Search Results (3,482)

Background/Objectives: Cyclodextrin-based ternary systems are widely used to improve the solubility of poorly soluble drugs. Amino acids such as L-arginine may further increase dissolved drug concentrations and reduce precipitation under physiologically relevant conditions. In many systems, apparent solubility enhancement is influenced simultaneously by drug ionization, inclusion complex formation, multicomponent interactions, and solid–liquid equilibria. This study presents a physicochemical modeling approach for analyzing pH-dependent solubility enhancement and precipitation behavior in drug–cyclodextrin–L-arginine systems. Methods: The model combines acid–base equilibria, binary inclusion complexation, ternary association, and explicit solid-phase partitioning within a unified mass-balance treatment. The approach was applied to representative ternary systems containing repaglinide, sulfadiazine, cefixime, and meloxicam. Results: Quantitative comparison with published phase-solubility data for the repaglinide–HPβCD–L-arginine system confirmed the numerical consistency of the model. The calculated profiles showed that enhanced solubilization and reduced precipitation occur only within specific pH regions determined by coupled equilibrium effects. For cefixime and meloxicam, the calculations were interpreted as predictive applications because directly comparable validation datasets were not available. Outside the favorable pH regions, a substantial fraction of the drug remained in the solid phase. Conclusions: These observations support the importance of pH and multicomponent interactions in controlling formulation performance in cyclodextrin-containing systems. The obtained profiles may support preliminary optimization of formulation pH and excipient composition before experimental screening. Full article

Adaptive and adverse brain states are often assumed to lie on a shared molecular continuum, but this assumption has rarely been evaluated against explicit transcriptomic alternatives. This study aimed to compare two representations of cross-context brain transcriptomic organization: a transcriptome-wide global-axis model and a low-dimensional reciprocal model. We benchmarked these models across a curated cross-study brain cohort spanning exercise, alcohol-related adversity-like contexts, stress, aging, and neurodegeneration, using prespecified intervention-like and adversity-like directional contrast labels rather than assuming homogeneous biological states. We assessed the competing representations using signed-effect correlations, permutation analyses, non-linear fitting, and held-out reconstruction, and we then examined the resulting structure through region-specific human bulk evaluation and exploratory cellular, single-nucleus, spatial, and chromatin projection analyses. These downstream analyses were used to examine localization and biological interpretability and were not treated as independent evaluation of the module 1/module 2 (M1/M2) partition. The combined signed-effect statistics were interpreted as representation-level directional summaries rather than estimates of a homogeneous cross-study biological effect. The global-axis model received limited support: intervention-like and adversity-like signed-effect summaries were only weakly correlated, were not stronger than permutation null expectations, and were not improved by non-linear fitting. Within the selected reciprocal-gene space, a rank-1 latent profile reconstructed held-out genes more accurately than the hard M1/M2 partition, whereas the M1/M2 discretization provided a more interpretable but selection-conditioned directional summary. Human analyses yielded an asymmetric pattern: a significant M1 association was observed only in the hippocampal dataset, whereas M2, the reciprocal index, and the other examined brain regions showed no consistent corresponding effects; leave-one-stratum-out analyses indicated poor cross-stratum reproducibility of the exact gene-level partition. These findings motivate a low-dimensional reciprocal representation as an exploratory framework while emphasizing context dependence, cohort dependence, and heterogeneity. Full article

Identifying driving styles is essential for personalizing driving assistance systems and enhancing intelligent transportation services. However, existing approaches predominantly rely on experience-driven feature engineering and annotated data, limiting objectivity and hindering the exploitation of unlabeled naturalistic driving datasets. To address these limitations, this paper proposes an unsupervised framework for driving style identification from naturalistic driving data through context normalization. A Constrained Convolutional Autoencoder (CCAE) integrated with a global self-attention mechanism is developed to map unlabeled driving sequences onto a standardized dynamic reference defined by the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). This process extracts Driving Adaptability Characteristics (DACs) as WLTC-anchored latent representations that characterize normalized driver-specific response patterns across heterogeneous naturalistic contexts. To ensure feature robustness, frequency-domain refinement is applied to eliminate high-frequency noise. The extracted DAC sequences are subsequently partitioned into distinct driving styles using a kernel-mapped clustering algorithm. To evaluate the external relevance and physical interpretability of the identified styles, actual vehicle accident records and raw CAN-bus feature backtracking are introduced as validation evidence. The results show that the identified driving styles exhibit different historical accident probabilities. The proposed CCAE model achieves clearer cluster-level differentiation than traditional feature engineering and unconstrained deep learning models, and the ablation analysis confirms the contribution of the WLTC-based constraint. These findings indicate that the context-normalization framework can extract interpretable and externally relevant driving style representations from unlabeled naturalistic data. Full article

Tesla valves are passive hydraulic devices capable of producing directional flow resistance without moving components, making them attractive for applications in microfluidics, thermal systems, and high-reliability hydraulic circuits. Despite extensive experimental and numerical studies, an analytical formulation capable of describing the hydrodynamic behavior of Tesla valves under varying operating and geometric conditions remains limited. In this work, a comprehensive analytical model is developed to describe the pressure losses, flow redistribution, and diodicity behavior of Tesla valves through a physics-based formulation derived from conservation laws, dimensional analysis, and inertial scaling principles. The proposed model incorporates the influence of Reynolds number, flow partition, geometric ratios, branch inclination angle, and number of diode stages within a unified nonlinear framework. A closed structural equation is obtained that relates hydraulic losses and directional asymmetry to the internal geometry of the valve. The formulation reveals the existence of geometric and energetic constraints governing rectification efficiency, including bounds associated with stage number, channel scaling, and angular momentum exchange. The results show that Tesla valve performance emerges from a delicate balance between inertial amplification and dissipative mechanisms, providing an analytical framework for the design and optimization of Tesla-type hydraulic systems across multiple scales. Full article

MicroRNA (miRNA) dysregulation is a crucial pathogenic factor that extensively participates in the occurrence and progression of various human diseases, especially cancers. Identifying unknown miRNA–disease connections is essential for understanding disease pathogenesis and improving clinical treatment strategies. Traditional biological experiments are often expensive and technically restricted, so computational prediction has become a widely used auxiliary research tool. In this study, we develop a novel predictive model called Attribute-Aware Message Passing Graph Convolutional Network (AAMPGCN) to identify potential miRNA–disease associations. The advantage of AAMPGCN lies in integrating miRNA and disease attribute information into the message-passing process: it partitions the miRNA–disease heterogeneous graph that incorporates miRNA functional similarity, disease semantic similarity, and Gaussian interaction kernel similarity into attribute-homogeneous subgraphs, while restricting high-order message propagation within each subgraph. This mechanism effectively filters cross-attribute noise, preserves the discriminability of miRNA and disease embeddings during deep convolution, and is thus well-adapted to miRNA–disease heterogeneous networks. The AAMPGCN prioritizes miRNA and disease attributes, aggregating messages specifically among nodes with similar attribute characteristics that are relevant to miRNA–disease interactions. Experimental results show that the AAMPGCN model achieves AUC and AUPR values of 94.06 and 93.52 on the HMDD2.0 dataset, which outperforms existing methods. The proposed AAMPGCN provides a new and effective method for miRNA–disease association prediction, and also offers theoretical support for the research on disease molecular mechanisms and the screening of clinical therapeutic targets. Full article

Empirical power-law relationships are widely used in reliability physics to describe degradation kinetics and predict lifetime. Such behavior appears across diverse failure mechanisms, including time-dependent dielectric breakdown (TDDB), hot-carrier injection (HCI), bias temperature instability (BTI), electromigration (EM), and fatigue. In this work, a thermodynamic framework for reliability kinetics is developed from Gibbs free energy and entropy partitioning, leading to a generalized kinetic equation that incorporates thermal activation, stress acceleration, and accumulated degradation. The formulation introduces two parameters: a stress coefficient, $\gamma$, which describes the influence of externally applied stress, and a correlation coefficient, $\chi$, which describes how accumulated degradation influences subsequent degradation. Negative values of $\chi$ correspond to self-limiting evolution, positive values correspond to self-amplifying evolution, and $\chi =0$ represents statistically independent accumulation. Representative reliability mechanisms are interpreted within this framework, with TDDB approaching independent evolution, HCI exhibiting weak self-limiting behavior, BTI showing strong self-limiting behavior, and fatigue exhibiting self-amplifying behavior. Electromigration illustrates the complementary role of stress acceleration through $\gamma$. The proposed framework provides a common thermodynamic interpretation of empirical power-law degradation kinetics and introduces degradation correlation as a complementary descriptor for reliability modeling and lifetime prediction. Full article

This paper investigates a class of Hadamard variable-order fractional differential equations in which the fractional order is determined by the cumulative distribution function (CDF) of a continuous random variable. The proposed framework establishes a novel connection between probability theory and variable-order fractional calculus by allowing the memory index of the fractional operator to evolve according to a prescribed distribution law. To facilitate the analysis, the CDF-based variable order is considered through a piecewise-constant representation on a finite partition of the interval, which transforms the original problem into a family of Hadamard fractional differential equations of constant order on successive subintervals. Existence and uniqueness results are established by converting the differential problem into an equivalent fractional integral equation and applying the Banach contraction principle in suitable Banach spaces. Sufficient conditions ensuring the well-posedness of the problem are derived in terms of explicit bounds involving the fractional order and the nonlinear term. In addition, the Ulam–Hyers stability of the proposed model is investigated, and stability criteria are obtained under the same analytical framework. To illustrate the applicability of the theoretical results, a numerical example involving a CDF-generated variable-order function is presented. The example verifies the assumptions of the existence, uniqueness, and stability theorems and demonstrates the effect of piecewise-constant approximations of the cumulative distribution function on the resulting numerical solutions. The obtained results show that the proposed CDF-based Hadamard variable-order framework provides a mathematically consistent setting for studying fractional differential equations whose memory characteristics depend on probabilistic distributions. Full article

Although non-structural walls do not determine the structural safety of a building, they are responsible for its functionality by serving as acoustic, thermal, and fire-resistant partitions. They may be freely located and relocated and are typically constructed during the finishing stage of building works. Reliable performance of non-structural walls depends on appropriate connections to floors and adjacent walls. Connections to walls are most commonly achieved using traditional masonry bonding or sufficiently durable wall connectors, usually made of steel. An alternative to steel connectors may be connectors made of polymer-based materials or meshes. This paper proposes an innovative method for connecting non-structural masonry walls using a spatially arranged mesh, which serves not only as reinforcement of the wall connection but also as reinforcement of the bed joints. The aim of the study was to evaluate the effectiveness of this method in comparison with other connection techniques, including traditional solutions. Experimental investigations were carried out using an original test setup on 12 specimens made of AAC masonry units, divided into three series: series P—traditional connection (reference series), series H—connection with mesh placed in bed joints, and series SHP—connection with spatially arranged mesh. Silica Glass Mesh (SGM), intended for reinforcement of bed joints in AAC masonry, was used in the study. The experiments focused on the analysis of connection behavior and load-bearing capacity, with particular emphasis on maximum load values and failure mechanisms. Individual stages of the behavior of mesh-reinforced connections were identified, and empirical relationships enabling estimation of maximum loads were developed. The results confirmed that the traditional connection achieved the highest load-bearing capacity. However, as expected, the mesh-reinforced connections—particularly those with the spatial mesh arrangement—exhibited a more stable response and a greater ability for progressive load transfer. The SHP series connections with spatially arranged meshes exhibited significantly lower load-bearing capacity compared to the reference unreinforced connections, while at the same time demonstrating substantially greater deformability. The stiffness degradation in the mesh-reinforced connections did not occur abruptly, as observed in the reference models, which makes them an effective alternative for practical applications. Technical models for predicting forces and displacements of connections reinforced with spatially arranged meshes and meshes placed in bed joints were also developed. Full article

Construction cost estimation often relies on subjective expert judgment, which introduces variability and inconsistency. Standardizing data and procedures can improve reliability and enable repeatable workflows. This research investigates how price lists used for public construction can be semantically linked to Building Information Modeling (BIM) via the Industry Foundation Classes (IFC) standard to support objective, repeatable, semi-automated model-to-cost estimation. By an inductive case-based design, the work uses Veneto Region price list and maps selected cost items to IFC properties. Six representative price list items (slabs, partition walls, plasterboards, plasters, doors, and windows) are examined to identify discriminating parameters (e.g., material, thickness, dimensions, fire rating) that are mappable to IFC entities and property sets. The methodology distinguishes primary charges from surcharges, then assesses the model-ability of parameters and their semantic coherence within BIM’s object-based paradigm. Findings show that through formalization and standardization of cost item characteristics via IFC properties, the approach reduces subjectivity, enabling structured and objective matching and laying the groundwork for future automated workflows. Limitations are discussed, including incomplete representation of some cost-driving attributes, reliance on naming conventions, and opportunities associated with Digital Product Passport implementation (DPP). Full article

The Internet of Medical Things (IoMT) has become a core component of the modern healthcare system, but its increasing connectivity also exposes medical networks to diverse cyber threats. Although recent threat detection frameworks have demonstrated strong predictive performance, many still operate as black-box models. They offer limited or no interpretability of their decisions. This paper proposes an explainable hybrid IDS framework for multiclass IoMT intrusion detection. The proposed framework partitions network traffic features into semantically related groups and employs specialized expert networks to learn complementary traffic representations. A gate-balanced Mixture-of-Experts (MoE) routing mechanism adaptively aggregates expert outputs, while a cross-expert self-attention module captures contextual dependencies among expert representations. Furthermore, the proposed framework incorporates multi-level interpretability through SHAP, LIME, and expert-routing analysis to explain both feature contributions and internal decision behavior. We evaluate the proposed framework on two recent IoMT benchmarks, namely CICIoMT2024 and IoMT-TrafficData, under 6-class, 19-class, and 9-class multiclass settings, respectively. On CICIoMT2024, the proposed IDS achieves 99.76% accuracy and an MCC of 0.9951 in the 6-class setting, while attaining 99.07% accuracy and an MCC of 0.9892 in the 19-class setting. On IoMT-TrafficData, the proposed framework achieves 99.92% accuracy and an MCC of 0.9988 in the 9-class setting. The explainability results further show that the model identifies meaningful traffic features and exhibits class-dependent expert specialization, thereby improving transparency in its decisions. These findings confirm that the proposed framework provides an effective and interpretable solution for securing IoMT systems. Full article

Accurate electricity consumption forecasting is essential for the efficient planning and operation of modern power systems. The development of predictive models based on machine learning and deep learning strongly depends on the availability of well-documented and publicly accessible electricity consumption datasets. However, most existing databases are concentrated in Europe and North America and are typically focused on residential measurements obtained from smart meters, resulting in limited representation of equatorial regions. This work presents a structured review of public electricity consumption repositories, analyzing characteristics such as geographical coverage, temporal resolution, user type, and accessibility. Based on the limitations identified in the literature, a new electricity consumption dataset obtained from real measurements collected at distribution substations located in an equatorial region is presented. The dataset was organized through a systematic preprocessing workflow that included temporal standardization, construction of 48-hour sliding windows, normalization, and stratified partitioning into training, validation, and test subsets. The descriptive statistical analysis confirms the consistency of the generated subsets and reveals differences between working-day and non-working-day consumption patterns. The proposed dataset provides a reproducible resource for the development and evaluation of multi-horizon electricity demand forecasting models, as well as for load analysis and energy management studies in equatorial regions. Full article

Fused deposition modeling (FDM) is a material-extrusion additive manufacturing technology that is widely used in rapid prototyping, complex product modeling, and functional part fabrication. However, process-parameter drift and environmental disturbances may induce underfilling, overfilling, warping, delamination, and other defects, thereby reducing part quality or interrupting the manufacturing process. Since FDM is characterized by point-wise extrusion and layer-by-layer deposition, layer-surface images naturally contain both spatial morphology and temporal evolution information. Existing image-based diagnostic methods often treat layer images as independent samples, and the selection of the image-stream length is still insufficiently supported by experimental evidence. Moreover, spatial compression in spatiotemporal neural networks may remove local defect information that is important for distinguishing similar process-parameter drifts. This study provides a deployment-oriented analysis of FDM image-stream diagnosis by systematically examining how layer-window length, spatial feature preservation, and strict data partitioning influence process-parameter drift recognition. To address these issues, this paper studies ConvLSTM-based FDM image-stream process-parameter drift diagnosis. Continuous region-of-interest image streams are constructed for one nominal condition and six process-parameter drift conditions. In this paper, the time step T denotes the number of consecutive layer-surface images, or, equivalently, the number of consecutive printed layers, contained in one diagnostic image stream. A ConvLSTM-Flatten baseline is first developed to preserve complete spatial feature maps and to evaluate the effect of different time-step lengths. Then, a ConvLSTM model with adaptive spatial pooling and temporal attention (ASP-TA) is constructed to analyze the influence of spatial pooling granularity and temporal feature fusion. The experiments show that the ConvLSTM-Flatten model achieves the highest average test accuracy of 0.7288 at $T=9$, whereas $T=3$ is identified as a practical optimal time step when test accuracy, image-frame computation, diagnosis latency, and convergence behavior are considered together. The paired trial-wise accuracy difference between $T=9$ and $T=3$ is small and not statistically significant over ten repeated trials. Thus, the diagnostic window corresponding to $T=3$ covers three consecutive deposited layers; after the initial window is available, stride-one stream construction allows the diagnosis to be updated with each newly acquired layer image. ASP-TA with a pooling size of eight consistently outperforms ASP-TA with a pooling size of four, but both are lower than the Flatten baseline, indicating that preserving sufficient spatial information is essential for distinguishing FDM process-parameter drift states. The results reveal the non-monotonic influence of time-step length and clarify the tradeoff between spatial feature preservation and model compactness in FDM image-stream process-parameter drift diagnosis. Full article

Military conflict generates a qualitatively distinct category of market shock that is sudden, geographically concentrated, and channeled directly through physical energy supply routes. This paper examines investor herding and cross-sectional return dispersion across five commodity markets (Brent crude, WTI crude, Henry Hub natural gas, spot gold, and the Baltic Dry Index) using 475 daily observations from January 2024 through April 2026, covering the sustained escalation phase of the Iran–Israel conflict. The empirical analysis incorporates eight complementary specifications: (1) baseline CSAD regression; (2) GARCH(1,1) conditional volatility augmentation; (3) volatility regime partitioning (high versus low); (4) quantile regression across the CSAD distribution; (5) asset-level disaggregation; (6) interaction with the geopolitical risk (GPR) index; (7) asymmetric analysis distinguishing between up- and down-market conditions; and (8) rolling 240-day estimation to capture time-varying dynamics. The results tend to reject the herding hypothesis and provide suggestive evidence of positive cross-commodity dispersion. The baseline model shows that large market movements significantly increase cross-sectional dispersion. At the asset level, natural gas exhibits the highest dispersion coefficient, reflecting its structural independence from oil-related geopolitical fundamentals. Moreover, gold provides evidence consistent with a positive but comparatively smaller coefficient, consistent with its role as a stabilizing safe-haven asset. Dispersion effects are broadly symmetric across market conditions. Furthermore, the geopolitical risk index does not exert a significant marginal effect. However, the analysis is restricted to five commodity assets and a single geopolitical conflict episode (the Iran–Israel conflict), which may limit the generalizability of the findings to other markets or conflict contexts. Full article

Road traffic injuries remain a central challenge for sustainable transport, public health, and mobility governance. The task of monitoring these injuries requires indicators that jointly capture harm severity, road and environmental context, and patterns of vehicle involvement at scale. Using harmonised English-language Romanian police crash exports (2023–2025), we build 92,790 records with 36 variables and estimate two complementary k-means typologies: a severity partition based on the fatality, injury, and vehicle-count fields (a register proxy for involvement, not vehicle-type attributes) and a context partition based on the road, environment, mechanism, and cause fields with one-hot encoding and TruncatedSVD. Reported tables and figures reproduce the archived MiniBatch pipeline for replication; for context, full-batch k-means clustering on the same embedding is the recommended default when cross-year prevalence stability is required (train–test TVD 0.039 versus 0.569 under MiniBatch). We report silhouette-guided choices ($k=6$ severity, $k=4$ context), cross-seed stability, feature ablations, and a 2023–2024 versus 2025 prevalence comparison. A Pearson ${\chi }^2$ test on severity × context labels reveals strong statistical significance, yet Cramér’s V remains small—statistical association with limited practical coupling, consistent with complementary rather than redundant partitions. Limitations include police-reported injury counts; a coarse vehicle proxy; weak context geometry; and large MiniBatch context drift, which binds inference to within-year descriptive profiling unless analysts refit the model, add version labels, or adopt full-batch context clustering. The contribution is an integrated, reproducible profiling and governance workflow for dashboards and follow-on modelling—not a fixed multi-year cluster taxonomy. Full article

3D Gaussian Splatting (3DGS) offers efficient explicit rendering, but large-scale remote-sensing use remains fragmented across UAV/aerial photogrammetry, satellite reconstruction, large-scene scaling, surface modeling, and geospatial evaluation. We present a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-informed scoping review based on 55 core studies identified through Web of Science, Scopus, IEEE Xplore, and supplementary searches completed on 3 June 2026. A faceted taxonomy organizes the literature by platform, sensor model, scalability strategy, and geometric supervision. The synthesis shows that partitioning, hierarchy, compression, and feed-forward inference improve scalability but do not guarantee metric geometry. Reliable deployment additionally requires sensor-consistent projection, geometric or georeferencing constraints, explicit supervision labels, and product-level evaluation. In control-point-free settings, internal consistency should be distinguished from independently validated accuracy. We therefore propose a platform-aware benchmark framework that jointly records visual fidelity, computational cost, metric geometry, product utility, failure behavior, and reproducibility metadata for UAV/aerial, satellite, and hybrid settings. Full article