Search Results (2,336)

This report focuses on cognitive gaps of the pressure dependence of melting temperature, presenting: (i) a coherent discussion on $T_m\left(P\right)$ behavior for both ‘Hard Matter’ (‘standard’ solid state) and Soft Matter systems, to reveal the importance of the strength of intermolecular interactions; (ii) a model picture linking systems with $d{T}_m/dP>0$, $d{T}_m/dP<0$, and the $T_m\left(P\right)$ curve maximum, which also shows the relevance of the negative-pressure domain; and (iii) the ultimate validation test of the recent ‘universal’ parabolic scaling of $P_m\left(T\right)$ against empirical data. The evidence for ‘Hard Matter’ focuses on systems relevant to the semiconductor industry: silicon, germanium, and gallium nitride. For Soft Matter, these include unique polymeric systems and liquid-crystalline pentylcyanobiphenyl (5CB). For the latter, symmetry-selected melting/freezing takes place. Finally, the report presents the specific case of graphite and diamond. The report also presents an innovative solution for melting-temperature and pressure detection. Full article

Structural analysis using X-rays and electron beams has long provided the average arrangement of atoms and molecules—that is, “structural information”—with high precision. By contrast, static measurements cannot directly yield dynamic information on how a material changes over time; instead, information on motion is convolved into a single numerical value such as the B-factor (atomic displacement parameter). Taking this limitation as its starting point, this review surveys the recent trend of introducing a time axis into measurements to observe material dynamics directly. First, we outline the technological foundations that have made the transition from static to time-resolved measurement possible. It rests on the dramatic shortening of exposure times, enabled by the increased brilliance of X-ray and electron sources and by advances in detection technology such as direct photon-counting detectors. Next, we survey dynamic measurement techniques, including time-resolved X-ray crystallography, coherent X-ray scattering, neutron scattering, and time-resolved electron microscopy. We also point out the essential limitation that most of them still return ensemble or volume averages. Building on this, we systematically describe diffracted X-ray tracking (DXT), diffracted X-ray blinking (DXB), small-angle X-ray blinking (SAXB), transmitted X-ray blinking (TXB), and electron-beam molecular dynamics (EBMD), which use gold nanocrystals and gold nanoparticles as motion probes. We distinguish throughout between methods that follow individual objects—DXT and EBMD, which yield trajectories of single labeled molecules or single particles—and methods that analyze intensity fluctuations arising from many contributors within one pixel or illuminated volume—DXB, SAXB and TXB. The latter are not single-molecule measurements; rather, they replace a global ensemble average by a spatially localized statistical one, retaining local heterogeneity that a bulk measurement would average away. Finally, we discuss the implementation and prospects of the large-volume data analysis—principal component analysis, Bayesian inference, machine learning, and autonomous measurement—needed to handle the explosively increasing amount of information that the time axis introduces. We close with the outlook that time-resolved measurement incorporating AI and big-data analysis will become established as a new measurement platform that complements and extends conventional static structural analysis. Full article

Retrieval-augmented fine-tuning (RAFT) can improve small language models (sLMs) on retrieval-grounded question answering, but the synthetic training data produced by commercial large language models (LLMs) vary in quality. This paper contributes a quality-aware selection protocol—rather than a new RAFT or QLoRA method—that scores LLM-generated alternatives along four embedding-based dimensions (question relevance, answer faithfulness, QA coherence, and semantic similarity) and selects one alternative per task before parameter-efficient fine-tuning. Under pre-specified paired-bootstrap contrasts with Holm correction, the parameter-free faithfulness-based selector only-AF significantly exceeds random selection on Gemma-2-9B-IT (ΔF1 = +0.106, 95% CI [+0.043, +0.174], Holm-corrected p = 0.019), and its pre-specified weighted companion af-70 (w_AF = 0.70) shows the same confirmed pattern (Holm-corrected p = 0.002). Both effects persist under a Korean character-level F1 that removes particles and punctuation (Holm-corrected p = 0.004 and p = 0.042), indicating robustness to the choice of lexical metric. Relative to training on the full 150-row augmented pool, the quality-selected 50-row sets are statistically indistinguishable while using one third of the training data, which we interpret as data efficiency rather than superiority. Across six instruction-tuned models (2B–27B), a significant selector-by-model interaction indicates that the optimal quality axis is model-dependent, and the two smallest models show no benefit from selection. The study’s confirmatory contrasts use a small controlled Korean corpus under a transductive design; two pre-registered validation experiments probe external validity. On an independent five-fold larger corpus with a passage-level train/test split, fine-tuning transfers strongly and the selected one-third subsets show no significant difference from the full pool, while the advantage over random selection is directionally positive but small and not significant; under controlled corruption of 35% of the pool, the metrics detect the damaged rows, and for the score-sum selector the selection-versus-random benefit is significantly larger than on the clean pool (difference-in-differences p = 0.0014; directionally consistent but not significant for the faithfulness selectors). Within this scope, quality-aware selection is a promising, data-efficient safeguard for synthetic RAFT data—performing comparably to full-pool training at one third of the cost, with growing value as pool quality degrades—and larger-scale external validation remains future work. Full article

Backgroundand Objectives: Retinal microcirculation may serve as a surrogate marker of systemic vascular status and can be non-invasively assessed using optical coherence tomography angiography (OCTA). Emerging evidence suggests that retinal and choroidal microvascular alterations may occur following coronavirus disease 2019 (COVID-19) and multisystem inflammatory syndrome in children (MIS-C), potentially reflecting systemic vascular and inflammatory processes. This review summarizes the current evidence regarding retinal and choroidal microvascular changes detected by OCTA in pediatric patients with COVID-19 and MIS-C. Materials and Methods: A structured literature search of the National Center for Biotechnology Information (NCBI) PubMed database was conducted from inception until April 2026. Original English-language studies evaluating retinal and choroidal microcirculation using OCTA in pediatric patients with COVID-19 or MIS-C were identified and reviewed. Results: Available studies suggest variable retinal and choroidal microvascular alterations following COVID-19 and MIS-C, including changes in vessel density, perfusion, and foveal avascular zone parameters. Several investigations reported reduced vessel density, particularly within the deep capillary plexus, whereas others demonstrated increased peripapillary vascular parameters or no significant differences during the observation period. Differences in patient characteristics, disease severity, timing of imaging, OCTA protocols, and study design likely contribute to the heterogeneity of the reported findings. Conclusions: OCTA represents a promising research tool for investigating retinal and choroidal microvascular alterations associated with pediatric COVID-19 and MIS-C. However, the available evidence remains limited by small observational studies, methodological heterogeneity and inconsistent findings. Larger prospective longitudinal studies using standardized imaging protocols are needed to determine the clinical significance and reproducibility of OCTA-derived parameters and to establish whether they may have potential as biomarkers of ocular or systemic vascular involvement. Full article

Reliable short-packet transmission is essential for MIMO-based wireless sensor systems, where low-power terminals upload short data blocks to multi-antenna gateways under stringent latency and complexity constraints. Although multi-antenna combining improves the equivalent received signal-to-noise ratio, the post-combining recovery of sparse-vector-coded packets is still limited by the high inter-column correlation of short binary spreading matrices and redundant searches over invalid indices in conventional orthogonal matching pursuit. To address these issues, this paper proposes a low-complexity sparse vector coding scheme for reliable short-packet transmission in MIMO-based wireless sensor systems. Specifically, at the transmitter, a low-coherence binary spreading matrix (LCB-SM) construction algorithm is designed based on Hadamard initialization and column-wise correlation optimization, which improves the distinguishability of sparse support positions while preserving multiplication-free encoding. At the receiver, a frozen-index-pruned orthogonal (FIP-OMP) matching pursuit algorithm is developed to exploit the predefined sparse mapping rule, thereby excluding invalid indices during atom selection and reducing noise-induced false support detection. Simulation results show that, for $N=24$, $L_s=16$, and $K=2$, the LCB-SM achieves a signal-to-noise (SNR) ratio gain of approximately $1.2$ dB over the optimized partial hadamard matrix (OPHM) at a BLER of ${10}^{-2}$; for $K=3$, it reduces the high-SNR BLER by more than two orders of magnitude compared with OPHM. Meanwhile, the FIP-OMP achieves an approximately $0.5$–$0.8$ dB gain over conventional orthogonal matching pursuit at a BLER of ${10}^{-2}$ with an average decoding time close to that of orthogonal matching pursuit (OMP) and much lower than that of multipath matching pursuit, Dynamic OMP and compressive sampling matching pursuit. A software defined radio-based wireless experiment further validates the reconstruction capability of LC-SVC for continuous sensing data, achieving an RMSE of $0.0612$. These results demonstrate that LC-SVC improves the reliability of the considered short-packet recovery task with lightweight transmitter-side encoding and low gateway-side decoding overhead. Full article

Aquaculture requires continuous environmental monitoring, yet low-cost IoT sensing in marine conditions remains poorly characterised, and the data it produces is rarely accompanied by mechanisms establishing its provenance. This paper presents an IoT sensor system for marine and coastal aquaculture, comprising solar-powered 4G multiparameter nodes, a cloud-native back-end with a RESTful layer, and integration with a blockchain-based change-detection mechanism supplying a GS1-compliant digital product passport. Four nodes in adjacent cages were deployed at a marine site on the Montenegrin Adriatic for eight weeks, measuring temperature, pH, dissolved oxygen, oxidation–reduction potential and conductivity at five-minute resolution. Lacking reference instrumentation, we use agreement between nodes for validation. Temperature showed the closest cross-node agreement, with nodes agreeing to within 0.28 °C, and resolved a coherent cold, low-salinity intrusion detected simultaneously by all four nodes. The electrochemical and optical channels proved precise but not accurate: they tracked relative change coherently while their absolute values diverged, with oxidation–reduction potential moving from 9 mV of agreement to 71 mV over the following weeks. Cross-node coherence in conductivity and dissolved oxygen degraded progressively over the deployment, with no electrochemical or optical channel remaining coherent beyond roughly six weeks. Such sensors suit anomaly detection without calibration but require periodic recalibration for absolute reporting. Tamper-evident provenance is therefore necessary but not sufficient: sensor-level quality assurance is its missing half. Full article

Background: Optical coherence tomography angiography (OCTA) provides quantitative metrics of the retinal microvasculature, most prominently vessel density (VD), that are increasingly used as biomarkers in ocular, systemic, and cerebrovascular disease. Because OCTA relies on the detection of flow-related motion contrast, image quality has emerged as a pervasive determinant of these metrics, yet its effect has been reported in fragmentary and non-comparable ways across the literature. Methods: We identified OCTA studies indexed in PubMed that examined the relationship between image quality and quantitative OCTA parameters, and we summarised their methods and findings across the macular, foveal avascular zone, and peripapillary regions and across vascular layers. We integrated directly comparable per-unit scan-quality effects using random-effects meta-analysis and separately synthesized direct cross-sectional Pearson correlations between manufacturer-reported image quality and macular vessel-density outcomes from independent healthy cohorts. Results: Higher image quality was associated with higher measured vessel density in every contributing dataset. On the Optovue scan-quality (SQ, 0–10) scale, superficial macular VD increased by 3.46% (95% CI 1.85–5.07) per SQ unit in controlled signal-attenuation experiments and by covariate-adjusted observational estimates ranging from 0.90% to 2.16% per SQ unit; a combined order-of-magnitude estimate across both estimator types was 1.64% (95% CI 1.15–2.12). Peripapillary estimates were heterogeneous and derived from only two independent cohorts; they were therefore summarised descriptively rather than pooled. Across three independent externally authored healthy cohorts reporting direct cross-sectional Pearson correlations, higher image quality was strongly associated with higher macular vessel-density outcomes (random-effects pooled r = 0.64, 95% CI 0.50–0.75; I² = 49%). Conclusions: Image quality is an important, directional confounder of VD-based OCTA metrics whose magnitude can rival the biological or physiological signal of interest. Platform-appropriate standardisation, transparent reporting, and consideration of image quality in acquisition and analysis are therefore important for the valid interpretation of vessel-density measurements, particularly in functional and longitudinal studies. Full article

In the field of 3D reconstruction for photovoltaic scenes, current texture mapping techniques frequently encounter significant texture segmentation and apparent joins in uniform plane regions, such as solar panels, because they lack geometric structural assumptions. To tackle these challenges, we introduce a new texture-mapping strategy for 3D solar panel scene reconstruction that focuses on planar structure awareness. We term the proposed method PSATM. Initially, we suggest a global constraint and a local refinement process to incorporate clear geometric structure details. This process automatically detects and labels planar regions through a region-growing approach. Next, we integrate a planar structure-aware module into the smoothness term of the Markov Random Field (MRF) energy function. This module uses dihedral angles and plane membership to adjust label transition costs, enhancing texture coherence within planar regions and maintaining smooth transitions at genuine geometric breaks. Furthermore, we establish a boundary treatment technique relying on local geometric support. This method combines area-based weighting and normal consistency to modify erroneous labels, successfully removing small remnants and smoothing texture edges. We tested the proposed PSATM with texture patch counts and visual quality measures on actual solar panel scenes. The results indicate that the proposed PSATM considerably reduces texture segmentation errors and improves texture flow and overall visual quality compared to the existing method. Full article

Discrete energy-level dynamics provide a fundamental framework for understanding driven two-state systems. Here, we propose and experimentally demonstrate a classical microwave-photonic analog based on two active fiber loops. Reciprocal electro-optic sidebands coherently couple two independently sustained lasing modes. When the input RF power is increased from 11.7 to 18.7 dBm, the measured oscillation frequency increases from 0.55 to 1.00 MHz, consistent with the modulation-induced coupling predicted by the coupled-mode model. The experiment therefore demonstrates RF-power-controlled Rabi-like oscillation in an active dual-loop system. Because the optical fields and detected intensities are classical, the platform reproduces reduced two-state dynamics rather than quantum-state evolution. This proof-of-concept provides a basis for quantum-inspired microwave-photonic signal processing and future short-cavity implementations. Full article

In psychotherapy, case formulation can organize clinically relevant information into a coherent account of how psychological difficulties emerge, persist, and may change. The Personal Meaning System Fuzzy Cognitive Map (PMS-FCM) represents the client’s bipolar construct system as a weighted directed graph, whereas the FCM-FRHP (Fuzzy Cognitive Map of Human Problem Formation and Resolution) provides a professional functional reference model for problem formation and resolution. This paper proposes a semantically constrained method for identifying systemic archetype-inspired configurations in PMS-FCM representations enriched with FCM-FRHP semantics. Rather than importing classical systemic archetypes directly, the method reformulates them as configurable graph templates adapted to intrapersonal bipolar construct systems. Detection combines FCM-FRHP functional roles, predefined semantic-affinity rules and PB-based structural criteria, edge-weight thresholds, and ranking criteria. The goal is to support the traceable identification of static structures that may inform the examination of clinically relevant systemic hypotheses, without treating them as diagnoses or evidence of observed temporal dynamics. The pipeline combines property-graph querying, RDF/SHACL conformance checking, ranked materialization, and rule-based trace generation. A local language model is used only after detection and conformance checking, as a constrained graph-to-text layer grounded in graph evidence and FCM-FRHP semantics. The approach offers a formally specified and reproducible method for conducting explicit, auditable pattern-level analysis of psychological case formulations. Full article

Thin film printed electronics, such as flexible circuits and sensor sheets, require non-contact inspection to detect defects and edge degradation. The present paper presents a laser-enhanced machine vision framework for detecting and analyzing the edges of printed conductive tracks using Canny edge detection and Otsu thresholding. Using a coherent laser source, Otsu’s method enhances contrast at the ink–substrate interface, enabling robust segmentation of edge lines. Canny operator is applied to thresholded images to extract precise edge profiles. Multiple printed tracks are analyzed to calculate four lateral edge roughness values (Ra). As a result, the values are 40.43 µm, 40.09 µm, 50.26 µm and 40.94 µm. The results show that the suggested method can detect and qualify variations in edge parameters. Printed electronics are produced using an inline inspection and quality control system based on non-contact, high-resolution, and scalable technologies. Full article

3D sonar is the primary technical means for the detection and health assessment of underwater structures. However, due to the harsh constraints of underwater imaging conditions, these point clouds experience severe noise interference and inconspicuous features. This complicates structural surface extraction and hinders the engineering monitoring process. To address this challenge, we propose an object-based energy-optimization segmentation method for precise extraction. First, the Voxel Cloud Connectivity Segmentation (VCCS) algorithm transforms massive discrete point clouds into semantically coherent supervoxel objects, significantly reducing computational complexity. Next, a global energy optimization framework is constructed, integrating a data term that characterizes structural surfaces and a smoothness term based on spatial neighborhood constraints. Through a graph-cut optimization procedure, this model enables the joint extraction of conspicuous and inconspicuous surfaces. Finally, a region-growing algorithm with multi-attribute constraints effectively filters typical sonar noise. Experimental results demonstrate the method’s effectiveness, achieving an average F1-score of 89.19%. This approach maintains high segmentation accuracy and provides reliable technical support for underwater structure detection and assessment. Full article

Under complex environmental disturbances and visual measurement noise, globally coherent components, damage-induced local anomalies, and random disturbances are coupled in full-field vertical displacement responses. This coupling limits conventional low-rank–sparse decomposition because of its lack of mechanics-based constraints and can obscure weak damage-induced anomalies. To address this issue, this study proposes a physics-regularized low-rank–sparse damage identification method incorporating a physics prior derived from curvature-strain-energy perturbation. The method first extracts deflection curvature from the full-field displacement responses of the healthy and damaged states. A normalized physical evidence field is then constructed from the curvature-energy difference through Gaussian spatial regularization and mapped into spatially varying sparsity weights to modulate anomaly separation. Subsequently, the Physics-Regularized Differential Damage Index (PRDDI) is constructed from the difference in physics-regularized sparse anomaly intensity between the two states for damage localization and severity-sensitive characterization. The proposed method is primarily intended for beam-like structures satisfying the small-deformation bending assumption. For more complex structures, such as continuous beams, frames, plates, and shells, the corresponding mechanics-based physical evidence and spatial neighborhood relationships can be extended according to their load-transfer mechanisms and spatial geometries. Experimental and numerical results show that the peak-to-background ratio of the physics-regularized sparse anomaly field reaches approximately 2.77 times that of conventional robust principal component analysis (RPCA), while the background level is reduced by approximately 60%, and spurious peaks in non-damaged regions are markedly suppressed. For local stiffness reductions of 5–30%, the PRDDI localization error remains within 0–1 spatial measurement points. Both the peak value and local integrated area within the damaged region increase consistently with the degree of stiffness reduction, with coefficients of determination $R^2$ exceeding 0.99 and Spearman rank correlation coefficients of 1.00. For representative dual-damage cases, the proposed method maintains good dual-peak resolution. Under 10 dB noise, the complete dual-damage detection rate is approximately 87%, while the missed-detection rate for weak damage is approximately 10%. The physics prior derived from curvature-strain-energy perturbation improves consistency with structural mechanics, spatial separability, and the identification reliability of local damage anomaly extraction under complex measurement conditions. By exploiting spatially continuous, vision-based full-field displacement measurements, the proposed method can identify local damage regions in bridges and characterize variations in damage severity, providing a basis for subsequent detailed inspection and condition assessment. Full article

Background/Objectives: Infective endocarditis (IE) is a life-threatening cardiovascular infection with in-hospital mortality of 15–30% despite modern therapy. Contemporary IE demonstrates non-random valve involvement: aortic and mitral 35–45%, tricuspid 5–10% (30–50% in intravenous drug users [IVDU]), and pulmonary < 1%. These patterns implicate valve-specific anatomy and hemodynamics as central determinants of susceptibility. This narrative review examines the reported distribution of IE across the aortic, mitral, tricuspid and pulmonary valves and summarises the anatomical, haemodynamic, structural, microbial and patient-related factors associated with valve-specific susceptibility. Methods: A structured narrative review of English-language literature was conducted using PubMed/MEDLINE, Embase, Scopus, and Google Scholar (January 1990–March 2026). Search terms included “infective endocarditis,” “valve anatomy,” “hemodynamics,” “bicuspid aortic valve,” “prosthetic valve endocarditis,” and “transcatheter aortic valve replacement (TAVR) endocarditis.” We included anatomical studies, clinical cohorts, surgical series, imaging research, and international guidelines. Evidence was synthesized narratively using Oxford Centre for Evidence-Based Medicine (CEBM) levels. Results: IE susceptibility follows a biologically coherent gradient determined by the interaction between valve anatomy, hemodynamic stress, endothelial injury, and structural substrate. The aortic valve is most vulnerable because of high shear stress, congenital abnormalities such as bicuspid aortic valve, and direct continuity with the cardiac fibrous skeleton, predisposing to peri-annular extension. Mitral valve IE is largely conditional upon pre-existing structural disease, particularly mitral valve prolapse, rheumatic heart disease, and mitral annular calcification, and is characterized by a high risk of systemic embolization. Tricuspid valve IE reflects the interaction between low-pressure hemodynamics and acquired patient-specific modifiers, including intravenous drug use, cardiovascular implantable electronic devices, and congenital heart disease. Pulmonary valve IE remains uncommon because of favorable native hemodynamics but occurs predominantly in repaired congenital heart disease, right ventricular outflow tract reconstruction, and prosthetic pulmonary valves. Across all valve types, multimodality imaging and anatomical assessment consistently influence complication detection, surgical planning, and long-term surveillance. Conclusions: IE involvement is unevenly distributed among the cardiac valves. Aortic and mitral involvement predominate, tricuspid involvement is strongly influenced by injection drug use and intracardiac devices, and pulmonary-valve IE remains rare and is principally associated with congenital abnormalities or prosthetic material. These patterns highlight the possible contributions of haemodynamic stress, pre-existing structural abnormalities, and age-related valvular changes to the greater susceptibility of left-sided valves. Full article

Background/Objectives: Comparisons of abdominal Surgical-Site Infection (SSI) rates during COVID-19 often treat recorded incidence as a direct measure of biological risk, although the pandemic altered operative volume and case mix, prevention practices, and post-discharge detection. This review examines when reported SSI changes are interpretable. Methods: We conducted a critical narrative review, informed by SANRA and critical interpretive synthesis, of MEDLINE/PubMed, Embase, Scopus, and Europe PMC (January 2018–25 June 2026). Comparative SSI studies formed the core evidence set, supported by contextual studies on emergency presentation, biological risk, prevention, and surveillance. Results: Studies reported lower, unchanged, non-monotonic, and higher SSI rates. These differences became more coherent when the recorded rate was separated into operative composition, true infection probability, and detection probability. No core study measured all domains required to isolate a biological pandemic effect. Evidence from acute pancreatitis and abdominal trauma showed that disease severity, care access, inflammatory status, and prognostic relationships varied with health-system context. Conclusions: We propose the Surgical-Site Infection Signal-Validity Framework (SSI-SVF). It classifies findings as probable true improvement, apparent improvement, masked deterioration, mixed deterioration, or indeterminate and requires joint assessment of operative composition, true infection risk, and detection probability before interpreting a calendar-period change as altered surgical safety. Full article