Search Results (193)

The evolution of 193 nm deep-ultraviolet (DUV) lithography toward high numerical aperture (NA > 1.35) presents challenges approaching physical limits for antireflective (AR) coatings on strongly curved lens elements. In this study, a full-stack multi-objective optimization framework is developed by coupling the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the Transfer Matrix Method (TMM) to optimize a 7-layer LaF₃/MgF₂ system on strongly curved substrates ($R=150$ mm). The model integrates material dispersion, thermo-optic effects, deposition flux deviations, and manufacturing thickness constraints. Following 1500 generations of optimization and TOPSIS-based decision-making, the selected Pareto optimal solution achieves a full-aperture average reflectance of 1.3633% and a radial uniformity of 9.5037%. The design further exhibits high environmental robustness with a thermal drift of 0.0019% and a residual stress of 39.23 MPa. These results demonstrate that the proposed method overcomes the critical process bottleneck of achieving full-aperture uniformity below 10% on strongly curved optics. This framework provides a general paradigm for the robust design of next-generation ultra-precision DUV optical systems, effectively balancing theoretical depth with engineering feasibility. Full article

Aortic stenosis is predominantly treated through transcatheter bioprosthetic heart valve implantation. However, the materials used in these devices are prone to premature failure. Polymer heart valves provide an alternative to current commercial devices, offering materials with greater durability and customisation through fibre reinforcement. Given the wide range of available materials and structures, there is a need for a systematic and efficient approach to designing and optimising novel bioinspired polymeric leaflets. This work presents a framework that employs computational modelling and Design of Experiments (DOE) tools to optimise bioinspired, 3D-printed, fibre-reinforced polymer leaflets made using melt electrowriting (MEW). Here, finite element (FE) models are created to represent MEW fibre-reinforced polymer leaflets for application in a transcatheter aortic heart valve. The behaviour of this valve under physiological loading conditions is modelled to predict valve performance and leaflet material response. These models were first used to investigate the impact of fibre orientation on valve performance and leaflet response, thereby demonstrating the benefits of a bioinspired fibre reinforcement structure. Using a DOE approach, the structural combination of MEW fibre reinforcement and an elastomeric matrix was optimised to improve valve performance and reduce leaflet stress and strain. Overall, the framework offers an efficient and versatile methodology for optimising fibre-reinforced polymer leaflets using an in silico approach, thereby reducing the need for physical prototyping and testing of these next-generation devices during early product development. Full article

The parameter space in a regression model for multinomial time series data contains the regression parameters those explain the effects of the time dependent covariates, and the dynamic dependence or category transition parameters those explain the influence of the past responses on the multinomial response at a given time. The estimation of the regression parameters can be negatively affected when higher dimension of the parameter space is considered specially for the transition parameters. In this paper we propose a parameter dimension-split approach where a conditional generalized quasi-likelihood (CGQL) estimating function is first developed for the dynamic dependence parameters in terms of unknown regression parameters which is exploited in the next step to develop an observed information matrix based maximum likelihood (ML) estimating equation for the main regression parameters. More specifically, this split approach helps to write the actual joint likelihood function of regression and dynamic dependence parameters as a likelihood function of regression parameters only by replacing the dynamic dependence parameters with their CGQL estimates obtained in the first step. As the time series length is generally large in practice, we have made sure that the proposed CGQL and ML estimators are asymptotically reliable, that is consistent for the respective parameters. Full article

Phase-inversion in situ implants (PIISIs) represent a versatile polymer platform in which the rational choice of matrix former and solvent system directly governs the macroscopic properties of the resulting depot. This study applied a Quality by Design (QbD) approach to rationalize a bleached shellac–based PIISI, with particular focus on the physicochemical interactions between the polymer and the injection vehicle. Bleached shellac—a natural, low-cost, biodegradable oligomeric resin bearing –COOH, –OH, and ester functional groups—was selected as the matrix former and screened in seven neat solvents and five 1:1 binary combinations at 25% (m/m). Twelve formulations were evaluated against a predefined set of critical quality attributes, including injectability, phase-inversion kinetics, solvent diffusion volume, and implant structure (n = 5 per formulation; mean ± standard deviation (SD); one-way analysis of variance (ANOVA) with Tukey’s post hoc test, p < 0.05). Three lead solvent systems—propylene glycol/N-methylpyrrolidone (PG+NMP), PG/dimethyl sulfoxide (PG+DMSO), and DMSO/benzyl alcohol (DMSO+BA)—were identified as those providing an optimal balance between hydrogen-bond donor/acceptor solvation and controlled solvent extraction. In the second stage, shellac concentration (20–35%) was optimized, with 30% shellac in PG+NMP yielding the fastest phase inversion (~50 s), a structurally uniform matrix, and the lowest swelling (22%). A working mechanistic framework consistent with all observed critical quality attribute (CQA) trends in which solvent hydrogen-bond donor/acceptor balance and water miscibility govern implant architecture is proposed, and it is intended as a hypothesis-generating basis for the rational design of PIISI formulations; direct validation by spectroscopic, thermal-analytical, and biological methods is identified as the next step. The developed formulations are presented as a preliminary physicochemical platform; biological validation (in vitro cytocompatibility and inflammatory response assessment) is required before the system can be considered a validated formulation for dental drug delivery. Full article

In distributed renewable energy systems, load fluctuations caused by energy resources and energy storage increase the overload risk of distribution transformers, which may accelerate insulation aging and cause overheating, and undermine operational reliability. For transformer condition monitoring, this risk is reflected not by a single variable but by heterogeneous sensing observations acquired from electrical, thermal, and equipment status monitoring channels. Because full-scale inspection of latent defects is impractical under limited staffing and equipment resources, accurate overload risk prediction is important for sensor-driven maintenance allocation. With such motivations, this paper proposes a Transformer Overload Risk Assessment (TORA) approach for robust overload risk prediction under nonstationary load conditions. First, a feature matrix is constructed by jointly incorporating static features that capture long-term drift and dynamic features extracted from multisource sensing and supervisory signals that reflect short-term fluctuations. Then, static and dynamic features are assessed with Edge-based Static Feature Risk Assessment (E-SFRA) model and Cloud-based Dynamic Feature Risk Assessment (C-DFRA) model, respectively, according to their temporal and statistical characteristics. Next, a periodic calibration model (CE-PAA) is established through a cloud–edge loop, which uses low-latency edge updates and high-capacity cloud computation as feedback. Finally, risk score fusion (RSF) fuses generated static and dynamic risk scores to integrate cloud and edge strengths. The case study results indicate that TORA can transform heterogeneous monitoring signals into calibrated risk information in the studied single power plant scenario, providing useful support for multisource sensor data fusion, transformer condition monitoring, and maintenance decision making. Further validation using multi source field datasets is still needed to assess its cross scenario generalization ability. Full article

The development of stable and efficient essential oil delivery systems remains a persistent challenge in active food packaging applications. This research aimed to develop a multi-functional soy protein isolate (SPI)-based composite gel film integrating a tea tree oil micro emulsion (TME) via a microemulsion-in-gel approach, featuring sustained antioxidant release. The TME was first optimized using pseudo-ternary phase diagrams and exhibited excellent physicochemical stability. It maintained a droplet size ranging from 10 to 13 nm, with a polydispersity index (PDI) less than 0.2 under diverse stress situations (such as dilution, heat treatment, pH change, centrifugation, and 30-day storage). Afterward, TME-SPI composite gel films containing 1 to 3% TME were fabricated through solution casting and subsequent gelation of the protein matrix. The incorporation of TME markedly improved the properties of the gel film network. It raised the opacity by around 2.5 times, boosted the elongation at break to 144% (which is three times that of the control), and distinctively enhanced both water solubility and the water vapor barrier. Importantly, the 2% TME-SPI gel film exhibited sustained antioxidant activity from within the gel matrix, retaining more than 50% of its original 1,1-diphenyl-2-picrylhydrazyl (DPPH) scavenging activity after 72 h, significantly outperforming films containing free TTO. The microemulsion-in-gel approach was shown to be effective in creating SPI-based gel films that possess combined light-barrier characteristics, adjustable moisture resistance, improved flexibility, and extended antioxidant release. This offers a promising framework for the next generation of active food packaging. Furthermore, the composite gel films exhibited concentration-dependent antibacterial activity against Staphylococcus aureus, with the 3% TME-SPI film achieving an 82% inhibition rate, thus experimentally validating its active packaging potential. Full article

This study examines how urban transportation systems influence the spatial spread of infectious diseases by developing a modified Susceptible–Exposed–Infected–Recovered (SEIR) model with explicit intercity travel dynamics. The model distinguishes between two mobility mechanisms: travel volume, represented by the departure rate g, and travel speed, represented by the arrival rate $\alpha$. Using the next-generation matrix (NGM) approach, we derive the basic reproduction number $R_0$ and analyse how within-city and transit-phase transmission contribute to epidemic spread. The results show that travel volume and travel speed affect mobility-driven transmission through distinct mechanisms. Increasing g increases the number of travelers entering the transit system and therefore amplifies the aggregate number of transit-mediated infections, although the per-capita transit reproduction expression is governed primarily by $\alpha$ and ${\beta }_d^T$ under the reduced next generation matrix formulation formulation. By contrast, increasing $\alpha$ shortens the time spent in transit, reduces the exposure window during travel, and lowers the per-capita contribution of transit-based infection to $R_0$. Numerical simulations illustrate these effects and support the conclusion that reducing travel volume can mitigate intercity epidemic spread by decreasing the number of potentially exposed travelers. Comparative case studies for Brazil, New Zealand, China, and Algeria are used to evaluate the model under different epidemiological settings and socioeconomic contexts. These socioeconomic indicators are treated as contextual background rather than as direct inputs to the mathematical model. The qualitative predictions of the ordinary differential equation (ODE) model are further cross-validated using an agent-based simulation implemented in NetLogo. Overall, the study shows that separating travel volume from travel speed provides a more precise understanding of mobility-driven disease transmission and can support the design of targeted travel-related control measures. Full article

Emerging contaminants (ECs) and microplastics (MPs) are increasingly detected in surface waters, wastewaters, and drinking water, often as complex mixtures, transformation products, and particle-associated burdens that challenge routine monitoring. This critical review examines current analytical strategies for the detection and characterization of both molecular and particulate emerging contaminants in aquatic systems, with particular emphasis on their relevance to environmental and human health risk assessment. For molecular ECs, targeted LC–MS/MS and GC–MS and GC–MS/MS approaches are evaluated alongside high-resolution mass spectrometry (HRMS)-based suspect and non-target screening, retrospective data mining, and transformation-product elucidation. For MPs, particle-resolved vibrational spectroscopy including µ-FTIR and µ-Raman is critically assessed in comparison with complementary thermal analysis methods, such as pyrolysis–GC–MS and thermal extraction–desorption GC–MS (TED–GC–MS). Particular attention is given to the influence of sampling design, matrix-adapted sample preparation, analytical confidence, and method-dependent size and polymer coverage on data quality and interstudy comparability. The review further highlights the risks of ECs in relation to exposure pathways, mixture effects, and the potential carrier role of MPs for ECs, additives, and microorganisms. Finally, key priorities are identified for next-generation monitoring frameworks, including harmonized workflows, transparent confidence reporting, and stronger integration of analytical evidence with fate, exposure, and risk assessment. Full article

Hyaluronic acid (HA), a native non-sulfated glycosaminoglycan of the extracellular matrix, has emerged as a central biomaterial in tissue engineering due to its biocompatibility, hydration capacity, and receptor-mediated bioactivity. Beyond its structural role, HA actively regulates cellular behaviors through interactions with receptors such as CD44 and RHAMM, with outcomes highly dependent on molecular weight, degradation state, and matrix context. Recent advances in chemical modification and crosslinking strategies have enabled the development of HA-based hydrogels, nanofibers, and composite systems with tunable mechanics and degradation profiles, supporting applications in bone, cartilage, vascular, and skin regeneration, as well as in emerging platforms such as 3D bioprinting and nanomedicine. However, inconsistent biological responses and limited clinical translation remain key challenges. This review integrates current understanding of HA synthesis, physicochemical properties, degradation, and receptor-mediated signaling, and establishes a mechanistic framework linking molecular characteristics, matrix mechanics, and cell responses. Building on this framework, we outline design strategies for multifunctional HA composites, advanced biofabrication approaches, and receptor-targeted systems, providing a basis for the rational engineering of next-generation HA-based biomaterials with improved translational potential. Full article

Hydrogel scaffolds have emerged as a central platform in tissue engineering due to their ability to mimic the extracellular matrix and support cellular functions. Among natural polymers, chitin and its derivative chitosan have emerged as valuable candidates for hydrogel scaffold development because of their biodegradability, compatibility with living tissues, and inherent biological functionality; however, their distinct and complementary roles in hydrogel scaffold design are often insufficiently differentiated in the literature. This review provides a comprehensive and mechanism-driven analysis of chitin- and chitosan-based hydrogel scaffolds, emphasising how their molecular structure governs network formation, mechanical performance, and biological functionality. Chitin is highlighted primarily as a structurally robust and crystalline component suitable for reinforcement. In contrast, chitosan serves as a versatile, soluble, and chemically reactive matrix enabling various crosslinking and functionalization strategies. Recent advances in physical, ionic, and covalent crosslinking as well as composite scaffold engineering, biofunctionalization, and emerging fabrication approaches such as injectable systems and three-dimensional bioprinting are systematically examined. The relationships between scaffold architecture, degradation behaviour, and cellular responses are discussed in key tissue engineering applications, including bone, cartilage, skin, and nerve regeneration. Importantly, this review introduces a unified structure–property–function framework that distinguishes the roles of chitin and chitosan within hydrogel systems and links crosslinking mechanisms to application-specific performance requirements, an aspect not comprehensively addressed in previous studies. Current challenges related to mechanical limitations, material variability, and clinical translation are critically evaluated, and future perspectives for the rational design of next-generation biomimetic hydrogel scaffolds are proposed. Full article

Gastric cancer (GC) is one of the most aggressive malignancies with a dismal prognosis, late diagnosis, and limited therapy efficacy. Biologically, GC is associated with multiple barriers to therapeutic response including gastric mucosal layer, acidic tumor microenvironment (TME), high accumulation of extracellular matrix (ECM) components, and limited penetration depth of anticancer drugs into tumor tissue. Furthermore, inherent or acquired drug resistance associated with drug efflux transporters, deregulated autophagy, tumor heterogeneity, and cell survival pathways severely compromise treatment response. Nanotechnology has been widely used to develop next-generation nanotherapeutic delivery systems to overcome these biological barriers. Currently available nanoplatforms such as liposomes, polymeric nanoparticles, dendrimers, and inorganic nanocarriers have improved drug loading capacity, aqueous solubility, circulation time stability, tumor-targeted delivery, and sustained release of chemotherapeutics. Smart and stimuli-responsive nanocarriers can also take advantage of pathological hallmarks of tumors including low pH, redox potential, and overexpressed enzymes for enhanced selective delivery to the tumor site. Nanotherapeutics have also shown promise for co-delivery of multiple therapeutic agents to overcome drug resistance, manipulation of TME, and suppression of autophagy and apoptosis signaling pathways associated with drug resistance. This review discusses recent advances in nanotherapeutics for GC including approaches to overcome biological barriers and drug resistance and highlights translational gaps for clinical development. Full article

Human neutrophil elastase (HNE) is a key driver of inflammatory lung disorders, promoting extracellular matrix degradation and tissue damage. Although inhibitors such as Sivelestat and Alvelestat are clinically relevant, their performance within the oxidative microenvironment of diseased lungs remains poorly understood. Here, we employed an integrated in vitro and in silico approach to investigate their behavior under physiological and oxidative conditions and to provide a molecular-level interpretation. Under physiological conditions, enzymatic assays and steady-state kinetics confirmed that both compounds act as competitive inhibitors, with Sivelestat displaying higher baseline potency. Under oxidative stress, however, Sivelestat exhibited a marked reduction in inhibitory potency, whereas Alvelestat retained its efficacy. Molecular modeling and molecular dynamics simulations of native and oxidized HNE variants provided a structural rationale for this divergence. Alvelestat, as a non-covalent inhibitor, maintains stable binding despite increased flexibility of the active site, whereas Sivelestat, acting via a reversible covalent mechanism, requires a precise pre-acylation geometry. Oxidation-induced remodeling of the S1 pocket disrupts the near-attack configuration required for covalent bond formation, thereby impairing inhibition. Overall, these findings indicate that oxidative stress may selectively compromise covalent inhibition while preserving enzymatic activity, and suggest that context-dependent redox-related structural effects may represent a consideration for the design of next-generation HNE inhibitors. Full article

In this work, sodium alginate (NaAlg) membranes were enhanced with synthesized zinc oxide (ZnO) nanoplates to enable efficient pervaporation dehydration of isopropyl alcohol (IPA). A comprehensive suite of characterisation techniques—scanning electron (SEM) and atomic force (AFM) microscopy, Fourier-transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), contact angle and liquid uptake measurements—along with density functional theory (DFT) calculations, was employed to establish robust structure–property relationships and to elucidate filler–polymer interactions. Membranes with different ZnO contents were prepared, and membranes based on the optimal NaAlg-ZnO(5%) composite were cross-linked with CaCl₂ to improve stability in aqueous solutions, and supported membranes were developed for prospective applications by applying this composite onto the prepared porous cellulose acetate (CA) substrate. This developed cross-linked supported NaAlg-ZnO(5%)/CA membrane had a permeation flux increased by 2 times or more compared to a dense NaAlg membrane during dehydration of IPA (12–30 wt.% water) with a permeate water content above 99 wt.%. The integrated experimental–theoretical approach provides mechanistic insight into ZnO–NaAlg interactions and demonstrates the strong potential of these mixed matrix membranes for high-efficiency alcohol dehydration, offering a rational design paradigm for next-generation pervaporation membranes. Full article

Bacterial biofilms are structured microbial communities enclosed within a self-produced extracellular polymeric substance matrix composed of polysaccharides, proteins, extracellular DNA, and lipids. This matrix promotes adhesion, structural stability, and the development of heterogeneous microenvironments that restrict antimicrobial penetration and shield bacteria from host immune responses. As a result, biofilms are major contributors to chronic, recurrent, device-related, and difficult-to-treat infections, posing a major challenge for clinical management and antimicrobial stewardship. This review summarizes current understandings of biofilm biology, its clinical relevance, including the stages of biofilm development, the composition and protective roles of the matrix, and the physiological heterogeneity that arises during maturation. It also examines key mechanisms underlying biofilm tolerance and resistance, such as limited antibiotic diffusion, and sequestration, enzymatic inactivation, efflux pump upregulation, persister cell formation, and horizontal gene transfer. In addition, it highlights important clinical settings in which biofilms are implicated, including cystic fibrosis, chronic wounds, osteomyelitis, implant- or device-associated infections, and breast implant illness, in which persistent implant-associated biofilms and the resulting chronic inflammatory milieu have been hypothesized to contribute to local and systemic manifestations in a subset of patients. The review further discusses conventional and emerging approaches for biofilm detection alongwith real-time monitoring. Biofilm-associated infections remain difficult to eradicate because persistence is driven by multiple interconnected protective mechanisms. Effective management therefore requires integrated strategies that combine accurate detection with multifaceted therapies, including antibiotics alongside matrix-disrupting enzymes, quorum-sensing inhibitors, bacteriophages, metabolic reactivators, and nanotechnology-based delivery systems. Advances in multi-omics and system-level modeling will be essential for developing next-generation strategies to prevent, monitor, and treat biofilm-associated disease. Full article

Directly modulated VCSEL transmitters are widely deployed in short-reach optical interconnects. However, further scaling of per-lane symbol rates in AI/HPC data center fabrics requires modulation schemes beyond the practical limits of direct current modulation. We demonstrate a laterally integrated VCSEL–electro-absorption modulator (EAM) transmitter enabled by resonance-detuned coupling on an oxide-confined half-VCSEL platform. A localized 20 nm surface etch produces > 5 nm resonance detuning, confirmed by measured spectra and supported by transfer-matrix and mode-matching simulations, which indicate strong slow-light-assisted lateral coupling into the modulator. Experimentally, the measured spectra confirm a 5 nm resonance separation. Static characterization shows a coupling ratio of 63% extracted from near-field profiles and an extinction ratio of 4 dB (based on modulator-side power) under a −2 V modulator bias, with an apparent 1 mW absorption at a 6 mA VCSEL drive current. Dynamic measurements demonstrate a small-signal 3 dB bandwidth of approximately 23 GHz and clear NRZ eye openings at 25 Gbps and 30 Gbps. These results validate resonance-detuned lateral integration as a compact and manufacturable approach to VCSEL-based externally modulated transmitters for next-generation short-reach interconnects. Full article