Observation Window Research Articles

Latent classes among substance-involved families in child welfare: Associations with treatment completion and reunification

BackgroundWhile often treated as a single group in previous studies, families involved with the child welfare system (CWS) because of parental substance use disorder (SUD) present with various combinations of risk factors and needs (e.g., housing instability, criminal justice involvement, single parenthood, etc.) that affect treatment and child welfare outcomes. Understanding these differential risk and need factors is important for effective case planning and service provision. The generalizability of existing research on this topic is limited by geographic and/or demographic homogeneity, small sample sizes, or short observation windows. The current study sought to examine latent risk/need profiles among families with parental SUD in child welfare using a large, longitudinal multi-state sample and compare treatment and reunification outcomes across classes. Participants & SettingThis study included N = 13,291 parent–child dyads served by the Regional Partnership Grant (RPG) program, which funded implementation of 53 demonstration projects from 29 states between 2007 and 2012. RPG projects aimed to increase well-being and permanency for children in CWS due to parental SUD. MethodsLatent class analysis was used to identify groupings among the study sample on the basis of six key parent-level risk and need factors. A latent class regression model (LCR, Bandeen-Roche et al., 1997) was used to examine differences in the likelihood of treatment completion and reunification across classes controlling for covariates. ResultsModel fit statistics supported a four-class solution: Moderate Risk/Need, Criminal Justice Risk, Complex Risk & Need, and Opioid Use Disorder. The LCR suggested that the Criminal Justice Risk class had the highest odds for treatment completion but lowest odds of reunification. The Complex Risk & Need class had the lowest odds for treatment completion. A significant interaction indicated that the individuals in the Opioid Use Disorder class were more likely to reunify if they completed treatment. ConclusionsHeterogeneity exists among families involved with the child welfare system due to parental substance use. The findings suggest, however, that they present with distinct patterns of risk/need characteristics. Moreover, these combined characteristics are associated with the likelihood that a parent completes SUD treatment and is reunified with their child. Comprehensive and collaborative service delivery must respond to the varied risks and needs of these families.

CZT-Based Harmonic Analysis in Smart Grid Using Low-Cost Electronic Measurement Boards

This paper validates the use of a harmonic analysis algorithm on a microcontroller to perform measurements of non-stationary signals in the context of smart grids. The increasing presence of electronic devices such as inverters of distributed generators (DG), power converters of charging stations for electric vehicles, etc. can drain non-stationary currents during their operation. A classical fast Fourier transform (FFT) algorithm may not have sufficient spectral resolution for the evaluation of harmonics and inter-harmonics. Thus, in this paper, the implementation of a chirp-Z transform (CZT) algorithm is suggested, which has a spectral resolution independent from the observation window. The CZT is implemented on a low-cost commercial microcontroller, and the absolute error is evaluated with respect to the same algorithm implemented in the LabVIEW environment. The results of the tests show that the CZT implementation on a low-cost microcontroller allows for accurate measurement results, demonstrating the feasibility of reliable harmonic analysis measurements even in non-stationary conditions on smart grids.

Different Ignition Modes and Temperature Evolution of Typical Hypergolic Ionic Liquids

The hypergolic ignition behaviors of three ionic liquids [BMIm][DCA], [EMIm][DCA], and [EMIm][CDB] reacting with white fuming nitric acid (WFNA) were investigated using the drop test approach. Two high-speed cameras with/without a long-distance microscope and a time-resolved infrared camera were used to simultaneously record the hypergolic ignition process and the reaction region temperature evolution. Results showed two distinct hypergolic ignition modes. Specifically, for [BMIm][DCA] and [EMIm][DCA], some “vapor smoke” is generated after the fuel droplet contacts the WFNA pool, followed by sudden ejection of liquids induced by microexplosion. The measured temperature in the observation window is found to first increase, then decrease, and rise again. For [EMIm][CDB], no microexplosion is observed and the temperature monotonically increases before hypergolic ignition. It is inferred that microexplosion is caused by sufficient accumulation of gaseous intermediates or products underneath the liquid surface, and the monotonic or nonmonotonic increasing temperature is a characteristic of ignition modes. A conceptual model is proposed to illustrate the two ignition modes. In addition, the droplet impact velocity (U0) was varied from 1.1 to 2.1 m/s in the experiment. It is found that the vapor delay time (VDT), explosion delay time (EDT), and ignition delay time (IDT) are all reduced with U0 by at most 50%. For a given U0, the three time scales decrease in the order [BMIm][DCA], [EMIm][DCA], and [EMIm][CDB] (IDT as low as 14.4 ms).

Advanced In Situ TEM Microchip with Excellent Temperature Uniformity and High Spatial Resolution.

Transmission electron microscopy (TEM) is a highly effective method for scientific research, providing comprehensive analysis and characterization. However, traditional TEM is limited to observing static material structures at room temperature within a high-vacuum environment. To address this limitation, a microchip was developed for in situ TEM characterization, enabling the real-time study of material structure evolution and chemical process mechanisms. This microchip, based on microelectromechanical System (MEMS) technology, is capable of introducing multi-physics stimulation and can be used in conjunction with TEM to investigate the dynamic changes of matter in gas and high-temperature environments. The microchip design ensures a high-temperature uniformity in the sample observation area, and a system of tests was established to verify its performance. Results show that the temperature uniformity of 10 real-time observation windows with a total area of up to 1130 μm2 exceeded 95%, and the spatial resolution reached the lattice level, even in a flowing atmosphere of 1 bar.

Mapping understory plant communities in deciduous forests from Sentinel-2 time series

Understory plant communities are an integral component of deciduous forests, playing a vital role in the overall health of the ecosystem. However, remote sensing of understory plant communities is challenging due to the obstruction by the forest canopy. In this study, we proposed an automated dense Sentinel-2 time series-based approach for understory plant communities and created maps of four understory classes (i.e., native shrubs of greenbrier and mountain laurel, and invasive shrubs of barberry and the assemblage of mixed invasive) at 10 m resolution in Connecticut's deciduous forests in 2020. A harmonic time series model and three years of Sentinel-2 time series from 2019 to 2021 were used to classify understory species based on their unique, intra-annual phenology characteristics. The time series model coefficients captured the subtle phenology differences and created synthetic cloud-free images within a short temporal window in the spring prior to canopy leaf-on (hereafter called “observation window”). During the observation window, Sentinel-2 data penetrated the deciduous overstory canopy and observed the unique trajectories of different understory species due to their phenology differences. We also calculated spatial texture features (i.e., mean, second moment, and contrast from gray level co-occurrence matrix) based on the synthetic images created within the observation window to capture the different conditions of leaf growth and distinct spatial patterns within deciduous forests. By using the spectral, temporal, and spatial features as input variables from dense Sentinel-2 data, auxiliary data (i.e., LiDAR and soil drainage layer), a random forest classifier, and a new strategy to iteratively select representative sample (namely ISRS), understory species maps were created with an overall accuracy of approximately 93%, and the user's and producer's accuracies varied from 39% to 99% for the three mapped understory species and one assemblage of species. The proposed method created an accurate binary map of understory presence with an overall accuracy of 95%, a producer's accuracy of 84%, and a user's accuracy of 68%. Additionally, we separated the invasive (i.e. barberry and mixed invasive of multi-flora rose, oriental bittersweet, honeysuckle, winged euonymus, and autumn olive) and native (greenbrier and mountain laurel) species with an overall accuracy of 94%. We estimated that the invasive species cover an area of 649.33 ± 140.59 km2, which occupied a large proportion (∼53%) of the shrub understory in Connecticut's deciduous forests.

Inflation from a chaotic potential with a step

In this work, we study the effects on the relevant observational parameters of an inflationary universe from a chaotic potential with a step. We numerically evolve the perturbation equations within both cold inflation and warm inflation. On the one hand, in a cold inflation scenario we analyse the scalar power spectrum $P_{\mathcal{R}}$ in terms of the number of e-folds $N_{e}$, and in terms of the ratio $k/k_{0}$, where $k_{0}$ is our pivot scale. We show how $P_{\mathcal{R}}$ oscillates around $0.2< k/k_{0} < 20$. Additionally, we present the evolution of two relevant parameters: the scalar spectral index $n_\mathrm{s}$ and the tensor-to-scalar ratio $r$. In fact, more than one region of $(n_\mathrm{s},r)$ lies within the observable window (Planck 2018). On the other hand, in the warm inflationary case, we also examine the evolution of $P_{\mathcal{R}}$ in terms of $N_{e}$ and $k/k_{0}$. Perturbations are amplified in WI; in fact, $P_{\mathcal{R}}$ can be much larger than the CMB value $P_{\mathcal{R}}> 2.22\times 10^{-9}$. This time, the spectral index $n_\mathrm{s}$ is clearly blue-tilted, at smaller scales, and the tensor-to-scalar ratio $r$ becomes too low. However, $n_\mathrm{s}$ can change from blue-tilted towards red-tilted, since $P_{\mathcal{R}}$ starts oscillating around $k/k_{0}\sim 40$. Indeed, the result from the step potential skims the Planck contours. Finally, one key aspect of this research was to contrast the features of an inflationary potential between both paradigms, and, in fact, they show similarities and differences. Due to a featured background and a combined effect of entropy fluctuations (only in warm inflation), in both scenarios certain fluctuation scales are not longer ``freeze in'' on super-horizon scales.

Gas Accretion Can Drive Turbulence in Galaxies

The driving of turbulence in galaxies is deeply connected with the physics of feedback, star formation, outflows, accretion, and radial transport in disks. The velocity dispersion of gas in galaxies therefore offers a promising observational window into these processes. However, the relative importance of each of these mechanisms remains controversial. In this work we revisit the possibility that turbulence on galactic scales is driven by the direct impact of accreting gaseous material on the disk. We measure this effect in a disk-like star-forming galaxy in IllustrisTNG, using the high-resolution cosmological magnetohydrodynamical simulation TNG50. We employ Lagrangian tracer particles with a high time cadence of only a few million years to identify accretion and other events. The energies of particles are measured by stacking the events in bins of time around the event. The average effect of each event is measured by fitting explicit models for the kinetic and turbulent energies as a function of time. These measurements are corroborated by cross-correlating the turbulent energy with other time series and searching for signals of causality, i.e., asymmetries across zero time lag. We find that accretion contributes to the large-scale turbulent kinetic energy even if it does not dominate in this ∼5 × 109 M ⊙ stellar mass galaxy. Extrapolating this finding to a range of galaxy masses, we find that there are regimes where energy from direct accretion may dominate the turbulent energy budget, particularly in disk outskirts, galaxies less massive than the Milky Way, and at redshift ∼2.

Implications of quantum gravity for dark matter in the brane-world scenario

Based on the swampland program establishing the constraints that an effective field theory must satisfy in order to come from quantum gravity, we point to that if this program is true the brane-world scenario with the branon dark matter (DM) would be ruled out without needing the experimental observations. In other words, the constraints of quantum gravity imply that the branons must be absorbed by the Kaluza-Klein (KK) gauge bosons which are the off-diagonal components of the bulk metric to become their longitudinal modes. Interestingly, the KK gauge bosons behave as the DM and hence it leads to a geometric unification of gravity and the DM, as a natural feature of the brane-world scenario completed into quantum gravity in the ultraviolet. In addition, the KK gauge boson DM would open a particularly promising observation window to search for the DM coupling very weakly to the SM in the cosmic microwave background and the spectrum of the primordial gravitational waves.

Backer behaviors – changing investment dynamics in equity crowdfunding markets

PurposeThis study aims to establish the shape of investment dynamics in equity crowdfunding to better understand backer behavior.Design/methodology/approachThis study provides insights into when backers invest in successful funding campaigns. It uses t-tests to compare differences in means between observation windows during successful funding campaigns. It is based on 4,938 transactions from 61 campaigns, focusing on the first and last tail ends.FindingsIn contrast to previous findings, the current investment dynamics seem more U-shaped than L-shaped. This supports previous findings about a strong start but also suggests a late collective attention effect. The strength is higher at the first tail end. However, differences in the later tail ends are statistically significant and emphasize the presence of late investment activities, especially in crowded or less complex campaigns.Practical implicationsThese findings emphasize the importance of signaling during the entire funding window. This encourages platforms to invest in user-friendly functionalities that guide entrepreneurs and help backers when investing in successful campaigns.Originality/valueThis study improves the understanding of backer behavior and suggests changing investment dynamics in equity crowdfunding. In addition, this pattern contrasts with previous findings on dynamic collective attention effects in rich digitally informative markets, implying two attention effects when uncertainty is high.

Deep learning techniques for imaging air Cherenkov telescopes

Very-high-energy (VHE) gamma rays and charged cosmic rays (CCRs) provide an observational window into the acceleration mechanisms of extreme astrophysical environments. One of the major challenges at imaging air Cherenkov telescopes (IACTs) designed to look for VHE gamma rays, is the separation of air showers initiated by CCRs which form a background to gamma-ray searches. Two other less well-studied problems at IACTs are (a) the classification of different primary nuclei among the CCR events, and (b) identification of anomalous events initiated by beyond-the-Standard-Model (BSM) particles that could give rise to shower signatures which differ from the standard images of either gamma rays or CCR showers. The problems of categorizing the primary particle that initiates a shower image, or the problem of tagging anomalous shower events in a model-independent way, are problems that are well suited to a machine learning approach. Traditional studies that have explored gamma-ray/CCR separation have used a multivariate analysis based on derived shower properties, which contains significantly reduced information about the shower. In our work, we address the problems outlined above by using machine learning architectures trained on full simulated shower images, as opposed to training on just a few derived shower properties. We illustrate the techniques of binary and multicategory classification using convolutional neural networks, and we also pioneer the use of autoencoders for anomaly detection at VHE gamma-ray experiments. The latter technique has been studied previously in the context of collider physics, to tag anomalous BSM candidates in a model-independent way. In this study, for the first time, we demonstrate the efficacy of these techniques in the domain of VHE gamma-ray experiments. As a case study, we apply our techniques to the High Energy Stereoscopic System experiment. However, the real strength of the techniques that we broach here in the context of VHE gamma-ray observatories, is that these methods can be applied broadly to any other IACT---such as the upcoming Cherenkov Telescope Array---or can even be suitably adapted to CCR experiments.

Alleviating the negative effects of salesperson depression on performance during a crisis: Examining the role of job resources

Depression is an important and prevalent issue often overlooked in the sales literature. Although prior research provides evidence that depression negatively impacts a variety of job-related outcomes, little is known about the factors that can mitigate such relationships—particularly that between depression and salesperson performance over time. To remedy this, we draw on Job Demands-Resources theory and propose a unique framework that examines job resources (i.e., work adaptability, family work support, and supervisor support) that may mitigate the negative effects of depression. Using salesperson surveys matched with objective sales performance data during the early stages of the COVID-19 pandemic (i.e., January, February, and March 2020), we show that depression is negatively related to salesperson performance over time, work adaptability and family work support both mitigate the negative effects of depression, and contrary to expectations, supervisor support aggravates the relationship between depression and performance for a time but helps boost performance at the end of the observation window. The article's broader contribution is that depression is a crucial issue to consider and that sales managers should pay special attention to job resources that may mitigate the negative relationship between depression and salesperson performance over time.

Hybrid method of speech signals spectral analysis based on the autoregressive model and Schuster periodogram

The task of measuring the power spectral density of a speech signal in the regime of a sliding observation window is considered. A parametric approach to solving this task using an autoregressive data model is studied. The problem of optimizing the order of an autoregressive model under conditions of small samples is studied. The problem is proposed to be solved using a hybrid method of spectral analysis based on sequential enumeration of a fi nite number of alternatives. The optimization criterion is formulated in terms of the inverse problem: from a speech signal to a voice source. It uses the scaleinvariant measure of the spectral distance as the objective function, and the Schuster periodogram as the reference sample. The effectiveness of the hybrid method has been experimentally evaluated on the basis of author's software. It is shown that with the duration of the observation window of no more than 10 ms, the use of the hybrid method increases the accuracy of spectral analysis by more than 30 % compared to the well-known Berg method, the order of which is established according to the Akaike information criterion.

Bayes estimation and analysis of GNSS SIS fault rate

GNSS SIS fault probabilities Psat and Pconst are the key parameters contained in the Integrity Support Message (ISM) which will be broadcast to GNSS Advanced Receiver Autonomous Monitoring (ARAIM) users. The accurate and reliable SIS fault rate is crucial to ensure the trustiness of ARAIM. Although prior published works have developed the methods to estimate the fault rate, there are lack of a metric to assess the reliability of the fault rate, and then determine the optimal length observation window. Therefore, based on the prior works, we aim to establish the method of reliability assessment for the SIS fault rate estimation. First, the SIS fault rate estimator is derived and its variance are formulated. Then the equivalence between 2-sigma and the error bound of 95% confidence are verified by exposing the inside properties of the posteriori PDF of fault rate. The reliability assessment provides a criterion to determine the optimal length of observation window and finally obtain the reliable fault rate estimators. GPS performance in the period of 2008–2019 is assessed by varying the length of observation window. The optimal estimators of GPS NF rate indicate that the specified fault rate of 1×10-5/sat/hour was achieved in the middle of 2011 and GPS has had a trend of performance improvement since 2011.

What Do MBA Program in Southeast Asia Scholars Propose for Future COVID-19 Research in Academic Publications? A Topic Analysis Based on Autoencoder.

To analyze the directions for future research suggested and to project future research plans, we extract relevant text from these publications with respect to COVID-19-related research based on 54,136 relevant academic journals published from the initial outbreak of COVID-19 in January 2020 until December 2020. First, we extract and preprocess the corpus and then determine that, according to the Elbow method, the optimal number of clusters is 7. Then, we construct a text clustering model based on an autoencoder, with the support of an artificial neural network. Distance measurements, such as correlation, cosine, Braycurtis, and Jaccard are compared, and the clustering results are evaluated with normal mutual information. The results show that cosine similarity has the best effect on clustering of COVID-19-related documents. A topic model analysis shows that the directions of future research can mainly be grouped into the following seven categories: infectivity testing, genome analysis, vaccine testing, diagnosis and infection characteristics, pandemic management, nursing care, and clinical testing. Among them, the topics of pandemic management, diagnosis and infection characteristics, and clinical testing trended upward in proportion to future directions. The topic of vaccine testing remains steady over the observation window, whereas other topics (infectivity testing, genome analysis, and nursing care) slowly trended downward. Among all the topics, medical research comprises 80%, and about 20% of the topics are related to public management, government functions, and economic development. This study enriches our scientific understanding of COVID-19 and helps us to effectively predict future scientific research output on COVID-19.

Modeling individual differences in the timing of change onset and offset.

Individual differences in the timing of developmental processes are often of interest in longitudinal studies, yet common statistical approaches to modeling change cannot directly estimate the timing of when change occurs. The time-to-criterion framework was recently developed to incorporate the timing of a prespecified criterion value; however, this framework has difficulty accommodating contexts where the criterion value differs across people or when the criterion value is not known a priori, such as when the interest is in individual differences in when change starts or stops. This article combines aspects of reparameterized quadratic models and multiphase models to provide information on the timing of change. We first consider the more common situation of modeling decelerating change to an offset point, defined as the point in time at which change ceases. For increasing trajectories, the offset occurs when the criterion attains its maximum ("inverted J-shaped" trajectories). For decreasing trajectories, offset instead occurs at the minimum. Our model allows for individual differences in both the timing of offset and ultimate level of the outcome. The same model, reparameterized slightly, captures accelerating change from a point of onset ("J-shaped" trajectories). We then extend the framework to accommodate "S-shaped" curves where both the onset and offset of change are within the observation window. We provide demonstrations that span neuroscience, educational psychology, developmental psychology, and cognitive science, illustrating the applicability of the modeling framework to a variety of research questions about individual differences in the timing of change. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

WEAVE-StePS: A stellar population survey using WEAVE at WHT

Context. The upcoming new generation of optical spectrographs on four-meter-class telescopes will provide valuable opportunities for forthcoming galaxy surveys through their huge multiplexing capabilities, excellent spectral resolution, and unprecedented wavelength coverage. Aims. WEAVE is a new wide-field spectroscopic facility mounted on the 4.2 m William Herschel Telescope in La Palma. WEAVE-StePS is one of the five extragalactic surveys that will use WEAVE during its first five years of operations. It will observe galaxies using WEAVE MOS (∼950 fibres distributed across a field of view of ∼3 square degrees on the sky) in low-resolution mode (R ∼ 5000, spanning the wavelength range 3660 − 9590 Å). Methods. WEAVE-StePS will obtain high-quality spectra (S/N ∼ 10 Å−1 at R ∼ 5000) for a magnitude-limited (IAB = 20.5) sample of ∼25 000 galaxies, the majority selected at z ≥ 0.3. The survey goal is to provide precise spectral measurements in the crucial interval that bridges the gap between LEGA-C and SDSS data. The wide area coverage of ∼25 square degrees will enable us to observe galaxies in a variety of environments. The ancillary data available in each of the observed fields (including X-ray coverage, multi-narrow-band photometry and spectroscopic redshift information) will provide an environmental characterisation for each observed galaxy. Results. This paper presents the science case of WEAVE-StePS, the fields to be observed, the parent catalogues used to define the target sample, and the observing strategy that was chosen after a forecast of the expected performance of the instrument for our typical targets. Conclusions. WEAVE-StePS will go back further in cosmic time than SDSS, extending its reach to encompass more than ∼6 Gyr. This is nearly half of the age of the Universe. The spectral and redshift range covered by WEAVE-StePS will open a new observational window by continuously tracing the evolutionary path of galaxies in the largely unexplored intermediate-redshift range.

Early decoding of walking tasks with minimal set of EMG channels

Objective. Powered lower-limb prostheses relying on decoding motor intentions from non-invasive sensors, like electromyographic (EMG) signals, can significantly improve the quality of life of amputee subjects. However, the optimal combination of high decoding performance and minimal set-up burden is yet to be determined. Here we propose an efficient decoding approach obtaining high decoding performance by observing only a fraction of the gait duration with a limited number of recording sites. Approach. Thirteen transfemoral amputee subjects performed five motor tasks while recording EMG signals from four muscles and inertial signals from the prosthesis. A support-vector-machine-based algorithm decoded the gait modality selected by the patient from a finite set. We investigated the trade-off between the robustness of the classifier’s accuracy and the minimization of (i) the duration of the observation window, (ii) the number of EMG recording sites, (iii) the computational load of the procedure, measured the complexity of the algorithm. Main results. When including pre-foot-strike data in the decoding, the combination of three EMG recording sites and the inertial signals led to correct rates above 94% at the 20% of the gait cycle, showing the best trade-off between invasiveness of the setup and accuracy of the classifier. The complexity of the algorithm proved to be significantly higher when applying a polynomial kernel compared to a linear one, while the correct rate of the classifier generally showed no differences between the two approaches. The proposed algorithm led to high performance with a minimal EMG set-up and using only a fraction of the gait duration. Significance. These results pave the way for efficient control of powered lower-limb prostheses with minimal set-up burden and a rapid classification output.

A Bayesian Approach to Network Monitoring for Progressive Failure Localization

Boolean Network Tomography (BNT) aims at identifying failures of internal network components by means of end-to-end monitoring paths. However, when the number of failures is not known a priori, failure identification may require a huge number of monitoring paths. We address this problem by designing a Bayesian approach that progressively selects the next path to probe on the basis of its expected information utility, conditioned on prior observations. As the complexity of the computation of posterior probabilities of node failures is exponential in the number of failed paths, we propose a polynomial-time greedy strategy which approximates these values. To consider aging of information in dynamic failure scenarios where node states can change during a monitoring period, we propose a monitoring technique based on a sliding observation window of adaptive length. By means of numerical experiments conducted on real network topologies we demonstrate the practical applicability of our approach, and the superiority of our algorithms with respect to state of the art solutions based on classic BNT as well as sequential group testing.

P‐7.3: Unidirectional Observable Window Signage Using Structural Colors Which Enable to Make Images Invisible from Back Side

It stands to reason that someone observes mirrored imaging according to the direction of viewing if you draw something on the transparent material. The authors dislike this mirrored imaging problem, although it is not a fatal defect. In 2021, our research group proposed window signage using dye‐doped thin film layer to avoid this problem of mirrored imaging. But our 2021 model had a defect that the transparency of a glass window with half mirror layer was less than the general glass material without half mirror because of image generating mechanism. To solve this “reflect surroundings” problem, we needed to find new technology for generating color using a transparent material. This paper shows a unidirectional observable window signage using holographic imaging or holographic optical elements for unidirectional color generating by light controls, how to make structural colors using holographic recording technology and material development.

Gadolinium(III) Complex-Backboned Branched Polymers as Imaging Probes for Contrast-Enhanced Magnetic Resonance Angiography.

Compared to traditional branched polymers with Gd(III) chelates conjugated on their surface, branched polymers with Gd(III) chelates as the internal skeleton are considered to be a reasonable strategy for preparing efficient magnetic resonance imaging contrast agents. Herein, the Gd(III) ligand DOTA was chosen as the internal skeleton; four different molecular weights (3.5, 5.3, 8.6, and 13.1 kDa) and degrees of branching poly-DOTA branched polymers (P1, P2, P3, and P4) were synthesized by a simple "A2 + B4"-type one-pot polymerization. The Gd(III) chelates of these poly-DOTA branched polymers (P1-Gd, P2-Gd, P3-Gd, and P4-Gd) display excellent kinetic stability, which is significantly higher than those of linear Gd-DTPA and cyclic Gd-DOTA-butrol and slightly lower than that of cyclic Gd-DOTA. The T1 relaxivities of P1-Gd, P2-Gd, P3-Gd, and P4-Gd are 29.4, 38.7, 44.0, and 47.9 Gd mM-1 s-1, respectively, at 0.5 T, which are about 6-11 times higher than that of Gd-DOTA (4.4 Gd mM-1 s-1). P4-Gd was selected for in vivo magnetic resonance angiography (MRA) because of its high kinetic stability, T1 relaxivity, and good biosafety. The results showed excellent MRA effect, sensitive detection of vascular stenosis, and prolonged observation window as compared to Gd-DOTA. Overall, Gd(III) chelates of poly-DOTA branched polymers are good candidates of MRI probes, providing a unique design strategy in which Gd chelation can occur at both the interior and surface of the poly-DOTA branched polymers, resulting in excellent relaxivity enhancement. In vivo animal MRA studies of the probe provide possibilities in discovering small vascular pathologies.