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Halo-structured nuclei are examples of many-body open quantum system. Here the authors use a complete kinematics measurement and find an elastic breakup of proton halo nucleus 8B.

Indoor localization for pedestrians, which relies solely on inertial odometry, has been a topic of great interest. Its significance lies in its ability to provide positioning solutions independently, without the need for external data. Although traditional strap-down inertial navigation shows rapid drift, the introduction of pedestrian dead reckoning (PDR), and artificial intelligence (AI) has enhanced the applicability of inertial odometry for indoor localization. However, inertial odometry continues to be affected by drift, inherent to the nature of dead reckoning. This implies that even a slight error at a given moment can lead to a significant decrease in accuracy after continuous integration operations. In this paper, we propose a novel approach aimed at enhancing the positioning accuracy of inertial odometry. Firstly, we derive a learning-based forward speed using inertial measurements from a smartphone. Unlike mainstream methods where the learned speed is directly used to determine the position, we use the forward speed combined with non-holonomic constraint (NHC) as a measurement to update the state predicted within a strap-down inertial navigation framework. Secondly, we employ an invariant extended Kalman filter (IEKF)-based state estimation to facilitate fusion to cope with the nonlinearity arising from the system and measurement model. Experimental tests are carried out in different scenarios using an iPhone 12, and traditional methods, including PDR, robust neural inertial navigation (RONIN), and the EKF-based method, are compared. The results suggest that the method we propose surpasses these traditional methods in performance.

Abstract Background A carotid-cavernous fistula is an abnormal communication between the arteries and veins within the cavernous sinus. While conservative management may be prudent in low risk cases, many patients require intervention and endovascular embolization has evolved as the preferred method of treatment. Embolization can be performed via either the transarterial or transvenous approach. One major challenge of the transvenous approach is the complex and variable compartmentation of the cavernous sinus, which often requires the use of low profile microcatheters to navigate and reach the fistulous point. Fibered coils are also preferred when performing transvenous embolization of carotid-cavernous fistula, as they are of higher thrombogenicity and allow for faster occlusion of the fistula. However, most low profile (0.017-inch) microcatheters are not able to deploy fibered coils based on the manufacturer’s instructions. Case presentation We present two successful cases of off-label use of Medtronic Concerto fibered coils via a 0.017-inch microcatheter during transvenous embolization of carotid-cavernous fistula in a 60-year-old and an 80-year-old Chinese female, respectively. Conclusion Our case series highlight the possibility of deploying large diameter (up to 10 mm) Concerto fibered coils through a low profile (0.017-inch) microcatheter in an off-label manner for transvenous embolization of indirect carotid-cavernous fistula.

Global sustainable agricultural systems are under threat, due to increasing and co-occurring drought and salinity stresses. Combined effects of these stresses on agricultural crops have traditionally been evaluated in small-scale experimental studies. Consequently, large-scale studies need to be performed to increase our understanding and assessment of the combined impacts in agricultural practice in real-life scenarios. This study aims to provide a new monitoring approach using remote-sensing observations to evaluate the joint impacts of drought and salinity on crop traits. In our tests over the Netherlands at large spatial scale (138.74 km2), we calculated five functional traits for both maize and potato from Sentinel-2 observations, namely leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (FAPAR), the fraction of vegetation cover (FVC), leaf chlorophyll content (Cab), and leaf water content (Cw). Individual and combined effects of the stresses on the seasonal dynamics in crop traits were determined using both one-way and two-way analyses of variance (ANOVAs). We found that both stresses (individual and co-occurring) affected the functional traits of both crops significantly (with R2 ranging from 0.326 to 0.796) though with stronger sensitivities to drought than to salinity. While we found exacerbating effects within co-occurrent stresses, the impact level depended strongly on the moment in the growing season. For both crops, LAI, FAPAR, and FVC dropped the most under severe drought stress conditions. The patterns for Cab and Cw were more inhibited by co-occurring drought and salinity. Consequently, our study constitutes a way towards evaluating drought and salinity impacts in agriculture, with the possibility of potential large-scale application for sustainable food security.

Abstract Using a sample of $$1.31\times 10^{9} ~J/\psi $$ 1.31×109J/ψ events collected with the BESIII detector, we perform a study of $$J/\psi \rightarrow \gamma K{\bar{K}}\eta '$$ J/ψ→γKK¯η′ . X(2370) is observed in the $$K{\bar{K}}\eta '$$ KK¯η′ invariant-mass distribution with a statistical significance of $$8.3\sigma $$ 8.3σ . Its resonance parameters are measured to be $$M=2341.6\pm 6.5 \, \text {(stat.)} \pm 5.7 \, \text {(syst.)}~ \hbox {MeV}/c^{2}$$ M=2341.6±6.5(stat.)±5.7(syst.)MeV/c2 and $$\Gamma = 117\pm 10 \, \text {(stat.)}\pm 8 \, \text {(syst.)}~\hbox {MeV}$$ Γ=117±10(stat.)±8(syst.)MeV . The product branching fractions for $$J/\psi \rightarrow \gamma X(2370),X(2370)\rightarrow K^{+}K^{-}\eta '$$ J/ψ→γX(2370),X(2370)→K+K-η′ and $$J/\psi \rightarrow \gamma X(2370),X(2370)\rightarrow K_{S}^{0}K_{S}^{0}\eta '$$ J/ψ→γX(2370),X(2370)→KS0KS0η′ are determined to be $$(1.79\pm 0.23\, \text {(stat.)}\pm 0.65\,\text {(syst.)})\times 10^{-5}$$ (1.79±0.23(stat.)±0.65(syst.))×10-5 and $$(1.18\pm 0.32\, \text {(stat.)}\pm 0.39\, \text {(syst.)})\times 10^{-5}$$ (1.18±0.32(stat.)±0.39(syst.))×10-5 , respectively. No evident signal for X(2120) is observed in the $$K{\bar{K}}\eta '$$ KK¯η′ invariant-mass distribution. The upper limits for the product branching fractions of $${\mathcal {B}}(J/\psi \rightarrow \gamma X(2120)\rightarrow \gamma K^{+} K^{-} \eta ')$$ B(J/ψ→γX(2120)→γK+K-η′) and $${\mathcal {B}}(J/\psi \rightarrow \gamma X(2120)\rightarrow \gamma K_{S}^{0} K_{S}^{0} \eta ')$$ B(J/ψ→γX(2120)→γKS0KS0η′) are determined to be $$1.49\times 10^{-5}$$ 1.49×10-5 and $$6.38\times 10^{-6}$$ 6.38×10-6 at the 90% confidence level, respectively.

Ca3Ti2O7 with Ruddlesden-Popper structure exhibits the largest polarization among the known hybrid improper ferroelectrics. However, the high Curie temperature impedes the thorough study of phase transition through dielectric characterization. According to the previous theoretical design rule, the Curie temperature can be suppressed by increasing the tolerance factor. So, in the present work, high-quality Ca3-xLaxTi2-xAlxO7 (x = 0.0, 0.1, 0.2, 0.3) ceramics with increased tolerance factors were successfully prepared. The amplitude of oxygen octahedral tilt mode indeed decreases with increasing tolerance factors, leading to a degeneration of ferroelectric polarization. However, the unexpected rigid rotation mode causes the composition-invariable coercive fields. The Curie temperatures decrease linearly with increasing x and tolerance factors. The variable-temperature dielectric constant confirms first-order improper ferroelectric transitions in Ca3Ti2O7-based ceramics. The results of variable temperature X-ray diffraction reveal the coexistence of two-phases below Curie temperature. The present work confidently confirms the first-order improper ferroelectric transition in Ca3Ti2O7-based ceramics by combining results of variable-temperature dielectric response and in-situ X-ray powder diffraction. Keywords: Hybrid improper ferroelectricity, Ca3Ti2O7, Rigid rotation mode, First-order phase transition, Dielectric response

To comprehensively evaluate the effects of the recent “2+26” regional strategy for air quality improvement, we compared the variations in PM2.5 concentrations in Beijing during four pollution episodes with different emission-reduction strategies. The 2+26 strategy implemented in March 2018 led to a mean PM2.5 concentration 16.43 % lower than that during the pollution episode in March 2013, when no specific emission-reduction measures were in place. The same 2+26 strategy implemented in November 2017 led to a mean PM2.5 concentration 32.70 % lower than that during the pollution episode in November 2016, when local emission-reduction measures were implemented. The results suggested that the effects of the 2+26 regional emission-reduction measures on PM2.5 reductions were influenced by a diversity of factors and could differ significantly during specific pollution episodes. Furthermore, we found the proportions of sulfate ions decreased significantly, and nitrate ions were the dominant PM2.5 components during the two 2+26 orange alert periods. Meanwhile, the relative contribution of coal combustion to PM2.5 concentrations in Beijing during the pollution episodes in March 2013, November 2016, November 2017 and March 2018 was 40 %, 34 %, 28 % and 11 %, respectively, indicating that the recent “Coal to Gas” project and the contingent 2+26 strategy led to a dramatic decrease in coal combustion in the Beijing–Tianjin–Hebei region. On the other hand, the relative contribution of vehicle exhaust during the 2+26 orange alert periods in November 2017 and March 2018 reached 40 % and 54 %, respectively. The relative contribution of local emissions to PM2.5 concentrations in Beijing also varied significantly and ranged from 49.46 % to 89.35 % during the four pollution episodes. These results suggested that the 2+26 regional emission-reduction strategy should be implemented with red air pollution alerts during heavy pollution episodes to intentionally reduce the dominant contribution of vehicle exhausts to PM2.5 concentrations in Beijing, while specific emission-reduction measures should be implemented accordingly for different cities within the 2+26 framework.

Alzheimer's disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, “The Virtual Brain (TVB)”, based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioural spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes.

Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the data- and computing- intensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop Distributed File System (HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data.

Endothelial cell (EC)-derived neoplasias range from benign hemangioma to aggressive metastatic angiosarcoma, which responds poorly to current treatments and has a very high mortality rate. The development of treatments that are more effective for these disorders will be expedited by insight into the processes that promote abnormal proliferation and malignant transformation of human ECs. The study of primary endothelial malignancy has been limited by the rarity of the disease; however, there is potential for carefully characterized EC lines and animal models to play a central role in the discovery, development and testing of molecular targeted therapies for vascular neoplasias. This review describes molecular alterations that have been identified in EC-derived neoplasias, as well as the processes that underpin the immortalization and tumorigenic conversion of ECs. Human EC lines, established through the introduction of defined genetic elements or by culture of primary tumor tissue, are catalogued and discussed in relation to their relevance as models of vascular neoplasia.

The classical paradigm of liver injury asserts that hepatic stellate cells (HSC) produce, remodel and turnover the abnormal extracellular matrix (ECM) of fibrosis via matrix metalloproteinases (MMPs). In extrahepatic tissues MMP production is regulated by a number of mechanisms including expression of the glycoprotein CD147. Previously, we have shown that CD147 is expressed on hepatocytes but not within the fibrotic septa in cirrhosis [1]. Therefore, we investigated if hepatocytes produce MMPs, regulated by CD147, which are capable of remodelling fibrotic ECM independent of the HSC.Non-diseased, fibrotic and cirrhotic livers were examined for MMP activity and markers of fibrosis in humans and mice. CD147 expression and MMP activity were co-localised by in-situ zymography. The role of CD147 was studied in-vitro with siRNA to CD147 in hepatocytes and in-vivo in mice with CCl4 induced liver injury using ãCD147 antibody intervention.In liver fibrosis in both human and mouse tissue MMP expression and activity (MMP-2, -9, -13 and -14) increased with progressive injury and localised to hepatocytes. Additionally, as expected, MMPs were abundantly expressed by activated HSC. Further, with progressive fibrosis there was expression of CD147, which localised to hepatocytes but not to HSC. Functionally significant in-vitro regulation of hepatocyte MMP production by CD147 was demonstrated using siRNA to CD147 that decreased hepatocyte MMP-2 and -9 expression/activity. Further, in-vivo α-CD147 antibody intervention decreased liver MMP-2, -9, -13, -14, TGF-β and α-SMA expression in CCl4 treated mice compared to controls.We have shown that hepatocytes produce active MMPs and that the glycoprotein CD147 regulates hepatocyte MMP expression. Targeting CD147 regulates hepatocyte MMP production both in-vitro and in-vivo, with the net result being reduced fibrotic matrix turnover in-vivo. Therefore, CD147 regulation of hepatocyte MMP is a novel pathway that could be targeted by future anti-fibrogenic agents.

Precise and robust localization is critical for many navigation tasks, especially autonomous driving systems. The most popular localization approach is global navigation satellite systems (GNSS). However, it has several shortcomings such as multipath and nonline- of-sight reception. Vision-based localization is one of the approaches without using GNSS which is based on vision. This paper used visual localization with a prior 3D LiDAR map. Compared to common methods for visual localization using camera-acquired maps, this paper used the method that tracks the image feature and poses of a monocular camera to match with prior 3D LiDAR maps. This paper reconstructs the image feature to several sets of 3D points by a local bundle adjustment-based visual odometry system. Those 3D points matched with the prior 3D point cloud map to track the globe pose of the user. The visual localization approach has several advantages. (1) Since it only relies on matching geometry, it is robust to changes in ambient luminosity appearance. (2) Also, it uses the prior 3D map to provide viewpoint invariance. Moreover, the proposed method only requires users to use low-cost and lightweight camera sensors.

This paper discusses ubiquitous smartphone pedestrian positioning challenges in urban canyons and GNSS-denied areas such as indoor spaces. Existing sensor-based techniques, including GNSS, INS, and VIO, have limitations that affect positioning accuracy and reliability. A machine learning-based approach is suggested to employ Support Vector Machine (SVM) to classify indoor/outdoor (IO) detection using GNSS measurement data. The proposed system integrates local estimates on VIO and 3D mapping aided (3DMA) GNSS measurements using Factor Graph Optimization (FGO) with an IO detection switch to estimate precise pose and eliminate global drift. The effectiveness of the system is evaluated through real-world experiments that produce notable outcomes.

We first use the multi-robot SLAM framework DiSCo-SLAM to evaluate the performance of cooperative SLAM based on the complicated dataset in urban scenes. Besides, we perform comparisons of single-robot SLAM and multi-robot SLAM to explore whether the cooperative framework can noticeably improve robot localization performance and the influence of inter-robot constraints in local pose graph, utilizing an identical dataset generated via the Carla simulator. Our findings indicate that under specific conditions, the integration of inter-robot constraints may effectively mitigate drift in local pose estimation. The extent to which inter-robot constraints affect the correction of local SLAM is related to various factors, such as the confidence level of the constraints and the range of keyframes imposed by the constraint.