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• Ktrans as a priori information is beneficial for the segmentation of NPC in DCE-MRI. • A network model MBRU-Net for multi-branch feature extraction is proposed. • Multi-branch networks outperform single-branch networks. Accurate segmentation of nasopharyngeal carcinoma (NPC) lesion areas from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) facilitates subsequent diagnostic and prognostic workups. However, in previous studies, little consideration has been given to incorporating the pharmacokinetic feature Ktrans as auxiliary information for segmenting NPC in DCE-MRI. Therefore, this paper proposes the use of a pharmacokinetic extended Tofts and Kermode (ETK) model to obtain the Ktrans feature from DCE-MRI and combine it with MRI images for nasopharyngeal tumor segmentation. Additionally, this paper proposes a multi-input branch residual U-Net (MBRU-Net) model that effectively fuses DCE-MRI features and Ktrans features. The effectiveness of the multibranch network is validated by comparing MBRU-Net with ResU-Net with DCE-MRI + Ktrans data. Additionally, different models are trained with DCE-MRI and DCE-MRI + Ktrans data separately and compared to validate the effectiveness of multimodal data using the Dice coefficient. Our proposed MBRU-Net achieves the best Dice in this study (67.39 ± 15.79%), higher than ResU-Net's Dice (65.57 ± 17.52) based on DCE-MRI and Ktrans data. U-Net, SegNet, R2U-Net, and ResU-Net achieve better results in terms of segmenting tumor regions with DCE-MRI + Ktrans data than those of the corresponding models with DCE-MRI data alone, where U-Net has the best performance (DCE-MRI + Ktrans: DCE-MRI = 66.31 ± 17.80%: 61.10 ± 24.14%). The results show that it is beneficial to add a pharmacokinetic parametric (Ktrans) map as prior information to the conventional anatomical MRI-based segmentation task, and multibranch network structures perform better than single-branch network structures in terms of NPC segmentation. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the prevalence and risk factors for carbapenem-resistant Enterobacterales colonisation/infection at admission and acquisition among patients admitted to the intensive care unit. A prospective and multicentre study. This study was conducted in 24 intensive care units in Anhui, China. Demographic and clinical data were collected, and rectal carbapenem-resistant Enterobacterales colonisation was detected by active screening. Multivariate logistic regression models were used to analyse factors associated with colonisation/infection with carbapenem-resistant Enterobacterales at admission and acquisition during the intensive care unit stay. There were 1133 intensive care unit patients included in this study. In total, 5.9% of patients with carbapenem-resistant Enterobacterales colonisation/infection at admission, and of which 56.7% were colonisations. Besides, 8.5% of patients acquired carbapenem-resistant Enterobacterales colonisation/infection during the intensive care stay, and of which 67.6% were colonisations. At admission, transfer from another hospital, admission to an intensive care unit within one year, colonisation/infection/epidemiological link with carbapenem-resistant Enterobacterales within one year, and exposure to any antibiotics within three months were risk factors for colonisation/infection with carbapenem-resistant Enterobacterales. During the intensive care stay, renal disease, an epidemiological link with carbapenem-resistant Enterobacterales , exposure to carbapenems and beta-lactams/beta-lactamase inhibitors, and intensive care stay of three weeks or longer were associated with acquisition. The prevalence of colonisation/infection with carbapenem-resistant Enterobacterales in intensive care units is of great concern and should be monitored systematically. Particularly for the 8.5% prevalence of carbapenem-resistant Enterobacterales acquisition during the intensive care stay needs enhanced infection prevention and control measures in these setting. Surveillance of colonisation/infection with carbapenem-resistant Enterobacterales at admission and during the patient's stay represents an early identification tool to prevent further transmission of carbapenem-resistant Enterobacterales. Carbapenem-resistant Enterobacterales colonization screening at admission and during the patient's stay is an important tool to control carbapenem-resistant Enterobacterales spread in intensive care units. [ABSTRACT FROM AUTHOR]

• The choking phenomenon is enhanced/suppressed by a heat/cold gas flow. • The choking phenomenon is weakly dependent on the absolute instability. • The nonexistence of steady-traveling wave solution is a good proxy for the choking phenomenon. This paper considers a thick liquid film of mean thickness h 0 coating the interior surface of a uniformly heated vertical tube of radius a under the influence of gravity ( h 0 is not far thinner than the radius a). An asymptotic model based on the long-wave assumption is derived. Linear stability analysis shows that the Rayleigh-Plateau instability can be inhibited when the core air phase is warmer than the liquid phase; while the Rayleigh-Plateau instability is enhanced when the air phase is colder than the liquid phase. Linear stability analysis also shows that the absolute instability can be enhanced by the Marangoni effect. Traveling wave study reveals that two different phenomena could exist in this system: choke phenomenon and saturated traveling waves. For small tube radius α ≲ 3.38 (α = a / h 0 is the dimensionless pipe radius), that the air phase always chokes. A cold air flow (the Marangoni number Ma > 0) promotes the height of traveling wave, which therefore enhances the choke phenomenon. The choke phenomenon can be suppressed by a warm air phase (Ma < 0). Numerical simulation shows that the traveling wave study predicts an upper bound of the critical tube radius, below which the air flow chokes. A self-similar analysis is carried out to investigate the dynamics of choke and the scaling (t c - t) 1 / 5 is obtained. The power exponent 1 / 5 is independent of Marangoni effect, but the choke time t c decreases as Ma increases. [ABSTRACT FROM AUTHOR]

Highlights • The instability is enhanced by the van der Waals attractions and Marangoni effect. • The Marangoni effect does not cause the breakup and the film thins as t −1 without van der Waals attractions. • The film thins as Δ t 1/5 due the van der Waals attractions, which ruptures in finite time. Abstract An ultra-thin liquid film flowing down a vertical uniformly heated cylinder under the influence of gravity is investigated. A thin liquid film model is derived, assuming that the film thickness h is much smaller than the fiber radius a. To predict the breakup of film, the van der Waals attraction, proportional to h - 3 , is taken into account. Linear stability analysis shows that the Rayleigh-Plateau instability is enhanced by the long-range attractions and Marangoni effect. The spatial-temporal stability analysis shows that the instability is absolute when A + M > 0.17 (A is a composite Hamaker number accounting for the strength of van der Waals attractions and M is the Marangoni number). A self-similarity analysis shows that the film thins as h ∼ (t r - t) 1 / 5 ( t r is the breakup time), which is supported by the numerical simulations of the thin film model. Although the scaling is independent on the Marangoni effect, nonlinear simulations demonstrate that the breakup time t r decreases as the Marangoni effect becomes stronger, demonstrating that the breakup process is accelerated by the Marangoni effect. Nonlinear simulation also shows that the thin heated or non-heated film mainly breaks up in the absolutely unstable regime. [ABSTRACT FROM AUTHOR]

• Tuberculosis is a global problem with an increasing impact on the public health. • Tuberculosis needs to be monitored or followed-up over time. X-ray based CT has become a standard examination, but radiation caused by CT is a concern. • Lung MRI with the motion correction provides a non-radiation alternative to the clinical standard CT. To evaluate the utility of magnetic resonance imaging (MRI) with an advanced motion correction technique in characterizing lung tissue changes and lesions induced by pulmonary tuberculosis (TB). Sixty-three subjects with computed tomography (CT) features of pulmonary TB underwent lung MRI. All subjects with pulmonary TB were confirmed by acid-fast bacillus (AFB) testing or the detection of Mycobacterium tuberculosis. T2-weighted turbo spin echo (TSE) sequence MRI with the MultiVane motion correction technique was used to image the lungs. Routine lung CT images were obtained as reference. MRI and CT images were reviewed by multiple readers independently. The performance of MRI in depicting abnormalities induced by pulmonary TB and their morphological changes were evaluated and compared with the performance of CT. Lung MRI found pulmonary abnormalities in all 63 TB subjects, with satisfactory quality. With the implementation of MultiVane for T2-weighted TSE sequences to reduce the motion correction effect, MRI showed excellent agreement with CT in detecting abnormal imaging features of pulmonary TB (κ = 0.88, p < 0.001), such as tree-in-bud sign, ground-glass opacity, consolidation, mass, and cavitation. MRI was advantageous in identifying caseation and liquefactive necrosis based on inhomogeneous signal distribution within consolidations and also in identifying mild pleural effusion. The optimized lung MRI was comparable to CT in detecting non-calcified nodules (κ = 0.90), with overall sensitivity of 50.0%, 91.1%, and 100% for nodules of size <5 mm, 5–10 mm, and >10 mm, respectively. However, MRI was less effective in identifying lesions with calcification. The clinical implementation of an optimized MRI protocol with the MultiVane motion correction technique for imaging pulmonary TB is feasible. Lung MRI without ionizing radiation is a promising alternative to the clinical standard CT, especially for pregnant women, children, adolescents, and patients requiring short-term and repeated follow-up observations. [ABSTRACT FROM AUTHOR]

Highlights • Robust System model of a mechanical DC circuit breaker with current injection circuit in RTDS environment. • Comparison with PSCAD model. • Refined modelling of energy absorber in RTDS. Abstract The main goal of the paper is the modeling of the mechanical circuit breaker (MCB) that can replicate the breaker characteristics in real time environment. The proposed MCB with active current injection is modelled for a system level, which provides adequate representation of the circuit breakers for system analysis studies. External current-voltage characteristics of the proposed MCB models replicate the ones of the devices in the real world. It is well known that the DC circuit breaker (DCCB) needs to interrupt DC faults very quickly in order to avoid converter damages. The total current interruption time consists of fault detection time, time needed for the DC protection to provide command to the DCCB, and DCCB arc clearing time. Thus, it is necessary to demonstrate the system performance of associated protective devices through real time simulation, before these devices can be implemented and commissioned in practice. This paper presents a detailed modeling of the mechanical DCCB in real time simulation environment based on RTDS. The performance of the model is verified by the simulations based on PSCAD and meaningful conclusions are drawn. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • BSA-Cu 2 SeNPs-DOX exhibits excellent water solubility and photothermal effect. • Cu 2 SeNPs can be use as a drug delivery system with dual temperature/pH response. • Upon NIR laser irradiation, Cu 2 SeNPs induces ROS generation significantly. • Cu 2 SeNPs-DOX can be used for synergistic chemo-photothermal therapy. Abstract Functionalized-nanoparticles have been developed as novel therapeutic delivery platform for simultaneous drug loading and therapy over the past decade. Rationally-designed biocompatible nanosystem simultaneously with multistimuli-responsive property and synergistic therapeutic potential are highly desirable for modern biological applications. Herein, Cu 2 Se nanoparticles (Cu 2 SeNPs) with suitable size have been functionalized by bull serum albumin (BSA) through a simply, facile and controllable method. As a result, Cu 2 SeNPs modified by BSA (BSA-Cu 2 SeNPs) showed excellent biocompatibility and stability. The strong absorbance of BSA-Cu 2 SeNPs at near infrared region imparts them with high photothermal efficiency. Then loading doxorubicin (DOX, anticancer drug) on the surface of BSA-Cu 2 SeNPs, and consequently, a novel multifunctional nanosystem of BSA-Cu 2 SeNPs-DOX is designed. The BSA-Cu 2 SeNPs can achieve high DOX loading capacity (approximately 157 μg DOX per mg of Cu 2 Se). Furthermore, a rational and precise release of DOX from the BSA-Cu 2 SeNPs-DOX could be easily realized under the stimulates of the pH and temperature, which remarkably improved antitumor efficacy of combined chemotherapy and photothermal therapy triggered by 808 nm NIR laser. Thus, the BSA-Cu 2 SeNPs-DOX could serve as an ideal nanoplatform for cancer diagnosis and treatment in future. The results of cell experiments show that the BSA-Cu 2 SeNPs-DOX exhibited favorable selective cellular uptake cells. Under the NIR laser irradiation, BSA-Cu 2 SeNPs-DOX could induce the excessive expression of ROS, eventually leading to the death of U251 cells. Both in vitro and in vivo experiments indicate that the nanosystem of BSA-Cu 2 SeNPs-DOX showed excellent synergistic therapeutic effect and multistimuli-responsive drug vehicle, which will exert huge potential for future clinical application. [ABSTRACT FROM AUTHOR]

Flexible photovoltaic devices have wide applications in many unique areas. However, the interparticle connection of photo-electrode in flexible photovoltaic devices still needs to be improved. Here, TiO 2 slurries have been prepared by a three-step process, which are employed to prepare the photo-anode for flexible CuInS 2 quantum dot sensitized solar cells (QDSSCs). 5 nm-sized TiO 2 nanoparticles and hydrochloric acid (HCl) have been introduced in 20 nm-sized TiO 2 based photo-electrodes under TiO 2 /HCl mass ratio of 3:1, which exhibits excellent photovoltaic performance and charge transfer property. Due to the interparticle connection enhancement by suitable content of 5 nm-sized TiO 2 nanoparticles, photo-anodes with the 20/5-TiO 2 nanoparticles mass ratio of 3:1 exhibits the better optical absorption property and photovoltaic performance of flexible QDSSCs. Moreover, there has maintained 98% photovoltaic conversion efficiency of flexible QDSSCs with the seriously curving condition, which can be considered the great foreground in future applications. [ABSTRACT FROM AUTHOR]

• Frequency containment reserve using wind farm and contingency state analysis. • An enhanced model of Frequency-constrained Contingency-State-Analysis is developed. • Both conventional generators and converter-interfaced generators as well as Under-Frequency-Load-Shedding are considered in the new models. • A set of new frequency dynamic indexes are calculated based on a Cross-Entropy-based Monte Carlo simulation. With the increasing penetration of converter-interfaced generators, the frequency containment reserve (FCR) from conventional generators keeps going down, leading to a potential risk of frequency instability under contingencies. Consequently, Converter-interfaced generators are required to provide FCR and participate in the corrective rescheduling. Nevertheless, how to assess the FCR and quantify the adequacy of FCR under contingencies is a big challenge in modern new power system. To address this challenge, a cross-entropy-based frequency-constrained contingency-state-analysis (FC-CSA) model is proposed in this paper. Notably, both frequency control (FC) of units (i.e., conventional synchronous generators and converter-interfaced generators), and under frequency load shedding (UFLS) are incorporated in the primary frequency response. Then a unified system frequency response (SFR) function representing frequency dynamic is derived. This SFR function is extracted and reformulated as a group of mixed-integer linear constraints and participates in the traditional CSA model. Moreover, a set of frequency dynamic indexes, i.e., Expectation of UFLS risk, Expectation of FCR from conventional and converter-interfaced generators, is extended to depict the FCR that the power system requires. These indexes are calculated by the FC-CSA in a cross-entropy-based monte carlo simulation (CE-MCs). Case studies on a modified IEEE 6-bus test system and IEEE 118-bus test system are carried out to demonstrate the effectiveness of the proposed FC-CSA model. [ABSTRACT FROM AUTHOR]

• DAE (deep auto-encoder) is implemented to extract key fault feature and re-divide sample dataset. Facing complex power system operation data, this process can be regarded as automatic data cleaning for system operation information. • Supervised deep networks learn key fault samples extraction through the knowledge of unsupervised process. In this way, the samples containing key fault features are automatically extracted by the fault identification network and indicate the system status. • Instead of increasing the complexity of the model, this article improves the performance of framework from the perspective of sample space and the union of multiple special networks. The samples with key features required by the tasks ensure that the models can learn the correct knowledge and have better performance. • The proposed framework can resist the influence of disturbance and distinguish fault state and fault types. In addition, it provides a kind of idea based on samples distribution for diagnosis tasks. To prevent serious malfunctions and reduce the impact of faults during an emergency state of a power system, protection systems are required to have disturbance and fault state identification abilities. In this study, a novel fault diagnosis framework based on deep learning with anti-disturbance ability is proposed to identify the fault state and fault type information, even under the influence of system disturbance. The framework consists of two parts: unsupervised and supervised learning. Specifically, an unsupervised deep auto-encoder (DAE) is applied for offline feature selection and data cleaning. The DAE can extract key fault features and significantly improve the fault detection accuracy. Furthermore, two supervised convolutional neural networks are used to learn key fault feature extraction online from complex operation information in power systems and assess the fault situation and type. Using case studies, the proposed method was implemented and compared with existing intelligent methods. The results indicate that the proposed framework has a better performance in terms of fault state identification and protection malfunction prevention. [ABSTRACT FROM AUTHOR]