EvLog: Evolving Log Analyzer for Anomalous Logs Identification

Software logs record system activities, aiding maintainers in identifying the underlying causes for failures and enabling prompt mitigation actions. However, maintainers need to inspect a large volume of daily logs to identify the anomalous logs that reveal failure details for further diagnosis. Thus, how to automatically distinguish these anomalous logs from normal logs becomes a critical problem. Existing approaches alleviate the burden on software maintainers, but they are built upon an improper yet critical assumption: logging statements in the software remain unchanged. While software keeps evolving, our empirical study finds that evolving software brings three challenges: log parsing errors, evolving log events, and unstable log sequences. In this paper, we propose a novel unsupervised approach named Evolving Log analyzer (EvLog) to mitigate these challenges. We first build a multi-level representation extractor to process logs without parsing to prevent errors from the parser. The multi-level representations preserve the essential semantics of logs while leaving out insignificant changes in evolving events. EvLog then implements an anomaly discriminator with an attention mechanism to identify the anomalous logs and avoid the issue brought by the unstable sequence. EvLog has shown effectiveness in two real-world system evolution log datasets with an average F1 score of 0.955 and 0.847 in the intra-version setting and inter-version setting, respectively, which outperforms other state-of-the-art approaches by a wide margin. To our best knowledge, this is the first study on tackling anomalous logs over software evolution. We believe our work sheds new light on the impact of software evolution with the corresponding solutions for the log analysis community

AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection

The rapid progress of modern computing systems has led to a growing interest in informative run-time logs. Various log-based anomaly detection techniques have been proposed to ensure software reliability. However, their implementation in the industry has been limited due to the lack of high-quality public log resources as training datasets. While some log datasets are available for anomaly detection, they suffer from limitations in (1) comprehensiveness of log events; (2) scalability over diverse systems; and (3) flexibility of log utility. To address these limitations, we propose AutoLog, the first automated log generation methodology for anomaly detection. AutoLog uses program analysis to generate run-time log sequences without actually running the system. AutoLog starts with probing comprehensive logging statements associated with the call graphs of an application. Then, it constructs execution graphs for each method after pruning the call graphs to find log-related execution paths in a scalable manner. Finally, AutoLog propagates the anomaly label to each acquired execution path based on human knowledge. It generates flexible log sequences by walking along the log execution paths with controllable parameters. Experiments on 50 popular Java projects show that AutoLog acquires significantly more (9x-58x) log events than existing log datasets from the same system, and generates log messages much faster (15x) with a single machine than existing passive data collection approaches. We hope AutoLog can facilitate the benchmarking and adoption of automated log analysis techniques.Comment: The paper has been accepted by ASE 2023 (Research Track

Degradation of conventional, biodegradable and oxo-degradable microplastics in a soil using a δ13C technique

Context. A significant amount of conventional plastics waste, especially in the form of microplastics (MPs), has accumulated in soils due to its limited degradation. Oxo-degradable and biodegradable plastics have also contributed to MP contamination in soils. Aims. In this study, we examined the degradation of a conventional plastic [fruit and vegetable (F&V) bag], two biodegradable plastics (bin liner and mulch film) and an oxo-degradable plastic (drinking straw). Methods. These plastics (5 mm) were mixed into a soil and incubated in the laboratory at 37 ± 1°C for 185 days. The CO2-carbon (C) mineralisation of the four plastics was determined using a δ13C technique, because the difference in the δ13C values of studied plastics and the experimental soil was ≥10‰. Key results. Bin liner showed the greatest C mineralisation (5.7%), followed by mulch film (4.1%), straw (0.4%) and F&V bag (0.3%) at the end of the incubation period. All plastics, except the mulch film for 23–77 days of incubation, caused a positive priming effect on soil organic carbon (SOC). Fourier transform infra-red spectroscopy and scanning electron microscopy analyses were consistent with theC mineralisation data. Conclusions. This study determines the degradation of various MPs in soil using a reliable and practical δ13C method, which has been lacking in this field of study. The priming effect of various MPs on SOC is a significant finding. Implications. The lack of consideration of priming effect on SOC may overestimate the mineralisation of plastics in soil

Microplastics contamination and decomposition in soils

A significant amount of conventional plastic waste, especially in the form of microplastics (MPs), has accumulated in soils due to its limited degradation. However, the existence, persistence and impacts of plastics on soil ecosystem services have received little attention compared to aquatic environments. First, this thesis provides a review of plastics in soils including plastic characteristics, methodologies to isolate and characterise plastics from soils, occurrence, sources, impacts of plastics to soil ecosystems and degradation of plastics in soils. Second, the interactions of plastics and soil organisms are complex and inconsistent observations have been made in published studies. Thus, we assessed the effects of plastic on plants, fauna and microbial communities, with a metaanalysis. it was found that overall plastics caused substantial detrimental effects to plants and fauna, but less so to microbial diversity and richness. Third, MP degradation fate and priming effect (PE) on native soil organic matter, are not well known. We examined the impacts of MP composition (i.e, biodegradable bin liner and mulch film, oxo-degradable straw and conventional F&V bag) and size (i.e., <100, 100-250, 250-500, 500-1000, 1000-2000, 2000-4750 μm), soil type (i.e., Ferralsol, Vertisol and Solonetz) and temperature (i.e., 20, 30 and 40°C) on its C mineralisation and PE using a δ13C method. The results demonstrated that MP composition and size, soil type, temperature and their interactions are all key factors on its mineralisation and PE. Furthermore, hitherto no research has been conducted on the abundance of MPs in Australian agricultural soils. We investigated the MP concentration and characteristics in soils treated with sludge, compost and mulch film in New South Wales, Australia. The largest number of MPs were observed in the compost soil with 20776 particles kg-1 soil, followed by soils with sludge (17449) and mulch film (13731) applications

Diode-Pumped Solid-State Q-Switched Laser with Rhenium Diselenide as Saturable Absorber

We report a solid-state passively Q-switched Nd:YVO4 laser adopting rhenium diselenide (ReSe2) as saturable absorber (SA) materials. ReSe2 belongs to a type of transition metal dichalcogenides (TMDs) materials and it has the weak-layered dependent feature beneficial for the preparation of few-layer materials. The few-layer ReSe2 was prepared by ultrasonic exfoliation method. Using a power-dependent transmission experiment, its modulation depth and saturation intensity were measured to be 1.89% and 6.37 MW/cm2. Pumped by diode laser and based on few-layer ReSe2 SA, the Q-switched Nd:YVO4 laser obtained the shortest Q-switched pulse width of 682 ns with the highest repetition rate of 84.16 kHz. The maximum average output power was 125 mW with the slope efficiency of 17.27%. Our experiment, to the best of our knowledge, is the first demonstration that used ReSe2 as SA materials in an all-solid-state laser. The results show that the few-layer ReSe2 owns the nonlinear saturable absorption properties and it has the capacity to act as SA in an all-solid-state laser

TL1A promotes metastasis and EMT process of colorectal cancer

Background: Metastasis is the major problem of colorectal cancer (CRC) and is correlated with the high mortality. Tumor necrosis factor-like cytokine 1A (TL1A) is a novel regulatory factor for inflammatory diseases. This work aimed to investigate the role of TL1A in CRC metastasis. Method: AOM/DSS-induced mouse model, xenograft tumor model and metastasis murine model were established to mimic the colitis-associated CRC and investigate CRC growth and metastasis in vivo. Colon tissues were assessed by hematoxylin/eosin (HE) staining and immunohistochemistry (IHC). CRC cell metastasis in vivo was observed using in vivo imaging system (IVIS). Cell viability and proliferation were examined using cell counting kit 8 (CCK-8) and EdU experiments. The expression of tumor growth factor β (TGFβ) and metastatic biomarkers were detected using western blotting experiment. The in vitro cell metastasis was measured by Transwell. Results: Knockdown of TL1A notably suppressed the generation of colonic tumors in azoxymethane/dextran sodium sulfate (AOM/DSS) model, suppressed in vivo CRC cell growth, as well as lung and liver metastasis. The inflammation response and inflammatory cell infiltration in tumor sites were decreased by TL1A depletion. The in vitro CRC cell growth and metastasis was also suppressed by shTL1A, along with altered expression of epithelial mesenchymal transition (EMT) biomarkers. TL1A depletion suppressed the level of the TGF-β1 receptor (TβRI) and phosphorylation of Smad3 in CRC cells. Stimulation with TGF-β recovered the CRC cell migration and invasion that suppressed by shTL1A. Conclusion: Our work implicated TL1A as a promoter of CRC generation and metastasis and defines TGF-β/Smad3 signaling as mediator of TL1A-regualated CRC cell metastasis

Influence of Meteorological Factors and Chemical Processes on the Explosive Growth of PM2.5 in Shanghai, China

In order to explore the mechanism of haze formation, the meteorological effect and chemical reaction process of the explosive growth (EG) of PM2.5 were studied. In this study, the level of PM2.5, water-soluble inorganic ions, carbonaceous aerosols, gaseous precursors, and meteorological factors were analyzed in Shanghai in 2018. The EG event is defined by a net increase of PM2.5 mass concentration greater than or equal to 100 &mu;g m&minus;3 within 3, 6, or 9 h. The results showed that the annual average PM2.5 concentration in Shanghai in 2018 was 43.2 &mu;g m&minus;3, and secondary inorganic aerosols and organic matter (OM) accounted for 55.8% and 20.1% of PM2.5, respectively. The increase and decrease in the contributions of sulfate, nitrate, ammonium (SNA), and elemental carbon (EC) to PM2.5 from clean days to EG, respectively, indicated a strong, secondary transformation during EG. Three EG episodes (Ep) were studied in detail, and the PM2.5 concentration in Ep3 was highest (135.7 &mu;g m&minus;3), followed by Ep2 (129.6 &mu;g m&minus;3), and Ep1 (82.3 &mu;g m&minus;3). The EG was driven by stagnant conditions and chemical reactions (heterogeneous and gas-phase oxidation reactions). This study improves our understanding of the mechanism of haze pollution and provides a scientific basis for air pollution control in Shanghai