Search Results (2,227)

Background and Objectives: Psychosocial symptoms and oral behaviors can complicate routine dental care, yet available screeners yield multiple separate scores. Explainable artificial intelligence offers a pragmatic way to integrate such multidomain measures into a single, auditable output that can support screening-oriented stratification and standardized documentation (non-diagnostic). Therefore, we aimed to develop an interpretable, deterministic Mamdani fuzzy inference system (FIS) integrating GAD-7, PHQ-9, and OBC-21 into a 0–10 psychobehavioral composite score (PCS) to support screening-oriented stratification and standardized documentation (non-diagnostic). Materials and Methods: Cross-sectional multicenter study in 18 private dental clinics in Romania (October 2024–March 2025; n = 460). A rule-based Mamdani Type-1 FIS was specified a priori (48 rules; triangular membership functions; centroid defuzzification) without supervised training. Internal evaluation assessed coherence across severity strata, robustness to predefined input perturbations (±1 point; ±5%) and membership-function variation (±10%), and benchmarking against linear composites (Z-mean; PCA PC1). Results: Median PCS was 2.30 (IQR 2.03–3.56). PCS correlated with GAD-7 (Spearman ρ = 0.886), PHQ-9 (ρ = 0.792), and OBC-21 (ρ = 0.687) (all p < 0.001), increased monotonically across anxiety and depression severity strata, and was higher in high OBC-21 risk. Robustness was excellent under input perturbations (ICC(3,1) = 0.983 for ±1 point; 0.992 for ±5%) and high under ±10% membership-function variation (ICC(3,1) = 0.959). Concordance with linear baselines was high (Spearman ρ = 0.956 for Z-mean; 0.955 for PCA PC1), with a small systematic nonlinearity at higher scores. Conclusions: PCS provides a fully auditable, rule-based integration of three patient-reported measures with coherent internal behavior and robustness to plausible measurement noise and specification changes. This study reports internal evaluation of a deterministic, rule-based aggregation; external clinical validation against independent outcomes is required before any clinical utility claims. Full article

Rolling-element bearing faults are a primary mechanical failure mode in rotating systems. In Permanent Magnetic DC (PMDC) motor applications operating under variable torque, vibration-based diagnosis is affected by load-dependent excitation and commutation-induced disturbances, which introduce amplitude bias and reduce the reliability of conventional statistical features. This study proposes Cross-Reference Energy Attention (CREA), a physics-guided dual-sensor feature framework for three-class bearing states in PMDC motor systems. CREA isolates fault-relevant content within a hardware-agnostic, empirically selected mid-frequency carrier band and incorporates a spatially separated reference sensor to evaluate transmission consistency. This design suppresses disturbances generated locally by the motor while retaining structurally transmitted bearing signatures. Experiments were conducted on a PMDC motor dynamometer with seeded bearing defects under controlled torque variation. GroupKFold cross-validation was implemented using the acquisition run as the grouping variable to prevent data leakage across runs. Under per-run normalization designed to eliminate amplitude memorization, conventional motor-side baseline features degraded to 0.495 ± 0.110 window-level accuracy, whereas the four-feature CREA representation maintained 0.999 ± 0.002. Systematic ablation and SHAP analysis demonstrate that carrier-band energy features provide the dominant discriminatory contribution, while cross-sensor interaction metrics supply complementary transmission validation consistent with the underlying mechanical model. Full article

Background/Objectives: Adeno-associated viruses (AAVs) are widely used gene therapy vectors; yet their physicochemical stability and chromatographic behavior are highly sensitive to the solution conditions they are in. Effective separation of full (F), empty (E), and partially filled (P) capsids—most commonly achieved by anion exchange (AEX) chromatography—is essential for standard analytical characterization, process development, and product safety. However, conventional AEX methods rely on low-conductivity alkaline mobile phases with low salt, which promote capsid binding and therefore higher resolution, at the expense of structural stability. Conversely, formulations such as near-neutral buffers might preserve capsid integrity but often impair AEX retention and separation resolution. Methods: Here, we extend a mechanistic investigation using AAV8 capsids as a model system, focusing on detailed capsid interactions with strong AEX, and present novel AAV8 separation strategies on a weak AEX stationary phase. Results: By systematically varying buffer pH and ionic strength, we identify operational regimes that balance capsid stability with chromatographic separation efficiency. In parallel, we introduce an integrated two-dimensional (2D) in-line buffer exchange configuration that decouples AEX performance from sample formulation, enabling robust separation of stability-optimized, high-salt matrices without off-line desalting. Conclusions: By elucidating the roles of capsid charge modulation, ligand physicochemical properties, and local microenvironmental buffering, this study establishes practical design principles for stability-preserving chromatography. It lays a foundation for more reliable analytical and future preparative AAV workflows. Full article

We perform a systematic analysis of the nuclear dependence of two-particle–two-hole meson-exchange current contributions to inclusive lepton-nucleus scattering within the relativistic mean-field framework. We present microscopic calculations of nuclear responses for a set of 17 nuclei, ranging from helium to uranium, using a model with different Fermi momenta for protons and neutrons. We propose a novel scaling prescription based on the two-particle phase space and key nuclear parameters. The resulting description is accurate over a wide range of nuclear targets, with typical deviations below 10%, and allows for a separate treatment of the different emission channels. In addition, a consistent benchmark against electron-scattering data is provided. The parametrization presented provides a practical framework for extending the responses to different nuclear targets in neutrino event generators. Full article

We have investigated the major- and trace-element composition of hydrothermal pyrite, magnetite, and Ti-magnetite, and of the principal Cu-minerals chalcopyrite and chalcocite, to constrain ore-forming processes in the northeastern Saveh district (central Urumieh-Dokhtar magmatic arc, Iran). Our data provide new constraints on the magmatic–hydrothermal evolution and subsequent hydrothermal–supergene modification of the ore system. Ti-magnetites hosted in monzodioritic intrusions are enriched in Ti–V–Al, plot below the magnetite–ulvöspinel join and record high crystallization temperatures (>500 °C) under relatively low oxygen fugacity. By contrast, magnetite from silica-rich hydrothermal veins is Fe-rich with very low TiO₂; it formed at intermediate temperatures (~200–300 °C) under higher fO₂ and is markedly depleted in Ti and V compared with the intrusive oxides. Textures and oxide systematics (Al + Mn vs. Ti + V; V/Ti–Fe) document repeated hydrothermal pulses, Fe²⁺ leaching and element redistribution during cooling and fluid–rock interaction. Geochemical trends indicate progressive evolution from a magmatic fluid to later meteoric water overprint, with increasing As contents reflecting cooling and mixing with meteoric waters. Vertical elemental zoning suggests that most samples represent mid- to deep-level sections of the epithermal system. Elevated Cu contents (up to 0.95 wt.%) highlight pyrite as a significant Cu host. Co/Ni ratios between 1 and 10 further corroborate a magmatic–hydrothermal origin. Chalcopyrite is the principal economic Cu carrier at Northeast Saveh. Replacement follows a temperature- and fluid-controlled pathway (chalcopyrite → covellite → chalcocite). At lower temperatures (<~200 °C) replacement proceeds more slowly, producing chalcocite/digenite under prolonged reaction conditions. Chalcocite commonly occurs as thin replacement rims and fracture fills that concentrate remobilized copper. Collectively, the investigated oxide and sulfide proxies provide robust discriminants for separating magmatic versus hydrothermal domains and for vectoring toward higher-temperature feeders and zones of remobilized copper. Full article

The primary objective of this research was to systematically investigate how bond–slip behavior affects the flexural behavior of alkali-activated slag concrete (AASC) beams reinforced with steel fibers. To this end, a finite element model incorporating the steel–concrete interface bond–slip effect was formulated in Abaqus using a separated modeling approach, grounded in a thorough analysis of established bond–slip constitutive models. Numerical simulations were conducted on both reinforcing bar pull-out specimens and beam members to examine the bond–slip interaction between steel reinforcement and steel fiber-reinforced alkali-activated slag concrete (SFR-AASC), as well as its influence on the flexural behavior of the beams. The results indicate that the bond–slip interaction at the steel–concrete interface can be effectively captured using nonlinear spring elements. The proposed modeling approach is simple to implement and demonstrates stable numerical convergence. For the pull-out specimens, the numerically obtained stress contours along the loading direction, together with the corresponding load–displacement curves, show good agreement with experimental observations. Further comparisons between numerical predictions and experimental results for beam specimens reveal that the prediction errors of the fully bonded model range from 0.2% to 9.7%, whereas those of the model accounting for bond–slip effects are reduced to 0.1–4.7%. The bond–slip model provides more accurate predictions of cracking load, ultimate load, and overall load–displacement behavior, thereby verifying the validity and accuracy of the developed finite element modeling strategy. Full article

Hydrogen energy is acknowledged as being the cleanest energy source. As hydrogen fuel cell technology advances, the development of low-cost, high-quality hydrogen purification technologies has grown increasingly critical. Targeting the separation of steam methane reforming gas mixture with a typical composition of H₂/CO₂/CH₄/CO = 76%/20%/3.5%/0.5%, a 6-bed-13-step pressure-swing adsorption process featuring four pressure-equalization steps was designed, in which a multi-layer adsorbent packing strategy was adopted to investigate the purification performance. The effects of feed flow rate, adsorbent packing combination, and purge-to-feed ratio on hydrogen purity and recovery, and on the impurity content level were analyzed. Furthermore, the gas-phase and solid-phase concentration distributions of each adsorbent layer under cyclic steady state were studied in detail, and the variation characteristics of their adsorption–desorption behaviors were systematically elaborated. Eventually, the optimal adsorbent combination and process condition configurations were determined. The results show that the proposed process can achieve a hydrogen purity of 99.99971%, with a concentration of CO of less than 0.2 ppm, which meets the fuel cell-grade hydrogen standard. Full article

Natural gas separation and purification are critical stages for ensuring product quality, operational safety, and economic efficiency in the energy sector. However, a significant research gap exists: conventional control systems, predominantly based on a proportional-integral-derivative (PID) controller, are often static and lack the adaptability required to handle fluctuations in raw gas composition and operating conditions. This review aims to systematically analyze modern control strategies to identify the most influential parameters and effective methodologies for enhancing process efficiency. The methods involve a comparative assessment of classical PID control against advanced intelligent approaches, including adaptive control, fuzzy logic, and machine learning (ML) models, based on a synthesis of the recent literature and industrial case studies. The key finding is that data-driven and intelligent methods (e.g., neural networks, adaptive fuzzy controllers) demonstrate superior performance in achieving precise parameter adjustment, improving responsiveness, and optimizing energy consumption compared to traditional static systems. Such an integrated strategy transforms decision-making into a multivariable optimization framework with objectives encompassing minimizing pollutants, lowering energy usage, and enhancing end-product specifications. The present work argues for employing methodologies like systemic analyses and advanced computational techniques—particularly artificial neural networks—to forecast gas stream attributes. Full article

Spiral separators commonly face the issue of particle misplacement during fine particle separation, which severely limits separation accuracy. This study employs a coupled CFD-DEM numerical simulation method to systematically investigate the influence mechanism of transverse inclination angle (10°, 15°, 20°) on particle moving behavior. The results show that the separation process exhibits distinct stage characteristics, which can be divided into an initial stage (first 1/3 turn), a transition stage (1/3 to 2 turns), and a quasi-steady stage (after 2 turns). A steeper angle (20°) optimizes the flow field, reducing the inner low-velocity zone and widening the high-velocity core, which promotes inward migration of particles. This enhances the enrichment of high-density particles while effectively suppressing their mixing into the clean coal product at the outer edge. For difficult-to-separate fine particles below 0.1 mm, although complete separation is challenging, increasing the transverse inclination angle still shows a clear reduction in the misplacement of high-density particles, providing a controllable approach for improving the quality of the outer edge product. This study offers theoretical insights and design guidance for optimizing spiral separator structures and enhancing fine coal separation efficiency. Full article

Background: Improving container terminal efficiency requires a comprehensive understanding of the interactions between vessel, truck, and container operations, yet existing studies often analyzed these components separately. In Japanese container terminals, where digitalization initiatives are progressing, empirical evidence based on integrated operational data remains limited. Methods: This study empirically analyzes turnaround times for vessels, trucks, and containers at five major Japanese container terminals using a composite dataset that integrates terminal operating system data, automatic identification system data, and liner service information. Descriptive statistical analyses and regression models are applied to examine vessel berthing time, truck arrival patterns and turnaround time, container dwell time within terminals, and container round-trip time outside terminals. Results: The analysis reveals distinct temporal patterns in terminal operations, including systematic morning–afternoon asymmetries and differences across cargo flows. Truck turnaround times increase with vessel calls and vary by time of day, while container dwell times are strongly influenced by terminal policies such as free-time rules. Regression analyses indicate that turnaround times are primarily affected by terminal-controlled factors. Conclusions: These findings demonstrate the importance of synchronizing quayside and landside operations. The study contributes integrated empirical evidence to the port digitalization literature and provides actionable insights for enhancing container terminal efficiency. Full article

Natural and human-made disasters threaten cities increasingly, thus requiring a combination of disaster risk reduction and sustainable development strategies. Although vulnerability assessment methods and urban sustainability policies have improved significantly, these two fields remain separate, leading to fragmented policies that may work against resilience objectives. This paper provides an overview by conducting a systematic review of 87 peer-reviewed studies published between 2000 and 2024 that describe and analyze the intersection of disaster prevention policies and sustainability practices in urban planning. Thematic analysis was employed, and five major themes were revealed: policy implementation frameworks, climate adaptation strategies, preparedness mechanisms, vulnerability assessment approaches, and sustainability evaluation systems. The findings reveal a critical disconnect: on the one hand, vulnerability assessments highlight the structural–technical aspect and, at the same time, ignore the sustainability indicators (resource efficiency, social equity, and ecosystem services), while on the other hand, sustainability frameworks deliberately shut out disaster risk awareness from the core evaluation criteria. This methodological separation produces policy conflicts where disaster interventions may compromise environmental goals, and sustainability initiatives may increase hazard vulnerability. This review concludes that resilient cities require assessment methodologies synthesizing disaster risk and sustainability dimensions. A novel conceptual integration framework is suggested that combines hazard-exposure-vulnerability analysis with environmental–social–economic sustainability pillars, thus laying the groundwork for future operational tools. This joint viewpoint accepts that hazards mainly affect development that is not sustainability-oriented, while sustainable systems through adaptive design and equitable resource distribution inherently lower the vulnerability. Full article

Aromatase inhibitors (AIs) are the standard adjuvant endocrine therapy for postmenopausal women with hormone receptor-positive breast cancer; however, their effects on lipid metabolism remain incompletely characterized. In this study, we investigated AI-associated alterations in the plasma lipidome using mass spectrometry-based lipidomics. Plasma samples were collected from 30 patients prior to AI initiation and 29 patients receiving non-steroidal AI therapy for at least 24 months. Ultra-high-performance liquid chromatography–tandem mass spectrometry identified and relatively quantified 649 lipid species across 23 lipid classes and subclasses. Lipidomic analysis revealed significant differences in specific lipid species. Several phosphatidylcholine, sphingomyelin, and lysophosphatidylethanolamine species were significantly more abundant in patient plasma prior to AI therapy, whereas higher levels of selected ceramides, hexosylceramides, phosphatidylinositol (PI 16:0_16:0), and a polyunsaturated diacylglycerol species were observed in patients receiving AI therapy. Multivariate analyses revealed patient group separation, and a Naive Bayes classification model based on lipid-class levels achieved an area under the curve of 0.79. Additionally, lipid network and hierarchical clustering analyses identified systematic lipid-class trends. Protein–protein interaction network analysis based on lipidomic profiles highlighted enzymes associated with sphingolipid metabolism pathways. These findings demonstrate that long-term AI therapy is associated with specific alterations in the plasma lipidome, consistent with estrogen-deprivation-related metabolic differences. Targeted lipidomic profiling may provide mechanistic insights into therapy-associated metabolic effects and support future efforts to optimize long-term management of breast cancer survivors. Full article

Heliostats constitute essential elements within concentrating solar power (CSP), where their structure, load profiles, and operational environment render wind loads a critical factor in their design considerations, as these loads directly impact the cost of energy generation. The aerodynamics significantly influence wind-induced effects, resulting in considerable variability in wind loads among different heliostat geometries. This study utilizes the Computational Fluid Dynamics (CFD) methodology to systematically examine the aerodynamic behavior of an isolated pentagonal heliostat. Employing the Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations with an atmospheric boundary layer inlet condition, the investigation focuses on the flow field and wind load characteristics at four representative pitch angles: 0° (stow position), 30°, 60°, and 90°. Findings indicate that the pitch angle exerts a decisive impact on flow separation patterns. Specifically, as the elevation angle decreases, the flow regime shifts from being predominantly influenced by the mirror surface to being governed by the support structure, mediated through an interactive coupling between these components. At the 60° operational pitch angle, the pentagonal heliostat’s distinctive corner geometry induces an asymmetric vortex configuration—characterized by a smaller vortex at the top and a larger one at the bottom—thereby disrupting the conventional vortex distribution observed in symmetric heliostat designs. A further analysis of wind load characteristics indicates that, compared to a quadrilateral heliostat, the pentagonal mirror exhibits a significantly lower Elevation Moment Coefficient, despite a slight increase in the normal force coefficient. This reduction is attributed to a balancing mechanism: the “vortex structure asymmetry” creates an upper-large–lower-small distribution of absolute negative pressure on the support surface, while the “stagnation point position” shift with elevation angle produces an upper-small–lower-large distribution of absolute positive pressure on the reflector. The interaction between these opposing trends minimizes the net pressure differential across the mirror height, thereby contributing to superior overall aerodynamic performance. The reduction in the elevation moment coefficient contributes to enhanced structural wind resistance, thereby improving the overall energy efficiency and economic viability of concentrating solar power. Full article

The photonic spin Hall effect (PSHE) originates from the spin–orbit interaction (SOI) of light. The literature indicates that the transverse spin-dependent shift, ${\delta }_H^{-}$ (SDS), from the PSHE is weak (in the nanometer range) and difficult to measure directly. This study utilizes a plasmonic structure to improve the ${\delta }_H^{-}$ in the PSHE. The obtained results of this study demonstrate that the inclusion of silicon nitride (Si₃N₄) significantly enhances the ${\delta }_H^{-}$ relative to its absence; however, plasmonic material is present in both cases. The enhanced shifts exhibit a significant dependence on the resonance angle (${\theta }_r$) and the thickness of layers of the PSHE structure to attain the maximum increase in ${\delta }_H^{-}$ of 350.82 µm at the plasmonic resonance condition. A systematic analysis of the centroid positions of the reflected beam indicates a distinct and constant separation of opposing spin components. Further, the improved ${\delta }_H^{-}$ is utilized in cancer cell detection, as changes in the refractive index (RI) of cells facilitate the identification of cancer cells from healthy to cancerous. All examined cell types demonstrate that cancerous cells had a greater ${\delta }_H^{-}$ than normal cells, owing to their elevated effective RI. These results illustrate that the proposed plasmonic-assisted PSHE structure offers significant enhancement and a high sensitivity of 439.30 µm/RIU for label-free detection of cancer cells. Full article

The ability to precisely control the morphology of polylactide (PLA) microparticles is crucial for their biomedical applications, yet it is a challenge due to the interdependent nature of key parameters such as size, porosity, and surface topology. This study presents a systematic approach to fabricating PLA microparticles with tunable architecture via emulsion-solvent evaporation by investigating the interplay of polymer molecular weight (44–442 kDa), solution concentration (0.5–20% w/v), and porogen type (PEG, alkanes, lithium salts). We achieved precise size control from 5 to 500 μm, dictated by solution viscosity and the polymer’s crystallization tendency, with poly(L-lactide) yielding irregular particles and poly(D,L-lactide) forming perfect spheres. Furthermore, porogen selection was critical for porosity: alkanes enabled tailored pore networks, with longer chains (e.g., decane) producing larger pores via enhanced phase separation, whereas the double-emulsion method with Li₂CO₃ proved superior for macroporosity due to its slow leaching kinetics. This work provides a foundational guideline for the rational design of PLA microparticles with customized properties for targeted applications in drug delivery and tissue engineering. Full article