A novel series of chromone-based alpha-aminophosphonates (7a-j) as potential dual inhibitors of alpha-amylase and alpha-glucosidase were developed and synthesized using a solvent-free, ultrasonic-assisted Kabachnik-Fields reaction catalyzed by nano-ZnO, with high yields. Before synthesis, molecular docking simulations and in silico ADMET evaluation were used to estimate the pharmacokinetic properties and inhibitory interactions with alpha-amylase and alpha-glucosidase enzymes. Spectroscopy was utilized to determine structural integrity. The compounds were tested in vitro for inhibitory effects on two enzymes linked to type 2 diabetes. When compared to acarbose, the in silico ADME analysis showed favorable pharmacokinetic characteristics suggesting promising oral drug-likeness. Stronger binding affinities were found in molecular docking studies toward pancreatic alpha-amylase and alpha-glucosidase than acarbose. Compounds 7e, 7d, and 7i inhibited alpha-amylase and alpha-glucosidase with IC50 values comparable to or better than acarbose, suggesting their promise as scaffolds for anti-diabetic drug development.
Wastewater treatment plants (WWTPs) must operate efficiently under varying influent conditions and rigorous regulations due to growing populations and industrial expansion. For complex processes such as nutrient removal, effluent quality, activated sludge behaviour, and aeration control, data collection is laborious, expensive, and expert-dependent, which slows machine learning and deep learning for water quality parameter prediction, fault detection, and process optimization. Transfer learning (TL) provides an efficient approach by allowing pre-trained models, developed on extensive generic or wastewater datasets, to be tailored for WWTP applications. TL enhances wastewater processes by transferring learned features that identify hydraulic patterns, pollutant indicators, reactor state transitions, and sludge characteristics, thus improving prediction accuracy in data-scarce environments. Recent studies indicate that TL improves the estimation of nutrient concentrations, sensor calibration, anomaly detection in aeration and settling units, and the early identification of sensor fouling and equipment faults. A comprehensive review focused on TL in WWTP is currently lacking, despite these benefits. Addressing this gap is crucial for informing the development and implementation of TL-based intelligent WWTP strategies. This review summarizes the framework of TL applications in WWTPs, examining previously explored pre-trained models, data sources, sensing and control modalities, computing platforms, and TL strategies. The primary challenges identified include data heterogeneity, the scarcity of benchmark datasets, model generalization, and dynamic operational conditions. Additionally, potential research directions for the integration of TL into next-generation WWTP are discussed.
Early clinical decisions increasingly rely on heterogeneous IoT streams (wearables, bedside waveforms, point-of-care imaging), yet current multimodal systems remain vulnerable to retrieval errors and unsupported LLM assertions. This paper proposes a topology-aware cross-modal retrieval and retrieval-augmented inference pipeline that fuses image and time-series embeddings via a domain-adapted contrastive dual encoder, regularizes embedding neighbourhood geometry with persistent homology, and conditions a frozen LLM through a BLIP-2-style Q-former for evidence-grounded generation. Evaluation on MIMIC-CXR and ROCO-style radiology corpora demonstrates substantial gains: Recall@1 $\cong 0.78$ ($\approx+9$ percentage points vs. a BiomedCLIP baseline) and mAP @ 10 $\approx$ 0.72, together with an absolute +0.15 increase in evidencecoverage (0.81 vs. 0.66), corresponding to $\approx 23 \%$ relative reduction in unsupported LLM assertions. Key contributions are: (i) a persistent-homology embedding regularizer for modality-invariant geometric alignment; (ii) an IoT-aware hybrid retrieval index and pruning strategy for bounded latency; and (iii) comprehensive empirical validation including ablations and clinician-adjudicated concordance analyses on MIMIC-derived datasets.
This study explores the performance, combustion, and emission parameters of a neem biodiesel enhanced with zinc oxide (ZnO) Nanoparticles (NPs) synthesized through a green method in a compression ignition (CI) engine. Neem oil was transesterified to produce biodiesel, while ZnO NPs were synthesized using neem leaf extract and dispersed at concentrations of 50 ppm and 100 ppm, denoted as NB25Zn50 and NB25Zn100, respectively. The outcomes showed that the addition of ZnO NPs significantly enhanced combustion efficiency, resulting in Brake Thermal Efficiency (BTE) increases of 1.51% for NB25Zn50 and 3.64% for NB25Zn100, along with Specific Fuel Consumption (SFC) decreases of 2.54% and 5.28%, respectively. Combustion analysis revealed a higher peak cylinder pressure (CP), increased Heat Release Rate (HRR), and quicker Mass Fraction Burning (MFB) progression for fuels blended with NPs, indicating a shorter Ignition Delay (ID) and more complete combustion. Emission analysis showed considerable reductions in Carbon monoxide (CO), Hydrocarbons (HC), and smoke opacity, whereas the optimal ZnO concentration (100 ppm) effectively controlled Nitrogen Oxides (NOx) emissions. Additionally, machine learning models such as K-Nearest Neighbors (KNN), Rrandom Forest (RF), Ssupport Vector Regression (SVR), and Extreme Gradient Boosting (XGB) were used to predict engine performance, emissions, and energy parameters. Among these, extreme gradient boosting demonstrated superior predictive accuracy with high correlation coefficients (R ≈ 0.99) and minimal error. Overall, neem biodiesel blended with ZnO NPs, especially at 100 ppm, exhibited strong potential as a sustainable fuel for enhanced overall engine performance and cleaner emissions.
