Absorption and Transmission Power Coefficients for Millimeter Waves in a Weakly Ionised Vegetation Fire

A vegetation fire plume is a weakly ionised gaseous medium. Electrons in the plume are mainly due to thermal ionisation of incumbent alkali impurities. The medium is highly collisional with free electron - neutral particle been the dominant particle interaction mechanism. Signal strength of an incident millimetre wave (MM-Wave) may be significantly attenuated in the plume depending on the extent of ionisation. A numerical experiment was set to investigate signal power loss of a MM-Wave incident on a simulated weakly ionised fire plume with flame maximum (seat) temperature ranging from 1000–1150 K. The simulated fire plume had alkali impurities (potassium) content of 1.0% per unit volume. MM-Wave frequency range investigated in the experiment is from 30–60 GHz. The simulation has application in the prediction of MM-Wave propagation in a crown forest fire and may also be applied in remote sensing studies of forest fire environments. Simulated attenuation per unit path length for the MM-Wave frequencies ranged from 0.06–24.00 dBm⁻¹. Phase change per unit path length was simulated to range from 2.97–306.17°m⁻¹ while transmission power coefficients ranged from maximum of 0.9996 for a fire plume at 1000 K to a minimum value of 0.8265 for a plume at a temperature of 1150 K over a plume depth of 1.20 m. Absorption power coefficient ranged from a minimum value of 0.0004 to maximum value of 0.1585 at a seat temperature of 1150 K over the plume depth.     

Prediction of protein structural class based on symmetrical recurrence quantification analysis

Protein structural class prediction for low similarity sequences is a significant challenge and one of the deeply explored subjects. This plays an important role in drug design, folding recognition of protein, functional analysis and several other biology applications. In this paper, we worked with two benchmark databases existing in the literature (1) 25PDB and (2) 1189 to apply our proposed method for predicting protein structural class. Initially, we transformed protein sequences into DNA sequences and then into binary sequences. Furthermore, we applied symmetrical recurrence quantification analysis (the new approach), where we got 8 features from each symmetry plot computation. Moreover, the machine learning algorithms such as Linear Discriminant Analysis (LDA), Random Forest (RF) and Support Vector Machine (SVM) are used. In addition, comparison was made to find the best classifier for protein structural class prediction. Results show that symmetrical recurrence quantification as feature extraction method with RF classifier outperformed existing methods with an overall accuracy of 100% without overfitting.     

荒漠绿洲区带状防护林遥感提取方法研究——以磴口为例

防护林是我国荒漠绿洲区主要植被类型，可为该地区防风固沙、水盐调控、水热平衡提供有力保障，调查防护林空间分布信息十分重要。然而荒漠绿洲防护林条带较窄、斑块面积小、分布广且零散，不易大尺度准确提取。为解决此难点，以磴口县荒漠绿洲为研究区，基于GF-2号遥感影像，采用面向对象分类技术提取防护林空间分布信息。分类前，首先基于局部方差（LV）和LV变化率（ROC）曲线，选取分割防护林的最优分割尺度。然后采用随机森林（RF）算法的袋外误差率（OOB error）及基尼系数（Gini）对包含光谱、形状和纹理的分类特征进行重要性评估并筛选特征、优化模型参数；最后，基于随机森林、CART决策树、支持向量机（SVM）、K近邻（KNN）四种分类器提取防护林空间分布信息并对比验证。结果表明：(1）采用ROC-LV曲线方法相比于遍历分割参数，可更客观更高效地筛选最优分割参数的可能值；(2）基于RF算法计算的袋外误分率和基尼系数可以有效筛除冗余特征，配合分类器参数优化，在保证分类精度的同时，有效提高分类器性能，大幅提升数据处理速度；(3）基于实测数据集对分类结果进行验证，结果显示基于随机森林算法的特征优化结合SVM分类器提取的防护林空间分布信息精度最高，生产者精度达到97.14%，总体防护林面积为208.99 km2，与实际210 km2接近，在小区块中，SVM分类器的分类效果优于其他三种分类器；(4）因GF-2影像分辨率高，并且含有近红外波段，通过波段合成得到亚米级信息，故基于面向对象的方法能够以单条林带为基本单位研究防护林林网属性，例如提取断带信息等林网结构特征。研究结论可为荒漠绿洲区带状防护林提取提供重要技术支撑。     

Species-Related Difference to Noise Reduction Between Trees in Urban Forest: The Abidar Forest Park (Case Study)

Sound pollution is one of the most important urban problems which endangers mental and physical health of the residents. This study was aimed to assess the influence of different tree species, including Fraxinus rotundifolia, Robinia pseudoacacia, Platanus orientalis, Platycladus orientalis, and Pinus eldarica, in reducing noise pollution in the Abidar Forest Park. A further objective was to identify the contaminated areas of Sanandaj city and to propose suitable noise absorbent tree species in consistent conditions. For each tree stands the noise measurements were performed during intervals at frequencies of 250, 500 and 1000 Hz, besides an open area with the same topography. With regards to the second purpose, a total of 50 stations with residential, commercial, residentialcommercial, and green space applications were selected across the city. Equivalent Continuous Sound Pressure Level (Leq) was determined in five replicates for 30 min. The measurements were performed under stable weather conditions and low wind velocity at 17:00 (traffic peak) in summer and fall. All of the Leq values were above the threshold noise level. The highest noise reduction was recorded in summer (i.e., green season); Platanus and Platycladus species demonstrated the highest and lowest noise absorption (31.43 dB and 22.28 dB, respectively). Furthermore, a meaningful difference was observed between Leq values of commercial, residential, commercial-residential, and green space urban applications, and the central parts of the city showed noticeable noise pollution. Taken together, due to being exposed to higher than the acceptable threshold noise level, the residents of Sanandaj will be endangered to health problems in the near future; thus consideration should be given to the noise pollution sources.     

Alphavirus protease inhibitors from natural sources: A homology modeling and molecular docking investigation

Alphaviruses such as Chikungunya virus (CHIKV), O’Nyong–Nyong virus (ONNV), Ross River virus (RRV), Eastern equine encephalitis virus (EEEV), Venezuelan equine encephalitis virus (VEEV), and Western equine encephalitis virus (WEEV), are mosquito-transmitted viruses that can cause fevers, rash, and rheumatic diseases (CHIKV, ONNV, RRV) or potentially fatal encephalitis (EEEV, VEEV, WEEV) in humans. These diseases are considered neglected tropical diseases for which there are no current antiviral therapies or vaccines available. The alphavirus non-structural protein 2 (nsP2) contains a papain-like protease, which is considered to be a promising target for antiviral drug discovery. In this work, molecular docking analyses have been carried out on a library of 2174 plant-derived natural products (290 alkaloids, 664 terpenoids, 1060 polyphenolics, and 160 miscellaneous phytochemicals) with the nsP2 proteases of CHIKV, ONNV, RRV, EEEV, VEEV, WEEV, as well as Aura virus (AURV), Barmah Forest Virus (BFV), Semliki Forest virus (SFV), and Sindbis virus (SINV) in order to identity structural scaffolds for inhibitor design or discovery. Of the 2174 phytochemicals examined, a total of 127 showed promising docking affinities and poses to one or more of the nsP2 proteases, and this knowledge can be used to guide experimental investigation of potential inhibitors.     

Does faecal matter reflect location? An initial assessment of isotopic variability between consumed prey remains and faecal matter for wild jaguars*

ABSTRACT

Faecal isotopic analysis may complement other non-invasive wildlife survey tools for monitoring landscape use by carnivores, such as motion-detecting cameras and non-invasive genetic sampling. We analysed carbon, nitrogen, and strontium isotopes in faecal matter produced by jaguars (Panthera onca) as well as bones from consumed prey at the Mountain Pine Ridge Forest Reserve (MPR) in Belize, Central America. The MPR is ideally suited for a spatial isotope study as vegetation and geology both vary considerably. The isotopic composition of faecal matter should reflect the habitat and geology where consumed prey lived. We used bone from consumed prey recovered from jaguar scats as a proxy for diet. Faecal matter and bone showed comparable spatial isotopic trends, suggesting that the isotopic composition of jaguar faeces can be used to detect foraging in different habitats (pine forest versus broadleaf forest) or on different geologies (Mesozoic carbonates; Palaeozoic granite, contact metamorphics, and metasediments). This result is reassuring as bones are not always present in carnivore scats. Studying landscape use by cryptic and wide-ranging carnivore species like jaguars remains challenging. Isotopic analysis of faecal matter complements the existing array of non-invasive spatial monitoring tools.     

Investigations on the Nitrogen Metabolism of Forest Trees by Mathematical Modelling of Natural Isotope Ratios

Abstract

Based on an experimental investigation of nitrogen concentrations and δ¹⁵N-values in different parts of Picea abies (L.) trees and soil samples and of their dependence on the age of plant parts and on damaging stress, mathematical models which represent main features of nitrogen turnover in the forest tree - soil system and associated isotope variations are developed. These models consider parts of 3 cycles of nitrogen: (1) transport of soluble nitrogen within trees, (2) turnover of protein nitrogen in different plant parts, and (3) the exchange of N between tree and soil via litter and the uptake of mineralized soil N by plant roots. Isotope effects of protein synthesis and mineralization of organic soil N are included. The models give a qualitative explanation of the data and lead to a better understanding of isotopic variations as indicators of forest damage.     

A simple proof of an inequality connecting the alternating number of independent sets and the decycling number

If s_k denotes the number of independent sets of cardinality k and α(G) is the size of a maximum independent set in graph G, then I(G;x)=s₀+s₁x+?+s_α(G)x^α(G) is the independence polynomial of G (Gutman and Harary, 1983) [8].In this paper we provide an elementary proof of the inequality|I(G;−1)|≤2^φ(G) (Engström, 2009) [7], where φ(G) is the decycling number of G (Beineke and Vandell, 1997) [3], namely, the minimum number of vertices that have to be deleted in order to turn G into a forest.     

A gradient theory of small-deformation, single-crystal plasticity that accounts for GND-induced interactions between slip systems

This paper develops a gradient theory of single-crystal plasticity based on a system of microscopic force balances, one balance for each slip system, derived from the principle of virtual power, and a mechanical version of the second law that includes, via the microscopic forces, work performed during plastic flow. When combined with thermodynamically consistent constitutive relations the microscopic force balances become nonlocal flow rules for the individual slip systems in the form of partial differential equations requiring boundary conditions. Central ingredients in the theory are densities of (geometrically necessary) edge and screw dislocations, densities that describe the accumulation of dislocations, and densities that characterize forest hardening. The form of the forest densities is based on an explicit kinematical expression for the normal Burgers vector on a slip plane.     

Intelligent computing based forecasting of deforestation using fire alerts: A deep learning approach

Deforestation is depletion of the forest cover and degradation in forest quality mainly through repeated fires, over-exploitation, and diseases. In a forest ecosystem, occurrence of wildfires is a natural phenomena. The curse of global warming and man-made interventions have made the wildfires increasingly extreme and widespread. Though, extremely challenging due to rapidly changing climate, accurate prediction of these fire events can significantly improve forestation worldwide. In this paper, we have addressed this issue by proposing a deep learning (DL) framework using long short term memory (LSTM) model. The proposed mechanism accurately forecasts weekly fire alerts and associated burnt area (ha) utilizing historical fire data provided by GLOBAL FOREST WATCH. Pakistan is taken as a case study since its deforestation rate is among the highest in the world while having one of the lowest forest covers. Number of epochs, dense layers, hidden layers and hidden layer units are varied to optimize the model for high estimation accuracy and low root mean square error (RMSE). Simulation results show that the proposed method can predict the forest fire occurrences with 95% accuracy by employing a suitable hyperparameter tuning.