Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Optical Fiber Sensing Signal Recognition Based on Deep Residual Shrinkage Network

Deep residual shrinkage network (DRSN) is proposed to enhance the recognition effect of distributed optical fiber sensing signals. The experimental data are collected by distributed optical fiber sensing perimeter security system in which six kinds of vibration signals are studied, including slapping, walking, rolling, noise, climbing and raining. The experimental results show that the recognition accuracy of DRSN method is higher than that of CNN and ResNet methods. The average test accuracy of DRSN method is up to 98.017%. Compared with ResNet method, the average test accuracy of DRSN method is improved by about 1.1 %. Compared with CNN method, the average test accuracy of DRSN method is improved by about 2.5%. Moreover, DRSN method is obviously superior to CNN and ResNet methods according to the evaluation indexes of precision, recall and F1-score.
A Review of 3D Printing Technology in Pharmaceutics: Technology and Applications, Now and Future

Three-dimensional printing technology, also called additive manufacturing technology, is used to prepare personalized 3D-printed drugs through computer-aided model design. In recent years, the use of 3D printing technology in the pharmaceutical field has become increasingly sophisticated. In addition to the successful commercialization of Spritam® in 2015, there has been a succession of Triastek’s 3D-printed drug applications that have received investigational new drug (IND) approval from the Food and Drug Administration (FDA). Compared with traditional drug preparation processes, 3D printing technology has significant advantages in personalized drug manufacturing, allowing easy manufacturing of preparations with complex structures or drug release behaviors and rapid manufacturing of small batches of drugs. This review summaries the mechanisms of the most commonly used 3D printing technologies, describes their characteristics, advantages, disadvantages, and applications in the pharmaceutical industry, analyzes the progress of global commercialization of 3D printed drugs and their problems and challenges, reflects the development trends of the 3D printed drug industry, and guides researchers engaged in 3D printed drugs.
Comparative transcriptomic and metabolomic study reveal that exogenous 24‐epiandrosterone mitigate alkaline stress in broomcorn millet (Panicum miliaceum L.) via regulating photosynthesis and antioxidant capacity

Globally, land alkalinization affecting agricultural development. Considering the increasingly serious effects of alkaline stress on agriculture and environment, phytoremediation may be an efficient way to addressed land alkalinization. Broomcorn millet (Panicum miliaceum L.) is a saline‐alkaline‐tolerant crop and bioenergy crop. However, the molecular mechanism of alkaline response on broomcorn millet remains large gap. To explore the alkaline stress on broomcorn millet and the mitigation of 24‐epicastasterone (BR), the effects of CK (nutrient solution only), CB (nutrient solution + 0.5 mg L−1 BR), AS (alkaline stress), and AB (alkaline stress + 0.5 mg L−1 BR) on TA289 (alkaline‐tolerant) and SA223 (alkaline‐sensitive) were investigated. Alkaline stress enhanced reactive oxygen species and membrane lipid peroxidation. BR boosted antioxidant enzyme activities to reduce oxidative stress. Simultaneously, BR attenuated Na+ toxicity and maintained ion homeostasis. Additionally, BR significantly maintained the physiological structure and photosynthetic properties. Transcriptomic and metabolomic analyses were applied to further evaluate the effect of BR on photosynthetic and antioxidant defense. The results showed that BR significantly reduced the transcriptional responses of photosynthesis and antioxidant defense and promoted the accumulation of effective metabolites such as biliverdin, l‐glutamate, and phosphoric acid. Taken together, BR application can significantly alleviate the damage of alkaline stress to broomcorn millet by altering transcriptional expression and metabolite accumulation and is a simple and effective strategy to alleviate alkaline stress. This study reveals the molecular mechanism of BR to enhance photosynthetic capacity and antioxidant defense of broomcorn millet under alkaline stress, which provides theoretical support for the cultivation of bioenergy crops on alkaline lands and the breeding of alkaline‐tolerant bioenergy varieties.
Polarization-selective transmission in stacked two-dimensional complementary plasmonic crystal slabs

It has been experimentally and numerically shown that transmission at near infrared wavelengths is selectively controlled by polarizations in two-dimensional complementary plasmonic crystal slabs (2D c-PlCSs) of stacked unit cell. This feature is naturally derived by taking account of Babinet’s principle. Moreover, the slight structural modification of the unit cell has been found to result in a drastic change in linear optical responses of stacked 2D c-PlCSs. These results substantiate the feasibility of 2D c-PlCSs for producing efficient polarizers with subwavelength thickness.
A wideband antenna based on self‐packaged suspended coplanar waveguide technology

In this article, a wideband antenna is implemented by using a multilayer self‐packaged suspended coplanar waveguide (SCPW). The lower air cavity of the SCPW is not only used for antenna suspending but also as an antenna reflector, while the reflector structure can be expanded by copper coating on the periphery of the multidielectric substrates, which improves the antenna radiation and antenna gain effectively, while the antenna profile is not increased. An extra patch is introduced for generating more resonances, which extends the antenna bandwidth importantly. The proposed antenna is designed to work at 5.42 GHz with a fractional bandwidth of 26.2% (4.71–6.13 GHz), and a maximum gain of 8.4 dBi. The SCPW antenna has been fabricated and measured, and the design has been demonstrated with the advantages of self‐packaging, wideband, favorable gain, and even low profile as well as miniature circuit size.
Rapid identification of antimicrobial drug resistance strains of E-coli using SERS nanowire chip

We have investigated the enhanced Raman spectra of AMR bacteria strains of E. coli using silver coated silicon nanowires SERS assay. Three different E. coli strains, E. coli CCUG17620, NCTC 13441, and A239, were detected using two different excitation laser wavelengths. We found stable and enhanced SERS spectrum using 785 nm laser as opposed to 532 nm. Future development of SERS-chip could offer a reliable platform for direct identification of the pathogen in bio-fluid samples at strains level.
Separation of interlayer resistance in multilayer MoS2 field-effect transistors

We extracted the interlayer resistance between two layers in multilayer molybdenum disulfide (MoS2) field-effect transistors by confirming that contact resistances (Rcontact) measured using the four-probe measurements were similar, within ∼30%, to source/drain series resistances (Rsd) measured using the two-probe measurements. Rcontact values obtained from gated four-probe measurements exhibited gate voltage dependency. In the two-probe measurements, the Y-function method was applied to obtain the Rsd values. By comparing those two Rcontact (∼9.5 kΩ) and Rsd (∼12.3 kΩ) values in strong accumulation regime, we found the rationality that those two values had nearly the same properties, i.e., the Schottky barrier resistances and interlayer resistances. The Rsd values of devices with two-probe source/drain electrodes exhibited thickness dependency due to interlayer resistance changes. The interlayer resistance between two layers was also obtained as ∼2.0 Ω mm.
Sediment resuspension due to density currents caused by a temperature difference: application to the Flix reservoir (Spain)

Density currents occur in lakes and sea environments when two masses of water interact giving rise to a relative movement between them. Depending on the initial and boundary conditions, turbulence associated with them is able to lift the sediment. This fact can be a problem in places where the sediment is contaminated due to industrial activities. In 2001, a pollution episode occurred in the Flix reservoir (Tarragona, Spain), in which thousands of fish died and the water supply of cities such as Tarragona was affected. This study analyses the possibility of a density current due to a temperature difference as the explanation for this event. The previously developed one-dimensional, Lagrangian model BANG 1D of subaqueous turbid surges has been modified to incorporate temperature evolution. The obtained results show that a temperature difference can generate a density current that is able to resuspend the sediment although the grade of erosion highly depends on the initial velocity of the current.
The Risks and Rewards of Collegiate Esports: A Multi-Case Study of Gamers in the United States and Italy

As participation in electronic sports (esports) continues to expand globally, colleges and universities are considering how such competitive video gaming might impact recruitment, retention, and the overall student engagement experience. This mixed-methods study focuses on the perceptions of college students in both the United States and Italy regarding the esports phenomenon. A compilation of the research findings from two case studies compared and contrasted themes associated with the benefits and risks to college students participating in esports. The findings highlighted the notion that even during challenging times such as the COVID-19 pandemic, the college students in this study were still driven to invest their time in esports play. However, the risks such as those of addiction, health impairment, and social isolation may outweigh the perceived benefits to game play. Included are critical considerations and policy recommendations for campus esports programs as well as future directions in research.
Where is agency in the context of urban transformation? Exploring the narratives of institutional stakeholders and community activists in Birmingham

How do institutional stakeholders and community activists differ in their perception of their agency to affect change? We explore this question by synthesising Archer’s theory of structure–agency dynamics’ and Easterly’s development models as it is now, and debates on the ‘just city’ to explore narratives around urban transformation in data from two projects in England’s second city, Birmingham. Our results show that, whereas institutional stakeholders affiliated with local government feel rather disempowered and defer to signals from national government or investors, community activists are focused on opportunities to use their agency and create change in their local areas. The implications of this divergence for the aims of achieving empowerment and social justice are discussed. Also, the benefits of a co-production model used in one of the research projects are reflected upon. Building on this model through policies of collaboration, forming social connections and active civic engagement could use the social energy and potential we identify to reinvigorate agency and the motivation of institutional stakeholders and generate change that is more bottom-up than top-down. To this end, we encourage greater reflection on notions of agency and participation in discussions on the ‘just city’. However, in view of prevailing structural forces, we acknowledge that such efforts ultimately remain aspirational and difficult to achieve.
Electroporation Pulse Generator for Biomedical Applications With Improved Output Voltage Ripple and Reduced Bus Capacitor

Electroporation is a promising cancer treatment based on applying short electric field pulses. In this context, highly versatile pulse generators are required to generate high-voltage low-ripple pulses in the microsecond range. In the past, several multilevel converters have been proposed to achieve this aim. Achieving constant output voltage and, consequently, electric field is essential to obtain predictable treatments and minimize the risks of tumor relapse. However, significant bus capacitance is required to achieve the required performance, leading to bulky equipment with serious safety implications due to the stored energy. In this letter, a high-performance pulse generator for electroporation is proposed that takes advantage of a multilevel structure with linear voltage regulation to achieve the desired performance with reduced bus capacitance. The proposed converter has been designed and tested experimentally during both laboratory and operation room treatments.
Simultaneous Destriping and Image Denoising Using a Nonparametric Model With the EM Algorithm

Digital images often suffer from the common problem of stripe noise due to the inconsistent bias of each column. The existence of the stripe poses much more difficulties on image denoising since it requires another ${n}$ parameters, where ${n}$ is the width of the image, to characterize the total interference of the observed image. This paper proposes a novel EM-based framework for simultaneous stripe estimation and image denoising. The great benefit of the proposed framework is that it splits the overall destriping and denoising problem into two independent sub-problems, i.e., calculating the conditional expectation of the true image given the observation and the estimated stripe from the last round of iteration, and estimating the column means of the residual image, such that a Maximum Likelihood Estimation (MLE) is guaranteed and it does not require any explicit parametric modeling of image priors. The calculation of the conditional expectation is the key, here we choose a modified Non-Local Means algorithm to calculate the conditional expectation because it has been proven to be a consistent estimator under some conditions. Besides, if we relax the consistency requirement, the conditional expectation could be interpreted as a general image denoiser. Therefore other state-of-the-art image denoising algorithms have the potentials to be incorporated into the proposed framework. Extensive experiments have demonstrated the superior performance of the proposed algorithm and provide some promising results that motivate future research on the EM-based destriping and denoising framework.
In vivo longitudinal two-photon intravital kidney imaging of a dynamic DHA crystal formation and tubular deterioration in the adenine-induced chronic kidney disease mouse model

Chronic kidney disease (CKD) is one of the most prevalent kidney disorders with no distinct symptoms and gradual loss of kidney function over time. The underlying mechanism in the pathogenesis of CKD with various causes such as high blood pressure, diabetes, high cholesterol, and kidney infection is not well understood. Following up on the pathophysiological alterations, longitudinal in vivo cellular-level imaging during CKD progression can provide unique insights and valuable information for diagnosing and managing CKD. In this study, with two-photon kidney intravital imaging, we achieved a repetitive longitudinal visualization of the kidney in an adenine diet-induced CKD mouse model over 30 days. Interestingly, based on a second-harmonics generation signal, we clearly visualized and quantified the formation of 2,8-dihydroxyadenine (DHA) crystals over time in a label-free manner. The imaging results were well correlated with CKD progression monitored by a standard blood test of blood urea nitrogen (BUN) and cystatin C levels. This result suggests the potential of the label-free second-harmonics generation crystal imaging as a novel optical technique for in vivo CKD progression monitoring.
Acute bacterial encephalitis complicated with recurrent nasopharyngeal carcinoma associated with Elizabethkingia miricola infection: A case report

Elizabethkingia miricola (E. miricola) is an extremely rare pathogenic bacterium, which causes serious infections in patients with primary immunodeficiency or tumors, and it is often misdiagnosed. E. miricola has rarely been known to cause a neurologic infection. We describe the first case of acute bacterial encephalitis associated with E. miricola infection in a man with recurrent nasopharyngeal carcinoma, which was successfully cured by antibiotics. The patient initially presented with recurrent episodes of fever and later showed impaired consciousness but these symptoms were alleviated with antibiotic therapy including cefoperazone/sulbactam. This study highlights that rapid and accurate pathogen detection via metagenomic next-generation sequencing and early use of appropriate antibiotics can improve the prognosis of patients with suspected neurologic E. miricola infection. Early treatment for underlying primary diseases can also significantly improve the outcomes of patients.
Parameter Estimation of Polynomial Phase Signal by Unscented Kalman Filtering

The parameter estimation of the Polynomial Phase Signals (PPS) is one of the core issues. In this paper, UKF-based algorithm is proposed to estimate the parameter of PPS embedded in Gaussian noise. The algorithm constructs an adequate state-space model to represent the PPS and the model can also be implied in real radar signal. Unscented Kalman filtering is applied to estimate the signal parameters. The method achieves the lower SNR threshold, the faster convergence speed, the higher accuracy and more stable estimation performance compared with the existing methods. Simulation also verifies the efficiency of the proposed method.
Posterior Simulation of Normalized Random Measure Mixtures

This article describes posterior simulation methods for mixture models whose mixing distribution has a Normalized Random Measure prior. The methods use slice sampling ideas and introduce no truncation error. The approach can be easily applied to both homogeneous and nonhomogeneous Normalized Random Measures and allows the updating of the parameters of the random measure. The methods are illustrated on data examples using both Dirichlet and Normalized Generalized Gamma process priors. In particular, the methods are shown to be computationally competitive with previously developed samplers for Dirichlet process mixture models. Matlab code to implement these methods is available as supplemental material.
An efficient implementation of particle methods for the incompressible Euler equations

A fast algorithm is presented that reduces the amount of work necessary for computing pairwise interactions in a system of n particles from $\mathcal{O}(n^2 )$ to $\mathcal{O}(n\log ^{d + 1} n)$, where d is the dimension of the computational domain. Theoretical error and work estimates are given.The main advantages of the new method over other fast algorithms of this kind are its applicability to a class of kernels not necessarily associated with the Laplace equation and the straightforward extension to three-dimensional problems. The algorithm is used to obtain a fast summation of the velocity field induced by (a) point vortices and (b) vortex blobs in the approximation of the incompressible Euler equations. It is shown that, with a proper choice of parameters, this technique preserves the accuracy of these methods. Numerical results are presented for the two-dimensional case.
WISE solid hydrogen cryostat design overview

The Wide-Field Infrared Survey Explorer (WISE) is a MIDEX mission that is being developed by the Jet Propulsion Laboratory (JPL) to address several of NASA's Astronomical Search of Origins (ASO) objectives. The WISE instrument, developed by the Space Dynamics Laboratory (SDL), includes a cryogenically cooled telescope (at < 13K) and four focal plane assemblies (2 at 7.6K, 2 at 32K). Cooling of the instrument is accomplished by a dual-stage solid hydrogen cryostat that is developed by the Lockheed Martin Advanced Technology Center (LM-ATC). This paper provides a combined overview of the WISE cryostat design and thermal support system.
Rapid Positioning Method of Civil Aircraft Assembly Scene Based on Vision

The assembly process of civil aircraft is complex and the amount of information is huge. When quality problems occur, the traceability of information investigation is low, especially for unstructured data such as images and videos. Manual positioning can range from hours to days. Therefore, solving the problem of information matching and rapid positioning of civil aircraft assembly scene can improve the efficiency of quality problem tracing and prevent the recurrence of the same problem in the production process. This paper proposes a vision-based method for information matching and rapid positioning of civil aircraft assembly scene. By uploading scene images, all the same or similar images can be quickly retrieved from the scene database. And the positioning experiment is carried out in the civil aircraft assembly production line and laboratory construction simulation scene. The positioning method proposed in this paper can locate the retrieval scene within 1 second, which has high positioning efficiency and high practicability.
Influence of measurement uncertainty on machine learning results demonstrated for a smart gas sensor

Abstract. Humans spend most of their lives indoors, so indoor air quality (IAQ) plays a key role in human health. Thus, human health is seriously threatened by indoor air pollution, which leads to 3.8 ×106 deaths annually, according to the World Health Organization (WHO). With the ongoing improvement in life quality, IAQ monitoring has become an important concern for researchers. However, in machine learning (ML), measurement uncertainty, which is critical in hazardous gas detection, is usually only estimated using cross-validation and is not directly addressed, and this will be the main focus of this paper. Gas concentration can be determined by using gas sensors in temperature-cycled operation (TCO) and ML on the measured logarithmic resistance of the sensor. This contribution focuses on formaldehyde as one of the most relevant carcinogenic gases indoors and on the sum of volatile organic compounds (VOCs), i.e., acetone, ethanol, formaldehyde, and toluene, measured in the data set as an indicator for IAQ. As gas concentrations are continuous quantities, regression must be used. Thus, a previously published uncertainty-aware automated ML toolbox (UA-AMLT) for classification is extended for regression by introducing an uncertainty-aware partial least squares regression (PLSR) algorithm. The uncertainty propagation of the UA-AMLT is based on the principles described in the Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements. Two different use cases are considered for investigating the influence on ML results in this contribution, namely model training with raw data and with data that are manipulated by adding artificially generated white Gaussian or uniform noise to simulate increased data uncertainty, respectively. One of the benefits of this approach is to obtain a better understanding of where the overall system should be improved. This can be achieved by either improving the trained ML model or using a sensor with higher precision. Finally, an increase in robustness against random noise by training a model with noisy data is demonstrated.