Previous Work Research Articles

Variations in Student Approaches to Problem Solving in Undergraduate Biology Education.

Existing research has investigated student problem-solving strategies across science, technology, engineering, and mathematics; however, there is limited work in undergraduate biology education on how various aspects that influence learning combine to generate holistic approaches to problem solving. Through the lens of situated cognition, we consider problem solving as a learning phenomenon that involves the interactions between internal cognition of the learner and the external learning environment. Using phenomenography as a methodology, we investigated undergraduate student approaches to problem solving in biology through interviews. We identified five aspects of problem solving (including knowledge, strategy, intention, metacognition, and mindset) that define three qualitatively different approaches to problem solving; each approach is distinguishable by variations across the aspects. Variations in the knowledge and strategy aspects largely aligned with previous work on how the use or avoidance of biological knowledge informed both concept-based and nonconcept-based strategies. Variations in the other aspects revealed intentions spanning complete disengagement to deep interest with the course material, different degrees of metacognitive reflections, and a continuum of fixed to growth mindsets. We discuss implications for how these characterizations can improve instruction and efforts to support development of problem-solving skills.

In-vehicle battery capacity fade: a follow-up study on six European regions

This work presents a follow up of previous case studies from the authors on the estimation of the in-vehicle battery capacity fade with performance-based models of Lithium-ion batteries applied to real-world vehicles’ activity data. The mobility pattern database is significantly enlarged compared to previous works from the authors, encompassing now six European regions, including 508,609 private vehicles which corresponds to 1.78 billion GPS records, 9.1 million trips and parking events and a total driven distance of 106.1 million kilometres. The capacity fade is derived at given years and mileage thresholds under different driving and recharging scenarios, supporting the concept of setting minimum performance requirements for traction batteries in future road transport policies. The vast majority of the considered scenarios do not lead to a battery capacity drop below 80% of its nominal value in less than five calendar years or 100,000 driven kilometres and below 70% in less than 8 years or 160,000 driven kilometres. The results of this work contributed to inform the new UN ECE Global Technical Regulation No. 22 on in-vehicle battery durability.

Nasopharyngeal carcinoma with non-squamous phenotype may be a variant of nasopharyngeal squamous cell carcinoma after inhibition of EGFR/PI3K/AKT/mTOR pathway.

Nasopharyngeal carcinoma (NPC) is a cancerous tumor that develops in the nasopharynx epithelium and typically has squamous differentiation. The squamous phenotype is evident in immunohistochemistry, with diffuse nuclear positivity for p63 and p40. Nonetheless, a few NPCs have been identified by clinicopathological diagnosis that do not exhibit the squamous phenotype; these NPCs are currently referred to as non-squamous immunophenotype nasopharyngeal carcinomas (NSNPCs). In a previous work, we have revealed similarities between the histological appearance, etiology, and gene alterations of NSNPC and conventional NPC. According to ultrastructural findings, NSNPC still falls under the category of non-keratinized squamous cell carcinoma that is undifferentiated. NSNPC has an excellent prognosis and a low level of malignancy, according to a retrospective investigation. Based on prior research, we investigated the molecular mechanism of NSNPC not expressing the squamous phenotype and its biological behavior. IHC was used to determine the expression of EGFR, PI3K, AKT, p-AKT, mTOR, p-mTOR, Notch, STAT3 and p-STAT3 in a total of 20 NSNPC tissue samples and 20 classic NPC tissue samples. We obtained human NPC cell lines (CNE-2,5-8F) and used EGFR overexpression plasmid and shRNAs to transfect them. To find out whether mRNA and proteins were expressed in the cells, we used Western blotting and qRT-PCR. Cell biological behavior was discovered using the CCK-8 assay, cell migration assay, and cell invasion assay. EGFR, PI3K, p-AKT and p-mTOR proteins were lowly expressed in NSNPC tissues by immunohistochemistry, compared with classical NPC. In the classical NPC cell lines CNE-2 and 5-8F, overexpression EGFR can up-regulate the expression of p63 through the PI3K/AKT/mTOR pathway, and promote the proliferation, migration, and invasion of nasopharyngeal carcinoma cells. At the same time, knockout of EGFR can down-regulate p63 expression through the PI3K/AKT/mTOR pathway, and inhibit the proliferation, migration, and invasion of nasopharyngeal carcinoma cells. The lack of p63 expression in NSNPC was linked with the inhibition of the EGFR/PI3K/AKT/mTOR pathway, and NSNPC may be a variant of classical NPC.

A Deep Learning Framework for Start-End Frame Pair-Driven Motion Synthesis.

A start-end frame pair and a motion pattern-based motion synthesis scheme can provide more control to the synthesis process and produce content-various motion sequences. However, the data preparation for the motion training is intractable, and concatenating feature spaces of the start-end frame pair and the motion pattern lacks theoretical rationality in previous works. In this article, we propose a deep learning framework that completes automatic data preparation and learns the nonlinear mapping from start-end frame pairs to motion patterns. The proposed model consists of three modules: action detection, motion extraction, and motion synthesis networks. The action detection network extends the deep subspace learning framework to a supervised version, i.e., uses the local self-expression (LSE) of the motion data to supervise feature learning and complement the classification error. A long short-term memory (LSTM)-based network is used to efficiently extract the motion patterns to address the speed deficiency reflected in the previous optimization-based method. A motion synthesis network consists of a group of LSTM-based blocks, where each of them is to learn the nonlinear relation between the start-end frame pairs and the motion patterns of a certain joint. The superior performances in action detection accuracy, motion pattern extraction efficiency, and motion synthesis quality show the effectiveness of each module in the proposed framework.

Weakly Supervised Video Anomaly Detection via Self-Guided Temporal Discriminative Transformer.

Weakly supervised video anomaly detection is generally formulated as a multiple instance learning (MIL) problem, where an anomaly detector learns to generate frame-level anomaly scores under the supervision of MIL-based video-level classification. However, most previous works suffer from two drawbacks: 1) they lack ability to model temporal relationships between video segments and 2) they cannot extract sufficient discriminative features to separate normal and anomalous snippets. In this article, we develop a weakly supervised temporal discriminative (WSTD) paradigm, that aims to leverage both temporal relation and feature discrimination to mitigate the above drawbacks. To this end, we propose a transformer-styled temporal feature aggregator (TTFA) and a self-guided discriminative feature encoder (SDFE). Specifically, TTFA captures multiple types of temporal relationships between video snippets from different feature subspaces, while SDFE enhances the discriminative powers of features by clustering normal snippets and maximizing the separability between anomalous snippets and normal centers in embedding space. Experimental results on three public benchmarks indicate that WSTD outperforms state-of-the-art unsupervised and weakly supervised methods, which verifies the superiority of the proposed method.

Reversible assembly and disassembly of V-ATPase during the lysosome regeneration cycle.

Regulation of the luminal pH of late endocytic compartments in continuously fed mammalian cells is poorly understood. Using normal rat kidney fibroblasts, we investigated the reversible assembly/disassembly of the proton pumping V-ATPase when endolysosomes are formed by kissing and fusion of late endosomes with lysosomes and during the subsequent reformation of lysosomes. We took advantage of previous work showing that sucrosomes formed by the uptake of sucrose are swollen endolysosomes from which lysosomes are reformed after uptake of invertase. Using confocal microscopy and subcellular fractionation of NRK cells stably expressing fluorescently tagged proteins, we found net recruitment of the V1 subcomplex during sucrosome formation and loss during lysosome reformation, with a similar time course to RAB7a loss. Addition of invertase did not alter mTORC1 signalling, suggesting that the regulation of reversible V-ATPase assembly/disassembly in continuously fed cells differs from that in cells subject to amino acid depletion/refeeding. Using live cell microscopy, we demonstrated recruitment of a fluorescently tagged V1 subunit during endolysosome formation and a dynamic equilibrium and rapid exchange between the cytosolic and membrane bound pools of this subunit. We conclude that reversible V-ATPase assembly/disassembly plays a key role in regulating endolysosomal/lysosomal pH in continuously fed cells.

Modeling Rationality: Toward Better Performance Against Unknown Agents in Sequential Games.

Opponent modeling is necessary for autonomous agents to capture the intents of others during strategic interactions. Most previous works assume that they can access enough interaction history to build the model. However, it may not be realistic. To solve this problem, we present a novel rationality-consistent opponent modeling (ROM) method for games with imperfect information. In our approach, a game-theoretical concept of consistence about rationality is proposed to take advantage of the characteristic of imperfect information sequential games that rational behavior at disjoint information sets is correlated through anticipated opponent's behavior. With the correlation between different information sets, agents could infer the opponents' strategies at information sets correlated to observed behavior. To exploit the correlation, ROM attempts to conduct reasoning from the opponent's perspective and rationalize its past behavior. In this way, ROM acquires the ability to better adapt to different opponents and achieves a more accurate opponent model with insufficient observation history, which is verified by experiments in different settings. A heuristic adaptation approach is also applied in ROM, which updates the opponent model in an online manner and significantly reduces the computation cost. We evaluate ROM in both a grid world game and a poker game. Compared with other opponent modeling methods, ROM shows better performance and has more accurate predictions in both games against different types of opponents with limited action interactions. Experimental results also show that ROM's time cost is significantly reduced through heuristic adaptation.

Learning to Detect Attended Objects in Cultural Sites with Gaze Signals and Weak Object Supervision

Cultural sites such as museums and monuments are popular tourist destinations worldwide. Visitors come to these places to learn about the cultures, histories, and arts of a particular region or country. However, for many cultural sites, traditional visiting approaches are limited and may fail to engage visitors. To enhance visitors’ experiences, previous works have explored how wearable devices can be exploited in this context. Among the many functions that these devices can offer, understanding which artwork or detail the user is attending to is fundamental to provide additional information on the observed artworks, understand the visitor’s tastes, and provide recommendations. This motivates the development of algorithms for understanding visitor attention from egocentric images. We considered the attended object detection task, which involves detecting and recognizing the object observed by the camera wearer, from an input RGB image and gaze signals. To study the problem, we collect a dataset of egocentric images collected by subjects visiting a museum. Since collecting and labeling data in cultural sites for real applications is a time-consuming problem, we present a study comparing unsupervised, weakly supervised, and fully supervised approaches for attended object detection. We evaluate the considered approaches on the collected dataset, assessing also the impact of training models on external datasets such as COCO and EGO-CH. The experiments show that weakly supervised approaches requiring only a 2D point label related to the gaze can be an effective alternative to fully supervised approaches for attended object detection. To encourage research on the topic, we publicly release the code and the dataset at the following url: https://iplab.dmi.unict.it/EGO-CH-Gaze/ .

Reinforcement Learning Based Approaches to Adaptive Context Caching in Distributed Context Management Systems

Real-time applications increasingly rely on context information to provide relevant and dependable features. Context queries require large-scale retrieval, inferencing, aggregation, and delivery of context using only limited computing resources, especially in a distributed environment. If this is slow, inconsistent, and too expensive to access context information, the dependability and relevancy of real-time applications may fail to exist. This paper argues, transiency of context (i.e., the limited validity period), variations in the features of context query loads (e.g., the request rate, different Quality of Service (QoS), and Quality of Context (QoC) requirements), and lack of prior knowledge about context to make near real-time adaptations as fundamental challenges that need to be addressed to overcome these shortcomings. Hence, we propose a performance metric driven reinforcement learning based adaptive context caching approach aiming to maximize both cost- and performance-efficiency for middleware-based Context Management Systems (CMSs). Although context-aware caching has been thoroughly investigated in the literature, our approach is novel because existing techniques are not fully applicable to caching context due to (i) the underlying fundamental challenges and (ii) not addressing the limitations hindering dependability and consistency of context. Unlike previously tested modes of CMS operations and traditional data caching techniques, our approach can provide real-time pervasive applications with lower cost, faster, and fresher high quality context information. Compared to existing context-aware data caching algorithms, our technique is bespoken for caching context information, which is different from traditional data. We also show that our full-cycle context lifecycle-based approach can maximize both cost- and performance-efficiency while maintaining adequate QoC solely based on real-time performance metrics and our heuristic techniques without depending on any previous knowledge about the context, variations in query features, or quality demands, unlike any previous work. We demonstrate using a real world inspired scenario and a prototype middleware based CMS integrated with our adaptive context caching approach that we have implemented, how realtime applications that are 85% faster can be more relevant and dependable to users, while costing 60.22% less than using existing techniques to access context information. Our model is also at least twice as fast and more flexible to adapt compared to existing benchmarks even under uncertainty and lack of prior knowledge about context, transiency, and variable context query loads.

Entity Footprinting: Modeling Contextual User States via Digital Activity Monitoring

Our digital life consists of activities that are organized around tasks and exhibit different user states in the digital contexts around these activities. Previous works have shown that digital activity monitoring can be used to predict entities that users will need to perform digital tasks. There have been methods developed to automatically detect the tasks of a user. However, these studies typically support only specific applications and tasks, and relatively little research has been conducted on real-life digital activities. This article introduces user state modeling and prediction with contextual information captured as entities, recorded from real-world digital user behavior, called entity footprinting —a system that records users’ digital activities on their screens and proactively provides useful entities across application boundaries without requiring explicit query formulation. Our methodology is to detect contextual user states using latent representations of entities occurring in digital activities. Using topic models and recurrent neural networks, the model learns the latent representation of concurrent entities and their sequential relationships. We report a field study in which the digital activities of 13 people were recorded continuously for 14 days. The model learned from this data is used to (1) predict contextual user states and (2) predict relevant entities for the detected states. The results show improved user state detection accuracy and entity prediction performance compared to static, heuristic, and basic topic models. Our findings have implications for the design of proactive recommendation systems that can implicitly infer users’ contextual state by monitoring users’ digital activities and proactively recommending the right information at the right time.

Linear and Centralised Net Vaults

This article presents an objective, point-cloud-based geometric description of the nave, apse, and sacristy vaults in the fortified church of Mediaş (RO; Medgyes, Mediasch). The description includes the global geometry of the rib systems and the connections between the webs and ribs in each vault. Based on these, we classify these structures into the geometry-based typologies for the global geometry of the rib system and the rib-web connections we established based on our previous works. Additionally, based on the vaults of Mediaş, two rib-system subtypes have been defined: centralising and linear vaults. The relationship between these subtypes and the concept of cylindric and spheric vaults, as well as their connection to the ‘pattern’ of the net vaults is discussed. Based on the geometric descriptions, we also discuss the possible building strategies and techniques of these vaults and consider the periodisation of the building.

Examining perceptions and outcomes of AI versus human course assistant discussions in the online classroom

ABSTRACT As technologies such as artificial intelligence (AI) and other forms of machine communication become more popular and readily available, the opportunities for use in an online class increase. This replication and extension sought to understand and test the use of AI versus human communication in an online learning space—specifically the learning management system (LMS) discussion board. More specifically, the differences in student perceptions and outcomes between those with a human course assistant and those assigned to an AI course assistant were examined. These perceptions were studied through the presence and absence of the identity heuristic and helper heuristic as outlined in previous literature. The study employed a four-condition randomized quasiexperimental design. Outcomes of interest included credibility, task attraction, social attraction, computer-mediated competence, student communication satisfaction, affect toward instructor, and affect toward taking classes with the instructor. Findings supported previous work regarding importance of helper heuristics and highlighted the preference for a human over AI/machine educational agent.

Design and Testing of a New Microinjector With Capacitive Force Sensor for Biological Microinjection

Microinjection with force sensing plays an important role in delivering foreign materials into biological entities. In this paper, a new force-sensing microinjector with compliant mechanism is presented. It provides a high sensitivity of force sensing by offering a small stiffness in the direction of microinjection. Meanwhile, it enables a sufficient load-bearing capability thanks to a large stiffness in the lateral direction. The small stiffness is realized by the combination of a positive-stiffness mechanism and a negative-stiffness mechanism, rather than a zero-stiffness mechanism. A custom-made capacitive force sensor based on tilted micropillar array is introduced to measure the microinjection force based on the microinjector’s output displacement. Moreover, a prototype has been fabricated for experimental testing. Experimental results show that the sensitivity of the proposed design has been improved by two-fold over previous work. The developed microinjector has been applied to successfully detect the microinjection force during the injection of zebrafish larvae, which renders a promising solution for regulating the microinjection force. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Force-assisted microinjection can provide force feedback, which is desirable to protect biological samples from excessive force and to judge successful penetration. Currently, most existing force sensor-based microinjectors can only puncture biological samples, rather than injecting materials, mainly due to the inability of bearing the weight of linker for the injection needle and actuator. In this work, a new microinjector with capacitive force sensor is presented for microinjection of zebrafish larvae. Based on the combination of a positive-stiffness mechanism and a negative-stiffness mechanism, a small-stiffness mechanism is introduced to design the microinjector. It simultaneously provides large axial sensitivity and large lateral loading ability. Moreover, a capacitive force sensor based on tilted micropillar array is devised to measure the microinjection force based on the overall displacement of the microinjector. For fixing zebrafhish larvae, a micro-array petri dish is fabricated by silicon wafer mould. Experimental results show that the microinjector is capable of delivering foreign materials and detecting the microinjection force, which is beneficial to the microinjection operation.

Improving Pareto Local Search Using Cooperative Parallelism Strategies for Multiobjective Combinatorial Optimization.

Pareto local search (PLS) is a natural extension of local search for multiobjective combinatorial optimization problems (MCOPs). In our previous work, we improved the anytime performance of PLS using parallel computing techniques and proposed a parallel PLS based on decomposition (PPLS/D). In PPLS/D, the solution space is searched by multiple independent parallel processes simultaneously. This article further improves PPLS/D by introducing two new cooperative process techniques, namely, a cooperative search mechanism and a cooperative subregion-adjusting strategy. In the cooperative search mechanism, the parallel processes share high-quality solutions with each other during the search according to a distributed topology. In the proposed subregion-adjusting strategy, a master process collects useful information from all processes during the search to approximate the Pareto front (PF) and redivide the subregions evenly. In the experimental studies, three well-known NP-hard MCOPs with up to six objectives were selected as test problems. The experimental results on the Tianhe-2 supercomputer verified the effectiveness of the proposed techniques.

Decentralized Scheduling for Concurrent Tasks in Mobile Edge Computing via Deep Reinforcement Learning

Mobile Edge Computing (MEC) is a promising solution to enhance the computing capability of resource-limited networks. A fundamental problem in MEC is efficiently offloading tasks from user devices to edge servers. However, there still exists a gap to deploy in real-world environments: 1) traditional centralized approaches needs complete information of edge network, ignoring the communication costs generated by synchronization, 2) previous works do not consider concurrent computation on edge servers, which may cause dynamic changes in the environment, and 3) the scheduling algorithm should deliver individualized decisions for different users independently and with high efficiency To solve this mismatch, we studied a multi-user task offloading problem where user devices make offloading decisions independently. We consider the concurrent execution of tasks and formulate a non-divisible and delay-aware task offloading problem to jointly minimize the dropped task ratio and long-term latency. We propose a decentralized task scheduling algorithm based on DRL that makes offloading decisions without knowing the information of other user devices. We employ Double-DQN, Dueling-DQN, Prioritized Replay Memory, and Recurrent Neural Network (RNN) techniques to improve the algorithm's performance. The results of simulation experiments show that our method can significantly reduce the long-term latency and dropped task ratio compared to the baseline algorithms.

Enhancing link prediction efficiency with shortest path and structural attributes

Link prediction is one of the most essential and crucial tasks in complex network research since it seeks to forecast missing links in a network based on current ones. This problem has applications in a variety of scientific disciplines, including social network research, recommendation systems, and biological networks. In previous work, link prediction has been solved through different methods such as path, social theory, topology, and similarity-based. The main issue is that path-based methods ignore topological features, while structure-based methods also fail to combine the path and structured-based features. As a result, a new technique based on the shortest path and topological features’ has been developed. The method uses both local and global similarity indices to measure the similarity. Extensive experiments on real-world datasets from a variety of domains are utilized to empirically test and compare the proposed framework to many state-of-the-art prediction techniques. Over 100 iterations, the collected data showed that the proposed method improved on the other methods in terms of accuracy. SI and AA, among the existing state-of-the-art algorithms, fared best with an AUC value of 82%, while the proposed method has an AUC value of 84%.

Online Learning Aided Decentralized Multi-User Task Offloading for Mobile Edge Computing

Mobile edge computing facilitates users to offload computation tasks to edge servers for meeting their stringent delay requirements. Previous works mainly explore task offloading when system-side information is given (e.g., server processing speed, cellular data rate), or centralized offloading under system uncertainty. But both generally fall short of handling task placement involving many coexisting users in an uncertain environment. In this paper, we develop a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-user</i> offloading framework considering <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unknown yet stochastic</i> system-side information to enable a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decentralized user-initiated</i> service placement under overlapping server coverage. Specifically, we formulate the dynamic task placement as an online multi-user multi-armed bandit process, and propose a decentralized epoch based offloading (DEBO) to optimize user rewards which are subjected under network delay. We consider both cases without and with neighboring edge feedback once users' tasks are processed, where the latter incorporates system-side information sharing among edge servers for an enhanced task placement. For both cases, we show that DEBO can gradually deduce the optimal user-server assignment during dynamic offloading, thereby achieving a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">close-to-optimal</i> service performance and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tight</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\log _{2} T)$</tex-math></inline-formula> regret. Moreover, we generalize DEBO to various common scenarios such as unknown reward gap, dynamic entering or leaving of clients, and fair reward distribution, while further exploring when users' offloaded tasks require <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heterogeneous</i> computing resources. Particularly, we accomplish a sub-linear regret for each of these instances. Real measurements based evaluations corroborate the superiority of our offloading schemes over state-of-the-art approaches in optimizing delay-sensitive rewards.

Few-Shot Object Detection With Self-Supervising and Cooperative Classifier.

Few-shot object detection (FSOD), which detects novel objects with only a few training instances, has recently attracted more attention. Previous works focus on making the most use of label information of objects. Still, they fail to consider the structural and semantic information of the image itself and solve the misclassification between data-abundant base classes and data-scarce novel classes efficiently. In this article, we propose FSOD with Self-Supervising and Cooperative Classifier ( [Formula: see text]) approach to deal with those concerns. Specifically, we analyze the underlying performance degradation of novel classes in FSOD and discover that false-positive samples are the main reason. By looking into these false-positive samples, we further notice that misclassifying novel classes as base classes are the main cause. Thus, we introduce double RoI heads into the existing Fast-RCNN to learn more specific features for novel classes. We also consider using self-supervised learning (SSL) to learn more structural and semantic information. Finally, we propose a cooperative classifier (CC) with the base-novel regularization to maximize the interclass variance between base and novel classes. In the experiment, [Formula: see text] outperforms all the latest baselines in most cases on PASCAL VOC and COCO.

SGTR+: End-to-End Scene Graph Generation With Transformer.

Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property. Most previous works adopt a bottom-up, two-stage or point-based, one-stage approach, which often suffers from high time complexity or suboptimal designs. In this paper, we propose a novel SGG method to address the aforementioned issues, formulating the task as a bipartite graph construction problem. To address the issues above, we create a transformer-based end-to-end framework to generate the entity and entity-aware predicate proposal set, and infer directed edges to form relation triplets. Moreover, we design a graph assembling module to infer the connectivity of the bipartite scene graph based on our entity-aware structure, enabling us to generate the scene graph in an end-to-end manner. Based on bipartite graph assembling paradigm, we further propose a new technical design to address the efficacy of entity-aware modeling and optimization stability of graph assembling. Equipped with the enhanced entity-aware design, our method achieves optimal performance and time-complexity. Extensive experimental results show that our design is able to achieve the state-of-the-art or comparable performance on three challenging benchmarks, surpassing most of the existing approaches and enjoying higher efficiency in inference.

Climatological Observation and Model Simulation of Near-Surface Hourly Maximum Gust Wind in Northern China

Abstract While previous work on the climatology of Northern China has focused on mean wind speed, wind gusts have received comparatively less attention but are equally important to various users. In this paper, an observed hourly maximum gust wind speeds (HMGS) dataset across North China has been created by using time series from 174 meteorological stations. The dataset offers superior quality, high spatiotemporal near-surface HMGS series for North China spanning from 2015 to 2022. The objective of this study is first to improve our understanding of the spatiotemporal gusts climatology in North China by analyzing the observed gust data. Second, we aim to supplement the observational data by using gust analysis and forecast data with a high spatial–temporal resolution from model simulations. The spatial characteristics of the seasonal cycle of the simulated analysis of mean HMGS and the performance in predicting gusts based on the geographical locations and elevations of the validation stations were investigated by comparing it with the observations. Results indicate the following: 1) Wind direction and intensity are affected by the terrain and climate conditions of different weather stations. Stations situated along the Bohai Bay coastal region and at higher-elevation areas of North China exhibit a higher mean HMGS than those located in the coastal and inland plains. 2) The probability density function curves for wind speed and wind direction exhibit notable variations across different elevation intervals. The contribution of moderate and strong gust wind speeds increases gradually with increasing altitude, while the gust directions in mountainous areas exhibit relatively consistent patterns due to the increased exposure to synoptic-scale forcing at higher elevations. 3) The nowcasting prediction system analysis of mean HMGS provides a higher horizontal resolution that is capable of capturing the contrasts between land and sea, as well as the influence of high HMGS associated with large-scale circulations in high-elevation regions. Significance Statement The purpose of this study is to better understand the spatiotemporal gust climatology in North China and the performance of the model-simulated gust analysis and forecast data. This is important because gusts conditions differ due to varying topographic and climatic conditions of different weather stations. Our results provide a valuable insight into the climatological variations of HMGS, their drivers, and identify the deficiencies in the model-simulation gusts.