Search Results (31,349)

The interaction of electromagnetic waves with atmospheric aerosols plays a significant role in communication systems and multimedia information transmission. Understanding the interaction of vortex light beams with an aerosol-laden atmosphere is indispensable for establishing a framework of the environmental channel. During the interaction, different optical effects such as absorption and scattering will result in energy attenuation, and this yields the deterioration of the transmission feature of the vortex beam signal. In this study, we present a theoretical analysis of Gaussian vortex beams (GVBs) scattering by diverse aerosol (unformed carbon, dust, sulphate, silicate, soot, and nitrate) particles in the atmosphere on the basis of the well-established generalized Lorenz–Mie theory (GLMT). Combined with the lognormal distribution model for aerosol particles, the attenuation and transmission characteristics of GVBs for different aerosol particles are analyzed. The extinction efficiency $\left(Q_{ext}\right)$ factor of GVB, caused by the absorption and scattering of various aerosols, becomes smaller compared to that of a basic Gaussian beam (GB). Increasing the OAM mode index, the energy attenuation and transmission caused by aerosol absorption and scattering further decrease. Moreover, this research provides a basis to analyze the optical characteristics of the twisted beams in different atmospheric channels, such as wireless communication networks over aerosol-laden systems and material interactions. Full article

Background/Objectives: Impacted maxillary canines are frequently associated with variations in maxillary arch morphology; however, the relationship between impaction position and three-dimensional arch characteristics remains unclear. This study aimed to evaluate the association between buccally and palatally impacted maxillary canines and maxillary arch morphology using CBCT and three-dimensional digital model analysis. Methods: This retrospective cross-sectional study included CBCT images and three-dimensional dental models of 86 individuals with unilateral impacted maxillary canines (mean age: 16.1 ± 0.72 years). Impacted canines were classified as buccal or palatal according to CBCT findings. Maxillary arch morphology was assessed using digital model analysis. Statistical comparisons between groups were performed using independent-samples t-tests (p < 0.05). Results: The buccally impacted group demonstrated significantly greater arch length, higher arch length-to-arch width ratios, greater mesiodistal width of the four maxillary incisors and increased tooth–arch discrepancy (p < 0.05). In contrast, intermolar width and available arch space were significantly greater in the palatally impacted group (p < 0.05). No significant differences were identified in arch width or palatal depth measurements between groups (p > 0.05). Intra-examiner reliability demonstrated excellent agreement (ICC > 0.90). Conclusions: Maxillary dental arch morphology differed according to the position of impacted maxillary canines. Buccal impaction was associated with sagittal arch elongation and increased tooth–arch discrepancy. In contrast, palatal impaction was not consistently associated with reduced transverse dental arch dimensions within the measurements evaluated in this study. These findings contribute to a better understanding of the association between impacted canine position and maxillary dental arch morphology and may assist clinicians in the morphological assessment of patients with impacted maxillary canines. Full article

Background: Electroencephalographic (EEG) functional connectivity analysis requires multiple signal-processing, source-modelling, and statistical steps that can limit its adoption in clinician-led randomised controlled trials (RCTs). NeuroStat was developed as a prototype research tool to integrate this workflow; formal usability validation with clinician end-users has not yet been conducted. Methods: NeuroStat is an open-source Python/PyQt6 desktop application that integrates automated artefact removal (a Generalised Eigenvalue Decomposition for Artefact Identification [GEDAI] pathway and a traditional Artefact Subspace Reconstruction (ASR)/Independent Component Analysis (ICA)/ICLabel pathway), boundary element model (BEM) source localisation using the Desikan–Killiany atlas (68 cortical regions), Phase Lag Index (PLI) connectivity estimation across five canonical frequency bands, and RCT-oriented statistical analysis. Evaluation separated sensor-space and source-space claims: a sensor-level simulation (repeated across five independent random seeds) tested preprocessing robustness, a repeated source-space simulation tested recovery of a known cortical parcel-pair contrast after forward projection and inverse reconstruction, a PhysioNet benchmark tested posterior Desikan–Killiany alpha PLI in 20 healthy adults, and an illustrative application to 20 sessions from a published chiropractic RCT demonstrated real-world workflow applicability. Results: In the sensor-level simulation benchmark, the Traditional pathway achieved a mean absolute error of $0.168\pm 0.017$ PLI units and root mean squared error of $0.219\pm 0.045$ (mean ± SD across five independent random seeds) across all artefact conditions. In the source-space simulation, reconstructed alpha PLI for the known bilateral lateral-occipital parcel pair exceeded anterior control edges across 60 repeated condition runs (mean known-control difference = 0.105 PLI units, 95% CI 0.096–0.114; $t\left(59\right)=22.61$, $p<0.001$). In the PhysioNet source-space benchmark, posterior Desikan–Killiany alpha PLI was higher during eyes-closed than eyes-open rest (Cohen’s $d=0.85$, $p=0.001$; 16/20 subjects showing the expected direction) after ICLabel-enabled preprocessing. In the pilot RCT application, all 20 sessions completed processing without manual intervention, with default-mode network alpha PLI showing a pre-to-post change of +0.071 in the intervention group versus +0.015 in the active control group. Conclusions: NeuroStat integrates preprocessing, source-space construction, connectivity estimation, and statistical reporting within a parameter-logged desktop workflow for EEG functional connectivity studies. Current evidence supports initial technical feasibility, sensor-level preprocessing robustness for one pathway in controlled simulations, source-space recovery of a known parcel-level contrast, source-space sensitivity to an expected posterior alpha resting-state contrast, and error-free processing across 20 real RCT sessions in a pilot workflow demonstration. Formal usability testing, test–retest reliability analysis, participant-specific source-model validation, and clinical-population validation remain necessary before clinician-facing or trial-deployment claims can be made. Full article

Background/Objectives: Neoadjuvant cisplatin-based chemotherapy (NAC) followed by radical cystectomy (RC) is a standard treatment for cisplatin-eligible patients with muscle-invasive bladder cancer (MIBC), yet baseline tools to refine prognostic stratification remain limited. We aimed to develop and internally validate a clinicopathological nomogram integrating the neutrophil-to-lymphocyte ratio (NLR) to estimate event-free survival (EFS) in patients with MIBC treated with NAC. Methods: We retrospectively analyzed 210 patients with cT2–T4aN0–1M0 MIBC treated with cisplatin-based NAC at two Spanish institutions between 2010 and 2021. Candidate predictors included demographic, clinicopathological, and routine laboratory variables. A multivariable Cox model with backward selection based on the Akaike information criterion (AIC) was used to derive the final model, and internal validation was performed using 1000 bootstrap resamples. Results: Sex, age, prior non–muscle-invasive bladder cancer (NMIBC), and NLR were retained in the final nomogram. The model showed moderate discrimination, with a Harrell’s c-index of 0.60 and an optimism-corrected c-index of 0.58. The nomogram stratified patients into low-, intermediate-, and high-risk groups, with median EFS not reached, 47.5 months, and 18.0 months, respectively. High-risk patients also showed lower pathological complete response (pCR) rates. Conclusions: This exploratory nomogram integrates an accessible systemic inflammatory marker with baseline clinical variables to identify patients with poorer outcomes despite NAC. External validation in contemporary cohorts is warranted before clinical implementation. Full article

Fast radio bursts (FRBs) are millisecond-duration radio transients of extragalactic origin, while ultra-high-energy cosmic rays (UHECRs; $E\gtrsim {10}^{18}$ eV) remain among the most important unresolved problems in astroparticle physics. This review examines the viability of FRBs and their central engines as sources of UHECRs within a comprehensive multi-messenger framework. We summarize the observational constraints on UHECR source populations imposed by the energy spectrum, nuclear composition, anisotropy measurements, diffuse $\gamma$-ray background, and high-energy neutrino observations, which, together, favor source classes capable of accelerating heavy nuclei with hard injection spectra, modest cosmological evolution, and sufficiently high source densities. We then review the current landscape of FRB progenitor and engine models, including magnetars, supramassive neutron stars, compact-object mergers, and accretion-powered systems, emphasizing their energetics, environments, and particle-acceleration capabilities through relativistic shocks, magnetic reconnection, magnetar wind nebulae, and direct electromagnetic acceleration by ultra-relativistic FRB pulses. We discuss how these scenarios are constrained by neutrino and $\gamma$-ray observations from IceCube, KM3NeT, and Fermi-LAT, as well as by large-scale UHECR anisotropy measurements from the Pierre Auger Observatory and Telescope Array. Finally, we examine the observational tests that will become possible in the coming decade through large samples of localized FRBs, composition-resolved UHECR measurements, next-generation neutrino observatories, and wide-field $\gamma$-ray facilities. We emphasize that FRB dispersion and rotation measures provide unique probes of the baryonic and magnetic environments relevant for UHECR acceleration and propagation, enabling a new form of multi-messenger tomography of cosmic-ray source environments and allowing the FRB–UHECR connection to become a quantitatively testable astrophysical framework. Full article

Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale. Full article

Background/Objectives: Immune surveillance is increasingly recognized as a modifier of myeloproliferative neoplasm (MPN) initiation and evolution, yet the contribution of the NKG2D receptor and its ligands MICA/MICB to CALR-mutated disease remains unclear. Methods: We performed high-resolution next-generation sequencing genotyping of MICA and MICB in 43 patients with CALR-mutated MPN (WHO 2022 criteria) and compared the allele and haplotype distributions with those of 156 healthy Bulgarian controls and 85 patients with JAK2 V617F-positive MPN. Associations were tested using age- and sex-adjusted additive generalized linear models; bi-locus haplotypes were evaluated using haplotype score methods. In a genotyped subgroup (35 CALR-mutated MPN patients and 105 controls), functional KLRK1 (NKG2D) polymorphisms were analyzed for haplotype-level associations. We also performed 700 ns molecular dynamics simulations of selected MICA variants in complex with NKG2D and reanalyzed publicly available single-cell RNA-sequencing data (GSE117826) and RNA-sequencing data from CRISPR/Cas9-edited CALR-mutant iPSC-derived megakaryocytes to evaluate MICA/MICB expression. Results: MICA*004:001 was significantly associated with CALR-mutated MPN versus controls (p = 0.004; Bonferroni-adjusted p = 0.047), while MICB*008:001 showed only nominal association. Exploratory haplotype analyses identified a MICA*009:01-MICB*004:001 haplotype associated with CALR-mutated status (p = 0.008) and a KLRK1 G-A-G-T haplotype (rs1049174-rs2617160-rs2246809-rs2617170) associated with increased CALR-mutated MPN risk (OR = 3.61; p = 0.029). Transcriptomic reanalysis indicated a higher fraction of CALR-mutant stem and progenitor cells expressing detectable MICA/MICB transcripts, and heterozygous CALR-mutant megakaryocytes exhibited higher MICA expression than the wild type. Conclusions: Together, these data support an exploratory immunogenetic and transcriptomic link between the NKG2D-MICA/MICB axis and CALR-mutated MPN, but direct protein-level and functional studies are required before mechanistic or therapeutic conclusions can be drawn. Full article

Background/Objectives: Non-specific low back pain (NSLBP) is the leading cause of disability worldwide. Artificial intelligence (AI) technologies are increasingly being integrated into healthcare interventions for NSLBP, yet their effectiveness remains uncertain. This systematic review and meta-analysis aimed to evaluate the effectiveness of AI-based Physical therapy (PT) interventions on pain intensity and disability outcomes in patients with NSLBP. Methods: We conducted a comprehensive search across six electronic databases. Randomised controlled trials (RCTs) evaluating AI-based interventions for NSLBP were only included. Mean differences (MD) with 95% confidence intervals (CIs) were calculated using random-effects models. Heterogeneity was assessed using I² statistics and Cochran’s Q test. Results: Five RCTs (n = 1939) met the inclusion criteria for systematic review. Three RCTs (n = 594 participants) provided data for meta-analysis. AI-based interventions significantly reduced pain (pooled MD −0.721, 95% CI −1.047 to −0.395; z = −4.34, p < 0.001; I² = 9.5%). Disability also significantly improved (pooled MD −1.031, 95% CI −2.020 to −0.042; t(2) = −4.48, p = 0.046; I² = 0%). Neither effect reached the minimal clinically important difference (1.0 for pain, 2–4 for disability). No serious adverse events were reported. Conclusions: AI-based PT interventions produce statistically significant but clinically small improvements in pain and disability for NSLBP. Certainty of evidence is low due to risk of bias and imprecision. Larger, blinded RCTs with standardised outcomes are needed. Full article

Power cycling reliability is one of the most widely used frameworks to evaluate the lifetimes of power semiconductor switching devices from a thermal stress perspective. Experimental tests can be used to predict their lifetimes under operating conditions. An estimation of the number of cycles to failure $N_f$ can also be given by several lifetime models, which express the number of cycles to end of life as a function of empirical coefficients. In the existing literature, these empirical coefficients are generally estimated using the classical least squares method (to find the best-fitting line through data points), where outliers are removed using the Random Sample Consensus algorithm. The aim of this paper is to present a general strategy for the calculation of empirical coefficients for different lifetime models, such as Coffin–Manson, Coffin–Manson–Arrhenius, Norris–Landzberg, and simplified Bayerer, aiming at minimizing the number of required experimental tests. The results show that the number of experimental trials required varies between two and four, depending on the number of empirical coefficients to be determined, which is specific to the lifetime model used. Furthermore, a limited number of experimental data points are selected to avoid any degradation in accuracy. The accuracy of coefficient estimation is significantly improved by excluding outliers: some relative errors decrease by 25%. Additionally, each empirical coefficient is determined under specific thermal stress conditions, such as a constant junction temperature swing $\mathsf{\Delta }{T}_j$, constant current per bond wire I, constant cycling frequency f, or constant mean junction temperature $T_{jm}$. Furthermore, a limited number of experimental data are selected to avoid any degradation in accuracy due to outliers. Moreover, this general method can be applied to all power devices, such as IGBTs or MOSFETs. Finally, the limitations of the analytical solution for the Scheuermann lifetime model are discussed. Full article

Aiming at the problem that complex underwater environments induce outliers in Doppler Velocity Log (DVL) measurements, which degrade the navigation accuracy of the Strapdown Inertial Navigation System (SINS)/DVL integrated system, this paper proposes a soft-gating Gaussian–Student’s t mixture robust Kalman filter (MRKF). Firstly, the measurement noise is modeled as a mixture of Gaussian and Student’s t distributions to adapt to normal stationary noise and abrupt outliers, respectively. Secondly, a logistic soft-gating weight is constructed based on the innovation Mahalanobis distance to adaptively balance the output contributions of the standard Kalman Filter (KF) and the variational Bayesian Student’s t filter. Finally, moment matching is adopted to realize the weighted fusion of two-branch posterior distributions, and an equivalent Gaussian posterior estimation is obtained. Simulation results under the considered SINS/DVL integrated navigation scenarios show that the proposed MRKF maintains estimation accuracy close to the standard KF under nominal Gaussian measurement noise. In the designed DVL outlier-injection scenario, the proposed MRKF achieves a position RMSE of $53.39\mathrm{m}$, compared with $878.75\mathrm{m}$, $58.84\mathrm{m}$, and $56.49\mathrm{m}$ for the nominal KF, Huber KF (HKF), and Student’s-t variational Bayesian KF (STVBKF), respectively. These results indicate that the proposed MRKF can improve robustness against DVL outliers while maintaining competitive estimation accuracy under the simulated conditions. Full article

The rapid digitalization of energy systems and the increasing integration of distributed energy resources, renewable energy technologies, and prosumer-oriented infrastructures have accelerated the development of IoT-enabled Smart Grids as a foundation for intelligent and adaptive energy management. Modern Smart Grids increasingly depend on the coordinated interaction of IoT architectures, artificial intelligence, distributed analytics, and decentralized control mechanisms to ensure reliability, scalability, and real-time operational flexibility. Despite extensive research activity, existing studies remain predominantly technology-centric, focusing on isolated architectural layers or individual intelligent methods without providing a unified system-level perspective on their coordinated operation and interoperability. This article presents a system-level integrative review and challenge-oriented comparative synthesis of intelligent operational and architectural foundations of IoT-enabled Smart Grids. The study analyzes data-driven, model-driven, knowledge-driven, agent-based, and hybrid-oriented intelligent paradigms within multi-layer IoT energy infrastructures. In addition, the research establishes a cross-layer mapping between Smart Grid operational challenges, enabling technologies, and corresponding analytical approaches while identifying interoperability constraints, scalability limitations, and coordination challenges associated with decentralized energy ecosystems. The conducted synthesis demonstrates that hybrid-oriented intelligent approaches represent the most promising direction for future Smart Grid evolution due to their ability to integrate AI, ML, digital twins, semantic reasoning, and decentralized multi-agent coordination within unified IoT architectures. The conducted comparative synthesis identifies the ongoing transition from isolated intelligent solutions toward integrated hybrid cyber–physical energy ecosystems and highlights key characteristics of future adaptive, interoperable, scalable, and explainable Smart Grid architectures. Full article

Intrusion detection in industrial cyber-physical systems is constrained by small labelled-attack corpora and by the subtler signal of physical-process attacks compared with classical IT-network intrusions, motivating renewed interest in foundation-model-based detectors; classical detectors are typically trained per dataset and degrade under the distribution shift that is common in operational technology, where attack repertoires evolve faster than retraining cycles. Two foundation-model families are now plausible candidates: open-source Large Language Models (LLMs) and recent tabular foundation models (TabPFN, TabICL) pre-trained for in-context tabular inference. We compare the two families head-to-head, alongside Random Forest and XGBoost classical anchors, across three established industrial security benchmarks (SWaT, HAI, WUSTL-IIoT-2021) under a controlled multi-seed full-holdout protocol with paired McNemar and cross-seed Mann–Whitney tests. The empirical picture is dataset-dependent rather than universal: tabular foundation models establish a strong, previously unreported baseline that is competitive with or superior to classical anchors on every dataset evaluated, while LLMs are complementary detectors with a specific advantage on schemas that carry process-engineering semantics (such as SWaT’s named sensor channels). A per-class analysis on the WUSTL five-class attack taxonomy shows that the two families have structurally different strengths: tabular methods dominate traffic-rich attacks (Denial-of-Service, Reconnaissance), whereas LLMs are competitive on rare attack types (Backdoor, Command Injection). A confidence-gated cascade that escalates only low-confidence tabular decisions to an LLM exceeds either detector alone at a small query budget, and a leave-one-attack-type-out analysis shows that foundation-model detectors generalise to unseen attack families substantially better than the classical anchors. The appropriate detector choice in industrial cyber-physical security is therefore informed by the dataset’s feature schema, the attack-type mix, and the operational cost envelope, rather than by a specific performance metric. Full article

Hepassocin, also known as fibrinogen-like protein 1 (FGL-1), is a liver-derived secretory protein initially identified as a mitogenic factor involved in hepatocyte proliferation and liver regeneration. Increasing evidence has subsequently suggested that FGL-1 functions as a hepatokine linking hepatic metabolic stress to systemic metabolic regulation. Experimental and clinical studies have demonstrated that circulating FGL-1 levels are associated with obesity, insulin resistance, metabolic dysfunction-associated steatotic liver disease (MASLD), and type 2 diabetes mellitus (T2DM). Mechanistically, FGL-1 appears to contribute to metabolic dysfunction by impairing insulin signaling and promoting hepatic lipid accumulation, although its precise molecular targets remain incompletely defined. In addition to its metabolic roles, FGL-1 has been identified as a major ligand of lymphocyte activation gene-3 (LAG-3), implicating it in immune modulation and tumor progression, particularly in hepatocellular carcinoma (HCC). However, most available human data are observational, and conflicting findings from experimental models suggest that FGL-1 may function as a context-dependent mediator rather than a purely pathogenic factor. Given the expanding but sometimes conflicting evidence, a comprehensive understanding of FGL-1 biology may provide important insights into the complex interactions among hepatic stress responses, metabolic dysfunction, and immune regulation. This review therefore examines the current evidence regarding the physiological and pathological roles of FGL-1 and highlights key unresolved questions that may influence future translational research and therapeutic development. Full article

The global prevalence of type 2 diabetes mellitus (T2DM) has increased more than twofold over the last thirty years. T2DM is associated with multiple complications, among which diabetic encephalopathy and accompanying cognitive impairment have drawn considerable interest. This systematic review synthesizes findings from advanced diffusion magnetic resonance imaging (dMRI) studies (published from 2009 to 2025) on T2DM-related brain microstructural abnormalities. The most common technique, diffusion tensor imaging (DTI), consistently reveals reduced white-matter integrity (lower fractional anisotropy, higher diffusivity) associated with cognitive impairment. DTI-based network analysis further identifies disrupted structural network topology, characterized by reduced global and nodal efficiency. To overcome DTI’s limitations, newer techniques provide more specific insights: diffusion kurtosis imaging shows reduced tissue complexity in white matter, gray matter, and crossing-fiber regions via non-Gaussian modeling; neurite orientation dispersion and density imaging quantifies decreased neurite density; intravoxel incoherent motion assesses combined microstructural and microvascular alterations; diffusion spectrum imaging maps complex fiber architecture. These dMRI metrics may provide promising imaging markers for characterizing T2DM-related brain microstructural alterations. However, most available evidence remains cross-sectional, and further longitudinal, multicenter validation is required before these measures can be considered clinically validated biomarkers for prediction, diagnosis, or monitoring. Full article

Hybrid RF-FSO-THz communication systems are promising candidates for future Internet of Things (IoT) and 6G networks because they combine the robustness of radio frequency links, the high-capacity potential of Free-Space Optical communications, and the ultra-wideband capabilities of terahertz transmission. Adaptive channel selection in such systems depends on multiple correlated environmental and physical-layer variables, including distance, rain intensity, humidity, visibility, turbulence strength, signal-to-noise ratio, channel capacity, and energy-efficiency metrics. This paper presents a physics-supported benchmark framework for supervised adaptive channel selection in hybrid RF-FSO-THz systems and systematically investigates the impact of linear and nonlinear dimensionality-reduction techniques on predictive performance, statistical robustness, computational complexity, and physical interpretability. A multi-scenario dataset comprising 5000 samples was generated using calibrated RF, FSO, and THz propagation models under clear, rain, fog, and worst-case environmental conditions. Principal Component Analysis (PCA) and Kernel PCA were evaluated together with Random Forest, Support Vector Machines (SVMs), XGBoost, Gradient Boosting (GB), Multi-Layer Perceptron (MLP), Logistic Regression, and Decision Trees. The results demonstrate that PCA preserves nearly all predictive capabilities while reducing the original 33-dimensional feature space by approximately 81.8%, maintaining accuracies close to 97–98% with the best-performing classifiers. Statistical significance analysis confirms that PCA introduces only modest degradations, whereas Kernel PCA consistently reduces the predictive performance while increasing memory requirements and inference latency. Additional environmental-only validation experiments indicate that adaptive channel selection remains highly learnable even when only pre-selection environmental descriptors are available, partially mitigating concerns regarding self-consistency bias. Overall, the results suggest that PCA provides an advantageous compromise among predictive accuracy, computational efficiency, statistical robustness, and physical interpretability for supervised adaptive channel selection in physics-supported hybrid wireless communication systems. Full article