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The filling stage of winter wheat is crucial for grain formation. Precise irrigation during this period can significantly enhance both grain yield and water productivity, especially in arid regions. This study introduces a method for precise irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content (LWC). Through the relationship between soil water content (SWC) and LWC, the optimal irrigation amounts at the filling stage are determined. We utilized two-year field irrigation experiments (2022–2023). The successive projection algorithm (SPA) was applied to select sensitive bands of LWC. Partial least squares regression (PLSR) and random forest (RF) were employed to establish an LWC inversion model. The SPA-RF model was found to be the most effective, with determination coefficients (R²) of 0.95 and 0.96, root mean square errors (RMSE) of 3.00 % and 2.70 %, and normalized root mean square errors (NRMSE) of 6.47 % and 6.01 %, respectively. The SPA algorithm also improved the inversion efficiency of LWC. A significant positive correlation between SWC and LWC during the filling stage was observed, and a conversion model was developed for the pre-, mid-, and late-filling stages. The R² values for pre-, mid-, and late-filling stages were 0.75, 0.80, and 0.73, respectively, with corresponding RMSE values of 28.79 m³/ha 17.26 m³/ha, and 37.35 m³/ha. The results indicate a high consistency between the SWC estimated via hyperspectral inversion and the irrigation quota based on measured SWC, making the proposed method a valuable tool for optimizing irrigation during this critical growth phase. The method for estimating irrigation amounts during the filling stage, based on UAV hyperspectral imagery proposed in this study, offers valuable support for achieving precise irrigation decisions for winter wheat.

Efficient use of scarce water resources to maximize yield and crop water productivity (WPc) is a common goal of sustainable and ecological agriculture in the North China Plain. To clarify whether light and frequent (LF) irrigation under the same or reduced amount of irrigation is beneficial to achieve this goal, and to further tap the water saving potential. Based on drip irrigation conditions, a three–year (2020–2023) field experiment was conducted to investigate the effect of four irrigation treatments (CK, conventional irrigation at the jointing and anthesis stages; LF1, 100 % ETc, ETc is the crop evapotranspiration; LF2, 75 % ETc, LF3, 50 % ETc) on yield, water consumption characteristics, leaf photosynthetic physiology and grain filling process. The results showed that compared with CK, LF1 and LF2 significantly increased the photosynthetic rate and chlorophyll content of flag leaves at 18 days after anthesis, promoted grain filling, and finally enhanced dry matter accumulation after anthesis, 1000–grain weight (TGW) and grain yield. LF irrigation management mode also increased the consumption of deep soil water (1.4–2.0 m), reduced soil water consumption and evapotranspiration, and significantly improved WPc and irrigation water productivity. However, LF1 and LF2 did not have significant yield enhancement in the wet year, but had one less irrigation than in the normal year. The LF2 achieved the highest WPc in three years (2.1 kg m–3), in the 25 % (37.5 mm) and 40 % (60 mm) less irrigation water in the normal and wet years, respectively. Meanwhile, the LF3 treatment reduced the number of spikes, TGW and dry matter accumulation to different degrees in three years, resulting in significantly lower yields (13.9 %–18.4 %), which cannot be used as a reference for an efficient irrigation system. Therefore, LF irrigation based on 75 % ETc is the best irrigation strategy to improve water utilization and yield in the North China Plain.

BackgroundAccurate estimation of reference crop evapotranspiration (ET0) is crucial for farmland hydrology, crop water requirements, and precision irrigation decisions. The Penman-Monteith (PM) model has high accuracy in estimating ET0, but it requires many uncommon meteorological data inputs. Therefore, an ideal method is needed that minimizes the number of input data variables without compromising estimation accuracy. This study aims to analyze the performance of various methods for estimating ET0 in the absence of some meteorological indicators. The Penman-Monteith (PM) model, known for its high accuracy in ET0 estimation, served as the standard value under conditions of adequate meteorological indicators. Comparative analyses were conducted for the Priestley-Taylor (PT), Hargreaves (H-A), McCloud (M-C), and FAO-24 Radiation (F-R) models. The Bayesian estimation method was used to improve the ET estimation model. ResultsResults indicate that, compared to the PM model, the F-R model performed best with inadequate meteorological indicators. It demonstrates higher average correlation coefficients (R2) at daily, monthly, and 10-day scales: 0.841, 0.937, and 0.914, respectively. The corresponding root mean square errors (RMSE) are 1.745, 1.329, and 1.423, and mean absolute errors (MAE) are 1.340, 1.159, and 1.196, with Willmott's Index (WI) values of 0.843, 0.862, and 0.859. Following Bayesian correction, R2 values remained unchanged, but significant reductions in RMSE were observed, with average reductions of 15.81%, 29.51%, and 24.66% at daily, monthly, and 10-day scales, respectively. Likewise, MAE decreased significantly, with average reductions of 19.04%, 34.47%, and 28.52%, respectively, and WI showed improvement, with average increases of 5.49%, 8.48%, and 10.78%, respectively. ConclusionTherefore, the F-R model, enhanced by the Bayesian estimation method, significantly enhances the estimation accuracy of ET0 in the absence of some meteorological indicators.

IntroductionField wheat ear counting is an important step in wheat yield estimation, and how to solve the problem of rapid and effective wheat ear counting in a field environment to ensure the stability of food supply and provide more reliable data support for agricultural management and policy making is a key concern in the current agricultural field.MethodsThere are still some bottlenecks and challenges in solving the dense wheat counting problem with the currently available methods. To address these issues, we propose a new method based on the YOLACT framework that aims to improve the accuracy and efficiency of dense wheat counting. Replacing the pooling layer in the CBAM module with a GeM pooling layer, and then introducing the density map into the FPN, these improvements together make our method better able to cope with the challenges in dense scenarios.ResultsExperiments show our model improves wheat ear counting performance in complex backgrounds. The improved attention mechanism reduces the RMSE from 1.75 to 1.57. Based on the improved CBAM, the R2 increases from 0.9615 to 0.9798 through pixel-level density estimation, the density map mechanism accurately discerns overlapping count targets, which can provide more granular information.DiscussionThe findings demonstrate the practical potential of our framework for intelligent agriculture applications.

Maize grain yield is drastically reduced by heat stress (HTS) during anthesis and early grain filling. However, the mechanism of HTS in reproductive organs and kernel numbers remains poorly understood. From 2018 to 2020, two maize varieties (ND372, heat tolerant; and XY335, heat sensitive) and two temperature regimens (HTS, heat stress; and CK, natural control) were evaluated, resulting in four treatments (372CK, 372HTS, 335CK, and 335HTS). HTS was applied from the nine-leaf stage (V9) to the anthesis stage. Various morphological traits and physiological activities of the tassels, anthers, and pollen from the two varieties were evaluated to determine their correlation with kernel count. The results showed that HTS reduced the number of florets, tassel volume, and tassel length, but increased the number of tassel branches. HTS accelerates tassel degradation and reduces pollen weight, quantity, and viability. Deformation and reduction in length and volume due to HTS were observed in both the Nongda 372 (ND372) and Xianyu 335 (XY335) varieties, with the average reductions being 22.9% and 35.2%, respectively. The morphology of the anthers changed more conspicuously in XY335 maize. The number of kernels per spike was reduced in the HTS group compared with the CK group, with the ND372 and XY335 varieties showing reductions of 47.3% and 59.3%, respectively. The main factors underlying the decrease in yield caused by HTS were reductions in pollen quantity and weight, tassel rachis, and branch length. HTS had a greater effect on the anther shape, pollen viability, and phenotype of XY335 than on those of ND372. HTS had a greater impact on anther morphology, pollen viability, and the phenotype of XY335 but had no influence on the appearance or dissemination of pollen from tassel.

BackgroundDrought-resistant varieties are an important way to address the conflict between wheat’s high water demand and the scarcity of water resources in the North China Plain (NCP). Drought stress impacts many morphological and physiological indicators in winter wheat. To increase the effectiveness of breeding drought-tolerant varieties, choosing indices that can accurately indicate a variety’s drought resistance is advantageous.ResultsFrom 2019 to 2021, 16 representative winter wheat cultivars were cultivated in the field, and 24 traits, including morphological, photosynthetic, physiological, canopy, and yield component traits, were measured to evaluate the drought tolerance of the cultivars. Principal component analysis (PCA) was used to transform 24 conventional traits into 7 independent, comprehensive indices, and 10 drought tolerance indicators were screened out by regression analysis. The 10 drought tolerance indicators were plant height (PH), spike number (SN), spikelet per spike(SP), canopy temperature (CT), leaf water content (LWC), photosynthetic rate (A), intercellular CO2 concentration (Ci), peroxidase activity (POD), malondialdehyde content (MDA), and abscisic acid (ABA). In addition, through membership function and cluster analysis, 16 wheat varieties were divided into 3 categories: drought-resistant, drought weak sensitive, and drought-sensitive.ConclusionJM418, HM19,SM22, H4399, HG35, and GY2018 exhibited excellent drought tolerance and,therefore, can be used as ideal references to study the drought tolerance mechanism in wheat and breeding drought-tolerant wheat cultivars.

IntroductionHigh rainfall and excessive urea application are counterproductive to summer maize growth requirements and lower grain yield and water/nitrogen (N) use efficiency. The objective of this study was to determine whether ETc irrigation based on summer maize demand and reduced nitrogen rate in the Huang Huai Hai Plain increased water and nitrogen use efficiency without sacrificing yield.MethodsTo achieve this, we conducted an experiment with four irrigation levels [ambient rainfall (I0) and 50% (I1), 75% (I2), and 100% (I3) of actual crop evapotranspiration (ETc)] and four nitrogen rates [no nitrogen fertilizer (N0), recommended nitrogen rate of urea (NU), recommended nitrogen rate of blending controlled-release urea with conventional urea fertilizer (BCRF) (NC), and reduced nitrogen rate of BCRF (NR)] in 2016–2018.ResultsThe results show that reduced irrigation and nitrogen rate reduced Fv/Fm, 13C-photosynthate, and nitrogen accumulation both in the kernel and plant. I3NC and I3NU accumulated higher 13C-photosynthate, nitrogen, and dry matter. However, 13C-photosynthate and nitrogen distribution to the kernel was decreased from I2 to I3 and was higher in BCRF than in urea. I2NC and I2NR promoted their distribution to the kernel, resulting in a higher harvest index. Compared with I3NU, I2NR increased root length density by 32.8% on average, maintaining considerable leaf Fv/Fm and obtaining similar kernel number and kernel weight. The higher root length density of I2NR of 40–60 cm promoted 13C-photosynthate and nitrogen distribution to the kernel and increased the harvest index. As a result, the water use efficiency (WUE) and nitrogen agronomic use efficiency (NAUE) in I2NR increased by 20.5%–31.9% and 11.0%–38.0% than that in I3NU, respectively.DiscussionTherefore, 75%ETc deficit irrigation and BCRF fertilizer with 80% nitrogen rate improved root length density, maintained leaf Fv/Fm in the milking stage, promoted 13C-photosynthate, and distributed nitrogen to the kernel, ultimately providing a higher WUE and NAUE without significantly reducing grain yield.

Abstract Water scarcity is a key constraint to crop production in North China Plain (NCP), which produces the majority of the country’s winter wheat (Triticum aestivum L.). The objective of this three-year field study was to see whether and when irrigation one-time in spring improved grain productivity and water use efficiency. Four sets of irrigation were established at the 3-leaf visible stage (L3) and the L4, L5, and L6 stages. When irrigation time was postponed, the spike number, 1000-grain weight, and water consumption increased progressively, whereas grain yield, grain number, dry matter, harvest, and WUE grew, then dropped, and peaked at L4. The increased grain number can be attributed to the L4's higher daily water consumption and water consumption percentage throughout the jointing-anthesis stages compared to the L3, L5, and L6. The cumulative (37 days), whereas it was longer in L3, L5, and L6(40, 42, and 43 days, respectively). Furthermore, flag leaf senescence was postponed in L4 with a higher post-anthesis leaf area index, photosynthetic rate, chlorophyll content, higher superoxide dismutase activity, and lower malondialdehyde concentration. As a result, single irrigation at the 4-leaf visible stage optimized water deficit and consumption before and after anthesis, resulting in higher yield and WUE in the NCP.

As an important type of interplant competition, line-spacing shrinkage and row-spacing expansion (LSRE) can increase the number of tillers and improve resource utilization efficiency in wheat. Wheat tillering is closely related to various phytohormones. However, it is unclear whether LSRE regulates phytohormones and their relationship to tillering and wheat yield. This study evaluated tillering characteristics, phytohormone content in tiller nodes at the pre-winter stage, and grain yield factors for the winter wheat variety Malan1. We used a two-factor randomized block trial design with two sowing spacings of 15 cm (15RS, conventional treatment) and 7.5 cm (7.5RS, LSRE treatment) at the same density and three sowing-date groups (SD1, SD2, and SD3). LSRE significantly promoted wheat tillering and biomass at the pre-winter stage (average increases of 14.5% and 20.9% in the three sowing-date groups, respectively) and shortened the accumulated temperature required for a single tiller. Changes in the levels of phytohormones, including decreased gibberellin and indole acetic acid and increased zeatin riboside and strigolactones, were determined by high-performance liquid chromatography and were shown to be responsible for the tillering process under LSRE treatment in winter wheat. LSRE treatment can improve crop yield by increasing the number of spikes per unit area and grain weight. Our results clarified the changes in tillering and phytohormones content of winter wheat under LSRE treatment and their correlation with grain yield. This study also provides insights into the physiological mechanisms of alleviating inter-plant competition to improve crop yield.

Purpose: The present study seeks to examine the efficacy of different training modalities on increasing workplace learning, representatives' intent to transfer what they learned into their work, and importantly how training impacts actual work performance. These relationships are tested in the context of a Chinese division of a multinational pharmaceutical company, where pharmaceutical representatives are tasked with relaying relevant efficacy and safety information on pharmaceutical products to health care professionals who prescribe them to patients. Methods: The present study employed a three-group between-subjects experimental design. Representatives received varying forms of training (instruction only, instruction plus reflection, and instruction, reflection, plus direct feedback) based on experimental conditions. After three training sessions over the course of six weeks, representatives were assessed on how much they learned in the training and their actual work performance through observer assessment of meetings with health care professionals, facilitated by the representatives. Findings: In this study, it was found that the process of actively reflecting on what was learned in training led to increased learning, as well as increased performance, compared to simply studying the material. However, receiving direct feedback on training performance, combined with active reflection training, did not provide any further benefits in terms of learning or work performance. Notably, there were no differences in intent to transfer learned material to work, as all conditions reported high levels of transfer intention. Conclusion: The finding provides insightful evidence to support the benefits of fostering trainees' active reflections for work-based learning in the Chinese industry training scenario. In contrast, receiving direct comments on how students performed from a manager or trainer, as well as advise on how do better in the future, had no effect on increasing learning or performance. Although the effect of direct feedback is not statistically significant in this context, further research should be done in understanding individuals' thoughts and behaviors when received direct feedbacks received in workplace training. Relatively little workplace research has assessed both workplace learning and performance in the same study, specifically in the Chinese context. While training efficacy likely varies across cultures to begin with, compensation structures in China do not provide the same monetary incentives for workplace learning (i.e. chance to increase income) as Western culture. This means that any way to increase workplace learning should be of extra value, as employees otherwise may not engage in it at all.

BackgroundPulmonary arterial hypertension (PAH) leads to progressive increases in pulmonary vascular resistance, right heart failure, and death if left untreated. Ocular complications secondary to PAH were less reported. In this study, we reported a case of bilateral visual loss and metamorphopsia in a patient with PAH, who developed central serous chorioretinopathy (CSCR)-like abnormalities and optic disc atrophy.Case summaryA 45-year-old man presented with decreasing central vision and metamorphopsia in both eyes. He had a history of PAH and 6-year history of low-dose oral sildenafil treatment. Slit-lamp examination revealed prominent dilated and tortuous episcleral and conjunctival vessels. Ultrawide-field color picture showed retinal pigment epithelial mottling and atrophy in ring-like configurations. Ultrawide-field autofluorescence showed multiple irregular hyper-autofluorescence with a constellation-like pattern surrounding the optic nerve head and macular region. Optical coherence tomography angiography (OCTA) b-scan demonstrated CSCR-like changes. Swept-source optical coherence tomography (SS-OCT) analysis showed optic nerve atrophy with enlarged cup/disc ratio in right eye, which was confirmed with perimetry. Fluorescein angiography (FA) showed marked leakage of macula and optic nerve head with time, cystoid macular edema, early blocking with late staining of the flecks as shown in the backgrounds of infrared and autofluorescence, and mild leakage in peripheral retina. Indocyanine green angiography (ICGA) showed dilation, tortuosity and congestion of all vortex veins without obvious leakage.ConclusionUndertreated PAH may cause the congestion of the choroid and induce CSCR-like abnormalities.

In this paper, an energy level analysis model of oil transfer station system is established, which is divided into oil transfer station subsystem and pipeline network subsystem. From the perspective of the level of energy, the system is evaluated and optimized by virtue of using energy level analysis method and combining with the ”three box” analysis model. Moreover, the energy level evaluation indexes of import and export, energy level difference and energy level balance coefficient, are calculated in terms of the calculation results. Then the energy utilization diagrams (EUD) are drawn, it can be seen that more than 95% of the supply energy in the oil transfer station subsystem is provided by gas. The energy level difference of the heating furnace is the largest, which can be seen intuitively that the heating furnace is the weak link of energy utilization in the whole system. Based on the influencing factors, the corresponding energy saving and consumption reduction measures are preliminarily proposed.

Diabetic retinopathy (DR), with increasing incidence, is the major cause of vision loss and blindness worldwide in working-age adults. Diabetic macular edema (DME) remains the main cause of vision impairment in diabetic patients, with its pathogenesis still not completely elucidated. Vascular endothelial growth factor (VEGF) plays a pivotal role in the pathogenesis of DR and DME. Currently, intravitreal injection of anti-VEGF agents remains as the first-line therapy in DME treatment due to the superior anatomic and functional outcomes. However, some patients do not respond satisfactorily to anti-VEGF injections. More than 30% patients still exist with persistent DME even after regular intravitreal injection for at least 4 injections within 24 weeks, suggesting other pathogenic factors, beyond VEGF, might contribute to the pathogenesis of DME. Recent advances showed nearly all the retinal cells are involved in DR and DME, including breakdown of blood-retinal barrier (BRB), drainage dysfunction of Müller glia and retinal pigment epithelium (RPE), involvement of inflammation, oxidative stress, and neurodegeneration, all complicating the pathogenesis of DME. The profound understanding of the changes in proteomics and metabolomics helps improve the elucidation of the pathogenesis of DR and DME and leads to the identification of novel targets, biomarkers and potential therapeutic strategies for DME treatment. The present review aimed to summarize the current understanding of DME, the involved molecular mechanisms, and the changes in proteomics and metabolomics, thus to propose the potential therapeutic recommendations for personalized treatment of DME.

The relationship between sign systems and the meaning potentials and affordances of multimodal technologies has received increasing attention in research on digital technology use in education. Students constantly adhere to and engage with semiotic shifts in sign systems when they work with digital technologies for learning purposes. This study explores students’ use of digital technologies in Swedish schools. We trace the way semiotic activity systems and cognitive processes are transformed and realized when students engage with shifts in sign systems into various meaning-making strategies. Methodologically, the study is based on a data set of video recordings, interviews, and observations of classroom practice in three primary schools. An analysis that draws on quantitative ethnography was applied to process and analyse the data. The main findings revealed that sign systems prompted by the technologies and the social space compete to some extent for students’ attention, and that technology design is monotonously rendering lower levels of mediation. These findings show that various sign system prompts need to be balanced and streamlined to support students in their meaning-making. This article conveys the importance of understanding sign systems, as they are the most common resources for technology-assisted learning, and change the prerequisites for meaning-making.

We report a simple realization of a compact linear electroacoustic field effect transistor (EA-FET) constructed by coupling a membrane-type acoustic metamaterial with a piezoelectric shunt circuit. As analogous to the electroelectronic field effect transistor, the proposed EA-FET is a four-terminal device in which the sound flux is regulated by the external electric field. The core function of the EA-FET relies on the electrical-elasticity-control mechanism stemming from the inside mechanical and electrical interactions. The effectiveness of the EA-FET concept is proved by investigating the intrinsic output and transfer properties and the sound transmission characteristics. The continuous and rapid regulation of sound flux, as well as the acoustic binary encoding via an EA-FET sample, were demonstrated experimentally. As an essential analog acoustic functional device, the EA-FET could play a significant role in engineering the integration of analog acoustic circuits, and it may be adapted to build distributed information processing systems.

This paper reports a small-scale study on the websites of 12 K-9 schools from four municipalities in Sweden. The purpose of the study is to explore, describe, and compare what and how information relevant for parental use is presented by local schools on their websites, which reflect the schools’ perceptions, intentions and strategies of communicating and cooperating with families. Epstein’s six key components regarding parental involvement are used as a theoretical framework in order to examine and analyse the content of school website settings. To evaluate the website design features, the website evaluation metrics suggested by Parajuli are adapted and applied. The results indicate that information on school websites for parental use is generally limited. It seems that schools’ expectations for parental involvement in education are based mainly on the social aspects of student development, rather than on pedagogical issues. In general, the websites of independent schools are more attractive than most public schools’ websites in terms of information richness and freshness, variations and friendliness. There is a need to develop websites that are more accessible for parents with immigrant backgrounds and non-Swedish speakers.

Abstract A multiscale regulation strategy has been demonstrated for synthetic energy storage enhancement in a tetragonal tungsten bronze structure ferroelectric. Grain refining and second-phase precipitation (perovskite phase) are introduced in the BaSrTiNb2-xTaxO9 ceramics by regulating the composition and sintering process. Disordered polarization and distribution, chemical inhomogeneity, and insulating boundary layers are achieved to provide the fundamental structural origin of the relaxation characteristic, high breakdown strength, and superior energy storage performance. Thus, an ultrahigh energy storage density of 12.2 J cm−3 with an low energy consumption was achieved at an electric field of 950 kV cm−1. This is the highest known energy storage performance in tetragonal tungsten bronze-based ferroelectric. Notably, this ceramic shows remarkable stability over frequency, temperature, and cycling electric fields. This work brings new material candidates and structure design for developing of energy storage capacitors apart from the predominant perovskite ferroelectric ceramics.

Glycation of proteins and DNA forms advanced glycation end products (AGEs) causing cell and tissue dysfunction and subsequent various chronic diseases, in particular, metabolic and age-related diseases. Targeted AGE inhibition includes scavengers of reactive carbonyl species (RCS) such as methylglyoxal (MG), glyoxalase-1 enhancers, Nrf2/ARE pathway activators, AGE/RAGE formation inhibitors and other antiglycatng agents. Citrus flavonoids have demonstrated antioxidant and anti-inflammatory effects and are also found to be effective antiglycating agents. Herein, we reviewed the up to date progress of the antiglycation effects of citrus flavonoids and associated mechanisms. Major citrus flavonoids, hesperedin and its aglycone, hesperetin, inhibited glycation by scavenging MG forming mono- or di-flavonoid adducts with MG, enhanced the activity of glyoxase-1, activated Akt/Nrf2 signal pathway while inhibiting AGE/RAGE/NF-κB pathway, reduced the formation of Nε-(carboxylmethyl)lysine (CML) and pentosidine, inhibited aldol reductase activity and decreased the levels of fructosamine. The antiglycating activity and mechanisms of other flavonoids was also summarized in this review. In conclusion, citrus flavonoids possess effective antiglycating activity via different mechanisms, yet there are many challenging questions remaining to be studied in the near future such as in vivo testing and human study of citrus flavonoids for efficacy, effectiveness and adverse effects of citrus flavonoids as a functional food in managing high levels of AGEs and controlling AGE-induced chronic diseases, diabetic complications in particular.

Abstract As a sport conducted in dynamically changing natural environments, orienteering places high demands on athletes' cognitive processing abilities and visual search efficiency. However, previous studies on orienteering have been primarily limited by the use of fixed stimulus materials on computer screens, which are unable to fully simulate authentic sports scenarios. To better understand the sports expertise of orienteering athletes in terms of their real scene image recognition performance and visual search characteristics, this study recruited 40 orienteering athletes, both experts and novices, as participants. By utilizing eye-tracking technology and setting observation points in real-world scenarios to conduct image recognition task tests, the ecological validity of the experiment was further enhanced. The results showed that the experts demonstrated a high level of accuracy and a short response time, with visual search characteristics including few saccade counts, low fixation frequency, concentrated fixation points, simple and clear fixation paths, and higher visual search efficiency. This study further reveals that long-term specialized training will lead to the formation of a unique cognitive structure related to the specific knowledge and long-term memory required by expert orienteering athletes, thereby promoting the development of expert advantage.

Abstract Modern radar systems often face various interference signals in complex and rapidly changing electronic environments. The task of suppressing this interference in the radar echo signal to extract vital information is challenging. A radar interference suppression method is proposed based on a generative adversarial network (GAN). This method effectively recovers the target signal from the echo signal, which contains interference and noise, by leveraging the powerful fitting ability of GAN. Specifically, this method was tested using coherent suppression interference, smart noise interference, and noise frequency modulation suppression interference. We compared the proposed GAN method with recurrent neural network, short‐time Fourier transform time‐varying filtering, short‐time fractional Fourier transform time‐varying filtering algorithms and RNN approach. The results show that the interference suppression algorithm based on GAN is superior to the other three algorithms.

In this article, we consider the parametric high-order fractional system with integral boundary value conditions involving derivatives of order $ p $-$ q $. With the aid of the fixed-point theorem, an exact interval from the existence to the solution of the system will be obtained, under the condition that the nonlinearities of the system may have singularities. Finally, we provide an instance to show the practicality of the primary outcomes.

This study aimed to determine the chemical composition of five Lavender essential oils (LEOs) using the gas chromatography–mass spectroscopy technique and to assess their antibacterial activity against four marine Vibrio species, including Shewanella algae, Shewanella maridflavi, Vibrio harveyi, and Vibrio alginolyticus. Sensitivity tests were performed using the disk diffusion and serial dilution methods. The results showed that all five LEOs exhibited antibacterial activity against the four tested marine Vibrio species. The antibacterial activities of all five LEOs were above moderate sensitivity. The five LEOs from French blue, space blue, eye-catching, and true Lavender showed high sensitivity, particularly against Shewanella maridflavi. The compounds of LEOs from different varieties of Lavender were similar and mainly comprised linalool, linalyl acetate, eucalyptol, and isoborneol. Different varieties of LEOs possessed unique components besides common components, and the percentage of each one was different, which led to different fragrance loads. The major fragrances were lily of the valley, an aromatic compound fragrance, and an herbal fragrance. The antibacterial activity of LEO from eye-catching Lavender was better than that of others, which could provide a reference for its application in the prevention and control of marine Vibrio spp. and the development of antibacterial products.

Infrared ship detection technology plays a crucial role in ensuring maritime transportation and navigation safety. However, infrared ship targets at sea exhibit characteristics such as multi-scale, arbitrary orientation, and dense arrangements, with imaging often influenced by complex sea–sky backgrounds. These factors pose significant challenges for the fast and accurate detection of infrared ships. In this paper, we propose a new infrared ship target detection algorithm, YOLO-IRS (YOLO for infrared ship target), based on YOLOv10, which improves detection accuracy while maintaining detection speed. The model introduces the following optimizations: First, to address the difficulty of detecting weak and small targets, the Swin Transformer is introduced to extract features from infrared ship images. By utilizing a shifted window multi-head self-attention mechanism, the window field of view is expanded, enhancing the model’s ability to focus on global features during feature extraction, thereby improving small target detection. Second, the C3KAN module is designed to improve detection accuracy while also addressing issues of false positives and missed detections in complex backgrounds and dense occlusion scenarios. Finally, extensive experiments were conducted on an infrared ship dataset: compared to the baseline model YOLOv10, YOLO-IRS improves precision by 1.3%, mAP50 by 0.5%, and mAP50–95 by 1.7%. Compared to mainstream detection algorithms, YOLO-IRS achieves higher detection accuracy while requiring relatively fewer computational resources, verifying the superiority of the proposed algorithm and enhancing the detection performance of infrared ship targets.

Reason: The article ‘The aesthetics of Gannan Hakka architecture in modern housing: A design psychology perspective’ by Xiang Lei, Hao Cao and Limin Guo has been retracted by AOSIS, as publisher, following an investigation undertaken by the publisher. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process. We cannot, therefore, vouch for the reliability or integrity of this article. Please note that this notice is intended solely to alert readers that the peer-review process of this article has been compromised. AOSIS regret that the usual quality checks did not identify these issues before publication and has since put additional measures in place to safeguard research integrity. The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement with this retraction. We have kept a record of any response received.

Based on properties of Green's function and the some conditions of $ f(t, u) $, we found a minimal and a maximal positive solution by the method of sequence approximation. Moreover, based on the properties of Green's function and fixed point index theorem, the existence of multiple positive solutions for a singular $ p $-Laplacian fractional differential equation with infinite-point boundary conditions was obtained and, at last, an example was given to demonstrate the validity of our main results.

The mathematical theories and methods of fractional calculus are relatively mature, which have been widely used in signal processing, control systems, nonlinear dynamics, financial models, etc. The studies of some basic theories of fractional differential equations can provide more understanding of mechanisms for the applications. In this paper, the expression of the Green function as well as its special properties are acquired and presented through theoretical analyses. Subsequently, on the basis of these properties of the Green function, the existence and uniqueness of positive solutions are achieved for a singular p-Laplacian fractional-order differential equation with nonlocal integral and infinite-point boundary value systems by using the method of a nonlinear alternative of Leray–Schauder-type Guo–Krasnoselskii’s fixed point theorem in cone, and the Banach fixed point theorem, respectively. Some existence results are obtained for the case in which the nonlinearity is allowed to be singular with regard to the time variable. Several examples are correspondingly provided to show the correctness and applicability of the obtained results, where nonlinear terms are controlled by the integrable functions 1π(lnt)12(1−lnt)12 and 1π(lnt)34(1−lnt)34 in Example 1, and by the integrable functions θ,θ¯ and φ(v),ψ(u) in Example 2, respectively. The present work may contribute to the improvement and application of the coupled p-Laplacian Hadamard fractional differential model and further promote the development of fractional differential equations and fractional differential calculus.

Abstract Traditional signal deinterleaving methods depend on preset parameters, use complicated steps and have limitations in handling complex electromagnetic environments where signals overlap in the space, time and frequency domains. This paper presents a novel approach using a deep segmentation network for radar signal deinterleaving. The pulse descriptor word data is transformed into a dot matrix image, which is then processed by a Cascade‐Recurrent Loop Network (CRLN) for segmentation. The CRLN consists of a Type Segmentation Network, an Amount Decision Network and a Recurrent Individual Segmentation Network. By recursively segmenting each target and determining the number of sources, the CRLN overcomes the challenges posed by the discontinuity and high overlap of individuals in the dot matrix image. Experimental results demonstrate the effectiveness of the proposed method, surpassing traditional deinterleaving methods, achieving high accuracy, low omission rate and effectively mitigates the increasing batch problem in complex scenarios. The experiments conducted on a randomly generated dataset yielded impressive results: over 90% accuracy when dealing with pulses from 15 radars of different types, over 96% accuracy when deinterleaving pulses from 4 radar individuals of a same type and over 90% accuracy when handling 5 radars of different types with 10% pulse loss.

Based on the spectral analysis method, Gelfand’s formula, and the cones fixed point theorem, some positive solutions with their existence of a nonlinear infinite-point Hadamard fractional-order differential equation is achieved on the interval [a, b] under some conditions, and particularly the nonlinear term allows singularities for time and spatial parameters in the present study. Finally, an analysis case is carried out to reveal the principal results.

Due to the development of digital radio frequency memory (DRFM), active jamming against the main lobe of the radar has become mainstream in electronic warfare. The jamming infiltrates the radar receiver via the main lobe, covering up the target echo information. This greatly affects the detection, tracking, and localization of targets by radar. In this study, we consider jamming suppression based on the independence of RF features. First, two stacked sparse auto-encoders (SSAEs) are built to extract the RF characteristics and signal features carried out by the actual radar signal for subsequent jamming suppression. This method can effectively separate RF features from signal features, making the extracted RF features more efficient and accurate. Then, an SSAE-based jamming suppression auto-encoder (JSAE) is proposed; the mixed signal, including the radar signal, jamming signal, and noise, is input to JSAE for dimensionality reduction. Therefore, the radar signal and RF features, extracted by the two SSAEs in the previous step, are used to constrain the features of the reduced mixed signal. Moreover, we integrate the feature level and signal level to jointly achieve jamming suppression. The original radar signal is used to assist the radar signal reconstructed by the decoder. By first filtering out interference-related features and then reconstructing the signal, we can achieve better jamming suppression performance. Finally, the effectiveness of the proposed method is verified by simulating the actual collected data.

Abstract Background Despite the increasing survival rates, liver transplant patients experience numerous postoperative complications and encounter significant challenges in long-term self-management. This study aims to examine the effectiveness of empowerment education in enhancing self-management skills and self-efficacy among liver transplant recipients. Methods A randomized, single-blind, single-center trial was conducted in China between August 2019 and September 2020, involving liver transplant recipients. The intervention group received 12 weeks of empowerment education, while the control group received 12 weeks of routine education. .The study assessed the patients’ self-management and self-efficacy using the Liver Transplant Recipient Self-Management Questionnaire and the Self-efficacy for Managing Chronic Disease 6-Item Scale. Follow-up assessments were conducted at 1, 3, and 6 months after the intervention. Results Eighty-four patients were initially randomized to either the intervention group (n1 = 42) or the routine education group (n2 = 42). Twelve patients were excluded from the analysis due to loss of follow-up or discontinuation of the intervention, leaving 72 patients (n1 = 35, n2 = 37) for the final analysis. The scores for exercise and lifestyle management were significantly higher in the intervention group than in the control group at 1, 3, and 6 months after the intervention (t = 3.047, 5.875, 8.356, and t = 5.759, 4.681, 11.759, respectively; P

In this paper we use the fixed point index theory to study the existence of positive radial solutions for a system of boundary value problems with semipositone second order elliptic equations. Some appropriate concave and convex functions are utilized to characterize coupling behaviors of our nonlinearities.

Background and objective Metastasis is the main cause of death in patients with lung cancer. Macrophages are innate immune cells that play important roles in cancer metastasis. Exosomes could play an important role of communication between tumor cells and macrophages. This study investigated the effect of miR-10b on cell growth invasion and epithelial mesenchymal transition (EMT) in lung adenocarcinoma A549 cell exosomes. Methods Exosomes were isolated from A549 cells and identified by transmission electron microscopy (TEM) and Western blot. CCK-8 assay and flow cytometry were used to detect cell proliferation and apoptosis. Cell migration and invasion were detected by Transwell assay. The expression of mRNA and protein were assessed by quantitative reverse transcription polymerase chain reaction (RT-qPCR) and Western blot, respectively. Results The expression of miR-10b was up-regulated in non-small cell lung cancer, and miR-10b inhibitor could inhibit the proliferation of A549 cell. Meanwhile, the tumor cell-derived exosome miR-10b promoted the invasion of A549 cell and EMT by promoting the M2 polarization of macrophages. Conclusion Tumor cell-derived exosome miR-10b promotes A549 cell invasion and EMT through M2 macrophage polarization.

Abstract The automatic modulation recognition (AMR) of radar signals has become a popular research topic in recent years. However, most algorithms focus on the type of signal modulation and lack further understanding of the signal. To address this gap, a network architecture for multi‐tasking intra‐pulse description words (MIDW‐Net) is proposed herein. In this framework, the denoising algorithm employs a convolutional denoising autoencoder, which is an effective method for suppressing noise interference and preserving signal information. The multiscale feature‐extraction capability of a feature pyramid network (FPN) is utilized to expand the perceptual domain without losing the high‐frequency features of the image. Finally, AMR and modulation parameter estimation are accomplished via multitask learning. Experiments performed on simulated radar signals using four intra‐pulse descriptors verified the effectiveness of the proposed algorithm.

The rapid acceleration of societal change has subjected contemporary individuals to prolonged periods of diverse pressures, leading to substantial psychological strain, resulting in anxiety, depression, and compromised mental well-being. Within this context, the home has evolved into a vital refuge for modern individuals, offering both physical and psychological respite. Through experimental intervention, this study examines two distinct residential groups: those adhering to traditional housing and those residing in characteristic folk houses, specifically Gannan Hakka architecture. Analysing the psychological state of contemporary individuals and integrating Gannan Hakka architectural elements into folk house design through a psychologically informed approach, this research aims to enhance residents’ aesthetic experiences and alleviate psychological distress. The study involved 120 volunteers with varying psychological stress levels, categorised into characteristic and traditional folk house groups. Psychological assessments evaluated stress, self-efficacy, and quality of life. Findings reveal significantly improved psychological well-being, self-efficacy, and quality of life among the characteristic residential group, affirming the benefits of Gannan Hakka architecture integration. This research underscores the importance of addressing modern individuals’ environmental needs and proposes the integration of Gannan Hakka cultural elements into residential design to enhance well-being. Contribution: This study demonstrates the application of Hakka culture in contemporary housing profoundly influences residents’ lives, enabling both relaxation and a deeper cultural connection. This integration facilitates a comprehensive understanding of Hakka culture, promoting its preservation. Notably recognised for hospitality and diligence, Hakka culture enhances individuals’ appreciation of tradition, fostering physical and mental rejuvenation. These findings offer valuable insights for practical theology scholars and practitioners exploring the impact of religious aspects on modern residential design from a psychological perspective.

Objective: The stability of antioxidant peptide activity of safflower seed protein with different molecular weight was studied. Methods: Using shelled safflower seed meal as experimental material, antioxidant peptides were prepared by compound enzymatic hydrolysis. The scavenging ability of 1-diphenyl-2-trinitrophenylhydrazine (DPPH) free radical, superoxide anion radical (O2−·) and hydroxyl radical (·OH) were used as indexes. The effects of environmental factors such as temperature, pH, food raw materials, metal ions and simulated gastrointestinal digestion on the activity of antioxidant peptides from safflower seeds with different molecular weight were studied. Results: The activities of the protease hydrolysates 10 kDa) were significantly lower than those before separation (PZn2+>K+>Mg2+>Ca2+, was stable after simulated gastrointestinal digestion, and the maintenance rate was 80% (P

ABSTRACT: Objectives: : To identify the carbapenemase and colistin resistance genes and epidemiological status of carbapenem-resistant Enterobacterales (CRE) among 7 secondary and 11 tertiary hospitals in Henan province, China. Methods: : CRE isolates and clinical data of infected patients were collected from 7 secondary and 11 tertiary hospitals in Henan from July to September 2019 and analysed retrospectively. Polymerase chain reaction (PCR) and gene sequencing were performed to detect carbapenemase and colistin resistance genes; multilocus sequence typing was also performed. Results: : In total, 238 nonduplicate CRE isolates were collected mainly from the respiratory tract (54.20%) and blood (18.91%) of CRE-infected patients, half of them aged >65 years (45.80%). Carbapenem-resistant Klebsiella pneumoniae (CRKP) was the most common CRE (184 isolates, 77.31%) with constituent ratios of 84.38% and 72.54% in secondary and tertiary hospitals, respectively. In 184 CRKP isolates, blaKPC-2 (89.13%) was the dominant carbapenemase gene, and ST11 (71.74%) was the most prevalent sequence type (ST), with constituent ratios of 83.95% and 62.14% in secondary and tertiary hospitals, respectively. In 29 carbapenem-resistant Escherichia coli (CREC) isolates, blaNDM-5 (58.62%) and ST2 (31.03%) were prevalent. Four CRKP isolates and one CREC isolate were colistin-resistant and carried the plasmid-mediated mcr-1 gene. Conclusion: : Our results showed a wide spread of CRKP-ST11 with KPC-2 carbapenemase in the analysed 18 hospitals. The CRKP constituent ratio, CRKP-STs, and CREC carbapenemase genes between secondary and tertiary hospitals showed significant differences. The emergence of a colistin-resistant CRKP with plasmid-mediated resistance gene mcr-1 is of serious concern due to the limited treatment options.

In the era of big data, unsupervised learning algorithms such as clustering are particularly prominent. In recent years, there have been significant advancements in clustering algorithm research. The Clustering by Density Peaks algorithm is known as Clustering by Fast Search and Find of Density Peaks (density peak clustering). This clustering algorithm, proposed in Science in 2014, automatically finds cluster centers. It is simple, efficient, does not require iterative computation, and is suitable for large-scale and high-dimensional data. However, DPC and most of its refinements have several drawbacks. The method primarily considers the overall structure of the data, often resulting in the oversight of many clusters. The choice of truncation distance affects the calculation of local density values, and varying dataset sizes may necessitate different computational methods, impacting the quality of clustering results. In addition, the initial assignment of labels can cause a ‘chain reaction’, i.e., if one data point is incorrectly labeled, it may lead to more subsequent data points being incorrectly labeled. In this paper, we propose an improved density peak clustering method, DPC-MS, which uses the mean-shift algorithm to find local density extremes, making the accuracy of the algorithm independent of the parameter dc. After finding the local density extreme points, the allocation strategy of the DPC algorithm is employed to assign the remaining points to appropriate local density extreme points, forming the final clusters. The robustness of this method in handling uncertain dataset sizes adds some application value, and several experiments were conducted on synthetic and real datasets to evaluate the performance of the proposed method. The results show that the proposed method outperforms some of the more recent methods in most cases.

Convolutional neural networks (CNNs) and transformers have boosted the rapid growth of object detection in synthetic aperture radar (SAR) images. However, it is still a challenging task because SAR images usually have the characteristics of unclear contour, sidelobe interference, speckle noise, multiple scales, complex inshore background, etc. More effective feature extraction by the backbone and augmentation in the neck will bring a promising performance increment. In response, we make full use of the advantage of CNNs in extracting local features and the advantage of transformers in capturing long-range dependencies to propose a Swin Transformer-based detector for arbitrary-oriented SAR ship detection. Firstly, we incorporate a convolution-based local perception unit (CLPU) into the transformer structure to establish a powerful backbone. The local-perception-enhanced Swin Transformer (LP-Swin) backbone combines the local information perception ability of CNNs and the global feature extraction ability of transformers to enhance representation learning, which can extract object features more effectively and boost the detection performance. Then, we devise a cross-scale bidirectional feature pyramid network (CS-BiFPN) by strengthening the propagation and integration of both location and semantic information. It allows for more effective utilization of the feature extracted by the backbone and mitigates the problem of multi-scale ships. Moreover, we design a one-stage framework integrated with LP-Swin, CS-BiFPN, and the detection head of R3Det for arbitrary-oriented object detection, which can provide more precise locations for inclined objects and introduce less background information. On the SAR Ship Detection Dataset (SSDD), ablation studies are implemented to verify the effectiveness of each component, and competing experiments illustrate that our detector attains 93.31% in mean average precision (mAP), which is a comparable detection performance with other advanced detectors.

In this paper, based on the properties of Green function and the eigenvalue of a corresponding linear operator, the existence of positive solutions is investigated by spectral analysis for a infinite-points singular p-Laplacian Hadamard fractional differential equation boundary value problem, and an example is given to demonstrate the validity of our main results.

Abstract The black TiO2 nanotubes arrays were prepared by anodizing and aluminothermic reduction process. The prepared samples were characterized by X‐ray diffraction (XRD), SEM, UV‐visible absorption spectroscopy and X‐ray photoelectron spectroscopy (XPS). The photochemical/electrical catalytic activity of the samples before and after blackening treatment was tested. Comparing with the raw TiO2, the macrostructure of the black TiO2 nanotube arrays changes a little, and the tubal walls become a little thicker and rough. The photocatalytic hydrogen production rate has an obvious enhancement (from 8.9 µmol cm−2 h−1 to 22.8 µmol cm−2 h−1) after the blackening treatment and the electrocatalytic H2 evolution rate is also improved with a lower onset potential. The increment of trivalent Ti3+ on the surface is confirmed by XPS and presented by the colour changing of the samples (from white colour to black). The hybrid energy band gap and reduced charge transfer barrier are proved to be essential in promoting the catalytic properties of the black TiO2 nanotube arrays.

Glycidol is a well-known food contaminant mainly formed in refined edible oils and various thermally processed foods. Here, we studied the toxicity effects and related mechanism of glycidol on Human umbilical vein endothelial cells (HUVECs). Glycidol was found to induce Gap period 2 (G2)/Mitosis (M) phase cell cycle arrest, apoptosis, and autophagy in HUVECs. Inhibition of autophagy by 3-methyladenine (3-MA) attenuated glycidol-induced cell death, suggesting that glycidol-induced apoptosis was autophagy-dependent. Moreover, glycidol treatment induced phosphorylation of extracellular signal-regulated kinase (ERK), c-Jun N-terminal protein kinase (JNK), and p38. Inhibition of ERK, JNK, and p38 phosphorylation by the inhibitors U0126, SP600125, and SB203580 attenuated glycidol-induced autophagy and prevented glycidol-mediated reduction in cell viability, demonstrating that glycidol inhibited HUVECs growth by inducing autophagic-dependent apoptosis through activation of the ERK, JNK and p38 signaling pathways. In addition, apigenin (API) and its octoic acid diester apigenin-7 (API-C8), 4′-O-dioctanoate were found to significantly attenuate glycidol-induced cell growth inhibition by inhibiting the above signaling pathways. Collectively, glycidol induces autophagic-dependent apoptosis via activating the ERK/JNK/p38 signaling pathways in HUVECs and API-C8 could attenuate the toxicity effects.

Abstract Aiming at the recognition of low‐probability‐of‐intercept (LPI) radar signals, a support vector machine (SVM)‐based algorithm is proposed, where the information geometry theory is utilised to optimise the kernel function of the SVM. Since signals with different modulations have different characteristics in the time‐frequency domain, the time‐frequency transformation result of the LPI radar signal is considered as an image, referred to as the time‐frequency image, and computer vision techniques are utilized to perform recognition. Specifically, the time‐frequency images of different LPI radar signals are obtained via the Choi‐Williams distribution (CWD) transform, and the AlexNet network, one improved convolutional neural network (CNN), is used to extract time‐frequency features. Then, an SVM is adopted to recognise LPI radar signals due to its superiority in addressing the dimension disaster and non‐linear inseparability issue. The extracted time‐frequency features are fed into the SVM for classification and recognition. Note that the classification performance of SVM depends on the kernel function. Therefore, in the proposed algorithm, information geometry theory is exploited to improve the Gaussian kernel function, and the maximum margin between different categories of samples is further enlarged. As a consequence, the recognition accuracy for LPI radar signals with similar time‐frequency images is effectively improved. In addition, the proposed algorithm has better robustness to small samples than other deep learning‐based algorithms, since the SVM method minimises the structural risk instead of the empirical risk. Simulation results verify the effectiveness of the proposed algorithm.

Various ZSM-5 zeolites with different morphologies (plate, brick, and spherical-like) supported Pt were synthesized as catalysts for the combustion of toluene. H2-TPR, SEM, XRD, N2 sorption, and NH3-TPD, among other methods, were adopted to measure their physicochemical characteristics. The findings show that zeolite shape substantially impacts the performance of the combustion of toluene. When compared to the other two catalysts, the plate-like ZSM-5 supported Pt (denoted as P-Pt/ZSM-5) exhibits better performance for toluene combustion due to its better dispersion of Pt species, smaller Pt particle sizes, and more potent acid sites. At 170 °C for 60 h, P-Pt/ZSM-5 exhibits the high stability with a 97% toluene conversion. Additionally, the toluene conversion only decreases by around 8% with the addition of 3.0 wt% H2O.

The pivotal role of oxygen electrocatalysis in the realm of energy conversion and storage is unmistakably significant. In an endeavor to diminish the reliance on precious metals, the development of innovative catalysts exhibiting exceptional bifunctional durability and heightened activity for both the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) has garnered considerable scholarly interest. Employing a straightforward two-step methodology, we have successfully synthesized uniformly distributed MnCo2O4 and CoMn2O4 nanoparticles of diminutive size, meticulously anchored onto carbon nanotubes (CNTs). Owing to the synergistic covalent interplay between the spinel oxide nanoparticles and CNTs, these nanocomposites demonstrate ORR activity on par with, and notably superior OER activity compared to, commercially available Pt/C catalysts. The onset potential of MnCo2O4-CNTs stands at 1.03 V vs. RHE, maintaining 95.76% of its initial current density following a 10,000-s chronoamperometry test. Furthermore, MnCo2O4-CNTs outperform CoMn2O4-CNTs in OER catalysis. The outstanding performance of MnCo2O4-CNTs is attributed to the higher content of Co3+ ions, which are active for the oxygen electrocatalysis.

With the rapid development of the economy, car ownership has grown rapidly, which causes many traffic problems. In recent years, intelligent transportation systems have been used to solve various traffic problems. To achieve effective and efficient traffic management, intelligent transportation systems need a large amount of complete traffic data. However, the current traffic data collection methods result in different forms of missing data. In the last twenty years, although many approaches have been proposed to impute missing data based on different mechanisms, these all have their limitations, which leads to low imputation accuracy, especially when the collected traffic data have a large amount of missing values. To this end, this paper proposes a latent-factor-model-based approach to impute the missing traffic data. In the proposed approach, the spatial information of the road network is first combined with the spatiotemporal matrix of the original traffic data. Then, the latent-factor-model-based algorithm is employed to impute the missing data in the combined matrix of the traffic data. Based on the real traffic data from METR-LA, we found that the imputation accuracy of the proposed approach was better than that of most of the current traffic-data-imputation approaches, especially when the original traffic data are limited.

Abstract Traumatic brain injury (TBI) is considered as the most common cause of disability and death, and therefore an effective intervention of cascade pathology of secondary brain injury promptly can be a potential therapeutic direction for TBI prognosis. Further study of the physiological mechanism of TBI is urgent and important. Phosphoglycerate mutase 5 (Pgam5), a mitochondrial protein, mediate mitochondrial homeostasis, cellular senescence, and necroptosis. This study evaluated the effects of Pgam5 on neurological deficits and neuroinflammation of controlled cortical impact-induced TBI mouse model in vivo and LPS + ATP-induced microglia model in vitro. Pgam5 was overexpressed post-TBI. Pgam5 depletion reduced pyroptosis-related molecules and improved microglia activation, neuron damage, tissue lesion, and neurological dysfunctions in TBI mice. RNA-seq analysis and molecular biology experiments demonstrated that Pgam5 might regulate inflammatory responses by affecting the post-translational modification and protein expression of related genes, including Nlrp3, caspase1, Gsdmd, and Il-1β. In microglia, Pgam5-sh abrogated LPS + ATP-induced Il-1β secretion through Asc oligomerization-mediated caspase-1 activation, which was independent of Rip3. The data demonstrate the critical role Pgam5 plays in nerve injury in the progression of TBI, which regulates Asc polymerization and subsequently caspase1 activation, and thus reveals a fundamental mechanism linking microglial inflammasome activation to Asc/caspase1-generated Il-1β-mediated neuroinflammation. Thus, our data indicate Pgam5 worsens physiological and neurological outcomes post-TBI, which may be a potential therapeutic target to improve neuroinflammation after TBI.

In this paper, the maximal and minimal iterative positive solutions are investigated for a singular Hadamard fractional differential equation boundary value problem with a boundary condition involving values at infinite number of points. Green's function is deduced and some properties of Green's function are given. Based upon these properties, iterative schemes are established for approximating the maximal and minimal positive solutions.