Miracle-dz/UniFAR-FTUs huggingface dataset:
Teak plantations are complementary habitat for the Green Peafowl movement within the fragmented Bago Yoma range, south-central Myanmar

ABSTRACT Habitat loss, degradation, and fragmentation following intensive agricultural expansion, and both legal and illegal logging, have contributed to the rapid decline of the Green Peafowl (Pavo muticus) over mainland Southeast Asia. Dry forest habitat in the Bago Yoma range (Myanmar) is considered one of the region's last remaining strongholds for the species. However, the area has been fragmented over the past years, mostly due to large-scale teak (Tectona grandis) plantations. This study aimed to estimate the Green Peafowl population in the Bago Yoma range with a particular focus on the use of large-scale teak plantations as possible complementary habitat for the species. The survey was conducted over 47 line transects located in the natural forest and teak plantations. The average estimated density, using distance sampling, was 0.800 calling males km–2 across the whole survey area. Estimated densities were similar within the natural forest and teak plantations (0.825 and 0.866 calling males km–2, respectively), suggesting an extensive use of this crop by Green Peafowl. The remaining suitable habitat covered an area of 8,554 km2 divided into 8 large forest patches (>40 km2). When including large-scale teak plantations in the landscape matrix, more suitable habitat fragments could be connected to each other, with the remaining suitable habitat grouped into 6 large forest patches. These results suggest teak plantations could be used for connecting remaining forest patches and improving fragmented natural habitats for Green Peafowl. How to Cite Win, L. , N. Sukumal, N. M. Shwe, and T. Savini (2022). Teak plantations are complementary habitat for the Green Peafowl movement within the fragmented Bago Yoma range, south-central Myanmar. Ornithological Applications 125:duac051. LAY SUMMARY Habitat loss, degradation and fragmentation following intensive agricultural expansion, and both legal and illegal logging, have contributed to the rapid decline of the Green Peafowl (Pavo muticus) over mainland Southeast Asia. Dry forest habitat in the BagoYoma range (Myanmar) is considered one of the region's last remaining strongholds for the species. However, the area has been fragmented over the past years, mostly due to large-scale teak (Tectona grandis) plantations. Our study aimed to estimate the Green Peafowl population in the BagoYoma range with a particular focus on the use of large-scale teak plantations as possible complementary habitat for the species. Our findings suggest teak plantations could be used for connecting remaining forest patches and improving fragmented natural habitats for Green Peafowl.
Mapping gray matter volume and cortical thickness in Alzheimer's disease

Gray matter volume and cortical thickness are two important indices widely used to detect neuropathological changes in brain structural magnetic resonance imaging. Using optimized voxel-based morphometry (VBM) protocol and surface-based cortical thickness measure, this study comprehensively investigated the regional changes in cortical gray matter volume and cortical thickness in Alzheimer's disease (AD). Thirteen patients with AD and fourteen age- and gender-matched healthy controls were included in this study. Results showed that voxel-based gray matter volume and cortical thickness reductions were highly correlated in the temporal lobe and its medial structure in AD. Moreover significant reduced cortical regions of gray matter volume were obviously more than that of cortical thickness. These findings suggest that gray matter volume and cortical thickness, as two important imaging markers, are effective indices for detecting the neuroanatomical alterations and help us understand the neuropathology from different views in AD.
Control of phosphatidylinositol‐3‐kinase signaling by nanoscale membrane compartmentalization

Phosphatidylinositol‐3‐kinases (PI3Ks) are lipid kinases that produce 3‐phosphorylated derivatives of phosphatidylinositol upon activation by various cues. These 3‐phosphorylated lipids bind to various protein effectors to control many cellular functions. Lipid phosphatases such as phosphatase and tensin homolog (PTEN) terminate PI3K‐derived signals and are critical to ensure appropriate signaling outcomes. Many lines of evidence indicate that PI3Ks and PTEN, as well as some specific lipid effectors are highly compartmentalized, either in plasma membrane nanodomains or in endosomal compartments. We examine the evidence for specific recruitment of PI3Ks, PTEN, and other related enzymes to membrane nanodomains and endocytic compartments. We then examine the hypothesis that scaffolding of the sources (PI3Ks), terminators (PTEN), and effectors of these lipid signals with a common plasma membrane nanodomain may achieve highly localized lipid signaling and ensure selective activation of specific effectors. This highlights the importance of spatial regulation of PI3K signaling in various physiological and disease contexts.
Two-Branch Generative Adversarial Network With Multiscale Connections for Hyperspectral Image Classification

Hyperspectral image (HSI) classification has always drawn great attention in the field of remote sensing. Various deep learning models are in the ascendant and gradually applied to HSI classification. Nevertheless, limited-labeled and class-imbalanced datasets largely make the classifier prone to overfitting. To address the above problem, this article proposes a two-branch generative adversarial network with multiscale connections (TBGAN), which includes two generators to produce the spectral and spatial samples, respectively. Thereinto, the spectral generator is imbued with the self-attention mechanism to maximumly capture the long-term dependencies across the spectral bands. And meanwhile, an elaborated discriminator with two branches is devised in TBGAN for extracting the joint spectral-spatial features. Besides, the multiscale connections are placed between the discriminator and two generators to alleviate the instability problems caused by the inherently backward propagation of gradients in GAN. Furthermore, a feature-matching term is added to the loss function to prevent the generators from overtraining upon the current discriminator, thereby further improving the stability of the network. Experiments upon three benchmark datasets demonstrate that TBGAN achieves an extremely competitive classification accuracy and exerts lower sensitivity to the training sample size compared with several state-of-the-art methods.
Monitoring Critical Infrastructure Using 3D LiDAR Point Clouds

Monitoring critical infrastructure is of great importance for resilience against current and emerging hazards. In Canada, at the national level, critical infrastructure is strategically classified into the following 10 sectors: energy and utilities, finance, food, transportation, government, information and communication technology, health, water, safety, and manufacturing. Many of these critical infrastructures may be effectively monitored using emerging technologies such as light detection and ranging (LiDAR). LiDAR technology enables active remote sensing from airborne and terrestrial systems. Accordingly, LiDAR can be used in a wide range of applications related to the monitoring of critical infrastructure to promote resilience. This survey provides a comprehensive, and structured literature overview of LiDAR technology currently available for commercial and research use, and also provides a detailed review of the relevant applications of LiDAR for critical infrastructure monitoring. Several LiDAR-based applications in areas of critical infrastructure such as terrestrial and air transportation, gas, oil, and water pipelines, energy generation and distribution, and security are discussed. A summary of the LiDAR data sets that are currently available for use is also presented.
Feeling the Pressure: The Influence of Vibrotactile Patterns on Feedback Perception.

Tactile feedback is necessary for closing the sen-sorimotor loop in prosthetic and tele-operable control, which would allow for more precise manipulation and increased acceptance of use of such devices. Pressure stimuli are commonly presented to users in haptic devices through a sensory substitution to vibration. The precise nature of this substitution affects pressure sensitivity, as well as the comfort and intuitiveness of the device for the user. This study determines the effects of different vibrational encodings for pressure on user-preference and performance in a 4-alternative absolute identification task. 4 different encoding patterns for pressure were examined: short pulse and long pulse amplitude modulation along with sine and square wave frequency modulation. Of the methods examined, frequency modulation methods led to the best discrimination of stimuli (p < 0.001). There was a notable difference in user preference between the two frequency modulated systems, with sinusoidal stimulation being highest ranked across all the preference metrics and square-wave being ranked lowest in two of the three. This difference trended towards, but did not achieve statistical significance (TLX rankings, $p$ = 0.098). This suggests that prostheses or teleoperated devices utilising vibrotactile feedback may benefit from implementing a discrete frequency-based sinusoidal pattern to indicate changes in grip force.
Cirrhosis Disease Classification by using Polynomial Feature and XGBoosting

Every year, over 2.8% of adult fatalities in India are caused by liver diseases. Discovering the liver illness is tough since its early phases, which will exhibit modest symptoms, are difficult to diagnose. Most often, cirrhosis disease doesn't manifest symptoms until an advanced stage, making it challenging to identify and diagnose. This paper shows different approaches to how they analyzed and visualized the performance. Medical issues can be resolved quickly and at a lower cost with the use of machine learning techniques. The major goal of this study is to increase the accuracy of result prediction and lower the price of diagnosis in the medical industry. As a result, we classified individuals with cirrhosis illness using several categorization methods. The biomedical field’s use of machine learning for the prognosis and diagnosis of cirrhosis disease has had a massive effect. In the biomedical area, machine learning provides a guarantee for better illness identification and prognosis, as well as for increasing the neutrality of the judgment process.
Assessment of the association of deep features with a polynomial algorithm for automated oral epithelial dysplasia grading

Oral epithelial dysplasia is a potentially malignant lesion that presents challenges for diagnosis. The use of digital systems in histological analysis can aid specialists to obtain data that allows a robust and fast grading process, but there are few methods in the literature proposing a grading system for this lesion. This study presents a method for oral epithelial dysplasia grading in histopathological images combining deep features and a polynomial classifier. The ResNet50 and AlexNet models were trained with the images and information was extracted from the convolutional layers, exploring convolutional neural networks via transfer learning. Then, the ReliefF algorithm was used to rank and select the most relevant features, which were given as an input to the polynomial classifier. The methodology was employed in a dataset with 296 regions of mice tongue images. The results were compared with the gold standard and other algorithms present in the literature. The classification stage presented AUC values ranging from 0.9663 to 0.9800. When compared to other algorithms present in the literature, our method provided relevant results regarding accuracy and AUC values. The proposed approach presented relevant results and can be used as a tool to aid pathologists in grading oral dysplastic lesions.
Pointwise accuracy of a stable Petrov-Galerkin approximation to the stokes problem

The finite-element approximation of the Stokes problem due to Hughes, Franca, and Balestra [Comput. Meth. App! Mech. Engrg. , 59 (1986), pp. 85–99] is analyzed in maximum norm. The method consists of modifying the usual bilinear form associated with the saddle-point structure to become coercive over the finite-element space. Exploiting the enhanced stability—thus getting rid of the inf sup condition—yields quasi-optimal $L^\infty $-error estimates for both velocity and pressure.
Methods Of Numerical Analysis Of One-Dimensional Two-Body Problem In Wheeler-Feynman Electrodynamics

Numerical methods for solutions of differential equations with deviating arguments describing one-dimensional ultra-relativistic scattering of two identical charged particles in Wheeler-Feynman electrodynamics with half-retarded/half-advanced interaction are developed. Utilization of the methods for the physical problem analysis leads to the discovery of a bifurcation of solutions and breaking of their reflectional symmetry for particles asymptotic velocity v>0.937c in their center-of-mass frame.
Phonological encoding and articulatory duration in spontaneous speech

Many studies have found that word duration is correlated with a word’s contextual predictability in conversational speech. Lindblom (1990)’s Hypo/Hyperarticulation Theory, Jurafsky et al. (2001)’s Probabilistic Reduction Hypothesis, and Aylett and Turk (2006)’s Smooth Signal Redundancy Hypothesis all suggest that such differences in duration are due to processes occurring primarily at the lexical level. The present study, however, suggests that these differences may be attributable to processes occurring at the phonological level. In this study, mixed modeling is used to examine the voice onset time and rime duration of monosyllabic /p t k/ words in spontaneous, connected speech (the Buckeye Corpus; Pitt et al., 2007). Higher contextual predictability given the previous word is found to be associated with shorter VOT, while higher contextual predictability given the following word is associated with shorter rime duration. VOT also varies according to the number and type of a word’s phonological neighbors;.
High power frequency comb based on mid-infrared quantum cascade laser at λ ∼ 9 μm

We investigate a frequency comb source based on a mid-infrared quantum cascade laser at λ ∼ 9 μm with high power output. A broad flat-top gain with near-zero group velocity dispersion has been engineered using a dual-core active region structure. This favors the locking of the dispersed Fabry-Perot modes into equally spaced frequency lines via four wave mixing. A current range with a narrow intermode beating linewidth of 3 kHz is identified with a fast detector and spectrum analyzer. This range corresponds to a broad spectral coverage of 65 cm−1 and a high power output of 180 mW for ∼176 comb modes.
Agent-based Simulation for Convenient Store’s Promotion Strategy Selection

Nowadays, convenience store becomes more important to urban life. It is also a competitive industry for retailers who would like to gain attention from customers and grow their profit. To achieve their goals, doing promotion is a way to go. However, promotion has two sides where the gain is also coming with the possibility of loss, so a thoughtful decision is concerned when doing the promotion. This study aims to develop agent-based simulation, an approach to deal with complex and high-dimension problems, to find how each strategy works on different price elasticities for strategic insights for the company’s future planning. The agent-based simulation is able to reach many possibilities of combinations between strategy and price elasticity instead of testing or gathering data in the real world. To achieve our purpose, we vary strategies while fixing the price elasticity and see how customers react in each situation. The customer’s decision is initially based two factors (advertisement effectiveness, and word of mouth), and then it is based on price reduction rate when they come to the store. The strategy is designed on two dimensions, percentage of price reduction and frequency of the promotion. The result shows that different strategies work on different price elasticity values where high price reduction rate strategy works well on high price elasticity, and vice versa. This study provides an insight about promotional strategy selection and future vision for a new method to approach complex problems.
Enhanced UV photoresponse from heterostructured Ag–ZnO nanowires

Photoactive material consisting of heterogeneous Ag–ZnO nanowires (NWs) was prepared by electrospinning. Tunable UV photodetectors fabricated using Ag–ZnO NWs have shown high sensitivity up to over four orders of magnitude with relatively fast and stable response speed. The mechanism for this colossal photoconductivity is elucidated by means of the efficient exciton dissociation under UV illumination due to accelerated electron transfer from conduction band of ZnO to Ag nanoparticles. The facile process proposed here may pave the way for designing sensing and imaging systems.
Controlled Fabrication of Micro-Chain Robot Using Magnetically Guided Arraying Microfluidic Devices

The magnetic microrobot has become a promising approach in many biomedical applications due to its small volume, flexible motion, and untethered micromachines. The micro-chain robot is one of the most popular magnetic microrobots. However, the uncontrollable magnetic moment direction and quantity of the magnetic beads consisted in the existing self-assembled micro-chain robot limit their locomotion and applications. This paper proposed an on-chip micro-chain robot fabrication method to assemble the magnetic beads with controllable magnetic moment direction and quantity. The bead quantity can be controlled by the structure limits of the microchannel, and the direction of the magnetic moment can be adj usted by the integrated external magnetic field. The assembled magnetic beads are then glued by the hydrogel under UV exposure. The micro-chain robots with different quantities and magnetic moment directions of the magnetic beads were successfully fabricated and tested in experiments. Due to the array structure of the microfluidic device, batch manufacturing of low-cost magnetic robots was achieved in our method. The movement of dual-bead microrobots with two orthogonal magnetic moment directions was analyzed and compared. One of the dual-bead microrobots was applied in the transportation of the hydrogel module using pushing and pulling modes. It indicated that the proposed controllable on-chip fabrication of the magnetic micro-chain robots has the potential to enhance the microrobot ability in biomedical applications.
Interpolation Errors in Wind Fields as a Function of Spatial and Temporal Resolution and Their Impact on Different Types of Kinematic Trajectories

Abstract This paper discusses some of the uncertainties that influence kinematic trajectory calculations. The interpolation errors due to different interpolation schemes are examined by degrading high-resolution wind fields from a numerical weather prediction model with respect to space and time. Under typical circumstances, the greatest errors are due to temporal interpolation, followed by horizontal and vertical interpolation. Relative errors in the vertical wind are higher than those in the horizontal wind components. These errors are quite substantial and severely affect the accuracy of trajectories. For instance, a decrease of the temporal resolution from 3 to 6 h leads to average relative interpolation errors of 16% in the horizontal wind components and 40% in the vertical wind component. These errors cause mean transport deviations of 280 km for two-dimensional model-level trajectories and 600 km for three-dimensional trajectories after 96-h travel time. The substantial deviations for three-dimensi.
Oppression by omission: An analysis of the #WhereIsTheInterpreter hashtag campaign around COVID-19 on Twitter

Critical to managing a crisis such as COVID-19 is the propagation of information to all vulnerable populations. Despite guidelines regarding communicating with people with differing accessibility needs during crises, some often find their needs unmet. Following a lack of assisted communications for d/Deaf people during the COVID-19 pandemic, a Twitter hashtag campaign, #WhereIsTheInterpreter, was launched in the UK, protesting the lack of accessibility during official press briefings around the epidemic. The campaign received support from across the globe. This study analyzes the discourse around the campaign in tweets published from March 1st, 2020 and September 30th, 2021 (N = 27,021) and analyzed the corpus using the Analysis of Topic Model Network (ANTMN) approach. We identified four major themes of discourse: discrimination, accessibility challenges, communication gaps and barriers, and Deaf rights. We analyze the discourse through the perspective of Critical Disability Theory (CDT) and hashtag activism, and discuss practical and theoretical implications.
The Eccentrics of CPU FIVR AGS supply noise debug and learnings

SoC design, validation, and manufacturing teams all need to work in tandem to ensure a successful product is released to the market. Pre-silicon and post-silicon validation is critical and is performed to ensure minimal Si re-spins at the fabrication and avoid any design bugs reaching the end customer. The post-silicon debug of various power failures issues related to analog supply exhibiting random signatures induced learnings that can help expedite changes in post-Si validation and high-volume screening, Discussed in detail are the debug efforts to identify one of the many Analog Generation Supply (AGS) noise failure and isolation. The paper elucidates reports of the proposed defects and their various bearings, issue root causes, and issue validation suite formation.
C$\mathsf{^{18}}$O abundance in the nearby globule Barnard 68

We have studied the radial variation of the CO abundance in the nearby isolated globule Barnard 68 (B68). For this purpose, B68 was mapped in the three rotational lines , and . Using the recent discovery of Alves et al. ([CITE]) that the density structure of B68 agrees with the prediction for a pressure bound distribution of isothermal gas in hydrostatic equilibrium (Bonnor-Ebert sphere), we show that the flat CO column density distribution can be explained by molecular depletion. By combining the physical model with the observed CO column density profile, it was found that the density dependence of the CO depletion factor $f_\mathrm{d}$ can be well fitted with the law , which is consistent with an equilibrium between the accretion and the desorption processes. In the cloud centre, between 0.5% and 5% of all CO molecules are in the gas phase. Our observations suggest a kinetic temperature of ≈ 8 K. In combination with the assumption that B68 is a Bonnor-Ebert sphere, this leads to a distance of 80 pc. The cloud mass consistent with these values is 0.7 $M_\odot$, considerably less than previously estimated. We find in B68 no clear deviance of the near-infrared reddening efficiency of dust grains per unit column density with respect to values derived in diffuse clouds.