Optical localization and polarization microscopy with angstrom precision based on position-ultra-sensitive giant Lamb shift

We propose an optical localization and polarization microscopy scheme with sub-nanometer precision for an emitter (atom/molecule/quantum dot) based on its Lamb shift. It is revealed that the position-ultra-sensitive giant Lamb shift with three or more orders of magnitude larger than that in the free space, can be induced by higher-order plasmonic dark modes of a metal nanoparticle. More importantly, this giant Lamb shift can be ultra-sensitively observed from the optical scattering spectrum of the nanoparticle via scanning an emitter by a sub-nanometer step, and the orientation of the Lamb shift image can be utilized to identify the dipole polarization of the emitter. They enable the optical spectrum microscope technology with angstrom precision and polarization identification, which will bring about broad applications in many fields, such as physics, chemistry, medicine, life science and materials science

Service selection strategic analysis for selfoperated e-commerce platforms under settlement

In order to study whether e-commerce platforms carry out service cooperation after settlement in-depth, this paper focuses on service selection strategic analysis for agent channels on some self-operated e-commerce platforms settled in hybrid e-commerce platforms. We present multi-leader-follower models in two different scenarios with the platforms as leaders and the manufacturers as followers and give some numerical experiments to analyze the impacts of service selection strategies for self-operated platforms on all supply chain members. Our finding shows that if the service cost efficiency is moderate or low, the self-operated platform prefers to provide its service for the agent; otherwise, its selection mainly depends on the unit product service fee. In addition, fierce service competition and high unit service fee are unfavorable to all members, while high service cost efficiency may hurt both the platform and the manufacturer

A novel approach for bilevel programs based on Wolfe duality

This paper considers a bilevel program, which has many applications in practice. To develop effective numerical algorithms, it is generally necessary to transform the bilevel program into a single-level optimization problem. The most popular approach is to replace the lower-level program by its KKT conditions and then the bilevel program can be reformulated as a mathematical program with equilibrium constraints (MPEC for short). However, since the MPEC does not satisfy the Mangasarian-Fromovitz constraint qualification at any feasible point, the well-developed nonlinear programming theory cannot be applied to MPECs directly. In this paper, we apply the Wolfe duality to show that, under very mild conditions, the bilevel program is equivalent to a new single-level reformulation (WDP for short) in the globally and locally optimal sense. We give an example to show that, unlike the MPEC reformulation, WDP may satisfy the Mangasarian-Fromovitz constraint qualification at its feasible points. We give some properties of the WDP reformulation and the relations between the WDP and MPEC reformulations. We further propose a relaxation method for solving WDP and investigate its limiting behavior. Comprehensive numerical experiments indicate that, although solving WDP directly does not perform very well in our tests, the relaxation method based on the WDP reformulation is quite efficient

In vitro culture method of powdery mildew (Oidium heveae Steinmann) of Hevea brasiliensis

A method for culturing powdery mildew (Oidium heveae) from isolated leaves of Hevea brasiliensis was evaluated, which included three steps: Leaves and fungi selection, nutrient solution and culture dish  preparation, fungi inoculation and culture. The culture time and produced conidia number were considered as decision index. We tested the influence of micro components of nutrient solution including 6-benzylaminopurine (6-BA), salicylic acid (SA) and vitamin C (VC) and evaluated the culture difference of various leaf phenological phases and rubber tree clones. The results show that the longest culture time of isolated leaves emerged on modified Murashige and Skoog (MS) macro elements with 4 mg/L 6-BA, 20 mg/L SA, 1 mg/L VC. The colour phase leaf was the preferable choice for culturing average 15 to 16 days and producing 3.2222 × 106 mL-1 conidia. The culture effects of using various rubber clones were different and higher resistance clones cultured less conidia. The method leading to mass production of powdery mildew was simple using a climate incubator to resolve problems linked to season and space limitation and preservation of powdery mildew. This method could improve rubber resistance breeding process.Key words: Hevea brasiliensis, Oidium heveae, in vitro culture, nutrient solution, phenological phase

Fuzzy superpixels for polarimetric SAR images classification

Superpixels technique has drawn much attention in computer vision applications. Each superpixels algorithm has its own advantages. Selecting a more appropriate superpixels algorithm for a specific application can improve the performance of the application. In the last few years, superpixels are widely used in polarimetric synthetic aperture radar (PolSAR) image classification. However, no superpixel algorithm is especially designed for image classification. It is believed that both mixed superpixels and pure superpixels exist in an image.Nevertheless, mixed superpixels have negative effects on classification accuracy. Thus, it is necessary to generate superpixels containing as few mixed superpixels as possible for image classification. In this paper, first, a novel superpixels concept, named fuzzy superpixels, is proposed for reducing the generation of mixed superpixels.In fuzzy superpixels ,not al lpixels are assigned to a corresponding superpixel. We would rather ignore the pixels than assigning them to improper superpixels. Second,a new algorithm, named FuzzyS(FS),is proposed to generate fuzzy superpixels for PolSAR image classification. Three PolSAR images are used to verify the effect of the proposed FS algorithm. Experimental results demonstrate the superiority of the proposed FS algorithm over several state-of-the-art superpixels algorithms

Biosensors for wastewater-based epidemiology for monitoring public health

Public health is attracting increasing attention due to the current global pandemic, and wastewater-based epidemiology (WBE) has emerged as a powerful tool for monitoring of public health by analysis of a variety of biomarkers (e.g., chemicals and pathogens) in wastewater. Rapid development of WBE requires rapid and on-site analytical tools for monitoring of sewage biomarkers to provide immediate decision and intervention. Biosensors have been demonstrated to be highly sensitive and selective tools for the analysis of sewage biomarkers due to their fast response, ease-to-use, low cost and the potential for field-testing. This paper presents biosensors as effective tools for wastewater analysis of potential biomarkers and monitoring of public health via WBE. In particular, we discuss the use of sewage sensors for rapid detection of a range of targets, including rapid monitoring of community-wide illicit drug consumption and pathogens for early warning of infectious diseases outbreaks. Finally, we provide a perspective on the future use of the biosensor technology for WBE to enable rapid on-site monitoring of sewage, which will provide nearly real-time data for public health assessment and effective intervention

Tetris: A compilation Framework for VQE Applications

Quantum computing has shown promise in solving complex problems by leveraging the principles of superposition and entanglement. The Variational Quantum Eigensolver (VQE) algorithm stands as a pivotal approach in the realm of quantum algorithms, enabling the simulation of quantum systems on quantum hardware. In this paper, we introduce two innovative techniques, namely "Tetris" and "Fast Bridging," designed to enhance the efficiency and effectiveness of VQE tasks. The "Tetris" technique addresses a crucial aspect of VQE optimization by unveiling cancellation opportunities within the logical circuit phase of UCCSD ansatz. Tetris demonstrates a remarkable reduction up to 20% in CNOT gate counts, about 119048 CNOT gates, and 30% depth reduction compared to the state-of-the-art compiler 'Paulihedral'. In addition to Tetris, we present the "Fast Bridging" technique as an alternative to the conventional qubit routing methods that heavily rely on swap operations. The fast bridging offers a novel approach to qubit routing, mitigating the limitations associated with swap-heavy routing. By integrating the fast bridging into the VQE framework, we observe further reductions in CNOT gate counts and circuit depth. The bridging technique can achieve up to 27% CNOT gate reduction in the QAOA application. Through a combination of Tetris and the fast bridging, we present a comprehensive strategy for enhancing VQE performance. Our experimental results showcase the effectiveness of Tetris in uncovering cancellation opportunities and demonstrate the symbiotic relationship between Tetris and the fast bridging in minimizing gate counts and circuit depth. This paper contributes not only to the advancement of VQE techniques but also to the broader field of quantum algorithm optimization

A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer

Purpose: To evaluate the potential of machine learning (ML)-based radiomics approach for predicting tumor mutation burden (TMB) in gastric cancer (GC).Methods: The contrast enhanced CT (CECT) images with corresponding clinical information of 256 GC patients were retrospectively collected. Patients were separated into training set (n = 180) and validation set (n = 76). A total of 3,390 radiomics features were extracted from three phases images of CECT. The least absolute shrinkage and selection operator (LASSO) model was used for feature screening. Seven machine learning (ML) algorithms were employed to find the optimal classifier. The predictive ability of radiomics model (RM) was evaluated with receiver operating characteristic. The correlation between RM and TMB values was evaluated using Spearman’s correlation coefficient. The explainability of RM was assessed by the Shapley Additive explanations (SHAP) method.Results: Logistic regression algorithm was chosen for model construction. The RM showed good predictive ability of TMB status with AUCs of 0.89 [95% confidence interval (CI): 0.85–0.94] and 0.86 (95% CI: 0.74–0.98) in the training and validation sets. The correlation analysis revealed a good correlation between RM and TMB levels (correlation coefficient: 0.62, p < 0.001). The RM also showed favorable and stable predictive accuracy within the cutoff value range 6–16 mut/Mb in both sets.Conclusion: The ML-based RM offered a promising image biomarker for predicting TMB status in GC patients