Maximizing the probability an aerial anti-submarine torpedo detects its target

As a result of the high speed of anti-submarine patrol aircraft as well as their wide range, high efficiency and other characteristics, aerial torpedoes released by anti-submarine patrol aircraft have become the key anti submarine tool. In order to improve operational efficiency, a deep study was made of the target detection probabilities for aerial torpedoes released by anti-submarine patrol aircraft. The operational modes of aerial torpedoes were analyzed and mathematical-simulation models were then established. The detection probabilities of three attacking modes were then calculated. Measures were developed for improving low probabilities of detection when attacking a probable target position. This study provides an important frame of reference for the operation of aerial torpedo released by anti-submarine patrol aircraft.

pFTAA: a high affinity oligothiophene probe that detects filamentous tau in vivo and in cultured neurons

Tauopathies describe a group of neurodegenerative diseases in which the protein tau,encoded by the gene MAPT,is aberrantly misfolded,leading to tau aggregation,neural dysfunction,and cell death（Spillantini and Goedert,2013）.In Alzheimer＇s disease（AD）,tau forms the characteristic intracellular neurofibrillary tangles（NFTs）,which are thought to be the major cause of neurodegeneration（Bloom,2014）.In other tauopathies,including frontotemporal dementia with Parkinsonism linked to chromo- some 17 （FTDP-17T）, corticobasal degeneration and progressive supranuclear palsy, there are specific forms of tau aggregates and filaments without any amyloid pathology, demonstrating tau＇s po- tent disease-causing potential （Spillantini and Goedert, 2013）. Tau is a microtubule （MT） binding protein, which becomes abnormally hyperphosphorylated on several residues prior/during the process of aggregation, thereby causing loss of its MT binding activity （Mandelkow and Mandelkow, 2012）.

Confidence Interval Estimation of the Correlation in the Presence of Non-Detects

This article deals with correlating two variables that have values that fall below the known limit of detection (LOD) of the measuring device;these values are known as non-detects (NDs). We use simulation to compare several methods for estimating the association between two such variables. The most commonly used method, simple substitution, consists of replacing each ND with some representative value such as LOD/2. Spearman’s correlation, in which all NDs are assumed to be tied at some value just smaller than the LOD, is also used. We evaluate each method under several scenarios, including small to moderate sample size, moderate to large censoring proportions, extreme imbalance in censoring proportions, and non-bivariate normal (BVN) data. In this article, we focus on the coverage probability of 95% confidence intervals obtained using each method. Confidence intervals using a maximum likelihood approach based on the assumption of BVN data have acceptable performance under most scenarios, even with non-BVN data. Intervals based on Spearman’s coefficient also perform well under many conditions. The methods are illustrated using real data taken from the biomarker literature.

Early identification of stroke through deep learning with multi-modal human speech and movement data

Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

Ultra‑High Sensitivity Anisotropic Piezoelectric Sensors for Structural Health Monitoring and Robotic Perception

Monitoring minuscule mechanical signals,both in magnitude and direction,is imperative in many application scenarios,e.g.,structural health monitoring and robotic sensing systems.However,the piezoelectric sensor struggles to satisfy the requirements for directional recognition due to the limited piezoelectric coefficient matrix,and achieving sensitivity for detecting micrometer-scale deformations is also challenging.Herein,we develop a vector sensor composed of lead zirconate titanate-electronic grade glass fiber composite filaments with oriented arrangement,capable of detecting minute anisotropic deformations.The as-prepared vector sensor can identify the deformation directions even when subjected to an unprecedented nominal strain of 0.06%,thereby enabling its utility in accurately discerning the 5μm-height wrinkles in thin films and in monitoring human pulse waves.The ultra-high sensitivity is attributed to the formation of porous ferroelectret and the efficient load transfer efficiency of continuous lead zirconate titanate phase.Additionally,when integrated with machine learning techniques,the sensor’s capability to recognize multi-signals enables it to differentiate between 10 types of fine textures with 100%accuracy.The structural design in piezoelectric devices enables a more comprehensive perception of mechanical stimuli,offering a novel perspective for enhancing recognition accuracy.

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.

MARIE:One-Stage Object Detection Mechanism for Real-Time Identifying of Firearms

Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.

Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

A novel detection method for warhead fragment targets in optical images under dynamic strong interference environments

A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,enabling the acquisition of full-process data of the fragment scattering process.However,mismatches between camera frame rates and target velocities can lead to long motion blur tails of high-speed fragment targets,resulting in low signal-to-noise ratios and rendering conventional detection algorithms ineffective in dynamic strong interference testing environments.In this study,we propose a detection framework centered on dynamic strong interference disturbance signal separation and suppression.We introduce a mixture Gaussian model constrained under a joint spatialtemporal-transform domain Dirichlet process,combined with total variation regularization to achieve disturbance signal suppression.Experimental results demonstrate that the proposed disturbance suppression method can be integrated with certain conventional motion target detection tasks,enabling adaptation to real-world data to a certain extent.Moreover,we provide a specific implementation of this process,which achieves a detection rate close to 100%with an approximate 0%false alarm rate in multiple sets of real target field test data.This research effectively advances the development of the field of damage parameter testing.

Procalcitonin and presepsin for detecting bacterial infection and spontaneous bacterial peritonitis in cirrhosis:A systematic review and meta-analysis

BACKGROUND Diagnosing bacterial infections(BI)in patients with cirrhosis can be challenging because of unclear symptoms,low diagnostic accuracy,and lengthy culture testing times.Various biomarkers have been studied,including serum procal-citonin(PCT)and presepsin.However,the diagnostic performance of these markers remains unclear,requiring further informative studies to ascertain their diagnostic value.AIM To evaluate the pooled diagnostic performance of PCT and presepsin in detecting BI among patients with cirrhosis.INTRODUCTION Bacterial infections(BI)commonly occur in patients with cirrhosis,resulting in poor outcomes,including the development of cirrhotic complications,septic shock,acute-on-chronic liver failure(ACLF),multiple organ failures,and mortality[1,2].BI is observed in 20%-30%of hospitalized patients,with and without ACLF[3].Patients with cirrhosis are susceptible to BI because of internal and external factors.The major internal factors are changes in gut microbial composition and function,bacterial translocation,and cirrhosis-associated immune dysfunction syndrome[4,5].External factors include alcohol use,proton-pump inhibitor use,frailty,readmission,and invasive procedures.Spontaneous bacterial peritonitis(SBP),urinary tract infection,pneumonia,and primary bacteremia are the common BIs in hospit-alized patients with cirrhosis[6].Early diagnosis and adequate empirical antibiotic therapy are two critical factors that improve the prognosis of BI in patients with cirrhosis.However,early detection of BI in cirrhosis is challenging due to subtle clinical signs and symptoms,low sensitivity and specificity of systemic inflammatory response syndrome criteria,and low sensitivity of bacterial cultures.Thus,effective biomarkers need to be identified for the early detection of BI.Several biomarkers have been evaluated,but their efficacy in detecting BI is unclear.Procalcitonin(PCT)is a precursor of the hormone calcitonin,which is secreted by parafollicular cells of the thyroid gland[7].In the presence of BI,PCT gene expression increases in extrathyroidal tissues,causing a subsequent increase in serum PCT level[8].Changes in serum PCT are detectable as early as 4 hours after infection onset and peaks between 8 and 24 hours,making it a valuable diagnostic biomarker for BI.Several studies have demonstrated the favorable diagnostic accuracy of PCT in the diagnosis of BI in individuals with cirrhosis[9-13]and without cirrhosis[14-16].Since 2014,two meta-analyses have been published on the diagnostic value of PCT for SBP and BI in patients with cirrhosis[17,18].Other related studies have been conducted since then[10-12,19-33].Serum presepsin has recently emerged as a promising biomarker for diagnosing BI.This biomarker is the N-terminal fraction protein of the soluble CD14 g-negative bacterial lipopolysaccharide–lipopolysaccharide binding protein(sCD14-LPS-LBP)complex,which is cleaved by inflammatory serum protease in response to BI[34].Presepsin levels increase within 2 hours and peaks in 3 hours[35].This is useful for detecting BI since presepsin levels increase earlier than serum Our systematic review and meta-analysis was performed with adherence to PRISMA guidelines[37].

Intelligent field monitoring system for cruciferous vegetable pests using yellow sticky board images and an improved Cascade R-CNN

Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.

Integration of wearable electronics and heart rate variability for human physical and mental well-being assessment

Heart rate variability(HRV)that can reflect the dynamic balance between the sympathetic nervous and parasympathetic nervous of human autonomic nervous system(ANS)has attracted considerable attention.However,traditional electrocardiogram(ECG)devices for HRV analysis are bulky,and hard wires are needed to attach measuring electrodes to the chest,resulting in the poor wearable experience during the long-term measurement.Compared with that,wearable electronics enabling continuously cardiac signals monitoring and HRV assessment provide a desirable and promising approach for helping subjects determine sleeping issues,cardiovascular diseases,or other threats to physical and mental well-being.Until now,significant progress and advances have been achieved in wearable electronics for HRV monitoring and applications for predicting human physical and mental well-being.In this review,the latest progress in the integration of wearable electronics and HRV analysis as well as practical applications in assessment of human physical and mental health are included.The commonly used methods and physiological signals for HRV analysis are briefly summarized.Furthermore,we highlighted the research on wearable electronics concerning HRV assessment and diverse applications such as stress estimation,drowsiness detection,etc.Lastly,the current limitations of the integrated wearable HRV system are concluded,and possible solutions in such a research direction are outlined.

Advances and challenges in molecular understanding, early detection, and targeted treatment of liver cancer

In this review,we explore the application of next-generation sequencing in liver cancer research,highlighting its potential in modern oncology.Liver cancer,particularly hepatocellular carcinoma,is driven by a complex interplay of genetic,epigenetic,and environmental factors.Key genetic alterations,such as mutations in TERT,TP53,and CTNNB1,alongside epigenetic modifications such as DNA methylation and histone remodeling,disrupt regulatory pathways and promote tumorigenesis.Environmental factors,including viral infections,alcohol consum-ption,and metabolic disorders such as nonalcoholic fatty liver disease,enhance hepatocarcinogenesis.The tumor microenvironment plays a pivotal role in liver cancer progression and therapy resistance,with immune cell infiltration,fibrosis,and angiogenesis supporting cancer cell survival.Advances in immune check-point inhibitors and chimeric antigen receptor T-cell therapies have shown po-tential,but the unique immunosuppressive milieu in liver cancer presents challenges.Dysregulation in pathways such as Wnt/β-catenin underscores the need for targeted therapeutic strategies.Next-generation sequencing is accele-rating the identification of genetic and epigenetic alterations,enabling more precise diagnosis and personalized treatment plans.A deeper understanding of these molecular mechanisms is essential for advancing early detection and developing effective therapies against liver cancer.

Topology Data Analysis-Based Error Detection for Semantic Image Transmission with Incremental Knowledge-Based HARQ

Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.

基于改进Detection Transformer的棉花幼苗与杂草检测模型研究

基于深度学习的目标检测技术在棉花幼苗与杂草检测领域已取得一定进展。论文提出了基于改进Detection Transformer的棉花幼苗与杂草检测模型,以提高杂草目标检测的准确率和效率。首先,引入了可变形注意力模块替代原始模型中的Transformer注意力模块,提高模型对特征图目标形变的处理能力。提出新的降噪训练机制,解决了二分图匹配不稳定问题。提出混合查询选择策略,提高解码器对目标类别和位置信息的利用效率。使用Swin Transformer作为网络主干,提高模型特征提取能力。通过对比原网络,论文提出的模型方法在训练过程中表现出更快的收敛速度,并且在准确率方面提高了6.7%。

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

Esophageal cancer screening,early detection and treatment:Current insights and future directions

Esophageal cancer ranks among the most prevalent malignant tumors globally,primarily due to its highly aggressive nature and poor survival rates.According to the 2020 global cancer statistics,there were approximately 604000 new cases of esophageal cancer,resulting in 544000 deaths.The 5-year survival rate hovers around a mere 15%-25%.Notably,distinct variations exist in the risk factors associated with the two primary histological types,influencing their worldwide incidence and distribution.Squamous cell carcinoma displays a high incidence in specific regions,such as certain areas in China,where it meets the cost-effect-iveness criteria for widespread endoscopy-based early diagnosis within the local population.Conversely,adenocarcinoma(EAC)represents the most common histological subtype of esophageal cancer in Europe and the United States.The role of early diagnosis in cases of EAC originating from Barrett's esophagus(BE)remains a subject of controversy.The effectiveness of early detection for EAC,particularly those arising from BE,continues to be a debated topic.The variations in how early-stage esophageal carcinoma is treated in different regions are largely due to the differing rates of early-stage cancer diagnoses.In areas with higher incidences,such as China and Japan,early diagnosis is more common,which has led to the advancement of endoscopic methods as definitive treatments.These techniques have demonstrated remarkable efficacy with minimal complications while preserving esophageal functionality.Early screening,prompt diagnosis,and timely treatment are key strategies that can significantly lower both the occurrence and death rates associated with esophageal cancer.

Superhydrophobic Surface-Assisted Preparation of Microspheres and Supraparticles and Their Applications

Superhydrophobic surface(SHS) has been well developed, as SHS renders the property of minimizing the water/solid contact interface. Water droplets deposited onto SHS with contact angles exceeding 150°, allow them to retain spherical shapes, and the low adhesion of SHS facilitates easy droplet collection when tilting the substrate. These characteristics make SHS suitable for a wide range of applications. One particularly promising application is the fabrication of microsphere and supraparticle materials. SHS offers a distinct advantage as a universal platform capable of providing customized services for a variety of microspheres and supraparticles. In this review, an overview of the strategies for fabricating microspheres and supraparticles with the aid of SHS, including cross-linking process, polymer melting,and droplet template evaporation methods, is first presented. Then, the applications of microspheres and supraparticles formed onto SHS are discussed in detail, for example, fabricating photonic devices with controllable structures and tunable structural colors, acting as catalysts with emerging or synergetic properties, being integrated into the biomedical field to construct the devices with different medicinal purposes, being utilized for inducing protein crystallization and detecting trace amounts of analytes. Finally,the perspective on future developments involved with this research field is given, along with some obstacles and opportunities.