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Several algorithms for nuclear power plant (NPP) break event detection, isolation, localization, and size estimation are proposed. A break event can be promptly detected and isolated after its occurrence by simultaneously monitoring changes in the sensing readings and by employing an interquartile range-based isolation scheme. By considering the multi-sensor data block of a break to be rank-one, it can be located as the position whose lead field vector is most orthogonal to the noise subspace of that data block using the Multiple Signal Classification (MUSIC) algorithm. Owing to the flexibility of deep neural networks in selecting the best regression model for the available data, we can estimate the break size using multiple-sensor recordings of the break regardless of the sensor types. The efficacy of the proposed algorithms was evaluated using the data generated by Maanshan NPP simulator. The experimental results demonstrated that the MUSIC method could distinguish two near breaks. However, if the two breaks were close and of small sizes, the MUSIC method might wrongly locate them. The break sizes estimated by the proposed deep learning model were close to their actual values, but relative errors of more than 8% were seen while estimating small breaks’ sizes.

Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy > 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD.

Security is a primary concern in Internet-of-Things (IoT) applications, and biometric recognition is considered to be a promising solution. In this article, we propose a novel electrocardiogram (ECG)-based biometric recognition scheme that can potentially strengthen the security of IoT-based patient monitoring systems. A biometric system is designed to operate in either verification or identification mode, and we concentrate on applying the proposed approach to the latter due to its difficulty and popularity in existing studies. Through the concept of “subspace oversampling,” we are able to create distinct and irreversible templates for an enrollee to avoid the cross-matching problem and privacy invasion. With the help of “subspace matching,” the identity of unknown subjects can be determined using only their beat bundles without any additional information required for template construction. Moreover, the proposed scheme includes a method for unregistered subject exclusion to avoid falsely linking an initially unidentifiable subject to someone in the database, further strengthening its security. The performance of the proposed scheme was evaluated using the ECGs of 287 subjects from the Physikalisch Technische Bundesanstalt data set. The experimental results demonstrated the linkability of the constructed templates as low as 0.0938, and beat bundles reconstructed from the templates of a given subject were more likely to be identified as those from another user. An identification rate of 99.02% was obtained even when the proposed exclusion scheme was incorporated; meanwhile, the corresponding false-positive identification error rate was 0.44% under a dictionary attack with real ECGs.

Deep learning (DL) is known for its excellence in feature learning and its ability to deliver high-accuracy results. Its application to ECG biometric recognition has received increasing interest but is also accompanied by several deficiencies. In this study, we focus on applying DL, especially convolutional neural networks (CNNs), to ECG biometric identification to address these deficiencies. Using prestored user-specific feature vectors, the proposed scheme can exclude unregistered subjects to realize ``open-set'' identification. With the help of its scalable structure and ``transfer learning,'' new subjects can be enrolled in an existing system without the need for storing the ECGs of those previously enrolled. Finally, schemes based on the quantum evolutionary algorithm (QEA) are presented to prune unnecessary filters in the proposed CNN model. The performance of the proposed scheme was evaluated using the ECGs of 285 subjects from the PTB dataset. The experimental results demonstrate an identification rate of more than 99% in closed-set identification. Although incorporating the proposed method for unregistered subject exclusion degraded the identification performance slightly, the ability of the approach to resist a dictionary attack was evident. Finally, using the QEA-based filter pruning method and its two-stage extension reduced the number of floating-point operations required to complete one identity recognition to 1.20% and 0.22% of the original value without significantly impacting the identification accuracy.

Hydrogen evolution reaction (HER) plays a key role in combating an ever-growing imbalance and adverse climatic issues. Thus far, Pt-based catalysts are treated as “the Holy Grail” for HER and none ...

Summary All-optical physiology (AOP) manipulates and reports neuronal activities with light, allowing for interrogation of neuronal functional connections with high spatiotemporal resolution. However, contemporary high-speed AOP platforms are limited to single-depth or discrete multi-plane recordings that are not suitable for studying functional connections among densely packed small neurons, such as neurons in Drosophila brains. Here, we constructed a 3D AOP platform by incorporating single-photon point stimulation and two-photon high-speed volumetric recordings with a tunable acoustic gradient-index (TAG) lens. We demonstrated the platform effectiveness by studying the anterior visual pathway (AVP) of Drosophila. We achieved functional observation of spatiotemporal coding and the strengths of calcium-sensitive connections between anterior optic tubercle (AOTU) sub-compartments and >70 tightly assembled 2-μm bulb (BU) microglomeruli in 3D coordinates with a single trial. Our work aids the establishment of in vivo 3D functional connectomes in neuron-dense brain areas., Graphical Abstract, Highlights • All-optical volumetric physiology = precise stimulation + fast volumetric recording • Precise single-photon point stimulation among genetically defined neurons • 3D two-photon imaging by an acoustic gradient-index lens for dense neural structures • Observation of 3D functional connectivity in Drosophila anterior visual pathway, Neuroscience; Techniques in Neuroscience; Biological Sciences Research Methodologies; Biological Sciences Tools

Relying on physiological or behavioral traits for identity recognition, biometric technologies offer several advantages over conventional possession- and knowledge-based approaches and are now widely used in diverse applications. However, as most biometrics ( e.g. , fingerprints, irises, etc.) in use are extrinsic, susceptible to replay attacks, and could result in the disclosure of individuals’ physiological and pathological conditions, security and privacy concerns must be considered. In this paper, several electrocardiogram (ECG)-based cancelable biometric schemes are proposed to mitigate such concerns. The intrinsic and dynamic nature of ECGs and their inherent indication of life make them extremely difficult to steal or counterfeit. Using the concept of “signal subspace collapsing,” distinct biometric templates associated with an individual’s ECGs can be constructed such that it is possible to revoke a compromised template like a password. By incorporating different strategies for common subspace suppression, the well-known multiple signal classification method can effectively determine the identity of any individual just via his/her ECGs. Unlike existing cancelable biometrics, the recognition can be accomplished without knowledge of the distortion transformation, which further increases the difficulty of recovering the original ECGs from their templates. Various experiments with real ECGs from 285 subjects are conducted to illustrate the efficacy of the proposed schemes.

Reactor-core loading pattern (LP) design is a task performed during refueling to rearrange the spent and newly refilled fuel assemblies (FAs). To attain sufficient cycle length while complying with safety constraints, LP design is not easy, with its difficulty growing exponentially as the number of FAs increases. Attempts to enable design automation have been made with various metaheuristic optimization algorithms. Among them, quantum-inspired algorithms have received much attention because of their small required population of individuals and superior search capability, although the problem of premature convergence also accompanies their applications in LP design. In this paper, a quantum evolutionary algorithm (QEA)-based automated scheme is proposed for LP design in pressurized water reactors. First, the premature convergence in QEA is relieved by modifying its evolutionary mechanism and the decoding scheme to construct an LP. Next, self-regulated learning is incorporated to acquire information that can expedite better LP search so that more satisfactory LPs can be found. The design capability of the proposed scheme is demonstrated with a reference cycle of the Maanshan nuclear power plant, and the results from several experiments illustrate the efficacy of the proposed approaches.

The widespread sensors in a nuclear power plant (NPP) provide vital support to its operation. Any fault in these sensors can be a severe issue, threatening the safety of the NPP. Thus, the detection and isolation of the problematic sensors and the reconstruction of their readings are essential. In this study, several algorithms for sensor fault detection, isolation, and signal reconstruction are proposed. Through simultaneously monitoring changes in the recordings from sensors of the same type, an anomaly can be detected while the preset thresholds are exceeded. No subjective reasoning is required in setting the thresholds. To prevent improper recovery actions, an interquartile range-based scheme is employed to avoid falsely linking the variations in sensor readings caused by an initiating event (IE) to those caused by sensor faults. Moreover, via a sequential backward selection-based approach, the faulty sensors can be effectively isolated without the need for checking all the installed ones when the variations are deemed to be caused by sensor faults. Lastly, multivariate autoregressive model-based approaches are introduced to reconstruct the readings of faulty sensors either when an NPP is in normal operation or during the occurrence of an IE. Results of experiments using data generated by a simulator of the Maanshan NPP in Taiwan are provided to illustrate the efficacy of the proposed approaches.

Catalytic carbon dioxide (CO2) hydrogenation to carbon monoxide (CO) via reverse water-gas shift (RWGS) reaction is of particular interest due to its direct use in various industrial processes as feedstock. However, the competitive CO2 methanation process severely limits the RWGS reaction in a lower temperature range. In this context, we propose a novel nanocatalyst (NC) comprising oxygen vacancy-enriched subnanometer-scale CoPd hybrid cluster (CoOxVPd)-anchored Pd nanoparticles (NPs) on cobalt oxide support underneath (denoted as CP-CoOxVPd) by using a galvanic replacement reaction-assisted wet chemical reduction method. As-developed CP-CoOxVPd NC initiated the RWGS reaction at 423 K temperature while showing an optimum CO production yield of ∼3414 μmol g−1catalyst and a CO selectivity as high as ∼99% at 523 K in the reaction gas of CO2:H2 = 1:3. The results of physical characterizations along with electrochemical and gas chromatography (GC) suggest that abundant oxygen vacancies in the surface-anchored CoOxVPd clusters are vital for CO2 adsorption and subsequent activation, while neighboring Pd domains facilitate the H2 dissociation. The obtained results are expected to provide a feasible design of Co-based NCs for the RWGS reaction.

A two-stage deep learning-based scheme is presented to predict the Hamilton Depression Scale (HAM-D) in this study. First, the cross-sample entropy (CSE) that allows assessing the degree of similarity of two data series are evaluated for the 90 brain regions of interest partitioned according to Automated Anatomical Labeling. The obtained CSE maps are then converted to 3D CSE volumes to serve as the inputs to the deep learning network models for the HAM-D scale level classification and prediction. The efficacy of the proposed scheme was illustrated by the resting-state functional magnetic resonance imaging data from 38 patients. From the results, the root mean square errors for the HAM-D scale prediction obtained during training, validation, and testing were 2.73, 2.66, and 2.18, which were less than those of a scheme having only a regression stage.

A unified framework for the analysis of fluorescence data taken by a two-photon imaging system is presented. As in the processing of blood-oxygen-level-dependent signals of functional magnetic resonance imaging, the acquired functional images have to be co-registered with a structural brain atlas before delineating the regions activated by a given stimulus. The voxels whose calcium traces are highly correlated with the predicted responses are demarcated without the need for subjective reasoning. Experimental data acquired while presenting olfactory stimuli are used to demonstrate the efficacy of the proposed schemes. The results indicate that the functional images of a Drosophila individual can be normalized into a standard stereotactic space, and the expected brain regions can be delineated adequately. This framework provides an opportunity to enable the development of a Drosophila functional connectome database.

Small cell lung cancer (SCLC) continues to cause poor clinical outcomes due to limited advances in sustained treatments for rapid cancer cell proliferation and progression. The transcriptional factor Forkhead Box M1 (FOXM1) regulates cell proliferation, tumor initiation, and progression in multiple cancer types. However, its biological function and clinical significance in SCLC remain unestablished. Analysis of the Cancer Cell Line Encyclopedia and SCLC datasets in the present study disclosed significant upregulation of FOXM1 mRNA in SCLC cell lines and tissues. Gene set enrichment analysis (GSEA) revealed that FOXM1 is positively correlated with pathways regulating cell proliferation and DNA damage repair, as evident from sensitization of FOXM1-depleted SCLC cells to chemotherapy. Furthermore, Foxm1 knockout inhibited SCLC formation in the Rb1fl/flTrp53fl/flMycLSL/LSL (RPM) mouse model associated with increased levels of neuroendocrine markers, Ascl1 and Cgrp, and decrease in Yap1. Consistently, FOXM1 depletion in NCI-H1688 SCLC cells reduced migration and enhanced apoptosis and sensitivity to cisplatin and etoposide. SCLC with high FOXM1 expression (N = 30, 57.7%) was significantly correlated with advanced clinical stage, extrathoracic metastases, and decrease in overall survival (OS), compared with the low-FOXM1 group (7.90 vs. 12.46 months). Moreover, the high-FOXM1 group showed shorter progression-free survival after standard chemotherapy, compared with the low-FOXM1 group (3.90 vs. 8.69 months). Our collective findings support the utility of FOXM1 as a prognostic biomarker and potential molecular target for SCLC.

Surgical removal of the head cuticle of Drosophila is necessary for optical brain imaging, leading to the Drosophila death in a short period of time and thus hindering long term monitoring. Targeting to the unmet need of surgery free procedure for Drosophila brain researches, in this study, laser scanning optical resolution photoacoustic microscopy (LSOR-PAM) of in vivo cuticle intact RCaMP labeled Drosophila mushroom body (MB) is presented. The MB is a higher-order olfactory center in Drosophila brain. This study paves the way toward exploring LSOR-PAM functional imaging capability of cuticle intact Drosophila brain under olfactory stimulus.