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As wireless technology continues its rapid evolution, the sixth-generation (6G) networks are capable of offering exceptionally high data transmission rates as well as low latency, which is promisingly able to meet the high-demand needs for digital twins (DTs). Quality-of-experience (QoE) in this situation, which refers to the users’ overall satisfaction and perception of the provided DT service in 6G networks, is significant to optimize the service and help improve the users’ experience. Despite progress in developing theories and systems for digital twin transmission under 6G networks, the assessment of QoE for users falls behind. To address this gap, our paper introduces the first QoE evaluation database for human digital twins (HDTs) in 6G network environments, aiming to systematically analyze and quantify the related quality factors. We utilize a mmWave network model for channel capacity simulation and employ high-quality digital humans as source models, which are further animated, encoded, and distorted for final QoE evaluation. Subjective quality ratings are collected from a well-controlled subjective experiment for the 400 generated HDT sequences. Additionally, we propose a novel QoE evaluation metric that considers both quality-of-service (QoS) and content-quality features. Experimental results indicate that our model outperforms existing state-of-the-art QoE evaluation models and other competitive quality assessment models, thus making significant contributions to the domain of 6G network applications for HDTs.

With the rapid advancements of the text-to-image generative model, AI-generated images (AGIs) have been widely applied to entertainment, education, social media, etc. However, considering the large quality variance among different AGIs, there is an urgent need for quality models that are consistent with human subjective ratings. To address this issue, we extensively consider various popular AGI models, generated AGI through different prompts and model parameters, and collected subjective scores at the perceptual quality and text-to-image alignment, thus building the most comprehensive AGI subjective quality database AGIQA-3K so far. Furthermore, we conduct a benchmark experiment on this database to evaluate the consistency between the current Image Quality Assessment (IQA) model and human perception, while proposing StairReward that significantly improves the assessment performance of subjective text-to-image alignment. We believe that the fine-grained subjective scores in AGIQA-3K will inspire subsequent AGI quality models to fit human subjective perception mechanisms at both perception and alignment levels and to optimize the generation result of future AGI models. The database is released on https://github.com/lcysyzxdxc/AGIQA-3k-Database.

Purpose: Advanced big data analysis and machine learning methods are concurrently used to unleash the value of the data generated by government hotline and help devise intelligent applications including automated process management, standard construction and more accurate dispatched orders to build high-quality government service platforms as more widely data-driven methods are in the process. Design/methodology/approach: In this study, based on the influence of the record specifications of texts related to work orders generated by the government hotline, machine learning tools are implemented and compared to optimize classify dispatching tasks by performing exploratory studies on the hotline work order text, including linguistics analysis of text feature processing, new word discovery, text clustering and text classification. Findings: The complexity of the content of the work order is reduced by applying more standardized writing specifications based on combining text grammar numerical features. So, order dispatch success prediction accuracy rate reaches 89.6 per cent after running the LSTM model. Originality/value: The proposed method can help improve the current dispatching processes run by the government hotline, better guide staff to standardize the writing format of work orders, improve the accuracy of order dispatching and provide innovative support to the current mechanism.

In the domain of infrared small target detection (IRSTD), the challenges revolve around detecting small and faint targets from infrared images. These targets lack distinct textures and morphology exist in complex backgrounds with numerous distractions. Current deep-learning methods typically prioritize preserving target features while neglecting the crucial background context, ultimately resulting in false alarms and miss detection. To tackle this issue, we propose a novel approach involving separately focusing on candidate target responses and background context during the encoding stage and aligning them during the decoding stage. Specifically, we introduce the progressive background-aware transformer (PBT) which adopts an asymmetric encoder-decoder architecture. The encoder with task-specific frequency domain priors extracts candidate target responses and background context features separately from shallow and deep blocks, respectively. The following hierarchical decoder progressively refines the candidate target responses under the guidance of rich background context stage by stage, leading to more accurate results. Our experiments demonstrate that PBT surpasses state-of-the-art IRSTD methods across various datasets. The code and dataset are available at https://github.com/Heron0625/PBT.

This brief focuses on the current sensorless output voltage control of the $LC$ filtered three-level T-type inverter (3LT2I). First, the 3LT2I dynamic models with lumped disturbances (integrating load disturbances, parameter uncertainties, and unmeasured inductor currents) are established. Then, finite-time observers are designed to estimate the lumped disturbances, providing the active anti-disturbance ability and eliminating current sensors. Based on this, continuous sliding-mode controllers are designed in the $dq$ -axis to achieve fast and accurate output voltage tracking. Test results demonstrate the effectiveness of the proposed strategy, which outperforms the adaptive method in terms of dynamic response and control accuracy when facing load disturbances and nonlinear loads.

Banding, also known as staircase-like contours, frequently occurs in flat areas of images/videos processed by compression or quantization algorithms. As undesirable artifacts, banding destroys the original image structure, thus inevitably degrading users’ quality of experience (QoE). In this paper, we systematically investigate the banding image quality assessment (IQA) problem, aiming to detect the image banding artifacts and evaluate their perceptual visual quality. Considering that the existing image banding databases only contain limited content sources and banding generation methods, and lack perceptual quality labels (i.e. mean opinion scores), we first build the largest banding IQA database so far, named B anding A rtifact N oticeable D atabase (BAND-2k), which consists of 2,000 banding images generated by 15 compression and quantization schemes. A total of 23 workers participated in the subjective IQA experiment, yielding over 214,000 patch-level banding class labels and 44,371 reliable image-level quality rating scores. Subsequently, we develop an effective no-reference (NR) banding evaluator for banding detection and quality assessment by leveraging frequency characteristics of banding artifacts. To be more specific, a dual convolutional neural network (CNN) is employed to concurrently learn the feature representation from the high-frequency and low-frequency maps, thereby enhancing the ability to discern banding artifacts. The quality score of a banding image is generated by pooling the banding detection maps masked by the spatial frequency filters. The experimental results demonstrate that our banding evaluator achieves remarkably high accuracy in banding detection and also exhibits high SRCC and PLCC results with the perceptual quality labels, even without directly learning a regression model for banding quality evaluation. These findings unveil the strong correlations between the intensity of banding artifacts and the perceptual visual quality, thus validating the necessity of banding quality assessment. The BAND-2k database and the proposed banding evaluator are available at https://github.com/zijianchen98/ BAND-2k.

Mobile games have played an increasingly significant role in people's leisure lives in recent years, thanks to the fast expansion of the gaming industry and the widespread use of mobile devices. The aesthetic quality of game pictures is a very important factor that attracts users' interest. However, evaluating the aesthetic quality of mobile game pictures is difficult since the painting styles of games vary greatly and the evaluation criteria are also diversified. In this article, we propose a multitask deep learning-based method, which is able to predict the aesthetic quality of mobile game images in multiple dimensions. The proposed model consists of two modules, a feature extraction module and a quality regression module. We extract quality-aware features from intermediate layers of the deep convolution neural network and then incorporate them into the final feature representation in the feature extraction module, allowing the model to fully use visual information from low to high levels. The quality regression module uses fully connected layers to map quality-aware features into quality scores across multiple dimensions. The multidimensional aesthetic quality scores are trained using a multitask learning approach, in which quality-aware features are shared across multiple dimensional quality prediction tasks. Finally, several key factors which help the proposed model perform better are analyzed. The experimental results indicate that our proposed method not only achieves the greatest performance on mobile game images, but also is applicable to natural scene images.

Byline: Dongni. Gao, Zicheng. Zhang, Qiwei. Yang, Baosheng. Li Purpose: The aim of this study was to investigate intratumoral genomic heterogeneity and subclonal structure of esophageal squamous cell carcinoma (ESCC). [...]

Although the meticulous design of functional diversity within the polymer interfacial layer holds paramount significance in mitigating the challenges associated with hydrogen evolution reactions and dendrite growth in zinc anodes, this pursuit remains a formidable task. Here, a large-scale producible zinc-enriched/water-lean polymer interfacial layer, derived from carboxymethyl chitosan (CCS), is constructed on zinc anodes by integration of electrodeposition and a targeted complexation strategy for highly reversible Zn plating/stripping chemistry. Zinc ions-induced crowding effect between CCS skeleton creates a strong hydrogen bonding environment and squeezes the moving space for water/anion counterparts, therefore greatly reducing the number of active water molecules and alleviating cathodic I3–attack. Moreover, the as-constructed Zn2+-enriched layer substantially facilitate rapid Zn2+migration through the NH2–Zn2+-NH2binding/dissociation mode of CCS molecule chain. Consequently, the large-format Zn symmetry cell (9 cm2) with a Zn-CCS electrode demonstrates excellent cycling stability over 1100 h without bulging. When coupled with an I2cathode, the assembled Zn–I2multilayer pouch cell displays an exceptionally high capacity of 140 mAh and superior long-term cycle performance of 400 cycles. This work provides a universal strategy to prepare large-scale production and high-performance polymer crowding layer for metal anode-based battery, analogous outcomes were veritably observed on other metals (Al, Cu, Sn).

Propylene carbonate (PC), due to its great dielectric constant, high breakdown stress, and strong adaption to the environment, will have great potential in the application of the pulsed power system as a liquid dielectric with high energy storage density. Therefore, it is of great practical significance and academic value to study the effect of external temperature variation on the breakdown characteristics of PC. Based on a pulsed power generator with a compact Tesla transformer as a high-voltage source and needle-plate electrode as a test specimen, the effect of external temperature ranging from −45 °C to 45 °C on the polarity of PC was experimentally investigated under microsecond pulse in this article. The breakdown voltage waveform is captured by the test probe using the capacitor voltage divider and the breakdown process of the specimen gap is recorded by the ultrahigh-speed camera. Experimental results show that the breakdown strength of PC increases with the rise of temperature in the different polarity, with an exceptive point at 0 °C. Meanwhile, the positive breakdown voltage of PC is greater than the negative one under this experimental condition, and the difference between them is the smallest at 18 °C. The analysis shows that microbubbles and water in PC are the main reasons for this phenomenon.

Evidence suggests that the intake of blueberry (poly)phenols is associated with improvements in vascular function and cognitive performance. Whether these cognitive effects are linked to increases in cerebral and vascular blood flow or changes in the gut microbiota is currently unknown.

In recent years, the development of mica paper capacitor (MPC) technology has dramatically improved the withstand voltage and energy storage density of capacitors, which is suitable for pulse power systems. The lifetime of capacitors is a key factor that ensures the reliability of a system. In this article, the investigation of the lifetime characteristics of MPCs under microsecond pulse is presented. The structure of an MPC is analysed, and two numerical simulations are performed to study its characteristics. A lifetime test platform is established to test MPCs, which has stable operation performance. Essential electrical degradation parameters of MPCs are obtained and analyzed. The operational factors that influence the lifetime of MPCs are analysed, and a lifetime empirical model under given operational factors is proposed based on the lifetime test results. The results suggest that the lifetime empirical model could match well with lifetime test results. Furthermore, the structure of a pulse forming network (PFN)-Marx is analyzed by the lifetime model, and constructive suggestions are proposed. This article is devoted to studying the characteristics of MPCs and analyzing the lifetime under various operational factors, which finally helps the device to specify operating conditions and ensure reliability.

The 4K content can deliver a more immersive visual experience to consumers due to the huge improvement in spatial resolution. However, the high spatial resolution brings a great challenge for video transmission and storage. Therefore, it is necessary to compress or downscale the 4K content before transmitting it to end-users. Existing blind image quality assessment (BIQA) methods are not suitable for 4K contents due to the high spatial resolution and specific distortions caused by upscaling methods. In this paper, we propose a deep learning-based BIQA model for 4K content, aiming to recognize true and pseudo 4K content and meanwhile evaluate their visual quality. Considering the characteristic that high spatial resolution can represent more abundant high-frequency information, we first propose a Grey-level Co-occurrence Matrix (GLCM) based texture complexity measure to select three representative image patches from a 4K image, which can reduce the computational complexity and is proven to be very effective for the overall quality prediction. Then, we extract various visual features from the intermediate layers of the convolutional neural network (CNN) and integrate them into the quality-aware feature representation. Finally, two multilayer perception (MLP) networks are utilized to map the quality-aware features into the class probability and the quality score of each patch respectively. The overall quality index is obtained through averaging the results of all patches. The proposed model is trained via the multi-task learning manner and the uncertainty principle is introduced to balance the losses of the classification and regression tasks. The experimental results show that the proposed model outperforms all compared BIQA metrics on four 4K content quality assessment databases.

Mica paper capacitors possess the characteristics of high energy storage density and reliability and have been widely applied in various power systems as energy storage components. The lifetime of capacitors is a critical factor that ensures the reliability of a system. The leakage resistance of a mica paper capacitor can be utilized to characterize its lifetime. However, the decrease of leakage resistance caused by a single test pulse is slight during a lifetime test. A large number of test pulses are needed to obtain complete lifetime data of mica paper capacitors. To obtain lifetime prediction of capacitors based on limited data, an improved iterative Grey–Markov chain model based on unbiased grey system theory, fuzzy classification with fuzzy C-mean (FCM) algorithm, and an iterative method is proposed. It can take advantage of the prediction power of the conventional Grey–Markov model and improve anti-interference performance simultaneously. As an example, six mica paper capacitors are utilized to validate the feasibility and practicability of this model. The working state and remaining lifetime of mica capacitors can be dynamically evaluated through this model in practical applications. This model is also applicable to forecasting in other lifetime testing situations. This helps to assess the reliability of a whole system.

Intense electron beams with pulse duration on the level of microseconds have been utilized in a number of areas, including the production of free electron laser, material surface modification, and high-energy-density physics research. The high-voltage pulse forming technology based on metal oxide varistors (MOVs) has been verified to be an effective way to produce quasi-square pulses in recent years. In this article, a high-energy-density Marx generator with output waveform modified by a flat-top compensation LC filtering branch and a low inductance MOV folding structure was designed and tested to generate microsecond pulses. Experimental results show that a quasi-square pulse with a voltage amplitude of more than 500 kV and a pulsewidth of $1~\mu \text{s}$ was obtained on a dummy load of $50~\Omega $ . Further experimental research was carried out using a magnetically insulated coaxial diode (MICD) electron beam diode, and as a result, a relativistic electron beam with an energy of 550 keV, a current of 8.3 kA, a rise time of less than 40 ns, and a flat-top of about 600 ns was produced. The Marx generator with a modified waveform has the advantages of simple design, compact construction, and high-quality flat-top, which is a good platform for the generation of intense microsecond electron beams.

Thermochemical energy storage technologies can be integrated with building heating to promote the application of renewable energy. In this study, to address the challenges between the substantial heating demand and the imbalanced supply for buildings in the low-latitude plateau regions of China, a novel and cost-effective hydrated salt composite has been developed based on volcanic from TengChong. The composite was prepared by impregnating acid-treatment volcanic into a 20 % concentration of a 1:1.5 mass ratio MgCl2-CaCl2solution. The BET results revealed that acid treatment increased the pore volume and the proportion of mesopores in the volcanic, which resulted in better absorption of the hydrated salts. The water adsorption capacity of the acid treatment's composites at 25 °C and RH 90 % reached 0.79 g/g. The energy storage density of the volcanic acid-treatment adsorbed hydrated salt (VAS) was 601.33 kJ/kg through DSC testing. VAS can achieve 84.15 % of the energy storage density at 68 % of the cost of MgCl2-CaCl2-zeolite-13×. Based on the subsequent environmental and economic analyses, the new composite was found to have significant potential for energy savings and carbon emission reduction if employed in building heating systems.

Green-emitting Mn2+-doped zinc silicate nanophosphor was synthesized by solvothermal method with the assistance of polyvinyl alcohol (PVA). The structure, morphology and optical properties of phosphors were characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Scanning electron microscope (SEM), Fourier transform infrared spectrometer (FT-IR) and Photoluminescence Spectroscopy (PL). Also, the application of Mn2+-doped zinc silicate phosphor in electronic devices was studied. Furthermore, Zn2SiO4: Mn2+phosphor was used to be a fluorescent sensor for the first time to detect inorganic ions and small molecules. The results indicated that pure α-Zn2SiO4phase nanoparticles with spherical morphology were obtained. The results also showed that the phosphor has excellent selectivity and sensitivity for detecting Cr3+ions, Cr2O72−ions and 4-nitrophenol (4-NP) with low detection limits (Cr3+ions: 1.34 μM, Cr2O72−ions: 5.86 μM, 4-NP: 2.38 μM). Thus, Mn2+-doped zinc silicate manganese phosphor has potential applications in light-emitting diodes (LEDs) and fluorescent sensors.

Cardiogenesis is a tightly regulated dynamic process through a continuum of differentiation and proliferation events. Key factors and pathways governing this process remain incompletely understood. Here, we investigate mice hearts from embryonic day 10.5 to postnatal week 8 and dissect developmental changes in phosphoproteome-, proteome-, metabolome-, and transcriptome-encompassing cardiogenesis and cardiac maturation. We identify mitogen-activated protein kinases as core kinases involved in transcriptional regulation by mediating the phosphorylation of chromatin remodeling proteins during early cardiogenesis. We construct the reciprocal regulatory network of transcription factors (TFs) and identify a series of TFs controlling early cardiogenesis involved in cycling-dependent proliferation. After birth, we identify cardiac resident macrophages with high arachidonic acid metabolism activities likely involved in the clearance of injured apoptotic cardiomyocytes. Together, our comprehensive multi-omics data offer a panoramic view of cardiac development and maturation that provides a resource for further in-depth functional exploration.

In the traditional manufacture methods of metal bellows, such as tube bulging and hydroforming, the dies and tools are generally used. In these methods, it is very inconvenient to change the shapes of bellows because the dies and tools should be changed, which often result in extremely high cost. In this study, a novel manufacture method without any dies and tools for producing the metal bellows with various shapes was proposed. This novel method is called as dieless bellows forming process with local heating technique. The factors which affect the shape of metal bellows and the deformation behavior of metal tubes during the dieless metal bellows forming process were investigated by experiment and finite element method simulation in this study. The metal tubes (SUS304, JIS) with the dimensions of 13.8 (D)×2 (t) mm and 25 (D)×2 (t) mm were used to study the availability of the newly proposed method for manufacturing the metal bellows with different tubular materials. As a result, the metal bellows with good shapes were successfully obtained using the newly proposed method. The results also showed that the shape of the metal bellows can be easily changed by adjusting the compression ratio. It was clarified that the deformation parameters, such as compression ratio and heating length, have the significant effects on the convolution height, pitch and deformation behavior of metal tube during dieless metal bellows forming process. The suitable parameters should be chosen to manufacture the metal bellows with the expected shape.

Background:Chemoradiotherapy (CRT) is currently performed for patients with advanced esophageal carcinoma. Sensitivity of tumours to CRT differs from one case to another and may be influenced by the expression of biological molecules. The aim of this study was to identify biological markers which could predict sensitivities of esophageal squamous cell carcinoma (ESCC) to CRT.Methods:A total of 84 patients with stage I–IV ESCC were evaluated. The cytokeratin 19 fragment antigen 21-1 (CYFRA21-1) and carcinoembryonic antigen (CEA) levels were measured before CRT by enzyme-linked immunosorbent assays in patients with primary ESCCs using 3.4 ng ml−1and 3.3 ng ml−1, respectively, as cut-off values. The relationships between pretreatment expression of CYFRA 21-1 and CEA and the effectiveness of CRT were analysed.Results:The complete response (CR) rates of the primary tumours estimated by computed tomography in patients with high levels of CYFRA21-1 and CEA were 10% (3/30) and 4.2% (1/24), while in cases with low CYFRA21-1 and CEA the CR rates were 50% (27/54) and 48.3% (29/60), respectively (p  0.002 and 0.003). The effective rates (CRPR) in CYFRA21-1 high and low groups were 60% (18/30) and 96.3% (52/54), while in CEA high and low groups they were 58.3% (14/24) and 93.3% (56/60), respectively (p  0.013 and 0.013).Conclusion:CYFRA21-1 and CEA may be helpful in predicting the responsiveness in ESCC of primary lesions to CRT, although the results should be confirmed in larger, more homogeneous studies.

Abstract: Objective: To evaluate the clinical efficacy and toxicities of simultaneous modulated accelerated radiotherapy (SMART) and concurrent chemotherapy for locally advanced nasopharyngeal carcinoma. Methods: Eightyseven patients with nasopharyngeal carcinoma received SMART from April 2002 to September 2006. According to the UICC staging system, 30 patients were diagnosed as stage IIb, 42 patients stage III, 13 patients stage IVa and 2 patients stage IVb. The intensitymodulated radiotherapy was delivered with the “step and shoot” SMART technique with the prescribed dose of 66–76 Gy (2.2–2.4 Gy/day) to the gross tumor volume (GTV) and positive neck lymph nodes (GTVLN), with 60 Gy (2.0 Gy/day) to the highrisk clinical target volume (CTV1), encompassing the area around the nasopharynx and the upper neck, and with 54 Gy (1.8 Gy/day) to the lowrisk clinical target volume (CTV2), including the lower neck and supraclavicular area. Among all the patients, 31 received 2 cycles of SMART concurrently with 5 fluorouracil (5-Fu) and cisplatin (the FP group) and 56 received 2 cycles of concurrent cisplatin. All the patients received 3 to 4 cycles of adjuvant combination chemotherapy of cisplatin and 5 fluorouracil starting from the 1st month after completion of SMART. Results: With a median follow up of 59 months (ranging from 19 to 85 months), the 1, 2 and 3 year overall survival rates were 100%, 94.6% and 91.3% respectively. Acute mucositis and pharyngitis were more frequently observed in the FP group than in the cisplatin group. Conclusion: SMART technique provides an excellent opportunity to spare normal tissue and is probably more biologically effective. Combination of single cisplatin was more tolerable.