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Abstract Analyzing the genetic diversity and selection characteristics of sheep (Ovis aries) holds significant value in understanding their environmental adaptability, enhancing breeding efficiency, and achieving effective conservation and rational utilization of genetic resources. In this study, we utilized Illumina Ovine SNP 50 K BeadChip data from four indigenous sheep breeds from the southern margin of the Taklamakan Desert (Duolang sheep: n = 36, Hetian sheep: n = 74, Kunlun sheep: n = 27, Qira black sheep: n = 178) and three foreign meat sheep breeds (Poll Dorset sheep: n = 105, Suffolk sheep: n = 153, Texel sheep: n = 150) to investigate the population structure, genetic diversity, and genomic signals of positive selection within the indigenous sheep. According to the Principal component analysis (PCA), the Neighbor-Joining tree (NJ tree), and Admixture, we revealed distinct clustering patterns of these seven sheep breeds based on their geographical distribution. Then used Cross Population Extended Haplotype Homozygosity (XP-EHH), Fixation Index (FST), and Integrated Haplotype Score (iHS), we identified a collective set of 32 overlapping genes under positive selection across four indigenous sheep breeds. These genes are associated with wool follicle development and wool traits, desert environmental adaptability, disease resistance, reproduction, and high-altitude adaptability. This study reveals the population structure and genomic selection characteristics in the extreme desert environments of native sheep breeds from the southern edge of the Taklimakan Desert, providing new insights into the conservation and sustainable use of indigenous sheep genetic resources in extreme environments. Additionally, these findings offer valuable genetic resources for sheep and other mammals to adapt to global climate change.

Abstract Tropical cyclones (TCs) have experienced poleward migration in recent years, but whether this exists in future projections with high-resolution climate models remains unclear. This study investigates the poleward migration of TCs over the western North Pacific (WNP) using CMIP6-HighResMIP models. We first assess the model performance in TC genesis frequency and latitude, which differ greatly from the observations, especially in winter and spring due to misinterpretation of extratropical storms. In this study, we put forward a revised constrained detection method based on the sea surface temperature (SST) and the atmospheric conditions to resolve this bias. Results indicate that the revised detection method has good performance in capturing the annual cycle of TC genesis frequency and latitude. Future projections constrained by this method show that the latitude of TC genesis and lifetime maximum intensity (LMI) both undergo a poleward shift, with the former being more significant. Spatial changes in the dynamic potential genesis index and large-scale environment could explain this shift. The regional changes of Hadley circulation and the role of global warming and internal variability are also discussed.

Abstract The main part of oil‐immersed power equipment is oil‐paper insulation. At lower ambient temperatures, transformer oil will become viscous. As a result, compared to those at room temperature or operating temperature, the frequency domain dielectric properties of oil‐paper insulation at low temperatures are drastically different. For evaluating the insulation condition of electrical equipment, it is essential to accurately determine their dielectric properties. This paper described the FDS test, the DC conductivity test, and the transformer oil viscosity test, which was carried out in a laboratory environment for different ageing of oil‐immersed pressboard and transformer oil at 233–373 K. The effects of temperature on the dielectric properties at the competing mechanisms of polarisation and conductivity loss were clarified based on variations of the FDS curves. Considering the viscosity change of ageing transformer oil at low temperatures, the Arrhenius and VFT viscosity equations were used to achieve a fitting calculation in the different temperature zones. Based on the molecular/ion transition model in the force field and electric field, the characterisation relationships between the dynamic viscosity, DC conductivity, and test temperature of transformer oil were established. The limitations of conventional transformer oil equivalent dielectric relaxation models over a wide temperature range were finally clarified by combining the test findings of the DC conductivity over a wide temperature range with observed and simulated calculations. This paper provides an essential theoretical reference when using the FDS test to diagnose the insulation performance of oil‐immersed power equipment in extremely cold regions.

In this work, two bainitic steels with medium carbon content were developed by adding different Si. The mechanical properties of the two test steels and the stability of their retained austenite were studied, and the influence of Si in the steels was discussed. The results show that the film-like retained austenite of the high Si bainitic steel is greatly increased compared with that of the low Si bainitic steel and that the C content is increased, effectively preventing crack propagation and significantly improving the comprehensive mechanical properties. Due to the different Si content in the test steels, the orientation relationship of austenite in the steels has changed, and when the Si content is increased from 0.7 wt% to 1.4 wt%, the variant selection of bainite becomes weaker. Moreover, the increase of Si content not only inhibits the precipitation of carbides but also promotes the accumulation of C in the austenite, which improves the stability of the retained austenite.

Abstract The observed Southern Ocean sea surface temperature (SST) has experienced prominent inter-decadal variability nearly in phase with the Inter-decadal Pacific Oscillation (IPO), but less associated with the Atlantic Multidecadal Variability (AMV), challenging the prevailing view of Pacific-Atlantic synergistic effects. Yet, the mechanisms of distinct trans-hemispheric connections to the Southern Ocean remain indecisive. Here, by individually constraining the observed cold-polarity and warm-polarity IPO and AMV SSTs in a climate model, we show that the IPO is influential in initiating a basin-wide Southern Ocean response, with the AMV secondary. A tropical Pacific-wide cooling triggers a basin-scale Southern Ocean cold episode through a strong Rossby wave response to the north-to-south cross-equatorial weakened Hadley circulation. By contrast, due to the competing role of tropical Pacific cooling, an Atlantic warming partly cools the Southern Ocean via a weak Rossby wave response to the south-to-north cross-equatorial enhanced Hadley circulation. Conversely, tropical Pacific warming leads to a warm Southern Ocean episode. Our findings highlight that properly accounting for the tropical Pacific SST variability may provide a potential for skillful prediction of Southern Ocean climate change and more reliable estimates of climate sensitivity, currently overestimated by the misrepresented Southern Ocean warming.

Throughout history, various odors have been harnessed to invigorate or relax the mind. The mechanisms underlying odors’ diverse arousal effects remain poorly understood. We conducted five experiments (184 participants) to investigate this issue, using pupillometry, electroencephalography, and the attentional blink paradigm, which exemplifies the limit in attentional capacity. Results demonstrated that exposure to citral, compared to vanillin, enlarged pupil size, reduced resting-state alpha oscillations and alpha network efficiency, augmented beta-gamma oscillations, and enhanced the coordination between parietal alpha and frontal beta-gamma activities. In parallel, it attenuated the attentional blink effect. These effects were observed despite citral and vanillin being comparable in perceived odor intensity, pleasantness, and nasal pungency, and were unlikely driven by semantic biases. Our findings reveal that odors differentially alter the small-worldness of brain network architecture, and thereby brain state and arousal. Furthermore, they establish arousal as a unique dimension in olfactory space, distinct from intensity and pleasantness.

Objective: To evaluate the effectiveness of low-level red light (LRL) in controlling the progression of myopia in children and adolescents. Methods: A randomized controlled trial was conducted from March 2022 to June 2022 at the Xuzhou First People's Hospital. A total of 73 children and adolescents with myopia, between the ages of 6 and 14, and meeting the inclusion criteria, were randomly divided into two groups. The experimental group wore single vision spectacles with LRL intervention, while the control group wore single vision spectacles alone. Spherical equivalent refraction (SER), axial length (AL), subfoveal choroidal thickness (SFCT), and best-corrected visual acuity (BCVA) were measured for the participants. Data analysis was performed using chi-square test, independent samples t-test, and Mann-Whitney U test. To compare the changes in SER and AL between groups, we utilized the Generalized Estimating Equations (GEE) model. Results: The experimental group was composed of 36 individuals, while the control group had 37. The mean age of the participants was 8.9 ± 2.0 years. No statistically significant distinctions in SER, AL and SFCT were observed between the two groups at baseline (P > 0.05). After 6 months of intervention, the experimental group's increase in SER (-0.01D; 95 % CI: -0.09, 0.06) was higher than that of the control group (-0.41D; 95 % CI: -0.51, -0.32), with a significance level of P < 0.001. Furthermore, the changes over time revealed significant differences between the two groups (Wald χ2group×time: 31.576, P < 0.001). The experimental group's AL increase (-0.02 mm; 95 % CI: -0.07, 0.03) was less than the control group's (0.22 mm; 95 % CI: 0.19, 0.25) (P < 0.001), with a significant difference over time between them (wald χ2group×time: 62.305, P < 0.001). SFCT change after 6 months in the experimental group was significantly greater (29.19 μm; 95 % CI: 18.48, 39.91) compared to that of the control group (-6.59 μm; 95 % CI: -14.28, 1.09) (P < 0.001). No adverse events were observed, and there was no evidence of fundus structural damage on OCT imaging. Conclusions: The findings suggest that low-level red light can effectively control myopia progression in children and adolescents within 6 months. No adverse reactions were observed.

Increasing the number of teats in sheep helps to improve the survival rate of sheep lambs after birth. In order to analyze the candidate genes related to the formation of multiple teats in Hu sheep, the present study was conducted to investigate the genetic pattern of multiple teats in Hu sheep. In this study, based on genome-wide data from 157 Hu sheep, Fst, xp-EHH, Pi and iHS signaling were performed, and the top 5% signal regions of each analyzed result were annotated based on the Oar_v4.0 for sheep. The results show that a total of 142 SNP loci were selected. We found that PTPRG, TMEM117 and LRP1B genes were closely associated with polypodium formation in Hu sheep, in addition, among the candidate genes related to polypodium we found genes such as TMEM117, SLC25A21 and NCKAP5 related to milk traits. The present study screened out candidate genes for the formation of multiple teats at the genomic level in Hu sheep.

Abstract Diabetic kidney disease (DKD) is the leading cause of end‐stage kidney disease. DKD is a heterogeneous disease with complex pathophysiology where early endothelial dysfunction is associated with disease progression. The Tie2 receptor and Angiopoietin 1 and 2 ligands are critical for maintaining endothelial cell permeability and integrity. Tie2 signaling is negatively regulated by the endothelial specific transmembrane receptor Vascular Endothelial Protein Tyrosine Phosphatase (VEPTP). Genetic deletion of VEPTP protects from hypertension and diabetes induced renal injury in a mouse model of DKD. Here, we show that VEPTP inhibition with an extracellular domain targeting VEPTP antibody induced Tie2 phosphorylation and improved VEGF‐A induced vascular permeability both in vitro and in vivo. Treatment with the VEPTP blocking antibody decreased the renal expression of endothelial activation markers (Angpt2, Edn1, and Icam1) but failed to improve kidney function in db/db uninephrectomized ReninAAV DKD mice.

BackgroundThe design of femoral stem prostheses requires a precise understanding of the femoral marrow cavity. Traditional measurements of morphological parameters in the upper femur, particularly the medullary cavity and cortical region, are primarily based on coronal and sagittal axes, which may not fully capture the true three-dimensional structure of the femur.MethodsPropose a Monte Carlo-based method for a more comprehensive analysis of the femoral marrow cavity, using CT scans of femurs from a selected group of patients. The study aimed to define and calculate anatomically semantic morphological parameters to enhance the understanding of the femoral marrow cavity's anatomical morphological changes, ultimately improving the design and clinical selection of femoral stem prostheses. To enhance the accuracy of femoral stem prosthesis design, this study aims to develop a Monte Carlo-based method for a more comprehensive analysis of the femoral marrow cavity. The proposed method transforms the non-random problem of determining cross-sectional size into a random issue, allowing for the calculation of the size of the medullary cavity and cortical region. Anatomically semantic morphological parameters are then defined, calculated, and analyzed.ResultsThe experimental results indicate that the newly defined parameters complement existing ones, providing a more rational scientific basis for understanding the anatomical morphological changes of the femoral marrow cavity.ConclusionThis research offers essential scientific theoretical support for improved morphologic research, design, and clinical selection of femoral stem prostheses. It holds significant importance and application value in clinical practice, contributing to a more accurate and comprehensive understanding of femoral anatomy for prosthetic design.

ABSTRACT The rapid expansion of antibiotic-resistant bacterial diseases is a global burden on public health. It makes sense to repurpose and reposition already-approved medications for use as supplementary agents in synergistic combinations with existing antibiotics. Here, we demonstrate that the anthelmintic drug nitazoxanide (NTZ) synergistically enhances the effectiveness of the lipopeptide antibiotic polymyxin B in inhibiting gram-negative bacteria, including those resistant to polymyxin B. Mechanistic investigations revealed that nitazoxanide inhibited calcium influx and cell membrane depolarization, enhanced the affinity between polymyxin B and the extracellular membrane, and promoted intracellular ATP depletion and an increase in reactive oxygen species (ROS), thus enhancing the penetration and disruption of the Escherichia coli cell membrane by polymyxin B. The transcriptomic analysis revealed that the combination resulted in energy depletion by inhibiting both aerobic and anaerobic respiration patterns in bacterial cells. The increased bactericidal effect of polymyxin B on the E. coli ∆nuoC strain further indicates that NuoC could be a promising target for nitazoxanide. Furthermore, the combination of nitazoxanide and polymyxin B showed promising therapeutic effects in a mouse infection model infected with E. coli. Taken together, these results demonstrate the potential of nitazoxanide as a novel adjuvant to polymyxin B, to overcome antibiotic resistance and improve therapeutic outcomes in refractory infections.IMPORTANCEThe rapid spread of antibiotic-resistant bacteria poses a serious threat to public health. The search for potential compounds that can increase the antibacterial activity of existing antibiotics is a promising strategy for addressing this issue. Here, the synergistic activity of the FDA-approved agent nitazoxanide (NTZ) combined with polymyxin B was investigated in vitro using checkerboard assays and time-kill curves. The synergistic mechanisms of the combination of nitazoxanide and polymyxin B were explored by fluorescent dye, transmission electron microscopy (TEM), and transcriptomic analysis. The synergistic efficacy was evaluated in vivo by the Escherichia coli and mouse sepsis models. These results suggested that nitazoxanide, as a promising antibiotic adjuvant, can effectively enhance polymyxin B activity, providing a potential strategy for treating multidrug-resistant bacteria.

Building use information is important for urban planning, city digital twins, and informed policy formulation. Prior research has predominantly focused on mapping building use in broad categories, offering general insight into their actual use. Our study investigates the extraction of hierarchical building categories, encompassing both broad and detailed classifications while accounting for mixed-use. To achieve this, we explore the fusion of building function information from satellite images, digital surface models (DSM), street view images, and point of interest (POI) data. We propose a novel multi-label multimodal transformer-based feature fusion network, which is capable of simultaneously predicting four broad categories and 13 detailed categories. Experimental results demonstrate the efficacy of our method, as it maps most of the building use categories, with the weighted average F1 score for four broad categories and 13 detailed categories of 91% and 77%, respectively. Our experiments underscore the critical role of satellite images in building use classification, with the inclusion of DSM data and POI significantly enhancing the classification accuracy. By considering detailed use categories and accounting for mixed-use, our method provides more detailed insights into land use patterns, thereby contributing to urban planning and management.

Abstract Urban water bodies can effectively mitigate the urban heat island effect and thus enhance the climate resilience of urban areas. The cooling effect of different water bodies varies, however, the cooling heterogeneity of different sections of a single watercourse or river network is rarely considered. Based on various satellite images, geospatial approaches and statistical analyses, our study confirmed the cooling heterogeneity from spatial and seasonal perspectives of the Suzhou Outer-city River in detail in the urban area of Suzhou, China. The cooling effect of the river was observed in the daytime in four seasons, and it is strongest in summer, followed by spring and autumn, and weakest in winter. The combination of the width of the river reach, the width and the NDVI value of the adjacent green space can explain a significant part of the cooling heterogeneity of the different river sections in different seasons. Land surface temperature (LST) variations along the river are more related to the width of the river reach, but the variations of the cooling distance are more related to the adjacent green space. The cooling effect of a river reach could be enhanced if it is accompanied by green spaces. In addition, the cooling effect of a looping river is stronger on the inside area than on the outside. The methodology and results of this study could help orient scientific landscape strategies in urban planning for cooler cities.

Abstract Background Missed nursing care is a pervasive issue in hospitals, nursing homes, and communities, posing a significant threat to patient safety and the quality of nursing care. It has adverse effects on patient satisfaction and the motivation of nursing staff. Understanding the causes and nature of these care omissions in clinical settings is essential for implementing effective interventions. This study aims to develop and validate a tool for assessing missed nursing care in adult intensive care units. Methods Semi-structured interviews, expert consultations conducted via the Delphi method and item analysis were used to develop the initial scale. Our analysis involved data collected from 400 nurses and employed correlation coefficient analysis, critical ratio assessment, Cronbach’s α coefficient evaluation, discrete trend analysis, and factor analysis, which were grounded in both classical test theory and item response theory, allowing us to scrutinize and refine the items in the scale. To validate the scale, we conveniently sampled 550 nurses and assessed structural validity, internal reliability, split-half reliability, and test-retest reliability to ensure the scale’s robustness and accuracy. Results The Missed Intensive Nursing Care Scale (MINCS) comprises three distinct components. Part A serves to collect general information about the participants. In Part B, the missed care elements are categorized into five domains, following the framework of Maslow’s hierarchy of needs theory: physiology, safety, belongingness, esteem, and cognition. Part C is dedicated to detailing the reasons behind missed care, which encompass labor resources, material resources, communication factors, and managerial factors. Remarkably, the Cronbach’s α coefficient for the MINCS stands at an impressive 0.951, with S-CVI values of 0.988 and 0.977 in Part B and C, respectively, underscoring the scale’s exceptional reliability and validity. This demonstrates the scale’s effectiveness in measuring missed nursing care while upholding rigorous standards of quality. Conclusions The MINCS emerges as a robust and dependable instrument for quantifying instances of missed care within the ICU. Its efficacy makes it a valuable resource for informing the development of strategies aimed at averting and mitigating the adverse effects associated with missed nursing care.

Bone defects caused by tumors, osteoarthritis, and osteoporosis attract great attention. Because of outstanding biocompatibility, osteogenesis promotion, and less secondary infection incidence ratio, stimuli-responsive biomaterials are increasingly used to manage this issue. These biomaterials respond to certain stimuli, changing their mechanical properties, shape, or drug release rate accordingly. Thereafter, the activated materials exert instructive or triggering effects on cells and tissues, match the properties of the original bone tissues, establish tight connection with ambient hard tissue, and provide suitable mechanical strength. In this review, basic definitions of different categories of stimuli-responsive biomaterials are presented. Moreover, possible mechanisms, advanced studies, and pros and cons of each classification are discussed and analyzed. This review aims to provide an outlook on the future developments in stimuli-responsive biomaterials.

The evolutionary conservation of non-core RAG regions suggests significant roles that might involve quantitative or qualitative alterations in RAG activity. Off-target V(D)J recombination contributes to lymphomagenesis and is exacerbated by RAG2’ C-terminus absence in Tp53−/− mice thymic lymphomas. However, the genomic stability effects of non-core regions from both Rag1c/c and Rag2c/c in BCR-ABL1+ B-lymphoblastic leukemia (BCR-ABL1+ B-ALL), the characteristics, and mechanisms of non-core regions in suppressing off-target V(D)J recombination remain unclear. Here, we established three mouse models of BCR-ABL1+ B-ALL in mice expressing full-length RAG (Ragf/f), core RAG1 (Rag1c/c), and core RAG2 (Rag2c/c). The Ragc/c (Rag1c/c and Rag2c/c) leukemia cells exhibited greater malignant tumor characteristics compared to Ragf/f cells. Additionally, Ragc/c cells showed higher frequency of off-target V(D)J recombination and oncogenic mutations than Ragf/f. We also revealed decreased RAG cleavage accuracy in Ragc/c cells and a smaller recombinant size in Rag1c/c cells, which could potentially exacerbate off-target V(D)J recombination in Ragc/c cells. In conclusion, these findings indicate that the non-core RAG regions, particularly the non-core region of RAG1, play a significant role in preserving V(D)J recombination precision and genomic stability in BCR-ABL1+ B-ALL.

Fluoride-resistant Streptococcus mutans (S. mutans) might affect the ecological balance of biofilms in the presence of fluoride. We used a S. mutans and Candida albicans (C. albicans) cross-kingdom biofilm model to investigate whether fluoride-resistant S. mutans in biofilms would support C. albicans growth under fluoride stress and attenuate the in vitro anti-caries effect of fluorine. The impact of fluoride-resistant S. mutans on formation of cross-kingdom biofilms by S. mutans and C. albicans in the presence of fluoride was investigated in vitro using the crystal violet staining assay. Biofilm constitution was determined using colony-forming unit (CFU) counts and fluorescent in situ hybridization (FISH). Extracellular polysaccharide (EPS) generation in biofilms was determined by EPS/bacterial dying and water-insoluble polysaccharide detection. Acid production and demineralization were monitored using pH, lactic acid content, and transversal microradiography (TMR). The gene expression of microorganisms in the cross-kingdom biofilm was measured using qRT-PCR. Our results showed that both C. albicans and fluoride-resistant S. mutans grew vigorously, forming robust cross-kingdom biofilms, even in the presence of sodium fluoride (NaF). Moreover, fluoride-resistant S. mutans-containing cross-kingdom biofilms had considerable cariogenic potential for EPS synthesis, acid production, and demineralization ability in the presence of NaF than fluoride-sensitive S. mutans-containing biofilms. Furthermore, the gene expression of microorganisms in the two cross-kingdom biofilms changed dissimilarly in the presence of NaF. In summary, fluoride-resistant S. mutans in cross-kingdom biofilms supported C. albicans growth under fluoride and might attenuate the anti-caries potential of fluorine by maintaining robust cross-kingdom biofilm formation and cariogenic virulence expression in vitro in the presence of NaF.

2020 and 1998 are the strongest Meiyu years in recent decades. The characteristics of the super-strong Meiyu precipitation and water vapor sources in 2020 and 1998 were compared, and the atmospheric circulation anomalies and the forcing factor SST were examined. (1) In 2020, the Meiyu duration, accumulated precipitation, and number of rainstorm days were greater than in 1998, and the highest since 1961. The Meiyu period in 2020 experienced 11 rainstorm processes. In 1998, a typical “second Meiyu” phenomenon occurred, and the area of heavy rainfall in 1998 was located further southward than that in 2020 (2) The contribution of the Bay of Bengal-South China Sea (BOB-SCS) to the total supply of water vapor in 2020 and 1998 was 43.0% and 42.0%, respectively, i.e., much higher than that of the climatological mean (25.5%). In 2020, the sources that provide most water vapor were the BOB, SCS, and central Pacific Ocean, while in 1998 were the Arabian Sea, BOB, and the western Pacific Ocean. (3) During the Meiyu period in 2020 and 1998, the position of atmospheric circulation pattern “two ridges and one trough” are different. Analysis of the vertical structure revealed that the specific humidity intensity above the area of heavy rainfall in 1998 was weaker than that in 2020, and the low-level convergence zone was further south and not as strong as in 2020. The positions of the western Pacific subtropical high (WPSH) and the western North Pacific anticyclone (WNPAC) in 1998 were both further south than those in 2020, which resulted in the more southerly locations of the southwesterly jet stream and rain belt. It should be pointed out that, the important contributions of the SST anomalies in the equatorial central eastern Pacific and the tropical Indian Ocean to the anomalous WNPAC in 1998 and 2020, respectively.

BackgroundIgA nephropathy (IgAN), the most common type of glomerulonephritis, has great individual differences in prognosis. Many studies showed the relationship between thyroid hormones and chronic kidney disease. However, the relationship between free thyroxine (FT4), as a thyroid hormone, and IgAN is still unclear. This study aimed to evaluate the impact of FT4 on IgAN prognosis.MethodsThis retrospective study involved 223 patients with biopsy-proven IgAN. The renal composite outcomes were defined as: (1) ESRD, defined as eGFR < 15 ml/(min·1.73 m2) or initiation of renal replacement therapy (hemodialysis, peritoneal dialysis, renal transplantation); (2) serum creatinine doubled from baseline; (3) eGFR decreased by more than 50% from baseline. The predictive value was determined by the area under the curve (AUC). Kaplan-Meier and Cox proportional hazards analyses assessed renal progression and prognosis.ResultsAfter 38 (26–54) months of follow-up, 23 patients (10.3%) experienced renal composite outcomes. Kaplan-Meier survival curve analysis showed that the renal survival rate of the IgAN patients with FT4

Abstract Although the relationship between the environmental factors, such as weather conditions and air pollution, and COVID-19 case fatality rate (CFR) has been found, the impacts of these factors to which infected cases are exposed at different infectious stages (e.g., virus exposure time, incubation period, and at or after symptom onset) are still unknown. Understanding this link can help reduce mortality rates. During the first wave of COVID-19 in the United Kingdom (UK), the CFR varied widely between and among the four countries of the UK, allowing such differential impacts to be assessed. We developed a generalized linear mixed-effect model combined with distributed lag nonlinear models to estimate the odds ratio of the weather factors (i.e., temperature, sunlight, relative humidity, and rainfall) and air pollution (i.e., ozone, $$N{O}_{2}$$ N O 2 , $$S{O}_{2}$$ S O 2 , $$CO$$ CO , $$P{M}_{10}$$ P M 10 and $$P{M}_{2.5}$$ P M 2.5 ) using data between March 26, 2020 and September 15, 2020 in the UK. After retrospectively time adjusted CFR was estimated using back-projection technique, the stepwise model selection method was used to choose the best model based on Akaike information criteria and the closeness between the predicted and observed values of CFR. The risk of death reached its maximum level when the low temperature (6 °C) occurred 1 day before (OR 1.59; 95% CI 1.52–1.63), prolonged sunlight duration (11–14 h) 3 days after (OR 1.24; 95% CI 1.18–1.30) and increased $$P{M}_{2.5}$$ P M 2.5 (19 μg/m3) 1 day after the onset of symptom (OR 1.12; 95% CI 1.09–1.16). After reopening, many COVID-19 cases will be identified after their symptoms appear. The findings highlight the importance of designing different preventive measures against severe illness or death considering the time before and after symptom onset.

Abstract East Asian floods and droughts in summer show a typical dipole pattern with a north-south oscillation centered near 30°N, called the southern drought–northern flood (SDNF) pattern, which has caused significant economic losses and casualties in the past three decades. However, effective explanations and predictions are still challenging, making suitable disaster prevention more difficult. Here, we find that a key predictor of this dipole pattern is the Quasi-Biennial Oscillation (QBO, tropical winds above 10 km). The QBO can modulate precipitation in East Asia, contributing the largest explained variation of this dipole pattern. A QBO-included statistical model can effectively predict summer floods and droughts at least three months in advance and explain at least 75.8% of precipitation variation. More than 30% of the SDNF pattern is attributed to the QBO in July-August 2020 and 2021. This result suggests a good prospect for using the tropical mid- to upper atmosphere in seasonal forecasts for summer.

Current land use classification models based on very high-resolution (VHR) remote sensing images often suffer from high sample dependence and poor transferability. To address these challenges, we propose an unsupervised multisource domain adaptation framework for cross-domain land use classification that eliminates the need for repeatedly using source domain data. Our method uses the Swin Transformer as the backbone of the source domain model to extract features from multiple source domain samples. The model is trained on source domain samples, and unlabeled target domain samples are then used for target domain model training. To minimize the feature discrepancies between the source and target domains, we use a weighted information maximization loss and self-supervised pseudolabels to alleviate cross-domain classification noise. We conducted experiments on four public scene datasets and four new land use scene datasets created from different VHR images in four Chinese cities. Results show that our method outperformed three existing single-source cross-domain methods (i.e., DANN, DeepCORAL, and DSAN) and four multisource cross-domain methods (i.e., M3SDA, PTMDA, MFSAN, and SHOT), achieving the highest average classification accuracy and strong stability. We conclude that our method has high potential for practical applications in cross-domain land use classification using VHR images.

In response to the challenge of three-dimensional path planning for unmanned aerial vehicles (UAVs), this paper introduces a Multi-Strategy Plant Growth Path Planning (MSPGPP) algorithm. Initially, a threat sphere and obstacle avoidance cone model are constructed to ensure safety in avoidance and enhance algorithm accuracy and convergence velocity, while B-spline curves are introduced to improve the quality of the path. Next, the concept of threat level is incorporated along with UAV maneuverability to establish a multi-objective optimization function, enabling the algorithm to plan optimal paths. Finally, an algorithm framework is developed, and comparative simulation results of the improved algorithm against the original algorithm and other optimization algorithms are presented in a three-dimensional environment. The results demonstrate that compared to the original algorithm, the improved algorithm better balances obstacle avoidance safety and UAV maneuverability, planning safer and more effective paths. Additionally, it achieves higher accuracy and faster convergence. Compared to other optimization algorithms, the improved algorithm has better optimization capabilities, higher computational efficiency, and superior path quality.

An approach based on refined critical plane methodology to fretting fatigue lifetime prediction for structures was proposed. A new shear stress-strain interaction (SSI) critical plane equation is developed, predicated on shear strain energy for predicting fretting fatigue lifetime. In order to obtain the fretting fatigue lifetime quickly and intelligently, the SSI critical plane method intelligent prediction software was developed using the Newton-Raphson iteration method. To corroborate the utility of the SSI critical plane method, we conducted a fretting fatigue lifetime test and then compared the calculated results of the SSI fretting fatigue lifetime prediction and traditional SWT prediction with the experimental values. The results indicate that SSI fretting fatigue lifetime prediction is closer to the experimental results than SWT.

With the integration of highly penetrative distributed photovoltaics into the distribution network, the imbalance between PV generation and load power becomes increasingly apparent, resulting in significant voltage security and stability issues in the distribution network. To overcome the limitations of traditional global voltage control and regional voltage cooperative control, a novel two-layer coordinated voltage control strategy, combining regional control and global control, is proposed for active distribution networks. Firstly, an adaptive partitioning of the active distribution network is achieved, without predefining the number of partitions, using the modular function gain change indicator and the Fast Unfolding algorithm. This adaptive partitioning ensures optimal regional control. Secondly, based on the voltage-power sensitivity principle, the reactive power control margin of PV sources is prioritized for regional-level reactive power control at critical nodes experiencing voltage violations. If the reactive power control margin within the region is insufficient, a combination of global-level reactive power control and active power control is employed. Lastly, simulation analysis is discussed by the IEEE 141-node traditional distribution network to confirm the effectiveness of the proposed method. Results demonstrate its ability to reduce the duration of voltage regulation and overcome the limitations of coordinated control range. The proposed method maximizes the utilization of the reactive power control margin of PV sources and significantly enhances PV penetration levels.

Self-organizing Map (SOM) neural network is a prominent algorithm in unsupervised machine learning, which is widely used for data clustering, high-dimensional visualization, and feature extraction. However, the hardware implementation of SOM is limited by the von Neumann bottleneck. Herein, a SOM neural network is implemented by the combination of 3D NAND flash memory arrays and in-memory Euclidean distance (ED) calculation. The weights in the SOM network are mapped to the conductance of the 3D NAND differential pair. It is experimentally demonstrated that the differential pair in 3D NAND flash array possesses superior characteristics for neuromorphic computing during increasing and decreasing synaptic weight. Using the 3D NAND-based SOM, a competitive learning neural network is established and used for the unsupervised classification of a set of Gaussian distribution data points. The experimental results illustrate the excellent performance and efficiency of the proposed architecture, highlighting the potential of 3D NAND-based in-memory computing for artificial intelligence applications.

Abstract In recent years, convolutional neural networks (CNN)‐based automatic segmentation of medical images has become one of the hot topics in clinical disease diagnosis. It is still a challenging task to improve the segmentation accuracy of the network model with the large variation of pathological regions in different patients and the fuzzy boundary of pathological regions. A Dual‐path Multi‐scale Attention Guided network (DMAGNet) for medical image segmentation is proposed in this paper. First, the Dual‐path Multi‐scale Attention Fusion Module (DMAF) is proposed as a novel skip connection strategy, which is applied to encode semantic dependencies between high‐level and low‐level channels. Second, the Multi‐scale Normalized Channel Attention Module (MNCA) based on the atrous convolution, normalization channel attention mechanism, and the Depthwise Separable Convolutions (DSConv) is developed to strengthen dependencies between channels. Finally, the encoder–decoder backbone employs the DSConv, as well as the pretrained Resnet34 block is combined in the encoder part to further improve the backbone network performance. Comprehensive experiments on brain, lung, and liver segmentation tasks show that the proposed DMAGNet outperforms the original U‐Net method and other advanced methods.

Some problems exist in classical optimization algorithms to solve multi-modal optimization problems and other complex systems. A Dynamic Niches-based Improved Hybrid Breeding Optimization (DNIHBO) algorithm is proposed to address the multi-modal optimization problem in the paper. By dynamically adjusting the niche scale, it effectively addresses the issue of niche parameter sensitivity. The structure of the algorithm includes three distinct groups: maintainer, restorer, and sterile lines for updating operations. However, the maintainer individuals often stagnate, leading to the risk of the local optima. To overcome this, neighborhood search and elite mutation strategies are incorporated, enhancing the balance between exploration and exploitation. To further improve individual utilization within niches, a niche restart strategy is introduced, ensuring sustained population diversity. The efficacy of DNIHBO is validated through simulations on 16 multi-modal test functions, followed by comparative analyses with various multi-modal optimization algorithms. The results convincingly demonstrate that DNIHBO not only effectively locates multiple global optima but also consistently outperforms other algorithms on test functions. These findings underscore the superiority of DNIHBO as a high-performing solution for multi-modal optimization.

Wind resources play a pivotal role in building sustainable energy systems, crucial for mitigating and adapting to climate change. With the increasing frequency of extreme events under global warming, effective prediction of extreme wind resource potential can improve the safety of wind farms and other infrastructure, while optimizing resource allocation and emergency response plans. In this study, we evaluate the seasonal prediction skill for summer extreme wind events over China using a 20-year hindcast dataset generated by a dynamical seamless prediction system designed by Shanghai Investigation, Design and Research Institute Co., Ltd. (Shanghai, China) (SIDRI-ESS V1.0). Firstly, the hindcast effectively simulates the spatial distribution of summer extreme wind speed thresholds, even though it tends to overestimate the thresholds in most regions. Secondly, high prediction skills, measured by temporal correlation coefficient (TCC) and normalized root mean square error (nRMSE), are observed in northeast China, central east China, southeast China, and the Tibetan Plateau (TCC is about 0.5 and the nRMSE is below 0.9 in these regions). The highest skills emerge in southeast China with a maximum TCC greater than 0.7, and effective prediction skill can extend up to a 5-month lead time. Ensemble prediction significantly enhances predictive skill and reduces uncertainty, with 24 ensemble members being sufficient to saturate TCC and 12–16 members for nRMSE in most key regions and lead times. Furthermore, we show that the prediction skill for extreme wind counts is strongly related to the prediction skill for summer mean wind speeds, particularly in southeast China. Overall, SIDRI-ESS V1.0 shows promising performance in predicting extreme winds and has great potential to provide services to the wind industry. It can effectively help to optimize wind farm operating strategies and improve power generation efficiency. However, further improvements are needed, particularly in areas where prediction skills for extreme winds are influenced by smaller-scale weather phenomena and areas with complex underlying surfaces and climate characteristics.

Due to the complexity and illegibility of medical images, it brings inconvenience and difficulty to the diagnosis of medical personnel. To address these issues, an optimization algorithm called GSL(Gradient sine linear) based on Adam algorithm improvement is proposed in this paper, which introduces gradient pruning strategy, periodic adjustment of learning rate, and linear interpolation strategy. The gradient trimming technique can scale the gradient to prevent gradient explosion, while the periodic adjustment of the learning rate and linear interpolation strategy adjusts the learning rate according to the characteristics of the sinusoidal function, accelerating the convergence while reducing the drastic parameter fluctuations, improving the efficiency and stability of training. The experimental results show that compared to the classic Adam algorithm, this algorithm can demonstrate better classification accuracy, the GSL algorithm achieves an accuracy of 78% and 75.2% on the MobileNetV2 network and ShuffleNetV2 network under the Gastroenterology dataset; and on the MobileNetV2 network and ShuffleNetV2 network under the Glaucoma dataset, an accuracy of 84.72% and 83.12%. The GSL optimizer achieved significant performance improvement on various neural network structures and datasets, proving its effectiveness and practicality in the field of deep learning, and also providing new ideas and methods for solving the difficulties in medical image recognition.

Multi-view scene matching refers to the establishment of a mapping relationship between images captured from different perspectives, such as those taken by unmanned aerial vehicles (UAVs) and satellites. This technology is crucial for the geo-localization of UAV views. However, the geometric discrepancies between images from different perspectives, combined with the inherent computational constraints of UAVs, present significant challenges for matching UAV and satellite images. Additionally, the imbalance of positive and negative samples between drone and satellite images during model training can lead to instability. To address these challenges, this study proposes a novel and efficient cross-view geo-localization framework called MSM-Transformer. The framework employs the Dual Attention Vision Transformer (DaViT) as the core architecture for feature extraction, which significantly enhances the modeling capacity for global features and the contextual relevance of adjacent regions. The weight-sharing mechanism in MSM-Transformer effectively reduces model complexity, making it highly suitable for deployment on embedded devices such as UAVs and satellites. Furthermore, the framework introduces a contrastive learning-based Symmetric Decoupled Contrastive Learning (DCL) loss function, which effectively mitigates the issue of sample imbalance between satellite and UAV images. Experimental validation on the University-1652 dataset demonstrates that MSM-Transformer achieves outstanding performance, delivering optimal matching results with a minimal number of parameters.

Hydrogenation of methane (CH4) is an important means of achieving energy savings and large-scale hydrogen (H2) transportation. Explosion venting is considered to be an effective method to reduce the risk of gas explosions. To investigate the parameters of the explosion characteristics of CH4/H2/Air premixed fuel under venting conditions, the explosion characteristics and the dynamic propagation law of venting flame in CH4/Air mixtures with 5% concentration were studied in 20 L and 40 L square explosion venting vessels. The development process of flames inside vessels, the evolution characteristics of explosive venting flames, and the changes in explosion pressure were tested using a high-speed camera and pressure sensors. The maximum explosion pressure, the maximum explosion pressure rise rate, and the maximum flame velocity increased first and then decreased with the addition of H2. However, the maximum flame length increased continuously with the increase of H2. With the increase of hydrogenation, the maximum flame area showed an exponential increase. At equivalence ratio less than 1, the effect of hydrogenation on the flame area was much attenuated than that at equivalence ratio was greater than 1.

Introduction Ulcerative colitis (UC) is a global chronic inflammatory bowel disease, and the poor efficacy of currently available pharmacological regimens makes the management of UC a great challenge. Moxibustion has shown great potential in the management of UC. However, its effectiveness and safety are still controversial. The purpose of this study is to synthesise the latest evidence regarding the clinical efficacy and safety of moxibustion for UC.Methods and analysis The Cochrane Library, PubMed, EMBASE, CNKI, Wanfang, VIP and SinoMed databases will be searched from inception to July 2023, to identify all randomised controlled trials with moxibustion for UC. The primary outcome will be clinical efficacy, as measured by validated scales. The serum inflammatory factor, colonoscopy results, quality of life, recurrence rate and adverse events will be the secondary outcomes. The Cochrane Risk of Bias 2.0 tool will be used to assess the methodological quality of each included trial. All data extraction will be carried out independently by two investigators. RevMan V.5.4 software will be used for data analysis and Cochran’s Q statistic and I2 test will be used to assess heterogeneity between studies. In addition, we will perform subgroup analyses, sensitivity analyses and publication bias if the available data are sufficient. The strength of evidence will be graded using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system.Ethics and dissemination Ethics approval is not required for this review. Our findings will be published in a peer-reviewed journal.PROSPERO registration number CRD42023425481.

Total internal reflection prevents photons from escaping deep‐ultraviolet (DUV) LED, resulting in serious energy waste and reduced service life. To lift the limitation of extraction ability of AlGaN‐based DUV LEDs, an inspiration was drawn from biological visual systems with wide field, which have obtained highly optimized features through evolution. By reconfiguring planar compound eye lens (PCEL) on the n‐AlGaN surface utilizing bio‐inspired features acquired from praying mantis, light extraction efficiency (LEE) enhancement over 180% is demonstrated both for transverse electric (TE) and magnetic fields by finite difference‐time domain (FDTD) simulation. Owing to its ultrathin planar structure and compatibility of material, PCEL provides a pathway to improve energy utilization efficiency of DUV‐LED utilizing one‐step nanoimprint.

Purpose: The interaction between inflammatory cells and integrin in the endothelium plays a key role during infiltration. Previous evidence has shown that synthetic C16 peptide selectively binds to integrins αvβ3 and α5β1 and exhibits a neuroprotective effect. It has also been reported to inhibit the differentiation of microglia into the M1 (pro-inflammatory) phenotype while promoting its differentiation to the M2 (anti-inflammatory) phenotype. This study aimed to investigate the mechanisms of action of the C16 peptide in multiple sclerosis using a rodent model.Methods: Molecular, morphological, and neurophysiological assays were used to investigate the neuroprotective effects of C16 peptide and related signaling pathways in a model of EAE.Results: The results showed that C16 significantly improved the clinical score and cortical somatosensory/motor evoked potential. It also alleviated inflammatory responses, including microglial activation and leukocyte infiltration, relieved the impairment of the brain blood barrier and edema, and reduced neuronal apoptosis, axonal loss, and demyelination induced by EAE. The C16 peptide increased the expressions of pTie-2 and Tie-2, integrin αvβ3, and α5β1 and activated the PI3K/Akt signal pathway but decreased the expression of Rho. Co-treatment of C16 with Tie-2 inhibitor and PI3K inhibitor LY294002 attenuated these effects of C16.Conclusion: The C16 peptide demonstrated neuroprotection in the EAE model through the integrin, Tie-2, and PI3K/Akt signaling pathways, and it could be a potential strategy for treating inflammation-related diseases in the central nervous system.

News dissemination is an important way for people to obtain information. With the development of new technologies, traditional news dissemination models have been impacted. It has problems with information filtering and bias, and has certain limitations in news quality, dissemination efficiency, etc., which makes it difficult to effectively meet people's information needs. In order to improve the quality and efficiency of news dissemination, promote the positive impact of news dissemination on society, this article combined artificial intelligence and big data technology to conduct in-depth research on the difficulties and coping strategies of news dissemination. This article first analyzed the characteristics and functions and influencing factors of news dissemination, then provided an overview of the difficulties and coping strategies in news dissemination. Finally, using association rule algorithms, personalized recommendations for news dissemination are achieved. To verify the effectiveness of artificial intelligence and big data in coping with the dilemma of news dissemination, this article conducted experimental analysis from the perspectives of news content quality, dissemination efficiency, objectivity, and dissemination cost. The experimental results show that under the application of news dissemination strategies based on artificial intelligence and big data, the quality of news content and dissemination efficiency have been improved by 4.76 % and 3.63 %, respectively. The conclusion indicates that artificial intelligence and big data can help improve the quality and dissemination efficiency of news content, and meet the diverse needs of the public for information.

The imbalance of follicular helper T (Tfh) cell subset is closely related to the occurrence of inflammatory bowel disease (IBD). Ginsenoside Rg1 (G-Rg1), the main component of ginseng, has excellent immunomodulatory and anti-inflammatory effects. Here, mice colitis was induced by dextran sulfate sodium and treated with G-Rg1 (200 mg/kg/day) for 14 days. Results showed that G-Rg1 can significantly alleviate symptoms of colitis in mice, regulate the balance of Tfh cell subsets, promote the secretion of IL-4 and IL-10, and inhibit the expression of IFN-γ, IL-17A, and IL-21. Molecular docking analysis revealed that G-Rg1 has excellent binding activity with target genes of the TLR/MyD88 signaling pathway, and and can reduce the expression levels of proteins such as TLR2, MyD88, IRAK4, TRAF6, and TAK1. These findings suggest that G-Rg1 can effectively regulate the balance of Tfh cell subsets, and its potential mechanism is related to the inhibition of the TLR/MyD88 signaling pathway.

Background: With the rapid development of Web3D, virtual reality, and digital twins, virtual trajectories and decision data considerably rely on the analysis and understanding of real video data, particularly in emergency evacuation scenarios. Correctly and effectively evacuating crowds in virtual emergency scenarios are becoming increasingly urgent. One good solution is to extract pedestrian trajectories from videos of emergency situations using a multi-target tracking algorithm and use them to define evacuation procedures. Methods: To implement this solution, a trajectory extraction and optimization framework based on multi-target tracking is developed in this study. First, a multi-target tracking algorithm is used to extract and preprocess the trajectory data of the crowd in a video. Then, the trajectory is optimized by combining the trajectory point extraction algorithm and Savitzky–Golay smoothing filtering method. Finally, related experiments are conducted, and the results show that the proposed approach can effectively and accurately extract the trajectories of multiple target objects in real time. Results: In addition, the proposed approach retains the real characteristics of the trajectories as much as possible while improving the trajectory smoothing index, which can provide data support for the analysis of pedestrian trajectory data and formulation of personnel evacuation schemes in emergency scenarios. Conclusions: Further comparisons with methods used in related studies confirm the feasibility and superiority of the proposed framework.

Abstract Background The CACNA1S gene encodes the alpha 1 S-subunit of the voltage-gated calcium channel, which is primarily expressed in the skeletal muscle cells. Pathogenic variants of CACNA1S can cause hypokalemic periodic paralysis (HypoPP), malignant hyperthermia susceptibility, and congenital myopathy. We aimed to study the clinical and molecular features of a male child with a CACNA1S variant and depict the molecular sub-regional characteristics of different phenotypes associated with CACNA1S variants. Case presentation We presented a case of HypoPP with recurrent muscle weakness and hypokalemia. Genetic analyses of the family members revealed that the proband had a novel c.497 C > A (p.Ala166Asp) variant of CACNA1S, which was inherited from his father. The diagnosis of HypoPP was established in the proband as he met the consensus diagnostic criteria. The patient and his parents were informed to avoid the classical triggers of HypoPP. The attacks of the patient are prevented by lifestyle changes and nutritional counseling. We also showed the molecular sub-regional location of the variants of CACNA1S which was associated with different phenotypes. Conclusions Our results identified a new variant of CACNA1S and expanded the spectrum of variants associated with HypoPP. Early genetic diagnosis can help avoid diagnostic delays, perform genetic counseling, provide proper treatment, and reduce morbidity and mortality.

In this study, the multi-scale microstructures of bainitic steel were refined to different degrees by austempering below Ms (martensite start temperature). The influence of multi-scale microstructures and their boundaries on impact toughness was analyzed. The results revealed that the impact toughness of bainitic steel significantly improved as the austempering temperature decreased. The block structure and Bain groups are refined by austempering below Ms, thereby introducing high-density HAGBs (high-angle grain boundaries) and increasing crack propagation resistance. The proportion of V2, V3, and V5 variants increased and formed HAGBs with the V1 variant, which regulated phase transformation strain and delayed crack formation. The improvement in impact toughness can be attributed to the formation of more HAGBs. This is mainly caused by the refinement of the block structure. Therefore, the block structure can be considered as the dominating and effective controlling unit of impact toughness.

To improve the performance of blanket: maximizing the tritium breeding rate (TBR) for tritium self-sufficiency, and minimizing the Dose of backplate for radiation protection, most previous studies are based on manual corrections to adjust the blanket structure to achieve optimization design, but it is difficult to find an optimal structure and tends to be trapped by local optimizations as it involves multi-physics field design, which is also inefficient and time-consuming process. The artificial intelligence (AI) maybe is a potential method for the optimization design of the blanket. So, this paper aims to develop an intelligent optimization method based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network to solve these problems mentioned above. This method has been applied on optimizing the radial arrangement of a conceptual design of CFETR HCSB blanket. Finally, a series of optimal radial arrangements are obtained under the constraints that the temperature of each component of the blanket does not exceed the limit and the radial length remains unchanged, the efficiency of the blanket optimization design is significantly improved. This study will provide a clue and inspiration for the application of artificial intelligence technology in the optimization design of blanket.

Doubly fed induction generator (DFIG) are vulnerable to unbalanced voltage and harmonic distortion, which will lead to the heating problem of stator/rotor windings and the damage of gearbox. This paper establishes the transient modeling of the DFIG under composite unbalanced voltage and harmonic distortion condition, and elaborates the dynamic response of DFIG from the perspectives of rotor voltage, active power and reactive power under voltage harmonics and unbalance. It also presents an improved control strategy for a dynamic voltage restorer (DVR) to accurately track the specific voltage harmonics, with targeted consideration of constraining unbalanced voltage and fifth- and seventh-order voltage harmonics. The extractions of each voltage signals are presented in this paper via multiple notch filter (MNF), which has a good low-pass characteristic and an excellent performance in trapping the specific AC signals. The presented notch-filter-based algorithm has the advantages of simper control structure and faster response speed, and is more accurate in harmonic extraction. A 1.5-MW DFIG has been employed to establish the object of protection in simulation analysis to verify the validity of the proposed control strategy. The results show that the proposed control strategy is effective which is consistent with the theoretical model, and the oscillations of each crucial parameter can be well-suppressed.

Abstract Chalcogenide phase-change materials (PCMs) are widely applied in electronic and photonic applications, such as non-volatile memory and neuro-inspired computing. Doped Sb2Te alloys are now gaining increasing attention for on-chip photonic applications, due to their growth-driven crystallization features. However, it remains unknown whether Sb2Te also forms a metastable crystalline phase upon nanoseconds crystallization in devices, similar to the case of nucleation-driven Ge-Sb-Te alloys. Here, we carry out ab initio simulations to understand the changes in optical properties of amorphous Sb2Te upon crystallization and post annealing. During the continuous transformation process, changes in the dielectric function are highly wavelength-dependent from the visible-light range towards the telecommunication band. Our finite-difference time-domain simulations based on the ab initio input reveal key differences in device output for color display and photonic memory applications upon tellurium ordering. Our work serves as an example of how multiscale simulations of materials can guide practical photonic phase-change applications.

Electronic components are the main components of PCBs (printed circuit boards), so the detection and classification of ECs (electronic components) is an important aspect of recycling used PCBs. However, due to the variety and quantity of ECs, traditional target detection methods for EC classification still have problems such as slow detection speed and low performance, and the accuracy of the detection needs to be improved. To overcome these limitations, this study proposes an enhanced YOLO (you only look once) network (EC-YOLOv7) for detecting EC targets. The network uses ACmix (a mixed model that enjoys the benefits of both self-attention and convolution) as a substitute for the 3 × 3 convolutional modules in the E-ELAN (Extended ELAN) architecture and implements branch links and 1 × 1 convolutional arrays between the ACmix modules to improve the speed of feature retrieval and network inference. Furthermore, the ResNet-ACmix module is engineered to prevent the leakage of function data and to minimise calculation time. Subsequently, the SPPCSPS (spatial pyramid pooling connected spatial pyramid convolution) block has been improved by replacing the serial channels with concurrent channels, which improves the fusion speed of the image features. To effectively capture spatial information and improve detection accuracy, the DyHead (the dynamic head) is utilised to enhance the model’s size, mission, and sense of space, which effectively captures spatial information and improves the detection accuracy. A new bounding-box loss regression method, the WIoU-Soft-NMS method, is finally suggested to facilitate prediction regression and improve the localisation accuracy. The experimental results demonstrate that the enhanced YOLOv7 net surpasses the initial YOLOv7 model and other common EC detection methods. The proposed EC-YOLOv7 network reaches a mean accuracy (mAP@0.5) of 94.4% on the PCB dataset and exhibits higher FPS compared to the original YOLOv7 model. In conclusion, it can significantly enhance high-density EC target recognition.

Yeast culture (YC) plays a significant role in enhancing the performance and health of poultry breeding. This study investigated the impact of different YC supplementation concentrations (basal diet with 1.0 g/kg and 2.0 g/kg of YC, YC1.0, and YC2.0) on egg production performance, egg quality, antioxidant properties, intestinal mucosal structure, and intestinal flora of laying hens. Both YC1.0 and YC2.0 groups significantly enhanced the egg protein height, Haugh unit, and crude protein content of egg yolks compared to the control group (p < 0.05). The supplementation with YC2.0 notably increased the egg production rate, reduced feed-to-egg ratio, and decreased the broken egg rate compared to the control group (p < 0.05). Additionally, YC supplementation enhanced serum total antioxidant capacity (T-AOC) and glutathione peroxidase (GSH-PX) activity while reducing malondialdehyde (MDA) content (p < 0.05). Moreover, YC supplementation promoted duodenal villus height and villus ratio in the duodenum and jejunum (p < 0.05). Analysis of cecal microorganisms indicated a decrease in Simpson and Shannon indices with YC supplementation (p < 0.05). YC1.0 reduced the abundance of Proteobacteria, while YC2.0 increased the abundance of Bacteroidales (p < 0.05). Overall, supplementation with YC improved egg production, quality, antioxidant capacity, intestinal morphology, and cecal microbial composition in laying hens, with significant benefits observed at the 2.0 g/kg supplementation level.

As a project construction organization model, whole-process engineering consulting (WPEC) is currently being promoted in China’s engineering consulting industry. However, various factors create numerous obstacles that hinder this model’s effective promotion and application. One essential task is to deeply analyze the transmission mechanism of the influencing factors in the promotion and application of WPEC, and explore effective pathways for promoting and applying this model. This paper systematically organizes and summarizes 10 primary influencing factors and 38 secondary influencing factors for promoting WPEC. Using the complex network analysis method, a network of influencing factors for promoting WPEC, a network structure, and motif analyses are conducted. The results show that the influencing factors have a complex transmission mechanism, and complex network analysis can reveal the laws of their construction and their causes. This paper provides a systematic basis and theoretical support for the effective formulation of policies related to the promotion and application of WPEC.

With the high proportion of renewable energy sources and power electronic devices accessed in the distribution network, the harmonic pollution problem has become increasingly serious. The traditional centralized harmonic mitigation strategy has difficulty in effectively dealing with these scattered and random harmonics. Therefore, a distributed harmonic mitigation strategy based on the dynamic points incentive of blockchain communities is proposed in this paper. Firstly, a comprehensive voltage sensitivity partitioning method with harmonic weight differentiation is proposed to realize the reasonable partitioning of each control node and controlled node in the distribution network concerning variability in harmonic components and their distribution. Then, a harmonic mitigation strategy based on the dynamic integral excitation of self-learning algorithms is constructed to promote self-organized optimization and active distributed coordinated control of mitigation devices. The strategy ensures that the total harmonic voltage distortion rate of each node meets the requirements by adjusting the partitioned collaboration to realize optimal harmonic mitigation. By setting optimized partitions in different scenarios and conducting simulation verification, the results demonstrate the effectiveness of the strategy in this paper. It stimulates synergy between devices through a dynamic incentive mechanism and significantly reduces the total harmonic voltage distortion rate across various test scenarios, reflecting the adaptability of the harmonic mitigation method presented.

PURPOSEThis study aimed to investigate the effect of using a deep neural network (DNN) in breast cancer (BC) detection.METHODSIn this retrospective study, a DNN-based model was constructed from a total of 880 mammograms that 220 patients underwent between April and June 2020. The mammograms were reviewed by two senior and two junior radiologists with and without the aid of the DNN model. The performance of the network was assessed by comparing the area under the curve (AUC) and receiver operating characteristic curves for the detection of four features of malignancy (masses, calcifications, asymmetries, and architectural distortions), with and without the aid of the DNN model and by the senior and junior radiologists. Additionally, the effect of utilizing the DNN on diagnosis time for both the senior and junior radiologists was evaluated.RESULTSThe AUCs of the model for the detection of mass and calcification were 0.877 and 0.937, respectively. In the senior radiologist group, the AUC values for evaluation of mass, calcification, and asymmetric compaction were significantly higher with the DNN model than those obtained without the model. Similar effects were observed in the junior radiologist group, but the increase in the AUC values was even more dramatic. The median mammogram assessment time of the junior and senior radiologists was 572 (357–951) s, and 273.5 (129–469) s, respectively, with the DNN model, and the corresponding assessment time without the model, was 739 (445–1003) s and 321 (195–491) s, respectively.CONCLUSIONThe DNN model exhibited high accuracy in detecting the four named features of BC and effectively shortened the review time by both senior and junior radiologists.

Abstract Although the trend of sea-ice extent under global warming has been studied extensively in recent years, most climate models have failed to capture the recent rapid change in the Arctic environment, which has brought into question the reliability of climate model projections of sea ice and suggested a potential shift in Arctic climate dynamics. Here, based on the results of a time-variant emergent constraint method with a weighting scheme, we show that an ice-free Arctic might occur earlier (by at least 5 ~ 10 years) than previously estimated. In other words, Arctic ice will likely disappear before the 2050 s. The observationally constrained date for an ice-free Arctic in September under fossil-fuel-based development (i.e., Shared Socioeconomic Pathway (SSP) 5–8.5) scenarios yields a central estimate of 2050–2054 with a 66% confidence range (equivalent to the IPCC’s ‘likely’ range) of 2037–2066, while an ice-free Arctic will likely occur for another 20 years and 11 years under ambitious mitigation scenarios (i.e., SSP2-4.5) and SSP3-7.0. An ice-free Arctic is unlikely to occur under the sustainable development scenario (i.e., SSP1-2.6). Looking forward, this time-variant emergent constraint may also help detect tipping points in the climate system. Our findings provide useful information to help policy makers cope with climate change.