Design of Protected Area by Tracking and Excluding the Effects of Climate and Landscape Change: A Case Study Using Neurergus derjugini

This study aimed to use the applications of Ensemble Species Distribution Modelling (eSDM), Geographical Information Systems (GISs), and Multi-Criteria Decision Analysis (MCDA) for the design of a protected area (PA) for the critically endangered yellow-spotted mountain newt, Neurergus derjugini, by tracking and excluding the effects of climate and landscape changes in western Iran and northeastern Iraq. Potential recent and future distributions (2050 and 2070) were reconstructed by eSDM using eight algorithms with MRI-CGCM3 and CCSM4 models. The GIS-based MCDA siting procedure was followed inside habitats with high eSDM suitability by eliminating the main roads, cities, high village density, dams, poor vegetation, low stream density, agricultural lands and high ridge density. Then, within the remaining relevant areas, 10 polygons were created as “nominations” for PAs (NPAs). Finally, for 10 different NPAs, the suitability score was ranked based on ratings and weights (analytical hierarchy process) of the number of newt localities, NPA connectivity, NPA shape, NPA habitat suitability in 2070, NPA size, genetic diversity, village density and distance to nearest PAs, cities, and main roads. This research could serve as a modern realistic approach for environmental management to plan conservation areas using a cost-effective and affordable technique.

On N-body simulations of globular cluster streams

ABSTRACT Stellar tidal streams are sensitive tracers of the properties of the gravitational potential in which they orbit and detailed observations of their density structure can be used to place stringent constraints on fluctuations in the potential caused by, e.g. the expected populations of dark matter subhaloes in the standard cold dark matter (CDM) paradigm. Simulations of the evolution of stellar streams in live N-body haloes without low-mass dark matter subhaloes, however, indicate that streams exhibit significant perturbations on small scales even in the absence of substructure. Here, we demonstrate, using high-resolution N-body simulations combined with sophisticated semi-analytical and simple analytical models, that the mass resolutions of 104–$10^5\, \rm {M}_{\odot }$ commonly used to perform such simulations cause spurious stream density variations with a similar magnitude on large scales as those expected from a CDM-like subhalo population and an order of magnitude larger on small, yet observable, scales. We estimate that mass resolutions of ${\approx}100\, \rm {M}_{\odot }$ (${\approx}1\, \rm {M}_{\odot }$) are necessary for spurious, numerical density variations to be well below the CDM subhalo expectation on large (small) scales. That streams are sensitive to a simulation’s particle mass down to such small masses indicates that streams are sensitive to dark matter clustering down to these low masses if a significant fraction of the dark matter is clustered or concentrated in this way, for example, in MACHO models with masses of 10–$100\, \rm {M}_{\odot }$.

An Improved Sign Language Translation Model with Explainable Adaptations for Processing Long Sign Sentences

Sign language translation (SLT) is an important application to bridge the communication gap between deaf and hearing people. In recent years, the research on the SLT based on neural translation frameworks has attracted wide attention. Despite the progress, current SLT research is still in the initial stage. In fact, current systems perform poorly in processing long sign sentences, which often involve long-distance dependencies and require large resource consumption. To tackle this problem, we propose two explainable adaptations to the traditional neural SLT models using optimized tokenization-related modules. First, we introduce a frame stream density compression (FSDC) algorithm for detecting and reducing the redundant similar frames, which effectively shortens the long sign sentences without losing information. Then, we replace the traditional encoder in a neural machine translation (NMT) module with an improved architecture, which incorporates a temporal convolution (T-Conv) unit and a dynamic hierarchical bidirectional GRU (DH-BiGRU) unit sequentially. The improved component takes the temporal tokenization information into consideration to extract deeper information with reasonable resource consumption. Our experiments on the RWTH-PHOENIX-Weather 2014T dataset show that the proposed model outperforms the state-of-the-art baseline up to about 1.5+ BLEU-4 score gains.

Ly α blobs from cold streams undergoing Kelvin–Helmholtz instabilities

ABSTRACT We present an analytic toy model for the radiation produced by the interaction between cold streams thought to feed massive haloes at high redshift and their hot CGM. We begin by deriving cosmologically motivated parameters for the streams, as they enter the halo virial radius, Rv, as a function of halo mass and redshift. For $10^{12}\, {\rm M}_\odot$ haloes at z = 2, we find the stream density to be $n_{\rm H,s}\sim (0.1{\!-\!}5)\times 10^{-2}\, {\rm cm}^{-3}$, a factor of δ ∼ (30–300) times denser than the hot CGM, while stream radii are in the range Rs ∼ (0.03−0.50)Rv. As streams accelerate towards the halo centre, they become denser and narrower. The stream–CGM interaction induces Kelvin–Helmholtz instability (KHI), which leads to entrainment of CGM mass by the stream and to stream deceleration by momentum conservation. Assuming the entrainment rates derived by Mandelker et al. (2020) in the absence of gravity can be applied locally at each halocentric radius, we derive equations of motion for the stream in the halo. Using these, we derive the net acceleration, mass growth, and energy dissipation induced by the stream–CGM interaction, as a function of halo mass and redshift, for different CGM density profiles. For the range of model parameters considered, we find that the interaction induces dissipation luminosities Ldiss > 1042 erg s−1 within ≲0.6Rv of haloes with $M_{\rm v}\gt 10^{12}\, {\rm M}_\odot$ at z = 2. The emission scales with halo mass and redshift approximately as $\propto M_{\rm v}\, (1+z)^2$. The magnitude and spatial extent of the emission are consistent with observed Ly α blobs, though better treatment of the UV background and self-shielding is needed to solidify this conclusion.

Estimation of Traffic Stream Density Using Connected Vehicle Data: Linear and Nonlinear Filtering Approaches

The paper presents a nonlinear filtering approach to estimate the traffic stream density on signalized approaches based solely on connected vehicle (CV) data. Specifically, a particle filter (PF) is developed to produce reliable traffic density estimates using CV travel-time measurements. Traffic flow continuity is used to derive the state equation, whereas the measurement equation is derived from the hydrodynamic traffic flow relationship. Subsequently, the PF filtering approach is compared to linear estimation approaches; namely, a Kalman filter (KF) and an adaptive KF (AKF). Simulated data are used to evaluate the performance of the three estimation techniques on a signalized approach experiencing oversaturated conditions. Results demonstrate that the three techniques produce accurate estimates—with the KF, surprisingly, being the most accurate of the three techniques. A sensitivity of the estimation techniques to various factors including the CV level of market penetration, the initial conditions, and the number of particles in the PF is also presented. As expected, the study demonstrates that the accuracy of the PF estimation increases as the number of particles increases. Furthermore, the accuracy of the density estimate increases as the level of CV market penetration increases. The results indicate that the KF is least sensitive to the initial vehicle count estimate, while the PF is most sensitive to the initial condition. In conclusion, the study demonstrates that a simple linear estimation approach is best suited for the proposed application.

Dam Site Suitability Mapping and Analysis Using an Integrated GIS and Machine Learning Approach

: Meeting water demands is a critical pillar for sustaining normal human living standards, industry evolution and agricultural growth. The main obstacles for developing countries in arid regions include unplanned urbanisation and limited water resources. Locating and constructing dams is a strategic priority of countries to preserve and store water. Recent advances in remote sensing, geographic information system (GIS), and machine learning (ML) techniques provide valuable tools for producing a dam site suitability map (DSSM). In this research, a hybrid GIS decision-making technique supported by an ML algorithm was developed to identify the most appropriate location to construct a new dam for Sharjah, one of the major cities in the United Arab Emirates. Nine thematic layers have been considered to prepare the DSSM, including precipitation, drainage stream density, geomorphology, geology, curve number, total dissolved solid elevation, slope and major fracture. The weights of the thematic layers were determined through the analytical hierarchy process supported by several ML techniques, where the best attempted ML technique was the random forest method, with an accuracy of 76%. Precipitation and drainage stream density were the most influential factors affecting the DSSM. The developed DSSM was validated using existing dams across the study area, where the DSSM provides an accuracy of 83% for dams located in the high and moderate zones. Three major sites were identified as suitable locations for constructing new dams in Sharjah. The approach adopted in this study can be applied for any other location globally to identify potential dam construction sites.

Distribution and classification of springs in Bansbari area of Melamchi Municipality, Sindhupalchowk, Nepal

Springs are the primary source of drinking water and irrigation in hills and mountains in Nepal. In recent years, the life sustaining springs are reported to be drying and degrading in quality. However, there is a lack of information and knowledge in various aspects of springs in Nepal. This has limited the scope for groundwater development and management in hills and mountains. The present study focuses on identifying, surveying and keeping records of spring sources in the Bansbari area of Melamchi Municipality, Sindhupalchowk, Nepal, and intends to understand the distribution of springs in the area. Assessment of spring water quality and classification of springs is other major component of this study. Out of forty-one springs observed in the area, 85% are perennial and others are seasonal in nature. It is found that majority of the springs are located in the middle altitude ranging from 1000 to 1350m above mean sea level and in moderate slope varying from 10° to 35°. In terms of land use, 37% are located in forest followed by 34%in bushes and 29% in agricultural land. In terms of stream density, 61% of springs are located in places with low stream density. In terms of type of deposit, 51% are located in colluviums, followed by 25% in rock and 24% in residual soil. The springs in the study area are classified as depression spring and fracture spring. They are mostly non-thermal with weakly mineralized water ranging from soft to hard. The general order of dominance of major cations in the sampled springs is Ca2+>Mg2+>Na+>K+ and major anions is HCO3-> Cl->SO42-. Springs of magnitude fifth and less are observed in the study area with significant decrease of discharge in the dry season. Regular monitoring of spring discharges is recommended to quantify the groundwater in the area.

Detecting the dark matter halos with star clusters in M31/M33 with PFS, SDSS-V and LAMOST

Since a great number of star stream and substructures near M31/M33 have been discovered in Pan-Andromeda Archaeological Survey (PAndAS) and variations of star stream density may trace the dark matter sub-halos, it is good opportunity to study the dark matter sub-halos with the star streams. Further it has been proved that dozens of halo star clusters have the relations with the star stream. As Prime Focus Spectroscopy (PFS) of the 8.2-m Subaru telescope have the powerful ability (I ∼ 22.3 mag) to observe ∼ 2400 objects at a time, it can be used to observe the giant star streams, faint halo star clusters and dwarf galaxies, which provides excellent opportunity to investigate the sub-halos of M31. In addition, we are involved with the Local Volume Mapper (LVM) of SDSS-V program, which may also provide more informations for the star clusters of the Local Group, especially for M31. Finally since we have done series of work with Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), we will continue the spectroscopic observations for more star clusters and giant stars of M31/M33.

New models of road transport system

Transport and operational conditions include: the road – a relief, width of the carriageway, type and a paving condition, factor of coupling and flatness of a covering, a condition of roadsides, existence and quality of elements of the engineering equipment; the transport – a sort of transported cargo, intensity, stream density, a high-speed mode of movement; the weather and climatic – visibility, a precipitation, temperature, pressure and humidity of air; culture of operation – level of the organization of works and management, qualification and discipline of drivers, material base, quality of operational materials. Considering novelty of the problem considered in this article, and impossibility of coverage of all circle of questions on CTRE, it is called as «Introduction in city transport-road ecology», and the main terms, definitions and necessary standard data are provided in appendices.