Search Results (3,260)

Skin cancer represents a growing global health burden. Current diagnostic pathways combine clinical examination and dermoscopy with histopathological confirmation; however, overlap between benign and malignant lesions can create diagnostic uncertainty and lead to potentially avoidable biopsies. Electrical impedance spectroscopy (EIS) has emerged as a non-invasive technique with potential for portable and cost-efficient implementation that quantifies the dielectric contrast between malignant and healthy tissue, providing objective information that may support clinical decision-making. This review synthesizes the field across four levels. First, we describe the biophysical origins of the impedance contrast in skin cancer, spanning the cellular, tissue architecture, and molecular scales, together with the equivalent circuit and Cole–Cole frameworks used to interpret it. Second, we examine hardware advances, including electrode–skin interface strategies, flexible and wearable architectures, computational electrode design, and the translation from laboratory prototypes to commercial systems such as Nevisense. Third, we critically appraise clinical evidence from large multicenter trials, focusing on the sensitivity–specificity trade-off and the demonstrated reduction in the number needed to excise. Finally, we discuss emerging frontiers, including artificial intelligence-driven analysis and multimodal fusion with dermoscopy, reflectance confocal microscopy, optical coherence tomography, and near-infrared spectroscopy. We conclude that EIS is most valuable as a complementary component within an integrated, AI-supported multimodal diagnostic framework. Full article

Background/Objectives: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease increasingly recognized for extra-motor manifestations, including cognitive dysfunction involving frontal cortical systems. Conventional clinical assessments are primarily behavioral and may not capture subtle alterations in the neural systems that support cognitive performance. This pilot study examined whether wearable functional near-infrared spectroscopy (fNIRS) could detect task-evoked prefrontal hemodynamic differences during the King-Devick Task (KDT), a rapid oculomotor number-naming task that engages visual scanning, attention, processing speed, and verbal response production. Methods: Sixteen participants, including seven individuals with ALS and nine age-matched healthy controls (HC), completed four progressively difficult KDT conditions while prefrontal cortical activity was recorded. Results: As task difficulty increased, response times became slower across participants, while accuracy remained preserved and behavioral performance did not differ significantly between groups. Prefrontal oxygenated hemoglobin (HbO) responses increased with task difficulty across all prefrontal optodes, supporting task-evoked cortical engagement. In contrast, deoxygenated hemoglobin (HbR) responses showed a group-specific pattern in the left dorsolateral prefrontal cortex: individuals with ALS demonstrated progressively increasing HbR responses across difficulty conditions, whereas HC showed relatively stable responses, with group differences emerging at higher difficulty levels. Conclusions: These findings suggest a dissociation between preserved behavioral performance and altered prefrontal hemodynamic regulation in ALS. The observed HbR pattern may reflect differences in cortical recruitment, neurovascular coupling, oxygen extraction, vascular responsiveness, or the efficiency of cortical resource allocation during increasing cognitive-motor demands. Wearable fNIRS may therefore complement behavioral assessments by revealing task-evoked neural alterations that are not evident from performance measures alone. Full article

Deep exploration in mature gold districts requires subsurface alteration evidence that can be related quantitatively to three-dimensional (3D) geological architecture. This study develops a workflow for the Tudui–Shawang deposit in the Muping–Rushan metallogenic belt that integrates drill-core short-wave infrared (SWIR) spectroscopy, 3D alteration modeling, ore-controlling geological constraints, positive–unlabeled (PU) learning, and ensemble prospectivity prediction. A total of 2140 spectra from 10 drillholes were processed to identify mineral assemblages, extract spectral scalars and feature-shape attributes, classify alteration facies, and construct continuous 3D alteration evidence. Discrete smooth interpolation and indicator kriging were used for continuous and categorical attributes, respectively, and CatBoost, LightGBM, XGBoost, and Random Forest were evaluated within a spatially separated PU-bagging design. Quantitative analyses show that individual SWIR attributes have weak deposit-scale relationships with Au grade. Nevertheless, local IC minima, relatively lower pos2200 values near several mineralized intervals, alteration-facies transitions, and a broader shift toward longer pos2250 wavelengths characterize relevant parts of the mineralized system. FUSE performed best under 1 km × 1 km spatial holdout validation, with an ROC AUC of 0.8900 and a PRAUC of 0.8926. Prediction-area analysis and the 3D probability volume delineated three ranked exploration targets (T1–T3). The results show that drill-core SWIR-derived 3D alteration evidence, when integrated with ore-controlling geology and spatially validated machine learning, provides a practical basis for target prioritization in mature gold districts. Full article

Two-dimensional GaS_xTe_1−x alloy nanosheets with different compositions were synthesized by chemical vapor deposition using GaS and GaTe powders as precursors. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) analyses confirmed their sheet-like morphology and the uniform distribution of S and Te, while Raman and photoluminescence measurements revealed composition-dependent vibrational and emission characteristics. Te-rich samples exhibited position-dependent emission ranging from the red to the near-infrared region, whereas increasing the S content gradually shifted the emission toward the blue-green region. Among the synthesized samples, GaS_0.9Te_0.1 showed a relatively stable photoluminescence peak near 520 nm and was therefore selected as a fluorescent powder for latent fingermark development. Its performance was evaluated on glass, stainless steel, plastic, and ceramic surfaces and compared with that of silver powder, gold powder, and commercial red fluorescent powder. GaS_0.9Te_0.1 produced clear fluorescent ridge patterns and strong background contrast, particularly on glass, plastic, and white ceramic. The mean contrast across the four substrates reached 25.83, exceeding that of the reference powders. These results demonstrate that GaS_xTe_1−x nanosheets possess tunable optical properties and that GaS_0.9Te_0.1 is a promising fluorescent material for latent fingermark development on non-porous surfaces. Full article

Thoracoabdominal aortic aneurysm (TAAA) is a complex vascular disorder involving both thoracic and abdominal segments of the aorta and remains associated with substantial morbidity and mortality. One of the most serious complications after thoracoabdominal aortic aneurysm (TAAA) repair is spinal cord ischemia (SCI), which may progress to spinal cord infarction and result in irreversible neurologic deficits including paraplegia, neurogenic bladder dysfunction, and bowel dysfunction. The incidence of SCI after TAAA repair varies with the extent of aneurysmal involvement, operative duration, and patient comorbidities. The primary pathophysiologic mechanism involves interruption of radiculomedullary arterial flow, compounded by systemic hypotension and limited collateral circulation. Because SCI profoundly affects functional recovery and long-term quality of life, prevention and early recognition are central to perioperative management. Established neuroprotective strategies emphasize maintenance of spinal cord perfusion through meticulous hemodynamic optimization, cerebrospinal fluid (CSF) drainage, and temperature modulation. Intraoperative neuromonitoring using motor and somatosensory-evoked potential facilitates early detection, while newer modalities such as near-infrared spectroscopy and CSF lactate monitoring may offer additional insight. When SCI occurs despite prophylaxis, rapid initiation of rescue measures including CSF drainage, hemodynamic augmentation, and other discussed treatments may improve neurologic outcomes. Long-term care focuses on rehabilitation, symptom management, and psychological support. Therefore, this review aims to consolidate medical evidence to improve the prevention, detection, and treatment of spinal cord infarction associated with thoracoabdominal aortic aneurysm repair. Full article

Seed quality profoundly affects productivity, marketability, and food security, yet conventional evaluation methods are destructive, slow, and unsuited to high-throughput screening. Non-destructive techniques, being rapid, non-invasive, and capable of measuring multiple indicators, have therefore gained substantial momentum. This review critically surveys the principles, applications, and limitations of major non-destructive techniques for seed quality assessment. Near-infrared spectroscopy (NIRS) enables fast, simultaneous multi-component analysis in portable formats, but its shallow penetration and poor sensitivity to subtle chemical shifts restrict single-seed vigor tests. Hyperspectral imaging (HSI) uniquely merges spectral with spatial data to map composition and surface defects, though large data volumes, high cost, and limited portability hinder practical use. Machine vision offers low-cost, high-throughput external sorting but captures only surface traits and is illumination-sensitive. X-ray/CT imaging visualizes internal cracks and insect damage, yet radiation safety and bulky hardware preclude field deployment. Complementary tools (NMR, electronic nose, Raman, dielectric, fluorescence, acoustic) address niche needs but face stability, sensitivity, or dimensionality trade-offs. Future breakthroughs demand multi-sensor data fusion, deep learning optimization, and ruggedized low-cost hardware. Bridging laboratory innovation and industrial reality requires concurrent algorithmic, optical, and engineering advances, ultimately transforming seed testing into a reliable, intelligent, and deployable ecosystem. Full article

Oxygen-related absorption features provide fundamental constraints for passive atmospheric remote sensing in the reflected-solar spectrum. Because molecular oxygen is well-mixed in the dry atmosphere, O₂ absorption links measured radiance to atmospheric mass, pressure, and effective photon path length, while O₂-O₂ (O₄) collision-induced absorption provides complementary sensitivity to lower-tropospheric photon paths. This review synthesizes the spectroscopic basis, radiative-transfer mechanisms, satellite implementations, retrieval algorithms, and atmospheric applications of O₂ and O₄ measurements from the ultraviolet to the shortwave infrared. Particular emphasis is placed on the O₂ B-band near 687 nm, the O₂ A-band near 760 nm, O₄ bands in the UV–visible range, and the O₂ band near 1.27 µm. These features support retrievals of cloud fraction, cloud pressure, optical centroid pressure, aerosol layer height, surface pressure, dry-air column abundance, and light-path corrections for greenhouse gas observations. We review major algorithmic approaches, including cloud-as-reflecting-boundary models, cloud-as-layer models, DOAS-based retrievals, optimal-estimation frameworks, photon path-length distribution methods, and machine learning or hybrid techniques. Key applications include cloud climatology, aerosol vertical characterization, air mass factor correction, XCO₂ and XCH₄ retrievals, carbon-cycle studies, and multi-mission data integration. Remaining challenges include spectroscopic uncertainty, aerosol and cloud scattering degeneracy, surface bidirectional reflectance, three-dimensional radiative-transfer effects, wavelength-dependent path mismatch, and inconsistent uncertainty characterization. Future progress will depend on improved spectroscopy, active–passive validation, multi-angle polarimetry, physically constrained machine learning, and harmonized multi-mission retrieval frameworks. Full article

Dendrobium polysaccharide is an important indicator for evaluating the quality of Dendrobium huoshanense. To improve the prediction accuracy of polysaccharide content, this study proposes an improved Artificial Lemming Algorithm (IALA) optimized BP neural network model. In IALA, a periodic mutation strategy and a fast hybrid opposition learning strategy (FHOBL) are introduced to enhance population diversity, improve global search ability, and avoid premature convergence. The proposed IALA was first evaluated on CEC2017 and CEC2020 benchmark functions. Experimental results show that IALA achieves better or competitive performance compared with seven other algorithms in terms of mean fitness, best fitness, and standard deviation. Statistical tests, including Wilcoxon rank-sum and Friedman tests, further verify the significant superiority and robustness of IALA. Then, IALA was used to optimize the initial weights and thresholds of BP neural networks for Dendrobium polysaccharide content prediction. The results show that IALA-BP achieves the best overall prediction performance, with an R² of 0.8731, RMSE of 2.1581, and MSE of 4.6683. Compared with standard BP and other optimized BP models, IALA-BP provides more accurate and stable prediction results. Therefore, the proposed IALA-BP model is effective for rapid prediction of Dendrobium polysaccharide content. Full article

This paper proposes a novel Topological Data Analysis (TDA) pipeline to extract robust structural features from functional near-infrared spectroscopy (fNIRS) signals for the classification of Alzheimer’s Disease (AD) stages. Alzheimer’s disease is increasingly understood as a disconnection syndrome, where the disruption of functional brain networks precedes gross anatomical atrophy. However, traditional graph-theoretic approaches rely on arbitrary connectivity thresholds, which can obscure critical multi-scale topological information, and are sensitive to noise. To address this, our framework leverages Persistent Homology (PH) to analyse the topological evolution of brain networks across a continuous range of scales. By modeling 48-channel hemoglobin concentration time-series as high-dimensional point clouds via Granger causality metrics, we construct filtration sequences of Vietoris–Rips complexes. The resulting topological invariants, including 0—dimensional connected components, 1—dimensional loops, and 2—dimensional voids, are first examined through Persistence Diagrams. For classification, significant H0 and H1 features are converted into Persistence Images using Gaussian kernel smoothing, while H2 features are retained for qualitative topological interpretation. This transformation enables the integration of complex topological features into standard machine learning workflows. Our experimental results were evaluated on a subject-level held-out test set consisting only of original, non-augmented recordings. Data augmentation was applied only to the training set to alleviate class imbalance. The proposed topology-driven feature extraction method achieved 86% accuracy in multi-class diagnosis (NC vs. MCI vs. AD). This study validates the efficacy of TDA as a sophisticated signal processing tool for revealing intrinsic neurodegenerative patterns in hemodynamic data, offering an exploratory methodological proof-of-concept for AD stage classification. Full article

The Kangaroo Escape Optimization Technique (KET) combines escape and safe-area searches, but its fixed stage allocation, restricted guidance range, limited refinement of low-ranked individuals, and insufficient use of population-state information can reduce its performance on complex problems. This study develops a Multi-Strategy Kangaroo Escape Optimization Technique (MSKET) through iteration-dependent stage switching, population-proportion-based candidate guidance, selective greedy DE/rand-to-best/1 refinement, and beta-distribution opposition-based global-best guidance. The contribution lies in assigning established mechanisms to specific KET limitations and coordinating them across different stages and population subsets. MSKET was evaluated through 30 independent runs on the 100-dimensional CEC2017 and CEC2020 suites. It ranked first on 22 of 29 CEC2017 functions and 8 of 10 CEC2020 functions, with the best average rank on both suites. Ablation, diversity, and nonparametric statistical analyses further supported the observed performance gains. MSKET also achieved the best overall repeated-run results on the piston–lever and three-bar truss design problems. For near-infrared prediction, MSKET-BP obtained an R² of 0.86440, an RMSE of 2.1377, and a MAPE of 5.0083% on the testing set. These results indicate improved search and prediction performance within the examined tasks, although at a higher computational cost than KET. Full article

Yb³⁺-containing glasses and glass-ceramics are attractive photonic materials because Yb³⁺ can act simultaneously as a near-infrared absorber, an energy-transfer sensitizer, a luminescent center, and a composition-dependent modifier of glass structure and crystallization. This brief review focuses on crystallization from parent glasses to glass-ceramics and examines glass-network chemistry, local Yb³⁺ coordination, phase separation, viscosity, heating rate, treatment temperature, holding time control nucleation, crystal growth, phase selection, rare-earth partitioning, transparency, and optical performance. Representative oxyfluoride, phosphate, oxyapatite, borosilicate, and aluminosilicate systems are compared using thermal analysis, X-ray diffraction, electron microscopy, vibrational spectroscopy, and optical spectroscopy. The available data show that Yb₂O₃ or YbF₃ does not have a universal effect on crystallization: low concentrations can promote fluoride-rich clustering or lower the apparent crystallization barrier, whereas higher concentrations can increase packing density, stabilize the residual glass, change the competitive phase assemblage, or suppress crystallization. Crystallization-enhanced luminescence is most consistently obtained when Yb³⁺ and the activator partition into low-phonon-energy nanocrystals while crystal size and refractive-index mismatch remain sufficiently small to preserve transparency. This review also identifies major reporting gaps, including limited quantification of crystalline fraction, partition coefficients, luminescence lifetime, quantum efficiency, and long-term thermal stability. Practical design guidelines and unresolved questions are proposed to support the rational development of transparent Yb³⁺-containing glass-ceramics for lasers, sensing, optical amplification, and related photonic applications. Full article

This study evaluates calibration strategies for predicting soluble solids content (SSC) in Ambrosia apples using a low-cost, portable short-wave near-infrared (SW-NIR) spectrometer (640–1050 nm) under interactance acquisitions. Replicate sampling spectral curves exhibited notable variability due to asymmetric illumination, peel color heterogeneity, and probe contact inconsistencies. Regression models were comparatively built on averaged spectra compared with those trained directly on multiply sampled replicate spectra, applying piecewise Savitzky–Golay smoothing and detrending as pretreatment. Variable selection was performed via uninformative variable elimination (UVE) and backward interval partial least squares (BiPLS). Models calibrated on replicate spectra demonstrated superior generalization to unseen replicate measurements, despite slightly higher cross-validation errors. The BiPLS model on replicate spectra achieved the best predictive performance (mean RMSEP = 0.677 °Brix, Rp = 0.796, RPD = 1.656), with improved trueness (lower relative absolute bias) and precision (lower relative standard deviation). For comparison, the BiPLS model on averaged spectra yielded a mean RMSEP = 0.899 °Brix, Rp = 0.593, RPD = 1.25; the replicate-spectra strategy thus reduced the RMSEP by 24.7% and increased Rp and RPD accordingly. This suggests that for low-cost NIR instruments, using replicate sampling spectral modeling combined with interval variable selection can provide better prediction performance and achieve the purpose of on-site sorting in food quality analysis. Full article

Background: Developmental topographical disorientation (DTD)-like navigational difficulties can impair everyday orientation and spatial information processing. Objective: We examined whether 8-week virtual reality (VR) orienteering training improves performance on a laboratory spatial memory task in college students with operationally defined DTD-like navigational difficulties and examined task-related cortical changes. Methods: In a randomized controlled trial, 96 students were assigned to VR orienteering, VR exercise, or control groups (n = 32/group). The exercise groups trained twice weekly for 45 min for 8 weeks at moderate intensity (64–76% HRmax). Before and after the intervention, all participants completed a delayed visuospatial recognition task designed to assess spatial-memory-related processing, while functional near-infrared spectroscopy measured hemodynamic changes in prefrontal and sensorimotor cortices. The post-test session was conducted 48 h after the final scheduled session, with no immediate post-exercise assessment. Results: VR orienteering produced greater improvement in spatial-memory-task accuracy and reaction time than the other groups. After BH-FDR correction across eight ROIs, training-specific reductions in task-related activation were observed in the right primary motor cortex and left frontopolar area. Within the VR orienteering group, pre-to-post change in left orbitofrontal activation was negatively associated with change in spatial-memory-task accuracy, Pearson r(30) = −0.819, 95% CI [−0.908, −0.658], p < 0.001, R² = 0.670; the association remained significant after eight-ROI BH-FDR correction (adjusted p < 0.001). Conclusions: VR orienteering improved performance on the laboratory spatial memory task and was accompanied by reduced task-related cortical recruitment. Greater reductions in L-OFC activation were linked to greater improvements in accuracy, identifying L-OFC activation change as a neural correlate of training-related spatial-memory improvement. Full article

Soluble solids content (SSC) is an important indicator of sweetness-related quality, maturity, and postharvest grading in cherry tomato. Destructive physicochemical measurements remain accurate but are labor-intensive and unsuitable for rapid batch assessment. This study developed an RGSW-SBL framework for predicting SSC in Zheyingfen cherry tomato using portable near-infrared (NIR) spectra. In this framework, region-guided stable wavelength selection (RGSW) was used to select informative wavelengths, whereas sparse Bayesian learning (SBL) served as the quantitative regression model for SSC prediction. RGSW integrates adaptive candidate waveband estimation with robust competitive wavelength screening, thereby retaining continuous spectral regions while reducing redundant and unstable variables. The prediction performance of SBL was compared with that of partial least squares (PLS) regression under full-spectrum and different wavelength-selection conditions. Model performance was evaluated using R² and RMSE, with the principal test-set results reported in terms of R² and RMSE. Under the training–validation–test evaluation protocol, RGSW-SBL achieved the numerically best internal-test result among the reported SBL combinations, with a test-set R² of 0.847 and an RMSE of 0.369 °Brix using 157 selected wavelengths. The selected and high-contribution wavelengths were concentrated in chemically meaningful NIR regions related to C-H and O-H overtone absorption, sugar responses, and water-related tissue information. These results support RGSW-SBL as an interpretable framework for controlled-condition SSC prediction in cherry tomato, although multi-season, multi-cultivar, and multi-instrument validation remains necessary before deployment. Full article

Near-infrared (NIR) spectroscopy-based adulteration detection approaches are still dominated by one-dimensional (1D) spectral analysis, which inherently limits the exploration of complex patterns and nonlinear interactions in spectral data. Therefore, the objective of this study is to use a two-dimensional (2D) NIR spectrogram, combined with Explainable Artificial Intelligence (XAI), to predict the level of adulteration in palmyrah sap. The dataset matrix dimension is 110 × 1101, derived from the sample adulteration level (0–100%) and the NIR wavenumber (4000–12,500 cm⁻¹). Following Kennard–Stone partitioning, the evaluated preprocessing methods were applied using parameters derived exclusively from the training set. For the 2D modeling branch, the resulting training and testing spectra were subsequently transformed separately using the Continuous Wavelet Transform (CWT). A total of six AI algorithms, three from machine learning (PLS, kNN, ANN) and three from deep learning (CNN, AlexNet, ResNet), were applied in this study. The best model AI was interpreted using Shapley Additive Explanations (SHAP) for 1D NIRs and the Gradient-weighted Class Activation Mapping (Grad-CAM) for 2D NIR spectrograms. The four best-performing model configurations can predict the level of palmyrah sap adulteration, with R² values ranging from 0.969 to 0.994 and RMSE ranging from 2.333% to 5.547% in the training. In the testing, the model’s performance is in the R² range of 0.959–0.990, RMSE of 3.093–6.396%, MAE of 2.358–4.252%, RPD of 5.06–10.46 and Bias of 0.03–0.93%. The SHAP and Grad-CAM XAI revealed that the wavenumber associated with this sap counterfeiting is critical to the level of adulteration of palmyrah sap. This approach provides a quantitative method that accounts for advanced dimensions and treats them as essential information to support large-scale data matrices in AI modeling. The application of this method is an alternative that is easy to interpret and implement, and can be applied to long- and short-wavelength data from continuous NIR or discrete multi-wavelength NIR. Full article