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In recent years, with the rapid development of distributed photovoltaic systems (DPVS), the shortage of data monitoring devices and the difficulty of comprehensive coverage of measurement equipment has become more significant, bringing great challenges to the efficient management and maintenance of DPVS. Virtual collection is a new DPVS data collection scheme with cost-effectiveness and computational efficiency that meets the needs of distributed energy management but lacks attention and research. To fill the gap in the current research field, this paper provides a comprehensive and systematic review of DPVS virtual collection. We provide a detailed introduction to the process of DPVS virtual collection and identify the challenges faced by virtual collection through problem analogy. Furthermore, in response to the above challenges, this paper summarizes the main methods applicable to virtual collection, including similarity analysis, reference station selection, and PV data inference. Finally, this paper thoroughly discusses the diversified application scenarios of virtual collection, hoping to provide helpful information for the development of the DPVS industry.

No-reference image quality assessment aims to evaluate image quality based on human subjective perceptions. Current methods face challenges with insufficient ability to focus on global and local information simultaneously and information loss due to image resizing. To address these issues, we propose a model that combines Swin-Transformer and natural scene statistics. The model utilizes Swin-Transformer to extract multi-scale features and incorporates a feature enhancement module and deformable convolution to improve feature representation, adapting better to structural variations in images, apply dual-branch attention to focus on key areas, and align the assessment more closely with human visual perception. The Natural Scene Statistics compensates information loss caused by image resizing. Additionally, we use a normalized loss function to accelerate model convergence and enhance stability. We evaluate our model on six standard image quality assessment datasets (both synthetic and authentic), and show that our model achieves advanced results across multiple datasets. Compared to the advanced DACNN method, our model achieved Spearman rank correlation coefficients of 0.922 and 0.923 on the KADID and KonIQ datasets, respectively, representing improvements of 1.9% and 2.4% over this method. It demonstrated outstanding performance in handling both synthetic and authentic scenes.

Surface filling during the mining of steeply inclined thick coal seams is an efficient method for restraining disasters caused by the cascading movement of overburden rocks. This study aims to control rock damage during the mining of thick coal seams steeply inclined at typically more than 45° in fully mechanized coal caving work surfaces with high section heights. Based on the green mining concept, we analyzed the movement of roof strata after filling using multiple methods, including field investigation, theoretical analysis, numerical calculation, and field monitoring. Results show that, in dynamic mine disasters caused mainly by complex coal conditions and strong disturbances in fully mechanized coal caving in large sections, the strength of the filling material is dependent on the features of the surrounding rock and burial depth. Also, the mining-induced peak stress shows a linear increase after filling, with the goafs in stress-free conditions, and failure zones occur in the roof and floor strata after mining. The stability of the rock pillars and overburden strata are better, and there are no large-scale tensile fissures in the ground surface. We adopted an intelligent underground radar detection technique that can reflect the rock-failure characteristics through the propagation characteristics of the electromagnetic spectrum. The detection results show that the coal goafs were filled properly as they were matched with the caving roof, which will collapse along with the release of the top coal, with the filling body able to move downward along with the discharge of top coal. The use of surface filling can restrain the dynamic disaster induced by a fully mechanized coal caving surface with a large section when mining steeply inclined thick coal seams, thereby ensuring safety and promoting the use of green mining practices.

Diabetic peripheral neuropathy (DPN) is a common complication of both type 1 and 2 diabetes. DPN lacks accurate early diagnostic indicators, prompting united identification of biomarkers through transcriptomics and Mendelian randomization (MR) to inform DPN prevention and treatment strategies. Differential expression analysis pinpointed DPN-related genes (DE-DPN-RGs) by screening differentially expressed genes (DEGs) across GSE95849 and GSE185011 datasets. MR approach validated DE-DPN-RGs causally linked to DPN as potential biomarkers, with sensitivity analysis and Steiger test reinforcing the findings. These biomarkers' expressions were verified via RT-qPCR, while their biological roles, pathways influencing DPN progression, and possible therapeutic targets were comprehensively investigated. 124 DE-DPN-RGs were identified from 5340 DEGs1 and 896 DEGs2, among them, , , , , , and , showing significant causal relationships with DPN. Sensitivity analysis along with the Steiger test validated the reliability of the results, demonstrating their resilience against reverse causation. Furthermore, , , , and demonstrated significant differential expression between DPN and control groups in both the GSE95849 and GSE185011 datasets, with consistent expression trends across both datasets, thereby warranting their designation as biomarkers. Biomarkers functioned in metabolic reactions of amino acids, rRNA processing, and translation, with potential therapeutic candidates including rosuvastatin, nitrofurfurylhydrazide, and neostigmine bromide. All four biomarkers exhibited significant upregulation in the DPN group as confirmed by RT-qPCR analysis, with the exception of , which displayed a non-significant difference between the groups. In conclusion, , , and emerge as promising biomarkers, elucidating roles in DPN pathogenesis and offering potential therapeutic targets.

Scene Text Recognition (STR) faces significant challenges under complex degradation conditions, such as distortion, occlusion, and semantic ambiguity. Most existing methods rely heavily on language priors for correction, but effectively constructing language rules remains a complex problem. This paper addresses two key challenges: (1) The over-correction behavior of language models, particularly on semantically deficient input, can result in both recognition errors and loss of critical information. (2) Character misalignment in visual features, which affects recognition accuracy. To address these problems, we propose a Deformable-Alignment-based Dual Correction Mechanism (DADCM) for STR. Our method includes the following key components: (1) We propose a visually guided and language-assisted correction strategy. A dynamic confidence threshold is used to control the degree of language model intervention. (2) We designed a visual backbone network called SCRTNet. The net enhances key text regions through a channel attention module (SENet) and applies deformable convolution (DCNv4) in deep layers to better model distorted or curved text. (3) We propose a deformable alignment module (DAM). The module combines Gumbel-Softmax-based anchor sampling and geometry-aware self-attention to improve character alignment. Experiments on multiple benchmark datasets demonstrate the superiority of our approach. Especially on the Union14M-Benchmark, where the recognition accuracy surpasses previous methods by 1.1%, 1.6%, 3.0%, and 1.3% on the Curved, Multi-Oriented, Contextless, and General subsets, respectively.

Skin color plays an important role in color image processing and human–computer interaction. However, factors such as rapidly changing illumination, various color styles, and camera characteristics also make skin detection a challenging task. In particular, the real-time requirement of practical applications is a challenging task in skin detection. In this paper, face detection and alignment are applied to select facial reference points for modeling the skin color distribution. Moreover, we propose the conception and detection approach of skin color model updating unit (SCMUU) according to the fact of skin color distribution remains consistent in a range of frames. The redundant operation of frame by frame updating is avoided using one model in frames of SCMUU. When no reliable faces are detected, two strategies are introduced to remedy and reduce the computational cost. It uses the corresponding model parameters if a similar previous SCMUU is found. Otherwise, we use fixed thresholds instead and increase the interval between two consecutive face detection. Besides, the time-consuming steps are accelerated using a graphic processing unit (GPU) with CUDA in this paper. Experimental results show that, compared with other existing methods, the proposed method has good real time and accuracy for skin detection of various resolution videos under different illumination conditions.

As the consumption of traditional energy sources intensifies and their adverse environmental impacts become more pronounced, wave energy stands out as a highly promising member of the renewable energy family due to its high energy density, stability, widespread distribution, and environmental friendliness. The key to its development lies in the precise prediction of Significant Wave Height (WVHT). However, wave energy signals exhibit strong nonlinearity, abrupt changes, multi-scale periodicity, data sparsity, and high-frequency noise interference; additionally, physical models for wave energy prediction incur extremely high computational costs. To address these challenges, this study proposes a hybrid model combining STL-FFT-STFT-TCN-LSTM. This model exploits the Seasonal-Trend Decomposition Procedure based on Loess (STL), Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Temporal Convolutional Network (TCN), and Long Short-Term Memory (LSTM) technologies. The model aims to optimize multi-scale feature fusion, capture extreme wave heights, and address issues related to high-frequency noise and periodic signals, thereby achieving efficient and accurate prediction of significant wave height. Experiments were conducted using hourly data from NOAA Station 41008 and 41047 spanning 2019 to 2022. The results showed that compared with other single models and hybrid models, the STL-FFT-STFT-TCN-LSTM model achieved significantly higher prediction accuracy in capturing extreme wave heights and suppressing high-frequency noise, with MAE reduced by 15.8\\%-40.5\\%, SMAPE reduced by 8.3\\%-20.3\\%, and R increased by 1.31\\%-2.9\\%; in ablation experiments, the model also demonstrated the indispensability of each component step, validating its superiority in multi-scale feature fusion.

We have identified DNA polymorphisms in the gene of insulin-like growth factor 2 by PCR-SSCP in a resource population, which was generated by Silky reciprocally crossing to Broilers. A C →G mutation was detected in the exon 2 (at position 71) by sequencing. This single nucleotide polymorphism (SNP) was found to be associated with production traits. Chicken with BB genotype showed more chest angle width but less 3 week body weight and glandular stomach weight than chicken with AA genotype (P<0.05); while the heterozygote (AB genotype) chicken had more abdominal fat weight, eviscerated yield with giblet than AA homozygote chicken. Further analysis showed that there were different genetic effects on some traits between heterozygote AB (paternal allele given first) and heterozygote BA: chickens with genotype BA had more birth weight and breast weight but less abdominal fat weight than chickens with genotype AB (P<0.05), which could be hypothetically contributed by genome imprinting. Therefore, Silky chickens were selected for production of heterozygotes to confirm whether IGF2 locus was imprinting. Progeny from heterozygote × homozygote reciprocal cross was assayed for expression after the genotype was determined. The transcription of IGF2 was detected by RT-PCR-SSCP. IGF2 gene was expressed bialleleically in 1-day-old neonatal liver and 90-day-old liver, kidney, heart, and muscle of both heterozygote AB and BA chickens. Therefore, IGF2 was not an imprinting gene in chicken. The different genetic effects between the heterozygote AB and BA remain to be elucidated.

This paper mainly introduces an infrared optical communication system based on stable and handstand pendulum. This system adopts the method of loading the infrared light emitting end on an handstand pendulum to realize the stability and controllability of the infrared light transmission light path. In this system, 940nm infrared light is mainly used for audio signal transmission, and an handstand pendulum based on PID is used to control the angle and stability of infrared light emission. Experimental results show that the system can effectively ensure the stability of the transmission optical path and is suitable for accurate and stable signal transmission in bumpy environments.

Fast-charging metal-ion batteries are essential for advancing energy storage technologies, but their performance is often limited by the high activation energy ( E a ) required for ion diffusion in solids. Addressing this challenge has been particularly difficult for multivalent ions like Zn 2+ . Here, we present an amorphous organic-hybrid vanadium oxide (AOH-VO), featuring one-dimensional chains arranged in a disordered structure with atomic/molecular-level pores for promoting hierarchical ion diffusion pathways and reducing Zn 2+ interactions with the solid skeleton. AOH-VO cathode demonstrates an exceptionally low E a of 7.8 kJ·mol −1 for Zn 2+ diffusion in solids and 6.3 kJ·mol −1 across the cathode-electrolyte interface, both significantly lower than that of electrolyte (13.2 kJ·mol −1 ) in zinc ion battery. This enables ultrafast charge-discharge performance, with an Ah-level pouch cell achieving 81.3% of its capacity in just 9.5 minutes and retaining 90.7% capacity over 5000 cycles. These findings provide a promising pathway toward stable, ultrafast-charging battery technologies with near-barrier-free ion dynamics. Promoting solid ion-diffusion is essential for fast-charging battery. Here, authors present near-barrier-free ion dynamics in an amorphous organic-hybrid vanadium oxide-based zinc ion battery and developed Ah-level fast-charging pouch cell.