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The versatile synthetic intermediate (2S,4R)-4-hydroxylysine can be produced using l-lysine hydroxylase. However, the wild-type enzyme cannot effectively catalyze the C4 hydroxylation of l-lysine t...

9α-Hydroxy-4-androstene-3,17-dione (9OHAD), formed by the 9α-hydroxylation reaction catalyzed by 3-ketosteroid-9-hydroxylase (KSH), is an important precursor for the synthesis of adrenocortical hor...

Instead of using traditional intrusive surveillance technologies such as cameras or body sensors, we propose a smart home system sensing the footstep-induced vibrations to feature the same functions which can be used for the assisted-living purpose. Based on distributed sensing and processing units, we adopt a distributed computing approach to preprocess the time-series data, recognize footstep signals, and extract vibration features locally. Through communications over the sensor networks, our system is capable to estimate the occupancy via counting the number of occupants. Besides, based on the multicomponent seismometer sensing system, we propose a novel indoor footstep localization method called angle constrained time difference of arrivals relying on both angle and arrival information of the recorded waveforms. According to separated pedestrian locations, different trajectories of multiple people can be tracked. From the location-tracking history, the resident's daily activities and social interactions can be inferred. In our experiments, the proposed system obtains promising results. Specifically, the location error is 0.14 m with a 0.11 m standard deviation. And the multipeople identification accuracy is above 87.83%.

With the dramatic growth of seismic data volume, efficient and accurate seismic source location has become a significant challenge to seismologists. Recently, time reversal imaging (TRI) has been widely applied in automatic seismic source location for its robustness and accuracy, but its wave-equation-based implementation is usually computationally expensive. To achieve an efficient in situ and real-time source location, the emerging sensor network is a good option. In this article, we propose a simplified Gaussian beam TRI (SGTRI) method to implement the seismic source location in a distributed sensor network. Gaussian beam (GB) is a high-frequency asymptotic solution of the wave equation, which can help reduce the computation costs of the wavefield extrapolation in conventional TRI. Traditionally, the GB construction for reflection seismic imaging covers the entire subsurface space. However, for certain source localization, only limited areas contribute. Thus, we propose a beamforming-technique-based simplified GB construction to further boost efficiency. Then, we propose an imaging condition for the SGTRI to construct the final source location map. Using synthetic experiments, we demonstrate the accuracy, robustness, and efficiency of the proposed method compared with conventional TRI. In the end, a field application also shows promising results.

Structured light is a widely employed approach for profile measurement in active stereo vision. Recent methods have developed learning-based single pattern structured light systems and unstructured light stereo vision systems. However, their methods require additional cameras or a diffractive optical element. In addition, the calibration methods of the learning-based structured light systems require a large number of images, while the stereo vision method cannot detect the blocked region. Classically, a digital projector is treated as a pseudocamera, and the stripe pattern is employed to measure the depth. However, an additional pattern is employed to calibrate the camera-projector system in the stripe-based method. In this paper, 24 reliable features and a novel calibration algorithm are proposed. With the proposed calibration algorithm, the stripe patterns are calibrated without additional patterns, and with the proposed RGB features, the number of encoded columns is enlarged significantly. Experiments demonstrate that the proposed method is accurate and robust.

The recent advancement of motion tracking technology offers better treatment tools for conditions, such as movement disorders, as the outcome of the rehabilitation could be quantitatively defined. The accurate and fast angular information output of the inertial measurement unit tracking systems enables the collection of accurate kinematic data for clinical assessment. This article presents a study of a low-cost microelectromechanical system inertial measurement unit-based tracking system in comparison with the conventional optical tracking system. The system consists of seven microelectromechanical system inertial measurement units, which could be mounted on the lower limbs of the subjects. For the feasibility test, 10 human participants were instructed to perform three different motions: walking, running, and fencing lunges when wearing specially designed sleeves. The subjects’ lower body movements were tracked using our inertial measurement unit-based system and compared with the gold standard—the NDI Polaris Vega optical tracking system. The results of the angular comparison between the inertial measurement unit and the NDI Polaris Vega optical tracking system were as follows: the average cross-correlation value was 0.85, the mean difference of joint angles was 2.00°, and the standard deviation of joint angles was ± 2.65°. The developed microelectromechanical system–based tracking system provides an alternative low-cost solution to track joint movement. Moreover, it is able to operate on an Android platform and could potentially be used to assist outdoor or home-based rehabilitation.

In this article, a systematic and generalized methodology is originally proposed to assess the vulnerability of electric drive systems due to sensor data integrity attacks. Novel evaluation metrics from the perspectives of steady-state and transient performance of electric drive systems are established to evaluate the system condition under different attacks. By using these metrics, innovative index-based resilience and security criteria, together with the stability theorem, are proposed specifically for electric drive systems, which can then be used for cyber-attack detection and diagnosis in a more systematic manner. Then, based on the simulation results under 15 attack cases (five typical types), the qualitative attack impacts on the dynamic characteristics and the statistical damage of different cyber-attacks to the defined metrics are analyzed, which can serve as useful guidelines for attack detection, diagnosis, and countermeasures.

Islanded nanogrids (NGs) are autonomous systems consisting of small-scale generation units including renewable energy sources and traditional fuel generators and energy storage systems (ESS) that typically serve few buildings or loads. This work aims at developing and validating a new optimal energy management (EM) algorithm for an islanded NG. To minimize the generator's operating cost and maximize battery availability at each operating cycle, dynamic programming (DP) framework is employed to solve the underlying optimization problem. The goal of the proposed approach is to ensure the use of maximum available solar power and to achieve optimal battery state of charge. To meet that goal, the management of the ESS is formulated as a stochastic optimal control problem, where nonlinearities in the battery discharging process are considered. A Markov model is constructed for predicting the probability distribution of the solar production used in the stochastic DP formulation. Simulation results are given to illustrate the efficacy of the proposed DP-based approach compared to a rule-based algorithm. Finally, a hardware-in-the-loop system is used to evaluate the real-time operation of the proposed EM algorithm.

Understanding earthquakes and volcano eruptions phenomena require the analysis of large amounts of subsurface data. Currently, data collected from these events need post-processing in an off-line mode due to hardware limitations and strong bandwidth constraints. Furthermore, analytics may take days, even weeks, in being processed after collection. In some cases, underground structures require previous studies and subsurface knowledge from experts to establish certain input parameters. By leveraging current IoT technologies, we introduce an autonomous seismic imaging system (ASIS), a real-time system for monitoring and generating analytics of seismic data based on a sensor network. ASIS processes real-time analytics near where the events are generated (fog computing) to mitigate bandwidth limitations. Calculations for earthquake detection, location, and magnitude are processed in situ . ASIS allows monitoring sensor status, visualizing the data of each sensor, and generating two dimensional/three dimensional (2-D/3-D) subsurface structures. The system is able to learn the underground structure by taking advantage of ambient noise data analysis, which significantly reduces the need of initial parameters. We incorporated a renewable energy source to extend sensor functionality, and we made the system self-healing and fault-tolerant. Indoor and outdoor evaluation of the system showed the error of earthquake detection is 13.7 ms and the spatial location accuracy is 5.1 ft.

To achieve accurate and robust object detection in the real-world scenario, various forms of images are incorporated, such as color, thermal, and depth. However, multimodal data often suffer from the position shift problem, i.e., the image pair is not strictly aligned, making one object has different positions in different modalities. For the deep learning method, this problem makes it difficult to fuse multimodal features and puzzles the convolutional neural network (CNN) training. In this article, we propose a general multimodal detector named aligned region CNN (AR-CNN) to tackle the position shift problem. First, a region feature (RF) alignment module with adjacent similarity constraint is designed to consistently predict the position shift between two modalities and adaptively align the cross-modal RFs. Second, we propose a novel region of interest (RoI) jitter strategy to improve the robustness to unexpected shift patterns. Third, we present a new multimodal feature fusion method that selects the more reliable feature and suppresses the less useful one via feature reweighting. In addition, by locating bounding boxes in both modalities and building their relationships, we provide novel multimodal labeling named KAIST-Paired. Extensive experiments on 2-D and 3-D object detection, RGB-T, and RGB-D datasets demonstrate the effectiveness and robustness of our method., Journal extension of the previous conference paper arXiv:1901.02645, see IEEE page https://ieeexplore.ieee.org/document/9523596

Human semen is a heterogeneous group containing a portion of low-motility sperm, which may determine the sperm quality evaluation. Abnormally expressed proteins in low-motility spermatozoa will be the candidates for sperm biology research. By comparing proteomes of high- or low-motility spermatozoa from the same semen of normal fertile men, 21 differentially expressed proteins were identified. Proteins with molecular chaperone function were significantly over-represented, of which HSPA1L and HSPA9 significantly decreased in low-motility sperm. Compared with young adult testes with normal spermatogenesis, HSPA1L and HSPA9 had decreased expressions in elderly testis characterised with poor spermatogenesis, suggesting their associations with spermatogenesis. Decreased expressions of HSPA1L and HSPA9 in low-motility spermatozoa were validated by Western Blot and immunofluorescence quantification analysis. HSPA1L was mainly expressed on sperm post-acrosome and midpiece, whilst HSAP9 was mainly expressed on acrosome and sperm tail. HSPA1L antibody could inhibit sperm motility validated by antibody blocking experiment, whilst HSPA9 antibody showed no significant effect on sperm motility. The study demonstrated that low-motility spermatozoa from fertile men had poor sperm quality, in which differential expressed proteins were promising markers for evaluating sperm quality, understanding mechanism of male infertility with unexplained causes, and providing new idea for male infertility research.

Geophysics-focused missions and improved geophysical data sets are critical for the future exploration of icy ocean worlds. Of particular interest is the exploration of the Galilean moon, Europa, and the Saturnian moons, Titan and Enceladus. These bodies likely have geologically active surfaces and may harbor habitable subsurface environments. Placing any candidate signatures for life in context requires further knowledge of the interior of these worlds. While the surfaces of these bodies have been mapped, their interiors remain poorly understood. Geophysical approaches such as geodesy, seismology, ice-penetrating radar and altimetry measurements, and electromagnetometry would provide critical information regarding the interior of these icy ocean worlds. The approaches described below would answer key science questions regarding ice shell and ocean dynamics, internal structure and interior layer thicknesses, near-surface structure, and how material from the deep interior might be exchanged with the surface. Here we outline the geophysical environments of Enceladus, Europa, and Titan; some outstanding science questions that remain to be addressed; and examples of the geophysical approaches that can provide the context to better understand icy ocean worlds.