Visibility Conditions Research Articles

Rekonstrukcja miejsca zdarzenia drogowego w oparciu o dane geoprzestrzenne Geoportalu Krajowego

The purpose of the article is to analyze the potential use of free spatial data available in the National Geoportal to support traffic accident reconstruction. The research focuses on the capabilities and limitations of using LiDAR and orthophotomaps data in the context of reconstructing traffic accident scenes. A comparative methodology was employed, involving the analysis of geospatial data in relation to traditional surveying techniques, exemplified by a real traffic accident analysis using the PC-Crash soft-ware. The main findings of the research indicate high precision of orthophotos and LiDAR data in reproducing the actual topography and elements of road infrastructure. The analysis conducted on a 3D model created from LiDAR data showed that modern measurement techniques allow a more accurate reconstruction of the accident's trajectory and assessment of road visibility conditions. Integrating these data with traditional measurement methods can significantly enhance the standards of forensic experts' work, contributing to an increased accuracy in determining accident scenarios and, more broadly, to a better understanding of accident causes.

A multi-center study on the use of lidocaine thermogel for pain control in outpatient operative hysteroscopy

ObjectivesThis study aimed to explore the use of an anesthetic lidocaine thermogel in outpatient operative hysteroscopies. Specifically, it assessed the safety, tolerability, and ease of use of the gel, as well as its potential for pain reduction during the procedure. Study designThis was a multicenter observational study conducted in 9 gynecological units between March 2023 and January 2024. The study included adult women scheduled for outpatient operative hysteroscopies, excluding those with hypersensitivity to the product or recent participation in other clinical studies. The target sample size was 60 to account for potential dropouts. Data collection was electronic, and SPSS was used for analysis. The study assessed visibility conditions, procedure duration, pain scores at different stages of the procedure, and adverse event frequency. Statistical analyses utilized descriptive statistics, Student’s t-tests, Wilcoxon and Friedman tests, and Chi-Square or Fisher tests as appropriate. Binary logistic regression was applied to identify factors influencing gel volume. ResultsAll 60 participants met the inclusion criteria. The mean age was 45.5 (SD 8.8) years, with a mean BMI of 27.0 (SD 5.6) kg/m2. Medical histories were reported in 46.7% of participants, and 50% had undergone previous gynecological surgeries. Prior to the procedure, 51.8% of participants took analgesics. The average procedure duration was 13.9 (SD 15.1) minutes. Pain scores were collected at different stages of the procedure, with median VAS scores ranging from 0 to 5 out of 10. In 50% of cases, the quality of vision during the procedure was rated 9 or higher on a 10-point scale. The full recommended dose of thermogel was administered in 91.7% of cases. The mean gel volume used was 7.0 (SD 1.9) milliliters. Regression analysis showed that younger age and a history of abortions or childbirth were significantly associated with higher gel volume use. ConclusionsThe anesthetic thermogel demonstrated effectiveness in managing pain during outpatient operative hysteroscopies, with median pain scores ranging from 0 to 5 out of 10 across different stages of the procedure. The gel showed a favorable safety profile, with only 15% of participants reporting adverse effects, all of which were minor and resolved satisfactorily. The high rate of complete gel application (91.7% of cases) and positive physician feedback suggest good tolerability and ease of use. Further research is recommended to evaluate the gel’s efficacy in other gynecological procedures and to optimize application protocols based on patient-specific factors such as age and reproductive history.

Rescue guiders layout study based on a two-layer optimization framework

In an emergency evacuation, the free evacuation of pedestrians can make the entire evacuation process slow and dangerous. To limit the free behavior of pedestrians and reduce the interaction between pedestrians, a reasonable layout of the guider can improve the efficiency and safety of evacuation. How to set the number, location, and exit allocation of guiders requires further investigation. In the current study, we transform the evacuation into a multi-objective optimization problem. A two-layer optimization framework is developed. In the upper level, the improved NSGA-II multi-objective algorithm is introduced to generate the favorable guider layout, and a chromosome fragment deletion operator is added to improve the optimization efficiency. In the lower layer, the agent movement simulation model is used to simulate the evacuation dynamic of crowd under the favorable guider layout. The variables of this multi-objective solution model in the upper layer are the number and location of the guiders. The evacuation time and agent movement cost are calculated by the lower layer simulation as the objective values of the solution sample, and guide the iterative search process to obtain more reasonable optimization results. The developed model is verified and then applied to a fictional scenario. The number, initial position and exit allocation of guiders are obtained by optimizing the iterative process. The results show that the near optimal solution can be applied in various visibility conditions, and the evacuation efficiency is much higher than that of unguided evacuation. This optimization framework can provide theoretical and methodological support for emergency evacuation planning.

Investigation on the transmission attenuation of Bessel-Gaussian beams in a dusty environment

This paper delves into the transmission dynamics of Bessel-Gaussian (BG) beams in three distinct dusty environments, leveraging the Generalized Lorenz-Mie Theory (GLMT) alongside a single scattering model for a comprehensive analysis. Through numerical simulations, the study explores the interaction between dust particle scattering and the attenuation and transmittance behaviors of BG beams, elucidating the influences of varying particle concentrations and visibility conditions typical of floating dust, blowing sand, and sandstorms. The findings reveal numerous determinants, including particle number concentration, optical visibility, wavelength, orbital angular momentum (OAM) modes, waist radius, cone angle, and polarization states, which significantly affect the transmission performance of BG beams in dusty conditions. Notably, the attenuation rate decreases with increasing wavelengths and higher OAM modes, thereby extending effective transmission distances. Furthermore, the strategic use of linear polarization emerges as an optimal approach for enhancing BG beam transmission efficiency in dust-rich environments. These insights are crucial for optimizing BG beam transmission in real-world applications, marking a significant advancement in the field.

Visual Enhancement of Aerial Flight in High Plateau Low Visibility Conditions

A method for automatic detection of airport runways in low visibility, forward and downward looking aerial airport images is proposed. Firstly, the recursive Otsu segmentation method is used to extract the main contour of the airport from the complex background; then the morphological filtering is performed by using the morphology-based open and swell operations, the open operation is used to filter out the isolated noise points and the swell operation is used to connect the segmented airport components into a complete whole; finally, the morphological filtered image is subjected to the region growth to localize the main runway of the airport. Finally, the morphology filtered image is processed to localize the main airport runway by its region growth. It is proved that this method can quickly and accurately detect and localize the main and auxiliary runways of airports in low visibility and forward and downward looking aerial images of airports.

Optimization of the methods of conducting fire training classes for cadets of the National Police of Ukraine in conditions of limited visibility

Optimization of the methods of conducting fire training classes for cadets of the National Police of Ukraine in conditions of limited visibility

GOI-YOLOv8 Grouping Offset and Isolated GiraffeDet Low-Light Target Detection.

In the realm of computer vision, object detection holds significant importance and has demonstrated commendable performance across various scenarios. However, it typically requires favorable visibility conditions within the scene. Therefore, it is imperative to explore methodologies for conducting object detection under low-visibility circumstances. With its balanced combination of speed and accuracy, the state-of-the-art YOLOv8 framework has been recognized as one of the top algorithms for object detection, demonstrating outstanding performance results across a range of standard datasets. Nonetheless, current YOLO-series detection algorithms still face a significant challenge in detecting objects under low-light conditions. This is primarily due to the significant degradation in performance when detectors trained on illuminated data are applied to low-light datasets with limited visibility. To tackle this problem, we suggest a new model named Grouping Offset and Isolated GiraffeDet Target Detection-YOLO based on the YOLOv8 architecture. The proposed model demonstrates exceptional performance under low-light conditions. We employ the repGFPN feature pyramid network in the design of the feature fusion layer neck to enhance hierarchical fusion and deepen the integration of low-light information. Furthermore, we refine the repGFPN feature fusion layer by introducing a sampling map offset to address its limitations in terms of weight and efficiency, thereby better adapting it to real-time applications in low-light environments and emphasizing the potential features of such scenes. Additionally, we utilize group convolution to isolate interference information from detected object edges, resulting in improved detection performance and model efficiency. Experimental results demonstrate that our GOI-YOLO reduces the parameter count by 11% compared to YOLOv8 while decreasing computational requirements by 28%. This optimization significantly enhances real-time performance while achieving a competitive increase of 2.1% in Map50 and 0.6% in Map95 on the ExDark dataset.

Blue skies: Does the visual composition of sky guide subjective judgments of naturalness in the environment?

Expanding on previous findings, that highlighted the significance of sky in environmental perception, our analysis investigated whether the visual composition of the sky shapes perceptions of environmental naturalness. The study employed a novel, free-selection task in which participants viewed a series of environmental images with varying levels of natural and urban elements, as well as different sky visibility conditions, and were asked to identify “nature” within these images. The task procedure also involved subjective ratings of each scene. Using previously gathered data, we reassessed 105 participants' selection of the sky as “nature” across 96 photos of diverse outdoor scenes to understand which visuospatial features influence these perceptions. Utilizing the Boruta feature selection algorithm, we identified key characteristics—fractal dimensions, brightness, and entropy in brightness, hue, and saturation—that significantly predicted the selection of sky as “nature”, irrespective of the environment type (urban or natural). Results indicated that lower fractal dimensions are preferred for sky selected as “nature”, inversely affecting the naturalness judgment of scenes with the additional effect of brightness. These findings enhance our understanding of how visuospatial features influence environmental perception, offering implications for future research directions and theoretical advancements in understanding environmental perception.

Filament-induced breakdown spectroscopy of solids through highly scattering media.

Ultrafast laser pulse filamentation in the air can be used for remote sensing by exciting a characteristic optical emission, which is usually referred to as filamentation-induced breakdown spectroscopy. In environments that impede light propagation, such as fog, haze, or clouds, scattering makes it challenging to propagate laser beams and retrieve generated optical signatures. We demonstrate the effectiveness of laser filamentation for simultaneously clearing the path for intense femtosecond pulse propagation in a highly scattering medium, generation of luminous plasma on a solid target, and counter-propagation of a characteristic spectroscopic signal over a cleared channel along the filament path. In a dense cloud, the counter-propagating signal predominantly transits the cleared on-axis path but is highly affected by the negative thermal lensing of a Gaussian beam. These insights enhance our understanding of laser filamentation in atmospheric sensing and could substantially improve remote detection capabilities in poor visibility conditions.

KCS-YOLO: An Improved Algorithm for Traffic Light Detection under Low Visibility Conditions

KCS-YOLO: An Improved Algorithm for Traffic Light Detection under Low Visibility Conditions

Consensus Control of Heterogeneous Uncertain Multiple Autonomous Underwater Vehicle Recovery Systems in Scenarios of Implicit Reduced Visibility

This paper investigates consensus control in heterogeneous and uncertain multiple autonomous underwater vehicle (AUV) systems under implicit reduced visibility conditions. We address challenges such as environmental uncertainties and system nonlinearity by utilizing a unified connectivity approach to model low-visibility interactions and heterogeneous multi-AUV dynamics. Our main contributions include developing a feedback linearization model for heterogeneous multi-AUV systems that accounts for uncertainties, introducing an adaptive consensus controller based on relative positioning that effectively manages implicit visual interaction limitations and validating our strategies through stability analysis and numerical simulations. Our simulations demonstrate approximately a 60% improvement in accuracy compared to previous algorithms, highlighting the practical value of our approach in AUV recovery operations. These advancements provide a robust solution for consensus control in complex underwater environments.

A Geant4 Monte Carlo toolkit-based radiative transfer model for studying the impact of aerosols

Radiative transfer models are of prime importance in quantifying the earth-atmosphere energy budget. A radiative transfer model based on the Geant4 Monte Carlo toolkit was developed. This model is able to perform calculations of the spectral atmospheric transmittance, surface solar irradiance, as well as the Top of the Atmosphere (TOA) reflected irradiance over the spectral range from 280 nm to 3000 nm. Through this study, the Geant4 toolkit was used to study the effect of rural aerosols concentration on different radiative characteristics. Geant4 radiative calculations in the presence and absence of aerosols were compared to that of DISORT code. For both surface and Top of the atmosphere simulations, good agreement was observed between the two models. Specifically, under atmospheric visibility conditions of 23 km, the mean relative difference between Geant4 and DISORT code was found to be less than 2.46 % for Global irradiance calculations and less than 7.9 % for TOA reflected irradiance. A comparative analysis between the measured total global irradiance in Adrar city and the irradiance estimated by Geant4 demonstrated the general effectiveness of Geant4, with a coefficient of determination of 0.991 and a Mean Absolute Percentage Error (MAPE) of 5.851 %.

A Step-Wise Domain Adaptation Detection Transformer for Object Detection under Poor Visibility Conditions

A Step-Wise Domain Adaptation Detection Transformer for Object Detection under Poor Visibility Conditions

RCRFNet: Enhancing Object Detection with Self-Supervised Radar-Camera Fusion and Open-Set Recognition.

Robust object detection in complex environments, poor visual conditions, and open scenarios presents significant technical challenges in autonomous driving. These challenges necessitate the development of advanced fusion methods for millimeter-wave (mmWave) radar point cloud data and visual images. To address these issues, this paper proposes a radar-camera robust fusion network (RCRFNet), which leverages self-supervised learning and open-set recognition to effectively utilise the complementary information from both sensors. Specifically, the network uses matched radar-camera data through a frustum association approach to generate self-supervised signals, enhancing network training. The integration of global and local depth consistencies between radar point clouds and visual images, along with image features, helps construct object class confidence levels for detecting unknown targets. Additionally, these techniques are combined with a multi-layer feature extraction backbone and a multimodal feature detection head to achieve robust object detection. Experiments on the nuScenes public dataset demonstrate that RCRFNet outperforms state-of-the-art (SOTA) methods, particularly in conditions of low visual visibility and when detecting unknown class objects.

Single-file movement of pedestrians at different visibility levels

Previous studies indicate that the movement of pedestrians under poor visibility conditions is significantly different from that under good visibility conditions. To investigate the influence of visibility factor on the longitudinal movement of pedestrian, series of single-file experiments were performed under the quantitative control of visibility level. Based on the extracted trajectories, typical evacuation behavior and movement characteristics of pedestrians under different visibility conditions are explored. It is found that the lateral swing amplitude of pedestrians during movement is within 20 cm under low densities while it is close to 30 cm at high densities. The free velocity under the light transmission of 100 %, 2.2 %, 0.5 % and 0.1 % is 0.856 m/s, 0.823 m/s, 0.741 m/s and 0.712 m/s respectively. Individuals cannot accurately perceive the surrounding situations with the worsening of visual condition, therefore the movement speed decreases under the poor visibilities. Besides, two regimes including the constrained regime and free regime are identified in the headway-velocity relation at different visibility levels. The maximum specific flow under the light transmission of 100 %, 2.2 %, 0.5 % and 0.1 % is 0.70 s−1, 0.67 s−1, 0.62 s−1 and 0.55 s−1 respectively. The study is helpful to reveal the pedestrian dynamics of single-file movement under the limited visibility conditions.

Neural patient-specific 3D-2D registration in laparoscopic liver resection.

Augmented reality guidance in laparoscopic liver resection requires the registration of a preoperative 3D model to the intraoperative 2D image. However, 3D-2D liver registration poses challenges owing to the liver's flexibility, particularly in the limited visibility conditions of laparoscopy. Although promising, the current registration methods are computationally expensive and often necessitate manual initialisation. The first neural model predicting the registration (NM) is proposed, represented as 3D model deformation coefficients, from image landmarks. The strategy consists in training a patient-specific model based on synthetic data generated automatically from the patient's preoperative model. A liver shape modelling technique, which further reduces time complexity, is also proposed. The NM method was evaluated using the target registration error measure, showing an accuracy on par with existing methods, all based on numerical optimisation. Notably, NM runs much faster, offering the possibility of achieving real-time inference, a significant step ahead in this field. The proposed method represents the first neural method for 3D-2D liver registration. Preliminary experimental findings show comparable performance to existing methods, with superior computational efficiency. These results suggest a potential to deeply impact liver registration techniques.

Spatio-temporal adaptive algorithm using Hamming code based on 3D-MIMO wireless channel model

Space-time coding is an effective method for constructing communication systems while saving bandwidth, taking into account the shortage of frequency resources and power in fading channels, which is implemented based on the advantages of using adaptive antenna arrays. Currently, WiMAX radio access technology has become widespread; its use is justified in conditions of direct visibility between the base and mobile stations, but in a city with dense buildings and in the presence of organized interference, its use leads to an increase in the likelihood of bit errors. At the same time, the combination of OFDM (orthogonal frequency division multiplexing) and MIMO (spatial coding) technologies based on adaptive antenna arrays significantly reduces the probability of bit errors, and therefore increases the throughput of the communication system. The use of adaptive space-time devices at the outputs of antenna arrays makes it possible to select a signal path with maximum power and with a minimum error in receiving OFDM pilot subcarriers, even in the absence of direct visibility between the base and mobile stations and in conditions of active interference. In addition, the use of a 3D model of the communication channel will allow us to evaluate the effectiveness of the communication system model in real operating conditions. The goal of the work is to solve the problem of additionally increasing noise immunity in communication systems in conditions of numerous reflections caused by dense buildings in urban environments, using a combination of Hamming code, OFDM and an algorithm for adaptive spatio-temporal signal processing in receivers such as SISO (Singl Input – Singl Output), and MIMO (Multiply Input – Multiply Output), built on adaptive antenna arrays. The work calculated the weight coefficients of the adaptation algorithm using the theory of eigenvalues and eigenvectors of the spatial correlation matrix of signals at the outputs of the adaptive antenna arrays. Modeling of algorithms and a 3D model of the communication channel in Matlab using the Hamming code was carried out, as a result, an increase in the efficiency of error correction for the Hamming code was demonstrated, as well as a significant reduction in the probability of code errors and an increase in the performance of the communication system when using an adaptive algorithm. At the same time, the article uses the term “multipath” channel in contrast to the generally accepted, but incorrect “multibeam” channel, since multibeam can be a characteristic of an antenna system. The presented results confirm that when processing signals in a receiving system in a multipath channel with dense urban development, the use of adaptive spatiotemporal algorithms provides increased noise immunity and radio communication capacity, especially for image transmission when using unmanned aerial vehicles.

Analysis of the peculiarities of the method of combining an electronic cartographic navigation information system with a ship's radar station

The article analyses the importance of radar image overlay on the screen of an electronic charting navigation information system (ECNIS) for improving safety and efficiency of navigation. Based on a review of scientific publications and professional literature, the article discusses the technical aspects of the overlay function, its advantages and limitations, and the impact on the decision-making process of a navigator. The overlay of radar information on an electronic navigation chart ensures the integration of planned and actual data on the environment, which contributes to the increase of situational awareness of the navigator. The combination of the systems makes it possible to visually compare radar targets with cartographic objects, clarify the vessel's location, control drift and orientation, and detect navigation hazards. The overlay function is especially valuable when sailing in confined waters and in conditions of limited visibility. At the same time, the use of overlay technology is associated with a number of technical difficulties and limitations related to ensuring the accuracy of overlap, harmonisation of data formats, and synchronisation of information updates. Errors in the operation of positioning systems, direction guidance, and cartography errors can lead to inconsistencies in image layers and incorrect interpretation of the navigation situation. The issues of equipment reliability, display parameters settings, competence and training of navigators are of critical importance. It is emphasised that radar image overlay on ECDIS is an effective tool for improving safety of navigation, but it does not replace traditional navigation practices and does not eliminate the need for critical evaluation of data. Over-reliance on technology, ignoring its limitations and disregard for the principles of good maritime practice can lead to erroneous decisions and have a negative impact on safety. Further prospects for the development of technology can be associated with the improvement of information processing algorithms, optimisation of human-machine interaction, and the development of training methods. The article emphasises the need for an integrated approach combining the improvement of technical systems and the human factor to fully realise the potential of the overlay function in ensuring safety of navigation. Only a balanced use of technological innovations and traditional navigational practices can guarantee the reliability and efficiency of modern navigation

Virtual/augmented reality-based human–machine interface and interaction modes in airport control towers

The concept of an innovative human–machine interface and interaction modes based on virtual and augmented reality technologies for airport control towers has been developed with the aim of increasing the human performances and situational awareness of air traffic control operators. By presenting digital information through see-through head-mounted displays superimposed over the out-of-the-tower view, the proposed interface should stimulate controllers to operate in a head-up position and, therefore, reduce the number of switches between a head-up and a head-down position even in low visibility conditions. This paper introduces the developed interface and describes the exercises conducted to validate the technical solutions developed, focusing on the simulation platform and exploited technologies, to demonstrate how virtual and augmented reality, along with additional features such as adaptive human–machine interface, multimodal interaction and attention guidance, enable a more natural and effective interaction in the control tower. The results of the human-in-the-loop real-time validation exercises show that the prototype concept is feasible from both an operational and technical perspective, the solution proves to support the air traffic controllers in working in a head-up position more than head-down even with low-visibility operational scenarios, and to lower the time to react in critical or alerting situations with a positive impact on the human performances of the user. While showcasing promising results, this study also identifies certain limitations and opportunities for refinement, aimed at further optimising the efficacy and usability of the proposed interface.

Managerial and Organizational Implications Arising from the Implementation of Blockchain Technology in Supply Chains: An AS-IS and To-Be Analysis

Purpose: The main objective of this research is to highlight the managerial and organizational implications for businesses resulting from the implementation of blockchain logic and technologies. The functionalities of blockchain technologies are compared with the traditional approach to supply chain management to emphasize the impact they can have on managing intra-supply chain relationships and the organizational structure of companies involved in supply channels. Design/methodology/approach: To conduct this study, a conceptual study was carried out, starting from an analysis of the main characteristics of blockchain technologies, to build an AS-IS scenario compared with the TO-BE scenario resulting from the implementation of these technologies within supply chains. To conduct this study, conceptual categories of the viable system approach and process management perspective were utilized. The conceptual analysis was performed using a matrix representation, which correlates material, informational, and financial flows with the dimensions of traceability, transparency, and efficiency, before and after the implementation of blockchains in managing supply chain transactions. Findings: The study highlighted how the principles underlying blockchain can lead to overcoming the traditional indispensable search for trust among supply chain operators. Therefore, through these results, it is possible to hypothesize an improvement in the resilience conditions of companies operating in block–supply chains. Originality/value: This study contributes to investigating the relationship between blockchain and operational consonance among operators in managing interorganizational processes to improve conditions of efficiency, traceability, and transparency, which are functional in the resilience of companies involved in the supply chain compared to the changing dynamics of the context. Research implications: Trustless logic in the implementation of blockchain technologies leads to the configuration of a new concept of trust, connected to the entire supply chain system, as a result of the improvement in the security, visibility, and efficiency conditions of transactions. The increased visibility and transparency developed in block–supply chains prompt all actors involved in the supply chains to rethink information management, given the reduction in informational asymmetries and, thus, the optimization of transaction costs.