Effect of low-degree astigmatism on the objective visual quality

AIM:To evaluate the effect of low-degree astigmatism on objective visual quality through the Optical Quality Analysis System(OQAS).METHODS:This study enrolled 46 participants(aged 23 to 30y,90 eyes)with normal or corrected-to-normal vision.The cylindrical lenses(0,0.5,0.75,1.0,and 1.25 D)were placed at the axial direction(180°,45°,90°,and 135°)in front of the eyes with the best correction to form 16 types of regular low-degree astigmatism.OQAS was used to detect the objective visual quality,recorded as the objective scattering index(OSI),OQAS values at contrasts of 100%,20%,and 9%predictive visual acuity(OV100%,OV20%,and OV9%),modulation transfer function cut-off(MTFcut-off)and Strehl ratio(SR).The mixed effect linear model was used to compare objective visual quality differences between groups and examine associations between astigmatic magnitude and objective visual quality parameters.RESULTS:Apparent negative relationships between the magnitude of low astigmatism and objective visual quality were observed.The increase of OSI per degree of astigmatism at 180°,45°,90°,and 135°axis were 0.38(95%CI:0.35,0.42),0.50(95%CI:0.46,0.53),0.49(95%CI:0.45,0.54)and 0.37(95%CI:0.34,0.41),respectively.The decrease of MTFcut-off per degree of astigmatism at 180°,45°,90°,and 135°axis were-10.30(95%CI:-11.43,-9.16),-12.73(95%CI:-13.62,-11.86),-12.75(95%CI:-13.79,-11.70),and-9.97(95%CI:-10.92,-9.03),respectively.At the same astigmatism degree,OSI at 45°and 90°axis were higher than that at 0°and 135°axis,while MTFcut-off were lower.CONCLUSION:Low astigmatism of only 0.50 D can significantly reduce the objective visual quality.

Management of Penetrating Cranioencephalic Trauma Caused by Sharp Metal Objects—Therapeutic and Evolutionary Aspects: 12 Cases at the Renaissance University Hospital in N’Djamena

Introduction: Cranioencephalic trauma caused by bladed weapons is rare, and that caused by sharp objects is exceptional. The aim of our study was to describe the clinical, therapeutic and evolutionary aspects. Materials and method: This was a descriptive and analytical study over a 48-month period at CHU la Renaissance from January 1, 2018 to December 31, 2021, concerning patients admitted for penetrating cranioencephalic trauma by pointed object. Results: Twelve cases, all male, of penetrating cranioencephalic sharp-force trauma were identified. The mean age was 34 ± 7 years, with extremes of 11 and 60 years. Farmers and herders accounted for 31% and 25% of cases respectively. The average admission time was 47 hours. Brawls were the circumstances of occurrence in 81.2% of cases. Knives (33%), arrows (25%) and iron bars (16.6%) were the objects used. Altered consciousness was present in 43.8% of cases, and focal deficit in 50%. Scannographic lesions were fracture and/or embarrhment (12 cases), intra-parenchymal haematomas (6 cases) and presence of object in place (4 cases). Surgery was performed in 11 patients. Postoperative outcome was favorable in 9 patients. After 12 months, 2 patients were declared unfit. Conclusion: Penetrating head injuries caused by sharp objects are common in Chad. Urgent surgery can prevent disabling after-effects.

The Fusion of Temporal Sequence with Scene Priori Information in Deep Learning Object Recognition

For some important object recognition applications such as intelligent robots and unmanned driving, images are collected on a consecutive basis and associated among themselves, besides, the scenes have steady prior features. Yet existing technologies do not take full advantage of this information. In order to take object recognition further than existing algorithms in the above application, an object recognition method that fuses temporal sequence with scene priori information is proposed. This method first employs YOLOv3 as the basic algorithm to recognize objects in single-frame images, then the DeepSort algorithm to establish association among potential objects recognized in images of different moments, and finally the confidence fusion method and temporal boundary processing method designed herein to fuse, at the decision level, temporal sequence information with scene priori information. Experiments using public datasets and self-built industrial scene datasets show that due to the expansion of information sources, the quality of single-frame images has less impact on the recognition results, whereby the object recognition is greatly improved. It is presented herein as a widely applicable framework for the fusion of information under multiple classes. All the object recognition algorithms that output object class, location information and recognition confidence at the same time can be integrated into this information fusion framework to improve performance.

Patterns of Interactions of the Complex City System:Emotional Urban Objects as Triggering Agents-A Secondary Publication

This article presents an analysis of the patterns of interactions resulting from the positive and negative emotional events that occur in cities,considering them as complex systems.It explores,from the imaginaries,how certain urban objects can act as emotional agents and how these events affect the urban system as a whole.An adaptive complex systems perspective is used to analyze these patterns.The results show patterns in the processes and dynamics that occur in cities based on the objects that affect the emotions of the people who live there.These patterns depend on the characteristics of the emotional charge of urban objects,but they can be generalized in the following process:(1)immediate reaction by some individuals;(2)emotions are generated at the individual level which begins to generalize,permuting to a collective emotion;(3)a process of reflection is detonated in some individuals from the reading of collective emotions;(4)integration/significance in the community both at the individual and collective level,on the concepts,roles and/or functions that give rise to the process in the system.Therefore,it is clear that emotions play a significant role in the development of cities and these aspects should be considered in the design strategies of all kinds of projects for the city.Future extensions of this work could include a deeper analysis of specific emotional events in urban environments,as well as possible implications for urban policy and decision making.

Research on the Relationship between the School-Running Characteristics of Private Colleges and Universities and the Development Objectives of the Government

The development objectives of the Chinese government have a guiding effect on the school-running characteristics of private colleges and universities.This paper explores the relationship between the characteristics of private colleges and universities and the development objectives of the provincial government,using Jiangsu Province as a case study.All the schools have the consciousness of fully considering the development objectives of Jiangsu Province when formulating the school-running characteristics.Their characteristics all contain some training methods,training courses,or institutional construction aiming at the development objectives.Most universities can support the government’s objectives on employment and innovation.Only a third of the schools provide government support for science research.Private colleges and universities should make full use of the free and flexible advantages of private education,combine them with the continuous changes of policies and markets,and create clear,scientific,and dynamic school-running characteristics,in order to achieve long-term development.

Real-Time Object Detection and Face Recognition Application for the Visually Impaired

The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe,navigable routes.Traditional approaches primarily focus on broad applications such as wayfinding,obstacle detection,and fall prevention.However,there is a notable discrepancy in applying these technologies to more specific scenarios,like identifying distinct food crop types or recognizing faces.This study proposes a real-time application designed for visually impaired individuals,aiming to bridge this research-application gap.It introduces a system capable of detecting 20 different food crop types and recognizing faces with impressive accuracies of 83.27%and 95.64%,respectively.These results represent a significant contribution to the field of assistive technologies,providing visually impaired users with detailed and relevant information about their surroundings,thereby enhancing their mobility and ensuring their safety.Additionally,it addresses the vital aspects of social engagements,acknowledging the challenges faced by visually impaired individuals in recognizing acquaintances without auditory or tactile signals,and highlights recent developments in prototype systems aimed at assisting with face recognition tasks.This comprehensive approach not only promises enhanced navigational aids but also aims to enrich the social well-being and safety of visually impaired communities.

Detecting the core of a network by the centralities of the nodes

Many networks exhibit the core/periphery structure.Core/periphery structure is a type of meso-scale structure that consists of densely connected core nodes and sparsely connected peripheral nodes.Core nodes tend to be well-connected,both among themselves and to peripheral nodes,which tend not to be well-connected to other nodes.In this brief report,we propose a new method to detect the core of a network by the centrality of each node.It is discovered that such nodes with non-negative centralities often consist in the core of the networks.The simulation is carried out on different real networks.The results are checked by the objective function.The checked results may show the effectiveness of the simulation results by the centralities of the nodes on the real networks.Furthermore,we discuss the characters of networks with the single core/periphery structure and point out the scope of the application of our method at the end of this paper.

General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline.Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve.Considering the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system.Furthermore,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and adaptability.Extensive simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants.Moreover,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

A Simple and Effective Surface Defect Detection Method of Power Line Insulators for Difficult Small Objects

Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable scale,and fuzzy edge morphology of insulator defects,we construct an insulator dataset with 1600 samples containing flashovers and breakages.Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed.Firstly,a high-resolution featuremap is introduced and a small object prediction layer is added so that the model can detect tiny objects.Secondly,a simplified adaptive spatial feature fusion(SASFF)module is introduced to perform cross-scale spatial fusion to improve adaptability to variable multi-scale features.Finally,we propose an enhanced deformable attention mechanism(EDAM)module.By integrating a gating activation function,the model is further inspired to learn a small number of critical sampling points near reference points.And the module can improve the perception of object morphology.The experimental results indicate that concerning the dataset of flashover and breakage defects,this method improves the performance of YOLOv5,YOLOv7,and YOLOv8.In practical application,it can simply and effectively improve the precision of power line insulator defect detection and reduce missing detection for difficult small objects.

A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.

Objective Identification and Climatic Characteristics of Heavy-Precipitation Northeastern China Cold Vortexes

The northeastern China cold vortex(NCCV)plays an important role in regional rainstorms over East Asia.Using the National Centers for Environmental Prediction Final reanalysis dataset and the Global Precipitation Measurement product,an objective algorithm for identifying heavy-precipitation NCCV(HPCV)events was designed,and the climatological features of 164 HPCV events from 2001 to 2019 were investigated.The number of HPCV events showed an upward linear trend,with the highest frequency of occurrence in summer.The most active region of HPCV samples was the Northeast China Plain between 40°–55°N.Most HPCV events lasted 3–5 days and had radii ranging from 250 to 1000 km.The duration of HPCV events with larger sizes was longer.About half of the HPCV events moved into(moved out of)the definition region(35°–60°N,115°–145°E),and half initiated(dissipated)within the region.The initial position was close to the western boundary of the definition region,and the final position was mainly near the eastern boundary.The locations associated with the precipitation were mostly concentrated within 2000 km southeast of the HPCV systems,and they were farther from the center in the cold season than in the warm season.

Cluster Analysis of the Roma-BZCAT Blazars

Based on the collected multiwavelength data, namely in the radio(NVSS, FIRST, RATAN-600), IR(WISE),optical(Pan-STARRS), UV(GALEX), and X-ray(ROSAT, Swift-XRT) ranges, we have performed a cluster analysis for the blazars of the Roma-BZCAT catalog. Using two machine learning methods, namely a combination of PCA with k-means clustering and Kohonen's self-organizing maps(SOMs), we have constructed an independent classification of the blazars(five classes) and compared the classes with the known Roma-BZCAT classification(FSRQs, BL Lacs, galaxy-dominated BL Lacs, and blazars of an uncertain type) as well as with the high synchrotron peaked(HSP) blazars from the 3HSP catalog and blazars from the TeVCat catalog. The obtained groups demonstrate concordance with the BL Lac/FSRQ classification along with a continuous character of the change in the properties. The group of HSP blazars stands out against the overall distribution. We examine the characteristics of the five groups and demonstrate distinctions in their spectral energy distribution shapes. The effectiveness of the clustering technique for objective analysis of multiparametric arrays of experimental data is demonstrated.

About Some Aspects of Use of Optical Sensors for Monitoring the Aquatic Environment

Multi-channel polarization optical technology is increasingly used for prompt monitoring of water systems.Optical devices during the assessment of water quality determine the intensity of light through the studied aquatic environment.Spectrophotometric devices measure the spectrum of weakening of light through the aquatic environment.Spectroellipsometric devices receive spectra in vertical and horizontal polarizations.The presented article develops an adaptive optical hardware and image system for monitoring water bodies.The system is combined.It consists of 2 parts:1)automated spectrophotometer-refractometer,and 2)adaptive spectroellipsometer.The system is equipped with a corresponding algorithmic and software,including algorithms for identifying spectral curves,databases and knowledge of spectral curves algorithms for solving reverse problems.The presented system is original since it differs from modern foreign systems by a new method of spectrophotometric and spectroellipsometric measurements,an original elemental base of polarization optics and a comprehensive mathematical approach to assessing the quality of a water body.There are no rotating polarization elements in the system.Therefore,this makes it possible to increase the signal-to-noise ratio and,as a result,improve measurement stability and simplify multichannel spectrophotometers and spectroellipsometers.The proposed system can be used in various water systems where it is necessary to assess water quality or identify the presence of a certain set of chemical elements.

An improved YOLOv7 for the state identification of sliding chairs in railway turnout

The sliding chairs are important components that support the switch rail conversion in the railway turnout.Due to the harsh environmental erosion and the attack from the wheel vibration,the failure rate of the sliding chairs accounts for up to 10%of the total failure number in turnout.However,there is little research carried out in the existing literature to diagnose the deterioration states of the sliding chairs.To fill out this gap,by utilizing the images containing the sliding chairs,we propose an improved You Only Look Once version 7(YOLOv7)to identify the state of the sliding chairs.Specifically,to meet the challenge brought by the small inter-class differences among the sliding chair states,we first integrate the Convolutional Block Attention Module(CBAM)into the YOLOv7 backbone to screen the information conducive to state identification.Then,an extra detector for a small object is customized into the YOLOv7 network in order to detect the small-scale sliding chairs in images.Meanwhile,we revise the localization loss in the objective function as the Efficient Intersection over Union(EIoU)to optimize the design of the aspect ratio,which helps the localization of the sliding chairs.Next,to address the issue caused by the varying scales of the sliding chairs,we employ K-means++to optimize the priori selection of the initial anchor boxes.Finally,based on the images collected from real-world turnouts,the proposed method is verified and the results show that our method outperforms the basic YOLOv7 in the state identification of the sliding chairs with 4%improvements in terms of both mean Average Precision@0.5(mAP@0.5)and F1-score.

Development and optimization of object detection technology in pavement engineering: A literature review

Due to the rapid advancement of the transportation industry and the continual increase in pavement infrastructure,it is difficult to keep up with the huge road maintenance task by relying only on the traditional manual detection method.Intelligent pavement detection technology with deep learning techniques is available for the research and industry areas by the gradual development of computer vision technology.Due to the different characteristics of pavement distress and the uncertainty of the external environment,this kind of object detection technology for distress classification and location still faces great challenges.This paper discusses the development of object detection technology and analyzes classical convolutional neural network(CNN)architecture.In addition to the one-stage and two-stage object detection frameworks,object detection without anchor frames is introduced,which is divided according to whether the anchor box is used or not.This paper also introduces attention mechanisms based on convolutional neural networks and emphasizes the performance of these mechanisms to further enhance the accuracy of object recognition.Lightweight network architecture is introduced for mobile and industrial deployment.Since stereo cameras and sensors are rapidly developed,a detailed summary of three-dimensional object detection algorithms is also provided.While reviewing the history of the development of object detection,the scope of this review is not only limited to the area of pavement crack detection but also guidance for researchers in related fields is shared.

Smart object recommendation based on topic learning and joint features in the social internet of things

With the extensive integration of the Internet,social networks and the internet of things,the social internet of things has increasingly become a significant research issue.In the social internet of things application scenario,one of the greatest challenges is how to accurately recommend or match smart objects for users with massive resources.Although a variety of recommendation algorithms have been employed in this field,they ignore the massive text resources in the social internet of things,which can effectively improve the effect of recommendation.In this paper,a smart object recommendation approach named object recommendation based on topic learning and joint features is proposed.The proposed approach extracts and calculates topics and service relevant features of texts related to smart objects and introduces the“thing-thing”relationship information in the internet of things to improve the effect of recommendation.Experiments show that the proposed approach enables higher accuracy compared to the existing recommendation methods.

Fermi Blazars in the Zwicky Transient Facility Survey:Properties of Large Optical Variations

We analyze the optical light curve data,obtained with the Zwicky Transient Facility(ZTF)survey,for 47 γ-ray blazars monitored by the Large Area Telescope onboard the Fermi Gamma-ray Space Telescope(Fermi).These 47sources are selected because they are among the Fermi blazars with the largest optical variations in the ZTF data.Two color-magnitude variation patterns are seen in them,with one being redder-to-stable-when-brighter(RSWB;in 31 sources)and the other being stable when brighter(in 16 sources).The patterns fit with the results recently reported in several similar studies with different data.Moreover,we find that the colors in the stable state of the sources share similar values,for which(after being corrected for the Galactic extinction)most sources are in a range of 0.4–0.55.This feature could be intrinsic and may be applied in,for example,study of the intragalactic medium.We also determine the turning points for the sources showing the RSWB pattern,after which the color changes saturate and become stable.We find a correlation between optical fluxes and γ-ray fluxes at the turning points.The physical implications of the correlation remain to be investigated,probably better with a sample of high-qualityγ-ray flux measurements.

Research of Future Directions and Considerations for Implementation of the Forest City Strategy

A “Forest City” (FC) is an urban area that has a significant amount of forest cover. It is now a green urban development strategy that is supported by numerous nations. This essay compares the many FC implementation strategies used in developed and developing countries and explores potential future paths for this tactic. The variations between FC in terms of measurement targets, air purification, street trees, and forestry development are thoroughly compared in this research. This essay goes on to explore FC’s potential in the future regarding policy changes and the environment based on this comparison. Therefore, this essay focuses on the necessity of considering industrial innovation, encouraging biodiversity, lowering greenhouse gas emissions, paying attention to forest restructuring, and being more responsive to the issues provided by urbanization in the future global implementation of FC.

Real Time Thermal Image Based Machine Learning Approach for Early Collision Avoidance System of Snowplows

In an effort to reduce vehicle collisions with snowplows in poor weather conditions, this paper details the development of a real time thermal image based machine learning approach to an early collision avoidance system for snowplows, which intends to detect and estimate the distance of trailing vehicles. Due to the operational conditions of snowplows, which include heavy-blowing snow, traditional optical sensors like LiDAR and visible spectrum cameras have reduced effectiveness in detecting objects in such environments. Thus, we propose using a thermal infrared camera as the primary sensor along with machine learning algorithms. First, we curate a large dataset of thermal images of vehicles in heavy snow conditions. Using the curated dataset, two machine-learning models based on the modified ResNet architectures were trained to detect and estimate the trailing vehicle distance using real-time thermal images. The trained detection network was capable of detecting trailing vehicles 99.0% of the time at 1500.0 ft distance from the snowplow. The trained trailing distance network was capable of estimating distance with an average estimation error of 10.70 ft. The inference performance of the trained models is discussed, along with the interpretation of the performance.

Study of Complex Nitrogen and Oxygen-bearing Molecules toward the High-mass Protostar IRAS 18089–1732

The observation of oxygen(O)-and nitrogen(N)-bearing molecules gives an idea about the complex prebiotic chemistry in the interstellar medium.Recent millimeter and submillimeter wavelength observations have shown the presence of complex O-and N-bearing molecules in the star formation regions.So,the investigation of those molecules is crucial to understanding the chemical complexity in the star-forming regions.In this article,we present the identification of the rotational emission lines of N-bearing molecules ethyl cyanide(C_(2)H_(5)CN)and cyanoacetylene(HC_(3)N),and O-bearing molecule methyl formate(CH_(3)OCHO)toward high-mass protostar IRAS18089–1732 using the Atacama Compact Array.We also detected the emission lines of both the N-and O-bearing molecule formamide(NH_(2)CHO)in the envelope of IRAS 18089–1732.We have detected the v=0 and 1 state rotational emission lines of CH_(3)OCHO.We also detected the two vibrationally excited states of HC_(3)N(v7=1 and v7=2).The estimated fractional abundances of C_(2)H_(5)CN,HC_(3)N(v7=1),HC_(3)N(v7=2),and NH_(2)CHO toward IRAS 18089–1732 are(1.40±0.5)×10^(-10),(7.5±0.7)×10^(-11),(3.1±0.4)×10^(-11),and(6.25±0.82)×10^(-11)respectively.Similarly,the estimated fractional abundances of CH_(3)OCHO(v=0)and CH_(3)OCHO(v=1)are(1.90±0.9)×10^(-9)and(8.90±0.8)×10^(-10),respectively.We also created the integrated emission maps of the detected molecules,and the observed molecules may have originated from the extended envelope of the protostar.We show that C_(2)H_(5)CNand HC_(3)N are most probably formed via the subsequential hydrogenation of the CH_(2)CHCNand the reaction between C_(2)H_(2)and CN on the grain surface of IRAS 18089–1732.We found that NH_(2)CHO is probably produced due to the reaction between NH_(2)and H_(2)CO in the gas phase.Similarly,CH_(3)OCHO is possibly created via the reaction between radical CH_(3)O and radical HCO on the grain surface of IRAS 18089–1732.