Continuous Profile Research Articles

A systematic approach for synthesizing 3D-printable all-dielectric devices

We present a systematic approach for synthesizing 3D-printable all-dielectric devices. Inverse design approaches yield, in many cases, configurations with a continuous range of dielectric constant values. However, 3D printer resins usually provide a very limited set of such values; commonly, a single resin and air are the only available materials. We propose a methodology for transforming a device with a continuous range of material properties to a manufacturable one, while preserving the device’s performance as close as possible to the continuous case. We develop an algorithm that takes the continuous range of dielectric constant profile as input and generates a binary and connected device that can be 3D-printed using a single resin. Our methodology advances state-of-the-art algorithms by using manufacturable configurations of prescribed local air/resin composition to realize each designed dielectric material instead of being limited to a predetermined shape. The additional degrees of freedom provided by our approach may be particularly useful in devices of conformal complex-shaped dielectric constant profiles. We demonstrate the proposed methodology by designing a 3D-printable wide-angle refraction metagrating with performance very close to the inversely designed device of a continuous dielectric constant profile. The approach can be adapted to accommodate three-dimensional devices and other applications.

Episodic marine anoxia, primary production, and weathering controls on carbon‑sulfur cycling in the Early Cambrian Yangtze Block

The Early Cambrian Period has received significant attention for decades owing to its geological records of unprecedented skeletal fossils, suggesting a major bio-event known as “the Cambrian Explosion.” Notwithstanding, related primary production and marine redox variation with respect to evolution of biota remain elusive. These factors are intimately linked to marine carbon‑sulfur cycling, promoting an extensive use of related isotopes (e.g., δ13C and δ34S) as paleoenvironmental proxies. Nevertheless, bulk-rock δ34S and δ13C may reflect multigenerational 34S/32S and 13C/12C isotopic fractionations. Herein, we analyzed drill cores from continuous depth profiles in the recently discovered Qingchuan-Ziyang-Yibin intrashelf basin for δ34S of sedimentary sulfate (δ34Ssulf), δ13C of carbonates (δ13Ccarb), and bulk-rock elements. We find that highly positive (+23.60 ‰ to +29.90 ‰) and negative (as low as −12 ‰) δ34Ssulf V-CDT are associated with samples exhibiting low and high detrital inputs, respectively. In contrast, total sulfur and δ13Ccarb V-PDB show no relationship with terrigenous materials. Enrichment factors of molybdenum (MoEF) and uranium (UEF), biogenic contents of nickel (Nibio) and copper (Cubio), as well as total organic carbon (TOC), are all widely distributed among the samples. Integration of these findings with existing data from shallow to deep marine environments in the Yangtze Block, supports the following conclusions: (1) Five episodes of widespread marine anoxia coincide with extensive primary production. (2) Inputs of terrigenous materials promote 32S-enriched sulfate, which adds to subsequent biochemical fractionations, resulting in generally complex pathways for δ34S of pyrite. (3) Terrigenous nutrients were critical to extensive marine primary production.

Learning continuous scattering length density profiles from neutron reflectivitiesusing convolutional neural networks

Abstract Interpreting neutron reflectivity (NR) data using ad hoc multi-layer models and
physics-based models provides information about spatially resolved neutron scattering
length density (NSLD) profiles. Recent improvements in data acquisition systems
have allowed acquiring thousands of NR curves in a couple of hours, which has led to
a need for automated data analysis tools to interpret NR measurements in real-time.
Here, we present a machine learning analysis workflow that uses a series of models,
based on a Convolutional Neural Network (CNN), to learn the relation between the
NSLDs and the NRs, and subsequently produce continuous NSLD profiles directly
from NRs. The usefulness of our CNN-based models is demonstrated by constructing
NSLDs from NRs of several films containing homopolymer polyzwitterions (PZs) and
diblock copolymers mixed with different types of salts. Comparisons of the NSLDs
with those constructed using ad hoc multi-layer models reveal a very good agreement,
suggesting the potential of CNN-based models for real-time automated data analysis
of NRs.

PD-L1 antibody conjugated dihydrotanshinone I-loaded polymeric nanoparticle for targeted cancer immunotherapy combining PD-L1 blockade with immunogenic cell death

PurposeCombination immune checkpoint inhibitors (ICI) with chemotherapeutic agents has proven to be highly promising in cancer therapy. However, low response rate, immune-related adverse events, and lack of effectively targeted co-delivery strategy are still major hurdles to overcome for this combination therapeutic regimen. Herein, programmed death-L1 (PD-L1) antibody modified and dihydrotanshinone I (DHT) loaded nanoparticle was prepared for tumor targeting drug delivery, thus achieving immune checkpoint blockade (ICB) and immunogenic cell death (ICD) synergistic anti-tumor effects. MethodsThe DHT-loaded nanoparticle (DHT NP) was prepared by the emulsion solvent diffusion method. Atezolizumab (ATEZO) was thiolated with 2-iminothiolane and conjugated to the surface of DHT NP to prepare the ATEZO DHT NP. The drug encapsulation efficiency, drug loading, particle size and drug release were determined. The in vitro cellular uptake, cell proliferation inhibition and apoptosis were evaluated on the HGC-27 tumor cell. The in vivo tumor targeting, anti-tumor efficiency and immune regulation were assessed on tumor bearing mice. ResultsThe optimized ATEZO DHT NP was a spherical nanoparticle of about 250 nm with a continuous drug release profile. It was selectively taken up by the tumor cells through PD-L1 receptor-mediated endocytosis, which resulted in enhanced cytotoxicity and cell apoptosis. In vivo imaging further demonstrated its superior tumor tissue targeting ability. When tumor bearing mice were treated with the ATEZO DHT NP, its synergistic anti-tumor effect was much stronger than that of a single drug. Moreover, the tumor targeting delivery of DHT caused tumor necrosis and initiated ICD with release of tumor-associated antigens, which efficiently up-regulated the population of CD4+ and CD8+ T cells. Notably, there were no obvious system toxicity or tissue damage occur during the whole treatment period. ConclusionThe ATEZO DHT NP could specifically target to tumor and enhance treatment efficiency through combination of PD-L1 blockade with ICD effect.

Population Digital Health: Continuous Health Monitoring and Profiling at Scale.

This paper introduces population digital health (PDH)-the use of digital health information sourced from health internet of things (IoT) and wearable devices for population health modeling-as an emerging research domain that offers an integrated approach for continuous monitoring and profiling of diseases and health conditions at multiple spatial resolutions. PDH combines health data sourced from health IoT devices, machine learning, and ubiquitous computing or networking infrastructure to increase the scale, coverage, equity, and cost-effectiveness of population health. This contrasts with the traditional population health approach, which relies on data from structured clinical records (eg, electronic health records) or health surveys. We present the overall PDH approach and highlight its key research challenges, provide solutions to key research challenges, and demonstrate the potential of PDH through three case studies that address (1) data inadequacy, (2) inaccuracy of the health IoT devices' sensor measurements, and (3) the spatiotemporal sparsity in the available digital health information. Finally, we discuss the conditions, prerequisites, and barriers for adopting PDH drawing on from real-world examples from different geographic regions.

Analytical modeling of III-V heterojunction source-all-around vertical tunnel FET and its inverter circuit application

Abstract This paper presents a comprehensive analytical modeling framework for the III-V heterojunction source-all-around vertical tunnel field-effect transistor (SAA V-TFET). Using Kane’s model, our approach involves solving Poisson’s equations to obtain a continuous surface potential profile, followed by the derivation of drain current. These models demonstrate excellent accuracy across all operating regions, precisely predicting the potential profile, output, and transfer characteristics of SAA V-TFETs. We implemented the models in MATLAB and validated them against Sentaurus TCAD simulations. Furthermore, we present a comprehensive performance analysis of SAA V-TFET-based digital inverters.

Machine-learning-based prediction of cardiovascular events for hyperlipidemia population with lipid variability and remnant cholesterol as biomarkers.

Dyslipidemia poses a significant risk for the progression to cardiovascular diseases. Despite the identification of numerous risk factors and the proposal of various risk scales, there is still an urgent need for effective predictive models for the onset of cardiovascular diseases in the hyperlipidemic population, which are essential for the prevention of CVD. We carried out a retrospective cohort study with 23,548 hyperlipidemia patients in Shenzhen Health Information Big Data Platform, including 11,723 CVD onset cases in a 3-year follow-up. The population was randomly divided into 70% as an independent training dataset and remaining 30% as test set. Four distinct machine-learning algorithms were implemented on the training dataset with the aim of developing highly accurate predictive models, and their performance was subsequently benchmarked against conventional risk assessment scales. An ablation study was also carried out to analyze the impact of individual risk factors to model performance. The non-linear algorithm, LightGBM, excelled in forecasting the incidence of cardiovascular disease within 3years, achieving an area under the 'receiver operating characteristic curve' (AUROC) of 0.883. This performance surpassed that of the conventional logistic regression model, which had an AUROC of 0.725, on identical datasets. Concurrently, in direct comparative analyses, machine-learning approaches have notably outperformed the three traditional risk assessment methods within their respective applicable populations. These include the Framingham cardiovascular disease risk score, 2019 ESC/EAS guidelines for the management of dyslipidemia and the 2016 Chinese recommendations for the management of dyslipidemia in adults. Further analysis of risk factors showed that the variability of blood lipid levels and remnant cholesterol played an important role in indicating an increased risk of CVD. We have shown that the application of machine-learning techniques significantly enhances the precision of cardiovascular risk forecasting among hyperlipidemic patients, addressing the critical issue of disease prediction's heterogeneity and non-linearity. Furthermore, some recently-suggested biomarkers, including blood lipid variability and remnant cholesterol are also important predictors of cardiovascular events, suggesting the importance of continuous lipid monitoring and healthcare profiling through big data platforms.

Breaking degeneracies in exoplanetary parameters through self-consistent atmosphere--interior modelling

With a new generation of observational instruments largely dedicated to exoplanets (i.e. JWST, ELTs, PLATO, and Ariel) providing atmospheric spectra and mass and radius measurements for large exoplanet populations, the planetary models used to understand the findings are being put to the test. We seek to develop a new planetary model, the Heat Atmosphere Density Evolution Solver (HADES), which is the product of self-consistently coupling an atmosphere model and an interior model, and aim to compare its results to currently available findings. We conducted atmospheric calculations under radiative-convective equilibrium, while the interior is based on the most recent and validated ab initio equations of state. We pay particular attention to the atmosphere--interior link by ensuring a continuous thermal, gravity, and molecular mass profile between the two models. We applied the model to the database of currently known exoplanets to characterise intrinsic thermal properties. In contrast to previous findings, we show that intrinsic temperatures (T$_ int $) of 200-400 K ---increasing with equilibrium temperature--- are required to explain the observed radius inflation of hot Jupiters. In addition, we applied our model to perform `atmosphere--interior' retrievals by Bayesian inference using observed spectra and measured parameters. This allows us to showcase the model using example applications, namely to WASP-39 b and 51 Eridani b. For the former, we show how the use of spectroscopic measurements can break degeneracies in the atmospheric metallicity (Z) and intrinsic temperature. We derive relatively high values of Z = 14.79$_ and int $K, which are necessary to explain the radius inflation and the chemical composition of WASP-39 b. With this example, we show the importance of using a self-consistent model with the radius being a constrained parameter of the model and of using the age of the host star to break radius and mass degeneracies. When applying our model to 51 Eridani b, we derive a planet mass M$_p=3.13_ $ M$_ J $ and a core mass M$_ core $ M$_ E $, suggesting a potential formation by core accretion combined with a `hot start' scenario. We conclude that self-consistent atmosphere--interior models efficiently break degeneracies in the structure of both transiting and directly imaged exoplanets. Such tools have great potential to interpret current and future observations, thereby providing new insights into the formation and evolution of exoplanets.

In Vivo and In Vitro Response to a Regenerative Dental Scaffold.

As dental pulp contains the stem cells necessary for regeneration, the tooth should hold the intrinsic capacity for self-repair. A triphasic hybrid dental biocomposite (3HB) composed of biocompatible biopolymers to provide strength, antibacterial properties and protein-based cell support could provide a conducive microenvironment for the regeneration of dental structures. 3HB was incorporated into Mineral Trioxide Aggregate (ProRoot MTA) to construct a malleable injectable implant. Human tooth pulp cells (hDPCs) significantly increased proliferation in the presence of 3HB+MTA compared to 3HB or MTA alone. Cell viability decreased with MTA alone but increased with 3HB and 3HB+MTA. 3HB+MTA was implanted into the residual tooth of drilled Wistar rat M2 molars for up to 45 days. Stereological analysis from micro-CT images showed the volume of the tooth remaining. Histologically, regenerative pulpal architecture was seen invading 3HB. A continuous odontoblastic profile lined a deposit of dentin-like material suggesting reparative dentinogenesis. Overall, no infection or encapsulation was seen. Immunohistochemically, odontoblasts were seen along the margins of the wounded tooth undergoing repair. Mesenchymal cells (MSCs) were seen at the base of the drilled tooth and by 21 days had translocated into the implant itself. Cells stimulating remineralization were highly expressed in the tooth undergoing repair. CD146-positive MSCs were seen in the center of the implant, possibly stimulating remineralization. In conclusion, behavior of 3HB+ in vitro and in vivo provided a promising start as 3HB+MTA may serve as a viable regenerative scaffold for pulp regeneration; however, this should be further studied before clinical use can be considered.

A Geophysical Investigation in Which 3D Electrical Resistivity Tomography and Ground-Penetrating Radar Are Used to Determine Singularities in the Foundations of the Protected Historic Tower of Murcia Cathedral (Spain)

This study presents a procedure in which 3D electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) were used to determine singularities in the foundations of protected historic towers, where space is limited due to their characteristics and location in highly populated areas. This study was carried out on the Tower of the Cathedral “Santa Iglesia Catedral de Santa María” in Murcia, Spain. The novel distribution of a continuous nonlinear profile along the outer and inner perimeters of the Tower allowed us to obtain a 3D ERT model of the subsoil, even under its load-bearing walls. This nonlinear configuration of the electrodes allowed us to reach adequate investigation depths in buildings with limited interior and exterior space for data collection without disturbing the historic structure. The ERT results were compared with GPR measurements and with information from archaeological excavations conducted in 1999 and 2009. The geometry and distribution of the cavities in the entire foundation slab of the Tower were determined, verifying the proposed procedure. This methodology allows the acquisition of a detailed understanding of the singularities of the foundations of protected historic towers in urban areas with limited space, reducing time and costs and avoiding the use of destructive techniques, with the aim of implementing a more efficient and effective strategy for the protection of other tower foundations.

Continuous glucose monitoring profile in COVID-19 patients with and without diabetes receiving methylprednisolone.

Methylprednisolone is widely used during the COVID-19 epidemic. We aimed to evaluate the glucose profile of COVID-19 patients with and without diabetes receiving methylprednisolone. 36 patients with COVID-19 admitted to hospital were included: 17 with and 19 without diabetes. Methylprednisolone 40 mg was administered at about 9:00 a.m. Glucose levels were assessed by blinded intermittently scanned continuous glucose monitoring (isCGM) for an average of 6.8 ± 2.4 days. Excess hyperglycemia was defined as time above range (TAR) > 10.0 mmol/L (TAR>10.0) ≥ 25%, or TAR > 13.9 mmol/L (TAR>13.9) ≥ 10%. Glucose management indicator (GMI) was significantly higher than the admission glycated hemoglobin A1c (HbA1c) level in patients without diabetes [6.7 (6.1-7.0) % vs. 5.9 (5.9-6.1) %, P < 0.001], while no significant difference was found in patients with diabetes [9.0 (7.5-9.5) % vs. 8.9 (7.5-10.2) %, P > 0.05]. The difference between GMI and HbA1c (∆GMI-HbA1c) in patients without diabetes was significantly higher than in patients with diabetes [0.7 (0.2-1.0) % vs. -0.2 (-1.5-0.5) %, P = 0.005]. The circadian patterns of glucose were similar in the two groups. In patients without diabetes, excess hyperglycemia occurred in 31.6% (6/19) of participants, with 31.6% (6/19) having a TAR>10.0 ≥ 25%, while 21.1% (4/19) had a TAR>13.9 ≥ 10%. The impact of methylprednisolone on glycemia was more pronounced in COVID-19 patients without diabetes, compared to those with diabetes. A significant burden of methylprednisolone-induced hyperglycemia was observed in patients without diabetes.

Mesoscale eddy in situ observation and characterization via underwater glider and complex network theory.

Mesoscale eddies have attracted increased attention due to their central role in ocean energy and mass transport. The observations of their three-dimensional structure will facilitate the understanding of nonlinear eddy dynamics. In this paper, we propose a novel framework, the mesoscale eddy characterization from ordinal modalities recurrence networks method (MeC-OMRN), that utilizes a Petrel-II underwater glider for in situ observations and vertical structure characterization of a moving mesoscale eddy in the northern South China Sea. First, higher resolution continuous observation profile data collected throughout the traversal by the underwater glider are acquired and preprocessed. Subsequently, we analyze and compute these nonlinear data. To further amplify the hidden structural features of the mesoscale eddy, we construct ordinal modalities sequences rich in spatiotemporal characteristics based on the measured vertical density of the mesoscale eddy. Based on this, we employ ordinal modalities recurrence plots (OMRPs) to depict the vertical structure inside and outside the eddy, revealing significant differences in the OMRPs and the unevenness of density stratification within the eddy. To validate our intriguing findings from the perspective of complex network theory, we build the multivariate weighted ordinal modalities recurrence networks, through which network measures exhibit a more random distribution of vertical density stratification within the eddy, possibly due to more intense vertical convection and oscillations within the eddy's seawater micelles. These framework and intriguing findings are anticipated to be applied to more data-driven in situ observation tasks of oceanic phenomena.

Gas Occurrences within the Southeastern Shelf of Crimea according to Continuous Seismoacoustic Profiling Data

Gas Occurrences within the Southeastern Shelf of Crimea according to Continuous Seismoacoustic Profiling Data

Elucidating key factors in regulating budgets of ozone and its precursors in atmospheric boundary layer

The vertical variations and key drivers of ozone and its precursors, namely NOx and VOCs, in the atmospheric boundary layer, have vital impacts on surface ozone budgets but are poorly understood so far. Using online gradient measurements from a 356 m tower, we obtained continuous vertical profiles of ozone and its precursors, which exhibited strong gradients throughout the day. In the daytime, the vertical gradients of ozone precursors are significantly regulated by reactions with OH radicals. At night, our observations confirmed more intense VOC reactions with NO3 radicals in the residual layer than in the boundary layer. Additionally, we found that residual layer entrainment could contribute to over half of the boundary-layer ozone enhancements in the morning periods. Our results underscore the importance of considering vertical changes of ozone and its precursors in the atmospheric boundary layer when developing future ozone mitigation strategies.

Spatial and seasonal fluctuations in fresh submarine groundwater discharge revealed by marine continuous resistivity profiling

Submarine Groundwater Discharge (SGD) is a major pathway for the discharge of fresh and saline groundwater and associated dissolved compounds into marine environments. However, assessing SGD processes in coastal aquifers is challenging due to inaccessibility, dynamic conditions, complex subsurface geology, and the need for long-term monitoring to capture temporal and spatial variations in SGD rates accurately. This study employs marine continuous resistivity profiling (MCRP) as a main method to assess the presence of freshwater or brackish SGD offshore and to examine its potential seasonal variations. The method has been applied in the coastal alluvial aquifer of Maresme (Spain) and validated with other methods to trace SGD, including salinity profiles, Ra isotopes, and piezometric levels. Several MCRP transects of 700 m long, perpendicular to the coastline, were performed in a coastal marine area to obtain electrical resistivity data of the seabed covering an area of 3 km2. The data was acquired in two field campaigns with contrasting hydrological conditions (dry and wet seasons). The MCRP results allow the identification of areas of fresh SGD in marine sediments, with a clear seasonal variability that indicates a higher discharge of fresh groundwater in the wet season.

The Paris low-level jet during PANAME 2022 and its impact on the summertime urban heat island

Abstract. The low-level jet (LLJ) and the urban heat island (UHI) are common nocturnal phenomena. While the UHI has been studied extensively, interactions of the LLJ and the urban atmosphere in general (and the UHI in particular) have received less attention. In the framework of the PANAME (PAris region urbaN Atmospheric observations and models for Multidisciplinary rEsearch) initiative in the Paris region, continuous profiles of horizontal wind speed and vertical velocity were recorded with two Doppler wind lidars (DWLs) – for the first time allowing for a detailed investigation of the summertime LLJ characteristics in the region. Jets are detected for 70 % of the examined nights, often simultaneously at an urban and a suburban site, highlighting the LLJ regional spatial extent. Emerging at around sunset, the mean LLJ duration is ∼ 10 h, the mean wind speed is 9 m s−1, and the average core height is 400 m above the city. The temporal evolution of many events shows signatures that indicate that the inertial oscillation mechanism plays a role in the jet development: a clockwise veering of the wind direction and a rapid acceleration followed by a slower deceleration. The horizontal wind shear below the LLJ core induces variance in the vertical velocity (σw2) above the urban canopy layer. It is shown that σw2 is a powerful predictor for regional contrast in air temperature, as the UHI intensity decreases exponentially with increasing σw2 and strong UHI values only occur when σw2 is very weak. This study demonstrates how DWL observations in cities provide valuable insights into near-surface processes relevant to human and environmental health.

Geotechnical Characterization of the Magdalena River Subsoil in Magangué, Colombia: A Study Using CPTu and SPT Tests

This study employs Cone Penetration Tests (CPTu) and Standard Penetration Tests (SPT) to analyze the geotechnical properties of the Magdalena River’s riverbed and banks. While these methods are standard in soil characterization, this research innovatively combines CPTu’s continuous profiling with SPT’s localized sampling to develop a nuanced stratigraphic model of the subsurface. This integrated approach provides a comprehensive view of the soil conditions, which is crucial for understanding sediment variability and stability along the riverbanks. The findings from this methodological integration enhance our ability to predict soil behavior under dynamic riverine conditions, offering valuable insights for erosion control and sustainable river management. The study underscores the practical benefits of synergizing traditional testing methods to address geotechnical challenges in river environments.

Quantifying Creep on the Laohushan Fault Using Dense Continuous GNSS

The interseismic behavior of faults (whether they are locked or creeping) and their quantitative kinematic constraints are critical for assessing the seismic hazards of faults and their surrounding areas. Currently, the creep of the eastern segment of the Laohushan Fault in the Haiyuan Fault Zone at the northeastern margin of the Tibetan Plateau, as revealed by InSAR observations, lacks confirmation from other observational methods, particularly high-precision GNSS studies. In this study, we utilized nearly seven years of observation data from a dense GNSS continuous monitoring profile (with a minimum station spacing of 2 km) that crosses the eastern segment of the Laohushan Fault. This dataset was integrated with GNSS data from regional continuous stations, such as those from the Crustal Movement Observation Network of China, and multiple campaign measurements to calculate GNSS baseline change time series across the Laohushan Fault and to obtain a high spatial resolution horizontal crustal velocity field for the region. A comprehensive analysis of this primary dataset indicates that the Laohushan Fault is currently experiencing left-lateral creep, characterized by a partially locked shallow segment and a deeper locked segment. The fault creep is predominantly concentrated in the shallow crustal region, within a depth range of 0–5.7 ± 3.4 km, exhibiting a creep rate of 1.5 ± 0.7 mm/yr. Conversely, at depths of 5.7 ± 3.4 km to 16.8 ± 4.2 km, the fault remains locked, with a loading rate of 3.9 ± 1.1 mm/yr. The shallow creep is primarily confined within 3 km on either side of the fault. Over the nearly seven-year observation period, the creep movement within approximately 5 km of the fault’s near field has shown no significant time-dependent variation, instead demonstrating a steady-state behavior. This steady-state creep appears unaffected by postseismic effects from historical large earthquakes in the adjacent region, although the deeper (far-field) tectonic deformation of the Laohushan Fault may have been influenced by the postseismic effects of the 1920 Haiyuan M8.5 earthquake.

Characteristics of gusts with different velocity profiles and control parameters

The characteristics of gust flow are essential for gust response and alleviation. To investigate the influence of control parameters on gusts with different velocity profiles, four vertical gust profiles were designed. Methods were proposed to generate them with two pitching airfoils in a low-speed water tunnel. The velocity field was measured via phase-locked particle image velocimetry. The coefficient of determination R2 was proposed to evaluate the generated gust profile quality, which referred to the quality of the vertical velocity profile. The influence of control parameters on different gust profiles was investigated, and the cause of the profile distortion was explored. For continuous sine gusts, the gust ratio GR increased approximately linearly with the pitching amplitude, while the gust ratio initially increased and then decreased with increasing frequency. As the two control parameters increased, the flow uniformity decreased because the airfoil wakes disturbed the measured flow field. In terms of continuous 1-cosine gusts, the gust ratio increased nonlinearly with pitching amplitude. Compared with those of the sine gusts, the GR values of the 1-cosine gusts were higher, whereas the R2 values were lower. In addition, the discrete and continuous gust profiles had similar distortion near the peaks. However, discrete gusts had lower R2 values than continuous gusts because the starting and stopping vortices of the pitching airfoils disturbed the gust flow. Based on these findings, a method to improve the profile quality and field uniformity by increasing the spacing of the pitching airfoils was proposed. This work can support further studies of gust response and alleviation during complex gust encounters.

Jerk limited continuous tool path generation for flexible systems in non-cartesian coordinate systems

Inspired by computer numerical control (CNC) technology and drastic changes in the world of electronics and microcontrollers, the idea of using non-cartesian coordinate system for CNC machines became a topic of interest. Non-cartesian coordinate systems provide freedom of movement in systems with large working spaces where high dimensional accuracy is not a prime concern. Applications of such systems can be found in murals, where a painting is applied to a large wall of any arbitrary build material. In the present paper, a new design was introduced to automate the process of drawing murals on large surfaces. The proposed mechanism utilized chain and sprocket to overcome the size limitations of traditionally available x-y tables. In addition to that, the developed mechanism does not utilize the common robust structures used in the conventional CNC systems and the tool motion is created using flexible arms that are not heavily constrained. The effect of systems unwanted vibration has been eliminated using continuous trajectory and kinematic profiles. Parametric curve interpolation algorithms for trajectory planning were implemented to increase the flexibility of drawing complex curves. Parametric curves such as B-spline and non-uniform rational B-spline (NURBS) were employed to generate a jerk limited trajectory for motion control and extraction of kinematic profiles. Such trajectory constrains the jerk of the system and guarantees a smooth motion free of feed-rate fluctuations. Compared to other methods available for extracting the kinematic profiles, jerk limited method decreases the travel time and trajectory error. Ultimately, motion commands were produced by using kinematic profiles and the second order Taylor interpolator. STM32746ZG card was used as the main processor and two stepper motors controlled by a Bit Pattern interpolation algorithm were utilized to drive the proposed mechanism. The input pulses of stepper motors were stored as binary bits and transmitted to drivers in real-time. The feedback was also evaluated by two rotary encoders, placed at the end of the motors’ shafts. Encoder data were acquired and transmitted using a TCP/IP protocol to guarantee zero data loss during the transmission.