Rainy Scenarios Research Articles

Weathering words: a virtual reality study of environmental influence on reading dynamics.

Reading is a fundamental cognitive activity that is influenced by both textual and external environmental factors, although the latter has been less thoroughly explored. This study aims to examine the impact of environmental visual conditions on reading performance using Virtual Reality (VR) technology. We conducted two experiments to assess the effects of visual contrast and simulated weather conditions on reading dynamics. In Experiment 1, we measured single-word recognition speed using a lexical decision task under different visual contrasts and weather conditions. In Experiment 2, we assessed reading dynamics during a sentence reading task, analyzing how visual contrast and simulated sunny versus rainy weather conditions affected reading behavior, particularly focusing on reading speed and eye fixations. In Experiment 1, high visual contrast, particularly under sunny conditions, significantly enhanced single-word recognition speed, indicating a notable influence of environmental visual conditions. In Experiment 2, visual contrast had minimal effect on sentence reading; however, sunny weather facilitated faster reading times, while rainy scenarios increased the number of eye fixations. These findings suggest that environmental factors, such as weather conditions, can significantly affect reading behavior. The study contributes to the understanding of key environmental influences on reading in everyday life contexts and has implications for the ergonomic design of reading materials, especially for outdoor settings and VR environments. Additionally, the integration of controlled stimuli within VR increases the ecological validity of reading research, underscoring the potential of VR as a powerful tool for cognitive research.

Cropping calendar, agroclimatic and agroecological zoning for rainfed maize (Zea mays L.) under different rainfall scenarios in a semi-arid region of Brazil

Context The productivity of crops is influenced by the available net water balance, which relies on a complex interplay of edaphic and climatic factors. In this sense, rainfall is the leading water input for crop growth and yield, especially in water-scarce regions under semi-arid climates worldwide. Aims This study aimed to define the optimum cropping calendar and the agroclimatic and agroecological zones for rainfed maize in the state of Ceará, which is predominantly located within the Brazilian semi-arid region, assuming different rainfall scenarios. Methods We considered the subdivision of the study area into eight homogeneous precipitation regions according to the regional rainfall pattern. The climatic water balance for each region over two decades was calculated from historical weather data series, assuming three rainy scenarios: dry, regular, and rainy. The agroclimatic zoning was defined through the crop water balance results, soil classification, land use and occupation, slope and temperature, weighted through a multi-criteria decision analysis based on the Analytic Hierarchy Process. Key results The recommended planting period was broader in rainy conditions. However, there may be some dry spells between two 10-day intervals of the calendar, which impair plant growth depending on their intensity. For the dry scenario, the agroclimatic zoning showed that the marginal water deficiency covered 96.55% of the study area. Conclusions Rainfed maize cultivated under the semi-arid climate of Ceará state is viable in rainy conditions, partially restricted in regular years, and not recommended in dry years. Implications This study emphasises the importance of climate monitoring in effective planning for rainfed maize cultivation in semi-arid regions.

A Robust Lightweight Network for Pedestrian Detection Based on YOLOv5-x

Pedestrian detection is a crucial task in computer vision, with various applications in surveillance, autonomous driving, and robotics. However, detecting pedestrians in complex scenarios, such as rainy days, remains a challenging problem due to the degradation of image quality and the presence of occlusions. To address this issue, we propose RSTDet-Lite (a robust lightweight network) for pedestrian detection on rainy days, based on an improved version of YOLOv5-x. Specifically, in order to reduce the redundant parameters of the YOLOv5-x backbone network and enhance its feature extraction capability, we propose a novel approach named CBP-GNet, which incorporates a compact bilinear pooling algorithm. This new net serves as a new backbone network, resulting in significant parameter reduction and enhancing the fine-grained feature fusion capability of the network. Additionally, we introduce the Simple-BiFPN structure as a replacement for the original feature pyramid module based on the weighted bidirectional feature pyramid to further improve feature fusion efficiency. To enhance network performance, we integrate the CBAM attention mechanism and introduce the idea of structural reparameterization. To evaluate the performance of our method, we create a new dataset named RainDet3000, which consists of 3000 images captured in various rainy scenarios. The experimental results demonstrate that, compared with YOLOv5, our proposed model reduces the network size by 30 M while achieving a 4.56% increase in mAP. This confirms the effectiveness of RSTDet-Lite in achieving excellent performance in rainy-day pedestrian detection scenarios.

PVformer: Pedestrian and Vehicle Detection Algorithm Based on Swin Transformer in Rainy Scenes

Pedestrian and vehicle detection plays a key role in the safe driving of autonomous vehicles. Although transformer-based object detection algorithms have made great progress, the accuracy of detection in rainy scenarios is still challenging. Based on the Swin Transformer, this paper proposes an end-to-end pedestrian and vehicle detection algorithm (PVformer) with deraining module, which improves the image quality and detection accuracy in rainy scenes. Based on Transformer blocks, a four-branch feature mapping model was introduced to achieve deraining from a single image, thereby mitigating the influence of rain streak occlusion on the detector performance. According to the trouble of small object detection only by visual transformer, we designed a local enhancement perception block based on CNN and Transformer. In addition, the deraining module and the detection module were combined to train the PVformer model through transfer learning. The experimental results show that the algorithm performed well on rainy days and significantly improved the accuracy of pedestrian and vehicle detection.

Lightweight single image deraining algorithm incorporating visual saliency

There are still some challenges in the task of single image rain removal, such as artefact remnant, background over-smooth, and increasingly complex and heavy-weight network architecture. Especially too heavy-weight network to fit outdoor detection devices or mobile devices. To address the above challenges, we propose a lightweight single image Deraining algorithm incorporating visual attention saliency mechanisms (LDVS). The proposed network consists of five blocks and two convolution operations, where each block consists of a dilation convolution module and a convolutional block attention module (CBAM). Specifically, visual saliency module CBAM is used for accurate localization of rain streak, and further the combinations of dilated convolution with CBAM is used to extract feature maps of rain streaks faithfully, which is able to remove artefact remnant while maintaining background details. A good tradeoff is presented between the network's weight size and effect of rain removal. Specifically, with only 48,268 parameters, the proposed model can achieve a guaranteed performance. Extensive experiments on a few typical rainy scenarios on synthetic and real-world datasets have demonstrated that to achieve the same level of performance, the proposed method has far smaller size than most of the baselines under both qualitative and quantitative analyses.

Single-Pixel Near-Infrared 3D Image Reconstruction in Outdoor Conditions.

In the last decade, the vision systems have improved their capabilities to capture 3D images in bad weather scenarios. Currently, there exist several techniques for image acquisition in foggy or rainy scenarios that use infrared (IR) sensors. Due to the reduced light scattering at the IR spectra it is possible to discriminate the objects in a scene compared with the images obtained in the visible spectrum. Therefore, in this work, we proposed 3D image generation in foggy conditions using the single-pixel imaging (SPI) active illumination approach in combination with the Time-of-Flight technique (ToF) at 1550 nm wavelength. For the generation of 3D images, we make use of space-filling projection with compressed sensing (CS-SRCNN) and depth information based on ToF. To evaluate the performance, the vision system included a designed test chamber to simulate different fog and background illumination environments and calculate the parameters related to image quality.

A Novel Algorithm for Detecting Pedestrians on Rainy Image.

Pedestrian detection is widely used in cooperative vehicle infrastructure systems. Traditional pedestrian detection methods perform sufficiently well under sunny scenarios and obtain trustworthy traffic data. However, the detection drastically decreases under rainy scenarios. This study proposes a pedestrian detection algorithm with a de-raining module that improves detection accuracy under various rainy scenarios. Specifically, this algorithm determines the density information of rain and effectively removes rain streaks through the de-raining module. Then the algorithm detects pedestrians as a pair of keypoints through the pedestrian detection module to solve the problem of occlusion. Furthermore, a new pedestrian dataset containing rain density labels is established and used to train the algorithm. For the scenarios of light, medium, and heavy rain, extensive experiments on synthetic datasets demonstrate that the proposed algorithm increases AP (average precision) of pedestrian detection by 21.1%, 48.1%, and 60.9%. Moreover, the proposed algorithm performs well on real datasets and achieves improvements over the state-of-the-art methods, which reveals that the proposed algorithm can significantly improve the accuracy of pedestrian detection in rainy scenarios.

A GPS water vapour tomography method based on a genetic algorithm

Abstract. Water vapour is an important substituent of the atmosphere but its spatial and temporal distribution is difficult to detect. Global Positioning System (GPS) water vapour tomography, which can sense three-dimensional water vapour distribution, has been developed as a research area in the field of GPS meteorology. In this paper, a new water vapour tomography method based on a genetic algorithm (GA) is proposed to overcome the ill-conditioned problem. The proposed approach does not need to perform matrix inversion, and it does not rely on excessive constraints, a priori information or external data. Experiments in Hong Kong under rainy and rainless conditions using this approach show that there is a serious ill-conditioned problem in the tomographic matrix by grayscale and condition numbers. Numerical results show that the average root mean square error (RMSE) and mean absolute error (MAE) for internal and external accuracy are 1.52∕0.94 and 10.07∕8.44 mm, respectively, with the GAMIT-estimated slant water vapour (SWV) as a reference. Comparative results of water vapour density (WVD) derived from radiosonde data reveal that the tomographic results based on GA with a total RMSE ∕ MAE of 1.43∕1.19 mm are in good agreement with that of radiosonde measurements. In comparison to the traditional least squares method, the GA can achieve a reliable tomographic result with high accuracy without the restrictions mentioned above. Furthermore, the tomographic results in a rainless scenario are better than those of a rainy scenario, and the reasons are discussed in detail in this paper.

Power allocation in multibeam satellites based on particle swarm optimization

In this paper, a power allocation in multibeam satellite (MBS) communication based on heuristic particle swarm optimization (PSO) is proposed. The PSO algorithm is evocated to solve the problem of power allocation in the multiple narrow spotbeams aiming to provide the minimum signal-to-noise plus interference ratio (SNIR) required by Earth station users to establish reliable communication. In the developed model it is considered the multibeam interference and the different channel conditions of each beam by rain attenuation. The numerical results have been generated taking into account different sky situations, including clear and rainy scenarios; such results have revealed the viability and accuracy of the PSO algorithm deployment in solving the power allocation problem. In addition, with the proposed scheme it is observed the decrement of transmitted power for non-rainy beams with guarantee of the minimum SNIR at the Earth receivers input, while increase the power availability to the rainy beams; as a consequence, the overall energy efficiency of the MBS system has been improved substantially. Moreover, the convergence of the proposed heuristic PSO-based algorithm is discussed while such heuristic approach comes with computational complexity reduction when compared with others efficient power allocation schemes.

Diurnal and partitioned heat-flux patterns of coupled green-building roof systems

Coupled green-building roof system can assess bidirectional heat-flux with indoor space. Two high-rise residential blocks in humid-subtropical Hong Kong, with and without roof building thermal insulation (BTI), were monitored for thermal performance across seasons and weather conditions. Each block had three plots: Sedum and herbaceous Peanut green roofs, plus bare Control. Diurnal heat-flux data were partitioned into daytime and nighttime, and positive and negative fractions. On hot summer-sunny day, Control without BTI facilitates notable daytime influx and nighttime efflux of heat. Sedum augments thermal-mass effect pushing more heat indoor than Control. With balance between transpiration-cooling and heat-sink formation, Peanut registers intermediate heat gain. Adding BTI raises heat-gain and eliminates Control heat-loss, curtails Sedum heat-gain, and maintains Peanut heat-gain. BTI partnering with green roof offer synergistic thermal benefit by increasing thermal mass, thermal capacity and thermal resistance to furnish thermal buffering, thermal lag, and extending heat ingress to nighttime. Lower solar-radiation input on summer-cloudy day significantly trims heat gain at Control, with limited reduction at both green roofs. Summer-rainy day brings further drop in heat gain. For cloudy and rainy scenarios, heat-gain suppression is more notable with BTI. Interpretations for other season-weather conditions and implications of the findings are elaborated.

Risk assessment of PM2.5 to child residents in Brazilian Amazon region with biofuel production

BackgroundExposure to fine fractions of particulate matter (PM2.5) is associated with increased hospital admissions and mortality for respiratory and cardiovascular disease in children and the elderly. This study aims to estimate the toxicological risk of PM2.5 from biomass burning in children and adolescents between the age of 6 and 14 in Tangará da Serra, a municipality of Subequatorial Brazilian Amazon.MethodsRisk assessment methodology was applied to estimate the risk quotient in two scenarios of exposure according to local seasonality. The potential dose of PM2.5 was estimated using the Monte Carlo simulation, stratifying the population by age, gender, asthma and Body Mass Index (BMI).ResultsMale asthmatic children under the age of 8 at normal body rate had the highest risk quotient among the subgroups. The general potential average dose of PM2.5 was 1.95 μg/kg.day (95% CI: 1.62 – 2.27) during the dry scenario and 0.32 μg/kg.day (95% CI: 0.29 – 0.34) in the rainy scenario. During the dry season, children and adolescents showed a toxicological risk to PM2.5 of 2.07 μg/kg.day (95% CI: 1.85 – 2 .30).ConclusionsChildren and adolescents living in the Subequatorial Brazilian Amazon region were exposed to high levels of PM2.5 resulting in toxicological risk for this multi-pollutant. The toxicological risk quotients of children in this region were comparable or higher to children living in metropolitan regions with PM2.5 air pollution above the recommended limits to human health.