Miracle-dz/UniFAR-FTUs huggingface dataset:
Protaetia brevitarsis Extract Attenuates RANKL-Induced Osteoclastogenesis by Inhibiting the JNK/NF-κB/PLCγ2 Signaling Pathway

Protaetia brevitarsis (PB)-derived bioactive substances have been used as food and medicine in many Asian countries because of their antioxidant, antidiabetic, anti-cancer, and hepatoprotective properties. However, the effect of PB extracts (PBE) on osteoclast differentiation is unclear. In this study, we investigated the effect of PBE on RANKL-induced osteoclastogenesis in mouse bone marrow-derived macrophages (BMMs). To investigate the cytotoxicity of PBE, the viability of BMMs was confirmed via MTT assay. Tartrate-resistant acid phosphatase (TRAP) staining and pit assays were performed to confirm the inhibitory effect of PBE on osteoclast differentiation and bone resorption. The expression levels of osteoclast differentiation-related genes and proteins were evaluated using quantitative real-time PCR and Western blotting. PBE attenuated osteoclastogenesis in BMMs in TRAP and pit assays without cytotoxicity. The expression levels of osteoclast marker genes and proteins induced by RANKL were decreased after PBE treatment. PBE suppressed osteoclastogenesis by inhibiting the RANKL-induced activated JNK/NF-κB/PLCγ2 signaling pathway and the expression of NFATc1 and c-Fos. Collectively, these results suggest that PBE could be a potential therapeutic strategy or functional product for osteoclast-related bone disease.
Enhancing laser speckle contrast imaging based on adaptive scale and directional kernel during V-PDT

Laser Speckle Contrast Imaging (LSCI) as the advantages of high speed, wide field of view, and ease of implementation has been widely used for imaging blood flow and vascular deformation during Vascular targeted Photodynamic Therapy (V-PDT). However, uncorrected direction and or scale kernel failed to improve the visualization of blood vessels derived from poor Signal Noise Ratio (SNR) of micro vessels during V-PDT. In this study, we extracted the adaptive scale and directional kernel using dual-wavelength reflection imaging to enhance laser speckle imaging. The in vivo data showed that the proposed method improved the 4%-29% performance of contrast-to-noise ratio (CNR) and provide a powerful monitoring tool for blood flow and vascular deformation during V-PDT.
Projected Wirtinger gradient descent for spectral compressed sensing

In modern data and signal acquisition, one main challenge arises from the growing scale of data. The data acquisition devices, however, are often limited by physical and hardware constraints, precluding sampling with the desired rate and precision. It is thus of great interest to reduce the sensing complexity while retaining recovery resolution. And that is why we are interested in reconstructing a signal from a small number of randomly observed time domain samples. The main contributions of this thesis are as follows. First, we consider reconstructing a one-dimensional (1-D) spectrally sparse signal from a small number of randomly observed time-domain samples. The signal of interest is a linear combination of complex sinusoids at R distinct frequencies. The frequencies can assume any continuous values in the normalized frequency domain [0, 1). After converting the spectrally sparse signal into a low-rank Hankel structured matrix completion problem, we propose an efficient feasible point approach, named projected Wirtinger gradient descent (PWGD) algorithm, to efficiently solve this structured matrix completion problem. We give the convergence analysis of our proposed algorithms. We then apply this algorithm to a different formulation of structured matrix recovery: Hankel and Toeplitz mosaic structured matrix. The algorithms provide better recovery performance; and faster signal recovery than existing algorithms including atomic norm minimization (ANM) and Enhanced Matrix Completion (EMaC). We further accelerate our proposed algorithm by a scheme inspired
Recycling plastics from e‐waste: Implications for effective eco‐design

This paper presents five case studies on waste electrical and electronic equipment (WEEE) recycling to provide a coherent overview on the likely impact of eco‐design measures on recycling of plastics used in energy‐related products within the EU. Whilst some eco‐design measures, such as improving disassembly of plastic parts, may generally benefit recycling operations, other measures were found to be ineffective or requiring further investigation. For example, product polymer marking, and provision of product‐specific information was rarely utilized by participant organizations, if at all. Additionally, this study highlights a disconnect between the aims of substance bans as an eco‐design measure and the impact upon plastics recycling in practice. Future research could help with quantitative and/or statistical analysis of WEEE processing to investigate across a wider selection of recyclers and recycling processes. Despite 20 years of research on eco‐design, it appears that EU eco‐design policies and voluntary initiatives are still being devised without adequate understanding of their impact on different types of recycling practices. Empirical research on recycling processes can provide important insight to ensure eco‐design measures are effective and avoid unintended consequences for the environment.
Dempster-Shafer evidence theory-based multi-feature learning and fusion method for non-rigid 3D model retrieval

This study introduces a novel multi-feature-based non-rigid three-dimensional (3D) model retrieval method. First, for each 3D model, compute the scale-invariant heat kernel signature (SI-HKS) descriptor and the wave kernel signature (WKS) descriptor of each vertex. Then, the normalised weighted bags of phrases feature is obtained and they are fed to the convolutional neural networks. The trust degree of each kind of descriptor is computed, and the total trust degree can be obtained. Finally, the fusion network is trained and the retrieval results can be obtained according to the ranking of the total trust degrees. For the training phase and the testing phase, the authors define different computation methods of the trust degrees and the total trust degrees. The Dempster-Shafer (DS) evidence-based total trust degrees are used not only in the feature layer but also in the decision layer. The final decision results of the total trust degrees are used in the process of the network learning. So the proposed method can make full use of the complementary information of the SI-HKS descriptor and the WKS descriptor. Extensive experiments have shown that the proposed multi-feature fusion method has better performance than a single feature-based method, and also outperforms other existing state-of-the-art methods.
A Sub-50-fs RMS Jitter, 103.5-GHz Fundamental-Sampling PLL With an Extended Loop Bandwidth

A sub-THz phase-locked loop (PLL) with a power-gating injection-locked frequency multiplier-based phase detector and extended loop bandwidth was designed to achieve ultralow jitter performance and low power consumption. The loop bandwidth of the PLL was extended in two ways to reduce the out-of-band phase noise of a sub-THz voltage-controlled oscillator (VCO) with a low-quality factor. First, the PLL used a separate proportional path to correct the jitter of the VCO directly without passing through the gm-amplifier. Second, a reset switch was used to nullify the effect of the intrinsic infinite-impulse-response (IIR) pole from charge-sharing operation on the loop bandwidth, thus allowing for the further extension of the bandwidth. The proposed sub-THz PLL was fabricated in a 65-nm CMOS process, and it occupied a silicon area of 0.13 mm 2. The proposed sub-THz PLL achieved an RMS jitter of 47 fs using 26.6-mW power, thereby obtaining a jitter FoM of–252 dB at 103.5 GHz.
Adaptive descriptor based on the geometric consistency of local image features: application to flower image classification

Abstract. Geometric consistency is, usually, considered as a postprocessing step to filter matched sets of local features in order to discard outliers. In this work, it is used to propose an adaptive feature that describes the geometric dispersion of keypoints. It is based on a distribution computed by a nonparametric estimator so that no assumption is made about the data. We investigated and discussed the invariance properties of our descriptor under the most common two- and three-dimensional transformations. Then, we applied it to flower recognition. The classification is performed using the precomputed kernel of support vector machines classifier. Indeed, a similarity computing framework that uses the Kullback–Leibler divergence is presented. Furthermore, a customized layout for each flower image is designed to describe and compare separately the boundary and the central area of flowers. Experimentations made on the Oxford flower-17 dataset prove the efficiency of our method in terms of classification accuracy and computational complexity. The limits of our descriptor are also discussed on a 10-class subset of the Oxford flower-102 dataset.
A multi-space error estimation approach for meshfree methods

: Error-controlled adaptive meshfree methods are presented for both global error measures, such as the energy norm, and goal-oriented error measures in terms of quantities of interest. The meshfree method chosen in this paper is the reproducing kernel particle method (RKPM), since it is based on a Galerkin scheme and therefore allows extensions of quality control approaches as already developed for the ﬁnite element method. Our approach of goal-oriented error estimation is based on the well-established technique us-ing an auxiliary dual problem. To keep the formulation general and to add versatility, a multi-space approach is used, where the dual problem is solved numerically using a diﬀerent approximation space than the one employed in the associated primal problem. This can be realized with meshfree methods at no additional cost. Possible merits of this multi-space approach are discussed and an illustrative numerical example is presented.
Modeling Lateral Control Behaviors of Distracted Drivers for Haptic-Shared Steering System

Haptic-shared steering (HSS) systems have been reported to enhance vehicle safety and reduce the workload for drivers. However, few studies have focused on modeling the lateral vehicle control behaviors of distracted drivers for HSS. The current study models this type of behavior in a series of high-fidelity driving simulator experiments with 18 participants. Two experimental conditions for a double lane change task are tested: HGT-Constant (haptic guidance torque with a constant gain) and HGT-Adaptive (haptic guidance torque with an adaptive gain). A gated recurrent unit (GRU) network is used to model lateral control behavior during driving. The effectiveness of the GRU network-based lateral control model of distracted drivers is benchmarked using a state-of-the-art long short-term memory network, a back propagation network, an extreme learning machine, and a traditional two-point visual model with neuromuscular dynamics. Experimental results indicate that the GRU network has the highest accuracy in terms of root mean square error, mean absolute error, mean absolute percentage error, and determination coefficient. In addition, a simulation model of the driver-vehicle-road closed-loop system demonstrates that the proposed model predicts driver behavior with acceptable lateral position error.
Self-Supervised Spectral–Spatial Graph Prototypical Network for Few-Shot Hyperspectral Image Classification

In recent years, deep learning has been widely applied to hyperspectral image (HSI) classification (HIC) with great success. However, since labeling hyperspectral images (HSIs) is time-consuming and labor-intensive, a limited number of labeled HSIs are available, making it difficult to train feature extractors and classifiers. To address this challenge, this article introduces a self-supervised spectral–spatial graph prototypical network (S4GPN) for few-shot HIC. In addition, we combine self-supervised learning (SSL) with few-shot learning (FSL) to provide additional semantic information to improve classification accuracy. Our method consists of three stages, including the prototypical network (PN) stage, the SSL stage, and the fusion stage, with each stage progressively improving the classification performance. In the PN stage, we perform supervised learning using a PN structure and leverage the supervised information to guide SSL. In the SSL stage, we use the SimSiam structure in a novel way for model training after the augmentation of spectral and spatial data separately. Finally, the features learned in the first two stages are fused to improve the quality of the feature representation. These three stages use the same structured feature extractors. To extract more diverse and discriminative feature representations for the HIC task, our method uses the graph convolution network (GCN) and the dense network (Densenet) to extract spectral information and spatial information, respectively. Experiments on four datasets and comparisons with the state-of-the-art methods demonstrate that our proposed S4GPN outperforms other methods for HIC.
Evaluation of Two Online Risk Prediction Models for the Mortality Rate of Acute Type A Aortic Dissection Surgery: The German Registry of Acute Aortic Dissection Type A Score and the European System for Cardiac Operative Risk Evaluation II

EuroSCORE II is one of the most widely utilized cardiovascular surgery risk scoring systems. Recently, a new online score calculator, namely the German Registry of Acute Aortic Dissection Type A (GERAADA), was launched to predict 30-day surgical mortality for acute type A aortic dissection (ATAAD) patients. The aim of this study is to evaluate the predictive performance of these two scores. We calculated the two scores for 1346 ATAAD patients from January 2012 to December 2021. The overall performance was evaluated using Brier scores and Hosmer-Lemeshow statistics. Receiver Operating Characteristic (ROC) curves were employed to assess diagnostic ability, and the standardized mortality ratio (SMR) was utilized to evaluate calibration. The GERAADA score and EuroSCORE II predicted 30-day mortality rates of 14.7% and 3.1%, respectively, while the observed rate was 12.5%. The predictive ability of EuroSCORE II (AUC 0.708, 95% CI: 0.664–0.792) was superior to that of the GERAADA score (0.648, 95% CI: 0.605–0.692). The GERAADA score had higher sensitivity but lower specificity than EuroSCORE II. And the GERAADA score may overestimate mortality (0.76, 95% CI: 0.65–0.89), while EuroSCORE II may underestimate the mortality rate (3.17, 95% CI: 2.92–3.44). The EuroSCORE II was superior in predicting surgical mortality among ATAAD patients. But the observed 30-day mortality rate certified a good calibration for the GERAADA score.
Valuation of Guaranteed Unitized Participating Life Insurance under MEGB2 Distribution

Crisis events have significantly changed the view that extreme events in financial markets have negligible probability. Especially in the life insurance market, the price of guaranteed participating life insurance contract will be affected by a change in asset volatility which leads to the fluctuations in embedded option value. Considering the correlation of different asset prices, MEGB2 (multivariate exponential generalized beta of the second kind) distribution is proposed to price guaranteed participating life insurance contract which can effectively describe the dependence structure of assets under some extreme risks. Assuming the returns of two different assets follow the MEGB2 distribution, a multifactor fair valuation pricing model of insurance contract is split into four components: the basic contract, the annual dividend option, the terminal dividend option, and the surrender option. This paper studies the effect of death rate, minimum guaranteed yield rate, annual dividend ratio, terminal dividend ratio, and surrender on the embedded option values and calculates the single premium of the insurance contract under different influence factors. The Least-Squares Monte Carlo simulation method is used to simulate the pricing model. This article makes a comparison in the sensitivity of the pricing parameters under the MEGB2 distribution and Multivariate Normal distribution asset returns. Finally, an optimal hedging strategy is designed to cover the possible risks of the underlying assets, which can effectively hedge the risks of portfolio.
Low-power, ultrafast, and dynamic all-optical tunable plasmon induced transparency in two stub resonators side-coupled with a plasmonic waveguide system

We theoretically and numerically investigate a low-power, ultrafast, and dynamic all-optical tunable plasmon induced transparency (PIT) in two stub resonators side-coupled with a metal-dielectric-metal (MDM) plasmonic waveguide system. The optical Kerr effect is enhanced by the local electromagnetic field of surface plasmon polaritons (SPPs) and the plasmonic waveguide based on graphene-Ag composite material structures with large effective Kerr nonlinear coefficient. An ultrafast response time of the order of 1 ps is reached because of ultrafast carrier relaxation dynamics of graphene. With dynamically tuning the propagation phase of the plasmonic waveguide, π-phase shift of the transmission spectrum in the PIT system is achieved under excitation of a pump light with an intensity as low as 5.8 MW cm−2. The group delay is controlled between 0.14 and 0.67 ps. Moreover, the tunable bandwidth of about 42 nm is obtained. For the indirect coupling between two stub cavities or the phase coupling scheme, the phase shift multiplication effect of the PIT effect is found. All observed schemes are analyzed rigorously through finite-difference time-domain simulations and coupled-mode formalism. This work not only paves the way towards the realization of on-chip integrated nanophotonic devices but also opens the possibility of the construction of ultrahigh-speed information processing chips based on plasmonic circuits.
Checkerboard image processing under uneven illumination for robust Harris corner detection in camera calibration

Harris corner detection in checkerboard images for camera calibration often suffer from uneven illumination. The key to camera calibration lies in that how to robustly detect corners from the degraded images. To this end, an image processing method is proposed to deal with non-uniform illumination problems. Experiments show that the stabilities of Harris corner detection under uneven illumination are improved obviously.
Nudged Elastic Band in Analysis of Range Image High-contrast Patches

Range images are more interested in lately for several reasons, such as, range images could be used in object recognition; the scene geometry of the 3D world could be comprehended more effectively by using range images. As there is a distance between the laser scanner and the nearest object for each range image pixel, hence, every range image may be thought as a vector in a high dimensional space W. It is very difficult to study a set of range images X ⊆ W, because X has very high dimension and it is very sparse in W. An efficient way for analysing range images is to study the space of small range image patches. The nudged elastic band method is a main tool for searching minimum energy paths in computational chemistry. In this paper, Morse functions are created by the sampled data from range image high contrast small patches, and then one-dimensional cell complexes are built from the Morse functions by using the nudged elastic band method, topological features of range image data are detected by a sequence of cell complexes. Particularly, we experimentally show that there exist subspaces of high-contrast 3×3, 4×4, 6×6 and 7×7 range image patches whose homology is that of a circle.
Efficient Outage Probability Evaluation of Diversity Receivers Over Generalized Gamma Channels

In this paper, we are interested in determining the cumulative distribution function of the sum of generalized Gamma in the setting of rare event simulations. To this end, we present an efficient importance sampling estimator. The main result of this work is the bounded relative property of the proposed estimator. This result is used to accurately estimate the outage probability of multibranch maximum ratio combining and equal gain combining diversity receivers over generalized Gamma fading channels. Selected numerical simulations are discussed to show the robustness of our estimator compared to naive Monte Carlo.
Randomized Subspace Learning with Structure Preservation

Modeling data as being sampled from a union of independent or disjoint subspaces has been widely applied to a number of real world applications. Recently, high dimensional data has come into focus because of advancements in computational power and storage capacity. However, a number of algorithms that assume the aforementioned data model have high time complexity which makes them slow. Dimensionality reduction is a commonly used technique to tackle this problem. In this paper, we first formalize the concept of Independent Subspace Structure and then we propose two randomized algorithms (supervised and unsupervised) for subspace learning that theoretically preserve this structure for any given dataset. Under supervised setting, we show that 2K projection vectors are sufficient for structure preservation of K class data. On the other hand for unsupervised framework, we show that random projection preserves this structure without the knowledge of labels. We support our theoretical analysis with empirical results on both synthetic and real world data.
In the shoes of a farmer: (re)connecting the public with animal health and welfare in livestock production

The UK public is becoming increasingly disconnected from farming, yet shows an interest in how food is produced and how farm animals are raised. This article reflects upon a novel engagement activity aimed at facilitating conversations about farm animal health and welfare in the UK. We focus upon how: (1) the interactive design encouraged participation, and made the activity enjoyable; (2) the content and presentation facilitated non-judgemental conversations; (3) the activity was an opportunity to discuss an important topic; (4) the location and timing allowed the researchers to reach different publics; and (5) the activity enhanced the subsequent research and engagement programme. We conclude by highlighting the value of creative engagement methods for engaging members of the public with food and farming issues.