Search Results (311,378)

Rare genetic disorders of the central nervous system (CNS) remain some of the most complex and challenging diseases to treat for several reasons. Targeting the CNS, especially the brain, presents one of the greatest obstacles in gene therapy using adeno-associated virus (AAV) vectors. Although various AAVs have been identified for their ability to transduce different cells in the CNS, their effectiveness and efficiency are significantly limited by the presence of neutralising antibodies (NAbs) and restricted cargo capacity. Despite these challenges, our understanding of AAV structure and technological advances continue to enable researchers to develop innovative strategies that have resulted in groundbreaking, FDA-approved therapeutic products now available for Leber congenital amaurosis (LCA) (Luxturna^®), spinal muscular atrophy (SMA) (Zolgensma^®), and the two recent gene therapy products for aromatic L-amino acid decarboxylase (AADC) deficiency, Kebilidi^® and Upstaza^®, which currently hold FDA and EMA approval, respectively. This review aims to highlight recent advances in the field of AAV gene therapy for neurological disorders, identify research gaps, and suggest areas for future investigation to enable potential breakthroughs particularly in neurodegenerative, neurodevelopmental, and neuromuscular disorders. We foresee that more tissue- and cell-specific AAV vectors designed using AI-powered platforms will emerge to precisely and efficiently target specific brain regions, transforming how CNS disorders are treated. Full article

Trajectory optimization during the terminal area energy management (TAEM) phase is pivotal for achieving accurate runway alignment and enhancing landing safety in autonomous aircraft operations. In the presence of initial state uncertainties in TAEM phase, conventional pseudo-spectral methods still suffer from robustness limitations and exhibit a strong dependence on the quality of the initial guess. Therefore, this paper proposes the composite African vulture optimization algorithm (CAVOA), a meta-heuristic framework designed to automate trajectory optimization. An in-depth examination of the heading alignment cone (HAC) trajectory model enables effective heading adjustments prior to landing, augmented by a tailored dynamic pressure profile to ensure safe touchdown velocities. By incorporating dynamic opposition learning, intelligent boundary processing, and composite exploration, CAVOA enhances global search efficiency. These enhancements are substantiated through comparisons with benchmark function optimization, Wilcoxon rank sum tests, and convergence analysis. Numerical simulations validate that CAVOA reliably directs autonomous aircraft to predefined touchdown states, demonstrating superior performance in complex aerial environments. Full article

Background: Human growth hormone (hGH) deficiency (GHD) is typically treated with daily injections of recombinant human growth hormone (rhGH), which do not fully replicate physiological secretion patterns. This study evaluates a novel approach using synthetic mRNA encoding hGH encapsulated in lipid nanoparticles (LNPs) and designated VTRC-01 to enable endogenous hormone production. Methods: VTRC-01 was administered intramuscularly to hypophysectomized (Hypox) prepubertal Wistar rats, and its efficacy was compared with rhGH. A cohort of healthy rats was included to assess anabolic effects and safety. Results: VTRC-01 stimulated longitudinal growth in both Hypox and healthy rats, achieving effects comparable to rhGH. Treatment induced a significant anabolic response that exceeded the basal growth rate of healthy controls. Conclusions: These findings provide proof-of-concept for hGH mRNA-based therapy as a promising alternative to rhGH. Further improvements in mRNA and LNP technologies are expected to enhance safe hormone production. These promising results underscore the potential of reprogramming via therapeutic mRNA the synthesis of key endocrine regulators (such as hGH) directly within the organism, offering for the first time a powerful pathway for the potential treatment for endocrine therapies targeting growth hormone deficiency. Full article

Considering the issue of noise generated by equipment that requires high air permeability for operation, a louvered noise barrier was designed. In accordance with sustainability principles, hemp fiber was used in the louvers. The aim of this experimental research was to investigate the effectiveness of the louvered noise barrier in a semi-anechoic chamber and to evaluate the influence of the number of louvers, the angle of inclination of the louvers, and the hemp fiber density on the performance of the barrier. An investigation of the barrier in a semi-anechoic chamber was carried out, using the rotating microphone method. The louvers in the barrier were tilted at angles of 0, 15, 30, or 45 degrees, and the density of fiber used in the different structures was 50, 100, 150, or 200 kg/m³. The highest insertion loss (IL) of the barrier reached 18.13 dB, and the sound reduction index (R′) reached up to 23.0 dB. The highest determined weighted sound reduction index (R′w) was 14.1 dB, and the equivalent sound level loss (LAeq) reached 9.9 dB (A). Full article

This study investigates a polymorphic underwater vehicle designed to combine long-range cruising with stable underwater operation, reducing dependence on surface support vessels. By introducing a foldable polymorphic structure, the vehicle can switch configurations, including serial and parallel. However, underwater environments often contain obstacles, and the vehicle may collide with them during the folding process. To prevent collisions between the vehicle and surrounding obstacles during the folding process, this paper investigates the motion envelope of the vehicle and examines how motion parameters and mass distribution influence the motion envelope. In this work, the polymorphic underwater vehicle is modeled as a multibody system operating under a neutrally buoyant condition. Based on space robot modeling methodologies and the linear and angular momentum theorems, the equations of motion of the polymorphic underwater vehicle are derived and verified using the Adams software 2020. In summary, the present study establishes a clear relationship between motion parameters, mass distribution, hydrodynamic effects, and the resulting motion envelope of a polymorphic underwater vehicle. The results show that the attitude of the vehicle during the folding process is uniquely determined by the joint angles, and a larger relative speed between the outer and inner folding motions produces a more compact attitude during the folding process. Mass distribution further influences the motion envelope of the vehicle: concentrating mass toward the center of the vehicle shifts the overall motion envelope upward, whereas concentrating mass toward both ends of the vehicle shifts it downward. In addition, hydrodynamic forces introduce an upward velocity component of the vehicle in the vertical direction during the folding process, which leads to an upward shift in the overall center of mass of the vehicle. Full article

Online apparel size recommendation remains difficult because consumers cannot physically assess garment fit before purchase. It is a multi-dimensional fit evaluation problem, particularly for complex garments such as jackets, where multiple body areas jointly influence perceived fit. Existing methods often rely on limited anthropometric measures, heuristic rules, or behavioral data, restricting both accuracy and interpretability. To address this issue, this study proposes an interpretable multi-dimensional fit evaluation framework based on garment ease theory. The framework defines ideal ease as the target fit condition and quantifies deviations through a segment-based weighting mechanism. Section-level mappings between body and garment measurements are established, and differentiated penalties are assigned according to the semantic fit interval of each body area. Section-specific evaluations are aggregated into an overall fit score (OFS) for candidate size ranking and Top-K recommendation, while also providing detailed fit feedback. Experiments involving 270 female participants and two jacket styles show high recommendation accuracy, achieving Top-3 accuracies of 99.6% for the regular-fit jacket and 98.9% for the tight-fit jacket. Compared with traditional heuristic methods, the proposed approach demonstrates clear advantages in both performance and interpretability, offering a practical solution that balances accuracy, transparency, and deployability. Full article

Cellulose is the most promising abundant renewable polymer material with the highest potential for the future low-carbon biorefineries. However, its utilization in industry is limited by the structural recalcitrance as a result of organization of crystalline domains, fibrillar architecture hierarchy and intramolecular and intermolecular hydrogen bonding which is responsible for access restriction for the catalysts and consequent cleavage of the glycosidic bonds. Therefore, efficient depolymerization of cellulose is of paramount importance as a step in biomass conversion into the low molecular products. In this review, the recent advances in cellulose depolymerization are discussed. The chemical, enzymatic, thermal, thermochemical, mechanochemical, oxidative and hybrid catalytic method is thoroughly discussed. Attention is paid to the mechanism of the depolymerization reaction steps as glycosidic bond activation as hydrolytic, radical mediated, and energy assisted pathways. Selectivity and conversion efficiency based on substrate morphology, solvent system and catalyst design are also discussed. Further, there is a comparison of key performance metrics which are relevant for the industrial process as product yield, carbon efficiency, energy demand, stability of the catalyst, solvent recyclability and impact to the environmental lifecycle. The pros and cons of the various methods are also represented. Processes based on mineral acids enable rapid conversion. However, they suffer from corrosion, waste handling issues and degradation by-products. On the other hand, enzymatic depolymerization processes offer relatively high selectivity but they are limited in terms of feedstock sensitivity and slow reaction kinetics. The downstream valorization mechanisms are also described with the result being that no single available technology is capable of satisfying all industrial requirements. Thus, future progress expects integrated circular processes where advanced catalysis, process intensification and digital optimization strategies take place. Full article

Background/Objectives: To explore potential associations between pediatric obesity and retinal and anterior segment ocular structures using OCT and ocular biometry. This study was designed as an exploratory, hypothesis-generating analysis without a pre-specified primary endpoint; all findings should be interpreted accordingly. Methods: This retrospective cross-sectional study included 52 children (104 eyes): 27 obese children (body mass index (BMI) percentile ≥95%) and 25 healthy controls (BMI percentile 5–85%). Optical coherence tomography (OCT) and ocular biometry were used to assess retinal nerve fiber layer (RNFL), ganglion cell complex (GCC), focal loss volume (FLV), global loss volume (GLV), Early Treatment Macular Map 5 (EMM5), corneal parameters, axial length (AL), anterior chamber depth (ACD), and white-to-white corneal diameter (WTOW). Group comparisons and cluster-robust bootstrap regression adjusted for inter-eye dependency, age, and sex; Bonferroni correction was applied. Results: Obese children showed nominally higher GCC average thickness, RNFL, and EMM5 values and shallower ACD; however, no parameter survived Bonferroni correction. ACD showed the most internally consistent exploratory pattern (unadjusted p = 0.006; adjusted p = 0.018; Bonferroni p = 0.249); however, this finding did not survive Bonferroni correction and should not be interpreted as a confirmed association. Other corneal and biometric parameters were not significantly different. Conclusions: Pediatric obesity may be associated with subtle ocular structural variations, but all findings are exploratory and hypothesis-generating. Larger prospective, pre-registered studies are needed to determine whether pediatric obesity is associated with structural ocular changes. Full article

Artificial intelligence (AI) is increasingly applied to healthcare decision-making; however, many persistent patient safety risks arise from sociotechnical conditions such as communication breakdowns, coordination failures, and organisational culture rather than diagnostic or decision error alone. While simulation can engage these dimensions of care, AI-supported simulation remains limited by heterogeneity and a lack of explicit conceptual structure. This study presents a narrative and conceptual review of the healthcare simulation and AI literature to identify structural barriers to coherent AI reasoning about simulation. Drawing on this synthesis, we introduce Transformative Simulation (TfS) as an intentional framework that can be formalised as an ontology for AI-supported simulation focused on cultural and systems-level change. TfS structures simulation through explicit Simulation-Based Intentions, an aligned design–delivery–data–debrief process, and foundational considerations of purpose, perspective, power, preparation, and possibility. Framed in this way, TfS enables AI systems to interpret simulation artefacts in relation to declared intent, sociotechnical context, and ethical boundaries. We further describe an Intentionality–Simulation–Intelligence triad and a continuous learning loop that align human values, simulation structure, and AI reasoning. The findings of this review suggest that an important challenge in applying AI to healthcare simulation may be ontological as well as technical, and that explicit representation of intention and context is necessary to support coherent, context-sensitive, and system-aligned AI reasoning in healthcare. Full article

The mechanical behavior of a composite is determined by the value of the engineering constants for the composite under consideration. If we study a homogeneous and isotropic composite, then two engineering elastic constants are needed to characterize the material; if we refer to a transversely isotropic composite, five elastic constants are needed. For more complex materials it can be necessary to determine more elastic constants in order to obtain the behavior of the composite in practical applications. In this paper, the authors present the main classic methods for calculating the engineering constants of a fiber composite material that are used in parallel with the finite element method (FEM) and highlight the advantages (and disadvantages) of using direct FEM to achieve this. The arrangement of identical fibers provides regularities that allow for easier calculations and, in some cases, the application of simple methods. The results that have already become classics, current results, and unusual examples are all critically presented in this study. All of these findings are discussed in relation to the use of the FEM, either as the primary calculation method or as a useful aid in the application of classical methods. The paper focuses on presenting research on the use of FEM for this purpose. For the different approaches discussed and for the area overall, future research directions are emphasized. Full article

Optimizing the injection molding of short-fiber-reinforced thermoplastics (SFRTs) is a persistent challenge due to the complex interplay between processing parameters and final mechanical performance. To address this, we developed and validated a machine learning (ML) pipeline to maximize both the tensile strength and Charpy impact resistance in polyamide 6 with 30% glass fiber (PA6-GF30). Through a designed experimental campaign, we systematically varied four key process parameters—melt temperature (260–300 °C), injection pressure (600–1000 bar), packing pressure (400–800 bar), and cooling time (15–35 s). The resulting dataset was used to train and compare three different regression models: Random Forest (RF), Gradient Boosting (GB), and Support Vector Regression (SVR). Our findings indicate that the Gradient Boosting (GB) algorithm yielded the most reliable predictions, significantly outperforming the other evaluated models. Further analysis using SHAP (Shapley Additive exPlanations) identified packing pressure as the dominant factor influencing tensile strength (contributing approximately 40% to the prediction), while melt temperature emerged as the key driver for impact resistance (around 35% contribution). By integrating our best-performing GB model with a multi-objective genetic algorithm, we identified an optimal set of parameters that simultaneously enhances both mechanical properties. Among the evaluated models (Random Forest, Support Vector Regression, and Gradient Boosting), the Gradient Boosting algorithm achieved the highest predictive accuracy. Compared to the baseline condition (280 °C melt temperature, 800 bar injection pressure, 600 bar packing pressure, 25 s cooling time), experimental validation of these optimized settings demonstrated substantial improvement: tensile strength increased from 145 MPa to 171 MPa (an 18% enhancement), and impact resistance rose from 45 kJ/m² to 55 kJ/m² (a 22% gain). This work establishes that an integrated ML and optimization framework can serve as a transformative approach for high-precision manufacturing of advanced engineering polymers. The primary novelty of this work lies in the development of a fully integrated, bias-free methodological framework that explicitly couples physical interpretability with multi-objective optimization, bridging the critical gap between black-box predictions and actionable industrial insights. Full article

Background/Objectives: Osteonecrosis of the femoral head is a progressive disease that frequently leads to femoral head collapse and secondary osteoarthritis. Although total hip arthroplasty provides reliable outcomes, its use in younger patients is limited due to concerns regarding implant longevity. Joint-preserving procedures such as core decompression have been widely used; however, their efficacy remains controversial. This study aims to evaluate a combined approach using arthroscopy-assisted core decompression and an osteoconductive bone substitute. Methods: This study is designed as a single-center, externally controlled trial conducted at Tohoku University Hospital. Patients with osteonecrosis of the femoral head (Japanese Investigation Committee Stage 1–3B, Type B–C2) will undergo arthroscopy-assisted core decompression combined with octacalcium phosphate/gelatin composite implantation. A total of 25 patients will be prospectively enrolled. Outcomes will be compared with a propensity score-matched historical control cohort. The primary outcome is disease progression within 1 year, defined as radiographic progression or conversion to total hip arthroplasty. Secondary outcomes include radiographic changes, clinical outcomes, and bone remodeling assessed by computed tomography. Expected Results: This study is expected to provide preliminary clinical evidence regarding the feasibility and potential effectiveness of arthroscopy-assisted core decompression combined with octacalcium phosphate/gelatin composite implantation for osteonecrosis of the femoral head. The intervention may promote bone remodeling and contribute to the prevention of femoral head collapse. Conclusions: The findings of this study may contribute to the development of improved minimally invasive joint-preserving treatment strategies for osteonecrosis of the femoral head and provide a basis for future large-scale clinical trials. Full article

As the complexity, scale, and nonlinearity of modern engineering optimization problems continue to increase, traditional optimization algorithms face significant challenges in achieving high solution accuracy, fast convergence, and robust performance. To address these issues, this paper proposes a Rural Tourism Migration-based Improved Equilibrium Optimizer (RTM-IEO), aiming to enhance the global search capability and adaptive balance between exploration and exploitation. Specifically, an adaptive lens imaging opposition-based learning strategy is introduced to effectively expand the search space and maintain population diversity. A dynamic elite-guided elimination mechanism is designed to strengthen exploitation capability and accelerate convergence by reconstructing inferior individuals using high-quality solutions. In addition, a multi-stage rural tourism migration strategy is developed to dynamically regulate the search behavior across different optimization phases, enabling a more flexible and efficient search process. The effectiveness of the proposed algorithm is comprehensively validated on the CEC2021 and CEC2022 benchmark suites, where RTM-IEO demonstrates superior performance in terms of convergence accuracy, convergence speed, and robustness compared with several representative state-of-the-art algorithms. The statistical superiority of the proposed method is further confirmed through Friedman mean ranking and Wilcoxon rank-sum tests. To further evaluate its practical applicability, RTM-IEO is applied to the sustainable economic dispatch problem of a microgrid integrating renewable energy sources, including wind power and photovoltaic generation, along with energy storage systems and controllable units. The optimization objective simultaneously considers economic cost minimization and sustainable operation requirements, such as improving renewable energy utilization and reducing dependence on fossil-fuel-based generation. Experimental results indicate that the proposed method achieves a significant reduction in daily operating cost (exceeding 52% compared with benchmark algorithms), while effectively promoting low-carbon energy utilization and enhancing overall system sustainability. Overall, the proposed RTM-IEO provides an efficient and reliable optimization framework for addressing complex global optimization problems, particularly in scenarios requiring a coordinated balance between economic performance and sustainable development. Full article

Background/Objectives: The accuracy and reliability of automated clinical analyzers are fundamental to patient safety and effective medical decision-making. Traditional quality control (QC) methods, which rely on periodic manual calibration and reactive maintenance, are inherently limited by high latency and labor costs; furthermore, they fail to provide continuous, real-time monitoring. This paper introduces a novel hybrid ensemble learning framework for the automated quality inspection of medical devices through the analysis of time-series reaction curves. Methods: Our system integrates three heterogeneous anomaly detection paradigms: an Enhanced Dynamic Time Warping (DTW) detector for robust non-linear pattern matching, a Shape Template Matching (STM) detector that mimics expert clinical logic by analyzing morphological features in a normalized shape space, and a specialized Time-series Variational Autoencoder (TimeVAE) for deep representation learning. The outputs of these detectors are fused using a weighted ensemble strategy, which is specifically designed to prioritize the minimization of false negatives—a critical requirement in medical diagnostics. Results: We evaluate our framework on a comprehensive, multi-center real-world dataset comprising seven distinct biochemical assays. Experimental results demonstrate that our proposed method achieves superior performance, attaining a $0\%$ false negative rate on CRE and DBIL assays and outperforming all baseline methods on the other five datasets. An ablation study confirms the model’s robustness even with limited training data, and a comparative analysis against eight state-of-the-art baseline methods further validates the effectiveness of our domain-optimized ensemble approach. Conclusions: The system provides a robust, interpretable, and highly automated solution for transitioning from reactive maintenance to proactive, real-time quality assurance in clinical laboratories. Full article

Background/Objectives: The aim of current study was the significant improvement of both the flowability and the compressibility of mesoporous silica microparticles (MSMs), to enable the formulation a potential drug delivery system. MSMs are of emerging interest in the pharmaceutical industry, due to their numerous advantages and versatile applicability, such as improvement in aqueous solubility and epithelial permeability, thus enhancing the oral bioavailability of drugs. However, the formulation of these types of materials has been a major challenge. This problem originates from poor powder flow characteristics due to particle properties. Methods: A binder-free high-shear wet granulation (HSWG) process was performed to improve the flowability and compressibility of the model material, meanwhile preserving its porosity. The prepared granules were characterized by particle size, size distribution, yield percentage, particle morphology, porosity, powder flowability, crushing strength, and stability. Micro-CT measurements were performed to examine the structure of the granules and to see the internal segmentation resulted by the two-step granulation process. The granules were compressed into tablets to evaluate the compressibility behavior based on the models of Kawakita and Walker. The physical parameters of the compressed tablets, such as breaking hardness, tensile strength, and thickness, were tested. Results: The prepared granules were evaluated successfully according to the mentioned properties and found to be satisfactory compared to the raw materials. The binder-free method appeared to be effective, thus the use of binders may be avoided if the process is designed well and critical process parameters (CPPs) selected carefully. The granules showed good stability over a one-year testing period. The micro-CT test also verified the success of the initial concept of preparing core–shell structured granules, and enabled the determination of macropores. Nevertheless, the results were completed with BET measurements to determine specific surface area of the granules. Conclusions: The effect of the critical process parameters of the granulation process on all the mentioned attributes was investigated and since major differences were observed between the batches, the effect of the selected CPPs were also verified. Full article