Time Path Research Articles

Spatio-Temporal Dynamics of Carbon Emissions and Their Influencing Factors at the County Scale: A Case Study of Zhejiang Province, China

Significant carbon emissions, a key contributor to global climate warming, pose risks to ecosystems and human living conditions. It is crucial to monitor the spatial and temporal patterns of carbon emissions at the county level to reach the goals of carbon peak and neutrality. This study examines carbon emissions and economic and social problems data from 89 counties in Zhejiang Province. It employs analytical techniques such as LISA time path, spatio-temporal transition, and standard deviational ellipse to investigate the trends of carbon emissions from 2002 to 2022. Furthermore, it utilizes the GTWR model to evaluate the factors that influence these emissions on a county scale. The findings reveal the following: (1) The LISA time path analysis indicates a pronounced local spatial structure in the distribution of carbon emissions in Zhejiang Province from 2002 to 2022, characterized by increasing stability, notable path dependency, and some degree of spatial integration, albeit with a diminishing trend in overall integration. (2) The LISA spatio-temporal transition analysis indicates significant path dependency or lock-in effects in the county-level spatial clustering of carbon emissions. (3) Over the period 2002–2022, the centroid of carbon emissions in Zhejiang’s counties mainly oscillated between 120°55′15″ E and 120°57′01″ E and between 29°55′52″ N and 29°59′11″ N, with a general northeastward shift forming a “V” pattern. This shift resulted in a stable “northeast–southwest” spatial distribution. (4) Factors such as population size, urbanization rate, and economic development level predominantly accelerate carbon emissions, whereas industrial structure tends to curb them. It is crucial to customize carbon mitigation plans to suit the circumstances of each county. This study provides insight into the spatial and temporal patterns of carbon emissions at the county level in Zhejiang Province. It offers crucial guidance for developing targeted and practical strategies to reduce carbon emissions.

Designing a time path tracking stabilizing controller for a tractor-trailer robot in the presence of uncertainties

In this article, the discussion of track tracking in tractor-trailer systems with the condition of non-slip and non-holonomic multi-controllers is presented. One of the significant issues in the control of this system is the limitation and saturation of operators, which in real conditions causes serious challenges in the discussion of control and instability of the system. Therefore, the control of these systems, taking into account the considerations of the operator, is of great importance. The aim of this research is to design a stabilizing controller for the tractor-trailer robot in the presence of uncertainties and considering the limitations of operator saturation. In order to track the time path, the controller is presented in two kinematic and dynamic stages. The focus of this research is on the application of operator restrictions in dynamic controllers, and two control approaches are presented for that. Dynamic modeling of the robot is done using Lagrange’s equations. At first, in order to use the anti-jackknife control of the nonlinear tractor-trailer system, it is estimated by identifying the system with a linear and uncertain system. This article investigates the use of neural networks to solve kinematic problems in an omnidirectional mobile robot. The neural network is trained to learn the speed of desired angles of the robot tires, corresponding to different routes. The results show that the neural network can control the robot’s movement without dynamic and kinematic knowledge. In the next section, a neural network is used as compensation for the malfunction of the operators in a feedback linear controller in order to prevent the saturation of the operators. The weights of the neural network are updated in such a way that the stability of the system is guaranteed using the Lyapunov candidate. This article investigates the use of neural networks to solve kinematic problems in an omnidirectional mobile robot. The neural network is trained to learn the speed of desired angles of the robot tires, corresponding to different routes. The results show that the neural network can control the robot’s movement without dynamic and kinematic knowledge. Also, a comparison has been made to validate the method used in Adams and MATLAB software and the simulation conditions of the proposed method with a reference article. The simulation results show the efficiency of the designed controllers. According to the investigations carried out, it can be seen from the angular and planar error of the robot that the neural network method has a better performance than the comparative methods with an approximation between 10% and 15% and the anti-jackknife method also because of the reference method and also The anti-jack knife shows between 13% and 16% due to the complexity of the angular movement of the steering wheel and the rotation and connections of the trailer to the tractor.

Proximity Queries on Point Clouds using Rapid Construction Path Oracle

The prevalence of computer graphics technology boosts the developments of point clouds in recent years, which offer advantages over terrain surfaces (represented by Triangular Irregular Networks, i.e., TINs) in proximity queries, including the shortest path query, the k-Nearest Neighbor (kNN) query and the range query. Since (1) all existing on-the-fly and oracle-based shortest path query algorithms on a TIN are very expensive, (2) all existing on-the-fly shortest path query algorithms on a point cloud are still not efficient, and (3) there are no oracle-based shortest path query algorithms on a point cloud, we propose an efficient (1+ε)-approximate shortest path oracle that answers the shortest path query for a set of Points-Of-Interests (POIs) on the point cloud, which has a good performance (in terms of the oracle construction time, oracle size and shortest path query time) due to the concise information about the pairwise shortest paths between any pair of POIs stored in the oracle. Our oracle can be easily adapted to answering the shortest path query for any points on the point cloud if POIs are not given as input, and also achieve a good performance. Then, we propose efficient algorithms for answering the (1+ε)-approximate kNN and range query with the assistance of our oracle. Our experimental results show that when POIs are given (resp. not given) as input, our oracle is up to 390 times, 30 times and 6 times (resp. 500 times, 140 times and 50 times) better than the best-known oracle on a TIN in terms of the oracle construction time, oracle size and shortest path query time, respectively. Our algorithms for the other two proximity queries are both up to 100 times faster than the best-known algorithms.

Economic growth in China and India: The potential role of population

From 1980 through 2020, the rate of population growth declined in both China and India, but the decline was far more pronounced in China. During the same period, per capita income increased substantially in both China and India, but the increase was far more pronounced in China. The fact that China and India are similar in many important respects (ancient cultures, large populations, etc.), but implemented substantially different population control policies during the 1980–2020 interval, suggests an equivalence to a quasi-controlled experiment, of the sort that very rarely occurs in the real world. The control would be India, with a relatively conventional population control policy, and the experiment would be China, with its relatively drastic population control policy. This research investigates the possibility of a causal relation between differential population growth and differential economic growth in China and India. It is shown that the simulation of a basic economic growth model in which population growth is a key exogenous determinant, and which utilizes the same economic relationships and numerical parameter values for both China and India, produces time paths of growth in per capita income that closely resemble the empirical Chinese and Indian time paths. This finding supports the hypothesis that a significant factor in China's remarkable economic growth over the last four decades has been its equally remarkable population control policy.

Memorized Rapidly Exploring Random Tree Optimization (MRRTO): An Enhanced Algorithm for Robot Path Planning

Abstract With the advancement of the robotics world, many path-planning algorithms have been proposed. One of the important algorithms is the Rapidly Exploring Random Tree (RRT) but with the drawback of not guaranteeing the optimal path. This paper solves this problem by proposing a Memorized RRT Optimization Algorithm (MRRTO Algorithm) using memory as an optimization step. The algorithm obtains a single path from the start point, and another from the target point to store only the last visited new node. The method for computing the nearest node depends on the position, when a new node is added, the RRT function checks if there is another node closer to the new node rather than that is closer to the goal point. Simulation results with different environments show that the MRRTO outperforms the original RRT Algorithm, graph algorithms, and metaheuristic algorithms in terms of reducing time consumption, path length, and number of nodes used.

Optimization of group control of robots in Arctic conditions

The article discusses the principles of optimization of group control of robots in Arctic conditions. The concept of optimization in the classical formulation is inextricably linked with the basic concept of efficiency, which is the most complete and comprehensive characteristic of any purposeful system and expresses the expected degree of achievement of the goals of its functioning. Efficiency can be considered an integral, generalized characteristic of the system, covering all the properties, relationships and connections between its parts and elements for all time stages, paths and goals of functioning. The effectiveness of any system is quantified using an integral indicator or a set of partial indicators.

Door to door space-time path planning of intercity multimodal transport network using improved ripple-spreading algorithm

The main objective of this study is to solve the problem of recommending door-to-door space–time paths in an intercity multimodal transport network. The available modes include railway, metro, bus, rapid-bus, and walking. In terms of mathematical modeling, the reconstructed space–time path planning model considers multiple constraints, such as acyclic constraint, flow balance constraint and transfer constraints, to ensure the viability of the path, which is significantly different from the path in the traditional graph theory and road network. To attain the K shortest paths in two situations, the improved ripple-spreading algorithm (hereafter referred to as IRSA) is introduced, which is inspired by the ripple-spreading phenomenon and can find global optimal paths through only one iteration. A method for reducing the search space is applied to speed up the computing process. We demonstrate the feasibility and effectiveness of the proposed model and algorithm by comparing it with Gurobi Solver and Dijkstra algorithm using a small-scale network. The experiment results are evaluated by solving the door-to-door itinerary problem from Taiyuan to Beijing. Results indicate that the proposed approach can find constrained K shortest paths within an acceptable computing time. A set of experiments are conducted to test the performance of different methods, and results show that the speed-up method provides a significant improvement in problem-solving efficiency, while attempting to guarantee path optimality, which could reduce the CPU time by 51% to 92% for the tested OD pairs.

Paleoproterozoic ultrahigh‐temperature mafic granulites with a high‐pressure prograde path from the Alxa Block: Implications on the tectonic evolution of the Khondalite Belt, North China Craton

AbstractUltrahigh‐temperature (UHT) granulites, a prominent feature of Paleoproterozoic orogenic belts, preserve a record of geodynamic processes during the Precambrian (Archean–Paleoproterozoic). Quantitative pressure–temperature–time (P–T–t) paths of these UHT granulites can constrain the tectonic processes and metamorphic evolution in such a tectonic regime. Here, UHT mafic granulites with a high‐pressure (HP) prograde path are first reported in the Diebusige Complex in the Alxa Block, western part of the Khondalite Belt (KB), North China Craton (NCC). The detailed petrographic studies show that two mafic granulite samples preserve corona textures around relict garnet or garnet pseudomorphs (completely replaced by plagioclase), and a third mafic granulite sample has a relatively simple mineralogy with a granoblastic‐polygonal texture. These mafic granulites have similar peak (Tmax) assemblages of clinopyroxene + orthopyroxene + plagioclase + ilmenite ± garnet ± amphibole ± quartz + melt. Phase equilibrium modelling and Ti‐in‐amphibole and rare earth element (REE)‐based thermometries all constrain similar peak conditions of ~880–950°C/~8.5–10 kbar implying an ~100°C/kbar apparent geothermal gradient for these mafic granulites. Based on the corona textures or pseudomorphs of garnet and mineral assemblages, we identified a Pmax (~14 kbar) prograde stage before the Tmax stage. Thus, a clockwise P–T path with heating and decompression followed by near‐isobaric cooling (IBC) is recorded from these UHT mafic granulites. In addition, zircon and apatite SHRIMP or LA–ICP–MS U–Pb dating yields an age interval of ~1.81–1.7 Ga, which is interpreted as representing the cooling time from ~900–800°C to ~575°C at the middle‐upper crustal levels (<25 km deep) for these mafic granulites, with an ~1.5–2.5°C/Myr cooling rate. The new P–T–t path of these rocks includes high‐pressure prograde, UHT peak, and slow cooling retrograde processes, which implicates a post‐collisional tectonic setting for UHT metamorphism in the KB and the processes of collision, exhumation, and cooling of the KB.

Training simulator efficacy in developing thoracic and general surgical skills in a residency programme: a pilot study.

Virtual training simulators have been introduced in several surgical disciplines to improve residents' abilities. Through the use of the LapSim® virtual training simulator (Surgical Science, Göteborg, Sweden), this study aims to plan an effective learning path in minimally invasive thoracic and general surgery. All thoracic and general surgery trainees in their 1st and 2nd year of residency at the University of Insubria were enrolled and randomized into 2 groups: residents undergoing an intensive twice-a-week virtual training programme (group A: n = 8) and those undergoing a once-weekly non-intensive virtual training programme (group B: n = 9). The virtual training programme was divided into 4 modules, each of 12 weeks. In the 1st module, trainees repeated grasping, cutting, clip application, lifting and grasping, and fine dissection exercises during each training session. Seal-and-cut exercise was performed as the initial and final test. Data on surgical manoeuvres (time and on mistakes) were collected; intra- and inter-group comparisons were planned. No significant differences were observed between groups A and B at the 1st session, confirming that the 2 groups had similar skills at the beginning. After 12 weeks, both groups showed improvements, but comparing data between initial and final test, only Group A registered a significant reduction in total time (P-value = 0.0015), left (P-value = 0.0017) and right (P-value = 0.0186) instrument path lengths, and in left (P-value = 0.0010) and right (P-value = 0.0073) instrument angular path lengths, demonstrating that group A acquired greater precision in surgical manoeuvres. Virtual simulator training programme performed at least twice a week was effective for implementing basic surgical skills required for the trainee's professional growth. Additional virtual training modules focused on more complex exercises are planned to confirm these preliminary results.

Frame potential of Brownian SYK model of Majorana and Dirac fermions

We consider the Brownian SYK, i.e. a system of N Majorana (Dirac) fermions with a white-noise q-body interaction term. We focus on the dynamics of the Frame potentials, a measure of the scrambling and chaos, given by the moments of the overlap between two independent realisations of the model. By means of a Keldysh path-integral formalism, we compute its early and late-time value. We show that, for q > 2, the late time path integral saddle point correctly reproduces the saturation to the value of the Haar frame potential. On the contrary, for q = 2, the model is quadratic and consistently we observe saturation to the Haar value in the restricted space of Gaussian states (Gaussian Haar). The latter is characterised by larger system size corrections that we correctly capture by counting the Goldstone modes of the Keldysh saddle point. Finally, in the case of Dirac fermions, we highlight and resolve the role of the global U(1) symmetry.

Government expenditure, informality, and economic growth: Evidence from Eastern and Southern African countries

AbstractThis study investigates the dynamic linkages among government expenditure, informality, and economic growth in 15 Eastern and Southern African countries over a 25‐year period during 1991–2015. The analysis exploits panel vector autoregressive models to construct the time path of one variable following an orthogonal shock to another variable using impulse response functions. The analysis is complemented by panel quantile regressions to investigate the potential role of informality in the expenditure–economic growth nexus. The findings are threefold. First, both government consumption and investment expenditure have positive and significant effects on real per capita GDP. Second, informality has a direct negative impact on real per capita GDP. Third, the informal sector dampens the impact of government expenditure on real per capita GDP. These results are robust to the inclusion of selected control variables and are unchanged across GDP quantiles. The study's findings suggest that government policies aimed at reducing informality can have direct impacts on economic growth and indirect impacts through improvements in the effectiveness of government expenditure.

Real-time dynamics of false vacuum decay

We investigate false vacuum decay of a relativistic scalar field initialized in the metastable minimum of an asymmetric double-well potential. The transition to the true ground state is a well-defined initial-value problem in real time, which can be formulated in nonequilibrium quantum field theory on a closed time path. We employ the nonperturbative framework of the two-particle irreducible (2PI) quantum effective action at next-to-leading order in a large-N expansion. We also compare to classical-statistical field theory simulations on a lattice in the high-temperature regime. By this, we demonstrate that the real-time decay rates are comparable to those obtained from the conventional Euclidean (bounce) approach. In general, we find that the decay rates are time dependent. For a more comprehensive description of the dynamics, we extract a time-dependent effective potential, which becomes convex during the nonequilibrium transition process. By solving the quantum evolution equations for the one- and two-point correlation functions for vacuum initial conditions, we demonstrate that quantum corrections can lead to transitions that are not captured by classical-statistical approximations. Published by the American Physical Society 2024

Research on Intelligent Analysis Model of Tourism Consumers’ Purchase Intention and Its Application Practice in E-commerce Platforms

Abstract Tourism consumption, as a special type of daily consumption, has significant features that distinguish it from other types of consumption. The purpose of this paper is to summarize and extract the feature engineering in tourism consumption, encode the feature engineering in tourism consumption behaviors using the Transformer model, and predict consumer purchase intention. After the performance evaluation of the Transformer-based consumer purchase intention analysis model constructed in this paper, it is applied to the e-commerce platform, taking the dynamic pricing strategy of the e-commerce platform as the entry point and analyzing the application effect of the model in the optimal inventory time path, the optimal price of the platform, and the expected revenue. The transformer-based consumer purchase intention analysis model in this paper is significantly better than other prediction models in AUC, accuracy, precision, recall, and F1. When applied to e-commerce platforms, this paper’s model helps to realize the inventory equilibrium state between inbound enterprises and e-commerce platforms, which improves the overall revenue of both parties. When time t=40 or inventory n=30, the optimal online store price of the platform after the model is lower than the price before the model is applied, and consumers can enjoy a better purchasing experience. t=40 or n=30, the total expected revenue of both parties after the model is higher than the total expected revenue before the model is applied. This paper shows that the Transformer-based consumer purchase intention analysis model can positively impact the dynamic pricing of e-commerce platforms.

Metamorphic evolution of high-pressure and ultrahigh-temperature granulites from the Alxa Block, North China Craton: Implications for the collision and exhumation of Paleoproterozoic orogenic belts

High-pressure (HP) and ultrahigh-temperature (UHT) granulites with a high geothermal gradient (greater than 500 °C/GPa) are prominent features of Paleoproterozoic orogenic belts and may represent paired metamorphic belts present during the early stages of plate tectonics. Understanding their pressure−temperature−time (P-T-t) paths and metamorphic evolutionary relationships could provide valuable constraints on the tectonic processes of Paleoproterozoic orogenic belts. Here, we describe garnet mafic and clinopyroxene-orthopyroxene (Cpx-Opx) granulites from the Diebusige area of the Alxa Block in the western part of the Khondalite Belt, North China Craton. Through detailed petrographic, phase equilibrium modeling, and Ti-in-amphibole thermometric studies, we obtained the preserved peak mineral assemblages of two types of mafic granulites: garnet + clinopyroxene + amphibole + plagioclase + quartz + ilmenite, and clinopyroxene + orthopyroxene + plagioclase + amphibole + garnet (rare) + ilmenite. The preserved peak P-T conditions were determined to be 850−890 °C/11.4−13.2 kbar (HP granulite-facies) and 950−970 °C/8.2−9.2 kbar (UHT conditions), with thermal gradients of ∼70 °C/kbar (moderate differential temperature/differential pressure, dT/dP) and ∼110 °C/kbar (high dT/dP), respectively. Using sensitive high-resolution ion microprobe U-Pb dating and rare earth element analysis of zircons, we found that the garnet mafic granulite recorded an HP granulite-facies metamorphic age of ca. 1.95 Ga and a retrograde cooling age of ca. 1.8 Ga, while the Cpx-Opx granulite recorded a consistent retrograde cooling age of ca. 1.8 Ga. By combining these results with the metamorphic evolution and timing (ca. 1.92−1.91 Ga) of UHT rocks from the Khondalite Belt, we suggest that the garnet (HP) mafic and Cpx-Opx (UHT) granulites may represent different stages of the same metamorphic event, shedding light on the processes involved in the collision and subsequent exhumation of Paleoproterozoic orogenic belts.

Formulation and heuristic method for urban cold-chain logistics systems with path flexibility – The case of China

The focus of this paper is on achieving a win-win situation regarding the economic, environmental, and social impacts of the cold chain logistics terminal distribution system. This paper proposes three multi-objective models to investigate the above effects by incorporating soft time windows, heterogeneous fleets, and path flexibility, with defining the objectives of examining logistics costs, fuel consumption, carbon emissions, quality damage to perishable commodities, and customer satisfaction using six evaluation functions. To solve the proposed models, an efficient optimization framework is developed by combining domain operators with versatile multi-objective evolutionary algorithms (MOEA) to obtain Pareto solutions. Extensive experiments are conducted to test the validity of the concerned model and algorithms. The results demonstrate that: (1) the proposed algorithm is effective in solving the proposed model; (2) the proposed multi-path strategy can effectively improve the performance of cold-chain logistics systems compared to single-path strategies; (3) evaluation functions that assess customer satisfaction greatly affect the performance of cold-chain logistics systems; and (4) the trade-off relationship between the objectives should be investigated to define the model. The paper also provides valuable managerial insights for improving the efficiency and sustainability of cold-chain logistics operations.

노년의 시간성과 자긍심의 정치적 가능성

This article sheds light on the marginalization of elderly characters in Wan-Seo Park's novels about old age, a phenomenon attributed to their illness and disability. The elderly characters find themselves ensnared in the normative time path, helplessly subjected to the violence of preemptive attack and alienation imposed upon them. On the other hand, some people recognize aging as a disease and disability and even embrace their identity as a person with a disability. This article analyzes the intersectionality of aging and disability and explore the potential for resistance among those who confront discriminatory violence through temporal and affective lenses.
 In 「Twilight」, an elderly character is referred to as “old man” in a preemptive manner that compels them to acknowledge his shameful existence. Through this, it was affirmed that life in old age is governed by the impact of existential power, which preemptively controls and manages society and culture as a whole. In contrast, the characters in 「Reunion of Evening」 and 「The Twilight at Dusk」 derive the strength to embrace themselves by means of positivity and pride in their own bodies. This study has found that the affirmation of the ‘sick body’ and appropriation of the sense of heterogeneity or queerness within the community can be a means of resisting the violence of healing.
 Wan-Seo Park's novel about aging attempts to challenge the dominant system that excludes the sick elderly body by redefining the female body. Moreover, the suggestion is put forth that when the elderly body is acknowledged as an entity with its own temporality, the perception of disability identity in old age can undergo a transformation.

Pursuit Path Planning for Multiple Unmanned Ground Vehicles Based on Deep Reinforcement Learning

Path planning plays a crucial role in the execution of pursuit tasks for multiple unmanned ground vehicles (multi-UGVs). Although existing popular path-planning methods can achieve the pursuit goals, they suffer from some drawbacks such as long computation time and excessive path inflection points. To address these issues, this paper combines gradient descent and deep reinforcement learning (DRL) to solve the problem of excessive path inflection points from a path-smoothing perspective. In addition, the prioritized experience replay (PER) method is incorporated to enhance the learning efficiency of DRL. By doing so, the proposed model integrates PER, gradient descent, and a multiple-agent double deep Q-learning network (PER-GDMADDQN) to enable the path planning and obstacle avoidance capabilities of multi-UGVs. Experimental results demonstrate that the proposed PER-GDMADDQN yields superior performance in the pursuit problem of multi-UGVs, where the training speed and smoothness of the proposed method outperform other popular algorithms. As a result, the proposed method enables satisfactory path planning for multi-UGVs.

Speech Perception Improvement Algorithm Based on a Dual-Path Long Short-Term Memory Network.

Current deep learning-based speech enhancement methods focus on enhancing the time-frequency representation of the signal. However, conventional methods can lead to speech damage due to resolution mismatch problems that emphasize only specific information in the time or frequency domain. To address these challenges, this paper introduces a speech enhancement model designed with a dual-path structure that identifies key speech characteristics in both the time and time-frequency domains. Specifically, the time path aims to model semantic features hidden in the waveform, while the time-frequency path attempts to compensate for the spectral details via a spectral extension block. These two paths enhance temporal and spectral features via mask functions modeled as LSTM, respectively, offering a comprehensive approach to speech enhancement. Experimental results show that the proposed dual-path LSTM network consistently outperforms conventional single-domain speech enhancement methods in terms of speech quality and intelligibility.

Hierarchical Area-Based and Path-Based Heuristic Approaches for Multirobot Coverage Path Planning with Performance Analysis in Surveillance Systems.

In this study, we present a systematic exploration of hierarchical designs for multirobot coverage path planning (MCPP) with a special focus on surveillance applications. Unlike conventional studies centered on cleaning tasks, our investigation delves into the realm of surveillance problems, specifically incorporating the sensing range (SR) factor equipped on the robots. Conventional path-based MCPP strategies considering SR, primarily rely on naive approaches, generating nodes (viewpoints) to be visited and a global path based on these nodes. Therefore, our study proposes a general MCPP framework for surveillance by dealing with path-based and area-based structures comprehensively. As the traveling salesman problem (TSP) solvers, our approach incorporates not the naive approach but renowned and powerful algorithms such as genetic algorithms (GAs), and ant colony optimization (ACO) to enhance the planning process. We devise six distinct methods within the proposed MCPP framework. Two methods adopt area-based approaches which segments areas via a hierarchical max-flow routing algorithm based on SR and the number of robots. TSP challenges within each area are tackled using a GA or ACO, and the result paths are allocated to individual robots. The remaining four methods are categorized by the path-based approaches with global-local structures such as GA-GA, GA-ACO, ACO-GA, and ACO-ACO. Unlike conventional methods which requires a global path, we further incorporate ACO- or GA-based local planning. This supplementary step at the local level enhances the quality of the path-planning results, particularly when dealing with a large number of nodes, by preventing any degradation in global path-planning outcomes. An extensive comparative analysis is conducted to evaluate the proposed framework based on execution time, total path length, and idle time. The area-based approaches tend to show a better execution time and overall path length performance compared to the path-based approaches. However, the path-based MCPP methods have the advantage of having a smaller idle time than the area-based MCPP methods. Our study finds that the proposed area-based MCPP method excels in path planning, while the proposed path-based MCPP method demonstrates superior coverage balance performance. By selecting an appropriate MCPP structure based on the specific application requirements, leveraging the strengths of both methodologies, efficient MCPP execution becomes attainable. Looking forward, our future work will focus on tailoring these MCPP structures to diverse real-world conditions, aiming to propose the most suitable approach for specific applications.

Pressure–temperature–time path of ultrahigh-pressure metamorphic terranes constrained by zircon petrochronology: A case study of orthogneisses from the northern Sulu belt, China

Reconstruction of pressure–temperature–time (P–T–t) path of volumetrically-dominant orthogneisses in deeply subducted continental crusts is challenging due to their simple amphibolite-facies mineral assemblages. To provide new insights into this issue, a detailed study of mineral inclusions, trace elements and U-Pb ages within robust zircon containers, together with titanite U-Pb dating and petrology were conducted on ultrahigh-pressure (UHP) orthogneisses from the northern Sulu belt. The following P–T–t conditions of three metamorphic stages were constrained: (i) cathodoluminescence (CL)-grey zircon cores (ca. 238–237 Ma) with flat heavy rare earth element (HREE) patterns include garnet, phengite and omphacite that yield estimated P–T conditions of 2.3–2.8 GPa/730–800 °C, corresponding to prograde high-pressure (HP) metamorphism before the UHP peak. (ii) CL-dark zircon mantles (ca. 224 Ma) contain multiphase solid inclusions of plagioclase + K-feldspar + quartz and show extremely low Th/U ratios (≤0.08) and negative Eu anomalies, demonstrating their crystallization from hydrous melt at 2.0–1.8 GPa/890–820 °C during HP eclogite-facies exhumation. In addition, titanite grains are characterized by high LREE contents (1939–5239 ppm), Th/U ratios (0.37–0.81) and crystallization temperatures (830–801 °C), suggesting their peritectic origin. The lower intercept age (220 ± 4 Ma) of titanite is broadly consistent with the ages of the zircon mantles, indicating that they both crystallized from the melt at the same stage. (iii) CL-bright zircon rims (ca. 212–211 Ma) preserve low-pressure mineral inclusions of plagioclase, K-feldspar and quartz, and they show low Th/U ratios (0.03–0.12) and steep HREE patterns with negative Eu anomalies, witnessing amphibolite-facies overprinting at 1.0–0.8 GPa/610–550 °C. Combined with the metamorphic peak conditions of 3.2 GPa/840 °C at ca. 232 Ma constrained by previous studies from the same area, the P–T–t path of the northern Sulu UHP belt is reconstructed. The data reflects two-stage exhumation of this terrane, including an early-stage (5.8 km/m.y.) and a later-stage slower (3.1 km/m.y.) exhumation processes. The faster exhumation during early decompression was probably enhanced by extensive melting activity recorded by the CL-dark zircon mantles from the orthogneisses.