Search Results (3,352)

Generative artificial intelligence (GenAI) tools such as ChatGPT have attracted growing attention in higher education, particularly in relation to how students perceive their usefulness, usability, and educational value. However, existing studies are often descriptive and rarely translate perception data into exploratory quantitative indicators that can support structured evaluation under uncertainty. To address this gap, this study develops an exploratory Monte Carlo simulation framework for quantifying perception-based student success in the context of GenAI use. The term Perception-Based Student Success Score is used here as an exploratory proxy indicator derived from students’ positive evaluations of usability, efficiency, learnability, and perceived integration; it does not represent direct academic achievement, grades, retention, or objectively measured learning outcomes. A PRISMA-informed structured literature search in Scopus identified nineteen empirical studies published between 2023 and 2025, of which six reported item-level means and standard deviations suitable for probabilistic modelling. One coherent 10-item, 5-point Likert-scale usability-oriented instrument was selected as a canonical proof-of-concept dataset and used to parameterise an inverse-variance-weighted Monte Carlo simulation generating 10,000 synthetic observations. The results show that the weighting structure substantially influences the simulated outcome. In particular, System Efficiency and Learning Burden received the largest inverse-variance weight and therefore had the strongest influence on the composite score. This dominance should be interpreted cautiously because low variance in Likert-scale data may reflect response homogeneity or ceiling effects rather than substantive importance alone. The study offers a transparent, reproducible, and privacy-preserving proof-of-concept framework linking structured literature search, item-level summary statistics, and probabilistic modelling. Full article

Background/Objectives: White matter (WM) tract detection is critical in the presurgical planning of tumor resection. However, standard-of-care imaging techniques including T₁-weighted, T₂-weighted, and Diffusion Tensor Imaging (DTI) often fail to identify WM tracts within edematous regions. In T₁/T₂-weighted imaging, edema increases extracellular water and reduces tissue contrast, and in diffusion-weighted imaging, edema elevates isotropic diffusion, reducing sensitivity to anisotropic diffusion along WM tracts. Advanced biophysical diffusion modeling techniques such as Neurite Orientation Dispersion and Density Imaging (NODDI) and the Standard Model (SM) address this limitation by compartmentalizing the diffusion signal into free-water, intra-neurite, and extra-neurite contributions. Here, we test if biophysical multi-compartment models can robustly identify WM tracts and recover tractography streamlines within edematous regions. Methods: In this study, we use multi-shell diffusion-weighted MRI data obtained from patients with meningiomas—a pathology allowing for isolation of the effects of edema without the confounding effects of tumor cell invasion. We compared FA from standard and free-water-corrected DTI, the orientation dispersion index (ODI) from NODDI, and P₂ (a scalar descriptor of fiber orientation coherence) from the SM fODF in edematous and unaffected contralateral WM regions. As a proof of concept, we visually evaluated the tractography performance across models. Results: Our results show that (1 − ODI) and P₂ values in edema remained close to within-subject contralateral measurements, contrasting with substantial reductions in FA and FW-FA. (1 − ODI) showed a small but statistically significant increase in edema (~8%, p = 0.02), while P₂ was unchanged. Conclusions: These results highlight the potential of biophysical diffusion models for preoperative mapping in edema. Full article

Ultra-fast electric vehicle (EV) charging systems are among the most demanding converter-dominated applications due to their high power levels, wide battery-voltage range, strict thermal constraints, and the need for adaptive charging control. Conventional design and tuning approaches often rely on fixed control policies and computationally expensive iterative optimization, which limits their ability to address nonlinear multi-objective trade-offs across the full charging envelope. This paper proposes a hybrid AI–quantum co-design framework for a SiC-based dual active bridge (DAB) converter intended for ultra-fast EV charging applications. The proposed approach combines a physical converter model, an AI surrogate-learning layer for rapid prediction of converter performance, and a quantum-assisted optimization layer for multi-objective exploration of design and control variables. To demonstrate the framework, a representative modular 350 kW ultra-fast charging case study is considered, implemented by four parallel 87.5 kW SiC-based DAB modules and including converter-level optimization and adaptive charging-policy refinement. The revised manuscript introduces a complete system schematic, an explicit DAB converter topology, a clarified methodological workflow, and a simulation-based proof-of-concept evaluation. Representative results indicate improved design-space exploration and more balanced trade-offs between efficiency, thermal stress, ripple, and dynamic response compared with a conventional baseline tuning approach. Although the study does not claim hardware-level quantum advantage, it provides a structured and practically interpretable computational framework for intelligent co-design of high-power charging converters. Full article

Background/Objectives: Accurate localisation of manifest accessory pathways from the 12-lead electrocardiogram remains clinically relevant in Wolff–Parkinson–White syndrome, particularly for pre-procedural planning. Although purpose-built artificial intelligence models have shown promise in ECG interpretation, the reliability of general-purpose multimodal large language models for accessory pathway localisation is unknown. We evaluated two contemporary general-purpose AI systems against an electrophysiology-confirmed reference standard and assessed reproducibility across repeated analyses. Methods: In this retrospective, single-centre proof-of-concept diagnostic accuracy study, 49 consecutive patients with manifest accessory pathways confirmed during electrophysiology study/ablation were included. Anonymised pre-procedural 12-lead ECGs were compiled into a single PDF and analysed by ChatGPT 5 Thinking and Gemini 2.5 Pro using predefined EASY-WPW anatomical categories. Each model was tested in three independent context-reset runs. The primary outcome was repeated-run diagnostic accuracy against the electrophysiology-confirmed pathway location, with confidence intervals calculated using an ECG-clustered approach. Secondary outcomes included majority-vote accuracy, pathway-specific descriptive accuracy, exact output consistency, no-consensus outputs, and “unable to identify” responses. Results: Each model generated 147 repeated outputs from the same 49 ECGs. ChatGPT 5 Thinking correctly localised 28/147 outputs, corresponding to a repeated-run accuracy of 19.0% (ECG-clustered 95% CI 11.5–26.6), while Gemini 2.5 Pro correctly localised 18/147 outputs, corresponding to 12.2% accuracy (95% CI 6.8–17.7). Both models performed below the no-information majority-class baseline of 36.7%. Majority-vote accuracy was 7/49 for ChatGPT 5 Thinking and 2/49 for Gemini 2.5 Pro. Exact output consistency across all three runs was observed in 2/49 ECGs for ChatGPT 5 Thinking and 0/49 ECGs for Gemini 2.5 Pro. Complete no-consensus outputs occurred in 30/49 and 26/49 ECGs, respectively. “Unable to identify” responses were infrequent: 8/147 outputs for ChatGPT 5 Thinking and 2/147 outputs for Gemini 2.5 Pro. Pathway-specific estimates were descriptive only because of class imbalance and small subgroup denominators. Conclusions: General-purpose multimodal large language models demonstrated poor repeated-run accuracy, very low reproducibility, frequent no-consensus outputs, and limited abstention when localising manifest accessory pathways from 12-lead ECGs. These findings do not support their current clinical use for accessory pathway localisation. Future progress is more likely to come from purpose-built, signal-native, or rigorously validated multimodal cardiac AI systems. Full article

Conventional colorimetric sensing methods typically rely on signal intensity at a single wavelength, often selected heuristically based on peak visual modulation. This approach overlooks the structured information embedded in full-spectrum transmission profiles, particularly in intensity-based systems where linear models may be highly effective. In this study, we experimentally demonstrate that applying a forward feature selection strategy to normalized transmission spectra, combined with linear regression and ten-fold cross-validation, yields significant improvements in predictive accuracy. Using food dye dilutions as a model system, the mean squared error was reduced from over 22,000 with a single wavelength to 3.87 using twelve selected features, corresponding to a more than 5700-fold enhancement. These results validate that full-spectrum modeling enables precise concentration prediction without requiring changes to the sensing hardware. The approach provides a proof-of-concept framework that may be extended to colorimetric assays used in medical diagnostics, environmental monitoring, and industrial analysis following broader validation with real analytes and heterogeneous sample matrices. Full article

Drying in granular porous media is governed by coupled thermal and hydraulic processes that can be substantially modified by biological activity. This proof-of-concept study investigated how surface heating and fungal colonization influence the evolution of thermal conductivity (λ) and matric suction (ψ) as functions of volumetric water content ${\theta }_v$ in Ottawa 20/30 sand. Four treatments were examined: sterile sand at 22 °C (T1), sterile sand at 28 °C (T2), fungal-amended sand with 10% biomass and 9-day incubation (T3), and fungal-amended sand with 15% biomass and 30-day incubation (T4). Samples were instrumented to monitor ${\theta }_v$, λ, and ψ during controlled evaporation using synchronized HYPROP and VARIOS measurements on the same specimen. Across all treatments, λ increased with ${\theta }_v$ (that is, λ declined as drying progressed), and ψ reflected the transition from hydraulically connected to disconnected pore water. Heating shortened the drying time but did not materially change the form of the λ–${\theta }_v$ relationship or generate strong matric gradients in sterile sand. Low biomass (T3) produced thermal and hydraulic responses comparable to the heated sterile control (T2), indicating limited pore-scale modification at early colonization. In contrast, high biomass (T4) widened the effective saturation range, maintained low and nearly uniform ψ across depth, and exhibited the steepest mid-range λ–${\theta }_v$ slope with a higher peak λ (~4 Wm⁻¹K⁻¹), consistent with hyphae and extracellular polymers stabilizing thin water films. A soil water retention curve (SWRC) analysis using the van Genuchten model further indicated increased water retention and delayed air entry with an increasing fungal biomass, with approximate air-entry values increasing from ~1.8 kPa (T3) to ~3.0 kPa (T4). Tests were terminated upon tensiometer cavitation rather than complete gravimetric dryness, constraining observations at very low ${\theta }_v$. These results indicate that heating primarily affects the rate of drying, whereas fungal networks alter the pathway by preserving hydraulic and thermal continuity at relatively high ${\theta }_v$. This behavior suggests a potential role of bio-mediated structuring in influencing near-surface thermo-hydraulic processes relevant to energy foundations, soil covers, and desiccation management in biologically active or bio-engineered soils. Full article

A TiO₂/hydrazine system was investigated as a proof-of-concept platform for coupling chemical CO₂ capture with light-driven H₂ evolution under UV irradiation. Hydrazine served as the CO₂ capture agent, leading to the formation of carbamate-type intermediates, while TiO₂ acted as the photoresponsive solid. FT-IR, UV-Vis, and mass spectrometry analyses supported carbamate formation after CO₂ uptake and confirmed H₂ generation during irradiation, reaching a maximum of 33.2 μmol under the conditions evaluated. Deuterated experiments showed no detectable HD or D₂, indicating that H₂ evolution predominantly proceeded via hydrazine dehydrogenation rather than direct water splitting. On the basis of the available spectroscopic evidence, a tentative pathway involving carbamate intermediates and nitrogen-containing oxidation products is proposed. However, key control experiments required to confirm a strictly photocatalytic origin of H₂ evolution were not performed in the present exploratory study. Therefore, the observed behavior is more appropriately interpreted as preliminary photoinduced reactivity in a TiO₂/hydrazine/CO₂ system rather than definitive proof of a fully established photocatalytic mechanism. Overall, the results establish a preliminary proof of concept, while the limitations related to control experiments, product identification, quantification, and reproducibility are recognized. Full article

Cancer is a leading cause of death in dogs, and incidence rates in dogs exceed those in humans. Current therapeutic options for canine cancer patients remain limited, with most treatments focused on palliative care. Immune checkpoint inhibitors such as anti-PD-1, anti-PD-L1, and anti-CTLA-4 antibodies that have transformed cancer therapy and expanded the therapeutic options in humans could offer the same clinical benefit in canine cancer patients. This study details the engineering and functional characterization of mouse and chimeric mouse–canine anti-canine PD-1 (cPD-1) monoclonal antibodies. We demonstrate that anti-cPD-1 antibodies block the interaction between cPD-1 and its ligand cPD-L1, thereby inhibiting this immune signaling pathway. In a proof-of-concept study in seven companion canine cancer patients, intratumoral therapy with the lead anti-cPD-1 antibody (HugPetmab) was safe, well-tolerated, had no observed adverse events, and showed evidence of tumor control in a subset of injected tumors. These findings support the potential of HugPetmab antibody as an immunotherapeutic option for treating canine cancer patients. Full article

We evaluated the effects of a 12-week multidisciplinary program on health-related physical fitness and depressive symptoms in overweight and obese women (aged 45–64 years) diagnosed with noncommunicable diseases (NCDs). Methods: A longitudinal, pre-experimental, proof-of-concept study was conducted. Thirty-one women completed multidisciplinary interventions [nutritional education or psychoeducation (each once a week), and resistance training (twice a week)]. Body composition (bioelectrical impedance), physical fitness (maximal isometric strength, lower limb strength–endurance, flexibility, and aerobic fitness), and depressive symptoms (PHQ-9) were measured at baseline and post-intervention. Results: Significant improvements in body composition were observed in terms of lean mass (Δ% = 3.7; p < 0.001), fat-free mass (Δ% = 3.6; p < 0.001), skeletal muscle mass (Δ% = 5.2; p < 0.001), fat mass (Δ% = −3.5; p < 0.001), body fat percentage (Δ% = −4.7; p < 0.001), and visceral fat level (Δ% = −2.9; p = 0.012). Physical fitness exhibited a large effect size in the chair stand test (d = 0.91) and the 6 min walk test (d = 1.22). Depressive symptom scores substantially decreased (p < 0.001). Conclusion: The program demonstrated potential efficacy in mitigating sarcopenic obesity, enhancing functional capacity, and reducing depressive symptoms, indicating potential clinical viability for the integrated management of multimorbidity. Full article

(1) Background: Cerium silicate (CeSi) nanoparticles (NPs) have potential as a restorative filler particle with multifunctional properties to improve longevity. To increase the biological activity, these nanoparticles can be fabricated with ultrasmall pores (mesoporous) (MPCeSi-NP) that can be loaded with a polyphosphate inhibitor, such as gallein. (2) Methods: MPCeSi-NPs were custom-synthesized with a microemulsion method, using cetyltrimethylammonium bromide (CTAB) as a template for self-assembly. Biocompatibility with oral keratinocytes/fibroblasts was tested, with the addition of examining the biomineralization potential with human bone-marrow-derived mesenchymal stromal cells (BM-MSCs). MPCeSi-NP, loaded with gallein, was tested against Rothia dentocariosa (Rd). MPCeSi-NP was added to a resin matrix of triethylene glycol dimethacrylate (TEGDMA) and Bisphenol A-glycidyl methacrylate (BisGMA) with subsequent mechanical properties evaluation. (3) Results: MPCeSi-NPs had high biocompatibility with oral keratinocytes and fibroblasts, especially at concentrations below 300 µg/mL. MPCeSi-NPs induced the biomineralization of BM-MSCs. Higher cerium levels increased mineralization. MPCeSi-NP had weak antimicrobial activity against Rd. At 1% wt, MPCeSi-NPs did not reduce the polymerization potential and mechanical properties of a TEGDMA:BisGMA polymer material, with controlled release of gallein in a simulated degradation model. (4) Conclusions: MPCeSi-NPs are highly biocompatible and bioinductive and have the potential to improve the biological response of current restorative materials. Full article

Precision medicine is increasingly applied in the cancer clinic, adapting treatment to genomic alterations of the tumor. However, whether alterations disrupt the function of a protein can depend on if both alleles of a gene are altered. While massively parallel sequencing technologies can identify sequence aberrations, they are limited in resolving the corresponding haplotype information. In this proof-of-concept case study, we applied the linked-read droplet barcode sequencing (DBS) technology to resolve the haplotype complexity of colorectal cancer genomes on paired tumor and normal samples. Several cancer-related genes carried multiple mutations in either one or both haplotypes. Additionally, a number of haplotype-resolved large structural variants and copy number alterations were detected and phased with short somatic variants. Nearly all characterized oncogenic pathways harbored some of the identified short somatic variants. The study demonstrates that linked-read DBS technology can characterize complex genetic variations in a haplotype context and may provide essential information for personalized approaches. Full article

Acorns (Quercus spp.) are an underutilized forest resource with recognized nutritional and bioactive potential, making them promising candidates for the development of sustainable plant-based functional foods. This study aimed to valorize acorns through the formulation of two novel acorn-based products, a plant-based beverage, and a pudding, and to assess their nutritional properties, sensory acceptability, and, for the beverage, refrigerated shelf-life stability. The beverage was optimized as a neutral-flavored milk alternative, using sodium alginate as a natural clean-label stabilizer to enhance emulsion stability and physicochemical properties. The final formulation exhibited low energy density and a lipid profile rich in monounsaturated fatty acids, contributing to its nutritional and functional value. Throughout 63 days of storage at 4 °C, sodium alginate effectively prevented phase separation and supported the retention of antioxidant capacity, as evidenced by stable ferric reducing antioxidant power (FRAP) and total phenolic content, although ABTS radical scavenging activity declined over time. No microbial growth was detected during storage, confirming the adequacy of the applied thermal treatment and aseptic filling procedures applied. The acorn-based pudding, developed by adapting a traditional egg-based recipe, functioned as a proof of concept illustrating the technological versatility of acorns across distinct plant-based matrices, exhibiting a nutritional profile comparable to commercial counterparts and high consumer acceptability. Overall, this work demonstrates the technological feasibility and versatility of incorporating acorns into plant-based food matrices, supporting their potential as sustainable ingredients for the development of innovative value-added foods and contributing to the valorization of forest resources. Full article

Efficient electricity distribution in rural areas is strongly affected by seasonal agricultural energy demand, particularly in irrigation-intensive regions where electricity consumption increases substantially during summer periods. Conventional transformer operation strategies in such rural grids often fail to adapt to seasonal load variability, leading to unnecessary idle operation, increased technical losses, and reduced infrastructure efficiency. Existing approaches generally rely on static assumptions or simulated data, limiting their ability to represent real irrigation-driven seasonal load asymmetry. To address this issue, this study proposes a data-driven multi-objective seasonal transformer scheduling framework using a bio-inspired Artificial Immune System (AIS) algorithm. The model was developed using two years of empirical hourly SCADA load data and transformer operation records obtained from a real 380/154 kV TEİAŞ transmission substation in Central Anatolia, Türkiye. Hourly SCADA measurements were used for seasonal load characterization and objective-function evaluation, while transformer scheduling decisions were defined at the seasonal operational level. The proposed AIS-based scheduling strategy reduced annual technical energy losses by approximately 5.4 GWh, decreased operational costs by 10.81 million TL (≈360,000 USD), and lowered carbon emissions by about 2270 metric tons of CO₂ compared with conventional static transformer operation. The study presents a proof-of-concept framework integrating empirical SCADA measurements with AIS-assisted seasonal transformer scheduling for practical utility-scale operational planning in irrigation-dominated rural electricity networks. Full article

This study introduces a novel physics-inspired framework to quantify defensive pressure in football from tracking data. We model defender–attacker interactions as a variable-mass dynamical system, translating Newtonian mechanics into operational metrics that combine spatial configuration and motion. From this formulation we derive interpretable quantities at dyad, player, and team level, including a Center of Pressure (CP), Defensive Momentum, Defensive Force, and Defensive Work. We illustrate the framework in a single-match proof-of-concept using professional optical tracking data, analysing both full-match behaviour and football-specific phases such as counter-pressing, set-pieces, and throw-ins. Results show how the proposed metrics separate persistent spatial constraint (pressure) from energetically demanding defensive actions (work), enable identification of high-cost match-ups and workload concentration, and support time-resolved descriptions of coordinated pressing sequences. The framework provides a transferable, mechanically grounded toolkit for applied defensive performance analysis and motivates future validation on larger datasets. Full article

In this study, poly(vinylpyrrolidone) (PVP)-modified liquid metal (LM) particles were formulated into a mixed-solvent system comprising ethanol (EtOH), 1,2-propanediol (1,2-PG), and a trace amount of N,N-dimethylformamide (DMF). This design addresses the instability, poor wetting/adhesion on polydimethylsiloxane (PDMS), and limited rheological tunability of conventional LM inks for screen printing. By regulating solvent evaporation during drying, the system enables coordinated control over wettability, viscosity, shear-thinning behavior, and drying-induced film formation. At an LM:PVP weight ratio of 20:1, the contact angle on PDMS decreased from 115° to 17.8°. The ink exhibited pronounced shear-thinning characteristics with tunable viscosity in the range of 1000–3000 cP, meeting the screen-printing requirements of facile mesh passage and rapid setting. Following laser activation, the printed conductive patterns demonstrated stable electrical performance, with a resistance drift of less than 1% after 14 days of storage and a ΔR/R₀ of less than 1% after 3000 bending cycles at a bending diameter of 1 cm. Furthermore, a resistance drift of less than 3% was observed after 1000 stretching cycles at 30% strain. This study proposes a viable materials and processing strategy for the reliable screen printing of LM:PVP ink on PDMS substrates toward flexible conductive electronics. The motion-monitoring test is presented only as a preliminary proof-of-concept demonstration of motion-induced electrical resistance response, rather than as a sensor performance evaluation. Full article