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Adequate separation of photogenerated carriers plays an important role in the photocatalytic degradation of pollutants. Here, a noble-free spatially separated photocatalyst rGO-CdS-MnO x (GCM) has been designed for removing environmental pollutants efficiently. rGO and MnO x are employed as the collectors of photoinduced electrons and holes, respectively. Under light irradiation, the electrons and holes motivated by CdS can directionally transfer to the rGO and MnO x , leading to the exposure of both reduction and oxidation surfaces, thereby enhancing the photocatalytic activity. Our results demonstrated that GCM-30 can remove 86.56 % of carbamazepine and 95.83 % of tetracycline hydrochloride within 30 min and 15 min, respectively, and 95.49 % of Cr (VI) within 21 min. Our work demonstrates that this dual functional photocatalyst may provide new strategies for the spatially separated photocatalysts for removing environmental pollutants. [ABSTRACT FROM AUTHOR]

Optical lattices for coherently manipulating ultracold atoms demand high-power, low-noise, narrow-line-width, and continuous-wave lasers. Here, we report the implementation of a 30 W 532 nm low-noise laser by second harmonic generation from a 1064 nm fiber laser, which is capable to generate optical lattices for a quantum gas microscope of R b 87 atoms. The overall conversion efficiency is 59% at an input power of 51 W with a lithium triborate crystal coupled to a ring cavity. The relative intensity noise of the output laser is suppressed to −120 dBc/Hz in the range of 10 Hz–100 kHz with a high dynamic range of over 50 dB, which is suitable for long-term trapping and coherent manipulation of the quantum gases. [ABSTRACT FROM AUTHOR]

In clinical practice, multi-modal magnetic resonance imaging (MRI) with different contrasts is usually acquired in a single study to assess different properties of the same region of interest in the human body. The whole acquisition process can be accelerated by having one or more modalities under-sampled in the ${k}$ -space. Recent research has shown that, considering the redundancy between different modalities, a target MRI modality under-sampled in the ${k}$ -space can be more efficiently reconstructed with a fully-sampled reference MRI modality. However, we find that the performance of the aforementioned multi-modal reconstruction can be negatively affected by subtle spatial misalignment between different modalities, which is actually common in clinical practice. In this paper, we improve the quality of multi-modal reconstruction by compensating for such spatial misalignment with a spatial alignment network. First, our spatial alignment network estimates the displacement between the fully-sampled reference and the under-sampled target images, and warps the reference image accordingly. Then, the aligned fully-sampled reference image joins the multi-modal reconstruction of the under-sampled target image. Also, considering the contrast difference between the target and reference images, we have designed a cross-modality-synthesis-based registration loss in combination with the reconstruction loss, to jointly train the spatial alignment network and the reconstruction network. The experiments on both clinical MRI and multi-coil ${k}$ -space raw data demonstrate the superiority and robustness of the multi-modal MRI reconstruction empowered with our spatial alignment network. Our code is publicly available at https://github.com/woxuankai/SpatialAlignmentNetwork. [ABSTRACT FROM AUTHOR]

Comprehensive performance evaluation is a fundamental part of the design and operation optimization of distributed integrated energy systems (IES), and the evaluation is currently focused on three types of elements: economic, energy efficiency and environmental protection. In this paper, energy use quality elements are proposed from the high energy supply requirements of users for distributed IES and characterized by the supply-demand imbalance rate indicator. Then the Analytic Hierarchy Process (AHP) and the information entropy method are combined to form a combined evaluation method by weighing the subjective and objective methods. The comprehensive performance evaluation of distributed IES with nine indicators considering four elements of economy, energy efficiency, environmental protection and energy use quality is realized. The feasibility and validity of the proposed indicators and the difference between the proposed combination method and the existing combination method are verified by the calculation examples. In the single-objective optimization with optimal energy use quality, the supply-demand imbalance rates for heat and cold are −0.05 and −0.035, respectively, which are the best among the five optimization schemes and fully guarantee the stability and quality of energy supply. However, its CO2 emission of 5738.91 (t/year) shows that this indicator is not suitable for the limitation of single-objective optimization, and it would be more suitable for comprehensive optimization with the above three indicators. The weights of operation cost, heat and cold supply-demand imbalance in the AHP method are 0.2247,0.0539,0.0539. The corresponding weights in the information entropy method are 0.0930,0.1670,0.1864. The corrected values are 0.1999, 0.0861, 0.0961. The results show the corrective effect of the information entropy method on the AHP. In addition, the combination method proposed in this paper corrects the configuration cost term to 0.2032, which is larger than both 0.1588 in the AHP method and 0.1388 in the information entropy method, highlighting the difference from existing combination methods. The above data show that the comprehensive evaluation by the AHP-information entropy method can effectively consider the four elements' role. It provides a new idea for the research on the impact of distributed IES energy storage on the energy use quality of the system and the construction and operation optimization of the comprehensive performance evaluation indicator of the system. [ABSTRACT FROM AUTHOR]

The development of highly efficient passive cooling technology has garnered significant attention. However, there is a mismatch between the cooling power of radiative cooling materials and actual cooling demand due to temperature variations. Therefore, there is a necessity for radiative cooling materials that can adjust to diverse weather conditions. In this study, a scalable self-adaptive radiative cooling film was proposed. By incorporating VO 2 -based core-shell nanoparticles into PE matrix, the radiative cooling film with self-adaptive ability has been achieved. The radiative properties of the film were predicted through the Mie theory and the Monte Carlo (MC) method. Two shell materials, BaF 2 and ZnS, were considered. The effects of shell material, particle diameter D , inner/outer diameter ratio r / R , doping concentration f v , and film thickness t on the radiative properties of the film were evaluated. An optimal design of the film was carried out. The change in cooling power was calculated to evaluate the self-adaptive performance. The films achieved a maximum change in cooling power of 61.7 W/ m 2 (BaF 2) and 60.7 W/ m 2 (ZnS). The energy-saving performance of the film was simulated by TRNSYS. This study provides a promising solution for scalable radiative cooling film with high self-adaptive cooling performance and durability. • A self-adaptive radiative cooling film is proposed and optimally designed. • The RC film achieves a cooling power difference over 60 W/m2. • The cooling/heating load of a building using the proposed RC film is evaluated. [ABSTRACT FROM AUTHOR]

The photo-Fenton system degrades pollutants, which can not only take the advantages of photocatalysis and Fenton reaction, but also avoid the drawbacks of each. However, removing pollutants without the addition of hydrogen peroxide (H 2 O 2) in photo-Fenton systems is still a challenge. Therefore, it is an outstanding strategy that inner-generate H 2 O 2 can provide more hydroxyl radicals to enhance the degradation of pollutants. This study reports a ZnIn 2 S 4 /Fe-POMs photo-Fenton system capable of degrading environmental pollutants due to the reaction between inner-generate H 2 O 2 (0.0248 mM in 15 min) and the stable circulation of Fe3+/Fe2+. As a result, the photodegradation efficiency of carbamazepine by ZnIn 2 S 4 /Fe-POMs can reach 93.92% within 60 min simulated solar irradiation even under neutral conditions. In addition, ZnIn 2 S 4 /Fe-POMs can remove Cr (VI) completely within 10 min under visible light irradiation. This work not only experimentally verifies the inner-generate H 2 O 2 can accelerate the Fe3+/Fe2+ conversion, but also broadens the initial pH range (pH=3–9) for constructing efficient photo-Fenton systems. • ZnIn 2 S 4 /Fe-POMs can generate H 2 O 2 in situ to excite the photo-Fenton reaction. • ZnIn 2 S 4 /Fe-POMs exhibited remarkable photo-Fenton performance. • ZnIn 2 S 4 /Fe-POMs can effectively overcome the pH limitation in the Fenton system. [ABSTRACT FROM AUTHOR]

Inorganic magnesium hydroxide nanoparticles (Mg(OH) 2 NPs) offer a promising halogen-/phosphorus-free flame-retardant solution for silk. However, achieving effective and durable flame retardancy remains a challenge. In this study, silane-modified Mg(OH) 2 NPs were prepared using the double drop-reverse precipitation method with the silane coupling agent 3-aminopropyltriethoxysilane as a surface modifier. The chemical bonding between silane-modified Mg(OH) 2 NPs and silk fabric was beneficial for improving the stabilization and durability of the functional particles on the silk surface. The surface morphology and structure of the silane-modified Mg(OH) 2 NPs, as well as the smoke and heat generation performance, flame retardancy, washing durability, and flame-retardant mechanism of the coated silk fabrics were investigated. The coated silk fabric exhibited high flame-retardant efficiency and significantly reduced smoke and heat generation, indicating a lower fire hazard. Additionally, the coated silk fabric displayed self-extinguishing behavior even after 15 robust washings, demonstrating high washing durability. The coated silk exhibited fibrous and weave structures after combustion, indicating a strong charring ability. Overall, this study presents a promising approach for the development of flame-retardant silk fabrics with excellent washing durability using inorganic Mg(OH) 2 NPs. [Display omitted] • Silane-modified magnesium hydroxide nanoparticles (Mg(OH) 2 NPs) is applied as inorganic flame-retardant coating for silk. • Silane-modified Mg(OH) 2 NPs coating endows silk textile with effective and durable flame retardancy. • Silane-modified Mg(OH) 2 NPs coating significantly suppresses the smoke and heat generation of silk textile. • Silane-modified Mg(OH) 2 NPs coating serves as a sustainable and promising technique to minimize fire hazards of silk textile. [ABSTRACT FROM AUTHOR]

Quantum gas microscopy has enabled the study on intriguing properties of ultracold atoms in optical lattices. It provides the cutting-edge technology for manipulating quantum many-body systems. In such experiments, atoms have to be prepared into a two-dimensional (2D) system for being resolved by microscopes with limited depth of focus. Here we report an experiment on slicing a single layer of the atoms trapped in a few layers of pancake-shaped optical traps to create a 2D system. This technique is implemented with a microwave "knife", i.e., a microwave field with a frequency defined by the resonant condition with the Zeeman-shifted atomic levels related to a gradient magnetic field. It is crucial to keep a stable preparation of the desired layer to create the 2D quantum gas for future experimental applications. To achieve this, the most important point is to provide a gradient magnetic field with low noises and slow drift in combination with a properly optimized microwave pulse. Monitoring the electric current source and the environmental magnetic field, we applied an actively stabilizing circuit and realized a field drift of 0.042(3) mG/hour. This guarantees creating the single layer of atoms with an efficiency of 99.92(3)% while atoms are hardly seen in other layers within 48 hours, satisfying future experimental demands on studying quantum many-body physics. [ABSTRACT FROM AUTHOR]

Since Zr-Fe-Sn is one of the key ternary systems for cladding and structural materials in nuclear industry, it is of significant importance to understand physicochemical properties related to Zr-Fe-Sn system. In order to design the new Zr alloys with advanced performance by CALPHAD method, the thermodynamic model for the lower order systems is required. In the present work, first-principles calculations are employed to obtain phonon, thermodynamic and elastic properties of Zr 6 FeSn 2 with C22 structure and the end-members (C22-Zr 6 FeFe 2 , C22-Zr 6 SnSn 2 and C22-Zr 6 SnFe 2 ) in the model of (Zr) 6 (Fe, Sn) 2 (Fe, Sn) 1 . It is found that the imaginary phonon modes are absent for C22-Zr 6 FeSn 2 and C22-Zr 6 SnSn 2 , indicating they are dynamically stable, while the other two end-members are unstable. Gibbs energies of C22-Zr 6 FeSn 2 and C22-Zr 6 SnSn 2 are obtained from the quasiharmonic phonon approach and can be added in the thermodynamic database: Nuclearbase. The C22-Zr 6 FeSn 2 ’s single-crystal elasticity tensor components along with polycrystalline bulk, shear and Young’s moduli are computed with a least-squares approach based upon the stress tensor computed from first-principles method. The results indicate that distortion is more difficult in the directions normal the c -axis than along to it. [ABSTRACT FROM AUTHOR]

• A transient electric arc furnace model is established to analyze heat-mass transfer characteristics. • The mathematical model is validated by comparing with experiments. • The influence of typical operating parameters on brown corundum smelting process are investigated. • A set of indicators are proposed to comprehensively evaluate the EAF performance. • BP neural network predicting model and multi-objective genetic optimization on EAF are carried out. • A set of optimization operating parameters about EAF is put forward. It is crucial to reveal the energy conversion and heat and mass transfer characteristics in the electric arc furnaces (EAFs). In this paper, a transient mathematical model was built to adequately capture the distribution of multi-physical fields in EAF. The numerical modeling method was verified against brown corundum smelting experiment data. In addition, the influence on the brawn corundum smelting process of the typical operating parameters has been examined, and the major problems which limit the performance improvement of smelting were analyzed. Moreover, four indicators were proposed to comprehensively evaluate the EAF performance, consisting of liquid volume fraction β , energy efficiency η , temperature rise index ς, and temperature uniformity index ξ. Furthermore, the optimal combination of operating parameters was selected automatically based on the back propagation (BP) neural network predicting model and non-domestic sorting genetic algorithm II (NSGA-II). The results showed that the local overheating phenomenon is serious and that homogenizing the energy distribution in the furnace is the key to improving the melting performance. Increasing electrode immersion and voltage can significantly increase the rate of heat and mass transfer. In the case of ψ = 0.75, υ = 0.6, when χ is increased from 0.2 to 0.6, τ max is shortened by 13.3% and η max is increased by 12.0%. While with ψ = 0.75 and χ = 0.4, τ max is reduced by 36.0% when υ increases from 0.2 to 1.0, but η max is also decreased by 7.0%. It was found that decreasing the height diameter ratio will improve the overall EAF smelting performance, but it also increases the area of heat loss. In the case of χ = 0.4, υ = 0.6, ξ ave and η max improved by 3.1 and 14.5%, respectively, when ψ decreases from 1.0 to 0.5. The EAF performances are considerably enhanced in comparison to the operating basic parameters (ψ = 0.75, χ = 0.4 and υ = 0.6), subject to optimal operation parameters (ψ = 0.61, χ = 0.59 and υ = 0.59). In this case, τ max and ς max reduced by 6.2 and 4.1%, respectively, while η max and ξ ave increased by 6.8 and 1.0%, respectively. [ABSTRACT FROM AUTHOR]

• A novel fence electrode design is proposed to optimize energy allocation in the EAF. • The influence of typical operating parameters on F-EAF are investigated. • BP neural network predicting model and multi-objective genetic optimization on F-EAF are carried out. • A set of optimization operating parameters about F-EAF is put forward. • The advantages of F-EAF are proved by industrial experiments. In this paper, a fence electrode design was proposed to optimize energy allocation in the EAF. The fence electrode includes a cylindrical main electrode (M-electrode) and circular smaller cylindrical assisted electrodes (A-electrodes). Five working parameters affecting the smelting performance were determined afterward, which include the height diameter ratio ψ , electrode immersion ratio χ , main electrode voltage parameter υ m , assisted electrode voltage parameter υ a , and smelting time parameter τ. Furthermore, the orthogonal analysis method was used to investigate the synergetic mechanism of operating parameters on four indicators. The four indicators consisting of liquid volume fraction β , energy efficiency η , temperature rise index ς, and temperature uniformity index ξ. The optimal fence electrode electric arc furnace (F-EAF) operating parameters were then automatically given by the back propagation (BP) neural network predicting model and non-dominated sorting genetic algorithm II (NSGA-II), and compared with the optimal traditional single electrode electric arc furnace (S-EAF). The fence electrode design was finally verified by industrial experiments. The results show that the fence electrode can effectively homogenize the energy distribution and provide arc heat directly to the side zone in EAF. Increasing A-electrode voltage has a significant advantage in terms of increasing the smelting rate, but it also significantly raises the overall temperature, which is not conducive to smelting safety. In the case of ψ = 0.75 and χ = 0.4, υ a increases from 0.1 to 0.3, τ max and η max decrease by 34.2 and 8.0%, respectively. By correlation analysis, it can be known that the A-electrode is an important factor for the F-EAF adjustment ability. Compared with the S-EAF, the F-EAF smelting performance is improved significantly. After multi-objective optimization, F-EAF τ max and ς max reduce by 8.9 and 0.6%, respectively. At the same time, η max and ξ ave have increased by an average of 1.4 and 2.6%, respectively. The industrial experiments reveal that the fence electrode design can not only effectively homogenize the EAF energy supply, but also can overcome the shortcoming of arc deflection, making the smelting process more stable. In conclusion, F-EAF contributes to the EAF smelting performance evaluation, and this new proposal opens up novel ideas for the future development of EAF design. [ABSTRACT FROM AUTHOR]

High-quality magnetic resonance (MR) image, i.e., with near isotropic voxel spacing, is desirable in various scenarios of medical image analysis. However, many MR images are acquired using good in-plane resolution but large spacing between slices in clinical practice. In this work, we propose a novel deep-learning-based super-resolution algorithm to generate high-resolution (HR) MR images of small slice spacing from low-resolution (LR) inputs of large slice spacing. Notice that real HR images are needed in most existing deep-learning-based methods to supervise the training, but in clinical scenarios, usually they will not be acquired. Therefore, our unique goal herein is to design and train the super-resolution network without real HR ground-truth. Specifically, two-staged training is used in our method. In the first stage, HR images of reduced slice spacing are synthesized from real LR images using variational auto-encoder (VAE). Although these synthesized HR images of reduced slice spacing are as realistic as possible, they may still suffer from unexpected morphing induced by VAE, implying that the synthesized HR images cannot be paired with the real LR images in terms of anatomical structure details. In the second stage, we degrade the synthesized HR images to generate corresponding LR-HR image pairs and train a super-resolution network based on these synthesized pairs. The underlying mechanism is that such a super-resolution network is less vulnerable to anatomical variability. Experiments on knee MR images successfully demonstrate the effectiveness of our proposed solution to reduce the slice spacing for better rendering. • MR images are usually acquired with large inter-slice spacing in clinical practice. • A deep-learning-based algorithm to reduce slice spacing for better rendering. • No real high-resolution ground-truth is required for training neural-networks. • Outperform current self super-resolution algorithms for MR images. [ABSTRACT FROM AUTHOR]

• CaC 2 electric furnace model is developed with feed configuration considered. • The multi-physics field exhibits the heterogeneous characteristic within furnace. • The application of compressed feed benefits 18.6% enhancement in CaC 2 production. • Small-size compressed feeds have a significant advantage in increasing the output. It is of particular important to optimize the feed of calcium carbide (CaC 2) production by improving the productivity and reducing the energy consumption. In this present paper, a transient mathematical model was established to analyze the effects of feed on heat and mass transfer characteristics of CaC 2 electric furnace. The model was adopted to study the effects of powder and compressed feed on heat and mass transfer characteristics. In addition, the influence of the size of compressed feed particles on physical field distribution in the equipment was also investigated. The results displayed that the application of compressed feed is better than traditional powder feed in reducing serious local overheating and increasing the CaC 2 production. When the three-phase alternating current (I) level increases from 10,000 A to 14,000 A, the average maximum temperature of the compressed feed furnace is about 8.8% lower than that of the traditional powder feed furnace. However, when furnace feed switches from powder feed to compressed feed, the average CaC 2 production increases by about 18.6%. Small sizes of compressed feed is qualified with significant advantages in increasing the output. When I = 12,000 A and the size of compressed feed particle decreases from 5.0 mm to 0.4 mm, CaC 2 production increases by about 26.5%, while when compressed feed particle size≤ 1.0 mm, with further size reduction, the differences in CaC 2 output is not obvious. The study on the feed of CaC 2 electric furnace on heat and mass transfer characteristics contributes to the optimization of performance in CaC 2 electric furnace smelting. [ABSTRACT FROM AUTHOR]

Purpose: Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images. Methods: A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV). Results: Overall, 86 and 54 patients with MVI− (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725–0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696–0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893–0.987) and testing sets (AUC = 0.913, 95% CI: 0.785–0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917). Conclusion: The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI. [ABSTRACT FROM AUTHOR]

In recent years, peroxisome proliferator-activated receptor γ (PPARγ) has been found to be closely associated with hypoxia renal disease. The aim of this study was to investigate the relationship between rosiglitazone and mitochondrial apoptosis in renal tissue and its associated mechanisms. Twenty-four male Sprague-Dawley rats were randomly divided into three groups (n = 8 in each): normal control group, hypoxia injury group (equal volume of 0.9% saline), and PPARγ agonist group (Rosiglitazone, 10 mg/kg · d, intraperitoneally). The hypoxia injury group and PPARγ agonist group were placed in a hypoxia chamber and the simulated altitude was set at 7,000 m for 7 days. Blood and kidney samples were collected after 7 days. The quantitative real-time polymerase chain reaction and Western blot methods were used to determine the expression of PPARγ, nuclear factor kappa-B (NF-κB), B-cell lymphoma-2 (Bcl-2), and Bax. The results showed that compared with the normal control group, the renal tissue of rats after hypoxia was severely damaged, as shown by massive renal tubular epithelial cell degeneration and detachment, and renal tubular dilation. The NF-κB protein expression significantly increased, the Bcl-2 protein and mRNA expression significantly decreased, and Bax protein and mRNA expression significantly increased (p <.05 for all). Renal injury was much less severe in the PPARγ agonist group compared to the hypoxia injury group. Rosiglitazone can alleviate hypoxia renal injury, with the possible mechanism involving attenuation of apoptosis by inhibiting the activation of the NF-κB signaling pathway in a PPARγ-dependent manner and increasing Bcl-2 and decreasing Bax expression. [ABSTRACT FROM AUTHOR]

A Gram-negative, aerobic, chemoheterotrophic, rod-shaped, and motile bacterium, designated as LST-1T, was isolated from wild Stevia rebaudiana Bertoni and subjected to a polyphasic taxonomic analysis. The LST-1 strain grew optimally at 37 °C and pH 6.0–7.0 in the presence of 0.5% (w/v) NaCl. Phylogenetic analysis based on the 16S rDNA sequence indicated that LST-1 is closely related to Lelliottia jeotgali PFL01T (99.85%), Lelliottia nimipressuralis LMG10245T (98.82%), and Lelliottia amnigena LMG2784T (98.54%). Multi-locus sequence typing of concatenated partial atpD, infB, gyrB, and rpoB genes was performed to improve the resolution, and clear distinctions between the closest related type strains were observed. The results of average nucleotide identify analyses and DNA–DNA hybridization with four species (16S rDNA similarity > 98.65%) were less than 90 and 40%, respectively, verifying the distinct characteristics from other species of Lelliottia. The cellular fatty acid profile of the strain consisted of C16:0, Summed Feature3, and Summed Feature8 (possibly 16:1 w6c/16:1 w7c and 18:1 w6c) as major components. The major polar lipids included phosphatidylethanolamine, phosphatidylglycerol, an aminophospholipid, three non-characteristic phospholipids, and a non-characteristic lipid. The genome of LST-1T was 4,611,055 bp in size, with a G + C content of 55.02%. The unique combination of several phenotypic, chemotaxonomic, and genomic characteristics proved that strain LST-1T belongs to a novel species, for which the name Lelliottia steviae sp. nov. is proposed. The type strain is LST-1T (= CGMCC 1.19175T = JCM 34938T). Repositories: The genbank accession numbers for the 16S rRNA gene and genome sequences of strain LST-1T are MZ497264 and CP063663, respectively. [ABSTRACT FROM AUTHOR]

Magnetic resonance (MR) images collected in 2D clinical protocols typically have large inter-slice spacing, resulting in high in-plane resolution and reduced through-plane resolution. Super-resolution technique can enhance the through-plane resolution of MR images to facilitate downstream visualization and computer-aided diagnosis. However, most existing works train the super-resolution network at a fixed scaling factor, which is not friendly to clinical scenes of varying inter-slice spacing in MR scanning. Inspired by the recent progress in implicit neural representation, we propose a Spatial Attention-based Implicit Neural Representation (SA-INR) network for arbitrary reduction of MR inter-slice spacing. The SA-INR aims to represent an MR image as a continuous implicit function of 3D coordinates. In this way, the SA-INR can reconstruct the MR image with arbitrary inter-slice spacing by continuously sampling the coordinates in 3D space. In particular, a local-aware spatial attention operation is introduced to model nearby voxels and their affinity more accurately in a larger receptive field. Meanwhile, to improve the computational efficiency, a gradient-guided gating mask is proposed for applying the local-aware spatial attention to selected areas only. We evaluate our method on the public HCP-1200 dataset and the clinical knee MR dataset to demonstrate its superiority over other existing methods. • A novel SR method named SA-INR for continuous slice interpolation of MR images. • Local-aware spatial attention in combination with a gradient-guided gating mask. • Improvement over other state-of-the-art single-scale and arbitrary-scale SR methods. [ABSTRACT FROM AUTHOR]

Purpose: Our purposes were (1) to explore the methodologic quality of the studies on the deep learning in knee imaging with CLAIM criterion and (2) to offer our vision for the development of CLAIM to assure high-quality reports about the application of AI to medical imaging in knee joint. Materials and methods: A Checklist for Artificial Intelligence in Medical Imaging systematic review was conducted from January 1, 2015, to June 1, 2020, using PubMed, EMBASE, and Web of Science databases. A total of 36 articles discussing deep learning applications in knee joint imaging were identified, divided by imaging modality, and characterized by imaging task, data source, algorithm type, and outcome metrics. Results: A total of 36 studies were identified and divided into: X-ray (44.44%) and MRI (55.56%). The mean CLAIM score of the 36 studies was 27.94 (standard deviation, 4.26), which was 66.53% of the ideal score of 42.00. The CLAIM items achieved an average good inter-rater agreement (ICC 0.815, 95% CI 0.660–0.902). In total, 32 studies performed internal cross-validation on the data set, while only 4 studies conducted external validation of the data set. Conclusions: The overall scientific quality of deep learning in knee imaging is insufficient; however, deep learning remains a promising technology for diagnostic or predictive purpose. Improvements in study design, validation, and open science need to be made to demonstrate the generalizability of findings and to achieve clinical applications. Widespread application, pre-trained scoring procedure, and modification of CLAIM in response to clinical needs are necessary in the future. Key Points: • Limited deep learning studies were established in knee imaging with mean score of 27.94, which was 66.53% of the ideal score of 42.00, commonly due to invalidated results, retrospective study design, and absence of a clear definition of the CLAIM items in detail. • A previous trained data extraction instrument allowed reaching moderate inter-rater agreement in the application of the CLAIM, while CLAIM still needs improvement in scoring items and result reporting to become a wide adaptive tool in reviews of deep learning studies. [ABSTRACT FROM AUTHOR]

Automatic brain segmentation of magnetic resonance images (MRIs) from severe traumatic brain injury (sTBI) patients is critical for brain abnormality assessments and brain network analysis. Construction of sTBI brain segmentation model requires manually annotated MR scans of sTBI patients, which becomes a challenging problem as it is quite impractical to implement sufficient annotations for sTBI images with large deformations and lesion erosion. Data augmentation techniques can be applied to alleviate the issue of limited training samples. However, conventional data augmentation strategies such as spatial and intensity transformation are unable to synthesize the deformation and lesions in traumatic brains, which limits the performance of the subsequent segmentation task. To address these issues, we propose a novel medical image inpainting model named sTBI-GAN to synthesize labeled sTBI MR scans by adversarial inpainting. The main strength of our sTBI-GAN method is that it can generate sTBI images and corresponding labels simultaneously, which has not been achieved in previous inpainting methods for medical images. We first generate the inpainted image under the guidance of edge information following a coarse-to-fine manner, and then the synthesized MR image is used as the prior for label inpainting. Furthermore, we introduce a registration-based template augmentation pipeline to increase the diversity of the synthesized image pairs and enhance the capacity of data augmentation. Experimental results show that the proposed sTBI-GAN method can synthesize high-quality labeled sTBI images, which greatly improves the 2D and 3D traumatic brain segmentation performance compared with the alternatives. Code is available at. • Introduce sTBI-GAN for augmenting traumatic brain segmentation data using image inpainting. • Develop image inpainting for simultaneous grayscale MR images and label map synthesis. • Adopt a coarse-to-fine synthesis strategy with edge information to enhance TBI image textures. • Develop a registration-based template pipeline to diversify synthesized brain trauma variance. • sTBI-GAN shows superior data augmentation and effectively aids segmentation in experiments. [ABSTRACT FROM AUTHOR]

Low-noise, high-power 532-nm lasers are of great interest in many scientific research studies, such as gravitational wave detection and ultracold atom experiments. In particular, in the experiments of quantum gas microscopy, a large power of laser is necessary during the imaging process, while low noise is important for preventing the atoms from being heated up. In this work, we report on the generation of such a 532-nm continuous-wave laser by coherently combining two laser beams produced by single-pass second-harmonic generation. The power of the combined laser is up to 17 W. With the help of intensity stabilization, we are able to suppress the relative intensity noise to below −120 dBc/Hz. The generated laser satisfies the experimental requirements for integrating optical superlattices with a quantum gas microscope. [ABSTRACT FROM AUTHOR]

Objective(s): Sirt3 may regulate ROS production and might be involved in β‑cell apoptosis, which plays an important role in the progression of type 2 diabetes mellitus (T2DM). Quercetin is a potent antioxidative bioflavonoid, but its effects on T2DM remain to be explored. This study aimed to investigate the effects of quercetin on β‑cell apoptosis and explore its mechanisms. Materials and Methods: The effects of quercetin were conducted on db/db mice and INS1 cells. Fasting blood glucose was determined by the colorimetric method, serum insulin was measured by enzyme‑linked immunosorbent assay (ELISA). Meanwhile, Sirt3 in INS1 cells was knocked down by plasmid transfection. The antioxidant proteins (SOD2 and CAT), apoptosis proteins (cleaved Caspase-3, Bax, and BCL-2), and Sirt3 protein in pancreases and INS1 cells were determined by western blotting. Results: When INS1 cells and diabetic mice were treated with quercetin, the levels of SOD2, CAT, and Sirt3 proteins were increased, the levels of cleaved Caspase-3 and the ratio of Bax to BCL-2 were decreased at different degrees, along with reduced blood glucose levels and elevated insulin levels in diabetic mice. When Sirt3 was knocked down in INS1 cells, increase of two antioxidants and decrease of cell apoptosis generated by quercetin could not occur. Conclusion: Quercetin protected islet β‑cells from oxidation-induced apoptosis via Sirt3 in T2DM, which would be beneficial to develop new strategies for preventing β‑cell failure in T2DM. [ABSTRACT FROM AUTHOR]

In various leading-edge ultracold atom experiments in fields such as quantum simulation, quantum computing, and quantum precision measurement, precise and fast control of the magnetic field is important and a current source with a low noise level and high control bandwidth is required. Conventional commercial current sources, even the expensive ones, may not meet the requirements. Here, we present a battery-powered current source (BPCS) solution that has a maximum output of 100 A with a root-mean-square noise of about only 0.35 mA (integrated from 1 Hz to 3 MHz) and a control bandwidth of about 10 kHz. The large output current is sourced from a lead-acid battery, a fluxgate current sensor is used to detect the output current, and fast feedback regulation is done by using an insulated-gate bipolar transistor under the control of an analog proportional-integral controller. The battery power method eliminates the switching noise. A prepulse method is applied to shorten the settling time when the current steps down from a high level to a low level that is close to zero. These features make the BPCS a suitable analog-controlled floating current source for cold-atom experiments such as the preparation of a single 2D quantum gas and optical lattice atomic clocks. [ABSTRACT FROM AUTHOR]

Objective: To investigate associations between sleep duration and health-related measures, and factors associated with short sleep, in adolescents in an East Asian society with strong emphasis on academic achievement.Methods: Adolescents aged 13-19 years (n = 2346) from eight schools in Singapore (five local, three international) took part in a cross-sectional survey of sleep habits, school life, and health. Self-rated health, overweight (International Obesity Task Force Criteria), and depression symptoms were compared in adolescents with short (<7 h), moderately short (7 to <8 h), or an appropriate amount of sleep (8-10 h) on school nights.Results: Short sleep on school nights was associated with poorer self-rated health, increased odds of being overweight (adjusted odds ratio [ORadj] = 2.56, 95% confidence interval = 1.39-4.70), and increased odds of feeling depression symptoms (sadness, irritability, worthlessness, low motivation, difficulty concentrating, anhedonia, anxiety, and thoughts of self-harm/suicide) compared with an appropriate sleep duration (ORadj = 2.10-4.33, p < 0.05 for each symptom). Barriers to healthy sleep included later preferred sleep timing (a relative indicator of later chronotype), lower parental supervision of bedtime, longer study time, early school start time, and longer travel time. Students at local schools were less likely to have a parent-set bedtime, and spent more time on homework/studying. Later bedtime in local schools attenuated the benefit of later school start time on nocturnal sleep duration.Conclusions: Short sleep may contribute to poorer adolescent health and well-being. Strategies for improving sleep in hard-driving East Asian societies should take into account sociocultural factors that may impede removal of barriers to healthy sleep. [ABSTRACT FROM AUTHOR]

Abstract A thermodynamic assessment of the Zr-Fe-Cu system has been performed by using the first-principles calculations and the CALPHAD approach, which is capable of combining experimental and theoretical data of thermodynamics and phase equilibria. The isothermal sections at 853, 973, 1273, 1373 and 1473 K together with the mixing enthalpies of liquid phase at 1873 K were well evaluated, and a consistent set of thermodynamic parameters was obtained. The combination of three thermodynamic criteria has been applied to predict the composition dependency of the glass forming range (GFR), the glass forming ability (GFA) and the glass forming behaviour in the Zr-Fe-Cu system based on the thermodynamic description assessed in this work. Highlights • The Zr-Fe-Cu thermodynamic database has been constructed by CALPHAD for the first time. • Employing ab-initio calculations (VASP) to obtain the enthalpies of intermetallic phases. • The glass forming ability prediction by three thermodynamic criteria can provide a guideline for the Zr-Fe-Cu BMG design. [ABSTRACT FROM AUTHOR]

• g-C 3 N 4 /Ti 3 C 2 /MnFe 2 O 4 can achieve peroxymonosulfate-assisted photocatalysis. • Ti 3 C 2 serves as the co-catalyst to improve the degradation performance. • Singlet oxygen acts as the main active species in naphthalene degradation. • g-C 3 N 4 /Ti 3 C 2 /MnFe 2 O 4 has good stability and low metal ion leaching rate. Given the urgency of polycyclic aromatic hydrocarbons (PAHs) remediation and a lack of recoverable efficient catalysts for advanced oxidation processes (AOPs), a novel g-C 3 N 4 /Ti 3 C 2 /MnFe 2 O 4 (CTM) catalyst based on the co-catalyst function of Ti 3 C 2 (MXene) was fabricated via in situ calcination and liquid surface self-assembly, employed in a peroxymonosulfate (PMS) -assisted photocatalytic system to degrade naphthalene (NAP). Almost 100% of the NAP was removed within 45 min with pseudo-first-order kinetic constants about 2.74 and 1.97 times of those in the absence of Ti 3 C 2 or visible light, respectively, confirming the positive role of the moderate Ti 3 C 2 and the synergism between PMS activation and photocatalysis. The effect of initial solution pH, various inorganic anions, and actual water conditions on the reaction and stability tests revealed the potential of CTM catalysts in practical applications. There existed both free radical and non-radical pathways for NAP degradation, in which singlet oxygen (1O 2) was identified as the main reactive oxygen species. This study provided a promising perspective for the rational design of efficient and stable Ti 3 C 2 -based heterojunction catalysts and their application in PMS-assisted photocatalytic systems for the effective degradation of persistent organic pollutants. [ABSTRACT FROM AUTHOR]

[Display omitted] • A visible light-enhanced 0.6 %GO-FSNT was developed for removing of phenanthrene. • The high degradation rate is obtained by using a less amount of catalyst within a shorter time. • The phenanthrene adsorbed on the GO while the water adhered to N-TiO 2 shell. • A small amount of GO facilitates charge separation and visible light utilization. • The photoinduced hydrophilicity contributes to the increase of photodegradation rate. One kind of novel magnetically separable graphene-based titanium dioxide photocatalytic nano-adsorbents (GO-FSNTs) were prepared for the adsorption-photodegradation of phenanthrene under visible light. The GO-FSNTs displayed the high adsorption-photodegradation efficiency and 10 mg of 0.6% GO-FSNT (wt%) can remove 94.9% of phenanthrene within 4 h. The introduction of a small amount of GO enables 0.6% GO-FSNT to selectively adsorb phenanthrene through the π-π interaction, reduce the light-shielding effect and improve separation of photogenerated carriers effectively. Meanwhile, the N-TiO 2 shell is occupied by adsorbed water molecules which can spontaneously repair lattice oxygen. Furthermore, the more hydrophilic surface of 0.6% GO-FSNT is formed by increased photoinduced hydroxyl groups under visible light during photodegradation. The photoinduced hydroxyl groups can further generate OH and improve photocatalytic activity. The degradation pathway of phenanthrene was proposed based on intermediates analysis and density functional theory calculations. This work helps to reveal the relationship between the adsorption-photodegradation and the photoinduced hydrophilicity. [ABSTRACT FROM AUTHOR]

• A semi-supervised learning framework called Dual-Consistency Mean Teacher (DC-MT) is designed for the knee cartilage defect assessment task. • A novel attention loss is defined to make the DC-MT model focus on the cartilage regions, yielding accurate attention masks for knee cartilage defect assessment. • A novel dual-consistency strategy is developed in the DC-MT model to penalize the inconsistent attention masks obtained from the student and the teacher sub-networks. • An aggregation network is proposed to ensemble features from all the slice-level by the DC-MT model, and finally yields the subject-level diagnosis. Knee cartilage defects caused by osteoarthritis are major musculoskeletal disorders, leading to joint necrosis or even disability if not intervened at early stage. Deep learning has demonstrated its effectiveness in computer-aided diagnosis, but it is time-consuming to prepare a large set of well-annotated data by experienced radiologists for model training. In this paper, we propose a semi-supervised framework to effectively use unlabeled data for better evaluation of knee cartilage defect grading. Our framework is developed based on the widely-used mean-teacher classification model, by designing a novel dual-consistency strategy to boost the consistency between the teacher and student models. The main contributions are three-fold: (1) We define an attention loss function to make the network focus on the cartilage regions, which can both achieve accurate attention masks and boost classification performance simultaneously; (2) Besides enforcing the consistency of classification results, we further design a novel attention consistency mechanism to ensure the focusing of the student and teacher networks on the same defect regions; (3) We introduce an aggregation approach to ensemble the slice-level classification outcomes for deriving the final subject-level diagnosis. Experimental results show that our proposed method can significantly improve both classification and localization performances of knee cartilage defects. Our code is available on https://github.com/King-HAW/DC-MT. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: Radial spherical ZnO structures with nanorods grown on both inside and outside of a hollow sphere-like core along the c-axis direction have been produced by thermal evaporation of Zn powder in the absence of catalysts. Scanning electron microscopy and X-ray diffraction investigation show that the radial spherical ZnO structures are of high purity nanocrystal with a wurtzite structure. It is found that the formation of the sphere-shaped liquid Zn droplets before adding oxygen is a key factor to control the morphology of the radial spherical ZnO structures. The radial spherical structures could possibly be used as a building block and open up new opportunities for the self-assembly of functional nanodevices. [Copyright &y& Elsevier]

A low‐phase‐noise transformer‐based gate‐coupled quadrature voltage‐controlled oscillator (TGC‐QVCO) using 0.13 μm CMOS technology is presented. The TGC‐QVCO consumes 7 mW at 1 V power supply with 18% frequency tuning. The measured phase noise at 1 MHz offset varies from about −122 to −119 dBc/Hz for frequency tuning from 1.34 to 1.6 GHz. The achieved figure of merit (FoM) is 179 dB and the chip area is 800 × 900 μm2. [ABSTRACT FROM AUTHOR]

This paper presents a numerical study on the interaction between the forward and aft propellers of hybrid CRP pod propulsion systems (CRP-POD) in open water with a focus on both global quantities, that is, thrust and torque, and flow details, such as vortical structure, slipstream evolution, and lateral load on the pod unit. Improved delayed detached eddy simulation (IDDES) method is adopted and combined with the γ − R e θ transition model. The open-water characteristics of CRP-POD obtained by CFD are in satisfactory agreement with model test data. Two sets of CRP-PODs with different propeller geometries are considered. It is found independently of propeller geometry that (i) in terms of thrust and torque, the forward propeller is slightly affected by the aft propeller, while it has a large effect on the aft propeller; (ii) the wake of the forward propeller is accelerated in the axial direction and weakened in the tangential direction by the aft propeller; and (iii) the time-averaged lateral force on the pod unit is reduced remarkably by the propeller-propeller interaction. However, the vortex interaction between the forward and aft propellers depends significantly on the propeller geometry. In the case when the tip vortices shed by the forward and aft propellers can directly collide with each other, the vortical structure in the wake becomes unstable and rapidly breaks down. • DES method is adoptedto simulatethe vortexevolutionin the wake of CRP-POD. • Vortex interaction between the forward and aft propellers and its effect on the stability of vortical structure are discussed. • Interaction of lateral hydrodynamicforce on pod unit between the forward and aft propellers is analyzed. [ABSTRACT FROM AUTHOR]