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KnitLoRA: bridging low-rank adaptation as interwoven layers for deeper semantic reasoning
Photocatalytic degradation of Congo red dye using innovative cerium titanate nanorods embedded in a cellulose-based hydrogel
Abstract This work used a simple technique to prepare a novel cerium titanate nano-rods embedded in a cellulose-based hydrogel with high photo catalytic degradation activity. The composite hydrogel was prepared by embedding different amounts of cerium titanate nano-rods (Ce/Ti-NRs) into a crosslinked carboxymethyl cellulose/polyacrylamide polyelectrolyte complex (CMC/PAM) as a matrix material. The physico-chemical properties of the Ce/Ti-NRs, CMC/PAM, and cerium titanate nano-rods embedded in a cellulose-based hydrogel (Ce/Ti-NRs/CMC/PAM) were investigated by FT-IR, XRD, BET, TEM, SEM, and EDX techniques. The photo-degradation efficiency of the prepared composite hydrogel was investigated for their ability to simultaneously adsorb-degrade Congo red dye (CR) under different conditions such as the dosage of composite hydrogel, dye solution pH and temperature, degradation time, initial dye concentration, and agitation rate. Also, the kinetics of the CR degradation process was evaluated. The obtained data fits well using pseudo-first order kinetic model with R 2 equal to 0.911 and calculated equilibrium capacity value (22.44 mg/g) closer to the corresponding experimental values (23.193 mg/g) than those of pseudo-second order model with R 2 equal to 0.888 and calculated equilibrium capacity value (34.78 mg/g). The linear plot of the intra-particle model indicated that the simultaneously adsorb-degrade of CR dye by the composite hydrogel is likely to be complex. It involves both film diffusion (boundary layer diffusion) and intra-particle diffusion. The optimum developed composite hydrogel shows high simultaneously adsorb-degrade photo catalytic activity superior to other published results as it degraded 91.68% of CR dye only after 90 min.
Soft computing models for predicting polymer viscosity across wide thermal and ionic-strength conditions
Method comparison of microscopy, metabarcoding, and multispectral imaging flow cytometry for identification and relative abundance analysis of insect-dispersed pollen
Abstract Pollen identification and quantification are essential in ecological and evolutionary research to address plant-pollinator relationships, pollination services, and plant reproduction. Research into pollen transfer patterns has been mainly based on traditional light microscopy, however, it is time consuming, labour intensive, and requires taxonomic expertise. High-throughput methods allow automated pollen identification across large temporal and spatial scales. This study compares the accuracy of species identification and quantification of their relative pollen abundance using traditional microscopy, and two high-throughput methods, namely multispectral imaging flow cytometry (MIFC) and metabarcoding. Method performance was tested using artificial samples with known pollen species composition and pollen samples from pollinators with unknown pollen composition. After checking the agreement against the line of identity, the coefficient of determination (R 2 ) values of linear models between the method estimates were compared. Metabarcoding performed best at identifying the taxa from artificial mixtures, while the two other methods assessed the relative abundance most accurately when there was information about species identity. Comparability between methods was overall low when assessing pollen composition on pollinators. To acquire both pollen identity and relative abundance, we recommend a metabarcoding-guided MIFC analysis that can be used as a high throughput approach for pollen research at large spatial and temporal scales.
‘Treasure trove’ of antiviral proteins could inspire powerful molecular tools
Galectin-4 contributes to the maintenance of expression and activation of multiple receptor-type kinases involved in peritoneal metastasis
Phase structure and machine learning identification in one dimensional systems with power law correlated disorder and long range hopping
Comparison of the long-term impact of laparoscopy and open surgery on the prognosis of patients with locally advanced gastric cancer
Study on mechanism of air distribution volume’s influence on airflow, dust migration and spray dust reduction in fully mechanized caving face
Sleep quality and quality of life in patients with dermatological diseases during the war in Gaza
Feasibility and acceptability of the MentiParent AI chatbot for training parental reflective functioning
Abstract This study presents a novel generative AI-based chatbot, MentiParent, designed to enhance Parental Reflective Functioning (PRF) through interactive, simulated parent-child interactions. Addressing existing limitations of traditional PRF interventions this proof-of-concept investigation assessed the feasibility and acceptability of implementing AI-driven technology within reflective parenting support. Two pilot studies were conducted. In Study 1, sixty mental health practitioners and graduate students engaged with MentiParent, simulating realistic interactions with a virtual adolescent (“Danny”). In Study 2, thirty-six parents from the general population interacted with an updated version of MentiParent featuring a new virtual child (“Evelyn”). All participants evaluated the MentiParent usability and relevance for real-world parenting contexts on a scale of 1–7, as well as provided qualitative feedback. Both practitioners and parents rated the chatbot highly for feasibility (M practitioners = 5.61, SD = 1.45; M parents = 6.03, SD = 1.18) and acceptability (M practitioners = 5.56, SD = 1.20; M parents = 5.53, SD = 1.37), with no significant differences between groups. Qualitative feedback highlighted engaging and realistic interactions, valued feedback, and identified limitations in authenticity and technical fluidity. Ethical themes of transparency, trust, and cultural sensitivity emerged as central. Findings indicate strong feasibility and acceptability of MentiParent among both professionals and parents, supporting its potential as a scalable, ethically grounded tool for enhancing reflective parenting skills.
Design optimization of battery enclosures under nonlinear impact loading using a K-Kriging hybrid surrogate model
Phenome-wide analysis of copy number variants in 470,727 UK Biobank genomes
Differential effect of antiseizure medications eslicarbazepine and carbamazepine on hippocampal synaptic transmission and plasticity
Longitudinal evaluation of neurocognitive outcomes in a cohort with persistent post-COVID olfactory dysfunction
Asymmetric Total Synthesis and Structure Revision of (+)-Mangicol D
Trajectories and associations between wearable-derived mobility, patient-reported outcomes, clinical measures and clinical indicators following total knee arthroplasty: The IMPACT project protocol
Background Total Knee Arthroplasty (TKA) is one of the most frequently performed elective surgical procedures worldwide. In the United States, annual procedure volumes are projected to exceed 3.5 million by 2030, while demand in Ireland is expected to rise by 49% by 2036. Despite its overall effectiveness, up to 20% of patients remain dissatisfied post-operatively. Conventional follow-up methods rely on intermittent Patient-Reported Outcome Measures (PROMs) and clinic-based performance tests, which provide only a partial view of recovery. Wearable-derived mobility data offers the opportunity to capture daily activity patterns in free-living conditions, complementing PROMs and clinical assessments to refine understanding of recovery trajectories following TKA. Methods This prospective observational cohort study will recruit up to 160 participants scheduled for unilateral TKA at the Beacon Hospital (Dublin, Ireland). Participants will wear a Garmin Vivosmart 5 continuously from up to four weeks pre-operatively to 6 months post-operatively. Continuous, minute-level wearable data will be collected including daily step count. PROMs will be collected using the Labfront Companion mobile application and will include weekly numeric ratings of pain, fatigue, stiffness and sleep quality; the Oxford Knee Score (OKS) at 30-day intervals; and the EQ-5D-5L at 60-day intervals. Clinical indicators and measures will be abstracted from the electronic health record (Meditech). Clinical and demographic measures include age, sex, height, weight, body mass index (BMI), and participation in prehabilitation exercise classes. Primary statistical analysis will evaluate longitudinal trend analysis of step count and PROMs over time after surgery, while covariate-adjusted Spearman-type correlations will evaluate cross-sectional associations between daily step count, OKS, EQ-5D-5L and pain. Expected outcomes This study will characterise wearable-derived mobility metrics across early and mid-stage recovery following TKA, and evaluate their associations with PROMs. Conclusions Integrating continuous wearable data with PROMs and clinical information may refine post-operative monitoring and support personalised rehabilitation following TKA.