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The Caldor fire burned ~222,000 acres of the Eastern Sierra Nevada during summer–fall 2021. We evaluated the effects of this “megafire” on the physical properties of a sandy soil developed from glacial tills to document fire-induced soil modifications in this region. We measured soil water retention and hydraulic conductivity functions as well as the thermal properties of five core samples from control (unburned) areas and eight core samples from burned soil of the same soil unit. Soil water repellency was measured in terms of water drop penetration time (WDPT) in the field and apparent contact angle in the laboratory on control and burned soil as well as ash samples. Soil organic matter (SOM) and particle and aggregate size distributions were determined on control and burned soil samples. Additionally, scanning electron microscopy (SEM) was used to image microaggregates of control and burned soil samples. We found a significant difference in SOM content and sand and silt aggregate size distribution between control and burned samples, which we associated with the disintegration of microaggregates due to the fire. We found no significant difference between soil water retention and hydraulic conductivity functions of control and burned soil but observed greater variation in saturated hydraulic conductivity and systematic shifts in thermal conductivity functions of burned compared to control samples. WDPT and apparent contact angle values were significantly higher for burned soils, indicating the occurrence of fire-induced soil hydrophobicity (FISH). Interestingly, the average apparent contact angle of the control soil was >90°, indicating that even the unburned soil was hydrophobic. However, the ash on top of the burned soil was found to be hydrophilic, having apparent contact angles <10°. Our results indicate that SOM and microaggregates were readily affected by the Caldor fire, even for sandy soil with a weakly developed structure. The fire seemed to have moderated thermal properties, significantly and soil wettability but had only minimal effects on water retention and hydraulic conductivity functions. Our findings demonstrate the complex nature of fire-soil interactions in a natural environment and highlight the need for additional investigation into the causes and processes associated with FISH and structure alterations due to fire to improve our ability to rapidly determine potential problem areas in terms of hazards commonly associated with fire-affected soils. 

Dispersed computing is a new resource-centric computing paradigm, which makes use of idle resources in the network to complete the tasks. Effectively allocating tasks between task nodes and networked computation points (NCPs) is a critical factor for maximizing the performance of dispersed computing. Due to the heterogeneity of nodes and the priority requirements of tasks, it brings great challenges to the task allocation in dispersed computing. In this paper, we propose a task allocation model based on incomplete preference list. The requirements and permissions of task nodes and NCPs are quantitatively measured through the preference list. In the model, the task completion rate, response time, and communication distance are taken as three optimizing parameters. To solve this NP-hard optimization problem, we develop a new many-to-many matching algorithm based on incomplete preference list. The unilateral optimal and stable solution of the model are obtained. Taking into account the needs for location privacy-preserving, we use the planar Laplace mechanism to produce obfuscated locations instead of real locations. The mechanism satisfies ε-differential privacy. Finally, the efficacy of the proposed model is demonstrated through extensive numerical analysis. Particularly, when the number of task nodes and NCPs reaches 1:2, the task completion rate can reach 99.33%. 

This paper investigates the short-term wind farm generation forecast. It is observed from the real wind farm generation measurements that wind farm generation exhibits distinct features, such as the non-stationarity and the heterogeneous dynamics of ramp and non-ramp events across different classes of wind turbines. To account for the distinct features of wind farm generation, we propose a Drifting Streaming Peaks-over-Threshold (DSPOT)-enhanced self-evolving neural networks-based short-term wind farm generation forecast. Using DSPOT, the proposed method first classifies the wind farm generation data into ramp and non-ramp datasets, where time-varying dynamics are taken into account by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events. We then train different neural networks based on each dataset to learn the different dynamics of wind farm generation by the NeuroEvolution of Augmenting Topologies (NEAT), which can obtain the best network topology and weighting parameters. As the efficacy of the neural networks relies on the quality of the training datasets (i.e., the classification accuracy of the ramp and non-ramp events), a Bayesian optimization-based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Based on the developed self-evolving neural networks, both distributional and point forecasts are developed. The experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. The experiment results indicate that the accuracy improvement in a 60 min horizon forecast in terms of the mean absolute error (MAE) is at least 33.6% for the whole year data and at least 37% for the ramp events. Moreover, the distributional forecast in terms of the continuous rank probability score (CRPS) is improved by at least 35.8% for the whole year data and at least 35.2% for the ramp events. 

Due to the rapidly evolving nature of the Virtual Reality field, many frameworks for multiuser interaction have become outdated, with few (if any) designed to support mixed virtual and non-virtual interactions. We have developed a framework that lays an exten- sible and forward-looking foundation for mixed interactions based upon a novel method of ensuring that inputs, visuals, and networking can all communicate without needing to understand the others’ internals. We tested this framework in the development of several applications and proved that it can easily be adapted to support application requirements it was not originally designed for. 

Virtual Reality (VR) has existed for many years; however, it has only recently gained wide spread popularity and commercial use. This change comes from the innovations in head mounted displays (HMDs) and from the work of many software engineers making quality user experiences (UX). In this work we present a brief history, current research areas, and areas for improvement in virtual reality