Miracle-dz/UniFAR-FTUs huggingface dataset:
Metasurface cloaks for decoupling electromagnetic interference within highly dense areas

This paper presents the results of intensive investigations of electromagnetic interference caused primarily by closely arranged antennas within subwavelength distance; potential applications involve the principle of scattering cancellation. A cloaked planar monopole antenna (CPMA) is newly proposed in this work. The performance of the CPMA is compared with that of a bare planar monopole antenna. Numerical simulations with the two antenna types were conducted, and various configurations with the potential for practical applications in the industrial and defense sectors were tested.
Comparison of bacterial carbon production estimates from dilution and 3H‐leucine methods across a strong gradient in ocean productivity

The uptake of 3H‐labeled leucine into proteins, a widely used method for estimating bacterial carbon production (BCP), is suggested to underestimate or overestimate bacterial growth in the open ocean by a factor of 40 uncertainty. Meanwhile, an alternative BCP approach, by the dilution method, has untested concerns about potential overestimation of bacterial growth from dissolved substrates released by filtration. We compared BCPDil and BCPLeu estimates from three cruises across a broad trophic gradient, from offshore oligotrophy to coastal upwelling, in the California Current Ecosystem. Our initial analyses based on midday microscopical estimates of bacterial size and a priori assumptions of conversions relationships revealed a mean two‐fold difference in BCP estimates (BCPDil higher), but no systematic bias between low and high productivity stations. BCPDil and BCPLeu both demonstrated strong relationships with bacteria cell abundance. Reanalysis of results, involving a different cell carbon‐biovolume relationship and informed by forward angle light scatter from flow cytometry as a relative cell size index, demonstrated that BCPDil and BCPLeu are fully compatible, with a 1 : 1 fit for bacteria of 5 fg C cell−1. Based on these results and considering different strengths of the methods, the combined use of 3H‐labeled leucine and dilution techniques provide strong mutually supportive constraints on bacterial biomass and production.
A unifying framework for specifying generalized linear models for categorical data

In the context of categorical data analysis, the case of nominal and ordinal data has been investigated in depth while the case of partially ordered data has been comparatively neglected. We first propose a new specification of generalized linear models (GLMs) for categorical response variables which en- compasses all the classical models such as multinomial logit, odds proportional or continuation ratio models but also led us to identify new GLMs. This unifying framework makes the different GLMs easier to compare and combine. We then define the more general class of partitioned conditional GLMs for categorical re- sponse variables. This new class enables to take into account the case of partially ordered data by combining nominal and ordinal GLMs.
Semi-infectious particles contribute substantially to influenza virus within-host dynamics when infection is dominated by spatial structure

Abstract Influenza is an ribonucleic acid virus with a genome that comprises eight segments. Experiments show that the vast majority of virions fail to express one or more gene segments and thus cannot cause a productive infection on their own. These particles, called semi-infectious particles (SIPs), can induce virion production through complementation when multiple SIPs are present in an infected cell. Previous within-host influenza models did not explicitly consider SIPs and largely ignore the potential effects of coinfection during virus infection. Here, we constructed and analyzed two distinct models explicitly keeping track of SIPs and coinfection: one without spatial structure and the other implicitly considering spatial structure. While the model without spatial structure fails to reproduce key aspects of within-host influenza virus dynamics, we found that the model implicitly considering the spatial structure of the infection process makes predictions that are consistent with biological observations, highlighting the crucial role that spatial structure plays during an influenza infection. This model predicts two phases of viral growth prior to the viral peak: a first phase driven by fully infectious particles at the initiation of infection followed by a second phase largely driven by coinfections of fully infectious particles and SIPs. Fitting this model to two sets of data, we show that SIPs can contribute substantially to viral load during infection. Overall, the model provides a new interpretation of the in vivo exponential viral growth observed in experiments and a mechanistic explanation for why the production of large numbers of SIPs does not strongly impede viral growth. Being simple and predictive, our model framework serves as a useful tool to understand coinfection dynamics in spatially structured acute viral infections.
Effect of the pectin contents and nanostructure on the stem straightness of two Paeonia lactiflora cultivars

Herbaceous peony (Paeonia lactiflora Pall.) is an ancient ornamental crop and, in recent decades, an emerging popular cut flower. Straight stems are a vital criterion for cut herbaceous peony selection, while many cultivars bend as the plant develops. Pectin helps maintain the mechanical strength of the cell wall. However, little is known about its role in the stem bending of herbaceous peony. Two herbaceous peony cultivars with contrasting stem morphologies (‘Dong Fang Shao Nv’, upright; ‘Lan Tian Piao Xiang’, bending gradually) at five developmental stages were used as materials to investigate the effects of pectin content and nanostructure on straightness using the carbazole colorimetric method and atomic force microscopy observations. The contents of water-soluble pectin (WSP), CDTA-soluble pectin (CSP), and sodium carbonate-soluble pectin (SSP) differed significantly between the two cultivars, and the contents and angle of the flower and branch showed correlations. For the pectin nanostructure, WSP showed agglomerates and long chains, with a higher proportion of broad agglomerates at the later stages of the bending cultivar than the upright cultivar. CSP showed branched chains, and the proportion of broad chains was higher in the upright cultivar at later stages, while CSP shape changed from agglomerates to chains in the bending cultivar. SSP mainly consisted of short linear main chains, and side chains in the upright stem were stacked, and the bent cultivar had more broad and short chains. It can be concluded that the contents, nanometric shape, and size of the three kinds of pectin are highly likely to affect herbaceous peony stem straightness. This study provides a theoretical basis for the role of pectin in the production and breeding of herbaceous peony cut flowers.
Modeling Fuzzy Data in Qualitative Marketing Research

In marketing, qualitative data are used in theory development to investigate marketing phenomena in more depth. After qualitative data are collected, the judgment-based classification of items into categories is routinely used to summarize and communicate the information contained in the data. In this article, the authors provide marketing researchers with a method that (1) provides useful substantive information about the proportion and degree to which items belong to several categories and (2) measures the classification accuracy of the judges. The model is called the fuzzy latent class model (FLCM), because it extends Dillon and Mulani's (1984) latent class model by freeing it from the restrictive assumption that all items are crisp for a given categorization. Instead, FLCM allows for items to be either crisp or fuzzy. Crisp items belong exclusively to one category, whereas fuzzy items belong—in varying degree—to multiple categories. This relaxation in the assumption about the nature of qualitative data makes FLCM more widely applicable: Qualitative data in marketing research are often fuzzy, because they involve open-ended descriptions of complex phenomena. The authors also propose a moment-based measure of overall data fuzziness that is bounded by 0 (completely crisp) and 1 (completely fuzzy).
Display Optimization for Vertically Differentiated Locations Under Multinomial Logit Preferences

We introduce a new optimization model, dubbed the display optimization problem, that captures a common aspect of choice behavior, known as the framing bias. In this setting, the objective is to optimize how distinct items (corresponding to products, web links, ads, etc.) are being displayed to a heterogeneous audience, whose choice preferences are influenced by the relative locations of items. Once items are assigned to vertically differentiated locations, customers consider a subset of the items displayed in the most favorable locations before picking an alternative through multinomial logit choice probabilities. The main contribution of this paper is to derive a polynomial-time approximation scheme for the display optimization problem. Our algorithm is based on an approximate dynamic programming formulation that exploits various structural properties to derive a compact state space representation of provably near-optimal item-to-position assignment decisions. As a byproduct, our results improve on existing constant-factor approximations for closely related models and apply to general distributions over consideration sets. We develop the notion of approximate assortments that may be of independent interest and applicable in additional revenue management settings. Lastly, we conduct extensive numerical studies to validate the proposed modeling approach and algorithm. Experiments on a public hotel booking data set demonstrate the superior predictive accuracy of our choice model vis-à-vis the multinomial logit choice model with location bias, proposed in earlier literature. In synthetic computational experiments, our approximation scheme dominates various benchmarks, including natural heuristics—greedy methods, local search, priority rules—and state-of-the-art algorithms developed for closely related models. This paper was accepted by Yinyu Ye, optimization.
Recent Advances in Water-Splitting Electrocatalysts Based on Electrodeposition

Green hydrogen is being considered as a next-generation sustainable energy source. It is created electrochemically by water splitting with renewable electricity such as wind, geothermal, solar, and hydropower. The development of electrocatalysts is crucial for the practical production of green hydrogen in order to achieve highly efficient water-splitting systems. Due to its advantages of being environmentally friendly, economically advantageous, and scalable for practical application, electrodeposition is widely used to prepare electrocatalysts. There are still some restrictions on the ability to create highly effective electrocatalysts using electrodeposition owing to the extremely complicated variables required to deposit uniform and large numbers of catalytic active sites. In this review article, we focus on recent advancements in the field of electrodeposition for water splitting, as well as a number of strategies to address current issues. The highly catalytic electrodeposited catalyst systems, including nanostructured layered double hydroxides (LDHs), single-atom catalysts (SACs), high-entropy alloys (HEAs), and core-shell structures, are intensively discussed. Lastly, we offer solutions to current problems and the potential of electrodeposition in upcoming water-splitting electrocatalysts.
Generation of plant dyeing stylized artwork based on diffusion model

With the improvement of people's living standards, people have higher clothing safety and artistry requirements. Plant dyeing artworks use plant sap to dye natural and woven fabrics, thus creating a print close to nature, gorgeous in style, and free from the harmful components of chemical dyeing, which is recognized and widely used by highly knowledgeable people. However, the type and the design of plant dyeing artworks need to incorporate the artist's creative inspiration, which is limited by human work efficiency. It constrains the creation of plant-dyeing artworks and limits the speed of innovation. Deep learning has made significant progress in the field of artistic creation. In this paper, we propose to combine the disco diffusion model with the creation of plant-dyeing artworks. The high-quality image generation capability of the diffusion model and multi-modal content description is utilized to design and generate plant dyeing artworks. The original style is firstly modeled after photographs of the original plant dyeing artwork. The style type is further expanded using text descriptions. The disco diffusion model is then used to create the final plant-dyeing piece of art. The experiment results show that generating plant dyeing paintings based on the disco diffusion model achieves better visual results. Through the controlled generation process and high-quality work generation results, the stylized plant dyeing design works is obtained automatically. It provides strong support for the creation of plant dyeing art.
The Effect of Glycine Betaine on Nitrogen and Polyamine Metabolisms, Expression of Glycoside-Related Biosynthetic Enzymes, and K/Na Balance of Stevia under Salt Stress

The beneficial role of glycine betaine (GB) in the adaptation of plants to abiotic stresses is well known; therefore, the study of physiological and molecular responses induced by exogenous GB under NaCl stress can provide a suitable reference for the application of this compound to enhance the adaptation of plants to salinity. The present study was conducted under in vitro conditions to evaluate the effect of GB (25 and 50 mM) on the growth, physiological, and molecular traits of Stevia rebaudiana during NaCl toxicity (50 mM). The results showed that applying NaCl treatment increased Na accumulation, induced oxidative stress, and disrupted N metabolism and K/Na homeostasis, which, as a result, decreased the stevia plant’s growth and biomass. However, application of GB improved the adaptation of NaCl-stressed plants by improving N metabolism and modulating the metabolism of polyamines. By increasing the activity of antioxidant enzymes, GB diminished oxidative stress, protected the plasma membrane, and restored photosynthetic pigments under NaCl toxicity. By reducing Na accumulation and increasing K accumulation, GB maintained the K/Na balance and reduced the effects of toxicity caused by the high Na concentration in stevia leaves. GB increased the leaf accumulation of rebaudioside A in NaCl-stressed plants by modulating the expression of genes (KAH, UGT74G1, UGT76G1, and UGT85C2) involved in the sugar compounds of the stevia plants. Our results provide a broad understanding of GB-induced responses in NaCl-stressed plants, which can help increase our knowledge of the role of GB in the defense mechanisms of plants under abiotic stresses.
Immunity of multiplex networks via acquaintance vaccination

How to find the effective approach of immunizing a population is one open question in the research of complex systems. Up to now, there have been a great number of works focusing on the efficiency of various immunization strategies. However, the majority of these existing achievements are limited to isolated networks, how immunization affects disease spreading in multiplex networks seems to need further exploration. In this letter, we explore the impact of the acquaintance immunization in multiplex networks, where two kinds of immunization strategies, multiplex node-based acquaintance immunization and layer node-based acquaintance immunization, are proposed. With the generating function method, our theoretical framework is able to accurately calculate the critical immunization threshold which is one of the most important indexes to predict the epidemic regime. Moreover, we further uncover that, with the increment of degree correlation between network layers, the immunization threshold declines for multiplex node-based acquaintance immunization, but slowly increases for layer node-based acquaintance immunization.
Workload Allocation Toward Energy Consumption-Delay Trade-Off in Cloud-Fog Computing Using Multi-Objective NPSO Algorithm

The Internet of Things (IoT) generates massive data from smart devices that demand responses from cloud servers. However, sending tasks to the cloud reduces the power consumed by the users’ devices, but increases the transmission delay of the tasks. In contrast, sending tasks to the fog server reduces the transmission delay due to the shorter distance between the user and the server. However, this occurs at the user end’s expense of higher energy consumption. Thus, this study proposes a mathematical framework for workload allocation to model the power consumption and delay functions for both fog and clouds. After that, a Modified Least Laxity First (MLLF) algorithm was proposed to reduce the maximum delay threshold. Furthermore, a new multi-objective approach, namely the Non-dominated Particle Swarm Optimization (NPSO), is proposed to reduce energy consumption and delay compared to the state-of-the-art algorithms. The simulation results show that NPSO outperforms the state-of-the-art algorithm in reducing energy consumption, while NGSA-II proves its effectiveness in reducing transmission delay compared to the other algorithms in the experimental simulation. In addition, the MLLF algorithm reduces the maximum delay threshold by approximately 11% compared with other related algorithms. Moreover, the results prove that metaheuristics are more appropriate for distributed computing.
Stability and instability of generalized Kolmogorov flows on the two-dimensional sphere

The Navier–Stokes equations on the two-dimensional rotating sphere with a family of forcing terms, whose stream functions are the Legendre polynomials P s , are considered. The stability and instability properties of the corresponding generalized Kolmogorov ﬂows are studied both analytically and numerically. Logarithmically sharp lower bounds for the dimension of the global attractor are obtained. The ef-fect of rotation on the stability properties of the Kolmogorov ﬂows is discussed.
Musculoskeletal Fitness for Identifying Low Physical Function in Older Women

Aims: This cross-sectional study aimed to analyze the relevance of musculoskeletal fitness for identifying low physical functioning in community-dwelling older women. Methods: Sixty-six older women (73.62 ± 8.23 yrs old) performed a musculoskeletal fitness assessment of the upper and lower limbs. A handheld dynamometer was used to evaluate upper-limb muscle strength through a handgrip (HG) test. Lower-limb power and force were assessed from a two-leg countermovement vertical jump (VJ) on a ground reaction force platform. Physical functioning was assessed subjectively using the Composite Physical Function (CPF) questionnaire and objectively by daily step count measured by accelerometry and gait speed/agility assessed by the 8-Foot Up-and-Go (TUG) test. Logistic regressions and ROC curves were carried out to define odds ratios and ideal cutoff values for discriminatory variables. Results: VJ power showed the ability to identify low physical functioning when evaluated through the CPF (14 W/kg, 1011 W), gait speed/agility (15 W/kg, 800 W), or daily accumulated steps (17 W/kg). Considering that VJ power was normalized for body mass, the increase of 1 W/kg corresponds to a decrease of 21%, 19%, or 16% in the chance of low physical functioning when expressed by these variables, respectively. HG strength and VJ force did not show a capacity to identify low physical functioning. Conclusions: The results suggest that VJ power is the only marker of low physical functioning when considering the three benchmarks: perception of physical ability, capacity for mobility, and daily mobility.
The study of middle school teachers' understanding and use of mathematical representation in relation to teachers' zone of proximal development in teaching fractions and algebraic functions

The Study of Middle School Teachers’ Understanding and Use of Mathematical Representation in Relation to Teachers’ Zone of Proximal Development in Teaching Fractions and Algebraic Functions. (August 2004) Zhonghe Wu, B.S., Wuhan University Marine Mechanical Engineering Institute; M.B.A., Texas A&M International University Chair of Advisory Committee: Dr. Gerald Kulm This study examined teachers’ learning and understanding of mathematical representation through the Middle School Mathematics Project (MSMP) professional development, investigated teachers’ use of mathematics representations in teaching fractions and algebraic functions, and addressed patterns of teachers’ changes in learning and using representation corresponding to Teachers’ Zone of Proximal Development (TZPD). Using a qualitative research design, data were collected over a 2-year period, from eleven participating 6 and 7 grade mathematics teachers from four school districts in Texas in a research-designed professional development workshop that focused on helping teachers understand and use of mathematical representations. Teachers were given two questionnaires and had lessons videotaped before and after the workshop, a survey before the workshop, and learning and discussion videotapes during the workshop. In addition, ten teachers were interviewed to find out the patterns of their changes in learning and using mathematics representations.
A Survey on Consensus Algorithms in Blockchain-Based Applications: Architecture, Taxonomy, and Operational Issues

Recently, Blockchain-based applications have become immensely popular because of limited reliance on a single entity, unlike a centralized system. However, reaching a consensus among blockchain networks is a challenging and vital aspect of blockchain-based applications. There are various types of blockchain networks for different kinds of application scenarios. Among all of them, the consensus algorithm is the most crucial part of reaching an agreement in the complex blockchain network. Over the years, researchers have focused on dealing with the challenges like distributed computing, storage, transaction speed, security, validity, interoperability, and many more. However, only some of them are appropriate for all domains. Therefore, this paper presents an extensive study of different types of consensus protocols used in existing blockchain solutions with the strength and limitations of each algorithm. We also provide an inherent comparison among different algorithms to understand consensus protocol selection better. Moreover, we investigate operational and interoperability issues in existing blockchain-based applications to understand challenges and provide recommendations for future developers.