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Artificial photosynthesis of H 2 O 2 using earth-abundant water and oxygen is a promising approach to achieve scalable and cost-effective solar fuel production. Recent studies on this topic have made significant progress, yet are mainly focused on using  organic polymers. This set of photocatalysts is susceptible to potent oxidants (e.g. hydroxyl radical) that are inevitably formed during H 2 O 2 generation. Here, we report an inorganic Mo-doped faceted BiVO 4 (Mo:BiVO 4 ) system that is resistant to radical oxidation and exhibits a high overall H 2 O 2 photosynthesis efficiency among inorganic photocatalysts, with an apparent quantum yield of 1.2% and a solar-to-chemical conversion efficiency of 0.29% at full spectrum, as well as an apparent quantum yield of 5.8% at 420 nm. The surface-reaction kinetics and selectivity of Mo:BiVO 4 were tuned by precisely loading CoO x and Pd on {110} and {010} facets, respectively. Time-resolved spectroscopic investigations of photocarriers suggest that depositing select cocatalysts on distinct facet tailored the interfacial energetics between {110} and {010} facets and enhanced charge separation in Mo:BiVO 4 , therefore overcoming a key challenge in developing efficient inorganic photocatalysts. The promising H 2 O 2 generation efficiency achieved by delicate design of catalyst spatial and electronic structures sheds light on applying robust inorganic particulate photocatalysts to artificial photosynthesis of H 2 O 2 . An inorganic and robust photocatalytic system based on Mo-doped faceted BiVO4 particles exhibits a solar-to-chemical conversion efficiency of 0.29% for H 2 O 2 generation, a new record among inorganic systems.

Circadian rhythms regulate a wide range of biological processes, and their precise characterization is essential for understanding behavioral and physiological fluctuations. However, existing tools to analyze circadian data often require coding expertise or rely on specific data acquisition software, limiting their general applicability. Here, we present easyClock, an intuitive and interactive application designed to streamline circadian rhythm analysis and visualization. The easyClock application enables simultaneous processing of multiple files, allowing users to batch-analyze and visualize diverse sets of time series data. To enhance data analysis efficiency and provide comparable results, this application integrates comprehensive methods for handling data with various waveforms and noises. Additionally, easyClock can assess inter-individual variability and group differences using linear mixed-effects modeling. All statistical results and graphs are easily viewed and exported for any selected range of data. As a demonstration, we present a re-analysis of a time-series transcriptomic dataset, highlighting the value of easyClock as an accessible, open-source tool. This easy-to-use application requires no programming expertise and can be directly installed on Windows and macOS machines in a single step.

The concentrations of heavy metals (mercury (Hg), cadmium (Cd), lead (Pb), chromium (Cr), copper (Cu) and arsenic (As)) in surface water and sediments were investigated in two adjacent drinking water reservoirs (Hongfeng and Baihua Reservoirs) on the Yunnan-Guizhou Plateau in Southwest China. Possible pollution sources were identified by spatial and statistical analyses. For both reservoirs, Cd was most likely from industrial activities, and As was from lithogenic sources. For the Hongfeng Reservoir, Pb, Cr and Cu might have originated from mixed sources (traffic pollution and residual effect of former industrial practices), and the sources of Hg included the inflows, which were different for the North (industrial activities) and South (lithogenic origin) Lakes, and atmospheric deposition resulting from coal combustion. For the Baihua Reservoir, the Hg, Cr and Cu were primarily derived from industrial activities, and the Pb originated from traffic pollution. The Hg in the Baihua Reservoir might also have been associated with coal combustion pollution. An analysis of ecological risk using sediment quality guidelines showed that there were moderate toxicological risks for sediment-dwelling organisms in both reservoirs, mainly from Hg and Cr. Ecological risk analysis using the Hakanson index suggested that there was a potential moderate to very high ecological risk to humans from fish in both reservoirs, mainly because of elevated levels of Hg and Cd. The upstream Hongfeng Reservoir acts as a buffer, but remains an important source of Cd, Cu and Pb and a moderately important source of Cr, for the downstream Baihua Reservoir. This study provides a replicable method for assessing aquatic ecosystem health in adjacent plateau reservoirs.

Two new compounds, named rhizoaspergillin A (1) and rhizoaspergillinol A (2), were isolated from the mangrove endophytic fungus Aspergillus sp. A1E3, associated with the fruit of Rhizophora mucronata, together with averufanin (3). The planar structures and absolute configurations of rhizoaspergillinol A (2) and averufanin (3) were established by extensive NMR investigations and quantum-chemical electronic circular dichroism (ECD) calculations. Most notably, the constitution and absolute configuration of rhizoaspergillin A (1) were unambiguously determined by single-crystal X-ray diffraction analysis of its tri-pivaloyl derivative 4, conducted with Cu Kα radiation, whereas those of averufanin (3) were first clarified by quantum-chemical ECD calculations. Rhizoaspergillin A is the first orsellinic acid–ribose–pyridazinone-N-oxide hybrid containing a unique β-oxo-2,3-dihydropyridazine 1-oxide moiety, whereas rhizoaspergillinol A (2) and averufanin (3) are sterigmatocystin and anthraquinone derivatives, respectively. From the perspective of biosynthesis, rhizoaspergillin A (1) could be originated from the combined assembly of three building blocks, viz., orsellinic acid, β-D-ribofuranose, and L-glutamine. It is an unprecedented alkaloid-N-oxide involving biosynthetic pathways of polyketides, pentose, and amino acids. In addition, rhizoaspergillinol A (2) exhibited potent antiproliferative activity against four cancer cell lines. It could dose-dependently induce G2/M phase arrest in HepG2 cells.

Does urban density always boost smart productivity? Based on panel data from 28 major Chinese cities (2011–2021), this study reveals an inverted U-shaped relationship between urban density and smart productivity. Using entropy weight method, we construct comprehensive indices to measure both urban density and smart productivity levels. Our findings demonstrate that urban density positively influences smart productivity up to a threshold of 0.497, beyond which the relationship becomes negative. The results from fixed effects modeling show that a 1% increase in urban density is associated with a 0.114% increase in smart productivity before reaching the threshold. Through mediation analysis, we find that urbanization level serves as a significant mediator, accounting for 49.1% of the total effect. Furthermore, heterogeneity analysis reveals distinct regional patterns: urban density exhibits stronger positive effects in western regions (coefficient = 0.181) compared to central regions (coefficient = 0.156), while showing negative impacts in eastern regions. These findings suggest that optimal urban density levels vary across regions, and cities should adopt differentiated development strategies accordingly. Our study contributes to the literature by quantifying the non-linear relationship between urban density and smart productivity, while providing empirical evidence for urban planning policies.

Oversized rings in wind turbines are regarded as crucial components because they often serve as the main load-bearing and connector structures. Surface defects on these rings can disrupt the normal operation of the entire unit. Detecting surface defects on oversized rings in wind turbine generators (WTGs) is highly challenging due to the huge ring size and small target defects, which will cause the detection process to be very time-consuming and difficult to achieve the expected accuracy. To address this challenge, we propose a new lightweight multiscale high-efficiency detector (LMHD) that balances accuracy and model size. The framework utilizes RepViT as the detection backbone and incorporates a bi-directional feature pyramid network (BiFPN) in the neck to achieve bi-directional feature transfer and aggregation. Additionally, it includes a new lightweight, efficient, multi-scale cross-stage partition module called the Diverse View Group Shuffle Cross Stage Partial Network (DVOV-GSCSPM), which employs a rational architecture and multiscale information fusion to ensure that the overall model is lightweight while maintaining a rich gradient flow. Self-Calibrated Convolutions (SCConv) and Efficient Local Attention (ELA) modules are introduced into the neck network to reduce computational complexity and the number of parameters while ensuring model accuracy. Ultimately, we incorporate the Powerful-IoUv2 loss function to enhance the rate of model convergence and generalization capabilities. The model is experimentally validated on the public dataset NEU-DET, achieving a detection accuracy of 87.0% with 70.4 frames per second (FPS).

To address the challenges of low prediction accuracy and insufficient capture of temporal dynamic variations in new energy electricity demand, this paper proposes a chaos-optimized least squares support vector machine (LSSVM) neural network model for multi-temporal and spatial forecasting. First, leveraging an edge computing framework, data collected at the metering side are processed, and redundant time records are cleaned. By integrating chaos theory with Takens’ theorem, the refined data sequence undergoes phase space reconstruction, producing a new energy electricity demand dataset with spatial correlation features. In an innovative step, the spatial transformation results are used as input, combining long short-term memory (LSTM) networks and least squares support vector machines to construct a hybrid LSSVM neural network model for electricity demand forecasting. This enables accurate and dynamic multi-temporal and spatial prediction of new energy electricity demand. Experimental results show that the proposed method achieves an MAE of 0.355 kWh and a MAPE of 1.32% for short-term new energy electricity demand forecasting, while for mid-term forecasting, the MAE and MAPE reach 25.36 kWh and 2.15%, respectively. These results verify the robustness and accuracy of the proposed method in dynamic multi-temporal and spatial electricity demand prediction.

Common PCB (Printed Circuit Board) defects include missing holes, shorts, spurs, etc., which may lead to product performance degradation, malfunction or safety hazards. Within the framework of Smart Manufacturing and Industry 4.0, industry strives to achieve automated and intelligent PCB defect inspection by using advanced machine vision systems and artificial intelligence algorithms. However, PCB defect detection faces challenges such as high density and miniaturization, complex background interference, and multiscale targets. For this reason, this paper proposes a new method for PCB defect detection according to a hierarchical scale-aware attention (HSA) mechanism based on RT-DETR (Real-Time Detection Transformer), and thus the method is coded as HSA-RTDETR. The core of the new method resides in the enhancement of feature information of small target defects in a feature fusion network. Firstly, a new backbone network, R18-Faster-EMA, is designed to make the overall model more efficient; Secondly, the AIFI (Attention-based Intra-scale Feature Interaction) module is redesigned to replace the original multihead self-attention mechanism with cascaded group attention to highlight important features. Thirdly, a hierarchical scale-aware pyramid attention network (HS-PAN) is designed to realize multi-scale feature fusion and learn more comprehensive feature arrays. Finally, to improve the efficiency of the model, a new loss function is designed to speed up convergence and prioritize small target defects. Experiments show that the HSA-RTDETR method achieves a mean average precision of 96.9% for six defects in a PCB dataset, which outperforms other existing models in terms of precision and recall. Compared with the original RT-DETR algorithm, the proposed method improves precision, recall, and mAP50 by 5.8%, 7.9% and 5.4%, respectively, In addition, the inference speed reaches 66.2 frames per second (FPS), which is deemed effective for the detection of small target defects in PCBs.

Unidirectional SiC f /SiC minicomposite with a scheelite (CaWO 4 ) interphase coating was fabricated through the precursor infiltration and pyrolysis method. Fractography of the SiC f /SiC minicomposites indicated that weak fiber/matrix bonding can be provided by the CaWO 4 interphase. Furthermore, interfacial debonding stress of SiC f /CaWO 4 /SiC minicomposite was evaluated through the fiber push-out test, and estimated to be 80.7 ± 4.6 MPa. In-situ tensile SEM observation of SiC f /CaWO 4 /SiC minicomposite after oxidation at 1000–1100 °C was carried out, and thermal compatibility between CaWO 4 interphase coating and SiC fiber or matrix after heat treatment at 1300 °C was investigated.

Background Renal involvement in undifferentiated connective tissue disease (UCTD) is rare and not well characterized, with membranous nephropathy (MN) representing an uncommon but clinically significant manifestation. While exostosin 1/exostosin 2 (EXT 1/EXT 2)-associated MN has been increasingly recognized in systemic autoimmune diseases such as lupus and Sjögren’s syndrome, its association with UCTD has not been previously reported. Case presentation We present the case of a 32-year-old male with UCTD who developed nephrotic syndrome and was found to have exostosin 1/exostosin 2 (EXT 1/EXT 2)-associated membranous nephropathy (MN) on renal biopsy. The patient exhibited serological markers including ANA, anti-SSA/Ro, and anti-ribosomal P protein antibodies, alongside characteristic pathological features of secondary MN with IgG1-dominant deposits and negative PLA2R/THSD7A staining. He was successfully treated with prednisone, tacrolimus, and hydroxychloroquine, achieving significant reduction in proteinuria within two months. Conclusions This case underscores the diagnostic utility of EXT 1/EXT 2 immunohistochemistry in identifying autoimmune-mediated MN in UCTD and supports the use of combined immunosuppressive therapy. To our knowledge, this is the first reported case of EXT 1/EXT 2-associated MN in a patient with UCTD, thereby expanding the spectrum of renal pathology in this condition and underscoring the importance of precise histological classification to guide targeted treatment.