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Adaptive optimization with reinforcement learning for high utility itemset extraction
Extraction of High Utility Itemsets (HUI) plays a vital role in data mining that comprises several techniques developed to address it efficiently. However, when dealing with large itemsets and diverse items in a dataset, the problem's search space becomes notably complex and expansive. This makes the task of identifying HUIs more computationally expensive and time-consuming. In this paper, a novel Optimized Coverage list unit utilities-based High Utility Itemset (OCHUI) extraction approach is introduced for High Utility Itemset extraction. The extraction of high utility patterns and the extraction of qualified high utility itemsets are the two main processes in the suggested method. In the first step, high utility patterns are identified by mining metrics such as Redefined transaction-weighted utility, positive and negative Unit profit, Purchase quantity, and Coverage (RUPC) from the dataset. In the second step, qualified high utility itemsets are obtained optimally using an adaptive optimization algorithm called Cuckoo search Assisted Ant colony Optimization with Reinforcement Learning (CAAO-RL) is proposed. The Reinforcement Learning (RL) uses the On and Off policy method to intelligently leverage the tuning parameter of optimization. The RUPC model obtained the pattern score of 13600, runtime rate of 10.256 s and memory usage of 198 MB, respectively. 2025 Elsevier B.V. -
A novel wide slice kronecker forward fractional network for osteoporosis detection using knee X-ray image
Osteoporosis is an asymptomatic and progressive skeletal disorder that maximizes the risk of fractures in people aged 50 to 60. Early and accurate detection is critical, yet challenging, due to the fine structural changes in bone that are often difficult to identify in routine medical images. Knee X-rays are commonly used diagnostic tools, but interpreting them for osteoporosis detection remains complex because of variations in bone geometry and trabecular patterns. To solve these challenges, the novel Wide Slice Kronecker Forward Fractional Network (WKFF-Net) is developed to detect osteoporosis efficiently. Initially, the input image is taken from the database for detection. Here, the denoising process is done using the Non-Local Means (NLM) filter, and the Otsu thresholding method is considered for the segmentation process. Further, a template search method is used for analyzing the femur geometry. Next, features, like spatial, adaptive Local Binary Patterns (aLBP), Convolutional Neural Networks (CNN), and medical-level features, are extracted, and osteoporosis detection is accomplished by the hybrid WKFF-Net model that integrates Deep Kronecker Network (DKN), Wide Slice Residual Network (WISeR), and fractional calculus. The experimental results obtained by the WKFF-Net are 90.868% accuracy, 92.876% True Positive Rate (TPR), 87.766% True Negative Rate (TNR), 89.888% precision, and 91.357% F1-score, for 90% of the training samples. 2026 Elsevier B.V. -
Tri-projection gated cross-modal fusion for robust multilingual emotion recognition
Existing multimodal approaches in emotion recognition (ER) rely on static or pairwise fusion strategies. These systems do not adequately address the challenges in real-world conversational systems, which require resilience to both multilingual code-switching and variable reliability of multiple modalities. We propose a transformer-based tri-modal emotion identification framework with a novel Tri-Projection Gated Cross-Modal Fusion (T-GCMF) module the first multimodal emotion recognition architecture explicitly designed for code-switched conversational input. T-GCMF simulates tri-modal interactions by explicitly calculating modality-specific confidence and cross-modal consistency, allowing for dynamic suppression of unreliable modalities during inference. Acoustic and visual cues are retrieved using CNNLSTM and deep CNN encoders, respectively. Textual representations are generated using XLM-RoBERTa to handle code-switched language reliably. We introduce Hinglish-MELD, the first multimodal emotion recognition dataset with aligned text, audio, and visual streams containing code-switched conversational content, filling a critical gap in the literature. With an accuracy of 88.3% and an F1-score of 87.0, the suggested confidence-aware fusion technique greatly surpasses unimodal, monolingual, and non-gated multimodal baselines. These findings demonstrate T-GCMF as a successful approach for emotion recognition in linguistically heterogeneous, real-world interactive systems and emphasize the significance of confidence-driven tri-modal integration. 2026 -
Characterization of Erd?s matrices by their zero entries
An Erd?s matrix E is a bistochastic matrix whose sum of squares of entries (Frobenius norm squared) equals its maxtrace (maximum value of the trace of ?E, as ? varies over permutation matrices). We characterize all Erd?s E by the patterns of their zero entries; showing that each such skeleton has at most one E. We present an algorithm to find all n Erd?s matrices, which finds them up to n?5 quickly and also size n=6. We further show some presently known RCDS matrices (E in which the trace of ?E remains constant across all the permutations that avoid every zero-entry position in E) to be Erd?s. 2026 Elsevier Inc. -
Electrochemical cobalt extraction from grinding sludge for supercapacitor applications via hydro- and solvometallurgical processes
Cobalt is a critical material for energy storage applications, including supercapacitors, but its supply is constrained by geopolitical and environmental challenges. This study presents a sustainable approach for cobalt recovery from cemented tungsten carbide grinding sludge via deep eutectic solvents (DESs) and evaluates the electrochemical performance of the recovered materials in supercapacitors. Electrochemical extraction was optimized at 4 V and 10 mA/cm2, achieving a cobalt concentration of 2900 mg/L in the DES. The cobalt was then recovered as cobalt oxalate via solvometallurgical (Route 1) and hydrometallurgical (Route 2) processes and subsequently calcined into cobalt oxide. Characterization revealed that the solvometallurgical route yielded finer, porous particles with enhanced electrochemical properties. The recovered cobalt oxalate and cobalt oxide were utilized in supercapacitor electrodes, demonstrating superior electrochemical performance when combined with activated carbon (AC). Supercapacitors incorporating cobalt oxide from route 1 with AC achieved a specific capacitance of 95 F/g, outperforming cobalt oxalate-based electrodes (89 F/g) at 1 mA/g. The AC-modified electrodes exhibited improved energy and power densities, with stable capacitance retention over 1000 cycles. Comparative analysis with direct deposition methods highlighted the multistep recovery process as a promising route for scalable cobalt recycling. This study underscores the potential of DES-based electrochemical extraction as an environmentally friendly alternative for critical metal recovery, aligning with circular economy principles and sustainable energy storage solutions. 2025 The Authors -
Mathematical and computational analysis of a fractional-order drug abuse model with nonlinear incidence and logistic growth
This paper presents a novel mathematical model for analyzing the dynamics of drug addiction using a fractional-order system based on the LiouvilleCaputo derivative. The proposed model incorporates a nonlinear saturated incidence rate, logistic growth in the addiction compartments, and seven interconnected subpopulations representing different stages of drug use and recovery, including relapse and awareness. We conduct a rigorous mathematical analysis to establish the existence, uniqueness, positivity, and boundedness of solutions, ensuring the epidemiological and physical validity of the model. The basic reproduction number R0 is derived, and the local and global stability of the equilibrium points is analyzed. A major contribution of this work is the application of a new domain decomposition spectral method based on second-kind Dickson polynomials, combined with the quasilinearization technique, to efficiently solve the complex nonlinear system. The convergence of the numerical method is theoretically validated. Numerical simulations are provided to illustrate the model's dynamics and to explore the impact of various parameters and intervention strategies. Compared to existing models, this study offers an improved framework for understanding memory-dependent behavior in addiction dynamics and introduces a computationally efficient approach to solve fractional-order systems with high accuracy. 2025 International Association for Mathematics and Computers in Simulation (IMACS) -
Manganese telluride quantum dot decorated 3D printed structures for dye-degradation
The disastrous result of fast industrialization and uncontrolled industrial effluent discharge is the lack of fresh water. Scholars have endeavored to extract water from heavily contaminated industrial effluent by creating several materials capable of effective and environmental friendly treating of tainted water. In the subject of water treatment, three-dimensional (3D) printed complex architecture has shown to be an emerging technique. Recently, nanomaterials have reformed filter technology because of their improved morphological characteristics. The current study explores the uses of two-dimensional (2D) Manganese Telluride (MnTe2) quantum dots (QDs) to decorate the 3D printed architecture for wastewater treatment. The photocatalytic performance of the QDs decorated 3D printed structures was demonstrated through the degradation of organic dyes (methylene blue (MB) and methyl orange (MO) dye) in both dark and light exposure conditions. The coated structures exhibited the ability to adsorb the organic pollutant and clean the contaminated water. We observe ?78 % degradation efficiency for MB and ?48 % for MO in dye concentrations of 10 mg/100 ml. A colorimetric detection method was used for real-time detection of degradation efficacy. The obtained results indicated that QDs decorated 3D printed system can be a significant system for wastewater treatment. 2025 -
10-camphor sulfonic acid: A simple and efficient organocatalyst to access anti-SARS-COV-2 Benzoxanthene derivatives
10-Camphor sulfonic acid (10-CSA) has gained popularity as an organocatalyst due to its broad range of solubility and user-friendliness. Affordable multicomponent reactions (MCRs) for the preparation of benzoxanthenes (4a-4 h) (5a-5i) are presented in this work. Extensive investigations and records have been conducted on the diverse biological features exhibited by xanthenes and benzoxanthenones, such as their antiviral, antibacterial, and anti-inflammatory capabilities.Using ?-naphthol, dimedone, and aldehydes, we demonstrate a cost-effective and environmentally friendly catalytic method. Under ideal circumstances, the 10-CSA catalyzes one-pot reaction, procuring impressive amounts of benzoanthenes (8595 %). All the synthesized compounds were characterized by 1H NMR and 13C NMR. A wide variety of suitable chemicals, simple work-up procedures, and solvent-free synthesis outperforms numerous existing methods for procuring biologically relevant benzoxanthene derivatives are some of the interesting features of this organocatalyzed bronsted acid process. Therefore this synthesis is industrially inevitable. Furthermore, computational studies such as molecular docking and ADMET data analysis were performed on a number of the synthesized benzoxanthene molecules. This has led to the identification of the most potent synthetic against the SARS-CoV-2 spike protein. Additionally, to mimic how medicinal compounds interact to target proteins, computational docking and dynamics techniques were used. These studies showed that, in terms of binding affinity and other crucial traits, 4a, 4b, and 5a are potential possibilities. Overall, the current study should be of great help in the development of benzoxanthene analogs which can be potential drugs for treatment of COVID-19. 2024 Elsevier B.V. -
Hemin-functionalised conducting polymer as a unique host matrix for the electrochemical synthesis of benzothiazole derivatives: A sustainable approach
An electrocatalyst based on the non-toxic and biologically derived metalloporphyrin hemin, immobilized on poly 3,4-diaminobenzoic acid (PDABA) was utilized for the sustainable electrochemical synthesis of benzothiazole derivatives. Electrochemical and topographical attributes of the electrocatalyst were analyzed critically using a ferricyanide probe, electrochemical impedance spectroscopy (EIS), X-ray photoelectron spectroscopy (XPS), optical profilometry, FTIR, and FE-SEM techniques. The modified electrode was employed for the electrochemical synthesis of benzothiazole derivatives using various aromatic aldehydes and 2-aminothiophenol. The reactions were performed in a three-electrode system, at oxidation potentials derived from cyclic voltametric elucidations, using lithium perchlorate as the supporting electrolyte and ethanol as the solvent. The products obtained were crystallized, purified and confirmed with the help of 1H-NMR spectroscopy, showing yields ranging from 78-92 %. The hemin based heterogenous electrocatalyst enhances the efficacy of the reaction by reducing reaction time, and negating tedious work-up procedures, thereby making the method highly facile and environmentally benign. 2025 -
Redox-active tetra-amino cobalt phthalocyanine electrocatalyst for sustainable electrochemical synthesis of 2-(pyridin-4-yl)-1H-benzo[d]imidazole
An electrocatalyst bearing a cobalt phthalocyanine derivative was developed by modifying carbon fibre paper electrode with polythiophene-3-acetic acid (pTAA) and further immobilizing tetra-amino cobalt phthalocyanine (TACoPc). The electrode was topographically and electrochemically characterized to validate its surface modification and functional suitability. This energy efficient electrocatalyst (CFP-TAA-TACoPc) was explored, for the sustainable synthesis of 2-(pyridin-4-yl)-1 H -benzo[ d ]imidazole at 1.35 V, with 87.6 1.267 % product yield at ambient conditions. o -phenylenediamine and pyridine-4-carboxaldehyde were used as starting materials, with ethanol as solvent, and lithium perchlorate as supporting electrolyte, using a three-electrode system, in a single compartment cell. The benzimidazole derivative was observed to crystallize out after completion of the reaction, negating the need for any further purification and was characterized using 1HNMR and GCMS. This work highlights the potential of electrochemical strategies as a sustainable and efficient alternative to conventional methods for heterocyclic synthesis. 2026 Elsevier B.V. -
Experimental investigation of plain and nano-graphene oxide mixed dielectric for sustainable EDM of Nimonic alloy using Cu and Brass electrode: A comparative study
The current research investigates the machinability of a novel Nimonic alloy through electro-discharge machining by assessing Material Removal Rate, Tool Wear Rate and Surface Roughness. The machining was conducted using plain dielectric and Graphene Oxide (GO) nanoparticles (5 g/l) mixed dielectric considering both Copper and Brass electrode. The novelity lies when machining with nano GO mixed dielectric. It was observed that the use of Copper electrode in machining of the alloy in nano GO mixed dielectric results in superior quality machining, demonstrating enhanced performance. The key findings include the identification of optimal parameters, where Vg of 70 V, Ton of 200 s, Fp of 0.5 kgf/mm2 maximize MRR (16.231 mm3/min) and minimize TWR (0.0062 mm3/min) and SR (5.1423 m). The microstructural study of the machined surface and sustainability study along with the detailed comparative analysis of responses assures the superiority of machining in Nano GO mixed dielectric-Cu electrode environment. 2024 Elsevier Ltd -
Power quality enhancement of renewable energy systems using a hybrid orangutan optimization algorithm and continuous spiking graph neural network with series active power filter
Interconnected renewable energy systems (RES) often experience power quality (PQ) issues, such as harmonics and voltage disturbances. Nevertheless, conventional Series Active Power Filter (SAPF) control schemes have disadvantages, such as slow adaptation and reduced accuracy in a fluctuating renewable environment. To overcome these limitations, this work proposes a hybrid adaptive SAPF-based PQ optimization technique. The proposed method combines the Orangutan Optimization Algorithm (OOA) and Continuous Spiking Graph Neural Network (CSGNN), referred to as the OOA-CSGNN method. Reduction of total harmonic distortion (THD), increase of PQ, and stabilize of voltage profiles in interconnected RES are the goals of the proposed technique. The OOA offers the best SAPF control parameters to maximize convergence and dynamic tracking, and the CSGNN is effective to predict the compensation signals using graph-based spiking computations. The suggested technique is implemented in MATLAB and evaluated against existing approaches, such as the Gorilla Troops Algorithm (GTA), Genetic Algorithm (GA), Adaptive Bald Eagle Optimization Algorithm (ABE-OA), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN). The proposed OOA-CSGNN approach achieves a load voltage THD of 0.11% under steady-state operating conditions after SAPF compensation, while maintaining voltage THD well within IEEE-519 limits during transient disturbances such as voltage sag, swell, and dip. These results demonstrate the efficiency and robustness of the proposed hybrid architecture for PQ optimization in renewable-integrated systems. 2026 Elsevier Ltd -
Enhancing image compression through a novel Structural Fidelity Weighted Ensemble (SFWE) model
With the explosion of digital images across multiple sectors like social media, health care, medical imaging, and remote sensing, there is a demand to optimise the storage and transmission of images. In this paper, a novel Structural Fidelity Weighted Ensemble model is proposed to dynamically adjust the weights between SVD and PCA outputs to enhance the quality of reconstructed images.Unlike traditional static fusion techniques, the proposed SFWE deploys a fast bounded scalar optimization strategy so as to dynamically estimate the optimal fusion weights thereby ensuring non-negativity and simplex constraints while significantly reducing computational overhead compared to Sequential Quadratic Programming(SQP) or constrained gradient descent methods.Validation was done across multiple benchmarks datasets namely, USC-SIPI Sequences (grayscale TIFF), Kodak, BSDS500, DRIVE (Digital Retinal Images for Vessel Extraction), and ISPRS Potsdam which cover natural, medical, and remote-sensing images. Per-image processing, runtime measurement, and compressed ratio (CR) were produced automatically by the provided evaluation pipeline;The SFWE method provides greater image quality and structural fidelity across diverse datasets, attaining a PSNR of 40 dB and SSIM of 0.95, outperforming existing approaches such as Discrete Cosine Transform (DCT), Wavelet Transform, Singular Value Decomposition (SVD), and Principal Component Analysis and JPEG2000 + CNN models. In addition, it also maintains a good compression ratio leading to an effective balance between the reduction in file size as well as visual quality of the images, which confirms enhanced structural preservation across diverse image types. To implement a novel ensemble model (SFWE) that optimally balances the outputs of SVD and PCA for doing effective image compression. To achieve a higher SSIM (0.95) and good PSNR (40 dB) compared to compression techniques such as DCT, Wavelet, SVD, PCA, and JPEG2000 + CNN. To ensure adaptive high-quality reconstruction across multiple datasets, demonstrating its suitability for diverse image-intensive applications. 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/ -
Actor-critic guided CDBN with GAN augmentation for robust facial emotion recognition
Facial emotion recognition (FER) remains challenging under limited data, noise, and occlusion. This study introduces an ActorCritic Convolutional Deep Belief Network (ACCDBN) that unifies Generative Adversarial Network (GAN)based augmentation, deep probabilistic feature learning, and reinforcement-driven optimization. Conditional GANs expand minority emotion classes, enhancing data diversity, while the CDBN extracts hierarchical texture features through convolutional and restricted Boltzmann layers. An ActorCritic module dynamically refines representations by rewarding accurate emotion classification and penalizing uncertain predictions. Trained and validated on the CK+ dataset with five-fold cross-validation, the proposed model achieves higher accuracy and stability than CNN, LSTM, and ResNet-50 baselines, maintaining strong performance under noise and occlusion. The approach demonstrates how reinforcement-guided generative learning can improve both accuracy and robustness in FER tasks.1. To implement this, the research utilised the publicly available Cohn-Kanade+ dataset, consisting of eight classes with samples of 920 grey-scale images.2. An improved ACCDBN model outperformed with 90.4% accuracy and 0.69 MCC (Mathews Correlation Coefficient) in 5-fold cross-validation using the cGAN-generated dataset and 87% on the CK+ dataset3. The main objective is to present an advanced facial emotion recognition (FER) system that combines a Convolution Deep Belief Network (CDBN) with a model-free reinforcement learning technique, namely the actor-critic approach. 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
Efficacy of digital MBCT-PD in preventing postpartum depression and enhancing work motivation: A study protocol
Background: Postpartum depression (PPD) is a significant challenge for women transitioning back to work. While preventive measures are essential, the effectiveness of Mindfulness-Based Cognitive Therapy (MBCT) in this context remains underexplored. This study will assess the efficacy of a digital MBCT program (MBCT-PD) in preventing PPD, enhancing well-being, and motivating work resumption after childbirth. Methods: A randomized controlled trial (RCT) with repeated measures will evaluate MBCT-PD, a digitally delivered intervention designed to promote mindfulness and emotional resilience. Eighty consenting pregnant women aged 18+, between 16 and 32 weeks gestation, residing in urban India will be recruited and randomized to either the MBCT-PD group or an enhanced treatment-as-usual (TAU) control group, which includes additional prenatal wellness resources. The intervention will span eight weeks, with assessments at baseline, post-intervention (T1), and six weeks postpartum (T2). Primary outcomes are depression (Edinburgh Postnatal Depression Scale), well-being (Pregnancy Experience Scale-Brief), and work motivation (Multidimensional Work Motivation Scale). Secondary outcome is mindfulness level (Three Facet Mindfulness Questionnaire-Short Form). Descriptive statistics, repeated measures ANOVA, and regression analyses will determine the effect of MBCT-PD on these outcomes. Expected Results: We anticipate that the MBCT-PD group will show reduced PPD symptoms, improved well-being, and greater motivation to resume work than the control group, consistent with previous findings on mindfulness-based interventions. Conclusion: The findings from this study are expected to support the efficacy of MBCT-PD as a cost-effective, scalable intervention for enhancing postpartum mental health and work reintegration, with potential applications in maternal mental health practices and policies worldwide. Trial Registration: Clinical Trial Registry of India. CTRI/2024/03/064,831 2025 -
Green synthesis, characterization, and biological applications of silver nanoparticles from Pachira glabra leaf extract
Green synthesis of silver nanoparticles (AgNPs) offers an environmentally sustainable approach to nanoparticles (NPs) production, utilizing plant extracts as reductant and stabilizer. This method helps minimizes the involvement of toxic chemicals, making it an economical and eco-friendly substitute to traditional synthetic techniques. The objective of this study is to synthesize and analyse AgNPs formed from Pachira glabra Pasq. leaf extract and to evaluate their biological applications. This study presents the first report on the green synthesis of AgNPs using P. glabra leaf extract, demonstrating its antibacterial, antioxidant, and cytotoxic potential. In this study, 1 mM silver nitrate (AgNO3) was used for synthesizing AgNPs. The shift in colour of the solution from pale green to brown indicates NP synthesis which was further confirmed by spectrophotometric analysis, exhibiting a peak at 424 nm. Fourier-transform infrared spectroscopy (FTIR) analysis was done to recognize the functional groups present in both P. glabra leaf extract and AgNPs synthesized. FTIR analysis revealed key functional groups such as hydroxyl, carbonyl, and amine groups. Peaks were observed in the range of 504 ?3351 cm?1. Dynamic Light Scattering (DLS) and zeta potential of biosynthesized AgNPs showed the size distribution and stability in suspension. Field Emission Scanning Electron Microscopy (FE-SEM) revealed that the biosynthesized AgNPs exhibited both cubic and spherical morphologies. Energy-dispersive X-ray spectroscopy (EDS) confirmed the presence and distribution of silver (Ag) and other elemental contents. The crystallinity of biosynthesized AgNPs was confirmed through X-ray diffraction (XRD) pattern. These results helped to characterize the biosynthesized AgNPs. Antibacterial activity of AgNPs was tested against Gram-negative Escherichia coli (MTCC 443) and Gram-positive Staphylococcus aureus (MTCC 3160) bacterial strains, with the AgNPs showing maximum effectiveness against E. coli, exhibiting an inhibition zone of 6.5 1.5 mm. The DPPH assay was used to evaluate antioxidant activity, and the biosynthesized AgNPs demonstrated a scavenging activity of 82.99 %, showing strong antioxidant potential compared to the standard. The cytotoxicity of both AgNPs and P. glabra leaf extract was tested against HCT-116 colorectal carcinoma cell line (ATCC-CCL-247). 2025 The Authors -
Neural network-assisted carbon nanotube electrochemical sensors for automated environmental risk assessment
The current research proposes an intelligent network system that continuously tracks the quality of water flow, with particular attention to pollutants frequently occurring in runoff water from agricultural practices. It deploys high-performance electrochemical sensors based on carbon nanotubes (CNT) combined with a small neural network that functions directly on the built-in microcontroller. It is deployed on floating buoys powered by solar energy, where it can detect some critical contaminants in the rural water bodies, including nitrates, phosphates, atrazine, cadmium, and lead. The sensors work by transmitting their electrical signals through the sensors to the neural network, which provides precise identification of the level of pollutants as one of three risk levels: safe (below detection levels), manageable (within regulatory levels), and hazardous (exceeding regulations). Regarding power performance, results can be delivered over a relatively small-time delay (1.2 milliseconds per reading) and with low memory usage (1.8 MB), making it ideal for remote and low-powered sensors. It is more accurate (93.6 %) than typical machine learning models. Should pollutants exceed the above-prescribed limits, an automated warning will be generated, and the information will be immediately uploaded to a cloud-based dashboard. The dashboard will be closely monitored via remote control, and trend analysis will be conducted. By eliminating the need for manual water sampling, the system offers a scalable and energy-saving method for autonomous environmental testing, particularly in inaccessible locations. In the future, the study will focus on the use of federated learning, a technique that retains data locally to protect privacy, enabling more intelligent and collaborative conclusions across sensor networks. This prepares the ground for more intelligent and secure environmental surveillance systems in the future. 2025 Elsevier B.V. -
Amine-functionalized MIL-101(Fe)-NH2@ZIF-8 composite for efficient adsorption of Pb2+ ions
Heavy metal contamination of water resources poses a serious environmental and public health threat, necessitating the development of efficient and selective adsorbent materials. In this study, a hierarchical MIL-101(Fe)-NH2@ZIF-8 composite was successfully fabricated via an interfacial growth strategy, integrating amine-functionalized MIL-101(Fe)-NH2 and ZIF-8 to achieve a synergistic micro-mesoporous architecture with accessible functional sites. The composite was thoroughly characterized by FTIR, PXRD, TGA, BET, and SEM-EDX analyses, with elemental mapping confirming the structural integration and resulting in enhanced porosity, thermal stability, and functional group availability. The material exhibited a remarkable Pb2+ adsorption efficiency of 94.9 % and a maximum adsorption capacity of 297 mg/g, significantly superior to the adsorption of other metal ions (Cd2+, Cu2+, Ni2+, and Cr2+). Atomic absorption spectroscopy (AAS) validated the exceptional selectivity of MIL-101(Fe)-NH2@ZIF-8 for Pb2+ ions. The enhanced performance is attributed to the synergistic effect of accessible amine (?NH2) functionalities, Fe?O coordination sites, and hierarchical porosity enabling strong metal binding and rapid diffusion. These findings highlight the exceptional potential of MIL-101(Fe)-NH2@ZIF-8 as an advanced adsorbent for Pb2+ removal from water, offering a practical pathway to address critical environmental challenges and promote sustainable human health and ecological protection. 2025 Elsevier B.V. -
Biogenic Synthesis of Zinc Oxide Nanoparticles using Coffea arabica Fruit Peel Extract for Electrochemical Detection and Photocatalytic Degradation of Methylene blue Dye
Methylene blue is an ecologically toxic, carcinogenic, and mutagenic dye. Due to extensive industrial use, a significant quantity of effluent containing methylene blue dye is released into a water source. It may cause toxicity to humans and aquatic fauna. Therefore, detecting and removing MB dye from the effluent is essential. For this goal, we synthesized dual application zinc oxide nanoparticles using coffee fruit (Coffea arabica) peel biomass as a reducing agent. SEM scans revealed spherical nanoparticles. The EDX spectral data indicated the existence of zinc and oxygen elements. The X-ray diffraction pattern exhibited crystallinity of ZnO. Under optimized conditions, the electrochemical impedance spectroscopy (EIS), cyclic voltammetry, and Differential Pulse Voltammetry (DPV) study was studied for the detection of MB, an impressively low detection limit (LOD) of 0.01771 ?M was recorded, The photocatalytic efficacy of ZnO nanoparticles demonstrated a significant 92.43% degradation of methylene blue under UV light. So, Coffea arabica biomass may play a vital role in synthesizing eco-friendly ZnO nanomaterials for environmental remediation applications. 2026 Elsevier B.V. -
Predictive value of IL-6, IL-1?, TNF-?, and vaginal pH in diagnosing vaginal microbial infections: A host-inflammatory axis perspective
Microbial-associated vaginal infections are common among women of reproductive age and are linked to alterations in the local immune environment. Inflammatory biomarkers such as IL-6, IL-?, and TNF-?, along with vaginal pH have emerged as potential indicators of microbial dysbiosis. This study aimed to statistically evaluate the ability of these specific inflammatory cytokines and vaginal pH to identify infection status. Cytokine concentrations and vaginal pH were measured in clinically characterized samples. The group differences were analyzed using Mann-Whitney U tests and Cliff's Delta for effect size. ROC-AUC analysis was also performed to assess the discriminative power, and correlation heatmaps explored marker synergy. The infected individuals showed increased levels of all cytokines (p < 0.001), with large effect size (? > 0.9 for IL-6, IL-1?, TNF-?). Vaginal pH also differed significantly (? = 0.60). In addition, the combination of IL-6 and vaginal pH achieved excellent discriminative performance (AUC = 0.98). These findings suggest that IL-6, IL-1?, and TNF-?, when combined with vaginal pH, can function as reliable non-invasive biomarkers for the early detection and improved diagnostic triaging of vaginal microbial infections. 2024