Search Results (96)

Advanced rider assistance systems (ARAS) play a crucial role in enhancing motorcycle safety through features such as collision avoidance, blind-spot detection, and adaptive cruise control, which rely heavily on sensors like radar, cameras, and LiDAR. However, their performance is often compromised under adverse weather conditions, leading to sensor interference, reduced visibility, and inconsistent reliability. This study evaluates the effectiveness and limitations of ARAS technologies in rain, fog, and snow, focusing on how sensor performance, algorithms, techniques, and dataset suitability influence system reliability. A thematic analysis was conducted, selecting studies focused on ARAS in adverse weather conditions based on specific selection criteria. The analysis shows that while ARAS offers substantial safety benefits, its accuracy declines in challenging environments. Existing datasets, algorithms, and techniques were reviewed to identify the most effective options for ARAS applications. However, more comprehensive weather-resilient datasets and adaptive multi-sensor fusion approaches are still needed. Advancing in these areas will be critical to improving the robustness of ARAS and ensuring safer riding experiences across diverse environmental conditions. Full article

This article examines the impact of an undetected Instrument Landing System (ILS) failure on crew situational awareness. A literature review of similar aviation accidents is presented, highlighting the recurring challenge of misleading instrument indications and their influence on approach safety. The research environment consisted of flight simulator replicating both ideal and accident-weather conditions at two airports, with the final scenario involving a simulated ILS receiver malfunction providing erroneous yet seemingly valid indications. Six pilots with varying flight hours participated, conducting four simulated approaches under different conditions. Flight path stability, deviation from glide slope and course, approach speed, and decision-making were recorded and analyzed. The results indicate that experienced pilots detected inconsistencies more quickly, maintained more stable control inputs, and initiated go-arounds earlier, while less experienced pilots required more time but were still able to correctly assess the risks. The primary goal of this research was to identify cognitive mechanisms and operational decision-making processes under simulated conditions, not to establish universally generalizable outcomes. The findings underline the importance of simulator-based training incorporating unexpected navigation system failures to reinforce cross-checking habits, enhance situational awareness, and improve decision-making during critical phases of flight. Full article

Green Firebreaks (GFBs), strips of strategically placed low-flammability vegetation, represent a proactive complement to other approaches to wildfire management. This review, which summarises the literature to elucidate GFBs’ potential to reduce fire spread and intensity, revealed that empirical studies validating their effectiveness remain scarce. It also revealed that comparisons of GFB techniques are challenging due to spatial and temporal complexity combined with inconsistent methods and terminology. Several researchers note that GFB effectiveness requires that their design is appropriate for the site conditions. Furthermore, GFBs are not a stand-alone solution to the wildfire problem, and a lack of consideration for trade-offs may undermine their effectiveness, particularly under extreme weather conditions. As climate change intensifies drought and heat, vegetation moisture content must be a key design factor given that even low-flammability vegetation becomes fuel under extreme drought conditions. In addition, poorly designed GFBs may unintentionally alter wind dynamics and increase ember transport and fire spread. There is a broad consensus in the literature that appropriately designed GFBs can complement wildfire management while providing additional biodiversity and other benefits. To achieve their potential, research is required for GFB designs to be site-specific, responsive to trade-offs, and effective in providing multiple benefits under different climate change scenarios. Full article

In addition to the use of reference instruments, low-cost sensors (LCSs) are becoming increasingly popular for air quality monitoring both indoors and outdoors. These sensors provide real-time measurements of pollutants and facilitate better spatial and temporal coverage. However, these simpler devices are typically characterised by lower accuracy and precision and can be more sensitive to the environmental conditions than the reference instruments. It is therefore crucial to characterise the applicability and limitations of these instruments, for which a possible solution is their comparison with reference measurements in real-world conditions. To this end, a measurement campaign has been carried out to evaluate the PM_2.5 readings of several low- and medium-cost air quality instruments of different types and categories (IQAir AirVisual Pro, TSI DustTrak™ II Aerosol Monitor 8532, Xiaomi Mijia Air Detector, and Xiaomi Smartmi PM_2.5 Air Detector). A GRIMM EDM180 instrument was used as the reference. This campaign took place in Budapest, Hungary, from 12 November to 15 December 2020, during typically humid and foggy weather conditions, when the air pollution level was high due to the increased anthropogenic emissions, including wood burning for heating purposes. The results indicate that the individual sensors tracked the dynamics of PM_2.5 concentration changes well (in a linear fashion), but the readings deviated from the reference measurements to varying degrees. Even though the AirVisual sensors performed generally well (0.85 < R² < 0.93), the accuracy of the units showed inconsistency (13–93%) with typical overestimation, and their readings were significantly affected by elevated relative humidity levels and by temperature. Despite the overall overestimation of PM_2.5 by the Xiaomi sensors, they also exhibited strong correlation coefficients with the reference, with R² values of 0.88 and 0.94. TSI sensors exhibited slight underestimations with high explained variance (R² = 0.93–0.94) and good accuracy. The results indicated that despite the inherent bias, the low-cost sensors are capable of capturing the temporal variability of PM_2.5, thus providing relevant information. After simple and multiple linear regression-based correction, the low-cost sensors provided acceptable results. The results indicate that sensor data correction is a necessary prerequisite for the usability of the instruments. The ensemble method is a reasonable alternative for more accurate estimations of PM_2.5. Full article

Plastic containers used for diesel storage represent an underexplored but significant environmental challenge due to hydrocarbon retention and prolonged weathering. This study evaluates the capacity of Aspergillus flavus to colonize and grow on high-density polyethylene (HDPE) surfaces contaminated with weathered and fresh diesel residues. Circular plastic samples from HDPE tanks exposed to environmental conditions for over two years (weathered) and for less than two months (non-weathered) were inoculated with A. flavus and incubated at 20 °C, 25 °C, and 30 °C. Growth kinetics were assessed through radial expansion and halo formation, quantified via digital imaging and ImageJ analysis. Results showed the most robust fungal growth occurred on weathered diesel-contaminated gray plastics at 30 °C, with colony areas exceeding 350 mm² and halos over 3000 mm². Conversely, white HDPE with fresh diesel showed limited and inconsistent growth, likely due to the presence of volatile hydrocarbons and polymer additives. These findings underscore the critical role of diesel aging and polymer characteristics in shaping fungal adaptability, providing a foundation for the development of environmentally sustainable bioremediation strategies targeting diesel-contaminated HDPE plastics. Full article

The increasing frequency and intensity of heat waves, combined with urban heat islands (UHIs), pose significant public health challenges. Implementing low-cost, real-time monitoring networks with distributed stations within the smart city framework faces obstacles in transforming urban spaces. Accurate data are essential for assessing these effects. This paper compares different network types in a medium-sized city in western Spain and their implications for UHI identification quality. The study first presents a purpose-built monitoring network using Open-Source platforms, IoT technology, and LoRaWAN communications, adhering to World Meteorological Organization guidelines. Additionally, it evaluates two citizen weather observer networks (CWONs): one from a commercial smart device company and another from a global community connecting environmental sensor data. The findings highlight several advantages of bespoke monitoring networks over CWON, including enhanced data accessibility and greater flexibility to meet specific requirements, facilitating adaptability and scalability for future upgrades. However, specialization is crucial for effective deployment and maintenance. Conversely, CWONs face limitations in network uniformity, data shadow zones, and insufficient knowledge of real sensor situations or component characteristics. Furthermore, CWONs exhibit some data inconsistencies in probability distribution and scatter plots during extreme heat periods, as well as improbable UHI temperature values. Full article

Irish potato (Solanum tuberosum) is a critical crop for food security and economic growth in Tsangano and Angónia Districts, Central Mozambique. Challenges like inconsistent yields and variable quality are often linked to suboptimal soil conditions, which limit production. This study aimed to classify and evaluate the suitability of soils for potato cultivation in Tete Province, where detailed soil assessments remain limited. Four pedons—TSA-P01 and TSA-P02 in Tsangano and ANGO-P01 and ANGO-P02 in Angónia—were examined for bulk density, texture, pH, organic carbon, and nutrient content using a combination of pedological methods and laboratory soil analysis. To determine each site’s potential for growing Irish potatoes, these factors were compared to predetermined land suitability standards. The pedons were very deep (>150 cm) and had textures ranging from sandy clay loam to sandy loam. TSA-P02 had the lowest bulk density (0.78 Mg m⁻³) and the highest available water capacity (182.0 mm m⁻¹). The soil pH ranged from 5.6 to 7.9, indicating neutral to slightly acidic conditions. Nutrient analysis revealed low total nitrogen (0.12–0.22%), varying soil organic carbon (0.16–2.73%), and cation exchange capacity (10.1–11.33 cmol⁽⁺⁾ kg⁻¹). Pedons TSA-P01, ANGO-P1, and ANGO-P02 were characterized by eluviation and illuviation as dominant pedogenic processes, while in pedon TSA-P02, shrinking and swelling were the dominant pedogenic processes. Weathering indices identified ANGO-P01 as most highly weathered, while TSA-P02 was least weathered and had better fertility indicators. According to USDA Taxonomy, the soils were classified as Ultisols, Vertisols, and Alfisols, corresponding to Acrisols, Alisols, Vertisols, and Luvisols in the WRB for Soil Resources. All studied soils were marginally suitable for potato production (S3f) due to dominant fertility constraints, but with varying minor limitations in climate, topography, and soil physical properties. The findings hence recommended targeted soil fertility management to enhance productivity and sustainability in potato cultivation. Full article

Extreme weather events from climate change challenge infrastructure stability. While water-related engineering enhances disaster resilience, it also impacts ecosystems. Taiwan has implemented Ecological Check and Identification (ECI) since 2003, yet challenges remain in standards, resource allocation, and effectiveness. This study analyzes 35 coastal engineering cases and participated in two engineering projects from five key perspectives. The results show that there are regional differences in the types of projects implemented for ECI. Landscape engineering was the main type in northern Taiwan (31%), water resource engineering was the main type in southern Taiwan (43%), and no cases were found in eastern Taiwan. Most inspections occur in the proposal (24%), planning (22%), and design (22%) stages, with limited post-construction monitoring (14%). Furthermore, ecological assessments were lacking in 49% of cases, and aquatic ecosystems were underrepresented. Inconsistent inspection formats and low species documentation (57% of cases) reduce data comparability and conservation effectiveness. To address these gaps, some recommendations were made, including standardizing inspections, integrating Sustainable Development Goals (SDGs), promoting low-carbon approaches, strengthening public participation, and establishing long-term monitoring. The findings provide policy insights to enhance ECI, supporting sustainable coastal engineering while balancing infrastructure benefits and environmental conservation. Full article

Accurate PV power generation forecasting is critical to enable grid utilities to manage energy effectively. This study presents an approach that combines machine learning with a clustering methodology to improve the accuracy of predictions for energy management purposes. First, various machine learning models were compared, and multilayer perceptron (MLP) outperformed others by effectively capturing the complex relationships between weather parameters and PV power output, obtaining the following results: MSE: 3.069, RMSE: 1.752, and MAE: 1.139. To improve the performance of MLP, weather characteristics that are highly correlated with PV power outputs, such as irradiation and sun elevation, were grouped using K-means clustering. The elbow method identified four optimal clusters, and individual MLP models were trained on each, reducing data complexity and improving model focus. This clustering-based approach significantly improved the accuracy of the predictions, resulting in average metrics across all clusters of the following: MSE: 0.761, RMSE: 0.756, and MAE: 0.64. Despite these improvements, further research on optimizing the MLP architecture and clustering methodology is required to address inconsistencies and achieve even better performance. Full article

Wave power (WP) is a strategic oceanic resource. Previous studies have extensively researched the long-term variations in WP in the South China Sea (SCS) for energy planning and utilization. This study extends the analysis of long-term trends to the last year based on ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5) reanalysis data from 1979 to 2024. Our results mainly indicate that the trends in WP after 2011 are significantly different from those before 2011. Before 2011, the WP in the SCS primarily showed an increasing trend, but, after 2011, it shifted to a decreasing trend. This trend has seasonal differences, manifested as being consistent with the annual trend in winter and spring while being inconsistent with the annual trend in summer and autumn. It indicates that the opposite trend in WP before and after 2011 was mainly the result of WP variations in winter and spring. To illustrate the driving factor for the WP’s variations, the contemporary long-term trend of the wind fields is systematically analyzed. Analysis results reveal that, regardless of seasonal differences or spatial distribution, the two trends are consistent in most situations, indicating that wind fields are the dominant factor for the long-term variations in WP. Meanwhile, the effects of the wind fields on the WP variations can also be modulated by environmental factors such as oceanic swell propagation and local topography. This study contributes to the knowledge of the latest trends and driving factors regarding the WP in the SCS. Full article

The conventional hand-picking of tea buds is inefficient and leads to inconsistent quality. Innovations in tea bud identification and automated grading are essential for enhancing industry competitiveness. Key breakthroughs include detection accuracy and lightweight model deployment. Traditional image recognition struggles with variable weather conditions, while high-precision models are often too bulky for mobile applications. This study proposed a lightweight YOLOV5 model, which was tested on three tea types across different weather scenarios. It incorporated a lightweight convolutional network and a compact feature extraction layer, which significantly reduced parameter computation. The model achieved 92.43% precision and 87.25% mean average precision (mAP), weighing only 4.98 MB and improving accuracy by 6.73% and 2.11% while reducing parameters by 2 MB and 141.02 MB compared to YOLOV5n6 and YOLOV5l6. Unlike networks that detected single or dual tea grades, this model offered refined grading with advantages in both precision and size, making it suitable for embedded devices with limited resources. Thus, the YOLOV5n6_MobileNetV3 model enhanced tea bud recognition accuracy and supported intelligent harvesting research and technology. Full article

Synthetic Aperture Radar (SAR) is extensively utilized in ship detection due to its robust performance under various weather conditions and its capability to operate effectively both during the day and at night. However, ships in SAR images exhibit various characteristics including complex land scattering interference, variable scales, and dense spatial arrangements. Existing algorithms are insufficient in effectively addressing these challenges. To enhance detection accuracy, this paper proposes the Rotated model with Spatial Aggregation and a Balanced-Shifted Mechanism (R-SABMNet) built upon YOLOv8. First, we introduce the Spatial-Guided Adaptive Feature Aggregation (SG-AFA) module, which enhances sensitivity to ship features while suppressing land scattering interference. Subsequently, we propose the Balanced Shifted Multi-Scale Fusion (BSMF) module, which effectively enhances local detail information and improves adaptability to multi-scale targets. Finally, we introduce the Gaussian Wasserstein Distance Loss (GWD), which effectively addresses localization errors arising from angle and scale inconsistencies in dense scenes. Our R-SABMNet outperforms other deep learning-based methods on the SSDD+ and HRSID datasets. Specifically, our method achieves a detection accuracy of 96.32%, a recall of 93.13%, and an average level of accuracy of 95.28% on the SSDD+ dataset. Full article

Sewer infrastructure is vital for flood prevention, environmental protection, and public health. As part of sewer infrastructure, sewer systems are prone to degradation. Traditional maintenance methods for sewer systems are largely manual and reactive and rely on inconsistent data, leading to inefficient maintenance. The KaSyTwin research project addresses the urgent need for efficient and resilient sewer system management methods in Germany, aiming to develop a methodology for the semi-automated development and utilization of digital twins of sewer systems to enhance data availability and operational resilience. Using advanced multi-sensor robotic platforms equipped with scanning and imaging systems, i.e., laser scanners and cameras, as well as artificial intelligence (AI), the KaSyTwin research project focuses on generating digital twin-enabled representations of sewer systems in real time. As a project report, this work outlines the research framework and proposed methodologies in the KaSyTwin research project. Digital twins of sewer systems integrated with AI technologies are expected to facilitate proactive maintenance, resilience forecasting against extreme weather events, and real-time damage detection. Furthermore, the KaSyTwin research project aspires to advance the digital management of sewer systems, ensuring long-term functionality and public welfare via on-demand structural health monitoring and non-destructive testing. Full article

Meteor radar is a widely used technique for measuring wind in the mesosphere and lower thermosphere, with the key advantage of being unaffected by terrestrial weather conditions, thus enabling continuous operation. In all-sky interferometric meteor radar systems, amplitude and phase consistencies between multiple channels exhibit dynamic variations over time, which can significantly degrade the accuracy of wind measurements. Despite the inherently dynamic nature of these inconsistencies, the majority of existing research predominantly employs static calibration methods to address these issues. In this study, we propose a dynamic adaptive calibration method that combines normalized least mean square and correlation algorithms, integrated with hardware design. We further assess the effectiveness of this method through numerical simulations and practical implementation on an independently developed meteor radar system with a five-channel receiver. The receiver facilitates the practical application of the proposed method by incorporating variable gain control circuits and high-precision synchronization analog-to-digital acquisition units, ensuring initial amplitude and phase consistency accuracy. In our dynamic calibration, initial coefficients are determined using a sliding correlation algorithm to assign preliminary weights, which are then refined through the proposed method. This method maximizes cross-channel consistencies, resulting in amplitude inconsistency of <0.0173 dB and phase inconsistency of <0.2064°. Repeated calibration experiments and their comparison with conventional static calibration methods demonstrate significant improvements in amplitude and phase consistency. These results validate the potential of the proposed method to enhance both the detection accuracy and wind inversion precision of meteor radar systems. Full article

Background: Respiratory viral infections (RVIs) exhibit seasonal patterns influenced by biological, ecological, and climatic factors. Weather variables such as temperature, humidity, and wind impact the transmission of droplet-borne viruses, potentially affecting disease severity. However, the role of climate in predicting complications in pediatric RVIs remains unclear, particularly in the context of climate-change-driven extreme weather events. Methods: This retrospective cohort study analyzed 1610 hospitalization records of children (0–18 years) with lower respiratory tract infections in Rome, Italy, between 2018 and 2023. Viral pathogens were identified using nasopharyngeal molecular testing, and weather data from the week preceding hospitalization were collected. Several machine learning models were tested, including logistic regression and random forest, comparing the baseline (demographic and clinical) models with those including climate variables. Results: Logistic regression showed a slight improvement in predicting severe RVIs with the inclusion of weather variables, with accuracy increasing from 0.785 to 0.793. Average temperature, dew point, and humidity emerged as significant contributors. Other algorithms did not demonstrate similar improvements. Conclusions: Climate variables can enhance logistic regression models’ ability to predict RVI severity, but their inconsistent impact across algorithms highlights challenges in integrating environmental data into clinical predictions. Further research is needed to refine these models for use in reliable healthcare applications. Full article