Log Analysis
Log Analysis - Related Videos
01:19
The Mantel-Cox Log-Rank Test
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2025
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.
09:32
A New Straightforward Method for Lipophilicity (logP) Measurement using 19F NMR Spectroscopy
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Cited by 6 •
2019
A novel and straightforward variation of the shake-flask method was developed for accurate lipophilicity measurement of fluorinated compounds by 19F NMR spectroscopy.
11:38
Heat Exchanger Analysis
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2023
Source: Alexander S Rattner and Christopher J Greer; Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, University Park, PA Heat exchangers transfer thermal energy between two fluid streams, and are ubiquitous in energy systems. Common applications include car radiators (heat transfer from hot engine coolant to surrounding air), refrigerator evaporators (air inside refrigerator compartment to evaporating refrigerant), and cooling towers in power plants...
10:39
Automated Analysis of Intracellular Phenotypes of Salmonella Using ImageJ
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Cited by 3 •
2022
Salmonella invades and replicates inside intestinal epithelial cells both in Salmonella-specific vacuoles and free in the cytosol (hyper-replication). A high-throughput fluorescence microscopy-based protocol is described here to quantify the intracellular phenotypes of Salmonella by two complementary image analyses through ImageJ, reaching single-cell resolution and scoring.
08:12
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Cited by 2 •
2022
The protocol presents an experimental psychophysics paradigm to obtain large quantities of similarity judgments, and an accompanying analysis workflow. The paradigm probes context effects and enables modeling of similarity data in terms of Euclidean spaces of at least five dimensions.
FAQs
The process begins by parsing entries into usable fields, such as timestamps or event details, even when records do not share the same format. Engineers can then filter those fields to retain events relevant to a fault, performance issue, or investigation. This reduces the volume of information under review and makes meaningful patterns easier to identify.
Timestamps establish the order and timing of events, while correlation connects records produced by different components of an engineering system. Examining both dimensions can show whether separate entries describe one developing problem or unrelated activity. This context helps engineers trace failures, recognize system-wide patterns, and understand how behavior progressed across components.
Visualization makes recurring behavior, unusual activity, and changes easier to inspect across large collections of records. Alerting can draw attention to significant events, while automated pattern detection helps identify repeated or unusual log behavior without relying entirely on manual review. Together, these capabilities turn processed records into more timely evidence for monitoring and diagnosis.
By comparing filtered and correlated records, engineers can identify significant events, failures, performance behavior, and anomalies. The method is useful not only for locating an individual fault but also for recognizing patterns that may span multiple components. These findings provide evidence for understanding what happened and for selecting appropriate reliability or corrective actions.
A practical workflow collects the relevant timestamped records, parses structured and unstructured entries, and filters fields connected to the engineering question. Engineers then correlate events across components and examine the resulting patterns through suitable visualization or automated detection. The final evidence supports diagnosis, incident investigation, performance assessment, or evaluation of a system change.
Engineers can apply it when a system shows a fault, unexplained performance behavior, or an event requiring investigation. It also supports ongoing monitoring by revealing patterns in operational records and highlighting significant activity through alerting. Using the method in these situations helps replace isolated observations with evidence drawn from system behavior over time.
After a system change, engineers can examine timestamped records for patterns associated with the modified components and compare observed behavior with the intended outcome. Filtering and correlation help isolate relevant events, while visualization or automated detection can expose anomalies or failures. This evidence supports a more informed assessment of whether the change affected performance or reliability.
Findings can guide faster diagnosis, preventive maintenance, and broader reliability improvements. A recurring pattern may indicate that engineers should investigate a component, monitor a condition more closely, or address a developing failure before it becomes more significant. Because the conclusions derive from operational records, they provide evidence for selecting and prioritizing engineering actions.