Cu2O@PNIPAM core–shell microgels as novel inkjet materials for the preparation of CuO hollow porous nanocubes gas sensing layers

There has been long-standing interest in developing metal oxide-based sensors with high sensitivity, selectivity, fast response and low material consumption. Here we report for the first time the utilization of Cu2O@PNIPAM core–shell microgels with a nanocube-shaped core structure for construction of novel CuO gas sensing layers. The hybrid microgels show significant improvement in colloidal stability as compared to native Cu2O nanocubes. Consequently, a homogeneous thin film of Cu2O@PNIPAM nanoparticles can be engineered in a quite low solid content (1.5 wt%) by inkjet printing of the dispersion at an optimized viscosity and surface tension. Most importantly, thermal treatment of the Cu2O@PNIPAM microgels forms porous CuO nanocubes, which show much faster response to relevant trace NO2 gases than sensors produced from bare Cu2O nanocubes. This outcome is due to the fact that the PNIPAM shell can successfully hinder the aggregation of CuO nanoparticles during pyrolysis, which enables full utilization of the sensor layers and better access of the gas to active sites. These results point out great potential of such an innovative system as gas sensors with low cost, fast response and high sensitivitH. J. gratefully acknowledges financial support of the CSC scholarship. S. P. acknowledges funding from the Community of Madrid under grant number 2016-T1/AMB-1695

Loghub: A Large Collection of System Log Datasets towards Automated Log Analytics

Logs have been widely adopted in software system development and maintenance because of the rich system runtime information they contain. In recent years, the increase of software size and complexity leads to the rapid growth of the volume of logs. To handle these large volumes of logs efficiently and effectively, a line of research focuses on intelligent log analytics powered by AI (artificial intelligence) techniques. However, only a small fraction of these techniques have reached successful deployment in industry because of the lack of public log datasets and necessary benchmarking upon them. To fill this significant gap between academia and industry and also facilitate more research on AI-powered log analytics, we have collected and organized loghub, a large collection of log datasets. In particular, loghub provides 17 real-world log datasets collected from a wide range of systems, including distributed systems, supercomputers, operating systems, mobile systems, server applications, and standalone software. In this paper, we summarize the statistics of these datasets, introduce some practical log usage scenarios, and present a case study on anomaly detection to demonstrate how loghub facilitates the research and practice in this field. Up to the time of this paper writing, loghub datasets have been downloaded over 15,000 times by more than 380 organizations from both industry and academia.Comment: Dateset available at https://zenodo.org/record/322717

ROME: Testing Image Captioning Systems via Recursive Object Melting

Image captioning (IC) systems aim to generate a text description of the salient objects in an image. In recent years, IC systems have been increasingly integrated into our daily lives, such as assistance for visually-impaired people and description generation in Microsoft Powerpoint. However, even the cutting-edge IC systems (e.g., Microsoft Azure Cognitive Services) and algorithms (e.g., OFA) could produce erroneous captions, leading to incorrect captioning of important objects, misunderstanding, and threats to personal safety. The existing testing approaches either fail to handle the complex form of IC system output (i.e., sentences in natural language) or generate unnatural images as test cases. To address these problems, we introduce Recursive Object MElting (Rome), a novel metamorphic testing approach for validating IC systems. Different from existing approaches that generate test cases by inserting objects, which easily make the generated images unnatural, Rome melts (i.e., remove and inpaint) objects. Rome assumes that the object set in the caption of an image includes the object set in the caption of a generated image after object melting. Given an image, Rome can recursively remove its objects to generate different pairs of images. We use Rome to test one widely-adopted image captioning API and four state-of-the-art (SOTA) algorithms. The results show that the test cases generated by Rome look much more natural than the SOTA IC testing approach and they achieve comparable naturalness to the original images. Meanwhile, by generating test pairs using 226 seed images, Rome reports a total of 9,121 erroneous issues with high precision (86.47%-92.17%). In addition, we further utilize the test cases generated by Rome to retrain the Oscar, which improves its performance across multiple evaluation metrics.Comment: Accepted by ISSTA 202

Probiotics/synbiotics supplementation reduce the infection incidence in patients undergoing resection for colorectal cancer: an umbrella review

ObjectivesThis study reviews meta-analyses of perioperative supplementation with probiotics/synbiotics in colorectal cancer (CRC), systematically assessing the quality of meta-analyses and synthesizing study results to provide robust evidence.MethodsThis review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The search was conducted by two authors in four databases, PubMed, CENTRAL, EMBASE and Web of Science, up until August 3rd, 2025, and conducted independent assessments of the methodological quality of the meta-analysis via A Measure Tool to Assess Systematic Reviews (AMSTAR) 2.ResultsA total of 11 meta-analyses were included in this umbrella review. 3 meta-analyses rated “Critically low” shared ≥3 critical flaws and 2 high-rated reviews adhered to ≥80% AMSTAR 2 criteria. Compared with the control group, the probiotic/synbiotic group presented lower incidence rates of overall infections (OR 0.49, 95% CI: 0.43, 0.56; P < 0. 001, I2 = 6%), surgical site infections (OR 0.58, 95% CI: 0.50, 0.67; P < 0.00001, I2 = 0%), urinary tract infections (OR 0.39, 95% CI: 0.27, 0.54; P < 0.00001, I2 = 0%), and pneumonia infections (OR 0.34, 95% CI: 0.26, 0.45; P < 0.00001, I2 = 0%), and diarrhea incidence (OR 0.41, 95% CI: 0.34, 0.51; P < 0.00001, I2 = 0%).ConclusionAccording to the results of our analyses, perioperative probiotic/synbiotic supplementation in CRC patients is associated with a reduced incidence of overall infections, surgical site infections, urinary tract infections, pneumonia infections, and diarrhea.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42024619853