Patient satisfaction and follow-up adherence to glaucoma case management clinic in China

AIM:To assess glaucoma patient satisfaction and follow-up adherence in case management and identify associated predictors to improve healthcare quality and patient outcomes.METHODS:In this cross-sectional study,a total of 119 patients completed a Patient Satisfaction Questionnaire-18 and a sociodemographic questionnaire.Clinical data was obtained from the case management system.Follow-up adherence was defined as completing each follow-up within±30d of the scheduled time set by ophthalmologists during the study period.RESULTS:Average satisfaction scored 78.65±7,with an average of 4.39±0.58 across the seven dimensions.Age negatively correlated with satisfaction(P=0.008),whilst patients with follow-up duration of 2 or more years reported higher satisfaction(P=0.045).Multivariate logistics regression analysis revealed that longer follow-up durations were associated with lower follow-up adherence(OR=0.97,95%CI,0.95-1.00,P=0.044).Additionally,patients with suspected glaucoma(OR=2.72,95%CI,1.03-7.20,P=0.044)and those with an annual income over 100000 Chinese yuan demonstrated higher adherence(OR=5.57,95%CI,1.00-30.89,P=0.049).CONCLUSION:The case management model proves effective for glaucoma patients,with positive adherence rates.The implementation of this model can be optimized in the future based on the identified factors and extended to glaucoma patients in more hospitals.

Loss to Specialized Cardiology Follow-Up in Adults Living with Congenital Heart Disease

Background:Much has been written about the loss to follow-up in the transition between pediatric and adult Congenital Heart Disease(CHD)care centers.Much less is understood about the loss to follow-up(LTF)after a successful transition.This is critical too,as patients lost to specialised care are more likely to experience mor-bidity and premature mortality.Aims:To understand the prevalence and reasons for loss to follow-up(LTF)at a large Australian Adult Congenital Heart Disease(ACHD)centre.Methods:Patients with moderate or highly complex CHD and gaps in care of>3 years(defined as LTF)were identified from a comprehensive ACHD data-base.Structured telephone interviews examined current care and barriers to clinic attendance.Results:Overall,407(22%)of ACHD patients(n=1842)were LTF.The mean age at LTF was 31(SD 11.5)years and 54%were male;311(76%)were uncontactable.Compared to adults seen regularly,lost patients were younger,with a greater socio-economic disadvantage,and had less complex CHD(p<0.05 for all).We interviewed 59 patients(14%).The top 3 responses for care absences were“feeling well”(61%),losing track of time(36%),and not needing fol-low-up care(25%).Conclusions:A large proportion of the ACHD population becomes lost to specialised cardiac care,even after a successful transition.This Australian study reports younger age,moderate complexity defects,and socio-economic disadvantage as predictive of loss to follow-up.This study highlights the need for novel approaches to patient-centered service delivery even beyond the age of transition and resources to maintain patient engagement within the ACHD service.

The Impact of Central Environmental Inspections on the Perception of Respect for Human Rights and Its Empirical Analysis

To develop China’s human rights cause with a people-centered approach,we should pay close attention to the concrete experiences of the general public regarding the protection of human rights.Deepening the research on the perception of respect for human rights can contribute to a more comprehensive understanding of the practical achievements of the hu man rights cause.Public environmental rights,as a new type of human rights,have become an important aspect of the development of the human rights cause in the new era.The central envi ronmental inspection,as an authoritative and interventionist vertical governance mechanism,promotes the implementation of environmental policies by local Party committees and govern ments and strengthens environmental information disclosure and public participation in environ mental matters.As a result,it contributes to the realization of public environmental rights and stimulates public perception of respect for human rights.Among them,the“look-back inspec tion is an important component of the central environmental inspection,and its implemen tation consolidates and enhances the previous inspection work.An empirical analysis based on the World Values Survey’s data for China indicates that residents in the provinces that have underg one“look-back inspections are more inclined to believe that human rights are adequately re spected compared to residents in the provinces that have not underwent such inspections.It suggests that the advancement and improvement of the central environmental inspection system promote improvements in ecological environment quality and contribute to enhancing the public percep tion of respect for human rights.

Essential role of postoperative follow-up in the management of clear cell sarcoma

Clear cell sarcoma(CCS)is a rare melanocytic soft tissue sarcoma known for itspropensity to metastasize to the lymph nodes and typically has an unfavorableprognosis.Currently,surgical resection is the primary treatment for localizedCCS,while radiotherapy and chemotherapy are preferred for metastatic cases.The roles of adjuvant chemotherapy,radiotherapy,and lymph node dissection arecontroversial.Although immunotherapy has emerged as a promising avenue inCCS treatment research,there are no established clinical standards for postoperativefollow-up.This editorial discusses a recent article by Liu et al,with afocus on current diagnostic modalities,treatment approaches,and the challengingprognosis associated with CCS.Our aim is to underscore the importance of longtermpatient follow-up in CCS management.

A Railway Fastener Inspection Method Based on Abnormal Sample Generation

Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.

Central environmental protection inspection and carbon emission reduction: A tripartite evolutionary game model from the perspective of carbon neutrality

Since the carbon neutrality target was proposed,many countries have been facing severe challenges to carbon emission reduction sustainably.This study is conducted using a tripartite evolutionary game model to explore the impact of the central environmental protection inspection(CEPI)on driving carbon emission reduction,and to study what factors influence the strategic choices of each party and how they interact with each other.The research results suggest that local governments and manufacturing enterprises would choose strategies that are beneficial to carbon reduction when CEPI increases.When the initial willingness of all parties increases 20%,50%—80%,the time spent for the whole system to achieve stability decreases from 100%,60%—30%.The evolutionary result of“thorough inspection,regulation implementation,low-carbon management”is the best strategy for the tripartite evolutionary game.Moreover,the smaller the cost and the larger the benefit,the greater the likelihood of the three-party game stability strategy appears.This study has important guiding significance for other developing countries to promote carbon emission reduction by environmental policy.

Long-term assessment of collagenase treatment for Dupuytren’s contracture:A 10-year follow-up study

BACKGROUND Enzymatic fasciotomy with collagenase clostridium histolyticum(CCH)has revolutionized the treatment for Dupuytren’s contracture(DC).Despite its benefits,the long-term outcomes remain unclear.This study presented a comprehensive 10-year follow-up assessment of the enduring effects of CCH on patients with DC.AIM To compare the short-term(12 wk)and long-term(10 years)outcomes on CCH treatment in patients with DC.METHODS A cohort of 45 patients was treated with CCH at the metacarpophalangeal(MCP)joint and the proximal interphalangeal(PIP)joint and underwent systematic reevaluation.The study adhered to multicenter trial protocols,and assessments were conducted at 12 wk,7 years,and 10 years post-surgery.RESULTS Thirty-seven patients completed the 10-year follow-up.At 10 years,patients treated at the PIP joint exhibited a 100%recurrence.However,patients treated at the MCP joint only showed a 50%recurrence.Patient satisfaction varied,with a lower satisfaction reported in PIP joint cases.Recurrence exceeding 20 degrees on the total passive extension deficit was observed,indicating a challenge for sustained efficacy.Significant differences were noted between outcomes at the 7-year and 10-year intervals.CONCLUSION CCH demonstrated sustained efficacy when applied to the MCP joint.However,caution is warranted for CCH treatment at the PIP joint due to a high level of recurrence and low patient satisfaction.Re-intervention is needed within a decade of treatment.

Distributed Resource Allocation in Dispersed Computing Environment Based on UAV Track Inspection in Urban Rail Transit

With the rapid development of urban rail transit,the existing track detection has some problems such as low efficiency and insufficient detection coverage,so an intelligent and automatic track detectionmethod based onUAV is urgently needed to avoid major safety accidents.At the same time,the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of devices.As a result,the Dispersed Computing(DCOMP)architecture enables collaborative computing between devices in the Internet of Everything(IoE),promotes low-latency and efficient cross-wide applications,and meets users’growing needs for computing performance and service quality.This paper focuses on examining the resource allocation challenge within a dispersed computing environment that utilizes UAV inspection tracks.Furthermore,the system takes into account both resource constraints and computational constraints and transforms the optimization problem into an energy minimization problem with computational constraints.The Markov Decision Process(MDP)model is employed to capture the connection between the dispersed computing resource allocation strategy and the system environment.Subsequently,a method based on Double Deep Q-Network(DDQN)is introduced to derive the optimal policy.Simultaneously,an experience replay mechanism is implemented to tackle the issue of increasing dimensionality.The experimental simulations validate the efficacy of the method across various scenarios.

Calibration of CO and CO2 Monitors Used in Periodic Inspection of Vehicles at Fixed Stations for Environmental Control

Global efforts for environmental cleanliness through the control of gaseous emissions from vehicles are gaining momentum and attracting increasing attention. Calibration plays a crucial role in these efforts by ensuring the quantitative assessment of emissions for informed decisions on environmental treatments. This paper describes a method for the calibration of CO/CO2 monitors used for periodic inspections of vehicles in cites. The calibration was performed in the selected ranges: 900 - 12,000 µmol/mol for CO and 2000 - 20,000 µmol/mol for CO2. The traceability of the measurement results to the SI units was ensured by using certified reference materials from CO/N2 and CO2/N2 primary gas mixtures. The method performance was evaluated by assessing its linearity, accuracy, precision, bias, and uncertainty of the calibration results. The calibration data exhibited a strong linear trend with R² values close to 1, indicating an excellent fit between the measured values and the calibration lines. Precision, expressed as relative standard deviation (%RSD), ranged from 0.48 to 4.56% for CO and from 0.97 to 3.53% for CO2, staying well below the 5% threshold for reporting results at a 95% confidence level. Accuracy measured as percent recovery, was consistently high (≥ 99.1%) for CO and ranged from 84.90% to 101.54% across the calibration range for CO2. In addition, the method exhibited minimal bias for both CO and CO2 calibrations and thus provided a reliable and accurate approach for calibrating CO/CO2 monitors used in vehicle inspections. Thus, it ensures the effectiveness of exhaust emission control for better environment.

Environmental Impact Assessment Follow-Up of Seismic Survey Offshore Activities in Brazil

Follow-up of environmental impacts is an integral part of the Environmental Impact Assessment (EIA) process, closely related to the effectiveness of the instrument. EIA follow-up has been receiving a lot of interest from scientists and practitioners, though it is recognized as one of the weakest points of EIA systems globally. Also, EIA follow-up is influenced by the context, mainly in terms of the types of projects or activities and their related impacts on the environment. Therefore, the present paper is focused on the investigation of the follow-up stage applied to the activity of seismic survey coupled with offshore oil & gas exploitation in Brazil. Research was based on a qualitative approach that included document analysis and semi-structured interviews with analysts involved in EIA processes, and sought to generate evidence of effectiveness of the EIA follow-up as conducted by the Federal Environment Agency (Ibama) in order to situate the practice of follow-up in the broader context of international best practice principles. Based on the findings, it was concluded that, due to the peculiarities of offshore seismic survey, it is necessary to promote adaptations in the procedures for monitoring impacts in order to ensure proper alignment with the principles and conceptual foundations that guide EIA practice. Specifically, the timing of the execution of the activity imposes challenges for its integration into the “conventional” cycle that has guided the monitoring of the impacts in the EIA of projects.

Automatic Road Tunnel Crack Inspection Based on Crack Area Sensing and Multiscale Semantic Segmentation

The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of roadtunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combinedwith a deep neural network model is an effective means to realize the localization and identification of crackdefects on the surface of road tunnels.We propose a complete set of automatic inspection methods for identifyingcracks on the walls of road tunnels as a solution to the problem of difficulty in identifying cracks during manualmaintenance. First, a set of equipment applied to the real-time acquisition of high-definition images of walls inroad tunnels is designed. Images of walls in road tunnels are acquired based on the designed equipment, whereimages containing crack defects are manually identified and selected. Subsequently, the training and validationsets used to construct the crack inspection model are obtained based on the acquired images, whereas the regionscontaining cracks and the pixels of the cracks are finely labeled. After that, a crack area sensing module is designedbased on the proposed you only look once version 7 model combined with coordinate attention mechanism (CAYOLOV7) network to locate the crack regions in the road tunnel surface images. Only subimages containingcracks are acquired and sent to the multiscale semantic segmentation module for extraction of the pixels to whichthe cracks belong based on the DeepLab V3+ network. The precision and recall of the crack region localizationon the surface of a road tunnel based on our proposed method are 82.4% and 93.8%, respectively. Moreover, themean intersection over union (MIoU) and pixel accuracy (PA) values for achieving pixel-level detection accuracyare 76.84% and 78.29%, respectively. The experimental results on the dataset show that our proposed two-stagedetection method outperforms other state-of-the-art models in crack region localization and detection. Based onour proposedmethod, the images captured on the surface of a road tunnel can complete crack detection at a speed often frames/second, and the detection accuracy can reach 0.25 mm, which meets the requirements for maintenanceof an actual project. The designed CA-YOLO V7 network enables precise localization of the area to which a crackbelongs in images acquired under different environmental and lighting conditions in road tunnels. The improvedDeepLab V3+ network based on lightweighting is able to extract crack morphology in a given region more quicklywhile maintaining segmentation accuracy. The established model combines defect localization and segmentationmodels for the first time, realizing pixel-level defect localization and extraction on the surface of road tunnelsin complex environments, and is capable of determining the actual size of cracks based on the physical coordinatesystemafter camera calibration. The trainedmodelhas highaccuracy andcanbe extendedandapplied to embeddedcomputing devices for the assessment and repair of damaged areas in different types of road tunnels.

Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.

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.

High-speed railway track components inspection framework based on YOLOv8 with high-performance model deployment

Railway inspection poses significant challenges due to the extensive use of various components in vast railway networks,especially in the case of high-speed railways.These networks demand high maintenance but offer only limited inspection windows.In response,this study focuses on developing a high-performance rail inspection system tailored for high-speed railways and railroads with constrained inspection timeframes.This system leverages the latest artificial intelligence advancements,incorporating YOLOv8 for detection.Our research introduces an efficient model inference pipeline based on a producer-consumer model,effectively utilizing parallel processing and concurrent computing to enhance performance.The deployment of this pipeline,implemented using C++,TensorRT,float16 quantization,and oneTBB,represents a significant departure from traditional sequential processing methods.The results are remarkable,showcasing a substantial increase in processing speed:from 38.93 Frames Per Second(FPS)to 281.06 FPS on a desktop system equipped with an Nvidia RTX A6000 GPU and from 19.50 FPS to 200.26 FPS on the Nvidia Jetson AGX Orin edge computing platform.This proposed framework has the potential to meet the real-time inspection requirements of high-speed railways.

A path planning method for robot patrol inspection in chemical industrial parks

Safety patrol inspection in chemical industrial parks is a complex multi-objective task with multiple degrees of freedom.Traditional pointer instruments with advantages like high reliability and strong adaptability to harsh environment,are widely applied in such parks.However,they rely on manual readings which have problems like heavy patrol workload,high labor cost,high false positives/negatives and poor timeliness.To address the above problems,this study proposes a path planning method for robot patrol in chemical industrial parks,where a path optimization model based on improved iterated local search and random variable neighborhood descent(ILS-RVND)algorithm is established by integrating the actual requirements of patrol tasks in chemical industrial parks.Further,the effectiveness of the model and algorithm is verified by taking real park data as an example.The results show that compared with GA and ILS-RVND,the improved algorithm reduces quantification cost by about 24%and saves patrol time by about 36%.Apart from shortening the patrol time of robots,optimizing their patrol path and reducing their maintenance loss,the proposed algorithm also avoids the untimely patrol of robots and enhances the safety factor of equipment.

From manual to automated fabric inspection

Uster,Switzerland,28th March 2024–Uster Technologies offers a flexible solution to upgrade fabric inspection from manual to automated.Integration in existing production lines is quick and easy,and the data flow also brings extra benefits.It means fabric producers can significantly improve their yield with fast,accurate quality monitoring.

Analysis of the Application of Bridge Rapid Inspection and Evaluation Technology

Rapid bridge inspection and evaluation mainly uses information technology to test the quality of bridge infrastructure and structures,integrates the test results with the existing management system,completes the bridge status assessment,establishes information management files to provide bridge disease problem inspection and analysis,and provides support for the application of disposal measures.This paper briefly discusses the necessity of applying rapid inspection and evaluation technology and analyzes the bridge’s rapid inspection and evaluation content,inspection system,and application process.We look forward to the future application prospects of this technology and supporting those in this field.

Defect Inspection Technology for Steel Truss Suspension Bridges

Steel truss suspension bridges are prone to developing defects after prolonged use.These defects may include corrosion of the main cable or the steel truss.To ensure the normal and safe functioning of the suspension bridge,it is necessary to inspect for defects promptly,understand the cause of the defect,and locate it through the use of inspection technology.By promptly addressing defects,the suspension bridge’s safety can be ensured.The author has analyzed the common defects and causes of steel truss suspension bridges and proposed specific inspection technologies.This research is intended to aid in the timely discovery of steel truss suspension bridge defects.

基于PowerInspect五轴在机检测(OMV)的运用研究

本研究项目探讨了如何实现工艺和质量控制自动化,基于数控加工质量控制缺环的解决。在机检测技术(OMV)作为先进的数控加工质量控制技术,可以直接在机床上对工件进行检测,获取定位数据,并根据零件当前状态调整加工程序。这种技术可以大大提高生产自动化能力,减少人工干预和错误率,提高生产效率和产品质量。对于复杂、无传统找正特征的零件,自适应加工技术具有显著优势。

Optimal Maintenance Policy for a Storage System with Finite Number of Inspections

The expected cost per unit of time for a sequential inspection policy is derived. It still has some difficulties to compute an optimal sequential policy numerically, which minimizes the expected cost of a system with finite number of inspections. This paper gives the algorithm for an optimal inspection schedule and specifies the computing procedure for a Weibull distribution. Using this algorithm, optimal inspection times are computed as a numerical result. Compared with the periodic point inspection, the policies in this paper reduce the cost successfully.