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A new method for the analysis of four target flavonoids in two kinds of citrus samples by ultra-high performance supercritical fluid chromatography (UHPSFC) method was developed. Main variables affecting the UHPSFC separation were optimized, and under the optimized conditions the four target compounds (tangeretin, nobiletin, hesperetin and naringenin) can be separated within 10 min. The UHPSFC method allowed the determination of the four target compounds in the diluted stock solutions with limit of detection (LOD) ranging from 1.08 to 2.28 μg mL−1, and limit of quantification (LOQ) ranging from 1.45 to 4.52 μg mL−1, respectively. The coefficients of determination (R2) of the calibration curves were higher than 0.9950. The recoveries of the four target compounds at three different concentrations were in the range of 82.4–117.6%. The validation results demonstrated that the proposed method is simple, accurate, time-saving and environment friendly, and it is applicable to a variety of complex samples such as medicine-food dual purpose herbs and functional foods.

A new polydopamine-functionalized titanium dioxide nano/submicro-particles (PDA@TiO2NPs) coating was successfully constructed on the surface of NiTi substrate as a solid-phase microextraction (SPME) fiber. To obtain a stable and uniform PDA@TiO2NPs coating, the NiTi fiber was hydrothermally treated for in situ growth of titanium/nickel oxide composite nanosheets (TiNiOCNSs). Subsequently, the titanium dioxide nano/submicro-particles (TiO2NPs) were electrophoretically deposited on the TiNiOCNSs coating. Finally, the polydopamine (PDA) coating was modified on the TiO2NPs coating by self-polymerization of dopamine (DA). Scanning electron microscopy (SEM) of the new PDA@TiO2NPs@TiNiOCNSs@NiTi fiber showed a popcornlike nanostructure with a larger surface area and more adsorption sites. Benefitting from the abundant hydrogen bonding, rich π-electron system, and strong hydrophobicity, the functionalized PDA@TiO2NPs@TiNiOCNSs@NiTi fiber illustrated excellent extraction capability for benzotriazole ultraviolet filters (BUvFs) among typical aromatic compounds coupled to HPLC with UV detection. The main SPME conditions such as extraction and desorption time, extraction temperature, stirring rate, and ionic strength were examined and optimized one by one. Under the optimum conditions, the calibration curves presented good linearity for BUvFs in the ranges from 0.01 µg·L−1 to 300 µg·L−1, and the correlation coefficients (r) were above 0.9990. The limits of detection (LODs, S/N = 3) were between 0.005 µg·L−1 and 0.043 µg·L−1. RSD repeatability of intraday and interday for one fiber was less than 5.3% and 6.9%, respectively. Moreover enrichment factors (EFs) ranged from 63 to 218. The recommended method can completely extract and detect trace BUvFs in different environmental water samples. Furthermore, the new fiber shows good stability and high strength.

With consideration of false data injection (FDI) on the demand side, it brings a great challenge for the optimal defensive strategy with the security issue, voltage stability, power flow, and economic cost indexes. This article proposes a Takagi-Sugeuo-Kang (TSK) fuzzy system-based reinforcement learning approach for the resilient optimal defensive strategy of interconnected microgrids. Due to FDI uncertainty of the system load, TSK-based deep deterministic policy gradient (DDPG) is proposed to learn the actor network and the critic network, where multiple indexes' assessment occurs in the critic network, and the security switching control strategy is made in the actor network. Alternating direction method of multipliers (ADMM) method is improved for policy gradient with online coordination between the actor network and the critic network learning, and its convergence and optimality are proved properly. On the basis of security switching control strategy, the penalty-based boundary intersection (PBI)-based multiobjective optimization method is utilized to solve economic cost and emission issues simultaneously with considering voltage stability and rate-of-change of frequency (RoCoF) limits. According to simulation results, it reveals that the proposed resilient optimal defensive strategy can be a viable and promising alternative for tackling uncertain attack problems on interconnected microgrids.

During endovascular aneurysm repair, commercial iliac branch devices (IBDs) have become an inescapable alternative for preserving antegrade internal iliac artery (IIA) blood flow. Due to the different morphological features of aneurysms, commercial IBDs may not be suitable for all patients. Reported experience with the implantation of the new surgeon-modified IBD (sm IBD) is limited. This investigation describes the indications, efficacy, and safety of the sm IBD.Data from consecutive elective implantations of IBDs in patients between March 2011 and May 2021 in a single center were incorporated. The sm IBDs were indicated in patients with common iliac artery aneurysms (CIAAs) and with a challenging anatomy and in those patients with or without abdominal aortic aneurysm (AAA).Fifteen patients (15 male, mean age 67.6±7.9 years) were included. Fifteen sm IBDs were implanted in one procedure (100%). Fourteen (93.3%) patients had simultaneous endovascular aneurysm repair (EVAR), and 1 (6.7%) patient previously had a bilateral CIAAs repair by EVAR. The mean CIA diameter was 36.6 ± 12.5 mm. Technical success was obtained in all patients (100%). The median operation time was 189.7 ± 78.6 min, with a median fluoroscopy time of 45.3 ± 15.9 min. Axillary artery access was used in 11 (73.3%) procedures. The mean total hospital stay was 5.6 ± 2.8 days, and the postoperative follow-up was 35.4 months (range 2-120). The estimated IIA bridge stent patency at 1 year after operation was 100%, and was 85.7%±13.2% at 5 years postoperatively. One (6.7%) IIA branch was occluded, and this patient remained asymptomatic. One patient (6.7%) needed reintervention, and another (6.7%) patient had type II leakage, which is currently under close surveillance.Using an IBD to maintain the pelvic blood flow is an effective and feasible intravas-cular technique, especially for patients with an abnormal iliac artery anatomy. This novel technique has similar midterm procedural success rate compared to the use of commercial IBDs. Therefore, these devices are more suitable for patients with certain anatomic challenges and can be used as an alternative treatment.

Lithium-ion batteries (LiBs) are well-known power sources due to their higher power and energy densities, longer cycle life and lower self-discharge rate features. Hence, these batteries have been widely used in various portable electronic devices, electric vehicles and energy storage systems. The primary challenge in applying a Lithium-ion battery (LiB) system is to guarantee its operation safety under both normal and abnormal operating conditions. To achieve this, temperature management of batteries should be placed as a priority for the purpose of achieving better lifetime performance and preventing thermal failures. In this paper, fibre Bragg Grating (FBG) sensor technology coupling with machine learning (ML) has been explored for battery temperature monitoring. The results based on linear and nonlinear models have confirmed that the novel methods can estimate temperature variations reliably and accurately.

Parvalbumin (PV)-positive neurons are type of neurons in lateral globus pallidus (LGP) that play an important role in motor control. The present study investigated the effect of histamine on LGPHistamine level and histaminergic innervation in LGP were determined through brain stimulation, microdialysis, anterograde tracing and immunostaining. Receptor and ionic mechanisms of histamine action were detected by immunostaining, single-cell qPCR, whole-cell patch-clamp recording, optogenetic stimulation and CRISPR/Cas9 gene-editing techniques. The effect of histamine on motor behaviors were detected by animal behavioral test.A direct histaminergic innervation in LGP from the tuberomammillary nucleus (TMN), and histamine-induced increase in the intrinsic excitability of LGPOur results indicated that endogenous histaminergic innervation in the LGP could bidirectionally promote motor control by increasing the intrinsic excitability of LGP

[Au12Ag60(S-c-C6H11)31Br9(Dppp)6]Br2 has provided a breakthrough in icosahedral assemblies, i.e., achieving a twelve-mer assembly, helping to elucidate the fusion growth of M13-based assembled nanoclusters and their geometric/electronic structure correlations.

A hybrid Cu-ZnO/g-C3N4 nanocomposite was synthesized and introduced to fabricate photocatalytic cement mortar whose bactericidal activity was investigated under visible light.

Light is an essential natural factor of fish growth and development. A light regime in indoor aquaculture is critical for sustainable fish production. This research aimed to investigate the effect of light intensity and photoperiod on the growth performance and stress response of Nile tilapia (Oreochromis niloticus), which is an important commercial species, in a recirculating aquaculture system (RAS). Fingerlings with an average weight of 5 ± 0.9 g were randomly assigned to nine 1.5 m3 tanks with 145 fish in each tank, cultured under different light intensities (1000, 2000, and 3000 lx) and photoperiods (12L:12D, 18L:6D, 24L:0D), and fed to apparent satiation twice a day for 160 days. The growth of tilapia subjected to 2000 lx was significantly better than that exposed to 1000 and 3000 lx (P 0.05). Light intensity and photoperiod manipulation did not cause a significant chronic stress response in tilapia. This study demonstrated that light intensity, especially at 2000 lx, and photoperiod manipulation could stimulate the growth of tilapia in the RAS and significantly affect economic profitability. This study also served as an important reference for tilapia indoor aquaculture.

With the prosperous development of wearable electronics, flexible radio-frequency wireless antenna sensors have attracted considerable interest due to their various applications in the Internet of things (IoT).

The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification.Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models.Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images.The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.