Non-volatile dynamically switchable color display via chalcogenide stepwise cavity resonators

High-resolution multi-color printing relies upon pixelated optical nanostructures,which is crucial to promote color display by producing nonbleaching colors,yet requires simplicity in fabrication and dynamic switching.Antimony trisulfide(Sb_(2)S_(3))is a newly rising chalcogenide material that possesses prompt and significant transition of its optical characteristics in the visible region between amorphous and crystalline phases,which holds the key to color-varying devices.Herein,we proposed a dynamically switchable color printing method using Sb_(2)S_(3)-based stepwise pixelated Fabry-Pérot(FP)cavities with various cavity lengths.The device was fabricated by employing a direct laser patterning that is a less timeconsuming,more approachable,and low-cost technique.As switching the state of Sb_(2)S_(3) between amorphous and crystalline,the multi-color of stepwise pixelated FP cavities can be actively changed.The color variation is due to the profound change in the refractive index of Sb_(2)S_(3) over the visible spectrum during its phase transition.Moreover,we directly fabricated sub-50 nm nano-grating on ultrathin Sb_(2)S_(3) laminate via microsphere 800-nm femtosecond laser irradiation in far field.The minimum feature size can be further decreased down to~45 nm(λ/17)by varying the thickness of Sb_(2)S_(3) film.Ultrafast switchable Sb_(2)S_(3) photonic devices can take one step toward the next generation of inkless erasable papers or displays and enable information encryption,camouflaging surfaces,anticounterfeiting,etc.Importantly,our work explores the prospects of rapid and rewritable fabrication of periodic structures with nano-scale resolution and can serve as a guideline for further development of chalcogenide-based photonics components.

Coupling of reduced inorganic fertilizer with plant-based organic fertilizer as a promising fertilizer management strategy for colored rice in tropical regions

Colored rice is a type of high-quality,high-added-value rice that has attracted increasing attention in recent years.The use of large amounts of inorganic nitrogen fertilizer in rice fields results in low fertilizer use efficiency and high environmental pollution.Organic fertilizer is a promising way to improve soil quality and sustain high yields.However,most studies focus on the effect of animal-based organic fertilizers.The effects of different ratios of plantbased organic fertilizer and inorganic fertilizer on the grain yield and quality of colored rice have rarely been reported.Therefore,a two-year field experiment was conducted in 2020 and 2021 to study the effects of replacing inorganic N fertilizers with plant-based organic fertilizers on the yield,nitrogen use efficiency(NUE),and anthocyanin content of two colored rice varieties in a tropical region in China.The experimental treatments included no nitrogen fertilization(T1),100% inorganic nitrogen fertilizer(T2),30%inorganic nitrogen fertilizer substitution with plant-based organic fertilizer(T3),60%inorganic nitrogen fertilizer substitution with plant-based organic fertilizer(T4),and 100% plantbased organic fertilizer(T5).The total nitrogen provided to all the treatments except T1 was the same at 120 kg ha-1.Our results showed that the T3 treatment enhanced the grain yield and anthocyanin content of colored rice by increasing nitrogen use efficiency compared with T2.On average,grain yields were increased by 9 and 8%,while the anthocyanin content increased by 16 and 10% in the two colored rice varieties under T3 across the two years,respectively,as compared with T2.Further study of the residual effect of partial substitution of inorganic fertilizers showed that the substitution of inorganic fertilizer with plant-based organic fertilizer improved the soil physiochemical properties,and thus increased the rice grain yield,in the subsequent seasons.The highest grain yield of the subsequent rice crop was observed under the T5 treatment.Our results suggested that the application of plantbased organic fertilizers can sustain the production of colored rice with high anthocyanin content in tropical regions,which is beneficial in reconciling the relationship between rice production and environmental protection.

The protective effect of cyclodextrin on the color quality and stability of Cabernet Sauvignon red wine

The impact of cyclodextrins(CDs)on wine quality and stability remains largely unknown.This study systematically assessed the protective effect of the post-fermentation addition of CDs on color stability of red wine from the viewpoints of color characteristics,copigmentation and phenolic profiles.The grey relational analysis(GRA)and principal component analysis(PCA)methods were employed to dissect the key effective determinants related to color quality.The addition of CDs induced a significant hyperchromic effect of 8.19-25.40%,a significant bathochromic effect and an enhancement of the color intensity.Furthermore,the evolution of anthocyanin forms and the content of monomeric anthocyanins revealed that β-CD is a superior favorable cofactor during wine aging,but for long-term aging,2-HP-β-CD and 2-HP-γ-CD are more beneficial in promoting the formation of polymerized anthocyanins and color stability.This work provides an important reference for the use of CDs to enhance the color quality and stability of red wines.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

Matching Dyeing and Properties of Silk Fabrics with Natural Edible Pigments

The silk fabrics were matching dyed with three natural edible pigments(red rice red,ginger yellow and gardenia blue).By investigating the dyeing rates and lifting properties of these pigments,it was observed that their compatibilities were excellent in the dyeing process:dye dosage 2.5%(omf),mordant alum dosage 2.0%(omf),dyeing temperature 80℃and dyeing time 40 min.The silk fabrics dyed with secondary colors exhibited vibrant and vivid color owing to the remarkable lightness and chroma of ginger yellow.However,gardenia blue exhibited multiple absorption peaks in the visible light range,resulting in significantly lower lightness and chroma for the silk fabrics dyed with tertiary colors,thus making it suitable only for matte-colored fabrics with low chroma levels.In addition,the silk fabrics dyed with these three pigments had a color fastness that exceeded grade 3 in resistance to perspiration,soap washing and light exposure,indicating acceptable wearing properties.The dyeing process described in this research exhibited a wide range of potential applications in matching dyeing of protein-based textiles with natural colorants.

Synthesis and Coloring Mechanism of Metallic Glaze in R_(2)O-RO-Al_(2)O_(3)-SiO_(2)-P_(2)O_(5)System

A copper-red and silver-white metallic glaze of R_(2)O-RO-Al_(2)O_(3)-SiO_(2)-P_(2)O_(5)system was synthesized by adjusting the firing temperature and glaze components.The coloration mechanism of the metallic glaze was revealed via investigation of the microstructure of the glaze.Our research reveals that the metallic glaze with different colors is mainly due to the amount of Fe_(2)O_(3).The metallic glaze shows a silver-white luster due to a structural color ofα-Fe_(2)O_(3)crystals with a good orientation when the sample contains 0.0939 mol of Fe_(2)O_(3),maintaining temperatures at 1150℃for 0.5 h.The metallic glaze is copper-red which is dominated by the coupling of chemical and structural color ofα-Fe_(2)O_(3)crystals when the sample contains 0.0783 mol of Fe_(2)O_(3).After testing the amount of SiO_(2),we find that 4.0499 mol is the optimal amount to form the ceramic network,and 0.27 mol AlPO_(4)is the best amount to promote phase separation.

Physiological and psychological responses to tended plant communities with varying color characteristics

Exposure to plants has been reported to promote health and reduce stress,and plant color has direct impacts on physical and mental health.We used images of common types of tended plant communities in Shenyang,China,with combinations of yellow,green,and red foliage,as experimental stimuli.A total of 27 images were used as visual stimuli.We used electroencephalography to measureαwave activity(8-13 Hz)in 40 subjects while they viewed visual stimuli.These data were combined with subjective questionnaire data to analyze the relaxing effect of images of tended plant communities with different color types and proportions on people.The results revealed that,although there were slight differences between the electroencephalography and psychological findings,women were significantly more relaxed than men after viewing the images.Physiological and psychological responses varied with the types and proportions of colors in the tended plant communities:those of foliage with combinations of two or three colors induced stronger responses than images with a single color.Specifically,(1)for one-color plant communities,green or yellow plant communities induced a stronger relaxation effect than red plant communities;(2)for two-color plant communities,the optimal color proportion was 55%+45%,and the green+yellow and green+red color combinations induced a stronger relaxation effect;(3)for three-color plant communities,the relaxation effect was strongest when the color proportion was 55%green+25%yellow+20%red.These data would provide a plant color matching in future plant landscape design,which may be helpful for creating healthy and relaxing environments.

Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation parameter.In this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter estimation.The proposed model highlights the residual region with considerable information and constructs color saliency.Second,we incorporate the content-based color saliency as spatial information in the Gaussian mixture model.The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria.Finally,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic objectives.For experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset.In the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.

A Portfolio Selection Method Based on Pattern Matching with Dual Information of Direction and Distance

Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.

Identification of Shell Color-Associated Genes Using Mantle Branch-Specific RNA Sequencing of Yellow-Colored Line of Pearl Oyster Pinctada fucata martensii

The yellow-colored line of pearl oyster Pinctada fucata martensii displays a yellow prismatic layer and a white nacreous layer that can be used as an ideal model for research on shell color formation.Micro-Raman spectroscopy and transcriptome analyses were performed to explore the potential molecular mechanism underlying the phenotype differentiation.The micro-Raman spectroscopy results indicate that the prismatic layer exhibits distinct characteristic peaks of carotenoids,while these peaks are not prominent in the nacreous layer.In the transcriptome comparison of the central zone of mantle and mantle edge tissue,which function in nacreous and prismatic layer formation,respectively,935 significantly differentially expressed genes(DEGs)were identified,with 385 genes upregulated and 227 genes downregulated(|log_(2)(Fold change)|>1 and false discovery rate<0.05)in the mantle edge tissue.Among these genes,some were associated with melanoma/melanogenesis,such as tyrosinase,zinc metalloprotease,glutathione S-transferase,and ATP-binding cassette sub-family;some were associated with the carotenoid-related pathway,including scavenger receptors,cytochrome P450 and lipoprotein receptor.Genes associated with porphyrin metabolism,including porphobilinogen deaminase,and copper/zinc superoxide dismutase,and genes associated with shell matrix protein,including amorphous calcium carbonate binding protein,shematrin,PIF,and collagen,also exhibited significantly different expressions.It is speculated that the different colours between prismatic layer and nacreous layer in the yellow-colored line of P.f.martensii might be resulted from melanin,carotenoids and porphyrin metabolism,while genes related to shell structure and biomineralization might also affect coloration.Our results provide new insights to understand the mechanism of shell color formation in mollusca.

Feature Matching via Topology-Aware Graph Interaction Model

Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.

Vividly Colored Accessories and Mood Changes in Patients Undergoing Breast Surgery

Few studies have examined the impacts of color stimuli on perioperative mood and quality of recovery;thus, this randomized controlled trial aimed to assess impacts of vividly colored accessories on mood and quality of recovery after breast surgery. This single-center, single-blind randomized controlled trial included 36 participants (all aged ≥ 20 years) who were randomized into intervention (n = 19) and control groups (n = 17). The intervention group received vividly colored accessories. The primary and secondary study outcomes were patient mood, evaluated using a two-dimensional mood scale, and postoperative recovery, evaluated using Quality of Recovery-15, which were assessed on postoperative day 3. There were no statistical intergroup differences in the scores of the Two-Dimensional Mood Scale (11.2 [intervention group] vs. 10.4 [control group], P = 0.75) and Quality of Recovery-15 (126.8 [intervention group] vs. 129.3 [control group], P = 0.73). Thus, the use of vividly colored accessories by patients undergoing breast surgery was not found to affect patients’ mood and quality of recovery.

Multiple Matching Attenuation Based on Curvelet Domain Extended Filtering

The paper develops a multiple matching attenuation method based on extended filtering in the curvelet domain,which combines the traditional Wiener filtering method with the matching attenuation method in curvelet domain.Firstly,the method uses the predicted multiple data to generate the Hilbert transform records,time derivative records and time derivative records of Hilbert transform.Then,the above records are transformed into the curvelet domain and multiple matching attenuation based on least squares extended filtering is performed.Finally,the attenuation results are transformed back into the time-space domain.Tests on the model data and field data show that the method proposed in the paper effectively suppress the multiples while preserving the primaries well.Furthermore,it has higher accuracy in eliminating multiple reflections,which is more suitable for the multiple attenuation tasks in the areas with complex structures compared to the time-space domain extended filtering method and the conventional curvelet transform method.

Reconfigurable and polarization-dependent optical filtering for transflective full-color generation utilizing low-loss phase-change materials

All-dielectric metasurface, which features low optical absorptance and high resolution, is becoming a promising candidate for full-color generation. However, the optical response of current metamaterials is fixed and lacks active tuning. In this work, we demonstrate a reconfigurable and polarization-dependent active color generation technique by incorporating low-loss phase change materials(PCMs) and CaF_2 all-dielectric substrate. Based on the strong Mie resonance effect and low optical absorption structure, a transflective, full-color with high color purity and gamut value is achieved. The spectrum can be dynamically manipulated by changing either the polarization of incident light or the PCM state. High transmittance and reflectance can be simultaneously achieved by using low-loss PCMs and substrate. The novel active metasurfaces can bring new inspiration in the areas of optical encryption, anti-counterfeiting, and display technologies.

Perfect 1-k Matchings of Bipartite Graphs

Let k be a positive integer and G a bipartite graph with bipartition (X,Y). A perfect 1-k matching is an edge subset M of G such that each vertex in Y is incident with exactly one edge in M and each vertex in X is incident with exactly k edges in M. A perfect 1-k matching is an optimal semi-matching related to the load-balancing problem, where a semi-matching is an edge subset M such that each vertex in Y is incident with exactly one edge in M, and a vertex in X can be incident with an arbitrary number of edges in M. In this paper, we give three sufficient and necessary conditions for the existence of perfect 1-k matchings and for the existence of 1-k matchings covering | X |−dvertices in X, respectively, and characterize k-elementary bipartite graph which is a graph such that the subgraph induced by all k-allowed edges is connected, where an edge is k-allowed if it is contained in a perfect 1-k matching.

A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching

For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity matching.In the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and symmetry.Some com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.

Distributed Matching Theory-Based Task Re-Allocating for Heterogeneous Multi-UAV Edge Computing

Many efforts have been devoted to efficient task scheduling in Multi-Unmanned Aerial Vehicle(UAV)edge computing.However,the heterogeneity of UAV computation resource,and the task re-allocating between UAVs have not been fully considered yet.Moreover,most existing works neglect the fact that a task can only be executed on the UAV equipped with its desired service function(SF).In this backdrop,this paper formulates the task scheduling problem as a multi-objective task scheduling problem,which aims at maximizing the task execution success ratio while minimizing the average weighted sum of all tasks’completion time and energy consumption.Optimizing three coupled goals in a realtime manner with the dynamic arrival of tasks hinders us from adopting existing methods,like machine learning-based solutions that require a long training time and tremendous pre-knowledge about the task arrival process,or heuristic-based ones that usually incur a long decision-making time.To tackle this problem in a distributed manner,we establish a matching theory framework,in which three conflicting goals are treated as the preferences of tasks,SFs and UAVs.Then,a Distributed Matching Theory-based Re-allocating(DiMaToRe)algorithm is put forward.We formally proved that a stable matching can be achieved by our proposal.Extensive simulation results show that Di Ma To Re algorithm outperforms benchmark algorithms under diverse parameter settings and has good robustness.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

MXenes for Bioinspired Soft Actuators:Advancements in Angle-Independent Structural Colors and Beyond

Soft actuators have garnered substantial attention in current years in view of their potential appliances in diverse domains like robotics,biomedical devices,and biomimetic systems.These actuators mimic the natural movements of living organisms,aiming to attain enhanced flexibility,adaptability,and versatility.On the other hand,angle-independent structural color has been achieved through innovative design strategies and engineering approaches.By carefully controlling the size,shape,and arrangement of nanostructures,researchers have been able to create materials exhibiting consistent colors regardless of the viewing angle.One promising class of materials that holds great potential for bioinspired soft actuators is MXenes in view of their exceptional mechanical,electrical,and optical properties.The integration of MXenes for bioinspired soft actuators with angle-independent structural color offers exciting possibilities.Overcoming material compatibility issues,improving color reproducibility,scalability,durability,power supply efficiency,and cost-effectiveness will play vital roles in advancing these technologies.This perspective appraises the development of bioinspired MXene-centered soft actuators with angleindependent structural color in soft robotics.