Capture Time Research Articles

Movement behavior and morphological change of droplets on vertically crossed fibers

Droplet capture and coalescence are critical processes for enhancing emulsion separation efficiency in oily wastewater treatment. In this paper, four kinds of motion behaviors and secondary droplet formation behaviors of oil droplets on vertically crossed fibers in a water environment were investigated using high-speed camera technology. The research shows that during the droplet capture process, droplets on large-diameter horizontal fiber exhibit greater lateral spreading, and smaller vertical elongation. Increasing the diameter of horizontal fiber significantly shortens droplet capture time. The capture time on lipophilic fiber intersection of 190–420 μm is 14 ms less than that of 190–190 μm. During the droplet coalescence process, the liquid bridge on large-diameter horizontal fiber is wider, and the droplet contraction ratio is smaller. The droplet contraction ratio on fiber intersection of 190–420 μm is 0.3 smaller than that of 190–190 μm. Increasing the diameter of horizontal fiber can promote rapid fusion between droplets, but lipophobic fibers hinder the degree of fusion and deformation. Furthermore, the diameter and wettability of horizontal fibers have significant effects on the volume of secondary droplets. The volume of secondary droplets increases with the diameter of lipophilic horizontal fiber in the capture process. The volume of secondary droplets on lipophilic horizontal fiber is approximately three times larger than that on lipophobic horizontal fiber in the coalescence process. These findings contribute to understanding the details of coalescence separation and optimizing the design of fiber fillers.

Solid, structured composite neutron detectors with high dynamic range capability

Neutron detectors are essential across disciplines such as fundamental science, nuclear security, safeguards, and civilian applications. While 3He-filled gas proportional counters have long been revered for their efficacy in detecting thermal neutrons and praised for their efficiency, neutron/gamma discrimination, and stability, the scarcity of 3He has spurred a search for alternatives. Here, we explore a solid structured scintillating particle composite (SPC) consisting of 6Li-containing scintillating glass particles within an acrylic matrix as a neutron detector for high dynamic range applications. We show for the first time that an SPC neutron detector can boast an intrinsic detection efficiency of 0.261% for pure 252Cf fission neutrons and an overall neutron detection efficiency of (0.546 ± 0.003)% at the Neutron Free-in-Air facility while being able to function in an intense gamma-ray environment. We also show that the SPC neutron detector supports fast neutron capture times and enables a dual-readout scheme that extends the detector dynamic range to high incident neutron fluxes. A scalable fabrication process allows for tailoring the SPC detector properties to the requirements of specific applications. Good agreement is found between the experimental results taken with a National Institute of Standards and Technology traceable 252Cf source and the coupled MCNP6 and optical-ray-tracing simulations.

Multi-key optical anti-counterfeiting and information storage based on multi-mode excitation-emission of upconversion nanocrystals and metal chlorides

Anti-counterfeiting has become a crucial global issue. Traditional anti-counterfeiting technology is easy to be imitated because of its low security, so it is urgent to develop an optical information encryption strategy with higher security level. In this study, a multi-key optical anti-counterfeiting and information storage strategy based on upconversion nanocrystals (UCNPs) and metal chlorides under multi-mode excitation-emission is proposed. Based on the optical properties of four orange luminescent materials (OLM), the distinguishable excitation mode (UV, NIR) and good afterglow characteristics are used to encrypt, decrypt and store real information. Finally, based on this characteristic, we propose an information encryption strategy for binary digit-ASCII encoding and digital information display. Under the irradiation of 254 nm excitation light, the real information is hidden, the “excitation wavelength key” and “capture time key” are used to read the correct information. This multi-key anti-counterfeiting strategy hides real information by interfering with information, which greatly improves the security level of anti-counterfeiting and is very promising in anti-counterfeiting applications.

Rice Leaf Nitrogen Content Estimation Through A Methodological Framework Using Single-Sensor Multispectral Images

Using non-destructive evaluation tools based on imaging techniques, including single-sensor multispectral cameras, provides a cost-effective solution for optimizing rice nitrogen fertilization through site-specific nutrient management. However, their accuracy and precision have been identified as areas for improvement. This study aims to develop a methodology to improve the accuracy of estimations through field experiments. It utilizes multispectral images captured by MAPIR Survey3W Orange Cyan Near-Infrared and MAPIR Survey3W Red Edge cameras. The Normalized Difference Vegetation Index and Red Edge values derived from these images are correlated with Soil Plant Analysis Development values to assess rice nitrogen levels. A prediction model is then built using the Support Vector Regression algorithm. Findings from the experiments underscore the importance of addressing shadow effects, integrating the dataset on light intensity and image capture time, conducting radiometric calibration, filtering outlier data, employing image segmentation, and utilizing nonlinear Canova tests to enhance estimation accuracy. By configuring the Support Vector Regression model with RBF kernel, gamma set to 1.24, and epsilon set to 0.1, the R2 of the train data and validation data reaches 0.851, and 0.840 respectively. Meanwhile, the R2 of the test data achieves 0.793 with a mean absolute percentage error of 3.49 % and a root mean square error of 1.70. These findings underscore the potential of the proposed methodology to improve the estimation of rice nitrogen status based on single-sensor multispectral images, paving the way for more effective nutrient management strategies in rice cultivation.

Cabozantinib Plus Nivolumab in Adult Patients with Advanced or Metastatic Renal Cell Carcinoma: A Retrospective, Non-Interventional Study in a Real-World Cohort/GUARDIANS Project.

Combinations of immune-checkpoint inhibitors (ICIs) are the standard of care (SOC) for treatment-naive metastatic renal cell carcinoma (mRCC) patients. In this multicenter study, we evaluated the RW safety and efficacy of cabozantinib plus nivolumab in mRCC patients. Data were retrospectively collected from twelve cancer centers in Germany, Switzerland, and Austria. Patients with advanced or mRCC were eligible. The investigator-based objective response rate (ORR) and progression free survival (PFS) were calculated from the start of the treatment to progression or death. Descriptive statistics and Kaplan-Meier (KM) plots were utilized where appropriate. In total, 96 eligible patients (66.6% male) with a median age of 66.0 years were included. The most common histology was clear-cell RCC (ccRCC) in 63.4% (n = 61). A prior nephrectomy was performed in 60.4% (n = 58). ECOG 0-1 was 68.8% (n = 66). A partial response was documented in 43.8% of patients (n = 42), a stable disease in 32.3% (n = 31), and a progressive disease in 8.3% (n = 8) as the best overall response. Response data were not evaluable in 13.5% (n = 13). The median follow-up time was 12.7 months (95% CI, 10.0-15.3). The PFS rate at 6 months was 89.8% in the overall population (86.8% for ccRCC; 90.0% for non-ccRCC). Adverse events (AEs) were reported in 82.3% (n = 79) for all grades and 41.7% (n = 40) for grades 3-5. Elevated liver enzymes (34.4%), diarrhea (31.3%), and hand-foot syndrome (29.2%) were the three most frequent AEs of any grade and causality. In this real-world cohort of mRCC patients, the application of cabozantinib plus nivolumab was shown to be safe and feasible. Our data support the use of cabozantinib plus nivolumab as a first-line standard therapy in mRCC patients. Major limitations were the retrospective data capture and short follow-up time of our study.

ChromaFlash: Snapshot Hyperspectral Imaging Using Rolling Shutter Cameras

Hyperspectral imaging captures scene information across narrow, contiguous bands of the electromagnetic spectrum. Despite its proven utility in industrial and biomedical applications, its ubiquity has been limited by bulky form factors, slow capture times, and prohibitive costs. In this work, we propose a generalized approach to snapshot hyperspectral imaging that only requires a standard rolling shutter camera and wavelength-adjustable lighting. The crux of this approach entails using the rolling shutter as a spatiotemporal mask, varying incoming light quicker than the camera's frame rate in order for the captured image to contain rows of pixels illuminated at different wavelengths. An image reconstruction pipeline then converts this coded image into a complete hyperspectral image using sparse optimization. We demonstrate the feasibility of this approach by deploying a low-cost system called ChromaFlash, which uses a smartphone's camera for image acquisition and a series of LEDs to change the scene's illumination. We evaluated ChromaFlash through simulations on two public hyperspectral datasets and assessed its spatial and spectral accuracy across various system parameters. We also tested the real-world performance of our prototype by capturing diverse scenes under varied ambient lighting conditions. In both experiments, ChromaFlash outperformed state-of-the-art techniques that use deep learning to convert RGB images into hyperspectral ones, achieving snapshot performance not demonstrated by prior attempts at accessible hyperspectral imaging.

Long-Term Impacts of Intense Pulsed Light Therapy on Ocular Surface Health and Tear Film Dynamics in Patients with Dry Eye Disease: Detailed Analysis and Observations Over a 1-Year Follow-Up Period.

To evaluate the long-term effects of intense pulsed light (IPL) therapy on patients with dry eye disease (DED) associated with meibomian gland dysfunction (MGD). A retrospective case series was performed with 110 participants undergoing IPL therapy. Assessments included the eye fitness test (EFT) to gauge subjective symptoms, along with objective measures using the Tearcheck® device (ESW Vision, Houdan, France) noninvasive first breakup time (NIFBUT), noninvasive average breakup time (NIABUT), central tear meniscus height (CTMH), thinnest tear meniscus height (TTMH), and ocular surface inflammatory risk evaluation (OSIE) assessed using the SCHWIND SIRIUS device (SCHWIND eye-tech-solutions GmbH, Kleinostheim, Germany). This study documented significant improvements in subjective and objective symptoms associated with DED and MGD. Subjective symptoms measured by the EFT showed an average increase of 9.74 points (range -10 to 28, standard deviation [SD] ± 7.54), indicating reduced symptoms. Objective measures of tear film stability, represented by NIABUT, increased by an average of 4.04s (range -15.00 to 14.40, SD ± 4.91). Tear film stability evaluation (TFSE) scores decreased by 229.12 points on average (range -1775 to 528, SD ± 384.94), suggesting enhanced tear film stability. OSIE type 1 showed a reduction in inflammation, with a percentage decrease of 4.98% (range -45 to 5, SD ± 7.33). Additionally, OSIE capture time decreased by 3.25s on average (range -27 to 22, SD ± 10.35), further indicating an improvement in ocular surface health. IPL therapy was shown to be a promising, noninvasive approach for improving quality of life in patients with DED by effectively managing symptoms and stabilizing tear film. The findings support the use of IPL as a sustainable treatment modality for DED associated with MGD.

Coarse frequency offset estimation and compensation based on short-time spectrum analysis in FSO communication system

Due to the high-speed movement of low-orbit satellites, the Doppler frequency offset in 1550-nm wavelength satellite-to-ground coherent laser communication is as high as ±4.5 GHz, which greatly increases the difficulty of carrier frequency acquisition. Low complexity, short capture time, and stable coarse frequency offset estimation and compensation algorithms are technical bottlenecks that must be addressed. In this work, a coarse frequency offset estimation and compensation algorithm based on short-time spectrum analysis is proposed. In the proof-of-principle system, the feasibility of the coarse frequency offset capture is verified based on a 2.5-GBaud data rate PM-QPSK modulation FPGA-based coherent receiver. The results show that the frequency offsets capture range is extended to ±4.5 GHz, covering the Doppler frequency offset range. Moreover, the standard deviation of the residual frequency offset after coarse frequency offset compensation is less than 140 MHz, which is smaller than the accurate frequency offset compensation range of 312.5 MHz.

Application of fuzzy logic control theory combined with target tracking algorithm in unmanned aerial vehicle target tracking

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

Stochastic resetting can optimize the intermittent search strategy in a two-dimensional confined topography

Based on the intermittent two-state random walk model, by calculating and discussing the mean first capture time, the search efficiency of the intermittent search strategy in a two-dimensional confined topography in the absence and presence of stochastic resetting is studied. Results reveal that for the close target, the regular Brownian motion is the efficient search strategy, whereas, for the target far from the starting point, the typical intermittent search strategy with relocation times being power-law distributed turns out to be the advantageous one, and the search efficiency impressively enhances with the distance of the target from the starting point increasing. We demonstrate that though in the absence of stochastic resetting there does not exist a universal optimal search strategy in the confined topography, the introduction of stochastic resetting makes the Brownian motion surprisingly to be the universal optimal search process, and distinctly outperforms the ones without stochastic resetting.

The evolution of cooperative chasing on complex networks via a game-based chasing rule

Cooperative pursuit, which involves a set of multiple agents with combined effort to capture some target, is a typical kind of collective intelligent behavior in social populations. It is intriguing how autonomous agents communicate and interaction with each other to facilitate collaborative pursuit tactics. In this paper, we consider the scenario of a set of cops to capture a robber on complex networks. A game-based chasing rule is proposed to coordinate the action of cops. It is shown that cooperative pursuit behavior, such as encirclement, can be generated by the proposed game-based chasing rule. Through experiments on different kinds of networks, it is shown that the game-based chasing rule greatly reduces the required capture time, compared to that of independent chasing rule. This study provides new insight into the development of more sophisticated pursuit and evasion behaviors in complex environments.

Electronic oral health surveillance system for Egyptian preschoolers using District Health Information System (DHIS2): design description and time motion study

BackgroundEarly childhood caries (ECC) is a major global health issue affecting millions of children. Mitigating this problem requires up-to-date information from reliable surveillance systems. This enables evidence-based decision-making to devise oral health policies. The World Health Organization (WHO) advocates the adoption of mobile technologies in oral disease surveillance because of their efficiency and ease of application. The study describes developing an electronic, oral health surveillance system (EOHSS) for preschoolers in Egypt, using the District Health Information System (DHIS2) open-source platform along with its Android App, and assesses its feasibility in data acquisition.MethodsThe DHIS2 Server was configured for the DHIS2 Tracker Android Capture App to allow individual-level data entry. The EOHSS indicators were selected in line with the WHO Action Plan 2030. Two modalities for the EOHSS were developed based on clinical data capture: face-to-face and tele/asynchronous. Eight dentists in the pilot team collected 214 events using modality-specific electronic devices. The pilot’s team's feedback was obtained regarding the EOHSS's feasibility in collecting data, and a time-motion study was conducted to assess workflow over two weeks. Independent t-test and Statistical Process Control techniques were used for data analysis.ResultsThe pilot team reported positive feedback on the structure of the EOHSS. Workflow adaptations were made to prioritize surveillance tasks by collecting data from caregivers before acquiring clinical data from children to improve work efficiency. A shorter data capture time was required during face-to-face modality (4.2 ± 0.7 min) compared to telemodality (5.1 ± 0.9 min), p < 0.001). The acquisition of clinical data accounted for 16.9% and 21.1% of the time needed for both modalities, respectively. The time required by the face-to-face modality showed random variation, and the tele-modality tasks showed a reduced time trend to perform tasks.ConclusionsThe DHIS2 provides a feasible solution for developing electronic, oral health surveillance systems. The one-minute difference in data capture time in telemodality compared to face-to-face indicates that despite being slightly more time-consuming, telemodality still shows promise for remote oral health assessments that is particularly valuable in areas with limited access to dental professionals, potentially expanding the reach of oral health screening programs.

Deep Low-Carbon Economic Optimization Using CCUS and Two-Stage P2G with Multiple Hydrogen Utilizations for an Integrated Energy System with a High Penetration Level of Renewables

Integrating carbon capture and storage (CCS) technology into an integrated energy system (IES) can reduce its carbon emissions and enhance its low-carbon performance. However, the full CCS of flue gas displays a strong coupling between lean and rich liquor as carbon dioxide liquid absorbents. Its integration into IESs with a high penetration level of renewables results in insufficient flexibility and renewable curtailment. In addition, integrating split-flow CCS of flue gas facilitates a short capture time, giving priority to renewable energy. To address these limitations, this paper develops a carbon capture, utilization, and storage (CCUS) method, into which storage tanks for lean and rich liquor and a two-stage power-to-gas (P2G) system with multiple utilizations of hydrogen including a fuel cell and a hydrogen-blended CHP unit are introduced. The CCUS is integrated into an IES to build an electricity–heat–hydrogen–gas IES. Accordingly, a deep low-carbon economic optimization strategy for this IES, which considers stepwise carbon trading, coal consumption, renewable curtailment penalties, and gas purchasing costs, is proposed. The effects of CCUS, the two-stage P2G system, and stepwise carbon trading on the performance of this IES are analyzed through a case-comparative analysis. The results show that the proposed method allows for a significant reduction in both carbon emissions and total operational costs. It outperforms the IES without CCUS with an 8.8% cost reduction and a 70.11% reduction in carbon emissions. Compared to the IES integrating full CCS, the proposed method yields reductions of 6.5% in costs and 24.7% in emissions. Furthermore, the addition of a two-stage P2G system with multiple utilizations of hydrogen further amplifies these benefits, cutting costs by 13.97% and emissions by 12.32%. In addition, integrating CCUS into IESs enables the full consumption of renewables and expands hydrogen utilization, and the renewable consumption proportion in IESs can reach 69.23%.

STP-Model: A semi-supervised framework with self-supervised learning capabilities for downhole fault diagnosis in sucker rod pumping systems

In oilfield crude oil production, failing to detect and address downhole faults promptly can have severe consequences, including production interruptions, equipment damage, and resource wastage. Employing artificial intelligence to diagnose the operational status of oil wells is crucial. However, the high costs and specialized expertise required for data labeling make collecting sufficient training data challenging, limiting supervised learning approaches’ applicability. To address this, this paper introduces a semi-supervised framework with self-supervised learning capabilities for downhole fault diagnosis (STP-Model). It utilizes supervised learning modules for the limited labeled data available and constructs temporal association and prediction self-supervised modules for the extensive volume of unlabeled downhole fault data. These modules aim to learn the data’s intrinsic temporal relationship structure. The temporal association module splits and combines time-series data segments to construct positive/negative samples based on temporal consistency, learning the semantic information of time-series data. The temporal prediction module divides a continuous time series into two fixed-length sequences, aiming to minimize the gap between predicted and actual sequences and capture time series trend changes. These modules jointly compute the loss and update a shared-weight encoding network. Experimental results show the STP-Model achieves a 33.25% improvement in accuracy over supervised methods and an accuracy improvement of over 7.41% compared to other semi-supervised methods.

Three-dimensional modelling of artificial caves for geomechanical analysis

Abstract. Accurate cave surveying is crucial for understanding their genesis, current state, and potential hazards, especially in challenging environments marked by limited accessibility and poor visibility. This study applies geomatics techniques, including Terrestrial Laser Scanning (TLS), SLAM-based Mobile Mapping Systems (MMS), and digital photogrammetry, to create three-dimensional models of artificial caves in Gravina in Puglia, Apulia region, southern Italy. The research aims to assess these methodologies' accuracy, reliability, and performance for structural monitoring and hazard assessment. Despite challenges such as rough conditions, limited accessibility and poor visibility, the study reveals promising insights into the capabilities of these techniques for efficient surveying in complex underground environments. While highlighting the potential of MMS for cost-effective and rapid data acquisition, digital photogrammetry using spherical cameras also emerges as a viable alternative, offering comprehensive data collection capabilities with minimal capture time. Further research is warranted to optimize these techniques for enhanced hazard assessment and structural monitoring in challenging underground environments.

A cops and robber game and the meeting time of synchronous directed walks

AbstractIn a previous work, the authors showed that the maximum number of infinitely long synchronous directed walks that never meet is equal to the dimension of the no‐meet matroid, namely the largest order of a collection of vertex‐disjoint cycles. Given , we want to compute the meeting time of walks: the first time step such that, given any set of walks, at least two of them must meet no later than . We precisely prove that the meeting time is at most , where is the number of vertices. A connection is established with a cops and robber game on directed graphs with helicopter cops and an invisible slow robber. The meeting time of walks equals the capture time in this game, when at most capture attempts are allowed. While this capture time can be computed in polynomial time, we show that it is NP‐hard to compute the minimum number of cops needed to catch the robber. More insights are also given on the number and its relation to pathwidth and other graph parameters. Finally we analyze these game measures on digraph tensor products.

Kaolin-Derived Porous Silico-Aluminate Nanoparticles as Absorbents for Emergency Disposal of Toluene Leakage.

To rapidly eliminate toluene from aqueous environments during leakage accidents, this paper synthesized porous silico-aluminate nanoparticles (SANs) via a hydrothermal method, using cost-effective and non-toxic natural kaolin as the basic raw material. The morphology and structure of the porous SANs were characterized using scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and BET-specific surface area tests. The effects of different conditions, such as the dosage of porous SANs, initial concentration of toluene, temperature, capture time, and pH, on the adsorption performance of porous SANs were analyzed. The as-prepared SANs exhibited a high removal efficiency and rapid adsorption performance toward toluene in aqueous solution. Finally, the kinetics of the adsorption of toluene in aqueous solution by porous SANs were investigated. The mechanism of the adsorption of toluene by porous SANs was further discussed. These findings provide a cost-effective and highly efficient absorbent for the emergency disposal of toluene leakage accidents.

Second gadolinium loading to Super-Kamiokande

The first loading of gadolinium (Gd) into Super-Kamiokande in 2020 was successful, and the neutron capture efficiency on Gd reached 50%. To further increase the Gd neutron capture efficiency to 75%, 26.1 tons of Gd2(SO4)3⋅8H2O was additionally loaded into Super-Kamiokande (SK) from May 31 to July 4, 2022. As the amount of loaded Gd2(SO4)3⋅8H2O was doubled compared to the first loading, the capacity of the powder dissolving system was doubled. We also developed new batches of gadolinium sulfate with even further reduced radioactive impurities. In addition, a more efficient screening method was devised and implemented to evaluate these new batches of Gd2(SO4)3⋅8H2O. Following the second loading, the Gd concentration in SK was measured to be 333.5±2.5 ppm via an Atomic Absorption Spectrometer (AAS). From the mean neutron capture time constant of neutrons from an Am/Be calibration source, the Gd concentration was independently measured to be 332.7 ± 6.8(sys.) ± 1.1(stat.) ppm, consistent with the AAS result. Furthermore, during the loading the Gd concentration was monitored continually using the capture time constant of each spallation neutron produced by cosmic-ray muons, and the final neutron capture efficiency was shown to become 1.5 times higher than that of the first loaded phase, as expected.

150 Years of Soil

AbstractSoil archives capture time in a bottle and enable researchers to study soil change over time. A recently re‐discovered archive of soils across Illinois stretching back to 1862 offers a rare chance to probe how agriculture has changed soils in the Prairie State.

Deep Sky Objects Detection with Deep Learning for Electronically Assisted Astronomy

Electronically Assisted Astronomy is a fascinating activity requiring suitable conditions and expertise to be fully appreciated. Complex equipment, light pollution around urban areas and lack of contextual information often prevents newcomers from making the most of their observations, restricting the field to a niche expert audience. With recent smart telescopes, amateur and professional astronomers can capture efficiently a large number of images. However, post-hoc verification is still necessary to check whether deep sky objects are visible in the produced images, depending on their magnitude and observation conditions. If this detection can be performed during data acquisition, it would be possible to configure the capture time more precisely. While state-of-the-art works are focused on detection techniques for large surveys produced by professional ground-based observatories, we propose in this paper several Deep Learning approaches to detect celestial targets in images captured with smart telescopes, with a F1-score between 0.4 and 0.62 on test data, and we experimented them during outreach sessions with public in Luxembourg Greater Region.