Search Results (163)

Microalgae have emerged as sustainable biofactories producing diverse bioactive compounds with significant applications in nutrition and cosmetics. Their high metabolic versatility makes them promising alternatives to conventional resources for addressing global challenges such as malnutrition, food insecurity, and environmental degradation. This review provides an integrated perspective on microalgal bioactives, highlighting their role in functional foods, dietary supplements, and maternal and infant nutrition, as well as their incorporation into cosmetic formulations for anti-aging, photoprotection, hydration, and microbiome support. Mechanistic insights reveal antioxidant, anti-inflammatory, and extracellular matrix-preserving effects, alongside UV absorption and barrier reinforcement. The review also discusses their biochemical diversity, mechanisms of action, safety, regulatory considerations, and emerging technologies for formulation and delivery. AI-driven and machine-learning approaches using microalgae for cosmetic and nutritional applications have also been discussed. Overall, microalgae serve as a cornerstone for next-generation nutraceuticals and cosmeceuticals, aligning with sustainability and circular-economy principles. Full article

To meet the requirements of next-generation spacecraft thermal protection systems for lightweight materials with high strength, effective thermal insulation, and superior ablation resistance, a novel POSS-modified phenolic aerogel/quartz fiber composite (POSS-PR/QF) was developed using a thiol–ene click reaction combined with a sol–gel process. Covalent incorporation of polyhedral oligomeric silsesquioxanes (POSS) into the phenolic matrix effectively eliminates nanoparticle aggregation and improves interfacial compatibility. As a result, the modified resin is suitable for resin transfer molding (RTM) processes. The resulting composite exhibited an aerogel-like porous structure with enhanced crosslinking density, thermal stability, and oxidation resistance. At 7.5 wt% POSS loading, the composite achieved low density (~0.7 g·cm⁻³) and outstanding mechanical properties, with tensile, flexural, compressive, and interlaminar shear strengths increased by 114%, 79%, 29%, and 104%, respectively. Its thermal conductivity (0.0619 W/(m·K)) and ablation rates were also markedly reduced. Mechanistic studies revealed that POSS undergoes in situ ceramification to form SiO₂ and SiC phases, which create a dense protective barrier. In addition, this ceramification process promotes char graphitization, thereby enhancing oxidation resistance and thermal insulation. This work provides a promising approach for designing lightweight, high-performance, and multifunctional thermal protection materials for aerospace applications. Full article

Amid the growing global environmental challenges, precise and efficient vehicle emission management plays a critical role in achieving energy-saving and emission reduction goals. At the same time, the rapid development of connected vehicles and autonomous driving technologies has generated a large amount of high-dimensional vehicle operation data. This not only provides a rich data foundation for refined emission accounting but also raises higher demands for the construction of accounting models. Therefore, this study aims to develop an accurate and efficient emission accounting model to contribute to the precise nitrogen oxide (NO_x) emission accounting for hybrid electric vehicles (HEVs). A systematic approach is proposed that combines incremental dimensionality reduction with advanced regression algorithms to achieve refined and efficient emission accounting based on multiple variables. Specifically, the dimensionality of the real driving emission (RDE) data is first reduced using the feature selection and t-distributed stochastic neighbor embedding (t-SNE) feature extraction method to capture key parameter information and reduce subsequent computational complexity. Next, an incremental dimensionality reduction method based on dictionary learning is employed to efficiently embed new data into a low-dimensional space through straightforward matrix operations. Given the computational cost of the dictionary learning training process, this study introduces the FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) for accelerated iterative optimization and enhances the computational efficiency through parameter optimization, while maintaining the accuracy of dictionary learning. Subsequently, an NO_x emission factor correction factor prediction model is trained using the low-dimensional data obtained from t-SNE embeddings, enabling direct computation of the corresponding correction factor when presented with new incremental low-dimensional embeddings. Finally, validation on independent HEV datasets shows that parameter K improves to 1 ± 0.05 and R² increases up to 0.990, laying a foundation for constructing an emission accounting model with broad applicability based on multiple variables. Full article

Silicon windows in wafer-level packaged LWIR sensors suffer ~30% Fresnel reflection per interface, limiting optical throughput and detector sensitivity. We present an end-to-end design, fabrication, and validation framework for CMOS-compatible moth-eye anti-reflection coatings patterned directly on silicon wafers. Our approach integrates the effective medium theory, a transfer matrix analysis, full-wave FDTD simulations, and experimental Fourier-transform infrared (FTIR) measurements to optimize subwavelength pillar arrays for broadband (8–14 μm) and angle-tolerant performance. Fabricated structures demonstrate a 46.7% responsivity boost in CMOS–SOI–MEMS thermal sensors compared to bare silicon windows, while simulations predict up to 85.1% transmission and 57.1% responsivity enhancement for double-sided patterning. These results establish moth-eye metasurfaces as a scalable, CMOS-compatible solution for next-generation wafer-level processing and packaging infrared sensing platforms, transforming optical improvements into measurable electrical performance gains. The contribution of this work is the end-to-end framework for designing moth-eye wafer level processing and packaging for “real-life” CMOS-compatible infrared sensors manufacturing. Full article

Constructed wetlands (CWs) are signified as green, self-sustaining systems for wastewater treatment. To date, their conventional designs struggle with slow kinetics and poor removal of refractory pollutants. This review redefines CWs as photo-reactive engineered systems, integrating near-neutral Fenton and photo-Fenton processes and in-situ oxidant generation to overcome diffusion limits, acid dosing, and sludge formation. By coupling catalytic fillers, solar utilization, and plant–microbe–radical (ROS) synergies, the approach enables intensified pollutant degradation while preserving the low-energy nature of CWs. Bibliometric trends indicate a sharp rise in studies linking CWs with advanced oxidation and renewable energy integration, confirming the emergence of a circular treatment paradigm. A decision framework is proposed that aligns material selection, reactor hydrodynamics, and solar light management with sustainability indicators such as energy efficiency, Fe-leach budget, and ROS-to-photon yield. This synthesis bridges environmental biotechnology with solar-driven catalysis, paving the way for next-generation eco-engineered wetlands capable of operating efficiently beyond the classical Fenton constraints. This work introduces the concept of “Constructed Wetlands Beyond the Fenton Limit”, where CWs are reimagined as photo-reactive circular systems that unify catalytic, biological, and solar processes under near-neutral conditions. It provides the first integrated decision matrix and performance metrics connecting catalyst design, ROS efficiency, and circular sustainability that offers a scalable blueprint for real-world hybrid wetland applications. Full article

Text-to-image (T2I) generation, a core component of generative artificial intelligence(AI), is increasingly important for creative industries and human–computer interaction. Despite impressive progress in realism and diversity, diffusion models still exhibit critical security blind spots particularly in the Transformer key-value mapping mechanism that underpins cross-modal alignment. Existing backdoor attacks often rely on large-scale data poisoning or extensive fine-tuning, leading to low efficiency and limited stealth. To address these challenges, we propose two efficient backdoor attack methods AttnBackdoor and SemBackdoor grounded in the Transformer’s key-value storage principle. AttnBackdoor injects precise mappings between trigger prompts and target instances by fine-tuning the key-value projection matrices in U-Net cross-attention layers (≈5% of parameters). SemBackdoor establishes semantic-level mappings by editing the text encoder’s MLP projection matrix (≈0.3% of parameters). Both approaches achieve high attack success rates (>90%), with SemBackdoor reaching 98.6% and AttnBackdoor 97.2%. They also reduce parameter updates and training time by 1–2 orders of magnitude compared to prior work while preserving benign generation quality. Our findings reveal dual vulnerabilities at visual and semantic levels and provide a foundation for developing next generation defenses for secure generative AI. Full article

Accurate identification of oil–water two-phase flow patterns is essential for ensuring the safety and operational efficiency of oil and gas extraction systems. While traditional methods using empirical models and sensor technologies have provided basic insights, they often struggle to capture the nonlinear features of complex operational conditions. To address the challenge of data scarcity commonly found in experimental settings, this study employs a data augmentation strategy that combines the Synthetic Minority Over-sampling Technique (SMOTE) with Gaussian noise injection, effectively expanding the feature space from 60 original experimental nodes. Next, a physics-constrained attention mechanism model was developed that incorporates a physical constraint matrix to effectively mask irrelevant feature interactions. Experimental results show that while the standard attention model (83.88%) and the baseline BP neural network (84.25%) have limitations in generalizing to complex regimes, the proposed physics-constrained model achieves a peak test accuracy of 96.62%. Importantly, the model demonstrates exceptional robustness in identifying complex transition regions—specifically Dispersed Oil-in-Water (DO/W) flows—where it improved recall rates by about 24.6% compared to baselines. Additionally, visualization of attention scores confirms that the distribution of attention weights aligns closely with fluid-dynamic mechanisms—favoring inclination for stratified flows and flow rate for turbulence-dominated dispersions—thus validating the model’s interpretability. This research offers a novel, interpretable approach for modeling dynamic feature interactions in multiphase flows and provides valuable insights for intelligent oilfield development. Full article

As edge computing becomes increasingly central to modern digital infrastructure, it also creates opportunities for sophisticated malware attacks that traditional security systems struggle to address. This study proposes a natural language processing (NLP) framework integrated with ensemble learning into next-generation firewalls (NGFWs) to detect and mitigate malware attacks in edge computing environments. The approach leverages unstructured threat intelligence (e.g., cybersecurity reports, logs) by applying NLP techniques, such as TF-IDF vectorization, to convert textual data into structured insights. This process uncovers hidden patterns and entity relationships within system logs. By combining Random Forest (RF) and Logistic Regression (LR) in a soft voting ensemble, the proposed model achieves 95% accuracy on a cyber threat intelligence dataset augmented with synthetic data to address class imbalance, and 98% accuracy on the CSE-CIC-IDS2018 dataset. The study was validated using ANOVA to assess statistical robustness and confusion matrix analysis, both of which confirmed low error rates. The system enhances detection rates and adaptability, providing a scalable defense layer optimized for resource-constrained, latency-sensitive edge environments. Full article

Atherosclerosis is a chronic, multifactorial vascular disease and the leading global cause of cardiovascular morbidity. Its development reflects interconnected disturbances in lipid metabolism, endothelial function, inflammation, smooth muscle cell (SMC) phenotypic switching, and extracellular matrix remodeling. Genetic predisposition, including monogenic disorders such as familial hypercholesterolemia and polygenic risk variants, modulates disease susceptibility by altering lipid homeostasis as well as inflammatory and thrombotic pathways. Epigenetic regulators and noncoding RNAs, such as histone modifications, microRNAs, and long noncoding RNAs, further shape gene expression and link environmental cues to vascular pathology. Endothelial injury promotes lipoprotein retention and oxidation, triggering monocyte recruitment and macrophage-driven foam cell formation, cytokine secretion, and necrotic core development. Persistent inflammation, macrophage heterogeneity, and SMC plasticity collectively drive plaque growth and destabilization. Emerging insights into immune cell metabolism, intracellular signaling networks, and novel regulatory RNAs are expanding therapeutic possibilities beyond lipid-lowering. Current and evolving treatments include statins, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, anti-inflammatory agents targeting interleukin-1 beta (IL-1β) or NOD-, LRR-, and pyrin domain-containing protein 3 (NLRP3), and advanced approaches such as gene editing, siRNA, and nanoparticle-based delivery. Integrating multi-omics, biomarker-guided therapy, and precision medicine promises improved risk stratification and next-generation targeted interventions. This review summarizes recent molecular advances and highlights translational opportunities for enhancing atherosclerosis prevention and treatment. Full article

With the proliferation of mobile internet and location-based services, location-based social networks (LBSNs) have accumulated extensive user check-in data, driving the advancement of next Point-of-Interest (POI) recommendation systems. Although existing approaches can model sequential dependencies and spatio-temporal patterns, they often fail to fully capture users’ dynamic preferences under varying spatio-temporal contexts and lack effective integration of fine-grained semantic information. To address these limitations, this paper proposes Language-Guided Spatio-Temporal Context Learning for Next POI Recommendation (LSCNP). It employs a pre-trained BERT model to encode multi-dimensional spatio-temporal context—including geographic coordinates, visiting hours, and surrounding POI categories—into structured textual sequences for semantic understanding; constructs dual-graph structures to model spatial constraints and user transition patterns; and introduces a contrastive learning module to align spatio-temporal context with POI features, enhancing the discriminability of representations. A Transformer-based sequential encoder is adopted to capture long-range dependencies, while a neural matrix factorization decoder generates final recommendations. Experiments on three real-world LBSN datasets demonstrate that LSCNP consistently outperforms state-of-the-art baselines. Ablation studies and hyperparameter analyses further validate the contribution of each component to the overall performance. Full article

Chronic and complex wounds, including diabetic foot ulcers, venous leg ulcers, burns and post-surgical defects, remain difficult to manage due to persistent inflammation, impaired angiogenesis, microbial colonization and insufficient extracellular matrix (ECM) remodeling. Conventional dressings provide protection, but they do not supply the necessary biochemical and structural signals for effective tissue repair. This review examines recent advances in hydroxyapatite–collagen (HAp–Col) composite dressings, which combine the architecture of collagen with the mechanical reinforcement and ionic bioactivity of hydroxyapatite. Analysis of the literature indicates that in situ and biomimetic mineralization, freeze-drying, electrospinning, hydrogel and film processing, and emerging 3D printing approaches enable precise control of pore structure, mineral dispersion, and degradation behavior. Antimicrobial functionalization remains critical: metallic ions and locally delivered antibiotics offer robust early antibacterial activity, while plant-derived essential oils (EOs) provide broad-spectrum antimicrobial, antioxidant and anti-inflammatory effects with reduced risk of resistance. Preclinical studies consistently report enhanced epithelialization, improved collagen deposition and reduced bacterial burden in HAp–Col systems; however, translation is limited by formulation variability, sterilization sensitivity and the lack of standardized clinical trials. Overall, HAp–Col composites represent a versatile framework for next-generation wound dressings that can address both regenerative and antimicrobial requirements. Full article

Diabetic wounds remain chronically non-healing due to impaired angiogenesis, persistent inflammation, and defective extracellular matrix remodelling. In recent years, stem cell-derived exosomes have emerged as a potent cell-free regenerative strategy capable of recapitulating the therapeutic benefits of mesenchymal stem cells while avoiding risks associated with direct cell transplantation. This review critically evaluates the preclinical evidence supporting the use of exosomes derived from adipose tissue, bone marrow, umbilical cord, and induced pluripotent stem cells for diabetic wound repair. These exosomes deliver bioactive cargos such as microRNAs, proteins, lipids, and cytokines that modulate key signalling pathways, including Phosphatidylinositol 3-kinase/Protein kinase (PI3K/Akt), Nuclear factor kappa B (NF-κB), Mitogen-activated protein kinase (MAPK), Transforming growth factor-beta (TGF-β/Smad), and Hypoxia inducible factor-1α/Vascular endothelial growth factor (HIF-1α/VEGF), thereby promoting angiogenesis, accelerating fibroblast and keratinocyte proliferation, facilitating re-epithelialization, and restoring immune balance through M2 macrophage polarization. A central focus of this review is the recent advances in exosome-based delivery systems, including hydrogels, microneedles, 3D scaffolds, and decellularized extracellular matrix composites, which significantly enhance exosome stability, retention, and targeted release at wound sites. Comparative insights between stem cell therapy and exosome therapy highlight the superior safety, scalability, and regulatory advantages of exosome-based approaches. We also summarize progress in exosome engineering, manufacturing, quality control, and ongoing clinical investigations, along with challenges related to standardization, dosage, and translational readiness. Collectively, this review provides a comprehensive mechanistic and translational framework that positions stem cell-derived exosomes as a next-generation, cell-free regenerative strategy with the potential to overcome current therapeutic limitations and redefine clinical management of diabetic wound healing. Full article

Poly(vinylidene fluoride) (PVDF) nanofibers have emerged as promising materials for flexible piezoelectric sensors, yet their performance is fundamentally constrained by the limited formation and alignment of the electroactive β-phase. In this study, we report a phase-engineering strategy that integrates ionic functionalization, inorganic nanofiller incorporation, and post-fabrication corona poling to achieve enhanced crystalline ordering and electromechanical coupling in electrospun PVDF nanofibers. Tetrabutylammonium perchlorate increases solution conductivity, enabling uniform, bead-free fiber formation, while barium titanate nanoparticles act as nucleation centers that promote β-phase crystallization at the expense of the non-polar α-phase. Subsequent corona poling further aligns molecular dipoles and strengthens remnant polarization within both the PVDF matrix and embedded nanoparticles. Structural analyses confirm the synergistic evolution of crystalline phases, and piezoelectric measurements demonstrate a substantial increase in peak-to-peak output voltage under dynamic loading conditions. This combined phase-engineering approach provides a simple and scalable route to high-performance PVDF-based piezoelectric sensors and highlights the importance of coupling crystallization control with dipole alignment in designing next-generation wearable electromechanical materials. Full article

Metalloproteinases (MPs) are zinc-dependent endopeptidases, including matrix metalloproteinases (MMPs) and a disintegrin and metalloproteinases (ADAMs), implicated in various diseases such as cancer, neurodegenerative disorders, and cardiovascular conditions. Among MPs, ADAM-17, also known as tumor necrosis factor-α (TNF-α)-converting enzyme (TACE), plays a crucial role in extracellular matrix remodeling and cytokine release. Dysregulation of ADAM-17 contributes to inflammatory diseases, cancer progression, and immune modulation. While small-molecule inhibitors have been limited by off-target effects and instability, antibody-based approaches offer a more selective strategy. Monoclonal antibodies show promise in blocking ADAM-17 activity, but there are concerns about toxicity due to the lack of selectivity. Enhancing the binding affinity and selectivity of single-chain antibodies requires unraveling the structural details that drive MP targeting. This study uses yeast surface display (YSD) and fluorescence-activated cell sorting (FACS) to engineer single-chain variable fragment (scFv) antibodies with optimized complementarity-determining region 3 of the heavy chain (CDR-H3) conformations. Next-generation sequencing (NGS) was used to identify key residues contributing to high-affinity ADAM-17 binding. These findings offer a framework for designing monoclonal antibodies against ADAM-17 and other MPs, paving the way for novel antibody-based designer scaffolds with applications in developing therapeutics. Full article

The search for multifunctional wound dressings that combine structural integrity with biological activity remains an important challenge in modern biomedicine. In this study, electrospun chitosan/polyethylene oxide (CS/PEO) nanofibers incorporating Centella asiatica extract were developed and evaluated in vitro as potential wound-healing materials. Nanofibers were fabricated using various CS/PEO ratios, and the 1:2 w/w composition loaded with 1% extract was selected as the optimal formulation based on morphological homogeneity and processing efficiency. Comprehensive characterization demonstrated that the nanofiber matrix provided sustained release of asiaticosides over several days, fitting best with Hixson–Crowell and Higuchi kinetic models, suggesting a combined diffusion–erosion mechanism. Biological assays confirmed that the optimized formulation displayed strong antioxidant and anti-inflammatory activity, with synergistic effects observed between chitosan and C. asiatica. Moreover, chitosan contributed intrinsic antimicrobial properties against Staphylococcus aureus and Klebsiella pneumoniae, while the extract provided additional antioxidant and regenerative potential. Biocompatibility studies in human fibroblasts showed no cytotoxic effects, and scratch assays confirmed that extract-loaded nanofibers significantly accelerated wound closure compared to the control and CS/PEO base. Taken together, the results highlight the potential of CS/PEO nanofibers with C. asiatica extract as multifunctional wound dressings that integrate structural support, controlled release, antimicrobial protection, and regenerative bioactivity. Future work should address in vivo evaluation, scale-up of electrospinning, and potential incorporation of synergistic antimicrobial agents to further enhance clinical applicability. This approach underlines the value of combining natural product pharmacology with biopolymer engineering in the design of next-generation wound-healing biomaterials. Full article