Search

This paper presents a comprehensive study of hydrogenated amorphous silicon (a-Si)-based detectors, utilizing electrical characterization, Raman spectroscopy, photoemission, and inverse photoemission techniques. The unique properties of a-Si have sparked interest in its application for radiation detection in both physics and medicine. Although amorphous silicon (a-Si) is inherently a highly defective material, hydrogenation significantly reduces defect density, enabling its use in radiation detector devices. Spectroscopic measurements provide insights into the intricate relationship between the structure and electronic properties of a-Si, enhancing our understanding of how specific configurations, such as the choice of substrate, can markedly influence detector performance. In this study, we compare the performance of a-Si detectors deposited on two different substrates: crystalline silicon (c-Si) and flexible Kapton. Our findings suggest that detectors deposited on Kapton exhibit reduced sensitivity, despite having comparable noise and leakage current levels to those on crystalline silicon. We hypothesize that this discrepancy may be attributed to the substrate material, differences in film morphology, and/or the alignment of energy levels. Further measurements are planned to substantiate these hypotheses.

In this study, we propose a novel unsupervised Change Detection (CD) model to detect flood extent using Synthetic Aperture Radar (SAR) time series data. The proposed model is based on a spatiotemporal variational autoencoder, trained with reconstruction and contrastive learning techniques. The change maps are generated with a proposed novel algorithm that utilizes differences in latent feature distributions between pre-flood and post-flood data. The model is evaluated on nine different flood events by comparing the results with reference flood maps collected from the Copernicus Emergency Management Services (CEMS) and Sen1Floods11 dataset. We conducted a range of experiments and ablation studies to investigate the performance of our model. We compared the results with existing unsupervised models. The model achieved an average of 70% Intersection over Union (IoU) score which is at least 7% better than the IoU from existing unsupervised CD models. In the generalizability test, the proposed model outperformed supervised models ADS-Net (by 10% IoU) and DAUSAR (by 8% IoU), both trained on Sen1Floods11 and tested on CEMS sites. Our implementation will be available here https://github.com/RituYadav92/CLVAE-Unsupervised_Change_Detection_TimeSeriesSAR.

The large variation in vertebral shape and calcification observed among elasmobranch species prevents using a single method for enhancing growth bands and reading age. Further, estimating age and growth parameters can be difficult or impractical when samples are incomplete due to the bycatch of a size-selective fishery. Using a single and rapid method, age readings were obtained on the vertebrae of four batoid species, namely 53 individuals of Dasyatis pastinaca, 51 of Raja asterias, 15 of Torpedo marmorata, and 55 specimens of Torpedo torpedo, from the local small-scale trammel net fishery in the coastal waters (5–20 m depth) of the Central Tyrrhenian Sea during 2019–2021. Based on these data, a statistical routine was developed to obtain multiple estimates of age and growth parameters for incomplete samples due to size-selective fishing. The acceptable agreement between and within readers (intra and inter-reader disagreement < 5%) and the rate of increase in vertebral size with body size (differently ranked across species) demonstrated the consistency of the enhancing method. The parameters estimated by the Von Bertalanffy and Gompertz growth models matched the data available in the Mediterranean Sea for the species studied, with D. pastinaca, T. torpedo, and R. asterias showing the lowest (k = 0.05–0.12), intermediate (k = 0.112–0.19), and highest (k = 0.18–0.23) growth rates, respectively, in line with the life history traits of these species. Overall, the method proved effective both in delineating band pairs in vertebrae of different species and in reliably estimating the age and growth parameters of problematic samples due to size-selective fishing. The proposed method supports the collection of comparable demographic data from other areas where similar multi-specific assemblages are bycatch of size-selective fisheries impacting potential nursery areas and other essential habitats for elasmobranchs.

Abstract Hybrid zones occur where genetically distinct populations meet, mate and produce offspring with mixed ancestry. In Plethodontid salamanders, introgressive hybridization is a common phenomenon, where hybrids backcross with parental populations leading to the spread of new alleles into the parental genomes. Whereas many hybrid zones have been reported in American Plethodontid salamanders, only a single hybrid zone has been documented in European plethodontids so far, which is located at the Apuan Alps in the Italian Peninsula. Here, we describe a previously unreported hybrid zone in the Northern Apennines involving all the three Plethodontid salamander species inhabiting the Italian Peninsula. We found 21 new Speleomantes sites of occurrence, from a hitherto unexplored area located at the boundaries between three Speleomantes species ranges. Using mitochondrial (Cytb and ND2 genes) and nuclear markers (two diagnostic SNPs at the NCX1 gene), we revealed a three‐way contact zone where all the three mainland species hybridize: S. strinatii, S. ambrosii and S. italicus. We observed a strong mitonuclear discordance, with mitochondrial markers showing a conspicuous geographic pattern, while diagnostic nuclear SNPs coexisted in both the same populations and individuals, providing evidence of hybridization in many possible combinations. The introgression is asymmetric, with S. italicus mitogenome usually associated with S. a. ambrosii and, to a lesser extent, to S. strinatii nuclear alleles. This finding confirms that Plethodontid are a group of choice to investigate hybridization mechanisms and suggests that behavioural, genetic and ecological components may concur in determining the direction and extent of introgression.

New cosmetic formulations are continuously requested by the market and the ingredients are constantly evolving. Recently the use of antioxidants has gained success and, in this context, analytical methods able to quickly and easily assess the antioxidant activity of cosmetics would make it possible to carry out analyses on new formulations even within the manufacturing process without the need for specialized laboratories and personnel, thus evaluating directly on-site the effectiveness and the shelf life of products. In this work, a chemiluminescent inhibition assay was developed for determining the total antioxidant activity in cosmetic products. The method was based on the luminol/enhancers/hydrogen peroxide/horseradish peroxidase chemiluminescent system, which generates light signals measurable through simple and compact instrumentation. The formation of the chemiluminescent signal is inhibited by the presence of antioxidant substances while it is restored once all the antioxidant molecules have been oxidized. The time of appearance of the light signal is related to the total antioxidant activity. The assay was carried out exploiting an integrated device comprising a microwell plate coupled with an array of amorphous silicon hydrogenated photosensors enclosed in a mini-dark box. The method was optimized in terms of concentrations and volumes of the required reagents and sample pre-treatment. A calibration curve was generated taking as a reference the antioxidant activity of ascorbic acid obtaining a detection limit of 10 µM. The developed method was applied to cosmetic products currently on the market as well as on spiked samples in order to evaluate the performance of the methods in terms of sensitivity, accuracy, and reproducibility.

One of the main challenges to be faced in deep space missions is to protect the health and ensure the maximum efficiency of the crew by preparing methods of prevention and in situ diagnosis. Indeed, the hostile environment causes important health problems, ranging from muscle atrophy, osteopenia, and immunological and metabolic alterations due to microgravity, to an increased risk of cancer caused by exposure to radiation. It is, therefore, necessary to provide new methods for the real-time measurement of biomarkers suitable for deepening our knowledge of the effects of space flight on the balance of the immune system and for allowing the monitoring of the astronaut’s health during long-term missions. APHRODITE will enable human space exploration because it fills this void that affects both missions in LEO and future missions to the Moon and Mars. Its scientific objectives are the design, production, testing, and in-orbit demonstration of a compact, reusable, and reconfigurable system for performing the real-time analysis of oral fluid samples in manned space missions. In the frame of this project, a crew member onboard the ISS will employ APHRODITE to measure the selected target analytes, cortisol, and dehydroepiandrosterone sulfate (DHEA-S), in oral fluid, in four (plus one additional desired session) separate experiment sessions. The paper addresses the design of the main subsystems of the analytical device and the preliminary results obtained during the first implementations of the device subsystems and testing measurements on Earth. In particular, the system design and the experiment data output of the lab-on-chip photosensors and of the front-end readout electronics are reported in detail along with preliminary chemical tests for the duplex competitive CL-immunoassay for the simultaneous detection of cortisol and DHEA-S. Different applications also on Earth are envisaged for the APHRODITE device, as it will be suitable for point-of-care testing applications (e.g., emergency medicine, bioterrorism, diagnostics in developing countries, etc.).

Wildfires play a crucial role in the transformation of forest ecosystems and exert a significant influence on the global climate over geological timescales. Recent shifts in climate patterns and intensified human–forest interactions have led to an increase in the incidence of wildfires. These fires are characterized by their extensive coverage, higher frequency, and prolonged duration, rendering them increasingly destructive. To mitigate the impact of wildfires on climate change, ecosystems, and biodiversity, it is imperative to conduct systematic monitoring of wildfire progression and evaluate their environmental repercussions on a global scale. Satellite remote sensing is a powerful tool, offering precise and timely data on terrestrial changes, and has been extensively utilized for wildfire identification, tracking, and impact assessment at both local and regional levels. The Canada Centre for Mapping and Earth Observation, in collaboration with the Canadian Forest Service, has developed a comprehensive National Burned Area Composite (NBAC). This composite serves as a benchmark for curating a bi-temporal multi-source satellite image dataset for change detection, compiled from the archives of Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2. To our knowledge, this dataset is the inaugural large-scale, multi-source, and multi-frequency satellite image dataset with 20 m spatial resolution for wildfire mapping, monitoring, and evaluation. It harbors significant potential for enhancing wildfire management strategies, building upon the profound advancements in deep learning that have contributed to the field of remote sensing. Based on our curated dataset, which encompasses major wildfire events in Canada, we conducted a systematic evaluation of the capability of multi-source satellite earth observation data in identifying wildfire-burned areas using statistical analysis and deep learning. Our analysis compares the difference between burned and unburned areas using post-event observation solely or bi-temporal (pre- and post-event) observations across diverse land cover types. We demonstrate that optical satellite data yield higher separability than C-Band and L-Band Synthetic Aperture Radar (SAR), which exhibit considerable overlap in burned and unburned sample distribution, as evidenced by SAR-based boxplots. With U-Net, we further explore how different input channels influence the detection accuracy. Our findings reveal that deep neural networks enhance SAR’s performance in mapping burned areas. Notably, C-Band SAR shows a higher dependency on pre-event data than L-Band SAR for effective detection. A comparative analysis of U-Net and its variants indicates that U-Net works best with single-sensor data, while the late fusion architecture marginally surpasses others in the fusion of optical and SAR data. Accuracy across sensors is highest in closed forests, with sequentially lower performance in open forests, shrubs, and grasslands. Future work will extend the data from both spatial and temporal dimensions to encompass varied vegetation types and climate zones, furthering our understanding of multi-source and multi-frequency satellite remote sensing capabilities in wildfire detection and monitoring.

Abstract Parasite biodiversity in cetaceans represents a neglected component of the marine ecosystem. This study aimed to investigate the distribution and genetic diversity of anisakid nematodes of the genus Anisakis sampled in cetaceans from the Northeast Atlantic Ocean and the Mediterranean Sea. A total of 478 adults and pre-adults of Anisakis spp. was identified by a multilocus genetic approach (mtDNA cox2, EF1 α − 1 nDNA and nas 10 nDNA gene loci) from 11 cetacean species. A clear pattern of host preference was observed for Anisakis spp. at cetacean family level: A. simplex (s.s.) and A. pegreffii infected mainly delphinids; A. physeteris and A. brevispiculata were present only in physeterids, and A. ziphidarum occurred in ziphiids. The role of cetacean host populations from different waters in shaping the population genetic structure of A. simplex (s.s.), A. pegreffii and A. physeteris was investigated for the first time. Significant genetic sub-structuring was found in A. simplex (s.s.) populations of the Norwegian Sea and the North Sea compared to those of the Iberian Atlantic, as well as in A. pegreffii populations of the Adriatic and the Tyrrhenian Seas compared to those of the Iberian Atlantic waters. Substantial genetic homogeneity was detected in the Mediterranean Sea population of A. physeteris. This study highlights a strong preference by some Anisakis spp. for certain cetacean species or families. Information about anisakid biodiversity in their cetacean definitive hosts, which are apex predators of marine ecosystems, acquires particular importance for conservation measures in the context of global climate change phenomena.

Urban Heat Islands (UHI) phenomenon is a pressing problem for highly industrialized areas with serious risks for public health. Weather stations guarantee long-term accurate observations of weather parameters, such Air Temperature (AT), but lack appropriate spatial coverage. Numerous studies have argued that satellite Land Surface Temperature (LST) is a relevant parameter for estimating AT maps, exploring both linear regression and Machine Learning algorithms. This study proposes a Neural Network (NN) regression model for estimating the maximum AT from Landsat-8 data. The approach has been tested in a variegated morphological region (Puglia, Italy) using a large stack of data acquired from 2018 to 2020. The algorithm uses the median values of LST and Normalized Difference Vegetation Index (NDVI) computed using different buffer radius around the location of each reference weather station (250 m, 1000 m, and 2000 m) to train the NN model with a K-fold cross-validation strategy. The reference dataset was split into three sets using a stratified sampling approach considering the different station categories: rural, High- and Low-density Urban areas respectively. The algorithm was tested with different learning rates (LR) (0.001 and 0.005). The results show that our NN model accuracy improves with the increase of the buffer radius, minimizing the difference in terms of R^2 between training and evaluation data, with an overall accuracy consistently higher than 0.84. Future research could investigate more input variables in the NN model such as morphology or climate variables and test the algorithm on larger areas.

Building change detection is essential for monitoring urbanization, disaster assessment, urban planning and frequently updating the maps. 3D structure information from airborne light detection and ranging (LiDAR) is very effective for detecting urban changes. But the 3D point cloud from airborne LiDAR(ALS) holds an enormous amount of unordered and irregularly sparse information. Handling such data is tricky and consumes large memory for processing. Most of this information is not necessary when we are looking for a particular type of urban change. In this study, we propose an automatic method that reduces the 3D point clouds into a much smaller representation without losing necessary information required for detecting Building changes. The method utilizes the Deep Learning(DL) model U-Net for segmenting the buildings from the background. Produced segmentation maps are then processed further for detecting changes and the results are refined using morphological methods. For the change detection task, we used multi-temporal airborne LiDAR data. The data is acquired over Stockholm in the years 2017 and 2019. The changes in buildings are classified into four types: ‘newly built’, ‘demolished’, ‘taller’ and ’shorter’. The detected changes are visualized in one map for better interpretation.

Urbanization is progressing at an unprecedented rate in many places around the world. The Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) missions, combined with deep learning, offer new opportunities to accurately monitor urbanization at a global scale. Although the joint use of SAR and optical data has recently been investigated for urban change detection, existing data fusion methods rely heavily on the availability of sufficient training labels. Meanwhile, change detection methods addressing label scarcity are typically designed for single-sensor optical data. To overcome these limitations, we propose a semi-supervised urban change detection method that exploits unlabeled Sentinel-1 SAR and Sentinel-2 MSI data. Using bitemporal SAR and optical image pairs as inputs, the proposed multi-modal Siamese network predicts urban changes and performs built-up area segmentation for both timestamps. Additionally, we introduce a consistency loss, which penalizes inconsistent built-up area segmentation across sensor modalities on unlabeled data, leading to more robust features. To demonstrate the effectiveness of the proposed method, the SpaceNet 7 dataset, comprising multi-temporal building annotations from rapidly urbanizing areas across the globe, was enriched with Sentinel-1 SAR and Sentinel-2 MSI data. Subsequently, network performance was analyzed under label-scarce conditions by training the network on different fractions of the labeled training set. The proposed method achieved an F1 score of 0.555 when using all available training labels, and produced reasonable change detection results (F1 score of 0.491) even with as little as 10% of the labeled training data. In contrast, multi-modal supervised methods and semi-supervised methods using optical data failed to exceed an F1 score of 0.402 under this condition. Code and data are made publicly available.

Floods are occurring across the globe, and due to climate change, flood events are expected to increase in the coming years. Current situations urge more focus on efficient monitoring of floods and detecting impacted areas. In this study, we propose two segmentation networks for flood detection on uni-temporal Sentinel-1 Synthetic Aperture Radar data. The first network is “Attentive U-Net”. It takes VV, VH, and the ratio VV/VH as input. The network uses spatial and channel-wise attention to enhance feature maps which help in learning better segmentation. “Attentive U-Net” yields 67% Intersection Over Union (IoU) on the Sen1Floods11 dataset, which is 3% better than the benchmark IoU. The second proposed network is a dual-stream “Fusion network”, where we fuse global low-resolution elevation data and permanent water masks with Sentinel-1 (VV, VH) data. Compared to the previous benchmark on the Sen1Floods11 dataset, our fusion network gave a 4.5% better IoU score. Quantitatively, the performance improvement of both proposed methods is considerable. The quantitative comparison with the benchmark method demonstrates the potential of our proposed flood detection networks. The results are further validated by qualitative analysis, in which we demonstrate that the addition of a low-resolution elevation and a permanent water mask enhances the flood detection results. Through ablation experiments and analysis we also demonstrate the effectiveness of various design choices in proposed networks. Our code is available on Github at https://github.com/RituYadav92/UNI_TEMP_FLOOD_DETECTION for reuse.