggle with intra-class heterogeneity and inter-class similarity,necessitating more robust classification models.This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning(DL)and Bat Swarm Optimization(BSO)hyperparameter optimization to improve breast cancer histopathology(BCH)image classification.A dataset of 804 Hematoxylin and Eosin(H&E)stained images classified as Benign,in situ,Invasive,and Normal categories(ICIAR2018_BACH_Challenge)has been utilized.ResNet50 was utilized for feature extraction,while Support Vector Machines(SVM),Random Forests(RF),XGBoosts(XGB),Decision Trees(DT),and AdaBoosts(ADB)were utilized for classification.BSO was utilized for hyperparameter optimization in a soft voting ensemble approach.Accuracy,precision,recall,specificity,F1-score,Receiver Operating Characteristic(ROC),and Precision-Recall(PR)were utilized for model performance metrics.The model using an ensemble outperformed individual classifiers in terms of having greater accuracy(~90.0%),precision(~86.4%),recall(~86.3%),and specificity(~96.6%).The robustness of the model was verified by both ROC and PR curves,which showed AUC values of 1.00,0.99,and 0.98 for Benign,Invasive,and in situ instances,respectively.This ensemble model delivers a strong and clinically valid methodology for breast cancer classification that enhances precision and minimizes diagnostic errors.Future work should focus on explainable AI,multi-modal fusion,few-shot learning,and edge computing for real-world deployment.展开更多
Bacterial soft rot(BSR)caused by Pectobacterium carotovorum subsp.brasiliense(Pcb)is a serious bacterial disease which negatively impact yield and quality in cucumber.However,the genetic mechanism of BSR resistance in...Bacterial soft rot(BSR)caused by Pectobacterium carotovorum subsp.brasiliense(Pcb)is a serious bacterial disease which negatively impact yield and quality in cucumber.However,the genetic mechanism of BSR resistance in cucumber has not been reported.Here,we investigated the BSR resistance of 119 cucumber core germplasm worldwide at the seedling stage and identified 26 accessions highly resistant to BSR.A total of 1642740 single-nucleotide polymorphisms(SNPs)were used to conduct GWAS,and five loci associated with BSR resistance were detected on four chromosomes:gBSR2.1,gBSR2.2,gBSR3.1,gBSR4.1 and gBSR5.1.Based on haplotype analysis,sequence polymorphisms,functional annotation and qRT-PCR analysis,six candidate genes were identified within the five loci.CsaV3_2G014450,CsaV3_2G014490,CsaV3_2G016000,CsaV3_3G000850,CsaV3_4G033150,and CsaV3_5G000390 each had nonsynonymous SNPs,and were significantly up-regulated in the resistant genotypes after inoculation.And CsaV3_5G000390 in the susceptible genotype was significantly up-regulated after inoculation.The identification of these candidate genes lays a foundation for understanding the genetic mechanism of BSR resistance in cucumber.Generally,our study mined genes associated with BSR resistance in cucumber seedlings and will assist the breeding of BSR-resistant cucumber cultivars.展开更多
Coking at the fractionating tower bottom and the decant oil circulation system disrupts the heat balance,leading to unplanned shutdown and destroying the long period stable operation of the Fluid Catalytic Cracking Un...Coking at the fractionating tower bottom and the decant oil circulation system disrupts the heat balance,leading to unplanned shutdown and destroying the long period stable operation of the Fluid Catalytic Cracking Unit(FCCU).The FCCU operates through interconnected subsystems,generating high-dimensional,nonlinear,and non-stationary data characterized by spatiotemporally correlated.The decant oil solid content is the crucial indicator for monitoring catalyst loss from the reactor-regenerator system and coking risk tendency at the fractionating tower bottom that relies on sampling and laboratory testing,which is lagging responsiveness and labor-intensive.Developing the online decant oil solid content soft sensor using industrial data to support operators in conducting predictive maintenance is essential.Therefore,this paper proposes a hybrid deep learning framework for soft sensor development that combines spatiotemporal pattern extraction with interpretability,enabling accurate risk identification in dynamic operational conditions.This framework employs a Filter-Wrapper method for dimensionality reduction,followed by a 2D Convolutional Neural Network(2DCNN)for extracting spatial patterns,and a Bidirectional Gated Recurrent Unit(BiGRU)for capturing long-term temporal dependencies,with an Attention Mechanism(AM)to highlight critical features adaptively.The integration of SHapley Additive exPlanations(SHAP),Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN),2DCNN,and expert knowledge precisely quantifies feature contributions and decomposes signals,significantly enhancing the practicality of risk identification.Applied to a China refinery with processing capacity of 2.80×10^(6) t/a,the soft sensor achieved the R^(2) value of 0.93 and five-level risk identification accuracy of 96.42%.These results demonstrate the framework's accuracy,robustness,and suitability for complex industrial scenarios,advancing risk visualization and management.展开更多
Soft magnetic alloys are extensively used in various power electronic devices due to their advantageous properties,including high saturation magnetic induction,low coercivity,and high permeability.In certain applicati...Soft magnetic alloys are extensively used in various power electronic devices due to their advantageous properties,including high saturation magnetic induction,low coercivity,and high permeability.In certain applications,complex-shaped components are increasingly required for performance enhancement.Additive manufacturing technique,particularly selective laser melting(SLM),has emerged as an effective method for fabricating such complex-shaped soft magnetic components.SLM,a laserbased additive manufacturing technique,employs high-power-density lasers to melt and fuse metal powders within a powder bed selectively.This approach enables rapid prototyping,precise geometrical control,and the integration of multi-material designs.This review highlights recent advancements in the application of SLM technique for the production of soft magnetic alloys,focusing on Fe-Si,Fe-Ni,Fe-Co,and amorphous alloy systems.Moreover,it explores the implementation of SLM in manufacturing processes and evaluates both the opportunities and challenges associated with SLM-based production of soft magnetic alloys.展开更多
Dear Editor,This letter presents a new approach to developing interpretable and reliable soft sensors for Industry 5.0 applications.Although sophisticated machine learning methods have made remarkable strides in soft-...Dear Editor,This letter presents a new approach to developing interpretable and reliable soft sensors for Industry 5.0 applications.Although sophisticated machine learning methods have made remarkable strides in soft-sensor predictive accuracy,ensuring interpretability and reliable performance across varying industrial operating conditions remains a challenge[1]–[4].This is precisely what Industry 5.0,proposed by the European Commission in 2021,advocates[5],[6].It integrates various cutting-edge technologies,such as human-machine interaction,digital twins,cybersecurity and artificial intelligence,to facilitate the development of better soft sensors.展开更多
Soft machines harness material-level physical intelligence to perform adaptive tasks,enabling advancements in biomedical and human-machine interaction fields.Soft switches are the basic building blocks to achieve inte...Soft machines harness material-level physical intelligence to perform adaptive tasks,enabling advancements in biomedical and human-machine interaction fields.Soft switches are the basic building blocks to achieve intelligent functions like autonomous decisions and mechanical computation.However,current soft switches suffer from complex fabrication processes,limited performance,and a lack of multimodal control,which hinder their practical application and the realization of machine intelligence.Herein,by harnessing the unique self-pinch and self-healing effects of the gallium-based liquid metals(LMs),we describe a soft high-performance electric switch composed of an LM line encapsulated within an elastomer.Applying pressure to deform the LM switch can increase local current density,leading to the electromagnetic self-pinch effect for switching off.After releasing pressure,the LM can spontaneously heal with the elastic recovery of the elastomer for switching on.This LM switch shows comprehensive advantages,including a compact design(0.5 mm×1.5 mm×10 mm),good stretchability(100%),high on/off ratio(~10^(9)),rapid response time(<100 ms),and excellent durability(>12000 cycles).Moreover,the LM switches enable multiple control modes,including magnetic and optical stimulation,through the integration of responsive materials.We demonstrate various LM switch-enabled functional soft machines,such as an interactive flexible gripper,a self-oscillating soft crawler,and wearable logic gates.This work will open new avenues for the application of LM in intelligent soft machines and advanced wearable electronics.展开更多
In this research,a comparative analysis was conducted on the performance and efficiency of the dual-anchor soft robot(DASR)and the extension-contraction soft robot(ECSR).These robots were constructed by imitating the ...In this research,a comparative analysis was conducted on the performance and efficiency of the dual-anchor soft robot(DASR)and the extension-contraction soft robot(ECSR).These robots were constructed by imitating the locomotion of razor clams.The penetration force for extension actuators and the anchorage force for expansion actuators in dry sand with distinct relative densities were tested by differentiating input air pressure and length-to-diameter ratios(λ).On the basis of the findings,a DASR and an ECSR were developed.DASR comprised two expansion actuators as the head and the tail segments at two ends,and one extension actuator as the middle segment.ECSR was composed of an extension actuator.A method based on the force equilibrium was introduced to ascertain and adjust the geometric parameters(length of each segment)of DASR.The burrowing-out performance and efficiency of DASR and ECSR in sands with distinct relative densities were explored.The results revealed that DASR exhibited high efficiency in dense sand in terms of lower time of burrowing-out,slip-to-advancement ratio,and cost of transport.ECSR might perform better in looser sand in terms of higher average burrowing-out velocity,higher advancement in each cycle,and lower energy consumption.However,it had larger slips than DASR.DASR could realize steady advancement and net displacement in each cycle and effectively decrease slips.These findings demonstrate the benefits and usability of the dual-anchor motion and offer new insights into the application of the dual-anchor mechanism in the burrowing of robots.展开更多
Wireless Sensor Networks(WSNs)play a crucial role in numerous Internet of Things(IoT)applications and next-generation communication systems,yet they continue to face challenges in balancing energy efficiency and relia...Wireless Sensor Networks(WSNs)play a crucial role in numerous Internet of Things(IoT)applications and next-generation communication systems,yet they continue to face challenges in balancing energy efficiency and reliable connectivity.This study proposes SAC-HTC(Soft Actor-Critic-based High-performance Topology Control),a deep reinforcement learning(DRL)method based on the Actor-Critic framework,implemented within a Software Defined Wireless Sensor Network(SDWSN)architecture.In this approach,sensor nodes periodically transmit state information,including coordinates,node degree,transmission power,and neighbor lists,to a centralized controller.The controller acts as the reinforcement learning(RL)agent,with the Actor generating decisions to adjust transmission ranges,while the Critic evaluates action values to reflect the overall network performance.The bidirectional Node-Controller feedback mechanism enables the controller to issue appropriate control commands to each node,ensuring the maintenance of the desired node degree,reducing energy consumption,and preserving network connectivity.The algorithmfurther incorporates soft entropy adjustment to balance exploration and exploitation,alongwith an off-policy mechanism for efficient data reuse,making it well-suited to the resource-constrained conditions ofWSNs.Simulation results demonstrate that SAC-HTC not only outperforms traditional methods and several existing RL algorithms but also achieves faster convergence,optimized communication range control,global connectivity maintenance,and extended network lifetime.The key novelty of this research lies in the integration of the SAC method with the SDWSN architecture forWSNs topology control,providing an adaptive,efficient,and highly promisingmechanism for large-scale,dynamic,and high-performance sensor networks.展开更多
Soft-tissue motion introduces significant challenges in robotic teleoperation,especially in medical scenarios where precise target tracking is critical.Latency across sensing,computation,and actuation chains leads to ...Soft-tissue motion introduces significant challenges in robotic teleoperation,especially in medical scenarios where precise target tracking is critical.Latency across sensing,computation,and actuation chains leads to degraded tracking performance,particularly around high-acceleration segments and trajectory inflection points.This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking.Three models—autoregressive(AR),long short-term memory(LSTM),and temporal convolutional network(TCN)—were implemented and evaluated on both synthetic and real datasets.By aligning the prediction horizon with the end-to-end system delay,we demonstrate that prediction-based compensation significantly reduces tracking errors.Among the models,TCN achieved superior robustness and accuracy on complex motion patterns,particularly in multi-step prediction tasks,and exhibited better latency–horizon compatibility.The results suggest that TCN is a promising candidate for real-time latency compensation in teleoperated robotic systems involving dynamic soft-tissue interaction.展开更多
In this study,the dynamic characteristics and microstructures of lacustrine soft clays were studied.Dynamic character tests were conducted on undisturbed,remolded,and saturated lacustrine soft clays,using a dynamic tr...In this study,the dynamic characteristics and microstructures of lacustrine soft clays were studied.Dynamic character tests were conducted on undisturbed,remolded,and saturated lacustrine soft clays,using a dynamic triaxial tester.A scanning electron microscope(SEM)was employed to assess the soil samples after dynamic testing.The results indicate that the dynamic characteristics of lacustrine soft clay were significantly affected by confining pressure and water content.A quantitative relationship was established among confining pressures,water content,and the dynamic shear modulus ratio.The dynamic characteristic parameters of undisturbed,remolded and saturated soil are obviously different,and the original structure can enhance the shear strength of soil.By comparing the results with those from other studies,we found that the dynamic characters of soft clays were considerably varied in different regions,and lacustrine soft clays had a larger dynamic shear modulus ratio and a smaller damping ratio when the dynamic shear strain was large.Using IPP software to process the microstructural images,we found that the soil was dominated by small pores and medium particles,and the roundness of pores and particles had an apparently positive correlation with the maximum diameter.Moreover,the pores and particles of the soil showed fractal characteristics and directionality,and the fractal dimensions and probability entropy were strongly correlated with the macrostructural parameters.Finally,we developed a prediction model for macrostructural and microstructural parameters.展开更多
In recent years,the rising incidence of gastrointestinal(GI)cancer has triggered an urgent need for effective early intervention strategies.Traditional endoscopic techniques often cause patient discomfort,and it is di...In recent years,the rising incidence of gastrointestinal(GI)cancer has triggered an urgent need for effective early intervention strategies.Traditional endoscopic techniques often cause patient discomfort,and it is difficult to navigate deep regions of complex organ structures.This work proposes a kind of bio-inspired magnetic soft robot(BMSR)to address these challenges.The design of the BMSRs is inspired by the rolling motion of the golden wheel spider.Two six-degree-of-freedom(6-DOF)robotic arms are used,where one arm is responsible for real-time manipulation of the BMSRs,and the other is dedicated to monitoring their status.Under the actuation of an external rotating magnetic field,the BMSRs can flexibly climb on inclined surfaces at any angle,involving the inverted surface.Through the powerful output force,the BMSRs can overcome the mobility barrier induced by different human organs,including mucus,folds,and height differences of up to 8 cm.Such an exceptional mobility enables the BMSRs to deliver drugs in the targeted complex GI environment.Moreover,in combination with an endoscope,it provides real-time visual feedback for precise navigation.In vitro animal experiments validate the feasibility of BMSRs,paving a way for their usage in minimally invasive GI treatment.This work advances the potential applications of magnetic soft robots in the biomedical field.展开更多
This paper develops a semi-analytical solution for pile penetration in natural soft clays using the strain path method(SPM).The stress-strain behavior of soils is characterized by the S-CLAY1S model,which can capture ...This paper develops a semi-analytical solution for pile penetration in natural soft clays using the strain path method(SPM).The stress-strain behavior of soils is characterized by the S-CLAY1S model,which can capture the anisotropic evolution and destructuring nature of soft clays.By integrating the S-CLAY1S model into the theoretical framework of the SPM,a set of ordinary differential equations is formulated with respect to the vertical coordinate of soil particles.The distribution of excess pore water pressure(EPWP)following pile installation is approximated through one-dimensional(1D)radial integration around the pile shaft.The distribution of stresses and EPWP,along with the evolution of fabric anisotropy within the soil surrounding the pile,is presented to illustrate the response of pile penetration in natural soft clays.The proposed solution is validated against existing theoretical solutions using the SPM and cavity expansion method(CEM),along with experimental data.The findings demonstrate that the SPM reveals lower radial effective stresses and EPWP at the pile shaft than that of CEM.Pile penetration alters the soil's anisotropic properties,inducing rotational hardening and affecting post-installation stress distribution.Soil destructuration eliminates bonding among particles near the pile,resulting in a complete disruption of soil structure at the pile surface,which is particularly pronounced for higher initial soil structure ratios.Minimal variation was observed in the three principal stresses and shear stress on the cone side surface as the angle increased from 18°to 60°,except for a slight reduction in EPWP.展开更多
Polyurethane elastomers exhibit high dielectric constants owing to their polar groups,and can be used as energy storage capacitors.Energy storage depends not only on the dielectric constant but also on the dielectric ...Polyurethane elastomers exhibit high dielectric constants owing to their polar groups,and can be used as energy storage capacitors.Energy storage depends not only on the dielectric constant but also on the dielectric loss.However,the relationship between chain structure and dielectric properties is not yet clear.Ketal-containing crosslinked polyurethane elastomers were prepared using cyclic ketal diol as a chain extender.The effect of the soft segment length on the dielectric properties and energy storage was investigated.The cause of the change in the dipolar polarization with the soft segment length was analyzed.As the soft segment length increased,the hard-soft hydrogen bonding decreased,whereas the hard-hard hydrogen bonding increased.Under the action of an electric field,the polar bonds in the ketal-containing polyurethane elastomer overcome the hydrogen bonding between hard-soft segments to produce polarization;meanwhile,they also experience crankshaft motions to generate polarization.The former has a relatively high relaxation activation energy of approximately 10-20 k J·mol^(-1),resulting in a large dielectric loss.The latter has a relatively low relaxation activation energy,approximately 0.7-1.7 kJ·mol^(-1),leading to low dielectric loss.As a result,the dielectric constant showed a decreasing trend,and the dielectric loss gradually decreased.This study provides a theoretical foundation for improving the dielectric properties of polyurethane elastomers.展开更多
Soft robots,characterized by compliance,adaptability,and multimodal responsiveness,represent a rapidly advancing frontier in biomedical applications,wearable technologies,and environmental exploration.This review summ...Soft robots,characterized by compliance,adaptability,and multimodal responsiveness,represent a rapidly advancing frontier in biomedical applications,wearable technologies,and environmental exploration.This review summarizes recent progress in soft robotics with a focus on material innovation,structural design,functional integration,and intelligent responsiveness.Emphasis is placed on the development of bioinspired and stimuli-responsive materials,the construction of modular and reconfigurable architectures,and the integration of actuation,sensing,and energy systems.Microneedle array-based soft robots and hydrogel-based 4D-printed systems are introduced as representative platforms for drug delivery,wound healing,and environmental monitoring.Key challenges,including limited durability,power autonomy,and multifunctional synergy,are critically analyzed in relation to practical operation and long-term reliability.Future directions involve the convergence of self-healing materials,intelligent control algorithms,and multiscale integration strategies to achieve enhanced adaptability and clinical translation.This review provides a comprehensive overview of the interdisciplinary development of next-generation soft robots that bridge materials science,biomedical engineering,and intelligent systems,paving the way toward real-world applications.展开更多
Conductive elastomers combining micromechanical sensitivity,lightweight adaptability,and environmental sustainability are critically needed for advanced flexible electronics requiring precise responsiveness and long-t...Conductive elastomers combining micromechanical sensitivity,lightweight adaptability,and environmental sustainability are critically needed for advanced flexible electronics requiring precise responsiveness and long-term wearability;however,the integration of these properties remains a significant challenge.Here,we present a biomass-derived conductive elastomer featuring a rationally engineered dynamic crosslinked network integrated with a tunable microporous architecture.This structural design imparts pronounced micromechanical sensitivity,an ultralow density(~0.25 g cm^(−3)),and superior mechanical compliance for adaptive deformation.Moreover,the unique micro-spring effect derived from the porous architecture ensures exceptional stretchability(>500%elongation at break)and superior resilience,delivering immediate and stable electrical response under both subtle(<1%)and large(>200%)mechanical stimuli.Intrinsic dynamic interactions endow the elastomer with efficient room temperature self-healing and complete recyclability without compromising performance.First-principles simulations clarify the mechanisms behind micropore formation and the resulting functionality.Beyond its facile and mild fabrication process,this work establishes a scalable route toward high-performance,sustainable conductive elastomers tailored for next-generation soft electronics.展开更多
Soft robotic manipulators represent a rapidly evolving field characterized by inherent compliance,adaptability,and safe interactions within unstructured environments.Over the past decade(2015-2025),significant advance...Soft robotic manipulators represent a rapidly evolving field characterized by inherent compliance,adaptability,and safe interactions within unstructured environments.Over the past decade(2015-2025),significant advancements have trans-formed their capabilities through novel designs inspired by biological systems,advanced modeling frameworks,sophisti-cated control strategies,and integration into diverse real-world applications.Recent innovations in multifunctional mate-rials and emerging actuation technologies have markedly expanded manipulator performance,reliability,and dexterity.Concurrently,developments in modeling have progressed from simplified geometric methods toward highly accurate physics-based and hybrid data-driven approaches,substantially improving real-time prediction and controllability.Coupled with these developments,adaptive and robust control strategies-including learning-based techniques-have enabled unprec-edented autonomy and precision in challenging application domains such as Minimally Invasive Surgery(MIS),precision agriculture,deep-sea exploration,disaster recovery,and space missions.Despite these remarkable strides,key challenges remain,notably regarding scalability,long-term material durability,robust integrated sensing,and standardized evaluation procedures.This review comprehensively synthesizes recent advances,critically evaluates state-of-the-art methodologies,and systematically identifies existing gaps to provide a clear roadmap and targeted research directions,guiding future developments toward the broader adoption and optimal utilization of soft robotic manipulators.展开更多
文摘The soft cancellation decoding of polar codes achieves a better performance than the belief propagation decoding with lower computational time and space complexities.However,because the soft cancellation decoding is based on the successive cancellation decoding,the decoding efficiency and performance with finite-length blocks can be further improved.Exploiting the idea of the successive cancellation list decoding,the soft cancellation decoding can be improved in two aspects:one is by adding branch decoding to the error-prone information bits to increase the accuracy of the soft information,and the other is through using partial iterative decoding to reduce the time and computational complexities.Compared with the original method,the improved soft cancellation decoding makes progress in the error correction performance,increasing the decoding efficiency and reducing the computational complexity,at the cost of a small increase of space complexity.
文摘The ＂soft canning＂ heat preservation technique is invented by Baoshan Iron ＆ Steel Co., Ltd. （Hereafter referred to Baosteel）, using the flexible insulation material which can be stuck on the surface of the heated ingot or billet in the superalloy thermal process. This adhesive insulation material can reduce the temperature drop of the ingot or billet during the transferring or hot working process, and can deform with the billet without dropping. The surface temperature drop can be effectively reduced, so the products can be obtained a good surface quality and the structural uniformity can be improved. The technique is applied to hard-wrought superalloy forging of cogging, superalloy rods finished forging fire and superalloy hot die forging processing,and good results have been achieved.
基金supported by National Natural Science Foundation of China(No.82161138029)Liaoning Revitalization Talents Program(No.XLYC2402040)the Project of China-Japan Joint International Laboratory of Advanced Drug Delivery System Research and Translation of Liaoning Province(No.2024JH2/102100007).
文摘Local precise drug delivery is conducive to improving therapeutic efficacy and minimizing off-target toxicity.Current local delivery approaches are focused mostly on superficial or postoperative tumor lesions,due to the challenges posed by the inaccessibility of deep-seated tumors.Herein,we report a magnetic continuum soft robot capable of non-invasive and site-specific delivery of prodrug nanoassemblies-loaded hydrogel.The nanoassemblies are co-assembled from redox-responsive docetaxel prodrug and oxaliplatin prodrug,and subsequently embedded into a hydrogel matrix.The hydrogel precursor and crosslinker are synchronously delivered using the soft robot under magnetic guidance and in situ crosslinked at the gastric cancer lesions,forming a drug depot for sustained release and long-lasting treatment.As the hydrogel gradually degrades,the nanoassemblies are internalized by tumor cells.The redox response ability enables them to be selectively activatedwithin tumor cells to trigger the release of docetaxel and oxaliplatin,exerting a synergistic anti-tumor effect.We find that the combination effectively induces immunogenic cell death of gastric tumor,enhancing antitumor immune responses.This strategy offers an intelligent and controllable integration platform for precise drug delivery and combined chemo-immunotherapy.
文摘Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine learning(ML)methods struggle with intra-class heterogeneity and inter-class similarity,necessitating more robust classification models.This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning(DL)and Bat Swarm Optimization(BSO)hyperparameter optimization to improve breast cancer histopathology(BCH)image classification.A dataset of 804 Hematoxylin and Eosin(H&E)stained images classified as Benign,in situ,Invasive,and Normal categories(ICIAR2018_BACH_Challenge)has been utilized.ResNet50 was utilized for feature extraction,while Support Vector Machines(SVM),Random Forests(RF),XGBoosts(XGB),Decision Trees(DT),and AdaBoosts(ADB)were utilized for classification.BSO was utilized for hyperparameter optimization in a soft voting ensemble approach.Accuracy,precision,recall,specificity,F1-score,Receiver Operating Characteristic(ROC),and Precision-Recall(PR)were utilized for model performance metrics.The model using an ensemble outperformed individual classifiers in terms of having greater accuracy(~90.0%),precision(~86.4%),recall(~86.3%),and specificity(~96.6%).The robustness of the model was verified by both ROC and PR curves,which showed AUC values of 1.00,0.99,and 0.98 for Benign,Invasive,and in situ instances,respectively.This ensemble model delivers a strong and clinically valid methodology for breast cancer classification that enhances precision and minimizes diagnostic errors.Future work should focus on explainable AI,multi-modal fusion,few-shot learning,and edge computing for real-world deployment.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFD1200101)the Earmarked Fund for Modern Agro-industry Technology Research System(Grant No.CARS-23)Science and Technology Innovation Program of the Chinese Academy of Agricultural Science(Grant No.CAAS-ASTIP-IVFCAAS).
文摘Bacterial soft rot(BSR)caused by Pectobacterium carotovorum subsp.brasiliense(Pcb)is a serious bacterial disease which negatively impact yield and quality in cucumber.However,the genetic mechanism of BSR resistance in cucumber has not been reported.Here,we investigated the BSR resistance of 119 cucumber core germplasm worldwide at the seedling stage and identified 26 accessions highly resistant to BSR.A total of 1642740 single-nucleotide polymorphisms(SNPs)were used to conduct GWAS,and five loci associated with BSR resistance were detected on four chromosomes:gBSR2.1,gBSR2.2,gBSR3.1,gBSR4.1 and gBSR5.1.Based on haplotype analysis,sequence polymorphisms,functional annotation and qRT-PCR analysis,six candidate genes were identified within the five loci.CsaV3_2G014450,CsaV3_2G014490,CsaV3_2G016000,CsaV3_3G000850,CsaV3_4G033150,and CsaV3_5G000390 each had nonsynonymous SNPs,and were significantly up-regulated in the resistant genotypes after inoculation.And CsaV3_5G000390 in the susceptible genotype was significantly up-regulated after inoculation.The identification of these candidate genes lays a foundation for understanding the genetic mechanism of BSR resistance in cucumber.Generally,our study mined genes associated with BSR resistance in cucumber seedlings and will assist the breeding of BSR-resistant cucumber cultivars.
基金supported by the Innovative Research Group Project of the National Natural Science Foundation of China(22021004)Sinopec Major Science and Technology Projects(321123-1)。
文摘Coking at the fractionating tower bottom and the decant oil circulation system disrupts the heat balance,leading to unplanned shutdown and destroying the long period stable operation of the Fluid Catalytic Cracking Unit(FCCU).The FCCU operates through interconnected subsystems,generating high-dimensional,nonlinear,and non-stationary data characterized by spatiotemporally correlated.The decant oil solid content is the crucial indicator for monitoring catalyst loss from the reactor-regenerator system and coking risk tendency at the fractionating tower bottom that relies on sampling and laboratory testing,which is lagging responsiveness and labor-intensive.Developing the online decant oil solid content soft sensor using industrial data to support operators in conducting predictive maintenance is essential.Therefore,this paper proposes a hybrid deep learning framework for soft sensor development that combines spatiotemporal pattern extraction with interpretability,enabling accurate risk identification in dynamic operational conditions.This framework employs a Filter-Wrapper method for dimensionality reduction,followed by a 2D Convolutional Neural Network(2DCNN)for extracting spatial patterns,and a Bidirectional Gated Recurrent Unit(BiGRU)for capturing long-term temporal dependencies,with an Attention Mechanism(AM)to highlight critical features adaptively.The integration of SHapley Additive exPlanations(SHAP),Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN),2DCNN,and expert knowledge precisely quantifies feature contributions and decomposes signals,significantly enhancing the practicality of risk identification.Applied to a China refinery with processing capacity of 2.80×10^(6) t/a,the soft sensor achieved the R^(2) value of 0.93 and five-level risk identification accuracy of 96.42%.These results demonstrate the framework's accuracy,robustness,and suitability for complex industrial scenarios,advancing risk visualization and management.
基金National Natural Science Foundation of China(52171191,52371198)Project of Constructing National Independent Innovation Demonstration Zones(XM2024XTGXQ05)。
文摘Soft magnetic alloys are extensively used in various power electronic devices due to their advantageous properties,including high saturation magnetic induction,low coercivity,and high permeability.In certain applications,complex-shaped components are increasingly required for performance enhancement.Additive manufacturing technique,particularly selective laser melting(SLM),has emerged as an effective method for fabricating such complex-shaped soft magnetic components.SLM,a laserbased additive manufacturing technique,employs high-power-density lasers to melt and fuse metal powders within a powder bed selectively.This approach enables rapid prototyping,precise geometrical control,and the integration of multi-material designs.This review highlights recent advancements in the application of SLM technique for the production of soft magnetic alloys,focusing on Fe-Si,Fe-Ni,Fe-Co,and amorphous alloy systems.Moreover,it explores the implementation of SLM in manufacturing processes and evaluates both the opportunities and challenges associated with SLM-based production of soft magnetic alloys.
文摘Dear Editor,This letter presents a new approach to developing interpretable and reliable soft sensors for Industry 5.0 applications.Although sophisticated machine learning methods have made remarkable strides in soft-sensor predictive accuracy,ensuring interpretability and reliable performance across varying industrial operating conditions remains a challenge[1]–[4].This is precisely what Industry 5.0,proposed by the European Commission in 2021,advocates[5],[6].It integrates various cutting-edge technologies,such as human-machine interaction,digital twins,cybersecurity and artificial intelligence,to facilitate the development of better soft sensors.
基金financial support from the Natural Science Foundation of Jiangsu Province(BK20220859)the Jiangsu Provincial Scientific Research Center of Applied Mathematics(BK20233002)+2 种基金the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_0473)the SEU Innovation Capability Enhancement Plan for Doctoral Students(CXJH_SEU 24144)supported by Open Research Fund of State Key Laboratory of Analytical Chemistry for Life Science,School of Chemistry and Chemical Engineering,Nanjing University。
文摘Soft machines harness material-level physical intelligence to perform adaptive tasks,enabling advancements in biomedical and human-machine interaction fields.Soft switches are the basic building blocks to achieve intelligent functions like autonomous decisions and mechanical computation.However,current soft switches suffer from complex fabrication processes,limited performance,and a lack of multimodal control,which hinder their practical application and the realization of machine intelligence.Herein,by harnessing the unique self-pinch and self-healing effects of the gallium-based liquid metals(LMs),we describe a soft high-performance electric switch composed of an LM line encapsulated within an elastomer.Applying pressure to deform the LM switch can increase local current density,leading to the electromagnetic self-pinch effect for switching off.After releasing pressure,the LM can spontaneously heal with the elastic recovery of the elastomer for switching on.This LM switch shows comprehensive advantages,including a compact design(0.5 mm×1.5 mm×10 mm),good stretchability(100%),high on/off ratio(~10^(9)),rapid response time(<100 ms),and excellent durability(>12000 cycles).Moreover,the LM switches enable multiple control modes,including magnetic and optical stimulation,through the integration of responsive materials.We demonstrate various LM switch-enabled functional soft machines,such as an interactive flexible gripper,a self-oscillating soft crawler,and wearable logic gates.This work will open new avenues for the application of LM in intelligent soft machines and advanced wearable electronics.
基金financially supported by the Natural Science Foundation of Jiangsu Province,China(No.BK 20221502)the National Natural Science Foundation of China(No.42477147)。
文摘In this research,a comparative analysis was conducted on the performance and efficiency of the dual-anchor soft robot(DASR)and the extension-contraction soft robot(ECSR).These robots were constructed by imitating the locomotion of razor clams.The penetration force for extension actuators and the anchorage force for expansion actuators in dry sand with distinct relative densities were tested by differentiating input air pressure and length-to-diameter ratios(λ).On the basis of the findings,a DASR and an ECSR were developed.DASR comprised two expansion actuators as the head and the tail segments at two ends,and one extension actuator as the middle segment.ECSR was composed of an extension actuator.A method based on the force equilibrium was introduced to ascertain and adjust the geometric parameters(length of each segment)of DASR.The burrowing-out performance and efficiency of DASR and ECSR in sands with distinct relative densities were explored.The results revealed that DASR exhibited high efficiency in dense sand in terms of lower time of burrowing-out,slip-to-advancement ratio,and cost of transport.ECSR might perform better in looser sand in terms of higher average burrowing-out velocity,higher advancement in each cycle,and lower energy consumption.However,it had larger slips than DASR.DASR could realize steady advancement and net displacement in each cycle and effectively decrease slips.These findings demonstrate the benefits and usability of the dual-anchor motion and offer new insights into the application of the dual-anchor mechanism in the burrowing of robots.
文摘Wireless Sensor Networks(WSNs)play a crucial role in numerous Internet of Things(IoT)applications and next-generation communication systems,yet they continue to face challenges in balancing energy efficiency and reliable connectivity.This study proposes SAC-HTC(Soft Actor-Critic-based High-performance Topology Control),a deep reinforcement learning(DRL)method based on the Actor-Critic framework,implemented within a Software Defined Wireless Sensor Network(SDWSN)architecture.In this approach,sensor nodes periodically transmit state information,including coordinates,node degree,transmission power,and neighbor lists,to a centralized controller.The controller acts as the reinforcement learning(RL)agent,with the Actor generating decisions to adjust transmission ranges,while the Critic evaluates action values to reflect the overall network performance.The bidirectional Node-Controller feedback mechanism enables the controller to issue appropriate control commands to each node,ensuring the maintenance of the desired node degree,reducing energy consumption,and preserving network connectivity.The algorithmfurther incorporates soft entropy adjustment to balance exploration and exploitation,alongwith an off-policy mechanism for efficient data reuse,making it well-suited to the resource-constrained conditions ofWSNs.Simulation results demonstrate that SAC-HTC not only outperforms traditional methods and several existing RL algorithms but also achieves faster convergence,optimized communication range control,global connectivity maintenance,and extended network lifetime.The key novelty of this research lies in the integration of the SAC method with the SDWSN architecture forWSNs topology control,providing an adaptive,efficient,and highly promisingmechanism for large-scale,dynamic,and high-performance sensor networks.
基金Support by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004]Guangzhou Huashang University[2024HSZD01,HS2023JYSZH01].
文摘Soft-tissue motion introduces significant challenges in robotic teleoperation,especially in medical scenarios where precise target tracking is critical.Latency across sensing,computation,and actuation chains leads to degraded tracking performance,particularly around high-acceleration segments and trajectory inflection points.This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking.Three models—autoregressive(AR),long short-term memory(LSTM),and temporal convolutional network(TCN)—were implemented and evaluated on both synthetic and real datasets.By aligning the prediction horizon with the end-to-end system delay,we demonstrate that prediction-based compensation significantly reduces tracking errors.Among the models,TCN achieved superior robustness and accuracy on complex motion patterns,particularly in multi-step prediction tasks,and exhibited better latency–horizon compatibility.The results suggest that TCN is a promising candidate for real-time latency compensation in teleoperated robotic systems involving dynamic soft-tissue interaction.
基金National Natural Science Foundation of China under Grant No.52278340Natural Science Foundation of Hebei Province under Grant No.E2023202028。
文摘In this study,the dynamic characteristics and microstructures of lacustrine soft clays were studied.Dynamic character tests were conducted on undisturbed,remolded,and saturated lacustrine soft clays,using a dynamic triaxial tester.A scanning electron microscope(SEM)was employed to assess the soil samples after dynamic testing.The results indicate that the dynamic characteristics of lacustrine soft clay were significantly affected by confining pressure and water content.A quantitative relationship was established among confining pressures,water content,and the dynamic shear modulus ratio.The dynamic characteristic parameters of undisturbed,remolded and saturated soil are obviously different,and the original structure can enhance the shear strength of soil.By comparing the results with those from other studies,we found that the dynamic characters of soft clays were considerably varied in different regions,and lacustrine soft clays had a larger dynamic shear modulus ratio and a smaller damping ratio when the dynamic shear strain was large.Using IPP software to process the microstructural images,we found that the soil was dominated by small pores and medium particles,and the roundness of pores and particles had an apparently positive correlation with the maximum diameter.Moreover,the pores and particles of the soil showed fractal characteristics and directionality,and the fractal dimensions and probability entropy were strongly correlated with the macrostructural parameters.Finally,we developed a prediction model for macrostructural and microstructural parameters.
基金supported in part by the National Natural Science Foundation of China under grant 52175556the Macao Science and Technology Development Fund under grant 0004/2022/AKP,0102/2022/A2,and 0078/2023/RIB3+1 种基金the Research Committee of the University of Macao under grants MYRG2022-00068-FST and MYRG-CRG202200004-FST-ICIthe Guangdong Basic and Applied Basic Research Foundation under grant 2023A1515011178。
文摘In recent years,the rising incidence of gastrointestinal(GI)cancer has triggered an urgent need for effective early intervention strategies.Traditional endoscopic techniques often cause patient discomfort,and it is difficult to navigate deep regions of complex organ structures.This work proposes a kind of bio-inspired magnetic soft robot(BMSR)to address these challenges.The design of the BMSRs is inspired by the rolling motion of the golden wheel spider.Two six-degree-of-freedom(6-DOF)robotic arms are used,where one arm is responsible for real-time manipulation of the BMSRs,and the other is dedicated to monitoring their status.Under the actuation of an external rotating magnetic field,the BMSRs can flexibly climb on inclined surfaces at any angle,involving the inverted surface.Through the powerful output force,the BMSRs can overcome the mobility barrier induced by different human organs,including mucus,folds,and height differences of up to 8 cm.Such an exceptional mobility enables the BMSRs to deliver drugs in the targeted complex GI environment.Moreover,in combination with an endoscope,it provides real-time visual feedback for precise navigation.In vitro animal experiments validate the feasibility of BMSRs,paving a way for their usage in minimally invasive GI treatment.This work advances the potential applications of magnetic soft robots in the biomedical field.
基金support from the National Natural Science Foundation of China(Grant No.42407256)the State Key Laboratory of Hydraulics and Mountain River Engineering,China(Grant No.SKHL2113)the Sichuan Science and Technology Program(Grant No.2024YFHZ0341).
文摘This paper develops a semi-analytical solution for pile penetration in natural soft clays using the strain path method(SPM).The stress-strain behavior of soils is characterized by the S-CLAY1S model,which can capture the anisotropic evolution and destructuring nature of soft clays.By integrating the S-CLAY1S model into the theoretical framework of the SPM,a set of ordinary differential equations is formulated with respect to the vertical coordinate of soil particles.The distribution of excess pore water pressure(EPWP)following pile installation is approximated through one-dimensional(1D)radial integration around the pile shaft.The distribution of stresses and EPWP,along with the evolution of fabric anisotropy within the soil surrounding the pile,is presented to illustrate the response of pile penetration in natural soft clays.The proposed solution is validated against existing theoretical solutions using the SPM and cavity expansion method(CEM),along with experimental data.The findings demonstrate that the SPM reveals lower radial effective stresses and EPWP at the pile shaft than that of CEM.Pile penetration alters the soil's anisotropic properties,inducing rotational hardening and affecting post-installation stress distribution.Soil destructuration eliminates bonding among particles near the pile,resulting in a complete disruption of soil structure at the pile surface,which is particularly pronounced for higher initial soil structure ratios.Minimal variation was observed in the three principal stresses and shear stress on the cone side surface as the angle increased from 18°to 60°,except for a slight reduction in EPWP.
基金financially supported by the Hubei Key Laboratory of Pollutant Analysis&Reuse Technology(No.PA230102)。
文摘Polyurethane elastomers exhibit high dielectric constants owing to their polar groups,and can be used as energy storage capacitors.Energy storage depends not only on the dielectric constant but also on the dielectric loss.However,the relationship between chain structure and dielectric properties is not yet clear.Ketal-containing crosslinked polyurethane elastomers were prepared using cyclic ketal diol as a chain extender.The effect of the soft segment length on the dielectric properties and energy storage was investigated.The cause of the change in the dipolar polarization with the soft segment length was analyzed.As the soft segment length increased,the hard-soft hydrogen bonding decreased,whereas the hard-hard hydrogen bonding increased.Under the action of an electric field,the polar bonds in the ketal-containing polyurethane elastomer overcome the hydrogen bonding between hard-soft segments to produce polarization;meanwhile,they also experience crankshaft motions to generate polarization.The former has a relatively high relaxation activation energy of approximately 10-20 k J·mol^(-1),resulting in a large dielectric loss.The latter has a relatively low relaxation activation energy,approximately 0.7-1.7 kJ·mol^(-1),leading to low dielectric loss.As a result,the dielectric constant showed a decreasing trend,and the dielectric loss gradually decreased.This study provides a theoretical foundation for improving the dielectric properties of polyurethane elastomers.
基金financial support from the National Key Research and Development Program of China(2024YFA0919100)the National Natural Science Foundation of China(32371435)+2 种基金the Qinglan Project of Jiangsu Province(2025 Excellent Young Scholar,Bingbing Gao)the Jiangsu government scholarship for overseas studies(Bingbing Gao)the Nanjing Tech University Teaching Reform Project(20250281)。
文摘Soft robots,characterized by compliance,adaptability,and multimodal responsiveness,represent a rapidly advancing frontier in biomedical applications,wearable technologies,and environmental exploration.This review summarizes recent progress in soft robotics with a focus on material innovation,structural design,functional integration,and intelligent responsiveness.Emphasis is placed on the development of bioinspired and stimuli-responsive materials,the construction of modular and reconfigurable architectures,and the integration of actuation,sensing,and energy systems.Microneedle array-based soft robots and hydrogel-based 4D-printed systems are introduced as representative platforms for drug delivery,wound healing,and environmental monitoring.Key challenges,including limited durability,power autonomy,and multifunctional synergy,are critically analyzed in relation to practical operation and long-term reliability.Future directions involve the convergence of self-healing materials,intelligent control algorithms,and multiscale integration strategies to achieve enhanced adaptability and clinical translation.This review provides a comprehensive overview of the interdisciplinary development of next-generation soft robots that bridge materials science,biomedical engineering,and intelligent systems,paving the way toward real-world applications.
基金supported by National Natural Science Foundation of China(No.52103044)Double First-Class Initiative University of Science and Technology of China(KY2400000037)the Young Talent Programme(GG2400007009).
文摘Conductive elastomers combining micromechanical sensitivity,lightweight adaptability,and environmental sustainability are critically needed for advanced flexible electronics requiring precise responsiveness and long-term wearability;however,the integration of these properties remains a significant challenge.Here,we present a biomass-derived conductive elastomer featuring a rationally engineered dynamic crosslinked network integrated with a tunable microporous architecture.This structural design imparts pronounced micromechanical sensitivity,an ultralow density(~0.25 g cm^(−3)),and superior mechanical compliance for adaptive deformation.Moreover,the unique micro-spring effect derived from the porous architecture ensures exceptional stretchability(>500%elongation at break)and superior resilience,delivering immediate and stable electrical response under both subtle(<1%)and large(>200%)mechanical stimuli.Intrinsic dynamic interactions endow the elastomer with efficient room temperature self-healing and complete recyclability without compromising performance.First-principles simulations clarify the mechanisms behind micropore formation and the resulting functionality.Beyond its facile and mild fabrication process,this work establishes a scalable route toward high-performance,sustainable conductive elastomers tailored for next-generation soft electronics.
基金Open access funding provided by The Science,Technology&Innovation Funding Authority(STDF)in cooperation with The Egyptian Knowledge Bank(EKB).
文摘Soft robotic manipulators represent a rapidly evolving field characterized by inherent compliance,adaptability,and safe interactions within unstructured environments.Over the past decade(2015-2025),significant advancements have trans-formed their capabilities through novel designs inspired by biological systems,advanced modeling frameworks,sophisti-cated control strategies,and integration into diverse real-world applications.Recent innovations in multifunctional mate-rials and emerging actuation technologies have markedly expanded manipulator performance,reliability,and dexterity.Concurrently,developments in modeling have progressed from simplified geometric methods toward highly accurate physics-based and hybrid data-driven approaches,substantially improving real-time prediction and controllability.Coupled with these developments,adaptive and robust control strategies-including learning-based techniques-have enabled unprec-edented autonomy and precision in challenging application domains such as Minimally Invasive Surgery(MIS),precision agriculture,deep-sea exploration,disaster recovery,and space missions.Despite these remarkable strides,key challenges remain,notably regarding scalability,long-term material durability,robust integrated sensing,and standardized evaluation procedures.This review comprehensively synthesizes recent advances,critically evaluates state-of-the-art methodologies,and systematically identifies existing gaps to provide a clear roadmap and targeted research directions,guiding future developments toward the broader adoption and optimal utilization of soft robotic manipulators.
