Quantifying tropical forest disturbances using canopy structural traits derived from terrestrial laser scanning

Forest disturbances can reduce the potential of ecosystems to provide resources and services. Despite the urgent need to understand the effects of logging on tropical ecosystems, the quantification of disturbances arising from selective logging remains a challenge. Here, we used canopy-three-dimensional information retrieved from Terrestrial Laser Scanner (TLS) measurements to investigate the impacts of logging on key structural traits relevant to forest functioning. We addressed the following questions: 1) Which canopy structural traits were mostly affected by logging? 2) Can remotely-sensed canopy structural traits be used to quantify forest distur-bances? Fourteen canopy structural traits were applied as input to machine learning models, which were trained to quantify the intensity of logging disturbance. The plots were located in Malaysian Borneo, over a gradient of logging intensity, ranging from forest not recently disturbed by logging, to forest at the early stage of recovery following logging. Our results showed that using the Random Forest regression approach, the Plant Area Index (PAI) between 0 m -5 m aboveground, Relative Height at 50 %, and metrics describing plant allocation in the middle-higher canopy layer were the strongest predictors of disturbance. In particular, PAI between 35 m and 40 m explained 12 % to 19 % of the structural variability between plots, followed by the relative height at 50 %, (10.5 % -18.6 %), and the foliage height diversity (7.5 % -16.9 %). The approach presented in this study allowed a spatially explicitly characterization of disturbances, providing a novel approach for quantifying and monitoring the integrity of tropical forests. Our results indicate that canopy structural traits can provide a robust indication of disturbances, with strong potential to be applied at regional or global scales. The data used in this study are openly available and we encourage other researchers to use them as a benchmark data set to test larger scale approaches based on satellite and airborne platforms.Peer reviewe

Enhancing Enterprise Network Security: Comparing Machine-Level and Process-Level Analysis for Dynamic Malware Detection

Analysing malware is important to understand how malicious software works and to develop appropriate detection and prevention methods. Dynamic analysis can overcome evasion techniques commonly used to bypass static analysis and provide insights into malware runtime activities. Much research on dynamic analysis focused on investigating machine-level information (e.g., CPU, memory, network usage) to identify whether a machine is running malicious activities. A malicious machine does not necessarily mean all running processes on the machine are also malicious. If we can isolate the malicious process instead of isolating the whole machine, we could kill the malicious process, and the machine can keep doing its job. Another challenge dynamic malware detection research faces is that the samples are executed in one machine without any background applications running. It is unrealistic as a computer typically runs many benign (background) applications when a malware incident happens. Our experiment with machine-level data shows that the existence of background applications decreases previous state-of-the-art accuracy by about 20.12% on average. We also proposed a process-level Recurrent Neural Network (RNN)-based detection model. Our proposed model performs better than the machine-level detection model; 0.049 increase in detection rate and a false-positive rate below 0.1.Comment: Dataset link: https://github.com/bazz-066/cerberus-trac

How anxiety and incremental secondary task demands impact processing efficiency, visual search, and gait kinematics in older adults

How anxiety and incremental secondary task demands impact processing efficiency, visual search, and gait kinematics in older adult

FMDV replicons encoding green fluorescent protein are replication competent

The study of replication of viruses that require high bio-secure facilities can be accomplished with less stringent containment using non-infectious 'replicon' systems. The FMDV replicon system (pT7rep) reported by Mclnerney et al. (2000) was modified by the replacement of sequences encoding chloramphenicol acetyl-transferase (CAT) with those encoding a functional L proteinase (Lpro) linked to a bi-functional fluorescent/antibiotic resistance fusion protein (green fluorescent protein/puromycin resistance, [GFP-PAC]). Cells were transfected with replicon-derived transcript RNA and GFP fluorescence quantified. Replication of transcript RNAs was readily detected by fluorescence, whilst the signal from replication-incompetent forms of the genome was >2-fold lower. Surprisingly, a form of the replicon lacking the Lpro showed a significantly stronger fluorescence signal, but appeared with slightly delayed kinetics. Replication can, therefore, be quantified simply by live-cell imaging and image analyses, providing a rapid and facile alternative to RT-qPCR or CAT assays

Cogent: uniqueness types and certifying compilation

This paper presents a framework aimed at significantly reducing the cost of proving functional correctness for low-level operating systems components. The framework is designed around a new functional programming language, Cogent. A central aspect of the language is its uniqueness type system, which eliminates the need for a trusted runtime or garbage collector while still guaranteeing memory safety, a crucial property for safety and security. Moreover, it allows us to assign two semantics to the language: The first semantics is imperative, suitable for efficient C code generation, and the second is purely functional, providing a user-friendly interface for equational reasoning and verification of higher-level correctness properties. The refinement theorem connecting the two semantics allows the compiler to produce a proof via translation validation certifying the correctness of the generated C code with respect to the semantics of the Cogent source program. We have demonstrated the effectiveness of our framework for implementation and for verification through two file system implementations

The motion of trees in the wind: a data synthesis

Interactions between wind and trees control energy exchanges between the atmosphere and forest canopies. This energy exchange can lead to the widespread damage of trees, and wind is a key disturbance agent in many of the world\u27s forests. However, most research on this topic has focused on conifer plantations, where risk management is economically important, rather than broadleaf forests, which dominate the forest carbon cycle. This study brings together tree motion time-series data to systematically evaluate the factors influencing tree responses to wind loading, including data from both broadleaf and coniferous trees in forests and open environments. We found that the two most descriptive features of tree motion were (a) the fundamental frequency, which is a measure of the speed at which a tree sways and is strongly related to tree height, and (b) the slope of the power spectrum, which is related to the efficiency of energy transfer from wind to trees. Intriguingly, the slope of the power spectrum was found to remain constant from medium to high wind speeds for all trees in this study. This suggests that, contrary to some predictions, damping or amplification mechanisms do not change dramatically at high wind speeds, and therefore wind damage risk is related, relatively simply, to wind speed. Conifers from forests were distinct from broadleaves in terms of their response to wind loading. Specifically, the fundamental frequency of forest conifers was related to their size according to the cantilever beam model (i.e. vertically distributed mass), whereas broadleaves were better approximated by the simple pendulum model (i.e. dominated by the crown). Forest conifers also had a steeper slope of the power spectrum. We interpret these finding as being strongly related to tree architecture; i.e. conifers generally have a simple shape due to their apical dominance, whereas broadleaves exhibit a much wider range of architectures with more dominant crowns

Agricultural mitigation and adaptation to climate change in Yolo County, CA

This place‐based case study in an agricultural county in California’s Central Valley focused on the period of 2010–2050, and dealt with biophysical and socioeconomic issues related to both mitigation of greenhouse gas (GHG) emissions and to adaptation to an uncertain climate. In the past 100 years, changes in crop acreage has been more related to crop price and availability of irrigation water than to growing degree days during summer, and in fact, summer temperatures have increased less than winter temperatures. Econometric analysis indicated that warmer winters, as projected by Geophysical Fluid Dynamics Laboratory‐Bias Corrected Constructed Analog during 2035–2050, could result in less wheat acreage, more alfalfa and tomato acreage, and slight effects on tree and vine crops. The Water Evaluation and Planning (WEAP) model showed that these econometric projections did not reduce irrigation demand under either the B1 or A2 scenarios, but a diverse, water‐efficient cropping pattern combined with improved irrigation technology reduced demand to 12 percent below the historic mean. Collaboration during development of Yolo County’s Climate Action Plan showed that nitrous oxide (mainly from nitrogen fertilizers) was the main source (≅40 percent) of agricultural emissions. Emissions from cropland and rangeland were several orders of magnitude lower than urbanized land per unit area. A survey distributed to 570 farmers and ranchers achieved a 34 percent response rate. Farmers concerned about climate change were more likely to implement water conservation practices, and adopt voluntary GHG mitigation practices. Use of the urban growth model (UPlan) showed that channeling much or all future urban development into existing urban areas will increase ecosystem services by preserving agricultural land and open space, immensely reducing the Yolo County’s GHG emissions, and greatly enhancing agricultural sustainability