Browse Articles

TEA–modified anthill clay for tannery wastewater treatment: COD and BOD adsorption

One-pot aqueous synthesis of DHLA-capped CuInS2 quantum dots with defect-mediated photoluminescence

Abstract Aqueous synthesis of I–III–VI 2 quantum dots (QDs) remains challenging due to variability associated with post-synthetic ligand exchange. Here, a direct one-pot aqueous synthesis of dihydrolipoic acid (DHLA)-capped CuInS 2 (CIS) QDs is presented, enabling simultaneous nanocrystal growth and surface passivation under mild conditions (75 °C) without ligand exchange. Ligand-control experiments using ligand-free and 3-mercaptopropionic acid (MPA)-capped CIS QDs confirm the role of DHLA in improving photoluminescence (PL) efficiency, colloidal stability, and chalcopyrite-related diffraction features. The optimized QDs exhibit sizes of 2.5–5.0 nm and broad defect-mediated PL centered at 580 nm, with a PL quantum yield (PLQY) of ~ 9%. Time-resolved PL (TRPL) analysis further supports long-lived donor–acceptor-type recombination involving In Cu -related donor and V Cu -related acceptor states. PL modulation by urea shows an approximately linear concentration-dependent response over 0–5.0 mM, with a sensitivity of 0.0850 mM − 1 and an estimated detection limit of 0.64 mM, without measurable spectral shift. Selectivity and Fourier transform infrared (FTIR) analyses indicate a preferential surface-mediated response toward urea through weak non-covalent interactions. This work establishes a reproducible strategy for defect-controlled aqueous CIS QDs and supports their use in surface-sensitive PL modulation studies.

The role of artificial intelligence in facilitating superwood utilization among furniture craft producers for sustainable smart manufacturing

Abstract Despite the demonstrated potential of superwood materials (engineered or modified wood products, such as densified, thermally modified, or cross-laminated timber, that exhibit enhanced strength, dimensional stability, and resource efficiency relative to conventional timber) and artificial intelligence (AI) to advance sustainable manufacturing, their adoption remains negligible among artisanal furniture producers in resource-constrained developing economies. This study investigates whether and how AI can facilitate superwood utilization within Nigeria’s furniture artisan sector, a context characterized by rising timber scarcity, environmental pressure, and limited technological integration. A convergent parallel mixed-methods design was employed, comprising a survey of 196 furniture artisans and in-depth interviews with 30 practitioners across South-East Nigeria, analyzed using the Technology Acceptance Model (TAM) and thematic analysis. Quantitative results revealed low awareness of superwood (34.2%) and AI (45.4%), with substantive AI understanding only 7.7%. Knowledge deficits constituted the most severe barrier (mean > 4.3), followed by financial constraints and infrastructure limitations. Structural equation modeling confirmed TAM relationships: perceived usefulness strongly predicted attitude and behavioural intention, while perceived ease of use remained low (superwood M = 2.87, AI M = 2.65), indicating that anticipated complexity hinders adoption. Qualitative findings elaborated on economic pressures, cultural attachment to traditional hardwoods, cooperative procurement preferences, and unreliable electricity/internet as enabling conditions. In response, the study developed the Contextually Appropriate Framework for AI-Facilitated Superwood Utilization (CAFAISU), integrating knowledge ecosystems, phased technology implementation, financial accessibility mechanisms, sociocultural integration, and infrastructure support. Overall, the findings indicate that AI may serve as a facilitating mechanism particularly through design optimization, mobile-based knowledge dissemination, and cooperative procurement but only once foundational knowledge, financial, infrastructural, and cultural barriers are addressed. Realizing this potential requires targeted, multipronged interventions that address these deficits simultaneously, beginning with the knowledge and infrastructure foundations on which subsequent technology adoption depends.

Identification of CCL4-positive macrophages and their associated immune microenvironment in thymic epithelial tumors

Machine learning model for the detection of autism spectrum disorder using electroretinogram signals

A time-lagged psychological model of linguistic uncertainty, cognitive flexibility, and academic well-being among English major students

BabyFlow: 3D modeling of realistic and expressive infant faces

Genotype-dependent profile and developmental dynamics of pyridine alkaloids in Areca catechu L.

Modeling multidimensional perceived risk in HIV-related social media: a multi-label transformers framework with longitudinal analysis

Large language models generate diagnostic likelihood ratios with low mean bias but wide dispersion

Integrating morphophysiology, gene expression and machine learning to characterize salt and drought stress responses in dragon fruit

A simulated dataset and evaluation framework for assessing AI detection limits in digital holographic microscopy

Abstract The integration of artificial intelligence (AI) with digital holographic microscopy (DHM) is transforming optical methods for particle detection and classification, particularly in biosecurity and biosafety. However, existing AI-DHM methods are typically developed or fine-tuned on hardware-specific experimental datasets, limiting their generalizability across different configurations and applications. A standardized and hardware-agnostic benchmarking framework for systematically evaluating AI performance in DHM is currently lacking. This study introduces a simulation and evaluation framework designed to generate reproducible, parameter-controlled synthetic datasets of raw holographic images, configurable to replicate specific optical setups while remaining independent of any particular hardware implementation. The framework provides a flexible environment for pre-training and testing open-source and proprietary machine learning (ML) and deep learning (DL) models, enabling transfer learning strategies and guiding the design and optimization of optical setups and computational pipelines. Here, the framework is demonstrated for the recognition of micrometric and submicrometric particles relevant to biosecurity scenarios, where particle size and concentration are critical operational parameters that directly influence sampling, filtration strategies and downstream AI analysis. By systematically varying these parameters alongside optical configurations, the framework is used to investigate their individual and combined effects on detection and classification accuracy. Results demonstrate the robustness of DL models, even under challenging conditions with small particles and high concentrations, while ML approaches are more sensitive to fringe overlap. Overall, this work provides a reproducible and extensible environment for the development of AI-driven microscopy systems, offering quantitative insights for optimizing experimental design, algorithm development and sample preparations in both laboratory and field applications, prior to the deployment on specific optical setups.

Emergency vehicle signal priority control method for arterial intersections in an intelligent connected environment

Designing a chimeric multi-epitope vaccine against Candida auris using reverse vaccinology approach targeting the agglutinin-like protein N-terminal domain

Prevalence and risk factors of foodborne disease among Iranian pilgrims during the Arbaeen mass gathering in Karbala, Iraq

Understanding phototexts through an eye-tracking study on visual and emotional interactions between text and photo

Comparative efficacy of eyelid cleansing wipes, hypochlorous acid, and saline for blepharitis-associated ocular surface disease: a randomized trial

Genome assembly and SNP resources of Anisakis simplex enable cost-effective population assessment and discrimination of species and hybrids

Abstract Anisakis spp are parasitic nematodes that can infect fish during larval stages, and, if consumed by humans, can cause anisakiasis and severe allergic reactions, including anaphylaxis. Here, we generated a 168 Mb genome assembly of A . s implex , the most abundant species in the Northeast Atlantic, which was used to develop SNP resources using a whole genome pooling resequencing strategy. 1,824,098 SNPs and 151,767 short structural variants were identified, showing the high polymorphism and complexity of A . simplex genome. A 481 SNP panel was developed for population screening using the Agriseq technology, through strict filtering and homogeneous distribution across the genome. This panel was used for genotyping 823 larvae and adults of A . simplex , 775 A . pegreffii and 402 putative hybrids collected from different hosts across the Northeast Atlantic, 275 SNPs being consistently genotyped in both species. Among them, 10 SNPs showing extreme genetic differentiation between A . simplex and A . pegreffii and no linkage disequilibrium in A . simplex , were used to design a cost-effective SNP tool for species and hybrid identification in population surveys. The genomic resources obtained will facilitate efficient surveillance programs, accurate discrimination of species and hybrids, effective anisakiasis management in fisheries, and improved seafood safety.

U-shaped association between non-protein calorie-to-nitrogen ratio and mortality in adults with overweight and obesity: a population-based cohort study

Abstract The non-protein calorie-to-nitrogen ratio (NPC/N, kcal/g) is recognized as a valuable metric for assessing the balance between non-protein energy intake and nitrogen derived from protein. This study aimed to investigate the association between NPC/N and mortality among overweight and obese adults. Data were obtained from the National Health and Nutrition Examination Survey (1999–2018), with mortality follow-up through December 31, 2019. Among 22,892 participants, 3,332 all-cause deaths occurred over up to 20 years of follow-up. Kaplan–Meier curves showed that the intermediate NPC/N group (90–160 kcal/g) had lower mortality than both the low (< 90 kcal/g) and high (≥ 160 kcal/g) groups (log-rank test P  = 0.012). Restricted cubic spline analysis demonstrated a U-shaped association between NPC/N and all-cause mortality, with the risk nadir at approximately 120 kcal/g ( P for non-linearity = 0.027, P for overall = 0.022). In fully adjusted models, NPC/N was inversely associated with all-cause mortality below the nadir (HR = 0.61, 95% CI: 0.39–0.94, P  = 0.030) and positively associated above it (HR = 1.12, 95% CI: 1.01–1.25, P  = 0.040); log-likelihood ratio test P  = 0.006. These suggest that both lower and higher NPC/N are associated with increased mortality.

Machine learning assisted spectroscopic investigation of fluorescence quenching in Hypocrellin B with magnetic nanoparticles

Abstract The present study investigates the interaction behavior of the photosensitizer Hypocrellin B (HB) with Fe 3 O 4 nanoparticles and Fe 3 O 4 /CdTe nanocomposites using a combined spectroscopic and machine learning approach. The structural characteristics of the synthesized nanoparticle were confirmed by UV-Visible and Fourier transform infrared (FTIR) spectroscopy. Steady-state and time-resolved fluorescence studies revealed the formation of ground-state complexes accompanied by static fluorescence quenching. Fluorescence titration experiments demonstrated that HB exhibits stronger binding affinity towards Fe 3 O 4 nanoparticles compared with Fe 3 O 4 /CdTe nanocomposites. The calculated bimolecular quenching constant (k q ) in the range of 5.4 × 10 13 to 9.4 × 10 13 M − 1 s − 1 further supported the static quenching mechanism. The thermodynamic feasibility of possible photoinduced electron- transfer interaction was also evaluated using the Rehm-Weller equation and the energy level analysis. In addition, principal component analysis (PCA) and Support vector regression (SVR) were employed to analyze spectral variation and predict interaction-related parameters. The PCA results provided insight into the spectral changes associated with HB-nanoparticle complex formation, while the SVR model demonstrated promising predictive capability with R 2 values above 0.98 and prediction errors below 5%. The combined experimental and multivariate analysis offers valuable insight into HB-nanoparticle interactions and highlights the potential of integrating spectroscopic techniques with data-driven modelling for fluorescence quenching studies relevant to photodynamic and biomedical applications.