Miracle-dz/UniFAR-FTUs huggingface dataset:
Heavy Metal Accumulation and The Possible Correlation with Acetylcholinesterase Levels in Honey Bees from Polluted Areas of Alexandria, Egypt

This study aimed to use honey bees, Apis mellifera L. , as bioindicator for a regional survey of heavy metal pollution in Alexandria city, Egypt. Honeybees were obtained from four different sites namely Abu Qir (AQ), Agricultural Road (AR), Khorshid (KR) and El-Sabahia (SB). Correlation between heavy metals and acetylcholinesterase (AChE) as a biomarker was also examined. The mean concentration of measured elements (µg/g) ranged from 2.20–5.78, 1.53–0.83, 3.95–11.40, 19.10–44.43 and ND–4.25 (ND = not detected) for Cr, Ni, Cu, Zn, Pb and Cd, respectively. The highest mean value of metal quantities over a period of 5 months was Zn and the highest concentration mean for Zn and metals as a total occurred at the KR site. The highest mean values of AChE activity were noted in honey bees sampled from SB and KR followed by AQ and AR. However, AChE was found to positively correlate significantly with Zn. Our results suggest that AChE could be useful as a biomarker in honey bees exposed to Zn and may constitute a valuable biomonitor to assess environmental pollution by metals.
Evaluating the Spectral Indices Efficiency to Quantify Daytime Surface Anthropogenic Heat Island Intensity: An Intercontinental Methodology

The surface anthropogenic heat island (SAHI) phenomenon is one of the most important environmental concerns in urban areas. SAHIs play a significant role in quality of urban life. Hence, the quantification of SAHI intensity (SAHII) is of great importance. The impervious surface cover (ISC) can well reflect the degree and extent of anthropogenic activities in an area. Various actual ISC (AISC) datasets are available for different regions of the world. However, the temporal and spatial coverage of available and accessible AISC datasets is limited. This study was aimed to evaluate the spectral indices efficiency to daytime SAHII (DSAHII) quantification. Consequently, 14 cities including Budapest, Bucharest, Ciechanow, Hamburg, Lyon, Madrid, Porto, and Rome in Europe and Dallas, Seattle, Minneapolis, Los Angeles, Chicago, and Phoenix in the USA, were selected. A set of 91 Landsat 8 images, the Landsat provisional surface temperature product, the High Resolution Imperviousness Layer (HRIL), and the National Land Cover Database (NLCD) imperviousness data were used as the AISC datasets for the selected cities. The spectral index-based ISC (SIISC) and land surface temperature (LST) were modelled from the Landsat 8 images. Then, a linear least square model (LLSM) obtained from the LST-AISC feature space was applied to quantify the actual SAHII of the selected cities. Finally, the SAHII of the selected cities was modelled based on the LST-SIISC feature space-derived LLSM. Finally, the values of the coefficient of determination (R2) and the root mean square error (RMSE) between the actual and modelled SAHII were calculated to evaluate and compare the performance of different spectral indices in SAHII quantification. The performance of the spectral indices used in the built LST-SIISC feature space for SAHII quantification differed. The index-based built-up index (IBI) (R2 = 0.98, RMSE = 0.34 °C) and albedo (0.76, 1.39 °C) performed the best and worst performance in SAHII quantification, respectively. Our results indicate that the LST-SIISC feature space is very useful and effective for SAHII quantification. The advantages of the spectral indices used in SAHII quantification include (1) synchronization with the recording of thermal data, (2) simplicity, (3) low cost, (4) accessibility under different spatial and temporal conditions, and (5) scalability.
Inter-Calibration of AMSU-A Window Channels

More than one decade of observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard the polar-orbiting satellites NOAA-15 to NOAA-19 and European Meteorological Operational satellite program-A (MetOp-A) provided global information on atmospheric temperature profiles, water vapor, cloud, precipitation, etc. These observations were primarily intended for weather related prediction and applications, however, in order to meet the requirements for climate application, further reprocessing must be conducted to first eliminate any potential satellites biases. After the geolocation and cross-scan bias corrections were applied to the dataset, follow-on research focused on the comparison amongst AMSU-A window channels (e.g., 23.8, 31.4, 50.3 and 89.0 GHz) from the six different satellites to remove any inter-satellite inconsistency. Inter-satellite differences can arise from many error sources, such as bias drift, sun-heating-induced instrument variability in brightness temperatures, radiance dependent biases due to inaccurate calibration nonlinearity, etc. The Integrated microwave inter-calibration approach (IMICA) approach was adopted in this study for inter-satellite calibration of AMSU-A window channels after the appropriate standard deviation (STD) thresholds were identified to restrict Simultaneous Nadir Overpass (SNO) data for window channels. This was a critical step towards the development of a set of fundamental and thematic climate data records (CDRs) for hydrological and climatological applications. NOAA-15 served as the main reference satellite for this study. For ensuing studies that expand to beyond 2015, however, it is recommended that a different satellite be adopted as the reference due to concerns over potential degradation of NOAA-15 AMSU-A.
Fire Detection and Fire Radiative Power in Forests and Low-Biomass Lands in Northeast Asia: MODIS versus VIIRS Fire Products

Fire omission and commission errors, and the accuracy of fire radiative power (FRP) from satellite moderate-resolution impede the studies on fire regimes and FRP-based fire emissions estimation. In this study, we compared the accuracy between the extensively used 1-km fire product of MYD14 from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the 375-m fire product of VNP14IMG from the Visible Infrared Imaging Radiometer Suite (VIIRS) in Northeastern Asia using data from 2012–2017. We extracted almost simultaneous observation of fire detection and FRP from MODIS-VIIRS overlapping orbits from the two fire products, and identified and removed duplicate fire detections and corresponding FRP in each fire product. We then compared the performance of the two products between forests and low-biomass lands (croplands, grasslands, and herbaceous vegetation). Among fire pixels detected by VIIRS, 65% and 83% were missed by MODIS in forests and low-biomass lands, respectively; whereas associated omission rates by VIIRS for MODIS fire pixels were 35% and 53%, respectively. Commission errors of the two fire products, based on the annual mean measurements of burned area by Landsat, decreased with increasing FRP per fire pixel, and were higher in low-biomass lands than those in forests. Monthly total FRP from MODIS was considerably lower than that from VIIRS due to more fire omission by MODIS, particularly in low-biomass lands. However, for fires concurrently detected by both sensors, total FRP was lower with VIIRS than with MODIS. This study contributes to a better understanding of fire detection and FRP retrieval performance between MODIS and its successor VIIRS, providing valuable information for using those data in the study of fire regimes and FRP-based fire emission estimation.
Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices

The ability to monitor post-fire ecological responses and associated vegetation cover change is crucial to understanding how boreal forests respond to wildfire under changing climate conditions. Uncrewed aerial vehicles (UAVs) offer an affordable means of monitoring post-fire vegetation recovery for boreal ecosystems where field campaigns are spatially limited, and available satellite data are reduced by short growing seasons and frequent cloud cover. UAV data could be particularly useful across data-limited regions like the Cajander larch (Larix cajanderi Mayr.) forests of northeastern Siberia that are susceptible to amplified climate warming. Cajander larch forests require fire for regeneration but are also slow to accumulate biomass post-fire; thus, tall shrubs and other understory vegetation including grasses, mosses, and lichens dominate for several decades post-fire. Here we aim to evaluate the ability of two vegetation indices, one based on the visible spectrum (GCC; Green Chromatic Coordinate) and one using multispectral data (NDVI; Normalized Difference Vegetation Index), to predict field-based vegetation measures collected across post-fire landscapes of high-latitude Cajander larch forests. GCC and NDVI showed stronger linkages with each other at coarser spatial resolutions e.g., pixel aggregated means with 3-m, 5-m and 10-m radii compared to finer resolutions (e.g., 1-m or less). NDVI was a stronger predictor of aboveground carbon biomass and tree basal area than GCC. NDVI showed a stronger decline with increasing distance from the unburned edge into the burned forest. Our results show NDVI tended to be a stronger predictor of some field-based measures and while GCC showed similar relationships with the data, it was generally a weaker predictor of field-based measures for this region. Our findings show distinguishable edge effects and differentiation between burned and unburned forests several decades post-fire, which corresponds to the relatively slow accumulation of biomass for this ecosystem post-fire. These findings show the utility of UAV data for NDVI in this region as a tool for quantifying and monitoring the post-fire vegetation dynamics in Cajander larch forests.
Fast Total Variation Method Based on Iterative Reweighted Norm for Airborne Scanning Radar Super-Resolution Imaging

The total variation (TV) method has been applied to realizing airborne scanning radar super-resolution imaging while maintaining the outline of the target. The iterative reweighted norm (IRN) approach is an algorithm for addressing the minimum Lp norm problem by solving a sequence of minimum weighted L2 norm problems, and has been applied to solving the TV norm. However, during the solving process, the IRN method is required to update the weight term and result term in each iteration, involving multiplications and the inversion of large matrices. Consequently, it suffers from a huge calculation load, which seriously restricts the application of the TV imaging method. In this work, by analyzing the structural characteristics of the matrix involved in iteration, an efficient method based on suitable matrix blocking is proposed. It transforms multiplications and the inversion of large matrices into the computation of multiple small matrices, thereby accelerating the algorithm. The proposed method, called IRN-FTV method, is more time economical than the IRN-TV method, especially for high dimensional observation scenarios. Numerical results illustrate that the proposed IRN-FTV method enjoys preferable computational efficiency without performance degradation.
Assessing Salt Marsh Vulnerability Using High-Resolution Hyperspectral Imagery

Change in the coastal zone is accelerating with external forcing by sea-level rise, nutrient loading, drought, and over-harvest, leading to significant stress on the foundation plant species of coastal salt marshes. The rapid evolution of marsh state induced by these drivers makes the ability to detect stressors prior to marsh loss important. However, field work in coastal salt marshes can be challenging due to limited access and their fragile nature. Thus, remote sensing approaches hold promise for rapid and accurate determination of marsh state across multiple spatial scales. In this study, we evaluated the use of remote sensing tools to detect three dominant stressors on Spartina alterniflora. We took advantage of a barrier island salt marsh chronosequence in Virginia, USA, where marshes of different ages and level of stressor exist side by side. We collected hyperspectral imagery of plants along with salinity, sediment redox potential, and foliar nitrogen content in the field. We also conducted a greenhouse study where we manipulated environmental conditions. We found that models developed for stressors based on plant spectral response correlated well with salinity and foliar nitrogen within the greenhouse and field data, but were not transferable from lab to field, likely due to the limited range of conditions explored within the greenhouse experiments and the coincidence of multiple stressors in the field. This study is an important step towards the development of a remote sensing tool for tracking of ecosystem development, marsh health, and future ecosystem services.
Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture

Remote sensing is a useful tool for monitoring spatio-temporal variations of crop morphological and physiological status and supporting practices in precision farming. In comparison with multispectral imaging, hyperspectral imaging is a more advanced technique that is capable of acquiring a detailed spectral response of target features. Due to limited accessibility outside of the scientific community, hyperspectral images have not been widely used in precision agriculture. In recent years, different mini-sized and low-cost airborne hyperspectral sensors (e.g., Headwall Micro-Hyperspec, Cubert UHD 185-Firefly) have been developed, and advanced spaceborne hyperspectral sensors have also been or will be launched (e.g., PRISMA, DESIS, EnMAP, HyspIRI). Hyperspectral imaging is becoming more widely available to agricultural applications. Meanwhile, the acquisition, processing, and analysis of hyperspectral imagery still remain a challenging research topic (e.g., large data volume, high data dimensionality, and complex information analysis). It is hence beneficial to conduct a thorough and in-depth review of the hyperspectral imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing hyperspectral information, and recent advances of hyperspectral imaging in agricultural applications. Publications over the past 30 years in hyperspectral imaging technology and applications in agriculture were thus reviewed. The imaging platforms and sensors, together with analytic methods used in the literature, were discussed. Performances of hyperspectral imaging for different applications (e.g., crop biophysical and biochemical properties’ mapping, soil characteristics, and crop classification) were also evaluated. This review is intended to assist agricultural researchers and practitioners to better understand the strengths and limitations of hyperspectral imaging to agricultural applications and promote the adoption of this valuable technology. Recommendations for future hyperspectral imaging research for precision agriculture are also presented.
Traffic Congestion Aware Route Assignment

Traﬃc congestion emerges when traﬃc load exceeds the available capacity of roads. It is challenging to prevent traﬃc congestion in current transportation systems where vehicles tend to follow the shortest/fastest path to their destinations without considering the potential congestions caused by the concentration of vehicles. With connected autonomous vehicles, the new generation of traﬃc management systems can optimize traﬃc by coordinating the routes of all vehicles. As the connected autonomous vehicles can adhere to the routes assigned to them, the traﬃc management system can predict the change of traﬃc ﬂow with a high level of accuracy. Based on the accurate traﬃc prediction and traﬃc congestion models, routes can be allocated in such a way that helps mitigating traﬃc congestions eﬀectively. In this regard, we propose a new route assignment algorithm for the era of connected autonomous vehicles. Results show that our algorithm outperforms several baseline methods for traﬃc congestion mitigation.
Imaging of the Upper Mantle Beneath Southeast Asia: Constrained by Teleseismic P-Wave Tomography

It is of great significance to construct a three-dimensional underground velocity model for the study of geodynamics and tectonic evolution. Southeast Asia has attracted much attention due to its complex structural features. In this paper, we collected relative travel time residuals data for 394 stations distributed in Southeast Asia from 2006 to 2019, and 14,011 seismic events were obtained. Then, teleseismic tomography was applied by using relative travel time residuals data to invert the velocity where the fast marching method (FMM) and subspace method were used for every iteration. A novel 3D P-wave velocity model beneath Southeast Asia down to 720 km was obtained using this approach. The tomographic results suggest that the southeastern Tibetan Plateau, the Philippines, Sumatra, and Java, and the deep part of Borneo exhibit high velocity anomalies, while low velocity anomalies were found in the deep part of the South China Sea (SCS) basin and in the shallow part of Borneo and areas near the subduction zone. High velocity anomalies can be correlated to subduction plates and stable land masses, while low velocity anomalies can be correlated to island arcs and upwelling of mantle material caused by subduction plates. We found a southward subducting high velocity body in the Nansha Trough, which was presumed to be a remnant of the subduction of the Dangerous Grounds into Borneo. It is further inferred that the Nansha Trough and the Dangerous Grounds belong to the same tectonic unit. According to the tomographic images, a high velocity body is located in the deep underground of Indochina–Natuna Island–Borneo–Palawan, depth range from 240 km to 660 km. The location of the high velocity body is consistent with the distribution range of the ophiolite belt, so we speculate that the high velocity body is the remnant of thee Proto-South China Sea (PSCS) and Paleo-Tethys. This paper conjectures that the PSCS was the southern branch of Paleo-Tethys and the gateway between Paleo-Tethys and the Paleo-Pacific Ocean. Due to the squeeze of the Australian plate, PSCS closed from west to east in a scissor style, and was eventually extinct under Borneo.
Measurement of Quasi-Static and Dynamic Displacements of Footbridges Using the Composite Instrument of a Smartstation and an Accelerometer: Case Studies

Monitoring the dynamic responses of bridge structures has received considerable attention. It is important to synchronously measure both the quasi-static and dynamic displacements of bridge structures. However, the traditional accelerometer method cannot capture the quasi-static displacement component, although it can detect the dynamic displacement component. To this end, a novel composite instrument of a smartstation was proposed to monitor vibration displacements of footbridges. Full-scale experiments were conducted on a footbridge to validate the feasibility of the composite instrument-based monitoring method. A Chebyshev filter and wavelet algorithms were developed to process the composite instrument measurements. It was concluded that the measurement noise of the composite instrument was mainly distributed in a frequency range of 0–0.1 Hz. In two case studies with displacement peaks of 5.7–10.0 mm and 1.3– 2.5 mm, the composite instrument accurately identified the quasi-static and dynamic displacements. The composite instrument will be a potential tool for monitoring structural dynamics because of its enhanced overall performance.
Molecular analysis of the recently described lizardfish Saurida lessepsianus (Synodontidae) from the Red Sea and the Mediterranean, with remarks on its phylogeny and genetic bottleneck effect

ABSTRACT The opening of the Suez Canal in 1869 led to a massive influx of Red Sea species that invaded the Mediterranean; this was termed ‘Lessepsian migration'. Among these species was a species of lizardfish, identified by some authors as Saurida undosquamis and by others as S. macrolepis. Recently, the Red Sea and the Mediterranean populations were described according to external characteristics as a unique taxon, Saurida lessepsianus. Our molecular study confirms this finding and determines that all previous records of S. undosquamis and S. macrolepis in the Red Sea and the Mediterranean are misidentifications of S. lessepsianus. The Mediterranean population of S. lessepsianus exhibits a lower genetic variability than that of the Red Sea population, suggesting a bottleneck effect.
Ensemble Modelling of Skipjack Tuna (Katsuwonus pelamis) Habitats in the Western North Pacific Using Satellite Remotely Sensed Data; a Comparative Analysis Using Machine-Learning Models

To examine skipjack tuna’s habitat utilization in the western North Pacific (WNP) we used an ensemble modelling approach, which applied a fisherderived presence-only dataset and three satellite remote-sensing predictor variables. The skipjack tuna data were compiled from daily point fishing data into monthly composites and re-gridded into a quarter degree resolution to match the environmental predictor variables, the sea surface temperature (SST), sea surface chlorophyll-a (SSC) and sea surface height anomalies (SSHA), which were also processed at quarter degree spatial resolution. Using the sdm package operated in RStudio software, we constructed habitat models over a 9-month period, from March to November 2004, using 17 algorithms, with a 70:30 split of training and test data, with bootstrapping and 10 runs as parameter settings for our models. Model performance evaluation was conducted using the area under the curve (AUC) of the receiver operating characteristic (ROC), the point biserial correlation coefficient (COR), the true skill statistic (TSS) and Cohen’s kappa (k) metrics. We analyzed the response curves for each predictor variable per algorithm, the variable importance information and the ROC plots. Ensemble predictions of habitats were weighted with the TSS metric. Model performance varied across various algorithms, with the Support Vector Machines (SVM), Boosted Regression Trees (BRT), Random Forests (RF), Multivariate Adaptive Regression Splines (MARS), Generalized Additive Models (GAM), Classification and Regression Trees (CART), Multi-Layer Perceptron (MLP), Recursive Partitioning and Regression Trees (RPART), and Maximum Entropy (MAXENT), showing consistently high performance than other algorithms, while the Flexible Discriminant Analysis (FDA), Mixture Discriminant Analysis (MDA), Bioclim (BIOC), Domain (DOM), Maxlike (MAXL), Mahalanobis Distance (MAHA) and Radial Basis Function (RBF) had lower performance. We found inter-algorithm variations in predictor variable responses. We conclude that the multi-algorithm modelling approach enabled us to assess the variability in algorithm performance, hence a data driven basis for building the ensemble model. Given the inter-algorithm variations observed, the ensemble prediction maps indicated a better habitat utilization map of skipjack tuna than would have been achieved by a single algorithm.