Real-time Observation Research Articles

Analyzing the Relationship Between User Feedback and Traffic Accidents Through Crowdsourced Data

Identifying road segments with a high crash incidence is essential for improving road safety. Conventional methods for detecting these segments rely on historical data from various sensors, which may inadequately capture rapidly changing road conditions and emerging hazards. To address these limitations, this study proposes leveraging crowdsourced data alongside historical traffic accident records to identify areas prone to crashes. By integrating real-time public observations and user feedback, the research hypothesizes that traffic accidents are more likely to occur in areas with frequent user-reported feedback. To evaluate this hypothesis, spatial autocorrelation and clustering analyses are conducted on both crowdsourced data and accident records. After defining hotspot areas based on user feedback and fatal accident records, a density analysis is performed on such hotspots. The results indicate that integrating crowdsourced data can complement traditional methods, providing a more dynamic and adaptive framework for identifying and mitigating road-related risks. Furthermore, this study demonstrates that crowdsourced data can serve as a strategic and sustainable resource for enhancing road safety and informing more effective road management practices.

Sustaining 500,000 Folding Cycles Through Bioinspired Stress Dispersion Design in Sodium-Ion Batteries.

Developing super-foldable electronic materials and devices presents a significant challenge, as intrinsic conductive materials are unable to achieve numerous true-folding operations (super-foldable) due to limitations from short-range forces of chemical bonds. Consequently, super-foldable batteries remain unexplored. This work focused on sodium-ion batteries as a breakthrough point to advance super-foldable devices. By employing a "2+1" bioinspired strategy, we stepwise designed and assembled super-foldable components, from substrates to electrodes, and to ultimately device. This bioinspired approach completely disperses folding stress and thus prevents the breakage of chemical bonds, enabling the successful fabrication of the first super-foldable ion battery. This battery can withstand true-folding at any angle, in any direction, and for an unprecedented number of cycles-far outperforming current foldable phones with hinge structures. Remarkably, after 500,000 true-folding cycles, the battery's microstructure remains intact with no significant degradation of electrochemical performance. Real-time dynamic folding observations reveal an M-shaped folding structure within the bioinspired materials, which effectively disperses stress via bulged layers, dispersed arcs, and slidable microgrooves that work together across different directions and dimensions to achieve super-foldability. Mechanical simulations vividly verify this principle. This work represents a breakthrough in super-foldable devices, offering valuable insights and promoting practical application for future super-foldable devices.

Bimolecular Fluorescence Complementation as a Tool to Study Specific Dynamic Interactions Between Proteins in Fission Yeast.

Bimolecular fluorescence complementation (BiFC) is a technique that enables real-time observation within living cells of the interaction between two proteins forming a complex, determining the location where such interaction occurs within the cell, and even the association and dissociation cycles in response to physiological cues. Here, we describe in detail the use of bimolecular fluorescence complementation to visualize the assembly and disassembly of cohesin over the fission yeast cell cycle.

An Exploratory Study of Small-Group Learning Interactions in Pre-Clerkship Medical Education: Uncovering a Mismatch Between Student Perceptions and Real-Time Observations

Small-group learning is a mainstay of medical education, and group functioning can have a major influence on these learning experiences. Our objective was to explore verbal exchange patterns within small-group learning sessions and examine how different patterns related to tutor involvement, tutor expertise, and participants’ perceptions. A non-participant observer collected group interactivity data using a real-time mobile device-based system. Verbal interaction patterns were visualized and analyzed using social network analysis and correlated with participant survey data and aggregate course grades. There were 46 observations across 30 separate groups. Group interactions clustered into four patterns defined by (1) tutor involvement (high vs. low) and (2) interactivity (high vs. low). Interaction patterns were largely stable for a given group and groups with content expert facilitators were generally less interactive. Students reported objectively fewer interactive groups as more interactive and enjoyable. There were no significant intergroup differences in aggregate course grades. Paradoxically, student perceptions were not aligned with observed interactivity data, and tutor content expertise influenced group interactivity. These findings suggest the need to better manage learner expectations of small-group learning, and to explicitly reflect on and develop skills for effective collaborative learning with both faculty and students.

In-situ understanding on the formation of fibrillar morphology in green solvent processed all-polymer solar cells

Abstract Solid additive engineering has been intensively explored on morphology tuning for highly efficient all-polymer solar cells (all-PSCs), a promising photovoltaic technology towards multi-scenario application. Although the nano-fibrillar network of the active layer induced by additive treatment is confirmed as the key factor for power conversion efficiency (PCE) of all-PSCs, its formation mechanism is not clearly revealed, for lack of precise and convincing real-time observation of crystallization and phase separation during the liquid-to-solid transition process of spin-coating. Herein we report an in-situ grazing incidence wide-angle/small-angle X-ray scattering (GIWAXS/GISAXS) screening that reveals the fact that naphthalene derived solid additives can suppress the aggregation of the polymer acceptor (PY-IT) at the beginning stage of spin coating, which provides sufficient time and space for the polymer donor (PM6) to form the fibril structure. Moreover, guided by this knowledge, a ternary all-polymer system is proposed, which achieves cutting-edge level PCEs for both small-area (0.04 cm2) (also decent operational stability) and large-area (1 cm2) devices.

Application of Internet of Things (IoT) on Monitoring EC and pH Values for Fertigation System

This paper introduces a novel Internet of Things (IoT) based fertigation control system designed to enhance traditional agricultural practices in Malaysia, particularly in rural areas. The system integrates a fertigation approach, combining fertilization and irrigation, with smart devices and a NodeMCU ESP32 microcontroller to automate the tracking of data from liquid fertilizer mixtures. By utilizing EC and pH sensors, the system accurately monitors the nutrient concentration and pH levels of the fertilizer mix, with data displayed on an IoT platform for real-time observation and control. This approach mitigates common issues such as overfertilization and inefficient nutrient use, offering significant improvements over manual data collection methods. The system's performance was validated, showing successful monitoring and control of the fertilization process, leading to enhanced plant health and crop quality. Key findings indicate that the system's integration with IoT technology provides a more efficient, automated solution for managing fertilizer applications, reducing labor and time while ensuring optimal plant nutrient levels. The study also highlights the potential for future development, including the integration of additional sensors and advanced data analytics to optimize fertigation practices further. The implementation of this IoT-based system represents a significant advancement towards sustainable agriculture, offering practical benefits for Malaysian farmers and setting a foundation for further innovation in fertigation technology.

To evaluate coronary microvascular obstruction by myocardial contrast echocardiography and its influence on prognosis in patients with ST-segment elevation myocardial infarction

Abstract Background The goal of primary PCI (pPCI) is not only to restore epicardial coronary blood flow quickly but also to provide continuous myocardial tissue level reperfusion through coronary microcirculation. However, the incidence of coronary microvascular obstruction (MVO) after pPCI is as high as 50%-60%, which is closely related to the occurrence of heart failure and increased mortality of patients. Myocardial contrast echocardiography (MCE) is a new noninvasive technique for real-time observation of myocardial microcirculation perfusion in a specific imaging mode. At present, there are few studies on the application of MCE in the evaluation of coronary MVO in STEMI patients in China. Purpose To evaluate coronary MVO by MCE and its influencing factors and prognosis value after pPCI in patients with STEMI. Methods Patients with STEMI who received pPCI from October 2020 to June 2023 in the CCU were enrolled. MCE examination was performed on the 5th to 7th day after pPCI. The patients were divided into the MVO group and Non-MVO group. Major adverse cardiovascular events (MACE) were observed by 6-month follow-up after STEMI onset. Multiple Logistic regression analysis was used to analyze the risk factors for MVO, Multiple Cox regression analysis of the risk factors for MACE and Kaplan-Meier survival curve to compare the cumulative event-free survival of the two groups. Results A total of 98 patients were included in the analysis. The age was 59.88±11.71 years old, and 76(77.55%) were male. MCE examination showed 52 cases in the MVO group and 46 cases in the Non-MVO group. The incidence of MVO was 53.1% (52/98). There were 15 patients with MACE during follow-up. The results of stepwise Multivariate Logistic regression analysis showed that the peak value of cardiac troponin T (cTnT) (OR=1.354,95%CI 1.103-1.661, P=0.004) and without thrombus aspiration (TA) were independent risk factors for MVO (OR=5.588,95%CI 1.915-16.308, P=0.002). Multivariate Cox regression analysis indicated that female (HR=2.891, 95%CI 1.027-8.135, P=0.044), left ventricular ejection fraction (LVEF) <40% (HR=9.960, 95%CI 3.370-29.435, P<0.001) and MVO (HR=5.759,95%CI 1.279-25.935, P=0.023) were independent risk factors for MACE. Kaplan-Meier survival curve showed that the cumulative event-free survival rate in the Non-MVO group was higher than that in the MVO group, and the difference was statistically significant (P=0.0047). Conclusion The incidence of coronary MVO was higher in STEMI patients after pPCI, the peak value of cTnT and without TA were independent risk factors of MVO. The independent risk factors for MACE were female, LVEF < 40%, and MVO.

Design an Agricultural Soil and Environment Monitoring System Based on IoT

One of the problems farmers face is the inability to make complete, real-time, and accurate observations of their farmland. The system proposed in this paper helps farmers to know the condition of farmland from anywhere and anytime by using a web-based application. The main objective of this prototype is to reduce the failure of the growth process of farming commodities by knowing the conditions inside and outside the soil with a total of 14 parameters. Internet of Things (IoT) technology is used to implement the prototype, which consists of Sensor Panels, Controllers, Message Broker, and Backend Service. All obtained data, created and tested in real-time, are displayed on the application. In addition to real-time data display, the system also includes monitoring history, alerts, and site location management.

Genetically encoded green fluorescent sensor for probing sulfate transport activity of solute carrier family 26 member a2 (Slc26a2) protein

Genetically encoded fluorescent biosensors became indispensable tools for biological research, enabling real-time observation of physiological processes in live cells. Recent protein engineering efforts have resulted in the generation of a large variety of fluorescent biosensors for a wide range of biologically relevant processes, from small ions to enzymatic activity and signaling pathways. However, biosensors for imaging sulfate ions, the fourth most abundant physiological anion, in mammalian cells are still lacking. Here, we report the development and characterization of a green fluorescent biosensor for sulfate named Thyone. Thyone, derived through structure-guided design from bright green fluorescent protein mNeonGreen, exhibited a large negative fluorescence response upon subsecond association with sulfate anion with an affinity of 11 mM in mammalian cells. By integrating mutagenesis analyses with molecular dynamics simulations, we elucidated the molecular mechanism of sulfate binding and revealed key amino acid residues responsible for sulfate sensitivity. High anion selectivity and sensitivity of Thyone allowed for imaging of sulfate anion transients mediated by sulfate transporter heterologously expressed in cultured mammalian cells. We believe that Thyone will find a broad application for assaying the sulfate transport in mammalian cells via anion transporters and exchangers.

Nanoplasma formation and expansion ignited by an intense FEL: a study using pump-probe electron spectroscopy

Abstract We demonstrate real-time observations of nanoplasma formation and expansion using intense extreme-ultraviolet (XUV) and near-infrared (NIR) pump--probe electron spectroscopy. We identified the formation of a nanoplasma by the sudden enhancement of low-energy electron emission within a few tens of femtoseconds after XUV excitation, which indicates considerable heating of the clusters by the NIR field. 
We probed the subsequent expansion of the nanoplasma by monitoring the transient resonant enhancement of high-energy electron emission. The dependence of the resonance on the XUV intensity is explained by the expansion speed of the XUV-induced nanoplasma.

Experimental method for internal deformation measurement of 3D solids with embedded cracks based on 3D printing and digital speckle techniques

Quantification of the internal deformation of fractured solids such as rock masses under external loads, especially the deformation around internal fractures, is crucial for understanding and predicting the failure of fractured solids. However, it is very difficult to achieve the goal using conventional experimental methods. In this study, we proposed a novel internal deformation measurement method based on three-dimensional (3D) printing and digital speckle techniques. The 3D printing technology was applied to fabricate a 3D transparent model with an embedded elliptical crack and to set a speckle pattern on the internal section of the transparent model. The digital image correlation (DIC) method was used to determine the internal deformation field of the 3D fractured model. The measured displacements and strains of the internal sections in the fractured model were compared with the numerical simulation results. The comparison indicates that the proposed experimental method can well determine the deformation inside the 3D model. This built-in speckle method can realize real-time observation of the internal deformation of a 3D solid model in a non-contact and non-destructive manner.

Detection And Accident Prevention Of Animals in Railway Track Using AI And IoT

Railway is the most widespread and welcoming transportation system of most of the large cities in the world. The real-time observation and control methods proposed in this research are based on the Internet of Things and function autonomously without external intervention. There are several ways that IoT might improve the railway system. Railway automation has the potential to revolutionize existing legacy systems and significantly reduce railway-related incidents. The proposed method for identifying and recognizing animals and humans has been tried and refined. Implement real-time control and automated monitoring of different parameters relevant to the railway. Build a fully automatic process that reduces human involvement and retains energy.

GRRIS: A Real-time Intrasite Observation Scheduling Scheme for Distributed Survey Telescope Arrays

The distributed telescope array offers promise for conducting large-sky-area, high-frequency time-domain surveys. Multiple telescopes can be deployed at each observation site, so intrasite observation task scheduling is crucial for enhancing observation efficiency and quality. Efficient use of observable time and rapid response to special situations are critical to maximize scientific discovery in time-domain surveys. Besides, the competing scientific priorities, time-varying observation conditions, and capabilities of observation equipment, lead to a vast search space of the scheduling. So with the increasing number of telescopes and observation fields, balancing computational time with solution quality in observation scheduling poses a significant challenge. Informed by the seminal contributions of earlier studies on a multilevel scheduling model and global scheduler for a time-domain telescope array, this study is devoted to further exploring the site scheduler. Formulating the observation scheduling of multiple telescopes at the site as a cooperative decision-making problem, this paper proposes GRRIS, a real-time intrasite observation scheduling scheme for the telescope array using graph and reinforcement learning (RL). It employs a graph neural network to learn node features that can embed the spatial structure of the observation scheduling. An algorithm based on multi-agent RL is designed to efficiently learn the optimum allocation policy of telescope agents to field nodes. Through numerical simulations with real-world scenarios, GRRIS can achieve up to a 22% solution improvement over the most competitive scheme. It offers better scalability and subsecond decision speed, meeting the needs of observation scheduling control for future distributed telescope arrays.

Limb Temperature Observations in the Stratosphere and Mesosphere Derived from the OMPS Sensor

Molecular scattering (Rayleigh scattering) has been extensively used from the ground with lidars and from space to observe the limb, thereby deriving vertical temperature profiles between 30 and 80 km. In this study, we investigate how temperature can be measured using the new Ozone Mapping and Profiler Suite (OMPS) sensor, aboard the Suomi NPP and NOAA-21 satellites. The OMPS consists of three instruments whose main purpose is to study the composition of the stratosphere. One of these, the Limb Profiler (LP), measures the radiance of the limb of the middle atmosphere (stratosphere and mesosphere, 12 to 90 km altitude) at wavelengths from 290 to 1020 nm. This new data set has been used with a New Simplified Radiative Transfer Model (NSRTM) to derive temperature profiles with a vertical resolution of 1 km. To validate the method, the OMPS-derived temperature profiles were compared with data from four ground-based lidars and the ERA5 and MSIS models. The results show that OMPS and the lidars are in agreement within a range of about 5 K from 30 to 80 km. Comparisons with the models also show similar results, except for ERA5 beyond 50 km. We investigated various sources of bias, such as different attenuation sources, which can produce errors of up to 120 K in the UV range, instrumental errors around 0.8 K and noise problems of up to 150 K in the visible range for OMPS. This study also highlighted the interest in developing a new miniaturised instrument that could provide real-time observation of atmospheric vertical temperature profiles using a constellation of CubeSats with our NSRTM.

Learning-based adaptive neural control for safer navigation of unmanned surface vehicle with variable mass

This paper presents a novel approach to the precise control of variable mass unmanned surface vehicles (USVs) during payload deployment, where both mass and draught undergo unpredictable changes. We propose a draught observation method and an adaptive control strategy that leverages the strong coupling between the USV's motion states, mass, and draught. Our method employs a radial basis function neural network (RBF-NN) for real-time draught observation, using an offline training strategy based on gradient descent, combined with an adaptive online training strategy to improve observation accuracy. An adaptive control strategy based on the Backstepping method is then developed, incorporating real-time draught data from the RBF-NN to address unknown variations in mass and draught. The stability of both the RBF-NN observer and the adaptive control algorithm is rigorously verified using the Lyapunov method. Simulation results demonstrate that the proposed draught observation method achieves up to 30% faster convergence compared to traditional methods, with a significant improvement in observation accuracy. Furthermore, the adaptive control strategy effectively manages real-time adjustments in dynamic scenarios, maintaining robust control performance even under significant mass changes, where conventional approaches fail.

Novel FRET-Based Biosensors for Real-Time Monitoring of Estrogen Receptor Dimerization and Translocation Dynamics in Living Cells.

Estrogen receptors (ERs), comprising ER α and ER β, are crucial for regulating cell growth and differentiation via homo- and hetero-dimer formation. However, accurately detecting ER dimerization with precise spatiotemporal resolution remains a significant challenge. In this study, fluorescence resonance energy transfer-based biosensors to monitor ER dynamics in real-time, are developed and optimized.This approach involves comprehensive structural analysis, linker comparison, and the selection of optimal fluorescent protein pairs, resulting in three distinct biosensors capable of detecting all ER homo- and hetero-dimerizations within the nucleus. These biosensors are utilized to reveal interactions between ER α/β and calmodulin during dimer formation.Furthermore, by leveraging the ligand-binding domain (LBD) of ER β, ER ββ LBD biosensor is designed for real-time analysis of ER ββ homodimerization in the cytoplasm, enhancing the ability to screen ER dimerization-related drugs.Additionally, we developed a novel ER ββ translocation biosensor, which enables real-time observation of ER ββ translocation to the nucleus-a capability previously unavailable, is developed. This spatiotemporal analysis demonstrates the relevance of ER translocation in response to drug binding efficacy and extracellular matrix changes. Our biosensors offertransformative tools for studying ER dynamics, providing valuable insights for drug screening and the investigation of ER-related cellular processes.

Real-time Observation of Stress-strain Behavior beyond Necking in Martensitic Steel by <i>in-situ</i> Synchrotron X-ray Diffraction

Real-time Observation of Stress-strain Behavior beyond Necking in Martensitic Steel by <i>in-situ</i> Synchrotron X-ray Diffraction

Yeast Solutions and Hyperpolarization Enable Real-Time Observation of Metabolized Substrates Even at Natural Abundance.

Metabolic changes in an organism often occur much earlier than macroscopic manifestations of disease, such as invasive tumors. Therefore, noninvasive tools to monitor metabolism are fundamental as they provide insights into in vivo biochemistry. NMR represents one of the gold standards for such insights by observing metabolites. Using nuclear spin hyperpolarization greatly increases the NMR sensitivity, enabling μmol/L sensitivity with a time resolution of about one second. However, a metabolic phantom with reproducible, rapid, and human-like metabolism is needed to progress research in this area. Using baker's yeast as a convenient metabolic factory, we demonstrated in a single study that yeast cells provide a robust and rapidly metabolizing phantom for pyruvate and fumarate, including substrates with a natural abundance of 13C: we observed the production of ethanol, carbon dioxide, bicarbonate, lactate, alanine from pyruvate, malate, and oxaloacetate from fumarate. For observation, we hyperpolarized pyruvate and fumarate via the dissolution dynamic nuclear polarization (dDNP) technique to about 30% 13C polarization that is equivalent to 360,000 signal enhancement at 1 T and 310 K. Major metabolic pathways were observed using tracers at a natural abundance of 13C, demonstrating that isotope labeling is not always essential in vitro. Enriched [1-13C]pyruvate revealed minor lactate production, presumably via the D-lactate dehydrogenase (DLD) enzyme pathway, demonstrating the sensitivity gain using a dense yeast solution. We foresee that yeast as a metabolic factory can find application as an abundant MRI phantom standard to calibrate and optimize molecular MRI protocols. Our study highlights the potential of using hyperpolarization to probe the metabolism of yeast and other microorganisms even with naturally abundant substrates, offering valuable insights into their response to various stimuli such as drugs, treatment, nourishment, and genetic modification, thereby advancing drug development and our understanding of biochemical processes.

Ah-Aloud Method to Comprehend Time-Series Emotion Observation During Gameplay: An Initial Investigation with Japanese Speakers

Emotional dimensions significantly impact user experience (UX) and are essential in assessing interactions in entertainment systems. However, relying solely on subjective post-event indicators fails to capture dynamic emotional changes during the system experience. Kawashima and Watanabe introduced the "ah-aloud" method, enabling real-time emotion observation by having participants vocalize "ah" during system experiences. However, this method remains conceptual and preliminary, lacking in specific experimental procedures, analysis methods, and utilization guidelines. Our study delineates the requirements for practical "ah-aloud" application and validates its potential for game experience assessment. Consequently, the "ah-aloud" method demonstrates the potential for observing time-series emotional changes during gameplay. Based on these findings, we deliberated its utility for system evaluation and reliability as an evaluation experiment.

Observation of reversible conformational interconversion accompanied by 3p internal conversions in Rydberg-excited N,N-dimethylethylamine

Conformational dynamics has been well observed in the 3s Rydberg state of amines, whereas its observation in higher-energy, non-equilibrium 3p Rydberg states is very rare, especially for a reversible conformational transition that could compete with other non-adiabatic transitions. Herein, we report the observation of a reversible conformational interconversion phenomenon in the 3p Rydberg excited-state dynamics of N,N-dimethylethylamine (DMEA). Upon electronic excitation, a forward and backward interconversion between the initially prepared 3p_l and 3p_h conformers accompanied by 3p internal conversions occurs, resulting in a 3p_l/3p_h equilibrium ratio of 61 %/39 % within ∼1.5 ps. The ensuing parallel internal conversions from the 3p_l to 3s_l and 3p_h to 3s_h deposit about 1.80 eV of vibrational energy into the 3s state, enabling a fast conformational interconversion between the 3s_h and 3s_l conformers to proceed within ∼2.0 ps. The final 3s_l/3s_h equilibrium ratio was determined to be 76 %/24 %. This work presents a real-time observation of the entire conformational interconversion process initiating from the higher-energy 3p states and finally reaching an equilibrium on the lower-energy 3s state.