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Shear Waves Induced Vibration in a Size-dependent Loosely-bonded ViscoelasticFlexoelectric Material Structure Subjected to Fractional Derivative
The present study investigates the dispersive and damping limitations of shear horizontal waves (SH-waves) in an imperfectly bonded size-dependent layer over layer (LoL) structure. The LoL model consists of a nonlocal flexoelectric layer (NFL) coated by a thin nonlocal viscoelastic layer (NVL) with fractional elastic and viscoelastic properties. Utilizing Eringen's nonlocal elasticity theory, the governing equations for both NVL and NFL have been established and a complex frequency relation through analytical methods is obtained by applying appropriate boundary conditions at the imperfect interface and free surfaces. The complex frequency relation was then separated into dispersion and attenuation equations to represent the dispersive and damping characteristics of SH-waves in the LoL model. The study presents the classical case as a particular instance along with various other cases obtained by relaxing certain assumptions from the present model. To visualize the impact of key parameters such as viscosity, NVL thickness, permittivity, piezoelectricity, nonlocality parameters of NVL and NFL, imperfectness, fractional-order derivative, and flexoelectricity on dispersive and damping natures, several graphs have been plotted and discussed the distinguished region of existence for dispersion and attenuation curves. This was achieved by deriving the lower and upper bounds for SH-wave velocity. Additionally, the influence of key parameters on the surface response of nonlocal shear stresses and particle displacement within the LoL structure is graphically depicted as a function of depth. The findings reveal that SH-wave characteristics are significantly more diverse in the size-dependent LoL model compared to the classical LoL model. The findings of this study hold significant promise for advancing the design and functionality of various technological applications. By enhancing our understanding of surface wave dynamics in size-dependent structures combining flexoelectric and viscoelastic materials, this research paves the way for innovations in sensor technology, energy harvesting systems, and devices capable of manipulating waves. 2025 Elsevier Masson SAS -
Innovative fish peptide-loaded chitosomes: Advancing bioactive delivery through comprehensive in vitro and in vivo assessments
Considerable advancements have been achieved in controlled delivery systems, yet ensuring optimal stability, bioavailability, and precise targeting of bioactive compounds continues to present challenges. Addressing this gap, the present study explores chitosomeslipid vesicles stabilized by chitosanas a promising approach to enhance the delivery efficiency of bioactive molecules. This study investigate fish peptide-loaded chitosomes, leveraging chitosan's biocompatibility, biodegradability, and encapsulation capabilities. A comprehensive evaluation was conducted under both in vitro and in vivo conditions to assess their potential applications. In vitro studies using L929 cell lines demonstrated high biocompatibility, efficient cellular uptake, and sustained cell viability, with a dose-dependent cytotoxicity profile, leading to early and late apoptosis. The tolerance, favourable metabolic stability, and biomarker responses were validated by in vivo evaluations in Albino Wistar rats, demonstrating systemic efficacy and safety. Moreover, the peptide-chitosome formulation demonstrated a lipid-lowering impact, as evidenced by increases in high-density lipoprotein (HDL) and unsaturated fatty acids and decreases in triglycerides, saturated fatty acid content, and low-density lipoprotein (LDL). These findings highlight the potential of fish peptide-loaded chitosomes as an advanced bioactive delivery system, addressing existing limitations and expanding their applicability in nutraceutical and therapeutic formulations. 2025 The Author(s) -
Early bruise detection, classification and prediction in strawberry using Vis-NIR hyperspectral imaging
The most frequent kind of damage to strawberries is bruising. However, most of the bruises are so barely perceptible at an early stage on the surface, that detection of them with the human eye is quite challenging. This study proposes a method for accurately detecting and classifying the damage using reflectance imaging spectroscopy. In order to carry out the study, an experiment was devised to artificially induce bruises and a dataset was generated at different bruise intervals. A model for detecting and classifying bruises at their latent stage was developed using machine learning classifiers, including support vector machines (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), to investigate the changes over time after bruise occurrence on the detection performance. Regression models for the prediction of bruising time were developed using partial least square regression (PLSR), RF, gradient boosting (GB), support vector regression (SVR), and DT. Among the compared models, both SVM and LDA could achieve 99.99 % classification accuracy. RF was regarded as being the most advisable for detection and prediction jobs due to its high performance. It achieved MSE of 0.052 and R2 of 0.989 for prediction. 2024 Elsevier Ltd -
SS-CNN BruiseFinder: Hyperspectral imaging and CNN-driven spatial-spectral fusion for non-destructive plum bruise analysis
Plum fruit is susceptible to damage at various stages, from growth to packaging, and such bruising is often difficult to detect visually due to its subtle surface appearance. This research seeks to develop a convolutional neural network (CNN) model that leverages 3D convolutional layers to integrate spatial and spectral features from hyperspectral data, enabling accurate bruise analysis in plum fruit. In this study, plums sourced from a Norwegian fruit store were intentionally bruised and then imaged using hyperspectral technology at various time intervals (30 min to 48 h post-bruising). A novel CNN model, dubbed SS-CNN BruiseFinder, is developed to harness the spatial and spectral characteristics of these hyperspectral images for accurate bruise detection and classification. The SS-CNN BruiseFinder model demonstrates detection accuracy ranging from 68.5% to 91.5% and categorization accuracy between 67.39% and 98.16%. To further establish the effectiveness of this approach, three additional deep learning models a custom spectral CNN, ResNet 101, and a bidirectional LSTM model are developed and evaluated on the same dataset, providing a comprehensive validation of the proposed method's superiority. Timely detection of bruising helps prevent contaminated plums from entering the supply chain during transportation or storage. By categorizing plums based on bruise age, retailers can offer consumers more accurate freshness and quality information, enabling them to make better-informed purchasing choices and ultimately enhancing the overall shopping experience. To encourage community engagement and re-implementation, our code is available at https://github.com/SS-CNN BruiseFinder. 2025 Elsevier Ltd -
Analysis of passive bloodstain morphology across surface textures and drop heights using deep learning
Bloodstain pattern analysis (BPA) is a critical forensic science tool for reconstructing crime scene events. In this study, the effect of substrate type and drop height on the morphology of passive bloodstains was examined under controlled laboratory conditions. Blood samples were dropped vertically at 90 angle from three different heights, and the drops were permitted to strike five different surfaces, including curved cups, crushed chart paper, jute cloth, jelly stone, and concrete. These substrates were chosen to represent a realistic range of porous, semi-porous, non-porous, textured, and curved materials that are commonly encountered in crime scenes. The features of the substrate affect stain morphology, including shape irregularity and satellite formation, but not the measured angle of impact. These findings validate the consistency of impact angle determination using BPA, wherein the nature of the substrate primarily affects stain morphology but not necessarily the accuracy of angles. The large image data sets were tested using deep learning approaches, which effectively differentiate bloodstain patterns generated from varying fall heights. MobileNet model, leveraging pretrained ImageNet features, achieved superior accuracy and generalisation, underscoring the value of transfer learning for small forensic datasets. Future extensions of this work will include multiple impact angles, motion-related effects and temperature-controlled conditions to represent the actual crime scene scenarios. Deep learningbased analysis of these data may improve the understanding of bloodstain morphology and strengthen the forensic applicability. 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Quantum-enhanced neuro-fusion framework for intelligent decision-making in smart home IoT surveillance
Smart-home surveillance systems increasingly rely on heterogeneous IoT data streams, requiring efficient fusion, scalability, and robustness under noisy sensing conditions. This paper proposes a Quantum-Inspired Deep Neuro-Fusion Architecture (QDNFA) for anomaly detection in edgecloud IoT environments. The framework integrates modular encoders, temporal alignment, and a quantum-inspired optimisation mechanism to support multi-modal data processing while maintaining real-time performance. Experimental evaluation is conducted on the CASAS Smart Home dataset to validate sensor-centric anomaly detection, scalability across multiple devices, and edgecloud inference efficiency. While the architecture is designed to support audio and video modalities, the present study focuses on low-dimensional sensor data, and large-scale benchmarking on audiovisual surveillance datasets is identified as future work. Results demonstrate improved detection accuracy and reduced latency compared to baseline methods in sensor-driven smart-home scenarios. 2026 The Author(s). -
Surface modified Cobalt Oxide Nanostructures for hydrogen generation from catalytic dissociation of NaBH4
Liquid chemical hydrides, such as aqueous sodium borohydride (NaBH4), offer a safer, energy-dense alternative for fuel cell vehicles, enabling on-demand hydrogen release under ambient conditions. However, achieving large-scale viability for this system requires the development of a cost-effective and durable catalyst to improve hydrogen release efficiency. In this study, three distinct nanostructured Co3O4 catalysts (nanorods (NR), nanosheets (NS), and nanocubes (NC)) were synthesised via a hydrothermal method and further modified by incorporating B and P heteroatoms on the surface. Among these, the B/P-Co3O4-NS catalyst with its 2D nanosheet structure exhibited the highest catalytic activity, achieving an activation energy of 17.7 kJ/mol and a maximum hydrogen generation rate (HGR) of 5.6 L/min/g for hydrolysis of NaBH4. All three B/P-modified Co3O4 catalysts outperformed both CoPB nanoparticles and unmodified Co3O4, attributed to enhanced electronic interactions and induced lattice strain from B and P incorporation, with the nanosheet morphology providing a large surface area for improved efficiency. The B/P- Co3O4-NS catalyst also demonstrated notable stability, successfully enduring recycling and high-temperature treatment (773 K). These results highlight B/P-Co3O4-NS as a promising candidate for practical hydrogen generation, combining high catalytic performance with robust stability. 2025 Elsevier Ltd -
Instigating the mixed phases of cobalt oxide in nanowires for electrolysis of urea-based water
The urea oxidation reaction (UOR) offers a more energy-efficient alternative to water splitting, with a lower theoretical potential of 0.37 V and the possibility of using urea-based wastewater as an electrolyte. In this study, phosphorus/boron-incorporated cobalt oxide nanowires supported on nickel foam (P,B-CoxOy NW@NF) are synthesized by hydrothermal and reduction methods as an electrocatalyst for UOR. The P,B-CoxOy NW@NF demonstrates exceptional electrocatalytic performance with a low UOR potential of 1.33 V at 50 mA/cm2 in alkaline media. Comprehensive structural and morphological analyses reveal the formation of mixed Co3O4-CoO phases with abundant oxygen vacancies (Ov) and Co2+ species, which synergistically enhance conductivity and provide ideal surroundings for active M?OOH species formation. In-situ electrochemical kinetic studies highlight the superior catalytic activity of P,B-CoxOy NW@NF, attributed to a high density of active sites, improved reactant adsorption, and efficient desorption of byproducts, including CO2. The catalyst exhibits excellent long-term stability with minimal degradation of 7 % over 100 h of continuous chronoamperometry testing and 2 % loss after 10,000 cycles. Furthermore, the activity of P,B-CoxOy NW@NF is evaluated in alkaline natural cow urine, requiring just 1.35 V at 50 mA/cm2 for UOR, demonstrating its practical relevance for real-world applications. These findings showcase the significant potential of P,B-CoxOy NW@NF as a scalable and stable electrocatalyst for sustainable hydrogen production from wastewater. 2025 Elsevier Ltd -
Biomass-derived N-doped carbon to anchor bimetallic-phospho boride for hydrogen evolution from alkaline seawater
Seawater electrolysis offers a sustainable pathway for hydrogen production, but is hindered by the limited activity and stability of electrocatalysts, with Pt-based materials being highly active yet costly and scarce. To address these issues, we synthesize nitrogen-doped carbon (NC) via a solvent-free method from golden shower biomass. NC is integrated with CoMoPB catalysts using a facile chemical reduction process. The resulting CoMoPB/NC catalyst exhibited superior HER activity, achieving a low overpotential of 34 mV at 10 mA/cm2 in alkaline natural seawater, outperforming the commercial Pt/C catalyst under similar conditions. The CoMoPB/NC catalyst demonstrated considerable stability at ?500 mA/cm2 for 100 h and showed strong HER performance in seawater electrolyzers, reaching ?1.98 V at 500 mA/cm2. This study explores the potential of biomass-derived catalysts to rival and surpass commercial noble metal-based systems, offering a cost-effective and sustainable solution for industrial-scale seawater electrolysis and renewable energy applications. 2025 Elsevier Ltd -
Aqueous symmetric supercapacitor based on hydrothermally grown reduced graphene oxide wrapped cobalt oxide nanocomposites: An efficient paradigm for enhanced performance in supercapacitors
Extensive research has been carried out on the development of electrode materials for energy storage applications, especially in the field of supercapacitors. The present work is the first report on the effect of rGO concentration on the electrochemical properties of reduced graphene oxide-cobalt oxide (rGO-Co3O4) nanocomposites. A symmetric supercapacitor device is assembled by compounding two hydrothermally grown rGO-Co3O4 nanocomposite electrodes separated by a membrane dipped in a 3 M KOH aqueous electrolyte solution. The device delivered a specific capacitance of 1006 Fg?1, energy density of 357.44 W h kg?1, and a power density of 1600 W kg?1 at a current density of 2 Ag?1. It showed a cyclic stability of 80 % during 10,000 cycles at a very high current density of 5 Ag?1 and a coulombic efficiency of 100 %, which maintained a better electrochemical performance, implying that the as-synthesized electrodes are useful for portable energy storage devices. 2025 Elsevier Ltd. -
A heavy metal tolerant Thiopseudomonas alkaliphila strain as a potential plant growth promoter isolated from Bengaluru region
Thiopseudomonas alkaliphila, a Pseudomonadaceae has diverse environmental role that has not been much explored. Current study highlights, the isolated strain from industrial sites of Bengaluru with heavy metal tolerance against lead, chromium and cadmium. The antibiotic susceptibility test (AST) and minimum inhibitory concentration (MIC) showed sensitive against all the antibiotics used in the study. Subsequently, 16s rRNA analysis established and closely related to T. alkaliphila D2441 strain, whole genome was submitted, GenBank SRA database accession number is as follows PRJNA1258058. The unravelling of genetic determinants analyzed for heavy metals, antibiotic resistance and plant growth promoting traits were compared with related strains. A single chromosome with 2,400,551 bp length, average GC ratio 49.44 % and with 1941 protein-encoding genes (PEGs), the strain can bioremediate different heavy metals (354 genes/proteins), along with an aptitude as plant growth promoting rhizobacteria (PGPR) evidenced by genes showcasing tolerance against adverse environmental conditions under stress for phytohormones, plant nutrient acquisition, heat and shock chaperones, siderophore etc. The study highlights, T. alkaliphila as a non-pathogenic, potential heavy metal remediator with potential activity for PGPR traits at genetic levels. 2025 -
Analysing the impact of oil prices, economic activity, and trade policy uncertainty on CO2 emissions in the US context: A wavelet approach
This study examines the simultaneous co-movements between oil prices, economic activity, trade policy uncertainty, and CO2 emissions in the United States using a series of wavelet methodologies. Unlike traditional approaches, the wavelet approach is appropriate for understanding the time-varying associations at different frequencies and is designed to efficiently handle the non-stationary nature of economic and environmental time series data. The empirical results highlight the potential of a leading relationship where economic activities and trade policy uncertainties drive CO2 emissions in the US during the period from January 1990 to January 2022. Contrarily, the link between oil prices and CO2 emissions is characterized by intricate dynamics, exhibiting both lagging and leading co-movements at different frequencies. Moreover, economic activities have a positive impact on CO2 emissions, while in the high quantile tails, trade policy uncertainty decreases CO2 emissions. This means economic activity is slowing down during the period of high trade policy uncertainty. Our findings highlight the necessity of specific policies that reconcile economic growth with environmental sustainability, manage the effect of oil price changes on CO2 emissions, and match trade policies with emission-minimizing goals. Based on the results, this research offers important implications for policymakers to ensure the equilibrium between economic activity and environmental management within the scope of sustainable development goals. 2025 International Association for Gondwana Research -
Recent developments in melamine detection: Applications of gold and silver nanostructures in colorimetric and fluorometric assays
The purity of milk, traditionally regarded as a symbol of health and nourishment, has been undermined by the alarming issue of melamine (MLM) adulteration. This nitrogen-rich compound is illicitly introduced to falsely enhance protein content, posing significant health risks. Traditional detection methods are often labor-intensive, time-consuming, or require expensive equipment. In response, researchers have developed colorimetric detection techniques to efficiently screen milk for MLM contamination. These methods are particularly promising due to their ease of preparation, rapid detection, high sensitivity, and capability for naked-eye detection. Furthermore, the unique optical properties of advanced nanomaterials have facilitated fluorometric detection, wherein the presence of contaminants induces detectable changes in fluorescence intensity or wavelength. This study offers an in-depth review of recent advancements in colorimetric and fluorometric probes based on silver (Ag) and gold (Au) nanostructures, exploring their application in food analysis. It delves into the underlying sensing mechanisms of these probes, showcasing their efficacy in detecting food contaminants. Despite the numerous advantages of Ag and Au nanostructure-based probes, challenges remain, particularly in addressing the complexity of food matrices, achieving simultaneous detection of multiple analytes, and mitigating interference from testing conditions. Additionally, this review highlights the emergence of immunoassay-based sensors, noting that many commercially available MLM testing kits utilize ELISA and LFIA platforms. For the first time, a comprehensive list of MLM testing devices and assay kits is presented, accompanied by key findings from recent studies and recommendations for future research directions. 2024 The Author(s) -
Engineered biocorona on microplastics as a toxicity mitigation strategy in marine environment: Experiments with a marine crustacean Artemia salina
The marine environment has become a major sink for microplastics (MPs) wastes. When MPs interact with biological macromolecules, the biocorona forms on their surface, which can alter their biological reactivity and toxicity. In this study, we investigated the impact of biocorona formation on the toxicity of aminated (NH2) and carboxylated (COOH) polystyrene MPs towards the marine crustacean Artemia salina. Biocoronated MPs were prepared using cell-free extracts (CFEs) from microalgae Chlorella sp. (phytoplankton) and the brine shrimp Artemia salina (zooplankton). The results revealed that biocorona formation effectively reduced the toxicity of MPs. Pristine NH2-MPs exhibited higher reactive oxygen species production (ROS) (182%) compared to COOH-MPs (162%) in Artemia salina. Notably, NH2-MPs coronated with brine shrimp CFE exhibited a substantial reduction in ROS production (127%) than those coronated with algal CFE, with COOH-MPs showing a similar trend (120%). Biocorona formation also significantly decreased malondialdehyde (MDA) levels and antioxidant activity compared to pristine MPs. Molecular docking and dynamics simulations demonstrated a strong binding between polystyrene and acetylcholinesterase (AChE). In vitro studies indicated that pristine NH2-MPs exhibited more reduction in AChE activity (84%) compared to COOH-MPs (95%). However, no significant reduction in AChE activity was observed upon exposure to MPs coronated with either algal or brine shrimp cell-free extracts. Independent action modeling indicated an antagonistic interaction for MPs coronated with both the CFEs. Pearson correlation and cluster heatmap analysis suggested that the toxicity reduction in Artemia salina might be driven by decreased oxidative stress followed by the corona formation. Overall, this study provides valuable insights into the potential of biomolecules from phytoplankton and zooplankton to reduce MPs toxicity in Artemia salina, while highlighting their role in modulating the toxicity of other marine pollutants. 2024 The Author(s) -
Impact of plastic contaminants on marine ecosystems and advancement in the detection of micro/nano plastics: A review
Micro/nanoplastics pollute all levels of the food web, beginning from aquatic algae, invertebrates, and other fish, through bioaccumulation or even physical and chemical damages augmenting degradation of the marine ecosystem. Besides plastic litter, other toxic chemicals employed in the manufacture of plastics also destroy stable ecosystems. Micro/nanoplastics are toxic to marine organisms through induction of blockage of ingestion, oxidative stress, and reproductive effects. Bivalves such as oysters accumulate microplastics in tissues, which decreases filtration rates. Polystyrene nanoplastics induce endocrine disturbance and neurotoxicity in fish. Seabirds suffer from gut inflammation ("plasticosis"), and zooplankton suffers from decreased feeding rates, which impacts trophic transfer. This review identifies some of the recent developments in electrochemical detection techniques, with a focus on electrochemical sensors and surface-enhanced Raman spectroscopy (SERS). Electrochemical sensors like CdS/CeO? heterojunction-based sensors have been able to detect 0.38 ng/mL of polystyrene nanoplastics. Biochar-modified electrodes and nanoporous gold sensors have also become more sensitive to trace detection levels (?0.44 nM) for microplastics. SERS-based techniques, for instance, membranes with Ag nanoparticles on anodic aluminium oxide (AAO) and metalphenolic networks with luminescence, have facilitated detection of polystyrene, polyethylene, and polypropylene nanoplastics in environmental matrices, with detection limits of 0.1 ?g/mL for 500 nm polystyrene and 1 ?g/mL for smaller plastic mimics. Although portable Raman spectrometers are sufficient for larger particulates, they need SERS enhancement for detecting oceanic matrix-bound nanoparticles. This article presents a critical overview of recent progress in the application of electrochemical sensors, Raman spectroscopy, and commercially available hardware to investigate their extended applications. Challenges and future directions for improved real-time monitoring with improved sensitivity and selectivity are also presented along with interference mitigation. 2025 The Author(s) -
An attention-based loss function and synthetic minority oversampling technique for alleviating class imbalance in predicting diabetes
Diabetes is a chronic disease due to higher blood sugar (or Glucose) levels in the blood. This study proposes a novel attention-based loss function and a lightweight artificial neural network (ANN) called Diabetic Lite (DB-Lite) for diabetes prediction in the Pima Indian Diabetes Dataset (PIDD). We show that the Pima dataset has many challenges. It is a small and imbalanced dataset; moreover, many features are non-linearly correlated in this dataset. The novelties of this research work are as follows: (i) A novel loss function of attention-based binary cross entropy (ABCE) is proposed for the first time to alleviate the statistical imbalance present within the Pima dataset. This ABCE loss function is incorporated in the DB-Lite model, which is trained from scratch. (ii) A Swish activation function is deployed in the hidden layer of DB-Lite instead of Rectified Linear Unit (ReLU) to deal with the non-linear dependency of features with the final outcome. (iii) The synthetic minority oversampling technique (SMOTE) is used as a pre-processing technique to mitigate the class imbalance problem from the Pima dataset. (iv) An adaptive learning rate is utilized while training the model to speed up the convergence of the DB-Lite model. Our final proposed framework has achieved 99.7% accuracy, 99.4% precision, 99.8% recall, and 99.6% F1 score in testing, which is the best result on this Pima dataset. The Welch t-testing (as a statistical hypothesis testing) and 10-fold cross-validation are utilized to prove the validity of the proposed loss function. 2025 -
Appraisal of prolyl 4-hydroxylase alpha subunit gene polymorphisms in Spondyloepimetaphyseal dysplasia of Handigodu type (SEMDHG)
Background: The Handigodu variant of Spondyloepimetaphyseal Dysplasia (SEMDHG) is a severe, progressive osteoarthritic disorder characterized by chronic pain and joint degeneration. Clinically, the disorder presents in three distinct phenotypic forms, each exhibiting varying degrees of stature reduction and disease severity. Urine analysis of affected individuals reveals an elevated peptide-bound proline to 4-hydroxyproline ratio relative to controls, suggesting disruptions in collagen metabolism. Given the critical role of prolyl 4-hydroxylase enzymes in stabilizing collagen structure, this study undertook a comprehensive sequence analysis of all three isoforms of prolyl 4-hydroxylase in both affected and unaffected individuals to elucidate potential molecular underpinnings of the disorder. Method: The entire exonic regions and 2000 base pairs upstream of the translation start sites of the P4HA1, P4HA2, and P4HA3 genes were sequenced in a cohort of 300 individuals, comprising 166 affected and 134 unaffected individuals. Results: Sequence analysis of the ? (I), ? (II), and ? (III) subunit genes identified three novel SNPs and a 39-bp deletion variant, in addition to ten previously reported SNPs catalogued in dbSNP. The SNP rs28384495 in P4HA1, the 39-bp deletion variant, and a novel mutation (SNP3) in P4HA3 exhibited significantly different allele frequencies between patients and controls. Genotype association analysis revealed that SNPs in P4HA1 and P4HA3 were associated with Type 2 and Type 3 HD under various genetic models. Notably, all Type 2 HD patients were heterozygous for the 39-bp deletion, whereas all Type 3 HD patients were homozygous for the variant. Haplotype analysis corroborated the findings of the genotype association analysis. Conclusion: This study is the first to account an association between the P4H gene and disease. Further research is needed to evaluate the functional implications of the identified mutations. 2024 -
Comprehensive investigation on mechanical properties of mango seed shell short fiber-reinforced epoxy based polymer composites
The mechanical properties of discarded Mango Seed Shell Fiber (MSF)-reinforced epoxy composites are studied in this work. MSF, which was obtained through agricultural wastes, was added to the epoxy matrix in varying weight fractions viz., 5%, 10%, 15%, 20%, and 25% using the hand lay-up method. The outcome shows that the best mechanical performance is reached at the 15% MSF content, i.e., the tensile strength of 29.35?MPa and tensile modulus of 758?MPa, an improvement of 24% in comparison with the unreinforced (neat) epoxy. The modulus and flexural strength were 2962?MPa and 48.13?MPa for 15% MSF content which was 68% and 42% more than neat epoxy. The highest impact strength of 75.93?J/m for 15% MSF which corresponds to 148% higher than the neat epoxy, and the hardness was between 47 RHN and 56 RHN and was maximum for 10% MSF. The novelty of the current study lies in the utilization of mango seed shell fiber, which is an underutilized agro-waste product that has been utilized systematically as a reinforcing element in epoxy composite and the determination of optimal fiber loading by thorough mechanical testing is accomplished in the present work. The results provide the base of mechanical performance data of MSF-reinforced eco-composites and confirm its opportunities as sustainable and cost-efficient reinforcement for lightweight and environmental-friendly structural applications. 2026 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/ -
Multimodal artificial intelligence for early cancer detection via liquid biopsy, imaging, and clinical records
Tumours are diverse and multiscale, making it difficult for modern medicine to diagnose early cancer. Using structured clinical data, radiologic imaging features, and liquid samples, this research presents a multimodal AI framework for the early and reliable detection of cancer. The proposed approach surpasses single-modality approaches by integrating signals from various domains, including cancer genetic, anatomical, and physiological data. Using attention-based fusion, representation learning, and better preprocessing, we developed a prediction model that fine-tuned the weights of different modes. The results of the experiments demonstrated that it outperformed unimodal models on all datasets in terms of sensitivity, specificity, and generalisation. The framework has potential for screening purposes because of its ability to detect cancer at an early stage. Clinical confidence and interpretability were both boosted by the results of explainability tests, which revealed substantial feature contributions. The suggested multimodal framework outperformed unimodal baselines across all assessment cohorts with an AUC of 0.94, sensitivity of 0.91, and specificity of 0.88. Experimental results confirm multimodal fusion's clinically interpretable early cancer detection and precision oncology decision assistance. Copyright 2026. Published by Elsevier B.V. -
Molecular and electronic structure of 5-coordinated [Fe(CO)?(X?)] complexes: A quantum chemical study
Quantum mechanical DFT calculations were performed on Fe(CO)5 and for the axial and equatorial isomers of 5-coordinated [Fe(CO)4(X2)] (where X = N, P, As, and Sb) complexes. The equatorially substituted complexes of 5-coordinated [Fe(CO)4(X2)] are more stable than the axially substituted complexes, as seen from their energy values. These complexes were further studied to understand their bonding nature using results from Natural population analysis (NPA) and energy decomposition analysis (EDA) calculations. The Wiberg bond indices (WBI) analysis provides the bond index of the bond between Fe and X2. The Frontier molecular orbital (FMO) studies show these complexes have a HOMO-LUMO energy gap in the values ranging from 1.99 to 5.04 eV, which is less than that of [Fe(CO)5]. From the Natural bond order (NBO) analysis, the contribution of the X?-atom is smaller in the ? bond formation compared to X? in P2, As2, and Sb2. Similar contributions are seen with the carbonyl group (in the Fe[sbnd]C bond), although the contribution of the carbon atom is larger than that of the X atom. 2025 Elsevier B.V.