Multiple Modes Research Articles

JargonFM: A Framework With Multiple Interpretation Modes for Jargon Understanding in Online Communities

Jargon words are commonly used in the communication of online communities. These words are characterized by special and implicit meanings that can only be comprehended by a small group of users, which brings challenges to community regulation and user engagement. For this problem, we present JargonFM, a framework with multiple interpretation modes for jargon understanding in online communities. JargonFM is designed based on the scientific explanation framework and supports three interpretation modes: jargon category prediction based on a jargon classifier, similar word identification based on a jargon synonyms selector, and representative text selection based on an example sentence selector. A jargon interpreter was also implemented to demonstrate the usage and usefulness of the interpretation framework. Automatic and human evaluations suggest that JargonFM can explain jargon words more accurately and more efficiently than the existing interpretation methods, leading to its wide acceptance among the evaluation participants.

Towards Attack Detection in Multimodal Cyber-Physical Systems with Sticky HDP-HMM based Time Series Analysis

Automatic detection of the precise occurrence and duration of an attack reflected in time-series logs generated by cyber-physical systems is a challenging problem. This problem is exacerbated when performing this analysis using logs with limited system information. In a realistic scenario, multiple and differing attack methods may be employed in rapid succession. Modern or legacy systems operate in multiple modes and contain multiple devices recording a variety of continuous and categorical data streams. This work presents a non-parametric Bayesian framework that addresses these challenges using the sticky Hierarchical Dirichlet Process Hidden Markov Model (sHDP-HMM) . Additionally, we explore metrics for measuring the accuracy of the detected events, their timings and durations, and compare the computational efficiency of different inference implementations of the model. The efficacy of attack detection is demonstrated in two settings: an avionics testbed and a consumer robot.

Investigating pharmaceuticals and personal care products in Musi River: A exploratory study in Hyderabad, India

This study investigated the presence of 46 pharmaceuticals and personal care products (PPCPs), including antibiotics, anti-inflammatory, antifungal, surfactant, antidepressants, and anticonvulsants, in wastewater samples collected from 11 sites along the Musi River in Hyderabad, India. These samples were obtained from diverse points along the river, representing urban, industrial, and hospital-related sources. This strategic selection ensures a comprehensive assessment of water quality, covering areas of varying population density and industrial activity. The samples were extracted for PPCPs using solid-phase extraction and quantified using ultra-performance liquid chromatography-tandem mass spectrometry in multiple reaction mode. Of the 46, 16 target analytes were detected at concentrations ranging from 0.01 ± 0.001 μg/L (for fluoxetine) to 19.295 ± 4.16 μg/L (for ciprofloxacin). An ecological risk assessment was conducted in terms of risk quotient (RQ) for acute and chronic effects for three trophic levels of aquatic organisms (algae, fish, and invertebrates) for each detected contaminant at every site. In the case of acute toxicity, high RQ values (>>1) were obtained for the antibiotics, naproxen, diclofenac, caffeine, fluconazole, propyl paraben, fluoxetine, and benzalkonium chloride C-12. In the case of chronic toxicity, high RQ values (>>1) were estimated for some of the commonly used PPCPs. This study aimed to investigate the contamination levels of PPCPs in various wastewater discharge points along the Musi River in Hyderabad, a densely populated urban center with a population density of up to 45,000/sq km. Additionally, the research conducted an ecotoxicological risk assessment to evaluate the risk posed by these pollutants to three trophic levels (algae, fish, and invertebrates) using RQ analysis. Notably, this study represents the first attempt to conduct such an assessment in this region, providing valuable insights into environmental health and pollution management.

Analysis of cardiovascular function in diabetic patients using EEMD-ICA fusion multi-scale percussion entropy.

Diabetes is a chronic disease that can lead to a variety of complications and even cause death. The signal characteristics of the photoplethysmography signals (PPG) and electrocardiogram signals (ECG) can reflect the autonomic and vascular aspects of the effects of diabetes on the body. Based on the complex mechanism of interaction between PPG and ECG, a set of ensemble empirical mode decomposition-independent component analysis (EEMD-ICA) fusion multi-scale percussion entropy index (MSPEI) method was proposed to analyze cardiovascular function in diabetic patients. Firstly, the original signal was decomposed into multiple Intrinsic Mode Function (IMFs) by ensemble empirical mode decomposition EEMD, principal components of IMF were extracted by independent component analysis (ICA), then the extracted principal components were reconstructed to eliminate the complex high and low frequency noise of physiological signals. In addition, the MSPEI was calculated for the ECG R-R interval and PPG amplitude sequence.(RRI and Amp) The results showed that, compared with EEMD method, the SNR of EEMD-ICA method increases from 2.1551 to 11.3642, and the root mean square error (RMSE) decreases from 0.0556 to 0.0067. This algorithm can improve the performance of denoising and retain more feature information. The large and small scale entropy of MSPEI (RRI,Amp) was significantly different between healthy and diabetic patients (p< 0.01). Compared with arteriosclerosis index (AI) and multi-scale cross-approximate entropy (MCAE): MSPEISS (RRI,Amp) indicated that diabetes can affect the activity of human autonomic nervous system, while MSPEILS (RRI,Amp) indicated that diabetes can cause or worsen arteriosclerosis. Multi-scale Percussion Entropy algorithm has more advantages in analyzing the influence of diabetes on human cardiovascular and autonomic nervous function.

Knittable Electrochemical Yarn Muscle for Morphing Textiles.

Morphing textiles, crafted using electrochemical artificial muscle yarns, boast features such as adaptive structural flexibility, programmable control, low operating voltage, and minimal thermal effect. However, the progression of these textiles is still impeded by the challenges in the continuous production of these yarn muscles and the necessity for proper structure designs that bypass operation in extensive electrolyte environments. Herein, a meters-long sheath-core structured carbon nanotube (CNT)/nylon composite yarn muscle is continuously prepared. The nylon core not only reduces the consumption of CNTs but also amplifies the surface area for interaction between the CNT yarn and the electrolyte, leading to an enhanced effective actuation volume. When driven electrochemically, the CNT@nylon yarn muscle demonstrates a maximum contractile stroke of 26.4%, a maximum contractile rate of 15.8% s-1, and a maximum power density of 0.37 W g-1, surpassing pure CNT yarn muscles by 1.59, 1.82, and 5.5 times, respectively. By knitting the electrochemical CNT@nylon artificial muscle yarns into a soft fabric that serves as both a soft scaffold and an electrolyte container, we achieved a morphing textile is achieved. This textile can perform programmable multiple motion modes in air such as contraction and sectional bending.

Fast, effective, and coherent time series modelling using the sparsity-ranked lasso

The sparsity-ranked lasso (SRL) has been developed for model selection and estimation in the presence of interactions and polynomials. The main tenet of the SRL is that an algorithm should be more sceptical of higher-order polynomials and interactions a priori compared to main effects, and hence the inclusion of these more complex terms should require a higher level of evidence. In time series, the same idea of ranked prior scepticism can be applied to characterize the potentially complex seasonal autoregressive (AR) structure of a series during the model fitting process, becoming especially useful in settings with uncertain or multiple modes of seasonality. The SRL can naturally incorporate exogenous variables, with streamlined options for inference and/or feature selection. The fitting process is quick even for large series with a high-dimensional feature set. In this work, we discuss both the formulation of this procedure and the software we have developed for its implementation via the fastTS R package. We explore the performance of our SRL-based approach in a novel application involving the autoregressive modelling of hourly emergency room arrivals at the University of Iowa Hospitals and Clinics. We find that the SRL is considerably faster than its competitors, while generally producing more accurate predictions.

Social strategies to access land influence crop diversity in northwestern Morocco

Abstract Much evidence supports the ecological and agronomic benefits of diversity, of both crops and environments, for building resilience and sustainability in agroecosystems. Farmers' knowledge about crop diversity is well‐documented, but aside from studies on how farmers exchange seeds and knowledge through networks, the interactions of social factors and the diversity of crops and cultivated environments have been mainly overlooked. One factor receiving attention is farmers' access to land, but in only one of its dimensions, the security of access. Here we address the different strategies by which farmers gain access to land. How does the plurality of modes of access to land influence crop choices, and thereby crop diversity? How does this plurality influence the range of environments available to individual farmers for cultivating crop diversity? Analysing data from 51 interviews with farmers and 312 plots in agrosilvopastoral systems in northwestern Morocco, we described eight different modes of access. Each mode offers different opportunities and constraints concerning the kind of crops that can be grown on the plot. We found that an increase in the number of modes of access to land increases the crop diversity of farmers' holdings, regardless of the total area each farmer cultivates. Accessing additional plots contributed to both environmental heterogeneity and to crop diversity of farms. In striving to gain access to land and to grow diverse crops, farmers are motivated by their notion of what it means to be a ‘real farmer’, that is, the relation to their identity. Farmers mobilize not only their economic power but also their social relationships to gain access to plots of land. Their choices are also based on their relationships to tree crops such as olive, which are economic and cultural keystone species, as well as markers of land ownership and control. Multiple modes of access to land characterize many smallholder farming systems, which support a large fraction of the world's population. Recognizing diverse social practices of access to land that allow farmers to continue to mobilize multiple modes of access can increase resilience against unpredictable events and help maintain sustainable agroecosystems. Read the free Plain Language Summary for this article on the Journal blog.

Effect of W on the Mechanical Properties and Tribological Behavior of CoCrFeNiWx Compositionally Complex Alloy

Introducing the hard second phase into soft matrix and reducing the grain size are key strategies to enhance the strength and wear performance of metallic alloys. Herein, to investigate the effect of hard second phase on the mechanical properties and tribological behavior of face‐centered‐cubic (FCC) CoCrFeNi compositionally complex alloy (CCA), a series of ultrafine‐grained CoCrFeNiWx (x = 0, 0.3, 0.5, and 1.0) CCAs with varying W contents are fabricated. The results show that the single FCC CCA transforms to the dual phase of FCC and μ phase after addition of W. With increasing W content, the hardness and yield strength show a linear increase due to the additional solid solution strengthening and secondary phase strengthening, but the fracture strain shows an opposite trend. Dry sliding wear tests show that both coefficients of friction and wear rates decrease with the W content. The wear mode changes from abrasive wear for CoCrFeNi CCA to multiple wear modes of abrasive wear, oxidative wear, and fatigue wear for W‐containing CCAs. These results suggest that adding W into the FCC CCA matrix could strengthen the FCC alloy and simultaneously improve wear performance.

Searching for ringdown higher modes with a numerical relativity-informed post-merger model

Robust measurements of multiple black hole vibrational modes provide a unique opportunity to characterise gravity in extreme curvature and dynamical regimes, to better investigate the nature of compact objects and search for signs of new physics. We use a numerically-tuned quasicircular non-precessing ringdown model, TEOBPM, and the pyRing analysis infrastructure to perform a time-domain spectroscopic analysis of the third catalog of transient gravitational-wave signals, GWTC-3, searching for higher angular modes. The TEOBPM model effectively includes non-linearities in the early post-merger signal portion, and carries information about the progenitors parameters through time-dependent excitation amplitudes of the black hole quasinormal modes. Such a strategy allows us to accurately model the full post-merger emission, recovering higher signal-to-noise ratios compared to templates based on more agnostic superpositions of damped-sinusoids. We find weak evidence for the presence of (l,m)=(3,3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(l,m)=(3,3)$$\\end{document} [(l,m)=(2,1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(l,m)=(2,1)$$\\end{document}] mode in several events, with the largest Bayes factor in favour of this mode being B≃2.6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {B}} \\simeq 2.6$$\\end{document} [B≃1.2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {B}} \\simeq 1.2$$\\end{document}] within the support of the peak time distribution. For GW190521, we observe B≃5.1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {\\mathcal {B}} \\simeq 5.1$$\\end{document}, but only for times outside the peak time support reconstructed using the highly accurate NRSur7dq4 model, indicating significant systematics affecting such putative detection. Allowing for deviations from general relativity under the assumption of the presence of two modes, we find tentative support for the Kerr “final state conjecture”. Our work showcases a systematic methodology to robustly identify and characterise higher angular modes in ringdown-only signals, highlighting the significant impact of modelling assumptions and peak time uncertainty on spectroscopic measurements, at current signal-to-noise ratios.

Simultaneous Quantification of Nine Target Compounds in Traditional Korean Medicine, Bopyeo-Tang, Using High-Performance Liquid Chromatography-Photodiode Array Detector and Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry.

Bopyeo-tang (BPT) is composed of six medicinal herbs (Morus alba L., Rehmannia glutinosa (Gaertn.) DC., Panax ginseng C.A.Mey., Aster tataricus L.f., Astragalus propinquus Schischkin, and Schisandra chinensis (Turcz.) Baill.) and has been used for the treatment of lung diseases. This study focused on establishing an analytical method that can simultaneously quantify nine target compounds (i.e., hydroxymethylfurfural, mulberroside A, chlorogenic acid, calycosin-7-O-glucoside, 3,5-dicaffeoylquinic acid, quercetin, kaempferol, schizandrin, and gomisin A) from a BPT sample using high-performance liquid chromatography with a photodiode array detector (HPLC-PDA) and ultra-performance liquid chromatography with tandem mass spectrometry (UPLC-MS/MS). The separation of compounds in both analyses was performed on a C18 reversed-phase column using the gradient elution of water-acetonitrile as the mobile phase. In particular, the multiple reaction monitoring mode was applied for quick and accurate detection in UPLC-MS/MS analysis. As a result of analyzing the two methods, HPLC-PDA and UPLC-MS/MS, the coefficient of determination of the regression equation for each compound was ≥0.9952, and recovery was 85.99-106.40% (relative standard deviation (RSD) < 9.58%). Precision testing of the nine compounds was verified (RSD < 10.0%). The application of these analytical assays under optimized conditions for quantitative analysis of the BPT sample gave 0.01-4.70 mg/g. Therefore, these two assays could be used successfully to gather basic data for clinical research and the quality control of BPT.

Efficient opto- and chemogenetic control in a single molecule driven by FRET-modified bioluminescence.

Bioluminescent optogenetics (BL-OG) offers a unique and powerful approach to manipulate neural activity both opto- and chemogenetically using a single actuator molecule (a LuMinOpsin, LMO). To further enhance the utility of BL-OG by improving the efficacy of chemogenetic (bioluminescence-driven) LMO activation. We developed novel luciferases optimized for Förster resonance energy transfer when fused to the fluorescent protein mNeonGreen, generating bright bioluminescent (BL) emitters spectrally tuned to Volvox Channelrhodopsin 1 (VChR1). A new LMO generated from this approach (LMO7) showed significantly stronger BL-driven opsin activation compared to previous and other new variants. We extensively benchmarked LMO7 against LMO3 (current standard) and found significantly stronger neuronal activity modulation ex vivo and in vivo, and efficient modulation of behavior. We report a robust new option for achieving multiple modes of control in a single actuator and a promising engineering strategy for continued improvement of BL-OG.

Construction and Multi-Mode Motion Analysis of Single-Degree-of-Freedom Four-Bar Multi-Mode Planar Mechanisms Based on Singular Configuration

Abstract The traditional four-bar mechanism is renowned for its simple structure, dependable performance, and wide range of applications. The single-degree-of-freedom (DOF) four-bar multi-mode planar mechanism (MMPM) is a type of four-bar mechanism that not only has the structural characteristics of the traditional four-bar mechanisms but also can achieve multiple motion modes by changing its structure. It has the advantage of performing diverse functions while conserving resources, which opens up new possibilities for research and application of the four-bar mechanism. However, due to the lack of a systematic configuration construction method, the design and application of single-DOF four-bar MMPMs are seriously limited. This paper presents a systematic method to construct a set of single-DOF four-bar MMPMs based on the loop equations and the proposed multi-mode modules (MMMs). First, depending on the loop equations, the four-bar planar mechanism containing two branches is identified by the corresponding branch graphs. Then, three kinds of MMMs are systematically proposed for the first time, helping the identified mechanism realize multiple motion modes. Subsequently, single-DOF four-bar MMPMs are constructed by replacing the specific component of the planar mechanism with the MMMs. Furthermore, the replacement rules of MMMs and the corresponding construction steps are summarized. Finally, 14 kinds of single-DOF four-bar MMPMs are listed, and the corresponding multi-mode motion analysis is discussed at the end of this paper. The proposed method is a straightforward one, which will provide great convenience for the configuration design of single-DOF four-bar MMPMs and promote the development and application of MMPMs.

A treelike framework combining fault diagnosis and RUL prediction

Most existing deep learning methods consider the remaining useful life (RUL) prediction problem under a single failure mode and cannot solve the RUL prediction problem with multiple failure modes coexisting caused by component coupling in actual engineering systems. Thus, considering these issues, this paper proposes a novel tree network framework to address fault classification and RUL prediction in parallel, and the RUL prediction results are fused output, which are suitable for bearing RUL prediction with multiple faults. First, this paper develops a fault recognizer combining a frequency domain classifier and deep convolutional neural network to improve model selection accuracy. Secondly, this paper proposes a feature fusion algorithm based on the Gini coefficient, and the fused indicators are input into the RUL prediction sub-network for model training. Finally, the RUL sub-network prediction results are dynamically weighted and fused with the fault classification results to obtain the RUL based on SoftMax. The bearing dataset XJTU-SY is introduced to verify the efficiency of the proposed method, and computational results show that the developed framework can effectively predict RUL compared with other traditional methods, especially for RUL prediction under multiple failure modes.

Experimental demonstration of a photonic reservoir computing system based on Fabry Perot laser for multiple tasks processing

Abstract Photonic reservoir computing (RC) is a simple and efficient neuromorphic computing framework for human cortical circuits, which is featured with fast training speed and low training cost. Photonic time delay RC, as a simple hardware implementation method of RC, has attracted widespread attention. In this paper, we present and experimentally demonstrate a time delay RC system based on a Fabry Perot (FP) laser for multiple tasks processing. Here, the various tasks are attempted to perform in parallel in the multiple longitudinal modes of the FP laser. It is found that the time delay RC system based on the FP laser can successfully handle different tasks across multiple longitudinal modes simultaneously. The experimental results demonstrate the potential of the time delay RC system based on the FP laser to achieve multiple tasks processing, providing various possibilities for improving the information processing ability of neural morphology RC systems, and promoting the development of RC systems.

How do stochastic processes and genetic threshold effects explain incomplete penetrance and inform causal disease mechanisms?

Incomplete penetrance is the rule rather than the exception in Mendelian disease. In syndromic monogenic disorders, phenotypic variability can be viewed as the combination of incomplete penetrance for each of multiple independent clinical features. Within genetically identical individuals, such as isogenic model organisms, stochastic variation at molecular and cellular levels is the primary cause of incomplete penetrance according to a genetic threshold model. By defining specific probability distributions of causal biological readouts and genetic liability values, stochasticity and incomplete penetrance provide information about threshold values in biological systems. Ascertainment of threshold values has been achieved by simultaneous scoring of relatively simple phenotypes and quantitation of molecular readouts at the level of single cells. However, this is much more challenging for complex morphological phenotypes using experimental and reductionist approaches alone, where cause and effect are separated temporally and across multiple biological modes and scales. Here I consider how causal inference, which integrates observational data with high confidence causal models, might be used to quantify the relative contribution of different sources of stochastic variation to phenotypic diversity. Collectively, these approaches could inform disease mechanisms, improve predictions of clinical outcomes and prioritize gene therapy targets across modes and scales of gene function. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Functional characterization of Francisella tularensis subspecies holarctica genotypes during tick cell and macrophage infections using a proteogenomic approach.

Tularemia is a vector-borne disease caused by the Gram-negative bacterium Francisella tularensis. Known hosts and vectors in Europe are hare and ticks. F. tularensis is transmitted from ticks and animals, but also from the hydrotelluric environment and the consumption of contaminated water or food. A changing climate expands the range in which ticks can live and consequently might contribute to increasing case numbers of tularemia. Two subspecies of F. tularensis are human pathogenic. Francisella tularensis tularensis (Ftt) is endemic in North America, while Francisella tularensis holarctica (Fth) is the only subspecies causing tularemia in Europe. Ft is classified as a category A bioterrorism agent due to its low infectious dose, multiple modes of transmission, high infectivity and potential for airborne transmission and has become a global public health concern. In line with the European survey and previous phylogenetic studies, Switzerland shows the co-distribution of B.6 and B.12 strains with different geographical distribution and prevalence within the country. To establish itself in different host environments of ticks and mammals, F. tularensis presumably undergoes substantial changes on the transcriptomics and proteomic level. Here we investigate the transcriptomic and proteomic differences of five strains of Fth upon infection of rabbit macrophages and tick cells.

Decoding low-frequency climate variations: A case study on ENSO and ocean surface warming

Current perspectives on lower frequency variations and secular warming have predominantly been shaped by traditional anomalies that assume an annual cycle (AC) with a time-invariant amplitude. However, this anomaly framework falls short in capturing the complexity of multiple periodic modes with intricate waveforms and time dependent amplitude—the traits, in general, shared by externally forced responses of complex dynamical systems. By allowing interannual amplitude modulation of ACs, we show that the monotonic amplitude increases of the first AC of Sea Surface Temperature (SST) are manifested as the basin-wide secular warming of the ocean surface. Notably, the first SST-AC exhibits significant interannual variances and the largest linear warming rates in the Pacific Warm Pool. While the linear warming pattern mirrors that of a long-term mean SST, it depicts an entirely different warming pattern on the surface of the tropical Pacific Ocean compared to those reported so far. Moreover, all interannual warm (El Niño) and cold (La Niña) events in tropical Pacific regions are abnormal interannual modulations in the third and fourth ACs of SST, respectively. Specifically, a strong El Niño event occurs when a positive amplitude modulation leads to the overlap of two consecutive positive phases of the third AC of SST. Conversely, the absence of such overlaps during negative amplitude modulations significantly contributes to the positive skewness of SST anomalies. No systematic decadal changes in the zonal propagation characteristics of SST in the eastern Pacific (EP) and central Pacific (CP) regions were detected. These findings underscore that the secular warming and low-frequency events in EP and CP are intrinsic to three distinct ACs.

New RMC Energy Deposition Treatment and its Application in Multi-Physics Simulation

Accurate estimation of energy deposition is important in core physics and severe accident analyses for design optimizations. In this study, a new energy deposition treatment is implemented in the Reactor Monte Carlo (RMC) code, offering multiple modes with varying levels of fidelity and computational requirements. The most precise mode is utilized in coupling simulations between RMC and the subchannel thermal-hydraulic analysis code SUBCHAN, incorporating an explicit moderator heating fraction in the coupling interface. The new treatment is verified against references from MCNP, Serpent, and OpenMC for three light water reactor (LWR) assembly cases, and great agreement is achieved. Energy deposition in different materials and components is emphasized in Kilowatt Reactor Using Stirling TechnologY (KRUSTY) modeling, and the results obtained using different modes are compared. The RMC-SUBCHAN coupling calculations for the three LWR assembly cases, employing the most accurate model, reveal a maximum increase of 94.6 K in the control rod centerline temperature, with a normalized energy deposition of 35.9% in the control rod regions. In the assembly case with gadolinium (Gd) burnable poison, a temperature increase of 7.3 K is observed in the Gd rod centerline, while the coolant outlet temperature decreases by 1.6 K due to the reduced explicit moderator heating fraction of 2.1%, compared to the constant 2.6% in the previous coupling scheme.

Polarization-controlled generation of multiple orbital angular momentum modes

By concurrently manipulating the degrees of freedom associated with polarization and orbital angular momentum (OAM), a variety of vector fields can be generated, which exhibit unique characteristics and have found extensive application in both classical and quantum optics. However, the OAM dimensions in these fields have been predominantly confined to two. Different from high-order OAM with a large topological charge, broadening the OAM dimensions beyond this limit and generating OAM spectra with multiple OAM modes can significantly enhance the scope of research. In this study, we explore vector fields with OAM dimensions exceeding ten, achieving polarization-controlled spectra in higher-dimensional OAM. Our findings not only offer a method for controlling high-dimensional OAM through polarization but also pave the way for potential applications in both classical and quantum realms utilizing high-dimensional vector states.

Multiple Evolution Modes of Megaripples in the Qaidam Basin and Implications for Ripple‐Like Aeolian Landforms on Mars

AbstractAeolian landforms provide valuable insights into the planetary surface environment and its evolutionary history. In this study, the formation and evolution of megaripples in the Qaidam Basin and their relationship with the development environment are analyzed. By quantifying the wind environment, morphology, grain‐size distribution, sedimentary structure, and luminescence age of megaripples, we propose for the first time that there are multiple megaripple evolution modes. Investigation revealed that three evolution modes were responsible for forming megaripples in different equilibrium states: transient, stable, and metastable. Well‐sorted coarse sand grains accumulate on ridges and overlay poorly sorted fine sand grains to form transient megaripples. Stable megaripples have alternating sedimentary bedding of coarse and fine sand grains. Metastable megaripples have a secondary ripple formation on the surface. Throughout their formation, coarse and fine sand grains undergo regrouping. The response of coarse grains to the change in wind speed lags behind that of fine grains. This process controls the erosion and accumulation of megaripples and affects their size and sedimentary structures. The evolution mode, scale, and sedimentary structure of megaripples are influenced by the grain‐size range under the same wind conditions. The luminescence ages of the coarse‐grained megaripple sediments are less than 700 years. This study provides a fresh perspective on the coexistence of various sand ripples and transverse aeolian ridges found on Mars.