Separation and purification of plant terpenoids from biotransformation.

The production of plant terpenoids through biotransformation has undoubtedly become one of the research hotspots, and the continuous upgrading of the corresponding downstream technology is also particularly important. Downstream technology is the indispensable technical channel for the industrialization of plant terpenoids. How to efficiently separate high-purity products from complex microbial fermentation broths or enzyme-catalyzed reactions to achieve high separation rates, high returns and environmental friendliness has become the focus of research in recent years.

Bloch Surface Waves Mediated Micro-Spectroscopy.

Micro-spectroscopy is a critical instrument for spectrum analysis in various applications such as chemical and biological analysis, environment detection, and hyperspectral imaging. However, current micro-spectral technique requires bulky and costly spectrometer. In this report, a new type of Bloch surface wave (BSW) based micro-spectrometer is proposed.

Circall: fast and accurate methodology for discovery of circular RNAs from paired-end RNA-sequencing data.

Background: Circular RNA (circRNA) is an emerging class of RNA molecules attracting researchers due to its potential for serving as markers for diagnosis, prognosis, or therapeutic targets of cancer, cardiovascular, and autoimmune diseases. Current methods for detection of circRNA from RNA sequencing (RNA-seq) focus mostly on improving mapping quality of reads supporting the back-splicing junction (BSJ) of a circRNA to eliminate false positives (FPs). We show that mapping information alone often cannot predict if a BSJ-supporting read is derived from a true circRNA or not, thus increasing the rate of FP circRNAs.

Deep ReLU neural networks in high-dimensional approximation.

We study the computation complexity of deep ReLU (Rectified Linear Unit) neural networks for the approximation of functions from the Hölder-Zygmund space of mixed smoothness defined on the d-dimensional unit cube when the dimension d may be very large. The approximation error is measured in the norm of isotropic Sobolev space. For every function f from the Hölder-Zygmund space of mixed smoothness, we explicitly construct a deep ReLU neural network having an output that approximates f with a prescribed accuracy ɛ, and prove tight dimension-dependent upper and lower bounds of the computation complexity of the approximation, characterized as the size and depth of this deep ReLU neural network, explicitly in d and ɛ.

Fuzzy Guided Autonomous Nursing Robot through Wireless Beacon Network.

Robotics is one of the most emerging technologies today, and are used in a variety of applications, ranging from complex rocket technology to monitoring of crops in agriculture. Robots can be exceptionally useful in a smart hospital environment provided that they are equipped with improved vision capabilities for detection and avoidance of obstacles present in their path, thus allowing robots to perform their tasks without any disturbance. In the particular case of Autonomous Nursing Robots, major essential issues are effective robot path planning for the delivery of medicines to patients, measuring the patient body parameters through sensors, interacting with and informing the patient, by means of voice-based modules, about the doctors visiting schedule, his/her body parameter details, etc.

Dynamical and static multisynchronization analysis for coupled multistable memristive neural networks with hybrid control.

This paper investigates the dynamical multisynchronization (DMS) and static multisynchronization (SMS) problems for a class of delayed coupled multistable memristive neural networks (DCMMNNs) via a novel hybrid controller which includes delayed impulsive control and state feedback control. Based on the state-space partition method and the geometrical properties of the activation function, each subnetwork has multiple locally exponential stable equilibrium states. By employing a new Halanay-type inequality and the impulsive control theory, some new linear matrix inequalities (LMIs)-based sufficient conditions are proposed.

DAGM: A novel modelling framework to assess the risk of HER2-negative breast cancer based on germline rare coding mutations.

Background: Breast cancers can be divided into HER2-negative and HER2-positive subtypes according to different status of HER2 gene. Despite extensive studies connecting germline mutations with possible risk of HER2-negative breast cancer, the main category of breast cancer, it remains challenging to obtain accurate risk assessment and to understand the potential underlying mechanisms.

Methods: We developed a novel framework named Damage Assessment of Genomic Mutations (DAGM), which projects rare coding mutations and gene expressions into Activity Profiles of Signalling Pathways (APSPs).

From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development.

Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high.

Highly In-Plane Anisotropic Two-Dimensional Ternary TaNiSe for Polarization-Sensitive Photodetectors.

Intriguing anisotropic electrical and optoelectrical properties in two-dimensional (2D) materials are currently gaining increasing interest both for fundamental research and emerging optoelectronic devices. Identifying promising new 2D materials with low-symmetry structures will be rewarding in the development of polarization-integrated nanodevices. In this work, the anisotropic electron transport and optoelectrical properties of multilayer 2D ternary TaNiSe were systematically researched.

Plasmonic tweezers: for nanoscale optical trapping and beyond.

Optical tweezers and associated manipulation tools in the far field have had a major impact on scientific and engineering research by offering precise manipulation of small objects. More recently, the possibility of performing manipulation with surface plasmons has opened opportunities not feasible with conventional far-field optical methods. The use of surface plasmon techniques enables excitation of hotspots much smaller than the free-space wavelength; with this confinement, the plasmonic field facilitates trapping of various nanostructures and materials with higher precision.

On-surface preparation of coordinated lanthanide-transition-metal clusters.

The study of lanthanide (Ln)-transition-metal (TM) heterometallic clusters which play key roles in various high-tech applications is a rapid growing field of research. Despite the achievement of numerous Ln-TM cluster compounds comprising one Ln atom, the synthesis of Ln-TM clusters containing multiple Ln atoms remains challenging. Here, we present the preparation and self-assembly of a series of Au-bridged heterometallic clusters containing multiple cerium (Ce) atoms via on-surface coordination.

A Varifocal Graphene Metalens for Broadband Zoom Imaging Covering the Entire Visible Region.

The ever-increasing demand for miniaturized optical systems has placed stringent requirements on the core element: lenses. Developing ultrathin flat lenses with a varifocal capability and broadband spectral response is critical for diverse applications, but remains challenging and has been the focus of intensive research. The recent demonstration of tunable focal length for a single wavelength with metalenses marked an important milestone for transforming the complex and bulky tunable lens kit into a single flat lens.

Transverse spin dynamics in structured electromagnetic guided waves.

Spin-momentum locking, a manifestation of topological properties that governs the behavior of surface states, was studied intensively in condensed-matter physics and optics, resulting in the discovery of topological insulators and related effects and their photonic counterparts. In addition to spin, optical waves may have complex structure of vector fields associated with orbital angular momentum or nonuniform intensity variations. Here, we derive a set of spin-momentum equations which describes the relationship between the spin and orbital properties of arbitrary complex electromagnetic guided modes.

High-performance imaging of cell-substrate contacts using refractive index quantification microscopy.

Non-invasive imaging of living cells is an advanced technique that is widely used in the life sciences and medical research. We demonstrate a refractive index quantification microscopy (RIQM) that enables label-free studies of glioma cell-substrate contacts involving cell adhesion molecules and the extracellular matrix. This microscopy takes advantage of the smallest available spot created when an azimuthally polarized perfect optical vortex beam (POV) is tightly focused with a first-order spiral phase, which results in a relatively high imaging resolution among biosensors.

Event-triggered impulsive synchronization of discrete-time coupled neural networks with stochastic perturbations and multiple delays.

This paper deals with the synchronization for discrete-time coupled neural networks (DTCNNs), in which stochastic perturbations and multiple delays are simultaneously involved. The multiple delays mean that both discrete time-varying delays and distributed delays are included. Time-triggered impulsive control (TTIC) is proposed to investigate the synchronization issue of the DTCNNs based on the recently proposed impulsive control scheme for continuous neural networks with single time delays.

Packing Sierpiński Triangles into Two-Dimensional Crystals.

Fractals are of fundamental importance in science and technology. Theoretical simulations indicate that Sierpiński triangles (STs) possess specific optical and electronic properties. To study their properties and uncover their potential applications, it is necessary to pack STs into large-scale two-dimensional crystalline structures.

Mapping the near-field spin angular momenta in the structured surface plasmon polariton field.

Optical spin angular momenta in a confined electromagnetic field exhibit a remarkable difference with their free space counterparts; in particular, the optical transverse spin that is locked with the energy propagating direction lays the foundation for many intriguing physical effects such as unidirectional transportation, quantum spin Hall effects, photonic Skyrmions, etc. In order to investigate the underlying physics behind the spin-orbit interactions as well as to develop the optical spin-based applications, it is crucial to uncover the spin texture in a confined field, yet it faces challenges due to their chiral and near-field vectorial features. Here, we propose a scanning imaging technique which can map the near-field distributions of the optical spin angular momenta with an achiral dielectric nanosphere.

ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET.

One of the crucial problems in Industry 4.0 is how to strengthen the performance of mobile communication within mobile ad-hoc networks (MANETs) and mobile computational grids (MCGs). In communication, Industry 4.

Analysis of Outbreak and Global Impacts of the COVID-19.

Corona viruses are a large family of viruses that are not only restricted to causing illness in humans but also affect animals such as camels, cattle, cats, and bats, thus affecting a large group of living species. The outbreak of Corona virus in late December 2019 (also known as COVID-19) raised major concerns when the outbreak started getting tremendous. While the first case was discovered in Wuhan, China, it did not take long for the disease to travel across the globe and infect every continent (except Antarctica), killing thousands of people.

The Combination of Adaptive Convolutional Neural Network and Bag of Visual Words in Automatic Diagnosis of Third Molar Complications on Dental X-Ray Images.

In dental diagnosis, recognizing tooth complications quickly from radiology (e.g., X-rays) takes highly experienced medical professionals.

Cosine similarity measures of bipolar neutrosophic set for diagnosis of bipolar disorder diseases.

Similarity plays a significant implicit or explicit role in various fields. In some real applications in decision making, similarity may bring counterintuitive outcomes from the decision maker's standpoint. Therefore, in this research, we propose some novel similarity measures for bipolar and interval-valued bipolar neutrosophic set such as the cosine similarity measures and weighted cosine similarity measures.

Design a prototype for automated patient diagnosis in wireless sensor networks.

It is indeed necessary to design of an elderly support mobile healthcare and monitoring system on wireless sensor network (WSN) for dynamic monitoring. It comes from the need for maintenance of healthcare among patients and elderly people that leads to the demand on change in traditional monitoring approaches among chronic disease patients and alert on acute events. In this paper, we propose a new automated patient diagnosis called automated patient diagnosis (AUPA) using ATmega microcontrollers over environmental sensors.

Linear quadratic regulator problem governed by granular neutrosophic fractional differential equations.

Quadratic cost functions estimation in the linear optimal control systems governed by differential equations (DEs) or partial differential equations (PDEs) has a well-established discipline in mathematics with many interfaces to science and engineering. During its development, the impact of uncertain phenomena to objective function and the complexity of the systems to be controlled have also increased significantly. Many engineering problems like magnetohydromechanical, electromagnetical and signal analysis for the transmission and propagation of electrical signals under uncertain environment can be dealt with.

Redundant feature pruning for accelerated inference in deep neural networks.

This paper presents an efficient technique to reduce the inference cost of deep and/or wide convolutional neural network models by pruning redundant features (or filters). Previous studies have shown that over-sized deep neural network models tend to produce a lot of redundant features that are either shifted version of one another or are very similar and show little or no variations, thus resulting in filtering redundancy. We propose to prune these redundant features along with their related feature maps according to their relative cosine distances in the feature space, thus leading to smaller networks with reduced post-training inference computational costs and competitive performance.

A New Approach for Construction of Geodemographic Segmentation Model and Prediction Analysis.

Customer retention is invariably the top priority of all consumer businesses, and certainly it is one of the most critical challenges as well. Identifying and gaining insights into the most probable cause of churn can save from five to ten times in terms of cost for the company compared with finding new customers. Therefore, this study introduces a full-fledged geodemographic segmentation model, assessing it, testing it, and deriving insights from it.