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Partial results: at least one source did not answer. Available results are shown rather than treating an upstream outage as zero matches.
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Biomarkers Selection and Combination Based on the Weighted Youden Index.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Using different evolutionary algorithms and artificial neural networks to predict the rheological behavior of a new nano-lubricant containing multi-walled carbon nanotube and zinc oxide nano-powders in oil 10W40 base fluid
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Thermo-diffusional influence on Cu₃Zn₂-Co nanomaterial with porous medium using intelligent Levenberg-Marquardt method.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Comprehensive framework of machine learning and deep learning architectures with metaheuristic optimization for high-fidelity prediction of nanofluid specific heat capacity
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
The role of precision tool positioning in enhancing machining accuracy
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 4 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
scYOU: a multi-scale yoked optimization and unsupervised framework for analysis of cellular heterogeneity and complex-associated protein module recovery.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Can CT Phantoms Produce Realistic CT Images of Human Anatomy? Physics-Based Evaluation of Multimaterial 3D-Printed CT Phantoms From CT Data.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Gray Wolf Optimizer With Communication Strategy Based on DV-Hop for Nodes Location of Wireless Sensor Networks
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 4 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Scaling Bayesian Optimization up to Higher Dimensions: a Review and Comparison of Recent Algorithms
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Guest Editorial Special Issue on Benchmarking Sampling-Based Optimization Heuristics: Methodology and Software
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Diagnostic performance and clinical utility of an HCV antigen-antibody combination assay across diverse real-world cohorts.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Advanced optimal GWO-PID controller for DC motor
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Deep learning-based automated segmentation of target volumes for pediatric craniospinal irradiation in proton therapy.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Advancing preeclampsia prediction: a tailored machine learning pipeline integrating resampling and ensemble models for handling imbalanced medical data
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Recent advances in volumetric super-resolution imaging through computational adaptive optics.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Review of Deep Learning for Language Modeling
uncertain
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
A Comparison of Metaheuristic Methods for the Vehicle Routing Problem
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Trends in Intra-Aortic Balloon Pump Technology: Patentometric Analysis.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Nurses' Attitudes and Experiences With Generative AI in Clinical Practice: Qualitative Systematic Review.
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Quantum Machine Learning: Exploring Quantum Algorithms for Enhancing Deep Learning Models
limited evidence
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Comparison of Optimal Control Techniques for Building Energy Management
uncertain
Show all credibility signals
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- supportingOpen access status: Normalized open-access status: open.
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- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
CEP-MI: a novel cue-execution dual-stage motor imagery paradigm with PGCANet decoding architecture.
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Developing an Optimized Energy-Efficient Sustainable Building Design Model in an Arid and Semi-Arid Region: A Genetic Algorithm Approach
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Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade
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Forecasting Algorithms and Optimization Strategies for Building Energy Management & Demand Response
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Sequence Decision Transformer for Adaptive Traffic Signal Control
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- supportingOpen access status: Normalized open-access status: open.
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AI-Enabled Supply Chain Optimization
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A Comprehensive Review of FACTS Controllers in Nigeria Power Systems Network for Enhanced Performance on Variable Loads
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Geo-trax: A Comprehensive Framework for Georeferenced Vehicle Trajectory Extraction from Drone Imagery
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- supportingPublication license: A publication license was supplied: https://opensource.org/licenses/MIT. Presence improves reuse transparency, not research validity.
- not scoredPublication version: A publication version was supplied but is not scored.
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Deep Neural Control Module (DNCM) AI-Driven Adaptive Deep Learning Control Framework for Islanded DC Microgrids in Space Habitats and UAVs
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modOpt: A modular development environment and library for optimization algorithms
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NEW ALGORITHMIC SOLUTIONS AND THEIR MATHEMATICAL FOUNDATIONS
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A Systematic Review on Optimization and Intelligent Performance Enhancement of Solar and Hybrid Renewable Energy Systems for Sustainable Development
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pyOptiGTest
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HYBRID MODELS AND ALGORITHMS FOR SELECTING OPTIMAL FREQUENCIES BASED ON ARTIFICIAL INTELLIGENCE
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Project Zion
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- supportingOpen access status: Normalized open-access status: open.
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STARLI | F101–F110 / 300 Flagship 13 Fission · Volume II · Distributed Microrobotic Coordination, Safety and Sample Integrity Author: 33 | Nonclinical Research Design Working Edition v1.2 | English Zenodo Ten-Volume Overview This collection presents ten prospective research-design monographs in micro/nanorobotic collective systems. Research Unit I (F101–F105) examines self-organizing sampling, load balancing, safe-stop behavior, local sensor fusion and asynchronous control. Research Unit II (F106–F110) investigates competing tasks, adaptive routing, contamination control, collaboration reliability and heterogeneous-agent coordination. The editorial aim is to define independently testable, resource-bounded questions, not to claim demonstrated autonomous performance or clinical efficacy. The ten English working editions comprise 7,770 PDF pages: 300 prospective mathematical research plates, 820 chapter openings, 4,100 independently paginated study sections, 2,000 Research Gates and 330 Phoenix Answer Embryos. Each Gate has six annotation categories: causal mechanism, measurement witness, blind falsifier, rival explanation, stopping boundary and successor interface. Architecture counts are not evidence of experimental validation or independently certified scientific originality. The Ten Research Directions F101 Nanorobotic Collective Self-Organization Sampling At The Limit — Explores whether locally coordinated sampling can preserve sample identity while keeping collision exposure within a bounded envelope. The proposed witness is harmful collision frequency [events/(agent*min)], alongside the atlas collision-rate outcome. Equal-energy fixed-rule behavior is the baseline; leaked global tracking is a rival explanation. Confidence maintained despite a concealed communication partition rejects the claim. F102 Microrobotic Collective Load Balancing At The Limit — Studies how task redistribution changes spatial coverage without relying on unmetered additional actuation. Coverage spatial entropy [nat] is the proposed endpoint, linked to the original coverage-entropy outcome. A single-agent controller with matched total actuation tests whether the apparent gain comes merely from redundancy. Failure to recover a known-safe state ends the trial. F103 Nanorobotic Collective Safe Stop At The Limit — Defines a test of independently observed transition to a safe state after a collective disturbance. The witness is time to demonstrably safe state [s], preserving the atlas recovery-time field. A fully observing centralized controller provides the comparator; increased agent count is an alternative cause of faster recovery. Crossing any registered safety invariant terminates evaluation. F104 Microrobotic Collective Local Sensor Fusion At The Limit — Examines whether local sensor exchange improves accepted mission decisions under limited communication. Communication traffic per accepted task [bytes/task] is a proposed measurement, with the original communication-load outcome retained. Equal-energy fixed rules challenge the fusion benefit; unacknowledged global state tracking is a competing explanation. Hidden partitions test whether confidence remains appropriately bounded. F105 Nanorobotic Collective Asynchronous Control At The Limit — Investigates decision timing when individual robots update at different rates rather than using a single synchronized command. Total metered energy per completed task [J/task] is the proposed observable, preserving the atlas energy-per-task field. The control comparison matches total actuation to a single-agent system; redundancy without true adaptation is the alternative. Failure to regain a known-safe state requires stopping. F106 Microrobotic Collective Task Competition And Coordination At The Limit — Proposes experiments in which agents must choose between competing tasks under bounded control authority. Selective abstention and service coverage [fraction] is the candidate measurement, anchored to the atlas abstention outcome. A fully observing centralized controller is the strong comparator, with simple agent-number advantage as a rival. Breach of registered safety invariants invalidates any coordination gain. F107 Nanorobotic Collective Adaptive Path Planning At The Limit — Tests whether locally coordinated route changes improve mission completion when obstacles or task geometry vary. Task-success fraction under equal energy [fraction] is the proposed endpoint, linked to the original task-completion measure. Fixed behavior at the same energy budget is the baseline; leaked external tracking may falsely suggest adaptation. Concealed network partitions probe erroneous collective confidence. F108 Microrobotic Collective Contamination Control At The Limit — Addresses a different physical constraint: preserving sample separation during repeated collective handling. The new proposed endpoint is cross-sample chemical carryover [mass fraction], while the source atlas retains consensus error. A matched-actuation single-agent control tests claimed collective benefit; redundancy alone may explain task performance. Inability to return to a known-safe state is a mandatory stop. F109 Nanorobotic Collective Collaboration Reliability At The Limit — Examines whether distributed cooperation can remain safe and reproducible across adverse trials. Hazardful collision frequency [events/(agent*min)] is the candidate observable alongside the original collision-rate outcome. A fully observing centralized controller is the baseline, and additional robots are a rival source of apparent performance. Safety-invariant crossing overrides aggregate success statistics. F110 Microrobotic Collective Heterogeneous Agents Coordination At The Limit — Studies coordination across agents with different sensing, mobility or task roles, while preserving traceable collective outcomes. Coverage entropy under heterogeneous roles [nat] is the proposed witness. Equal-energy fixed behavior is the baseline; outside tracking that silently supplies global state is a competing explanation. Unjustified high confidence during a concealed partition rejects the proposal. Scope, Falsifiability and Evidence Boundaries These are prospective nonclinical research designs restricted to authorized bench-scale, ex-vivo or synthetic reference settings with traceable sample identity, calibrated measurement and bounded actuation. Proposed endpoints are editorial additions and do not replace original atlas outcomes. No laboratory experiments or clinical results are reported. Matched energy and actuation, blinded disturbances, full negative-trial records and noncompensable safety stops are needed before any advantage claim. PDF structure and archive integrity can be checked automatically, but derivation-level physics, causal independence, source-to-claim DOI alignment and specialist review remain incomplete. Selected Established Methodological References Yang & Zhang (2021) · Motion Control in Magnetic Microrobotics: From Individual and Multiple Robots to Swarms · DOI 10.1146/annurev-control-032720-104318 | Farivarnejad & Berman (2022) · Multirobot Control Strategies for Collective Transport · DOI 10.1146/annurev-control-042920-095844 | Seo, Paik & Yim (2019) · Modular Reconfigurable Robotics · DOI 10.1146/annurev-control-053018-023834 These existing methodological citations are reproduced from the source archive evidence register; they do not validate the STARLI-specific proposed systems. Identity-to-claim mapping for each flagship, including additional source DOI candidates, has not been exhaustively verified. Join the STARLI Research Community Explore more than 100 interdisciplinary research themes and follow the evolving STARLI Research Gate community: https://zenodo.org/communities/starli-research-gate/ We do not need to know which door future generations will choose to open. We only hope that when they open it, they will find a little light to guide their way.
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RESEARCH COLLECTION / FLAGSHIPS 231–240 OF 300 Optical Reliability, Causal Metrology & Bounded Autonomous Micro/Nano-Systems at the Limit Flagship 13 Fission | Volume III | Author 33 | Research Design Working Edition v0.7 (Three-Prose Working Draft) This ten-volume collection develops falsifiable research designs for optical microrobot fatigue, encapsulation reliability, photon transport loss, optical–magnetic control, material biofouling, photonic-chip thermal management, cross-instrument consistency, nanomaterial qualification, multiphysics coordination and evidence-bounded autonomous experimentation. It does not claim an experimentally established advantage. The ten English research-design manuscripts contain 7,770 pages, 300 model plates, 820 chapter openings, 4,100 independently paginated study designs, 2,000 Research Gates and 330 Phoenix Answer Embryos. Each Gate has a separately defined physical mechanism and concealed intervention; cross-volume scientific distinctness remains subject to expert review. Counts describe editorial coverage, not scientific proof. Research Unit I · F231–F235 F231 OPTICAL MICROROBOTIC MATERIALS FATIGUE AT THE LIMIT — Probes cyclic microbeam and coating fatigue through independent deflection, fracture and thermal witnesses. Concealed ageing challenges fixture relaxation and survivor selection; whole-cohort failure counts govern durability. F232 NANO-QUANTUM OPTICAL ENCAPSULATION RELIABILITY AT THE LIMIT — Tests water, ion and seam leakage, pinholes, seal swelling and reference drift through masked humidity and tracer studies. Edge diffusion, adhesive failure and photothermal limits remain live disqualifiers. F233 MICROSCALE PHOTON TRANSPORT LOSS AT THE LIMIT — Separates waveguide propagation loss from coupling, absorption, scattering and detector offsets. Length-resolved cutback and calorimetry establish a physical photon budget; high detected power alone is not proof of low loss. F234 NANO-OPTICAL AND MAGNETIC ACTUATION HYBRID CONTROL AT THE LIMIT — Challenges optical–magnetic actuation using field reversal, independently measured force and pose, synchronized timing faults and thermal budgets. The matched optical-only and magnetic-only controllers are real alternatives. F235 QUANTUM-MATERIAL BIOFOULING TOLERANCE AT THE LIMIT — Examines protein corona, non-specific adsorption, lipid deposition and regeneration-cycle damage. Independent mass, chemical, optical and hydraulic witnesses distinguish surface fouling from bulk-index drift and sample contamination. SECOND RESEARCH UNIT / TRANSFER, SAFETY & SUCCESSION Research Unit II · F236–F240 F236 MICROSCALE PHOTONIC CHIP THERMAL MANAGEMENT AT THE LIMIT — Investigates chip hot spots, interfacial resistance, pulse heating and shutdown latency. Independent thermometry and energy closure test cooling against passive heat sinking; temperature vetoes cannot be traded for optical output. F237 NANO-OPTICAL SENSOR CROSS-INSTRUMENT CONSISTENCY AT THE LIMIT — Tests calibration transfer across instruments with independently certified wavelength and power standards, masked instrument swaps and held-out laboratories. Vendor fitting and common-reference drift are direct rival explanations. F238 QUANTUM NANOMATERIALS FUNCTIONAL QUALIFICATION AT THE LIMIT — Builds functional nanomaterial qualification from independent structural, chemical and optical evidence across complete manufacturing lots. Hidden lot swaps probe selection bias; failed specimens remain in the denominator. F239 PHOTONIC MICROROBOTIC MULTIPHYSICS COORDINATION AT THE LIMIT — Resolves optical, magnetic, fluid and thermal coupling in physical microrobot control. Separate field, force, position and heat measurements test synchronization faults and safe retrieval under conservation limits. F240 NANO-QUANTUM PHOTONIC AUTONOMOUS EXPERIMENTAL PLATFORM AT THE LIMIT — Proposes an evidence-bounded autonomous photonic laboratory with frozen action logs, hidden instrument faults, abstention and independent successor replay. Confidence scores cannot replace physical reference or permission integrity. Scientific Scope, Comparators & Evidence Limits All proposals are nonclinical bench research on traceable materials, sealed microfluidic modules and authorized reference standards. Independent physical witnesses, unselected denominators, matched resource budgets, concealed faults and hard stopping conditions are mandatory. These manuscripts offer designs, not performed experiments, clinical demonstrations or proven quantum advantage. Mother scientific ancestry: Flagship 13 version DOI 10.5281/zenodo.23318840, all-versions DOI 10.5281/zenodo.23318839. Contextual DOI anchors mentioned in the collection include 10.1002/adpr.202500103 (optical propagation), 10.1039/D2LC00573E (optical/magnetic micro-robotics), 10.1002/admt.202401848 (packaging) and 10.1038/s41570-026-00847-2 (self-driving labs). Independent bibliographic verification and source-to-claim suitability are pending. Three-Prose Gate Evolution & QA Boundary Each of the 2,000 Research Gates now presents three independent scientific prose passages: Origin, Extreme Extrapolation, and Future Research Plan (approximately 100 English words each). The essays distinguish mechanism-specific predictions, orthogonal measurements, blind interventions, rival explanations, hard stops, and the next falsifiable successor experiment. This is a research-design evolution, not empirical validation; remaining semantic similarities and DOI matches require independent specialist review. Join the STARLI Research Community Explore more than 100 interdisciplinary research themes and follow the evolving STARLI Research Gate community: https://zenodo.org/communities/starli-research-gate/ We do not need to know which door future generations will choose to open. We only hope that when they open it, they will find a little light to guide their way. 🌈🚪✨
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Optimization of Plate girder using Genetic Algorithm by MATLAB
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Computational Fluid Dynamics and Machine Learning Integration for High Performance Mechanical Engineering Thermal Systems Analysis
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Development of an Enhanced Supply Chain Algorithm for ERP Systems Using ACO, Genetic, and Floyd-Warshall Algorithms
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Comprehensive Survey for Cloud Computing Based Nature-Inspired Algorithms Optimization Scheduling
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Eco-Performance Analytics: A Multi-Objective Optimization Framework for Sustainable Sports Engineering
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ISVHAAI AI Society Letters - Letter No. 5
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Neural Network and Deep Learning with Mathematica
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