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The Kashmir “seismic gap” in NW Himalaya is marked by hinterland‐to‐foreland reduction in GPS‐geodetic arc‐normal convergence‐velocity and increase in horizontal strain‐rate, associated with occurrence of moderate‐to‐small earthquakes. We analyze continuous waveforms from Jammu and Kashmir seismological network (2015–2017) to detect and preliminarily locate 1064 events, followed by probabilistic non‐linear relocation of 360 well‐located local earthquakes, with magnitudes 0.6–4.7 and hypocentral depths 0–60 km. Hypocenters shallower than 20 km lie on or above the Main Himalayan Thrust (MHT), clustered beneath the Kishtwar Higher‐Himalaya. Hinterlandward dipping seismicity‐clusters coincide with a MHT mid‐crustal frontal ramp, marking a ∼ ${\\sim} $50 km wide locked‐to‐creep transition. The frictionally‐locked up‐dip MHT segment is ∼ ${\\sim} $100 km wide, capable of hosting a Mw∼ ${M}_{w}\\sim $ 8.4 earthquake, if ruptured completely. Clustered seismicity SW of the Kishtwar Window and to its east illuminate MHT lateral‐ramps, which may limit the rupture width and/or modulate the rupture propagation. Entirely seismogenic underthrust Indian‐crust poses additional hazard from large earthquakes within it. Plain Language Summary In the NW Himalaya, the region between the 1905 Kangra and 2005 Kashmir earthquake rupture zones is known as the Kashmir ’seismic gap’. The last major earthquake in this “gap” was the 1555 magnitude ∼ ${\\sim} $8 event. From the measured convergence rate across the NW Himalaya it is estimated that this region has accumulated sufficient strain‐energy to cause a major‐to‐great earthquake. We established the Jammu and Kashmir seismological network in 2013 to study the seismicity in this “gap.” Continuous ground‐motion data from 2015 to 2017 has been used to detect and locate 1064 events. After visual assessment and accurate P‐ and S‐wave arrival‐time picks, we relocated earthquakes using a probabilistic non‐linear location method. These 360 well‐located local earthquakes have magnitudes of 0.6–4.7 and source depths of 0–60 km. Depth distribution of these earthquakes indicate that the entire crust deforms brittlely. The majority of these earthquakes are shallower than 20 km and are clustered beneath the Jammu‐Kishtwar Higher‐Himalaya. They originated on or above the Main Himalayan Thrust (MHT), a fault zone which separates the underthrusting Indian crust from the overriding Himalaya. The distribution of the clustered seismicity illuminates structural heterogeneities on the MHT, which could influence rupture initiation and propagation of future large earthquakes. Key Points We detect and (re)locate earthquakes with magnitudes 0.6–4.7, hypocentral depths 0–60 km, within the Kashmir “seismic gap,” NW Himalaya Shallow hypocenters (<20 km) cluster beneath Kishtwar Higher Himalaya, illuminating the Main Himalayan Thrust (MHT) frontal and lateral ramps The clustered seismicity marks a ∼50 km wide locked‐to‐creep transition on the MHT, with a ∼100 km wide frictionally locked up‐dip segment

Emissions inevitably approach zero with a “carbon law” Although the Paris Agreement's goals ( 1 ) are aligned with science ( 2 ) and can, in principle, be technically and economically achieved ( 3 ), alarming inconsistencies remain between science-based targets and national commitments. Despite progress during the 2016 Marrakech climate negotiations, long-term goals can be trumped by political short-termism. Following the Agreement, which became international law earlier than expected, several countries published mid-century decarbonization strategies, with more due soon. Model-based decarbonization assessments ( 4 ) and scenarios often struggle to capture transformative change and the dynamics associated with it: disruption, innovation, and nonlinear change in human behavior. For example, in just 2 years, China's coal use swung from 3.7% growth in 2013 to a decline of 3.7% in 2015 ( 5 ). To harness these dynamics and to calibrate for short-term realpolitik, we propose framing the decarbonization challenge in terms of a global decadal roadmap based on a simple heuristic—a “carbon law”—of halving gross anthropogenic carbon-dioxide (CO 2 ) emissions every decade. Complemented by immediately instigated, scalable carbon removal and efforts to ramp down land-use CO 2 emissions, this can lead to net-zero emissions around mid-century, a path necessary to limit warming to well below 2°C.

We introduce a novel dataset containing 3-dimensional biomechanical and wearable sensor data from 22 able-bodied adults for multiple locomotion modes (level-ground/treadmill walking, stair ascent/descent, and ramp ascent/descent) and multiple terrain conditions of each mode (walking speed, stair height, and ramp inclination). In this paper, we present the data collection methods, explain the structure of the open dataset, and report the sensor data along with the kinematic and kinetic profiles of joint biomechanics as a function of the gait phase. This dataset offers a comprehensive source of locomotion information for the same set of subjects to motivate applications in locomotion recognition, developments in robotic assistive devices, and improvement of biomimetic controllers that better adapt to terrain conditions. With such a dataset, models for these applications can be either subject-dependent or subject-independent, allowing greater flexibility for researchers to advance the field.

How do closed quantum many-body systems driven out of equilibrium eventually achieve equilibration? And how do these systems thermalize, given that they comprise so many degrees of freedom? Progress in answering these—and related—questions has accelerated in recent years—a trend that can be partially attributed to success with experiments performing quantum simulations using ultracold atoms and trapped ions. Here we provide an overview of this progress, specifically in studies probing dynamical equilibration and thermalization of systems driven out of equilibrium by quenches, ramps and periodic driving. In doing so, we also address topics such as the eigenstate thermalization hypothesis, typicality, transport, many-body localization and universality near phase transitions, as well as future prospects for quantum simulation. Statistical mechanics is adept at describing the equilibria of quantum many-body systems. But drive these systems out of equilibrium, and the physics is far from clear. Recent advances have broken new ground in probing these equilibration processes.

Neurons in the macaque lateral intraparietal (LIP) area exhibit firing rates that appear to ramp upward or downward during decision-making. These ramps are commonly assumed to reflect the gradual accumulation of evidence toward a decision threshold. However, the ramping in trial-averaged responses could instead arise from instantaneous jumps at different times on different trials. We examined single-trial responses in LIP using statistical methods for fitting and comparing latent dynamical spike-train models. We compared models with latent spike rates governed by either continuous diffusion-to-bound dynamics or discrete \"stepping\" dynamics. Roughly three-quarters of the choice-selective neurons we recorded were better described by the stepping model. Moreover, the inferred steps carried more information about the animal's choice than spike counts.

On-ramps are one of common traffic congestion scenarios, which lead to reduced time and energy efficiency and cause range anxiety for electric vehicles. Connected and automated vehicle techniques, especially platoon techniques are conducive to relieve congestion by effective communication and control. Therefore, this paper proposes a merging control strategy for platoon formation of connected and automated electric vehicles (CAEVs) to improve time and energy efficiency at-ramps. A vehicle automatic-cluster approach based on virtual rotation classifies CAEVs into multiple clusters to avoid the excessive acceleration and to reduce computational cost. A global optimal merging algorithm is designed for a clustered CAEVs to determine the optimal merging sequence and corresponding velocity trajectory, which guarantees a clustered vehicles arrive at the merging point with consistent speed and time interval to form pre-platoons. Subsequently, a consensus-based platoon controller is structured to promptly form tightly-coupled platoons with stability from the pre-platoons. The simulation for three different traffic flow states validates the effectiveness of the proposed algorithm and demonstrates its potential to improve time and energy efficiency compared to another platoon-based merging algorithm. The stability of platoons is also verified through further simulation.

Purpose>On-ramp merging areas are typical bottlenecks in the freeway network since merging on-ramp vehicles may cause intensive disturbances on the mainline traffic flow and lead to various negative impacts on traffic efficiency and safety. The connected and autonomous vehicles (CAVs), with their capabilities of real-time communication and precise motion control, hold a great potential to facilitate ramp merging operation through enhanced coordination strategies. This paper aims to present a comprehensive review of the existing ramp merging strategies leveraging CAVs, focusing on the latest trends and developments in the research field.Design/methodology/approach>The review comprehensively covers 44 papers recently published in leading transportation journals. Based on the application context, control strategies are categorized into three categories: merging into sing-lane freeways with total CAVs, merging into sing-lane freeways with mixed traffic flows and merging into multilane freeways.Findings>Relevant literature is reviewed regarding the required technologies, control decision level, applied methods and impacts on traffic performance. More importantly, the authors identify the existing research gaps and provide insightful discussions on the potential and promising directions for future research based on the review, which facilitates further advancement in this research topic.Originality/value>Many strategies based on the communication and automation capabilities of CAVs have been developed over the past decades, devoted to facilitating the merging/lane-changing maneuvers at freeway on-ramps. Despite the significant progress made, an up-to-date review covering these latest developments is missing to the authors’ best knowledge. This paper conducts a thorough review of the cooperation/coordination strategies that facilitate freeway on-ramp merging using CAVs, focusing on the latest developments in this field. Based on the review, the authors identify the existing research gaps in CAV ramp merging and discuss the potential and promising future research directions to address the gaps.

Highway exit ramps play a crucial role in ensuring the safe and efficient operation of road networks. As automated vehicles progressively integrate into highways, it is essential to investigate whether these exit ramps can accommodate the safe and efficient operation of heterogeneous traffic flows. This study constructed a basic simulation test using the SUMO simulation platform to analyze the adaptability of motorway exit ramps in a heterogeneous traffic environment. The simulation model incorporated the Krauss car-following model for the longitudinal dynamics of manual-driving vehicles, the ACC/CACC car-following model for automated vehicles, the LC2013 lane-changing model for manual-driving vehicles, and the game-theoretic lane-changing model for automated vehicles. The results reveal that in the absence of automated vehicles, the comprehensive cost is minimized with a deceleration lane length of 215 m, offering superior adaptability compared to the current standard of 180 m. As the proportion of automated vehicles gradually increases to surpass 40%, the rate of improvement in traffic flow, operational speed, and overall operational costs diminishes. Under these conditions, heterogeneous traffic flows exhibit limited adaptability to the existing road infrastructure. However, when the deceleration lane is extended to 200 m, the exit ramp shows optimal adaptability for heterogeneous traffic flows.

Mixing of initially distinct substances plays an important role in our daily lives as well as in ecological and technological worlds. From the continuum point of view, which we adopt here, mixing is complete when the substances come together across smallest flow scales determined in part by molecular mechanisms, but important stages of the process occur via the advection of substances by an underlying flow. We know how smooth flows enable mixing but less well the manner in which a turbulent flow influences it; but the latter is the more common occurrence on Earth and in the universe. We focus here on turbulent mixing, with more attention paid to the postmixing state than to the transient process of initiation. In particular, we examine turbulent mixing when the substance is a scalar (i.e., characterized only by the scalar property of its concentration), and the mixing process does not influence the flow itself (i.e., the scalar is “passive”). This is the simplest paradigm of turbulent mixing. Within this paradigm, we discuss how a turbulently mixed state depends on the flow Reynolds number and the Schmidt number of the scalar (the ratio of fluid viscosity to the scalar diffusivity), point out some fundamental aspects of turbulent mixing that render it difficult to be addressed quantitatively, and summarize a set of ideas that help us appreciate its physics in diverse circumstances. We consider the so-called universal and anomalous features and summarize a few model studies that help us understand them both.