Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal free-surface immersed-boundary lattice Boltzmann method for free surface flows with a liquid-solid phase transition

This paper reports on the progress of the liquid-solid phase transition modeling of water in open channel flow by using the lattice Boltzmann method with the immersed boundary modification. The phase transition in a fluid flow has a moving interface between the liquid and solid state, which leads complicated treatments in existing numerical models. By applying the immersed boundary modification in the lattice Boltzmann method and the non-iterative enthalpy approach for the separation of the states, the moving boundary of the melting or solidification front is solved without any difficulty. The ice bed and the submerged ice cover under dynamic flow conditions is exercised to demonstrate the model performance. The model is extremely suitable in the formulation in terms of its simple and compact framework extendable to any dimensions.
Dual stage probabilistic voice activity detector.

Voice activity detectors (VADs) are critical part of every speech enhancement and speech processing system. One of the major problems in practical realizations is to achieve robust VAD in conditions of background noise. Most of the statistical model‐based approaches employ the Gaussian assumption in the discrete Fourier transform domain, which deviates from the real observation. In this paper, we propose a class of VAD algorithms based on several statistical models of the probability density functions of the magnitudes. In addition, we evaluate several approaches for time smoothing the magnitude response to achieve a more robust estimate. A large data corpus of in‐car noise conditions is then used to optimize the parameters of the VAD, and the results are discussed.
Maximum familywise Type I error rate: The least significant difference, Newman-Keuls, and other multiple comparison procedures.

This article argues that the most reasonable and cautious definition of error rate in the multiple comparisons problem is the maximum familywise rate of Type I error (MFWER), that is, the maximum error rate attainable under all possible null hypotheses. This article shows how the original formulations of Fisher's least significant difference (LSD) and the Newman-Keuls procedures, which define the error rate with respect to only the complete null hypothesis, do not limit the MFWER to the level of significance. Modified LSD and Newman-Keuls procedures that do limit the MFWER are presented. Finally, additional multiple comparisons procedures that limits the MFWER and are more powerful than currently used tets are enumerated
Transport of Asian Desert Aerosol to the Hawaiian Islands

Abstract A cloud of aerosol with optical thickness τ ≈ 0.18 (500 nm wavelength), passed over the Hawaiian Islands from late April to early May 1979. Vertical profiles, taken by evaluating the optical extinction coefficient by sun photometry, showed that 80–90% of the aerosol was confined to a 1 km thick layer centered at 3.5–4km altitude. Trajectory analysis at the 500 mb pressure level (∼5 km) indicated that the aerosol probably had its origin in sandstorms in the eastern deserts of Asia nine days before the event. The dust cloud was first observed passing over Japan where sand particles fell out at Nagasaki on 21 April. By the time dust from the sandstone reached Hawaii, it has spread out to ∼1500 km and contained an estimated 1011 g of sand material, mainly in the 0.5 < r < 5.0 μm size range. This episode clearly indicates that substantial quantities of primary aerosol are being transported on global distance scales. The episode is used to obtain a desert aerosol surface particle flux which agrees to a.
Multi-Scale Recursive Semi-Supervised Deep Learning Fault Diagnosis Method with Attention Gate

The efficiency of deep learning-based fault diagnosis methods for bearings is affected by the sample size of the labeled data, which might be insufficient in the engineering field. Self-training is a commonly used semi-supervised method, which is usually limited by the accuracy of features for unlabeled data screening. It is significant to design an efficient training mechanism to extract accurate features and a novel feature fusion mechanism to ensure that the fused feature is capable of screening. A novel training mechanism of multi-scale recursion (MRAE) is designed for Autoencoder in this article, which can be used for accurate feature extraction with a small amount of labeled data. An attention gate-based fusion mechanism was constructed to make full use of all useful features in the sense that it can incorporate distinguishing features on different scales. Utilizing large numbers of unlabeled data, the proposed multi-scale recursive semi-supervised deep learning fault diagnosis method with attention gate (MRAE-AG) can efficiently improve the fault diagnosis performance of DNNs trained by a small number of labeled data. A benchmark dataset from the Case Western Reserve University bearing data center was used to validate this novel method which shows that 7.76% accuracy improvement can be achieved in the case when only 10 labeled samples was available for supervised training of the DNN-based fault diagnosis model.
Experimental Demonstration of the Small Pixel Effect in an Amorphous Photoconductor using a Monolithic Spectral Single Photon Counting Capable CMOS-Integrated Amorphous-Selenium Sensor

We directly demonstrate, for the first time, the small pixel effect in an amorphous photoconductor by measuring the intrinsic transient response of an amorphous selenium (a-Se) photoconductor pixel of various sizes monolithically combined with a CMOS sensor. Our front-end circuitry leverages the lowest electronic noise reported for aSe photon detection, $\sim 300 \mu \text{V}_{rms}$ measured, to demonstrate the first-ever fully CMOS integrated a-Se pulse heigh spectroscopy results. Fabricated using $0.18 \mu \text{m}$ CMOS mixedsignal technology, the chip contains four pixel arrays with 30, 60, 90, and $120 \mu \text{m}$-pitch, each pixel having its own dedicated charge-sensitive amplifier. Our measured results from a monoenergetic Gamma source demonstrate the capability of a-Se/CMOS pixel arrays to achieve a high count rate when the small pixel effect is leveraged in the high spatial resolution pixel arrays. The results demonstrated can expedite the development of energy discriminating single photon counting imaging detectors for large area mammography tomosynthesis and dedicated breast computed tomography, something that has not as yet been achieved at the commercial scale.
Moderately exponential time and fixed parameter approximation algorithms

We survey approximation issues matching ideas and tools from the fields of polynomial approximation, moderately exponential computation and fixed parameter tractability aiming at designing approximation algorithms achieving ratios unachievable in polynomial time (unless a very unlikely complexity conjecture is confirmed) with worst-case complexity much lower (though super-polynomial) than that of an exact computation. This research programme, despite its relative youth, is becoming very active in computer science and in combinatorial optimization and motivates extensive research.
Graphene Nanocomposite Membranes: Fabrication and Water Treatment Applications

Graphene, a two-dimensional hexagonal honeycomb carbon structure, is widely used in membrane technologies thanks to its unique optical, electrical, mechanical, thermal, chemical and photoelectric properties. The light weight, mechanical strength, anti-bacterial effect, and pollution-adsorption properties of graphene membranes are valuable in water treatment studies. Incorporation of nanoparticles like carbon nanotubes (CNTs) and metal oxide into the graphene filtering nanocomposite membrane structure can provide an improved photocatalysis process in a water treatment system. With the rapid development of graphene nanocomposites and graphene nanocomposite membrane-based acoustically supported filtering systems, including CNTs and visible-light active metal oxide photocatalyst, it is necessary to develop the researches of sustainable and environmentally friendly applications that can lead to new and groundbreaking water treatment systems. In this review, characteristic properties of graphene and graphene nanocomposites are examined, various methods for the synthesis and dispersion processes of graphene, CNTs, metal oxide and polymer nanocomposites and membrane fabrication and characterization techniques are discussed in details with using literature reports and our laboratory experimental results. Recent membrane developments in water treatment applications and graphene-based membranes are reviewed, and the current challenges and future prospects of membrane technology are discussed.
Doping-level-dependent optical properties of GaN:Mn

The optical properties of molecular-beam-epitaxy-grown GaN with different Mn-doping levels (5–23×1019 cm−3) were studied by cathodoluminescence (CL) and optical transmission spectroscopy. Transmission measurements at 2 K revealed an absorption peak at 1.414±0.002 eV, which was attributed to an internal 5T2→5E transition of the neutral Mn3+ state. The intensity of this Mn-related transmission peak was found to scale with the Mn3+ concentration. The CL measurements showed that Mn-doping concentrations around 1020 cm−3 reduced the near band edge emission intensity by around one order of magnitude. A complete quenching of the donor–acceptor-pair band at 3.27 eV and strong decrease of the yellow luminescence centered at 2.2 eV were attributed to a reduced concentration of VGa. In the infrared spectral range of 0.8–1.4 eV three broad, Mn-doping related CL emission bands centered at 1.01±0.02, 1.09±0.02, and 1.25±0.03 eV were observed. Their origin is attributed to deep donor complexes, which are generated as a .
Achieving UN SDGs in Food Supply Chain Using Blockchain Technology

Food supply chains are highly distributed, collaborative, heterogeneous, diverse, and varied by product, process, and destination. The global food supply chain (FSC) objective is to maintain a good balance between supply and demand and move products from producer to market. However, sustainability of the FSC has become a major concern as limited resources and increasing population pressure threaten its existence. Supply chain management is an important issue for FSC due to information flow throughout the supply chain. Industry-specific characteristics and extensive integration among multiple actors in an entire supply chain exacerbate this situation. The agri-food sector has one of the lowest rates of information technology penetration for innovation. Over the past thirty years, information and communication technology (ICT) has been introduced into the agricultural and food sectors, helping to improve food production and transportation. However, there are various challenges, such as transparency, accountability, food scandal, trust, and inefficient information flow, that the food supply chain is still facing in reaching sustainable goals. The complexity of food supply systems and the opportunities and challenges faced regarding desired sustainability performance need to be examined to achieve the United Nations Sustainable Development Goals (SDGs). Blockchain is an emerging and disruptive digital technology that can transform governance and sustainability in integrated food supply chains. It provides a transparent, immutable, and traceable ledger that minimizes anomalies and information fraud, making it a potential solution for designing a transparent, traceable food system. Blockchain can potentially improve the sustainability of the food supply chain by providing a transparent traceability system. Food traceability is important for managing the food supply chain and protecting public health. It allows quick and accurate traceability of contaminated food that causes foodborne illness outbreaks, leading to the withdrawal of contaminated food from markets. Blockchain can achieve traceability, provenance tracking, transparency, and reduce environmental impact in the food supply chain. It also helps in achieving sustainable development goals set by the UN. However, there is no scientific research on blockchain’s contribution to achieving these goals in the food supply chain. Therefore, this article presents a systematic literature review and thematic analysis to study the relationship between FSC sustainability, blockchain, and sustainable development goals.
Tag Localization by Handheld UHF RFID Reader With Optical and RFID Landmarks

In this paper a prototype method for manual inventorying and real-time 3D localization of RFID tagged products by a low-cost handheld UHF RFID reader is introduced. The device is equipped with a UHF RFID reader and an optical camera. “ArUco” markers with RFID tags are placed at known positions. Those optical and RF landmarks are used for the estimation of the pose of the handheld reader over time, by fusing the corresponding measurements in a Kalman filter. The reader also measures RFID tagged items at unknown locations. The positions of the tags are estimated by unwrapping the collected phase measurements, combined with the estimated antenna trajectory. Experimental results show that increased accuracy can be achieved by increasing the spatial density of the optical and RFID markers. The proposed method is expected to enhance low-cost manual inventorying, delivering 3D localization of the tagged products.
Performance Analysis of IEEE 802.11p MAC with Considering Capture Effect under Nakagami-m Fading Channel in VANETs

Vehicular ad hoc networks (VANETs) have recently drawn a large amount of attention because of their enormous potential in road safety improvement and traffic management as well as infotainment service support. As the standard of medium access control (MAC) and physical (PHY) layers for VANETs, IEEE 802.11p has been proposed for more than a decade. Though performance analyses of IEEE 802.11p MAC have been performed, the existing analytical methods still need to be improved. In this paper, to assess the saturated throughput and the average packet delay of IEEE 802.11p MAC in VANETs, a two-dimensional (2-D) Markov model is introduced by considering the capture effect under Nakagami-m fading channel. Moreover, the closed-form expressions of successful transmission, collided transmission, saturated throughput, and average packet delay are carefully derived. Finally, the simulation results are demonstrated to verify the accuracy of the proposed analytical model, which also proves that this analytical model is more precise than the existing ones in terms of saturated throughput and average packet delay.
Transglutaminase Activity Is Conserved in Stratified Epithelia and Skin Appendages of Mammals and Birds

The cross-linking of structural proteins is critical for establishing the mechanical stability of the epithelial compartments of the skin and skin appendages. The introduction of isopeptide bonds between glutamine and lysine residues depends on catalysis by transglutaminases and represents the main protein cross-linking mechanism besides the formation of disulfide bonds. Here, we used a fluorescent labeling protocol to localize the activity of transglutaminases on thin sections of the integument and its appendages in mammals and birds. In human tissues, transglutaminase activity was detected in the granular layer of the epidermis, suprabasal layers of the gingival epithelium, the duct of sweat glands, hair follicles and the nail matrix. In the skin appendages of chickens, transglutaminase activity was present in the claw matrix, the feather follicle sheath, the feather sheath and in differentiating keratinocytes of feather barb ridges. During chicken embryogenesis, active transglutaminase was found in the cornifying epidermis, the periderm and the subperiderm. Transglutaminase activity was also detected in the filiform papillae on the tongue of mice and in conical papillae on the tongue of chickens. In summary, our study reveals that transglutaminase activities are widely distributed in integumentary structures and suggests that transglutamination contributes to the cornification of hard skin appendages such as nails and feathers.
Interactive RGB Image Segmentation via Depth-modified Click Encoding and Estimated Depth

Interactive image segmentation separates the object of interest in the scene from the background, using the help of human annotations. Click-based interactive segmentation methods typically receive positive and negative clicks from the user indicating the object of interest and the background. These clicks are encoded into click-maps to be processed further by a neural network. Although the depth information is known to improve the image segmentation accuracy, the previous work encodes only the locations of the clicks into the click-maps. We propose two novel click-map generation methods that modify the conventional click-maps using relative depth information. This depth information is estimated from the monocular RGB image. After retraining the baseline interactive segmentation method with our novel click-maps, the segmentation accuracy improved without requiring any additional input or increasing the network size. Experimental evaluations showed that our method yields a better mean segmentation accuracy on the Berkeley and DAVIS datasets than the baseline using conventional click-maps, and a comparable performance on the GrabCut dataset.
Optimizing Resources and Increasing the Coverage of Internet-of-Things (IoT) Networks: An Approach Based on LoRaWAN

A resource optimization methodology is proposed for application in long range wide area networks (LoRaWANs). Using variable neighborhood search (VNS) and a minimum-cost spanning tree algorithm, it reduces the implementation and the maintenance costs of such low power networks. Performance evaluations were conducted in LoRaWANs with LoRa repeaters to increase coverage, in scenario where the number and the location of the repeaters are determined by the VNS metaheuristic. Parameters such as spread factor (SF), bandwidth and transmission power were adjusted to minimize the network’s total energy per useful bit (Ebit) and the total data collection time. The importance of the SF in the trade-off between (Ebit) and time on-air is evaluated, considering a device scaling factor. Simulation results, obtained after model adjustments with experimental data, show that, in networks with few associated devices, there is a preference for small values of SF aiming at reduction of Ebit. The usage of large SF’s becomes relevant when reach extensions are required. The results also demonstrate that, for networks with high number of nodes, the scaling of devices over time become relevant in the fitness function, forcing an equal distribution of time slots per SF to avoid discrepancies in the time data collection.
CAGE20-7 Cruise Report: Sediment and water column analyses around flares at Norskebanken, Hinlopen and offshore Prins Karls Forland

During the cruise CAGE 20-7 (November 2nd to 16th 2020), we observed and investigated a large active methane seepage area south from the Yermak plateau (Northern Svalbard), in particular to characterize the water column and the sediment near known flares in Norskebanken (Geissler et al., 2016). Some flares were targeted for gas composition, DNA and bubble size distribution. The combination of the R/V Kronprins Haakon and the ROV ÆGIR 6000 from the Norwegian Marine Robotics Laboratory at the University of Bergen allowed this investigation to happen in this rough environment, while collecting invaluable data from the ROV. The cruise may be known as: CAGE20_7
A Kubernetes-Based Edge Architecture for Controlling the Trajectory of a Resource-Constrained Aerial Robot by Enabling Model Predictive Control

In recent years, cloud and edge architectures have gained tremendous focus for offloading computationally heavy applications. From machine learning and Internet of Thing (IOT) to industrial procedures and robotics, cloud computing have been used extensively for data processing and storage purposes, thanks to its “infinite” resources. On the other hand, cloud computing is characterized by long time delays due to the long distance between the cloud servers and the machine requesting the resources. In contrast, edge computing provides almost real-time services since edge servers are located significantly closer to the source of data. This capability sets edge computing as an ideal option for real-time applications, like high level control, for resource-constrained platforms. In order to utilized the edge resources, several technologies, with basic ones as containers and orchestrators like kubernetes, have been developed to provide an environment with many different features, based on each application's requirements. In this context, this works presents the implementation and evaluation of a novel edge architecture based on kubernetes orchestration for controlling the trajectory of a resource-constrained Unmanned Aerial Vehicle (UAV) by enabling Model Predictive Control (MPC).
Force-Directed Graph Layouts Revisited: A New Force Based on the T-Distribution

In this paper, we propose the t-FDP model, a force-directed placement method based on a novel bounded short-range force (t-force) defined by Student's t-distribution. Our formulation is flexible, exerts limited repulsive forces for nearby nodes and can be adapted separately in its short- and long-range effects. Using such forces in force-directed graph layouts yields better neighborhood preservation than current methods, while maintaining low stress errors. Our efficient implementation using a Fast Fourier Transform is one order of magnitude faster than state-of-the-art methods and two orders faster on the GPU, enabling us to perform parameter tuning by globally and locally adjusting the t-force in real-time for complex graphs. We demonstrate the quality of our approach by numerical evaluation against state-of-the-art approaches and extensions for interactive exploration.
A Novel Deep Nearest Neighbor Neural Network for Few-Shot Remote Sensing Image Scene Classification

Remote sensing image scene classification has become more and more popular in recent years. As we all know, it is very difficult and time-consuming to obtain a large number of manually labeled remote sensing images. Therefore, few-shot scene classification of remote sensing images has become an urgent and important research task. Fortunately, the recently proposed deep nearest neighbor neural network (DN4) has made a breakthrough in few-shot classification. However, due to the complex background in remote sensing images, DN4 is easily affected by irrelevant local features, so DN4 cannot be directly applied in remote sensing images. For this reason, a deep nearest neighbor neural network based on attention mechanism (DN4AM) is proposed to solve the few-shot scene classification task of remote sensing images in this paper. Scene class-related attention maps are used in our method to reduce interference from scene-semantic irrelevant objects to improve the classification accuracy. Three remote sensing image datasets are used to verify the performance of our method. Compared with several state-of-the-art methods, including MatchingNet, RelationNet, MAML, Meta-SGD and DN4, our method achieves promising results in the few-shot scene classification of remote sensing images.