Search Results (6,669)

Background: Following a significant decline during the 2020–2021 SARS-CoV-2 pandemic, Mycoplasma pneumoniae (MP) experienced a resurgence across Europe in 2023–2024. Although macrolide-resistant MP has increased globally, severe disease can occur even in the absence of resistance, which highlights the importance of rapid molecular characterization for clinical purposes. In this context, clinical severity is often improperly used as a surrogate marker of macrolide resistance, potentially driving unnecessary antibiotic escalation. Methods: We report a severe MP pneumonia occurring during the 2023–2024 resurgence and evaluate macrolide resistance through a rapid two-step workflow (Real Time-PCR screening for A2063G/A2064G followed by confirmatory 23S rRNA sequencing), to assess whether severity predicts resistance and to support antibiotic stewardship. Results: The patient developed acute hypoxic respiratory failure (PaO₂ 54.9 mmHg; P/F ratio 110), extensive centrilobular micronodules on chest CT imaging, significant systemic inflammation and elevated liver enzymes. Respiratory support was escalated from a Venturi mask to a high-flow nasal cannula and BiPAP. MP infection was confirmed by multiplex Real Time-PCR (RT-PCR) and supported by positive IgM/IgG serology. RT-PCR targeting A2063G/A2064G mutations revealed no resistance-associated variants, and Sanger sequencing of an 807 bp 23S rRNA fragment confirmed a wild-type genotype. Despite severe hypoxemic respiratory failure, no resistance-associated variants were detected, documenting a clear severity–genotype mismatch. Clinical and radiological improvement followed second-line antibiotic therapy. Conclusions: Severe MP pneumonia can occur despite the absence of macrolide resistance. During MP re-emergence, clinical severity should not be used to infer macrolide resistance. Integrating nucleic acid amplification test (NAAT) diagnosis with rapid genotyping/confirmatory 23S rRNA sequencing can prevent misclassification, reduce unwarranted broad-spectrum escalation, and strengthen antimicrobial stewardship decisions. Full article

The highly nonlinear aerodynamic interference in tandem-airfoil configurations significantly hinders the precise exploitation of their aerodynamic potential. To address this issue, this study establishes a high-fidelity computational fluid dynamics benchmark. A high-quality sample set is constructed using Latin hypercube sampling combined with an intra-layer replacement strategy. Subsequently, a Gaussian process surrogate model and Bayesian optimization are employed to maximize the total system lift coefficient across a four-dimensional design space comprising longitudinal and vertical separations, fore airfoil angle of attack, and angle of attack difference. Global sensitivity analysis indicates that longitudinal separation dominates the interference modes. Optimization reveals a distinct mode switching phenomenon using a longitudinal separation of twice the chord length as the critical threshold. In the close-coupled configuration, a negative optimal angle of attack difference enhances the slot effect and upwash induction, thereby delaying rear airfoil stall and achieving synergistic lift enhancement. Conversely, in the distant-coupled configuration, the system transitions to a decoupled compensation mode, where a positive angle of attack difference compensates for the effective angle of attack loss induced by wake downwash. This research elucidates the competitive mechanisms between inter-airfoil slot flow and wake interference, providing a theoretical reference for the aerodynamic layout optimization of tandem-airfoil aircraft. Full article

To realise high-speed free-space optical communication links in harsh space environments, it is crucial to consider the link’s operating wavelength, the performance of the optical receiver, and the radiation hardness of the avalanche photodiode (APD)—optical detectors in the optical receivers. In this work, we experimentally evaluated the radiation hardness of 2.5 Gb/s receivers based on InGaAs/AlGaAsSb APDs integrated with Ommic CGY2102UH/C2 transimpedance amplifiers. Proton energy (62 MeV) and fluence (up to 3.8 × 10¹⁰ p/cm²) representative of space environments were used to irradiate multiple receivers, ensuring rigour. After irradiation, the receivers maintained their avalanche gain and photocurrent, while exhibiting bandwidths exceeding 1.5 GHz. Despite a slight increase in APD’s dark current at high reverse bias, there was no degradation of the receiver’s bit error rate. At 2.5 Gb/s data rate and 1550 nm wavelength, the irradiated receivers achieved a bit error rate of 10⁻⁹ with an average optical power of −38.2 dBm, outperforming selected commercial receivers by ~3 dB. Since the displacement damage dose induced by the proton radiation levels used in this work are representative of those in Low Earth, Geostationary and Global Positioning System orbits, we demonstrated that InGaAs/AlGaAsSb APDs have sufficient radiation hardness to be employed as optical detectors of high-speed optical links in harsh space environments. Full article

Cooperative localization enables teams of robots to maintain better positioning in GNSS-denied environments by sharing state estimates and inter-robot range measurements to reduce the rate of proprioceptive odometry drift. In scenarios without fixed navigation beacons or pre-surveyed reference nodes, each robot functions as both a positioning client and a mobile ranging peer. A critical vulnerability in this architecture is silent fault propagation. A robot with a degraded localization solution may broadcast an incorrect, often overconfident position estimate, corrupting its peers’ localization. Classical Global Navigation Satellite System (GNSS) Receiver Autonomous Integrity Monitoring (RAIM) methods are ineffective in this context because meter-scale inter-robot separations introduce strong geometric nonlinearity and unstable Geometric Dilution of Precision (GDOP), resulting in scattered subset solutions rather than the coherent, biased clusters that RAIM is designed to detect. This paper addresses this vulnerability by proposing a two-stage distributed Fault Detection and Identification (FDI) architecture for peer-to-peer ranging-based cooperative localization. The first stage applies a global chi-square test on Weighted Least-Squares trilateration residuals to detect the presence of a fault. The second stage identifies the faulty robot by computing Leave-One-Out and Leave-Two-Out subset solutions, which are then partitioned using a clustering algorithm. The cluster that exempts measurements from the faulty robot is identified using either a maximum-cardinality or a minimum-variance criterion. A decentralized voting protocol that requires at least two independent corroborations is then employed for network-wide fault declaration. Monte Carlo simulations show that the clustering-based identification method outperforms classical residual-based methods across multiple fault types, with results reported for the planar (2D) case. No single clustering configuration dominates in terms of identification performance across all tested fault conditions, as performance varies with the fault profile. The proposed architecture operates fully in a distributed manner, requiring only the exchange of position estimates, covariances, and binary votes. Full article

Coffee is among the most widely consumed beverages globally being cultivated in nearly 80 countries. Its processing generates large quantities of by-products, including mucilage, pulp/husks, silverskin, parchment, and spent coffee grounds. Although traditionally treated as waste, these residues are increasingly recognized as valuable resources rich in bioactive compounds exhibiting antioxidant, antimicrobial, and health-promoting properties. This review explores the antimicrobial potential of coffee by-products, with particular emphasis on their chemical composition and mechanisms of action. Compounds such as caffeine, chlorogenic acids, polyphenols, and melanoidins have demonstrated inhibitory effects against a broad spectrum of bacteria, including both Gram-positive and Gram-negative bacteria. Many of these compounds, which originate from plant’s defensive system or result from Maillard reactions, are known to disrupt microbial membranes, inhibit DNA repair, and interfere with pathogen metabolism. However, the available literature on their antimicrobial effectiveness remains limited. In the context of the rising worldwide concern over antimicrobial resistance, coffee by-products represent a sustainable and promising source of novel antimicrobial agents. Their valorization may support advances in food preservation, pharmaceutical innovation, and waste management practices, contributing to the implementation of a circular economy framework in the coffee industry while promoting environmental, economic, and social sustainability. Full article

Iterative learning contouring control (ILCC) improves contouring accuracy in multi-axis motion systems via the equivalent contour error formulation. However, its convergence strongly depends on the learning gain. Large gains may induce overly aggressive updates and local divergence, degrading performance, whereas small gains lead to slow convergence. Moreover, contour error convergence is typically non-uniform along the trajectory, and local divergence may still occur despite global convergence, particularly near error saturation regions. To address these issues, a fuzzy inference mechanism is integrated into the online ILCC framework, yielding an online ILCC with fuzzy-regulated convergence parameters (online ILCCf), enabling adaptive regulation of the learning gain. Two regulation strategies are developed: (i) online ILCCf${}_{\mathrm{i}}$, an independent multi-parameter regulation scheme; and (ii) online ILCCf${}_{\mathrm{u}}$, a unified single-parameter regulation scheme. The fuzzy mechanism adaptively adjusts the convergence parameters online according to the instantaneous magnitude of the equivalent contour error. Experimental results on a six-axis industrial robot demonstrate fast convergence while maintaining satisfactory contouring performance. Among all comparison cases , online ILCCf${}_{\mathrm{i}}$ achieves the best performance, reducing the RMS position error from $7.26\times {10}^{-1}$ mm to $5.93\times {10}^{-2}$ mm and the RMS orientation error from $6.95\times {10}^{-4}$ rad to $5.64\times {10}^{-5}$ rad, without oscillation or local divergence. Further simulations confirm robustness under model uncertainty and measurement noise. Full article

Background/Objectives: The integration of Artificial Intelligence (AI) into clinical workflows raises critical questions regarding decision-making responsibility, as fully autonomous systems inevitably carry a margin of error that can be fatal in high-stakes fields like surgery. This study addresses this challenge by evaluating a “Human-in-the-Loop” (HITL) workflow, using intraoperative Optical Coherence Tomography (OCT) for glioma detection. We aimed to determine if integrating Machine Learning (ML)-generated segmentation maps with human contextual analysis resolves the tension between automation and clinical responsibility, yielding superior diagnostic reliability compared to structural or quantitative imaging alone. Methods: We retrospectively analyzed 86 intraoperative OCT scans from 27 patients. Five neurosurgeons blindly assessed the data across three progressive levels of processing: (1) structural scans, (2) physics-based parametric maps, and (3) SVM-based generated segmentation maps. Crucially, the HITL inference performance on segmentation maps was benchmarked against “models-only” inference pipeline: a SVM and a state-of-the-art multimodal reasoning model, Gemini 3.1 Pro. To evaluate interpretability and the operator’s ability to confidently exercise their authority, we measured inter-rater consistency alongside diagnostic performance. Results: The results demonstrate that, while quantitative parametric maps improved Global Accuracy (87% [95% CI: 82–92%]) compared to structural scans (80% [95% CI: 73–86%]), they suffered from an “interpretability gap,” resulting in a moderate inter-rater consistency of 0.68 [95% CI: 0.59–0.78]. In contrast, the HITL approach using segmentation maps maximized consensus to 0.98 [95% CI: 0.95–1.00] and achieved the highest performance (Accuracy 94% [95% CI: 88–98%] and Sensitivity 98% [95% CI: 92–100%]). Compared to the standalone models, the HITL approach significantly outperformed the SVM baseline (Accuracy 84% [95% CI: 81–87%]; Sensitivity 83% [95% CI: 78–88%]). Furthermore, it surpassed the SOTA Gemini 3.1 Pro model (Accuracy 90% [95% CI: 83–95%]; Sensitivity 86% [95% CI: 74–95%]). While the HITL sensitivity demonstrated a definitive and statistically significant edge over the Gemini model, the accuracy improvement fell just slightly short of undisputed statistical significance due to overlapping confidence intervals. Conclusions: By utilizing their clinical domain knowledge of tumor invasion patterns and topological priors, surgeons effectively filtered algorithmic noise—overriding ML errors in 69% (9 out of 13) false positive cases that models alone could not resolve. This demonstrates exactly how and where HITL optimally utilizes human contextual intelligence to outperform autonomous “models-only” pipelines, confirming a human-ML synergy that augments the objectivity of machine learning with human domain knowledge. This paradigm ensures that the ultimate responsibility for diagnostic inference remains safely and practically in human hands. Open Data Initiative: To ensure essential reproducibility, enable independent multi-center validation and support open science, all examples of intraoperative in vivo OCT brain scans used in this study are made publicly available. To the best of our knowledge, this represents the first open-access data of its kind globally. Full article

This study examines how digital transformation in higher education institutions (HEIs) contributes to public value creation, moving beyond efficiency-oriented narratives toward broader societal outcomes. Using a PRISMA-ScR approach, the study systematically reviews 47 peer-reviewed articles published between 2013 and 2025 across major academic databases. The review maps the evolution of scholarship and identifies the key mechanisms through which digital transformation influences public value. The findings reveal three interrelated dimensions shaping outcomes: digital competencies, institutional readiness, and governance alignment. Digital competencies enable the effective adoption and use of technologies, while institutional readiness—comprising digital infrastructure, leadership capacity, and organizational culture—acts as a mediating condition influencing implementation success. Governance alignment, including regulatory coherence, accountability mechanisms, and stakeholder engagement, plays a moderating role in determining whether digital transformation initiatives generate inclusive and socially beneficial outcomes. In addition to positive outcomes such as improved access, service quality, and transparency, the review identifies critical risks—including digital inequality, data governance challenges, and algorithmic bias—that may constrain public value creation, particularly in resource-constrained and Global South contexts. Building on these findings, the study develops the Global Digital Transformation—Public Value Creation (G-DTPVC) framework as an evidence-synthesized model derived from the reviewed literature. The framework specifies key constructs, causal relationships, and indicative measures to support future empirical research and policy application. By linking digital transformation processes in HEIs to broader public value outcomes and Sustainable Development Goals (SDGs 4, 9, and 16), this study advances theoretical understanding and provides actionable, context-sensitive guidance for policymakers and institutional leaders seeking to foster inclusive, accountable, and resilient higher education systems. Full article

In this paper, we consider a reaction–diffusion system governed by incommensurate fractional time derivatives based on the Degn–Harrison model. Its formulation incorporates various memory effects on axial position through Caputo derivatives of variable orders, producing a more realistic modeling of the temporal dynamics. This paper starts with a study of the spatially homogeneous system and establishes conditions for local stability by using the Matignon criterion. The spectral decomposition method under Neumann boundary condition is then applied to study the complete reaction–diffusion system and describe diffusion-induced instabilities. Our results indicate that the noninteger fractional orders lead to significant changes in stability regions, as well as the initiation of pattern formation. Specifically, the orders of fractions induced as a control variable are regarded to be effective in controlling the stability of the system, thus they are global (or positive) control variables when their values achieved at some levels apply to the entire saturation, etc. Our numerical simulations are in excellent agreement with the theoretical predictions and show that memory asymmetry induces complex spatiotemporal dynamics not seen for classical integer-order systems. Full article

Cervical cancer remains a major global health challenge, particularly in low- and middle-income countries, where access to screening, vaccination, and timely treatment may be limited. Cervical cytology has played an important historical role in prevention, but it is labor-intensive, time-consuming, and subject to observer variability and limited sensitivity. In many contemporary screening programs, HPV testing is now used as the primary screening test, while cytology is used mainly for the triage of HPV-positive women. In recent years, artificial intelligence (AI), particularly deep learning (DL), has shown considerable potential in medical image analysis and computer-aided diagnosis. This review summarizes current applications of AI in cervical cytology and related diagnostic workflows, including automated and assisted slide screening, liquid-based cytology, the triage of equivocal or HPV-positive cases, and colposcopy support. Across these settings, AI-assisted systems may improve efficiency, standardization, and diagnostic consistency, and may reduce workload in resource-constrained environments. However, the evidence is heterogeneous, and important challenges remain, including the need for large and diverse datasets, prospective validation, regulatory approval, digital infrastructure, workflow integration, and the resolution of ethical and legal issues. AI should therefore be regarded as a promising adjunct to human expertise rather than a replacement in cervical cytology and related clinical diagnostic pathways. Full article

The study investigates position-dependent variability in the slitting and rewinding process of filtration paper rolls under industrial conditions. Although individual cutting positions operate under identical machine settings, systematic differences between them lead to quality deviations and reduced process performance. Spatial variability was analyzed using descriptive statistics, control charts, and process performance indices (P_p, P_pk), complemented by non-parametric statistical testing. The results revealed a significant spatial effect, with one slitting position responsible for most nonconforming products, highlighting the limitations of global capability indices, which may mask local systematic deviations in a multi-stream process. Potential root causes were identified using the 5 Whys method within the Quick Response Quality Control (QRQC) methodology. Following the implementation of corrective actions, including parameter adjustments, position-dependent control, and revised operating procedures, the observed proportion of nonconforming products reduced from 14.7% to 6.0%. Furthermore, after excluding the first rolls from the start-up phase, process performance improved to P_p = 1.36 and P_pk = 1.21. The study suggests that integrating global and position-level analysis in multi-stream manufacturing systems enables more targeted identification and mitigation of quality deviations. Full article

Background: Patient navigation has been recognized as a promising strategy to address fragmented cancer care; however, evidence from low- and middle-income countries (LMICs) remains limited, particularly regarding how navigation-related needs are associated with patient-reported outcomes. Objective: This study aimed to examine the association between multidimensional patient navigation needs and quality of life (QoL) among women with gynecological cancer in Indonesia. Methods: A cross-sectional study was conducted among 128 women diagnosed with gynecological cancer at a referral hospital in Indonesia. Patient navigation needs were assessed using a 37-item multidimensional instrument developed based on international frameworks, while QoL was measured using the EORTC QLQ-C30. Data were analyzed using Pearson correlation and multiple linear regression to evaluate the relationships and relative contributions of navigation need domains to QoL. Results: The mean global health status score indicated relatively low QoL (Mean = 41.7, SD = 31.0). Most domains of patient navigation needs were significantly and negatively associated with QoL (p < 0.001), with the strongest correlation observed for total navigation needs (r = −0.657). Multivariable analysis showed that administrative and financial needs showed the strongest association with poorer QoL (β = −0.373, p < 0.001), followed by psychosocial, cultural, and family support needs (β = −0.356, p < 0.001). In contrast, late-stage clinical needs were positively associated with QoL (β = 0.206, p = 0.005). The model explained 59.5% of the variance in QoL. Conclusions: Patient navigation needs are strongly associated with QoL among women with gynecological cancer, highlighting the critical role of system-level and psychosocial factors in shaping patient outcomes. Addressing administrative complexity, financial burden, and psychosocial support gaps is essential for improving QoL in LMIC settings. These findings provide novel evidence for developing context-specific, integrated patient navigation models to enhance cancer care delivery. Full article

The autoantibody reactome refers to the multidimensional repertoire of antibody reactivities against self-antigens across the human proteome or selected antigenic compartments. This offers a scalable systemic layer for precision immunology across spontaneous autoimmunity and treatment-induced immune toxicity. Autoimmune diseases and immune-related adverse events (irAEs) share major features of dysregulated immunity, yet clinically useful tools for risk stratification, early detection, endotyping, and treatment guidance remain limited and slow. A central challenge is that tissue pathology is highly informative but not uniformly accessible across diseases and organ systems, whereas routine serology captures only a narrow fraction of immune heterogeneity. In this perspective, I argue that a global autoantibody reactome can serve as a central unifying framework linking systemic immune history, tissue pathology, and clinical trajectories across autoimmune disorders and irAEs. Rheumatoid arthritis (RA) provides a strong prototype because its serological diversity, major role of post-translationally modified autoantigens, and marked synovial heterogeneity allow reactome features to be interpreted against tissue biology. Immune checkpoint inhibitor-associated inflammatory arthritis serves as an illustrative rheumatic irAE and a model of treatment-induced immune dysregulation with clear opportunities for longitudinal blood-based profiling. Spatial transcriptomics and proteomics are therefore positioned not as stand-alone solutions, but as mechanistic tools that can decode reactome-defined immune states within tissue microenvironments where tissue is accessible. Clinical translation will require integration of autoantibody reactomes with tissue, circulating proteomic, imaging, genetic, and clinical data through transparent multimodal models, as well as a shift from exploratory resources such as AAgAtlas toward analytically validated and clinically interpretable biomarker panels for risk prediction, endotyping, monitoring, and biomarker-guided intervention. This perspective outlines technical and strategic steps toward clinically actionable decision support, including risk stratification before ICI initiation and treatment guidance for patients who develop ICI-induced inflammatory arthritis, through integration of autoantibody reactome profiling, spatial omics and transparent multimodal AI. Full article

We study the incompressible fractional viscous–resistive magnetohydrodynamic system on ${\mathbb{R}}^n$ with fractional diffusion ${(-\Delta )}^{\alpha }$, where $\alpha \in (1/2,1]$, and with positive viscosity and resistivity coefficients $\mu ,\nu >0$. The problem is treated at the scale-invariant regularity $s_c={\displaystyle \frac{n}{p}}+1-2\alpha .$ For small divergence-free initial data in the critical Triebel–Lizorkin–Lorentz space ${\dot{F}}_{p,r}^{s_c,q}$, we construct a unique global mild solution. The main contribution is the use of the single-norm time–frequency space $m_m^{\prime }{\dot{F}}_{p,r}^{s_c,q}$, built on Meyer wavelets and the parabolic gauge $t{2}^{2\alpha j}$. This space keeps the critical spatial size, the short-time behavior, and the high-frequency decay in one norm. By using a Gevrey-weighted Duhamel formulation, we prove boundedness of the corresponding fractional heat propagators and establish the bilinear paraproduct estimate required for the fixed-point argument. Consequently, $e^{{\left(t{(-\Delta )}^{\alpha }\right)}^{\gamma }}(u,b)\in {\left(m_m^{\prime }{\dot{F}}_{p,r}^{s_c,q}\right)}^{2n}$ for some $\gamma >0$ depending on the parameters. This gives a Gevrey-type spatial smoothing effect, which is stronger than ordinary analyticity in the adopted scale. The restriction $\alpha >{\displaystyle \frac{1}{2}}$ enters through the factor $2^{j(1-2\alpha )}$, which supplies the high-frequency gain needed to close the critical bilinear estimates; in this sense it is sharp for the present method. The classical viscous–resistive case is recovered when $\alpha =1$. Full article

The agricultural industry is at a critical juncture, experiencing global pressures in the form of climate volatility, a shortage of labor, and an increase in production costs. Although artificial intelligence (AI) has the potential for revolution due to its predictive analytics and self-controlled machinery, it has not achieved widespread and even distribution for use, especially among small-to-medium-sized farms in the Midwestern United States. This study formulates and empirically examines a comprehensive socio-technical model to determine the drivers and barriers to the adoption of AI in this agricultural region. Based on a synthesized framework of the “Unified Theory of Acceptance and Use of Technology” (UTAUT) and “Task–Technology Fit” (TTF), the study incorporates agriculture-specific contextual factors such as “environmental risk, access to broadband, economic constraints, and policy support”. The analyses of the 489 farmers in the U.S. Midwest were conducted through the “partial least squares structural equation modeling” (PLS-SEM) “SmartPLS v.3.9”. The findings provide full empirical evidence of the proposed model, which supports 11 hypothesized relationships. The key results show that the strongest positive predictors of adoption intention are “performance expectancy, effort expectancy, and trust”. On the other hand, data security concerns and financial restrictions are strong deterrents. The paper also outlines the significant facilitating functions of the broadband infrastructure and policy support in building farmer perceptions of technology’s ease-of-use and facilitating conditions. These lessons can provide policymakers, ag-tech developers, and extension agencies with a roadmap on how to create more equitable and contextual interventions that overcome the rural digital divide and create resilient data-driven farming systems. Full article