Miracle-dz/UniFAR-FTUs huggingface dataset:
Learn Generalization Feature via Convolutional Neural Network: A Fault Diagnosis Scheme Toward Unseen Operating Conditions

In recent years, Convolutional neural networks (CNNs) have achieved start-of-art performance in the fault diagnosis field. If there is no available information on the unseen operating conditions, the model trained on the seen operating condition cannot perform well. One of the feasible strategies is to enhance the generalization ability of the network on various seen operating conditions. We introduce the center loss to the traditional CNN and build an end-to-end fault diagnosis framework (called CNN-C). By minimizing the intra-class variations, center loss cluster the learned features across various seen operating conditions. With the joint supervision of the center loss and the softmax loss, the learned features of the same class could minimize the domain difference across various seen operating conditions while the features of different classes are separable. The generalization ability of network is improved on unseen operating conditions. Compared with the shallow methods and traditional CNN, the proposed method is promising to deal with the fault diagnosis tasks of the bearing and gearbox.
Deep Multimodal Brain Network Learning for Joint Analysis of Structural Morphometry and Functional Connectivity

Learning from the multimodal brain imaging data attracts a large amount of attention in medical image analysis due to the proliferation of multimodal data collection. It is widely accepted that multimodal data can provide complementary information than mining from a single modality. However, unifying the image-based knowledge from the multimodal data is very challenging due to different image signals, resolution, data structure, etc. In this study, we design a supervised deep model to jointly analyze brain morphometry and functional connectivity on the cortical surface and we name it deep multimodal brain network learning (DMBNL). Two graph-based kernels, i.e., geometry-aware surface kernel (GSK) and topology-aware network kernel (TNK), are proposed for processing the cortical surface morphometry and brain functional network. The vertex features on the cortical surface from GSK is pooled and feed into TNK as its initial regional features. In the end, the graph-level feature is computed for each individual and thus can be applied for classification tasks. We test our model on a large autism imaging dataset. The experimental results prove the effectiveness of our model.
Topic-Oriented Bucket-Based Fast Multicast Routing in SDN-Like Publish/Subscribe Middleware

In traditional IP-based publish/subscribe middlewares, a detour to overlay network is demanded to match events with defined filters, which introduces more latency overhead for delivering events from publishers to subscribers. The emerging Software Defined Networking (SDN) creates boundless possibilities to improve the efficiency of event delivery because of its centralized control and customized programmability. In this paper, we propose a topic-oriented bucket-based fast multicast routing to improve the efficiency of delivering events in SDN-like publish/subscribe middlewares. First, we design an SDN-like publish/subscribe middleware hierarchical architecture with an implementation framework in SDN controller to deliver events. Topic encoding, topic prefix matching, and the subscription coverage relationships between topics are considered to reduce the number of flow entries and improve the matching abilities of SDN switches. Then, a topic-oriented multicast tree construction algorithm is proposed to build publish/subscribe overlay networks with the minimal total link delay of event transmission and minimal switches in SDN controllers to deliver events fast. A topic-oriented bucket-based multicast forwarding algorithm is designed to achieve efficient multicast forwarding in SDN switches. Finally, experiments are conducted to verify that our construction algorithm has the minimal total delay of event transmission and our bucket-based multicast forwarding algorithm is effective.
A Deep Learning Approach to Video Fluoroscopic Swallowing Exam Classification

Dysphagia, or difficulty swallowing, is a serious health problem that reduces the quality of life of those affected. The standard method to diagnose dysphagia is the x-ray video fluoroscopic swallowing exam (VFSE). In this paper we investigate the use of deep learning networks to classify VFSE as normal or abnormal. The proposed network is based on a long term recurrent convolutional network (LRCN). This network was trained and validated using 1154 VFSE. Using 10-fold cross-validation, the accuracy of classification was 85% and the area under the ROC curve was 0.89. This work shows the promise of using deep learning networks as a screening tool to detect dysphagia in VFSE.
RF Cloud for Cyberspace Intelligence

Wireless information networks have become a necessity of our day-to-day life. Over a billion Wi-Fi access points, hundreds of thousands of cell towers, and billions of IoT devices, using a variety of wireless technologies, create the infrastructure that enables this technology to access everyone, everywhere. The radio signal carrying the wireless information, propagates from antennas through the air and creates a radio frequency (RF) cloud carrying a huge amount of data that is commonly accessible by anyone. The big data of the RF cloud includes information about the transmitter type and addresses, embedded in the information packets; as well as features of the RF signal carrying the message, such as received signal strength (RSS), time of arrival (TOA), direction of arrival (DOA), channel impulse response (CIR), and channel state information (CSI). We can benefit from the big data contents of the messages as well as the temporal and spatial variations of their RF propagation characteristics to engineer intelligent cyberspace applications. This paper provides a holistic vision of emerging cyberspace applications and explains how they benefit from the RF cloud to operate. We begin by introducing the big data contents of the RF cloud. Then, we explain how innovative cyberspace applications are emerging that benefit from this big data. We classify these applications into three categories: wireless positioning systems, gesture and motion detection technologies, and authentication and security techniques. We explain how Wi-Fi, cell-tower, and IoT wireless positioning systems benefit from big data of the RF cloud. We discuss how researchers are studying applications of RF cloud features for motion, activity and gesture detection for human-computer interaction, and we show how authentication and security applications benefit from RF cloud characteristics.
Gastrointestinal: Bowel ischemia in a suspected coronavirus disease (COVID‐19) patient

A 70-year-old male was admitted to our hospital for abdominal pain, nausea, fever, pharyngodinia, and cough for 3 days. His oxygen saturation was 95% and body temperature 37.5°C. Moreover, diffuse abdominal tenderness and positive Blumberg sign were observed. Laboratory test showed increased white blood cell count (15.3 × 10 μ/L, with 90% neutrophils) and increased C-reactive protein (149 mg/L), with normal levels of total bilirubin and transaminase. Reverse transcription polymerase chain reaction (RT-PCR) performed on nasopharyngeal swab resulted negative for SARSCoV-2 infection, and therefore, suspected COVID-19 was the final diagnosis, according to the European Center for Disease Control criteria. Computed tomography (CT) demonstrated findings classified as common in COVID-19 pneumonia, according to the recommendations of the Radiologic Society of North America. Contrast-enhanced CT of the abdomen showed acute small bowel hypoperfusion (Fig. 1a). The patient was declared inoperable and passed away 2 days after admission, despite medical treatments. The case above described had clinical signs and symptoms and chest CT findings consistent with COVID-19, but the final diagnosis was suspected COVID-19 due to the negative result of RTPCR. However, this clinical scenario is not unusual due to the well-known low specificity of RT-PCR on nasopharyngeal swab. The clinical course of COVID-19 may be complicated by coagulopathy. Pulmonary embolism was recently described as a possible complication in patients with severe bilateral pneumonia from COVID-19. Furthermore, a significantly higher incidence of disseminated intravascular coagulation was recently demonstrated in the group of non-survivors of a population of patients affected by COVID-19 pneumonia. To the best of our knowledge, this is the first report on acute bowel ischemia due to thromboembolism, in a non-survivor patient with suspected COVID-19. This case highlights the concept that it is possible to find extrapulmonary symptoms and signs of thromboembolism also in patients with the diagnosis of suspected COVID-19. Therefore, in suspected and confirmed COVID-19, it is mandatory for radiologists to acquire CT also in the arterial phase and to carefully analyze all vascular structures, in order to detect any sign of thromboembolism, in both pulmonary and systemic circulation.
A Modified Four-Component Decomposition Method With Refined Volume Scattering Models

In this article, a modified four-component decomposition method with refined volume scattering models is proposed for polarimetric synthetic aperture radar (SAR) image processing. In the new decomposition method, after the orientation angle compensation, the orientation angle is placed in the probability density functions. General forms of the volume scattering models and branch conditions can be obtained. Similar to the four-component scattering power decomposition with extended volume scattering model (S4R) proposed by Sato et al., refined volume scattering models can be used for various land covers based on the criteria. Since the orientation angles are contained in the refined volume scattering models, the oriented buildings can be discriminated from the vegetation areas and the overestimation problem of volume scattering is substantially overcome. In this article, the performance of the proposed method is evaluated by the spaceborne C-band Gaofen-3 data and airborne L-band E-SAR data. Several approaches are employed as a comparison of the proposed methods. Experimental results show that, compared with the existing decomposition methods, the proposed method can effectively represent the scattering characteristics of the ambiguous regions, and the double-bounce scattering contributions over the urban areas can be substantially enhanced.
Continuous Microfluidic Encapsulation of Single Mesenchymal Stem Cells Using Alginate Microgels as Injectable Fillers for Bone Regeneration

The encapsulation of cells in microscale hydrogels can provide a mimic of a three-dimensional (3D) microenvironment to support cell viability and functions and to protect cells from the environmental stress, which have been widely used in tissue regeneration and cell therapies. Here, a microfluidics-based approach is developed for continuous encapsulation of mesenchymal stem cells (MSCs) at the single-cell level using alginate microgels. This microfluidic technique integrated on-chip encapsulation, gelation, and de-emulsification into a one-step fabrication process, which enables scalable cell encapsulation while retaining the viability and functionality of loaded cells. Remarkably, we observed MSCs encapsulated in Ca-alginate microgels at the single-cell level showed significantly enhanced osteogenesis and accelerated mineralization of the microgels which occurred only after 7 days of induction. Furthermore, MSCs laden in alginate microgels displayed significantly enhanced bone formation compared to MSCs mixed with microgels and acellular microgels in a rat tibial ablation model. To conclude, the current microfluidic technique represents a significant step toward continuous single cells encapsulation, fabrication, and purification. These microgels can boost bone regeneration by providing a controlled osteogenic microenvironment for encapsulated MSCs and facilitate stem cell therapy in the treatment of bone defects in a minimally invasive delivery way. Statement of significance The biological functions and therapeutic activities of single cells laden in microgels for tissue engineering remains less investigated. Here, we reported a microfluidic-based method for continuous encapsulation of single MSCs with high viability and functionality by integrating on-chip encapsulation, gelation, and de-emulsification into a one-step fabrication process. More importantly, MSCs encapsulated in alginate microgels at the single-cell level showed significantly enhanced osteogenesis, remarkably accelerated mineralization in vitro and bone formation capacity in vivo. Therefore, this single-cell encapsulation technique can facilitate stem cell therapy for bone regeneration and be potentially used in a variety of tissue engineering applications.
An improved recombinase polymerase amplification assay for visual detection of Vibrio parahaemolyticus with lateral flow strips.

Vibrio parahaemolyticus is an important pathogenic bacterium in both food safety management and mariculture. Rapid and accurate detection technologies are critical for effective control of its outbreak and spreading. Conventional technologies and polymerase chain reaction (PCR)-based approaches have limited usage because of the requirement of laboratory instruments and trained personnel. Using the isothermal recombinase polymerase amplification (RPA) technology, several detection assays have been developed with added convenience. Combining the lateral flow strip (LFS) test with RPA can further simplify the detection. In this study, an improved RPA assay using LFS for visual detection of V. parahaemolyticus was developed. Primers were designed targeting the virulence genes and screened for amplification efficiency, nonspecific amplification, and primer-dimer formation. Probes were designed for the best primer pairs, and the weakness of LFS tests, being easily affected by primer-dependent artifacts, was overcome by sequence modifications on primers and probe. The RPA-LFS assay took 25 min at 35 to 45 °C, and showed excellent specificity. It detected as low as one colony forming unit (CFU) of V. parahaemolyticus per reaction without DNA purification, or 10 CFU/10 g spiked food samples with 2 hr of enrichment. The detection limit was better than the currently available RPA-based detection methods. Application of the RPA-LFS assay for simulated samples or real clinical samples showed accurate and consistent detection results compared to bioassay and quantitative PCR. The RPA-LFS assay provided a rapid, accurate, and convenient V. parahaemolyticus detection method suitable for on-site detection in resource-limited conditions. PRACTICAL APPLICATION: This research developed a rapid and visual detection technology for Vibrio parahaemolyticus that is not dependent on complicated equipment. The detection process takes 25 min and the result is read with the naked eye. A detection kit can be developed based on this technology for on-site detection of V. parahaemolyticus in resource-limited regions for food safety management and mariculture.
Optimal Control of AVR System With Tree Seed Algorithm-Based PID Controller

In this study, an optimal Tree-Seed Algorithm (TSA) algorithm-based Proportional-Integral-Derivative (PID) controller is proposed for automatic voltage regulator (AVR) system terminal tracking problem. PID controller gains <inline-formula> <tex-math notation="LaTeX">$K\text{p}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$K\text{i}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$K\text{d}$ </tex-math></inline-formula> are optimized with the proposed TSA algorithm based on different objective functions. The TSA-based optimal PID controller’s performance is compared with numerous PID controllers, which were developed using different meta-hermetic optimization algorithms in the literature. Several analysis methods including root locus, bode analysis, robustness, and disturbance rejection are studied and compared with reported works in the literature. It is shown that there is still a research gap to improve the tracking performance of the AVR system due to its importance in electrical systems. According to the obtained comparison results, it has been revealed that the proposed TSA-based PID controller improves tracking properties under load change thus it can be effectively used for synchronous generator automatic voltage regulator terminal voltage stability.
Continuous Sign Language Recognition Through Cross-Modal Alignment of Video and Text Embeddings in a Joint-Latent Space

Continuous Sign Language Recognition (CSLR) refers to the challenging problem of recognizing sign language glosses and their temporal boundaries from weakly annotated video sequences. Previous methods focus mostly on visual feature extraction neglecting text information and failing to effectively model the intra-gloss dependencies. In this work, a cross-modal learning approach that leverages text information to improve vision-based CSLR is proposed. To this end, two powerful encoding networks are initially used to produce video and text embeddings prior to their mapping and alignment into a joint latent representation. The purpose of the proposed cross-modal alignment is the modelling of intra-gloss dependencies and the creation of more descriptive video-based latent representations for CSLR. The proposed method is trained jointly with video and text latent representations. Finally, the aligned video latent representations are classified using a jointly trained decoder. Extensive experiments on three well-known sign language recognition datasets and comparison with state-of-the-art approaches demonstrate the great potential of the proposed approach.
Morphological and Biometric Features of Male Bluethroats Luscinia svecica in Central Iberia

Summary. In order to test biometrics and plumage variability in bluethroats Luscinia svecica in central Iberia, 185 birds were captured in four areas (Sierra-Béjar, Sierra-Piedrahita, Sierra-Gredos and Sierra-Paramera). Older bluethroats were significantly larger than second calendar year birds in wing, tail, and total body lengths, but not in tarsus and bill lengths. A multivariate analysis of variance (MANOVA) and a principal component analysis (PCA) were used. Total body length only varied significantly between sampling areas: the birds from Sierra-Béjar were larger than those from Sierra-Gredos and Sierra-Paramera, but were similar to those of Sierra-Piedrahita. Birds with a white spot, a chestnut spot or no spot were found. Therefore, the question arises whether there are three subspecies (L. s. cyanecula, L. s. svecica and L. s. azuricollis), or three forms of a single subspecies (L. s. azuricollis). The percentage of birds with and without a throat spot was similar in the four sampled areas, and the percentage of birds with a spot significantly increased with age. These observations suggest that some plumage characters, such as the throat spot, should not be used as taxonomic characters, at least in the area where this study was conducted.
Mechanisms of Asymmetrical Turn-On and Turn-Off and the Origin of Dynamic CGD Hysteresis for Hard-Switching Superjunction MOSFETs

The <italic>dV</italic> / <italic>dt</italic> in superjunction metal–oxide–semiconductor field-effect transistors (MOSFETs) during the turn-off transient has been shown to be higher than during the turn-on, which can be attributed to a shorter Miller plateau. In this article, we will show that these asymmetrical turn-on and turn-off characteristics are indirectly detected by the hysteresis of the dynamic gate-to-drain capacitance, <inline-formula> <tex-math notation="LaTeX">${C}_{\text {GD}}$ </tex-math></inline-formula>. Moreover, this article reveals the mechanisms behind the asymmetrical switching and the origin of the hysteresis, which have been shown to be caused by the difference in the <inline-formula> <tex-math notation="LaTeX">${C}_{\text {GD}}$ </tex-math></inline-formula> displacement current paths for the turn-off and the turn-on.
Tibial Intraosseous Administration of Epinephrine Is Effective in Restoring Return of Spontaneous Circulation in a Pediatric Normovolemic But Not Hypovolemic Cardiac Arrest Model

Objective We compared the efficacy of tibial intraosseous (TIO) administration of epinephrine in a pediatric normovolemic versus hypovolemic cardiac arrest model to determine the incidence of return of spontaneous circulation (ROSC) and plasma epinephrine concentrations over time. Methods This experimental study evaluated the pharmacokinetics of epinephrine and/or incidence of ROSC after TIO administration in either a normovolemic or hypovolemic pediatric swine model. Results All subjects in the TIO normovolemia cardiac arrest group experienced ROSC after TIO administration of epinephrine. In contrast, subjects experiencing hypovolemia and cardiac arrest were significantly less likely to experience ROSC when epinephrine was administered TIO versus intravenous (TIO hypovolemia: 14% [1/7] vs IV hypovolemia: 71% [5/7]; P = 0.031). The TIO hypovolemia group exhibited significantly lower plasma epinephrine concentrations versus IV hypovolemia at 60, 90, 120, and 150 seconds (P < 0.05). Although the maximum concentration of plasma epinephrine was similar, the TIO hypovolemia group exhibited significantly slower time to maximum concentration times versus TIO normovolemia subjects (P = 0.004). Conclusions Tibial intraosseous administration of epinephrine reliably facilitated ROSC among normovolemic cardiac arrest pediatric patients, which is consistent with published reports. However, TIO administration of epinephrine was ineffective in restoring ROSC among subjects experiencing hypovolemia and cardiac arrest. Tibial intraosseous–administered epinephrine during hypovolemia and cardiac arrest may have resulted in a potential sequestration of epinephrine in the tibia. Central or peripheral intravascular access attempts should not be abandoned after successful TIO placement in the resuscitation of patients experiencing concurrent hypovolemia and cardiac arrest.
Time Resolved Hyperspectral Quantum Rod Thermography of Microelectronic Devices: Temperature Transients in a GaN HEMT

The trend of miniaturization and rapid progress in the cost-competitive microelectronic industry require high resolution, fast, accurate and cost-effective thermal characterization techniques. These techniques aid the assessment of reliability and performance bench-marking of new device designs for the realistic operation conditions. We present a time resolved, surface sensitive, sub-micron resolution wide field thermal imaging technique, exploiting fast radiative recombination rates of quantum rod photoluminescence to probe temperature transients in semiconductor devices. We demonstrate a time resolution of $20~\mu \text{s}$ on a single finger AlGaN/GaN HEMT. This technique provides an image of the surface temperature transients regardless of the device design/material system under test. The results were verified with transient thermo-reflectance measurements.