Stereodivergent Total Synthesis of Tacaman Alkaloids.

This paper describes a concise, asymmetric and stereodivergent total synthesis of tacaman alkaloids. A key step in this synthesis is the biocatalytic Baeyer-Villiger oxidation of cyclohexanone, which was developed to produce seven-membered lactones and establish the required stereochemistry at the C14 position (92% yield, 99% ee, 500 mg scale). Cis- and trans-tetracyclic indoloquinolizidine scaffolds were rapidly synthesized through an acid-triggered, tunable acyl-Pictet-Spengler type cyclization cascade, serving as the pivotal reaction for building the alkaloid skeleton. Computational results revealed that hydrogen bonding was crucial in stabilizing intermediates and inducing different addition reactions during the acyl-Pictet-Spengler cyclization cascade. By strategically using these two reactions and the late-stage diversification of the functionalized indoloquinolizidine core, the asymmetric total syntheses of eight tacaman alkaloids were achieved. This study may potentially advance research related to the medicinal chemistry of tacaman alkaloids.

Predictive modeling for ubiquitin proteins through advanced machine learning technique.

Ubiquitination is an essential post-translational modification mechanism involving the ubiquitin protein's bonding to a substrate protein. It is crucial in a variety of physiological activities including cell survival and differentiation, and innate and adaptive immunity. Any alteration in the ubiquitin system leads to the development of various human diseases. Numerous researches show the highly reversibility and dynamic of ubiquitin system, making the experimental identification quite difficult. To solve this issue, this article develops a model using a machine learning approach, tending to improve the ubiquitin protein prediction precisely. We deeply investigate the ubiquitination data that is proceed through different features extraction methods, followed by the classification. The evaluation and assessment are conducted considering Jackknife tests and 10-fold cross-validation. The proposed method demonstrated the remarkable performance in terms of 100 %, 99.88 %, and 99.84 % accuracy on Dataset-I, Dataset-II, and Dataset-III, respectively. Using Jackknife test, the method achieves 100 %, 99.91 %, and 99.99 % for Dataset-I, Dataset-II and Dataset-III, respectively. This analysis concludes that the proposed method outperformed the state-of-the-arts to identify the ubiquitination sites and helpful in the development of current clinical therapies. The source code and datasets will be made available at Github.

Associations of sugary beverage consumption with chronic obstructive pulmonary disease (COPD), asthma, and asthma-COPD overlap syndrome: A prospective cohort study.

BACKGROUND: The associations between specific types of sugary beverages and major chronic respiratory diseases remain relatively unexplored. OBJECTIVE: To investigate the associations of sugar-sweetened beverages (SSBs), artificially sweetened beverages (ASBs), and natural juices (NJs) with chronic obstructive pulmonary disease (COPD), asthma, and asthma-COPD overlap syndrome (ACOS). METHODS: This prospective cohort study included 210,339 participants from the UK Biobank. Sugary beverage intake was measured in units (glasses/cans/cartons/250 ml) through 24-hour dietary questionnaires. Logistic regression and Cox proportional hazards models were used to analyze the prevalence and incidence, respectively. Quantile G-computation was employed to estimate the joint associations and relative contributions of the three types of sugary beverages. RESULTS: Over a median follow-up of 11.6 years, 3,491 participants developed COPD, 4,645 asthma, and 523 ACOS. In prevalence analysis, certain categories of SSB and NJ consumption were associated with increased asthma prevalence, while high ASB consumption (>2 units/day) was linked to higher risks of all three outcomes. In incidence analysis, high SSB consumption (>2 units/day) was associated with incident COPD [hazard ratio (HR) 95% confidence interval (CI): 1.53 (1.19, 1.98)] and asthma [HR (95% CI): 1.22 (0.98, 1.52)]. Dose‒response relationships were observed for ASB consumption with all three outcomes [continuous HR (95% CI): 1.98 (1.36, 2.87) for COPD; 1.65 (1.24, 2.20) for asthma; and 2.84 (1.20, 6.72) for ACOS]. Moderate NJ consumption (>0-1 unit/day) was inversely associated with COPD [HR (95% CI): 0.89 (0.82, 0.97)], particularly grapefruit and orange juice. Joint exposure to these beverages (per unit increase) was associated with COPD [HR (95% CI): 1.15 (1.02, 1.29)] and asthma [HR (95% CI): 1.16 (1.06, 1.27)], with ASBs having greater positive weights than SSBs. CONCLUSION: Consumption of SSBs and ASBs was associated with increased risks of COPD, asthma, and potentially ACOS, whereas moderate NJ consumption was associated with a reduced risk of COPD, depending on the juice type.

Application of Artificial Neural Network and Genetic Algorithm Modeling for In Vitro Regeneration of Seaweed Seedling Production.

Marine macro-algae, commonly known as "seaweed," are used in everyday commodity products worldwide for food, feed, and biostimulant for plants and animals and continue to be one of the conspicuous components of world aquaculture production. However, the application of ANN in seaweeds remains limited. Here, we described how to perform ANN-based machine learning modeling and GA-based optimization to enhance seedling production for implications on commercial farming. The critical steps from seaweed seedling explant preparation, selection of independent variables for laboratory culture, formulating experimental design, executing ANN Modelling, and implementing optimization algorithm are described.

Centimeter-Scale Tellurium Oxide Films for Artificial Optoelectronic Synapses with Broadband Responsiveness and Mechanical Flexibility.

Prevailing over the bottleneck of von Neumann computing has been significant attention due to the inevitableness of proceeding through enormous data volumes in current digital technologies. Inspired by the human brain's operational principle, the artificial synapse of neuromorphic computing has been explored as an emerging solution. Especially, the optoelectronic synapse is of growing interest as vision is an essential source of information in which dealing with optical stimuli is vital. Herein, flexible optoelectronic synaptic devices composed of centimeter-scale tellurium dioxide (TeO2) films detecting and exhibiting synaptic characteristics to broadband wavelengths are presented. The TeO2-based flexible devices demonstrate a comprehensive set of emulating basic optoelectronic synaptic characteristics; i.e., excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), conversion of short-term to long-term memory, and learning/forgetting. Furthermore, they feature linear and symmetric conductance synaptic weight updates at various wavelengths, which are applicable to broadband neuromorphic computations. Based on this large set of synaptic attributes, a variety of applications such as logistic functions or deep learning and image recognition as well as learning simulations are demonstrated. This work proposes a significant milestone of wafer-scale metal oxide semiconductor-based artificial synapses solely utilizing their optoelectronic features and mechanical flexibility, which is attractive toward scaled-up neuromorphic architectures.

Evaluation of electric vehicle response capability in power grid.

In recent years, frequent and substantial area-wide power outages have underscored the critical need for cities to possess robust backup power sources capable of swift response to prevent prolonged power system disruptions. Electric vehicles can contribute electricity to the power grid using vehicle-to-grid technology. The power delivered by electric vehicles in this context is termed as response capability. However, existing studies have overlooked response capability dynamics during transitions between electric vehicle states-such as the shift from charging or discharging to an idle state, thereby hindering a comprehensive understanding of this aspect. Hence, this paper introduces a multi-timescale response capability prediction model that evaluates the electric vehicle's state of charge to ensure users' requirements are met for upcoming trips. To better assess users' travel demand, the gravity model is employed as a precursor to response capability prediction to further enhance the validity of the prediction outcomes. Three neighborhoods in Los Angeles have been chosen for analysis: Downtown, Lincoln Heights, and Silver Lake. Predictions indicate that neglecting the response capability when electric vehicles undergo state transformation can lead to a differential response capability ranging from 2000 kWh to 4000 kWh, resulting in a loss of prediction accuracy by 20 % to 25 %.•The response capability of EV is non-zero during state transformations•Users' travel demand assessment•Seamless integration of vehicle-to-grid technology into the power grid.

Associations between individual and mixed urinary metal exposure and dyslipidemia among Chinese adults: Data from the China Multi-Ethnic Cohort Study.

The prevalence of dyslipidemia is increasing, and it has become a significant global public health concern. Some studies have demonstrated contradictory relationships between urinary metals and dyslipidemia, and the combined effects of mixed urinary metal exposure on dyslipidemia remain ambiguous. In this study, we examined how individual and combined urinary metal exposure are associated with the occurrence of dyslipidemia. According to the data from the 2018-2019 baseline survey database of the China Multi-Ethnic Cohort (CMEC) Study, a population of 9348 individuals was studied. Inductively coupled plasmamass spectrometry (ICP-MS) was used to measure 21 urinary metal concentrations in the collected adult urinary samples. The associations between urinary metals and dyslipidemia were analyzed by logistic regression, weighted quantile sum regression (WQS), and quantile-based g-computation (qgcomp), controlled for potential confounders to examine single and combined effects. Dyslipidemia was detected in 3231 individuals, which represented approximately 34.6 % of the total population. According to the single-exposure model, Al and Na were inversely associated with the risk of dyslipidemia (OR = 0.95, 95 % CI: 0.93, 0.98; OR = 0.89, 95 % CI: 0.83, 0.95, respectively), whereas Zn, Ca, and P were positively associated (OR = 1.69, 95 % CI: 1.42, 2.01; OR = 1.12, 95 % CI: 1.06, 1.18; OR = 1.21, 95 % CI: 1.09, 1.34, respectively). Moreover, Zn and P were significantly positively associated even after adjusting for these metals, whereas Al and Cr were negatively associated with the risk of dyslipidemia. The results of the WQS and qgcomp analyses showed that urinary metal mixtures were positively associated with the risk of dyslipidemia (OR = 1.26, 95 % CI: 1.15, 1.38; OR = 1.09, 95 % CI: 1.01, 1.19). This positive association was primarily driven by Zn, P, and Ca. In the sensitivity analyses with collinearity diagnosis, interaction, and stratified analysis, the results remained, confirming the reliability of the study findings. In this study, the individual and combined effects of urinary Zn, P, and Ca on dyslipidemia were determined, which provided novel insights into the link between exposure to metals and dyslipidemia.

Neuromorphic Computing-Assisted Triboelectric Capacitive-Coupled Tactile Sensor Array for Wireless Mixed Reality Interaction.

Flexible tactile sensors show promise for artificial intelligence applications due to their biological adaptability and rapid signal perception. Triboelectric sensors enable active dynamic tactile sensing, while integrating static pressure sensing and real-time multichannel signal transmission is key for further development. Here, we propose an integrated structure combining a capacitive sensor for static spatiotemporal mapping and a triboelectric sensor for dynamic tactile recognition. A liquid metal-based flexible dual-mode triboelectric-capacitive-coupled tactile sensor (TCTS) array of 4 × 4 pixels achieves a spatial resolution of 7 mm, exhibiting a pressure detection limit of 0.8 Pa and a fast response of 6 ms. Furthermore, neuromorphic computing using the MXene-based synaptic transistor achieves 100% recognition accuracy of handwritten numbers/letters within 90 epochs based on dynamic triboelectric signals collected by the TCTS array, and cross-spatial information communication from the perceived multichannel tactile data is realized in the mixed reality space. The results illuminate considerable application possibilities of dual-mode tactile sensing technology in human-machine interfaces and advanced robotics.

Voltage-Driven Fluorine Motion for Novel Organic Spintronic Memristor.

Integrating tunneling magnetoresistance (TMR) effect in memristors is a long-term aspiration because it allows to realize multifunctional devices, such as multi-state memory and tunable plasticity for synaptic function. However, the reported TMR in different multiferroic tunnel junctions is limited to 100%. This work demonstrates a giant TMR of -266% in La0.6Sr0.4MnO3(LSMO)/poly(vinylidene fluoride)(PVDF)/Co memristor with thin organic barrier. Different from the ferroelectricity-based memristors, this work discovers that the voltage-driven florine (F) motion in the junction generates a huge reversible resistivity change up to 106% with nanosecond (ns) timescale. Removing F from PVDF layer suppresses the dipole field in the tunneling barrier, thereby significantly enhances the TMR. Furthermore, the TMR can be tuned by different polarizing voltage due to the strong modification of spin-polarization at the LSMO/PVDF interface upon F doping. Combining of high TMR in the organic memristor paves the way to develop high-performance multifunctional devices for storage and neuromorphic applications.

Enhancing Earth data analysis in 5G satellite networks: A novel lightweight approach integrating improved deep learning.

Efficiently handling huge data amounts and enabling processing-intensive applications to run in faraway areas simultaneously is the ultimate objective of 5G networks. Currently, in order to distribute computing tasks, ongoing studies are exploring the incorporation of fog-cloud servers onto satellites, presenting a promising solution to enhance connectivity in remote areas. Nevertheless, analyzing the copious amounts of data produced by scattered sensors remains a challenging endeavor. The conventional strategy of transmitting this data to a central server for analysis can be costly. In contrast to centralized learning methods, distributed machine learning (ML) provides an alternative approach, albeit with notable drawbacks. This paper addresses the comparative learning expenses of centralized and distributed learning systems to tackle these challenges directly. It proposes the creation of an integrated system that harmoniously merges cloud servers with satellite network structures, leveraging the strengths of each system. This integration could represent a major breakthrough in satellite-based networking technology by streamlining data processing from remote nodes and cutting down on expenses. The core of this approach lies in the adaptive tailoring of learning techniques for individual entities based on their specific contextual nuances. The experimental findings underscore the prowess of the innovative lightweight strategy, LMAED2L (Enhanced Deep Learning for Earth Data Analysis), across a spectrum of machine learning assignments, showcasing remarkable and consistent performance under diverse operational conditions. Through a strategic fusion of centralized and distributed learning frameworks, the LMAED2L method emerges as a dynamic and effective remedy for the intricate data analysis challenges encountered within satellite networks interfaced with cloud servers. The empirical findings reveal a significant performance boost of our novel approach over traditional methods, with an average increase in reward (4.1 %), task completion rate (3.9 %), and delivered packets (3.4 %). This report suggests that these advancements will catalyze the integration of cutting-edge machine learning algorithms within future networks, elevating responsiveness, efficiency, and resource utilization to new heights.

Recent progress in the study on phonon heat transport property of Earth's lower mantle minerals.

The lattice thermal conductivities (κ_"lat" ^ ) of Earth's lower mantle (LM) minerals is a crucial parameter in the study of deep Earth dynamics and its determination is also one of the grand challenges in condensed matter physics. Here, we review recent progress on theoretical and experimental studies for the κ_"lat" ^ under high pressure (P) and high temperature (T) condition up to 150 GPa and 4000 K. After the critical parameters necessary to obtain converged values of the κ_"lat" ^ are summarized, the theoretical κ_"lat" ^ of the LM minerals, determined through various computational methodologies, is compiled along with experimental findings. Although significant scattering is found in the experimental results at LM P,T, the quantum anharmonic lattice dynamics theory combined with the phonon Boltzmann transport theory demonstrates a clear relationship in the κ_"lat" ^ of the end-member LM phases, MgO, MgSiO3 bridgmanite (Brg) and post-perovskite (PPv), κ_lat^MgO>>κ_lat^PPv>κ_lat^Brg, and a discontinuous change in the κ_"lat" ^ by ~20-50% expected across the Brg-PPv transition. Knowledge on the additional but geophysically important factors, such as the effects of iron solid solution, isotopic mass difference, and higher order crystal anharmonicity are also summarized in detail. Current problems and future perspectives are finally mentioned.

Rebuilding the Habitable Zone from the Bottom up with Computational Zones.

Computation, if treated as a set of physical processes that act on information represented by states of matter, encompasses biological systems, digital systems, and other constructs and may be a fundamental measure of living systems. The opportunity for biological computation, represented in the propagation and selection-driven evolution of information-carrying organic molecular structures, has been partially characterized in terms of planetary habitable zones (HZs) based on primary conditions such as temperature and the presence of liquid water. A generalization of this concept to computational zones (CZs) is proposed, with constraints set by three principal characteristics: capacity (including computation rates), energy, and instantiation (or substrate, including spatial extent). CZs naturally combine traditional habitability factors, including those associated with biological function that incorporate the chemical milieu, constraints on nutrients and free energy, as well as element availability. Two example applications are presented by examining the fundamental thermodynamic work efficiency and Landauer limit of photon-driven biological computation on planetary surfaces and of generalized computation in stellar energy capture structures (a.k.a. Dyson structures). It is suggested that CZs that involve nested structures or substellar objects could manifest unique observational signatures as cool far-infrared emitters. While these latter scenarios are entirely hypothetical, they offer a useful, complementary introduction to the potential universality of CZs.

Numerical computation and experimental assessment of a pressure-retaining gas-tight sediment sampler.

The pressure of the recovered sample is intricately connected to seawater temperature, the recovery velocity, and the pressure of the pre-charged gas. To better understand the sample pressure dynamics during the sampling recovery process, we focus on a gas-tight sediment sampler, delving into its pressure-compensation and pressure-retaining mechanisms. A comprehensive thermal and thermodynamic analysis is conducted throughout the entire pressure-retaining sampling process, examining the temporal variations in the temperatures of seawater and nitrogen within the sampler at various descending velocities. The heat transfer and thermodynamics are examined throughout the entire pressure-retaining sampling process to determine how changes in the temperatures of seawater and nitrogen, as well as the descent velocity, affect the pressure-retaining performance. The influence of pre-charging pressure and recovery velocities on the pressure-retaining performance of the sampler is examined. Then the proposed numerical model was well verified by the ultra-high-pressure vessel experiments of the sampler under 115 MPa. Finally, the sea trial results further verified the accuracy of the numerical model.

Multi-Server Multi-Function Distributed Computation.

The work here studies the communication cost for a multi-server multi-task distributed computation framework, as well as for a broad class of functions and data statistics. Considering the framework where a user seeks the computation of multiple complex (conceivably non-linear) tasks from a set of distributed servers, we establish the communication cost upper bounds for a variety of data statistics, function classes, and data placements across the servers. To do so, we proceed to apply, for the first time here, Körner's characteristic graph approach-which is known to capture the structural properties of data and functions-to the promising framework of multi-server multi-task distributed computing. Going beyond the general expressions, and in order to offer clearer insight, we also consider the well-known scenario of cyclic dataset placement and linearly separable functions over the binary field, in which case, our approach exhibits considerable gains over the state of the art. Similar gains are identified for the case of multi-linear functions.

Semiparametric g-computation for survival outcomes with time-fixed exposures: An illustration.

PURPOSE: Generalized (g-) computation is a useful tool for causal inference in epidemiology. However, in settings when the outcome is a survival time subject to right censoring, the standard pooled logistic regression approach to g-computation requires arbitrary discretization of time, parametric modeling of the baseline hazard function, and the need to expand one's dataset. We illustrate a semiparametric Breslow estimator for g-computation with time-fixed treatments and survival outcomes that is not subject to these limitations. METHODS: We compare performance of the Breslow g-computation estimator to the pooled logistic g-computation estimator in simulations and illustrate both approaches to estimate the effect of a 3-drug vs 2-drug antiretroviral therapy regimen among people with HIV. RESULTS: In simulations, both approaches performed well at the end of follow-up. The pooled logistic approach was biased at times between the endpoints of the discrete time intervals used, while the Breslow approach was not. In the example, both approaches estimated a 1-year risk difference of about 6 % in favor of the 3-drug regimen, but the shape of the survival curves differed. CONCLUSIONS: The Breslow g-computation estimator of counterfactual risk functions does not rely on strong parametric assumptions about the time-to-event distribution or onerous dataset expansions.

The Effect of Transverse Sinus Stenosis Caused by Arachnoid Granulation on Patients with Venous Pulsatile Tinnitus: A Multiphysics Interaction Simulation Investigation.

This study aimed to investigate the effect of the transverse sinus (TS) stenosis (TSS) position caused by arachnoid granulation on patients with venous pulsatile tinnitus (VPT) and to further identify the types of TSS that are of therapeutic significance for patients. Multiphysics interaction models of six patients with moderate TSS caused by arachnoid granulation and virtual stent placement in TSS were reconstructed, including three patients with TSS located in the middle segment of the TS (group 1) and three patients with TTS in the middle and proximal involvement segment of the TS (group 2). The transient multiphysics interaction simulation method was applied to elucidate the differences in biomechanical and acoustic parameters between the two groups. The results revealed that the blood flow pattern at the TS and sigmoid sinus junction was significantly changed depending on the stenosis position. Preoperative patients had increased blood flow in the TSS region and TSS downstream where the blood flow impacted the vessel wall. In group 1, the postoperative blood flow pattern, average wall pressure, vessel wall vibration, and sound pressure level of the three patients were comparable to the preoperative state. However, the postoperative blood flow velocity decreased in group 2. The postoperative average wall pressure, vessel wall vibration, and sound pressure level of the three patients were significantly improved compared with the preoperative state. Intravascular intervention therapy should be considered for patients with moderate TSS caused by arachnoid granulations in the middle and proximal involvement segment of the TS. TSS might not be considered the cause of VPT symptoms in patients with moderate TSS caused by arachnoid granulation in the middle segment of the TS.

The Influence of Microstructural Arrangement on the Failure Characteristics of 3D-Printed Polymers: Exploring Damage Behaviour in Acrylonitrile Butadiene Styrene.

This study investigated how printing conditions influence the fracture behaviour of 3D-printed acrylonitrile butadiene styrene (ABS) under tensile loading. Dog-bone-shaped ABS specimens were produced using the fusion filament fabrication technique, with varying printing angles. Tensile tests were conducted on pre-notched specimens with consistent pre-notch lengths but different orientations. Optical and scanning electron microscopies were employed to analyse crack propagation in the pre-notched specimens. In order to support experimental evidence, finite element computation was implemented to study the damage induced by the microstructural rearrangement of the filaments when subject to tensile loading. The findings revealed the simple linear correlation between the failure properties including elongation at break and maximum stress in relation to the printing angle for different pre-notch lengths. A more progressive damage was found to support the ultimate performance of the studied material. This experiment evidence was used to build a damage model of 3D-printed ABS that accounts for the onset, growth, and damage saturation. This damage modelling is able to capture the failure properties as a function of the printing angle using a sigmoid-like damage function and a modulation of the stiffness within the raster. The numerical results demonstrated that damage pattern develops as a result of the filament arrangement and weak adhesion between adjacent filaments and explains the diffuse damage kinetics observed experimentally. This study concludes with a topological law relating the notch size and orientation to the rupture properties of 3D-printed ABS. This study supports the idea of tailoring the microstructural arrangement to control and mitigate the mechanical instabilities that lead to the failure of 3D-printed polymers.

In2O3/ZnO heterojunction thin film transistor for high recognition accuracy neuromorphic computing and optoelectronic artificial synapses.

Solid electrolyte-gated transistors exhibit improved chemical stability and can fulfill the requirements of microelectronic packaging. Typically, metal oxide semiconductors are employed as channel materials. However, the extrinsic electron transport properties of these oxides, which are often prone to defects, pose limitations on the overall electrical performance. Achieving excellent repeatability and stability of transistors through the solution process remains a challenging task. In this study, we propose the utilization of a solution-based method to fabricate an In2O3/ZnO heterojunction structure, enabling the development of efficient multifunctional optoelectronic devices. The heterojunction's upper and lower interfaces induce energy band bending, resulting in the accumulation of a large number of electrons and a significant enhancement in transistor mobility. To mimic synaptic plasticity responses to electrical and optical stimuli, we utilize Li+-doped high-k ZrOxthin films as a solid electrolyte in the device. Notably, the heterojunction transistor-based convolutional neural network achieves a high accuracy rate of 93% in recognizing handwritten digits. Moreover, our research involves the simulation of a typical sensory neuron, specifically a nociceptor, within our synaptic transistor. This research offers a novel avenue for the advancement of cost-effective three-terminal thin-film transistors tailored for neuromorphic applications.

How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic-precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft-surface materials. In this paper, we focus on the potential for supporting SPM-based measurements, with an emphasis on the application of AI-based algorithms, especially Machine Learning-based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure-property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI-based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI-QC-based approach were also discussed. Finally, we outline a research path for improving AI-QC-powered SPM. RESEARCH HIGHLIGHTS: Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai-qc-powered scanning probe microscopy.

Flow sensing on dragonfly wings.

One feature of animal wings is their embedded mechanosensory system that can support flight control. Insect wings are particularly interesting as they are highly deformable yet the actuation is limited to the wing base. It is established that strain sensors on insect wings can directly mediate reflexive control; however, little is known about airflow sensing by insect wings. What information can flow sensors capture and how can flow sensing benefit flight control? Here, we use the dragonfly (Sympetrum striolatum) as a model to explore the function of wing sensory bristles in the context of flight control. Combining our detailed anatomical reconstructions of both the sensor microstructures and wing architecture, we used computational fluid dynamics simulations to ask the following questions. (1) Are there strategic locations on wings that sample flow for estimating aerodynamically relevant parameters such as the local effective angle of attack? (2) Is the sensory bristle distribution on dragonfly wings optimal for flow sensing? (3) What is the aerodynamic effect of microstructures found near the sensory bristles on dragonfly wings? We discuss the benefits of flow sensing for flexible wings and how the evolved sensor placement affects information encoding.