Rain Conditions Research Articles

Impact of Adverse Weather and Image Distortions on Vision-Based UAV Detection: A Performance Evaluation of Deep Learning Models

Unmanned aerial vehicle (UAV) detection in real-time is a challenging task despite the advances in computer vision and deep learning techniques. The increasing use of UAVs in numerous applications has generated worries about possible risks and misuse. Although vision-based UAV detection methods have been proposed in recent years, a standing open challenge and overlooked issue is that of adverse weather. This work is the first, to the best of our knowledge, to investigate the impact of adverse weather conditions and image distortions on vision-based UAV detection methods. To achieve this, a custom training dataset was curated with images containing a variety of UAVs in diverse complex backgrounds. In addition, this work develops a first-of-its-kind dataset, to the best of our knowledge, with UAV-containing images affected by adverse conditions. Based on the proposed datasets, a comprehensive benchmarking study is conducted to evaluate the impact of adverse weather and image distortions on the performance of popular object detection methods such as YOLOv5, YOLOv8, Faster-RCNN, RetinaNet, and YOLO-NAS. The experimental results reveal the weaknesses of the studied models and the performance degradation due to adverse weather, highlighting avenues for future improvement. The results show that even the best UAV detection model’s performance degrades in mean average precision (mAP) by −50.62 points in torrential rain conditions, by −52.40 points in high noise conditions, and by −77.0 points in high motion blur conditions. To increase the selected models’ resilience, we propose and evaluate a strategy to enhance the training of the selected models by introducing weather effects in the training images. For example, the YOLOv5 model with the proposed enhancement strategy gained +35.4, +39.3, and +44.9 points higher mAP in severe rain, noise, and motion blur conditions respectively. The findings presented in this work highlight the advantages of considering adverse weather conditions during model training and underscore the significance of data enrichment for improving model generalization. The work also accentuates the need for further research into advanced techniques and architectures to ensure more reliable UAV detection under extreme weather conditions and image distortions.

Comparative analysis of cloud properties over drought- and flood-prone regions of western India using machine learning techniques

ABSTRACT Cloud properties are pivotal in analyzing rainfall patterns globally, especially in monsoon-dependent countries such as India. The impact of climate change becomes more important in regions susceptible to hydrometeorological events due to different monsoon regimes. To examine regional heterogeneity of cloud properties, this study investigates long-term trends and predictive capabilities for cloud properties in drought-prone and flood-prone regions of western India, utilizing satellite data and employing various machine learning (ML) models to comprehend intricate data patterns and enhance predictive accuracy. The results show higher mean and variability in cloud parameters over the flood-prone area due to favorable rain conditions, reflecting higher cloud microphysical and optical properties. These parameters negatively correlate with some cloud macrophysical properties along with the aerosol property in the drought-prone area. Additionally, a moderate correlation exists between certain cloud characteristics of one region and another. Employing ML algorithms for regression analysis and comparing them for cloud effective radius across regions shows promising results, with random forest (RF) demonstrating high coefficient of determination (0.86, 0.93) and low root mean squared error (0.76, 1.15) due to its robustness and high accuracy. This research enhances the understanding of regional heterogeneity in India and shows that ML can be helpful in predicting future cloud dynamics and climate variables identifying the most suitable model.

Assessing hidden corrosion in AZ91 magnesium alloy via infrared thermography in simulated atmospheric acid rain conditions

Abstract Magnesium alloys have garnered significant attention in the aerospace and marine industries due to their exceptional mechanical properties, including low density and a high strength-to-weight ratio. Despite these advantages, their widespread adoption is hindered by susceptibility to corrosion, particularly under harsh environmental conditions. While considerable research has focused on uniform surface corrosion of magnesium alloys, studies addressing hidden corrosion remain limited. Hidden corrosion is a critical defect that can severely compromise the structural integrity of aging aircraft. In this study, the hidden corrosion behavior of the AZ91 magnesium alloy was explored using infrared thermographic non-destructive testing. Seven blind holes were drilled into the surface of the alloy, which was then exposed to simulated acid rain for ten days to examine the effects of corrosion. The rate of corrosion in the drilled samples was observed to be slightly higher compared to bare samples, likely due to the accumulation of acid in the crevices. Changes in the initial and final hole dimensions and volume were used to determine the corrosion rate, providing valuable insights into the mechanisms governing hidden corrosion in magnesium alloys. This work offers a deeper understanding of hidden corrosion, with potential implications for improving the longevity and safety of critical aerospace and marine components.

Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China

BackgroundInfluenza outbreaks have occurred frequently these years, especially in the summer of 2022 when the number of influenza cases in southern provinces of China increased abnormally. However, the exact evidence of the driving factors involved in the prodrome period is unclear, posing great difficulties for early and accurate prediction in practical work.MethodsIn order to avoid the serious interference of strict prevention and control measures on the analysis of influenza influencing factors during the COVID-19 epidemic period, only the impact of meteorological and air quality factors on influenza A (H3N2) in Xiamen during the non coronavirus disease 2019 (COVID-19) period (2013/01/01-202/01/24) was analyzed using the distribution lag non-linear model. Phylogenetic analysis of influenza A (H3N2) during 2013–2022 was also performed. Influenza A (H3N2) was predicted through a random forest and long short-term memory (RF-LSTM) model via actual and forecasted meteorological and influenza A (H3N2) values.ResultsTwenty nine thousand four hundred thirty five influenza cases were reported in 2022, accounting for 58.54% of the total cases during 2013–2022. A (H3N2) dominated the 2022 summer epidemic season, accounting for 95.60%. The influenza cases in the summer of 2022 accounted for 83.72% of the year and 49.02% of all influenza reported from 2013 to 2022. Among them, the A (H3N2) cases in the summer of 2022 accounted for 83.90% of all A (H3N2) reported from 2013 to 2022. Daily precipitation(20–50 mm), relative humidity (70–78%), low (≤ 3 h) and high (≥ 7 h) sunshine duration, air temperature (≤ 21 °C) and O3 concentration (≤ 30 µg/m3, > 85 µg/m3) had significant cumulative effects on influenza A (H3N2) during the non-COVID-19 period. The daily values of PRE, RHU, SSD, and TEM in the prodrome period of the abnormal influenza A (H3N2) epidemic (19–22 weeks) in the summer of 2022 were significantly different from the average values of the same period from 2013 to 2019 (P < 0.05). The minimum RHU value was 70.5%, the lowest TEM value was 16.0 °C, and there was no sunlight exposure for 9 consecutive days. The highest O3 concentration reached 164 µg/m3. The range of these factors were consistent with the risk factor range of A (H3N2). The common influenza A (H3N2) variant genotype in 2022 was 3 C.2a1b.2a.1a. It was more accurate to predict influenza A (H3N2) with meteorological forecast values than with actual values only.ConclusionThe extreme weather conditions of sustained low temperature and wet rain may have been important driving factors for the abnormal influenza A (H3N2) epidemic. A low vaccination rate, new mutated strains, and insufficient immune barriers formed by natural infections may have exacerbated this epidemic. Meteorological forecast values can aid in the early prediction of influenza outbreaks. This study can help relevant departments prepare for influenza outbreaks during extreme weather, provide a scientific basis for prevention strategies and risk warnings, better adapt to climate change, and improve public health.

Simultaneous lightwave information and power transfer for NLOS ultraviolet communications under different weather conditions

In this work, a power allocation scheme for ultraviolet communication (UVC) with simultaneous lightwave information and power transfer (SLIPT) is proposed. Based on the non-line-of-sight (NLOS) single scattering channel model, the path loss and bit error rate (BER) performance of the UVC system with different geometrical parameters are analyzed. The effects of three types of weather, light rain, fine weather and severe fog conditions are considered. It is proved that the path loss is minimized on foggy days with minimum BER, followed by sunny days. The proposed SLIPT approach that the receiving energy is in turn divided into two parts, one for energy harvesting (EH) and the other for information decoding (ID). The system is designed to optimize energy harvesting for autonomous power supply, while ensuring a specified BER threshold and maintaining spectral efficiency. The effects of three types of weather on the SLIPT system are further investigated. It is demonstrated that under shorter communication distance(r), smaller elevation angle of receiving terminal (βR), larger field of view (FoV) and lower spectral efficiency(η) conditions, a higher percentage of energy is allocated for EH, with foggy days being the most favorable. The extreme geometric parameters of rainy condition can lead to the inability of communication, which needs to be limited to r ≤ 10 m, elevation angle of Rx ≤ 6°, FOV ≥100°, spectral efficiency ≤3, under the preset parameters in this paper.

Nickel-plated cyanate Ester/melamine shape memory composites for stable electromagnetic shielding switch, infrared stealth, and flame retardancy

Military equipment must receive signals but is prone to malfunction due to electromagnetic interference. Moreover, operational temperature variations can be detected by infrared sensors, leading to potential exposure. Therefore, developing materials that integrate electromagnetic shielding switches with infrared stealth capabilities is crucial for enhancing target security. This study investigates a novel composite material that combines shape memory polymer (SMP) and conductive layers for integrated functionality. Utilizing melamine sponge as the substrate, a cyanate ester (CE)-nitrile rubber (NR) SMP coating is applied via dip-coating, allowing the sponge to self-fixate after deformation. The SMP demonstrates stable shape recovery. Subsequently, a conductive nickel layer is added through keratin modification and chemical plating, providing electromagnetic shielding effectiveness exceeding 30 dB and reducing infrared emissivity to 0.6. The composite material is capable of achieving controllable electromagnetic shielding switches of 35 dB and 15 dB. It maintains excellent electromagnetic shielding and infrared stealth capabilities even under acid rain conditions and extreme temperature variations. This innovative approach holds significant promise for applications requiring simultaneous electromagnetic and infrared protection.

Investigating high altitude platform stations communication in Tanzania under heavy rain conditions

AbstractHigh altitude platform station (HAPS) is one of the promising solutions to many of the developing and less developed countries. To a great extent, HAPS is relatively cost‐effective and can be easily moved on demand while observing the required quality of service. For tropical countries, rain attenuation is the main factor that affects HAPS performance. This study investigated HAPS performance in Tanzania under heavy rain conditions. Rain rate, rain attenuation, and carrier‐to‐noise ratio were used to quantify the system's performance. In this study, the rain rate exceeds by 0.01% of an average, was obtained using the Chebil model. Based on Tanzania Meteorological Agency data, the registered a high amount of rainfall for the five selected regions: Dar es Salaam, Kagera, Tanga, Unguja, and Pemba. By using ITU‐R model, rain attenuation for the downlink was predicted, assuming horizontal polarisation, for 31.0–31.3 GHz. Finally, the HAPS link budget was analysed with a platform altitude of 22 km at frequency of 31.0–31.3 GHz for different elevation angles. Results showed that the performance of the system was promising in the given rainfall scenarios.

Study of Millimeter-Wave Fuze Echo Characteristics under Rainfall Conditions Using the Monte Carlo Method

Due to the similarity in wavelength between millimeter-wave (MMW) signals and raindrop diameters, rainfall induces significant attenuation and scattering effects that challenge the detection performance of MMW fuzes in rainy environments. To enhance the adaptability of frequency-modulated MMW fuzes in such conditions, the effects of rain on MMW signal attenuation and scattering are investigated. A mathematical model for the multipath echo signals of the fuze was developed. The Monte Carlo method was employed to simulate echo signals considering multiple scattering, and experimental validations were conducted. The results from simulations and experiments revealed that rainfall increases the bottom noise of the echo signal, with rain backscatter noise predominantly affecting the lower end of the echo signal spectrum. However, rain conditions below torrential levels did not significantly impact the detection of strong reflection targets at the high end of the spectrum. The modeling approach and findings presented offer theoretical support for designing MMW fuzes with improved environmental adaptability.

Assessing the Impact of Adverse Weather on Performance and Safety of Connected and Autonomous Vehicles

Connected and Autonomous Vehicles (CAVs) might significantly enhance the transportation system by improving safety, accessibility, efficiency, and sustainability. However, a major challenge lies in ensuring CAVs can operate properly under diverse weather conditions, which have already proven to impair human driving capabilities. This pioneering study aims to bridge a crucial research gap by comprehensively assessing the performance of CAVs on traffic operations and safety across varying weather scenarios. Using microscopic traffic simulation in VISSIM and the Surrogate Safety Assessment Model (SSAM), this study evaluates key metrics, including average speed, delay, number of stops, travel time, and number of conflicts for different CAV market penetration rates. The analysis spans 21 scenarios under clear, light rain, heavy rain, and foggy conditions within a selected urban corridor in the United Arab Emirates. The results showed that the average speed rose by 55% in clear weather, while the average delay, the number of stops, travel time, and the number of accidents decreased by 50%, 50%, 95%, and 68%, respectively. In light rain, the average speed improved by 43%, while the average delay, number of stops, travel time, and the number of accidents reduced by 43%, 56%, 96%, and 74%, respectively. The average speed increased by 82% under heavy rain, while the average delay, the number of stops, the travel time, and the number of accidents all fell by 62%, 68%, 96%, and 74%, respectively. In fog, the average speed rose by 32%, while the average delay, average stop number, travel time, and the number of accidents decreased by 33%, 47%, 90%, and 83%, respectively. Overall, this paper highlights the need for resilient CAV systems adaptable to diverse environmental conditions. It helps advance the understanding of how CAVs can be optimized for safety and efficiency in urban settings, contributing to sustainable transportation solutions. It provides insights into the challenges and innovative approaches for CAV deployment in adverse weather, laying a foundation for future research and the broader implementation of these technologies in urban mobility. Doi: 10.28991/CEJ-2024-010-09-019 Full Text: PDF

Moderately cool environment with rain and wind increases cold strain and energy expenditure via carbohydrate oxidation during running exercise.

Outdoor exercise often proceeds despite rain and wind in cool conditions. The aim of this study was to investigate the effects of rain with wind on physiological responses during running exercise at 70% V̇O<inf>2max</inf> in cool conditions. Eleven healthy men exercised on a treadmill at 70% V̇O<inf>2max</inf> intensity for 60 min under conditions of 10 mm/h rain and 3 m/sec wind (RW) or not (CON) at 10°C in a climatic chamber able to simulate various temperature, humidity, rain, and wind conditions. Body temperature, expired air, and blood samples were measured at rest and exercise. Rectal temperature, mean weighted skin temperature, and thermal sensation were all significantly lower in RW than in CON during exercise (all P<0.05). Oxygen uptake was significantly higher in RW than in CON during exercise (all P<0.05). Participants' rating of perceived exertion was significantly higher in RW than in CON at 50 and 60 min (P<0.05). Respiratory exchange ratio was significantly higher in RW than in CON at 10, 20, and 40 min. Plasma lactate concentration and plasma norepinephrine levels were significantly higher in RW than in CON during exercise (both P<0.05). Rain with wind intensified body heat loss, and energy expenditure and carbohydrate oxidation increased as the body cooled. These conditions may decrease exercise performance.

An enclosed solid-liquid triboelectric nanogenerator based on Janus-type TPU nanofibers

Various environmental factors such as high humidity, rain, snow, and other conditions restrict the practical use of triboelectric nanogenerator (TENG). Herein, Janus-type thermoplastic polyurethane (TPU) nanofiber films with good hydrophobicity were fabricated by side-by-side electrospinning. The output performance of TENG based on Janus-type TPU nanofiber films was much higher than that made of normal TPU nanofiber films. A flexible enclosed TENG was prepared that maintained its output performance regardless of external humidity. What’s more, liquid metal gallium (Ga) was injected into the enclosed TENG and used as the tribo-material. The addition of Ga to the enclosed TENG increased its output performance to reach a peak voltage of 282 V and current of 6.0 μA. The enclosed TENG was able to power light-emitting diodes (LEDs) even when immersed in water, demonstrating its potential for using under high-humidity conditions.

Effect analysis of government intervention on scale-heterogeneous farmers' behavior of groundwater exploitation.

Government intervention has become an important measure to restrain groundwater overexploitation. This paper analyzes the effect of three types of government intervention measures, namely, guidance, incentive and constraint, on farmers' groundwater utilization behavior, from the perspective of scale-heterogeneity, using general quantile regression model, by survey data of 1122 households in well irrigation area of north China. The results showed that: (1) the incentive and guiding measures have negative effects on farmers' groundwater usage, while the effect of restrictive measures is not obvious. The guided policy is superior to the incentive measure as to governance effect. (2) With the increase of farmers' land scale, the influence of incentive measures shows a trend of weakening, and the effect of guided measures on groundwater demand reduction of farmers is stronger. When it comes to the different point of water consumption, when at the point level of 0.25, the incentive measures have the most obvious inhibitory effect. With the increase of water consumption of farmers, the guided measures begin to play a core role. The effect of restrictive measures is not obvious with the increase of water consumption. (3) In addition, farmers' irrigation water consumption also is affected by gender, cognition of water resources shortage, ecological cognitive level, acquisition ability of disaster information, village rain conditions, the degree of water rights market development, feelings of water fee increase, irrigated disputes in the village, collective economic level of village. The selection of policy tools is flexible according to the farmers' land scale for groundwater over-extraction control.

What Can Hurricane Sam (2021) Tell Us About Extreme Ocean Waves Under Tropical Cyclones?

AbstractWe investigate the ocean wave field under Hurricane Sam (2021). Whilst measurements of waves under Tropical Cyclones are rare, an unusually large number of quality in situ and remote measurements are available in that case. First, we highlight the good consistency between the wave spectra provided by the Surface Waves Investigation and Monitoring (SWIM) instrument onboard the China‐France Oceanography SATellite, the in situ spectra measured by National Data Buoy Center buoys, and a saildrone. The impact of strong rains on SWIM spectra is then further investigated. We show that whereas the rain definitely affects the normalized radar cross section, both the innovative technology (beam rotating scanning geometry) and the post‐processing processes applied to retrieve the 2D wave spectra ensure a good quality of the resulting wave spectra, even in heavy rain conditions. On this basis, the satellite, airborne and in situ observations are confronted to the analytical model proposed by Kudryavtsev et al. (2015, https://doi.org/10.1002/2015JC011284). We show that an extended fetch mechanism may be invoked to explain the large significant wave height observed in the right front quadrant of Hurricane Sam.

Analysis on the stability of a coal mine's dumping slope and its influence on oil pipeline

There is cross-interference between an-yu-meng line of Yunnan product oil pipeline about 920m and NO.1 dump of a coal mine. In order to analyze the stability of the dump slope under heavy rain and other working conditions, and the influence of the dump continued dumping on the pipeline safety, combined with the geological environment conditions, quantitative calculation of stability and numerical simulation in the study area, a comprehensive analysis is carried out. The results show that the south section of the dump is about 200m long and unstable under heavy rain, and the other sections are in a stable state, because the pipeline in the south section of the dump is located at the foot of the dump slope, the soil has the possibility of squeezing and shearing the pipeline, and the cross-interference pipeline in the north section of the dump is about 490m long, the northern section of the dump has a good stability. Most of the pipelines are buried at a depth of 1.5 m ~ 2 m, and the local depth is 7.5 m. the buried depth of the pipelines is in strongly weathered basalt, which is beneficial to the safety of the pipelines, the slope of the pipeline is inclined to the west, the slope of the pipeline is inclined in the opposite direction to the lateral compression of the dump, and the possibility of the pipeline forming lateral shear is small, the main hazards of the north slope instability to pipelines are burial and impact. The research results provide basis for safety evaluation and later treatment design of pipeline in cross-interference section of dump slope.

Crash Risk Monitoring Method for Highways in Rain Conditions

The slick road surface during rainy days necessitates an increased braking distance, posing prominent safety hazards for driving. Consequently, this paper researched on the monitoring method of collision risk posture of highway in rainy days. The impact of rain on the driving risk of vehicles is mainly the braking distance. In this paper, the ratio μ, defined as the quotient of stopping sight distance (St) and the spacing (Sr) between the front of a trailing vehicle and the rear of a leading vehicle, was adopted as the collision risk assessment metric. Employing the Expressway A-Rain segment from the Citysim dataset as the subject of investigation, traffic risk probability statistics diagrams and heatmaps had been generated. The results showed that the ratio μ&gt;1 has accounted for nearly 10% of the roadway, the traffic risk of this roadway is high, and the risk is mainly distributed on the middle lane of the freeway and entrance ramp.

An Algorithm for Ship Detection in Complex Observation Scenarios Based on Mooring Buoys

Marine anchor buoys, as fixed-point profile observation platforms, are highly susceptible to the threat of ship collisions. Installing cameras on buoys can effectively monitor and collect evidence from ships. However, when using a camera to capture images, it is often affected by the continuous shaking of buoys and rainy and foggy weather, resulting in problems such as blurred images and rain and fog occlusion. To address these problems, this paper proposes an improved YOLOv8 algorithm. Firstly, the polarized self-attention (PSA) mechanism is introduced to preserve the high-resolution features of the original deep convolutional neural network and solve the problem of image spatial resolution degradation caused by shaking. Secondly, by introducing the multi-head self-attention (MHSA) mechanism in the neck network, the interference of rain and fog background is weakened, and the feature fusion ability of the network is improved. Finally, in the head network, this model combines additional small object detection heads to improve the accuracy of small object detection. Additionally, to enhance the algorithm’s adaptability to camera detection scenarios, this paper simulates scenarios, including shaking blur, rain, and foggy conditions. In the end, numerous comparative experiments on a self-made dataset show that the algorithm proposed in this study achieved 94.2% mAP50 and 73.2% mAP50:95 in various complex environments, which is superior to other advanced object detection algorithms.

Environmental effects on faecal genotyping success in mesocarnivores

AbstractNon-invasive genetic sampling can be used in research, monitoring, and conservation of wild animals to, for example, provide insights into diets, identify individuals and estimate population sizes. Non-invasive genetic sampling may be especially useful to monitor elusive species because DNA can be derived from materials such as hairs and faeces without handling individuals. However, the reliability of the results derived from this technique is dependent on the quality of DNA obtained from samples, which can deteriorate from exposure to environmental conditions and sample age. While freshness of the sample is an important factor, the combined effect of different field conditions on the genotyping success is not fully understood. To address this gap, we systematically investigated the effects of sample age and environmental conditions on genotyping success of faeces, in an experimental setting of four treatments that combined rain and temperature conditions typical of central European climates. We compared the performance of a microsatellite marker set and a SNP panel for red fox (Vulpes vulpes), as well as a microsatellite marker set for pine marten (Martes martes) from faeces resampled over 21 days. We found that genotyping success decreased significantly with sample age, however environmental treatments did not impact the success. Furthermore, the SNPs we used amplified more successfully over time than the respective microsatellites. Therefore, the use of SNPs instead of conventional microsatellites, when using faecal samples for analysis relying on correct amplifications, could be advantageous. We recommend to prioritise the collection of fresh faeces regardless of environmental conditions.

Utilization of satellite data for landslide disaster mitigation in extreme event: a case study June 18, 2016, landslide in Banjarnegara Regency, Indonesia

Abstract Understanding the characteristics of landslides produced by rainfall and soil humidity is critical for reducing the occurrence of landslides. Extreme rain, as an external impact, has been suggested to play a role in landslide development. Many people were killed in a landslide that occurred in the Banjarnegara Regency on June 18, 2016, which was unexpected because June is not Indonesia’s rainy season. El Niño and La Niña events were observed in Indonesia in 2015-2016. The initial hypothesis is that such extreme rain conditions caused this landslide. This study aims to apply an alternative approach to detecting landslides by monitoring soil moisture and rainfall with remote sensing data during extreme events. An analysis of precipitation and moisture data has been carried out before the event and is associated with the El Niño and La Niña phenomena. The findings indicate that the natural environment of Banjarnegara Regency is unique. Landslides occur even during the extreme dry season. Rain still drops, and soil moisture is high. The impact of local variables is more significant than regional conditions. They are still at risk of a landslide during the El Niño phase. During that stage, Banjarnegara Regency’s potential landslide area would continue to be observed. Monitoring needs to be carried out rapidly, with a high level of spatial and temporal resolution. It is possible to resolve it using IMERG and SMAP data.

Elephant skin-inspired mycelium tiles for thermal regulation of buildings

Abstract Of all types of ecosystems, cities are the most polluting and this pollution affects more than 50% of the global population. One main cause for this pollution is related to the energy used to heat or cool down buildings. Currently, only 15% of households in Southeast Asia have an air conditioner, but this number is expected to rise, leading to an increase in demand in energy consumption, electricity and CO2 emissions which could further worsen global pollution and climate change. There is therefore an urgent need to find alternative solutions to cool buildings and regulate their temperatures. In this paper, inspiration is taken from elephants who live in very hot climates. Elephants can cool themselves thanks to the wrinkles on their skin that can limit heat gain, dissipate energy by evaporative cooling and store water. To emulate elephants’ cooling, tiles with elephant skin-inspired surface texture are designed. Computational simulations are performed to evaluate the effect of local shading due to the texture. Experimental tiles are produced using a biodegradable and natural material grown by a fungus, Pleurotus Ostreatus. These tiles are mycelium-bound composites (MBCs) where the fungus grew on bamboo microfibers, developing an interconnected web of cells called the mycelium that binds the microfibers together. The thermal properties of the tiles were measured for heating and cooling on the textured and flat side. The results show the tiles have anisotropic properties with a significant improvement by 25% in the cooling of the textured side over the flat side. In simulated rain conditions, the cooling is further improved by 42% as compared to dry conditions. The elephant-mycelium tiles are therefore promising for thermal regulation of building in Southeast Asia environments.

Bioinspired nanostructured hydroxyapatite-polyelectrolyte multilayers for stone conservation

Stone-built cultural heritage faces threats from natural forces and anthropogenic pollutants, including local climate, acid rain, and outdoor conditions like temperature fluctuations and wind exposure, all of which impact their structural integrity and lead to their degradation. The development of a water-based, environmentally-friendly protective coatings that meet a combination of requirements posed by the diversity of the substrates, different environmental conditions, and structures with complex geometries remains a formidable challenge, given the numerous obstacles faced by current conservation strategies. Here we report the structural, electrical, and mechanical characterization, along with performance testing, of a nanostructured hydrophilic and self-healing hybrid coating based on hydroxyapatite (HAp) nanocrystals and polyelectrolyte multilayers (PEM), formed in-situ on Greek marble through a simple spray layer-by-layer surface functionalization technique. The polyelectrolyte-hydroxyapatite multilayer (PHM) structure resembled the design of naturally forming trabecular bone, attained at a short procedural time. It exhibited chemical affinity, aesthetical compatibility and resistance to weathering while offering reversibility. The proposed method is able to generate micron-sized coatings with controlled properties, such as adhesion and self-healing, leading to less weathered surfaces. Our results show that the PHM is a highly effective protective material that can be applied for stone protection and other similar applications.