Bisphosphonates are highly effective drugs for the treatment of bone-related diseases; however, their oral administration is limited by very low bioavailability and gastrointestinal irritation. Polymer-based delivery systems may overcome these limitations by prolonging residence time and modulating drug release in the gastrointestinal tract. Among them, chitosan is particularly attractive due to its biocompatibility, biodegradability, and mucoadhesive properties. This study is the first to systematically investigate how batch-to-batch viscosity variability of commercially available chitosan affects the performance of oral drug delivery systems. Although the investigated chitosans were supplied under identical catalog number, significant differences in viscosity were observed between batches. Lyophilized and chemically crosslinked matrices (glutaraldehyde or genipin) were prepared using chitosan batches of different viscosities and characterized in terms of swelling, degradation, drug release kinetics, mucoadhesion, and intestinal permeability under simulated gastrointestinal conditions. Batch-dependent viscosity differences significantly influenced matrix structure and functional behavior, resulting in distinct swelling, degradation, and release profiles. In contrast, passive permeability assessed using a biomimetic PermeaPad (R) GIT system remained comparable across formulations. These findings highlight chitosan viscosity variability as a critical factor affecting matrix performance and reproducibility in polysaccharidebased drug delivery systems.
We review major developments in multi-objective optimization over the past decades. Although mathematical foundations and basic concepts have been established earlier, substantial progress in methods for constructing and identifying preferred solutions started in the late 1950s. We classify these approaches into two broad categories: mathematical programming-based and population-based. The former originated in the late 1950s, and its growth accelerated from the 1970s onward. We differentiate between approaches dealing with problems that operate in a continuous solution space and combinatorial problems where some variables are restricted to integer values. Population-based approaches flourished in the 1990s. Our focus is on evolutionary computation techniques that either aim to discover the entire Pareto front or incorporate the decision maker’s preferences to select the most favorable solution(s) or bias the search toward preferred regions. For all categories, we discuss those approaches that, in our opinion, have made major impacts. We examine current research trends and speculate on future directions in the field.
This paper studies hypothesis testing for structured scale matrices in the multivariate t distribution. We develop likelihood ratio and Rao score tests for general structural constraints on the scale matrix and derive the Wald test for hypotheses in which the scale matrix is restricted to a quadratic subspace. The latter class includes, in particular, diagonality, sphericity, compound symmetry, circular Toeplitz structures, and their block extensions with structured subblocks. Since the construction of the test statistics requires maximum likelihood estimation, we derive the likelihood equations under quadratic subspace restrictions. Although likelihood ratio and score tests have been investigated for covariance structure in the multivariate normal model, the Wald test has not been developed in that setting. Because the multivariate normal distribution arises as a limiting case of the multivariate t model, our results also provide a new contribution to the normal framework. Finite sample performance is examined via simulation, and a real data example illustrates the proposed methodology.
The development of the renewable energy sector, especially solar energy, is essential to mitigate the rapidly progressing climate change. However, the inherent uncertainty related to the stability of solar energy production capabilities prevents large-scale integration of solar-based sources into power grids. Therefore, this work addresses this practical challenge by presenting a systematic, large-scale synthesis and validation of a comprehensive neural network-based image enhancement pipeline for ground-based solar forecasting. This consolidated pipeline includes a Sun mask, optical flow, a cloud channel, and a novel irradiance value channel for early-stage spatio-temporal fusion. As a second key methodological novelty, we introduce the mean adaptive BerHu penalty as a loss function, which is shown to be uniquely suited for robustly handling the high dynamic range of solar irradiance values. The proposed comprehensive pipeline, when evaluated on a spatio-temporal model, outperforms existing methods from the literature. This strong performance is not architecture-specific, as demonstrated by our cross-architecture study, in which the pipeline’s portability achieves an average forecast skill of 19.35% among evaluated models for the 15-minute forecasting horizon. This is due to physically referenced, representation-level enhancements that act as an architecture-agnostic driver of forecasting skill going beyond incremental, model-specific tuning. Its practical applicability is further confirmed by its effectiveness across multiple intra-hour time horizons, with significant skill improvements for both ultra-short 5-minute (16.22%) and longer 30-minute (22.52%) periods. Finally, its practical deployability was confirmed through extensive validation on resource-constrained edge devices. The source code used in this study is publicly available to promote research reproducibility.
Neural Radiance Fields achieve high-fidelity scene representation but suffer from costly training and rendering, while 3D Gaussian splatting offers real-time performance with strong empirical results. Recently, solutions that harness the best of both worlds by using Gaussians as proxies to guide neural field evaluations, still suffer from significant computational inefficiencies. They typically rely on stochastic volumetric sampling to aggregate features, which severely limits rendering performance. To address this issue, a novel framework named IRIS (Intersection-aware Ray-based Implicit Editable Scenes) is introduced as a method designed for efficient and interactive scene editing. To overcome the limitations of standard ray marching, an analytical sampling strategy is employed that precisely identifies interaction points between rays and scene primitives, effectively eliminating empty space processing. Furthermore, to address the computational bottleneck of spatial neighbor lookups, a continuous feature aggregation mechanism is introduced that operates directly along the ray. By interpolating latent attributes from sorted intersections, costly 3D searches are bypassed, ensuring geometric consistency, enabling high-fidelity, real-time rendering, and flexible shape editing.
