Intensity prediction of tropical cyclones (TC) has been one of the major challenges for the operational forecast and warning service, as well as consequential assessment of impacts including high ...winds, storm surge and heavy rainfall caused by TC. With the advances in global numerical weather prediction (NWP) modelling systems, TC track and intensity forecasts for medium range are available every 6 or 12 h, and ensemble prediction system (EPS) outputs provide various scenarios for producing probabilistic forecasts. The TC intensity forecast from the EPS of the European Centre for Medium‐Range Weather Forecasts (ECMWF) has shown systematic negative biases, although the performance is better than other global models in general. A machine learning model based on XGBoost, a decision‐tree‐based machine learning algorithm, is introduced in this paper to post‐process ECMWF EPS outputs and generate an improved forecast of TC intensity. The predictors such as selected percentiles of ensemble members in maximum wind and minimum pressure of previous TC cases were applied in the XGBoost model to generate a calibrated forecast for the maximum wind of TCs. Verification of the XGBoost model was made using TCs over the western North Pacific during 2016–2019. It is found that the negative biases of the intensity forecast from ECMWF EPS and HRES can be reduced with improvement in the overall accuracy.
A machine learning model based on XGBoost is introduced to post‐process ECMWF EPS and HRES outputs and generate an improved forecast of TC intensity. The predictors such as selected percentiles of ensemble members in maximum wind and minimum pressure of previous TC cases were applied in the XGBoost model to generate a calibrated forecast for the maximum wind of TCs. It is found that the negative biases of the intensity forecast from ECMWF EPS and HRES can be reduced with improvement in the overall accuracy.
This study proposes a framework to diagnose stock market crashes and predict the subsequent price rebounds. Based on the observation of anomalous changes in stock correlation networks during market ...crashes, we extend the log-periodic power-law model with a metric that is proposed to measure network anomalies. To calculate this metric, we design a prediction-guided anomaly detection algorithm based on the extreme value theory. Finally, we proposed a hybrid indicator to predict price rebounds of the stock index by combining the network anomaly metric and the visibility graph-based log-periodic power-law model. Experiments are conducted based on the New York Stock Exchange Composite Index from 4 January 1991 to 7 May 2021. It is shown that our proposed method outperforms the benchmark log-periodic power-law model on detecting the 12 major crashes and predicting the subsequent price rebounds by reducing the false alarm rate. This study sheds light on combining stock network analysis and financial time series modeling and highlights that anomalous changes of a stock network can be important criteria for detecting crashes and predicting recoveries of the stock market.
Our ultimate goal of in vitro derivation of Schwann cells (SCs) from adult bone marrow stromal cells (BMSCs) is such that they may be used autologously to assist post-traumatic nerve regeneration. ...Existing protocols for derivation of SC-like cells from BMSCs fall short in the stability of the acquired phenotype and the functional capacity to myelinate axons. Our experiments indicated that neuro-ectodermal progenitor cells among the human hBMSCs could be selectively expanded and then induced to differentiate into SC-like cells. Co-culture of the SC-like cells with embryonic dorsal root ganglion neurons facilitated contact-mediated signaling that accomplished the switch to fate-committed SCs. Microarray analysis and in vitro myelination provided evidence that the human BMSC-derived SCs were functionally mature. This was reinforced by repair and myelination phenotypes observable in vivo with the derived SCs seeded into a nerve guide as an implant across a critical gap in a rat model of sciatic nerve injury.
•A protocol for in vitro derivation of fate-committed SCs from human BMSCs•The derived human SCs were functionally capable of myelination in vitro•The derived human SCs guided axonal regrowth and formed compact myelin in vivo
Ying-Shing Chan and colleagues demonstrate that the human bone marrow harbors neuro-ectodermal progenitors that can be enriched, expanded, and directed to differentiate into functionally mature, fate-committed SCs. This work holds promise for further development into an autologous cell source for implantation as a treatment strategy for nerve injuries or peripheral neuropathies.
Vestibular information processed first by the brainstem vestibular nucleus (VN), and further by cerebellum and thalamus, underlies diverse brain function. These include the righting reflexes and ...spatial cognitive behaviour. While the cerebellar and thalamic circuits that decode vestibular information are known, the importance of VN neurons and the temporal requirements for their maturation that allow developmental consolidation of the aforementioned circuits remains unclear. We show that timely unsilencing of glutamatergic circuits in the VN by NMDA receptor-mediated insertion of AMPAR receptor type 1 (GluA1) subunits is critical for maturation of VN and successful consolidation of higher circuits that process vestibular information. Delayed unsilencing of NMDA receptor-only synapses of neonatal VN neurons permanently decreased their functional connectivity with inferior olive circuits. This was accompanied by delayed pruning of the inferior olive inputs to Purkinje cells and permanent reduction in their plasticity. These derangements led to deficits in associated vestibular righting reflexes and motor co-ordination during voluntary movement. Vestibular-dependent recruitment of thalamic neurons was similarly reduced, resulting in permanently decreased efficiency of spatial navigation. The findings thus show that well-choreographed maturation of the nascent vestibular circuitry is prerequisite for functional integration of vestibular signals into ascending pathways for diverse vestibular-related behaviours.
•Unsilencing of NMDAR-only synapses in the vestibular nucleus at postnatal day 5–7 enables integration of inputs into ascending pathways.•Delayed unsilencing hinders pruning of inputs to Purkinje cells and reduces plasticity, impacting postural and motor coordination.•Such delay impairs cerebellar and thalamic connectivity of vestibular inputs into adulthood, with impacts on spatial cognition.
Local energy markets (LEMs) use online platforms and smart grid technologies to incentivize and coordinate a local supply of spatially-distributed renewable energy resources, which may not be ...directly controllable by power system operators. Socio-economic values are increasingly noted as prominent motivations for expected LEM users, but socio-economic aspects of user decision-making or market outcomes are not considered in current LEM mechanism design analyses. Here, agent-based simulation is used to analyze expected socio-economic outcomes from LEM operation under a double-sided auction with uniform pricing. The environment is modeled as a virtual LEM platform, operating independently from the underlying power grid. Socio-economic market inputs are produced by income-preference heterogeneous agents, and market outcomes are evaluated by two key socio-economic metrics: energy affordability, and market access. When LEM prices are not restricted to a common range considered by all agents (e.g., between external retail market prices), access disparities may arise; LEM price restriction addresses consumer disparities, but energy affordability gaps are expected to remain. The magnitude of affordability gaps is notably reduced, and bill assistance programs may eliminate remaining gaps, but a mechanism that efficiently realizes socio-economic standards for energy affordability may also reduce expected LEM operation costs. Remaining research gaps are noted, and LEM support for equitable and sustainable energy infrastructure is emphasized.
Efficient accurate Gaussian localization is an important topic in many applications, e.g. localization based super-resolution microscopy and image scanning microscopy, which requires large-scale ...Gaussian patterns localization for accurate super-resolution image reconstruction. Existing Gaussian localization methods usually require high signal-to-noise image and the existing standard fitting algorithm usually requires manually inputting a good initial value for all parameters, which could be not convenient to use and difficult to guarantee high robustness for large-scale Gaussian localizations with a computer. It would be even more challenge to detect all the Gaussian patterns with high-dynamic-range of amplitudes, as well as to estimate a good initial value for all parameters for efficient Gaussian fitting and guarantee high robustness of the localization algorithm for low signal-to-noise ratio image data with strong background. In this paper, we propose an efficient Gaussian patterns detection technique and a robust Gaussian fitting method for accurate Gaussian fitting without initial estimation. In our technique, a fast Pearson correlation algorithm is proposed to improve the efficiency of the calculation of normalized cross correlation for large scale object detection with template matching. By introducing blind background estimation, a modified iterative least-squares Gaussian fitting algorithm without initials estimation is proposed for robust Gaussian fitting with noisy data with strong background. The simulation shows that the performance of the proposed detection technique is high for low SNR image and an efficiency improvement of 27% can be achieved; the proposed Gaussian fitting algorithm is capable of calculating all parameters without initial estimation, and the resulting fitting accuracy is very close to exiting standard methods, which indicates that image signal-to-noise ratio higher than 10dB is required to obtain subpixel accuracy.
Self-similar growth and fractality are important properties found in many real-world networks, which could guide the modeling of network evolution and the anticipation of new links. However, in ...technology-convergence networks, such characteristics have not yet received much attention. This study provides empirical evidence for self-similar growth and fractality of the technology-convergence network in the field of intelligent transportation systems. This study further investigates the implications of such fractal properties for link prediction via partial information decomposition. It is discovered that two different scales of the network (i.e., the micro-scale structure measured by local similarity indices and the scaled-down structure measured by community-based indices) have significant synergistic effects on link prediction. Finally, we design a synergistic link prediction (SLP) approach which enhances local similarity indices by considering the probability of link existence conditional on the joint distribution of two scales. Experimental results show that SLP outperforms the benchmark local similarity indices in most cases, which could further validate the existence and usefulness of the synergistic effect between two scales on link prediction.
This paper studies the temporal evolution of the size distribution of China's listed companies. We first identify a Pareto distribution for the upper-tail distribution. Unexpectedly, we observe that ...overall the Pareto coefficients decreased over the years from 2001 to 2013, which has not been reported previously in the literature. In particular, the Pareto coefficients dropped significantly during 2001 to 2008, and then fluctuated at the lowest level after 2008. A decreasing Pareto coefficient implies that the firm size inequality of the China's listed companies continuously increases during these years. By analyzing the relationship between the growth and size of firms based on a panel data model, we find that one possible reason causing the Pareto coefficients to decrease is that large firms grow faster than small ones, which is in particularly true during the non-tradable shares reform period. Furthermore, estimation results of the panel data model show that after 2008 large firms grew not as fast as they would before 2008, indicating a possible negative outcome due to the global financial crisis, which affected the growth of large firms. In addition, we examine the newly listed companies and discover that the newly listed companies with size greater than the lower bound of Pareto distribution also contribute to the decrease of the Pareto coefficients.
•The size distribution of China’s listed companies measured by Pareto coefficients decreased from 2001 to 2013.•One possible reason for the decreasing of Pareto coefficients is that large firms grow faster in China.•The non-tradable shares reform and 2008 global financial crisis have great effects on the firm size-growth relationship.•Newly listed companies also contribute to the decrease of the Pareto coefficients.
At vertebrate neuromuscular junctions (NMJs), the synaptic basal lamina contains different extracellular matrix (ECM) proteins and synaptogenic factors that induce and maintain synaptic ...specializations. Here, we report that podosome-like structures (PLSs) induced by ubiquitous ECM proteins regulate the formation and remodeling of acetylcholine receptor (AChR) clusters via focal ECM degradation. Mechanistically, ECM degradation is mediated by PLS-directed trafficking and surface insertion of membrane-type 1 matrix metalloproteinase (MT1-MMP) to AChR clusters through microtubule-capturing mechanisms. Upon synaptic induction, MT1-MMP plays a crucial role in the recruitment of aneural AChR clusters for the assembly of postsynaptic specializations. Lastly, the structural defects of NMJs in embryonic MT1-MMP
mice further demonstrate the physiological role of MT1-MMP in normal NMJ development. Collectively, this study suggests that postsynaptic MT1-MMP serves as a molecular switch to synaptogenesis by modulating local ECM environment for the deposition of synaptogenic signals that regulate postsynaptic differentiation at developing NMJs.
Biomass, which its conversion into greener bio-based products, is able to achieve a more balanced carbon cycle through circular utilisation. The development of biomass industry, therefore, appears to ...be one priority area and is an important step to motivate the global circular economy and sustainability. However, due to the existence of commercialisation barriers, the biomass industry in developing country, such as Malaysia, is not on par with the increment of the country's gross domestic product. This paper overviews the barriers of development and challenges encountered by the biomass industry in Malaysia. Challenges are classified into four barrier categories, i.e., (i) technical barrier, (ii) financial barrier, (iii) social awareness barrier and (iv) misunderstanding and gaps between stakeholders. Based on the barriers identified, recommendations which embrace the areas of technology innovation, logistics management, interaction between academia and industry, policy and enforcement, social impact and international benchmarking, are proposed. These recommendations can act as good references for the development of biomass industry in Malaysia and reflection for other developing countries with biomass resources in the promotion of sustainability and commercialisation of biomass products. The role of the five key stakeholders in commercialising the biomass technologies are highlighted in this review.
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•Biomass industry in Malaysia is not well-developed due to several barriers.•Four key barriers categories are introduced and discussed.•Recommendations which address the identified barriers are proposed.•The stakeholders' role in the future deployment of biomass industry are highlighted.•Collaborative engagement of all stakeholders is the key success factor.
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