Miracle-dz/UniFAR-FTUs huggingface dataset:
Untangling multi‐scale habitat relationships of an endangered frog in streams to inform reintroduction programs

Successful reintroductions of endangered species rely on comprehensive knowledge of habitat requirements across spatial and temporal scales. To inform designs of reintroduction programs, we studied habitat selection of the federally endangered Sierra Nevada yellow-legged frog ( Rana sierrae ) in streams in its northern range. We quanti ﬁ ed multi-scale habitat use across diverse streams, habitat types within streams, and seasonal ﬂ ows. In one intermittent stream, we compared habitat selection between wild and captive-reared frogs that were released as a population augmentation. We analyzed habitat selection of seasonal habitat unit types (e.g., pool, rif ﬂ e, cascades) and dimensions, and microhabitat hydrology and cover. R . sierrae appeared able to meet its ecological requirements in a variety of habitats. Study streams ranged from third-order perennial streams to ﬁ rst-order intermittent headwater creeks. Perennial streams retained a variety of habitats across ﬂ ows whereas intermittent streams dried to just a few pools. Frogs used all seasonal habitat types but selected deeper pools most often relative to available habitats in intermittent streams and selected rif ﬂ es or showed no preference in perennial streams. Frogs avoided fast deep-water microhabitats, but, otherwise, preferred ﬂ owing water or deeper water without ﬂ ow. Our results suggest that diverse streams can be considered candidates for reintroductions. Within intermittent streams, deeper perennial pools may provide more stable release sites, whereas non-pool habitats may be safer in perennial streams with ﬁ sh. Importantly, captive-reared frogs selected similar habitats as wild frogs, suggesting that captivity does not alter habitat selection behaviors post-release and that reintroduction designs for this species can be based on wild frog preferences. By improving our understanding of how habitat use varies among different types of streams and differs seasonally in response to changing habitat conditions, our results inform the design of effective reintroduction programs for frogs in streams.
Tidal inlet morphodynamics through numerical prediction and measurements

Abstract The formation of sandbars, spits and shoals are common at the inlet mouth particularly along coasts dominated by longshore sediment transport. These formations act as barriers, thus preventing the ingress of tidal flow. Sandbar and spit formation can be preferably averted by the construction of a pair of training walls, after which monitoring the behaviour of the stability of the tidal inlet is of prime importance. In cases where the tidal inlet meets a riverine estuary, the inlet dimensions and rate of littoral transport are primarily governed by the volume of water exchanged at the inlet mouth and the ebbing tidal currents, respectively. This paper discusses the case study of a micro-tidal inlet at Karaikal (10°54′52″N; 79°51′09″E), situated along the southeast coast of the Indian peninsula. This inlet coincides with the mouth of the Arasalar estuary, a tributary of the Cauvery river. A numerical model using the finite volume method is applied to estimate morphodynamic changes in the inlet channel driven by tide-induced currents and riverine discharge during the North-East monsoon season and the same has been validated through field measured data. The ultimate aim is to arrive at mitigation measures to combat siltation within the inlet.
A Convolutional Neural Network-Based End-to-End Self-Driving Using LiDAR and Camera Fusion: Analysis Perspectives in a Real-World Environment

In this paper, we develop end-to-end autonomous driving based on a 2D LiDAR sensor and camera sensor that predict the control value of the vehicle from the input data, instead of modeling rule-based autonomous driving. Different from many studies utilizing simulated data, we created an end-to-end autonomous driving algorithm with data obtained from real driving and analyzing the performance of our proposed algorithm. Based on the data obtained from an actual urban driving environment, end-to-end autonomous driving was possible in an informal environment such as a traffic signal by predicting the vehicle control value based on a convolution neural network. In addition, this paper solves the data imbalance problem by eliminating redundant data for each frame during stopping and driving in the driving environment so we can improve the performance of self-driving. Finally, we verified through the activation map how the network predicts the vertical and horizontal control values by recognizing the traffic facilities in the driving environment. Experiments and analysis will be shown to show the validity of the proposed algorithm.
Computed Tomography Imaging Based on Edge Detection Algorithm in Diagnosis and Rehabilitation Nursing of Stroke Patients with Motor Dysfunction

This study was to explore the effect of computed tomography (CT) images processed by image edge detection technology based on the improved Canny algorithm in the diagnosis of stroke patients with mobility dysfunction and to evaluate the clinical application value of early rehabilitation nursing (ERN). 114 patients who were diagnosed and treated in hospital and were suspected of having stroke movement dysfunction were selected as the research objects, and they were randomly divided into two groups, each with 57 patients. Patients in the control group were diagnosed with conventional CT examination, and the patients in observation group were diagnosed based on the CT images processed by the image edge detection technology based on the improved Canny algorithm. Patients in the observation group were divided into a group C and a group O. Patients (27 cases) in group O received rehabilitation training within 3 days after their vital signs were stabilized, and patients (30 cases) in group C received rehabilitation training within 3∼7 days after their condition was stabilized. The CT image diagnosis effects on patients of the control group and the observation group were analyzed, and the ERN effect on patients of the C group and the O group was compared. The results showed that the mean square error (MSE) of the improved Canny algorithm (233.78) was smaller than that of the traditional Canny algorithm and Sobel and Prewitt algorithm, and the peak signal-to-noise ratio (PSNR) (27.89) was greater than that of the traditional Canny algorithm and Sobel and Prewitt algorithm ( P < 0.05 ). Compared with the control group, the sensitivity (85.00% vs. 62.12%), specificity (70.59% vs. 36.36%), and accuracy (80.70% vs. 54.39%) of the examination method of the observation group were much higher ( P < 0.05 ). In addition, the total effective rate of patients in group O was 89.47%, which was greatly higher than that of group C (70.18%), and the scores of Meyer index and Barthel index were also higher in contrast to those of group C ( P < 0.05 ). In conclusion, the improved Canny algorithm showed a clearer display on the edge detection of CT images and good application effect. It showed the effect of making conventional CT more accurate in the examination and diagnosis of stroke patients, and it was worthy of clinical application and promotion. The research showed that the timelier rehabilitation training, the better the treatment effect of patients.
Effects of Running Speed on Coupling between Pantograph of High-Speed Train and Tunnel Based on Aerodynamics and Multi-Body Dynamics Coupling

(1) Background: The ratio of railway tunnel to line is larger, which produces tunnel entrance and exit effect, aerodynamic resistance, and sudden pressure changes. When the train passes through the tunnels at high-speed, the interaction between the pantograph on it and its surrounding air intensifies and the coupling effects between the pantograph and tunnel become more significant; (2) Methods: A coupling method between aerodynamics and multi-body dynamics is proposed based on hybrid meshing and grid motion. The layered grid motion method is combined with the viscous mesh deformation method with swift, effective data exchange. The significant coupling effects between the pantograph and tunnel are revealed; (3) Results: The influence laws and evolution mechanism of running speed as it affects important service characteristics and behaviors of the pantograph are accurately quantified. Noteworthy factors include the temporal characteristics of panhead aerodynamic lift, the contact force between the pantograph and catenary, vertical displacement and acceleration of the contact strip, the phase diagram of the contact strip, and various frequency-domain characteristics. The action mechanism of running speed on the coupling effect between the pantograph and tunnel is comprehensively and accurately revealed by the proposed method; (4) Conclusions: The larger service characteristics amplitudes of the high-speed pantograph appear at low frequencies and are not multiple frequencies of the basic frequency. By comparisons, the coupling calculation results are closer to the test results than the non-coupling results regardless of the maximum, minimum, or mean.
PDGFR dimer-specific activation, trafficking and downstream signaling dynamics

Signaling through the platelet-derived growth factor receptors (PDGFRs) plays a critical role in multiple cellular processes during development. The two PDGFRs, PDGFRα and PDGFRβ, dimerize to form homodimers and/or heterodimers. Here, we overcome previous limitations in studying PDGFR dimer-specific dynamics by generating cell lines stably expressing C-terminal fusions of each PDGFR with bimolecular fluorescence complementation (BiFC) fragments corresponding to the N-terminal or C-terminal regions of the Venus fluorescent protein. We find that PDGFRβ receptors homodimerize more quickly than PDGFRα receptors in response to PDGF ligand, with increased levels of autophosphorylation. Further, we demonstrate that PDGFRα homodimers are trafficked and degraded more quickly, while PDGFRβ homodimers are more likely to be recycled back to the cell membrane. We show that PDGFRβ homodimer activation results in a greater amplitude of phospho-ERK1/2 and phospho-AKT signaling, as well as increased proliferation and migration. Collectively, our findings provide significant insight into how biological specificity is introduced to generate unique responses downstream of PDGFR engagement. Summary The authors utilize a novel bimolecular fluorescence complementation approach to investigate PDGFR homodimer-specific dynamics. They uncover differences in the timing and extent of receptor dimerization, activation and trafficking, which lead to changes in downstream signaling and cellular activity.
AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification

Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet connectivity massively grows in recent years. Conventional shallow machine learning-based techniques for DDoS attack classification tend to be ineffective when the volume and features of network traffic, potentially carry malicious DDoS payloads, increase exponentially as they cannot extract high importance features automatically. To address this concern, we propose a hybrid approach named AE-MLP that combines two deep learning-based models for effective DDoS attack detection and classification. The Autoencoder (AE) part of our proposed model provides an effective feature extraction that finds the most relevant feature sets automatically without human intervention (e.g., knowledge of cybersecurity professionals). The Multi-layer Perceptron Network (MLP) part of our proposed model uses the compressed and reduced feature sets produced by the AE as inputs and classifies the attacks into different DDoS attack types to overcome the performance overhead and bias associated with processing large feature sets with noise (i.e., unnecessary feature values). Our experimental results, obtained through comprehensive and extensive experiments on different aspects of performance on the CICDDoS2019 dataset, demonstrate both a very high and robust accuracy rate and F1-score that exceed 98% which also outperformed the performance of many similar methods. This shows that our proposed model can be used as an effective DDoS defense tool against the growing number of DDoS attacks.
Recent Advances on Functional Nucleic-Acid Biosensors

In the past few decades, biosensors have been gradually developed for the rapid detection and monitoring of human diseases. Recently, functional nucleic-acid (FNA) biosensors have attracted the attention of scholars due to a series of advantages such as high stability and strong specificity, as well as the significant progress they have made in terms of biomedical applications. However, there are few reports that systematically and comprehensively summarize its working principles, classification and application. In this review, we primarily introduce functional modes of biosensors that combine functional nucleic acids with different signal output modes. In addition, the mechanisms of action of several media of the FNA biosensor are introduced. Finally, the practical application and existing problems of FNA sensors are discussed, and the future development directions and application prospects of functional nucleic acid sensors are prospected.
A Performance Evaluation of DRAM Access for In-Memory Databases

The latest CPUs(computer cpu processors) employ multiple cores, massively superscalar pipelines, out-of-order execution of tons of instructions, and advanced SIMD capabilities, which can hide the memory access latency. And most of recent memory-oriented data structures have already benefit from these features. However, due to the complexity of data organization, these CPUs do not always work well in memory resident database systems (MMDBs), particularly regarding storing data in dynamic random-access memory (DRAM). This article studies memory-efficient data structures by analyzing the run time, access latency, cache misses, instructions per cycle (IPC), and DRAM reads (bytes). Then, we design and implement two data organization schemas in the main memory database: dispersing data block organization and clustering data block organization. Using algorithmic engineering and careful attention to internal parallelism, cache alignment can hide the memory access latency. However, we find that these data structures work well in some cases, though they have been eclipsed in the face of complex access paths. To determine the reasons, we study the impact of database techniques on memory access latency, such as data partitioning, storage models, and by processing algorithms. With the specific main memory database system, we estimate the performance of each data organization schema based on DRAM DDR4 and the latest Intel Haswell microarchitecture. In conclusion, this work will make DRAM access applicable in real-world situations by implementing the schema to systems, such as in-memory databases.
Excess Volatility in the Tunisian Stock Market: Explanation by Behavioral Finance

In this paper, we tried to show the existence of excess volatility of stock prices in the Tunisian stock exchange during the period 2000 - 2017, by applying the variance bounds of Shiller. We used data on daily closing prices and the transaction volume of 22 companies listed on Tunisian Financial market during the period 2016/2017 to identify the relationship between over confidence bias and the Excess Volatility via the Granger causality test. Based on Chuang and Lee’s approach, we studied the effect of the excess confidence component on volatility by the E-GARCH Model (1.1). Our results show that high market volatility resulted from overconfident investors.
Quantitative absorption imaging of single nanoparticles by widefield interferometric photothermal microscopy

We demonstrate widefield interferometric photothermal microscopy to detect the local refractive index changes of single nanoparticles induced by the dissipated heat due to light absorption (known as the thermal lens effect). The sensitivity of our system is sufficient for visualizing very small single gold nanoparticles, as small as 5 nm, with a dissipated heat of a few nanowatts. We establish a model to explain the generation mechanism of the photothermal signal. For small particles (< 40 nm), the photothermal signal is contributed by the scattered field of the thermal lens, independent of the particle size and materials. For large particles, we find that the strong scattered field of the sample would interfere with that of the thermal lens, leading to an enhancement of the photothermal contrast. This work shows that the complete scattering property of the sample is required for accurate absorption quantification through photothermal measurement.
Beyond prediction: methods for interpreting complex models of soil variation

Understanding the spatial variation of soil properties is central to many sub-disciplines of soil science. Commonly in soil mapping studies, a soil map is constructed through prediction by a statistical or non-statistical model calibrated with measured values of the soil property and environmental covariates of which maps are available. In recent years, the field has gradually shifted attention towards more complex statistical and algorithmic tools from the field of machine learning. These models are particularly useful for their predictive capabilities and are often more accurate than classical models, but they lack interpretability and their functioning cannot be readily visualized. There is a need to understand how these these models can be used for purposes other than making accurate prediction and whether it is possible to extract information on the relationships among variables found by the models. In this paper we describe and evaluate a set of methods for the interpretation of complex models of soil variation. An overview is presented of how model-independent methods can serve the purpose of interpreting and visualizing different aspects of the model. We illustrate the methods with the interpretation of two mapping models in a case study mapping topsoil organic carbon in France. We reveal the importance of each driver of soil variation, their interaction, as well as the functional form of the association between environmental covariate and the soil property. Interpretation is also conducted locally for an area and two spatial locations with distinct land use and climate. We show that in all cases important insights can be obtained, both into the overall model functioning and into the decision made by the model for a prediction at a location. This underpins the importance of going beyond accurate prediction in soil mapping studies. Interpretation of mapping models reveal how the predictions are made and can help us formulating hypotheses on the underlying soil processes and mechanisms driving soil variation.
Evaluation of Diameter and Distance of Sacrificial Piles from the Bridge Pier in Reducing Local Scour

In this research, it was analyzed influence of variation of sacrificial piles diameter, as well as the distance between front pile and the face of the pier in reducing local scour depth. For this purpose, it was calibrated a physical test of an isolated cylindrical pier developed in a previous research. For calibration, numerical models were made with Manning's roughness coefficients of the sand of 0.023, 0.024 and 0.025 and the application of Froechlich and Colorado State University (CSU) equations to calculate the local scour depth. After that, 12 numerical models were made with the addition of sacrificial piles in a 30° triangular configuration to the calibrated model. The diameters of sacrificial piles were 5, 6, 7.5 and 10 mm and distances between front pile and the face pier were 75, 90 and 105 mm. According to the results, the highest percentage of local scour reduction was obtained when the sacrificial piles had a diameter of 10 mm and the distance between the front pile and the pier was equal to 105 mm. In this sense, when the distance between the front pile and the face pier was greater, the percentage of local scour reduction was higher. Also, based on the results, it is concluded that, Froechlich's equations and CSU lose precision when there are sacrificial piles.
A Syscall-Level Binary-Compatible Unikernel

Unikernels are minimal single-purpose virtual machines. They are highly popular in the research domain due to the benefits they provide. A barrier to their widespread adoption is the difficulty/impossibility to port existing applications to current unikernels. HermiTux is the first unikernel providing system call-level binary compatibility with Linux applications. It is composed of a hypervisor and a lightweight kernel layer emulating the load- and runtime Linux ABI. HermiTux relieves application developers from the burden of porting software, while providing unikernel benefits such as security through hardware-assisted virtualized isolation, swift boot time, and low disk/memory footprint. Fast system calls and kernel modularity are enabled through binary rewriting and analysis techniques, as well as shared library substitution. HermiTux's design principles are architecture-independent and we present a prototype on both the x86-64 and ARM aarch64 ISAs, targeting various cloud as well as edge/embedded deployments. We demonstrate HermiTux's compatibility over a range of native C/C++/Fortran/Python Linux applications. We also show that it offers a similar degree of lightweightness compared to other unikernels, and that it performs similarly to Linux in many cases: its performance overhead averages 3% in memory- and compute-bound scenarios, and its I/O performance is acceptable.
SPTM-EC: A Security and Privacy-Preserving Task Management in Edge Computing for IIoT

Data/tasks of Industrial Internet of Things (IIoT) are extremely private and valuable to computing. Edge computing facilitates IIoT services by offering platforms of supportive facilities and functionalities. However, the convergence of intelligent edge computing and IIoT platforms has been held back by the need to develop systems for maintaining security and privacy. In this article, we propose a new framework for securely and confidentially sending, storing, and computing IIoT tasks. This framework employs a lightweight encryption scheme alongside modified ElGamal encryption and digital signature schemes. We analyzed the robustness of this framework in terms of security and privacy, and assessed its performance through simulations. Ultimately, the proposed framework outperformed the existing models in terms of time complexity, latency, and energy consumption.
Sparsity Signal Detection for Indoor GSSK-VLC System

In this article, the signal detection problem in indoor visible light communication (VLC) system aided by generalized space shift keying (GSSK) is modeled as a sparse signal reconstruction problem, which has lower computational complexity by exploiting the sparse reconstruction algorithms in compressed sensing (CS). In order to satisfy the measurement matrix property to perform sparse signal reconstruction, a preprocessing approach of measurement matrix is proposed based on singular value decomposition (SVD), which theoretically guarantees the feasibility of utilizing CS based sparse signal detection method in indoor GSSK-VLC system. Then, by adopting classical orthogonal matching pursuit (OMP) algorithm and compressed sampling matching pursuit (CoSaMP) algorithm, the GSSK signals are efficiently detected in the considered indoor GSSK-VLC system. Furthermore, a more efficient detection algorithm combined with OMP and maximum likelihood (ML) is also presented especially for SSK scenario. Finally, the effectiveness of the proposed sparsity aided detection algorithms in indoor GSSK-VLC system are verified by computer simulations. The results show that the proposed algorithms can achieve better bit error rate (BER) and lower computation complexity than ML based detection method. Specifically, a signal-to-noise ratio (SNR) gain as high as 12 dB is observed in the SSK scenario and about 5 dB in case of a GSSK scenario upon employing our proposed detection methods.