Triazole fungicides (TFs) are difficult to determine in environmental water due to their high polarity, water solubility, and complex matrix interferences, while conventional extraction techniques are often plagued by low enrichment efficiency and high organic solvent consumption. To address these limitations, a novel effervescent-assisted dispersive liquid-liquid microextraction (EA-DLLME) strategy based on ternary quasi-hydrophobic natural deep eutectic solvents (NADESs) was developed. The NADESs, composed of terpenoids, long-chain alcohols, and short-chain carboxylic acids, were synthesized and combined with Na2CO3 to form an effervescent system. The hydrophilicity of the short-chain carboxylic acid enabled in situ decomposition, while the agitation effect of CO2 bubbles facilitated rapid extraction. FT-IR and 1H NMR characterized the solvent structure and dispersion mechanism. Under optimized conditions via response surface methodology and GC-MS analysis, the method exhibited a linear range of 0.5-1000 ng mL-1 (R2 > 0.99) for the three target TFs (myclobutanil, tebuconazole, and epoxiconazole), with limits of detection of 0.017-0.030 ng mL-1. The recoveries of spiked samples ranged from 89.17% to 111.2%, with relative standard deviations below 9.5%. Density functional theory calculations including electrostatic potential, Independent Gradient Model, and Reduced Density Gradient reveal that the NADES forms through electrostatic complementarity and a hydrogen-bond network, and that the extraction with TFs occurs via directional hydrogen bonding between triazole nitrogen and the hydroxyl group of thymol, with a binding energy of -23.66 kcal mol-1, further stabilized by van der Waals forces. This approach effectively overcomes the poor retention and low efficiency associated with traditional reversed-phase extraction, offering a simple, green, and practical tool for monitoring trace TFs in complex water samples.

The rat middle cerebral artery occlusion (MCAO) model remains the reference standard for elucidating ischemic stroke pathophysiology and validating neuroprotective therapies. Subtle surgical deviations, however, can profoundly alter infarct volume and behavioral outcomes. Using a "visualize-then-systematize" framework, we re-examined every step of model establishment and validation. Detailed photographic documentation of key surgical landmarks and critical steps was performed to standardize anatomical orientation and filament placement. 2,3,5-Triphenyltetrazolium chloride (TTC) staining was used to precisely map the volume and topology of infarcts at 24 h and 72 h after reperfusion. Neurological deficits were graded with the Zea-Longa, Bederson, and modified Garcia scales. Twenty-four male Sprague-Dawley rats underwent 60 min transient MCAO; infarct volume and neurological scores were assessed at 24 h and 72 h. Approximately 87.50% of animals achieved successful cerebral blood-flow blockade (Zea-Longa ≥ 1 and unequivocal TTC-defined infarct), with an overall mortality of 12.50%, attesting to the model's high fidelity. This standardized protocol provides a technically refined, reproducible foundation for preclinical stroke research, facilitating inter-laboratory consistency in studies investigating stroke pathophysiology and therapeutic interventions.

As global demographics shift toward an increasingly aged society, the preservation of independent mobility and spatial navigation has emerged as a critical public health priority. This study investigates the spatial exploration behaviors of visually impaired older adults in complex airport terminals, utilizing real-world wayfinding tasks coupled with integrated eye-tracking technology. Through a comparative analysis between a visually impaired elderly cohort and a younger sighted control group, this research quantifies navigational effort via gaze angular velocity (AV), wayfinding duration, and gaze distribution heatmaps. Experimental results indicate that visually impaired older adults exhibit significantly higher gaze AV across all navigational sections, a phenomenon characterized here as a compensatory behavioral response to declining visuospatial function. However, this compensatory mechanism demonstrates a "saturation point" at structurally complex intersections, such as those with acute connection angles, where elderly participants fail to further scale their exploratory efforts. The analysis further reveals that dynamic traffic entities, specifically pedestrians, create an "attentional lock-in" effect that disproportionately suppresses peripheral awareness in older navigators. Correlating these behavioral markers with clinical indicators, including Mean Deviation (MD), Pattern Standard Deviation (PSD), and Useful Field of View (UFOV), this article establishes a logical framework for evidence-based inclusive design. Recommendations focus on geometric simplification of transit nodes, multisensory redundancy, and the integration of adaptive assistance technologies to mitigate the "environmental press" on the aging brain.

To address semantic prediction discontinuities and physical boundary distortions caused by furniture occlusion in complex indoor scenes, this paper proposes a semantic parsing method that leverages building-structure priors. The scheme uses a shifted-window hierarchical transformer encoder to extract multi-scale visual features and combines a gradient-direction-consistency line segment detection algorithm to construct a Manhattan 3D bounding box. This bounding box is transformed into a signed distance field (SDF) prior to encoding discrete geometric contours into a continuous physical potential field. A structure-guided cross-attention mechanism forces the visual signals to align with real 3D orthogonal geometric boundaries, restoring feature continuity in occluded areas. A spatial adjacency graph constructed from superpixel nodes drives a Graph Convolutional Network (GCN) to aggregate features, ensuring macroscopic semantic consistency within the physical load-bearing plane. A combination of pixel-level cross-entropy loss and a custom-designed structural consistency loss strengthens the constraints, penalizing out-of-bounds predictions. Experiments across multiple independent runs show that the mean intersection over union (mIoU) reaches 68.7% with a standard deviation of 0.2%, and the structural boundary F1 score reaches 76.4% with a standard deviation of 0.3%, confirming the robust performance of the proposed modules. With a single image node size of 256, the average inference time remained at 61 ms.

This protocol was designed to preliminarily validate Emoscape, a proprietary machine-learning algorithm based on the Navarasa framework, by testing whether its emotion scores change in concordance with standardized emotion-elicitation tasks in healthy adults. Seventy-two healthy adults were recruited after providing written informed consent. Participants completed the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7, then viewed facial emotion stimuli from the TRENDS set and Navarasa images, while the FEA algorithm recorded upper-body micro-movements throughout the task. Participants also identified the emotions displayed. Changes in the FEA algorithm scores from baseline were analyzed during exposure to neutral, happy, fear, and anger stimuli from TRENDS, as well as during exposure to Navarasa images. Receiver operating characteristic analysis was used to estimate sensitivity and specificity for discrimination across emotional categories. The FEA algorithm scores showed significant deviations from baseline for TRENDS images mapped to corresponding emotions (Neutral-Shanta, Happy-Haasya, Fear-Bhayanaka, and Anger-Raudra). Concordant changes were also observed in Navarasa images, with all mean differences statistically significant (p < 0.001). Area under the curve (AUC) values varied across categories. The FEA algorithm showed modest sensitivity for pleasant emotions (70-80%) and unpleasant emotions (60-70%). These findings provide preliminary support for the protocol and for the use of the FEA algorithm to assess emotion-related responses during controlled emotion-elicitation tasks in healthy adults.

Current neural image compression (NIC) methods primarily focus on signal fidelity optimization. While perceptually optimized codecs can generate decoded images that better align with human visual preferences at equivalent bitrates, they raise authenticity concerns due to potential deviations from the original content. Therefore, achieving controllable decoding is crucial in various applications. This study presents a novel plug-and-play framework that leverages large vision-language model (LVLM) priors to balance fidelity and perception for existing NICs. Our approach consists of two key components: a scalable Low-Rank Adaptation scheme to controllably enhance the semantics of initially decoded images, and a two-stage agent-assisted decoding strategy with vision-language priors utilization. Specifically, the first stage extracts textual semantic information from an LVLM using decoded images enhanced by flexible fidelity-perception decoding, while the second stage effectively integrates semantic priors from LVLMs, further mitigating decoding semantic uncertainty and achieving higher-quality decoding. Extensive experiments on multiple benchmark datasets demonstrate that our method enables off-the-shelf NICs to achieve flexible control between optimal perceptual quality and signal fidelity.

State space models (SSMs) have emerged as a compelling alternative to transformers for visual recognition, offering linear computational complexity while maintaining competitive accuracy. However, the lack of interpretability tools designed specifically for the recurrent dynamics of these models remains a significant gap: existing saliency methods originate from convolutional or attention-based architectures and do not account for the sequential state propagation that governs information flow in SSMs. We introduce controllability analysis, a framework grounded in control theory that quantifies the structural influence of each input position on the internal state dynamics of vision SSMs. We derive two complementary indices: a Jacobian-based measure that tracks sensitivity propagation through the full sequence via an efficient $O(LN)$ backward recursion, and a Gramian-based measure that admits a closed-form, fully parallelizable solution for diagonal state matrices. Unlike gradient-based attribution methods that produce class-specific explanations, controllability indices measure intrinsic model properties that are invariant to the target class. Experiments on seven image classification benchmarks spanning diverse visual domains (microscopy, optical coherence tomography (OCT), dermatoscopy, radiography, natural images, apparel, and remote sensing) demonstrate that controllability analysis produces perfectly class-agnostic explanations (cross-class correlation of 1.0 compared to -0.45 to 0.18 for Grad-CAM across datasets) and reveals layerwise information flow patterns that vary across imaging domains. On the dataset where the model is best trained (BloodMNIST, 98% test accuracy), the proposed Jacobian index also outperforms Grad-CAM on standard insertion/deletion faithfulness (Jacobian $0.63 {\,}\pm {\,}0.02$ versus Grad-CAM $0.47 {\,}\pm {\,}0.03$ , mean ± standard deviation across three training seeds, with nonoverlapping per-seed 95% bootstrap CIs); on the lower accuracy datasets Grad-CAM remains stronger on this class-specific metric, a gap we discuss as a diagnostic of incomplete alignment between the model's internal dynamics and the classification task rather than a failure of the structural framework. A complementary internal-attention IoU metric, measuring alignment between each method's saliency and the model's own L2-magnitude attention pattern, shows controllability outperforming Grad-CAM on six of the seven datasets. Directional decomposition of the controllability profiles further shows that influence progressively sharpens with depth, transitioning from diffuse responses in early layers to sparse, high-magnitude peaks in deep layers that align with semantically meaningful structures. The proposed framework provides a principled foundation for understanding, debugging, and improving vision SSMs across application domains.

The purpose of this brief report is to report on the outcome of glaucoma management in children after pars plana vitrectomy (PPV) in Alexandria Main University hospital. The Medical records of children presenting with glaucoma after PPV were reviewed. Data extracted included patients' demographic data, antenatal, natal and postnatal history, indication for PPV and surgical details, details of glaucoma surgery, clinical preoperative and postoperative follow up. Success of glaucoma surgery was defined as an IOP ≤ 16 mmHg and ≥ 20% reduction from the presenting IOP. The study included 9 eyes of 8 children, that had undergone PPV for ROP (6 eyes) and vitreous haemorrhage (3 eyes). All study eyes were subjected to combined trabeculectomy-trabeculotomy with Mitomycin C. The mean ± standard deviation of the age at glaucoma surgery and follow up duration were 7.9±4.43 and 11.1±10.0 months respectively. The mean ± standard deviation of the preoperative and last visit IOP and C/D ratio were 22.9±6.9 mmHg, 0.8±0.2, 16.9±5.8 mmHg (P=0.063) and 0.4±0.3 (P=0.036) respectively. The success rate at final follow up was 55.6%. In this study, glaucoma after PPV has a poor prognosis and does not generally respond to combined filtering and angle surgery.

Despite advancements in automating observation-based ergonomics assessments, most automations still rely on a single underlying construct (e.g. posture). This fragmented approach, however, derogates from the integrated nature of traditional assessment methods. The few studies that consider multiple aspects do so through indirect or manual procedures. This paper studies a novel approach of automating ergonomics assessment, making use of the well-established European Assembly Worksheet (EAWS), incorporating both posture and load manipulation. To this end, it comparatively examines observation-based and automated calculations, using paper-based documents for the former, and inertial measurement units and smart glasses for the latter. The comparative results show a strong, significant correlation between automated and observation-based EAWS calculations (r = 0.74; p < 0.05 for an assembly task, r = 0.82; p < 0.01 for a palletising task). Additional subjective survey results deviate from the EAWS calculations, but are complementary in identifying effects not detected by it.

Rational design of regenerants for perfluoroalkyl and polyfluoroalkyl substances (PFAS)-laden ion-exchange resins is hindered by the complex interplay among solution components, resin properties, and PFAS structures. This study applied machine learning (ML) with 916 experimental data points to predict the liquid-phase desorption efficiency (DE) of PFAS-laden resins and identify critical influencing factors. We observed that DE positively correlated with the application of polyacrylic resins and organic solvent concentration, but was negatively influenced by PFAS chain length. Subsequently, the interpretable ML model was employed to design targeted regeneration strategies, guiding a systematic assessment from single-component to multicomponent regenerants. The model revealed that polyacrylic resins achieve high DE (>95%) using single-chloride salts (e.g., 5-10% NaCl) through ion exchange. In contrast, most polystyrene resins require combined alkali-salt solutions to surpass 80% DE by disrupting hydrophobic interactions. Model-proposed optimal formulations for nine resins across four PFAS were experimentally validated, demonstrating a close match between predictions and outcomes with a mean absolute deviation of 10.5-16.7 percentage points. This study offers a transferable and data-driven strategy for optimizing resin regenerant formulations, highlighting interpretable ML's role in advancing efficient recovery practices.

A post-modification strategy was employed to fabricate Fe-CAU-10-OH@Cou for the trimodal detection of Zineb. Fe-CAU-10-OH@Cou was fabricated by post-modifying fluorescent CAU-10-OH with Fe3+ and coumarin aldehyde (Cou). In this composite, Fe-doped coordination centers provided peroxidase-like activity, while Cou enabled a ratiometric fluorescence response. In the presence of Zineb, the fluorescence emission at 360 nm remained stable, whereas that at 475 nm was quenched, providing ratiometric fluorescence detection with a detection limit of 0.36 µM. Fe-CAU-10-OH@Cou catalyzed the oxidation of colorless TMB to blue oxidized TMB (ox-TMB). Zineb significantly inhibited this oxidation process, leading to a marked decrease in UV-vis absorption at 650 nm, and concomitantly suppressed the photothermal conversion ability. Based on these phenomena, colorimetric and photothermal detection methods were developed, with detection limits of 15.0 µM and 1.03 µM, respectively. Furthermore, a smartphone-assisted visual detection platform was developed based on the colorimetric signal variation, enabling precise quantitative determination of Zineb (LOD = 10.9 µM). Spike recovery experiments on actual fruit and vegetable samples demonstrated recoveries ranging from 96.0% to 106.0% across the three detection modes, with relative standard deviations (RSDs) below 5%, confirming the practicality and reliability of the proposed method. Moreover, mechanistic studies revealed that the inner filter effect (IFE) constituted the primary mechanism for fluorescence quenching, whereas the suppressive effect of Zineb on the enzymatic activity of Metal-Organic Frameworks (MOFs) accounted for the colorimetric and photothermal detection modes.