Spin photonics on chip based on a twinning crystal metamaterial

Two-dimensional photonic circuits with high capacity are essential for a wide range of applications in next-generation photonic information technology and optoelectronics. Here we demonstrate a multi-channel spin-dependent photonic device based on a twinning crystal metamaterial. The structural symmetry and material symmetry of the twinning crystal metamaterial enable a total of 4 channels carrying different transverse spins because of the spin-momentum locking. The orientation of the anisotropy controls the propagation direction of each signal, and the rotation of the E-field with respect to energy flow determines the spin characteristics during input/output coupling. Leveraging this mechanism, the spin of an incident beam can be maintained during propagation on-chip and then delivered back into the free space, offering a new scheme for metamaterial-based spin-controlled nano-photonic applications

Towards model checking Android applications

As feature-rich Android applications (apps for short) are increasingly popularized in security-sensitive scenarios, methods to verify their security properties are highly desirable. Existing approaches on verifying Android apps often have limited effectiveness. For instance, static analysis often suffers from a high false-positive rate, whereas approaches based on dynamic testing are limited in coverage. In this work, we propose an alternative approach, which is to apply the software model checking technique to verify Android apps. We have built a general framework named DroidPF upon Java PathFinder (JPF), towards model checking Android apps. In the framework, we craft an executable mock-up Android OS which enables JPF to dynamically explore the concrete state spaces of the tested apps; we construct programs to generate user interaction and environmental input so as to drive the dynamic execution of the apps; and we introduce Android specific reduction techniques to help alleviate the state space explosion. DroidPF focuses on common security vulnerabilities in Android apps including sensitive data leakage involving a non-trivial flow- and context-sensitive taint-style analysis. DroidPF has been evaluated with 131 apps, which include real-world apps, third-party libraries, malware samples and benchmarks for evaluating app analysis techniques like ours. DroidPF precisely identifies nearly all of the previously known security issues and nine previously unreported vulnerabilities/bugs.NRF (Natl Research Foundation, S’pore

Optical vortices enabled by structural vortices

The structural symmetry of solids plays an important role in defining their linear and nonlinear optical properties. The quest for versatile, cost-effective, large-scale, and defect-free approaches and materials platforms for tailoring structural and optical properties on demand has been underway for decades. We experimentally demonstrate a bottom-up self-assembly-based organic engineered material comprised of synthesized molecules with large dipole moments that are crystallized into a spherulite structure. The molecules align in an azimuthal direction, resulting in a vortex polarity with spontaneously broken symmetry leading to strong optical anisotropy and nonlinear optical responses. These unique polarization properties of the judiciously designed organic spherulite combined with the symmetry of structured optical beams enable a plethora of new linear and nonlinear light-matter interactions, including the generation of optical vortex beams with complex spin states and on-demand topological charges at the fundamental, doubled, and tripled frequencies. The results of this work are likely to enable numerous applications in areas such as high-dimensional quantum information processing, with large capacity and high security. The demonstrated spherulite crystals facilitate stand-alone micro-scale devices that rely on the unique micro-scale spontaneous vortex polarity that is likely to enable future applications for high-dimensional quantum information processing, spatiotemporal optical vortices, and a novel platform for optical manipulation and trapping

Combination of Neutrophil Count and Gensini Score as a Prognostic Marker in Patients with ACS and Uncontrolled T2DM Undergoing PCI

Background: Several biomarkers have been studied as prognostic indicators among people with diabetes and coronary artery disease (CAD). The purpose of this study was to determine the prognostic value of neutrophil counts and the Gensini score in patients with diabetes and ACS undergoing percutaneous coronary intervention (PCI). Methods: A total of 694 people with ACS and T2DM who simultaneously had elevated HBA1c received PCI. Spearman rank correlation estimates were used for correlation evaluation. Multivariate Cox regression and Kaplan-Meier analysis were used to identify characteristics associated with major adverse cardiovascular and cerebrovascular events (MACCEs) and patient survival. The effects of single- and multi-factor indices on MACCEs were evaluated through receiver operating characteristic curve analysis. Results: The Gensini score and neutrophil count significantly differed between the MACCE and non-MACCE groups among patients receiving PCI who had concomitant ACS and T2DM with elevated HBA1c (P<0.001). The Gensini score and neutrophil count were strongly associated with MACCEs (log-rank, P<0.001). The Gensini score and neutrophil count, alone or in combination, were predictors of MACCEs, according to multivariate Cox regression analysis (adjusted hazard ratio [HR], 1.005; 95% confidence interval [CI], 1.002–1.008; P=0.002; adjusted HR, 1.512; 95% CI, 1.005–2.274; P=0.047, respectively). The Gensini score was strongly associated with neutrophil count (variance inflation factor ≥ 5). Area under the curve analysis revealed that the combination of multivariate factors predicted the occurrence of MACCEs better than any single variable. Conclusion: In patients with T2DM and ACS with elevated HBA1c who underwent PCI, both the Gensini score and neutrophil count were independent predictors of outcomes. The combination of both predictors has a higher predictability

Field measurement of the erosion threshold of silty seabed in the intertidal flat of the Yellow River Delta with a newly-developed annular flume

Accurately measuring the critical shear stress is crucial for numerous applications, such as sediment transport modeling, erosion prediction, and the design of sustainable coastal engineering structures. However, developing reliable and precise in-situ measurement devices faces significant challenges due to the harsh and dynamic nature of aquatic environments. Factors like turbulence and waves introduce complexities that must be considered when designing and calibrating these devices. The newly developed Openable Underwater Carousel In-situ Flume (OUC-IF) was used to determine the critical shear stress (τc) and quantify erosion rates. Acoustic Doppler Velocimeter (ADV) was employed to measure 3D near-bottom velocities, which were then used to estimate and pre-calibrate bed shear stress (τ) applied on the seabed in the annular flume. Three computation methods of shear stress were evaluated: turbulent kinetic energy (TKE), direct covariance (COV), and log profile (LP). In-situ erosion experiments were conducted for the first time at two sites in the tidal flat of the Yellow River Delta (site 1 with a water depth of 1.32 m and site 2 with a water depth of 0.75 m). The critical shear stress was found to be 0.10 Pa at site 1 and 0.19 Pa at site 2, and the erosion rates of the sediments were successfully measured. The effect of wave-seabed interactions on erosion resistance was explored by theoretically estimating the wave-induced pore pressure of the seabed based on the observed data. The max liquefaction degree of the seabed at site 1 and site 2 was 0.035 and 0.057, respectively, and the average erosion coefficient Me was 2.63E-05 kg m-2s-1 at site 1 and 3.48E-05 kg m-2s-1 at site 2

Sequence analysis of the Epstein-Barr virus (EBV) BRLF1 gene in nasopharyngeal and gastric carcinomas

<p>Abstract</p> <p>Background</p> <p>Epstein-Barr virus (EBV) has a biphasic infection cycle consisting of a latent and a lytic replicative phase. The product of immediate-early gene BRLF1, Rta, is able to disrupt the latency phase in epithelial cells and certain B-cell lines. The protein Rta is a frequent target of the EBV-induced cytotoxic T cell response. In spite of our good understanding of this protein, little is known for the gene polymorphism of BRLF1.</p> <p>Results</p> <p>BRLF1 gene was successfully amplified in 34 EBV-associated gastric carcinomas (EBVaGCs), 57 nasopharyngeal carcinomas (NPCs) and 28 throat washings (TWs) samples from healthy donors followed by PCR-direct sequencing. Fourteen loci were found to be affected by amino acid changes, 17 loci by silent nucleotide changes. According to the phylogenetic tree, 5 distinct subtypes of BRLF1 were identified, and 2 subtypes BR1-A and BR1-C were detected in 42.9% (51/119), 42.0% (50/119) of samples, respectively. The distribution of these 2 subtypes among 3 types of specimens was significantly different. The subtype BR1-A preferentially existed in healthy donors, while BR1-C was seen more in biopsies of NPC. A silent mutation A/G was detected in all the isolates. Among 3 functional domains, the dimerization domain of Rta showed a stably conserved sequence, while DNA binding and transactivation domains were detected to have multiple mutations. Three of 16 CTL epitopes, NAA, QKE and ERP, were affected by amino acid changes. Epitope ERP was relatively conserved; epitopes NAA and QKE harbored more mutations.</p> <p>Conclusions</p> <p>This first detailed investigation of sequence variations in BRLF1 gene has identified 5 distinct subtypes. Two subtypes BR1-A and BR1-C are the dominant genotypes of BRLF1. The subtype BR1-C is more frequent in NPCs, while BR1-A preferentially presents in healthy donors. BR1-C may be associated with the tumorigenesis of NPC.</p

Abnormal Cockpit Pilot Driving Behavior Detection Using YOLOv4 Fused Attention Mechanism

The abnormal behavior of cockpit pilots during the manipulation process is an important incentive for flight safety, but the complex cockpit environment limits the detection accuracy, with problems such as false detection, missed detection, and insufficient feature extraction capability. This article proposes a method of abnormal pilot driving behavior detection based on the improved YOLOv4 deep learning algorithm and by integrating an attention mechanism. Firstly, the semantic image features are extracted by running the deep neural network structure to complete the image and video recognition of pilot driving behavior. Secondly, the CBAM attention mechanism is introduced into the neural network to solve the problem of gradient disappearance during training. The CBAM mechanism includes both channel and spatial attention processes, meaning the feature extraction capability of the network can be improved. Finally, the features are extracted through the convolutional neural network to monitor the abnormal driving behavior of pilots and for example verification. The conclusion shows that the deep learning algorithm based on the improved YOLOv4 method is practical and feasible for the monitoring of the abnormal driving behavior of pilots during the flight maneuvering phase. The experimental results show that the improved YOLOv4 recognition rate is significantly higher than the unimproved algorithm, and the calling phase has a mAP of 87.35%, an accuracy of 75.76%, and a recall of 87.36%. The smoking phase has a mAP of 87.35%, an accuracy of 85.54%, and a recall of 85.54%. The conclusion shows that the deep learning algorithm based on the improved YOLOv4 method is practical and feasible for the monitoring of the abnormal driving behavior of pilots in the flight maneuvering phase. This method can quickly and accurately identify the abnormal behavior of pilots, providing an important theoretical reference for abnormal behavior detection and risk management