Search Results (2,392)

The discharge of dye-laden effluents remains an environmental challenge since conventional treatments remove color but not the organic load. This study systematically compared anodic oxidation (AO), electro-Fenton (EF), and photoelectro-Fenton (PEF) processes for three representative industrial dyes, such as Coriasol Red CB, Brown RBH, and Blue VT, and their ternary mixture, using boron-doped diamond (BDD) and Ti/IrO₂–SnO₂–Sb₂O₅ (MMO) anodes. Experiments were conducted in a batch reactor with 50 mM Na₂SO₄ at pH = 3.0 and current densities of 20–60 mA cm⁻². Kinetic analysis showed that AO-BDD was most effective at low pollutant loads, EF-BDD became superior at medium loads due to efficient H₂O₂ electrogeneration, and PEF-MMO dominated at higher loads by fast UVA photolysis of surface Fe(OH)²⁺ complexes. In a ternary mixture of 120 mg L⁻¹ of dyes, EF-BDD and PEF-MMO achieved >98% decolorization in 22–23 min with pseudo-first-order rate constants of 0.111–0.136 min⁻¹, whereas AO processes remained slower. COD assays revealed partial mineralization of 60–80%, with EF-BDD providing the most consistent reduction and PEF-MMO minimizing treatment time. These findings confirm that decolorization overestimates efficiency, and electrode selection must be tailored to dye structure and effluent composition. Process selection rules allow us to conclude that EF-BDD is the best robust dark option, and PEF-MMO, when UVA is available, offers practical guidelines for cost-effective electrochemical treatment of textile wastewater. Full article

In real-world fire scenarios, complex lighting conditions and smoke interference significantly challenge the accuracy and robustness of traditional fire detection systems. Fusion of complementary modalities, such as visible light (RGB) and infrared (IR), is essential to enhance detection robustness. However, spatial shifts and geometric distortions occur in multi-modal image pairs collected by multi-source sensors due to installation deviations and inconsistent intrinsic parameters. Existing multi-modal fire detection frameworks typically depend on pre-registered data, which struggles to handle modal misalignment in practical deployment. To overcome this limitation, we propose an end-to-end multi-modal Fire Salient Object Detection framework capable of dynamically fusing cross-modal features without pre-registration. Specifically, the Channel Cross-enhancement Module (CCM) facilitates semantic interaction across modalities in salient regions, suppressing noise from spatial misalignment. The Deformable Alignment Module (DAM) achieves adaptive correction of geometric deviations through cascaded deformation compensation and dynamic offset learning. For validation, we constructed an unregistered indoor fire dataset (Indoor-Fire) covering common fire scenarios. Generalizability was further evaluated on an outdoor dataset (RGB-T Wildfire). To fully validate the effectiveness of the method in complex building fire scenarios, we conducted experiments using the Fire in historic buildings (Fire in historic buildings) dataset. Experimental results demonstrate that the F1-score reaches 83% on both datasets, with the IoU maintained above 70%. Notably, while maintaining high accuracy, the number of parameters (91.91 M) is only 28.1% of the second-best SACNet (327 M). This method provides a robust solution for unaligned or weakly aligned modal fusion caused by sensor differences and is highly suitable for deployment in intelligent firefighting systems. Full article

Electrospinning is a versatile technique used to manufacture nanofibers by applying an electric field to a polymer solution. This method has gained significant interest in the biomedical, pharmaceutical, and materials engineering fields due to its ability to produce porous structures with a high specific surface area, making it ideal for applications such as wound dressings, controlled drug delivery systems, and tissue engineering. The materials used in electrospinning play a crucial role in determining the final properties of the obtained nonwoven nanofibers. Among the most studied substances are chitosan, collagen, and fish-derived gelatin, which are biopolymers with high biocompatibility. These materials are especially used in the medical and pharmaceutical fields due to their bioactive properties. In combination with synthetic polymers such as polyethylene glycol (PEG) and polyvinyl alcohol (PVA), these biopolymers can form electrospun fibers with improved mechanical characteristics and enhanced structural stability. The characterization of these materials was performed using modern characterization techniques, such as one-dimensional (1D) proton NMR spectroscopy (¹H), for which the spin–spin relaxation time distributions T₂ were characterized. Additionally, two-dimensional (2D) measurements were conducted, for which EXSY T₂-T₂ and COSY T₁-T₂ exchange maps were obtained. The characterization was complemented with FT-IR spectra measurements, and the nanofiber morphology was observed using SEM. As a novelty, machine learning methods, including artificial neural networks (ANNs), were applied to characterize the local structural order of the produced nanofibers. In this study, it was shown that the nanofiber nonwoven materials made from PVA are characterized by a degree of order in the range of 0.27 to 0.61, which are more ordered than the nanofibers made from chitosan and fish gelatin, characterized by an order degree ranging from 0.051 to 0.312, where 0 represents the completely unordered network and 1 a fully ordered fabric. Full article

Background/objectives: Pharmaceutical cocrystals have emerged as a promising strategy to enhance the solubility and bioavailability of poorly water-soluble drugs. Indomethacin (IND), classified as a Biopharmaceutics Classification System (BCS) Class II drug, exhibits low solubility but high permeability. This study aims to develop a synthesis method, evaluate cocrystal solubility/stability and the physicochemical properties of the pure components, and describe cocrystal solubility using a mathematical model. Methods: Cocrystals were synthesized via solvent evaporation, using ethanol, methanol, and ethyl acetate. The pure components, IND and benzoic acid (AcBz) were dissolved in each solvent and maintained in a thermostabilizer for 24 h. Cocrystal formation was confirmed by UV-V spectroscopy, differential scanning calorimetry (DSC), and infrared (IR) spectroscopy. Results: The results showed that the solubility of the cocrystals decreased with increasing benzoic acid concentration. Mathematical modelling revealed that solubility can be expressed as the product of the solubilities of the individual components and the stability constant of the solution complex. Among the solvents tested, ethanol exhibited the highest solubility and equilibrium constant (K_eq) for IND–AcBz cocrystals, suggesting a greater molecular affinity and enhanced cocrystal formation. Conclusions: These findings demonstrate that the formation of the novel INDAcBz cocrystal significantly enhances Indomethacin solubility and thermodynamic stability. These results validate benzoic acid as an effective coformer and establish phase solubility diagrams (PSD) as predictive tools for rational cocrystal design, supporting the future development of optimized pharmaceutical formulations. Full article

Positive (+) sense RNA viruses include many important pathogens that exploit noncanonical translation mechanisms to express their genomes within the host cells. Unlike DNA or negative (−) sense RNA viruses, (+) sense RNA viruses can directly function as mRNAs, even though they lack typical features of host mRNAs, such as the 5′ cap structure required for canonical translation initiation. Instead, they exploit structured RNA elements to recruit host translational machinery without the 5′ cap, bypassing the canonical translation initiation mechanism. Prominent examples include internal ribosome entry sites (IRESs) and 3′ cap-independent translation enhancers (3′ CITEs). These RNA modules facilitate translation initiation by recruiting the ribosomal subunits, either directly or through initiation factors, and mediating long-range RNA-RNA interactions. Other regulatory motifs, such as frameshifting signals, allow the ribosome to shift reading frames to regulate protein output. All these RNA elements function through RNA-protein interactions and often utilize host and virus-encoded proteins to hijack the host’s translational apparatus. Over the past several years, various structural biology approaches, including biochemical and enzymatic probing, X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and cryogenic electron microscopy (cryo-EM), have revealed the unique structural roles of these viral RNA elements and their protein complexes. Although a few structures of IRES and CITE domains have been solved through these methods, the structures of these RNA elements and their structure-function relationship have remained largely unknown. This review discusses the current understanding of translation-related RNA structures in (+) sense RNA viruses, the critical RNA-protein interactions they mediate, and various structural biology approaches used to study them. Since the genome of these viruses serves as a template for two mutually exclusive virological processes, namely genome translation and replication, the review also discusses how viruses can utilize RNA structure-based strategies to regulate the switch between genome translation and replication, highlighting future directions for exploring these fundamental virological processes to develop antiviral therapeutics able to combat diseases caused by these pathogens. Full article

Organophosphonates have manifold applications in the chemical industry, of which one of the most commonly used is 2-phosphonobutane-1,2,4-tricarboxylic acid (PBTC). It is widely used as a cement additive and may pose a potential risk of complexing radionuclides such as uranium in nuclear waste repositories. PBTC, in its fully deprotonated form, has four negatively charged groups, one phosphonate and three carboxylate groups, which makes it a superior ligand for metal ion complexation. In this study, for the first time, its complexation behavior towards hexavalent uranium, U(VI), in the pH range from 2 to 11, has been investigated using various spectroscopic methods. The structure-sensitive methods NMR, IR, and Raman spectroscopy were used to characterize the complex structure. The interpretation of the results was supported by density functional calculations. Over almost the entire pH range studied, U(VI) and PBTC form a chelate complex via the phosphonate and the geminal carboxylate group, highlighting the strong chelating ability of the ligand. UV-Vis spectroscopy combined with factor analysis was applied to determine the distribution of differently protonated chelate species and their stability constants. Time-resolved laser-induced luminescence spectroscopy (TRLFS) was additionally used as a complementary method. Full article

Space-based infrared (IR) detection, with wide coverage, all-time operation, and stealth, is crucial for aerial target surveillance. Under low signal-to-noise ratio (SNR) conditions, however, its small target size, limited features, and strong clutters often lead to missed detections and false alarms, reducing stability and real-time performance. To overcome these issues of energy-integration imaging in perceiving dim targets, this paper proposes a biomimetic vision-inspired Infrared Temporal Differential Detection (ITDD) method. The ITDD method generates sparse event streams by triggering pixel-level radiation variations and establishes an irradiance-based sensitivity model with optimized threshold voltage, spectral bands, and optical aperture parameters. IR sequences are converted into differential event streams with inherent noise, upon which a lightweight multi-modal fusion detection network is developed. Simulation experiments demonstrate that ITDD reduces data volume by three orders of magnitude and improves the SNR by 4.21 times. On the SITP-QLEF dataset, the network achieves a detection rate of 99.31%, and a false alarm rate of $1.97\times {10}^{-5}$, confirming its effectiveness and application potential under complex backgrounds. As the current findings are based on simulated data, future work will focus on building an ITDD demonstration system to validate the approach with real-world IR measurements. Full article

(1) Background. Printed circuit board (PCB) inspection is increasingly constrained by the cost and latency of reliable labels, owing to tiny/low-contrast defects embedded in complex backgrounds and severe class imbalance. (2) Methods. We proposed a semi-automatic labeling pipeline that converts anomaly detection proposals into class labels via small margin cropping from images, interchangeable embeddings (HOG, ResNet-50, ViT-B/16), clustering (k-means/GMM/HDBSCAN), and cluster-level verification using representative montages. (3) Results. On 9354 cropped defects spanning 10 categories (imbalance IR ≈ 1542, Gini ≈ 0.642), ResNet-50 + HDBSCAN achieved NMI ≈ 0.290, AMI ≈ 0.283, and purity ≈ 0.624 with ~47 clusters; ViT + HDBSCAN was comparable (NMI ≈ 0.281, AMI ≈ 0.274, ~44 clusters). With a fixed taxonomy, k-means (K = 10) yielded the strongest ARI (0.169 with ResNet-50; 0.158 with ViT). Macro-purity exceeded micro-purity, indicating many small, homogeneous clusters suitable for one-shot acceptance/rejection, enabling an upper-bound ~200× reduction in operator decisions relative to per-image labeling. (4) Conclusions. The workflow provides an auditable, resource-flexible path from normal-only localization to scalable supervision, prioritizing labeling productivity over detector state-of-the-art and directly addressing the industrial bottleneck in the development lifecycle for PCB inspection. Full article

High-quality image restoration (IR) is a fundamental task in computer vision, aiming to recover a clear image from its degraded version. Prevailing methods typically employ a static inference pipeline, neglecting the spatial variability of image content and degradation, which makes it difficult for them to adaptively handle complex and diverse restoration scenarios. To address this issue, we propose a novel adaptive image restoration framework named Hybrid Convolutional Transformer with Dynamic Prompting (HCTDP). Our approach introduces two key architectural innovations: a Spatially Aware Dynamic Prompt Head Attention (SADPHA) module, which performs fine-grained local restoration by generating spatially variant prompts through real-time analysis of image content and a Gated Skip-Connection (GSC) module that refines multi-scale feature flow using efficient channel attention. To guide the network in generating more visually plausible results, the framework is optimized with a hybrid objective function that combines a pixel-wise L1 loss and a feature-level perceptual loss. Extensive experiments on multiple public benchmarks, including image deraining, dehazing, and denoising, demonstrate that our proposed HCTDP exhibits superior performance in both quantitative and qualitative evaluations, validating the effectiveness of the adaptive restoration framework while utilizing fewer parameters than key competitors. Full article

4,7-di(N-morpholyl)-1,10-phenanthroline (morphen) was introduced for the first time as a ligand for the construction of metal–organic frameworks. The obtained MOF compound has the crystallographic formula {[Tb₂(morphen)₂Br₂(chdc)₂]}_n (1; chdc²⁻ = trans-1,4-cyclohexanedicarboxylate) and is based on binuclear {Tb₂(N^N)₂Br₂(OOCR)₄} carboxylate blocks, interlinked by ditopicchdc linkers into a layered coordination network with sql topology. Purity and integrity of the as-synthesized 1 were confirmed by common characterization techniques, such as PXRD, CHN, IR, and TGA. Compound 1 was found to be hydrolytically stable and possessing typical green emission for Tb(III) complexes. Exploiting its high stability, luminescent 1@PVA films were successfully prepared from 1 and polyvinyl alcohol (PVA) through the water solution drying approach. Full article

Resilience has become a central theme in organisational research, particularly in sectors such as construction that face frequent disruption, complexity, and uncertainty. Although individual resilience (IR) and organisational resilience (OR) have been widely explored, their relationship remains conceptually fragmented and often assumed to be either linear or inherently aligned. This study, thus, examines how the IR–OR relationship has been conceptualised in the literature, explores the nature of their interdependence, and identifies future research opportunities. A reflexive thematic analysis of peer-reviewed literature was conducted using Braun and Clarke’s framework, supported by NVivo 14 for data organisation and pattern identification. The analysis revealed gaps in how resilience is theorised and highlighted the absence of cohesive frameworks linking individual and organisational domains. In response, the study introduces three conceptual models: the stacked model, which treats IR and OR as hierarchical; the nested model, which reflects partial overlap; and the modified integrated model, indicating combined action of various factors. While this study draws on literature across all industries, the New Zealand construction sector is referenced as an illustrative example of a highly vulnerable sector where future empirical testing of the proposed models would be valuable. This research contributes to theory by reframing resilience as a relational construct shaped by numerous conditions. It also provides a foundation for future empirical studies and practical frameworks that embed resilience more holistically into organisational design, leadership development, and workforce strategy. Full article

Background/Objectives: Insulin resistance (IR) in adolescents contributes to the development of metabolic and immunological alterations. These alterations can lead to chronic, systemic, low-grade inflammation in adulthood. Evidence suggests that alterations in miRNA expression play a significant role in the onset of IR by influencing insulin signaling pathways. Therefore, identifying specific miRNAs may aid in the early diagnosis of cardiometabolic risk, particularly during the transition from adolescence to adulthood. Methods: This population-based study aimed to analyze the expression of 21 miRNAs in the plasma of adolescents. We considered IR status, overweight, sex, and age for the analyses. The study measured miRNA expression in plasma samples from 187 adolescents aged 12 to 19 years from the cross-sectional study of the 2015 São Paulo Health Survey (ISA-Nutrition). MiRNA expression was assessed using Exiqon^® assays on Fluidigm^® technology (Les Ulis, France). Statistical analyses were performed to identify differences in miRNA expression and correlations between variables, using a complex research design to ensure representativeness at the population level. Results: The incidence of IR and overweight was high in adolescents (44% and 33%, respectively). High-sensitivity C-reactive protein (hs-CRP) concentration was higher in overweight adolescents. IR was correlated with higher plasma expression of miR-122 and miR-139-3p. Furthermore, miR-486, miR-363, miR-30d, miR-28, miR-223, miR-21, miR-146, miR-130b, miR-126, miR-122, and miR-139-3p showed specific correlations with individual risk for IR, sex, and adolescent stage. Conclusions: The miRNAs showed differential expression according to sex and adolescent stage, and were correlated with cardiometabolic risk factors, suggesting their potential utility for early screening in adolescents. The study highlights age- and sex-related differences in miRNA levels between adolescents with IR and overweight. The cross-sectional design is a limitation of this study, as we cannot infer causality for the associations observed here. Full article

Influenced by various physical, chemical, and microbial factors, the aging process of Citri Reticulatae ‘Chachiensis’ Pericarpium (CRCP) poses a complex scientific challenge. Drawing inspiration from the perspective of traditional Chinese medicine, volatile oils were extracted from CRCP aged 1, 3, 5, and 7 years by steam distillation and subsequently analyzed by GC-MS. The results revealed that the relative percentage of 4-vinylguaiacol (4-VG) increased progressively with aging. Nineteen volatile oil components were further assessed for their glucose metabolism-enhancing activities, with 4-VG emerging as a key active compound. Notably, 4-VG remarkably enhanced insulin-stimulated glucose uptake in C2C12 myotubes. Moreover, 4-VG demonstrated potent antihyperglycemic effects by upregulating IRS-1/Akt/GSK-3β phosphorylation in the insulin signaling pathway on a high-fat diet and STZ-induced diabetic mouse model. In addition, the metabolic pathway of 4-VG, from ferulic acid and then to vanillin and guaiacol, was verified via HPLC-UV, metabolomics, and microbiome analyses, which confirmed the microbial conversion of 4-VG within CRCP. The metabolic pathway was ultimately validated by isolating and identifying Priestia aryabhattai, Bacillus velezensis, and Aspergillus fumigatus from CRCP, with further in vitro culture and biotransformation experiments confirming its functionality and efficiency. These findings provide new insights and experimental evidence that deepen our understanding of the aging process of CRCP. Full article

This study reports the synthesis and comprehensive spectroscopic characterization of 4-indolylcyanamide (4ICA), a novel indole-derived infrared (IR) probe designed for assessing local microenvironments in biological systems. 4ICA was synthesized via a two-step procedure with an overall yield of 43%, and its structure was confirmed using high-resolution mass spectrometry and ¹HNMR. Fourier Transform Infrared (FTIR) spectroscopy revealed that the cyanamide group stretching vibration of 4ICA exhibits exceptional solvent-dependent frequency shifts, significantly greater than those of conventional cyanoindole probes. A strong linear correlation was observed between the vibrational frequency and the combined Kamlet–Taft parameter, underscoring the dominant role of solvent polarizability and hydrogen bond acceptance in modulating its spectroscopic behavior. Quantum chemical calculations employing density functional theory (DFT) with a conductor-like polarizable continuum model (CPCM) provided further insight into the solvatochromic shifts and suppression of Fermi resonance in high-polarity solvents such as DMSO. Additionally, IR pump–probe measurements revealed short vibrational lifetimes (~1.35 ps in DMSO and ~1.13 ps in ethanol), indicative of efficient energy relaxation. With a transition dipole moment nearly twice that of traditional nitrile-based probes, 4ICA demonstrates enhanced sensitivity and signal intensity, establishing its potential as a powerful tool for site-specific environmental mapping in proteins and complex biological assemblies using nonlinear IR techniques. Full article

Background/Objectives: This study aimed to synthesize a novel, high-molecular-weight acyclic heterocyclic compound, compound 5, via a one-pot reaction between Trichloroisocyanuric acid (TCCA) and ethanolamine, and evaluate its anticancer, antioxidant, and antifungal activities. Methods: Its complex tetrameric structure, assembled through N-N linkages, was unequivocally confirmed by a full suite of spectroscopic techniques including IR, ¹H & ¹³C NMR, ²D-NMR, and high-resolution mass spectrometry (LC/Q-TOF/MS). The MTT assay was used to assess the anticancer activity of compound 5 against four different human cancer cell lines. Results: The findings indicate that human colon (HT29) and ovarian (OVCAR3) cancer cells were sensitive to the treatment, whereas brain (glioblastoma) (T98G) cancer cells were resistant. The most pronounced cytotoxic effect was observed in pancreatic (MiaPaCa2) cancer cells. Notably, compound 5 exhibited potent antifungal properties, achieving 100% inhibition of the pathogenic water mould Saprolegnia parasitica zoospores at 100 µM after 10 min. Molecular docking studies corroborated the biological data, revealing a high binding affinity for key cancer and fungal targets (Thymidylate Synthase and CYP51), providing a strong mechanistic basis for its observed activities. Conclusions: These findings establish compound 5 as a promising dual-action agent with significant potential as both a targeted anticancer lead and an eco-friendly antifungal for applications in aquaculture. Full article