Early diagnosis of breast cancer has always been a difficult clinical challenge. We developed a deep-learning model EDL-BC to discriminate early breast cancer with ultrasound (US) benign findings. This study aimed to investigate how the EDL-BC model could help radiologists improve the detection rate of early breast cancer while reducing misdiagnosis. In this retrospective, multicentre cohort study, we developed an ensemble deep learning model called EDL-BC based on deep convolutional neural networks. The EDL-BC model was trained and internally validated on B-mode and color Doppler US image of 7955 lesions from 6795 patients between January 1, 2015 and December 31, 2021 in the First Affiliated Hospital of Army Medical University (SW), Chongqing, China. The model was assessed by internal and external validations, and outperformed radiologists. The model performance was validated in two independent external validation cohorts included 448 lesions from 391 patients between January 1 to December 31, 2021 in the Tangshan People's Hospital (TS), Chongqing, China, and 245 lesions from 235 patients between January 1 to December 31, 2021 in the Dazu People's Hospital (DZ), Chongqing, China. All lesions in the training and total validation cohort were US benign findings during screening and biopsy-confirmed malignant, benign, and benign with 3-year follow-up records. Six radiologists performed the clinical diagnostic performance of EDL-BC, and six radiologists independently reviewed the retrospective datasets on a web-based rating platform. The area under the receiver operating characteristic curve (AUC) of the internal validation cohort and two independent external validation cohorts for EDL-BC was 0.950 (95% confidence interval [CI]: 0.909–0.969), 0.956 (95% [CI]: 0.939–0.971), and 0.907 (95% [CI]: 0.877–0.938), respectively. The sensitivity values were 94.4% (95% [CI]: 72.7%–99.9%), 100% (95% [CI]: 69.2%–100%), and 80% (95% [CI]: 28.4%–99.5%), respectively, at 0.76. The AUC for accurate diagnosis of EDL-BC (0.945 [95% [CI]: 0.933–0.965]) and radiologists with artificial intelligence (AI) assistance (0.899 [95% [CI]: 0.883–0.913]) was significantly higher than that of the radiologists without AI assistance (0.716 [95% [CI]: 0.693–0.738]; p < 0.0001). Furthermore, there were no significant differences between the EDL-BC model and radiologists with AI assistance (p = 0.099). EDL-BC can identify subtle but informative elements on US images of breast lesions and can significantly improve radiologists' diagnostic performance for identifying patients with early breast cancer and benefiting the clinical practice. The 10.13039/501100012166 National Key R&D Program of China .

This paper proposes an integrated, ontology-based agricultural information system (AIS) to provide all-round and precise information for efficiently guiding farmers and agri-professionals to conduct agricultural processing. Since the existing independent AIS platforms can only offer specific but incomplete agricultural information service, aiming at this issue, the newly proposed AIS system employs ontology techniques, including RDF-based representation and semantic reasoning, to integrate the index information provided by all involved independent agricultural information platforms. As a result, this newly proposed AIS system can provide users with integrated and accurate response information. Through a realistic case study and relevant experiments, it is clear that the accuracy ratio and integrity ratio of response information offered by this ontology-based integrated AIS can be enhanced to a great extent. In summary, not only agri-professionals, but also farmers, who might not understand information retrieval skills, can benefit from this newly proposed AIS to conduct activities in agricultural production life cycle.

This article proposes a pattern-based prefetching scheme with the support of adaptive cache management, at the flash translation layer of solid-state drives ( SSDs ). It works inside of SSDs and has features of OS dependence and uses transparency. Specifically, it first mines frequent block access patterns that reflect the correlation among the occurred I/O requests. Then, it compares the requests in the current time window with the identified patterns to direct prefetching data into the cache of SSDs. More importantly, to maximize the cache use efficiency, we build a mathematical model to adaptively determine the cache partition on the basis of I/O workload characteristics, for separately buffering the prefetched data and the written data. Experimental results show that our proposal can yield improvements on average read latency by 1.8 %– 36.5 % without noticeably increasing the write latency, in contrast to conventional SSD-inside prefetching schemes.

Most flash-based solid-state drives (SSDs) adopt an onboard dynamic random access memory (DRAM) to buffer hot write data. Then, the write or overwrite operations can be absorbed by the DRAM cache, given that there is sufficient locality in the applications’ I/O access pattern, to consequently avoid flushing the write data onto underlying SSD cells. After analyzing typical real-world workloads over SSDs, we observed that the buffered data of small-size requests are more likely to be reaccessed than those of large write requests. To efficiently utilize the limited space of DRAM cache, this article proposes an adaptive request granularity-based cache management scheme for SSDs. First, we introduce a request block corresponding to a write request, as the cache management granularity, and propose a dynamic manner for classifying small and large request blocks. Next, we design three-level linked lists for supporting different routines of upgradation for small and large request blocks, once their data have been hit in the cache. Finally, we present a scheme of evicting the request blocks having the minimum cost in cache replacement, by taking both factors of access hotness and time discounting into account. Experimental results show that our proposal can yield improvements on cache hits and the overall I/O latency by 21.8 % and 14.7 % on average, compared to state-of-the-art cache management schemes inside SSDs.

NAND flash-based high-density solid-state drives (SSDs) commonly have limited write endurance, as a single block becomes unavailable after a finite number of program/erase cycles. The (static) wear leveling algorithms migrate cold data to more worn blocks for yielding an even distribution of wears in SSDs. However, migrating cold data must delay normal I/O processing, and unnecessary wear leveling operations may damage SSD endurance as each operation causes a program/erase cycle. This article proposes an adaptive switch on wear leveling (called ASWL), to boost I/O performance and lifetime for high-density SSDs. The basic idea of ASWL is to adaptively switch the threshold function for intelligently triggering wear leveling operations at different stages of SSD lifetime. To this end, we build a mathematical model determining the wear leveling threshold condition with a dynamic manner, by considering the real-time factors of I/O intensity and the wear difference of SSD blocks. Through a series of emulation experiments on several realistic disk traces, we show that the proposed ASWL mechanism can not only greatly improve I/O performance but also noticeably extend the lifetime of high-density SSDs, in contrast to existing wear leveling methods.