Results 51 to 60 of about 612,057 (284)

Generalising quasinormal subgroups [PDF]

open access: yes, 2012
In Cossey and Stonehewer ['On the rarity of quasinormal subgroups', Rend. Semin. Mat. Univ. Padova 125 (2011), 81-105] it is shown that for any odd prime p and integer n >= 3, there is a finite p-group G of exponent p(n) containing a quasinormal subgroup
Stonehewer, Stewart Edward   +1 more
core   +1 more source

Multi‐Omics Integration Identifies a CDH3‐Associated Malignant Epithelial State and Immunosuppressive Niche to Predict Prognosis in Thymic Epithelial Tumors

open access: yesAdvanced Science, EarlyView.
Single‐cell, spatial, molecular, and pathology analyses identify a CDH3‐associated malignant epithelial state in thymic epithelial tumors. This state links stem‐like and EMT programs to M2 macrophage–rich immunosuppressive niches, genomic instability, poor survival, and drug vulnerability.
Yuntao Feng   +13 more
wiley   +1 more source

Zero overshoot and fast transient response using a fuzzy logic controller [PDF]

open access: yes, 2012
In some industrial process control systems it is desirable not to allow an overshoot beyond the setpoint or a threshold, this could be a safety constraint or the requirement of the system.
Mehrdadi, Bruce, Saeed, Bakhtiar I.
core   +3 more sources

An Interpretable, Data‐Driven, Hierarchical Multi‐Domain Fusion Framework for Classification and Motor Function Scoring in Chronic Ankle Instability

open access: yesAdvanced Science, EarlyView.
An AI‐enabled digital twin framework integrates wearable EMG sensing with hierarchical multi‐domain fusion to classify chronic ankle instability, distinguish clinically relevant subtypes, and generate continuous motor function scores. Clinically interpretable functional stratification and SHAP‐based biomarker analysis provide transparent decision ...
Tianle Jie   +12 more
wiley   +1 more source

HESITANT FUZZY SUBGROUPS

open access: yesJournal of New Theory, 2016
Hesitant fuzzy subgroup defined on a group G generalizes the idea of fuzzy subgroups. It mainly focuses on the multiplicity of values encountered when dealing with hesitant fuzzy sets.
Deepak Divakaran, Sunil Jacob John
doaj  

Metabolic Memory in Cardiovascular Disease: Encoding, Propagation, and Therapeutic Targeting

open access: yesAdvanced Science, EarlyView.
Cardiovascular risk often persists after metabolic abnormalities are corrected. This conceptual Review frames such persistence as metabolic memory, encoded through a narrowing therapeutic window from reversible marks to irreversible damage, with continuous input from peripheral organs.
Cheng Cheng   +12 more
wiley   +1 more source

When Biology Meets Medicine: A Perspective on Foundation Models

open access: yesAdvanced Intelligent Discovery, EarlyView.
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu   +3 more
wiley   +1 more source

Pinned-flags of some operations on fuzzy subgroups

open access: yesInternational Journal of Mathematics and Mathematical Sciences, 2005
Fuzzy subgroups of finite groups have been treated recently using the concept of pinned-flags. In this paper, we consider the operations of intersection, sum, product, and quotient of fuzzy subgroups of finite abelian groups in general, in terms of ...
B. B. Makamba, V. Murali
doaj   +1 more source

Matrix formulation of fuzzy rule-based systems [PDF]

open access: yes, 1996
In this paper, a matrix formulation of fuzzy rule based systems is introduced. A gradient descent training algorithm for the determination of the unknown parameters can also be expressed in a matrix form for various adaptive fuzzy networks.
Lotfi, A, Andersen, HC, Tsoi, AC
core  

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley   +1 more source

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