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RGBT tracking technology often struggles in noncooperative challenges, such as illumination variation, scale variation, fast motion, occlusion, and thermal crossover. In this work, we propose a novel RGBT tracking model, called challenge-based Feature complementary Fusion Network (FcFNet), especially aiming at noncooperative scenarios. In particular, we first decouple the mixed challenging attributes and adaptively combine features at each layer using the challenge branch fusion module (CBFM), forming more discriminative target representations. Subsequently, we employ two nonshared convolution kernels in each layer and one shared convolution kernel to, respectively, extract individual and common features of different modalities. By applying the dynamic convolution fusion module (DCFM), the nonshared and shared convolution kernels are reweighted dynamically. Finally, we embed the feature-enhanced aggregation and dynamic kernel weights into the backbone network, which forms the FcFNet. Experimental evaluations conducted on the GTOT and RGBT234 datasets demonstrate a notable enhancement in detection performance, with an absolute improvement of 2.4% in success rate and 3.3% in precision rate when leveraging the CBFM and DCFM. The proposed FcFNet exhibits competitive performance compared with other state-of-the-art tracking algorithms, especially in continuous tracking tasks for noncooperative targets.

Laser active detection technology utilizing the cat’s eye effect is widely applied in rapid reconnaissance, positioning, and target information acquisition. This paper examines the theory of the cat’s eye effect in optical systems and its application in the field of laser active detection. This study assesses the development trend of this technology and the feasibility of implementing long-range target laser active detection. Initially, the working principle of the laser active detection system based on the cat’s eye effect is elucidated. Additionally, this paper compares and analyzes three methods of laser active detection using the cat’s eye effect: geometric optics, physical optics, and angular spectrum propagation. Subsequently, the characteristics and processing methods of cat’s eye target echo data are investigated. Finally, the paper summarizes the main challenges of this technology and assesses its development trends based on engineering requirements. The application of the cat’s eye effect in laser active reconnaissance involves linking the cat’s eye optical system with the target’s optical structure and motion state, optimizing data extraction and recognition algorithms to acquire multidimensional feature information, and enabling real-time detection of high-speed moving targets.

To comprehensively understand the genetic information of Lactiplantibacillus plantarum SGJ-24 and thoroughly analyze the functional genes associated with its tolerance to challenging vinous environments, reduction of biological acid, and biogenic amine degradation activities. The study was conducted using whole-genome sequencing, which combined second-generation Illumina HiSeq technology with third-generation PacBio RS II technology. The results showed that, a total genome size of 3451191 bp and 3361 coding genes. Numerous genes were annotated for functions such as nutrient (sugar, amino acid and so on) transport and metabolism, protein synthesis, and nucleic acid metabolism. Additionally, specific functional genes related to the versatile activities of this strain were identified, including malolactic fermentation activity (MLE and so on), acid tolerance (gadB and so on), ethanol tolerance (PFK, GK and so on), and biogenic amine degradation (GAPDH) function. The research findings provided a deep insight into the genetic characteristics of L. plantarum SGJ-24, laying a theoretical foundation for further development and utilization of the strain.

Abstract 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.

Sm-Fe-N material holds immense promise as new material for developing next generation permanent magnets. However, the achievement of high coercivity in this material remains a big challenge, and moreover new coercivity theories beyond the commonly considered nucleation-type mechanism needs exploration. This study describes both the yielding of anisotropic Sm-Fe-N powders with high coercivities and the discovery of new coercivity mechanism by a systematic study using a designed milling preparation method. As a result, a random-direction cleavage behavior with unexpected XRD peak shifts and formation of new micron-sized flower-like particles were found in the yielded Sm-Fe-N powders. The grain sizes of the powders are of nanoscale dimension and they refine gradually as the milling time increases. The measured coercivity is up to 15.0 kOe, which is a substantial increase of approximately 36.4% compared to the original commercial powders and among the highest reported values via the milling preparation method (commonly below 13 kOe). The yielded powders also demonstrate a new dual-type coercivity mechanism (combing both nucleation-type and pinning-type control effect), and the improved pining effect contributes to the high coercivities. This study opens the door of utilizing pinning effect for achieving high coercivities in anisotropic Sm-Fe-N material.

Abstract Plants with the C4 photosynthesis pathway typically respond to climate change differently from more common C3-type plants, due to their distinct anatomical and biochemical characteristics. These different responses are expected to drive changes in global C4 and C3 vegetation distributions. However, current C4 vegetation distribution models may not predict this response as they do not capture multiple interacting factors and often lack observational constraints. Here, we used global observations of plant photosynthetic pathways, satellite remote sensing, and photosynthetic optimality theory to produce an observation-constrained global map of C4 vegetation. We find that global C4 vegetation coverage decreased from 17.7% to 17.1% of the land surface during 2001 to 2019. This was the net result of a reduction in C4 natural grass cover due to elevated CO2 favoring C3-type photosynthesis, and an increase in C4 crop cover, mainly from corn (maize) expansion. Using an emergent constraint approach, we estimated that C4 vegetation contributed 19.5% of global photosynthetic carbon assimilation, a value within the range of previous estimates (18–23%) but higher than the ensemble mean of dynamic global vegetation models (14 ± 13%; mean ± one standard deviation). Our study sheds insight on the critical and underappreciated role of C4 plants in the contemporary global carbon cycle.

ObjectivesThis study focused on the association between visceral adiposity index (VAI) and the prevalence of hypertension in a nationally representative population of American adults.MethodsThe study obtained data from the National Health and Nutrition Examination Survey (NHANES) database from 2003–2018 for a large-scale study. This study incorporated participants ≥18 years of age. Multivariate logistic regression modelling and smoothed curve fitting were applied to investigate the existence of a correlation between VAI and hypertension prevalence. Subgroups were analyzed to confirm the stationarity of the association between VAI and hypertension prevalence. In addition, an interaction test was conducted in this study.ResultsIn completely adapted sequential models, the risk of hypertension prevalence in the overall population increased 0.17-fold with each 1-unit increase in VAI [odds ratio (OR) = 1.17; 95% confidence interval (CI) 1.12–1.22]. In the wholly adapted categorical model, there was a 0.95-fold increased risk of hypertension in the population of VAI quartile 4 (Q4) vs. VAI quartile 1 (Q1) (OR = 1.95; 95% CI 1.62–2.35). These results indicate that VAI was strongly related to the occurrence of hypertension, and smoothed curve-fitting analysis showed nonlinearity. Adjustment for covariates revealed no apparent interactions in the subgroup analyses, and results were stable across subgroups.ConclusionThis cross-sectional study suggests a nonlinear and positive correlation between elevated VAI and the adult risk of developing hypertension in U.S. adults.

Abstract 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.

BackgroundThere is growing evidence that antioxidant-rich diets may prevent hyperlipidemia. However, the relationship between the Composite Dietary Antioxidant Index (CDAI) and hyperlipidemia is unclear. The CDAI is a composite score reflecting the antioxidant content of an individual’s diet, and this study aimed to investigate the relationship between CDAI and hyperlipidemia.MethodsThe study used the 2003–2018 National Health and Nutrition Examination Survey (NHANES) database for cross-sectional analyses and included 27,626 participants aged 20 years and older. The CDAI, which includes vitamins A, C, and E, zinc, selenium, and carotenoids, was calculated based on dietary intake reported in a 24-h recall interview. Hyperlipidemia was defined by the National Cholesterol Education Program (NCEP). Covariates included age, sex, race, education, marriage, household poverty-to-income ratio (PIR), glomerular filtration rate (eGFR), body mass index (BMI), energy, carbohydrates, total fat, cholesterol, smoking, alcohol consumption, hypertension, diabetes mellitus, coronary heart disease, and lipid-lowering medications. The association between CDAI and hyperlipidemia was explored through multiple logistic regression analyses and smoothed curve fitting. We also performed subgroup analyses and interaction tests to verify the relationship’s stability.ResultsAfter adjusting for potential confounders, CDAI was negatively associated with the risk of developing hyperlipidemia (OR 0.98, 95% CI 0.96–0.99, p

Abstract 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.

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 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

ObjectiveTo investigate the association between the visceral adiposity index and the prevalence of diabetes and prediabetes in the US adult population.MethodWe 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.ResultsIn 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), pConclusionIn 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.

The development of microwave absorption materials with good environmental adaptability is a hot topic in the electromagnetic protection field. In this research, Expanded graphite (EG)/ZIF-67 nanocubes-derived (Co@C) composites were successfully fabricated by electrostatic assembly and subsequent heat treatment. The results show that the involvement of an appropriate content of Co@C not only overcomes the problem of single EG impedance mismatch but also introduces additional magnetic loss for the absorber. At the thicknesses of 1.6 mm and 1.5 mm, an effective absorption bandwidth (EAB) of 4.52 GHz and a minimum reflection loss (RLmin) of −48.4 dB were achieved for the Z2-700 sample with a low filling ratio (20 wt%). The enhancement of EMW-absorption performance is attributed to the optimization of impedance matching and the synergistic action between multiple loss mechanisms. Electrochemical corrosion test showed that graphite nanoflakes of EG enhanced the transfer resistance during corrosion, and the corrosion resistance of pure Co@C was enhanced with the protection of EG. This work provides a reasonable attempt to design and fabricate efficient microwave absorption materials suitable for harsh environments.

The pure-MBBR process was applied to remove ammonia in a full-scale micro-polluted-water treatment plant with a daily treatment capacity of 260 × 104 m3/d, Guangdong, China. The relationship between treatment efficiency, physical and chemical properties and microbial diversity in the process of biofilm growth was explored, and the oxygen transfer model of biofilm was established. The results show that the effluent of two-stage pure MBBR process is stable and up to standard after 10 days' incubation. The nitrification loads of two-stage biofilm was stable on the 14th day. The biomass and biofilm thickness lagged behind the nitrification load, and reached a relatively stable level on the 28th day. The species richness of biofilm basically reached a stable level on the 21st day, and the microbial diversity of primary biofilm was higher. In the primary and secondary stage at different periods, the relative abundance of dominant nitrifying bacteria Nitrospira reaches 8.48–13.60%, 6.48–9.27%, and Nitrosomonas reaches 2.89–5.64%, 0.00–3.48%. The pure MBBR system mainly adopts perforated aeration. Through the cutting and blocking of bubbles by suspended carriers, the oxygen transfer rate of the system was greatly improved. HIGHLIGHTS The MBBR process was integrated into a micro-polluted-water treatment plant of 2.6 million tons daily.; The nitrification load, biomass, and biofilm thickness stabilized after 10, 14, and 28 d, respectively.; The dominant microorganisms at genus level were Nitrospira, Hyphomicrobium, Pedomicrobium, Nitrosomonas, and Pedobacter.;

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.

3D object detection is a critical technology in many applications, and among the various detection methods, pointcloud-based methods have been the most popular research topic in recent years. Since Graph Neural Network (GNN) is considered to be effective in dealing with pointclouds, in this work, we combined it with the attention mechanism and proposed a 3D object detection method named PointGAT. Our proposed PointGAT outperforms previous approaches on the KITTI test dataset. Experiments in real campus scenarios also demonstrate the potential of our method for further applications.

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.

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.

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.

With the development of automobile intelligence and connectivity, Intelligent and Connected Vehicle (ICV) is an inevitable trend in the transformation and upgrading of the automotive industry. The maturity of any advanced technology is inseparable from a large number of test verifications, especially the research and application of automotive technology require a large number of reliable tests for evaluation and confirmation. Therefore, the ICV Test Site (ICVTS) will become a key deployment area. In this paper, we analyze the development status of ICVTS outside and within China, summarize the shortcomings of the existing test sites, and put forward some targeted suggestions, in an effort to guide the development and construction of ICVTS towards the path that seems to be most promising.

IntroductionExisting multi-view-based 3D model classification methods have the problems of insufficient view refinement feature extraction and poor generalization ability of the network model, which makes it difficult to further improve the classification accuracy. To this end, this paper proposes a multi-view SoftPool attention convolutional network for 3D model classification tasks.MethodsThis method extracts multi-view features through ResNest and adaptive pooling modules, and the extracted features can better represent 3D models. Then, the results of the multi-view feature extraction processed using SoftPool are used as the Query for the self-attentive calculation, which enables the subsequent refinement extraction. We then input the attention scores calculated by Query and Key in the self-attention calculation into the mobile inverted bottleneck convolution, which effectively improves the generalization of the network model. Based on our proposed method, a compact 3D global descriptor is finally generated, achieving a high-accuracy 3D model classification performance.ResultsExperimental results showed that our method achieves 96.96% OA and 95.68% AA on ModelNet40 and 98.57% OA and 98.42% AA on ModelNet10.DiscussionCompared with a multitude of popular methods, our algorithm model achieves the state-of-the-art classification accuracy.

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.

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 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.

Understanding the potential mechanisms and processes of leaf photosynthesis in response to elevated CO2 concentration ([CO2]) and temperature is critical for estimating the impacts of climatic change on the growth and yield in crops such as maize (Zea mays L.), which is a widely cultivated C4 crop all over the world. We examined the combined effect of elevated [CO2] and temperature on plant growth, leaf photosynthesis, stomatal traits, and biochemical compositions of maize with six environmental growth chambers controlling two CO2 levels (400 and 800 μmol mol−1) and three temperature regimes (25/19°C, 31/25°C, and 37/31°C). We found that leaf photosynthesis was significantly enhanced by increasing growth temperature from 25/19°C to 31/25°C independent of [CO2]. However, leaf photosynthesis drastically declined when the growth temperature was continually increased to 37/31°C at both ambient CO2 concentration (400 μmol mol−1, a[CO2]) and elevated CO2 concentration (800 μmol mol−1, e[CO2]). Meanwhile, we also found strong CO2 fertilization effect on maize plants grown at the highest temperature (37/31°C), as evidenced by the higher leaf photosynthesis at e[CO2] than that at a[CO2], although leaf photosynthesis was similar between a[CO2] and e[CO2] under the other two temperature regimes of 25/19°C and 31/25°C. Furthermore, we also found that e[CO2] resulted in an increase in leaf soluble sugar, which was positively related with leaf photosynthesis under the high temperature regime of 37/31°C (R2 = 0.77). In addition, our results showed that e[CO2] substantially decreased leaf transpiration rates of maize plants, which might be partially attributed to the reduced stomatal openness as demonstrated by the declined stomatal width and stomatal area. These results suggest that the CO2 fertilization effect on plant growth and leaf photosynthesis of maize depends on growth temperatures through changing stomatal traits, leaf anatomy, and soluble sugar contents.

As a novel lactate-derived post-translational modification (PTM), lysine lactylation (Kla) is involved in diverse biological processes, and participates in human tumorigenesis. Identification of Kla substrates with their exact sites is crucial for revealing the molecular mechanisms of lactylation. In contrast with labor-intensive and time-consuming experimental approaches, computational prediction of Kla could provide convenience and increased speed, but is still lacking. In this work, although current identified Kla sites are limited, we constructed the first Kla benchmark dataset and developed a few-shot learning-based architecture approach to leverage the power of small datasets and reduce the impact of imbalance and overfitting. A maximum 11.7% (0.745 versus 0.667) increase of area under the curve (AUC) value was achieved in contrast to conventional machine learning methods. We conducted a comprehensive survey of the performance by combining 8 sequence-based features and 3 structure-based features and tailored a multi-feature hybrid system for synergistic combination. This system achieved >16.2% improvement of the AUC value (0.889 versus 0.765) compared with single feature-based models for the prediction of Kla sites in silico. Taken few-shot learning and hybrid system together, we present our newly designed predictor named FSL-Kla, which is not only a cutting-edge tool for Kla site profile but also could generate candidates for further experimental approaches. The webserver of FSL-Kla is freely accessible for academic research at http://kla.zbiolab.cn/.

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 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 (A n) with elevated CO2 concentrations. This down-regulation of leaf photosynthesis was evident in biochemical and photochemical processes since the maximum carboxylation rate (V cmax) and the maximum electron transport rate (J max) 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 (G s) 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.

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.

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.

In order to obtain high yield and purity, the synthesis method of agomelatine is studied. Agomelatine is synthesized with methyl-phenoxide and succinic anhydride by Friedel-crafts acylation, catalytic hydrogenation reduction, cyclization, Knoevenagel condensation, aromatization, NaBH4/NiCl2·6H2O reduction and acylation, and the reaction conditions and parameters are optimized and improved. The results show that the optimum conditions are as following: Catalytic hydrogenation reduction is applied instead of conventional Huang Minglong reduction or Clemenson reduction, with the amount of Pd/C is 20% in solvent anhydrous ethanol, under the pressure of 1 MPa, temperature of 35 ℃, and the yield is up to 86.8%. Knoevenagel condensation is optimized, with benzyl amine and formic acid as catalyst, n(7-methoxy-1-naphthalene ketone)∶n(cyanoacetic acid)∶n(benzyl amine)∶n(formic acid)=1∶1.8∶0.3∶0.3, and the transformation rate is 95.6%. In NaBH4/NiCl2·6H2O reduction step, Boc anhydride is specially added to protect the amino group and reduce by-product, and the yield of 72.5% is obtained. Under the optimized conditions, the total yield is 26%, and the purity is 99.79%. The structures of target compounds are confirmed by 1H-NMR and 13C-NMR. The process route of catalytic hydrogenation reduction method is green and environmentally friendly, the reaction conditions are mild, and the post-treatment is simple, which provides a theoretical reference for improving the reaction yield and purity of the product of agomelatine.

The detection of terahertz electromagnetic waves is crucial for emerging applications within this frequency band, such as spectroscopy, imaging, and communication. Extending the well-developed uncooled infrared focal plane array technology to terahertz frequency regime would be very attractive, but high absorption in the terahertz region with a subwavelength pixel is necessary. In this paper, we proposed a meander line nanoantenna (MLNA) absorber with a metal-insulator-metal structure for subwavelength terahertz absorption. 89% absorption is achieved at the wavelength of 155 μm with a 10 μm pitch size. The MLNA absorber is polarization insensitive and can maintain a high absorption when the incident angle is within 40°. We expect that the proposed MLNA absorber can be integrated with the small pixels of uncooled infrared focal plane array for terahertz detection.

Land albedo is an essential variable in land surface energy balance and climate change. Within regional land, albedo has been altered in Greenland as ice melts and runoff increases in response to global warming against the period of the pre-industrial revolution. The assessment of spatiotemporal variation in albedo is a prerequisite for accurate prediction of ice sheet loss and future climate change, as well as crucial prior knowledge for improving current climate models. In our study, we employed the satellite data product from the global land surface satellite (GLASS) project to obtain the spatiotemporal variation of albedo from 1981 to 2017 using the non-parameter-based M-K (Mann-Kendall) method. It was found that the albedo generally showed a decreasing trend in the past 37 years (−0.013 ± 0.001 decade−1, p < 0.01); in particular, the albedo showed a significant increasing trend in the middle part of the study area but a decreasing trend in the coastal area. The interannual and seasonal variations of albedo showed strong spatial-temporal heterogeneity. Additionally, based on natural and anthropogenic factors, in order to further reveal the potential effects of spatiotemporal variation of albedo on the regional climate, we coupled climate model data with observed data documented by satellite and adopted a conceptual experiment for detections and attributions analysis. Our results showed that both the greenhouse gas forcing and aerosol forcing induced by anthropogenic activities in the past 37 decades were likely to be the main contributors (46.1%) to the decrease of albedo in Greenland. Here, we indicated that overall, Greenland might exhibit a local warming effect based on our study. Albedo−ice melting feedback is strongly associated with local temperature changes in Greenland. Therefore, this study provides a potential pathway to understanding climate change on a regional scale based on the coupled dataset.

The rational design of porphyrin sensitizers is always crucial for dye-sensitized solar cells (DSSCs), since the change of only a single atom can have a significant influence on the photovoltaic performance. We incorporated the pyridothiadiazole group, as a stronger electron-withdrawing group, into the commonly well-established skeleton of D-porphyrin-triple bond-acceptor sensitizers by a single atom change for a well-known strong electron-withdrawing benzothiadiazole (BTD) unit as an auxiliary acceptor. The impact of the pyridothiadiazole group on the optical; electrochemical; and photovoltaic properties of D–π–A porphyrin sensitizers was investigated with comparison for a benzothiadiazole-substituted SGT-020 porphyrin. The pyridothiadiazole-substituted SGT-024 porphyrin dye was red-shifted so that the absorption range might be expected to achieve higher light harvest efficiency (LHE) than the SGT-020 porphyrin. However, all the devices were fabricated by utilizing SGT-020 and SGT-024, evaluated and found to achieve a cell efficiency of 10.3% for SGT-020-based DSSC but 4.2% for SGT-024-based DSSC under standard global AM 1.5G solar light conditions. The main reason is the lower charge collection efficiency of SGT-024-based DSSC than SGT-020-based DSSC, which can be attributed to the tilted dye adsorption mode on the TiO2 photoanode. This may allow for faster charge recombination, which eventually leads to lower Jsc, Voc and power conversion efficiency (PCE).