Search Results (22,171)

This study is dedicated to the application of neural network technologies for determining aeration parameters in order to predict the efficiency of flotation separation. Within the framework of the research, digital technology solutions were actively employed, including a neural network for segmentation at the stage of determining the granulometric characteristics of bubbles and a convolutional neural network module for determining the froth layer height. An analysis was conducted to examine the variation in the statistical parameter d₃₂, which characterizes the bubble size distribution, as a function of flotation time and measurement height. The analysis revealed that the d₃₂ values determined by neural network processing remained within the range of acceptable dispersion and are therefore suitable for subsequent analytical procedures. Furthermore, a comparative evaluation of the obtained size distributions indicated the absence of statistically significant differences between the neural network measurements and manually labelled data with a p-value equal to 0.64. A neural network for object detection was used to record the height of the froth layer during the experiment to obtain a time series, that were subsequently processed with data processing approaches including Savitzky–Golay and Singular Spectra Analysis. Based on the analysis of the sum of the obtained dependences, a criterion is proposed and modeled for evaluating the selectivity of frother by connecting the diameter of bubble in pulp and bubble in froth. Based on the modeling results, it was determined that the optimal range of bubble sizes and froth size ratios for MIBC is constrained to d₃₂ values ranging from 1.058 to 1.089 mm, with the ratio of froth bubble radius to d₃₂ ranging from 1.302 to 2.098, depending on the floatability ratios of the respective fractions. When employing OPF, the values for d₃₂ fall within the interval of 0.868 to 1.113 mm, while the Dₓ parameter ranges from 0.559 to 0.931. Full article

Background/Objectives: Surgery is the most effective treatment for early-stage lung cancer but imposes a greater physical burden than other therapies. We previously identified socioclinical factors associated with reduced perioperative health-related quality of life (HR-QOL) in patients undergoing anatomical pulmonary resection. This study aimed to evaluate the relationship between predictors of HR-QOL and long-term survival. Methods: In this prospective study, 87 patients undergoing anatomical pulmonary resection for non-small cell lung cancer at Shonan Kamakura General Hospital, Japan, were evaluated using the Short Form Health Survey 36. Multivariable analyses identified socioclinical factors associated with physical and mental QOL preoperatively and at 6 months postoperatively. Survival analyses were performed for factors showing differences in multivariable analysis and related trends in univariable analysis of HR-QOL. Results: Preoperatively, lower performance status (PS) and living alone were independently associated with poorer physical QOL (regression coefficient [95% CI]: −10.94 [−14.34–−7.54] and −9.86 [−13.89–−5.82], respectively; both p < 0.001) and mental QOL (−9.34 [−13.30–−5.37] and −10.33 [−15.30–−5.35]; both p < 0.001). At 6 months postoperatively, smoking cessation within 1 year, lower PS, and living alone predicted worse physical QOL, while smoking cessation and lower PS predicted worse mental QOL. Lower PS and higher comorbidity burden were also adverse prognostic factors for long-term survival (p < 0.001 and p = 0.015, respectively). Conclusions: Reduced physical activity and greater comorbidity are associated with poorer HR-QOL and survival after lung cancer surgery. These findings highlight the need for careful consideration of surgical indications in patients with these risk factors. Full article

Within industrialised and emerging industrialised economies people typically spend over 95% in industrialised and emerging industrialised economies typically spend over 95% of their time in built environments, yet the neurophysiological impact of architectural design remains poorly understood. While previous studies link visual patterning to cortical activity, the cortical-to-autonomic stress pathway remains largely unexplored—a key omission given that chronic stress contributes to allostatic overload. This study examined how architectural façade design influences neurophysiological stress through a multimodal approach combining functional near-infrared spectroscopy (fNIRS) to monitor occipital cortical activity with heart rate variability (HRV) as an index of autonomic regulation. Eighteen participants provided HRV data and subjective ratings for nine systematically varied façade images characterised by their deviation with respect to natural statistics, while a subset of twelve completed fNIRS recording due to signal acquisition constraints. Façade identity significantly affected discomfort, complexity, and interest ratings ($p<0.001$), and deviation from natural statistics predicted all three measures ($p<0.01$). Façade type also showed a small but significant effect on HRV ($p=0.003$), although variance was dominated by individual differences. No stimulus-specific occipital fNIRS differences were observed. However, due to the limited sample size, further research is needed to verify this observed result. Whilst global generalisations cannot be drawn due to the small sample size, these pilot research findings indicate that façades deviating from natural image statistics influence perceptual comfort and may modestly modulate autonomic balance. However, the present data does not provide clear evidence of stimulus-specific cortical effects, which, if present, likely remain below the detection thresholds of the current protocol given its methodological constraints. This study highlights methodological hurdles and establishes a scalable framework for linking computational visual metrics to physiological responses, informing future investigations into how architectural features influence human health. Full article

Metformin is an antidiabetic drug that has been tested widely as an anti-cancer agent. However, data from clinical trials have been mixed. Evidence for metformin’s efficacy in HER2+ breast cancer exists. Hence, we evaluated whether WBP2, a HER2-coamplified gene, can regulate the response of HER2+ breast cancer to metformin. Identification of biomarkers for predicting metformin response has implications in repurposing metformin for precision oncology. The effect of WBP2 on breast cancer response to metformin was studied using in vitro and mouse models. The mechanism of WBP2 on metformin-induced AMPK activation was elucidated, and its co-expression with p-AMPK was examined in clinical specimens using IHC. RNA-seq analyses were performed to elucidate WBP2’s mechanism in energy metabolism. WBP2 inhibited the metformin response of HER2+ breast cancer in vitro and in vivo. These effects were concomitant with WBP2-mediated repression of metformin-induced AMPK activation and mTOR inhibition in HER2+ breast cancer cells, a lower AMP:ATP ratio state, and enhanced glycolytic capacity and mitochondria respiration. Analysis of HER2-positive breast cancer samples supports the negative correlation between WBP2 expression and activated AMPK observed in vitro. RNA-seq analysis revealed the potential mechanism of WBP2 in regulating ATP production processes and preferential effect of WBP2 on metformin response in HER2+ breast cancer. This study reported a novel role of WBP2 in cancer metabolism and energetics that contributes new insights into the molecular etiology of cancer. WBP2 may be a biomarker for patient stratification, paving the way towards repurposing metformin for precision oncology. Full article

Environmental education awareness (EEA) often fails to translate into confident environmental action, representing a critical knowledge–action gap in sustainability education. This study investigated whether digital citizenship mediates the relationship between EEA and self-efficacy among university students. A cross-sectional design was employed with 879 Egyptian university students from Al-Azhar University. Participants completed validated instruments measuring EEA, digital citizenship competencies (including internet political activism, technical skills, critical perspectives, and networking agency), and general self-efficacy. Mediation analysis using Hayes’ PROCESS macro revealed that digital citizenship significantly partially mediated the relationship between environmental awareness and self-efficacy, accounting for 21.9% of the total effect. Environmental awareness directly predicted self-efficacy (β = 0.590) and indirectly through digital citizenship (β = 0.166). These findings demonstrate that digital competencies serve as “smart tools” enabling students to transform ecological knowledge into confident environmental agency. The results underscore the necessity of integrating digital citizenship training within environmental curricula to cultivate climate-literate, digitally empowered citizens capable of meaningful contributions to global sustainability and climate action initiatives. Full article

Early identification of the risk of malignant transformation in oral potentially malignant disorders (OPMDs) is critical for improving outcomes in oral squamous cell carcinoma (OSCC). This comprehensive review examines immunological biomarkers obtained from minimally invasive oral cytobrush (OCB) specimens for the early detection of OSCC within a precision medicine framework. The objectives were to (1) identify and characterise key immunological biomarkers associated with early oral carcinogenesis; (2) evaluate the diagnostic utility of OCB sampling for detecting these biomarkers; and (3) explore the potential of OCB-based profiling to support personalised screening and patient management. The review highlights the potential advantages of OCB compared with conventional diagnostic methods, as reported in the literature, particularly its ability to capture early malignant changes through immunological analysis. Evidence is discussed for biomarker pathways related to cell-cycle and differentiation dysregulation (p53, Ki-67, CKs), inflammation-driven epithelial transformation (IL-1β, IL-6, IL-8, TNF-α), and immune suppression and checkpoint activation (PD-L1, B7-H6). OCB provides reliable and patient-friendly cyto-salivary samples that are suitable for immunological and molecular analyses. Aberrant biomarker expression detected in OCB specimens correlates with epithelial dysplasia and reflects early non-invasive neoplastic transformation, supporting the diagnostic value of integrated biomarker panels. Overall, OCB-based immunoanalysis represents a practical, non-invasive approach for the early detection of OSCC. Emerging technologies, including AI and multi-omics approaches, may further support the precision and predictive values of immunological analysis for OSCC. When combined with relevant biomarker pathways reflecting tumour biology and host immune responses, this strategy could offer a strong foundation for precision-medicine screening. It may also support personalised monitoring in patients with OPMDs. Full article

To maximize competitive performance in motorsports, balancing high downforce for cornering with low drag for straight–line speed is essential. This paper presents the development and optimization of a sliding Drag Reduction System (DRS) integrated with a ducktail guide for a Student Formula racing car. To overcome the computational costs and time constraints of conventional CFD–based iterative design, a Graph Neural Network (GNN) surrogate model was developed to predict aerodynamic coefficients. Unlike traditional models, the GNN directly learns from the geometric graph structure of the multi–element wing, enabling near–instantaneous and highly accurate predictions. CFD results indicated that activating the DRS reduced drag from 82.68 N to 25.51 N, improving the lift–to–drag ratio from 1.67 to 2.67. The GNN surrogate model achieved an R² value exceeding 0.99, demonstrating exceptional predictive fidelity compared to high–resolution simulations. Physical track testing with a Formula SAE vehicle corroborated these findings, showing a 4.6% improvement in 50 m acceleration and a 5.8% increase in maximum speed. This research establishes that GNN–based surrogate models can significantly accelerate the design and optimization of complex variable aerodynamic systems, providing a robust framework for performance enhancement in racing applications. Full article

Background: Dynamic visual acuity (DVA) is functionally distinct from static visual acuity (SVA), though SVA is often used clinically as a reference. Methods: To identify EEG biomarkers for DVA, we presented participants with a high-contrast checkerboard moving horizontally at speeds ranging from 4°/s to 30°/s, engaging motion-sensitive pathways while preserving spatial detail. Six EEG features—ERPs (N200 and P300), TRCA, Hjorth activity, mean curve length, and Tsallis entropy—were extracted from eight occipito-parietal channels and evaluated for speed sensitivity. Results: Hjorth activity and Tsallis entropy showed consistent monotonic trends with respect to speed. Hjorth activity exhibited the strongest univariate correlation (r = 0.88, p < 0.05). In a Lasso regression model using all speed-sensitive features, the predicted speed correlated with actual speed at r = 0.588, with TRCA-weighted features retained for their multivariate contribution. Notably, Hjorth activity peaked at PO7/PO8 (3.558 and 1.478 µV² at 30°/s), aligning with V5/MT+ activation. Conclusion: Given its high sensitivity, neuroanatomical plausibility, and simplicity, Hjorth activity is recommended as a primary candidate for EEG-based DVA biomarker development. This study provides a foundation for objective neurophysiological evaluation of dynamic vision. Full article

The continuous scaling of microelectronic technology nodes has imposed fundamental physical constraints on conventional floating-gate (FG) non-volatile memory, driving the adoption of charge-trapping memory such as Silicon–Oxide–Nitride–Oxide–Silicon (SONOS) technology. SONOS devices offer advantages in scalability, endurance, and compatibility with advanced CMOS processes, yet their high-temperature reliability remains challenging due to charge loss mechanisms influenced by device structure and material properties. In this work, we systematically evaluate the reliability of two-transistor SONOS memory fabricated using a 28 nm high-K metal gate (HKMG) process. A refined temperature-dependent charge loss model (T-model) is introduced, which, by incorporating a characteristic temperature parameter (T₀) that captures the dynamic shift in activation energy, fundamentally departs from the constant-activation energy assumption of the conventional Arrhenius model. This approach more accurately describes charge retention behavior across a wide temperature range. Experimental results demonstrate excellent device performance, including endurance exceeding 10⁴ program/erase cycles at 85 °C and data retention over 10 years at 85 °C. The T-model shows strong agreement with measured data, providing a physically grounded framework for predicting long-term reliability. This study not only validated a novel charge loss model, providing insights for predicting the failure time of SONOS memory, but also demonstrated that HKMG-integrated SONOS memory exhibits high reliability. Full article

Background/Objectives: Postoperative early hypocalcemia (PEH) is a key postoperative issue after parathyroidectomy in primary hyperparathyroidism. It often leads to long-lasting hypocalcemia, requiring more calcium and active vitamin D supplements. This study aimed to determine whether the extent of intraoperative parathyroid hormone (PTH) decline, measured 15 min after parathyroid tumor excision, could serve as a reliable intraoperative rule-out marker for PEH. Methods: We conducted a retrospective review of 88 adult patients who underwent surgical intervention for a solitary parathyroid tumor at a single institution. Postoperative early hypocalcemia (PEH) was defined as a total serum calcium level <8.5 mg/dL within the postoperative 6th hour or on postoperative day 1, requiring clinical calcium supplementation (oral and/or intravenous), with active vitamin D when appropriate. The percentage decrease in PTH at 15 min post-excision was calculated using morning-of-surgery preoperative PTH values alongside the 15-min post-excision levels. Additional variables assessed included preoperative alkaline phosphatase (ALP), parathyroid tumor weight, and serum concentrations of calcium, phosphate, magnesium, and 25-hydroxyvitamin D. Predictive factors were identified by logistic regression, and the diagnostic accuracy of the 15-min PTH decline was evaluated using receiver operating characteristic (ROC) curve analysis, optimizing cutoff selection with Youden’s index. Odds ratios were standardized per 10-unit increments for ALP and parathyroid tumor weight for interpretability. Results: Of the studied cohort, 10 patients (11.4%) developed PEH. The intraoperative 15-min PTH decline was notably greater in those who developed PEH compared to those who did not (81.2 ± 4.4% vs. 69.9 ± 8.3%; p < 0.001). Univariate logistic regression showed a significant association between the 15-min PTH decline and PEH (OR 1.22 per 1% increment; 95% CI 1.08–1.38). That said, when we added ALP and parathyroid tumor weight to the multivariate models, PTH decline no longer predicted independently. In contrast, ALP (OR 3.11 per 10 U/L; 95% CI 1.34–7.93; p = 0.011) and parathyroid tumor weight (OR 1.22 per 10 mg; 95% CI 1.10–1.48; p = 0.004) stayed significant. Thus, the incremental prognostic contribution of the 15-min PTH decline beyond ALP and parathyroid tumor weight appears limited. The ROC curve for the 15-min PTH decline produced an AUC of 0.883, with an optimal cutoff of 75% providing 100% sensitivity and 74.4% specificity. No patients with a PTH decline below 75% developed PEH. Conclusions: Preoperative ALP and parathyroid tumor weight showed the strongest independent associations with PEH following parathyroid tumor surgery. An intraoperative PTH decline of less than 75% at 15 min may serve as a practical rule-out tool for PEH, although further validation in larger patient populations is warranted. Full article

Electromechanical Actuators (EMAs) are critical components in More-Electric Aircraft (MEA) and Reusable Launch Vehicles (RLVs), yet they remain vulnerable to jamming and fatigue failures under high-stress flight maneuvers. Existing Health-Aware Flight Control approaches often treat failure prediction and control allocation as separate processes, leading to suboptimal sortie generation rates. This paper presents a reliability-adaptive control framework that unifies trajectory tracking with online health management. Empowered by a hierarchical mission-to-control architecture, the system employs stochastic Model Predictive Control (SMPC) to actively modulate control surface deflection profiles in real time. A comparative case study on a coupled EMA drivetrain demonstrates that the proposed controller extends useful life by 65% compared to fixed-gain baselines, achieves 23% higher mission performance than reactive PID controllers, and it maintains zero constraint violations throughout the mission by optimally distributing the health budget across mission phases. Full article

Polytetrafluoroethylene (PTFE) is attractive for high-frequency communications but adheres very poorly to other materials due to its very low surface energy. Conventionally, surface treatments of PTFE are used to increase the polarity of the PTFE surface and enable bonding to materials with increased surface free energy. However, surface treatments are difficult to scale, can damage surfaces, and often lack reproducibility. Therefore, developing a material that can make PTFE adhere well to other materials without surface treatment is highly desirable. In this study, we aimed to develop a new material with strong adhesion to PTFE. We synthesized three polymer gels from dodecyl acrylate (DA) and 2-(dimethylamino) ethyl acrylate (DMAE): the homopolymer gels PDEAE and PDA, and the copolymer gel P(DEAE-co-DA). The copolymer gel P(DEAE-co-DA) exhibited high pressure-sensitive adhesion to PTFE, recording the highest adhesive strength (F = 430.0 N/m) and the highest peel energy (G = 713.4 J/m²) compared to the homopolymer gels PDEAE and PDA. Mechanical testing showed PDEAE had the greatest strength and toughness, PDA balanced stiffness and extensibility, and P(DEAE-co-DA) was the most flexible and extensible. The P(DEAE-co-DA) with the smoothest surface (Sz ≈ 0.176 µm) showed the highest F and G, implying that surface roughness did not contribute significantly to the interfacial adhesion between the gels and the PTFE. Based on the surface free energy ${\sigma }_s$ and work of adhesion $W_a$ values, the adhesive strength to PTFE was predicted to be PDEAE > P(DEAE-co-DA) > PDA, but the measured G in peel tests contradicted this, indicating that the gels’ viscoelastic deformation and energy dissipation dominate the measured F and G. The frequency-dependent viscoelastic data and relaxation times τ and activation energies $E_a$ suggested optimal adhesion requires a balance of adhesion (mobility for energy dissipation (short τ, low $E_a$)) and sufficient cohesion (high G′). P(DEAE-co-DA) achieved this balance, explaining its high measured F and G. With precise control of polymer chain mobility, the adhesion of P(DEAE-co-DA) gels can likely be improved further. Future work will employ block copolymerization and monomer-ratio control to tune molecular motion and enhance adhesion to PTFE. Full article

Ground-based three-dimensional motion testing of space manipulators typically relies on active suspension-based gravity compensation systems. The design of such systems faces two fundamental challenges: first, how multiple suspension winch units can precisely track the dynamic trajectories of the corresponding suspension interfaces on the manipulator; and second, how to achieve optimal collision avoidance among the suspension mechanisms themselves during the tracking process. To address these challenges, this paper presents a multi-point suspension system endowed with kinematic redundancy for the trajectory tracking task, thereby ensuring precise tracking of the manipulator’s complex three-dimensional motions. The key innovation of this work lies in formulating the internal collision avoidance constraints as safety distance functions and integrating them into the system states. These are then combined with the trajectory-tracking states to construct a unified state-extended system model that exhibits typical underactuated characteristics. For this model, and under the concurrent influence of external disturbances from both the manipulator’s motion and the proximity to collision boundaries, a dedicated Model Predictive Controller (MPC) is designed. The results demonstrate that the proposed controller can generate an optimal coordinated collision-avoidance motion plan for the suspension winch units while maintaining precise trajectory tracking, thereby effectively solving the coordinated motion-planning problem for such complex underactuated systems. The proposed MPC achieves maximum tracking errors of 0.64 mm (X) and 0.13 mm (Z)—substantially lower than the 1.3 mm and 1.9 mm results listed in the comparative scheme—while delivering optimal collision avoidance, which is only suboptimally realized in the baseline. Full article

With the advent of ubiquitous healthcare and advancements in textile industry, non-invasive wearable biomedical solutions are becoming an increasingly attractive alternative to in-hospital monitoring, allowing for timely diagnostics and prediction of severe medical conditions. Non-contact biopotential monitoring is particularly promising because non-contact biopotential electrodes can be applied over clothing or embedded in the material without almost any preparation. However, due to the intricacies of capacitive coupling they rely on, the design of such electrodes and their interface with the body plays a key role in achieving measurement repeatability and their widespread utilization in clinical-grade diagnostics. Based on exhaustive investigation of several decades of the literature on non-contact and capacitive biopotential electrodes and electric potential sensors, this study is intended to serve as a state-of-the-art overview of their historical development and design challenges, a collecting point for important research theories and development milestones, a starting point for anyone seeking for a soft head start into this research area, and a remedy for occasional misnomers and conceptual errors identified in the existing papers. The ultimate goal of this comprehensive analysis is to demystify phenomena of non-contact biopotential monitoring and capacitive coupling, systematically reconciliate terminological inconsistencies, and enhance accessibility to the most important findings for future research. To accomplish this, fundamental concepts are thoroughly revisited—from fundamentals of electrochemistry and working principles of capacitors and operational amplifiers to system stability and frequency-domain analysis. With the use of various mathematical tools (Laplace transform, phasors and Fourier analysis, and time-domain differential calculus), discussions on non-contact and capacitive biopotential electrodes, collected from the 1960s onward, are for the first time compiled into a unified, abstracted, bottom-up analysis. The laid-out inspection provides analytical explanation for various aspects of measurement results available in the referenced literature, but also serves an educative purpose by devising a methodological framework that can be easily applied to other similar research fields. Firstly, the differences and similarities between wet, dry, surface-contact, non-contact, capacitive, insulated, on-body, and off-body biopotential electrodes are clarified. For this purpose, equivalent electrical models of various non-invasive biopotential electrodes are analyzed and compared. As a result, a proposal for a revised classification of biopotential electrodes is given. Secondly, instead of using the concept of a purely capacitive biopotential electrode, a test is proposed for assessing the predominant coupling mechanism achieved with an electrode over an insulating layer. Thirdly, a fundamental model of a buffer active non-contact biopotential electrode and its interface with the body is built and generalized, and the proposed test is applied for analyzing the influence of voltage attenuation and phase shifts on signal morphology. Lastly, guidelines for designing the described electrode–body interfaces are proposed, along with a discussion on practical aspects of their implementation. Full article

Background: Water inertia load training using equipment such as water vests provides unstable resistance that enhances trunk muscle activation. However, practical methods for prescribing exercise intensity without expensive electromyography (EMG) equipment remain limited. This pilot study aimed to develop prediction models for estimating trunk muscle activation using rating of perceived exertion (RPE) during water inertia load exercises. Methods: Seventeen healthy adults (20.45 ± 2.02 years) performed lateral trunk flexion exercises wearing a water vest at five progressive loads (8–16 kg in 2 kg increments). Surface EMG was recorded from four trunk muscles (rectus abdominis, external oblique, internal oblique, erector spinae) and normalized to maximal voluntary isometric contraction (%MVIC). Rating of perceived exertion (RPE) was assessed using the Borg CR-10 scale. Load-dependent changes in muscle activation were examined using repeated-measures ANOVA, and relationships between RPE and EMG were analyzed using regression and linear mixed-effects models. Results: All trunk muscles showed significant increases in activation with increasing load (all p < 0.001, ηp² = 0.381). RPE demonstrated significant positive correlations with all abdominal muscles (r = 0.37–0.46, p < 0.001). Simple regression analyses indicated predictive accuracy (R² = 0.267), representing a 29% increase compared with the strongest individual muscle model. Linear mixed-effects modeling confirmed RPE as a significant predictor after accounting for inter-individual variability. Conclusions: This pilot study provides preliminary evidence that RPE can be used to estimate trunk muscle activation during water inertia load exercise. The proposed composite activation index enhances prescription when EMG measurement is not feasible. Full article