Strong cosmic censorship for the massless Dirac field in the Reissner-Nordstrom-de Sitter spacetime

We present the Fermi story of strong cosmic censorship in the near-extremal Reissner-Nordstrom-de Sitter black hole. To this end, we first derive from scratch the criterion for the quasi-normal modes of Dirac field to violate strong cosmic censorship in such a background, which turns out to be exactly the same as those for Bose fields, although the involved energy momentum tensor is qualitatively different from that for Bose fields. Then to extract the low-lying quasi-normal modes by Prony method, we apply Crank-Nicolson method to evolve our Dirac field in the double null coordinates. As a result, it shows that for a fixed near-extremal black hole, strong cosmic censorship can be recovered by the $l=\frac{1}{2}$ black hole family mode once the charge of our Dirac field is greater than some critical value, which is increased as one approaches the extremal black hole.Comment: JHEP published versio

Precision Control of Amphoteric Doping in Cu x Bi2Se3 Nanoplates.

Copper-doped Bi2Se3 (Cu x Bi2Se3) is of considerable interest for tailoring its electronic properties and inducing exotic charge correlations while retaining the unique Dirac surface states. However, the copper dopants in Cu x Bi2Se3 display complex electronic behaviors and may function as either electron donors or acceptors depending on their concentration and atomic sites within the Bi2Se3 crystal lattice. Thus, a precise understanding and control of the doping concentration and sites is of both fundamental and practical significance. Herein, we report a solution-based one-pot synthesis of Cu x Bi2Se3 nanoplates with systematically tunable Cu doping concentrations and doping sites. Our studies reveal a gradual evolution from intercalative sites to substitutional sites with increasing Cu concentrations. The Cu atoms at intercalative sites function as electron donors while those at the substitutional sites function as electron acceptors, producing distinct effects on the electronic properties of the resulting materials. We further show that Cu0.18Bi2Se3 exhibits superconducting behavior, which is not present in Bi2Se3, highlighting the essential role of Cu doping in tailoring exotic quantum properties. This study establishes an efficient methodology for precise synthesis of Cu x Bi2Se3 with tailored doping concentrations, doping sites, and electronic properties

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Activity-Aware Physiological Response Prediction Using Wearable Sensors

Prediction of physiological responses can have a number of applications in the health and medical fields. However, this can be a challenging task due to interdependencies between these responses, physical activities, environmental factors and the individual’s mental state. In this work, we focus on forecasting physiological responses in dynamic scenarios where individuals are performing exercises and complex activities of daily life. We minimize the effect of environmental and physiological factors in order to focus on the effect of physical activities. In particular, we focus on forecasting heart rate and respiratory rate due to their relevance in medical and fitness training. We aim to forecast these physiological responses up to 60 s into the future, study the effect of different predictors that incorporate different sensing modalities and different amounts of historical data and analyze the performance of various strategies for prediction. Activity information is incorporated by clustering the data streams and fitting different predictive models per cluster. The effect of clustering is also studied by performing a hierarchical analysis on the clustering parameter, and we observe that activity clustering does improve the performance in our proposed methodology when predicting physiological response across modalities

Fusion of Human Gaze and Machine Vision for Predicting Intended Locomotion Mode

Predicting the user’s intended locomotion mode is critical for wearable robot control to assist the user’s seamless transitions when walking on changing terrains. Although machine vision has recently proven to be a promising tool in identifying upcoming terrains in the travel path, existing approaches are limited to environment perception rather than human intent recognition that is essential for coordinated wearable robot operation. Hence, in this study, we aim to develop a novel system that fuses the human gaze (representing user intent) and machine vision (capturing environmental information) for accurate prediction of the user’s locomotion mode. The system possesses multimodal visual information and recognizes user’s locomotion intent in a complex scene, where multiple terrains are present. Additionally, based on the dynamic time warping algorithm, a fusion strategy was developed to align temporal predictions from individual modalities while producing flexible decisions on the timing of locomotion mode transition for wearable robot control. System performance was validated using experimental data collected from five participants, showing high accuracy (over 96% in average) of intent recognition and reliable decision-making on locomotion transition with adjustable lead time. The promising results demonstrate the potential of fusing human gaze and machine vision for locomotion intent recognition of lower limb wearable robots

Improving Performance and Quantifying Uncertainty of Body-Rocking Detection Using Bayesian Neural Networks

Body-rocking is an undesired stereotypical motor movement performed by some individuals, and its detection is essential for self-awareness and habit change. We envision a pipeline that includes inertial wearable sensors and a real-time detection system for notifying the user so that they are aware of their body-rocking behavior. For this task, similarities of body rocking to other non-related repetitive activities may cause false detections which prevent continuous engagement, leading to alarm fatigue. We present a pipeline using Bayesian Neural Networks with uncertainty quantification for jointly reducing false positives and providing accurate detection. We show that increasing model capacity does not consistently yield higher performance by itself, while pairing it with the Bayesian approach does yield significant improvements. Disparities in uncertainty quantification are better quantified by calibrating them using deep neural networks. We show that the calibrated probabilities are effective quality indicators of reliable predictions. Altogether, we show that our approach provides additional insights on the role of Bayesian techniques in deep learning as well as aids in accurate body-rocking detection, improving our prior work on this subject

Foraminifera optical microscope images with labelled species and segmentation labels

The dataset contains foraminifera images of over 1,000 forams taken under 16 different lighting directions with an optical microscope. The species and locations of the samples are also specified. It also contains manual segmentation of over 400 samples from the images described above. The segmentation labels are matched by their name. To capture these images, a visual identification system was developed in order to automate the identification of target microorganisms. The visual system incorporates a controllable LED lighting ring used to capture images by illuminating the specimens from several directions, mimicking an important step in the traditional identification process. The dataset was originally used for foraminifera identification and segmentation with machine learning and computer vision techniques. This work is a collaboration between the Dr. Edgar Lobaton (Associate Professor at the North Carolina State University), Dr. Thomas Marchitto (Associate Professor at the University of Colorado Boulder) and Dr. Ritayan Mitra (Assistant Professor at IIT Bombay). Please refer to https://research.ece.ncsu.edu/aros/foram-identification/ for more information about the datasets, related studies and downloading the dataset

Associating liver partition and portal vein ligation for staged hepatectomy versus conventional two-stage hepatectomy: a systematic review and meta-analysis

Abstract Background It is generally accepted that an insufficient future liver remnant is a major limitation of large-scale hepatectomy for patients with primary hepatocellular carcinoma. Conventional two-stage hepatectomy (TSH) is commonly considered to accelerate future liver regeneration despite its low regeneration rate. Associating liver partition and portal vein ligation for staged hepatectomy (ALPPS), which is characterized by a rapid regeneration, has brought new opportunities. Methods Relevant studies were identified by searching the selected databases up to September 2017. Then, a meta-analysis of regeneration efficiency, complication rate, R0 resection ratio, and short-term outcomes was performed. Results Ten studies, comprising 719 patients, were included. The overall analysis showed that ALPPS was associated with a larger hyperplastic volume and a shorter time interval (P < 0.00001) than TSH. ALPPS also exhibited a higher completion rate for second-stage operations (odds ratio, OR 9.50; P < 0.0001) and a slightly higher rate of R0 resection (OR 1.90; P = 0.11). Interestingly, there was no significant difference in 90-day mortality between the two treatments (OR 1.44; P = 0.35). Conclusions These results indicate that compared with TSH, ALPPS possesses a stronger regenerative ability and better facilitates second-stage operations. However, the safety, patient outcomes, and patient selection for ALPPS require further study

Research Progress in Biological Control of Soft Rot of Amorphophallus konjac

In this paper, the main control methods of soft rot of Amorphophallus konjac are reviewed, with a focus on the current research status of using plant growth promoting rhizobacteria for biological control of soft rot of A. konjac, and future research directions are looked forward to

Research Progress in Biological Control of Soft Rot of Amorphophallus konjac

In this paper, the main control methods of soft rot of Amorphophallus konjac are reviewed, with a focus on the current research status of using plant growth promoting rhizobacteria for biological control of soft rot of A. konjac, and future research directions are looked forward to