Browse Articles

Predictive modeling of coagulant dosing in drilling wastewater treatment using artificial neural networks

Increasing the fungal inoculation of mine tailings from 1 to 2% decreases plant oxidative stress and increases the soil respiration rate

Numerical study on bearing performance of rock socketed pile anchored by inclined anchors under uplift and horizontal combined load

Dihydroartemisinin induces tumor suppression in the Drosophila brain tumor with functional recovery and a rescue in lethality

Prediction of coal and gas outbursts based on physics informed neural networks and traditional machine learning models

Shared energy storage planning based on the adjustable potential of data center based on visual IOT platform

The relationship between liver stiffness, fat content measured by liver elastography, and coronary artery disease: a study based on the NHANES database

Integrating circular fuzzy approach with an interval valued EDAS approach to evaluate teaching effectiveness in virtual and augmented reality classrooms

Experimental investigation of the properties of collapsible soil stabilized by colloidal silica

Travel distance may have a negative impact on the outcome of deep brain stimulation in Parkinson’s disease

Abstract Potential beneficial effects of teleprogramming on the efficacy of deep brain stimulation (DBS) have not been studied. We aimed to explore the effects of travel distance on the outcome of DBS in Parkinson’s disease (PD). We retrospectively analyzed travel- and visit-related data of PD patients treated with DBS. Comparing the first-year and five-year outcomes of far-living patients (> 50 km) having the desired number of follow-up visits (n = 74) to those of far-living subjects having less visits than optimal (n = 54), patients with desired number of visits had better global health-related quality of life according to summary indexes of disease-specific and non-specific scales at five years. Far-living patients with desired number of visits had larger decrease in Parts II, III and IV of the Movement Disorder Society-sponsored Unified Parkinson’s Disease Rating Scale from baseline to the five-year follow-up and more simplified antiparkinsonian medication treatment. Even 40% of PD patients with DBS who live far from the movement disorder center may have less in-person visits than optimal and be on the risk of having worse long-term outcome of DBS than could be achieved. Teleprogramming may help maintaining the long-term efficacy of stimulation in this group of patients.

Essential minerals in colostrum of preterm and full-term Ghanaian mothers and related maternal factors

Exploring the role of lipid metabolism related genes and immune microenvironment in periodontitis by integrating machine learning and bioinformatics analysis

UPLC-Q-TOF/MS, network pharmacology, molecular docking, and experimental validation to explore the mechanisms of toad clothing on rheumatoid arthritis

Ligand-controlled growth and stabilization of doped ZnO2 nanoparticles for dual antibacterial and enzyme inhibition

Abstract Maintaining structural stability in multifunctional nanoparticles (NPs) remain a challenge in nanomedicine. To address this limitation, organic ligand-capped pristine and doped zinc peroxide (ZnO2) NPs were synthesized via co-precipitation method for enhanced antibacterial efficacy against Gram-positive bacteria (methicillin-resistant Staphylococcus aureus (MRSA) and Bacillus cereus (BC)), and inhibition of the acetylcholinesterase enzyme (AChE). The synthesized samples were characterized using complementary characterization techniques. In-situ studies confirmed citrate (cit) molecules slow down the nucleation kinetics, while manganese (Mn) and cobalt (Co) doping reduces the optical bandgap from 3.07 eV to 2.89 eV, and 2.79 eV, respectively. Critically, ligand engineering and doping substantially improved bioactivity. ZnO2 NPs exhibited dose-dependent antimicrobial activities, with 7.7 ± 0.9 mm and 8.6 ± 0.9 mm zones of inhibition (ZOIs) at 1000 µg/ml concentration against MRSA and BC, respectively. Incorporation of 3% Mn into the ZnO2 lattice improved the ZOIs to 8.9 ± 1.7 mm against MRSA and 11 ± 1.9 mm against BC at 1000 µg/ml concentration. Notably, 5% Co-doped with cit capping exhibits the ZOIs of 12.5 ± 2.0 mm against MRSA and 6.4 ± 1.5 mm BC at 1000 µg/ml. 3% Mn-doped ZnO2 NPs with dmlt as capping agent showed ZOIs of 10.3 ± 1.7 mm against MRSA and 12.3 ± 1.9 mm against BC at 1000 µg/ml concentration. Furthermore, the anti-acetylcholinesterase enzyme (AChE) activities of the synthesized NPs were assessed. At 125 µg/ml concentration, cit-capped ZnO2 NPs inhibits 75.5 ± 0.1% of AChE activity. 3% Mn-doped ZnO2 NPs show the inhibition of 73.2 ± 0.2% AChE, enhancing to 82 ± 0.3% upon pent capping. In contrast, 3% Co-doped ZnO2 NPs and dmlt-capped 5% Co-doped ZnO2 NPs exhibit modest AChE inhibition, with values of 62.4 ± 0.3% and 54.5 ± 0.2%, respectively. Molecular docking studies suggested moderate interaction of ZnO2 NPs with phenol-soluble modulins alpha2 (PSMα2), strong interaction with Phospholipase C Regulator (PlcR), and moderate interaction with 1EEA (acetylcholinesterase from Electrophorus electricus (electric eel)). This study introduced a novel approach utilizing highly stabilized ZnO2 NPs as potent antimicrobial agents and acetylcholinesterase inhibitors.

Finite element analysis of chloride ion penetration and service life prediction in concrete with supplementary cementitious materials

Influence of joint fissure occurrence on stress distribution of coal wall in large mining height

Hybrid compatible grid forming inverters with coordinated regulation for low inertia and mixed generation grids

Semantic web ontology for structured knowledge representation and clinical decision support in eye diseases

DFT simulation to study the mechanical, electronic, thermal and superconducting properties of borocarbides materials AX2B2C where A = Y, La, Th and X = Pd, Pt

Stacked ensemble model for NBA game outcome prediction analysis

Abstract This research presents a stacked ensemble approach that employs artificial intelligence (AI) techniques to predict the outcomes of NBA games. Several machine learning algorithms were utilized, including Naïve Bayes, AdaBoost, Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), XGBoost, Decision Tree, and Logistic Regression. The best-performing models were selected to serve as the base learners in the ensemble architecture. To improve the model’s interpretability and transparency, SHAP was used to clarify its decision-making process. The model was trained and evaluated using publicly available NBA datasets from 2021–2022,2022–2023, and 2023–2024. Experimental results indicate that the proposed ensemble approach is practical in predicting game outcomes. Furthermore, the SHAP analysis provides valuable insights into the underlying predictive mechanisms, offering actionable information for coaches and analysts.