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Introduction: While driving, drivers frequently adapt their driving behaviors according to their perception of the road's alignment features. However, traditional two-dimensional alignment methods lack the ability to capture these features from the driver's perspective. Method: This study introduces a novel method for road alignment recognition, employing image recognition technology to extract alignment perspective features, namely alignment perspective skewness (APS) and alignment perspective kurtosis (APK), from in-real driving images. Subsequently, the K-means clustering algorithm is utilized for road segment classification based on APS and APK indicators. Various sliding step length for clustering are employed, with step length ranging from 100m to 400m. Furthermore, the accident rates for different segment clusters are analyzed to explore the relationship between alignment perspective features and traffic safety. A 150 km mountain road section of the Erlianhaote-Guangzhou freewway from Huaiji to Sihui is selected as a case study. Results: The results demonstrate that using alignment perspective features as classification criteria produces favorable clustering outcomes, with superior clustering performance achieved using shorter segment lengths and fewer cluster centers. The road segment classification based on alignment perspective features reveals notable differences in accident rates across categories; while traditional two-dimensional parameters-based classification methods fail to capture these differences. The most significant differences in accident rates across categories are observed with segment length of 100m, with the significance gradually diminishing as segment length increases and disappearing entirely when the length exceeds 300m. Implication: These findings validate the reliability of using alignment perspective features (APS and APK) for road alignment classification and road safety analysis, providing valuable insights for road safety management. [ABSTRACT FROM AUTHOR]

Background: Functional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer's disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD. Methods: This multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers. Results: The STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status. Conclusions: This study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset. [ABSTRACT FROM AUTHOR]

In this work, LaNiO 3 epitaxial thin films on (001) single-crystal LaAlO 3 and SrTiO 3 substrates were deposited by polymer assisted deposition method, respectively. Additionally, LaNiO 3 polycrystalline film was coated on ceramic substrate using LaNiO 3 powder obtained from precursor solution in polymer assisted deposition. Due to the difference of lattice mismatch between LaNiO 3 and single-crystal substrates, LaNiO 3 thin films suffer different epitaxial strain, resulting in distortion of NiO 6 octahedrons which increases with the intensity of strain. Such phenomena lead to the decrease of the field splitting energy of NiO 6 octahedron and the band gap value of LaNiO 3 , further increasing the charge transition and enhancing the visible-light photoconductivity. The existence of epitaxial strain brings more Ni2+ and adsorbed oxygen into perovskite type LaNiO 3 , introduces more oxygen vacancy defects, reduces the complex rate of photo induced electron-hole pairs, and thereby improves the photoelectric activity of LaNiO 3. This work provides a feasible method to control the structure distortion and photoconductivity effect of all-inorganic perovskite. [Display omitted] [ABSTRACT FROM AUTHOR]

Toll-gates are crucial points of management and key congestion bottleneck for the freeway. In order to avoid traffic deterioration and alleviate traffic congestion in advance, it is necessary to predict and evaluate the congestion in toll-gates scattering in large-scale region of freeway network. In this paper, traffic volume and operational delay time are selected from various traffic indicators to evaluate congestion considering the particular characteristics of the traffic flow within the toll-gate area. The congestion prediction method is designed including two modules: a deep learning (DL) prediction and a fuzzy evaluation. We propose a modified deep learning method based on graph convolutional network (GCN) structure in the fusion of dilated causal mechanism and optimize the method for spatial feature extraction by constructing a new adjacency matrix. This new AI network could process spatiotemporal information of traffic volume and operational delay time, that extracted from large-scaled toll-gates spontaneously, and predict key indicators in 15/30/60 min future time. The evaluation module is proposed based on these predicted results. Then, fuzzy C-means algorithm (FCM) is further modified by determining coupling weight for these two key indicators to detect congestion state. Original traffic data are obtained from the current 186 toll-gates served on the freeway network in Shaanxi Province, China. Experimental tests are carried out based on historical data of four months after preprogressing. The comparative tests show the proposed CPT-DF (congestion prediction on toll-gates using deep learning and fuzzy evaluation) outperforms the current-used other models by 6-15%. The successful prediction could extend to the real-time prediction and early warning of traffic congestion in the toll system to improve the intelligent level of traffic emergency management and guidance on the key road of disasters to some extent. [ABSTRACT FROM AUTHOR]

Background: Deoxynivalenol (DON) is a widespread issue for feed and food safety, leading to animal and human health risks. The objective of this study was to determine whether ferroptosis is involved in DON-induced intestinal injury in piglets. Three groups of 21-day-old male weanling piglets (n = 7/group) were fed a control diet, or diet adding 1.0 or 3.0 mg DON/kg. At week 4, serum and small intestines were collected to assay for biochemistry, histology, redox status and ferroptosis-related genes expression. In addition, the involvement of ferroptosis and the role of FTL gene in DON-induced cell death were further verified in the IPEC-J2 cells. Results: Compared to the control, dietary supplementation of DON at 1.0 and 3.0 mg/kg induced different degrees of damage in the duodenum, jejunum and ileum, and increased (P < 0.05) serum lipopolysaccharide concentration by 46.2%–51.4%. Dietary DON supplementation at 1.0 and (or) 3.0 mg/kg increased (P < 0.05) concentrations of malondialdehyde (17.4%–86.5%) and protein carbonyl by 33.1%–92.3% in the duodenum, jejunum and ileum. In addition, dietary supplemented with DON upregulated (P < 0.05) ferroptotic gene (DMT1) and anti-ferroptotic genes (FTL and FTH1), while downregulated (P < 0.05) anti-ferroptotic genes (FPN, FSP1 and CISD1) in the duodenum of the porcine. Furthermore, the in vitro study has demonstrated that deferiprone, a potent ferroptotic inhibitor, mitigated (P < 0.05) DON-induced cytotoxicity in porcine small intestinal IPEC-J2 cells. Additionally, deferiprone prevented or alleviated (P < 0.05) the dysregulation of ferroptosis-related genes (ACSL4 and FTL) by DON in IPEC-J2 cells. Moreover, specific siRNA knockdown FTL gene expression compromised the DON-induced cell death in IPEC-J2 cells. Conclusions: In conclusion, this study revealed that ferroptosis is involved in DON-induced intestinal damage in porcine, and sheds a new light on the toxicity of DON to piglets. [ABSTRACT FROM AUTHOR]

Illustration of electrochemical and colorimetric dual-signal biosensor for ultrasensitive SARS-CoV-2 detection. [Display omitted] • Bifunctional probe NiFe-LDHs@AgCit was prepared. • A dual-mode strategy for determination of SARS-CoV-2 was developed. • The electrochemical detection method showed the signal of Ag+. • The colorimetric signal was the catalyst oxidation of 3,3′,5,5′-tetramethyl biphenyl. • The proposed biosensor realized highly sensitive detection of SARS-CoV-2. The COVID-19 pandemic caused by SARS-CoV-2 has precipitated a global public health emergency, necessitating the urgent development of rapid and accurate virus detection methods. Compared with the current single-signal detection methods commonly used, dual-mode detection has inherent self-verification and self-correction functions and is expected to provide more reliable and interference-resistant detection results. In this work, a rapid approach for SARS-CoV-2 detection was developed using an electrochemical and colorimetric immunosensor based on a NiFe layered double hydroxide (LDH) loaded with silver citrate (NiFe-LDH@AgCit). The electrochemical sensor is capable of measuring the electrical signal of the transition from Ag to Ag+ in the presence of SARS-CoV-2. At the same time, the color reaction of 3,3′,5,5′-tetramethylbenzidine with ⋅OH can be used to build a colorimetric sensor. In electrochemical and colorimetric sensing systems, the linear detection ranges were 0.0005–10 and 0.001–10 ng mL−1, respectively. The corresponding limits of detection reached as low as 0.167 and 0.333 pg mL−1, respectively. In addition, the dual-signal sensor has been favorably used for the detection of SARS-CoV-2 in human serum, and the recovery ranges were 99.0 %–101.6 % and 100.6 %–104.5 %, respectively. In conclusion, the proposed dual-signal sensing technique offers novel insights for the prompt diagnosis of virus-related illnesses, which has wide application prospects. [ABSTRACT FROM AUTHOR]

The aim of this study is to control the course and roll motion for ships advancing in a seaway through taking the rudder and propeller actions. A high-fidelity model of the container ship is adopted and validated by using the data of the free-running model test. The full-scale sea trial data are analyzed to improve the simulation characteristics of the actuator to avoid the severe wear problem caused by overusing of the steering machine. In the present study, the adaptive robust controllers are designed for the nonlinear hydrodynamic model to achieve the overall optimal control performances of the course and roll motion. Results show that the course performance can be tuned quantitatively to satisfy the different engineering requirements. For the combined system of the course and roll motion, it is found that the course control and roll stabilization performances conflict with each other due to their coupling. The adaptive robustness controllers can easily make a tradeoff between them due to a controller only with an adjustable parameter. Simulation shows that under the premise of the good course performance, the roll reduction rate (RRR) can decrease by nearly 32% through taking the rudder and propeller actions. • The course control performance can be tuned quantitatively only adjusting the performance degree parameter. • The simulation characteristics of the actuator are improved by analyzing the full-scale sea trial data. • Two adaptive robust controllers are designed for the nonlinear hydrodynamic model to achieve the overall optimal performances of the course control and roll stabilization. [ABSTRACT FROM AUTHOR]

Palpation localization is essential for detecting physiological parameters of the radial artery for pulse diagnosis of Traditional Chinese Medicine (TCM). Detecting signal or applying pressure at the wrong location can seriously affect the measurement of pulse waves and result in misdiagnosis. In this paper, we propose an effective and high accuracy regression model using 3-dimensional convolution neural networks (CNN) processing near-infrared picture sequences to locate radial artery upon radius at the wrist. Comparing with early studies using 2-dimensional models, 3Dcnn introduces temporal features with the third dimension to leverage pulsation rhythms, and had achieved superior performance accuracy as 0.87 within 50 pixels at testing resolution of 1024 × 544. Model visualization shows that the additional dimension of the temporal convolution highlights dynamic changes within image sequences. This study presents the great potential of our constructed model to be applied in real wrist palpation location scenarios to bring the key convenience for pulse diagnosis. [ABSTRACT FROM AUTHOR]

Modulational instability is one of the possible basic mechanisms for the generation of rogue waves in deep water. Under the condition of finite depth, this instability is reduced and disappears as k 1 h < 1. 363 (k 1 is the wavenumber of the carrier wave and h is the water depth). In this study, we addressed this phenomenon through three approaches, theoretical analysis of the Zakharov's equation and the nonlinear Schrödinger equation and direct numerical simulations of the Euler equation. Their results on the modulational instability over arbitrary depths, particularly the growth rates of sidebands, are analysed and compared. In addition, the consequences of the suppression of modulational instability for realistic ocean waves are illustrated by direct simulations of long-crested, irregular waves at finite water depth. The results show that many phenomena related to the third-order nonlinearity (i.e. the downshift of the spectral peak frequency, the enhancement of kurtosis of surface elevations and rouge wave occurrence probability) are suppressed with decreasing depth k 1 h and eventually cease to exist as k 1 h < 1. 363. Considering the dependence of wave evolution on water depth, two Benjamin–Feir indices for arbitrary depths derived from Zakharov's equation and nonlinear Schrödinger equation are used as indicators to estimate the modulational instability effect on nonlinear wave fields. Our results confirm that both parameters provide reasonable predictions of the modulational instability and probability of rogue wave occurrence. [ABSTRACT FROM AUTHOR]

[Display omitted] For the first time, nitrogen oxide (NOx) emissions were systematically investigated during different operating conditions from a single test article on a chassis dynamometer, under real-world (on-road) conditions, on an engine dynamometer after the engine was pulled out of the vehicle, and again on a chassis dynamometer when the engine was installed back in the vehicle. The main goal of this study was to understand the discrepancies between certification and in-use NOx emission rates from a heavy-duty diesel vehicle (HDDV) with an engine with diesel particulate filter (DPF) and selective catalytic reduction (SCR) aftertreatment. The results showed that in-use NOx emissions over urban driving cycles were above the 0.2 g/bhp-hr NOx standard for most test conditions, ranging from 0.16 to 0.7 g/bhp-hr, with the highest NOx emissions for the chassis dynamometer testing. For the freeway/steady-state testing, the results were generally lower compared with the results of urban driving cycles, with values of 0.3 g/bhp-hr or less. For the Urban Dynamometer Driving Schedule (UDDS) cycle, NOx emissions were lowest for the engine dynamometer testing, followed by the on-road testing, and the chassis dynamometer testing. The differences between the tailpipe NOx emissions for the different types of tests could be attributed to differences in SCR inlet temperatures and engine-out NOx emissions. [ABSTRACT FROM AUTHOR]

• Near-zero NOx emissions natural gas engines will result to 90%+ reductions compared to 2010 standard. • Cold-start shows little influence to NOx emissions. • Natural gas engines show a significant fraction of sub 23 nm particles in the tailpipe. • CO emissions are generally high for stoichiometric engines and dependent on cold-start. • Elevated CH 4 emissions during transient operation. In this study, a goods movement vehicle equipped with the ISX12N 400 near-zero natural gas engine and a yard tractor equipped with the B6.7 N 240 near-zero natural engine were evaluated over different test cycles on a chassis dynamometer. Both vehicles were operated stoichiometrically and equipped with a three-way catalyst (TWC). Experiments emphasized on nitrogen oxide (NOx) emissions, greenhouse gases (GHGs), and particulate emissions. Results showed substantially low levels of NOx emissions of both vehicles and dramatic reductions of up to 90% compared to the 2010 certification standard. The cold-start period did not influence the overall NOx emissions, suggesting that real-world NOx formation during cold-start will not contribute to ground level ozone considering the vocations of these vehicles. Carbon monoxide (CO) emissions were found at relatively high levels. Solid particle number emissions from both vehicles were seen to be above the European particle number limit, indicating that current natural gas engines could be an important source of nanoparticles, especially those of the sub 23 nm range. Both vehicles demonstrated elevated methane (CH 4) and ammonia (NH 3) emissions. Nitrous oxide (N 2 O) and NH 3 emissions were largely dependent of the cold-start fraction of the test cycles, showing greater concentrations compared to the hot-start tests. [ABSTRACT FROM AUTHOR]

[Display omitted] • F-containing groups tune the electronic microenvironment of adsorption sites. • The adsorption capacity of PCA-Nanofiber for In(III) ions reached 116.3 mg/g. • Effective charge transfer of adsorption sites promotes In(III) ions capturing. Enhancing adsorbent-adsorbate interactions via rational construction of adsorption sites is essential for the capture of the strategic metal indium. Herein, a nanofiber (PCA-Nanofiber) was successfully fabricated for In(III) ions recovery by introducing an electronegative fluorine-containing group to modulate the electronic structure of adsorption sites. Adsorption experiments exhibited that the maximum adsorption of PCA-Nanofiber for In(III) ions (116.3 mg/g) was 3.3 times higher than that of the pre-regulated nanofiber, exceeding most of the reported adsorbents. The PCA-Nanofiber maintained stable performance in five consecutive adsorption–desorption experiments and exhibited excellent selectivity. Furthermore, advanced spectroscopic characterization revealed that the adsorbed complex presented in a six-coordinated octahedral configuration, resembling an anti-triangular prism configuration centered on In(III) ions. The density functional theory demonstrates that the excellent adsorption performance benefits from the electronegative F atoms modulating the electronic microenvironment of the adsorption sites and optimizing the band-gap energy for charge migration, resulting in efficiently driven charge transfer. This work offers ideas for the rational design of adsorption sites in nanofiber from the perspective of regulation of electronic microenvironmental. [ABSTRACT FROM AUTHOR]

Liquefied natural gas (LNG) is becoming increasingly popular as a marine fuel as emission regulations become more stringent. However, very little data are available on the particulate matter (PM) emissions of modern marine natural gas engines. In this study, we present a first detailed characterization of the composition of the PM emitted by a modern, in-use, natural-gas-powered vessel. The vessel engines use compression-ignition and only a small amount of diesel fuel as pilot. These engines drive electrical generators, providing propulsion as well as auxiliary power for the vessel. Our emissions characterization includes six different techniques to measure black carbon (BC), including all methods determined as appropriate for measuring BC emissions from ships by the International Maritime Organization, as well as particle size distributions, metal concentrations, and organic particulate emissions. PM emissions differed significantly between idle and at-sea operating conditions. At idle, PM emission factors were primarily organic (approximately 1500 mg / kWh) , with BC emission factors over two orders of magnitude lower (5.6 ± 0.4 mg / kWh). At engine loads above 25%, all emissions were independent of load and substantially lower than at idle, at 4.4 ± 1.7 mg / kWh for organics and 0.8 ± 0.2 mg / kWh for black carbon. When operated only on diesel fuel, this engine emitted 8-fold more organic PM (38 ± 15 mg / kWh) and 37-fold more BC (30 ± 11 mg / kWh) at loads above 25%. At idle loads, the diesel-fuel emissions were comparable to the natural-gas emissions. In addition to organics and BC, a third category of non-volatile sub-10-nm particles was identified. A detailed consideration of our measurements indicated that the sources of the organic, BC, and sub-10-nm particles were lubrication oil, diesel pilot fuel, and lubrication-oil metals, respectively. Future studies should seek to quantify the emissions of other dual-fuel engines that will be entering the market. Image 1 • Detailed characterization of PM emissions from natural-gas-powered marine vessel. • PM was primarily volatile organics, which may originate from lubrication oil. • BC emissions were 37x higher when switching from liquid natural gas to diesel fuel. [ABSTRACT FROM AUTHOR]

Under the research background of China vigorously promoting the planning and construction of multi-energy complementary energy bases as well as the steady advancement of the electricity market, how to better promote the participation of large-scale multi-energy complementary bases in the electricity market has become a new research focus. In this paper, a joint optimization model for the participation of multi-energy systems in the electric energy market and auxiliary service market is proposed based on the Nash negotiation theory with coordinated wind-photovoltaic-pumped-storage-hydropower (WPPSH) generation systems as the research objects. Further, the joint optimization model is equated into two subproblems, namely, maximizing the net return of the alliance and negotiating the payment of the multi-energy complementary transaction, and the alternating direction method of multiplier algorithm (ADMM) is used to solve the above two subproblems separately. The simulation results show that the revenue of WPPSH increased by 25.82%, 30.67%, 49.35% and 3.78% respectively after joint operation, and the absorption rates of wind power (WD), photovoltaic (PV) and hydropower (HY) increased by 17.69%, 21.87% and 9.01% respectively. Through the cooperation of WPPSH generation systems, the income of each entity and the alliance can be improved, the fair distribution of incremental income is basically realized, and the consumption of clean energy can be promoted. In addition, the capacity allocation cap ratio of pumped storage (PS) units in the energy market and auxiliary service market will have a certain impact on the returns of WPPSH generation systems in multi-principal cooperation. [Display omitted] • The WPPSH can improve the profits of the alliance and individuals. • The WPPSH can improve the consumption rate of all kinds of clean energy. • The capacity allocation ceiling ratio of PS will have a certain impact on the cooperative benefits of WPPSH. • Nash negotiation theory is helpful for each agent to achieve the optimal trading strategy. [ABSTRACT FROM AUTHOR]

[Display omitted] • An entirely novel kind of GQDs-modified TpPa-1-COF photocatalyst was prepared. • GQDs were synchronously deposited onto the TpPa-1-COF by the in-situ method. • GQDs serve as a photosensitizer that enhances the optical absorption ability. • The 0.44% GQDs/TpPa-1-COF shows a better H 2 production than bare COF by 2.9 times. • DFT simulation indicates the highly efficient interfacial charge migration. The photocatalytic activity of covalent organic frameworks (COFs) to evolve hydrogen (H 2) triggered by visible light has been promising, but the separation and migration efficiency of photogenerated electron-hole pairs still have to be improved. In the study, we designed and synthesized a kind of graphene quantum dots (GQDs) modified β-ketoenamine-linked COF composite (GQDs/COF) by an in-situ method to improve its photocatalytic H 2 evolution for the first time. The resulting 0.44% GQDs/TpPa-1-COF shows a rate of H 2 production of 487 μmol g−1h−1 with the illumination of visible light, outperforming those of bare TpPa-1-COF by 2.9 times. It is found that the GQDs serve as a photosensitizer that effectively enhances the optical absorption ability. Moreover, GQDs serve as a means of collecting photoinduced electrons as for preventing the subsequent recombination of carriers excited by visible light. Furthermore, the Ohmic contact effect between the TpPa-1-COF and GQDs facilitates the highly efficient migration of photoinduced electrons, which has been demonstrated by the density functional theory (DFT) calculation results. Overall, this work provides a novel strategy to design COF-based composites and gives some insight into the role of GQDs on enhanced photocatalytic activity. [ABSTRACT FROM AUTHOR]

Microbial growth and reproduction can cause food spoilage. Developing the controlled release packaging films for food is an ideal solution. In this study, polyethyleneimine (PEI) was grafted to cellulose nanofibers (CNF) films by Schiff base, and when the CNF/PEI films were stimulated by pH, PEI released from the CNF/PEI films due to Schiff base hydrolysis, improving the antibacterial efficiency of PEI. Stimulated by acid with pH of 4, the PEI cumulative release rate of the CNF/PEI 800 and the CNF/PEI 2000 films reached to 92.90 % and 87.28 %, respectively. At the same time, the amino groups of PEI protonated by obtaining H+, the charge density increased, and PEI molecular chains extended, enhancing the antibacterial activity of films. The Zeta potential value on the surface of the CNF/PEI film increased with the decrease of pH value. Schiff base synergized with protonation of PEI to achieve smart antibacteria of CNF packaging films. The antibacterial rates of the film against L. monocytogenes and E. coli were 94.7 % and 90.6 % at pH 4, but 29.5 % and 23.6 % at pH 8, respectively. The developed films also had good barrier properties of oxygen, visible light and mechanical properties, and had an attractive application prospect in food preservation to control release of antibacterial agent. [Display omitted] • PEI was grafted to the CNF films by Schiff base bonding. • The developed CNF/PEI films sensitively responded to pH stimulation. • Schiff bases synergized with protonation of PEI to achieve smart antibacteria. • The films showed excellent antibacterial activity against E. coli and L. monocytogenes. [ABSTRACT FROM AUTHOR]

The application of conventional sensors is limited due to energy supply issues and environmental problems, and current triboelectric nanogenerator (TENG) based self-powered sensing systems is hard to meet the specific requirement. In this paper, TiO 2 /Ni/C composites were prepared using MOF-derived Ni/C materials for SO 2 F 2 detection at room temperature. The excellent pore structure and high electrical conductivity of Ni/C material greatly improve the sensitivity properties of TiO 2. The TiO 2 /Ni/C composites-based SO 2 F 2 sensor exhibits a wide detection range of 5–200 ppm concentration and a fast response/recovery time of 26 s/9 s, while its response value for 200 ppm SO 2 F 2 is 18.34 times higher than that of the pure TiO 2 -based sensor. Furthermore, by modifying the PVDF film with TiO 2 , the permittivity of PVDF is greatly improved, and the surface potential is 2.6 times higher than that of pure PVDF film. Eventually, a dual-layer hybrid TENG based on TiO 2 /PVDF composite film was constructed. Its upper TENG powers the TiO 2 /Ni/C composite sensor, and the lower hybrid TENG drives the thermohygrometer to realize a self-powered temperature and humidity-corrected SO 2 F 2 sensing system, which can accurately implement SO 2 F 2 gas concentration alarms at 10 ppm, 50 ppm, and 100 ppm at different humidity levels. This work broadens the application of TENG in the field of self-powered gas sensing while providing a new idea to achieve the trace detection of SO 2 F 2 at room temperature. [Display omitted] • Preparation of TiO 2 /Ni/C composites with high sensitivity SO 2 F 2 sensing effect. • TiO 2 -modified PVDF film achieves higher output. • An air-sensitive and temperature and humidity monitoring system for self-powered cabin SO2F2 de-worming. [ABSTRACT FROM AUTHOR]

The paper presents experimental studies on the effects of draft conditions, current headings and mooring stiffnesses on the Vortex-Induced Motion (VIM) responses of a Tension-Leg Platform (TLP) with four circular columns. The nominal transverse and yaw amplitudes, and the corresponding spectra obtained by fast Fourier Transform (FFT) and continuous wavelet transform (CWT) are analyzed to examine the characteristics of the motion responses. Four current headings in the range of 0° to 45° are adopted in the model test. The results show that the maximum response amplitude in both transverse and yaw directions are achieved at 0° current incidence. For the transverse response, the maximum nominal amplitude decreases and the lock-in range narrows as the current heading increases. The spectral analyses for the transverse and yaw motions at 0° current heading show that the dominant peak frequencies increase as the reduced velocity increases. It is observed that the low-frequency spectral peak of the in-line motion amplitude spectra increases as reduced velocity increases. A color band or a ribbon concentrates stably around the frequency of f < 0. 05 in the wavelet analysis. It is primarily caused by the initial acceleration of the model in the towing tank. The absolute values of correlations between the spring tensions and the transverse motion of the platform are much larger than those between the spring tensions and the in-line or yaw motions. Moreover, it is found that the VIM trajectories of the TLP are primarily always along the direction perpendicular to the current heading. • A model test was conducted to study the VIM responses of a TLP. • Amplitude spectra analysis was performed to examine the performances of motion and spring tension. • Wavelet analysis was performed to examine the transient effect. • The correlations between the mooring tension and the VIM are analyzed. • The motion trajectories of the TLP for different conditions are analyzed. [ABSTRACT FROM AUTHOR]

• A hybrid nanogenerator is proposed. • Structural optimization design for higher economic efficiency. • Structural optimization design for higher output. • Designed a real time sensing system at 6 m/s wind speed. The sustained development of intelligent agriculture has attracted widespread attention to more significant requirements for real-time sensing. In recent years, the emerging triboelectric nanogenerator (TENG) has proved to be a promising and renewable energy-harvesting device, which will provide a powerful boost for the development of intelligent agriculture. In this work, we aggregated TENG and electromagnetic generator (EMG) to form a stackable hybrid nanogenerator, withing the advantages of simple structure, easy manufacture, and extensive fields of application. The hybrid nanogenerator shows excellent output performance with wind speeds ranging from 3 m/s to 6 m/s. Through the membrane structure optimization, an optimal film structure was proposed that owns satisfactory output and high economic benefit. The output performance of EMG obtained further design in rationalization, which achieved a peak power of 118 mW at a wind speed of 6 m/s. It is of extraordinary significance to realize the real-time sensing of the fan status, temperature, and humidity in the smart agricultural greenhouse for monitoring the environmental parameters of the intelligent agricultural by integrating with a Bluetooth module. [ABSTRACT FROM AUTHOR]

In recent years, liquefied natural gas (LNG) has become a popular alternative to heavy fuel oils for use in marine engines due to its low cost and increasingly stringent emissions regulations from the International Maritime Organization (IMO). While natural gas (NG) combustion produces substantially less soot than other fossil fuels, its soot emissions are not negligible and have not been morphologically characterized. In this study, transmission electron microscopy (TEM) was used to interpret scanning mobility particle sizer (SMPS) distributions of catalytically-denuded particulate matter (soot) from an NG-powered commercial vessel. The vessel studied was powered by a dual-fuel compression ignition engine which uses a diesel pilot only to ignite the NG. Less than 2% of the engine's power is provided by the diesel pilot for engine loads above 50%. For comparison, tests were also run in diesel-only mode. During NG operation, the SMPS size distributions did not follow a lognormal distribution as expected. Instead, the distribution remained roughly constant from 10 nm to 100 nm. High resolution (HR)-TEM revealed particles as small as 30 nm in diameter were soot. From the TEM images, soot primary particle size and projected-area-equivalent diameter were estimated for individual soot particles. These data were then used to infer the effective density of the particles collected. In general, the primary particle size and correspondingly the estimated effective density of soot from this engine were larger than those from the "universal fit" proposed for soot in previous literature. For both operating conditions the source of the soot was likely the diesel pilot however, there were statistically significant differences between particles produced by the two fuelling modes. With LNG fueling, there were far fewer particles, and mean soot aggregate size decreased, but the primary particle size (for a 100 nm aggregate) increased, relative to diesel. Engine load did not have a statistically significant effect on soot morphology. • Real-world soot from a marine engine running on natural gas with a diesel pilot. • Statistically different soot produced by the engine burning diesel and natural gas. • Calculated effective density in good agreement with prior direct measurements. [ABSTRACT FROM AUTHOR]

Objectives: The 3 most frequently utilized frailty assessment measures are the Fried criteria, FRAIL scale, and Frailty Index (FI). This study aimed to compare predictive capabilities of these 3 measures regarding all-cause mortality in the United States and to identify the key predictive variables., Design: Cross-sectional study., Setting and Participants: From the National Health and Nutrition Examination Survey (NHANES) 2005-2018 cycles, a total of 39,631 participants aged 20 and older were included., Methods: Fried status, FRAIL status, and FI status were determined for each individual based on the cutoff values from the continuous scores of their respective scales. Univariate and multivariate models, incorporating 11 covariates-sex, age, body mass index, ethnicity, education, marital status, smoking status, alcohol intake, employment status, poverty-to-income ratio, and total energy intake-were fitted using Cox proportional hazards and 2 machine learning models. Model performance was assessed through Integrated Brier Score (IBS), concordance index (C-index), and area under the curve (AUC) values from 10-fold cross-validation. Key variable analysis was performed using permutation importance and C-index increment. Subgroup analysis was developed according to age., Results: In univariate analyses, FI consistently outperformed Fried and FRAIL, showing significantly lower IBS, and higher C-index and AUC values. In multivariate analyses, few significant differences were found. Permutation importance analysis identified age as the most important variable, followed by Fried status and FI status. Similarly, in C-index increment analysis, age was the top one variable. Subgroup analyses showed that FI status consistently performed best in all metrics across univariate analyses at least in 40-59 and 60-79 age groups. FI status consistently emerged as the most important variable in permutation analysis across all age groups., Conclusions and Implications: FI demonstrated the best performance as a single predictor in predicting all-cause mortality, with age being crucial for enhancing predictive performance. Future research should explore the applicability of FI in different populations and its relationship with cause-specific mortality., Competing Interests: Disclosure The authors declare no conflicts of interest., (Copyright © 2024 Post-Acute and Long-Term Care Medical Association. Published by Elsevier Inc. All rights reserved.)

Palm-vein has been widely used for biometric recognition due to its resistance to theft and forgery. However, with the emergence of adversarial attacks, most existing palm-vein recognition methods are vulnerable to adversarial image attacks, and to the best of our knowledge, there is still no study specifically focusing on palm-vein image attacks. In this paper, we propose an adversarial palm-vein image attack network that generates highly similar adversarial palm-vein images to the original samples, but with altered palm-identities. Unlike most existing generator-oriented methods that directly learn image features via concatenated convolutional layers, our proposed network first maps palm-vein images into multi-scale high-dimensional shallow representation, and then develops attention-based dual-path feature learning modules to extensively exploit diverse palm-vein-specific features. After that, we design visual-consistency and identity-aware loss functions to specially decouple the visual and identity features to reconstruct the adversarial palm-vein images. By doing this, the visual characteristics of palm-vein images can be largely preserved while the identity information is removed in the adversarial palm-vein images, such that high-aggressive adversarial palm-vein samples can be obtained. Extensive white-box and black-box attack experiments conducted on three widely used databases clearly show the effectiveness of the proposed network., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objectives: In this study, we propose an interpretable deep learning radiomics (IDLR) model based on [ 18 F]FDG PET images to diagnose the clinical spectrum of Alzheimer's disease (AD) and predict the progression from mild cognitive impairment (MCI) to AD., Methods: This multicentre study included 1962 subjects from two ethnically diverse, independent cohorts (a Caucasian cohort from ADNI and an Asian cohort merged from two hospitals in China). The IDLR model involved feature extraction, feature selection, and classification/prediction. We evaluated the IDLR model's ability to distinguish between subjects with different cognitive statuses and MCI trajectories (sMCI and pMCI) and compared results with radiomic and deep learning (DL) models. A Cox model tested the IDLR signature's predictive capability for MCI to AD progression. Correlation analyses identified critical IDLR features and verified their clinical diagnostic value., Results: The IDLR model achieved the best classification results for subjects with different cognitive statuses as well as in those with MCI with distinct trajectories, with an accuracy of 76.51% [72.88%, 79.60%], (95% confidence interval, CI) while those of radiomic and DL models were 69.13% [66.28%, 73.12%] and 73.89% [68.99%, 77.89%], respectively. According to the Cox model, the hazard ratio (HR) of the IDLR model was 1.465 (95% CI: 1.236-1.737, p < 0.001). Moreover, three crucial IDLR features were significantly different across cognitive stages and were significantly correlated with cognitive scale scores (p < 0.01)., Conclusions: Preliminary results demonstrated that the IDLR model based on [ 18 F]FDG PET images enhanced accuracy in diagnosing the clinical spectrum of AD., Key Points: Question The study addresses the lack of interpretability in existing DL classification models for diagnosing the AD spectrum. Findings The proposed interpretable DL radiomics model, using radiomics-supervised DL features, enhances interpretability from traditional DL models and improves classification accuracy. Clinical relevance The IDLR model interprets DL features through radiomics supervision, potentially advancing the application of DL in clinical classification tasks., Competing Interests: Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is J.J. Conflict of interest: The authors declare no potential conflicts of interest relevant to this article. Statistics and biometry: Fan Yang and J.S. kindly provided statistical advice for this manuscript. Informed consent: Written informed consent was obtained from all subjects (patients) in this study. Ethical approval: The research involved human participants. Data used in this project was approved by institutional review boards of ADNI and written informed consent was obtained from all participants or authorised representatives. Study subjects or cohorts overlap: Some study subjects or cohorts have been previously reported by Jiang et al [16]. Methodology: Prospective Experimental Multicentre study, (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

Background: The core clinical sign of Parkinson's disease (PD) is bradykinesia, for which a standard test is finger tapping: the clinician observes a person repetitively tap finger and thumb together. That requires an expert eye, a scarce resource, and even experts show variability and inaccuracy. Existing applications of technology to finger tapping reduce the tapping signal to one-dimensional measures, with researcher-defined features derived from those measures., Objectives: (1) To apply a deep learning neural network directly to video of finger tapping, without human-defined measures/features, and determine classification accuracy for idiopathic PD versus controls. (2) To visualise the features learned by the model., Methods: 152 smartphone videos of 10s finger tapping were collected from 40 people with PD and 37 controls. We down-sampled pixel dimensions and videos were split into 1 s clips. A 3D convolutional neural network was trained on these clips., Results: For discriminating PD from controls, our model showed training accuracy 0.91, and test accuracy 0.69, with test precision 0.73, test recall 0.76 and test AUROC 0.76. We also report class activation maps for the five most predictive features. These show the spatial and temporal sections of video upon which the network focuses attention to make a prediction, including an apparent dropping thumb movement distinct for the PD group., Conclusions: A deep learning neural network can be applied directly to standard video of finger tapping, to distinguish PD from controls, without a requirement to extract a one-dimensional signal from the video, or pre-define tapping features., Competing Interests: Declaration of competing interest No specific funding was received for this work. The authors declare that there are no conflicts of interest relevant to this work., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Low-temperature operation of sodium metal batteries (SMBs) at the high rate faces challenges of unstable solid electrolyte interphase (SEI), Na dendrite growth, and sluggish Na + transfer kinetics, causing a largely capacity curtailment. Herein, low-temperature and fast-charge SMBs are successfully constructed by synergetic design of the electrolyte and electrode. The optimized weak-solvation dual-salt electrolyte enables high Na plating/stripping reversibility and the formation of NaF-rich SEI layer to stabilize sodium metal. Moreover, an integrated copper sulfide electrode is in situ fabricated by directly chemical sulfuration of copper current collector with micro-sized sulfur particles, which significantly improves the electronic conductivity and Na + diffusion, knocking down the kinetic barriers. Consequently, this SMB achieves the reversible capacity of 202.8 mAh g -1 at -20 °C and 1 C (1 C = 558 mA g -1 ). Even at -40 °C, a high capacity of 230.0 mAh g -1 can still be delivered at 0.2 C. This study is encouraging for further exploration of cryogenic alkali metal batteries, and enriches the electrode material for low-temperature energy storage., (© 2024 Wiley‐VCH GmbH.)

Background: The association of cognitive function, its changes, and all-cause mortality has not reached a consensus, and the independence of the association between changes in cognitive function and mortality remains unclear. The purpose of this study was to evaluate the longitudinal association between baseline cognitive function and cognitive changes over 1 year with subsequent all-cause mortality among the older adults aged 60 and above., Methods: A prospective cohort study utilizing the Community Older Adults Health Survey data. Initiated in 2018, the study annually assessed all individuals aged 60+ in Dalang Town, Dongguan City. Cognitive function was assessed using the Chinese version of the Mini-Mental State Examination (MMSE). A total of 6,042 older adults individuals were included, and multivariate Cox proportional hazard models were used to examine cognitive function's impact on mortality., Results: Participants' median age was 70 years, with 39% men. Over a median 3.08-year follow-up, 525 died. Mortality risk increased by 6% per MMSE score decrease (adjusted HR = 1.06, 95% CI : 1.05-1.08). Compared to those with normal cognitive function at baseline, participants with mild cognitive impairment and moderate to severe cognitive impairment had significantly higher mortality risks (adjusted HR = 1.40, 95% CI : 1.07-1.82; HR = 2.49, 95% CI : 1.91-3.24, respectively). The risk of death was 5% higher for each one-point per year decrease in cognitive function change rate ( HR = 1.05, 95%CI: 1.02-1.08). Compared with participants with stable cognitive function, those with rapid cognitive decline had a 79% increased risk of death (adjusted HR = 1.79, 95% CI : 1.11-2.87), with baseline cognitive function influencing this relationship significantly ( P for interaction = 0.002)., Conclusion: Baseline cognitive impairment and rapid cognitive decline are associated with higher all-cause mortality risks in Chinese older adults. Baseline function influences the mortality impact of cognitive changes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, He, Wu, Tang, Ouyang, Jing, Yang, Yang, Che, Pan, Chen, Zhang, Zheng, Xu, Liao, Yin and Ni.)

Background: Linear associations between circulating insulin-like growth factor-1 (IGF-1) levels and Parkinson's disease (PD) have been evidenced in observational studies. Yet, the causal relationship between IGF-1 levels and PD remains obscure. We conducted Mendelian randomization to examine the correlation between genetically predicted IGF-1 levels and PD., Methods: By reviewing genome-wide association studies (GWAS) that are publicly accessible, we uncovered SNPs linked to both serum concentrations of IGF-1 and PD. A two-sample Mendelian randomization (MR) analysis was carried out to evaluate the individual effect of IGF-1 on PD., Results: In a primary causal effects model in MR analysis, employing the inverse-variance weighted (IVW) method, IGF-1 levels exhibited a notable association with the risk of PD (OR, 1.020, 95% CI, 1.003-1.038, p  = 0.0215). Multiple evaluations revealed that horizontal pleiotropy was improbable to distort the main results (MR-Egger: P PD intercept =0.719), and no bias was detected by leave-one-out analysis., Conclusion: This study unearthed evidence indicating that heightened IGF-1 levels might be causally correlated with an increased risk of PD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Xu, Fan, Yang, Yin, Wu, Chen and Ni.)

In this study, we employed laser-induced breakdown spectroscopy (LIBS) along with machine learning algorithms, which encompass partial least squares regression (PLSR), the deep convolutional neural network (CNN), the deep residual neural network (ResNet), and the deep residual shrinkage neural network (DRSN), to estimate the surface hardness of laser cladding layers. (The layers were produced using Fe316L, FeCrNiCu, Ni25, FeCrNiB, and Fe313 powders, with 45 steel and Q235 serving as substrates.) The research findings indicate that both linear and nonlinear models can effectively fit the relationship between LIBS spectra and surface hardness. Particularly, the model derived from the ResNet exhibits superior performance with an R 2 value as high as 0.9967. We hypothesize that the inclusion of numerous noises in the LIBS spectra contributes to the enhanced predictive capability for surface hardness, thereby leading to the superior performance of the ResNet compared to the DRSN.

Postnatal growth retardation (PGR) frequently occurs during early postnatal development of piglets and induces high mortality. To date, the mechanism of PGR remains poorly understood. Adipose tissue-derived microbes have been documented to be associated with several disorders of metabolism and body growth. However, the connection between microbial disturbance of adipose tissue and pig PGR remains unclear. Here, we investigated piglets with PGR and found that the adipose tissue of PGR piglets was characterized by metabolism impairment, adipose abnormality, and specific enrichment of culturable bacteria from Proteobacteria . Gavage of Sphingomonas paucimobilis , a species of Sphingomonas genus from the alphaproteobacteria , induced PGR in piglets. Moreover, this bacterium could also lead to metabolic disorders and susceptibility to acute stress, resulting in weight loss in mice. Mechanistically, multi-omics analysis indicated the changes in lipid metabolism as a response of adipose tissue to abnormal microbial composition. Further experimental tests proved that one of the altered lipids phosphatidylethanolamines could rescue the metabolism disorder and growth retardation, thereby suppressing the amount of Sphingomonas in the adipose tissue. Together, these results highlight that the microbe-host crosstalk may regulate the metabolic function of adipose tissue in response to PGR., Competing Interests: The authors declare that no conflict of interest exists., (© The Author(s) 2024. Published by Oxford University Press on behalf of Higher Education Press.)

Neurological disorders (NDs), such as Alzheimer's disease, have been a threat to human health all over the world. It is of great importance to diagnose ND through combining artificial intelligence technology and brain imaging. A graph neural network (GNN) can model and analyze the brain, imaging from morphology, anatomical structure, function features, and other aspects, thus becoming one of the best deep learning models in the diagnosis of ND. Some researchers have investigated the application of GNN in the medical field, but the scope is broad, and its application to NDs is less frequent and not detailed enough. This review focuses on the research progress of GNNs in the diagnosis of ND. Firstly, we systematically investigated the GNN framework of ND, including graph construction, graph convolution, graph pooling, and graph prediction. Secondly, we investigated common NDs using the GNN diagnostic model in terms of data modality, number of subjects, and diagnostic accuracy. Thirdly, we discussed some research challenges and future research directions. The results of this review may be a valuable contribution to the ongoing intersection of artificial intelligence technology and brain imaging.

Agricultural plastic films have caused serious plastic pollution. There are many studies that consider mechanical recycling an appropriate system for the recovery of post-consumption agricultural mulch film. The recovery effect of plastic film depends on the mechanical properties, the level of dirtiness of the post-consumption film, and the recycling process itself. In this study, the mechanical properties of four types of polyethylene plastic films with a thickness of 8, 10, 12, and 10 μm, weather-resistant, commonly used in Xinjiang cotton fields, were tested. As well as the friction coefficient between the film and soil, the cotton stalk, boll shell, and leaf with different moisture contents were measured. Then, the self-propelled straw chopping and residual film recycling combined machine collected the four types of mulch films. The results showed that the longitudinal mechanical properties of the plastic film were greater than the transversal ones, with the exception of the nominal tensile strain at break, and the tensile characteristics of the mulching film covered with soil were greater than those without soil. The dynamic or static friction coefficient between the film and the contact material had a linear relationship with the moisture content of the material. During the recycling operation, the better the mechanical properties of the plastic film, the higher the pick-up rate of the mulch film. The maximum longitudinal tensile force of 12-μm plastic film was 3.42 N, and the nominal tensile strain at break was 303.09%. The pick-up rate reached more than 93% when the 12-μm plastic film was recovered in autumn, which effectively reduced the residue of plastic film coverage in the current year. Moreover, the more soil that was present on the much film, the greater the soil content of the recycled film roll, and the stalk content also increased, but the change was small. The research provides a reference for the mechanical and the friction features of agricultural plastic film in Xinjiang, and provides a theoretical basis for the formulation of standards for film thickness and mechanical properties, as well as the design and optimization of a residual film collecting machine in the cotton field., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The key to "dual carbon" lies in low-carbon energy systems. The energy internet can coordinate upstream and downstream "source network load storage" to break energy system barriers and promote carbon reduction in energy production and consumption processes. This article first introduces the basic concepts and key technologies of the energy internet from the current situation of energy supply and demand in China. Second, this paper proposes to create an energy internet with coordinated and complementary "source network load storage" and to construct a new type of power system with six new characteristics in this context. Finally, combined with an example of the energy internet demonstration project, this paper analyses and summarizes the value creation and business type innovation of the energy internet from three aspects: power market mechanisms, comprehensive energy services, and low-carbon energy diversification, and it looks ahead to the future direction of energy internet construction., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)