Search

Abstract Background Angiogenesis is associated with tumour growth, infiltration, and metastasis. This study aimed to detect the mechanisms of angiogenesis-related genes (ARGs) in multiple myeloma (MM) and to construct a new prognostic model. Methods MM research foundation (MMRF)-CoMMpass cohort, GSE47552, GSE57317, and ARGs were sourced from public databases. Differentially expressed genes (DEGs) in the tumour and control cohorts in GSE47552 were determined through differential expression analysis and were enriched with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Weighted gene coexpression network analysis (WGCNA) was applied to derive modules linked to the ARG scores and obtain module genes in GSE47552. Differentially expressed ARGs (DE-ARGs) were selected for subsequent analyses by overlapping DEGs and module genes. Furthermore, prognostic genes were selected using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Depending on the prognostic genes, a risk model was constructed, and risk scores were determined. Moreover, MM samples from MMRF-CoMMpass were sorted into high- and low-risk teams on account of the median risk score. Additionally, correlations among clinical characteristics, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), immune analysis, immunotherapy predictions and the mRNA‒miRNA‒lncRNA network were carried out. Results A total of 898 DEGs, 211 module genes, 24 DE-ARGs and three prognostic genes (AKAP12, C11orf80 and EMP1) were selected for this study. Enrichment analysis revealed that the DEGs were related to 86 GO terms, such as ‘cytoplasmic translation’, and 41 KEGG pathways, such as ‘small cell lung cancer’. A prognostic gene-based risk model was created in MMRF-CoMMpass and confirmed with the GSE57317 dataset. Moreover, a nomogram was established on the basis of independent prognostic factors that have proven to be good predictors. In addition, the immune cell infiltration results suggested that memory B cells were enriched in the high-risk group and that immature B cells were enriched in the low-risk group. Finally, the mRNA‒miRNA‒lncRNA network demonstrated that hsa-miR-508-5p was tightly associated with EMP1 and AKAP12. RT‒qPCR was used to validate the expression of the genes associated with prognosis. Conclusion A new prognostic model of MM associated with ARGs was created and validated, providing a new perspective for exploring the connection between ARGs and MM.

Neurotoxic astrocytes are a promising therapeutic target for the attenuation of cerebral ischemia/reperfusion injury. Low-density lipoprotein receptor, a classic cholesterol regulatory receptor, has been found to inhibit NLR family pyrin domain containing protein 3 (NLRP3) inflammasome activation in neurons following ischemic stroke and to suppress the activation of microglia and astrocytes in individuals with Alzheimer’s disease. However, little is known about the effects of low-density lipoprotein receptor on astrocytic activation in ischemic stroke. To address this issue in the present study, we examined the mechanisms by which low-density lipoprotein receptor regulates astrocytic polarization in ischemic stroke models. First, we examined low-density lipoprotein receptor expression in astrocytes via immunofluorescence staining and western blotting analysis. We observed significant downregulation of low-density lipoprotein receptor following middle cerebral artery occlusion reperfusion and oxygen–glucose deprivation/reoxygenation. Second, we induced the astrocyte-specific overexpression of low-density lipoprotein receptor using astrocyte-specific adeno-associated virus. Low-density lipoprotein receptor overexpression in astrocytes improved neurological outcomes in middle cerebral artery occlusion mice and reversed neurotoxic astrocytes to create a neuroprotective phenotype. Finally, we found that the overexpression of low-density lipoprotein receptor inhibited NLRP3 inflammasome activation in oxygen–glucose deprivation/reoxygenation injured astrocytes and that the addition of nigericin, an NLRP3 agonist, restored the neurotoxic astrocyte phenotype. These findings suggest that low-density lipoprotein receptor could inhibit the NLRP3-meidiated neurotoxic polarization of astrocytes and that increasing low-density lipoprotein receptor in astrocytes might represent a novel strategy for treating cerebral ischemic stroke.

Abstract Rice blast is the primary disease affecting rice yield and quality, and its effective detection is essential to ensure rice yield and promote sustainable agricultural production. To address traditional disease detection methods’ time-consuming and inefficient nature, we proposed a method called Pyramid-YOLOv8 for rapid and accurate rice leaf blast disease detection in this study. The algorithm is built on the YOLOv8x network framework and features a multi-attention feature fusion network structure. This structure enhances the original feature pyramid structure and works with an additional detection head for improved performance. Additionally, this study designs a lightweight C2F-Pyramid module to enhance the model’s computational efficiency. In the comparison experiments, Pyramid-YOLOv8 shows excellent performance with a mean Average Precision (mAP) of 84.3%, which is an improvement of 9.9%, 4.3%, 7.4%, 6.1%, 1.5%, 3.7%, and 8.2% compared to the models Faster-RCNN, RT-DETR, YOLOv3-SPP, YOLOv5x, YOLOv9e, and YOLOv10x, respectively. Additionally, it reaches a detection speed of 62.5 FPS; the model comprises only 42.0 M parameters. Meanwhile, the model size and Floating Point Operations (FLOPs) are reduced by 41.7% and 23.8%, respectively. These results demonstrate the high efficiency of Pyramid-YOLOv8 in detecting rice leaf blast. In summary, the Pyramid-YOLOv8 algorithm developed in this study offers a robust theoretical foundation for rice disease detection and introduces a new perspective on disease management and prevention strategies in agricultural production.

Abstract Background The objective of this research is to clarify the impact of periodontitis on overall and cardiovascular-related death rates among hypertensive individuals. Method A total of 5665 individuals with hypertension were included from the National Health and Nutrition Examination Survey (NHANES) data spanning 2001–2004 and 2009–2014. These individuals were divided into two groups based on the presence or absence of periodontitis and further stratified by the severity of periodontitis. We employed weighted multivariate Cox proportional hazards regression and Kaplan-Meier curves (log-rank test) to evaluate the impact of periodontitis on all-cause and cardiovascular mortality. Additional analyses, including adjustments for various covariates, subgroups, and sensitivity analyses, were conducted to ensure the robustness and reliability of our results. Result Over an average follow-up duration of 10.22 years, there were 1,122 all-cause and 297 cardiovascular deaths. Individuals with periodontitis exhibited an elevated risk of all-cause mortality (HR = 1.33, 95% CI 1.18–1.51; p

Abstract In the field of information processing, all-optical routers are significant for achieving high-speed, high-capacity signal processing and transmission. In this study, we developed three types of structurally simple and flexible routers using the deep diffractive neural network (D2NN), capable of routing incident light based on wavelength and polarization. First, we implemented a polarization router for routing two orthogonally polarized light beams. The second type is the wavelength router that can route light with wavelengths of 1550, 1300, and 1100 nm, demonstrating outstanding performance with insertion loss as low as 0.013 dB and an extinction ratio of up to 18.96 dB, while also maintaining excellent polarization preservation. The final router is the polarization-wavelength composite router, capable of routing six types of input light formed by pairwise combinations of three wavelengths (1550, 1300, and 1100 nm) and two orthogonal linearly polarized lights, thereby enhancing the information processing capability of the device. These devices feature compact structures, maintaining high contrast while exhibiting low loss and passive characteristics, making them suitable for integration into future optical components. This study introduces new avenues and methodologies to enhance performance and broaden the applications of future optical information processing systems. Graphical Abstract

The effectiveness of rehabilitation is contingent upon the motor recovery process which typically involves long-term motor skill re-acquisition. Given that the learning process can be modulated by task difficulty, elucidating the underlying neural mechanism is essential for optimizing rehabilitation prescription to suit different patient conditions. This study aimed to investigate the impact of task difficulty on cortical response during force-control training via electroencephalography (EEG). An 8-day visuomotor force-tracking training experiment was conducted. Healthy right-handed participants (N=33) were recruited and randomly assigned to 3 groups, and each group was tasked with a different level of difficulty. The task difficulty was manipulated by variation in force-production complexity and execution sequence assignments, with real-time visual feedback provided to participants for self-output adjustment. Behavioral performance was quantitatively assessed using a pre-defined score metric related to performance accuracy. The EEG signals were collected, and corresponding event-related desynchronization (ERD) and relative functional connectivity (FC) during the task execution were analyzed within the alpha- (8–13 Hz) and beta- (15–30 Hz) bands. A post-training experiment was also performed to evaluate the near-transfer capability of learning. Results showed all the behavioral performances improved during practice, while higher task difficulty level was affiliated with better post-training near-transfer ability. The dynamic neural response to training could be mediated by changes in difficulty level, where increased task complexity corresponded with the heightened activities in the beta-band priorly within the right dorsolateral prefrontal area. Additionally, stronger alpha-band functional connectivity was observed to be predominantly associated with the left motor area (LMA) during challenging tasks, and the intensification in connectivity persisted selectively post-training which appeared to be acritical factor for skill transfer performance improvement. These findings illustrated the dynamic neural mechanism through which task difficulty affects behavioral performance during long-term motor training with accurate force-control purpose. The selectively strengthened functional connectivity may contribute to facilitating new task execution after training interventions. Therefore, beneficial neural modulation can be expected to be feasible by well-designed task difficulty strategies for effective motor rehabilitation.

We used powder hot extrusion followed by annealing to fabricate a dual-phase AlCoCrFeNi2.1 eutectic high-entropy alloy consisting of both non-recrystallized and recrystallized grains with dense nano-sized precipitates arranged in heterogeneous structures. The resulting alloy exhibits superior tensile properties with a yield strength of ∼1.2 GPa, an ultimate tensile strength of ∼1.5 GPa, and a uniform elongation of ∼18%. Such multiscale microstructure results in exceptional strain hardening of the AlCoCrFeNi2.1 alloy by activating remarkable hetero-deformation induced (HDI) stresses. Our study provides an effective approach to fabricating high-performance structural materials via dual-phase heterogeneous microstructure design.

Microbiota in the reproductive tract of cattle play a vital role in maintaining normal reproduction. However, the information on microbiota in different parts of reproductive tracts with different genetic background is few. The aim of the present study was to describe and compare the microbiota in vagina, cervix and uterus of Yanbian cattle and Yanhuang cattle. The results showed that microbial diversity increases from the vagina to the uterus. The top three bacterial phyla in bovine reproductive tract were Proteobacteria, Firmicutes and Bacteroidetes, accounting for more than 85%. From the vagina to the uterus, the relative abundance of Proteobacteria gradually decreased, while that of Firmicutes gradually increased. Phylum-level Firmicutes and genus-level UCG_010 were significantly enriched in the uterus of Yanbian cattle and Yanhuang cattle. Comparing the same parts of the two breeds, it was found that there was no significant difference in alpha diversity, but significant differences in beta diversity. In addition, microbiota with significant differences in the relative abundance of the reproductive tract were found. These findings lay a foundation for a comprehensive understanding of the structure of the genital tract microbiota of cows and its regulatory mechanisms.

Addressing the quintessential challenge of local feature matching in computer vision, this study introduces a novel fast sparse seed graph structure named LightSGM. This structure aims to refine the characterization of graph features while minimizing superfluous connections. Initially, a subset of high-quality seed feature points is curated using a confidence filter. Subsequently, keypoint features are assimilated into this seed set via graph pooling, and the composite features are further processed through a memory and computation-efficient seed transformer to capture rich contextual information about the keypoints. The seed feature points are then relayed back to the original keypoints using an inverse process known as graph unpooling. The paper also introduce an adaptive mechanism to determine the optimal number of model layers based on the intricacy of matching image pairs. A Matching Point Prediction Header is employed to extract the final set of matching points. Through extensive experimentation on image matching and position estimation, LightSGM has demonstrated its prowess in delivering competitive matching accuracy while maintaining a balance with real-time processing capabilities.

Aiming at the existing object detection models has difficulty effectively capturing relevant features when detecting small targets such as wire breakage faults in transmission lines, resulting in low detection accuracy. An improved algorithm, named DRS-YOLO, has been developed based on YOLOv8 for detecting broken wires in transmission lines. Firstly, by adding a deformable convolution module (C2f_DCNv3) and mixed attention convolution module (RFASEConv) to the backbone network of YOLOv8n, the receptive field of the model is expanded to improve the detection accuracy of small targets in complex backgrounds. Secondly, the Inner-SIOU loss based on auxiliary bounding boxes is adopted as the loss function to enhance the feature extraction efficiency of the model and further improve its accuracy in detecting wire breakage in transmission lines. Finally, DRS-YOLO is analysed in comparison with Faster-RCNN, SSD, YOLOv9-c, YOLOv5n and YOLOv8n. The results showed that compared with other detection methods, DRS-YOLO has higher detection accuracy smaller parameters and computational complexity in detecting wire breakage faults in transmission lines. Its average detection accuracy (mAP) is 92.5%, the recall is 85.8%, and the Precision is 95.4%. Compared with the original YOLOv8n network, it has improved by 7.6%, 4%, and 2.7% respectively, while the parameter quantity is 2.76M, which is 8% lower than the original YOLOv8n. Compared with other existing object detection models, the DRS-YOLO model has achieved good results in mAP, accuracy, and recall, and can efficiently and accurately complete the task of detecting broken wires in transmission lines.

Abstract It is believed that gravity can be considered as a quantum coherent mediator. In this study, we propose a plan to test the existence of extra dimensions using the quantum gravity induced entanglement of masses (QGEM) experiment. This experiment involves two freely falling test masses passing through a Stern–Gerlach-like device. We investigate the entanglement witness between these masses within the framework of the Randall–Sundrum II model (RS-II). Our findings indicate that the system reaches entanglement more rapidly in the presence of extra dimensions, particularly when the radius of the extra dimension is large.

We report a cast light-weight dual-phase eutectic high-entropy alloy (EHEA) of Ni49Fe28Al17V6 that comprises of alternating FCC and ordered B2 lamellae. Such lamellar EHEA exhibits a superior tensile strength of ∼ 1.3 GPa in combination with a large uniform elongation of ∼ 26%, well surpassing those of other state-of-the-art cast EHEAs. The high strength-ductility synergy originates largely from the extensive dislocation activities in both FCC and B2 lamellae. In addition, the martensitic transformation of the B2 lamellae during deformation further promotes the work hardening capability to enhance the tensile strength and ductility.

Buckwheat grains will suffer varying degrees of damage during the threshing process. Damaged grains are prone to changes in nutritional quality during storage. To explore the relationship between the threshing damage mechanism and nutritional quality, the mechanical properties of buckwheat with different moisture contents were determined through compression and friction tests, obtaining some conventional mechanical property indicators. On this basis, a 3D collision model of buckwheat–nail tooth was established, and the dynamic process of collision was simulated with LS-DYNA. During collision, the changes in energy, von Mises stress, and critical damage velocity of grains with different moisture contents were analyzed. After the threshing test, the grains were observed and classified according to the degree of damage. The differences in nutritional components of different types of grains were analyzed through physicochemical experiments. The experimental results showed that the failure force, elastic modulus, and ultimate strength of buckwheat grains were negatively correlated with moisture content, while the deformation and friction coefficient were positively correlated with moisture content. During collision, the von Mises stress of the grains showed a pattern of increase and then decrease. The maximum stress value occurred at the contact area center, spreading along the periphery and gradually decreasing. The maximum von Mises stress decreased with increasing moisture content and increased with increasing collision velocity. The critical damage velocities of grains at moisture contents of 11.98%, 15.77%, 18.04%, 20.82%, and 25.22% were 13.07, 11.72, 10.94, 10.55, and 10.15 m/s, respectively. After threshing, grains were divided into three types: undamaged, surface cracked, and shell damaged. The nutritional quality of the last two damaged grains decreased during the storage process. These results are of great significance for optimizing buckwheat threshing parameters, reducing buckwheat damage, and improving the economic performance of the buckwheat industry.

Blue carbon is an important driver to achieve the "carbon peak" and "carbon neutrality" goals in China and promote the development of the national carbon market. The revision of the Marine Environmental Protection Law of the People's Republic of China responded to China's current practical needs for a certain extent. Although it still did not explicitly mention the concept of blue carbon, it had indirectly considered the protection of blue carbon through blue carbon ecosystems in terms of marine resources and marine environment. The rules regarding the marine resources were general and principled, and the rules regarding the marine environment were more comprehensive. It confirmed the priority of restoration and improvement of blue carbon ecosystems in terms of both objects and means, and the priority of means was not yet complete. As for the future development of the Marine Environmental Protection Law of the People's Republic of China, improvements should be made in four aspects including clarifying the legal attributes of blue carbon, promoting institutional design that is conducive to the implementation of blue carbon sink technology, appropriately exerting active justice, and ensuring the blue carbon market rules, and ultimately build a more complete blue carbon protection system.

Abstract Background The therapeutic effect of poly (ADP-ribose) polymerase inhibitors (PARPi) monotherapy compared with platinum-based chemotherapy, and the impact to subsequent platinum-based chemotherapy after PARPi resistance were inconclusive in breast cancer susceptibility genes (BRCA)1/2-mutated ovarian cancer patients with secondary platinum-sensitive relapse. Methods BRCA1/2-mutated patients with secondary platinum-sensitive relapse included in this study did not receive any maintenance regimen after first- and second-line platinum-based chemotherapy, and the secondary platinum-free interval (PFI) was more than 6 months. Patients in study group were treated with PARPi monotherapy until disease progression, and patients in control group were treated with platinum-based chemotherapy without restriction. Progression-free survival (PFS) was defined as the time from third-line therapy to disease progression or death, PFS2 was defined as the time from platinum-based chemotherapy after PARPi resistance to next subsequent therapy or death. Post-recurrence survival (PRS) refers to the survival time after secondary platinum-sensitive relapse. Results A total of 119 patients were retrospectively analyzed, including 71 (59.7%) in study group and 48 (40.3%) in control group. The objective response rate (ORR: 77.5% vs. 80.0%, p=0.766) and PFS (median: 11.2 vs. 11.0 months, p=0.962) were comparable. The benefit of subsequent platinum-based chemotherapy after PARPi resistance was more pronounced in patients with PARPi treatment for more than 12 months (median PFS2: 8.6 vs. 4.3 months, p=0.040). PARPi monotherapy had no adverse effect on PRS compared with platinum-based chemotherapy (median PRS:41.2 vs. 42.8 months, p=0.323). Compared to patients in control group who had never received PARPi, PARPi monotherapy (median PRS: 41.2 vs. 33.7 months, p=0.019) and post-line treatment with PARPi in the control group (median PRS: 48.1 vs. 33.7 months, p=0.002) could prolong PRS for patients with secondary platinum-sensitive relapse. Conclusions PARPi monotherapy was similar to platinum-based chemotherapy for BRCA1/2-mutated ovarian cancer patients with secondary platinum-sensitive recurrence, and could improve prognosis.

In order to detect rice false smut quickly and accurately, a lightweight false smut detection model, YOLOv8n-MBS, was proposed in this study. The model introduces the C2f_MSEC module to replace C2f in the backbone network for better extraction of key features of false smut, enhances the feature fusion capability of the neck network for different sizes of false smut by using a weighted bidirectional feature pyramid network, and designs a group-normalized shared convolution lightweight detection head to reduce the number of parameters in the model head to achieve model lightweight. The experimental results show that YOLOv8n-MBS has an average accuracy of 93.9%, a parameter count of 1.4 M, and a model size of 3.3 MB. Compared with the SSD model, the average accuracy of the model in this study increased by 4%, the number of parameters decreased by 89.8%, and the model size decreased by 86.9%; compared with the YOLO series of YOLOv7-tiny, YOLOv5n, YOLOv5s, and YOLOv8n models, the YOLOv8n-MBS model showed outstanding performance in terms of model accuracy and model performance detection; compared to the latest YOLOv9t and YOLOv10n models, the average model accuracy increased by 2.8% and 2.2%, the number of model parameters decreased by 30% and 39.1%, and the model size decreased by 29.8% and 43.1%, respectively. This method enables more accurate and lighter-weight detection of false smut, which provides the basis for intelligent management of rice blast disease in the field and thus promotes food security.

For buckwheat, the optimal harvest period is difficult to determine—too early or too late a harvest affects the nutritional quality of buckwheat. In this paper, physical and chemical tests are combined with a method using near-infrared spectroscopy nondestructive testing technology to study buckwheat harvest and determine the optimal harvest period. Physical and chemical tests to determine the growth cycle were performed at 83 days, 90 days, 93 days, 96 days, 99 days, and 102 days, in which the buckwheat grain starch, fat, protein, total flavonoid, and total phenol contents were assessed. Spectral images of buckwheat in six different harvest periods were collected using a near-infrared spectral imaging system. Four preprocessing methods (SNV, S-G, DWT, and the normaliz function) and three dimensionality reduction algorithms (IVSO, VCPA, VISSA) were used to process the raw buckwheat spectral data, and the full and eigen spectra were established as a random forest (RF). Random forest (RF) and Least Squares Support Vector Machine (LS-SVM) classification models were used to determine the full and eigen spectra, respectively, and the optimal model for the buckwheat single harvest period was determined and validated. Through physical and chemical tests, it was concluded that the 90-day harvest buckwheat grain protein, fat, and starch contents were the highest, and that the total flavonoid and total phenolic contents were also high. The SNV preprocessing method was the most effective, and the feature bands extracted using the IVSO algorithm were more representative. The IVSO-RF model was the best discriminative model for the classification of buckwheat in different harvest periods, with the correct rates of the training and prediction sets reaching 100% and 96.67%, respectively. When applying the IVSO-RF model to the buckwheat single harvest period to verify the classification, the correct rate of the training set for each harvest period reached 96%, and that of the prediction set reached 100%. Near-infrared spectroscopy combined with the IVSO-RF modeling method for buckwheat harvest period detection is a rapid, nondestructive classification method. When this was combined with physical and chemical analyses, it was determined that a growth cycle of 90 days is the best harvest period for buckwheat. The results of this study can not only improve the quality of buckwheat crops but also be applied to other crops to determine their optimal harvest period.

Abstract Background The current study aimed to investigate the incidence and risk factors for postoperative acute ischemic stroke (PAIS) in advanced-aged patients (≥ 75 years) with previous ischemic stroke undergoing noncardiac surgery. Methods In this single-center retrospective cohort study, all advanced-aged patients underwent noncardiac surgery from 1 January, 2019, to 30 April, 2022. Data were extracted from hospital electronic medical records. Multivariable logistic regression analysis was performed to determine predictors of PAIS. Multivariable linear or logistic regression analysis was performed to determine predictors of outcomes due to PAIS. Results Twenty-four patients (6.0%) of the 400 patients developed PAIS. Carotid endarterectomy (CEA), length of surgery and preoperative Modified Rankin scale (mRS) ≥ 3 were significant predictors of PAIS. CEA was associated with increased risk of PAIS (OR 4.14; 95%CI, 1.43–11.99). Each additional minute in length of surgery had slightly increased the risk of PAIS (OR, 1.01; 95%CI, 1.00-1.01). Compared with reference (mRS

The high sensitivity to color browning during room-temperature storage was a significant factor in limiting the development of fresh spinach noodles (FSN). The practice of humidity-controlled heat treatment (HCHT) at varying temperatures, relative humidity, and time was carried out to limit enzyme activity and improve the quality of FSN. Results showed that HCHT could maximize the color preservation of fresh spinach noodle quality while effectively inactivating polyphenol oxidase and the yeasts, and mold count in FSN during storage was almost undetectable after mild conditions (80 °C). The hardness and chewiness of HCHT noodles were significantly increased, but the free sulfhydryl content was reduced. At 80 °C, 90 %, 5 min, protein structural aggregation was found in the microstructure of HCHT fresh spinach noodles. HCHT also caused partial gelatinization, as evidenced by the decrease in starch gelatinization enthalpy from 5.49 to 4.77 J/g, although the gelatinization degree of FSN was comparatively low.

Light emission with delicate wavelength control generates fundamental colors that are essential for vision-based applications such as displays, information communication, visual and augmented reality. Yet these light emitting technologies critically rely on the development of photonic sources for proper lighting and color matching. Halide perovskites have recently emerged as excellent and efficient photonic sources due to their outstanding photophysical properties, such as low defect trap densities, long carrier lifetime, large absorption coefficient, high quantum yield and optical gain. On a par with the rapid advances of light-emitting diodes (LEDs), perovskite-based optical amplifiers and lasers have made great strides in the past few years, which have much more accurate control of color-matched lighting, high power, spatial and wavevector control of color emission. In this review, we aim to review the recent progress of perovskite lasers at micro-, nano- and subwavelength scales, discuss the properties of halide perovskites and optical cavity structures that benefit color light emission, and examine the remaining challenges in the field for the future development of perovskite-based lasing technologies.

In the face of competitive pricing pressure from online retailers and high-quality service pressure from physical retailers, are retailers in these two channels willing to establish a service cooperation (SC) strategy? If so, then will they support or avoid consumer showrooming behavior? To address these questions, we use a game theoretic model to examine three strategic configurations: nonservice cooperation (SN), SC, and nonshowroom cooperation (NC). We recommend the optimal strategy based on factors influencing the service-level threshold, such as market information coverage factors and service characteristics. Our results show that the SC strategy is the best for physical retailers when their market coverage is low or the threshold requirement for service is high; however, when the service level is low, physical retailers should avoid supporting consumer showrooming behavior; that is, the NC strategy is their best choice. In contrast, online retailers are always willing to choose the SC strategy and support showrooming to reflect their lower price advantage. Finally, we explore the optimal strategies of the two retailers in the endogenous service-level scenario. The optimal strategy selection of online retailers remains unchanged. However, physical retailers prefer the SN strategy because of their dominance in service; these retailers have the incentive to choose the NC strategy when their markets have a high degree of information coverage.

Superpixel-based hyperspectral unmixing (HU) can effectively reduce spectral variability’s influence on unmixing performance. In the superpixel-based HU method, this study proposes a segmentation scale determination method to improve the accuracy of endmembers and fully constrained least squares based on distance strategy (D-FCLS) to improve the efficiency of abundance estimation. In the segmentation-scale determination method, this study establishes a segmentation scale division criterion to divide segmented images with similar quality into the same segmentation scale. The optimal segmentation scale is selected according to the actual situation of hyperspectral images. Moreover, the distance strategy is applied to fully constrained least squares (FCLS) using the spatial relationship between endmembers and the mixed pixel in abundance estimation. The proposed methods are evaluated on the synthetic and real datasets. The results show that the validity of the segmentation-scale determination method is verified by quantitative and qualitative evaluation on all datasets. In terms of abundance estimation, compared with FCLS, D-FCLS improves the efficiency by more than 10.30% on the synthetic dataset and 18.71% on the real dataset. In addition, this study’s proposed abundance estimation method and unsupervised superpixel-based HU method are superior to the other comparison methods.

Abstract Rice leaf blast is prevalent worldwide and a serious threat to rice yield and quality. Hyperspectral imaging is an emerging technology used in plant disease research. In this study, we calculated the standard deviation (STD) of the spectral reflectance of whole rice leaves and constructed support vector machine (SVM) and probabilistic neural network (PNN) models to classify the degree of rice leaf blast at different growth stages. Average accuracies at jointing, booting and heading stages under the full-spectrum-based SVM model were 88.89%, 85.26%, and 87.32%, respectively, versus 80%, 83.16%, and 83.41% under the PNN model. Average accuracies at jointing, booting and heading stages under the STD-based SVM model were 97.78%, 92.63%, and 92.20%, respectively, versus 88.89%, 91.58%, and 92.20% under the PNN model. The STD of the spectral reflectance of the whole leaf differed not only within samples with different disease grades, but also among those at the same disease level. Compared with raw spectral reflectance data, STDs performed better in assessing rice leaf blast severity.

Chemical-exfoliated borophene and its application in multi-imaging guided photothermal therapy.

Surface lattice resonances (SLRs) are the coherent collective interactions between periodically arranged nanoparticles, which are generally considered to be formed by the resonant electric dipole, magnetic dipole, or electric quadrupole moments of a single nanoparticle coupled with the Rayleigh anomaly (RA). Here we reveal the first observation of the chiral SLRs that are formed by the coupling of the chiral toroidal electric dipole (TED) moment and RA mode through the theoretical design and experimental fabrication of a nano-kirigami based propeller metasurface. By engineering the rotational symmetry of the propeller, e.g., from C2 (C3) symmetry to C4 symmetry, we find that the electric dipole (electric quadrupolar) chiral SLRs have evolved into the TED associated chiral SLRs. Furthermore, it is found that the excitation amplitude of the TED moment can be tailored by controlling the stereo twisted height of the propeller and the spin of the incident light. Finally, the chiral TED moment enhanced circular dichroism is verified in the near-infrared wavelength region. Our study provides an effective yet simple scheme to manipulate the TED-dependent chiral SLRs, paving the way toward exploring the unconventional physical properties of TED and advanced chiroptical physics.

ObjectiveThis study aims to investigate the situation of vertigo disorder combined with anxiety and depression in patients with different types of vestibular syndrome.MethodsA total of 330 patients with vertigo in otolaryngology outpatient department were selected, and clinical information such as age, gender, and scores of Dizziness handicap inventory (DHI), Generalized anxiety disorder-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9) were collected. Analyzed the differences among acute vestibular syndrome (AVS), episodic vestibular syndrome (EVS) and chronic vestibular syndrome (CVS) in terms of age, gender, comorbid anxiety and depression, and the multivariate ordered logistic regression analysis was used to evaluate the relationship between the above factors and the degree of vertigo disorder.ResultsThe three types of vestibular syndrome had no significant difference in age composition, sex composition, anxiety and depression. There was no significant difference in the probability of anxiety and depression among vertigo patients of different ages and genders. The total score of vertigo disorder and each sub-item score were higher in patients with anxiety and depression. Patients with anxiety mainly manifested in EVS and CVS, while patients with depression mainly manifested in EVS and AVS. The probability of increased vertigo in anxious patients was 4.65 times that of non-anxious patients, and the probability of increased vertigo in depressed patients was 3.49 times that of non-depressed patients. Age and gender had no statistically significant effect on the degree of vertigo. In patients with EVS, anxiety and depression had a significant effect on the degree of vertigo; in patients with CVS, anxiety had a significant effect on the degree of vertigo, but depression had no significant effect.ConclusionAge and gender do not significantly affect the degree of vertigo disorder and mental state in various vestibular syndromes. Instead, anxiety and depression are the risk factors for aggravating the degree of vertigo disorder, and manifest differently in each type of vestibular syndrome. Therefore, it is necessary to use a quick scale tool to conduct a standardized screening of the psychological status of patients with vertigo.

Leaf blast is recognized as one of the most devastating diseases affecting rice production in the world, seriously threatening rice yield. Therefore, early detection of leaf blast is extremely important to limit the spread and propagation of the disease. In this study, a leaf blast-specific spectral vegetation index RBVI = 9.78R816−R724 − 2.08(ρ736/R724) was designed to qualitatively detect the level of leaf blast disease in the canopy of a field and to improve the accuracy of early detection of leaf blast by remote sensing by unmanned aerial vehicle. Stacking integrated learning, AdaBoost, and SVM were used to compare and analyze the performance of the RBVI and traditional vegetation index for early detection of leaf blast. The results showed that the stacking model constructed based on the RBVI spectral index had the highest detection accuracy (OA: 95.9%, Kappa: 93.8%). Compared to stacking, the detection accuracy of the SVM and AdaBoost models constructed based on the RBVI is slightly degraded. Compared with conventional SVIs, the RBVI had higher accuracy in its ability to qualitatively detect leaf blast in the field. The leaf blast-specific spectral index RBVI proposed in this study can more effectively improve the accuracy of UAV remote sensing for early detection of rice leaf blast in the field and make up for the shortcomings of UAV hyperspectral detection, which is susceptible to interference by environmental factors. The results of this study can provide a simple and effective method for field management and timely control of the disease.

Narrow emission lines in a galaxy’s spectrum that show double peaks indicate the presence of distinct gas components with different velocities, and its physical origin remains uncertain. This study uses galaxies from the final MaNGA data release to detect double-peaked narrow emission-line spaxels (DPSs) by examining the double Gaussian profiles of the H α -[N ii ] doublets across all MaNGA spaxels. A total of 5420 DPSs associated with 304 double-peaked narrow emission-line galaxies (DPGs) are identified, with each DPG containing a minimum of five DPSs and being free from overlap with other galaxies. We find that DPSs can be categorized into three groups according to their central distance r / R _e and the velocity difference Δ v between their two components: the inner low-Δ v , inner high-Δ v , and outer DPSs. By incorporating the physical characteristics of the DPGs into their DPSs, we demonstrate for the first time the existence of statistical correlations between barred DPGs and inner low-Δ v DPSs, AGN-hosting DPGs, and inner high-Δ v DPSs, as well as tidal DPGs and outer DPSs.

Nanostructured multi-principal element alloys (MPEAs) have been explored as next-generation engineering materials due to unique mechanical and functional properties which have significant advantages over traditional dilute alloys. However, the practical applications of nanostructured MPEAs are still limited due to the lack of scalable processing approaches to prepare a large quantity of nanostructured MPEAs, as well as lack of an efficient pathway for high-throughput discovery of better functional nanostructured MPEAs within their vast compositional space. Here we tackle these challenges by presenting an integrated approach by combining direct-ink-writing-based additive manufacturing, solid-state sintering, and chemical dealloying to manufacture hierarchically porous MPEAs. The hierarchical structure is comprised of macro- and micro-scale pores introduced via extrusion printing and polymer decomposition during sintering, as well as nanoscale pores formed via chemical dealloying. The macro- and micro-scale pores allow efficient dealloying of a large mass of material as the diffusion length that the corroding medium must penetrate remains at the scale of the ligaments formed after sintering (∼10 μ m), despite the large volume of the 3D-printed samples. In addition, this integrated approach enables versatile control of the alloy composition via precisely tuning the ratio of elemental powders in the starting ink, thus offering a pathway for high-throughput discovery of novel functional MPEAs. As a case study, multiscale macro/micro/nanoporous NiFeMn MPEAs with three different compositions were investigated as catalysts to reduce the overpotential of oxygen evolution reaction (OER), where NiFeMn-based electrocatalysts display composition-dependent performance such that the overpotential measured at a current of 0.5 A g ^−1 for OER increases in the order of Ni _58 Fe _29 Mn _13 ⩽ Ni _64 Fe _26 Mn _10 < Ni _76 Fe _18 Mn _6 . This introduced manufacturing process offers new opportunities for scalable fabrication and rapid screening of nanostructured multi-component complex alloys.

We present a study of the molecular gas in early-mid stage major mergers, with a sample of 43 major-merger galaxy pairs selected from the Mapping Nearby Galaxies at Apache Point Observatory survey and a control sample of 195 isolated galaxies selected from the xCOLD GASS survey. Adopting kinematic asymmetry as a new effective indicator to describe the merger stage, we aim to study the role of molecular gas in the merger-induced star formation enhancement along the merger sequence of galaxy pairs. We obtain the molecular gas properties from CO observations with the James Clerk Maxwell Telescope, Institut de Radioastronomie Milimetrique 30 m telescope, and the MaNGA-ARO Survey of CO Targets survey. Using these data, we investigate the differences in molecular gas fraction ( ${f}_{{{\rm{H}}}_{2}}$ ), star formation rate (SFR), star formation efficiency (SFE), molecular-to-atomic gas ratio ( ${M}_{{{\rm{H}}}_{2}}$ / M _H _i ), total gas fraction ( f _gas ), and the SFE of total gas (SFE _gas ) between the pair and control samples. In the full pair sample, our results suggest the ${f}_{{{\rm{H}}}_{2}}$ of paired galaxies is significantly enhanced, while the SFE is comparable to that of isolated galaxies. We detect significantly increased ${f}_{{{\rm{H}}}_{2}}$ and ${M}_{{{\rm{H}}}_{2}}$ / M _H _i in paired galaxies at the pericenter stage, indicating an accelerated transition from atomic gas to molecular gas due to interactions. Our results indicate that the elevation of ${f}_{{{\rm{H}}}_{2}}$ plays a major role in the enhancement of global SFR in paired galaxies at the pericenter stage, while the contribution of enhanced SFE in specific regions requires further explorations through spatially resolved observations of a larger sample spanning a wide range of merger stages.

Quiescent galaxies generally possess denser cores than star-forming galaxies with similar mass. As a measurement of the core density, the central stellar mass surface density within a radius of 1 kpc (Σ _1 ) was thus suggested to be closely related to galaxy quenching. Massive star-forming galaxies with high Σ _1 do not fit into this picture. To understand the origin of such galaxies, we compare the spatially resolved stellar population and star formation properties of massive (>10 ^10.5 M _⊙ ) blue spiral galaxies with high and low Σ _1 , divided by Σ _1 = 10 ^9.4 M _⊙ kpc ^−2 , based on the final release of MaNGA integral field unit data. We find that both high-Σ _1 and low-Σ _1 blue spirals show large diversities in stellar population and star formation properties. Despite the diversities, high-Σ _1 blue spirals are statistically different from the low-Σ _1 ones. Specifically, the radial profiles of the luminosity-weighted age and Mgb/〈Fe〉 show that high-Σ _1 blue spirals consist of a larger fraction of galaxies with younger and less α -element-enhanced centers than their low-Σ _1 counterparts, ∼55% versus ∼30%. The galaxies with younger centers mostly have higher central specific star formation rates, which still follow the spaxel-based star formation main-sequence relation. Examinations of the H α velocity field and the optical structures suggest that galactic bars or galaxy interactions should be responsible for the rejuvenation of these galaxies. The remaining ∼45% of high-Σ _1 blue spirals are consistent with the inside-out growth scenario.

We investigate the suppression of star formation in galaxy pairs based on the isolated galaxy pair sample derived from the Sloan Digital Sky Survey survey. By comparing the star formation rate between late-type galaxies (LTGs) in galaxy pairs and those in the isolated environment, we detect a signal of the suppression of star formation in galaxy pairs at d _p < 100 kpc and 200 kpc < d _p < 350 kpc. The occurrence of the suppression of star formation in these LTGs requires their companion galaxies to have an early-type morphology ( n _s > 2.5). The suppression of star formation in wide galaxy pairs at 200 kpc < d _p < 350 kpc mainly occurs in massive LTGs, while in close galaxy pairs at d _p < 100 kpc, it only appears in LTGs with a massive companion ( $\mathrm{log}{M}_{\star }\gt 11.0$ ), nearly independent of their own stellar mass. Based on these findings, we infer that the suppression of star formation in wide galaxy pairs is actually a result of galaxy conformity, while in close galaxy pairs, it stems from the influence of hot circumgalactic medium surrounding companion galaxies.

The measurements of masses and luminosities of massive stars play an important role in understanding the formation and evolution of their host galaxies. In this work, we present the measurement of masses and luminosities of 2946 OB-type stars, including 78 O-type stars and 2868 B-type stars, based on their stellar parameters (effective temperature, surface gravity, and metallicity) and the Padova and Trieste Stellar Evolution Code isochrones model. Our results show that the median mass and luminosity of the 2946 OB-type stars are 5.4 M _⊙ and log( L / L _⊙ ) = 3.2 with median relative errors of 21.4% and 71.1%, respectively. A good agreement between our results estimated by using our method and those derived by using the orbital motions of binary stars from the literature is found for some B-type stars. In addition, we also fit the mass–luminosity relation of B-type stars by using our derived mass and the luminosity from Gaia Data Release 3.

The optimal operation of cascade hydropower station is a hydropower system based on the optimal operation method of a single reservoir and considering the water relations between reservoirs. Based on the allocation capacity of the reservoir, Water resources are redistributed in cascade basins, so that the power generation can reach the maximum under the condition of limited water resources. The optimal operation of cascade hydropower stations is a nonlinear optimization problem with many variables and constraints. When solving nonlinear problems, this paper considers using interior point method, which has good robustness characteristics. In this paper, interior point method is used to solve the optimal operation of cascade hydropower stations with intermountain runoff flows into the reservoir. The method used in this paper can shorten the optimal operation calculation time of 24 h 4 reservoir cascade hydropower station to 0.16 s. Compared with the traditional manual operation calculation, the calculation in this paper can be used for online operation to realize real-time operation. The manually scheduled power generation and the calculated power generation are 4328MW and 4617.77MW. In the calculation result, the power generation is 6.7% more than that of the manual calculation result.

BackgroundQuantitative electroencephalography (QEEG) has emerged as a promising monitoring method in cerebral ischemia, but the feasibility of QEEG in intraoperative cerebral perfusion-related ischemia monitoring is still uncertain. The purpose of this study was to investigate the value of QEEG monitoring in Carotid Endarterectomy (CEA) and the thresholds for intraoperative cerebral perfusion-related ischemia monitoring.MethodsSixty-three patients who underwent carotid endarterectomy with continuous Transcranial Doppler ultrasound (TCD) monitoring and QEEG monitoring at Xuanwu Hospital Capital Medical University from January 2021 to August 2021 were enrolled in this study. Each patient received total intravenous anesthesia. Middle cerebral artery blood flow velocity (V-MCA) was obtained by TCD. Relative alpha percentage (RA) and alpha-delta ratio (ADR) were obtained by QEEG monitoring. Patients were divided into ischemic and non-ischemic groups using a decline of more than 50% in the V-MCA monitored by TCD as the gold standard.ResultsOf the 63 patients, twenty patients were divided into the ischemic group, and forty-three patients into the non-ischemic group. Ipsilateral post-clamp RA and ADR values of QEEG were decreased for all patients in the ischemic group. The optimal threshold for RA and ADR to predict cerebral ischemia was a 14% decrease from baseline (sensitivity 90.0%, specificity 90.7%, Kappa value 0.786), a 21% decrease from baseline (sensitivity 85.0%, specificity 81.4%, Kappa value 0.622), respectively, indicated by TCD monitoring.ConclusionsOur study demonstrated that QEEG is a promising monitoring method undergoing CEA under general anesthesia and has good consistency with TCD.

Abstract Eucommia ulmoides Oliver is one of the commonly used traditional Chinese medicines for the treatment of osteoporosis, and iridoid glycosides are considered to be its active ingredients against osteoporosis. This study aims to clarify the chemical components and molecular mechanism of iridoid glycosides of Eucommia ulmoides Oliver in the treatment of osteoporosis by integrating network pharmacology and molecular simulations. The active iridoid glycosides and their potential targets were retrieved from text mining as well as Swiss Target Prediction, TargetNet database, and STITCH databases. At the same time, DisGeNET, GeneCards, and Therapeutic Target Database were used to search for the targets associated with osteoporosis. A protein–protein interaction network was built to analyze the interactions between targets. Then, DAVID bioinformatics resources and R 3.6.3 project were used to carry out Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis. Moreover, interactions between active compounds and potential targets were investigated through molecular docking, molecular dynamic simulation, and binding free energy analysis. The results showed that a total of 12 iridoid glycosides were identified as the active iridoid glycosides of Eucommia ulmoides Oliver in the treatment of osteoporosis. Among them, aucubin, reptoside, geniposide and ajugoside were the core compounds. The enrichment analysis suggested iridoid glycosides of Eucommia ulmoides Oliver prevented osteoporosis mainly through PI3K-Akt signaling pathway, MAPK signaling pathway and Estrogen signaling pathway. Molecular docking results indicated that the 12 iridoid glycosides had good binding ability with 25 hub target proteins, which played a critical role in the treatment of osteoporosis. Molecular dynamic and molecular mechanics Poisson–Boltzmann surface area results revealed these compounds showed stable binding to the active sites of the target proteins during the simulations. In conclusion, our research demonstrated that iridoid glycosides of Eucommia ulmoides Oliver in the treatment of osteoporosis involved a multi-component, multi-target and multi-pathway mechanism, which provided new suggestions and theoretical support for treating osteoporosis.

Evolutionary algorithms have shown great successes in various real-world applications ranging in molecule to astronomy. As a mainstream evolutionary algorithm, differential evolution (DE) possesses the characteristics of simple algorithmic structure, easy implement, and efficient search performance. Nevertheless, it still suffers from the issues of local optimal trapping and premature of evolution problems. In this study, we innovatively improve the performance of DE by incorporating a full utilization of information feedback, which includes the population’s holistic information and the direction of differential vectors. The proposed permutation-archive information directed differential evolution (PAIDDE) algorithm is verified on a set of 29 benchmark numerical functions and 22 real-world optimization problems. Extensive experimental and statistical results show that PAIDDE can significantly outperform other 12 state-of-the-art algorithms in terms of solution qualities. Additionally, the computational complexity, solution distribution, convergence speed, search dynamics, and population diversity of PAIDDE are systematically analyzed.

Under the background of the rapid development of digital economy, this paper empirically analyzes the impact of digital transformation on enterprise innovation and selects the panel data of China’s Shanghai and Shenzhen A-share listed companies from 2013 to 2021 as the research objective is to study the impact of digital transformation on enterprise innovation from theoretical and empirical perspectives. First, we find that digital transformation accelerates enterprise innovation, a conclusion that has been validated through robustness testing. Second, digital transformation impacts enterprise innovation by enhancing productivity and information transparency. Third, financing constraints and financial redundancy play distinct regulatory roles in the process. Fourth, heterogeneity analysis finds that the role of digital transformation in promoting enterprise innovation has different effects in state-owned and non-state-owned enterprises, high-tech and non-high-tech enterprises, and enterprises with different life cycles. Finally, the functional analysis suggests that further investigation is needed to determine whether digital transformation can significantly promote the sustainable development of enterprises through innovation while also recognizing that this function may have a lag effect. Overall, this study contributes to a deeper understanding of digital transformation and innovation-driven practices and encourages more significant integration of the real and digital economies.

Engineering the chiroptical responses of artificial nanostructures is vital for realizing applications in the fields of optical devices, enantioselective separation, and bio-sensing. Here, by utilizing the nano-kirigami based meta-molecule arrays, the flexible engineering of circular dichroism (CD) is achieved in the near-infrared wavelength region by the excitation of chiral surface lattice resonances (SLRs). It is found that the chiral SLRs can be flexibly tailored by a tiny structural perturbation. As a result, the wavelength, intensity, and sign of CD peak/dip can be abruptly engineered. Specifically, a CD peak with the value of +0.44 is evolved into a CD dip with an intensity of −0.66 when the etching silt length of the meta-molecules is simply decreased by 190 nm. Importantly, such CD reversal is experimentally demonstrated with the nano-kirigami method without requiring the inversion of geometric chirality. Moreover, it is found that the asymmetric dielectric environment around the meta-molecules can significantly suppress the chiral SLRs, providing an insightful understanding of the chiral SLR. Such flexible tailoring of the CD with chiral SLRs paves a versatile way toward the manipulation of chiral light–matter interactions and chiroptical functional devices.

The size dependence of ductile to brittle transition of Zr-based metallic glasses was systematically investigated by small punch test. It was found that the metallic glasses with thickness ranging from 50 to 700 μm displayed an obvious transition from ductility to brittleness. Compared with thick samples, thin samples exhibit higher strength and better plasticity, which is attributed to the size-dependent transformation from soft state to hard state dominated by the deformation factor (α=τ/σ). Specifically, two significant transitions in deformation behavior at sample thickness of 200 and 500 μm are attributed to two critical deformation factors of αc1 and αc2, which are responsible for the initiation of circumferential and radial shear bands, respectively. Specifically, only several micro-cracks can be observed on sample surface with α≤αc2. While αc1≥α≥αc2, multiple radial shear bands are activated and uniformly distributed on the sample surface. Further, when α≥αc1, multiple circumferential shear bands primarily initiate under high shear stress and large number of radial and circumferential shear bands can be observed on sample surface. The initiation and interaction of multiple radial and circumferential shear bands significantly enhance the ductility of the samples with α≥αc1.

BackgroundNovel motor skills are generally acquired through repetitive practices which are believed to be strongly related to neural plasticity mechanisms. This study aimed to investigate the learning-relative hemodynamic modulation of cortical plasticity induced by long-term motor training.MethodsAn 8-day participation-control program was conducted. Eighteen right-handed healthy participants were recruited and randomly assigned into the training (12) and control groups (6). The training group were arranged to undergo the 8-day block-designed motor training which required to repeat a visuomotor force-control task. The functional near-infrared spectroscopy (fNIRS) was used to continuously monitor the cortical hemodynamic response during training. Two transcranial magnetic stimulation (TMS) measurements were performed before and after training to evaluate the cortical excitability changes. The transfer effects of learning were also investigated.ResultsThe behavior performance was quantified via score execution accuracy to illustrate the fast/slow learning stages as experience cumulated. The cortical hemodynamic activations mapped by fNIRS exhibited a temporal evolution trends that agreed the expansion–renormalization model, which assumed the brain modulation against skill acquisition includes complex mechanisms of neural expansion, selection, and renormalization. Functional connectivity (FC) analysis showed the FC strength was maintained, while the measured homodynamic activation returned to baseline after certain level of skill acquisition. Furthermore, the TMS results demonstrated a significant increase of motor evoked potential (MEP) on the targeted muscle for the trained participants, who significantly outperformed the untrained subjects in learning transfer investigation.ConclusionThe study illustrated the expansion–renormalization trends during continuous motor training, and relative analysis showed the functional connectivity enhancement may be maintained after amplitude renormalization of cortical hemodynamic activations. The TMS findings further gave an implication of neural facilitations on the descending motor pathway when brain activation returned to renormalization status after certain level of learning stages was achieved, and the learning can transfer to enhance the performance while encountering similar tasks.

With the widespread application of new infrastructure and other technologies, demand responsive transit (DRT) is becoming the trend of urban traffic development in the future.In order to further clarify the research status of DRT at home and abroad, firstly, the classification of DRT and the operational production factors were analyzed.Secondly, the production process and operation practice of DRT mode were described.Thirdly, the DRT optimization problem was summarized and analyzed from the three dimensions including optimization objectives, constraint selection and solving algorithm, especially the meta heuristic algorithm.Finally, the prospect of DRT scenarios, model construction, algorithm solution, and the collaborative optimization were proposed.Several hotspots and possible directions of future research were suggested.

Leaf spot disease is a dangerous disease that affects peanut growth, and its severity can significantly impact peanut yield. Hyperspectral-based disease detection technology is a popular non-destructive technique due to its high efficiency, objectivity, and accuracy. In this study, peanut leaf spectra at different levels of severity of leaf spot disease were collected in Liaoning Province, China, in mid-August. This study analyzed the differences in wavelengths using mean spectral reflectance and sensitivity. Using improved principal component analysis loading (I-PCA loading) based on the contribution weight assignment approach, we identified three feature wavelengths of 570 nm, 671 nm, and 750 nm. We evaluated the ability of these feature wavelengths to detect the severity of leaf spot disease using k-nearest neighbor (KNN), support vector machine (SVM), and back-propagation (BP) neural network classifiers. Our experimental results showed that our improved PCA loading method achieved higher classification accuracy with fewer wavelengths than the seven commonly used feature selection methods. Among these classifiers, the SVM achieved the highest accuracy, with an overall accuracy (OA) of 96.88% and a Kappa of 95.81%. Therefore, our proposed method can accurately detect the severity of peanut leaf spot disease and provide scientific and technical support for accurately managing peanut crops.

We study the presale strategy and decision-making problems for a retailer selling products. Based on the penetration and skimming pricing strategies, we consider the characteristics of consumer market segmentation and valuation heterogeneity, focus on the implementation conditions and optimal decision results of different pricing strategies before and after the introduction of an online presale channel, and conduct a multidimensional comparative study on the impact of the introduction of the online presale channel. We find that the introduction of an online presale channel can always improve the retailer’s profitability, but its pricing strategy and sales effort should be adjusted to align with the consumer market segmentation. When the proportion of online consumers is high or their preference for the spot channel is high, the retailer should always adopt a penetration pricing strategy. If online consumers also show a high consumption preference for the presale channel, the retailer should reduce its sales effort to save costs after introducing the presale channel; otherwise, the retailer should increase its sales effort to increase demand in the offline spot channel. When the proportion of online consumers is low and their preference for the spot channel is low, the retailer should always adopt a skimming pricing strategy and keep sales effort unchanged. When the proportion of online consumers and their consumption preference for the spot channel are moderate, the retailer should adjust its pricing strategy from penetration to skimming after introducing the online presale channel and appropriately reduce the level of sales effort.

On-chip arbitrary-elliptical-polarization converter plays a key role in optical information processing. However, the traditional method of manually adjusting parameters for designing devices is limited in function. In this letter, on-chip ultra-small arbitrary-elliptical-polarization converters are realized through optimization algorithms based on genetic algorithm (GA) combined with finite element method (FEM). The converters can realize the function of arbitrary ellipticity angle conversion of full polarization states. The size of the core device is only 1 µm × 1 µm, which is very conducive to high-density integration. The difference between the ideal output ellipticity angle and the actual output ellipticity angle of each device is no more than 2 degrees. In addition, it is proved that the use of optimization algorithms can easily design arbitrary-elliptical-polarization converters with different materials, different structures and different wavelengths, and the design of three-dimensional arbitrary-elliptical-polarization converters is also verified by using the optimization algorithm. This work provides an efficient design method for arbitrary polarization state conversion, showing its significance in improving the information capacity in optical communication.

Cascaded nanophotonic devices play a vital role in all-optical connection, all-optical computation and all-optical network. However, there is almost no effective method for the direct design of on-chip cascaded nanophotonic devices, since current study of nanophotonic devices mostly focuses on single device. Here, on-chip cascaded nanophotonic devices are designed based on an intelligent algorithm by combining genetic algorithm, simulated annealing algorithm and finite element method for the first time, and verified experimentally by using silicon-based planar structures. The cascaded devices consist of a bandpass filter and a wavelength router operating in optical communication range. The operation bandwidth of the bandpass filter is 408 nm with transmission more than 80%, within which the communication wavelengths of 1,300 nm and 1,550 nm are routed into different output ports through the wavelength router component. The footprint is only 3.62 μm2 for the bandpass filter and only 2.56 μm2 for the wavelength router, which are easy for integration with planar structures and ultrasmall size. This work provides a highly-efficient scheme for the realization of on-chip cascaded nanophotonic devices on the same chip, and lays a foundation for the realization of photonic chip based on intelligent algorithm.

Chicoric acid has been widely used in food, medicine, animal husbandry, and other commercial products because of its significant pharmacological activities. However, the shortage of chicoric acid limits its further development and utilization. Currently, Echinacea purpurea (L.) Moench serves as the primary natural resource of chicoric acid, while other sources of it are poorly known. Extracting chicoric acid from plants is the most common approach. Meanwhile, chicoric acid levels vary in different plants as well as in the same plant from different areas and different medicinal parts, and different extraction methods. We comprehensively reviewed the information regarding the sources of chicoric acid from plant extracts, its chemical synthesis, biosynthesis, and bioactive effects.

Leaf blast is a disease of rice leaves caused by the Pyricularia oryzae. It is considered a significant disease is affecting rice yield and quality and causing economic losses to food worldwide. Early detection of rice leaf blast is essential for early intervention and limiting the spread of the disease. To quickly and non-destructively classify rice leaf blast levels for accurate leaf blast detection and timely control. This study used hyperspectral imaging technology to obtain hyperspectral image data of rice leaves. The descending dimension methods got rice leaf disease characteristics of different disease classes, and the disease characteristics obtained by screening were used as model inputs to construct a model for early detection of leaf blast disease. First, three methods, ElasticNet, principal component analysis loadings (PCA loadings), and successive projections algorithm (SPA), were used to select the wavelengths of spectral features associated with leaf blast, respectively. Next, the texture features of the images were extracted using a gray level co-occurrence matrix (GLCM), and the texture features with high correlation were screened by the Pearson correlation analysis. Finally, an adaptive-weight immune particle swarm optimization extreme learning machine (AIPSO-ELM) based disease level classification method is proposed to further improve the model classification accuracy. It was also compared and analyzed with a support vector machine (SVM) and extreme learning machine (ELM). The results show that the disease level classification model constructed using a combination of spectral characteristic wavelengths and texture features is significantly better than a single disease feature in terms of classification accuracy. Among them, the model built with ElasticNet + TFs has the highest classification accuracy, with OA and Kappa greater than 90 and 87%, respectively. Meanwhile, the AIPSO-ELM proposed in this study has higher classification accuracy for leaf blast level classification than SVM and ELM classification models. In particular, the AIPSO-ELM model constructed with ElasticNet+TFs as features obtained the best classification performance, with OA and Kappa of 97.62 and 96.82%, respectively. In summary, the combination of spectral characteristic wavelength and texture features can significantly improve disease classification accuracy. At the same time, the AIPSO-ELM classification model proposed in this study has sure accuracy and stability, which can provide a reference for rice leaf blast disease detection.

As the backdrop for contemporary international relations, globalization reflects the way economic and political power are distributed, and provides the grand context for China’s strategic planning. The history and logic of globalization have shown that underpinned by a system of nation-states, globalization proceeds according to an inescapable cyclical pattern. Globalization suffered major setbacks in the aftermath of the 2008 financial crisis and is likely to further lose steam amid an evolving Covid-19 pandemic. A low-ebb phase of globalization will present an increasingly complicated strategic environment featuring intensifying great power rivalry, regionalized supply chains, and growing technology competition. Beijing remains determined to integrate further into the world, but to adapt to a new strategic environment, will vigorously implement the newly unveiled dual circulation strategy. As China sees it, despite all the major setbacks, globalization is an irreversible mega-trend but it will be driven by a new underlying logic.