Saved Queries

Inflammatory Bowel Diseases (IBD), most notably ulcerative colitis (UC) and Crohn’s disease (CD), are long-standing disorders driven by dysregulated immune responses within the gastrointestinal tract and characterized by several metabolic disturbances, which are believed to influence disease progression. We have recently shown that a systemic deficiency of taurine, a semi-essential amino acid with anti-inflammatory properties, marks IBD. To characterize the temporal dynamics and determinants of taurine deficiency in IBD, we conducted a prospective longitudinal study assessing serum taurine levels in a cohort of 47 patients with IBD compared with 33 healthy controls. Serum taurine concentrations were measured at baseline and after a median follow-up period of 45 months using ELISA. Patients were stratified by disease subtype (UC and CD), age group, and clinical activity status at baseline and follow-up. Serum taurine levels were significantly lower in IBD patients at both baseline and the end of follow-up (p < 0.05), and remained stable over time within the CD and UC cohorts. In healthy individuals, but not in IBD patients, taurine concentrations declined with age, suggesting that age-related metabolic regulation of taurine is altered in the context of chronic intestinal inflammation. Stratification by disease activity revealed that taurine deficiency was present in both active and inactive IBD, particularly among younger patients, and differences between active and inactive disease were minimal. These findings indicate that the persistent reduction in serum taurine in IBD is independent of age, disease subtype, or clinical activity, and remains relatively constant over time across most patient subgroups, suggesting an underlying alteration in taurine metabolism or homeostasis associated with IBD pathophysiology. Further investigation is needed to elucidate the mechanisms linking taurine dysregulation to IBD progression. Full article

(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)

►▼ Show Figures

Figure 1

14 pages, 1746 KB

Open AccessArticle

Resistance Patterns in Gram-Negative Bacilli Isolated in a Secondary Care Hospital: A Therapeutic Challenge in Western Mexico

by César Ricardo Cortez-Álvarez, Benjamín de Jesús Gutiérrez-García, Pablo Ulises Romero-Mendoza, María del Rosario Cabral-Medina, Monserratt Abud-Gonzalez, Susana Olivia Guerra-Martínez, Livier Amalia Gutiérrez-Morales, María Luisa Muñoz-Almaguer, Santiago José Guevara-Martínez, Daniel Osmar Suárez-Rico, Marco Pérez-Cisneros and Martin Zermeño-Ruiz

Microbiol. Res. 2026, 17(1), 17; https://doi.org/10.3390/microbiolres17010017 (registering DOI) - 10 Jan 2026

Abstract

Antimicrobial resistance (AMR) continues to represent a significant global public health concern. Gram-negative bacilli (GNB) are the primary causative agents of severe nosocomial infections and possess a notable capacity to develop resistance mechanisms that restrict therapeutic options. The objective of this study was to characterize the antimicrobial susceptibility profiles of GNB isolated at a secondary-level hospital in Guadalajara, Mexico, with the aim of identifying predominant resistance patterns and the most effective therapeutic alternatives. A descriptive, retrospective, cross-sectional study was conducted using clinical isolates of Acinetobacter spp., Pseudomonas spp., Escherichia coli, Klebsiella spp., Morganella morganii, Proteus spp., and Enterobacter spp. collected during 2024. The identification and susceptibility testing were carried out using the VITEK^® 2 automated system, and the results were interpreted in accordance with CLSI guidelines. High resistance rates were observed in Acinetobacter spp. and Pseudomonas spp., particularly to carbapenems (>50% and >40%, respectively). Escherichia coli and Klebsiella spp. demonstrated resistance to third-generation cephalosporins and trimethoprim/sulfamethoxazole, exhibiting high susceptibility to amikacin and carbapenems (>90%). New-generation β-lactam/β-lactamase inhibitor combinations, such as ceftazidime/avibactam and ceftolozane/tazobactam, have demonstrated high efficacy against resistant strains. Overall, GNB isolates in this secondary-level hospital demonstrated elevated resistance levels, particularly to β-lactams and carbapenems, which pose a significant therapeutic challenge. Nevertheless, amikacin, carbapenems, and new-generation β-lactams persist as valuable therapeutic options. In order to contain the spread of multidrug-resistant organisms, it is imperative to strengthen local surveillance, optimize antibiotic stewardship, and reinforce infection control measures. Full article

►▼ Show Figures

Figure 1

10 pages, 264 KB

Open AccessBrief Report

The H159Y Variant of the BAFF-R Gene (TNFRSF13C) Is Unrelated to the Risk of Developing Systemic Lupus Erythematosus and Sjögren’s Disease in a Mexican Population

by Itzel María Borunda-Calderón, Jazz Alan Corona-Angeles, Noemí Espinoza-García, Miguel Marín-Rosales, Diana Celeste Salazar-Camarena, Edith Oregon-Romero, Ramsés Alejandro Morales-Zambrano and Claudia Azucena Palafox-Sánchez

Int. J. Mol. Sci. 2026, 27(2), 726; https://doi.org/10.3390/ijms27020726 (registering DOI) - 10 Jan 2026

Abstract

Systemic Lupus Erythematosus (SLE) and primary Sjögren’s Disease (SjD) are autoimmune diseases characterized by the presence of autoantibodies that lead to damage in healthy tissues. The production of autoantibodies requires the activation and differentiation of B-lymphocytes into plasma cells. To achieve this effect, BAFF (B-lymphocyte activating factor), APRIL (A proliferation-inducing ligand), and their receptors are key factors. BAFF is a cytokine recognized by BAFF-R (BAFF receptor), which is increased and related to disease activity in both SLE and SjD patients. The H159Y mutation (rs61756766) in the gene encoding the BAFF-R, TNFRSF13C (Tumor Necrosis Factor Receptor Superfamily) has been shown in vitro to cause receptor hyperactivation via the NF-κB2 pathway. This study evaluated the frequency of this variant in a western Mexican population and its association with the risk of developing SLE and SjD. Genotypes of the TNFRSF13C H159Y (rs61756766) variant were determined by PCR-RFLP assay. sBAFF levels were measured by ELISA. The study included 300 SLE patients, 110 SjD patients, and 300 healthy subjects (HS). HS were in Hardy–Weinberg equilibrium. The data distribution was assessed using the Kolmogorov–Smirnov test. Group comparisons were conducted using the Chi-square test, Fisher’s exact test, or the Mann–Whitney U test, as appropriate. A p-value of <0.05 was considered statistically significant. In the Mexican population, allelic and genotypic distribution frequencies of the H159Y variant (rs61756766) were similar between SLE patients and HSs, while the variant was not found in SjD patients. SLE patients carrying the heterozygous CT genotype showed a trend toward higher soluble BAFF (sBAFF) levels than wild-type genotype patients. This variant does not confer risk to SLE or SjD in the Mexican population. However, the heterozygous genotype may be associated with high levels of sBAFF in SLE patients. Full article

(This article belongs to the Special Issue Genetics and Omics in Autoimmune Diseases)

17 pages, 4818 KB

Open AccessArticle

Impact of PKC-MAPK Signaling on Cardiac Sympathetic Overactivation in Type-2 Diabetes Mellitus

by Jaswinder Singh, Afia Saabea Owusu Konadu, Yu Li, Boris Shabaltiy and Yu-Long Li

Int. J. Mol. Sci. 2026, 27(2), 723; https://doi.org/10.3390/ijms27020723 (registering DOI) - 10 Jan 2026

Abstract

Type-2 Diabetes Mellitus (T2DM) is related to cardiac arrhythmias. The stellate ganglion (SG), part of the sympathetic nervous system, regulates heart function. Within the SG, satellite glial cells (SGCs) have gap junction channels (Cx43). Increased Cx43 permeability induces SGC depolarization and activates the PKC-MAPK14-ADAM17 signaling pathway, releasing some endogenous factors that stimulate nearby cardiac postganglionic sympathetic neurons (CPSN). This study investigated the activation of the PKC-MAPK14-ADAM17 signaling pathway in T2DM SGs and SGCs as a novel mechanism of sympathetic overactivation. A total of 56 Sprague-Dawley rats were randomly assigned to sham and T2DM groups, and T2DM was induced using a high-fat diet combined with low-dose streptozotocin. Real-time RT-PCR, Western blot, and ELISA quantified mRNA/protein expression and enzymatic activity. The patch clamp technique assessed neuronal voltage-gated Ca²⁺ currents and action potentials, while electrophysiological recording measured cardiac sympathetic nerve activity (CSNA). T2DM rats exhibited marked upregulation of MAPK14, PKC-α, and ADAM17 mRNA/protein in the SG, alongside elevated enzymatic activities of PKC and ADAM17. T2DM also increased Ca²⁺ currents and neuronal excitability in the CPSN and induced the elevation of the CSNA. Upregulated PKC-MAPK-ADAM17 signaling in the SG might contribute to cardiac sympathetic overactivation in T2DM rats by enhancing the cell excitability of the CPSN. Full article

(This article belongs to the Special Issue New Trends in Diabetes, Hypertension and Cardiovascular Diseases: 4th Edition)

►▼ Show Figures

Figure 1

17 pages, 977 KB

Open AccessArticle

Effects of Pulsed Electric Field Technology on Whey Protein Concentrate

by Elizabeth L. Ryan and Owen M. McDougal

Molecules 2026, 31(2), 237; https://doi.org/10.3390/molecules31020237 (registering DOI) - 10 Jan 2026

Abstract

Whey protein concentrate (WPC-80) was reconstituted to 10% (m/v) and pumped through a pulsed electric field (PEF) system using three treatment conditions. The PEF-treated whey solution was assessed for viscosity, whereas dried whey was resolubilized and tested for protein structure integrity by circular dichroism (CD), fluorescence, and differential scanning calorimetry (DSC), and functionality was assessed by measuring solubility, foamability, emulsification, and particle size. PEF treatment resulted in a reduction in apparent viscosity (from 2.74 cP down to 2.57 cP) and particle size (from 325.9 nm down to 297.6 nm), and increased solubility (from 90.41% up to 92.34%) and emulsification stability (from 1727 min up to 4821 min), while emulsification stability decreased initially (from 1.645 m²/g to 1.283 m²/g) then increased at the high treatment level (1.915 m²/g). The foamability and molecular weight profile did not change with PEF treatment. Exposure to PEF resulted in no statistically significant changes to protein structure based on data obtained from CD, fluorescence, or DSC. This study represents the first instance of a WPC-80 being treated with a commercially available, scalable, continuous flow PEF system at a higher concentration (10% m/v), resulting in favorable changes to the physical and functional properties of the whey solution and dried powder. Full article

►▼ Show Figures

Figure 1

20 pages, 1258 KB

Open AccessArticle

Impacts of Hydrogen Blending on High-Rise Building Gas Distribution Systems: Case Studies in Weifang, China

by Yitong Xie, Xiaomei Huang, Haidong Xu, Guohong Zhang, Binji Wang, Yilin Zhao and Fengwen Pan

Buildings 2026, 16(2), 294; https://doi.org/10.3390/buildings16020294 (registering DOI) - 10 Jan 2026

Abstract

Hydrogen is widely regarded as a promising clean energy carrier, and blending hydrogen into existing natural gas pipelines is considered a cost-effective and practical pathway for large-scale deployment. Supplying hydrogen-enriched natural gas to buildings requires careful consideration of the safe operation of pipelines and appliances without introducing new risks. In this study, on-site demonstrations and experimental tests were conducted in two high-rise buildings in Weifang to evaluate the impact of hydrogen addition on high-rise building natural gas distribution systems. The results indicate that hydrogen blending up to 20% by volume does not cause stratification in building risers and leads only to a relatively minor increase in additional pressure, approximately 0.56 Pa/m for every 10% increase in hydrogen addition. While hydrogen addition may increase leakage primarily in aging indoor gas systems, gas meter leakage rates under a 10% hydrogen blend remain below 3 mL/h, satisfying safety requirements. In addition, in-service domestic gas alarms remain effective under hydrogen ratios of 0–20%, with average response times of approximately 19–20 s. These findings help clarify the safety performance of hydrogen-blended natural gas in high-rise building distribution systems and provide practical adjustment measures to support future hydrogen injection projects. Full article

(This article belongs to the Topic Advances in Building Sector for Achieving Net Zero Emission by 2050)

21 pages, 4327 KB

Open AccessArticle

A Multi-Data Fusion-Based Bearing Load Prediction Model for Elastically Supported Shafting Systems

by Ziling Zheng, Liang Shi and Liangzhong Cui

Appl. Sci. 2026, 16(2), 733; https://doi.org/10.3390/app16020733 (registering DOI) - 10 Jan 2026

Abstract

To address the challenge of bearing load monitoring in elastically supported marine shafting systems, a multi-data fusion-based prediction model is constructed. In view of the small-sample nature of measured bearing load data, transfer learning is adopted to migrate the physical relationships embedded in finite element simulations to the measurement domain. A limited number of actual samples are used to correct the simulation data, forming a high-fidelity hybrid training set. The system—supported by air-spring isolators mounted on the raft—is divided into multiple sub-regions according to their spatial layout, establishing local mappings from air-spring pressure variations to bearing load increments to reduce model complexity. On this basis, a Stacking ensemble learning framework is further incorporated into the prediction model to integrate multi-source information such as air-spring pressure and raft strain, thereby enriching the model’s information acquisition and improving prediction accuracy. Experimental results show that the proposed transfer learning-based multi-sub-region bearing load prediction model performs significantly better than the full-parameter input model. Furthermore, the strain-enhanced Stacking-based multi-data fusion bearing load prediction model improves the characterization of shafting features and reduces the maximum prediction error. The proposed multi-data fusion modeling strategy offers a viable approach for condition monitoring and intelligent maintenance of marine shafting systems. Full article

►▼ Show Figures

Figure 1

23 pages, 1453 KB

Open AccessArticle

SER-YOLOv8: An Early Forest Fire Detection Model Integrating Multi-Path Attention and NWD

by Juan Liu, Jiaxin Feng, Shujie Wang, Yian Ding, Jianghua Guo, Yuhang Li, Wenxuan Xue and Jie Hu

Forests 2026, 17(1), 93; https://doi.org/10.3390/f17010093 (registering DOI) - 10 Jan 2026

Abstract

Forest ecosystems, as vital natural resources, are increasingly endangered by wildfires. Effective forest fire management relies on the accurate and early detection of small–scale flames and smoke. However, the complex and dynamic forest environment, along with the small size and irregular shape of early fire indicators, poses significant challenges to reliable early warning systems. To address these issues, this paper introduces SER–YOLOv8, an enhanced detection model based on the YOLOv8 architecture. The model incorporates the RepNCSPELAN4 module and an SPPELAN structure to strengthen multi-scale feature representation. Furthermore, to improve small target localization, the Normalized Wasserstein Distance (NWD) loss is adopted, providing a more robust similarity measure than traditional IoU–based losses. The newly designed SERDet module deeply integrates a multi–scale feature extraction mechanism with a multi-path fused attention mechanism, significantly enhancing the recognition capability for flame targets under complex backgrounds. Depthwise separable convolution (DWConv) is utilized to reduce parameters and boost inference efficiency. Experiments on the M4SFWD dataset show that the proposed method improves mAP50 by 1.2% for flames and 2.4% for smoke, with a 1.5% overall gain in mAP50–95 over the baseline YOLOv8, outperforming existing mainstream models and offering a reliable solution for forest fire prevention. Full article

(This article belongs to the Section Natural Hazards and Risk Management)

16 pages, 5275 KB

Open AccessArticle

A Study of Absolute Pressure Inside the Cabins of Land Transport Vehicles—The Concept of a Ventilation System Regulating the Pressure in the Vehicle

by Tomasz Janusz Teleszewski and Katarzyna Gładyszewska-Fiedoruk

Sensors 2026, 26(2), 469; https://doi.org/10.3390/s26020469 (registering DOI) - 10 Jan 2026

Abstract

This paper presents the concepts of a vehicle pressure regulation ventilation system based on the results of absolute pressure measurements in land transport vehicles: passenger cars, buses and trains. Despite the fact that absolute pressure affects human well-being and health, this parameter is often overlooked in studies assessing thermal comfort. Absolute pressure measurements were taken during normal passenger transport operation. The studies were conducted for various terrain types: lowlands, highlands, and mountains. Absolute pressure fluctuations in land transport depended primarily on altitude, with the largest atmospheric pressure differences recorded in mountains and the smallest in lowlands. A pressure change of 8 hPa within a 24 h period constitutes an unfavorable mechanical stimulus for the human body and causes changes in the excitability of the nervous system. In all measurement series, absolute pressure fluctuations exceeded 8 hPa. Based on the results of absolute pressure measurements and altitude, a simplified model for predicting absolute pressure in transport vehicles was developed. To reduce absolute pressure fluctuations inside passenger land vehicle cabins, a ventilation scheme regulating pressure inside land vehicle cabins was proposed. Full article

(This article belongs to the Special Issue IoT Sensor Systems: Design, Interfaces, Signals, Processing, and Applications)

►▼ Show Figures

Figure 1

26 pages, 27748 KB

Open AccessArticle

LiDAR-Based Skin Depth Analysis of Subterranean RF Propagation in Sandstone and Limestone Caves

by Atawit Jantaupalee, Sirigiet Phunklang, Peerasan Khamsalee, Piyaporn Krachodnok and Rangsan Wongsan

Technologies 2026, 14(1), 53; https://doi.org/10.3390/technologies14010053 (registering DOI) - 10 Jan 2026

Abstract

This study investigates radio frequency (RF) wave propagation in sandstone and limestone cave environments, emphasizing the use of LiDAR-derived three-dimensional (3D) models to characterize cave geometry and support waveguide-based propagation analysis incorporating skin depth effects. RF transmission and reception measurements were conducted under line-of-sight (LOS) conditions across frequency bands from Low Frequency (LF) to Ultra-High Frequency (UHF). Comparative results reveal distinct attenuation behaviors governed by differences in cave geometry and electrical properties. The sandstone cave, with a more uniform geometry and relatively higher electrical conductivity, exhibits lower attenuation across most frequency bands, whereas the limestone cave shows higher attenuation due to its irregular structure. LiDAR-based 3D models are employed to extract key geometric parameters, including cavity dimensions, wall roughness, and wall inclination, which are incorporated into the proposed analytical framework. The model is further validated using experimental field measurements, demonstrating that the inclusion of LiDAR-derived geometry and skin depth effects enables a more realistic representation of underground RF propagation for communication system analysis. Full article

(This article belongs to the Section Information and Communication Technologies)

►▼ Show Figures

Figure 1

21 pages, 2158 KB

Open AccessArticle

Machine Learning-Based Prediction of Breakdown Voltage in High-Voltage Transmission Lines Under Ambient Conditions

by Mujahid Hussain, Muhammad Siddique, Farhan Hameed Malik, Zunaib Maqsood Haider and Ghulam Amjad Hussain

Eng 2026, 7(1), 36; https://doi.org/10.3390/eng7010036 (registering DOI) - 10 Jan 2026

Abstract

Reliability and safety of high-voltage transmission lines are essential for stable and continuous operation of a power system. Environmental factors such as pressure, temperature, surface contamination, humidity, etc., significantly affect the dielectric strength of air, often causing unpredictable voltage breakdowns. This research presents [...] Read more.

(V_{b k})

of transmission lines in various environmental conditions. The main contribution is to demonstrate that data-driven prediction of breakdown voltage

(V_{b k})

using a hybrid machine learning model is in agreement with physical discharge theory. The model achieved root mean square error (RMSE) of 5.2% and mean absolute error (MAE) of 3.5% when validated against field data. Despite the randomness of avalanche breakdown, model predictions strongly match experimental measurements. This approach enables early detection of insulation stress, real-time monitoring, and optimises maintenance scheduling to reduce outages, costs, and safety risks. Its robustness is confirmed experimentally. Overall, this work advances the prediction of avalanche breakdown behaviour using machine learning. Full article

34 pages, 951 KB

Open AccessReview

Life as a Categorical Information-Handling System: An Evolutionary Information-Theoretic Model of the Holobiont

by Antonio Carvajal-Rodríguez

Biology 2026, 15(2), 125; https://doi.org/10.3390/biology15020125 (registering DOI) - 10 Jan 2026

Abstract

Living systems can be understood as organized entities that capture, transform, and reproduce information. Classical gene-centered models explain adaptation through frequency changes driven by differential fitness, yet they often overlook the higher-order organization and causal closure that characterize living systems. Here we revisit several evolutionary frameworks, from the replicator equation to group selection and holobiont dynamics, and show that evolutionary change in population frequencies can be expressed as a Jeffreys divergence. Building on this foundation, we introduce a categorical model of Information Handlers (IHs), entities capable of self-maintenance, mutation, and combination. This abstract architecture illustrates the usefulness of category theory for framing evolutionary processes that range from very simple to highly complex. The same categorical scheme can represent basic allele-frequency change as well as more elaborate scenarios involving reproductive interactions, symbiosis, and other organizational layers. A key feature of the framework is that different levels of evolutionary change can be summarized through a measure that quantifies the information generated, thereby distinguishing diverse types of evolutionary transformation, such as individual and sexual selection, mate choice, or even holobiont selection. Finally, we show that the informational partition associated with host–microbiome pairings in holobionts generalizes the information-theoretic structure previously developed for non-random mating, revealing a common underlying architecture across biological scales. Full article

20 pages, 465 KB

Open AccessArticle

Cross-Assessment & Verification for Evaluation (CAVe) Framework for AI Risk and Compliance Assessment Using a Cross-Compliance Index (CCI)

by Cheon-Ho Min, Dae-Geun Lee and Jin Kwak

Electronics 2026, 15(2), 307; https://doi.org/10.3390/electronics15020307 (registering DOI) - 10 Jan 2026

Abstract

This study addresses the challenge of evaluating artificial intelligence (AI) systems across heterogeneous regulatory frameworks. Although the NIST AI RMF, EU AI Act, and ISO/IEC 23894/42001 define important governance requirements, they do not provide a unified quantitative method. To bridge this gap, we propose the Cross-Assessment & Verification for Evaluation (CAVe) Framework, which maps shared regulatory requirements to four measurable indicators—accuracy, robustness, privacy, and fairness— and aggregates them into a Cross-Compliance Index (CCI) using normalization, thresholding, evidence penalties, and cross-framework weighting. Two validation scenarios demonstrate the applicability of the approach. The first scenario evaluates a Naïve Bayes-based spam classifier trained on the public UCI SMS Spam Collection dataset, representing a low-risk text-classification setting. The model achieved accuracy 0.9850, robustness 0.9945, fairness 0.9908, and privacy 0.9922, resulting in a CCI of 0.9741 (Pass). The second scenario examines a high-risk healthcare AI system using a CheXNet-style convolutional model evaluated on the MIMIC-CXR dataset. Diagnostic accuracy, distribution-shift robustness, group fairness (finding-specific group comparison), and privacy risk (membership-inference susceptibility) yielded 0.7680, 0.7974, 0.9070, and 0.7500 respectively. Under healthcare-oriented weighting and safety thresholds, the CCI was 0.5046 (Fail). These results show how identical evaluation principles produce different compliance outcomes depending on domain risk and regulatory priorities. Overall, CAVe provides a transparent, reproducible mechanism for aligning technical performance with regulatory expectations across diverse domains. Additional metric definitions and parameter settings are provided in the manuscript to support reproducibility, and future extensions will incorporate higher-level indicators such as transparency and human oversight. Full article

(This article belongs to the Special Issue Artificial Intelligence Safety and Security)

►▼ Show Figures

Figure 1

25 pages, 546 KB

Open AccessArticle

Dynamic Analysis and Optimal Prevention Strategies for Monkeypox Spread Modeled via the Mittag–Leffler Kernel

by Mine Yurtoğlu, Dilara Yapışkan, Ebenezer Bonyah, Beyza Billur İskender Eroğlu, Derya Avcı and Delfim F. M. Torres

Fractal Fract. 2026, 10(1), 44; https://doi.org/10.3390/fractalfract10010044 (registering DOI) - 10 Jan 2026

Abstract

Monkeypox is a viral disease belonging to the smallpox family. Although it has milder symptoms than smallpox in humans, it has become a global threat in recent years, especially in African countries. Initially, incidental immunity against monkeypox was provided by smallpox vaccines. However, the eradication of smallpox over time and thus the lack of vaccination has led to the widespread and clinical importance of monkeypox. Although mathematical epidemiology research on the disease is complementary to clinical studies, it has attracted attention in the last few years. The present study aims to discuss the indispensable effects of three control strategies such as vaccination, treatment, and quarantine to prevent the monkeypox epidemic modeled via the Atangana–Baleanu operator. The main purpose is to determine optimal control measures planned to reduce the rates of exposed and infected individuals at the minimum costs. For the controlled model, the existence-uniqueness of the solutions, stability, and sensitivity analysis, and numerical optimal solutions are exhibited. The optimal system is numerically solved using the Adams-type predictor–corrector method. In the numerical simulations, the efficacy of the vaccination, treatment, and quarantine controls is evaluated in separate analyzes as single-, double-, and triple-control strategies. The results demonstrate that the most effective strategy for achieving the aimed outcome is the simultaneous application of vaccination, treatment, and quarantine controls. Full article

(This article belongs to the Special Issue Fractional Systems, Integrals and Derivatives: Theory and Application)

10 pages, 259 KB

Open AccessArticle

Kolmogorovian Censorship, Predictive Incompleteness, and the Locality Loophole in Bell Experiments

by Philippe Grangier

Entropy 2026, 28(1), 80; https://doi.org/10.3390/e28010080 (registering DOI) - 10 Jan 2026

Abstract

We revisit the status of quantum probabilities in light of Kolmogorovian Censorship (KC) and the Contexts, Systems, and Modalities (CSM) framework, and we discuss KC-based ideas with respect to superdeterminism, counterfactuality, and predictive incompleteness. After briefly recalling the technical content of KC and its scope, we show that KC correctly identifies that probabilities are classical within a fixed measurement context but does not by itself remove the conceptual tension that motivates nonlocal or conspiratorial explanations of Bell inequality violations. We argue that predictive incompleteness—the view that the quantum state is operationally incomplete until the measurement context is specified—provides a simple, minimal, and explanatory framework that preserves relativistic locality while matching experimental practice. Finally we clarify logical relations among these positions, highlight the assumptions behind them, and justify the move from Kolmogorov’s to Gleason’s framework for quantum probabilities. Full article

(This article belongs to the Special Issue Quantum Information and Probability: From Foundations to Engineering IV)

Show export options Show export options

Select all

Export citation of selected articles as:

Error

Oops... you haven't selected anything for export.

Displaying article 1-50 on page 1 of 1386.

Go to page 1 2 3 4 5

Search Results (69,282)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI