Search Results (1,798)

Wavelength modulation spectroscopy (WMS) is a representative implementation of tunable diode laser absorption spectroscopy (TDLAS), enabling reliable gas component analysis with concentration-related information derived from harmonic component extraction, while offering enhanced noise immunity for trace gas sensing in open environments. However, due to the strong coupling between laser wavelength and intensity, wavelength modulation inevitably introduces residual amplitude modulation (RAM), which significantly degrades measurement accuracy. To address this issue, this study introduces a RAM suppression algorithm based on multiple harmonic component decoupling (MHCD), using the second-harmonic lateral peak inclination angle (LPIA) as a characteristic indicator. Unit harmonic operators for the first, second, and third harmonics are designed, and an original harmonic reconstruction model is established via linear superposition of harmonic components. The optimal harmonic component ratio is determined at the composite operator with the maximum cross-correlation coefficient, and RAM noise is eliminated through a multi-harmonic decoupling matrix. Repetitive measurements on 22 mm pharmaceutical vials with 4% oxygen concentration demonstrate that MHCD reduces the second-harmonic LPIA from 18.07° to 8.56°. Concentration discrimination experiments conducted on seven groups of 22 mm vials with 2% concentration steps (0–12%) show that MHCD increases the true positive rate by 6–11% and decreases the false positive rate by 4–9%, confirming its effectiveness for pharmaceutical online inspection applications. Full article

The paper proposes a completely new geometric interpretation of intuitionistic fuzzy sets for confidence-aware decision making. Instead of directly using ordered pairs of membership and non-membership degrees, we reinterpret the intuitionistic fuzzy triangle as a structured coordinate framework that simultaneously represents the evaluation outcomes and the evaluators’ expertise in the following manner: experts’ confidence levels are modelled with line segments parallel to the hypotenuse, while evaluation scores are represented by line segments radiating from the origin of the coordinate system toward the hypotenuse. Their intersections form a finite lattice of points whose total number depends on the chosen confidence and assessment scales. The proposed construction preserves the semantic foundations of intuitionistic fuzziness: points closer to the origin reflect higher uncertainty in the evaluator’s confidence, while points onto the hypotenuse represent determinate judgments (with varying degrees of positivity or negativity) based on the complete evaluator’s confidence. The geometric distances between intersections provide a formal explanation of varying discriminative power: assessments from highly confident reviewers are more distinguishable than those from less confident ones. In addition, a colour-based visualization further supports the intuitive interpretation of confidence-weighted evaluations. The proposed framework offers an alternative yet fully consistent way to model expertise-dependent decision processes within the intuitionistic fuzzy setting, bridging geometric insight and practical evaluation scenarios via a structured system of parallels and meridians. Full article

Sustainability monitoring relies on environmental indicator systems that integrate heterogeneous multivariate measurements across space and time; however, collinearity, non-Gaussian variability, and influential observations frequently destabilize classical multiblock methods and may bias indicator-based assessment and decision support. This study proposes a robust extension of the STATICO (STATIS–CO-inertia) framework to model common structures among paired environmental indicator blocks under realistic data contamination. The approach preserves the original triadic algebraic formulation while incorporating robust covariance estimation and adaptive weighting to reduce the influence of outliers and structurally unstable blocks. Robustification is implemented at the interstructure stage through a reformulated Escoufier’s RV coefficient and in the construction of the compromise space via robust distances. The RV coefficient, a multivariate generalization of the squared Pearson correlation computed between cross-product matrices, is used to quantify structural similarity between paired data blocks and to evaluate the stability of the compromise structure. Performance is evaluated using simulated datasets calibrated to represent Ecuadorian coastal monitoring conditions. The results show that Robust STATICO increases compromise dominance and stability, redistributes inter-block similarities more coherently, and improves discriminative representation in the factorial space, yielding more interpretable and environmentally plausible structures. Overall, the proposed method provides a reliable analytical tool for sustainability-oriented environmental monitoring by supporting stable identification of persistent multivariate patterns and robust comparison of indicator structures in complex systems. Full article

Introduction: Gender-based violence and femicide represent the most extreme manifestation of a deep-rooted cultural distortion embedded within patriarchal social structures. In this study, adopting a comparative and multidisciplinary approach, we analyzed the evolution of international legislation and the major historical milestones in the protection of women’s rights and the prevention of gender-based violence at both the global and Italian levels. Specific protocols such as the “Pink code” were examined, with particular attention to medico-legal implications and the clinical management of victims, highlighting how violence against women continues to be fuelled by stereotypes, discrimination, and unequal power relations. Materials and Methods: Gender-based violence and femicide were examined from both national and international perspectives. A total of 73 scientific articles in English and 28 legal sources were selected from an initial pool of 918 publications, through a narrative review with a structured search strategy of international and Italian legislation and scientific literature. Electronic databases (PubMed and Google Scholar) were searched for the period 2000–2025. Only original observational studies, medico-legal analyses, epidemiological reports, and forensic case series were included. Cases primarily related to pregnancy, migration, infanticide, suicide, or substance abuse were excluded to reduce heterogeneity and focus on violence rooted in gender-based power asymmetries. Results: The legislative analysis shows a progressive strengthening of protection mechanisms, particularly between 2012 and 2023, following the ratification of the Istanbul Convention, the increase in intimate partner violence, and the COVID-19 pandemic. In Italy, the repeal of discriminatory norms and the introduction of specific legislative measures have led to increased attention toward prevention, protection, and prosecution of gender-based violence. Protocols such as the ‘Pink Code’, an Italian hospital-based multidisciplinary pathway activated mainly in emergency departments for the early identification, clinical care, medico-legal documentation, and judicial protection of victims of gender-based violence, have improved multidisciplinary management of victims within healthcare and judicial settings, although significant challenges remain regarding the full enforcement of legislation and the effective protection of women. The analysis focuses on female victims, in accordance with the Italian legal definition of gender-based violence, while other forms of gender-related violence were considered beyond the scope of this review. Conclusions: Despite substantial legal advances, combating gender-based violence clearly requires an integrated approach that combines prevention, assistance, and prosecution. Strengthening collaboration among institutions, healthcare services, and the judicial system—consistent with international recommendations—is essential to ensure an effective and rights-based response to victims. Overcoming the cultural and social barriers that perpetuate violence remains a fundamental priority, alongside promoting genuine gender equality. Full article

Semantic segmentation of remote sensing imagery is crucial for applications such as land resource management and urban planning, yet it remains challenging due to low intra-class variation, ambiguous boundaries, and the coexistence of multi-scale geospatial features. To tackle these issues, we propose GS-USTNet, a novel architecture that enhances both feature representation and boundary recovery. First, we introduce a Global–Local Adaptive Convolution (GLAConv) module that dynamically fuses global contextual cues with local responses to generate content-aware convolutional weights, thereby improving feature discriminability. Second, we design a Skip-Guided Attention (SGA) mechanism that leverages spatial–channel joint attention to guide the decoder, effectively mitigating attention dispersion in complex scenes or under class imbalance and significantly sharpening object boundaries. Built upon the efficient USTNet framework, our model achieves substantial performance gains without compromising computational efficiency. Extensive experiments on benchmark datasets demonstrate that GS-USTNet achieves consistent improvements over the original USTNet, with gains of approximately 3.5% in overall accuracy and 6.0% in F1-score across datasets. Ablation studies further confirm the effectiveness of the proposed GLAConv and SGA modules. This work provides an efficient and robust approach for fine-grained semantic segmentation of high-resolution remote sensing imagery. Full article

Text-based person retrieval (TBPR) aims to search for target person images from large-scale video clips or image databases based on textual descriptions. The quality of benchmarks is critical to accurately evaluating TBPR models for their ability in relation to cross-modal matching. However, we find that existing TBPR benchmarks have a common problem, which often leads to ambiguities where multiple images of persons with different identities have very similar or even identical textual descriptions. As a consequence, although TBPR models correctly retrieve the images corresponding to a given description, such matches may be erroneously evaluated as mismatches due to the above annotation problem. We argue that the main cause of this problem is that each person image is annotated individually without reference to other similar images, making it challenging to provide distinctive descriptions for each image. To address this problem, we propose an effective and efficient annotation refinement framework to improve the annotation quality of TBPR benchmarks and thereby mitigate annotation-induced mismatches. Firstly, sets of images prone to mismatches are automatically identified by TBPR models. Then, by leveraging multimodal large language models (MLLMs), multiple images are simultaneously processed and distinctive descriptions are generated for each image. Finally, the original descriptions are replaced to improve the annotation quality. Extensive experiments on three popular TBPR benchmarks (CUHK-PEDES, RSTPReid and ICFG-PEDES) validate the effectiveness of our proposed method for improving the quality of annotations, and demonstrate that the resulting more discriminative captions can truly benefit the mainstream TBPR models. The improved annotations of these benchmarks will be released publicly. Full article

To address the issues of sample imbalance, unstable generation quality, and insufficient feature extraction in spectrum anomaly signal detection under complex electromagnetic environments, this paper proposes a VAE-GANGP identification model that integrates a Variational Autoencoder (VAE) with a Gradient Penalty-based Generative Adversarial Network (GAN-GP). First, the VAE is employed to encode the original spectrum, generating structured latent features that follow a standard normal distribution. This replaces the random noise input in traditional GANs, significantly enhancing the semantic consistency of generated samples and training stability. Second, an adversarial training mechanism based on Wasserstein distance with gradient penalty (WGAN-GP) is introduced, effectively mitigating mode collapse and gradient vanishing, thereby improving the model’s capability to fit complex signal distributions. Furthermore, a multi-objective optimization function combining reconstruction error and adversarial loss is constructed, establishing an end-to-end integrated framework for feature learning, signal reconstruction, and anomaly discrimination. Experiments are conducted using a synthetic dataset comprising various modulation types and simulated environments with different signal-to-noise ratios for systematic validation. The results demonstrate that the spectrum data generated by VAE-GANGP closely matches the distribution of real signals. Under AWGN-dominated synthetic test conditions, the model achieves an anomaly detection accuracy of 98.1%. When evaluated under more realistic channel impairments (phase noise, multipath, impulsive interference), the model maintains competitive performance, outperforming existing methods and demonstrating promising potential for practical electromagnetic spectrum monitoring. Its performance significantly surpasses traditional detection methods and single deep learning models, providing a highly reliable and adaptive solution for spatial electromagnetic spectrum anomaly detection. Full article

Hyperspectral sensors provide high spectral resolution, enabling accurate material discrimination and effective target detection. However, their practical use is constrained by limited spatial resolution and high acquisition costs. This paper proposes a novel framework to enhance small-target detection in multispectral imagery by leveraging deep learning-based spectral reconstruction to generate high-resolution hyperspectral representations from multispectral inputs. Two state-of-the-art reconstruction networks, MST++ and MIRNet, are trained using paired multispectral–hyperspectral samples derived from AVIRIS-NG data through proper spectral response functions. To improve discriminative capability for the target of interest, a rapid, target-specific fine-tuning stage is introduced, allowing the models to adapt to spectral signatures that are poorly represented or absent in the original training data. Target detection is performed using a spectral signature-based detector applied to the reconstructed hyperspectral data. The proposed framework is evaluated in a real-world scenario involving known field-deployed targets and hyperspectral imagery acquired from an unmanned aerial vehicle. Experimental results demonstrate that the proposed approach significantly outperforms baseline detection applied directly to multispectral data. These findings underscore the effectiveness of spectral reconstruction for downstream tasks such as target detection, particularly in scenarios where hyperspectral data are expensive or unavailable. Full article

Background/Objectives: Fever can develop from several causes, including infectious diseases, noninfectious inflammatory diseases (NIID), malignancies, and other medical conditions. Although serum ferritin (SF) level can help differentiate infectious from non-infectious diseases, its discriminative ability (specificity) is far from satisfactory. The aim of this study was to develop a diagnostic prediction model to distinguish infectious diseases from other febrile illnesses using only common blood tests available on admission, in addition to SF level, in patients with undiagnosed fever. Methods: This single-center retrospective observational study included patients with fever of unidentified origin aged ≥18 years admitted to a Japanese acute care hospital between 1 January 2013, and 31 December 2022. They were divided into infectious and non-infectious disease groups based on their final diagnosis. Machine learning and multivariable logistic regression analysis were used to develop a model to differentiate infectious diseases from non-infectious diseases. Model performance was evaluated using area under the curve (AUC), shrinkage coefficient, and stratified likelihood ratio. Results: Among the 143 patients included, 73 had infectious diseases. A prediction model consisting of five factors—serum white blood cell count, neutrophil percentage, platelet count, lactate dehydrogenase level, and log-transformed SF level—was developed. The AUC of the model was 0.794 (95% confidence interval: 0.721–0.867) with a sensitivity of 77.1%, specificity of 68.5%, shrinkage coefficient of 0.876, and stratified likelihood ratio of 0.13–5.04. Conclusions: We developed a prediction model consisting of only five high-performing indicators, which would help differentiate infectious diseases from other fever causes early after admission. Full article

With evolving consumption trends, the coffee market is experiencing increasing demand for high-quality, traceable coffees, which, in turn, has led to price growth. Therefore, due to its increased economic value, coffee has become a constant target of fraudulent actions. As result, many analytical techniques have been explored as tools for coffee classification and authentication, of which the use of digital, hyperspectral and/or multispectral imaging is noteworthy. This type of analysis provides rapid, non-destructive, environmentally friendly, and increasingly accessible alternatives to conventional analytical methods. By consulting three different databases, this work systematically revised articles published in the last 10 years, which utilize digital image analysis and hyper/multispectral imaging for the botanical and geographical classification and authentication of coffees. The reviewed studies (n = 17) demonstrate that, when paired with classification algorithms, discrimination across species, origins, and quality categories can be achieved. A critical point to highlight is the importance of using whole beans and standardizes roast degree to avoid biasing the models. Concerning digital images, relying solely on color features limits the robustness of the classification models. Incorporating complementary textural and shape features is thus necessary to capture the coffee botanical or geographic information, as shown in a minor number of the selected studies. In a similar fashion, for hyper/multispectral imaging, there is still potential to further exploit the spatial information, thus achieving the technique’s full potential. The evidence indicates that image-based methods are steadily progressing into reliable tools for coffee authentication. Full article

To resolve the issue of degraded detection accuracy for unmanned aerial vehicle object detection under low-illumination environments, this paper introduces a parallel object detection model. First, a dual-branch architecture is established by parallelly integrating a Zero-Reference Deep Curve Estimation (Zero-DCE) illumination enhancement network with a You Only Look Once (YOLOv11n)-based object detection network, enabling collaborative feature training and real-time updates. Through a feature-sharing mechanism, the two branches are jointly optimized during training, thus enhancing the model’s generalization capability in low-illumination environments. Furthermore, to further improve detection accuracy, a Dynamic Pooling Synergy Attention (DPSA) module is introduced into the backbone of YOLOv11n. By integrating dynamic pooling-based channel attention with spatial attention, this module improves feature representation, improves performance under complex environments, and increases adaptability to multi-scale targets. In addition, a High and Low Frequency Spatially-adaptive Feature Modulation (HLSAFM) module is added to the detection network’s Neck. Through high- and low-frequency feature refinement, segmented feature processing, and dynamic modulation, the network is able to capture richer feature information, thereby strengthening feature representation and discriminative capability. Extensive experiments on the VisDrone (Night) and DroneVehicle (Night) datasets demonstrate superior performance over multiple existing methods under low-illumination object detection tasks. Compared with the original YOLOv11n model, the proposed model $mAP50$ increases by 6.0% and 1.0% and $mAP50:95$ increases by 3.1% and 0.8%, respectively. These results confirm the enhanced detection capability achieved by our method in challenging low-illumination unmanned aerial vehicle (UAV) scenarios. Full article

This study investigates whether dedicated tumor segmentation for radiomics (TRAD) offers any advantage over gross tumor volume (GTV) in CT radiomics for predicting adenoid cystic carcinoma (ACC) progression after proton therapy (PT). Fifty-six patients with histologically proven salivary gland ACC were included, and 107 original features were extracted using PyRadiomics v3.1.0. Signatures were selected (n = 3) with sequential backward elimination using multiple classifiers, all optimized for improving cross-validated area under the ROC curve (AUC). Signature similarity was quantified using the Spearman correlation coefficient. Random forest (RF) yielded the best discriminative performance, with no statistical difference in AUCs between contour choices (GTV: 0.87 vs. TRAD: 0.80; ΔAUC_median = 0.0, p = 0.589). Time-to-event analysis confirmed both signatures stratified patients into distinct progression-free survival risk groups (Log-rank p < 0.0001) and demonstrated robust prognostic accuracy (GTV: C-index = 0.74, HR = 11.63; TRAD: C-index = 0.72, HR = 7.01). Biologically, GTV and TRAD signatures were borderline associated with perineural spread (p = 0.056) and solid tumor patterns (p = 0.053), respectively. Overall, CT-based radiomics models performed comparably across both segmentation strategies, supporting GTV as a practical and efficient alternative to TRAD for predicting ACC progression after PT. Full article

LiDAR has been widely applied in autonomous driving and mobile robotics. Recently, many studies focus on real-time point cloud segmentation, aiming to achieve higher accuracy while maintaining real-time inference speed. Current real-time methods mostly rely on 2D projection, which inevitably leads to spatial information loss. To address the limitations of 2D projection methods, we propose a Geometry-Enhanced Network called GENet that exploits spatial priors. The network employs an Atrous Separable Range Attention (ASRA) module to explicitly utilize spatial priors from range images, enabling geometry-aware feature aggregation with large receptive field at linear complexity. A Geometry-Context Modulation (GCM) mechanism is then used to calibrate semantic features, incorporating geometric priors while preserving the discriminative ability of original features across different categories. Experiments show that our method achieves efficient information fusion while maintaining real-time performance. Compared to existing methods, GENet requires fewer parameters and less computation, achieving a favorable balance between accuracy and efficiency. Full article

Background: Kidney transplantation is the optimal treatment for end-stage renal disease; however, acute rejection remains a major determinant of long-term graft dysfunction and failure. Donor-derived cell-free DNA (dd-cfDNA) has emerged as a minimally invasive biomarker reflecting allograft injury, with growing evidence supporting diagnostic and prognostic utility. Objectives: This structured narrative review aimed to synthesize contemporary evidence (2020–2025) on the diagnostic and prognostic utility of plasma dd-cfDNA and its integration into kidney transplant rejection surveillance. Methods: A narrative literature review was conducted using PubMed to identify studies published or available online ahead of print, between January 2020 and September 2025, evaluating plasma dd-cfDNA in adult kidney transplant recipients. Manual screening of reference lists supplemented the search. Original clinical studies reporting diagnostic or prognostic outcomes were included, and the results were synthesized narratively due to methodological heterogeneity. Results: Across prospective and retrospective cohorts, elevated dd-cfDNA discriminated rejection from non-rejection biopsies, with strongest performance in antibody-mediated and microvascular rejection phenotypes. Longitudinal studies demonstrated that dd-cfDNA elevations often preceded histologically confirmed rejection and predicted adverse graft outcomes, while low levels were associated with immunologic quiescence. Assay variability limited cross-study comparability, whereas integration with donor-specific antibodies, gene expression profiling, or algorithm-based approaches improved diagnostic and prognostic discrimination. Conclusions: Dd-cfDNA is a clinically meaningful biomarker for kidney transplant rejection monitoring, providing diagnostic and prognostic information beyond conventional functional markers. When interpreted longitudinally and in clinical context, dd-cfDNA supports risk stratification and surveillance, with evidence supporting its expanding role in risk-adapted transplant care. Full article

Thermal groundwater resources constitute valuable health-oriented georesources, particularly when integrated into regional strategies for wellness, balneotherapy, and therapeutic tourism. This study presents the first comprehensive and integrated hydrochemical, geospatial, and balneological characterization of thermal groundwater systems in Uruguay, enabling their classification from a medical hydrology perspective and supporting the assessment of their potential use in balneotherapy. Seven thermal groundwater sources located in northwestern Uruguay were investigated, mainly associated with the Guaraní Aquifer System (GAS), together with the singular Almirón spring, which represents a distinct hydrogeological setting. Field measurements and laboratory analyses were conducted to determine physicochemical parameters, major ions, and gases. Hydrogeochemical facies were identified using Piper and Gibbs diagrams, while multivariate statistical techniques, including Principal Component Analysis (PCA) and hierarchical clustering, were applied to discriminate water types and support their balneological classification. The results indicate that most thermal waters associated with the GAS are characterized by sodium–bicarbonate facies, weak to medium mineralization. Dry residue to 180 °C, (311–734 mg/L), and mesothermal to hyperthermal temperatures (36.3–44.5 °C), reflecting deep confined circulation and prolonged water–rock interaction. By comparison, the Almirón spring exhibits a chloride–sodium facies with strong mineralization. Dry residue to 180 °C, (6590 mg/L) and hypothermal (32 °C), consistent with a distinct hydrogeological origin involving crystalline basement and Devonian sedimentary units and reflecting more evolved geochemical conditions. Based on the obtained results, and by analogy with comparable international hydrothermal profiles, the main balneological indications of these waters include musculoskeletal and rheumatic disorders, dermatological disorders, and other emerging indications such as stress, sleep disorders, obesity, and Long COVID. In conclusion, this study reveals the hydrochemical diversity of Uruguay’s thermal groundwater and its possible use in balneology. Future research should focus on controlled clinical and balneological studies to validate specific therapeutic effects. Full article