Distant Objects Research Articles

CORRELATION BETWEEN COMPLIANCE OF SPECTACLE WEAR AND MYOPIA PROGRESSIVITY RATE IN MEDICAL STUDENTS OF DIPONEGORO UNIVERSITY

Introduction Myopia is a refractive error that causes images of distant objects to be focused in front of the retina by the non-accommodating eye. Myopia is one of the most common visual disturbances in the world. It is believed that the use of spectacle can reduce the progressivity rate of myopia. Spectacle can help light fall on the retina and prevent axial elongation of the eye. This theory needs to be investigated further so that it can be used as a reference. This study aimed to know the correlation between compliance of spectacle wear and myopia progressivity rate in medical students of Diponegoro University (UNDIP). Methods This research was an observational analytical study with cross-sectional design on 54 subjects ranging from 18 to 22 years old. Data are primary data obtained from questionnaires and objective eye examinations using an autorefractometer. The research was conducted from October to November 2021. Data were analyzed using Spearman Rank test which p<0,05 was considered significant. Results The mean of compliance of spectacle wear and myopia progressivity rate in medical students of UNDIP are (1,57±0,108) and (0,259±0,515) dioptres respectively. The correlation between compliance of spectacle wear and myopia progressivity rate are p=0,082 and r=0,239. Conclusion There are no correlation between compliance of spectacle wear and myopia progressivity rate in medical students of UNDIP. Keywords : Compliance, Spectacle, Myopia progressivity rate, Student

CORRELATION BETWEEN COMPLIANCE OF SPECTACLE WEAR AND MYOPIA PROGRESSIVITY RATE IN MEDICAL STUDENTS OF DIPONEGORO UNIVERSITY

Introduction and Objective
 Myopia is a refractive error that causes images of distant objects to be focused in front of the retina by the non-accommodating eye. Myopia is one of the most common visual disturbances in the world. It is believed that the use of spectacle can reduce the progressivity rate of myopia. Spectacle can help light fall on the retina and prevent axial elongation of the eye. This theory needs to be investigated further so that it can be used as a reference. This study aimed to know the correlation between compliance of spectacle wear and myopia progressivity rate in medical students of Diponegoro University (UNDIP).
 Methods
 This research was an observational analytical study with cross-sectional design on 54 subjects ranging from 18 to 22 years old. Data are primary data obtained from questionnaires and objective eye examinations using an autorefractometer. The research was conducted from October to November 2021. Data were analyzed using Spearman Rank test which p<0,05 was considered significant.
 Results
 The mean of compliance of spectacle wear and myopia progressivity rate in medical students of UNDIP are (1,57±0,108) and (0,259±0,515) dioptres respectively. The correlation between compliance of spectacle wear and myopia progressivity rate are p=0,082 and r=0,239.
 Conclusion
 There are no correlation between compliance of spectacle wear and myopia progressivity rate in medical students of UNDIP.

Lorentz invariance violation from GRB 221009A

The Large High Altitude Air Shower Observatory (LHAASO) reported observation of photons with energies above 10[Formula: see text]TeV from gamma-ray burst GRB 221009A. A suggestion was proposed that this result may contradict our knowledge of special relativity (SR) and the standard model (SM), according to which photons of about 10[Formula: see text]TeV from such a distant object should be severely suppressed because of the absorption by extragalactic background light. As a result, a number of mechanisms have been proposed to solve this potential puzzle, including Lorentz invariance violation (LIV). In this work, we perform a detailed numerical calculation and show the feasibility to constrain LIV of photons from the LHAASO observation of GRB 221009A quantitatively.

The Condition of Decreased Eye Vision (Myopia) in Production Operators in The Workplace Impact on The Quality of Production Results

Myopia or nearsightedness is a vision condition that cannot see distant objects clearly. This condition is increasingly common around the world, including in Indonesia. In the workplace, myopia can be a problem for production operators who need sharp vision to perform their tasks well. The purpose of this study was to determine the condition of decreased eye vision (myopia) in production operators in the workplace and its impact on the quality of production results. This research uses qualitative research methods. The data collection technique in this research is by literature study. The data that has been collected is then processed through three stages, namely data reduction, data presentation and conclusion drawing. The results showed that operators suffering from myopia can experience a decrease in production output for three main reasons, including myopia sufferers tend to have difficulty seeing details that can affect the accuracy and quality of work, myopia conditions can cause eye fatigue which can reduce the efficiency and productivity of operators in carrying out production tasks and myopia can cause discomfort in physical members, such as the neck, shoulders and back, due to improper posture during work. In addition, myopia can also increase the risk of work accidents due to impaired vision.

Improving 3D Object Detection with Context-Aware and Dimensional Interaction Attention

Recently, 3D object detection technology based on point clouds has developed rapidly. However, too few points of distant and occluded objects are scanned by the sensor, and thus these objects suffer from too insufficient features to be detected. This case damages the detection accuracy. Therefore, we constitute a novel 3D object detection with Context-aware and dimensional Interaction Attention Network (CIANet) to explore vital geometric cues for enriching the feature representation of the object, thus boosting the overall detection performance. Specifically, in the first stage, we employ the 3D sparse convolution to extract voxel features, and then construct a Channel-Spatial Hybrid Attention (CSHA) module and a Contextual Self-Attention (CSA) module to enhance voxel features for generating proposals. The CSHA module aims to enhance the key information of the channel and spatial domains of 2D Bird’s Eye View (BEV) features, and the CSA module is applied to supplement contextual information to the enhanced BEV features, thus generating accurate proposals. In the second stage, we construct a Dimensional Interaction Attention (DIA) module to refine Region of Interest (RoI) features within the proposals. It enhances the interactions among the channel and spatial dimensions of RoI features to learn accurate boundaries of objects for proposal refinement. Extensive experiments on the KITTI and Waymo benchmarks show the superior detection performance of CIANet compared to recent methods, especially for objects such as pedestrians and cyclists.

Simulation of Vehicle Distance Detection for Traffic Order

The use of ultrasonic sensors, especially HC-SR04, in microcontroller projects is expanding thanks to its ability to accurately detect distance. In this final project, the HC- SR04 is connected to an ESP32 to measure the distance of an object and provide feedback in the form of sound when the object approaches the sensor within a certain distance. The HC-SR04 sensor works by emitting ultrasonic waves and measuring the time it takes for the waves to reflect back to the sensor. The ESP32, as the microcontroller connected to the sensor, processes this time data to calculate the object's distance from the sensor. When the distance of the object is below a predetermined threshold, the ESP32 will activate the buzzer as a sound signal. This implementation can be applied in various systems, for this test we used it on the zebra crossing system automatically. The test results show that this system is able to detect distance with sufficient accuracy and provide a fast and consistent sound response according to changes in object distance.

A Size Estimate for Galaxy GN-z11

GN-z11 is the highest redshift galaxy spectroscopically confirmed with the Hubble Space Telescope (HST). Previous measurements of the effective radius of GN-z11 utilized galfit, which is not optimized to measure structural parameters for such a faint, distant object. Using new software called forcepho, we derive a size from images in the F160W band obtained both from the complete CANDELS survey and additional midcycle observations in order to contribute to the knowledge base on the size evolution, size–luminosity, and size–mass relation of early galaxies. We find a half-light radius mean of 0.″036 ± 0.″006 corresponding to a physical size of 0.15 ± 0.025 kpc. This size, smaller than the point-spread function, is dramatically smaller than previous estimates with shallower HST data using galfit but consistent with recent measurements using forcepho on new JWST data. Such a small size suggests that GN-z11's high luminosity is dominated by an active galactic nucleus.

Analisis Faktor Penyebab Terjadinya Miopia pada Mahasiswa Fisika Angkatan 2020 Universitas Negeri Padang

The organ of sight plays a central role in acquiring necessary information, enabling us to carry out various daily activities normally. The eyes can experience refractive errors, one of which is nearsightedness or myopia. Myopia is a refractive eye disorder with a high prevalence worldwide. There are various factors that can influence the development of myopia. One highly influential factor is the habit of reading and writing at too close a distance. Additionally, other factors include a history of exposure to light from computers or gadgets, age, type of pregnancy, birth history, nutritional status, deficiency of vitamins A and D, as well as genetic factors or family history. This study aims to determine the factors causing myopia among physics students of the 2020 intake at Padang State University. This research employs a descriptive qualitative research method and was conducted in December 2023. Research findings show that the highest percentage of individuals unaware of distant objects' presence is 70%, while 20% are aware, and 10% occasionally notice distant objects. The highest percentage of smartphone usage duration is 100%, with usage exceeding 5 hours, while 0% use it for less than 5 hours. Regarding smartphone lighting, 50% prefer bright illumination, 30% opt for moderate brightness, and 20% prefer dim lighting.

Radiative loss of coherence in free electrons: a long-range quantum phenomenon

Quantum physics rules the dynamics of small objects as they interact over microscopic length scales. Nevertheless, quantum correlations involving macroscopic distances can be observed between entangled photons as well as in atomic gases and matter waves at low temperatures. The long-range nature of the electromagnetic coupling between charged particles and extended objects could also trigger quantum phenomena over large distances. Here, we reveal a manifestation of quantum mechanics that involves macroscopic distances and results in a nearly complete depletion of coherence associated with which-way free-electron interference produced by electron–radiation coupling in the presence of distant extended objects. This is a ubiquitous effect that we illustrate through a rigorous theoretical analysis of a two-path electron beam interacting with a semi-infinite metallic plate and find the inter-path coherence to vanish proportionally to the path separation at zero temperature and exponentially at finite temperature. The investigated regime of large distances originates in the coupling of the electron to radiative modes assisted by diffraction at material structures but without any involvement of material excitations. Besides the fundamental interest of this macroscopic quantum phenomenon, our results suggest an approach to measuring the vacuum temperature and nondestructively sensing the presence of distant objects.

A 6-DOF camera motion correction method using IMU sensors for photogrammetry and optical measurements

Environmental conditions such as wind and ground traffic introduce motion in camera measurement systems and affect measurement accuracy. Conventional camera motion correction methods track static reference points with one or multiple cameras, reducing applicability. This study proposes a novel 6-degree-of-freedom (DOF) camera motion correction method using only an inertial measurement unit (IMU) sensor. A Kalman filter is adopted as a data fusion method to estimate the camera orientation and translation using IMU data. Six pinhole camera models are built to evaluate and correct 6-DOF camera motions. The motion correction efficiency and robustness are tested for different object distances and focal lengths of optical lenses. The motion correction ratio is statistically analysed and reaches approximately 80%. The object distance has little effect on the motion correction ratio. The rotation-induced pixel movement is independent of the object distance. More than 90% of the pixel movement noise is caused by camera rotation. The translation-induced pixel movement is inversely correlated with the object distance.

Location and segmentation of important railway signs based on improved segmentation

Purpose With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection. Design/methodology/approach This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure. Findings This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs. Research limitations/implications The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate. Social implications The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security. Originality/value This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.

L[formula omitted]OP-ART: A linear-time adaptive random testing algorithm for object-oriented programs

Object-oriented (OO) programming offers useful and desirable characteristics in the resulting code. These OO characteristics, however, demand an effective testing method. Adaptive random testing (ART) serves as an effective testing technique where test cases are distributed uniformly according to a proper metric for difference (distance) between test cases. Recently proposed object and method invocation sequence similarity (OMISS) distance metric was incorporated into a new ART algorithm (OMISS-ART) for testing OO software. Unfortunately, OMISS-ART’s high effectiveness in finding faults comes at significantly higher computational overheads. To alleviate the problem, we present in this paper a linear-time ART algorithm called L′OP-ART (Linear-time Object-oriented Program testing by ART) based on a lightweight OMISS metric. L′OP-ART designs specific data structures that store information about executed test cases, thus, the differences between candidates and executed test cases can be obtained with reduced overheads. Empirical studies show that L′OP-ART detects failures more effectively than random testing (RT), and has comparable fault-detection effectiveness to other ART algorithms for OO testing (according to the F-measure, or the expected number of test case executions required to find a failure). Furthermore, the test case generation overhead of L′OP-ART is low, close to that of RT.

THE RELATIONSHIP OF JOB FACTORS AND CLOSE READING HABITS WITH MYOPIA SUFFERING IN TEENAGERS

Based on the concept of static refraction, myopia is a condition in which parallel rays entering the eyeball are refracted by the refractive media in front of the retina. As a result, objects that are far from the eyeball do not appear clearly in the patient's vision. The inability of a person to see distant objects clearly can occur, among others, due to heredity, work and reading habits or position, work environment, etc. To overcome refractive errors in myopia sufferers, minus spherical correction lenses can be given in front of the patient's eyes, so that the light that falls in front of the retina can be shifted and focused right on the retina. The purpose of this study, the authors want to explain the existence of a mutually influencing relationship between near work vision and close reading habits, as well as room lighting used by people with myopia. This research was conducted using a quantitative research method with a sampling technique "systematic random sampling". The results of this study found that myopic refractive errors occupied the highest percentage, namely 65.5%, that there was a mutually influencing relationship between work vision and close reading habits, as well as room lighting used by people with myopia

Microlidar Application for Object Detector to Support The Navigation System in Self-Driving Vehicle

The ability to detect and measure the distance of potential obstacles is importance for navigation system in self-driving vehicles. The measurement process needs to be fast and accurate since the controller requires real-time data to make quick decisions and respond to any potential disturbances on the vehicle's track. This research aims to develop Microlidar for detection system that can accurately measure the distance of a potential obstacle object. The Microlidar utilize Lidar Lite V3 proximity sensor which have range measurement specification of up to 40 meter. Microlidar rapidly rotate 360 degrees by using a stepper motor while in the same time continuously measure the real-time distance. The measurement data are sent to a microcontroller through I2C, and the Processing software plot the 2D image which work like radar visualization. The system is assessed for ranging the various distance object in static and dynamic measurement mode. The results show that the Microlidar has a good level of accuracy with an average error value at the distance of 300 cm is 4.99 cm or 1.7% while the average error value at the distance of 1000 cm is 15.69 cm or 1.6% obtained from 100 data sets collected. The communication from the sensor to Arduino requires a minimum baud rate of 115200 bits/second to minimize data loss and ensure that the measured distance can be processed in real time by the microcontroller. Real-time data with high speed is essential since it will be used on the vehicle in order to quickly decide whether there is a barrier or not on the vehicle’s track. The sensor analysis distance expected from this research could be used as a reference to support the navigation system performance of self-driving vehicles.

A Novel Method for Full‐Field Scale Factor Calculation under Off‐Axis Measurements for Vision‐Based Structural Multipoint Displacement Measurement and Health Monitoring

For vision‐based structural displacement measurement and health monitoring, the scale factor (SF) calculation plays a pivotal role in converting the pixel displacement into the actual one. On the other hand, for the current SF calculation methods, the object distance, between the to‐be‐measured points on the object plane and the optical center of the shooting instrument, has to be measured in advance due to the existence of the pitch and horizontal angle under off‐axis measurement. Unfortunately, it is usually inefficient, difficult, and even impossible to obtain the object distances for all the to‐be‐measured points, especially for the full‐field displacement monitoring on huge scale structures. In this paper, a novel SF calculation method was proposed to calculate the full‐field scale factor of the host structure under off‐axis measurement by combining with the similarity relation of the camera imaging model. With the help of this method, all the scale factors of the to‐be‐measured points can be calculated accurately and highly efficiently, as long as the object distance of any one point or the geometric dimensions of any feature object on the object surface is provided. In addition, to quantitatively assess the SF calculation accuracy of the method, a static vision‐based measurement investigation was firstly conducted, and then, two experimental investigations about the multipoint and full‐field structural displacement measurement on a cable‐stayed bridge model and a simply supported beam model were conducted to validate the effectiveness and feasibility of the proposed method. Finally, all the results demonstrated that the proposed method exhibits an excellent performance on the SF calculation under off‐axis measurement and provides a great potential to be utilized for the vision‐based structural multipoint and full‐field displacement measurement and health monitoring.

“Visibility” at Italian natural parks: preliminary data from the first-ever pilot project by ENEA and CUFAA

“Visibility” is meant as the greatest distance at which an observer can see a distant object in contrast with the horizon and, when referred to a landscape, it depends on the optical characteristics of the atmosphere, somehow linked to the presence of air pollutants. Visibility can hence become a useful indicator of air quality and the project Visibility, representing the first case of visibility monitoring in Europe, aims to apply this approach within the Italian National Parks as natural areas with a significant fruition vocation. The goal is to test the U.S. National Park Service protocol I.M.PRO.V.E. (Interagency Monitoring of PROtected Visual Environment) for the quantification of a coefficient that describes the light extinction (Bext) as a function of different chemical-physical parameters associated with molecules dispersed in the air, and the Circeo National Park (LT) was selected for the “pilot” action, with the positioning of the measuring instruments for atmospheric pollution evaluation at the Lago dei Monaci site. 24-hours samplings were performed in spring-summer 2021 and winter 2021/2022, to measure the air concentration of PM2.5 and PM10 particulate matter, sulfates and nitrates, elemental carbon and organic carbon, metals and trace elements and the gaseous species nitrogen dioxide, with the consequential calculation of the Bext. The visual detection of the degree of air transparency was performed by a panoramic camera pointed in the direction of the “landmark” Monte Circeo, taken as a reference for the definition of long-distance visibility.

Photometric analysis of Type Ia Supernova 2023jvj

The study of supernovae – large explosions at the end of a star’s life that release immense energy and various elements into space – is important in the fields of stellar evolution and the expansion of the universe. In the large cosmological time scale, supernovae allow for a short glimpse into the universe’s evolution. Upon the discovery of Supernova (SN) 2023jvj, located in an anonymous galaxy in the Boötes constellation, our team launched an investigation to further knowledge of transient objects and their role in the cosmos. In this study, we conducted a photometric analysis of SN 2023jvj to determine its type and distance. We hypothesized SN 2023jvj would be a Type Ia supernova based on reports that there were no hydrogen spectral lines. We observed SN 2023jvj and collected photometric data over four weeks using the 12-inch Meade Schmidt Cassegrain Telescope (SCT) and 16-inch Ritchey-Chretien Telescope (RCT) at the Leitner Family Observatory and Planetarium (LFOP). Images were additionally collected remotely from Australia and Utah via the iTelescope Network. Using this data, standard Sloan green and Sloan red magnitudes were found through color calibration and used to generate a light curve (a plot of supernova brightness over time). SN 2023jvj was then determined to be a Type Ia supernova — a standard candle — with a distance modulus of 35.36. Therefore, SN 2023jvj is approximately 1.246e8 parsecs away from Earth. Our findings have a broader impact on how astronomers analyze the relationship between the brightness and distance of transient objects in the ever-expanding universe.

A verification on accuracy of 3D models constructed by SfM software—In terms of reconstructing shape and measuring distance of objects and sites—

A verification on accuracy of 3D models constructed by SfM software—In terms of reconstructing shape and measuring distance of objects and sites—

Error assessment of subjective estimates of linear breast dimensions versus the objective method.

The study aimed to investigate the subjective method of estimating linear breast dimensions in comparison to the objective method. The reproducibility and accuracy of the subjective method of estimating linear breast dimensions during a simplified breast shape analysis were examined. Four linear breast dimensions including the distance from the sternal notch to the nipple, distance from the nipple to the inframammary fold, distance from the nipple to the midline and under-breast width were evaluated based on subjective estimates. Images from 100 women with natural breasts and without any history of breast surgery were reviewed by two examiners three times each. The cases were obtained from a large database of breast images captured using the Vectra Camera System (Canfield Scientific Inc., USA). The subjective data were then compared with the objective linear data from the Vectra Camera System in the automated analysis. Statistical evaluation was conducted between the three repeated estimates of each examiner, between the two examiners and between the objective and subjective data. The intra-individual variations of the three subjective estimates were significantly greater in one examiner than in the other. This trend was consistent across all eight parameters in the majority of the comparisons of the standard deviations and variation coefficients, and the differences were significant in 14 out of 16 comparisons (p<0.05). Conversely, in the comparison between the subjective and objective data, the estimates were closer to the measurements in one examiner than the other. In contrast to the reproducibility observed, the assessment of the accuracy revealed that the examiner who previously presented with less reproducibility of the estimated data overall showed better accuracy in comparison to the objective data. The overall differences were inconsistent, with some being positive and others being negative. Regarding the distances from the sternal notch to the nipple and breast width, both examiners underestimated the values. However, the deviations were at different levels, particularly when considering the objective data from the Vectra Camera System as the gold standard data for comparison. Regarding the distance from the nipple to the inframammary fold, one examiner underestimated the distance, while the other overestimated it. An opposite trend was noted for the distance from the nipple to the midline. There were no differences in the estimates between the right and left sides of the breasts. The correlations between the measured and estimated distances were positive: as the objective distances increased, the subjective distances also increased. In all cases, the correlations were significant. However, the correlation for the breast width was notably weaker than that for the other distances. The error assessment of the subjective method reveals that it varies significantly and unsystematically between examiners. This is true when assessing the reproducibility as well as the accuracy of the method in comparison to the objective data obtained with an automated system.

YOLO-CSM-Based Component Defect and Foreign Object Detection in Overhead Transmission Lines

Detecting component defects and attaching tiny-scaled foreign objects to the overhead transmission lines are critical to the national grid’s safe operation and power distribution. This urgent task, however, faces challenges, such as the complex working environment and the considerable amount of workforce investment, for which we propose a deep-learning-aided object detection approach, YOLO-CSM, to address the issue. Combined with two attention mechanisms (Swin transformer and CBAM) and an extra detection layer, the proposed model can effectively capture global information and key visual features and promote its ability to identify tiny-scaled defects and distant objects in the visual fields. In order to validate this model, this work consolidates a dataset composed of public images and our field-taken picture samples. The experiment verifies YOLO-CSM as a suitable solution for small and distant object detection that outperforms several well-used algorithms, featuring a 16.3% faster detection speed than YOLOv5 and a 3.3% better detection accuracy than YOLOv7. Finally, this work conducts an interpretability experiment to reveal the similarity between YOLO-CSM’s attention patterns and that of humans, aiming to explain YOLO-CSM’s advantages in detecting small objects and minor defects in the working environments of power transmission lines.