To tackle the long-standing dilemma between energy waste and thermal comfort in building HVAC operation, we develop a two-month sliding-window evaluation framework for deep reinforcement learning (DRL) controllers and, for the first time, embed it into a lightweight physics environment centred on heat-balance and equipment-efficiency equations. The simulator preserves physical interpretability while accelerating training by an order of magnitude; the control granularity is refined from 1h to 30min, and diurnal periodic features are added to strengthen temporal priors. Four algorithms—SAC, TD3, DDPG, and PPO—are trained independently on six consecutive windows and quantitatively compared over the entire year and by cold/hot seasons using three metrics: total energy consumption, comfort-violation ratio, and temperature-stability index. Results show that the three off-policy methods converge within 200 episodes, whereas on-policy PPO requires about 18000 iterations. SAC reduces annual energy use to 3.51MWh (42.8% below the rule-based baseline), confines the overall comfort-violation ratio to 0.11%, and achieves the lowest temperature fluctuation; TD3 attains the best summer comfort, while DDPG and PPO prove more sensitive to load variation. Hence, SAC occupies the annual and seasonal Pareto optimum across the energy–comfort–stability triad. The proposed framework offers a verifiable and transferable physical foundation for rapid screening and engineering deployment of DRL-based HVAC controllers.

Autonomous Underwater Vehicle (AUV)-assisted Underwater Wireless Sensor Networks (UWSNs) offer transformative potential for data collection, disaster prediction and environmental monitoring. However, these networks remain critically vulnerable to malware attacks due to inherent communication challenges and limited energy resources. To rigorously capture the spatiotemporal dynamics of malware propagation, this paper introduces a novel two-layer heterogeneous model rooted in Partial Differential Equations (PDEs). To suppress malware propagation under limited bandwidth, this paper proposes a spatiotemporal optimal control strategy integrating saturation functions. The existence of the optimal pair is demonstrated by the minimizing sequence method, and the first-order necessary condition for spatiotemporal control is derived based on convex perturbation methods. Further, extensive numerical experiments validate the effectiveness of the proposed two-layer heterogeneous model and spatiotemporal control strategy. Finally, the applications of the proposed model and control strategy in real-world scenarios are discussed.

Internet of Underwater Things (IoUT), widely utilized in data collection, ocean exploration and disaster prevention, are prone to malware attacks. To accurately describe the spatiotemporal dynamics of malware propagation in IoUT, a new Reaction-Diffusion Sleep Control Model (RDSCM) based on Partial Differential Equations (PDEs) is established in this paper. To target heavily infected regions and optimize control costs, a spatiotemporal hybrid optimal control strategy is implemented. First, based on the formulation of the spatiotemporal optimal control problem, the existence, uniqueness, and some estimates of the strong solution of the controlled system are obtained using the truncation method and semigroup theory. Then, the existence of the optimal pair is verified through the minimization sequence technique. Subsequently, the first-order necessary optimality condition for the optimal control problem of PDEs is derived by proving the differentiability of the control-to-state mapping. To validate the effectiveness of the proposed model and control methodology, three comparative experimental studies are conducted. Finally, some discussions are provided on the extension and application of the proposed method.

Abstract Fog computing is an emerging paradigm that supplies storage, computation, and networking resources between traditional cloud data centers and end devices. This article focuses on the resource provisioning problem in collaborative fog computing for multiple delay‐sensitive users. Our goal is to implement a resource provisioning strategy for network operators to minimize the total monetary cost by considering the deadline and capacity constraints. Two scenarios are considered: unlimited‐processor fog nodes (UPFN) and limited‐processor fog nodes (LPFN). In either scenario, we prove that the resource provisioning problem is NP‐hard. First, we consider the UPFN scenario that the processors of fog nodes are unlimited and users' requests can be ideally processed in parallel. Two algorithms are proposed which greedily delete fog nodes based on the local or global collaborative influences until there is no feasible provisioning to guarantee the deadline of users. Then we extend the resource provisioning problem to a more realistic and complicated scenario LPFN in which the scheduling delay cannot be ignored. Two types of tasks are considered. One is the arbitrarily divided tasks, and a near‐optimal solution bounded by has been found. m is the number of fog nodes, and is the upper bound on the Lipschitz constant of the delay function. Another one is the application‐driven tasks, and we propose a heuristic algorithm. Extensive experiments validate the efficiency of the proposed algorithms.

Contextualized neural language models have gained much attention in Information Retrieval (IR) with its ability to achieve better text understanding by capturing contextual structure. However, to achieve better document understanding, it is necessary to involve global structure of a document. In this paper, we take the advantage of Graph Convolutional Networks (GCN) to model global word-relation structure of a document to improve context-aware document ranking. We propose to build a graph for a document to model the global structure. The nodes and edges of the graph are constructed from contextual embeddings. Then we apply graph convolution on the graph to learning a new representation, and this representation covers both contextual and global structure information. The experimental results show that our method outperforms the state-of-the-art contextual language models, which demonstrate that incorporating global structure is useful for improving document ranking and GCN is an effective way to achieve it.

Abdominal electrocardiogram is an important means to obtain fetal health condition during high-risk pregnancy. In this paper, a novel method for extracting fetal heart rate from multi-channel mother abdomen electrocardiograms is proposed using fast multivariate empirical mode decomposition technique (FMEMD). Firstly, FMEMD decomposes the multichannel ECG signals into a set of modes. Two significant channels are selected according to the standard deviation of the fifth layer. Then the continuous wavelet transform technique (CWT) is applied to these two channels to denoise. The baseline is removed by zero-crossing rate. Following, the interference of the mother QRS complexes and non-overlapped fetal R-peaks can be eliminated and detected by CWT coefficient. The overlapped fetal R-peaks are obtained by combining the dynamic pattern matching program and creative algorithm. The proposed method achieves an accuracy of 99.9% on the existing data set, and the calculating time is only 1/6.39 of the MEMD-based method.

Contextualized neural language models have gained much attention in Information Retrieval (IR) with its ability to achieve better word understanding by capturing contextual structure on sentence level. However, to understand a document better, it is necessary to involve contextual structure from document level. Moreover, some words contributes more information to delivering the meaning of a document. Motivated by this, in this paper, we take the advantages of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAN) to model global word-relation structure of a document with attention mechanism to improve context-aware document ranking. We propose to build a graph for a document to model the global contextual structure. The nodes and edges of the graph are constructed from contextual embeddings. We first apply graph convolution on the graph and then use attention networks to explore the influence of more informative words to obtain a new representation. This representation covers both local contextual and global structure information. The experimental results show that our method outperforms the state-of-the-art contextual language models, which demonstrate that incorporating contextual structure is useful for improving document ranking.

We propose a salient-context based semantic matching method to improve relevance ranking in information retrieval. We first propose a new notion of salient context and then define how to measure it. Then we show how the most salient context can be located with a sliding window technique. Finally, we use the semantic similarity between a query term and the most salient context terms in a corpus of documents to rank those documents. Experiments on various TREC collections show the effectiveness of our model compared to the state-of-the-art methods.

In this paper, we propose a time-aware approach to promoting textual retrieval performance for Electronic Health Record (EHR) search. The proposed approach focuses on identifying patients cohorts from the perspective of EHR temporal correlation. First, an EHR temporal profile is created according to EHR distribution on time interval for each patient. Second, the temporal similarity is computed and used as a feature for discovering temporal cohorts. In each cohort, the highest-ranked profile in textual retrieval is considered as the centroid, and a temporal relevance score is computed by multiplying temporal similarity with the textual relevance of the centroid. Finally, the temporal relevance is combined linearly with the textual relevance for re-ranking. Extensive experiments are conducted to demonstrate the effectiveness of the proposed approach in promoting retrieval performance for EHR search.