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As a two-dimensional crystal, molybdenum trioxides (α-MoO 3) has been considered as a typical candidate for next-generation photodetectors (PDs) but with limited photodetection applications in the ultraviolet region. Here, a photo-induced thermoelectric (PTE) effect in α-MoO 3 is proposed as a practical approach to realize the broadband photodetection of α-MoO 3 /Si heterojunction PDs. High-quality α-MoO 3 films are grown on Si by using an e-beam evaporation method. By modulating the photo-induced thermoelectric potential along the c-axis on the transport properties, the α-MoO 3 /Si PDs can be operated as a self-powered device, showing broadband photoresponse beyond the bandgap limitation in the wavelength range of 405–1550 nm. The manipulation of the PTE effect in the heterojunction is investigated carefully, clarifying the corresponding physical mechanisms of the unique photoresponse behaviors. Furthermore, the fabricated device exhibits competitive photodetection performance with a high photoresponsivity of 63.3 mA/W, a high optical detectivity of 3.1 × 1011 cm Hz1/2W−1, fast response speeds with the rise/fall times of 0.47/0.76 ms, as well as high durability and environmental stability under 980-nm infrared illumination. These results not only provide a novel strategy to develop novel PDs with high performance, but also supply a deeply understanding of the PTE effect in α-MoO 3 /Si heterojunctions. [ABSTRACT FROM AUTHOR]

Two researchers independently assessed studies published up to February 5, 2023, across PubMed, Web of Science, Embase, and Cochrane Library, to investigate the associations of sleep traits with cardiometabolic risk factors, as well as with cardiovascular diseases. Fourteen systematic reviews consisting of 23 meta-analyses, and 11 Mendelian randomization (MR) studies were included in this study. Short sleep duration was associated with a higher risk of obesity, type 2 diabetes (T2D), hypertension, stroke, and coronary heart disease (CHD) in observational studies, while a causal role was only demonstrated in obesity, hypertension, and CHD by MR. Similarly, long sleep duration showed connections with a higher risk of obesity, T2D, hypertension, stroke, and CHD in observational studies, none was supported by MR analysis. Both observational and MR studies indicated heightened risks of hypertension, stroke, and CHD in relation to insomnia. Napping was linked to elevated risks of T2D and CHD in observational studies, with MR analysis confirming a causal role in T2D. Additionally, snoring was correlated with increased risks of stroke and CHD in both observational and MR studies. This work consolidates existing evidence on a causal relationship between sleep characteristics and cardiometabolic risk factors, as well as cardiovascular diseases. [ABSTRACT FROM AUTHOR]

In animal cells, the dysregulation of centrosome duplication and cohesion maintenance leads to abnormal spindle assembly and chromosomal instability, contributing to developmental disorders and tumorigenesis. However, the molecular mechanisms involved in maintaining accurate centrosome number control and tethering are not fully understood. Here, we identified coiled-coil domain-containing 102A (CCDC102A) as a centrosomal protein exhibiting a barrel-like structure in the proximal regions of parent centrioles, where it prevents centrosome overduplication by restricting interactions between Cep192 and Cep152 on centrosomes, thereby ensuring bipolar spindle formation. Additionally, CCDC102A regulates the centrosome linker by recruiting and binding C-Nap1; it is removed from the centrosome after Nek2A-mediated phosphorylation at the onset of mitosis. Overall, our results indicate that CCDC102A participates in controlling centrosome number and maintaining centrosome cohesion, suggesting that a well-tuned system regulates centrosome structure and function throughout the cell cycle. [Display omitted] • Centrosomal protein CCDC102A prevents excess procentriole assembly • CCDC102A restricts interactions between Cep192 and Cep152 on centrosomes • CCDC102A maintains centrosome cohesion by recruiting C-Nap1 to the centrosome • Nek2A-mediated phosphorylation promotes CCDC102A removal from the centrosome Wang et al. show that CCDC102A is a centrosome duplication and cohesion regulator. CCDC102A controls centriole number by governing formation of the Cep192-Cep152 complex; it promotes centrosome linker assembly by recruiting C-Nap1. At the onset of mitosis, CCDC102A is phosphorylated by Nek2A and removed from the centrosome. [ABSTRACT FROM AUTHOR]

This paper proposes a centralized relaxation strategy for cooperative lane changes on the freeway with a connected and automated vehicle (CAV) dedicated lane. Three numerical models are designed in this scenario. Firstly, a lane-changing decision (LCD) model is established considering the speed difference between merging CAV and the target platoon, and the number of following vehicles (FVs) in the platoon. The merging gap is decided by the gap choice model, which is based on the safe distance according to the Gipps model. The relaxation model controls the longitudinal accelerations of all FVs in the platoon to provide proper gaps for merging CAVs. The parameters of these three models are obtained by sensitivity analysis. The results show that safety risks are significantly reduced in the lane-changing (LC) process with centralized relaxation. Benefitting from extending relaxation time, the decrease in amplitude of acceleration alleviates the speed oscillation among the FVs. It also helps to improve traffic efficiency and driving comfort. However, these advantages are weakened when the number of FVs exceeds 5 or the speed difference is over 7 m/s. It is suggested that the single CAV (SV) should merge to the end of the platoon in these conditions from a systematic view. [ABSTRACT FROM AUTHOR]

• A two-view and multi-scale supervision network is proposed for pancreas segmentation. • A location branch for localization and a segmentation branch for segmentation. • Multi-scale supervision is used to learn multi-scale features. • Effectiveness verified on two pancreas datasets and one spleen dataset. Automatic organ segmentation systems can accelerate the development of computer-aided diagnosis (CAD) in clinical applications. In this paper, we focus on the challenging pancreas segmentation task. The tiny size, poor contrast, and blurred boundaries of the pancreas make it hard to detect. Current approaches emphasize decomposing this task into subtasks (localization and segmentation) and using the same network to solve different tasks. However, they overestimate the generalization ability of their models. In addition, current methods rely too much on the result of localization. To address these challenges, we propose a novel network by two-view feature learning based on attention mechanism and multi-scale supervision, which we term TVMS-Net. For localization, we adopt Attention Gate (AG) to distinguish appearance features of the pancreas in shallow layers. For segmentation, a simple and effective Residual Multi-Scale Dilated Attention (RMSA) module is designed to extract comprehensive inter-channel relationships and multi-scale spatial information. TVMS-Net is supervised in multi-scale to learn specific-level semantic information. Experimental results on two pancreas datasets show that TVMS-Net obtains remarkable performance. Importantly, TVMS-Net also achieves excellent segmentation accuracy on another tiny organ dataset, i.e., the spleen, which justifies the reliability and robustness of our method. [ABSTRACT FROM AUTHOR]

Magnetic molasses hydrothermal carbon (MMHC) with many amino groups was prepared in one step by in situ modification using molasses wastewater as an amino-rich carbon source and FeCl 3 and FeCl 2 as precursors of Fe 3 O 4. MMHC was characterized by FT-IR, SEM, XRD, TGA, XPS, and magnetic property tests, and the hydrothermal carbonization pattern of molasses wastewater was investigated. The adsorption behaviors of MMHC on heavy metal ions (Pb2+, Cu2+, Cd2+) were simulated and discussed to refine and deepen the adsorption mechanisms between different heavy metal cations and amine-based adsorbents. The compositional analysis showed that almost all the amino acids in the molasses wastewater were involved in the hydrothermal reaction. Characterization showed that amino groups dominated the adsorption of heavy metal ions, and the adsorption of Pb2+, Cu2+ and Cd2+ on MMHC satisfied the Langmuir model and the pseudo-secondary kinetic model. The maximum adsorption capacities of MMHC for Pb2+, Cu2+ and Cd2+ under ideal conditions were 51.81, 21.41 and 17.64 mg/g, respectively. This work provides an effective new direction for the treatment of heavy metal pollution in water bodies and the resourceful reuse of sugar-containing wastewater. The schematic of magnetic molasses hydrothermal carbon. [Display omitted] • Amine-rich magnetic molasses hydrothermal carbon (MMHC) was synthesized. • Molasses wastewater was used as an amino-rich carbon source. • The sorption capacities of MMHC for Pb2+, Cu2+ and Cd2+ were 51.81, 21.41 and 17.64 mg/g. • The Langmuir model and the pseudo-secondary kinetic model were satisfied. [ABSTRACT FROM AUTHOR]

[Display omitted] • Nanoscale confined-space stretching due to dimensional synergistic and interfacial effects was used in this work. • Monte Carlo simulation was used to theoretically verify the 2D nanofiller-enhanced 1D carbon nanofibres (CNF) in-film orientation behaviour. • 2D dielectric boron nitride enhanced the in-plane orientation of 1D CNF to boost dielectric properties. • 2D conductive graphene nanoplatelets enhanced the 1D CNF in-plane orientation to boost electromagnetic interference shielding properties. A nanoscale stretching-induced one-dimensional (1D) nanofiller orientation in a three-dimensional (3D) film (the films form cell walls of foamed materials) is beneficial for improving the dielectric and electromagnetic interference (EMI) shielding properties of composite systems. However, nanoscale stretching may cause nanofiller separation, negatively impacting their properties. In this study, a two-dimensional (2D) nanofiller was introduced into a carbon nanofibre/poly(vinylidene fluoride) (CNF/PVDF) nanocomposite system to improve the nanoscale confined-space stretching effect. As a result, the degree of in-film orientation of the CNFs was significantly improved. Based on this effect, boron nitride was used as a 2D dielectric nanofiller to improve the degree of in-plane orientation of CNFs and to prepare 3D nanocomposite films with improved dielectric properties (improved by 54.3 %). Furthermore, 2D conductive nanofiller graphene nanoplatelets (GNPs) were employed to improve the degree of in-film orientation of CNFs for the preparation of CNF/GNP/PVDF nanocomposite foams with improved electrical conductivity and EMI shielding performance (increased by 89.4 %, from 18.8 to 35.6 dB·g−1·cm3 at 10 GHz). A Monte Carlo simulation was used to verify that the 2D nanofiller improved the 1D nanofiller orientation in the 3D nanocomposite film. This study provides a guide for the design of high-performance foamed nanocomposite materials. [ABSTRACT FROM AUTHOR]

• A method for measuring absorption cross sections of unstable CS is proposed. • The experimental device for measuring the CS concentration is designed. • Different concentrations of CS are obtained by the photolysis of CS 2. • The absorption cross sections of CS are provided for the first time. The absorption cross sections, as important spectroscopic data, can be used for the identification and quantitative measurement of components. However, in view that carbon monosulfide (CS) is unstable and highly reactive, it is difficult to measure the absorption cross sections of CS. Therefore, a method for measuring absorption cross sections of CS based on the dynamic equilibrium between carbon disulfide (CS 2) and CS in the photolysis CS 2 is proposed in the paper, and the absorption cross sections of CS are obtained for the first time. Firstly, the method for evaluating CS 2 concentrations is introduced, and the calculation formula of CS 2 concentrations is given. Secondly, CS 2 is converted to CS by increasing the intensity of incident light. Factors affecting CS 2 conversion efficiency, such as the flow rate of CS 2 and the intensity of incident light, are studied. Next, according to the conversion relationship between CS 2 and CS, the CS concentration has been obtained by measuring the change of CS 2 concentration. Finally, the CS absorption cross sections of (0.29 ± 0.01) × 10−16 cm2/molecule and (2.15 ± 0.04) × 10−16 cm2/molecule are provided at wavelengths of 250.88 nm and 257.60 nm, respectively. [ABSTRACT FROM AUTHOR]

• We presented a concentricity measurement method based on laser ranging principle. • The improved Kalman filtering method is proposed to process the distance data. • The weighted least square fitting method is used to obtain the geometric center. • Concentricity measurement error of less than 0.5 mm is obtained. In the forging process of large forgings, the concentricity between forging billets and upsetting rod or piercing rod has an important influence on the forging accuracy and forging quality. Therefore, a concentricity measurement method based on laser ranging principle is proposed in the paper. Firstly, the distance data between the billets, piercing rod and the laser rangefinders is obtained using two pulse laser rangefinders with a measurement resolution of 1 mm. Secondly, the improved Kalman filtering method is proposed to process the distance data to weaken the influence of random noise on the measurement results. Finally, the weighted least square fitting method is employed to analyze the geometric centers of the workpieces, and then the concentricity is calculated using the obtained geometric centers. The experimental analysis results indicate that the measurement error of concentricity is less than 0.5 mm. The proposed concentricity measurement method is suitable for measuring concentricity in forging and piercing, which can improve the operation precision of upsetting or piercing. [ABSTRACT FROM AUTHOR]

A novel lane-changing trajectory control strategy in an iterative learning framework is proposed and its impact on fuel consumption of the off-ramp traffic system is analyzed in this paper. The iterative learning framework includes three layers. In the calibration layer, the lane-changing decision process is modelled by probabilistic neural network while a back-propagation neural network is designed to imitate the lane-changing trajectory. Moreover, we use a cost function and numerical simulation in the optimization layer to optimize the trajectory database and calibrate the proposed framework, respectively. In the application layer, simulation experiments are conducted to examine the fuel consumption of individuals and systems. The results indicate that connected automated vehicles (CAVs) can dissipate quickly after congestion is formed and complete lane-change and avoidance behavior in a more stable state after iterative learning. CAVs can reduce fuel consumption by 35% compared with human-driven vehicles. [ABSTRACT FROM AUTHOR]

• Lane-changing trajectories are modified by cost function and machine learning for connected and automated vehicles. • The area influenced by lane-changing behaviors upstream off-ramp bottlenecks is essentially reduced by iterative learning control. • The performance in safety and efficiency of all vehicles in off-ramp bottleneck is improved by applying iterative learning control on connected and automated vehicles. • Iterative learning framework is effective in scenarios with various market penetration rates of connected and automated vehicles. This paper proposes an iterative learning control framework for lane changing to improve traffic operation and safety at a diverging area nearby a highway off-ramp in an environment with connected and automated vehicles (CAVs). This framework controls CAVs in the off-ramp bottlenecks by imitating the trajectories optimized by machine learning algorithms. Next Generation Simulation (NGSIM) dataset is utilized as the raw data and filtered by cost function. The traffic models, including lane-changing decision (LCD) models and lane-changing execution (LCE) models, are completed by Random Forest (RF) and Back Propagation Neural Network (BPNN) algorithms. Based on simulation results, simulation data satisfying the predetermined criterion will be added to dataset in the next iteration. Various metrics are considered to evaluate the proposed framework systematically from both lateral and longitudinal aspects, including time exposed time-to collision (TET), time integrated time-to-collision (TIT), rear-end collision risk indexes (RCRI) and lane-changing risk index (LCRI). The results present that the iterative framework can decrease the longitudinal risk of the system by a factor of two times, and can reduce the lateral risk by 28.7%. When the CAVs market penetration rate (MPR) reaches 100%, the longitudinal and lateral risk values of the off-ramp system can be reduced by 90% and 35%, respectively. However, it is worth noting that only when the CAVs MPR reaches 50% does the system's value at risk change significantly. [ABSTRACT FROM AUTHOR]

• A modified method to detect melatonin in serum and milk with liquid chromatography tandem mass spectrometry (LC-MS/MS). • Comparisons of different pre-treatment procedures on melatonin extraction from milk. • Cold methanol is used to improve melatonin extraction efficiency. Since the global demanding of natural melatonin-enriched milk has been significantly increased in the populations of children and elder, the accurate and quick melatonin detection from milk is urgently required. Thus, the regular methods no longer satisfy this requirement. In the current study, we reported a novel method to extract melatonin from milk for liquid chromatography-tandem mass spectrometry melatonin detection. This novel method was to use cold methanol (-20℃) to precipitate proteins and fat in milk with one step to extract melatonin. Compared to the regular methods, it was devoid of procedures of sample drying, solid phase extraction and sample reconstitution. It could short the extraction time from the regularly 150 min to 60 min/per 24 milk samples. We believe that this novel method provides a possibility to detect large scale of milk samples in relatively short time with more efficiency and less cost compared to the regular method. [ABSTRACT FROM AUTHOR]