Search Results (5,634)

The present study investigates passive microdroplet generation in a T-junction microchannel using microscopic observations, microscale particle image velocimetry (Micro-PIV) visualization, and lattice Boltzmann simulations. The key flow regimes, i.e., dripping, threading, and parallel flow, are characterized by analyzing the balance between hydrodynamic forces and surface tension, revealing the critical role of the flow rate ratio of the continuous to dispersed fluids in regime transitions. Micro-PIV visualizes velocity fields and vortex structures during droplet formation, while a lattice Boltzmann model with wetting boundary conditions captures interface deformation and flow dynamics, showing good agreement with experiments in the dripping and threading regimes but discrepancies in the parallel flow regime due to neglected surface roughness. The present experimental results highlight non-monotonic trends in the maximum head interface and breakup positions of the dispersed fluid under various flow rates, reflecting the competition between the squeezing and shearing forces of the continuous fluid and the hydrodynamic and surface tension forces of the dispersed fluid. Quantitative analysis shows that the droplet size increases with the flow rate of continuous fluid but decreases with the flow rate of dispersed fluid, while generation frequency rises monotonically with the flow rate of dispersed fluid. The dimensionless droplet length correlates with the flow rate ratio, enabling tunable control over droplet size and flow regimes. This work enhances understanding of T-junction microdroplet generation mechanisms, offering insights for applications in precision biology, material fabrication, and drug delivery. Full article

Background and Clinical Significance: Patients with a neurodegenerative condition known as posterior cortical atrophy (PCA) can present with attention impairments across a variety of cognitive contexts, but the consequences of these are little explored in example of single word reading. Case Presentation: We present a detailed single-case study of KL, a local resident of South Wales, a patient diagnosed with posterior cortical atrophy (PCA) in 2018, whose reading and letter-naming abilities are selectively disrupted under non-canonical visual presentations. In particular, KL shows significantly impaired accuracy performance when reading words presented in tilted (rotated 90°) format. By contrast, his reading under conventional horizontal (canonical) presentation is nearly flawless. Whilst other presentation formats including, mixed-case text (e.g., TaBLe) and vertical (marquee) format led to only mild performance decrements—even though mixed-case formats are generally thought to increase attentional ‘crowding’ effects. Discussion: These findings indicate that impairments of word reading can emerge in PCA when visual-attentional demands are sufficiently high, and access to ‘top down’ orthographic information is severely attenuated. Next, we explored a cardinal feature of attentional dyslexia, namely the word–letter reading dissociation in which word reading is superior to letter-in-string naming. In KL, a similar dissociative pattern could be provoked by non-canonical formats. That is, conditions that similarly disrupted his word reading led to a pronounced disparity between word and letter-in-string naming performance. Moreover, different orientation formats revealed the availability (or otherwise) of distinct compensatory strategies. KL successfully relied on an oral (letter by letter) spelling strategy when reading vertically presented words or naming letters-in-strings, whereas he had no ability to engage compensatory mental rotation processes for tilted text. Thus, the observed impact of non-canonical presentations was moderated by the success or failure of alternative compensatory strategies. Conclusions: Importantly, our results suggest that an attentional ‘dyslexia-like’ profile can be unmasked in PCA under sufficiently taxing visual-attentional conditions. This approach may prove useful in clinical assessment, highlighting subtle reading impairments that conventional testing might overlook. Full article

Citrus diseases severely impact fruit yield and quality. To facilitate in-field, non-destructive, and rapid detection of citrus leaf physiological and pathological conditions, this study proposes a classification method for citrus leaf physiological and pathological statuses that integrates visible/near-infrared multispectral technology with deep learning. First, a handheld spectrometer was employed to acquire spectral images of five sample categories—Healthy, Huanglongbing, Yellow Vein Disease, Magnesium Deficiency and Manganese Deficiency. Mean spectral data were extracted from regions of interest within the 350–2500 nm wavelength range, and various preprocessing techniques were evaluated. The Standard Normal Variate (SNV) transformation, which demonstrated optimal performance, was selected for data preprocessing. Next, we innovatively introduced an adaptive spectral positional encoding mechanism into the Transformer framework. A lightweight, learnable network dynamically optimizes positional biases, yielding the ASTransformer (Adaptive Spectral Transformer) model, which more effectively captures complex dependencies among spectral features and identifies critical wavelength bands, thereby significantly enhancing the model’s adaptive representation of discriminative bands. Finally, the preprocessed spectra were fed into three deep learning architectures (1D-CNN, 1D-ResNet, and ASTransformer) for comparative evaluation. The results indicate that ASTransformer achieves the best classification performance: an overall accuracy of 97.7%, underscoring its excellent global classification capability; a Macro Average of 97.5%, reflecting balanced performance across categories; a Weighted Average of 97.8%, indicating superior performance in classes with larger sample sizes; an average precision of 97.5%, demonstrating high predictive accuracy; an average recall of 97.7%, showing effective detection of most affected samples; and an average F1-score of 97.6%, confirming a well-balanced trade-off between precision and recall. Furthermore, interpretability analysis via Integrated Gradients quantitatively assesses the contribution of each wavelength to the classification decisions. These findings validate the feasibility of combining a handheld spectrometer with the ASTransformer model for effective citrus leaf physiological and pathological detection, enabling efficient classification and feature visualization, and offer a valuable reference for disease detection of physiological and pathological conditions in other fruit crops. Full article

The repeated head impacts experienced by athletes have attracted significant interest from both the public and the scientific community; however, the neurobiological effects following the games are not well understood. For example, a single football match carries the risk of repeated concussive and subconcussive head impacts, which can increase the risk of developing neurodegenerative diseases. Chronic traumatic encephalopathy (CTE) is one of the neurodegenerative conditions athletes often face or are unaware of. However, addressing the disease progression in CTE is difficult to determine due to several reasons, such as the failure to identify risk factors, difficulty in differentiating CTE from other neurodegenerative diseases, and the lack of a specific mechanism by which CTE leads to tau protein accumulation. In addition, CTE symptoms overlap with other neurodegenerative conditions, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), which poses a challenge to producing specific targeted therapy. In this case, ultrasound represents a promising non-invasive technique that enables clear visualization of brain structures and may modulate neuronal activity. The term ultrasound encompasses various modalities; for example, high-intensity focused ultrasound (HIFU) employs thermal energy to ablate cells, whereas low-intensity pulsed ultrasound (LIPUS) delivers mechanical energy that activates molecular signaling pathways to impede the progression of CTE. Therefore, the LIPUS application could potentially minimize the risk of damage in the surrounding tissues of the brain and reduce the disease progression in individuals with CTE. Nevertheless, limited studies have been reported in the literature, with a poor mechanistic approach. Hence, this review aims to highlight the molecular signaling pathways, such as AKT, MAPK, and ERK, affected by LIPUS and emphasize the need for additional research to clarify its mechanistic effects in CTE management. Ultimately, this review aims to contribute to a nuanced understanding of LIPUS as a therapeutic strategy in addressing the complexities of CTE and its associated neurodegenerative disorders. Full article

Jñānakīrti (Tib. Ye shes grags pa), an eminent monk of the late Indian Buddhist period, composed the Tattvāvātara (De kho na nyid la ’jug pa, Realizing Reality), of which only a Tibetan translation exists in the Tibetan Tripiṭaka-Tanjur. The treatise is considered an exposition of the Mahāmudrā teachings, with the chapter entitled “Practices of the Secret Mantra Approach” (gSang sngags kyi sgo’i spyod pa) forms a large part. However, this part has been less frequently discussed. This chapter guides the practice of Mahāmudrā non-dual yoga, which is intended for practitioners with superior faculties. The core content of this chapter can be subsumed under the following two aspects: Mahāmudrā teachings involve practicing insight (prajñā), which represents the theory of meditation, i.e., the idea of emptiness (śūnyatā); it also involves practicing skillful means (upāya), which includes the methods of cultivation, such as tantric rituals such as Vajradhātu maṇḍala visualization. From the perspective of compositional length, the first half of the text contains numerous quotations of verses, with several being from Nāgārjuna’s Mūlamadhyamakakārikā, while the second half mainly draws references from the Tattvasaṃgrahatantra and the Guhyasamājatantra. More attention should be paid to the juxtaposition of the Mahāmudrā teachings with the Tattvasaṃgrahatantra and the Guhyasamājatantra, which reflect the early form of the Mahāmudrā teachings as they were introduced into Tibetan Buddhism. Full article

The historic landscape and the value of the ancient city in the stock era present a diversified and mixed problem; as such, this study explores a quantifiable spatial correlation method for landscape layering characteristics and value space, in order to provide support for the urban renewal paths that integrate historical and contemporary needs. Taking as an example Chaozhou Ancient City, a renowned historical and cultural city in China, this study draws on the theory of historical urban landscape layering and comprehensively uses historical graphic interpretation, GIS spatial quantitative analysis, the single-land-use dynamic degree model, the Analytic Network Process, and the Delphi method to quantitatively analyze and evaluate the landscape layering characteristics and value space of the ancient city. Meanwhile, it explores the relationship between the historical landscape layering characteristics and value space of ancient cities using the spatial autocorrelation model and the coupling coordination modulus model. The key findings are as follows: (1) The high-layer space (66.1%) and high-value space (31.1%) of the historic landscape of Chaozhou Ancient City show significant mismatch and imbalance. Spatially, layer spaces increase from the city center toward the periphery, whereas value spaces decrease from the center outward, demonstrating marked spatial heterogeneity. (2) The layer–value space shows a spatial distribution of agglomeration, with Moran’s I index values of 0.2712 and 0.6437, respectively. The agglomeration degree of the value space is much higher than that of the layer space, and both show significant non-equilibrium and associative coupling. (3) Coupling coordination: basically balanced (D = 0.56) indicates a transition toward a more integrated state, although 48% of the region remains in a state of severe dysfunction, mainly consisting of two types of spaces: “high-layer–high-value” and “low-layer–low-value.” These two dysfunctional types should be prioritized in future conservation and renewal strategies. This study provides a more comprehensive quantitative analysis path for identifying and evaluating the landscape layer–value space of the ancient city, providing visualization tools and decision-making support for the future protection and renewal of Chaozhou Ancient City and the declaration of the World Heritage. Full article

Background/Objectives: There is increasing interest within cognitive neuro-surgery in preserving domains not traditionally assessed during awake surgery. The study aims at proposing a specific protocol to assist surgical resection in right temporal areas. Patients were not evaluated during direct cortical stimulation; instead, assessments occurred during the resection itself. The real-time neuropsychological testing (RTNT) protocol employed tasks evaluating visuospatial and social cognition, administered repeatedly throughout the resection using varied items. Methods: A consecutive series of 24 patients (median age 44) performed RTNT. The aim of RTNT is to maintain high accuracy through resection. Lesions in the right temporal cortex and the subcortical white matter beneath can cause deficits; accordingly, not all of our patients had pre-surgery performance within the normal range. In this case, the aim of RTNT is to maintain the not perfect pre-surgery level. Results: We found a statistically significant between-tasks difference in the patients’ median values (across RTNT runs), in their minimum score reached during resection, and in the delta between performance at the last vs. the first RTNT run. The tasks that varied belonged to visual–spatial attention (landmark task), face processing (recognition of famous faces), and social cognition (theory of mind). The outcome was measured by pre- vs. post-surgery neuropsychological score comparison. The number of patients scoring below the normal range did not significantly differ between post- vs. pre-intervention. Conclusions: Results demonstrated the feasibility of implementing a continuous monitoring protocol during the resection phase, and the potential of the selected tasks to assess visuospatial and social functions associated with the non-dominant (right) hemisphere. Full article

Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder frequently associated with cognitive impairment. In the context of increasing interest in remote healthcare solutions, particularly after the COVID-19 pandemic, this preliminary study aimed to evaluate the feasibility of an online version of the Parkinson’s Disease—Cognitive Rating Scale (PD-CRS), a first-level neuropsychological screening tool for mild cognitive impairment (MCI) in individuals with PD. Methods: Seventy-nine patients with idiopathic PD were recruited between October 2020 and February 2024. A telematic version of the PD-CRS was administered via video call using adapted materials (e.g., slide-based instructions and webcam-mediated tasks). Both patients and examiners completed a Visual Analogue Scale (VAS) to rate perceived difficulty. Descriptive statistics and non-parametric tests were used to analyze data. Results: Difficulty ratings were low for both patients (mean VAS = 1.60, SD = 0.88) and the examiner (mean VAS = 1.43, SD = 0.61), with no significant difference (p = 0.176). No demographic or clinical variable predicted difficulty. Conclusions: These findings support the feasibility and usability of an online version of PD-CRS. This approach may facilitate wider access to cognitive screening for PD patients, particularly those with mobility limitations or living in underserved areas. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) is the most common primary malignant tumour of the liver. In a cirrhotic liver, each nodule larger than 10 mm demands further work-up using CT or MRI. The Liver Imaging Reporting and Data System (LI-RADS) is still based on visual assessment and measurements. The purpose of this study was to evaluate whether semi-automated quantification of visual LR-5 lesions is appropriate and can objectify HCC classification for personalized radiomic research. Methods: A total of 52 HCC patients (median age 67 years, 17% females, 83% males) from a retrospective data collection were evaluated visually and compared by the results using an oncology software with features of LI-RADS-based structured tumour evaluation and documentation, semi-automated tumour segmentation, and texture analysis. Results: Software-based evaluation of non-rim arterial-phase hyperenhancement (APHE) and non-peripheral washout, as well as the LI-RADS-score, showed no statistically significant differences compared with visual assessment (p = 0.2, 0.7, 0.17), with a consensus between a human reader and the software approach in 98% (APHE), 89% (washout), and 93% (threshold growth) of cases, respectively. The software provided automated LI-RADS classification, structured reporting, and quantitative features for HCC registries and radiomic research. Conclusions: The presented work may serve as an outlook for LI-RADS-based automated qualitative and quantitative evaluation. Future research may show if texture analysis can be used to foster personalized medical approaches in HCC. Full article

Background: Pelemir (Cephalaria syriaca) is a bitter-tasting ancestral legume with a high polyphenol content and emerging potential as a functional food ingredient. This study investigated the acute metabolic effects of pelemir-enriched bread in adults. Methods: In this two-phase non-randomized trial, 60 participants in three groups (n = 20 per group: healthy controls [HCs], individuals with obesity [OB], and individuals with type 2 diabetes [T2D]) consumed regular or pelemir-enriched bread on two separate test days. Postprandial glucose, insulin, C-peptide, GLP-1, PYY, ghrelin, leptin, triglyceride, and IL-6 were measured over 120 min. Subjective appetite ratings were evaluated using visual analog scales (VASs). The incremental area under the curve (iAUC) values were compared using Wilcoxon tests and linear mixed-effects models. Results: Pelemir-enriched bread significantly increased iAUCs for insulin (p = 0.014), C-peptide (p = 0.046), and GLP-1 (p = 0.039) compared to regular bread. There was no significant change in iAUC for glucose. Group-stratified analyses showed a higher postprandial iAUC of glucose, insulin, and C-peptide in the OB group compared to the HC group. VAS-based appetite ratings did not show significant changes in hunger, fullness, or desire to eat, but a borderline significant reduction was observed in prospective food consumption after pelemir-enriched bread (p = 0.050). Conclusions: Acute consumption of pelemir-enriched bread may modulate postprandial insulin and incretin responses. Its modest impact on subjective appetite regulation supports further investigation of pelemir as a functional food rich in polyphenols, especially in populations with metabolic dysfunction. Full article

Electromagnetic induction (EMI) devices have become increasingly popular for their soil bulk properties, soil nutrient status, and use in taking non-invasive soil salinity measurements. However, the high cost of data acquisition (DAQ) systems has been a significant barrier to the widespread adoption of these devices. In this study, we addressed this challenge by developing a cost-effective, easy-to-use, open-source DAQ system, transferable to the end user. This system employs a Raspberry Pi 4 model, paired with various components, to monitor the speed and position of the EM38 (Geonics Ltd, Mississauga, ON, Canada) and compare these with a proprietary CR1000 system. Through our results, we demonstrate that the low-cost DAQ system can successfully extract the analogical signal from the device, which is strongly responsive to the variation in the soil’s physical properties. This cost-effective system is characterized by increased flexibility in software processes and provides performance comparable to the proprietary system in terms of its geospatial data and ECb measurements. This was validated by the strong correlation (R² = 0.98) observed between the data collected from both systems. With our zoning analysis, performed using the Kriging technique, we revealed not only similar patterns in the ECb data but also similar patterns to the Normalized Difference Vegetation Index (NDVI) map, suggesting that soil physical characteristics contribute to variability in crop vigor. Furthermore, the developed web application enabled real-time data monitoring and visualization. These findings highlight that the open-source DAQ system is a viable, cost-effective alternative for soil property monitoring in precision farming. Future enhancements will focus on integrating additional sensors for plant vigor and soil temperature, as well as refining the web application, supporting zone classification based on the use of multiple parameters. Full article

Background/Objectives: Elastography is a non-invasive imaging technique that assesses tissue stiffness and elasticity. This study aimed to evaluate the diagnostic performance and clinical utility of elastography and S-detect in distinguishing benign from malignant thyroid nodules. S-detect (RS85) is a deep learning-based computer-aided diagnosis (DL-CAD) software that analyzes grayscale ultrasound 2D images to evaluate the morphological characteristics of thyroid nodules, providing a visual guide to the likelihood of malignancy. Method: This retrospective study included 159 patients (61 male and 98 female) aged 30–83 years (56.14 ± 11.35) who underwent thyroid ultrasonography between January 2023 and June 2024. All the patients underwent elastography, S-detect analysis, and fine needle aspiration cytology (FNAC). Malignancy status was determined based on the FNAC findings, and the diagnostic performance of the elasticity contrast index (ECI), S-detect, and evaluations by a radiologist were assessed. Based on the FNAC results, 101 patients (63.5%) had benign nodules and 58 patients (36.5%) had malignant nodules. Results: Radiologist interpretation demonstrated the highest diagnostic accuracy (area under the curve 89%), with a sensitivity of 98.28%, specificity of 79.21%, positive predictive value (PPV) of 73.1%, and negative predictive value (NPV) of 98.8%. The elasticity contrast index showed an accuracy of 85%, sensitivity of 87.93%, specificity of 81.19%, PPV of 72.9%, and NPV of 92.1%. S-detect yielded the lowest accuracy at 78%, with a sensitivity of 87.93%, specificity of 68.32%, PPV of 61.4%, and NPV of 90.8%. Conclusions: These findings offer valuable insights into the comparative diagnostic utility of elastography and AI-based S-detect for thyroid nodules in clinical practice. Although limited by its single-center design and sample size, which potentially limits the generalization of the results, the controlled environment ensured consistency and minimized confounding variables. Full article

This study aims to examine how organizations disclose Circular Economy (CE) information through multimodal communication. While conventional reporting often fails to capture the complexity of CE, we adopt a Multi-Discourse Analysis (MDA) framework that integrates textual, numerical, visual, spatial, and sensory dimensions. The methodology involves a qualitative content analysis of non-financial reports from 13 Italian electronics firms, a sector with a high environmental impact and low circularity. Key findings show a dominance of textual narratives and increasing use of numerical indicators aligned with the European Union Taxonomy. Visual elements are underutilized and largely symbolic, reflecting a product-centric rather than systemic view of circularity. The spatial dimension, operationalized through ESRS E5 categories, reveals fragmented CE integration and limited forward-looking financial disclosures. The sensory dimension, assessed via integrated thinking, highlights a polarization between firms that embed CE into strategy and those that do not. Recommendations are provided to enhance the clarity, comparability, and strategic relevance of CE disclosures, with implications for corporate practice, regulatory development, and future research. Overall, this study advances the understanding of CE by applying MDA to reveal the interplay of communicative modes, the gaps in systemic representation, and the degree of strategic integration in sustainability reporting. Full article

Background: Optic neuritis (ON) is a common manifestation of multiple sclerosis (MS), serving as a clinical window into central nervous system demyelination. Optical coherence tomography (OCT) and visual evoked potentials (VEPs) are complementary non-invasive tools for assessing structural and functional damage to the visual pathway. The objective of this paper is to evaluate correlations between OCT and VEP parameters in MS patients with and without a history of ON and assess their relationship with disease duration and disability (EDSS). Methods: This cross-sectional study included 54 eyes from 27 relapsing–remitting MS patients. OCT was used to measure circumpapillary and the temporal peripapillary retinal nerve fiber layer (pRNFL) and the foveal/parafoveal ganglion cell-inner plexiform layer (GCIPL) thickness. VEPs assessed P100 latency and amplitude. Patients were grouped by ON history. Results: Eyes without ON showed a significantly greater circumpapillary pRNFL thickness (mean difference: 18.27 ± 5.33 µm, p = 0.001), temporal pRNFL thickness (15.71 ± 5.49 µm, p = 0.006), and parafoveal GCIPL thickness (12.85 ± 5.3 µm, p = 0.019) compared to ON eyes. p100 latency was shorter and the amplitude was higher in NON eyes, but without statistical significance. Strong negative correlations were found between OCT thickness and EDSS and disease duration. p100 latency correlated negatively with OCT parameters, while amplitude showed a positive correlation with pRNFL thickness in ON eyes. Conclusions: OCT parameters, particularly pRNFL and GCIPL thickness, correlate with functional and clinical markers of MS. Combined OCT–VEP evaluation enhances the assessment of neurodegeneration and disease progression. Full article

Background: Interstitial lung disease (ILD) is a frequent and potentially progressive manifestation in patients with connective tissue diseases (CTDs). Accurate and reproducible quantification of parenchymal abnormalities on high-resolution computed tomography (HRCT) is essential for evaluating treatment response and monitoring disease progression, particularly in complex cases undergoing antifibrotic therapy. Artificial intelligence (AI)-based tools may improve consistency in visual assessment and assist less experienced radiologists in longitudinal follow-up. Methods: In this retrospective study, 48 patients with CTD-related ILD receiving antifibrotic treatment were included. Each patient underwent four HRCT scans, which were evaluated independently by two radiologists (one expert, one non-expert) using a semi-quantitative scoring system. Percentage estimates of lung involvement were assigned for four parenchymal patterns: hyperlucency, ground-glass opacity (GGO), reticulation, and honeycombing. AI-based analysis was performed using the Imbio Lung Texture Analysis platform, which generated continuous volumetric percentages for each pattern. Concordance between AI and human interpretation was assessed, along with mean absolute error (MAE) and inter-reader differences. Results: The AI-based system demonstrated high concordance with the expert radiologist, with an overall agreement of 81% across patterns. The MAE between AI and the expert ranged from 1.8% to 2.6%. In contrast, concordance between AI and the non-expert radiologist was significantly lower (60–70%), with higher MAE values (3.9% to 5.2%). McNemar’s and Wilcoxon tests confirmed that AI aligned more closely with the expert than the non-expert reader (p < 0.01). AI proved particularly effective in detecting subtle changes in parenchymal burden during follow-up, especially when visual interpretation was inconsistent. Conclusions: AI-driven quantitative imaging offers performance comparable to expert radiologists in assessing ILD patterns on HRCT and significantly outperforms less experienced readers. Its reproducibility and sensitivity to change support its role in standardizing follow-up evaluations and enhancing multidisciplinary decision-making in patients with CTD-related ILD, particularly in progressive fibrosing cases receiving antifibrotic therapy. Full article