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In the field of point cloud representation learning, many self-supervised learning methods aim to address the issue of conventional supervised learning methods relying heavily on labeled data. Particularly in recent years, contrastive learning-based methods have gained an increasing popularity. However, most of the current contrastive learning methods solely rely on conventional random augmentation, limiting the effectiveness of representation learning. Moreover, to prevent model collapse, they construct positive and negative sample pairs or explicit clustering centers, which adds complexity to data preprocessing operations. To address these challenges effectively and achieve accurate point cloud classification and segmentation, we propose PointSL, a self-learning network for point clouds based on contrastive learning. PointSL incorporates a learnable point cloud augmentation (LPA) module, which transforms samples with high precision, significantly improving the augmentation effect. To further enhance feature discrimination, PointSL introduces a self-learning process along a refined feature predictor (FFP). This innovative approach leverages the attention mechanism to facilitate mutual feature prediction between pairs of point clouds, thereby continuously improving discriminant performance. Additionally, the network constructed a simple yet effective self-adaptive loss function that optimizes the entire network through gradient feedback. For pretraining, it is beneficial to obtain encoders with a better generalization and a higher accuracy. We evaluate PointSL on benchmark datasets such as ModelNet40, Sydney Urban Objects and ShapeNetPart. Experimental results demonstrate that PointSL outperforms state-of-the-art self-supervised methods and supervised counterparts, achieving exceptional performance in classification and segmentation tasks. Notably, on the Sydney Urban Objects and ModelNet40 datasets, PointSL achieves OA and AA metrics of 80.6%, 69.9%, 94.2% and 91.4%, respectively. On the ShapeNetPart dataset, PointSL achieves Inst.mIoU and Cls.mIoU metrics of 86.3% and 85.1%, respectively. [ABSTRACT FROM AUTHOR]

Relationships between the visceral adiposity index (VAI) and type 2 diabetes mellitus (T2DM) have been underexplored. The purpose of this study is to explore association between VAI and T2DM in adults in the US. Based on the National Health and Nutrition Examination Survey 2007–2018, 11,214 participants aged 20 years or older were included in a cross-sectional study. Multifactorial logistic regression analysis and smoothed curve fitting analysis were performed to investigate links between VAI and the prevalence of T2DM, as well as the stability and incidence between subgroups. In a fully adjusted continuous model, the aggregate population risk of T2DM increased 0.43-fold with each 1-unit increase in VAI [odds ratio (OR) = 1.43; 95% confidence interval (CI) 1.35–1.50]. In the fully adjusted categorical model with VAI scores stratified by quartiles, results showed a higher T2DM advantage among participants in the second, third, and fourth quartiles (Q2: OR 1.35, 95% CI 1.06–1.71; Q3: OR 2.46, 95% CI 1.95–3.11; Q4: OR 4.42, 95% CI 3.55–05.50). Compared with Q1, the prevalence of T2DM in the total population increased 3.42-fold in Q4. The above results indicated that VAI was positively associated with the prevalence of T2DM, which was consistent and nonlinear with the smoothed curve-fitting analysis (P for non-linear = 0). Subgroup analyses after adjusting for covariates showed that keeping with the overall population results, it also was found that there was an interaction between sex and hypertension in the subgroups. VAI was positively associated with the prevalence of T2DM and was more prevalent in women, non-hypertensive than in men, hypertensive populations. [ABSTRACT FROM AUTHOR]

Antibiotic contamination has become a global environmental issue of widespread concern, among which oxytetracycline contamination is very severe. In this study, earthworm (Eisenia fetida) was exposed to oxytetracycline to study its impact on the soil environment. The total protein (TP), catalase (CAT), superoxide dismutase (SOD), peroxidase (POD), malondialdehyde (MDA), glutathione S-transferase (GST), and glutathione peroxidase (GPX) oxidative stress indicators in earthworms were measured, and the integrated biomarker response (IBR) approach was used to evaluate the toxic effect of oxytetracycline on earthworms. A Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and a path analysis model were used to explore the physiological and metabolic processes of earthworms after stress occurs. The results showed that SOD, GPX, and GST play important roles in resisting oxytetracycline stress. In addition, stress injury showed a good dose–effect relationship, and long-term stress from pollutants resulted in the most serious damage to the head tissue of earthworms. These results provide a theoretical basis for understanding the toxic effect of oxytetracycline on soil animals, monitoring the pollution status of oxytetracycline in soil, and conducting ecological security risk assessment. [ABSTRACT FROM AUTHOR]

Objective: To investigate the association between the visceral adiposity index and the prevalence of diabetes and prediabetes in the US adult population. Method: We conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018 for ten consecutive years, including 18745 eligible participants. The weighted multivariate logistic model and fitting curve were used to explore the correlation and dose-response relationship between visceral adiposity index (VAI) and diabetes (DM) and prediabetes in the general population and the prevalence of different subgroups. Results: In the fully adjusted continuous model, the risk of diabetes and prediabetes in the general population increased 0.15 times [1.15 (1.10,1.20), p<0.0001] with every increase of 1 unit of VAI. In the fully adjusted classification model, with the lowest quartile array Q1 of VAI as the reference group, Q2 of the second Quantile group, Q3 of the third Quantile group, and Q4 of the Quartile group increased 0.26 times [1.26 (1.10,1.44), p<0.001], 0.65 times [1.65 (1.43,1.89), p<0.0001], 1.60 times [2.60 (2.28,2.97), p<0.0001] respectively with the risk of diabetes and prediabetes. The above results showed that VAI was positively associated with the prevalence of diabetes and prediabetes, and the fitted curves showed a non-linear trend. (P for non-linear = 0<0.05). The results of the subgroup population were consistent with the total population and a significant interaction was found in gender (P for interaction<0.0001). Conclusion: In conclusion, we found a non-linear positive association between VAI and the risk of diabetes and prediabetes in the US adult population and found that women have a higher risk of diabetes and prediabetes than men; therefore, we should focus on the female population, and we call for the use of VAI to manage the development of diabetes and prediabetes in the clinical setting. [ABSTRACT FROM AUTHOR]

Convenient and accurate blood pressure (BP) measurement is of great importance in both clinical and daily life. Although deep learning (DL) can achieve cuff-less BP measurement based on Photoplethysmography (PPG), the performance of DL is affected by few-shot data. Data augmentation becomes an effective way to enhance the size of the training data. In this paper, we use cropping, flipping, DTW barycentric averaging (DBA), generative adversarial network (GAN) and variational auto-encoder (VAE) for the data augmentation of PPG. Furthermore, a PE–CNN–GRU model is designed for cuff-less BP measurement applying position encoding (PE), convolutional neural networks (CNNs) and gated recurrent unit (GRU) networks. Experiment results based on real-life datasets show that VAE is the most suitable method for PPG data augmentation, which can reduce the mean absolute error (MAE) of PE–CNN–GRU when measuring systolic blood pressure (SBP) and diastolic blood pressure (DBP) by 18.80% and 19.84%. After the data augmentation of PPG, PE–CNN–GRU achieves accurate and cuff-less BP measurement, thus providing convenient support for preventing cardiovascular diseases. [ABSTRACT FROM AUTHOR]

The reliability of irrigated area (IA) information dominates the performance of irrigation water use and crop modeling accuracy. IA is typically mapped using Food and Agriculture Organization (FAO) agricultural census and remote sensing indices. Recent advances in machine learning and sampling techniques further improve IA mapping. However, the relative performances of different IA mapping approaches and their capability in capturing long‐term IA temporal variability remain unknown. Here, 1861 county‐level IA information from Government Censored Data (GCD) during 2000–2021 are collected, cross‐validated, and employed to evaluate commonly used gridded IA data sets. Results show that IA data sets based on the direct interpolation of FAO agricultural census can accurately capture the spatial distribution of IA. However, FAO statistics are only available in a particular year, which cannot capture inter‐annual irrigation variations. In contrast, IA products solely based on vegetation indices are prone to positive biases over humid regions due to the lack of contrast in vegetation dynamics. Overall, the latest GCD‐based machine learning IA data sets are relatively more accurate, but they are also problematic in estimating IA trends due to the use of temporally static training samples. Such biases are tightly related to agricultural suitability (AS calculated using precipitation and potential evapotranspiration). This suggests that AS should be employed as an endogenous variable in future machine learning based IA mapping algorithms. Plain Language Summary: Understanding how much land is irrigated (Irrigated Area or IA) is crucial for accurate water use and crop modeling. Traditional IA maps are developed using data from the Food and Agriculture Organization (FAO) and remote sensing indices. Recent improvements, like machine learning, have enhanced IA mapping. However, the relative performances of these methods and their reliability in capturing long‐term changes in irrigated areas remain unknown. This study collects government‐censored county‐level IA data from 2000 to 2021, which is subsequently used for evaluating commonly used IA data sets. Results show that data sets directly using FAO data can reasonability capture the spatial distribution of IA but may miss year‐to‐year changes. On the other hand, data sets relying only on vegetation data tend to overestimate irrigated areas in humid regions. The latest machine learning‐based IA data sets are relatively more accurate but still contain significant uncertainties in IA trends. These biases are closely linked to agricultural suitability (calculated using precipitation and potential evapotranspiration), which may be useful for enhancing future IA mapping algorithms. Key Points: Remote sensing vegetation index is insufficient for identifying irrigated area over humid regionsMachine learning based irrigated area is sensitive to the quality of training sampleAll the irrigated area data sets are biased in representing the temporal dynamics of irrigated area [ABSTRACT FROM AUTHOR]

Stimuli-responsive hydrogels can respond to external stimuli with a change in the network structure and thus have potential application in drug release, intelligent sensing, and scaffold construction. Peptides possess robust supramolecular self-assembly ability, enabling spontaneous formation of nanostructures through supramolecular interactions and subsequently hydrogels. Therefore, peptide-based stimuli-responsive hydrogels have been widely explored as smart soft materials for biomedical applications in the last decade. Herein, we present a review article on design strategies and research progress of peptide hydrogels as stimuli-responsive materials in the field of biomedicine. The latest design and development of peptide hydrogels with responsive behaviors to stimuli are first presented. The following part provides a systematic overview of the functions and applications of stimuli-responsive peptide hydrogels in tissue engineering, drug delivery, wound healing, antimicrobial treatment, 3D cell culture, biosensors, etc. Finally, the remaining challenges and future prospects of stimuli-responsive peptide hydrogels are proposed. It is believed that this review will contribute to the rational design and development of stimuli-responsive peptide hydrogels toward biomedical applications. [ABSTRACT FROM AUTHOR]

Perovskite solar cells (PSCs) have been significantly improved by utilizing an inorganic hole-transporting layer (HTL), such as nickel oxide. Despite the promising properties, there are still limitations due to defects. Recently, research on self-assembled monolayers (SAMs) is being actively conducted, which shows promise in reducing defects and enhancing device performance. In this study, we successfully engineered a p-i-n perovskite solar cell structure utilizing HC-A1 and HC-A4 molecules. These SAM molecules were found to enhance the grain morphology and uniformity of the perovskite film, which are critical factors in determining optical properties and device performance. Notably, HC-A4 demonstrated superior performance due to its distinct hydrophilic properties with a contact angle of 50.3°, attributable to its unique functional groups. Overall, the HC-A4-applied film exhibited efficient carrier extraction properties, attaining a carrier lifetime of 117.33 ns. Furthermore, HC-A4 contributed to superior device performance, achieving the highest device efficiency of 20% and demonstrating outstanding thermal stability over 300 h. [ABSTRACT FROM AUTHOR]

The generation of moiré lattices by superimposing two identical sublattices at a specific twist angle has garnered significant attention owing to its potential applications, ranging from two-dimensional materials to manipulating light propagation. While macroscale moiré lattices have been widely studied, further developments in manipulating moiré lattices at the subwavelength scale would be crucial for miniaturizing and integrating platforms. Here, we propose a plasmonic metasurface design consisting of rotated nanoslits arranged within N + N′ round apertures for generating focused moiré lattices. By introducing a spin-dependent geometric phase through the rotated nanoslits, an overall lens and spiral phase can be achieved, allowing each individual set of round apertures to generate a periodic lattice in the focal plane. Superimposing two sets of N and N′ apertures at specific twist angles and varying phase differences allows for the superposition of two sublattices with different periods, leading to the formation of diverse moiré patterns. Our simulations and theoretical results demonstrate the feasibility of our proposed metasurface design. Due to their compactness and tunability, the utilization of metasurfaces in creating nanoscale photonic moiré lattices is anticipated to find extensive applications in integrated and on-chip optical systems. [ABSTRACT FROM AUTHOR]

Objectives: To explore the mediating role of self‐esteem in social anxiety and QoL during the COVID‐19 local epidemic in China. Design: A cross‐sectional survey. Methods: This study conveniently selected 344 hypertensive patients from a Grade‐A tertiary hospital in Wuhu, Anhui Province, as the study population. Participants completed the demographic questionnaire, the social phobia inventory, the self‐esteem scale and the 36‐item short‐form health survey. Results: Social anxiety, self‐esteem and QoL were significantly correlated with each other. Social anxiety showed no direct effect on QoL (β = 0.011, p > 0.05). Social anxiety showed an indirect effect on QoL (β = −0.248, p < 0.001). Self‐esteem fully mediated the association between social anxiety and QoL in hypertensive patients. Patient or Public Contribution: Hypertensive patients in this study were participants during the data collection process. Nursing staff from the cardiology department at the hospital of a hospital in Wuhu City, Anhui Province assisted in the recruitment phase of the data collection process. [ABSTRACT FROM AUTHOR]

Malolactic fermentation (MLF) by different lactic acid bacteria has a significantly influence on the aromatic and sensory properties of wines. In this study, four strains including two Oenococcus oeni (commercial O-Mega and native DS04) and two Lactiplantibacillus plantarum (commercial NoVA and native NV27) were tested for their performances over MLF and effects on the basic composition, volatile components and sensory property of black raspberry wine. Results of microbial growth kinetics showed Lactiplantibacillus strains had higher fermentation efficiency than Oenococcus. The volatile compounds were determined by GC-IMS; NoVA and NV27 had higher production of volatile esters, and DS04 synthesized more amounts of acetate esters and several alcohols. In terms of sensory evaluation, NV27 and DS04 showed great aroma properties due to the enhanced fruity and sweet aroma. Furthermore, PLS was used for the establishment of the relationship between volatiles and sensory odors and sensory data interpretation. [ABSTRACT FROM AUTHOR]

This paper presents a deep framework EddyDet to automatically detect oceanic eddies in Synthetic Aperture Radar (SAR) images. The EddyDet has been developed using the Mask Region with Convolutional Neural Networks (Mask RCNN) framework, incorporating two new branches: Edge Head and Mask Intersection over Union (IoU) Head. The Edge Head can learn internal texture information implicitly, and the Mask IoU Head improves the quality of predicted masks. A SAR dataset for Oceanic Eddy Detection (SOED) is specifically constructed to evaluate the effectiveness of the EddyDet model in detecting oceanic eddies. We demonstrate that the EddyDet is capable of achieving acceptable eddy detection results under the condition of limited training samples, which outperforms a Mask RCNN baseline in terms of average precision. The combined Edge Head and Mask IoU Head have the ability to describe the characteristics of eddies more correctly, while the EddyDet shows great potential in practice use accurately and time efficiently, saving manual labor to a large extent. [ABSTRACT FROM AUTHOR]

Amorphous high-entropy alloys (HEAs) as electromagnetic-wave absorbing materials have been rarely reported. In this work, amorphous FeCoNiTixSi HEAs were synthesized by introducing a high content of large-atom Ti using the high-energy ball-milling technique. This amorphous structure could improve the saturated magnetization and coercivity of HEAs, but slightly degraded the mechanical and oxidation resistance properties. In terms of electromagnetic properties, FeCoNiTi0.01Si and FeCoNiTiSi exhibit excellent electromagnetic-wave absorption performances, with significant absorptions of −68.4 dB at 6.14 GHz and −63.4 dB at 9.12 GHz, corresponding to bandwidths of 5.15 GHz (1.69 mm) and 3.64 GHz (1.43 mm), respectively. Overall, the prepared FeCoNiTixSi HEAs exhibited superior comprehensive performances compared to other HEA absorption materials. This work provided a novel strategy for the development of new electromagnetic-wave absorption materials with low weight, high absorption efficiency, and resistance to harsh environments. [ABSTRACT FROM AUTHOR]

Aims: To investigate loneliness, depression and sleep quality in patients with Type 2 Diabetes (T2DM) and to examine the mediating role of depression between loneliness and sleep quality among this group of patients with T2DM. Design: A cross‐sectional study. Methods: A group of T2DM patients was recruited from a tertiary hospital affiliated with a university in Wuhu City, Anhui Province from May to October 2021 by convenient sampling. Pearson correlation analysis and structural equation modelling were used for data analysis in this study. Results: The direct effect of loneliness on sleep quality was not statistically significant, but the indirect effect of depression on sleep quality was statistically significant. Depression mediated the relationship between loneliness and sleep quality. Depression can affect emotional health and reduce sleep quality. We should reduce the loneliness of patients, prevent the occurrence of depression and improve the quality of sleep. [ABSTRACT FROM AUTHOR]

We propose and experimentally demonstrate a tunable frequency-doubling optoelectronic oscillator (FD-OEO) based on a single-bandpass dispersion-induced microwave photonic filter (MPF) consisting of a Mach–Zehnder modulator (MZM), a linearly chirped fiber Bragg grating and polarization-multiplexed dual-loop. Thanks to the polarization dependence of the MZM, a special double sideband modulation is implemented where the optical carrier (OC) and subcarriers are orthogonally polarized. By simply tuning the PC in the OEO loop, the phase difference between the orthogonal polarization carrier and two sidebands can be controlled, and thus the center frequency of the fundamental OEO can be tuned. Furthermore, a PC and a polarizer are placed outside the OEO to achieve optical carrier suppression (OCS) modulation, which ensures that a frequency-tunable microwave signal at the second-harmonic frequency is generated. In the experiment, a fundamental frequency signal with tunable frequency from 3.6 to 6.85 GHz and FD-OEO with a tunable frequency range from 7.2 to 13.7 GHz are generated. [ABSTRACT FROM AUTHOR]

In the field of state-of-the-art object detection, the task of object localization is typically accomplished through a dedicated subnet that emphasizes bounding box regression. This subnet traditionally predicts the object's position by regressing the box's center position and scaling factors. Despite the widespread adoption of this approach, we have observed that the localization results often suffer from defects, leading to unsatisfactory detector performance. In this paper, we address the shortcomings of previous methods through theoretical analysis and experimental verification and present an innovative solution for precise object detection. Instead of solely focusing on the object's center and size, our approach enhances the accuracy of bounding box localization by refining the box edges based on the estimated distribution at the object's boundary. Experimental results demonstrate the potential and generalizability of our proposed method. [ABSTRACT FROM AUTHOR]

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Frequency‐adaptive prefilters are widely used in the phase‐locked loop (PLL) to suppress input disturbances. However, the parameters of the PLL and prefilters are often designed separately or based on an inaccurate model. The positive feedback effect introduced by the frequency adaption (FA) is usually ignored, leading to non‐optimal performance. Based on the accurate small‐signal model with a particular focus on the FA, this paper proposes an optimal design method for these advanced PLLs. The PLL based on dual second‐order generalized integrator (DSOGI) is taken as an example to demonstrate the principle. With different gain margins, the optimal damping factors of DSOGI and PLL, as well as the natural frequency of PLL, are obtained using the proposed method. Then the global optimal parameters are obtained by evaluating the settling time and overshoot. The analysis shows that the truly optimal parameters are quite different with those provided in the literature, and the dynamic performance is significantly enhanced without degrading the steady‐state performance. Experimental results are presented to verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

The compound pollutants formed by microplastics and cadmium present a significant potential threat to the soil-based ecosystem, and it is urgent to carry out relevant ecotoxicological studies. However, the lack of appropriate test methods and scientific mathematical analysis models has restricted the progress of research. Based on an orthogonal test design, a ternary combined stress test was performed to study the effect of microplastics and cadmium on earthworms. This study used the particle size and concentration of microplastics as well as the concentration of cadmium as test factors. Using the improved factor analysis model and the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, a new model was constructed according to the response surface methodology to analyze the acute toxic effects on earthworms under the combined stress of microplastics and cadmium. In addition, the model was tested in a soil-polluted environment. The results show that the model can perfectly integrate the spatiotemporal cross effects of the concentration and time of the applied stress, and the scientific data analysis process ensures the efficient development of ecotoxicological research in the actual compound pollution environment. Moreover, the results of the filter paper test and soil test showed that the equivalent toxicity ratio of cadmium concentration, microplastic concentration, and microplastic particle size to earthworms as 26:35:39 and 23:36:41, respectively. In terms of the interaction effect, a certain positive interaction was observed between the cadmium concentration and that of the microplastics and their particle size, while a negative interaction was observed between the concentration of microplastics and their particle size. This research provides a test basis and model reference for early monitoring of the health of contaminated soils and assessments of ecological safety and security. [ABSTRACT FROM AUTHOR]

Each of >20 independent evolutions of C4 photosynthesis in grasses required reorganization of the Calvin–Benson‐cycle (CB‐cycle) within the leaf, along with coordination of C4‐cycle enzymes with the CB‐cycle to maximize CO2 assimilation. Considering the vast amount of time over which C4 evolved, we hypothesized (i) trait divergences exist within and across lineages with both C4 and closely related C3 grasses, (ii) trends in traits after C4 evolution yield the optimization of C4 through time, and (iii) the presence/absence of trends in coordination between the CB‐cycle and C4‐cycle provides information on the strength of selection. To address these hypotheses, we used a combination of optimality modelling, physiological measurements and phylogenetic‐comparative‐analysis. Photosynthesis was optimized after the evolution of C4 causing diversification in maximal assimilation, electron transport, Rubisco carboxylation, phosphoenolpyruvate carboxylase and chlorophyll within C4 lineages. Both theory and measurements indicated a higher light‐reaction to CB‐cycle ratio (Jatpmax/Vcmax) in C4 than C3. There were no evolutionary trends with photosynthetic coordination between the CB‐cycle, light reactions and the C4‐cycle, suggesting strong initial selection for coordination. The coordination of CB‐C4‐cycles (Vpmax/Vcmax) was optimal for CO2 of 200 ppm, not to current conditions. Our model indicated that a higher than optimal Vpmax/Vcmax affects assimilation minimally, thus lessening recent selection to decrease Vpmax/Vcmax. Summary Statement: When C4 evolved in grass lineages affects the current optimization of photosynthetic traits, with two exceptions: 1. the coordination between light reactions, Calvin‐Benson and C4 cycles shows no evolutionary trend, suggesting strong initial selection for tight coordination, and 2. The coordination between Calvin‐Benson and C4 cycle is optimal for CO2 of 200 ppm, as opposed to current CO2. [ABSTRACT FROM AUTHOR]

This paper proposes the dynamic inoperability input–output model (DIIM) to analyze the economic impact of COVID-19 in Shanghai in the first quarter of 2022. Based on the input–output model, the DIIM model introduces the sector elasticity coefficient, assesses the economic loss of the system and the influence of disturbances on other sectors through sectoral dependence, and simulates the inoperability and economic loss changes through time series. A multi-evaluation examination of the results reveals that the degree of inoperability of sub-sectors is inconsistent with the ranking of economic losses and that it is hard to quantify the impact of each sector directly. Different from the traditional DIIM model that only considers the negative part of the disaster, the innovation of this paper is that the negative value of the inoperability degree is used to measure the indirect positive growth of sectors under the impact of the Shanghai pandemic shock. At the same time, policymakers need to consider multi-objective optimization when making risk management decisions. This study uses surrogate worth trade-off to construct a multi-objective risk management framework to expand the DIIM model to enable policymakers to quantify the trade-off between economic benefit and investment costs when making risk management decisions. [ABSTRACT FROM AUTHOR]

FeCoNiCuxMn (x = 0, 0.1, 0.3, 0.5, 0.7 and 0.9) and FeCoNiCu0.1MnB0.05 high entropy alloys (HEAs) were prepared via an improved swing ball-milling method. The BCC structure and FCC structure coexisted in the HEAs, and the proportion of FCC structure presented an increasing tendency with the increase of Cu content. In terms of corrosion resistance, Cu segregation generated galvanic corrosion, and weakened the corrosion resistance. A small amount of Cu had little effect on the oxidation resistance at high temperatures, but when the Cu content was high (FeCoNiCu0.9Mn), the oxidation resistance of the alloys at 1000 °C was enhanced due to the formation of CuO protective layers. The introduction of boron reduced the oxidation rate and enhanced the initial oxidation temperature but weakened the corrosion resistance and impedance matching. The optimal reflection loss (RLmin) of FeCoNiCu0.1Mn reached −71.0 dB as thin as 2.93 mm, and the widest bandwidth was 6.79 GHz (7.75–14.54 GHz). This work has important significance for the exploration of practical EMA materials that are resistant to harsh environments. [ABSTRACT FROM AUTHOR]

Summary: A three‐dimensional, two‐phase, non‐isothermal proton exchange membrane (PEM) water electrolyzer model was developed, aiming to reveal water and heat distribution characteristics and to explore the effects of various parameters on heat and mass transfer and performance of the electrolyzer. The results show that the electrolyzer performance depends on the combined effect of heat and mass, especially at high voltages. Although increasing the inlet velocity can accelerate the discharge of bubbles, it causes a larger temperature drop which degrades the performance. Increasing the inlet temperature can effectively improve the kinetic reaction rate of the catalyst layer and reduce the ohmic resistance of the membrane, which promotes the performance improvement. Decreasing the contact angle of anode gas diffusion layer (A‐GDL) and increasing its porosity is beneficial to the transport of liquid water and improves the performance, but excessive porosity leads to a rapid increase in the ohmic resistance of A‐GDL, and the optimal porosity range is 0.5 to 0.6. In addition, changes in A‐GDL porosity and contact angle have little effect on temperature. Decreasing the thickness of the membrane can significantly improve the performance, but accelerate the increase of the membrane temperature at high voltage. [ABSTRACT FROM AUTHOR]

Transition‐metal sulfides have gained considerable attention for the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) in rechargeable Zn‐air batteries owing to their natural abundance, chemical stability, and efficient activity. Here, cobalt sulfide nanocrystalline supported on the 3D hierarchically flower‐like porous nanocarbon (mC‐CoSx‐50) electrocatalysts have been synthesized through a combination of metal‐organic complexation and porous silica templates for efficient bifunctional oxygen catalysis. With the high specific surface area, high nitrogen content and heteroatom doping, the as‐synthesized mC‐CoSx‐50 exhibits an excellent bifunctional oxygen catalytic performance. Remarkably, the rechargeable Zn‐air batteries assembled with the cobalt sulfide‐based hierarchically porous electrocatalysts reveal a high peak power density and excellent cycling stability in comparison with Pt/C+RuO2 owing to the synergistic effects between cobalt sulfide nanocrystallines and hierarchically porous carbon. We envision that this work not only offers high‐performance metal sulfide for future clean energy conversion but also provides a facial strategy for constructing hierarchically porous carbon‐supported catalyst. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) point cloud classification methods based on deep learning have good classification performance; however, they adapt poorly to diverse datasets and their classification accuracy must be improved. Therefore, FSDCNet, a neural network model based on the fusion of static and dynamic convolution, is proposed and applied for multiview 3D point cloud classification in this paper. FSDCNet devises a view selection method with fixed and random viewpoints, which effectively avoids the overfitting caused by the traditional fixed viewpoint. A local feature extraction operator of dynamic and static convolution adaptive weight fusion was designed to improve the model's adaptability to different types of datasets. To address the problems of large parameters and high computational complexity associated with the current methods of dynamic convolution, a lightweight and adaptive dynamic convolution operator was developed. In addition, FSDCNet builds a global attention pooling, integrating the most crucial information on different view features to the greatest extent. Due to these characteristics, FSDCNet is more adaptable, can extract more fine-grained detailed information, and can improve the classification accuracy of point cloud data. The proposed method was applied to the ModelNet40 and Sydney Urban Objects datasets. In these experiments, FSDCNet outperformed its counterparts, achieving state-of-the-art point cloud classification accuracy. For the ModelNet40 dataset, the overall accuracy (OA) and average accuracy (AA) of FSDCNet in a single view reached 93.8% and 91.2%, respectively, which were superior to those values for many other methods using 6 and 12 views. FSDCNet obtained the best results for 6 and 12 views, achieving 94.6%, 93.3%, 95.3%, and 93.6% in OA and AA metrics, respectively. For the Sydney Urban Objects dataset, FSDCNet achieved an OA and F1 score of 81.2% and 80.1% in a single view, respectively, which were higher than most of the compared methods. In 6 and 12 views, FSDCNet reached an OA of 85.3% and 83.6% and an F1 score of 85.5% and 83.7%, respectively. [ABSTRACT FROM AUTHOR]

In this study, coral powder with different contents and levels of fineness were incorporated into cement; then, the pore structure of a coral powder–cement slurry was measured using the MIP test at days 3 days and 28, respectively. Neimark's model, Ji's model, and Pfeifer and Avnir's model were also used to analyze the fractal characteristics of the coral powder–cement slurry. The results show that the coral powder–cement slurry has multifractal characteristics when using Neimark's model, and the entire pore size range of the cement slurry can be divided into three parts: Region I (1–200 μm), Region II (70 nm–4 μm), and Region III (5–500 nm). The pore surface fractal dimension of both Regions I and III is less than 3, while that of Region II is greater than 3. This indicates that Regions I and III have obvious fractal characteristics, which Region II does not. Meanwhile, the pore surface fractal dimension of Region I is positively correlated with hydration age, while the pore surface fractal dimension of Region III is less affected by hydration age and coral powder contents. Ji's model reveals that coral powder–cement slurry also has multifractal characteristics, but the entire pore size range of the cement slurry can be divided into two parts: Region I (5.482 nm–500 m) and Region II (120 nm–370 μm). The pore volume fractal dimension of Region I is greater than 2 and less than 2.5, while that of Region II is greater than 2.9 and less than 3. Therefore, both Regions I and II have fractal characteristics. In addition, the coral powder admixture, fineness, and age have large effects on the pore volume fractal dimension and pore size range of Region II. Pfeifer and Avnir's model reveals that the entire pore size range of cement slurry can also be divided into Region I (5.482–600 nm), Region II (120 nm–10 μm), and Region III (5–365 μm), and that the pore surface fractal dimension of both Regions I and III is less than 3, while that of Region II is greater than 3. This indicates that Regions I and III have fractal characteristics, while Region II does not have fractal characteristics. [ABSTRACT FROM AUTHOR]

Background: Plant long non-coding RNAs (lncRNAs) play vital roles in many biological processes mainly through interactions with RNA-binding protein (RBP). To understand the function of lncRNAs, a fundamental method is to identify which types of proteins interact with the lncRNAs. However, the models or rules of interactions are a major challenge when calculating and estimating the types of RBP. Results: In this study, we propose an ensemble deep learning model to predict plant lncRNA-protein interactions using stacked denoising autoencoder and convolutional neural network based on sequence and structural information, named PRPI-SC. PRPI-SC predicts interactions between lncRNAs and proteins based on the k-mer features of RNAs and proteins. Experiments proved good results on Arabidopsis thaliana and Zea mays datasets (ATH948 and ZEA22133). The accuracy rates of ATH948 and ZEA22133 datasets were 88.9% and 82.6%, respectively. PRPI-SC also performed well on some public RNA protein interaction datasets. Conclusions: PRPI-SC accurately predicts the interaction between plant lncRNA and protein, which plays a guiding role in studying the function and expression of plant lncRNA. At the same time, PRPI-SC has a strong generalization ability and good prediction effect for non-plant data. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) recurrence is attributed to the presence of therapy‐resistant glioblastoma stem cells. Steroid receptor coactivator‐1 (SRC‐1) acts as an oncogenic regulator in many human tumors. The relationship between SRC‐1 and GBM has not yet been studied. Herein, we investigate the role of SRC‐1 in GBM. In this study, we found that SRC‐1 expression is positively correlated with grades of glioma and inversely correlated with glioma patient's prognosis. Steroid receptor coactivator‐1 promotes the proliferation, migration, and tumor growth of GBM cells. Notably, SRC‐1 knockdown suppresses the stemness of GBM cells. Mechanistically, long noncoding RNA X‐inactive specific transcript (XIST) is regulated by SRC‐1 at the posttranscriptional level and mediates the function of SRC‐1 in promoting stemness‐like properties of GBM. Steroid receptor coactivator‐1 can promote the expression of Kruppel‐like factor 4 (KLF4) through the XIST/microRNA (miR)‐152 axis. Additionally, arenobufagin and bufalin, SRC small molecule inhibitors, can reduce the proliferation and stemness of GBM cells. This study reveals SRC‐1 promotes the stemness of GBM by activating the long noncoding RNA XIST/miR‐152/KLF4 pathway and provides novel markers for diagnosis and therapy of GBM. [ABSTRACT FROM AUTHOR]

Solar-induced fluorescence (SIF) has become a milestone for estimating terrestrial gross primary productivity (GPP) at the global scale, and therefore it is important to understand how the relationship between SIF and GPP varies with environmental stresses and canopy structures. Our study investigated the impact of environmental conditions on the relationship between SIF and GPP at the global scale based on Global Ozone Monitoring Instrument 2 (GOME-2) SIF and Max Planck Institute for Biogeochemistry (MPI-BGC) GPP. GOME-2 SIF had strong correlations with gridded GPP data at instantaneous (R2 = 0.618, p < 0.001) and daily timescales (R2 = 0.621, p < 0.001) across the globe in eleven major biomes. Strong linear relationships were also consistently found for nine individual biomes at the daily timescale (R2 = 0.512–0.839, p < 0.001) but were not found for evergreen broadleaf forests (EBF) (R2 = 0.142, p < 0.001) and closed shrublands (R2 = 0.395, p < 0.001). Generally, high coefficients of determination (R2 > 0.8) were mainly distributed in southern Europe, southern and central Africa, northeastern and central China, North America, India and southern Russia, and the slope had a similar spatial distribution as the coefficients of determination. The relationship between SIF and GPP varied with air temperature (TAir), vapour pressure deficit (VPD) and leaf area index (LAI). The slope of the SIF-GPP relationship had a maximal value when the air temperature ranged between approximately 10°C and 20°C, when the linear relationships between SIF and GPP were also the strongest. Our analysis based on the Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model also indicated that the SIF-GPP relationship was complex and affected by TAir, VPD and LAI. Our study can improve our understanding of the functional relationship between SIF and GPP and the information contained in remotely sensed SIF. [ABSTRACT FROM AUTHOR]

The choice of basic epoxy resin (ER) is especially important for the design of epoxy adhesive formulations. In the present study, performance of several high-temperature ER systems, prepared using 4,4-diaminodiphenylsulfone as the curing agent by the same curing process, was investigated. The curing behavior was studied by dynamic rheometry and differential scanning calorimetry. The thermal properties of the cured resins were investigated by dynamic thermomechanical analysis, thermomechanical analysis, and thermogravimetric analysis. The peeling properties, mechanical behaviors, and moisture absorptivities of the cured resins were also studied. The results showed traditional diglycidyl ether of bisphenol A epoxy to be insufficient in heat resistance. Naphthalene-based ER and bifunctional cardo ER showed higher glass transition temperature values. However, the processability and adhesive properties may not comply with the application requirements. Biphenyl novolac ER has excellent performance in all aspects, which is suitable for use as a high-temperature adhesive. [ABSTRACT FROM AUTHOR]

Soil cadmium (Cd) pollution has received increasing attention from scholars. In the field of Cd pollution remediation, there is an urgent need to study the combined bioremediation technology of earthworms and microbes. In this paper, a short-term stress test and a long-term stress test were conducted. Eisenia fetida were inoculated into artificial soil that was contaminated with Cd. After different Cd stress times, the regulation process between the microbial communities in the earthworms and in the soil was studied. Canonical correlation analysis and the TOPSIS method were combined to establish a mathematical model for data analysis, and the changes in the carbon source utilization intensity by microbes were analysed. The results showed that in the short-term stress tests, the regulation process could be divided into five stages. Specifically, after 1–3 days of stress, the microbial community in the earthworms regulated the soil microbial community, but on the 3rd day, the regulation was weakened. On the 4th day, the soil microbial community was affected not only by the microbes in the earthworms but also by the increasing intensity of Cd stress. After 5 days of stress, the microbial communities in the earthworms and the soil were both greatly affected by Cd poisoning, and the microbes transitioned from stable to declining. At 6–7 days, the microbes in the earthworms gained control over those in the soil once again, and the Cd-tolerant microbes began to appear and proliferate. At 8–10 days, the regulation of the soil microbes by the earthworm microbes weakened, while the Cd-tolerant population in the soil microbial communities gradually evolved at this stage to adapt to the increasing Cd stress. The long-term stress tests showed that the difference between the microbial communities in the soil and in the earthworms increased, and there was almost no regulation between them. [ABSTRACT FROM AUTHOR]

A new series of 4‐hexyl‐4H‐thieno[3,2‐b]indole (HxTI) based organic chromophores is developed by structural engineering of the electron donor (D) group in the D–HxTI–benzothiadiazole‐phenyl‐acceptor platform with different fluorenyl moieties, such as unsubstituted fluorenyl (SGT‐146) and hexyloxy (SGT‐147), decyloxy (SGT‐148) and hexyloxy‐phenyl substituted (SGT‐149) fluorenyl moieties. In comparison to a reference dye SGT‐137 with a biphenyl‐based donor, the effects of the donating ability and bulkiness of the fluorenyl based donor in this D–π–A‐structured platform on molecular properties and photovoltaic performance are investigated to establish the structure–property relationship. The photovoltaic performance of dye‐sensitized solar cells (DSSCs) is improved according to the bulkiness of the donor groups. As a result, the DSSCs based on SGT‐149 show high power conversion efficiencies (PCEs) of 11.7% and 10.0% with a [Co(bpy)3]2+/3+ (bpy = 2,2′‐bipyridine) and an I−/I3− redox electrolyte, respectively. Notably, the co‐sensitization of SGT‐149 with a SGT‐021 porphyrin dye by utilizing a simple "cocktail" method, exhibit state‐of‐the‐art PCEs of 14.2% and 11.6% with a [Co(bpy)3]2+/3+ and an I−/I3− redox electrolyte, respectively. [ABSTRACT FROM AUTHOR]

A microwave photonics interferometric (MWPI) method for measuring the beat length in high birefringence polarization-maintaining fiber (PMF) is proposed. The approach is based on the radio frequency (RF) interference between the two polarization modes propagating on the polarization axis in the PMF. The period of the RF interference spectral is determined by the optical path difference of the two polarization modes. According to the principle that the phase difference in one free spectral range (FSR) of the interference spectral is 360 degree, the beat length of the PMF can be calculated by recording the RF interference spectral. Two different lengths of PMF have been tested and the measurement results show that the measurement precision is 0.02 mm. The wavelength dependent beat length has also been measured. Furthermore, the measurement precision of this method and the main source of errors are investigated in detail. [ABSTRACT FROM AUTHOR]

Measurements of photosynthetic assimilation rate as a function of intercellular CO2 (A/Ci curves) are widely used to estimate photosynthetic parameters for C3 species, yet few parameters have been reported for C4 plants, because of a lack of estimation methods. Here, we extend the framework of widely used estimation methods for C3 plants to build estimation tools by exclusively fitting intensive A/Ci curves (6–8 more sampling points) for C4 using three versions of photosynthesis models with different assumptions about carbonic anhydrase processes and ATP distribution. We use simulation analysis, out of sample tests, existing in vitro measurements and chlorophyll-fluorescence measurements to validate the new estimation methods. Of the five/six photosynthetic parameters obtained, sensitivity analyses show that maximal-Rubisco-carboxylation-rate, electron-transport-rate, maximal-PEP-carboxylation-rate, and carbonic-anhydrase were robust to variation in the input parameters, while day respiration and mesophyll conductance varied. Our method provides a way to estimate carbonic anhydrase activity, a new parameter, from A/Ci curves, yet also shows that models that do not explicitly consider carbonic anhydrase yield approximate results. The two photosynthesis models, differing in whether ATP could freely transport between RuBP and PEP regeneration processes yielded consistent results under high light, but they may diverge under low light intensities. Modeling results show selection for Rubisco of low specificity and high catalytic rate, low leakage of bundle sheath, and high PEPC affinity, which may further increase C4 efficiency. [ABSTRACT FROM AUTHOR]

Cadmium (Cd) contamination in soil has become the focus of widespread concern in society today. In this paper, with Eisenia fetida as research subjects, an indoor simulation experiment was conducted. A BIOLOG microplate technique was used to determine the carbon source (single-carbon) utilization of the microbial communities in the contaminated soil and earthworms under Cd stress. Contour line analysis was used for the first time to study the difference of carbon source metabolism in microbial communities. And the effects of Cd stress on the functional diversity of the microbial communities and the detoxification mechanism in earthworms were researched. With two test groups, a short-term test and the long-term test were performed. The former test lasted for 10 days, with the removal of an earthworm every day for analysis; the latter test lasted for 30 days, with the removal of an earthworm every 10 days. The Cd2+ concentration was set at 0, 50, 100, 125, 250, or 500 mg kg−1 dry weight, and 10 earthworms were inoculated in each concentration treatment. The earthworm homogenate and soil extracts were used to determine the carbon source utilization of the microbial communities. The results show that Cd stress changed the functional diversity of the microbial communities in the soil and earthworms. With the extension of stress time and the increase of stress concentration, earthworms will adjust their own physiological functions (including the microbial community structure and stress mechanism in the body) and regulate the microbial community structure in the external environment to obtain the necessary substances for growth. In addition, 2-hydroxybenzoic acid, γ-hydroxybutyric acid, glutamyl-L-glutamic acid, α-butyric acid, threonine, and α-cyclodextrin were important carbon sources for the earthworms to maintain their normal physiological metabolism under Cd stress. This study confirms that changes in microbial communities can be used to reveal the detoxification mechanisms of earthworm under heavy metal stress. [ABSTRACT FROM AUTHOR]

Background: Understanding the mechanisms of crops in response to elevated CO2 concentrations is pivotal to estimating the impacts of climate change on the global agricultural production. Based on earlier results of the "doubling-CO2 concentration" experiments, many current climate models may overestimate the CO2 fertilization effect on crops, and meanwhile, underestimate the potential impacts of future climate change on global agriculture ecosystem when the atmospheric CO2 concentration goes beyond the optimal levels for crop growth. Results: This study examined the photosynthetic response of soybean (Glycine max (L.) Merr.) to elevated CO2 concentration associated with changes in leaf structure, non-structural carbohydrates and nitrogen content with environmental growth chambers where the CO2 concentration was controlled at 400, 600, 800, 1000, 1200, 1400, 1600 ppm. We found CO2-induced down-regulation of leaf photosynthesis as evidenced by the consistently declined leaf net photosynthetic rate (An) with elevated CO2 concentrations. This down-regulation of leaf photosynthesis was evident in biochemical and photochemical processes since the maximum carboxylation rate (Vcmax) and the maximum electron transport rate (Jmax) were dramatically decreased at higher CO2 concentrations exceeding their optimal values of about 600 ppm and 400 ppm, respectively. Moreover, the down-regulation of leaf photosynthesis at high CO2 concentration was partially attributed to the reduced stomatal conductance (Gs) as demonstrated by the declines in stomatal density and stomatal area as well as the changes in the spatial distribution pattern of stomata. In addition, the smaller total mesophyll size (palisade and spongy tissues) and the lower nitrogen availability may also contribute to the down-regulation of leaf photosynthesis when soybean subjected to high CO2 concentration environment. Conclusions: Down-regulation of leaf photosynthesis associated with the changes in stomatal traits, mesophyll tissue size, non-structural carbohydrates, and nitrogen availability of soybean in response to future high atmospheric CO2 concentration and climate change. [ABSTRACT FROM AUTHOR]

Herein, we report an easy-to-prepare polyamic acid salt (PAAS) hydrogel with multiple functionalities including rapid self-healing, stimuli-responsive properties, and conductivity. This study describes a method for synthesizing PAAS hydrogel in aqueous solution. Phenylenediamine and 3,3′,4,4′-biphenyltetracarboxylic dianhydride were used to prepare a hydrogel with a fully aromatic backbone via gradual polymerization. It is possible to use the gel in the field of biomaterials in the absence of organic solvents. The prepared hydrogel was characterized using Fourier-transform infrared spectroscopy and dynamic rheometry. The gel has good toughness and the storage modulus and tensile stress are, respectively, about 0.25 and 0.45 MPa. Our study may pave the way for the manufacture of PAAS hydrogel, which may promote the development of hydrogel materials. [ABSTRACT FROM AUTHOR]

Two porphyrin sensitizers, SGT-023 and SGT-025, were synthesized through acceptor structural engineering. SGT-023 was designed by replacing the commonly used benzothiadiazole (BTD) unit in the well-known platform of D-porphyrin-triple bond-BTD-acceptor sensitizers (e.g., SM315 and SGT-021) with a pyridothiadiazole unit as a stronger electron-withdrawing group. As for SGT-025, an additional ethynylene bridge was inserted into the acceptor part of the well-known skeleton of D-porphyrin-triple bond-BTD-acceptor sensitizers. The impact of the auxiliary acceptors on the optical, electrochemical, and photovoltaic properties was investigated and compared with a benchmark dye, SGT-021, developed by our group. Consequently, both porphyrin sensitizers could red-shift the absorption range and downshift the lowest unoccupied molecular orbital (LUMO) energy level, which was supposed to achieve higher light harvest efficiency (LHE). Under standard global AM 1.5G solar light conditions, SGT-025 attained a relatively high maximum power conversion efficiency (PCE) of 11.0%, which was slightly lower than that of the reference dye SGT-021 (12.6%), and a moderate PCE of 5.6% was obtained by SGT-023. The main reason is the lower charge collection efficiency, which can be attributed to the tilted dye adsorption mode on TiO2. This may allow for faster charge recombination which eventually leads to lower Jsc, Voc and PCE. [ABSTRACT FROM AUTHOR]

One-step introduction of metallic Bi, non-metallic C into Bi2WO6 was successfully prepared via a facile hydrothermal synthetic route. The obtained materials were characterized by X-ray diffraction, scanning electron microscope, transmission electron microscope, X-ray photoelectron spectroscopy (XPS), nitrogen absorption-desorption isotherms, photoluminescence spectroscopy, photocurrent responses, electrochemical impedance spectroscopy (EIS), electron paramagnetic resonance (EPR), UV-vis diffuse reflectance spectra (DRS). As a result, C/Bi codecorated Bi2WO6 shows profoundly photocatalytic activity in comparison with undoped Bi2WO6 for decomposition of various industrial pollutants, including phenol, ciprofloxacin, bisphenol A, rhodamine B, and methyl orange under visible light (λ ≥ 420 nm). The enhanced photocatalytic properties may be ascribed to the fact that the synergistic effects of the SPR effect of Bi metal and the hybrid orbital constructed by C doping not only enhance the absorption of visible light but also accelerate the separation and migration of photo-generated carriers. Moreover, on the basis of the radical species trapping experiments, EPR analyses, DRS technique and Mott-Schottky plots, we proposed a simple photocatalytic mechanism. [ABSTRACT FROM AUTHOR]