Search Results (139)

This study aimed to develop innovative functional gummy candies enriched with protein hydrolysates derived from porcine liver, enhancing their antioxidant and antimicrobial properties. First, the overall consumer acceptability (OA) was assessed to determine the most suitable combination of gummy matrix components. Selected combinations were then analyzed for antioxidant activity (ABTS•+, DPPH•), antimicrobial effects, microbiological safety, and physicochemical characteristics. The incorporation of liver hydrolysates significantly increased antioxidant capacity. The highest activity was observed in sample GC5Pa24Ag, hydrolyzed with papain for 24 h and formulated with agar, showing ABTS•+ and DPPH• scavenging activities of (67.6 ± 0.98 µmol/g) and (49.14 ± 1.00%), respectively (p ≤ 0.05). Pepsin hydrolyzed samples (GC2Pe3Gl, GC2Pe6Gl, GC2Pe24Gl) exhibited significantly larger inhibition zones against Listeria monocytogenes ATCC 13932, Escherichia coli ATCC 25922, and Salmonella enterica subsp. enterica serovar Typhimurium ATCC 14028 compared to the control (p < 0.05). Among all, GC5Pa24Ag demonstrated the broadest antimicrobial activity, with a 29.0 ± 0.2 mm inhibition zone against all tested pathogens. These findings suggest that porcine liver hydrolysates can be successfully incorporated into confectionery products to create functional gummies with potential health benefits, offering antioxidant protection and antimicrobial effects in a consumer-friendly form. Full article

This study presents a multi-modal investigation into the wear behavior of bio-based epoxy composites reinforced with multi-walled carbon nanotubes (MWCNTs) at 0–0.75 wt%. A Taguchi L16 orthogonal array was employed to systematically assess the influence of MWCNT content, load (20–50 N), and sliding speed (1–2.5 m/s) on wear rate (WR), coefficient of friction (COF), and surface roughness (Ra). Statistical analysis revealed that MWCNT content contributed up to 85.35% to wear reduction, with 0.5 wt% identified as the optimal reinforcement level, achieving the lowest WR (3.1 mm³/N·m) and Ra (0.7 µm). Complementary morphological characterization via SEM and AFM confirmed microstructural improvements at optimal loading and identified degradation features (ploughing, agglomeration) at 0 wt% and 0.75 wt%. Regression models (R² > 0.95) effectively captured the nonlinear wear response, while a Random Forest model trained on GLCM-derived image features (e.g., correlation, entropy) yielded WR prediction accuracy of R² ≈ 0.93. Key image-based predictors were found to correlate strongly with measured tribological metrics, validating the integration of surface texture analysis into predictive modeling. This integrated framework combining experimental design, mathematical modeling, and image-based machine learning offers a robust pathway for designing high-performance, sustainable nanocomposites with data-driven diagnostics for wear prediction. Full article

In the wide range of human diseases, Parkinson’s disease (PD) has a high incidence, according to a recent survey by the World Health Organization (WHO). According to WHO records, this chronic disease has affected approximately 10 million people worldwide. Patients who do not receive an early diagnosis may develop an incurable neurological disorder. PD is a degenerative disorder of the brain, characterized by the impairment of the nigrostriatal system. A wide range of symptoms of motor and non-motor impairment accompanies this disorder. By using new technology, the PD is detected through speech signals of the PD victims by using the reduced instruction set computing 5th version (RISC-V) processor. The RISC-V microcontroller unit (MCU) was designed for the voice-controlled human-machine interface (HMI). With the help of signal processing and feature extraction methods, the digital signal is impaired by the impairment of the nigrostriatal system. These speech signals can be classified through classifier modules. A wide range of classifier modules are used to classify the speech signals as normal or abnormal to identify PD. We use Matrix Laboratory (MATLAB R2021a_v9.10.0.1602886) to analyze the data, develop algorithms, create modules, and develop the RISC-V processor for embedded implementation. Machine learning (ML) techniques are also used to extract features such as pitch, tremor, and Mel-frequency cepstral coefficients (MFCCs). Full article

This study presents a robust machine learning framework based on Gaussian process regression (GPR) to predict the tensile strength of polymer nanocomposites reinforced with various nanofillers and processed under diverse techniques. A comprehensive dataset comprising 25 polymer matrices, 22 surface functionalization methods, and 24 processing routes was constructed from the literature. GPR, coupled with Monte Carlo sampling across 2000 randomized iterations, was employed to capture nonlinear dependencies and uncertainty propagation within the dataset. The model achieved a mean coefficient of determination (R²) of 0.96, RMSE of 12.14 MPa, MAE of 7.56 MPa, and MAPE of 31.73% over 2000 Monte Carlo iterations, outperforming conventional models such as support vector machine (SVM), regression tree (RT), and artificial neural network (ANN). Sensitivity analysis revealed the dominant influence of Carbon Nanotubes (CNT) weight fraction, matrix tensile strength, and surface modification methods on predictive accuracy. The findings demonstrate the efficacy of the proposed GPR framework for accurate, reliable prediction of composite mechanical properties under data-scarce conditions, supporting informed material design and optimization. Full article

Urban air pollution poses significant risks to cultural heritage buildings, particularly in polluted megacities like Delhi, India. The Red Fort, a UNESCO World Heritage Site and a symbol of India’s rich history, is highly susceptible to degradation caused by air pollutants. Despite its great importance as an Indian and world heritage site, no studies have focused on characterizing its constituent materials or the degradation phenomena taking place. This study was developed in the framework of the MAECI (Italian Ministry of Foreign Affairs) and the Department of Science and Technology under the Ministry of Science and Technology, India, project: Indo—Italian Centre of Excellence for Restoration and Assessment of Environmental Impacts on Cultural Heritage Monuments. To understand their composition and degradation, Vindhyan sandstone and black crust samples were studied. Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (SEM-EDX), X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) indicated that the red sandstone predominantly consisted of quartz and microcline, while the black crusts mainly comprised gypsum, bassanite, weddellite, quartz, and microcline. The analysis attributed the formation of gypsum to exogenous sources, such as construction activities and cement factory emissions. This pioneering study provides a basis for further research into the impacts of air pollution on Indian patrimony and promotes conservation strategies. Full article

Diagnosing tuberculosis (TB) in children presents significant challenges, necessitating the identification of reliable biomarkers for accurate diagnosis. In this study, we investigated plasma matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) as potential diagnostic markers. A prospective case–control study involved 167 children classified into confirmed TB, unconfirmed TB, and unlikely TB control groups. Plasma levels of MMPs (MMP 1, 2, 3, 7, 8, 9, 12, and 13) and TIMPs (TIMP 1, 2, 3, and 4) were measured using multiplex assays. Elevated baseline levels of MMP-1, MMP-2, MMP-7, MMP-9, TIMP-1, TIMP-2, TIMP-3, and TIMP-4 were observed in active TB cases compared to unlikely TB controls. Receiver operating characteristics (ROC) analysis identified MMP-1, MMP-2, MMP-9, and TIMP-1 as potential biomarkers with over 80% sensitivity and specificity. A three-MMP signature (MMP-1, MMP-2, and MMP-9) demonstrated 100% sensitivity and specificity. The findings suggest that a baseline MMP signature could serve as an accurate biomarker for diagnosing pediatric TB, enabling early intervention and effective management. Full article

Due to the complementary operational features, photovoltaic (PV) and fuel cell (FC) systems are increasingly being integrated into hybrid microgrids. PV systems provide clean energy during the day, while FCs provide continuous power supply throughout the day and night; thus, FCs can address PV’s incapacity during the night. However, voltage instability, frequency deviation, and enhanced harmonic distortion can result from the intrinsic intermittency of solar energy, switching errors in power electronic equipment, and varying load demands. Thus, a fuzzy logic-based current-controlled voltage source inverter (CC-VSI) is proposed in this paper to overcome these issues and enhance power quality in PV-FC hybrid microgrids. As per IEEE 1547 regulations, the fuzzy controller dynamically modifies the inverter current to maintain steady voltage and frequency profiles. MATLAB/Simulink (R2022a) is used to model and simulate the system, and its performance is evaluated under various reactive load scenarios. To test the efficacy of the proposed control technique, various power quality metrics, viz., voltage profiles (sag and swell), frequency profile, and total harmonic distortions, are plotted when subjected to large reactive load variations. The simulation results that are obtained with the proposed fuzzy-based current control technique are compared with the conventional artificial neural networks-based controller to verify the effectiveness. From the comparison study, it is found that the proposed technique shows superior power quality performance over the conventional technique. This encourages the development of renewable energy-based sustainable hybrid microgrids worldwide. Full article

Renewable energy and voltage source inverter-driven microgrids generally lack natural inertia to provide transient energy support during sudden load demands. To address this, the virtual synchronous generator (VSG) is a state-of-the-art control technique applied in power controllers to emulate virtual inertia during sudden load changes. This allows for stable power delivery from the source to the loads during sudden active power load demands. However, in systems with large inductively dominant load demands, conventional VSG-based power controllers may exhibit a delayed reactive power response due to their inertia-emulating characteristics, potentially affecting the overall power-sharing performance. To address this limitation of VSG control, this paper proposes an advanced control scheme in which the VSG is supported by appropriately designed voltage and current controllers. Conventionally, classical tuning techniques are used to design the controllers in the forward paths of the voltage and current controllers (CVAs). Thus, the conventional control scheme is a combination of a VSG and CVAs. Recently, a hybrid modified pole-zero cancellation technique has been discussed in the literature for the design of voltage and current controllers (HVAs) to improve the vector control of the inverter. This method supports better tuning for controllers of both forward and cross-coupling paths. Therefore, to improve the power delivery with VSG-based control when subjected to inductive load changes, this paper proposes an advanced control scheme that is based on the combination of VSG and HVA. The performance of both conventional and proposed control schemes is verified through simulation in MATLAB/Simulink under two different test load conditions, namely good and poor power factor loadings. Based on the results obtained during these test cases, the response and power delivery capability of the proposed control scheme is comparable with that of the conventional control scheme. The results verify that the power delivery capability of the microgrid with the proposed control scheme is improved by 25% compared to the conventional control scheme. Full article

Power electronic converter-based microgrids generally suffer from poor power delivery/handling capability during sudden load changes, especially during islanded operations. This is due to the lack of transient energy support to compensate for sudden load changes. The literature has suggested the use of adaptive droop control to provide compensation during transient conditions, thereby improving the power delivery capability. In this context, fuzzy logic-based adaptive droop control is a state-of-the-art technique that was developed based on empirical knowledge of the system. However, this way of designing the droop coefficient values without considering the mathematical knowledge of the system leads to instability during transient conditions. This problem further aggravates when dominant inductive load changes occur in the system. To address this limitation, this paper proposes an improved fuzzy logic-based adaptive droop control method. In the proposed methodology, the values of droop coefficients that are assigned for different membership functions are designed based on the stability analysis of the microgrid. In this analysis, the feasible range of active power–frequency droop values that could avoid instability during large inductive load changes is identified. Accordingly, the infeasible values are avoided in the design of the fuzzy controller. The performance of the proposed and the conventional fuzzy logic methods is verified through simulation in MATLAB/Simulink. From the results, it is identified that the proposed method has improved the power delivery capability of the microgrid by 14% compared to the conventional method. Full article

Campylobacter jejuni (C. jejuni) is the primary Campylobacter species and a major cause of foodborne illness associated with poultry products. This review focuses on lactic acid bacteria (LAB), especially Lactobacillus species, and acid whey as a dairy by-product for C. jejuni control in poultry as a sustainable method. LAB strains L. crispatus exhibit a cecal colonization reduction of >90% by competitive exclusion and bacteriocin activity, while L. johnsonii FI9785 decrease bacterial load 4–5 log₁₀. Acid whey, which is abundant in organic acids (e.g., lactic acid) and bioactive peptides (e.g., lactoferrin), reduces C. jejuni viability, decreasing the food product contamination on the carcass for a short time by 40%. LAB antimicrobial function becomes more effective when used with acid whey, although specific farm-related variables require additional optimization. Some of the key strategies include co-encapsulating LAB with acid whey or plant-derived antimicrobials for improving survival, conducting in vivo trials in commercial farm conditions to evaluate scalability, and adding whey into feed (1–2% inclusion) or applying it as a pre-slaughter spray. These strategies enable the antibiotic-free production and circular economy goals through repurposing low-cost acid whey. Future studies should directly compare them with standard antimicrobials to confirm their scalability for poultry safety. Full article

Advanced biotherapeutic systems such as gene therapy, mRNA lipid nanoparticles, antibody–drug conjugates, fusion proteins, and cell therapy have proven to be promising platforms for delivering targeted biologic therapeutics. Preserving the intrinsic stability of these advanced therapeutics is essential to maintain their innate structure, functionality, and shelf life. Nevertheless, various challenges and obstacles arise during formulation development and throughout the storage period due to their complex nature and sensitivity to various stress factors. Key stability concerns include physical degradation and chemical instability due to various factors such as fluctuations in pH and temperature, which results in conformational and colloidal instabilities of the biologics, adversely affecting their quality and therapeutic efficacy. This review emphasizes key stability issues associated with these advanced biotherapeutic systems and approaches to identify and overcome them. In gene therapy, the brittleness of viral vectors and gene encapsulation limits their stability, requiring the use of stabilizers, excipients, and lyophilization. Keeping cells viable throughout the whole cell therapy process, from culture to final formulation, is still a major difficulty. In mRNA therapeutics, stabilization strategies such as the optimization of mRNA nucleotides and lipid compositions are used to address the instability of both the mRNA and lipid nanoparticles. Monoclonal antibodies are colloidally and conformationally unstable. Hence, buffers and stabilizers are useful to maintain stability. Although fusion proteins and monoclonal antibodies share structural similarities, they show a similar pattern of instability. Antibody–drug conjugates possess issues with conjugation and linker stability. This review outlines the stability issues associated with advanced biotherapeutics and provides insights into the approaches to address these challenges. Full article

Ceruminous glands are specialized apocrine sweat glands. Neoplastic transformation of these glands is often seen in the external ear canal. Tumors arising from these glands can present a diagnostic dilemma because of their varied clinical and histological manifestations. This study was conducted as little information is currently available on these neoplasms. The present study undertakes a scoping review of research on canine and feline CGTs according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for scoping reviews on three databases (NCBI-PubMed, Scopus and ScienceDirect) from 1980 to 2023 (43 years) to determine the extent of the existing literature on its clinicopathological characteristics, overall prognosis, survival rates, and biomarker studies. Seventeen canine and nineteen feline publications that met the inclusion criteria were analyzed. Eleven canine and twelve feline unpublished cases of CGTs managed in Malaysia were also reviewed. Our study concluded surgical excision as part of the treatment for this neoplasia may lengthen animals’ survival period and produce a satisfactory quality of life; however, a substantial risk of complications, especially after aggressive surgical excision, exists. Full article

Alzheimer’s disease (AD), an intense neurological illness, severely impacts memory, behavior, and personality, posing a growing concern worldwide due to the aging population. Early and accurate detection is crucial as it enables preventive measures. However, current diagnostic methods are often inaccurate in identifying the disease in its early stages. Although deep learning-based bioimaging has shown promising results in medical image classification, challenges remain in achieving the highest accuracy for detecting AD. Existing approaches, such as ResNet50, VGG19, InceptionV3, and AlexNet have shown potential, but they often lack reliability and accuracy due to several issues. To address these gaps, this paper suggests a novel bioimaging technique by developing a custom Convolutional Neural Network (CNN) model for detecting AD. This model is designed with optimized layers to enhance feature extraction from medical images. The experiment’s first phase involved the construction of the custom CNN structure with three max-pooling layers, three convolutional layers, two dense layers, and one flattened layer. The Adam optimizer and categorical cross-entropy were adopted to compile the model. The model’s training was carried out on 100 epochs with the patience set to 10 epochs. The second phase involved augmentation of the dataset images and adding a dropout layer to the custom CNN model. Moreover, fine-tuned hyperparameters and advanced regularization methods were integrated to prevent overfitting. A comparative analysis of the proposed model with conventional models was performed on the dataset both before and after the data augmentation. The results validate that the proposed custom CNN model significantly overtakes pre-existing models, achieving the highest validation accuracy of 99.53% after data augmentation while maintaining the lowest validation loss of 0.0238. Its precision, recall, and F1 score remained consistently high across all classes, with perfect scores for the Moderate Demented and Non-Demented categories after augmentation, indicating superior classification capability. Full article

The rapid growth of electric vehicle (EV) adoption presents significant challenges in planning efficient charging infrastructure, including suboptimal station placement, energy consumption, and rising infrastructural costs. The conventional methods, such as grey wolf optimization (GWO), fail to address real-time user demand and dynamic factors like fluctuating grid loads and environmental impact. These approaches rely on fixed models, often leading to inefficient energy use, higher operational costs, and increased traffic congestion. This paper proposes a novel framework that integrates deep Q networks (DQNs) for real-time charging optimization, coupled with multimodal bioinspired algorithms like ant lion optimization (ALO) and moth flame optimization (MFO). Unlike conventional geographic placement models that overlook evolving travel patterns, this system dynamically adapts to user behavior, optimizing both onboard and offboard charging systems. The DQN enables continuous learning from changing demand and grid conditions, while ALO and MFO identify optimal station locations, reducing energy consumption and emissions. The proposed framework incorporates dynamic pricing and demand response strategies. These adjustments help balance energy usage, reducing costs and preventing overloading of the grid during peak times, offering real-time adaptability, optimized station placement, and energy efficiency. To improve the performance of the system, the proposed framework ensures more sustainable, cost-effective EV infrastructural planning, minimized environmental impacts, and enhanced charging efficiency. From the results for the proposed system, we recorded various performance parameters such as the installation cost, which decreased to USD 1200 per unit, i.e., a 20% cost efficiency increase, optimal energy utilization increases to 85% and 92% during peak hours and off-peak hours respectively, a charging slot availability increase to 95%, a 30% carbon emission reduction, and 95% performance retention under the stress condition. Further, the power quality is improved by reducing the sag, swell, flicker, and notch by 2 V, 3 V, 0.05 V, and 0.03 V, respectively, with an increase in efficiency to 89.9%. This study addresses critical gaps in real-time flexibility, cost-effective station deployment, and grid resilience by offering a scalable and intelligent EV charging solution. Full article

The increasing demand for food safety and the need to combat emerging foodborne pathogens have driven the development of innovative packaging solutions. Active packaging, particularly those incorporating antimicrobial agents, has emerged as a promising approach to enhance food preservation and safety. Among these agents, bacteriophages (phages) have gained significant attention due to their specificity, efficacy, and natural origin. This manuscript explores the role of active packaging in protecting against foodborne pathogens, with a particular focus on bacteriophages. The review overviews recent advances in antimicrobials in food packaging, followed by a detailed discussion of bacteriophages, including their classification, mode of action, multidisciplinary applications, and their use as antimicrobial agents in active food packaging. The manuscript also highlights commercially available bacteriophage-based products and addresses the challenges and limitations associated with their integration into packaging materials. Despite their potential, issues such as stability, regulatory hurdles, and consumer acceptance remain critical considerations. In conclusion, bacteriophages represent a promising tool in active packaging for enhancing food safety, but further research and innovation are needed to overcome existing barriers and fully realize their potential in the food industry. Full article