Search Results (20,770)

Titanium dioxide nanoparticles (TiO₂ NPs) are incorporated into automotive coatings to enhance durability, corrosion, UV resistance, and, in some formulations, photocatalytic self-cleaning. While the toxicology of pristine TiO₂ is well studied, the behavior of TiO₂ NPs embedded in polymer matrices and subjected to real-world aging, maintenance, and removal remains poorly characterized. This narrative review synthesizes 24 publications spanning the lifecycle of TiO₂ nano-enabled automotive coatings, from synthesis and formulation through application, in-service weathering, repair, refinishing, and end-of-life environmental fate. Upstream properties, such as coating functionality and performance, have been examined as determinants of later-life release, exposure, and fate. Across studies, dispersion state, interfacial compatibility, and surface modification—together with transformations such as agglomeration, photocatalysis, weathering, and eco-corona formation—shape particle stability, release, exposure relevance, and toxicological risk. Evidence indicates that sanding and accelerated weathering predominantly generate matrix-associated, polymer-fragment-dominated aerosols rather than pristine TiO₂ NPs, while NP-specific exposure measurements during spray application remain limited. Hazard data suggest matrix embedding may attenuate, but does not eliminate, biological responses relative to pure particles. Wastewater treatment plants and biosolids have been shown to act as sinks with potential for soil accumulation following sludge application. Regulatory frameworks rarely account for aging, transformation, and release, stressing the need for synchronized testing of aged materials and nano-specific exposure metrics to support safer-by-design coatings and risk governance. Full article

From an epidemiological perspective, polymorphous low-grade neuroepithelial tumor (PLNTY) represents a small proportion of brain tumors encountered in epilepsy surgery series. Their rarity and relatively recent recognition likely contribute to underdiagnosis and poor prognosis. In terms of histopathological features, they are similar to oligodendrogliomas. Molecular analyses can be used to show the fusion between fibroblast growth factor receptor (FGFR3) and transforming acidic coiled coil (TACC) proteins, which most commonly results in progression towards glioblastoma (GBM). We report a case of a 62-year-old man who underwent left frontal craniotomy to remove a frontal mass. Histologically, the glial lesion consisted of elements associated with oligodendroglia-like features. Immunohistochemistry was positive for glial fibrillary acidic protein (GFAP), oligodendrocyte transcription factor 2 (OLIG2), and α-thalassemia X-linked mental retardation syndrome (ATRX) nuclear expression, but negative for isocitrate dehydrogenase 1 (IDH1) and BRAF-V600E. Next-generation sequencing showed the FGFR-TACC3 fusion, and taken together, these findings supported the final diagnosis of PLNTY. During follow-up, the patient underwent a second neurosurgery, where histological evaluation indicated a GMB. This article presents clinical and radiological data, morphology, immunohistochemistry, molecular features, and treatment to enhance the clinical and pathological understanding of PLNTY with FGFR3-TACC3 fusion for all professionals involved in medical decisions. Full article

Whether the risk of gastric neoplasm is modified by newer glucose-lowering therapies—dipeptidyl peptidase-4 inhibitors (DPP4is), glucagon-like peptide-1 receptor agonists (GLP1RAs), and sodium–glucose cotransporter 2 inhibitors (SGLT2is)—remains uncertain. Given their global uptake and long-term use in populations already predisposed to malignancy, decision-grade comparative safety evidence is needed. We conducted a systematic review and network meta-analysis (NMA) of randomized controlled trials (RCTs) in adults without baseline gastric neoplasms. PubMed, Embase, Cochrane CENTRAL, Web of Science, ClinicalTrials.gov, ClinicalKey, ProQuest, and ScienceDirect were searched from inception to 10 January 2026, without language restrictions. The primary outcome was incident gastric neoplasms (benign or malignant). Random-effects frequentist NMA estimated risk ratios (RRs) with 95% confidence intervals (CIs); Bayesian NMA served as sensitivity analysis. Certainty of evidence was assessed using GRADE adapted for NMA (PROSPERO CRD420261282728). Fifty-two RCTs (171,165 participants; mean age 63.6 years; 36.9% women; mean follow-up 141.8 weeks) were included. At the class level, GLP1RAs were associated with lower gastric neoplasm risk versus controls (RR = 0.51, 95% CI = 0.28–0.92), whereas DPP4is were associated with higher risk (RR = 1.77, 95% CI = 1.09–2.85). These signals persisted in prespecified subgroup analyses among participants with diabetes mellitus, in trials with duration ≥52 weeks (GLP1RA: RR = 0.52, 95% CI = 0.28–0.95; DPP4i: RR = 2.05, 95% CI = 1.19–3.55), and in older populations (age ≥60 years; DPP4i: RR = 2.08, 95% CI = 1.15–3.77). No class showed a significant association in younger participants (<60 years) or shorter trials (<52 weeks). Across available RCT evidence, GLP1RA prescription generally had a relatively lower gastric neoplasm risk than controls. In contrast, among patients with diabetes mellitus receiving longer-term therapy, GLP1RAs may be the preferable option from the perspective of gastric neoplasm risk, while DPP4is warrant heightened vigilance and mechanistic clarification. These findings support improved neoplasms ascertainment in future trials rather than immediate prescribing changes. Full article

Tight sandstone reservoirs are characterized by highly heterogeneous pore structures, in which multiscale pore–throat systems jointly control the shapes of capillary pressure curves and relative permeability, thereby exerting a fundamental influence on water production behavior and the overall development performance of gas reservoirs. The Ordos Basin is generally characterized by the development of tight sandstone. The tight sandstones exhibit porosities of 2–13% and permeabilities of 0.01–10 × 10⁻³ μm². To quantitatively elucidate the controlling mechanisms of multiscale pore structure on capillary pressure curve morphology and relative permeability, this study systematically investigates the fractal and multifractal characteristics of pore structures in tight sandstones based on high-pressure mercury intrusion (MICP) and nuclear magnetic resonance (NMR) experimental data, and establishes a quantitative relationship between fractal parameters and the capillary pressure curve shape parameter λ. First, capillary pressure curves were fitted using the Brooks–Corey model within the effective saturation interval to extract the shape parameter λ, which characterizes the concentration degree of pore-size distribution and the drainage behavior. Subsequently, based on NMR T₂ spectra, the small-pore fractal dimension D₁, large-pore fractal dimension D₂, and the multifractal singularity spectrum width Δα were extracted to quantitatively describe the geometric complexity of pore structures at different scales. On this basis, the correlations between λ and D₁, D₂, and Δα were systematically analyzed, and the predictive performance of λ under different parameter combinations was compared. The results indicate that: (1) the pore structures of tight sandstones exhibit pronounced fractal and multifractal characteristics at the NMR T₂ scale, with significant differences among samples; (2) λ shows an overall negative correlation with fractal parameters, among which the correlations with the large-pore fractal dimension D₂ and the multifractal spectrum width Δα are the most significant; (3) compared with models using a single fractal dimension, the multiparameter model incorporating Δα provides a more comprehensive characterization of multiscale pore heterogeneity, leading to a substantial improvement in the accuracy and stability of λ prediction; and (4) λ exerts a clear control on the shape of relative permeability curves, where a larger λ corresponds to earlier initiation and forward-shifted rising segments of water-phase flow, while a smaller λ results in overall flatter relative permeability curves. From the perspectives of fractal and multifractal theory, this study establishes an intrinsic linkage among pore structure, capillary pressure curve shape parameters, and relative permeability, providing a novel quantitative framework for constraining relative permeability curve morphology in tight sandstones under conditions where systematic relative permeability experiments are unavailable. Full article

Metasurface is a kind of artificial structure which can efficiently control the amplitude, phase, frequency, and polarization of the light field. Metasurface polarization holographic encryption is a holographic encryption technology with the polarization state as a key, which has been widely concerned in recent years with advantages such as sub-wavelength pixels, precision adjustment, and high security factor. In this paper, the design and optimization of the unit structure of metasurface have been carried out, and the clear double-channel holographic image reproduction and good encryption effects have been realized afterwards. The results show that the relatively good polarization holographic encryption can be achieved by employing the designed Si nanorods with the length of 148 nm and width of 55 nm, respectively, which have been beforehand grown on SiO₂ substrates. Note that the periodic angle deflection around the Z axis was adopted by using the dual-channel optical rotation incidence with the wavelength of 632.8 nm. It has been theoretically demonstrated that information transmittance loss should be less and the image restoration effect should be satisfactory. A novel encryption method has also been proposed for the optical information processing and optical encryption, and the huge application potential of our theme has been revealed as the next-generation optical control platform in the near future. Full article

Real-time PCR (qPCR) is today’s definitive quantitative technology in molecular biology and diagnostics. Until 30 years ago, PCR product analyses were generally performed after amplification using gel-based methods. Quantification typically relied on visual inspection or densitometry of end-point products and was therefore relatively unreliable and poorly suited to high-throughput automation. To celebrate real-time PCR’s 30-year anniversary of commercial availability, Professor Stephen Bustin, Guest Editor for the special edition, “Advancing Molecular Science Through Reproducible qPCR: MIQE Guidelines and Beyond,” asked Russell Higuchi to give a historical account on how his idea of real-time PCR was conceived and brought to fruition. Dr. Higuchi then asked his collaborator, Lincoln McBride, who drove the development of the ABI 7700—the high-throughput real-time PCR instrument that gave researchers access to this technology—to co-author this dual memoir. This story is told from the perspectives of the two scientists most directly responsible for making real-time PCR practical and widely accessible. Taking turns, Russell Higuchi describes the conceptual and experimental steps at Cetus and then Roche that led from homogeneous PCR detection to continuous fluorescence monitoring, whilst Lincoln McBride details ABI’s parallel efforts to commercialize Russ’s invention. Together, they trace how experimental insight, engineering constraints, product development, and commercial decision-making shaped the Applied Biosystems 7700 Sequence Detection System and established real-time PCR as a practical and reliable quantitative technology. Their team’s efforts persevered through technological uncertainty and within a complex corporate collaboration. They share key historical documents in their original form. Their accounts show how the 7700 system emerged as the convergence of chemistry, optics, software, and product development. The eventual global reliance on real-time PCR during the COVID-19 pandemic demonstrated, at unprecedented scale, the profound and enduring impact of these early technical and organizational choices. Full article

Depth conversion of underwater static electric fields refers to a mathematical approach that indirectly determines the distribution of planar electric fields at larger depths using measured planar electric field data obtained from a shallower region with finite depth and limited area. The complicated environment of high-latitude low-temperature sea areas further increases the difficulty of performing practical large-depth measurements of underwater electric fields. Therefore, depth conversion becomes an important technical strategy for overcoming the constraints of field measurements and for comprehensively understanding the distribution of underwater static electric fields of the target. This study begins with the mathematical formulation of the depth conversion problem, solves the related boundary value problem, and develops the corresponding depth conversion method. Subsequently, based on COMSOL simulation data of the underwater static electric field generated by a scaled-down submersible model, numerical analyses are conducted to investigate the effects of factors such as grid discretization, measurement plane dimensions, conversion depth, and data noise on the conversion accuracy. Finally, the reliability of the conversion method is validated in a laboratory environment by simulating a naturally frozen sea area and employing measured underwater static electric field data from the scaled-down submersible model. The results demonstrate that the developed conversion method can effectively achieve extrapolation of the underwater static electric field of the submersible from shallow regions to deeper water. Even when the noise amplitude is nearly twice that of the effective signal and the conversion depth reaches 8 times the outer diameter of the submersible, the relative root mean square error (RRMSE) of the conversion results can still be maintained below 0.10. These findings provide useful references for the advancement of technologies related to underwater electric field characteristic recognition and electric field stealth performance evaluation in high-latitude low-temperature sea areas. Full article

Arsenic (As) and mercury (Hg) are trace elements of major environmental and public health concern. Their relevance is due to their well-documented toxicological effects. In rapidly urbanizing port-industrial cities, soil contamination by these elements represents a critical challenge. This situation compromises sustainable urban development and environmental governance. This study had three main objectives: First, to evaluate the contamination status of As and Hg in urban soils using multiple geochemical indices; Second, to assess the potential human health risks associated with exposure in the urban environment of Talcahuano; Third, to identify the relative contributions of geogenic and anthropogenic sources based on spatial distribution patterns. A total of 420 soil samples were collected. These included 140 topsoil samples (TS; 0–10 cm), 140 subsoil samples (SS; 10–20 cm), and 140 deep-soil samples (DS; 150 cm). Arsenic concentrations were determined using hydride-generation atomic absorption spectrometry (HG-AAS). Mercury concentrations were measured by cold-vapour atomic absorption spectrometry (CV-AAS). Median As concentrations were 2.7 mg kg⁻¹ in TS, 3.1 mg kg⁻¹ in SS, and 2.5 mg kg⁻¹ in DS. The corresponding median Hg concentrations were 0.2 mg kg⁻¹ in TS and 1.4 mg kg⁻¹ in both SS and DS. Spatial distribution maps were generated through ordinary kriging interpolation. Geochemical baseline values were calculated using the median + 2 × MAD approach. The resulting baseline values were 7.8 mg kg⁻¹ for As and 3.6 mg kg⁻¹ for Hg. Contamination assessment was conducted using the geoaccumulation index (Igeo), enrichment factor (EF), and contamination factor (Cf). Results indicate that most soils are classified as uncontaminated. Enrichment levels were minimal and contamination factors were low. Nevertheless, isolated outliers were identified. These included one significantly enriched As sample and several moderately enriched or slightly contaminated Hg samples. Human health risk assessment incorporated the Hazard Index (HI) and Total Carcinogenic Risk (TCR). Results indicate that neither non-carcinogenic nor carcinogenic risks exceed acceptable thresholds at any investigated soil depth. Spatial analysis suggests that anthropogenic activities are the dominant sources of As and Hg in the study area. Traffic emissions and industrial activities appear to be the primary contributors. Full article

Accurate time-domain photovoltaic (PV) models are needed to evaluate performance under outdoor variability beyond STC datasheet conditions. This paper presents a traceable modeling workflow based on the standard single-diode formulation, implemented in MATLAB/Simulink (R2023a) as a modular white-box architecture that explicitly resolves photocurrent generation and loss mechanisms (diode recombination, shunt leakage, and series resistance effects) with temperature-consistent propagation through V_T(T) and saturation-current terms. The method couples optical boundary conditions to the electrical model by embedding plane-of-array (POA) excitation via the incidence angle Θ(t) and roof albedo directly into the photocurrent source term, preserving the causal chain from mounting geometry to electrical response. Calibration is separated from prediction by initializing key parameters using the standard Simulink PV block and then freezing them for time-domain evaluation. The workflow is validated on a 395 W rooftop prototype using 1 min resolved POA irradiance (ISO 9060:2018 Class A radiometric chain) and module temperature (IEC 60751 Class A Pt100), synchronized with electrical measurements. Over a multi-week campaign, the model exhibits high fidelity, with a worst-case relative current error of ~1.1% and a consistently low bias and dispersion, quantified by ME, MAE, RMSE, σ_e, and thresholded MAPE. Full article

We propose an agent-based, non-probabilistic framework for modeling the joint evolution of two discounted asset prices expressed in units of a third asset acting as numeraire. The framework is based on a trajectorial superhedging theory, in which pricing, arbitrage, and null events are defined purely in financial terms, without reference to probability measures or martingale assumptions. A central necessary theoretical requirement is that the global property $\left(L\right)$-a.e. holds, ensuring consistency of the model construction. Admissible price evolutions are described by multidimensional trajectory sets generated from observable price movements and operational rebalancing rules representing a prescribed class of agents. Within a fixed trajectory set, relative price bounds between the two assets are obtained via superhedging and subhedging by means of self-financing portfolios that trade one asset against the other. Full article

We propose Adaptive Inference, a portfolio management framework extending Active Inference to non-stationary financial environments. The framework integrates inference, control, and execution under endogenous uncertainty, modeling investment decisions as coupled dynamics of belief updating, preference encoding, and action selection rather than optimization over fixed objectives. In this approach, portfolio behavior is governed by the expected free energy (EFE) minimization, showing that classical valuation models emerge as limiting cases when epistemic components vanish. Using train–test evaluation on the ARKK Innovation ETF (2015–2025), we identify a Passivity Paradox: frozen belief transfer outperforms naive adaptive learning. A Professional Agent achieves a Sharpe ratio of 0.39 while its adaptive counterpart degrades to $-0.28$, reflecting belief contamination when learning from policy-dependent signals. Crucially, the architecture is not designed to generate alpha but to perform endogenous risk management that mitigates overtrading under regime ambiguity and distributional shift. Adaptive Inference Agents maintain long exposure most of the time while tactically reducing positions during high-entropy periods, implementing uncertainty-aware passive investing. All agents reduce realized volatility relative to ARKK Buy-and-Hold (43.0% annualized). Cross-asset validation on the S&P 500 ETF (SPY) shows that inference-guided risk shaping achieves a positive Entropic Sharpe Ratio (ESR), defined as excess return per unit of informational work, thereby quantifying the economic value of information under thermodynamic constraints on inference. Full article

Residential photovoltaic (PV)–battery systems are increasingly deployed to reduce electricity costs under time-of-use and demand-charge tariffs, yet their economic value depends critically on how storage is operated over time. Effective control must simultaneously address short-term energy costs, peak-demand exposure, and long-term battery degradation, all under substantial uncertainty in load and PV generation. While optimization-based approaches can achieve strong performance with accurate forecasts, they are sensitive to forecast errors, whereas learning-based methods often neglect degradation effects or deplete the battery prematurely, leading to suboptimal peak-shaving behavior. This paper proposes a forecast-free, degradation-aware reinforcement learning (RL) framework for residential PV–battery energy management that jointly addresses demand-charge mitigation and battery aging. The proposed controller internalizes both calendar aging and rainflow-based cycling degradation within its objective and incorporates demand-aware reward shaping with time-varying penalties on on-peak grid imports. In addition, a complementary state-of-charge reserve mechanism discourages premature battery depletion and improves responsiveness to late on-peak demand surges, despite the absence of explicit load or PV forecasts. Physical feasibility is guaranteed through an execution-time safety layer that enforces all device and operational constraints by construction. The proposed framework is evaluated on high-resolution residential datasets and compared against optimization-based baselines, including a day-ahead scheduler with perfect foresight and a receding-horizon MPC controller using short-horizon forecasts. Overall, the results show that the proposed RL controller substantially reduces demand charges and total electricity costs relative to forecast-based MPC while maintaining degradation-aware operation, demonstrating the potential of forecast-free reinforcement learning as a practical control strategy for residential PV–battery systems under demand-charge tariffs. Full article

Laser wire directed energy deposition (LWDED) has garnered significant attention for the fabrication of large metallic components. However, the complex coupling effects among its process parameters pose challenges for porosity control. Optimizing parameter combinations to effectively minimize porosity is therefore critical to the broader adoption of this technology. In this study, systematic experiments and modeling were conducted to optimize the LWDED process parameters and predict porosity. First, single-factor and orthogonal experiments were performed to evaluate the individual effects of laser power, scanning speed, wire feeding speed, and air pressure on porosity. Subsequently, range analysis and analysis of variance were employed to determine the influence of each parameter and the significance of their interactions. Four machine learning models—SVR, RF, GPR, and XGBoost—were then trained and compared. Among them, the SVR model exhibited the best predictive performance, achieving an R² of 0.8960, an RMSE of 0.19, and an MAE of 0.15, outperforming the other three models. Based on this, the SVR model was further utilized to establish the mapping between process parameters and porosity. Contour maps and three-dimensional surface plots were generated to visualize porosity variation patterns under interacting parameters. Validation experiments showed that the maximum relative error between model predictions and experimental measurements was 0.514%, with an average error of 0.251%. This study provides a reliable reference for selecting low-porosity parameter combinations in the LWDED fabrication of 5356 aluminum alloy components. Full article

Prior research on power-law distributions has primarily focused on modeling frequency patterns, with less attention given to rank distributions and how ranked positions reflect relative importance among elements. In discrete power-law distributions, frequency-based metrics often provide limited discrimination in the tail, where elements may exhibit similar counts but differ in relative dominance. These patterns are especially evident, for instance, in academic publishing, where keywords, affiliations, and citations commonly exhibit power-law behavior. To address this limitation, we introduce the Relative Importance Factor (RIF) Index, a statistical measure derived from the estimated discrete power-law rank distribution rather than an additional independent parameter. The RIF Index compares the probability of an element at a given rank with its probabilities at lower ranks, enabling explicit pairwise statistical comparison, particularly within the tail. We formalize the mathematical framework for discrete rank modeling and apply RIF to synthetic data and a Scopus dataset on social resilience. Our results show that RIF clarifies dominance relationships among ranked elements, providing stronger discrimination in the tail than frequency-based measures alone. We further introduce the RIF matrix and RIF network to represent these pairwise relationships structurally, supporting interpretation of prominence patterns. Although demonstrated in academic publishing, the method generalizes to domains where categorical variables follow discrete power-law behavior under appropriate model-fit validation. Full article

This study investigates algorithm-driven placement optimization of two aircraft-mounted VHF monopole antennas to mitigate mutual coupling under realistic installation constraints. A parameterized 3D aircraft model inspired by general-aviation platforms is analyzed using full-wave electromagnetic simulations over the 30–100 MHz band. The optimization problem is formulated to reduce inter-antenna coupling across the operating band while restricting the search space to physically installable regions on the airframe. Two global optimization methods, Genetic Algorithm and Particle Swarm Optimization, are applied and compared under the identical constraints and objective definitions. The results show that both optimizers achieve a significant reduction in coupling relative to non-optimized placements, with comparable overall performance. Installed far-field radiation characteristics are further evaluated to verify that the optimized solutions preserve, and in some cases improve, the omnidirectional coverage required for airborne VHF communication. The proposed workflow provides a practical, simulation-driven framework for electromagnetic compatibility (EMC)-oriented antenna integration on complex aircraft platforms. Full article