Spatial prediction of environmental suitability for vector-borne disease transmission is crucial for public health, with spatial connectivity being a critical factor. However, existing models often face a trade-off between model interpretability and the accurate representation of spatial connectivity. Simpler, interpretable models tend to oversimplify connectivity, while more advanced models can lack interpretability and often rely on difficult-to-obtain fine-grained mobility data. To address this challenge, this paper proposes an integrated framework (GES-SC) for the spatial prediction of environmental suitability for vector-borne disease transmission, which combines Geographical Environmental Similarity (GES) with quantified Spatial Connectivity (SC). The method operates on the principle that environmentally similar and highly spatially connected locations have similar transmission potential. It quantifies connectivity using available road network data and distance decay, providing an effective alternative to direct mobility data. A case study in Guangzhou, China, demonstrates that integrating this quantified spatial connectivity enhances prediction accuracy. Compared to Geographically Weighted Regression, Bayesian Conditional Autoregressive, XGBoost, and a Simple Neural Network, the GES-SC method performed better in cross-validation, achieving lower RMSE and MAE, and a higher R2. The proposed framework effectively addresses the accuracy-interpretability trade-off, improving spatial epidemiological prediction accuracy and providing an uncertainty measure for public health decisions.

The data consistency protocol is a core component of distributed data services that provide fault-tolerance and data consistency across distributed data centers and even edge networks. Raft is a popular approach due to its ease of implementation and superior performance. However, Raft adopts a sequential log entry processing strategy, where log entries without dependencies are not allowed to be processed in parallel, limiting system performance in high-concurrency scenarios. To address this challenge, researchers propose Raft-based protocols that supportout-of-orderapply(OOApply), which is called OORaft. Existing OORaft protocols adopt the Paxos-style election and replication process to merge missing entries on leader candidates. It leads to problems such as extra overhead on dependency analysis, availability when the network is partitioned, and incomplete correctness verification. This paper proposes aconciseparalleledRaftprotocol called CP-Raft, which is the first OORaft protocol to focus on dependency analysis overhead and to use full TLA$^+$validation for the leader election process. Specifically, 1) CP-Raft proposes a Raft-aligned three-step election method that significantly simplifies the difficulty of understanding and solves the availability problem when the network is partitioned. 2) CP-Raft applies a leader-side bitmap-based dependency analysis and representation method to break through the performance bottleneck caused by the high overhead of dependency analysis. 3) CP-Raft discusses why existing methods cannot achieve OORaft correctness verification using TLA$^+$in a limited time and uses phased verification methods to ensure its correctness. Finally, we implement CP-Raft based on an open-source Raft protocol and discuss its potential performance bottlenecks in various scenarios. The experimental results under high dependency strength workloads demonstrate that CP-Raft achieves 1.5× transaction per second (TPS) performance of DP-Raft and 2× of ParallelRaft-CE. It also provides better availability than state-of-the-art OORaft protocols.

Data read and write tail latency in distributed storage systems affects the quality of service of applications. In this paper, we focus on requests with latency around 99.99th tail latency and design a critical window. By analyzing the target storage device distribution of requests in the critical window, we design a simple but effective cache space allocation method to optimize tail latency. Unlike traditional methods, it schedules target cache space allocation instead of requests. Since it does not change the processing of requests and I/Os, it reduces the extra time consumption incurred by the scheduling algorithm. At the same time, it solves the problems of lag, tail latency fluctuation, and high resource consumption of the load balancing-based tail latency guarantee algorithm on request scheduling. Finally, we verify the optimization effect of the method’s tail-latency metrics.

The attention mechanism enables graph neural networks (GNNs) to learn the attention weights between the target node and its one-hop neighbors, thereby improving the performance further. However, most existing GNNs are oriented toward homogeneous graphs, and in which each layer can only aggregate the information of one-hop neighbors. Stacking multilayer networks introduces considerable noise and easily leads to over smoothing. We propose here a multihop heterogeneous neighborhood information fusion graph representation learning method (MHNF). Specifically, we propose a hybrid metapath autonomous extraction model to efficiently extract multihop hybrid neighbors. Then, we formulate a hop-level heterogeneous information aggregation model, which selectively aggregates different-hop neighborhood information within the same hybrid metapath. Finally, a hierarchical semantic attention fusion model (HSAF) is constructed, which can efficiently integrate different-hop and different-path neighborhood information. In this fashion, this paper solves the problem of aggregating multihop neighborhood information and learning hybrid metapaths for target tasks. This mitigates the limitation of manually specifying metapaths. In addition, HSAF can extract the internal node information of the metapaths and better integrate the semantic information present at different levels. Experimental results on real datasets show that MHNF achieves the best or competitive performance against state-of-the-art baselines with only a fraction of 1/10$\sim$1/100 parameters and computational budgets. Our code is publicly available athttps://github.com/PHD-lanyu/MHNF

Cache prefetching technology has become the mainstream data access optimization strategy in the Industrial Intelligent Systems (IIS) and the data centers. However, the rapidly increasing of unstructured data generates massive pairwise access relationships. Therefore, researchers have to make a choice between spatial locality and temporal locality to ensure an acceptable computational complexity. We propose cache-transaction-based data grouping model (CTDGM) to solve the problems described above by optimizing the feature representation method and grouping efficiency. First, we provide the definition of the cache transaction and propose the method for extracting the cache transaction feature (CTF). Second, we design a data chunking algorithm based on CTF and spatiotemporal locality to optimize the relationship calculation efficiency. Third, we propose CTDGM by constructing a relation graph that groups data into independent groups according to the strength of the data access relation. Based on the results of the experiment, compared with the state-of-the-art and traditional methods, our algorithm achieves an average increase in the cache hit rate of 5%–20% on the MSR, VDI-LUN, and KC data set, which in turn reduces the number of data I/O accesses by 30%–60%.

The private chain-based Internet-of-Things (IoT) system ensures the security of cross-organizational data sharing. As a widely used consensus model in private chains, the leader-based state-machine replication (SMR) model meets the performance bottleneck in IoT blockchain applications, where nontransactional sensor data are generated on a scale. We analyzed IoT private chain systems and found that the leader maintains too many connections due to high latency and client request frequency, which results in lower consensus performance and efficiency. To meet this challenge, we propose a novel solution for maintaining low request latency and high transactions per second (TPS): replicate nontransactional data by followers and confirm by the leader to achieve nonconfliction SMR, rather than all by the leader. Our solution, named leader confirmation replication (LCR), uses the newly proposed future log and confirmation signal to achieve nontransactional data replication on the followers, thereby reducing the leader’s network traffic and the request latency of transactional data. In addition, the generation replication strategy is designed to ensure the reliability and consistency of LCR when meeting membership changes. We evaluated LCR with various cluster sizes and network latencies. The experimental results show that in ms-network latency (2–30) environments, the TPS of LCR is 1.4X-1.9X higher than Raft, the transactional data response time is reduced by 40%–60%, and the network traffic is reduced by 20%–30% with acceptable network traffic and CPU cost on the followers. In addition, LCR shows high portability and availability since it does not change the number of leaders or the election process.

Seismic interpretation is a fundamental approach for obtaining static and dynamic information about subsurface reservoirs, such as geological faults/salt bodies and associated fluid types and distribution. Due to the exponential growth in seismic data volume and considerable uncertainty in manual interpretation, deep learning (DL) algorithms have been introduced to assist seismic interpretation. Our investigation of the trained neural networks suggests that they underperform on seismic data with different noise characteristics. One of the main issues is that the noise patterns of seismic data are highly inconsistent due to many factors, including geological features, sampling parameters and human intervention. To address this problem, we propose a noise pattern transfer (NPT) framework to transfer or remove seismic noise style between datasets by treating noise patterns as styles of image, which can also improve the generality of automatic seismic interpretation algorithms. Extensive experiments on three synthetic datasets and two field seismic datasets demonstrate the promising performance of our proposed NPT approach. Pairs of clean and stylised seismic data are generated by extending the use of the neural style transfer algorithm beyond the artistic domain. We then demonstrate how our method achieves superior noise pattern transferability between datasets and denoising performance on field datasets. Associated improvements in accuracy and generalisation of the neural network-based fault recognition tasks successfully demonstrate the practicality of our NPT approach. The source code is made publicly available online at https://github.com/Magnomic/npt-code.

With the advent of the era of the Internet of Things (IoT), a large number of interconnected smart devices form a huge network. The network can be abstracted as a graph, and we propose to identify similar IoT devices in different networks by graph alignment. However, most methods rely on prelabeled cross-network node pairs such as anchor links, which are difficult to obtain due to personal privacy and security restrictions, especially in IoT. In addition, existing network entity alignment methods focus on individual pairs of nodes but ignore the tightly connected group structure in the network, which is a significant feature of IoT devices. In this article, we propose a method of hierarchical unsupervised network alignment (HUNA) to identify similar IoT devices in different networks by a deep learning approach. First, we propose an unsupervised network alignment method based on cycle adversarial networks (UNA), which utilizes the adversarial characteristics of cycle adversarial networks to achieve entity alignment under unsupervised conditions. Second, we further expand the model by carefully designing the group structure aggregation optimization module to aggregate the nodes with closely related attributes and structures into a coarse-grained node and align the coarse-grained nodes. Finally, we evaluate HUNA with real and synthetic data sets. Experimental results show that this method can improve the accuracy of node alignment by 10% and perform well in terms of parameter sensitivity.