Cholesteric Liquid Crystal Network Composite Film with Structure, Dual-Band Responsive Luminescent Color for Multiple Anti-Counterfeiting and Information Encryption.

Anti-counterfeiting technology plays an indispensable role in the high-tech field and various critical application areas. However, traditional anti-counterfeiting approaches currently in widespread use are too simplistic and easily replicated or forged, while advanced technologies with multiple anti-counterfeiting functions remain in the developmental stage. This study presents a novel multiple anti-counterfeiting technique.

Pure spin current generation with photogalvanic effects in h-BN/graphene/h-BN van der Waals vertical heterostructures.

We have computationally demonstrated a new method for generating pure spin current with the photogalvanic effect (PGE) by constructing transport junctions using h-BN/graphene/h-BN van der Waals (vdW) heterostructure leads. It has been observed that the pure spin current without any accompanying charge current induced by the PGE can consistently be obtained, regardless of photon energy and polarization/helicity angle, as well as the specific type of polarization (linear, circular, or elliptical). The mechanism lies in the structural inversion symmetry and real space spin polarization antisymmetry of the junctions.

ASIP Performance Enhancement by Hazard Control through Scoreboard.

The application-specific instruction set processor (ASIP) has been gradually accepted in AI, communication, media, game and industry control. The digital signal processor (DSP) is a typical ASIP, whose benefits include high performance in specific domains, low power consumption, high flexibility and low silicon consumption. One of the challenges for DSP design is to handle problems induced by datapath acceleration.

New Families of Frequency-Hopping Sequence Sets with a Low-Hit-Zone.

As a means of spread spectrum communication, frequency-hopping technology has good performance in anti-jamming, multiple-access, security, covert communications, and so on. In order to meet the needs of different frequency-hopping multiple-access (FHMA) communication scenarios, the research on frequency-hopping sequence (FHS) sets with a low-hit-zone (LHZ) is now becoming more and more crucial. In this paper, a general construction to obtain new families of LHZ-FHS sets is achieved via interleaving technique.

Strip and boundary detection multi-task learning network for segmentation of meibomian glands.

Background: Automatic segmentation of meibomian glands in near-infrared meibography images is basis of morphological parameter analysis, which plays a crucial role in facilitating the diagnosis of meibomian gland dysfunction (MGD). The special strip shape and the adhesion between glands make the automatic segmentation of meibomian glands very challenging.

Purpose: A strip and boundary detection multi-task learning network (SBD-MTLNet) based on encoder-decoder structure is proposed to realize the automatic segmentation of meibomian glands.

Functional Monitoring of Patients With Knee Osteoarthritis Based on Multidimensional Wearable Plantar Pressure Features: Cross-Sectional Study.

Background: Patients with knee osteoarthritis (KOA) often present lower extremity motor dysfunction. However, traditional radiography is a static assessment and cannot achieve long-term dynamic functional monitoring. Plantar pressure signals have demonstrated potential applications in the diagnosis and rehabilitation monitoring of KOA.

Convolution spatial-temporal attention network for EEG emotion recognition.

In recent years, emotion recognition using electroencephalogram (EEG) signals has garnered significant interest due to its non-invasive nature and high temporal resolution. We introduced a groundbreaking method that bypasses traditional manual feature engineering, emphasizing data preprocessing and leveraging the topological relationships between channels to transform EEG signals from two-dimensional time sequences into three-dimensional spatio-temporal representations. Maximizing the potential of deep learning, our approach provides a data-driven and robust method for identifying emotional states.

Improving drug-target interaction prediction through dual-modality fusion with InteractNet.

In the drug discovery process, accurate prediction of drug-target interactions is crucial to accelerate the development of new drugs. However, existing methods still face many challenges in dealing with complex biomolecular interactions. To this end, we propose a new deep learning framework that combines the structural information and sequence features of proteins to provide comprehensive feature representation through bimodal fusion.

HyFormer: a hybrid transformer-CNN architecture for retinal OCT image segmentation.

Optical coherence tomography (OCT) has become the leading imaging technique in diagnosing and treatment planning for retinal diseases. Retinal OCT image segmentation involves extracting lesions and/or tissue structures to aid in the decisions of ophthalmologists, and multi-class segmentation is commonly needed. As the target regions often spread widely inside the retina, and the intensities and locations of different categories can be close, good segmentation networks must possess both global modeling capabilities and the ability to capture fine details.

Distributed containment control for first-order and second-order multi-agent systems with delays and position-constraints.

This work addresses the discrete-time containment control problem for first-order and second-order systems, incorporating position constraints, nonuniform time delays, and switching topologies. Projection operators are introduced to maintain agent states within the position constraints. Then, model transformation methods, along with stochastic properties of matrices, are employed to handle coupled nonlinearities stemming from position constraints and time delays.

Ampelopsis grossedentata tea alleviating liver fibrosis in BDL-induced mice via gut microbiota and metabolite modulation.

Liver fibrosis (LF) is a common sequela to diverse chronic liver injuries, leading to rising rates of cirrhosis and hepatocellular carcinoma (HCC). As the medicinal and edible homologous material, traditional teas have exhibited promising applications in the clinical management of liver fibrosis. Here, we generated a liver fibrosis mouse model to explore the potent therapeutic ability of Ampelopsis grossedentata (AG) tea on this condition by multi-omics analysis.

Towards faster and robust solution for dynamic LR and QR factorization.

Dynamic LR and QR factorization are fundamental problems that exist widely in the control field. However, the existing solutions under noises are lack of convergence speed and anti-noise ability. To this end, this paper incorporates the advantages of Dynamic-Coefficient Type (DCT) and Integration-Enhance Type (IET) Zeroing Neural Dynamic (ZND), and proposes an Adaptive and Robust-Enhanced Neural Dynamic (AREND).

A literature review of contacting force measurement methods for pedestrian crowds.

This article reviewed state-of-the-art achievements in pedestrian contacting force measurement as a hotspot survey closer to ground truth supporting pedestrian dynamics in mass-gathering environments. It analyzed different forces acting on pedestrian bodies, including normal external forces, self-driven forces, abnormal external forces, and pedestrian motion constraint forces from other obstacles, besides the crowding posture on the force distribution. This review covered main methodologies: sophisticated pressure sensors, modern technology for pedestrian motion-capturing systems, and advanced numerical simulations.

Adaptive quantized finite-time fault-tolerant control for uncertain multi-input multi-output systems and its application.

The article proposes a novel state-feedback control method for a multiple-input multiple-output (MIMO) nonlinear system with actuator faults and input quantization. The innovation of the design approach lies in the utilization of fuzzy logic systems (FLSs) to approximate the uncertain intermediate virtual control laws, thereby achieving a simplified virtual control design form. Additionally, finite-time control is employed to enhance the system's response speed.

Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays.

Background: Correctly diagnosing and accurately distinguishing mycoplasma pneumonia in children has consistently posed a challenge in clinical practice, as it can directly impact the prognosis of affected children. To address this issue, we analyzed chest X-rays (CXR) using various deep learning models to diagnose pediatric mycoplasma pneumonia.

Methods: We collected 578 cases of children with mycoplasma infection and 191 cases of children with virus infection, with available CXR sets.

Design and theoretical analysis of a tunable bifunctional metasurface absorber based on vanadium dioxide and photoconductive silicon.

A tunable bifunctional metasurface absorber based on vanadium dioxide (VO) and photoconductive silicon (PSi) is proposed in a terahertz (THz) band. When the conductivities of VO () and PSi () are 10 S m and 1 × 10 S m, the designed absorber has a function of dual-broadband absorption. The absorptivity rate of over 90% is in the dual-broadband of 2.

Deconvolution of cell-type-associated markers predictive of response to neoadjuvant radiotherapy.

Tumor microenvironent contains prognostic molecular markers and therapeutic targets from different cellular sources, which are still not fully revealed in the resistance and recurrence after radiotherapy for rectal cancer. By integrating the scRNA-seq data, we deconvoluted the bulk transcriptomics of rectal cancer collected before preoperative neoadjuvant radiotherapy (nRT) into fractions and gene expression of the six cell types. The inferred cell-type-associated DEGs, abbreviated as caDEGs, of myeloid and stromal cells were enriched for overlapping yet unique biological processes including immunity, angiogenesis, and metabolism, respectively.

Speed of Light in Hollow-Core Photonic Bandgap Fiber Approaching That in Vacuum.

A Fresnel mirror is introduced at a hollow-core photonic bandgap fiber end by fusion splicing a short single-mode fiber segment, to reflect the light backward to an optical frequency domain reflectometry. The backward Fresnel reflection is used as a probe light to achieve light speed measurement with a high resolution and a high signal-to-noise ratio. Thus, its group velocity is obtained with the round-trip time delay as well as the beat frequency at the reflection peak.

VNS-BA*: An Improved Bidirectional A* Path Planning Algorithm Based on Variable Neighborhood Search.

The A* algorithm is an effective method for path planning; however, it has certain drawbacks, such as a high number of turns, low planning efficiency, and redundant searches. To address these issues, this paper proposes an improved bidirectional A* global path planning algorithm based on a variable neighborhood search strategy, named VNS-BA*. The new algorithm first employs an 8-11-13 neighborhood search method for node expansion.

A numerical evaluation of real-time workloads for ramp controller through optimization of multi-type feature combinations derived from eye tracker, respiratory, and fatigue patterns.

Ramp controllers are required to manage their workloads effectively while handling complex operational tasks, a crucial part of improving aviation safety. The ability to detect their instantaneous workload is vital for ensuring operational effectiveness and preventing hazardous incidents. This paper introduces a novel methodology aimed at enhancing the evaluation of the ramp controller's cumulative workload by incorporating and optimizing the feature combination from eye movement, respiratory, and fatigue characteristics.

Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection.

Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in crop yield and quality caused by these problems. In recent years, the remote sensing (RS) areas has been prevailed over by unmanned aerial vehicle (UAV)-based applications. Herein, by using methods such as keyword co-contribution analysis and author co-occurrence analysis in bibliometrics, we found out the hot-spots of this field.