Search Results (11)

Soils play a central role in ecosystem functioning, and thus, mapped soil property information is indispensable to supporting sustainable land management. Digital Soil Mapping (DSM) provides a framework to spatially estimate soil properties. However, broad-scale DSM remains challenging because of non-purposively sampled soil data, large data volumes for processing extensive soil covariates, and high model complexities due to spatially varying soil–landscape relationships. This study presents a three-dimensional DSM framework for Switzerland, targeting the soil properties of clay content (Clay), organic carbon content (SOC), pH value (pH), and potential cation exchange capacity (CEC_pot). The DSM approach is based on machine learning and a comprehensive exploitation of soil and remote sensing data archives. Quantile Regression Forest was applied to link the soil sample data from a national soil data base with covariates derived from a LiDAR-based elevation model, from climate raster data, and from multispectral raster time series based on satellite imagery. The covariate set comprises spatially multiscale terrain attributes, climate patterns and their temporal variation, temporarily multiscale land use features, and spectral bare soil signatures. Soil data and predictions were evaluated with respect to different landcovers and depth intervals. All reference soil data sets were found to be spatially clustered towards croplands, showing an increasing sample density from lower to upper depth intervals. According to the R² value derived from independent data, the overall model accuracy amounts to 0.69 for Clay, 0.64 for SOC, 0.76 for pH, and 0.72 for CEC_pot. Reduced model accuracies were found to be accompanied by soil data sets showing limited sample sizes (e.g., CEC_pot), uneven statistical distributions (e.g., SOC), and low spatial sample densities (e.g., woodland subsoils). Multiscale terrain covariates were highly influential for all models; climate covariates were particularly important for the Clay model; multiscale land use covariates showed enhanced importance for modeling pH; and bare soil reflectance was a major driver in the SOC and CEC_pot models. Full article

In order to investigate the mechanism of environmental factors in soil salinization, this study focused on analyzing the temporal-spatial variation of soil salinity in the Ogan-Kuqa River Oasis in Xinjiang, China. The research aimed to predict soil salinity using a combination of satellite data, environmental covariates, and advanced modeling techniques. Firstly, Boruta and ReliefF algorithms were employed to select variables that significantly affect soil salinity from the Sentinel-2 satellite data and environmental covariates. Subsequently, a soil salinity inversion model was established using three advanced strategies: comprehensive variable analysis, a Boruta-based variable selection algorithm, and a ReliefF-based variable selection algorithm. Each strategy was modeled using a Light Gradient Boosting Machine (LightGBM), an Extreme Learning Machine (ELM), and a Support Vector Machine (SVM). Finally, the Boruta-LightGBM strategy was proven to be the most effective in predicting soil electrical conductivity (EC), with a coefficient of determination ($R^2$) of 0.72 and a Root Mean Square Error (RMSE) of 12.49 ds/m. The experimental results show that the red-edge band index is the foremost variable in predicting soil salinity, succeeded by the salinity index and soil attribute data, while the topographic index has the least influence, which further demonstrates that proper variable selection could significantly improve model functionality and predictive precision. Furthermore, the Multiscale Geographically Weighted Regression (MGWR) model was utilized to reveal the influence and temporal-temporal-spatial heterogeneity of environmental factors such as soil organic carbon (SOC), precipitation (PRE), pH value, and temperature (TEM) on soil EC. This research offers not just a viable methodological framework for monitoring soil salinization but also new perspectives on the environmental drivers of soil salinity changes, which have implications for sustainable land management and provide valuable information for decision-making in soil salinity control and mitigation efforts. Full article

A second-order delta-sigma modulator (DSM) is proposed for readout integrated circuits of sensor applications requiring a small area and low-power consumption. The proposed second-order CIFF DSM with the architecture of cascaded-of-integrator feedforward (CIFF) basically consists of two integrators, a 3-bit quantizer, data-weighted averaging (DWA) circuit, and clock generator. The use of the 3-bit quantizer instead of the single-bit quantizer reduces the size of the feedback capacitor in the first integrator. The 3-bit quantizer is designed based on a successive approximation register analog-to-digital converter for small area and low power implementation. Furthermore, the proposed second-order CIFF DSM has a single supply without an additional reference driver while having a wide analog input voltage range with rail to rail. The proposed second-order CIFF DSM, implemented using a 130 nm 1-poly 6-metal CMOS process with a supply of 1.5 V, has an area of 0.096 mm². It has a sampling frequency of 500 kHz for the implementation of an input bandwidth of 2 kHz and an oversampling ratio of 125. The measured peak signal-to-noise and distortion ratio is approximately 90 dB when the differential analog input signal has a frequency of 353 Hz and an amplitude of 1.2 Vpp. The measured dynamic range is approximately 96.3 dB. Full article

The reliability and security of an electric power supply have become pivotal to the proper functioning of modern society. Traditionally, the electric power supply system has been designed with the objective of being able to adequately meet present and future demand, with efforts to maintain supply reliability being focused primarily on the supply side. Over the decades, however, the value of demand-side management—efforts focused on enhancing the efficient and effective use of electricity in support of the power system and customer needs—has been widely acknowledged as being able to play a greater role in ensuring that the key objectives of power system operation are satisfied. This article presents a study of demand-side management and opportunities for incorporating it into network planning as an effective means of addressing supply capacity constraints in the South African electric grid. The main drivers, benefits and potential barriers to the effective implementation of demand-side management are examined, along with the main enabling technologies. The key finding of the study is that the effective integration of demand-side management into network planning requires a shift from the traditional network planning approach to one that is more suited to fully exploiting the flexibility resources available on the demand side of the network. Full article

The integration of multiple data sources, including satellite imagery, aerial photography, and ground-based measurements, represents an important development in the study of landslide processes. The combination of different data sources can be very important in improving our understanding of geological phenomena, especially in cases of inaccessible areas. In this context, the study of coastal areas represents a real challenge for the research community, both for the inaccessibility of coastal slopes and for the numerous drivers that can control coastal processes (subaerial, marine, or endogenic). In this work, we present a case study of the Conero Regional Park (Northern Adriatic Sea, Ancona, Italy) cliff-top retreat, characterized by Neogenic soft rocks (flysch, molasse). In particular, the study is focused in the area between the beach of Portonovo and Trave (south of Ancona), which has been studied using aerial orthophoto acquired between 1978 and 2021, Unmanned Aerial Vehicle (UAV) photographs (and extracted photogrammetric model) surveyed in September 2021 and 2012 LiDAR data. Aerial orthophotos were analyzed through the United States Geological Survey’s (USGS) tool Digital Shoreline Analysis System (DSAS) to identify and estimate the top-cliff erosion. The results were supported by the analysis of wave data and rainfall from the correspondent period. It has been found that for the northernmost sector (Trave), in the examined period of 40 years, an erosion up to 40 m occurred. Furthermore, a Digital Elevation Model (DEM) of Difference (DoD) between a 2012 Digital Terrain Model (DTM) and a UAV Digital Surface Model (DSM) was implemented to corroborate the DSAS results, revealing a good agreement between the retreat areas, identified by DSAS, and the section of coast characterized by a high value of DoD. Full article

A high-performance Driver State Monitoring (DSM) application for the detection of driver drowsiness is presented in this paper. The popular Ensemble of Regression Trees (ERTs) machine learning method has been employed for the alignment of 68 facial landmarks. Open-source implementation of ERTs for facial shape alignment has been ported to different platforms and adapted for the acceleration of the frame processing speed using reconfigurable hardware. Reducing the frame processing latency saves time that can be used to apply frame-to-frame facial shape coherency rules. False face detection and false shape estimations can be ignored for higher robustness and accuracy in the operation of the DSM application without sacrificing the frame processing rate that can reach 65 frames per second. The sensitivity and precision in yawning recognition can reach 93% and 97%, respectively. The implementation of the employed DSM algorithm in reconfigurable hardware is challenging since the kernel arguments require large data transfers and the degree of data reuse in the computational kernel is low. Hence, unconventional hardware acceleration techniques have been employed that can also be useful for the acceleration of several other machine learning applications that require large data transfers to their kernels with low reusability. Full article

Driver’s behavior characteristics (DBCs) influence car-following safety. Therefore, this paper aimed to analyze the effect of different DBCs on the car-following safety based on the desired safety margin (DSM) car-following model, which includes five DBC parameters. Based on the Monte Carlo simulation method, the effect of DBCs on car-following safety is investigated under a given rear-end collision (RECs) condition. We find that larger subjective risk perception levels can reduce RECs, a smaller acceleration sensitivity (or a larger deceleration sensitivity) can improve car-following safety, and a faster reaction ability of the driver can avoid RECs in the car-following process. It implies that DBCs would cause a traffic wave in the car-following process. Therefore, a reasonable value of DBCs can enhance traffic flow stability, and a traffic control strategy can improve car-following safety by using the adjustment of DBCs. Full article

Global economic growth, demographic explosion, digitization, increased mobility, and greater demand for heating and cooling due to climate change in different world areas are the main drivers for the surge in energy demand. The increase in energy demand is the basis of economic challenges for power companies alongside several socio-economic problems in communities, such as energy poverty, defined as the insufficient coverage of energy needs, especially in the residential sector. Two main strategies are considered to meet this increased demand. The first strategy focuses on new sustainable and eco-friendly modes of power generation, such as renewable energy resources and distributed energy resources. The second strategy is demand-side oriented rather than the supply side. Demand-side management, demand response (DR), and energy efficiency (EE) programs fall under this category. On the other hand, the decentralization and digitization of the energy sector convoyed by the emersion of new technologies such as blockchain, Internet of Things (IoT), and Artificial Intelligence (AI), opened the door to new solutions for the energy demand dilemma. Among these technologies, blockchain has proved itself as a decentralized trading platform between untrusted peers without the involvement of a trusted third party. This newly introduced Peer-to-Peer (P2P) trading model can be used to create a new demand load control model. In this article, the concept of an energy cap and trade demand-side management (DSM) model is introduced and simulated. The introduced DSM model is based on the concept of capping consumers’ monthly energy consumption and rewarding consumers who do not exceed this cap with energy tradeable credits that can be traded using blockchain-based Peer-to-Peer (P2P) energy trading. A model based on 200 households is used to simulate the proposed DSM model and prove that this model can be beneficial to both energy companies and consumers. Full article

Phelan–McDermid syndrome (PMS) is one of the most common genetic forms of autism spectrum disorder (ASD). While sensory reactivity symptoms are widely reported in idiopathic ASD (iASD), few studies have examined sensory symptoms in PMS. The current study delineates the sensory reactivity phenotype and examines genotype–phenotype interactions in a large sample of children with PMS. Sensory reactivity was measured in a group of 52 children with PMS, 132 children with iASD, and 54 typically developing (TD) children using the Sensory Assessment for Neurodevelopmental Disorders (SAND). The SAND is a clinician-administered observation and corresponding caregiver interview that captures sensory symptoms based on the DSM-5 criteria for ASD. Children with PMS demonstrated significantly greater hyporeactivity symptoms and fewer hyperreactivity and seeking symptoms compared to children with iASD and TD controls. There were no differences between those with Class I deletions or sequence variants and those with larger Class II deletions, suggesting that haploinsufficiency of SHANK3 is the main driver of the sensory phenotype seen in PMS. The syndrome-specific sensory phenotype identified in this study is distinct from other monogenic forms of ASD and offers insight into the potential role of SHANK3 deficiency in sensory reactivity. Understanding sensory reactivity abnormalities in PMS, in the context of known glutamatergic dysregulation, may inform future clinical trials in the syndrome. Full article

Monitoring anthropogenic disturbances on aboveground biomass (AGB) of tropical montane forests is crucial, but challenging, due to a lack of historical AGB information. We examined the use of spaceborne (Shuttle Radar Topographic Mission Digital Elevation Model (SRTM) digital surface model (DSM)) and airborne (Light Detection and Ranging (LiDAR)) digital elevation data to estimate tropical montane forest AGB changes in northern Borneo between 2000 and 2012. LiDAR canopy height model (CHM) mean values were used to calibrate SRTM CHM in different pixel resolutions (1, 5, 10, and 30 m). Regression analyses between field AGB of 2012 and LiDAR CHM means at different resolutions identified the LiDAR CHM mean at 1 m resolution as the best model (modeling efficiency = 0.798; relative root mean square error = 25.81%). Using the multitemporal AGB maps, the overall mean AGB decrease was estimated at 390.50 Mg/ha, but AGB removal up to 673.30 Mg/ha was estimated in the managed forests due to timber extraction. Over the 12 years, the AGB accumulated at a rate of 10.44 Mg/ha/yr, which was attributed to natural regeneration. The annual rate in the village area was 8.31 Mg/ha/yr, which was almost 20% lower than in the managed forests (10.21 Mg/ha/yr). This study identified forestry land use, especially commercial logging, as the main driver for the AGB changes in the montane forest. As SRTM DSM data are freely available, this approach can be used to estimate baseline historical AGB information for monitoring forest AGB changes in other tropical regions. Full article

Replacing fossil fuels with cellulosic biofuels is a valuable component of reducing the drivers of climate change. This leads to a requirement to develop more productive bioenergy crops, such as Arundo donax with the aim of increasing above-ground biomass (AGB). However, direct measurement of AGB is time consuming, destructive, and labor-intensive. Phenotyping of plant height and biomass production is a bottleneck in genomics- and phenomics-assisted breeding. Here, an unmanned aerial vehicle (UAV) for remote sensing equipped with light detection and ranging (LiDAR) was tested for remote plant height and biomass determination in A. donax. Experiments were conducted on three A. donax ecotypes grown in well-watered and moderate drought stress conditions. A novel UAV-LiDAR data collection and processing workflow produced a dense three-dimensional (3D) point cloud for crop height estimation through a normalized digital surface model (DSM) that acts as a crop height model (CHM). Manual measurements of crop height and biomass were taken in parallel and compared to LiDAR CHM estimates. Stepwise multiple regression was used to estimate biomass. Analysis of variance (ANOVA) tests and pairwise comparisons were used to determine differences between ecotypes and drought stress treatments. We found a significant relationship between the sensor readings and manually measured crop height and biomass, with determination coefficients of 0.73 and 0.71 for height and biomass, respectively. Differences in crop heights were detected more precisely from LiDAR estimates than from manual measurement. Crop biomass differences were also more evident in LiDAR estimates, suggesting differences in ecotypes’ productivity and tolerance to drought. Based on these results, application of the presented UAV-LiDAR workflow will provide new opportunities in assessing bioenergy crop morpho-physiological traits and in delivering improved genotypes for biorefining. Full article