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.

Masquelet technique using an allogeneic cortical bone graft for a large bone defect:A case report

BACKGROUND The induced-membrane technique was initially described by Masquelet as an effective treatment for large bone defects,especially those caused by infection.Here,we report a case of chronic osteomyelitis of the radius associated with a 9 cm bone defect,which was filled with a large allogeneic cortical bone graft from a bone bank.Complete bony union was achieved after 14 months of follow-up.Previous studies have used autogenous bone as the primary bone source for the Masquelet technique;in our case,the exclusive use of allografts is as successful as the use of autologous bone grafts.With the advent of bone banks,it is possible to obtain an unlimited amount of allograft,and the Masquelet technique may be further improved based on this new way of bone grafting.CASE SUMMARY In this study,we reported a case of repair of a long bone defect in a 40-year-old male patient,which was characterized by the utilization of allograft cortical bone combined with the Masquelet technique for the treatment of the patient's long bone defect in the forearm.The patient's results of functional recovery of the forearm were surprising,which further deepens the scope of application of Masquelet technique and helps to strengthen the efficacy of Masquelet technique in the treatment of long bones indeed.CONCLUSION Allograft cortical bone combined with the Masquelet technique provides a new method of treatment to large bone defect.

Exploring battery material failure mechanisms through synchrotron X-ray characterization techniques

Rechargeable battery cycling performance and related safety have been persistent concerns.It is crucial to decipher the capacity fading induced by electrode material failure via a range of techniques.Among these,synchrotron-based X-ray techniques with high flux and brightness play a key role in understanding degradation mechanisms.In this comprehensive review,we summarize recent advancements in degra-dation modes and mechanisms that were revealed by synchrotron X-ray methodologies.Subsequently,an overview of X-ray absorption spectroscopy and X-ray scattering techniques is introduced for charac-terizing failure phenomena at local coordination atomic environment and long-range order crystal struc-ture scale,respectively.At last,we envision the future of exploring material failure mechanism.

Advancements and challenges in neuroimaging for the diagnosis of intracranial aneurysms:Addressing false positive diagnoses and emerging techniques

Despite advancements in neuroimaging,false positive diagnoses of intracranial aneurysms remain a significant concern.This article examines the causes,prevalence,and implications of such false-positive diagnoses.We discuss how conditions like arterial occlusion with vascular stump formation and infundibular widening can mimic aneurysms,particularly in the anterior circulation.The article compares various imaging modalities,including computer tomography angiogram,magnetic resonance imaging/angiography,and digital subtraction angiogram,highlighting their strengths and limitations.We emphasize the im-portance of accurate differentiation to avoid unnecessary surgical interventions.The potential of emerging technologies,such as high-resolution vessel wall ima-ging and deep neural networks for automated detection,is explored as promising avenues for improving diagnostic accuracy.This manuscript underscores the need for continued research and clinical vigilance in the diagnosis of intracranial aneurysms.

The Application of Analytical Techniques to Alpha-Synuclein in Parkinson’s Disease

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia, as well as non-motor symptoms including cognitive impairment and mood disorders. A hallmark of PD is the accumulation of alpha-synuclein, a presynaptic neuronal protein that aggregates to form Lewy bodies, leading to neuronal dysfunction and cell death. The study of alpha-synuclein and its pathological forms is crucial for understanding the etiology of PD and developing effective diagnostic and therapeutic strategies. Analytical techniques play a pivotal role in elucidating the structure, function, and aggregation mechanisms of alpha-synuclein. Biochemical methods such as Western blotting and enzyme-linked immunosorbent assay (ELISA) are employed to detect and quantify alpha-synuclein in biological samples, offering insights into its expression levels and post-translational modifications. Imaging techniques like immunohistochemistry and positron emission tomography (PET) allow for the visualization of alpha-synuclein aggregates in tissue samples and in vivo, respectively, facilitating the study of its spatial distribution and progression in PD Spectroscopic methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry, provide detailed structural information on alpha-synuclein and its isoforms, aiding in the identification of conformational changes associated with aggregation. Emerging techniques such as cryo-electron microscopy (Cryo-EM) and single-molecule fluorescence enable high-resolution structural analysis and real-time monitoring of alpha-synuclein aggregation dynamics, respectively. The application of these analytical techniques has significantly advanced our understanding of the pathophysiological role of alpha-synuclein in PD. They have contributed to the identification of potential biomarkers for early diagnosis and the evaluation of therapeutic interventions targeting alpha-synuclein aggregation. Despite technical limitations and challenges in clinical translation, ongoing advancements in analytical methodologies hold promise for improving the diagnosis, monitoring, and treatment of Parkinson’s disease through a deeper understanding of alpha-synuclein pathology.

Bicuspidization Using the Open-Sleeve Technique for Congenital Aortic Stenosis during Infancy

Congenital aortic stenosis(cAS)frequently requires intervention during the neonatal or infantile period.However,surgical repair is challenging because of the narrow surgical space.We performed bicuspidization using the open-sleeve technique for cAS with a unicuspid aortic valve in two patients.Postoperatively,the patients were doing well without reintervention for the aortic valve for 8 and 6 years,respectively.Their aortic annular diameter increased along with somatic growth.Bicuspidization for neonates or infancy can be performed safely using the open-sleeve technique as its midterm results have been satisfactory.

Novel fabrication techniques for ultra-thin silicon based flexible electronics

Flexible electronics offer a multitude of advantages,such as flexibility,lightweight property,portability,and high durability.These unique properties allow for seamless applications to curved and soft surfaces,leading to extensive utilization across a wide range of fields in consumer electronics.These applications,for example,span integrated circuits,solar cells,batteries,wearable devices,bio-implants,soft robotics,and biomimetic applications.Recently,flexible electronic devices have been developed using a variety of materials such as organic,carbon-based,and inorganic semiconducting materials.Silicon(Si)owing to its mature fabrication process,excellent electrical,optical,thermal properties,and cost efficiency,remains a compelling material choice for flexible electronics.Consequently,the research on ultra-thin Si in the context of flexible electronics is studied rigorously nowadays.The thinning of Si is crucially important for flexible electronics as it reduces its bending stiffness and the resultant bending strain,thereby enhancing flexibility while preserving its exceptional properties.This review provides a comprehensive overview of the recent efforts in the fabrication techniques for forming ultra-thin Si using top-down and bottom-up approaches and explores their utilization in flexible electronics and their applications.

Electrospinning-hot pressing technique for the fabrication of thermal and electrical storage membranes and its applications

The combination of electrospinning and hot pressing,namely the electrospinning-hot pressing technique(EHPT),is an efficient and convenient method for preparing nanofibrous composite materials with good energy storage performance.The emerging composite membrane prepared by EHPT,which exhibits the advantages of large surface area,controllable morphology,and compact structure,has attracted immense attention.In this paper,the conduction mechanism of composite membranes in thermal and electrical energy storage and the performance enhancement method based on the fabrication process of EHPT are systematically discussed.Moreover,the state-of-the-art applications of composite membranes in these two fields are introduced.In particular,in the field of thermal energy storage,EHPT-prepared membranes have longitudinal and transverse nanofibers,which generate unique thermal conductivity pathways;also,these nanofibers offer enough space for the filling of functional materials.Moreover,EHPT-prepared membranes are beneficial in thermal management systems,building energy conservation,and electrical energy storage,e.g.,improving the electrochemical properties of the separators as well as their mechanical and thermal stability.The application of electrospinning-hot pressing membranes on capacitors,lithium-ion batteries(LIBs),fuel cells,sodium-ion batteries(SIBs),and hydrogen bromine flow batteries(HBFBs)still requires examination.In the future,EHPT is expected to make the field more exciting through its own technological breakthroughs or be combined with other technologies to produce intelligent materials.

Isothermal hydrogen absorption process of Pd-capped Mg films traced by ion beam techniques and optical methods

Pd-capped nanocrystalline Mg films were prepared by electron beam evaporation and hydrogenated under isothermal conditions to inves-tigate the hydrogen absorption process via ion beam techniques and in situ optical methods.Films were characterized by different techniques such as X-ray diffraction(XRD)and scanning electron microscopy(SEM).Rutherford backscattering spectrometry(RBS)and elastic recoil detection analysis(ERDA)provided a detailed compositional depth profile of the films during hydrogenation.Gas-solid reaction kinetics theory applied to ERDA data revealed a H absorption mechanism controlled by H diffusion.This rate-limiting step was also confirmed by XRD measurements.The diffusion coefficient(D)was also determined via RBS and ERDA,with a value of(1.1±0.1)·10^(−13)cm^(2)/s at 140℃.Results confirm the validity of IBA to monitor the hydrogenation process and to extract the control mechanism of the process.The H kinetic information given by optical methods is strongly influenced by the optical absorption of the magnesium layer,revealing that thinner films are needed to extract further and reliable information from that technique.

Prediction of high-embankment settlement combining joint denoising technique and enhanced GWO-v-SVR method

Reliable long-term settlement prediction of a high embankment relates to mountain infrastructure safety.This study developed a novel hybrid model(NHM)that combines a joint denoising technique with an enhanced gray wolf optimizer(EGWO)-n-support vector regression(n-SVR)method.High-embankment field measurements were preprocessed using the joint denoising technique,which in-cludes complete ensemble empirical mode decomposition,singular value decomposition,and wavelet packet transform.Furthermore,high-embankment settlements were predicted using the EGWO-n-SVR method.In this method,the standard gray wolf optimizer(GWO)was improved to obtain the EGWO to better tune the n-SVR model hyperparameters.The proposed NHM was then tested in two case studies.Finally,the influences of the data division ratio and kernel function on the EGWO-n-SVR forecasting performance and prediction efficiency were investigated.The results indicate that the NHM suppresses noise and restores details in high-embankment field measurements.Simultaneously,the NHM out-performs other alternative prediction methods in prediction accuracy and robustness.This demonstrates that the proposed NHM is effective in predicting high-embankment settlements with noisy field mea-surements.Moreover,the appropriate data division ratio and kernel function for EGWO-n-SVR are 7:3 and radial basis function,respectively.

Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.

A Systematic Review of Computer Vision Techniques for Quality Control in End-of-Line Visual Inspection of Antenna Parts

The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.

No-touch isolation technique in emergency pancreaticoduodenectomy for neoplastic hemorrhage: Two case reports and review of literature

BACKGROUND Emergency pancreaticoduodenectomy(EPD)is a rare event for complex periam-pullary etiology.Increased intraoperative blood loss is correlated with poor post-operative outcomes.CASE SUMMARY Two patients underwent EPD using a no-touch isolation technique,in which all arteries supplying the pancreatic head region were ligated and divided before manipulation of the pancreatic head and duodenum.The operative times were 220 and 239 min,and the blood loss was 70 and 270 g,respectively.The patients were discharged on the 14^(th) and 10^(th) postoperative day,respectively.Thirty-two patients underwent EPD for the treatment of neoplastic bleeding.The mean operative time was 361.6 min,and the mean blood loss was 747.3 g.The comp-lication rate was 37.5%.The in-hospital mortality rate was 9.38%.CONCLUSION The no-touch isolation technique is feasible,safe,and effective for reducing intraoperative blood loss in EPD.

Micro-power negative pressure wound technique reduces risk of incision infection following loop ileostomy closure

BACKGROUND Prophylactic loop ileostomy is an effective way to reduce the clinical severity of anastomotic leakage following radical resection of rectal cancer.Incisional surgical site infection(SSI)is a common complication after ileostomy closure.AIM To evaluate the efficacy and safety of the micro-power negative pressure wound technique(MPNPWT)in preventing incisional SSI.METHODS This was a prospective,randomized controlled clinical trial conducted at a single center.A total of 101 consecutive patients who underwent ileostomy closure after rectal cancer surgery with a prophylactic ileostomy were enrolled from January 2019 to December 2021.Patients were randomly allocated into an MPNPWT group and a control group.The MPNPWT group underwent intermittent suturing of the surgical incision with 2-0 Prolene and was covered with a micro-power negative pressure dressing.The surgical outcomes were compared between the MPNPWT(n=50)and control(n=51)groups.Risk factors for incisional SSI were identified using logistic regression.RESULTS There were no differences in baseline characteristics between the MPNPWT(n=50)and control groups(n=51).The incisional SSI rate was significantly higher in the control group than in the MPNPWT group(15.7%vs 2.0%,P=0.031).However,MPNPWT did not affect other surgical outcomes,including intra-abdominal complications,operative time,and blood loss.Postoperative hospital stay length and hospitalization costs did not differ significantly between the two groups(P=0.069 and 0.843,respectively).None of the patients experienced adverse effects of MPNPWT,including skin allergy,dermatitis,and pain.MPNPWT also helped heal the infected incision.Our study indicated that MPNPWT was an independent protective factor[odds ratio(OR)=0.005,P=0.025)]and diabetes was a risk factor(OR=26.575,P=0.029)for incisional SSI.CONCLUSION MPNPWT is an effective and safe way to prevent incisional SSI after loop ileostomy closure.

Standardizing MI-TLIF, a Proposal for a Reproducible Technique

Background: Minimally invasive transforaminal lumbar interbody fusion (MI TLIF) is a widely known and performed technique, however its versatility among different physicians continues to hinder its replication and results. Therefore, this study aimed to provide a step-by-step surgical guide to perform a safe MI-TLIF, based on the results obtained in patients operated on by a single surgeon over a period of 12 years. Patients and methods: A retrospective, single center, longitudinal, and observational cohort study was conducted with 931 patients who underwent MI TLIF by a single surgeon between 2010 and 2022 using the technique described on this paper, each with a minimum follow-up of 12 months. Criteria included Schizas classification, listhesis according to Meyerding classification, number of levels treated, cage size, and complications (screw repositioning or cerebrospinal fluid leak). Patient clinical outcomes were assessed using the Oswestry Disability Index (ODI), Visual Analog Scale (VAS) for pre- and postoperative radicular pain. Thin slice CT scans were used to assess the progression of the fusion using the Bridwell classification. In the statistical analysis, percentages, median, and interquartile range (IQR) were calculated. Results: Nine hundred and thirty one patients underwent MI TLIF using the technique described, eight hundred and eighty (94.5%) had a single level treated and fifty one (5.5%) had a 2 level procedure (982 levels), an 8mm cage was placed on five hundred and seventeenlevels (52.7%), six hundred and sixty three levels(67.6%) achieved grade I fusion, two hundred and sixty six levels (27.1%) achieved grade II fusion, 52 levels (5.3) achieved grade III fusion and one level (0.1) achieved a grade IV fusion or non-union. Revision surgery was performed on 3 patients (0.3%) for screw repositioning, cerebrospinal fluid leak was present on 2 patients during surgery and treated before closure. VAS scores and ODI were improved at 12 months postop (VAS from 8.70 to 2.30 and ODI from 34.2 to 14.1, (p = 0.001). Conclusions: The MI TLIF technique described could be a safe and easy to replicate way to achieved lumbar interbody fusion, providingclinical and radiological benefits.

Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.

Optimization Techniques for GPU-Based Parallel Programming Models in High-Performance Computing

This study embarks on a comprehensive examination of optimization techniques within GPU-based parallel programming models,pivotal for advancing high-performance computing(HPC).Emphasizing the transition of GPUs from graphic-centric processors to versatile computing units,it delves into the nuanced optimization of memory access,thread management,algorithmic design,and data structures.These optimizations are critical for exploiting the parallel processing capabilities of GPUs,addressingboth the theoretical frameworks and practical implementations.By integrating advanced strategies such as memory coalescing,dynamic scheduling,and parallel algorithmic transformations,this research aims to significantly elevate computational efficiency and throughput.The findings underscore the potential of optimized GPU programming to revolutionize computational tasks across various domains,highlighting a pathway towards achieving unparalleled processing power and efficiency in HPC environments.The paper not only contributes to the academic discourse on GPU optimization but also provides actionable insights for developers,fostering advancements in computational sciences and technology.

ML and DL-based Phishing Website Detection:The Effects of Varied Size Datasets and Informative Feature Selection Techniques

Onemust interact with a specific webpage or website in order to use the Internet for communication,teamwork,and other productive activities.However,because phishing websites look benign and not all website visitors have the same knowledge and skills to inspect the trustworthiness of visited websites,they are tricked into disclosing sensitive information and making them vulnerable to malicious software attacks like ransomware.It is impossible to stop attackers fromcreating phishingwebsites,which is one of the core challenges in combating them.However,this threat can be alleviated by detecting a specific website as phishing and alerting online users to take the necessary precautions before handing over sensitive information.In this study,five machine learning(ML)and DL algorithms—cat-boost(CATB),gradient boost(GB),random forest(RF),multilayer perceptron(MLP),and deep neural network(DNN)—were tested with three different reputable datasets and two useful feature selection techniques,to assess the scalability and consistency of each classifier’s performance on varied dataset sizes.The experimental findings reveal that the CATB classifier achieved the best accuracy across all datasets(DS-1,DS-2,and DS-3)with respective values of 97.9%,95.73%,and 98.83%.The GB classifier achieved the second-best accuracy across all datasets(DS-1,DS-2,and DS-3)with respective values of 97.16%,95.18%,and 98.58%.MLP achieved the best computational time across all datasets(DS-1,DS-2,and DS-3)with respective values of 2,7,and 3 seconds despite scoring the lowest accuracy across all datasets.

Clinical Efficacy of GBR Technique Combined with Temporary Bridgework-Guided Gingival Contouring in Treating Upper Anterior Tooth Loss with Labial Bone Defects

Objective:To investigate the clinical effect of the guided bone regeneration(GBR)technique combined with temporary bridgework-guided gingival contouring in treating upper anterior tooth loss with labial bone defects.Methods:From July 2023 to April 2024,80 patients with upper anterior tooth loss and labial bone defects were admitted to the hospital and selected as evaluation samples.They were divided into an observation group(n=40)and a control group(n=40)using a numerical table lottery scheme.The control group received treatment with the GBR technique,while the observation group received treatment with the GBR technique combined with temporary bridges to guide gingival contouring.The two groups were compared in terms of clinical red aesthetic scores(PES),labial alveolar bone density,labial bone wall thickness,gingival papillae,gingival margin levels,and patient satisfaction.Results:The PES scores of patients in the observation group were higher than those in the control group after surgery(P<0.05).The bone density of the labial alveolar bone and the thickness of the labial bone wall in the observation group were higher than those in the control group.The levels of gingival papillae and gingival margins were lower in the observation group after surgery(P<0.05).Additionally,patient satisfaction in the observation group was higher than in the control group(P<0.05).Conclusion:The GBR technique combined with temporary bridge-guided gingival contouring for treating upper anterior tooth loss with labial bone defects can improve the aesthetic effect of gingival soft tissue,increase alveolar bone density and the thickness of the labial bone wall,and enhance patient satisfaction.This approach is suitable for widespread application in healthcare institutions.

In-situ investigation of atmospheric corrosion behavior of bronze under thin electrolyte layers using electrochemical technique

The atmospheric corrosion behavior of bronze under thin electrolyte layer （TEL） with different thicknesses was monitored using cathodic polarization curves, open circuit potential （OCP） and electrochemical impedance spectroscopy （EIS）. Cathodic polarization result indicates that the cathodic limiting current density increases with decreasing the TEL thickness. EIS result shows that the corrosion rate increases with decreasing the TEL thickness at the initial stage because the corrosion is dominated by the cathodic process, whereas after long immersion time, the corrosion degree with the TEL thickness is in the sequence of 150 μm 〉 310 μm〉 10μm ≈ bulk solution 〉 57 μm. The measurements of OCP and EIS present in-situ electrochemical corrosion information and their results are in good agreement with that of physical characterizations.