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Abstract This paper presents a miniaturized circularly polarized (CP) implantable antenna Ultra-wide bandwidth for continuous blood pressure monitoring. The miniature and CP of the antenna are gained by using a new slot method. The symmetry slots in the radiation patch, two T-shape slots in the GND, coupled with the use of a short pin make the designed antenna with good physical and radiation properties. In simulation, the fractional effective axial ratio bandwidth is 23.6% (2.36–2.94 GHz), and the peak gain is − 28.9 dBi. Its total size is only 17.78 mm3 (0.056 × 0.04 × 0.004 λ0 3), which size is smaller compared to other antennas with similar performance. A prototype is fabricated and measured. The experimental results are in good agreement with the simulation results. And, the radiation patterns have good symmetry both in the simulation and measurement. In addition, the maximum SAR value is in accordance with the IEEE standard safety guidelines (IEEE C95.1-2019).

Abstract This study aimed to explore potential radiomics biomarkers in predicting the efficiency of chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC). Eligible patients were prospectively assigned to receive chemo-immunotherapy, and were divided into a primary cohort (n = 138) and an internal validation cohort (n = 58). Additionally, a separative dataset was used as an external validation cohort (n = 60). Radiomics signatures were extracted and selected from the primary tumor sites from chest CT images. A multivariate logistic regression analysis was conducted to identify the independent clinical predictors. Subsequently, a radiomics nomogram model for predicting the efficiency of chemo-immunotherapy was conducted by integrating the selected radiomics signatures and the independent clinical predictors. The receiver operating characteristic (ROC) curves demonstrated that the radiomics model, the clinical model, and the radiomics nomogram model achieved areas under the curve (AUCs) of 0.85 (95% confidence interval [CI] 0.78–0.92), 0.76 (95% CI 0.68–0.84), and 0.89 (95% CI 0.84–0.94), respectively, in the primary cohort. In the internal validation cohort, the corresponding AUCs were 0.93 (95% CI 0.86–1.00), 0.79 (95% CI 0.68–0.91), and 0.96 (95% CI 0.90–1.00) respectively. Moreover, in the external validation cohort, the AUCs were 0.84 (95% CI 0.72–0.96), 0.75 (95% CI 0.62–0.87), and 0.86 (95% CI 0.75–0.96), respectively. In conclusion, the radiomics nomogram provides a convenient model for predicting the effect of chemo-immunotherapy in advanced NSCLC patients.

Abstract The characteristic mode method is used to design a miniaturized dual-band dual circularly polarized (CP) implantable antenna operating in ISM bands. The miniaturization and dual-band characteristics are gained by using the slotting method and by inserting a short-circuit probe between the radiation patch and the ground plane. We use the characteristic mode method to study the current distribution of circular radiation patches with T-shaped slots in different modes. After opening a cross-shaped slot at the center of the radiation patch and the ground plane, we obtained two orthogonal modes with equal amplitude and phase difference of 90° in two operating frequency bands, ultimately achieving CP characteristics of the antenna. Its overall size is only $$\pi$$ π ×(0.014 $$\uplambda$$ λ 0)2 × 0.0027 $$\uplambda$$ λ 0, smaller than other CP implantable antennas with similar performances, and it has satisfactory radiation efficiency and gain characteristics. Measurements show that it can operate in the ISM bands of 0.9 and 2.4 GHz with an effective 3 dB axial ratio bandwidth greater than 220 MHz (0.87 to 1.09 GHz, 22.45%) and 230 MHz (2.31 to 2.54 GHz, 9.48%), and its peak gain is − 29.5 dBi and − 19.2 dBi, respectively. And, this design complies with IEEE safety guidelines.

Abstracts Objective The oxidative balance score (OBS) reflects the overall burden of oxidative stress in an individual, with a higher OBS indicating greater antioxidant exposure. This study aimed to explore the association between constipation and OBS. Methods Variables were extracted from participants who completed a constipation questionnaire as part of the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2010. The OBS was developed based on dietary and lifestyle factors, encompassing 16 nutrients and 4 lifestyle variables. Weighted logistic regression and restricted cubic spline (RCS) analyses were employed to evaluate the association between OBS and constipation. Results After adjusting for all covariates, weighted multivariate logistic regression analysis revealed a 4% reduction in the incidence of constipation for each additional unit of OBS (OR: 0.96, 95% CI: 0.95–0.97, p

Additive manufacturing (AM) presents promising prospects for the production of laser ceramics, particularly those with composite structures. However, achieving uniform density within these ceramics is a formidable challenge, primarily due to the intrinsic limitations of uneven forces within the mold. This paper introduces the Density Spatiotemporal Evolution (DSE) model, a novel predictive tool designed to map the density distribution in laser ceramics throughout the AM process. The DSE model elucidates that ceramic density increases in proximity to the indenter and mold wall and reveals an oscillatory pattern in particle density during pressing. These insights have been corroborated through experimental measurements and simulation analyses. Leveraging the DSE model and a Simulated Annealing (SA) algorithm, an innovative AM process is proposed. The pressure exerted to each layer of ceramics is gradient distributed, i.e. 18, 18.36, 19.31, 22.08, 33.02 MPa. Preliminary experimental results indicate a significant enhancement in density uniformity, with an improvement of up to 45% in the optimized ceramics. The advancement promises to yield more homogeneous ceramic materials, potentially accelerating the progress of high-energy laser technology.

Radio frequency (RF) energy harvester as an efficient tool for capturing and converting the flourishing ambient RF energy provides a promising solution for long-term powering the wireless sensor networks and the Internet of things (IoTs). However, the actual distribution of the environmental RF signals is dynamically frequency-dependent due to the diverse wireless terminals only interacting with specified frequencies. To take full advantage of the RF energy carrying this characteristic, an intelligent RF energy harvester is in demand to automatically sense the frequency information of an incident signal and conduct the corresponding RF-to-direct current transformation process. Here, to the best of my knowledge, a frequency-self-adaptive RF harvester is first presented with the help of the shape-reconfigurable liquid metal, which can precisely identify and efficiently convert an arbitrary signal from the frequency span of 1.8 to 2.6 GHz. Companied with a microcontroller unit and a tensile system, the dynamic functionality of the entire system is comprehensively demonstrated, showing promising potential to significantly advance various fields, including sustainable IoT applications, green wearable technologies, and self-powered devices.

Background: Retinoblastoma (RB) is a rare primary malignant tumor primarily affecting children. Our study aims to compare the overall survival (OS) between pediatric and adult RB patients and establish a predictive model for adult RB patients' OS to assist clinical decision-making. Methods: This study retrospectively analyzed data from 1938 RB patients in the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2000 to 2015. Propensity score matching (PSM) ensured balanced characteristics between pediatric and adult groups. A Cox proportional hazards regression model was used to assess prognostic factors, and selected variables were utilized to construct a predictive survival model. The Nomogram model's performance was evaluated through the C-index, time-dependent ROC curves, calibration curves, and decision curve analysis (DCA). Results: Following PSM, adult RB patients had lower OS compared to pediatric RB patients. Independent prognostic factors for adult RB OS included age, gender, disease stage, radiation therapy, income, and diagnosis confirmation. In the training cohort, the Nomogram achieved a C-index for OS of 0.686 and accurately predicted 2-year, 3-year, and 5-year OS with AUC values of 0.672, 0.680, and 0.660, respectively. The C-index, time-dependent ROC curves, calibration curves, and DCA in both training and validation cohorts confirmed the Nomogram's excellent performance. Conclusion: In this study, adult RB patients have worse OS than pediatric RB patients. Consequently, we constructed a Nomogram to predict the risk for adult RB patients. The Nomogram demonstrated good accuracy and reliability, making it suitable for widespread application in clinical practice to assist healthcare professionals in assessing patients’ prognoses.

Superhydrophobic coating is widely used due to its waterproof and self-cleaning properties. Carbon soot (CS) nanoparticles are naturally superhydrophobic and non-toxic which are superior to other superhydrophobic coating. However, the weak binding force of the CS nanoparticle layers hinders their practical application. In this study, micro-nanostructured PET-CS superhydrophobic coatings were prepared by a simple method. The obtained coatings presented durable superhydrophobicity and underwater stability, which are superior to PDMS-CS coatings and CS layers. The coating surfaces demonstrated superhydrophobicity under a water pressure of 13.72 kPa for up to 16 days. The surface could withstand water flush for more than 15 min. The coatings also demonstrated good mechanical stability and maintained superhydrophobicity after an abrasion length of 8 m. The stable long-lasting underwater superhydrophobic surface is of great importance for marine applications.

The abundance of petroleum resource is influenced by various factors and involves complex parameters and extensive data. Consequently, traditional geostatistical methods often lack precision in quantitative prediction. To address this issue, this study focuses on the third member of Paleogene Shahejie Formation (member Es3) in the Wenliu area of the Dongpu Sag and utilizes a multi-layer perceptron neural network (MLP) for predicting petroleum resource abundance and employed the Boosting ensemble learning algorithm to optimize the prediction model. The MLP and MLP-Boosting algorithm models were test on 66 sample groups, yielding correlation coefficients of 0.789 and 0.989 for the training set, 0.618 and 0.825 for the validation set and 0.689 and 0.845 for the test set. The analysis identified effective thickness, average permeability and effective porosity are the most significant geological factors influencing petroleum resource abundance, with importance coefficients of 33.93%, 20.12% and 19.53%, respectively. Other factors such as trap area, surface crude oil density and sedimentary facies assignment were found to be less influential. Overall, the Boosting ensemble learning algorithm significantly enhanced the prediction accuracy of the multi-layer perceptron model, offering valuable insights for target optimization, exploration planning and petroleum resource evaluation in other blocks in the sag.

Abstract Clarifying the geochemical characteristics of formation water and its origin is conducive to clarifying the gas migration path, elaborating the law of gas migration and accumulation, and further predicting the favourable area of gas accumulation. Taking Quaternary formation water from the Tainan-Sebei area of the Sanhu Depression as the research object, the chemical characteristics and origin of the region are clarified using anion analysis, cation analysis, hydrogen isotope analysis, oxygen isotope analysis and so on. The results are as follows. (1) The formation water in the study area has a high total dissolved solids (TDS) content and is mainly type IV and V of CaCl2. (2) Low r(Na+)/r(Cl−), low desulfurization coefficient, high r(Ca2+)/r(Mg2+) and high indices of base exchange indicate that the Qigequan Formation is in a stagnant zone, which is beneficial for the accumulation and preservation of biogenic gas. (3) Due to albitization and water–rock reactions, the formation water is rich in Ca2+, poor in Na+ and poor in Mg2+. (4) The formation water in the study area originates from the glacial meltwater of the Kunlun Mountains, which converts into groundwater and seeps into the formation along the piedmont slope zone. In the process of groundwater infiltration and convergence, many salt substances in the formation are dissolved, resulting in a gradual increase in TDS. Then, the formation water with a high TDS migrates to the anticline in the northern part of the depression and is finally trapped in the formation.

Objective: Finding biomarkers related to non-small cell lung cancer (NSCLC) is helpful for the diagnosis and precise treatment of lung cancer. The relationship between serum tumor M2-pyruvate kinase (TuM2-PK), carcinoembryonic antigen (CEA), and cytokeratin 19 fragment (CYFRA21-1) and NSCLC was analyzed. Methods: The serum levels of TuM2-PK, CEA, and CYFRA21-1 in 184 patients with the NSCLC group, 60 patients with the benign lung disease (BLD) group, and 90 healthy controls (HC) group were detected. The levels of TuM2-PK were measured by using an enzyme-linked immunosorbent assay. The detection methods of CEA and CYFRA21-1 were electrochemiluminescence. The receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic value of TuM2-PK, CEA, and CYFRA21-1 on NSCLC. The Kaplan–Meier survival curve was drawn to evaluate the survival status in NSCLC patients with different serum levels of TuM2-PK, CEA, and CYFRA21-1. Results: Serum levels of TuM2-PK, CEA, and CYFRA21-1 in the NSCLC group were significantly higher than those in the BLD group and the HC group ( P

BackgroundOxidative stress is acknowledged as a pivotal factor in the intricate pathophysiological processes and pathogenesis of constipation. Modifying dietary patterns can elevate in vivo antioxidant biomarker levels, consequently mitigating oxidative stress. The Composite Dietary Antioxidant Index (CDAI) provides a dependable scoring mechanism for quantifying the potential antioxidant capacity of diets. The association between CDAI levels and the risk of constipation remains uncertain.PurposeTo investigate the potential correlation between CDAI and constipation, aiming to improve constipation management through dietary guidance.MethodsA total of 11,165 adults aged ≥20 years, drawn from the 2005–2010 National Health and Nutrition Examination Survey, were enrolled in this cross-sectional study. We evaluated the correlation between CDAI levels and the risk of constipation through three weighted logistic regression models. Restricted cubic spline (RCS) analysis was employed to assess nonlinear trends, and stratified analyses were conducted.ResultsAfter adjusting for all confounding variables, the findings revealed an association between CDAI and constipation [OR = 0.937; 95% CI (0.892, 0.984), p = 0.012]. Moreover, individuals in the highest quartile of CDAI demonstrated a 40.1% lower likelihood of experiencing constipation compared to those in the lowest quartile [OR = 0.599; 95% CI (0.382, 0.939), p = 0.027]. The RCS analysis indicated a linear relationship between CDAI and constipation (P-non-linear =0.1016). Subgroup analysis by gender revealed a negative correlation in the male population [OR = 0.871; 95% CI (0.801, 0.947), p = 0.002], with men in the highest CDAI quartile exhibiting a 59.8% lower likelihood of experiencing constipation compared to those in the lowest quartile [OR = 0.402; 95% CI (0.206, 0.787), p = 0.010]. Furthermore, alterations in selenium [OR = 0.997; 95% CI (0.995, 1.000), p = 0.039] per milligram were independently linked to constipation. In a gender subgroup analysis of a single antioxidant, changes per milligram of vitamin E [OR = 0.904; 95% CI (0.838 to 0.975), p = 0.011] among males were independently associated with constipation.ConclusionThe fully adjusted model showed a correlation between CDAI and constipation and a significant correlation in quartiles. Meanwhile, subgroup analysis by gender showed that CDAI was negatively associated with constipation in the male population. Moreover, the findings of this study imply that investigations into antioxidant diets should be contextualized within dietary patterns.

BackgroundEvidence from observational studies and clinical trials has associated gut microbiota with infectious diseases. However, the causal relationship between gut microbiota and infectious diseases remains unclear.MethodsWe identified gut microbiota based on phylum, class, order, family, and genus classifications, and obtained infectious disease datasets from the IEU OpenGWAS database. The two-sample Mendelian Randomization (MR) analysis was then performed to determine whether the gut microbiota were causally associated with different infectious diseases. In addition, we performed reverse MR analysis to test for causality.ResultsHerein, we characterized causal relationships between genetic predispositions in the gut microbiota and nine infectious diseases. Eight strong associations were found between genetic predisposition in the gut microbiota and infectious diseases. Specifically, the abundance of class Coriobacteriia, order Coriobacteriales, and family Coriobacteriaceae was found to be positively associated with the risk of lower respiratory tract infections (LRTIs). On the other hand, family Acidaminococcaceae, genus Clostridiumsensustricto1, and class Bacilli were positively associated with the risk of endocarditis, cellulitis, and osteomyelitis, respectively. We also discovered that the abundance of class Lentisphaeria and order Victivallales lowered the risk of sepsis.ConclusionThrough MR analysis, we found that gut microbiota were causally associated with infectious diseases. This finding offers new insights into the microbe-mediated infection mechanisms for further clinical research.

This study addresses the challenges of low detection precision and limited generalization across various ripeness levels and varieties for large non-green-ripe citrus fruits in complex scenarios. We present a high-precision and lightweight model, YOLOC-tiny, built upon YOLOv7, which utilizes EfficientNet-B0 as the feature extraction backbone network. To augment sensing capabilities and improve detection accuracy, we embed a spatial and channel composite attention mechanism, the convolutional block attention module (CBAM), into the head’s efficient aggregation network. Additionally, we introduce an adaptive and complete intersection over union regression loss function, designed by integrating the phenotypic features of large non-green-ripe citrus, to mitigate the impact of data noise and efficiently calculate detection loss. Finally, a layer-based adaptive magnitude pruning strategy is employed to further eliminate redundant connections and parameters in the model. Targeting three types of citrus widely planted in Sichuan Province—navel orange, Ehime Jelly orange, and Harumi tangerine—YOLOC-tiny achieves an impressive mean average precision (mAP) of 83.0%, surpassing most other state-of-the-art (SOTA) detectors in the same class. Compared with YOLOv7 and YOLOv8x, its mAP improved by 1.7% and 1.9%, respectively, with a parameter count of only 4.2M. In picking robot deployment applications, YOLOC-tiny attains an accuracy of 92.8% at a rate of 59 frames per second. This study provides a theoretical foundation and technical reference for upgrading and optimizing low-computing-power ground-based robots, such as those used for fruit picking and orchard inspection.

Abstract Invasive plants exert significant ecological impacts on native plants, communities, and ecosystems. However, consistent conclusions regarding how traits of invasive plants, native plants, and their divergences affect invasion dynamics are still lacking. Here, we conducted a pairwise common garden experiment to investigate how invasion was influenced not only by invasive plants but also by native plants, aiming to elucidate the role of invasive‐plant traits, native‐plant traits, and their divergences in invasion processes. Our findings revealed variations in invasive stage depending on the combinations of invasive and native plants. Specifically, native plants such as A. argyi, A. lavandulifolia, and C. album exhibited competitive superiority when co‐occurring with the three invasive plants. S. viridis, A. vestita, and A. annua had competitive superiority when they co‐occurred with E. canadensis, G. quadriradiata, and E. annuus respectively. Furthermore, our results demonstrated that the competitive abilities of invasive plants were primarily influenced by factors such as height, diameter, and biomass allocation, while native plants' competitive abilities were mainly affected by diameter, biomass allocation, and function group differences. Moreover, our analysis revealed that invasive‐plant traits, native‐plant traits, their divergences, and their interactions together explained 36.88% of the variation in invasion dynamics, with invasive‐plant traits and the native‐plant traits explaining 10.19% and 6.88%, respectively. In conclusion, the traits of invasive and native plants, along with their divergences, significantly influence interspecific relationships, and influencing the invasive stages. Divergences in competitive strategies between the native plants and invasive plants facilitated invasion processes. Our study not only contributes to understanding the mechanisms underlying invasion, but also provides a scientific foundation for predicting and managing the negative effects of invasive plants.

Background: Tracheoesophageal fistula (TEF) remains a rare but significant clinical challenge, mainly due to the absence of established, effective treatment approaches. The current focus of therapeutic strategy is mainly on fistula closure. However, this approach often misses important factors, such as accelerating fistula contraction and fostering healing processes, which significantly increases the risk of disease recurrence. Methods: In order to investigate if Mesenchymal Stem Cells (MSCs) can enhance fistula repair, developed a TEF model in beagles. Dynamic changes in fistula diameter were monitored by endoscopy. Concurrently, we created a model of LPS-induced macrophage to replicate the inflammatory milieu typical in TEF. In addition, the effect of MSC supernatant on inflammation mitigation was evaluated. Furthermore, we looked at the role of TLR4/NF-κB pathway plays in the healing process. Results: Our research revealed that the local administration of MSCs significantly accelerated the fistula's healing process. This was demonstrated by a decline in TEF apoptosis and decrease in the production of pro-inflammatory cytokines. Furthermore, in vivo experiments demonstrated that the MSC supernatant was effective in suppressing pro-inflammatory cytokine expression and alleviating apoptosis in LPS-induced macrophages. These therapeutic effects were mainly caused by the suppression of TLR4/NF-κB pathway. Conclusion: According to this study, MSCs can significantly improve TEF recovery. They achieve this via modulating apoptosis and inflammatory responses, mainly by selectively inhibiting the TLR4/NF-κB pathway.

Abstract Two-dimensional (2D) superlattices, formed by stacking sublattices of 2D materials, have emerged as a powerful platform for tailoring and enhancing material properties beyond their intrinsic characteristics. However, conventional synthesis methods are limited to pristine 2D material sublattices, posing a significant practical challenge when it comes to stacking chemically modified sublattices. Here we report a chemical synthesis method that overcomes this challenge by creating a unique 2D graphene superlattice, stacking graphene sublattices with monodisperse, nanometer-sized, square-shaped pores and strategically doped elements at the pore edges. The resulting graphene superlattice exhibits remarkable correlations between quantum phases at both the electron and phonon levels, leading to diverse functionalities, such as electromagnetic shielding, energy harvesting, optoelectronics, and thermoelectrics. Overall, our findings not only provide chemical design principles for synthesizing and understanding functional 2D superlattices but also expand their enhanced functionality and extensive application potential compared to their pristine counterparts.

Traditionally, radio frequency detection or ambient spectrum sensing has required high-performance spectrum analyzers and RF signal analyzers, leading to relatively high costs due to the need for a local oscillator and signal mixer. To overcome this challenge, we propose a low-cost, substantially simplified solution utilizing a stretchable rectenna, a microcontroller unit (MCU), and feedback control systems. By exploiting the dynamic correlation between the resonant frequency and the tensile ratio of the stretchable antenna, the incoming frequency can be determined by recording the maximum rectifier DC power output as a function of the electromechanically controlled tension ratio of the stretchable antenna. Our measured results indicate that a frequency measurement range of 1.8 GHz to 2.5 GHz can be achieved through careful design of the stretchable antenna and broadband rectifier. We have experimentally demonstrated an over-the-air far-field frequency sensing system based on this concept, showcasing significant advantages in power consumption, cost-effectiveness, and simplicity when compared to state-of-the-art RF spectrum analyzers.

Invasive plants have already been observed in the understory of mountain forests, which are often considered a safe shelter for most native plants. Microorganisms might be drivers of plant invasions. Nevertheless, the mechanisms determining variations in microbial community composition (beta diversity) during invasion along altitudinal gradients remain to be elucidated. Here, the elevational patterns and the driving ecological processes (e.g., environmental filtering, co-occurrence patterns, and community assembly processes) of soil bacterial beta diversity were compared between invasive and native plants on the Qinling Mountains. The species turnover dominated bacterial compositional dissimilarities in both invasive and native communities, and its contribution to total beta diversity decreased during invasion. Total soil bacterial dissimilarities and turnover exhibited significant binominal patterns over an altitudinal gradient, with a tipping point of 1413 m. Further analysis showed that the contributions of assembly processes decreased in parallel with an increase in contributions of co-occurrence patterns during the invasion process, indicating that species interdependence rather than niche partitioning is strongly correlated with the bacterial biogeography of invasive communities. Plant invasion affects the relative contributions of stochastic processes and co-occurrence interactions through the regulation of the physiochemical characteristics of soil, and ultimately determines compositional dissimilarities and the components of the bacterial community along altitudinal gradients.

Plant restoration strategies are ubiquitously employed for the purposes of soil and water conservation and ecological improvement in forest ecosystems. Despite N and temperature being acknowledged as pivotal factors affecting plant restoration outcomes, their effects on soil fertility, microbial communities, and shrub biomass remain underexplored, particularly in the loess hilly regions of China. Here, we examined the growth patterns and nutrient acquisition abilities of three shrub species, Periploca sepium, Amorpha fruticosa, and Vitex negundo, along with the attendant alterations in soil properties and microbial community composition under controlled greenhouse conditions. Specifically, we imposed three levels of N fertilization (200, 400, and 600 kg ha−1; designated as N1, N2, and N3, respectively) and temperature regimes (18–23, 25–30, and 32–37 °C; labeled T1, T2, and T3, respectively). The results indicated a significant interplay between the combination of N fertilization and temperature significantly affecting shrub growth. Optimal growth conditions, as evidenced by the highest dry biomass accumulation, were identified as N3T1 for A. fruticosa, N1T1 for P. sepium, and N2T2 for V. negundo, with these conditions differentially influencing roots, stems, and leaves. Furthermore, soil microorganisms also responded significantly to the N fertilization and temperature. However, this was largely dependent on shrub species and soil nutrients. For A. fruticosa under N3T1 conditions, Actinobacteria and Basidiomycota abundances correlated strongly with soil C, N, and P contents, while leaf N uptake significantly correlated with the structure of both bacterial and fungal communities. For P. sepium at N1T1, Acidobacteriota was dominant in response to soil N and C, while leaf C uptake and leaf and stem N uptake positively correlated with bacterial and fungal communities, respectively. For V. negundo at N2T2, Chloroflexi had the greatest abundance, responding to the greatest variation in soil N and C, while its stem N uptake was significantly related to the structure of the fungal communities. Thus, our findings underscored the intricate interplay between abiotic factors, shrub growth, soil fertility, and microbial community dynamics, providing insights into the optimization of plant restoration efforts in ecologically sensitive regions.

The target is often obstructed by obstacles with the dynamic tracking scene, leading to a loss of target information and a decrease in tracking accuracy or even complete failure. To address these challenges, we leverage the capabilities of Geiger-mode Avalanche Photodiode (GM-APD) LiDAR to acquire both intensity images and point cloud data for researching a target tracking method that combines the fusion of intensity images and point cloud data. Building upon Kernelized correlation filtering (KCF), we introduce Fourier descriptors based on intensity images to enhance the representational capacity of target features, thereby achieving precise target tracking using intensity images. Additionally, an adaptive factor is designed based on peak sidelobe ratio and intrinsic shape signature to accurately detect occlusions. Finally, by fusing the tracking results from Kalman filter and KCF with adaptive factors following occlusion detection, we obtain location information for the central point of the target. The proposed method is validated through simulations using the KITTI tracking dataset, yielding an average position error of 0.1182m for the central point of the target. Moreover, our approach achieves an average tracking accuracy that is 21.67% higher than that obtained by Kalman filtering algorithm and 7.94% higher than extended Kalman filtering algorithm on average.

Spin‐orbital angular momentum conversion (SOC) of light has found applications in classical and quantum optics. However, the existing SOC elements suffer severe restrictions on broadband integrated applications at miniature scales, due to bulky configurations, single function, and failing to control the dispersion. Herein, a high‐efficiency broadband achromatic method for independently and elaborately engineering the dispersion and the SOC of light based on a cascaded metasurface device is proposed. The metadevice is capable of efficiently decoupling the SOC from the modulation of dispersion with high‐broadband focusing efficiency up to 75%. For the proof of concept, the generation of high‐efficiency achromatic‐focused and spin‐controlled optical vortices with switchable topological charge (lσ=+1=1 and lσ=−1=2) is successfully demonstrated. The presence of achromatically and highly concentrated optical vortices with tunable photonic angular momentum using spin as an optical knob makes the proposed ultracompact and multifunctional metadevice a promising platform for optical micromanipulation at nanoscale dimensions.

BackgroundDifferences in border zone contribute to different outcomes post-infarction, such as left ventricular aneurysm (LVA) and myocardial infarction (MI). LVA usually forms within 24 h of the onset of MI and may cause heart rupture; however, LVA surgery is best performed 3 months after MI. Few studies have investigated the LVA model, the differences in border zones between LVA and MI, and the mechanism in the border zone.MethodsThe LVA, MI, and SHAM mouse models were used. Echocardiography, Masson’s trichrome staining, and immunofluorescence staining were performed, and RNA sequencing of the border zone was conducted. The adipocyte-conditioned medium-treated hypoxic macrophage cell line and LVA and MI mouse models were employed to determine the effects of the hub gene, adiponectin (ADPN), on macrophages. Quantitative polymerase chain reaction (qPCR), Western blot analysis, transmission electron microscopy, and chromatin immunoprecipitation (ChIP) assays were conducted to elucidate the mechanism in the border zone. Human subepicardial adipose tissue and blood samples were collected to validate the effects of ADPN.ResultsA novel, simple, consistent, and low-cost LVA mouse model was constructed. LVA caused a greater reduction in contractile functions than MI owing to reduced wall thickness and edema in the border zone. ADPN impeded cardiac edema and promoted lymphangiogenesis by increasing macrophage infiltration post-infarction. Adipocyte-derived ADPN promoted M2 polarization and sustained mitochondrial quality via the ADPN/AdipoR2/HMGB1 axis. Mechanistically, ADPN impeded macrophage HMGB1 inflammation and decreased interleukin-6 (IL6) and HMGB1 secretion. The secretion of IL6 and HMGB1 increased ADPN expression via STAT3 and the co-transcription factor, YAP, in adipocytes. Based on ChIP and Dual-Glo luciferase experiments, STAT3 promoted ADPN transcription by binding to its promoter in adipocytes. In vivo, ADPN promoted lymphangiogenesis and decreased myocardial injury after MI. These phenotypes were rescued by macrophage depletion or HMGB1 knockdown in macrophages. Supplying adipocytes overexpressing STAT3 decreased collagen disposition, increased lymphangiogenesis, and impaired myocardial injury. However, these effects were rescued after HMGB1 knockdown in macrophages. Overall, the IL6/ADPN/HMGB1 axis was validated using human subepicardial tissue and blood samples. This axis could serve as an independent factor in overweight MI patients who need coronary artery bypass grafting (CABG) treatment.ConclusionThe IL6/ADPN/HMGB1 loop between adipocytes and macrophages in the border zone contributes to different clinical outcomes post-infarction. Thus, targeting the IL6/ADPN/HMGB1 loop may be a novel therapeutic approach for cardiac lymphatic regulation and reduction of cell senescence post-infarction.

Aims: Previous studies have demonstrated a significant upregulation of Integrin Beta 1 (ITGB1) in Telocytes. This study aims to explore the roles and underlying mechanisms of ITGB1 in inflammation and oxidative stress following Lipo-polysaccharide (LPS) administration in Telocytes. Methods: We observed an increase in reactive oxygen species (ROS) production, accompanied by a reduction in ITGB1 levels post-LPS treatment. Results: Notably, inhibiting ROS synthesis markedly reduced LPS-induced ITGB1 expression. Additionally, ectopic ITGB1 expression mitigated LPS-induced inflammation and oxidative stress, evident through decreased levels of pro-inflammatory markers such as Tumor Necrosis Factor-alpha (TNF-α), Interleukin (IL)-1β, IL-6, and Monocyte Chemoattractant Protein (MCP)-1. Depletion of endothelial Yes-Associated Protein 1 (YAP1) notably diminished the levels of inflammatory markers and ROS production. Furthermore, exosomes secreted by ITGB1-modified Telocytes promoted Human Umbilical Vein Endothelial Cells (HUVECs) proliferation and inhibited apoptosis. In vivo experiments revealed that exosomes from ITGB1-modified Telocytes modulated functional and structural changes, as well as inflammatory responses in Acute Lung Injury (ALI). Conclusion: These findings highlight the critical role of the YAP1/ROS axis in LPS-induced Telocyte injuries, underlining the therapeutic potential of targeting ITGB1 for mitigating inflammation and oxidative stress in these cells.

Abstract Harvested wood/bamboo products (HWP/HBP) constitute a large global carbon stock. However, the contribution of HBP to carbon stocks has been neglected in mixed wood and bamboo data, especially in China. Therefore, the production approach and the first-order decay method were used to estimate the spatiotemporal carbon stock change in HWP/HBP based on provincial production data from the China Forestry Statistical Yearbooks for 1987–2020. The results showed that China’s total carbon stocks of HWP and HBP were 328.7 teragram carbon (TgC) and 129.7 TgC between 1987 and 2020. Of this, the HWP carbon stock was mainly sourced from three provinces across the north and south: Guangxi (60.8 TgC), Heilongjiang (37.2 TgC), and Fujian (24.2 TgC), and HBP carbon stock was mainly sourced from three southern provinces: Fujian (33.4 TgC), Guangxi (20.3 TgC), and Zhejiang (13.7 TgC). The proportion of the HBP carbon stock in the total carbon stock increased from 20% in 2010 to 28% in 2020, indicating that bamboo products play an important role in the accumulation of carbon stocks in China. The differences in contributions to spatiotemporal trends between the provinces provide more specific information to make precise decisions about forest management and carbon sequestration.

We conducted an experiment of planting a dead cow and a metal-framed cage with cameras on the 1629 m deep sea floor off the southeast coast of Hainan Island in the northwestern South China Sea, using ROV diving and setting up a video camera on the cage to observe animals who came to eat the bait. The deep-sea cameras captured footage of eight Pacific sleeper sharks (Somniosus pacificus) swimming and feeding around the dead cow. To our knowledge, this is the first time the occurrence of such a shark species has been reported in the South China Sea. Eight individuals were differentiated based on the characteristic differences displayed in the images, with lengths of 1.9 to 5.1 m estimated. The video camera also recorded the predators’ behavior of tearing at the dead cow on the seabed. It was discovered that Pacific sleeper sharks are not strictly solitary and exhibit queue-feeding behavior. This study is significant as it documents a record of a data-scarce shark species, for which little information is available in the literature. It also documents an expansion of the species’ known habitat from the north Pacific Ocean into the South China Sea. Such sharks diving into the deep sea to predate on dead animals also suggests that occurrences of large chunks of dead organic bodies falling onto the deep sea might have been more frequent than we previously thought in the South China Sea. The findings have implications for understanding the geographic connectivity of large swimming animals between the South China Sea and the Pacific Ocean and provide scientific evidence for formulating conservation and management strategies for sharks and other large animals in the oceans.

Hemodialysis is achieved by implanting a smart arteriovenous graft (AVG) to build a vascular pathway, but reliability and stability in data transmission cannot be guaranteed. To address this issue, a miniaturized dual-band circularly polarized implantable antenna operating at 1.4 GHz (for energy transmission) and 2.45 GHz (for wireless telemetry), implanted in a wireless arteriovenous graft monitoring device (WAGMD), has been designed. The antenna design incorporates a rectangular serpentine structure on the radiation surface to reduce its volume to 9.144 mm3. Furthermore, matching rectangular slots on the radiation surface and the ground plane enhance the antenna’s circular polarization performance. The simulated effective 3 dB axial ratio (AR) bandwidths are 11.43% (1.4 GHz) and 12.65% (2.45 GHz). The simulated peak gains of the antenna are −19.55 dBi and −22.85 dBi at 1.4 GHz and 2.45 GHz, respectively. The designed antenna is implanted in a WAGMD both in the simulation and the experiment. The performance of the system is simulated in homogeneous human tissue models of skin, fat, and muscle layers, as well as a realistic adult male forearm model. The measurement results in a minced pork environment align closely with the simulation results.

Continuous planting is unavoidable in agricultural production, but continuous planting affects plant growth and physiological characteristics. In this study, we analyzed rhizosphere soil nutrients, physiological characteristics, hormone metabolome changes and their interactions of Casuarina equisetifolia (C. equisetifolia) with the increase of continuous planting number. The results found that C. equisetifolia root was significantly inhibited, the plant height was dwarfed and the biomass was significantly reduced as continuous planting number increased. Secondly, continuous planting caused a decrease in the rhizosphere soil nutrient transformation capacity, and a significant decrease in the total soil nutrient and available nutrient content. Analysis of physiological indexes showed that continuous planting resulted in a decrease in nitrogen, phosphorus, and potassium content, a decrease in the activity of physiological indexes of resistance, and a decrease in photosynthetic capacity of C. equisetifolia leaves. Hormone metabolome analysis showed that continuous planting critically affected the accumulation of five characteristic hormones in C. equisetifolia leaves, in which salicylic acid 2-O-β-glucoside (SAG), 2-oxindole-3-acetic acid (OxIAA), trans-zeatin-O-glucoside (tZOG) and gibberellin A3 (GA3) content decreased significantly while abscisic acid (ABA) content increased significantly. In conclusion, continuous planting lowered the rhizosphere soil nutrient transformation capacity of C. equisetifolia, lowered the soil available nutrient content, inhibited their root growth, and hindered the nutrient uptake and transportation by the root, thus led to the decrease of the nutrient accumulation capacity in the leaves of C. equisetifolia, and the decrease of SAG, OxIAA, and tZOG, GA3 synthesis ability decreased, ABA accumulated in large quantities, C. equisetifolia resistance and photosynthesis ability decreased, and their growth was impeded. This study provides insights for the effective management of continuous planting in the cultivation of C. equisetifolia.

IntroductionAs motor imagery is informed by the anticipated sensory consequences of action, including pain, we reasoned that motor imagery could provide a useful indicator of chronic back pain. We tested the hypothesis that mental motor imagery regarding body movements can provide a reliable assessment of low back pain.MethodsEighty-five subjects with back pain and forty-five age-matched controls were shown two names of body parts and asked to indicate if they could imagine moving so that the named body parts touched. Three types of imagined movements were interrogated: movements of arms, movements of legs and movements requiring flexion and/or rotation of the low back.ResultsAccuracy and reaction times were measured. Subjects with back pain were less likely to indicate that they could touch body parts than age-matched controls. The effect was observed only for those movements that required movement of the low back or legs, suggesting that the effect was not attributable to task difficulty or non-specific effects. There was an effect of pain severity. Compared to subjects with mild pain, subjects with severe pain were significantly less likely to indicate that they could move so that named body parts touched. There was a correlation between pain ratings and impaired performance for stimuli that involved the lower but not upper body.DiscussionAs the Can They Touch task is quick, easy to administer and does not require an explicit judgment of pain severity, it may provide useful information to supplement the assessment of subjects with chronic pain.

Object detection based on deep learning has made great progress in the past decade and has been widely used in various fields of daily life. Model lightweighting is the core of deploying target detection models on mobile or edge devices. Lightweight models have fewer parameters and lower computational costs, but are often accompanied by lower detection accuracy. Based on YOLOv5s, this article proposes an improved lightweight target detection model, which can achieve higher detection accuracy with smaller parameters. Firstly, utilizing the lightweight feature of the Ghost module, we integrated it into the C3 structure and replaced some of the C3 modules after the upsample layer on the neck network, thereby reducing the number of model parameters and expediting the model’s inference process. Secondly, the coordinate attention (CA) mechanism was added to the neck to enhance the model’s ability to pay attention to relevant information and improved detection accuracy. Finally, a more efficient Simplified Spatial Pyramid Pooling—Fast (SimSPPF) module was designed to enhance the stability of the model and shorten the training time of the model. In order to verify the effectiveness of the improved model, experiments were conducted using three datasets with different features. Experimental results show that the number of parameters of our model is significantly reduced by 28% compared with the original model, and mean average precision (mAP) is increased by 3.1%, 1.1% and 1.8% respectively. The model also performs better in terms of accuracy compared to existing lightweight state-of-the-art models. On three datasets with different features, mAP of the proposed model achieved 87.2%, 77.8% and 92.3%, which is better than YOLOv7tiny (81.4%, 77.7%, 90.3%), YOLOv8n (84.7%, 77.7%, 90.6%) and other advanced models. When achieving the decreased number of parameters, the improved model can successfully increase mAP, providing great reference for deploying the model on mobile or edge devices.

Background: Glioblastoma (GBM) is the most prevalent malignant brain tumor, significantly impacting the physical and mental wellbeing of patients. Several studies have demonstrated a close association between gut microbiota and the development of GBM. In this investigation, Mendelian randomization (MR) was employed to rigorously evaluate the potential causal relationship between gut microbiota and GBM.Methods: We utilized summary statistics derived from genome-wide association studies (GWAS) encompassing 211 gut microbiota and GBM. The causal association between gut microbiota and GBM was scrutinized using Inverse Variance Weighted (IVW), MR-Egger, and Weighted Median (WM) methods. Cochrane’s Q statistic was employed to conduct a heterogeneity test. MR-Pleiotropic Residuals and Outliers (MR-PRESSO) were applied to identify and eliminate SNPs with horizontal pleiotropic outliers. Additionally, Reverse MR was employed to assess the causal relationship between GBM and pertinent gut microbiota.Results: The MR study estimates suggest that the nine gut microbiota remain stable, considering heterogeneity and sensitivity methods. Among these, the family.Peptostreptococcaceae and genus.Eubacterium brachy group were associated with an increased risk of GBM, whereas family.Ruminococcaceae, genus.Anaerostipes, genus.Faecalibacterium, genus.LachnospiraceaeUCG004, genus.Phascolarctobacterium, genus.Prevotella7, and genus.Streptococcus were associated with a reduced risk of GBM. Following Benjamini and Hochberg (BH) correction, family.Ruminococcaceae (OR = 0.04, 95% CI: 0.01–0.19, FDR = 0.003) was identified as playing a protective role against GBM.Conclusion: This groundbreaking study is the first to demonstrate that family.Ruminococcaceae is significantly associated with a reduced risk of GBM. The modulation of family_Ruminococcaceae for the treatment of GBM holds considerable potential clinical significance.

Unmanned surface vehicle (USV)’s motion is represented by time-series data that exhibit highly nonlinear and non-stationary features, significantly influenced by environmental factors, such as wind speed and waves, when sailing on the sea. The accurate prediction of USV motion, particularly crucial parameters, such as the roll angle and pitch angle, is imperative for ensuring safe navigation. However, traditional and single prediction models often struggle with low accuracy and fail to capture the intricate spatial–temporal dependencies among multiple input variables. To address these limitations, this paper proposes a prediction approach integrating temporal convolutional network (TCN) and bi-directional long short-term memory network (Bi-LSTM) models, augmented with a temporal pattern attention (TPA) mechanism, termed the TCN-Bi-LSTM-TPA (TBT) USV motion predictor. This hybrid model effectively combines the strengths of TCN and Bi-LSTM architectures to extract long-term temporal features and bi-directional dependencies. The introduction of the TPA mechanism enhances the model’s capability to extract spatial information, crucial for understanding the intricate interplay of various motion data. By integrating the features extracted by TCN with the output of the attention mechanism, the model incorporates additional contextual information, thereby improving prediction accuracy. To evaluate the performance of the proposed model, we conducted experiments using real USV motion data and calculated four evaluation metrics: mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R-squared (R2). The results demonstrate the superior accuracy of the TCN-Bi-LSTM-TPA hybrid model in predicting USV roll angle and pitch angle, validating its effectiveness in addressing the challenges of multivariate USV motion prediction.

While convolutional operation effectively extracts local features, their limited receptive fields make it challenging to capture global dependencies. Transformer, on the other hand, excels at global modeling and effectively captures global dependencies. However, the self-attention mechanism used in Transformers lacks a local mechanism for information exchange within specific regions. This article attempts to leverage the strengths of both Transformers and convolutional neural networks (CNNs) to enhance the Swin Transformer V2 model. By incorporating both convolutional operation and self-attention mechanism, the enhanced model combines the local information-capturing capability of CNNs and the long-range dependency-capturing ability of Transformers. The improved model enhances the extraction of local information through the introduction of the Swin Transformer Stem, inverted residual feed-forward network, and Dual-Branch Downsampling structure. Subsequently, it models global dependencies using the improved self-attention mechanism. Additionally, downsampling is applied to the attention mechanism’s Q and K to reduce computational and memory overhead. Under identical training conditions, the proposed method significantly improves classification accuracy on multiple image classification datasets, showcasing more robust generalization capabilities.

BackgroundPositive surgical margin (PSM) or apical positive surgical margin (APSM) is an established predictive factor of biochemical recurrence or disease progression in prostate cancer (PCa) patients after radical prostatectomy. Since there are limited usable magnetic resonance imaging (MRI)-based models, we sought to explore the role of three-dimensional (3D) visualization for preoperative MRI in the prediction of PSM or APSM.MethodsFrom December 2016 to April 2022, 149 consecutive PCa patients who underwent radical prostatectomy were retrospectively selected from the Second Affiliated Hospital of Dalian Medical University. According to the presence of PSM or APSM, patients were divided into a PSM group (n=41) and a without PSM group (n=108) and into an APSM group (n=33) and a without APSM group (n=116). Twenty-one parameters, including prostate apical shape, PCa distance to the membranous urethra, and pubic angle, were measured on 3D visualization of MRI. The development of the nomogram models was built by the findings of multivariate logistic regression analysis for significant factors.ResultsTo predict the probability of PSM, a longer PCa distance to the membranous urethra (OR=0.136, p=0.019) and the distance from the anterior peritoneum to the anterior border of the coccyx (work space AP, OR=0.240, p=0.030) were independent protective factors, while a type 3 prostate apical shape (OR=8.262, p=0.025) and larger pubic angle 2 (OR=5.303, p=0.029) were identified as independent risk factors. The nomogram model presented an area under the curve (AUC) of the receiver operating characteristic curve (ROC) of PSM of 0.777. In evaluating the incidence of APSM, we found that the distance to the membranous urethra (OR=0.135, p=0.014) was associated with a low risk of APSM, while larger pubic angle 1 (OR=4.666, p=0.043) was connected to a higher risk of APSM. The nomogram model showed that the AUC of APSM was 0.755.ConclusionAs 3D visualization for preoperative MRI showed good performance in predicting PSM or APSM, the tool might be potentially valuable, which also needs to be validated by multicenter, large-scale, prospective studies.

Due to the constraints of agricultural computing resources and the diversity of plant diseases, it is challenging to achieve the desired accuracy rate while keeping the network lightweight. In this paper, we proposed a computationally efficient deep learning architecture based on the mobile vision transformer (MobileViT) for real-time detection of plant diseases, which we called plant-based MobileViT (PMVT). Our proposed model was designed to be highly accurate and low-cost, making it suitable for deployment on mobile devices with limited resources. Specifically, we replaced the convolution block in MobileViT with an inverted residual structure that employs a 7×7 convolution kernel to effectively model long-distance dependencies between different leaves in plant disease images. Furthermore, inspired by the concept of multi-level attention in computer vision tasks, we integrated a convolutional block attention module (CBAM) into the standard ViT encoder. This integration allows the network to effectively avoid irrelevant information and focus on essential features. The PMVT network achieves reduced parameter counts compared to alternative networks on various mobile devices while maintaining high accuracy across different vision tasks. Extensive experiments on multiple agricultural datasets, including wheat, coffee, and rice, demonstrate that the proposed method outperforms the current best lightweight and heavyweight models. On the wheat dataset, PMVT achieves the highest accuracy of 93.6% using approximately 0.98 million (M) parameters. This accuracy is 1.6% higher than that of MobileNetV3. Under the same parameters, PMVT achieved an accuracy of 85.4% on the coffee dataset, surpassing SqueezeNet by 2.3%. Furthermore, out method achieved an accuracy of 93.1% on the rice dataset, surpassing MobileNetV3 by 3.4%. Additionally, we developed a plant disease diagnosis app and successfully used the trained PMVT model to identify plant disease in different scenarios.

Most geological drilling rigs using asynchronous motor with gear box as the drive device of drawworks have problems of instability at low speed, complex mechanism, and lots maintenance work. This paper proposes an application of permanent magnet synchronous motor (PMSM) used in drawworks. Firstly, running characteristics of PMSM in steady state are analyzed and calculated theoretically, and mathematical models of motor’s power, torque, and power loss are given. Secondly, vector control principle of PMSM is analyzed and introduced, and maximum torque/current control method is finally decided as the basis for the design of control system. Thirdly, running parameters of PMSM in three stages (lowering drill string, constant speed/pressure drilling, and lifting drill string) of each test point were measured, and the curves of key parameters of PMSM are given. Finally, it is concluded that drawworks with PMSMs can not only improve control performance, reduce power consumption, and save energy, but also omit the gearbox, which makes structure of lifting system more compact and reduce lots maintenance work. The research work of this paper lays a foundation for the popularization and application of PMSM in the industry of deep well drilling rig.

Abstract Solar‐driven interfacial evaporation is a sustainable and economical technology for freshwater generation. Structural design of photothermal material is an effective strategy to improve the evaporation performance but is usually bothered by complicated processes and non‐adjustability. Herein, magnetic nanoparticles assembled photothermal evaporator was developed, which showed an adjustable spinal array surface under uniform magnetic field induction. By regulating position in the magnetic field, the desirable surface structures could be uniform at relatively low load density of magnetic nanoparticles to improve light absorption via multiple reflections. Magnetic field induced evaporator could accelerate evaporation to over 1.39 kg m−2 h−1 under 1‐sun illumination, which was 2.8 times that of natural evaporation. After coated by carbon layer, magnetic nanoparticles could overcome the oxidation to realize stable evaporation in long‐term desalination. The facile strategy to optimize the surface structure via magnetic field is appropriate for various fields with special requirements on surface structure.

This study examined the role of pretreatment albumin-to-fibrinogen ratio (AFR) in the prognosis of small-cell lung cancer (SCLC) patients receiving first-line platinum-based chemotherapy. A total of 131 SCLC patients were enrolled. The predictive value of the AFR for progression free survival (PFS) and overall survival (OS) were evaluated by receiver operating characteristic (ROC) curve analysis. The predictive factor of survival was assessed by univariate and multivariate Cox proportional regression analysis. The correlation between OS, PFS and AFR was determined by the log-rank test using the Kaplan-Meier method. AFR was an effective predictor of OS in SCLC patients with a cut-off value of 7.78. AFR was independent risk factors for OS and PFS. Kaplan Meier analysis showed that PFS and OS in patients with high AFR levels were significantly higher than those with low AFR levels. These results suggest that AFR could be an effective predictor of survival in patients with SCLC.

Self-driven solar-blind ultraviolet photodetectors (SBUVPDs) have attracted considerable interest for their superior sensitivity and operation flexibility. Herein, we demonstrate realization of a simple vertical-structured self-driven SBUVPD by pulsed laser deposition of β-Ga2O3 thin film on commercially available fluorine doped tin oxide (FTO) substrate, adopting multi-layer graphene (MLG) as the top electrode. On the one hand, the introduction of MLG with both high electrical conductance and UV transmittance greatly enhances photocurrent and responsivity of the photodetector. On the other hand, the dominating Schottky contact between Ga2O3 and MLG creates a net built-in electric field, leading to self-driven photoresponse of the device with improved response speed. With optimized thickness (8 ± 2 single layers of graphene) of the top electrode, the device exhibits the best detection performance that is superior to most of previous reports towards UV illumination at 0 V bias. It delivers a photocurrent as high as 31 nA towards 250 nm-light with ultra-fast response speed (τr = 2 ms, τd = 8.8 ms), and exhibits a maximum responsivity of 9.2 mA/W and detectivity of 5.27 × 1011 Jones under 230 nm-light illumination, while the response cuts off at light wavelength of 261 nm, falling completely within the solar blind band. In addition, the device has good multi-cycle repeatability and stability, showing great application potential in solar-blind UV detection.

To investigate the effects of millet flour on the farinaceous properties, viscoelastic properties and noodle quality of wheat dough, and the correlation analysis between dough rheological properties and noodle quality. Millet flour was added in the proportion of 0%, 10%, 20%, 30% and 40% to wheat flour, and the rheological properties of dough, cooking properties and texture properties of noodles with different amounts of millet flour were determined. The results showed that the weakening degree, elastic modulus and viscosity modulus of the dough, the breaking rate and the cooking loss rate of the noodles increased with the increase of the amount of millet flour; while the water absorption rate, stabilization time and forming time of the dough, the cohesion, recovery, stretching force and stretching distance of noodles showed a downward trend. When the addition amount of millet flour was 20%, the hardness, stickiness and chewiness of noodles were 777.8 g, 637.7, and 588.06, respectively, which were optimal compared with other additions of millet flour. The elastic modulus, viscous modulus and weakening degree of millet-wheat mixed flour dough were negatively correlated with the stretching force and stretching distance of the noodles, which proved the application potential of millet flour in the directional change of noodle quality.

IntroductionThe difficulties in tea shoot recognition are that the recognition is affected by lighting conditions, it is challenging to segment images with similar backgrounds to the shoot color, and the occlusion and overlap between leaves.MethodsTo solve the problem of low accuracy of dense small object detection of tea shoots, this paper proposes a real-time dense small object detection algorithm based on multimodal optimization. First, RGB, depth, and infrared images are collected form a multimodal image set, and a complete shoot object labeling is performed. Then, the YOLOv5 model is improved and applied to dense and tiny tea shoot detection. Secondly, based on the improved YOLOv5 model, this paper designs two data layer-based multimodal image fusion methods and a feature layerbased multimodal image fusion method; meanwhile, a cross-modal fusion module (FFA) based on frequency domain and attention mechanisms is designed for the feature layer fusion method to adaptively align and focus critical regions in intra- and inter-modal channel and frequency domain dimensions. Finally, an objective-based scale matching method is developed to further improve the detection performance of small dense objects in natural environments with the assistance of transfer learning techniques. Results and discussionThe experimental results indicate that the improved YOLOv5 model increases the mAP50 value by 1.7% compared to the benchmark model with fewer parameters and less computational effort. Compared with the single modality, the multimodal image fusion method increases the mAP50 value in all cases, with the method introducing the FFA module obtaining the highest mAP50 value of 0.827. After the pre-training strategy is used after scale matching, the mAP values can be improved by 1% and 1.4% on the two datasets. The research idea of multimodal optimization in this paper can provide a basis and technical support for dense small object detection.

IntroductionInsect pests from the family Papilionidae (IPPs) are a seasonal threat to citrus orchards, causing damage to young leaves, affecting canopy formation and fruiting. Existing pest detection models used by orchard plant protection equipment lack a balance between inference speed and accuracy.MethodsTo address this issue, we propose an adaptive spatial feature fusion and lightweight detection model for IPPs, called ASFL-YOLOX. Our model includes several optimizations, such as the use of the Tanh-Softplus activation function, integration of the efficient channel attention mechanism, adoption of the adaptive spatial feature fusion module, and implementation of the soft Dlou non-maximum suppression algorithm. We also propose a structured pruning curation technique to eliminate unnecessary connections and network parameters.ResultsExperimental results demonstrate that ASFL-YOLOX outperforms previous models in terms of inference speed and accuracy. Our model shows an increase in inference speed by 29 FPS compared to YOLOv7-x, a higher mAP of approximately 10% than YOLOv7-tiny, and a faster inference frame rate on embedded platforms compared to SSD300 and Faster R-CNN. We compressed the model parameters of ASFL-YOLOX by 88.97%, reducing the number of floating point operations per second from 141.90G to 30.87G while achieving an mAP higher than 95%.DiscussionOur model can accurately and quickly detect fruit tree pest stress in unstructured orchards and is suitable for transplantation to embedded systems. This can provide technical support for pest identification and localization systems for orchard plant protection equipment.

Covering about 30% of the global total land area, desert ecosystems have been influenced by warming and nitrogen deposition. However, it remains unclear how desert ecosystems respond to warming and nitrogen deposition. Therefore, we conducted a greenhouse experiment to examine the impacts of N addition and warming on the plant–soil system of Neotrinia splendens, the dominant plant in the desert ecosystem in Northern China. Our findings revealed that low-N dose (N1) and high-N dose additions (N2) increased the biomass by 22.83% and 54.23%, respectively; meanwhile, moderate warming (T2) and severe warming (T3) decreased the biomass by 39.07% and 45.47%, respectively. N addition did not significantly affect the C:N:P stoichiometry in the plant–soil system. T2 and T3 decreased the leaf N content by 17.50% and 16.20%, respectively, and decreased the leaf P content by 10.61% and 45.29%, respectively. This resulted in the plant C:N ratio, C:P ratio, and N:P ratio increasing with warming. Furthermore, warming or N addition not only decreased soil microbial diversity, but also inhibited microbial genera associated with nutrient cycling, such as that of Tumebacillus spp., Bacillus spp., and Mortierella spp.; it additionally influenced important bacterial functions, such as nitrate reduction and ureolysis. Moreover, warming and N addition induced P limitation in the plant–soil system by inhibiting soil microorganisms, such as Mortierella spp. and Bacillus spp., which are associated with P transformation; this was also brought about by increasing the effects of leaf P content on leaf N:P. In conclusion, our results showed that warming and N addition had significant effects on the C:N:P stoichiometry of the plant–soil system through microbial mediation and led to P limitation in the system, regardless of how they affected biomass. Soil microorganisms could mediate the impacts of environmental changes on the plant–soil system. Our findings may provide valuable insights for adjusting vegetation restoration strategies in desert ecosystems under environmental changes.

High-tilt reclined seats are one of the most popular configurations in highly automated vehicles; however, current restraint systems cannot protect out-of-position occupants in this type of seat. To reduce the risk of injury to reclined occupants, this study proposes a swiveling seat driven by occupant inertia and rotated in the sagittal plane during impact. The effectiveness of the swiveling seat was evaluated based on kinematics and injury to a human biomechanical model in a frontal sled test. A simulation matrix was constructed to design and optimize various safety devices, including the belt, pre-tensioner, knee constraint, and rotation stiffness for the swiveling seat. The results showed that (1) submarining occurred when the reclined occupant was on a fixed seat with a normal three-point belt during impact; (2) a fixed seat with a dynamic locking tongue and passenger lap pretension prevented the submarining, but produced a high lumbar force of 5359 N, which was higher than the spine injury criterion; and (3) the proposed swiveling seat with a matched restraint system could prevent submarining and produce lumbar force of 1787 N. The results demonstrated that the swiveling seat has high potential for occupant protection in intelligent driving scenarios.

Quasi-solid-state Zn-air batteries are limited by sluggish kinetics and low temperature incompatibility. Here, the authors use a single-atom catalyst and an organohydrogel electrolyte to enable a 100 mA cm−2 cycling current density at 25 °C and a −60 to 60 °C operational window.

Benshan tea is a kind of oolong tea, and Benshan (Camellia sinensis) tea tree originates from Anxi County of Fujian Province in China, which is a national tea tree breed. Tea processing is the key to the formation of its odor characteristics. It is extremely important to step by step analyze effects of tea processing on aroma intensity and the formation of odor characteristics for optimizing tea processing process and improving tea quality. The results of this study showed that processing resulted in a significant increase in the content of volatile compounds in tea leaves, i.e., from 25.213 μg/kg to 111.223 μg/kg, in which the volatile compounds were mainly terpenoids. Secondly, the analysis found that 20 kinds of key compounds constituted to odor characteristics of Benshan tea leaves, among which geraniol, trans-β-ionone, gerol, citronellol, benzeneacetaldehyde, and trans-nerolidol were the most key six. Floral and fruity aromas, especially floral aroma, mainly formed odor characteristics of Benshan tea after processing, while floral aroma mainly came from the contribution of geraniol, which was the foremost compound in the formation of floral aroma of Benshan tea.

The biogas of Quaternary weak diagenetic shale has a wide exploration prospect, and the understanding of pore structure and physical properties of these shales is conducive to promote the geological theory of shale biogas reservoir. The Quaternary weak diagenetic shale from the Sanhu area of Qaidam Basin was employed as an example in this study, to study the pore structure and physical properties by the means of scanning electron microscopy, thin section observation, X-ray diffraction of whole-rock samples, overpressure pore permeability determination, relative permeability of gas-water etc. Results show that there are three types of lithofacies for the Quaternary weak diagenetic shales including clay shales, mixed shales and felsic shales, and pores appeared to include intergranular pores, inner pores of brittle mineral grains, inner pores of clay mineral grains and organic pores. The pore size of felsic shale is large, mainly micron pores. The pore size of clay shale and mixed shale are dominated by nano-scale pores. The peak porosity and permeability of Quaternary weak diagenetic shale distribute in the range of 15%-30% and (0.1-10) ×10-3 μm2, respectively, and significantly reduce under overburden conditions. The residual water saturation after displacement is high (with an average value of 58.7%). Due to clay expansion, the effective pore space is reduced and the fluidity of gas and water is restrained, resulting in very low relative permeability of gas and water in the two-phase co-flow zone.

In this paper, a solution to the problem of following a curved path for underactuated unmanned surface vehicles (USVs) with unknown sideslip angle and model uncertainties is studied. A novel smooth sliding mode control (SSMC) based on a finite-time extended state observer (FTESO) for heading control is proposed. Firstly, the model of a USV with rudderless double thrusters is established. Secondly, the path-following error dynamics of a USV is established in a path-tangential reference frame. Thirdly, a finite-time observer is introduced to estimate the unidentified sideslip angle, and the line-of-sight (LOS) guidance law is applied to produce the desired heading angle. Finally, an SSMC controller is proposed to force USV tracking at the desired heading angle and surge speed, in which FTESO is used to estimate and compensate the unknown disturbance in sliding mode dynamics. The theoretical analysis for FTESO-SSMC verifies that the controller can provide finite-time convergence to and stability on the sliding surface. Simulation studies and contrast test are conducted to demonstrate the robustness and rapidity of the proposed FTESO-SSMC controller.

The shale reservoirs of the Lower Cambrian Qiongzhusi Formation are widely distributed in the Sichuan Basin and have abundant gas resources. However, the shale lithofacies of the Qiongzhusi Formation are complex due to frequent sea level changes. The reservoir pore structure characteristics and gas content of different shale lithofacies vary significantly, which makes identifying the ‘sweet spot’ a challenging task. In this study, core observation and X-ray diffraction (XRD) were used to analyze the lithofacies types and characteristics of the study area. The pore types of different shale lithofacies were observed using field emission-scanning electron microscopy. Pore structures were studied using low-temperature gas (including N2 and CO2) physisorption, and the pore volume (PV), specific surface area (SSA) and pore structure were systematically characterized. The primary factors influencing pore formation in different types of shale lithofacies were analyzed by combining geochemical experiments and mineral contents. The results indicate that the lithofacies of the Qiongzhusi Formation shale in the study area can be classified into five categories according to mineral compositions: Siliceous argillaceous shale (CM-1), Argillaceous siliceous mixed shale (M-2), Argillaceous siliceous shale (S-3), Siliceous rock (S) and Calcareous siliceous shale (S-2). Pores are abundant in S-3 shale, M-2 shale and CM-1 shale. The S-3 shale is more enriched in organic pores and clay mineral pores compared to other lithofacies shales, and the pore morphology is mainly wedge-shaped and plate-like. M-2 shale and CM-1 shale are rich in clay minerals and mainly develop clay mineral pores and are mainly wedge-shaped and plate-like. The S shale and S-2 shale mainly develop interparticle pores and clay mineral pores, which are mainly slit-like. The results show that TOC, pyrite content, quartz and feldspar mineral content, clay mineral type and content affect the pore structure in the study area. Quartz and feldspar content have a negative effect on micropore and mesopore volumes. TOCs have a weak positive correlation with micropore volume and micropore SSA. Clay mineral content has significant positive effects on the PV and SSA of micropores and mesopores, indicating that clay mineral content is the main factor affecting the pore structure of shale.