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Abstract In general, integer order differential equations fall short of adequately describing a range of phenomena in many disciplines of science and engineering when compared to fractional order differential equations. In the context of the Caputo fractional derivative, this study examines the nonlinear fractional order foam drainage problem as well as the fractional order modified regularized long wave equation. In this work, we offer two novel techniques by combining the Elzaki transform with the Adomian decomposition method (ADM) and the homotopy perturbation method (HPM). We first translate the problem into its differential partner using the Elzaki transform, and then we use He’s polynomials and the Adomian polynomials that are derived from HPM and ADM, respectively. These are two very powerful supports for nonlinear problems. The efficacy and precision of the recommended techniques are illustrated through the use of numerical and graphical findings. The numerical simulations of the derived solutions make use of a number of relevant values of the order of fractional parameter λ. It is important to keep in mind that the recommended techniques can achieve higher overall performance since they need less computational effort than alternative strategies while yet retaining a high degree of numerical precision. The solutions acquired through the suggested techniques have been compared with the classical solutions and the solution obtained by ADM, new iterative method (NIM). The considerable results show that these approaches minimize the heavy calculation without restricting variables while requiring no assumptions. The present study can confirm the applicability of the new generalized Caputo type fractional operator to mathematical real-world problems. This approach shows to be one of the most efficient methods to solve fractional order nonlinear differential equations that arise in related fields of engineering and science. These methods also make clear how to use fractal calculus in real life. Furthermore, the results of this study support the value and significance of fractional operators in real-world applications.

Abstract Spices played crucial and variable roles in traditions, culture, history, religious ceremonials and festivals along with fetching food flavor and microbial protection globally due to presence of structurally unique and multi-natured chemotypes. Their existence in dishes portrayed key roles in improving shelf life by regulating spoilage of cuisine with different synergistic mechanism. Histo-anatomically (A) sowa exhibited distinguished cellular attributes which created remarkable differences with T. ammi. HPTLC chemometrics of both fruits revealed several peaks for active metabolomics with unique isocratic combination of menstruum. GC-MS study of hydro-distillate exhibited presence of monoterpenic cyclic and aromatic hydrocarbons, alcoholic and ketonic groups along with phenolic derivative that covers majorly 90% of total metabolites. Combined essential oils (EOs 1 + 2) of both fruits showed excellent antimicrobial activity against various clinical pathogenic strains such as K. pneumoniae at 10 µL/mL, S. aureus at 2.5 µL/mL, E. coli and E. faecalis at 1.25 µL/mL, and MRSA and Bcereus at 0.625 µL/mL and (C) albicans at 0.312 µL/mL as the MIC. The antioxidant study of (EOs 1 + 2) with maximum inhibition percentage to DPPH assay was 84.02 ± 1.05 at 100 µg/mL, and minimal inhibition was 72.31 ± 0.63 at 5 µg/mL with IC50 value 4.69 ± 0.22 µg/mL, while ABTS assay extreme was 79.15 ± 2.14 µg/mL and minimal was 67 ± 1.34 with the IC50 value of 18.37 ± 0.15 µg/mL, in superoxide assay uppermost inhibition was 81.03 ± 0.27 µg/mL and lowest was at 65.16 ± 3.15 with the IC50 value 16.46 ± 0.54, and H2O2 radical scavenging activity, predominant value was 78.01 ± 0.47 and least was 64.1 ± 2.01 with IC50 15.58 ± 0.34. These comparative key diagnostic features and synergistic effect of multicomponent natural antimicrobials will provide profound intellect of ancient utility and further scientists to explore their multiple mechanistic modality and application in food and beverages industry.

Abstract Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental to the diagnosis because it contains detailed structural views of the brain, which is vital in identifying any of its abnormalities. The other option of performing an invasive biopsy is very painful and uncomfortable, which is not the case with MRI as it is free from surgically invasive margins and pieces of equipment. This helps patients to feel more at ease and hasten the diagnostic procedure, allowing physicians to formulate and practice action plans quicker. It is very difficult to locate a human brain tumor by manual because MRI scans produce large numbers of three-dimensional images. Complete applicability of pre-written computerized diagnostics, affords high possibilities in providing areas of interest earlier through the application of machine learning techniques and algorithms. The proposed work in the present study was to develop a deep learning model which will classify brain tumor grade images (BTGC), and hence enhance accuracy in diagnosing patients with different grades of brain tumors using MRI. A MobileNetV2 model, was used to extract the features from the images. This model increases the efficiency and generalizability of the model further. In this study, six standard Kaggle brain tumor MRI datasets were used to train and validate the developed and tested model of a brain tumor detection and classification algorithm into several types. This work consists of two key components: (i) brain tumor detection and (ii) classification of the tumor. The tumor classifications are conducted in both three classes (Meningioma, Pituitary, and glioma) and two classes (malignant, benign). The model has been reported to detect brain tumors with 99.85% accuracy, to distinguish benign and malignant tumors with 99.87% accuracy, and to type meningioma, pituitary, and glioma tumors with 99.38% accuracy. The results of this study indicate that the described technique is useful in the detection and classification of brain tumors.

Abstract Cancer has arisen from both genetic mutations and epigenetic changes, making epigenetics a crucial area of research for innovative cancer prevention and treatment strategies. This dual perspective has propelled epigenetics into the forefront of cancer research. This review highlights the important roles of DNA methylation, histone modifications and non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long non-coding RNAs, which are key regulators of cancer-related gene expression. It explores the potential of epigenetic-based therapies to revolutionize patient outcomes by selectively modulating specific epigenetic markers involved in tumorigenesis. The review examines promising epigenetic biomarkers for early cancer detection and prognosis. It also highlights recent progress in oligonucleotide-based therapies, including antisense oligonucleotides (ASOs) and antimiRs, to precisely modulate epigenetic processes. Furthermore, the concept of epigenetic editing is discussed, providing insight into the future role of precision medicine for cancer patients. The integration of nanomedicine into cancer therapy has been explored and offers innovative approaches to improve therapeutic efficacy. This comprehensive review of recent advances in epigenetic-based cancer therapy seeks to advance the field of precision oncology, ultimately culminating in improved patient outcomes in the fight against cancer.

Abstract Wound infections resulting from pathogen infiltration pose a significant challenge in healthcare settings and everyday life. When the skin barrier is compromised due to injuries, surgeries, or chronic conditions, pathogens such as bacteria, fungi, and viruses can enter the body, leading to infections. These infections can range from mild to severe, causing discomfort, delayed healing, and, in some cases, life-threatening complications. Zinc oxide (ZnO) nanoparticles (NPs) have been widely recognized for their antimicrobial and wound healing properties, while cinnamic acid is known for its antioxidant and anti-inflammatory activities. Based on these properties, the combination of ZnO NPs with cinnamic acid (CA) was hypothesized to have enhanced efficacy in addressing wound infections and promoting healing. This study aimed to synthesize and evaluate the potential of ZnO-CN NPs as a multifunctional agent for wound treatment. ZnO-CN NPs were synthesized and characterized using key techniques to confirm their structure and composition. The antioxidant and anti-inflammatory potential of ZnO-CN NPs was evaluated through standard in vitro assays, demonstrating strong free radical scavenging and inhibition of protein denaturation. The antimicrobial activity of the nanoparticles was tested against common wound pathogens, revealing effective inhibition at a minimal concentration. A zebrafish wound healing model was employed to assess both the safety and therapeutic efficacy of the nanoparticles, showing no toxicity at tested concentrations and facilitating faster wound closure. Additionally, pro-inflammatory cytokine gene expression was analyzed to understand the role of ZnO-CN NPs in wound healing mechanisms. In conclusion, ZnO-CN NPs demonstrate potent antioxidant, anti-inflammatory, and antimicrobial properties, making them promising candidates for wound treatment. Given their multifunctional properties and non-toxicity at tested concentrations, ZnO-CN NPs hold significant potential as a therapeutic agent for clinical wound management, warranting further investigation in human models.

Abstract The goal of this research is to create an ensemble deep learning model for Internet of Things (IoT) applications that specifically target remote patient monitoring (RPM) by integrating long short-term memory (LSTM) networks and convolutional neural networks (CNN). The work tackles important RPM concerns such early health issue diagnosis and accurate real-time physiological data collection and analysis using wearable IoT devices. By assessing important health factors like heart rate, blood pressure, pulse, temperature, activity level, weight management, respiration rate, medication adherence, sleep patterns, and oxygen levels, the suggested Remote Patient Monitor Model (RPMM) attains a noteworthy accuracy of 97.23%. The model's capacity to identify spatial and temporal relationships in health data is improved by novel techniques such as the use of CNN for spatial analysis and feature extraction and LSTM for temporal sequence modeling. Early intervention is made easier by this synergistic approach, which enhances trend identification and anomaly detection in vital signs. A variety of datasets are used to validate the model's robustness, highlighting its efficacy in remote patient care. This study shows how using ensemble models' advantages might improve health monitoring's precision and promptness, which would eventually benefit patients and ease the burden on healthcare systems.

Abstract Sleep deprivation is a critical issue that affects workers in numerous industries, including construction. It adversely affects workers and can lead to significant concerns regarding their health, safety, and overall job performance. Several studies have investigated the effects of sleep deprivation on safety and productivity. Although the impact of sleep deprivation on safety and productivity through cognitive impairment has been investigated, research on the association of sleep deprivation and contributing factors that lead to workplace hazards and injuries remains limited. To fill this gap in the literature, this study utilized machine learning algorithms to predict hazardous situations. Furthermore, this study demonstrates the applicability of machine learning algorithms, including support vector machine and random forest, by predicting sleep deprivation in construction workers based on responses from 240 construction workers, identifying seven primary indices as predictive factors. The findings indicate that the support vector machine algorithm produced superior sleep deprivation prediction outcomes during the validation process. The study findings offer significant benefits to stakeholders in the construction industry, particularly project and safety managers. By enabling the implementation of targeted interventions, these insights can help reduce accidents and improve workplace safety through the timely and accurate prediction of sleep deprivation.

Abstract In this work, we use two unique methodologies, the homotopy perturbation transform method and Yang transform decomposition method, to solve the fractional nonlinear seventh-order Kaup–Kupershmidt (KK) problem. The physical phenomena that arise in chemistry, physics, and engineering are mathematically explained in this equation, in particular, nonlinear optics, quantum mechanics, plasma physics, fluid dynamics, and so on. The provided methods are used to solve the fractional nonlinear seventh-order KK problem along with the Yang transform and fractional Caputo derivative. The results are significant and necessary for exploring a range of physical processes. This paper uses modern approaches and the fractional operator to develop satisfactory approximations to the offered problem. To solve the fractional KK equation, we first use the Yang transform and fractional Caputo derivative. He’s and Adomian polynomials are useful to manage nonlinear terms. It is shown that the suggested approximate solution converges to the exact one. In these approaches, the results are calculated as convergent series. The key advantage of the recommended approaches is that they provide highly precise results with little computational work. The suggested approach results are compared to the precise solution. By comparing the outcomes with the precise solution using graphs and tables we can verify the efficacy of the offered strategies. Also, the outcomes of the suggested methods at various fractional orders are examined, demonstrating that the findings get more accurate as the value moves from fractional order to integer order. Moreover, the offered methods are innovative, simple, and quite accurate, demonstrating that they are effective for resolving differential equations.

Abstract In automatic facial expression recognition (AFER) systems, modelling the spatio‐temporal feature information in a specific manner, coalescing, and its effective utilization is challenging. The state‐of‐the‐art studies have examined integrating multiple features to enhance the recognition rate of AFER systems. However, the feature variations between expressive and neutral face images are not fully explored to identify the expression class. The proposed research presents an innovative approach to AFER by modelling appearance variations in both expressive and neutral face images. The prominent contributions of the work are developing a novel and hybrid feature space by integrating the discriminative feature distribution derived from expressive and neutral face images; preserving the highly discriminative latent feature distribution using autoencoders. Local binary pattern (LBP) and histogram of oriented gradients (HOG) are the feature descriptors employed to derive the discriminative texture and shape information, respectively. The component‐based approach is employed, wherein the features are derived from the salient facial regions instead of the whole face. The three‐stage stacked deep convolutional autoencoder (SDCA) and multi‐class support vector machine (MSVM) are employed to address dimensionality reduction and classification, respectively. The efficacy of the proposed model is substantiated by empirical findings, which establish its superiority in terms of accuracy in AFER tasks on widely recognized benchmark datasets.

The group of nations which includes Brazil, Russia, India, China, and South Africa (BRICS) need to address rising energy demand, reduce carbon emissions, and sustain economic growth, necessitating a strategy that integrates environmental sustainability with economic development. The current study delves into the profound impact of energy mix, financial development, digitalization, and national income on carbon emissions in BRICS countries. The study uses a nonlinear panel quantile regression model utilizing panel data spanning from 2000 to 2023, by estimating the asymmetric impact of energy mix, financial development, and digitalization on carbon emissions of BRICS countries. The estimates assert that a higher energy mix ratio, favoring renewable sources for energy generation, serves as a potent catalyst in mitigating carbon emissions. Moreover, the study highlights the pivotal role of financial development in curbing carbon emissions by fostering investment in green energy and encouraging sustainable energy consumption. Furthermore, the study underscores the transformative effect of digitalization in BRICS countries, leading to a reduction in carbon emissions. This exemplifies how BRICS countries have strategically leveraged financial development and digitalization to significantly reduce carbon emissions. However, the study also prompts policymakers to address the challenges posed by increased energy consumption from non-renewable sources amid developmental activities. By providing fresh evidence on the impact of the energy mix, financial development, and digitalization on carbon emissions, this study augments the existing literature and underscores the imperative for concerted action in addressing environmental concerns.

Nanotechnology is a critical emerging technology that enables contributions, innovation and long-term impact in food, medicine and agriculture. Nanomaterials can improve the quantitative and qualitative development of safer, healthier and higher-quality semi-perishable or perishable functional foods. Nanotechnologies outperform traditional food manufacturing techniques in extending the shelf life of the food product, reducing contamination and producing higher-quality food. Nanotechnology applications improve the bioavailability, flavor, texture and stability of food by altering the particle diameter, potential clustering and charge density of food nanoparticles. The present review article highlights the role of nanotechnology in product development and quality maintenance as well as its potential role in agriculture.

This research aimed to explore natural, cost-effective alternatives to conventional antibiotics, focusing on Citrus sinensis peel and Moringa oleifera leaf extracts. The objectives were to extract, characterize, and evaluate the antimicrobial potential of these extracts against common poultry pathogens. The proximate analysis of M. oleifera leaf powder revealed 22% protein, 10% fat, 10% ash, and 10% fiber. Phytochemical screening of both aqueous and ethanol extracts of M. oleifera and orange peel indicated the presence of alkaloids, flavonoids, saponins, tannins, and total phenolics. These extracts exhibited significant antimicrobial activity against bacterial strains like E. coli, S. gallinarum, P. aeruginosa, B. cereus, Campylobacter, S. aureus and M. gallisepticum. In a study involving 30 broilers, those treated with M. oleifera extract showed superior antibacterial effects compared to other treatment groups. These findings suggest that M. oleifera leaf extract could serve as an effective, affordable alternative to antibiotics in broiler diets, potentially improving disease management in the poultry industry.

Cereal vinegars are unique and diverse condiments have long been among the center of culinary traditions, and their potential health advantages are becoming more widely recognized. Cereal vinegars add a distinct flavor to salad dressings, pickles, and marinades, balancing flavors and exciting taste perceptions. Production technologies of cereal vinegars and innovations are discussed in this article which are required for manufacturing of cereal-based vinegars. We discussed complicated procedures of fermentation and aging, highlighting the methods that impart these vinegars’ particular flavors and fragrances. Further, this paper explores into the nutritional wealth kept within these condiments and investigates the wide range of micronutrients, antio xidants, and bioactive substances found in them. It provides a brief summary of the potential health benefits of cereal vinegars and also discusses potential health hazards.

The research aimed to enhance the shelf life of sugarcane juice through carbonation, employing different concentrations of juice mixed with syrup. Treatments included T0 (100% juice), T1 (80% juice), T2 (60% juice), T3 (40% juice), and T4 (20% juice). Physicochemical analyses encompassed color, pH, total soluble solids, and titratable acidity, with T0 (pH: 5.2, total soluble solids: 18.5°Brix) showing significant values. However, T4 exhibited the most significant (p ≤ .05) results, suggesting its potential for shelf-life extension. Additionally, bioactive compounds such as ascorbic acid, total phenolic, and flavonoid contents were measured, with T4 demonstrating noteworthy levels. Antioxidant assays (DPPH: 65% inhibition, TFC: 45 mg QE/100 ml, TPC: 130 mg GAE/100 ml, and FRAP: 320 µmol Fe2+/L) highlighted T4 the most effective, showcasing robust antioxidant activity. Sensory evaluation, conducted at refrigeration temperatures of 4–10°C, favored T4, indicating its superior acceptability. In conclusion, Carbonated sugarcane juice (20% T4) extends shelf life, preserves quality, and boosts antioxidants.

The rapid identification and categorisation of breast cancers using low-contrast MRI images presents a significant challenge due to the disease’s prevalence among women of all ages. Nowadays, it is very difficult to classify and generalise models from low-contrast MRI datasets due to the prevalence of class imbalance caused by a wide range of symptoms and untrustworthy data sources. Using ensemble learning with optimised k-means helps with these problems; it reduces overfitting and improves reliability by using methods like k-means clustering, frog feap algorithm (FLA), boosting, and bagging to balance datasets and show the complexity of symptoms. In this research, we present a new strategy that makes use of a hybrid optimised K-Means algorithm combined with the FLA, along with thresholding and morphological approaches, and ensemble learning. There are two main stages to our work. In the initial phase, we preprocess low-contrast MRI images and delineate the breast tumour area from the segmented MRI images. Furthermore, we employ Discrete Wavelet Transformation (DWT) to extract the most salient features. In the next step, the retrieved features will be used as input parameters for an ensemble-learned breast tumour classifier. To determine whether a low-contrast breast tumour MRI picture has a benign or malignant tumour after model training, we use a variety of classifiers, such as k-nearest neighbours (KNN), decision trees (DT), gradient boosting (GB), random forests (RF), and artificial neural networks (ANN). Through a voting mechanism that averages the accuracy of all models, our suggested framework achieves a remarkable 98.8% accuracy rate. In addition, proposed model is quite efficient & reliable to detect the kind of breast tumour or cancer with a sensitivity of 98.02% and a specificity of 97.5%.

In the rapidly evolving gut and gastroenterological research field, postbiotics, a fermentation byproduct, are emerging as a cutting-edge alternative in the nutraceutical and pharmaceutical environment. These functional bioactive components, which include muropeptides, extracellular polymeric substances (EPS), teichoic acids, polysaccharides, cell-free supernatants, short-chain fatty acids, enzymes, bactericidal antibiotics, and vitamins, play an important role in immune system development. This review explores the intricate pathways through which gut microorganisms produce metabolites that contribute to the health-promoting properties of postbiotics. Postbiotics have a variety of appealing qualities, including anticancer, antibacterial, and immunological actions. Their multiple effects include influencing immunological responses, decreasing cancer cell growth, and preventing bacterial infections. Their presence in dairy and plant-based meals is noteworthy, as it provides a perfect matrix for fermentation and a varied range of antibacterial chemicals. This article delves into enhancing understanding of the concept of therapeutic properties of postbiotics in promoting human health.

Food safety assurance systems are growing increasingly strict in both industrialized and developing nations as a result of increased, both real and perceived food safety concerns. This is happening as a result of developments in both public (such as direct regulation and product liability) and private (such as self and third-party certification) quality control systems. This study assessed the microbial safety, heavy metals contamination, and sensorial characteristics of beef seekh kababs made by local and branded restaurants in Faisalabad. A total of 24 samples were collected from four regions (R1 Lyallpur town, R2 Madina town, R3 Jinnah town, and R4 Iqbal town) of Faisalabad, and analyzed. Physicochemical parameters including moisture, fat, pH, ash, proteins and color (L*, b* and a*), microbiological parameters such as total plate count (TPC), total Coliform count (TCC), E. coli O157:H7, and heavy metals (Cd, Fe, Pb, and Zn) content were determined. Moreover, the lab supervisor and 18 food technology students, who had prior tasting experience, conducted sensory tests on the samples. They evaluated the samples right after they reached the laboratory, before any other analyses. Results showed that the R2 (Local) sample had the significantly (p

In the realm of marketing, capturing the attention of Millennials and Generation Z individuals, known for their short attention spans and ad-skipping tendencies, presents a formidable challenge for marketers. Memes have emerged as a short form of content infused with humour, allowing brands to captivate their audience. Regardless of their popularity and effectiveness, memes are a nuanced and intricate marketing tool. Understanding the specific types of meme content that generate engagement is critical. Memes are not only expressions but also a platform for voicing opinions. This study compares brand-generated and user-generated memes from food delivery apps on Instagram (n = 813), using the uses and gratifications theory as an analytical framework. The findings reveal notable differences between these two categories of memes, offering valuable insights for brand managers seeking to craft compelling and impactful meme-based campaigns.

Despite their serious disadvantages, which include higher upfront costs, the possibility of malfunctions due to corrosiveness, and a negative impact on the organoleptic properties of the food and possibly its nutritional importance, conventional antibacterial techniques such as pasteurization, pressure preparation, and radioactive substances are also valid as synthetic antiseptics, in fact, reduce bacterial growth in food to varying degrees. Most importantly, these cleaning techniques remove all contaminants, including various (often helpful) microorganisms found naturally in food. One potential solution to some of these issues is bacteriophage bio-control, a common and inexpensive method that uses lytic bacteriophages taken from the environment to selectively target harmful bacteria and eliminate significantly reduce their stages of feeding. It has been claimed that using bacteriophages on food is a novel way to prevent the growth of germs in vegetables. This review highlights the role of bacteriophages in food safety and their advantages in detail.

AbstractTechnology and the internet have changed the Indian retail industry. To better serve customers, retailers are combining the benefits of physical locations with the vast amount of online information. Omnichannel commerce is a unique sales experience that combines the benefits of real venues with the information-rich experience of internet shopping. Thus, the main goal of this study is to understand how customers use omni-channel, identify the most important factors affecting their customer experiences, and examine how customer experience affects customer value. With the help of 37 item-based questionnaires a quantitative survey was conducted on consumers who had purchased from multichannel retailers at least one time in the past year. Demographics included age, gender, education, and family income. Further structural equation modeling technique was implied on data to analyze how integrated channels affect customers’ value from interactions. The findings indicate that both cognitive and affective customer experience have significant impact on value creation process of customers. However, the customer drew greater value from integrated promotions, product, and price in the presence of cognitive customer experience, whereas information access was more valuable in the presence of affective customer experience. The findings of this study demonstrate the relevance of comparing the effectiveness of various channel integration strategies.

AbstractDigital financial services play a crucial role in breaking down traditional barriers to financial access, promoting greater financial inclusion, and providing marginalized groups with equal access. These services offer innovative strategies designed to address the unique financial barriers and empower women to achieve financial access and independence. This study aims to examine the influence of the adoption of digital financial services on enhancing women’s financial independence. This study examines the responses of 426 women in North India using Partial Least Squares Structural Equation Modelling (PLS-SEM) as its research approach. This study also employs the PLS Predict technique to evaluate the ability of the model to predict women’s financial autonomy. The findings demonstrate a significant and favourable relationship between the utilization of digital financial services and the improvement of women’s ability to make financial decisions. This study contributes to the discussion on promoting gender equality and economic empowerment, aligning with United Nations Sustainable Development Goal 5; showcasing the potential of information and communication technology (ICT) in advancing women’s economic and social empowerment. The results offer vital perspectives for policymakers, financial service providers, and development agencies, emphasizing the substantial influence of digital financial services in promoting gender parity and enhancing women’s socio-economic circumstances.

AbstractThis study aims to validate a model on the green entrepreneurial intention (GEI), attitude, subjective norms (SN), perceived behavioral control (PBC) and green entrepreneurial behavior (GEB) among students pursuing higher education in Delhi &NCR. This study also captures the mediating role of GEI between attitude, SN, PBC and GEB. About 327 samples were used for data analysis purposes using Smart-PLS. The result of the study suggests that attitude, SN and PBC have a significant positive relationship with GEB. Whereas, GEI as a mediator has no effect between attitude and GEB, while GEI as a mediator has a significant effect between SN, PBC and GEB. GEB among young professionals has been found a changing paradigm in the area of entrepreneurial practices in India. It has a significant impact on the sustainability aspects of society as well as the organization. Millennials get ignited toward such orientation which improved global environmental dynamics. The result of the study provides a significant source to the policymakers to promote GEBs among professionals.

AbstractThe present study explores the relationship between career commitment and subjective career success among media journalism industry employees. Additionally, it investigates the mediating role of career resilience behavior while anchoring its theoretical foundation in the career self-determination theory. The media journalism industry, characterized by burnout, demanding schedules and the mismatch of personal and professional commitments, provides a fitting context to examine the dynamics of career commitment and subjective career success. Using a structural equation modeling approach, the hypotheses were tested on 303 participants from the Punjab and Chandigarh regions in India. The results of the structural equation modeling affirmed the career commitment’s substantial direct and indirect influence on subjective career success, mediated by the career resilience variable. The implications derived from this study are pertinent for the media journalism sector. It underscores the pivotal role of cultivating career-committed behavior among employees and advocating for strategic career development initiatives. This, in turn, fosters a resilient environment conducive to nurturing individual career accomplishments, transcending conventional benchmarks of organizational success. In conclusion, this research underscores the significance of transitioning from a traditional assessment of performance framework to a more subjective evaluation approach, ultimately contributing to cultivating a motivated and engaged workforce.

This study extended an existing semi-analytical technique, the Homotopy Perturbation Method, to the Block Homotopy Modified Perturbation Method by solving two [Formula: see text] crisp triangular intuitionistic fuzzy (TIF) systems of linear equations. In the original system, the coefficient matrix is considered as real crisp, while the unknown variable vector and right hand side vector are regarded as triangular intuitionistic fuzzy numbers. The Block Homotopy Modified Perturbation Method is found to be efficient and practical to solve [Formula: see text] TIF linear systems as it only requires the non-singularity of the [Formula: see text] TIF linear system's coefficient matrix, whereas the point Homotopy Perturbation Method and other classical numerical iterative methods typically require non-zero diagonal entries in the coefficient matrix. A set of theorems relevant to this study are presented and demonstrated. We solve an engineering application, i.e. a current flow circuit problem that is represented in terms of a triangular intuitionistic fuzzy environment, using the suggested method. The unknown current is then obtained as a triangle intuitionistic fuzzy number. The proposed semi-analytic method is used to solve some numerical test problems in order to validate their performance and efficiency in comparison to other existing techniques. The numerical results of the example are displayed on graphs with different degrees of uncertainty. The efficiency and accuracy of the proposed method are further demonstrated by comparisons to block Jacobi, Adomain Decomposition method, Successive Over-Relaxation method and the classical Gauss-Seidel numerical method.

AbstractCorporate disclosure is critical for many stakeholders to make the best decisions possible. Corporate disclosure practices may vary based on corporate attributes. This study focuses on analyzing the influence of corporate attributes on disclosure. We developed a disclosure index using the unweighted disclosure index method for Indian banks. The disclosure index is developed based on data collected from the annual reports covering 2011-2020. The panel regression model examined corporate attributes’ impact on disclosure practices, and the results reveal that corporate attributes significantly influence the disclosure practices of banks. The Disclosure index will help us understand disclosure compliance and the impact of corporate attributes on disclosure. This study reiterates that banks should be transparent and understand the relevance of corporate attributes and information disclosure. It advocates the importance of corporate disclosure, which helps practitioners and policymakers gain the trust of stakeholders, translating into business opportunities and reflecting on the bank’s performance.

AbstractThe purpose of this study was to examine the factors that influence career aspirations in students, including the role of university, gender, and parental education. The research methodology involved analyzing the attitudes of students at four universities in India using a closed-ended questionnaire and factor analysis, MANOVA, and Structural-Equation-Modeling (SEM) analysis. The results showed significant differences in the attitudes of students at private and government universities, and a significant effect of parental education, but no significant impact of gender. However, the study had a limitation of data collected from only four specific universities and may not be representative of the entire national university population. The findings have practical implications for universities to improve the positive attitudes of their students and increase their motivation levels. This study is original in its focus on the impact of parental education and socio-demographic factors on career aspirations in adolescent students in Delhi NCR, India, the first of its kind in this population in the Delhi NCR region of India.

Due to rising demand for energy-efficient buildings, advanced predictive models are needed to evaluate heating and cooling load requirements. This research presents a unified strategy that blends LSTM networks and GBM to improve building energy load estimates’ precision and reliability. Data on energy usage, weather conditions, occupancy trends, and building features is collected and prepared to start the process. GBM model attributes are created using sequential relationships and initial load projections using LSTM networks. Combining LSTM with GBM takes advantage of each model's strengths: LSTM's sequential data processing and GBM's complex nonlinear connection capture. Performance measures like RMSE and MAE are used to evaluate the hybrid model's validity. Compared to individual models, the integrated LSTM-GBM method improves prediction accuracy. This higher predictive capacity allows real-time energy management systems, improving building operations and reducing energy use. Implementing this integrated model in Building Management Systems (BMS) shows its practicality in achieving sustainable building energy efficiency.

Ankle and foot orthotics are suggested for gait rehabilitation therapy after a stroke. Clinical practice necessitates adjusting the ankle foot orthosis torque to accommodate each patient’s unique stride and body function. The objective of this paper is to develop a customized, cost-effective footplate orthosis from carbon fibers, manufactured using 3D printing, for individuals with partial foot amputations. The footplate is designed for use in developing nations and aimed to be made available with a reduced lead time. The experiment work carried out in this research is used to design, analyze, and validate a prosthetic footplate. A foot of size UK 7 with a 2 mm thick model of the human foot was modeled using Unigraphics-NX. The footplate’s design is being tested both conceptually and empirically. The footplate manufactured by applying load at toe and heel shows that the carbon fiber successfully restores leg length and reduces pressure on the feet’s delicate distal end.

Abstract Nutraceuticals are dietary supplements produced from food sources that are intended to improve health through immune system modulation or by helping to prevent and treat a variety of ailments. Their anti‐inflammatory, immunomodulatory, and antioxidant qualities are well recognized. In certain instances, they even show direct microbiological efficacy against viral infections. Because of their special capacity, they may be able to help slow the spread and effects of very infectious illnesses, such as pandemics like COVID‐19. Nutraceuticals work well against viral infections because of a number of intricate processes. Reducing the harm that infections do to the immune system is one of the main effects. Nutraceuticals have the power to stimulate and activate the immune system, improving the body's capacity to identify and eliminate infections. Additionally, they are essential in reducing oxidative damage, which can result in serious consequences and is frequently intensified during viral infections. Nutraceuticals also strengthen immunity by promoting regulatory T cell differentiation and proliferation, which is crucial for preserving immune system homeostasis and limiting hyperinflammatory reactions. They control the production of proinflammatory cytokines, which helps to avoid cytokine storms during viral infections that may seriously harm tissue. This regulation lowers the danger of hyperinflammation and enhances overall results by supporting a balanced immune response. Through a number of biochemical mechanisms, nutraceuticals can strengthen the body's defenses against viral infections. They promote a more effective and efficient immune response by regulating immune cell activities, oxidative stress reduction, and antioxidant pathway activation. Furthermore, by interacting with different cellular signaling pathways, nutraceuticals can affect the synthesis and function of important immune components. This study clarifies the molecular processes behind the immunomodulatory effects of nutraceuticals, highlighting their crucial role in both treating and preventing viral infections.

Abstract Background Dental pathogens play a crucial role in oral health issues, including tooth decay, gum disease, and oral infections, and recent research suggests a link between these pathogens and oral cancer initiation and progression. Innovative therapeutic approaches are needed due to antibiotic resistance concerns and treatment limitations. Methods We synthesized and analyzed piperine-coated zinc oxide nanoparticles (ZnO-PIP NPs) using UV spectroscopy, SEM, XRD, FTIR, and EDAX. Antioxidant and antimicrobial effectiveness were evaluated through DPPH, ABTS, and MIC assays, while the anticancer properties were assessed on KB oral squamous carcinoma cells. Results ZnO-PIP NPs exhibited significant antioxidant activity and a MIC of 50 µg/mL against dental pathogens, indicating strong antimicrobial properties. Interaction analysis revealed high binding affinity with dental pathogens. ZnO-PIP NPs showed dose-dependent anticancer activity on KB cells, upregulating apoptotic genes BCL2, BAX, and P53. Conclusions This approach offers a multifaceted solution to combatting both oral infections and cancer, showcasing their potential for significant advancement in oral healthcare. It is essential to acknowledge potential limitations and challenges associated with the use of ZnO NPs in clinical applications. These may include concerns regarding nanoparticle toxicity, biocompatibility, and long-term safety. Further research and rigorous testing are warranted to address these issues and ensure the safe and effective translation of ZnO-PIP NPs into clinical practice.

Abstract Diagnosing brain tumors is a complex and time-consuming process that relies heavily on radiologists’ expertise and interpretive skills. However, the advent of deep learning methodologies has revolutionized the field, offering more accurate and efficient assessments. Attention-based models have emerged as promising tools, focusing on salient features within complex medical imaging data. However, the precise impact of different attention mechanisms, such as channel-wise, spatial, or combined attention within the Channel-wise Attention Mode (CWAM), for brain tumor classification remains relatively unexplored. This study aims to address this gap by leveraging the power of ResNet101 coupled with CWAM (ResNet101-CWAM) for brain tumor classification. The results show that ResNet101-CWAM surpassed conventional deep learning classification methods like ConvNet, achieving exceptional performance metrics of 99.83% accuracy, 99.21% recall, 99.01% precision, 99.27% F1-score and 99.16% AUC on the same dataset. This enhanced capability holds significant implications for clinical decision-making, as accurate and efficient brain tumor classification is crucial for guiding treatment strategies and improving patient outcomes. Integrating ResNet101-CWAM into existing brain classification software platforms is a crucial step towards enhancing diagnostic accuracy and streamlining clinical workflows for physicians.

Abstract Parkinson’s Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and presenting symptoms such as gait difficulties and speech impairments. Although a cure remains elusive, symptom management through medication is possible. Timely detection is pivotal for effective disease management. In this study, we leverage Machine Learning (ML) and Deep Learning (DL) techniques, specifically K-Nearest Neighbor (KNN) and Feed-forward Neural Network (FNN) models, to differentiate between individuals with PD and healthy individuals based on voice signal characteristics. Our dataset, sourced from the University of California at Irvine (UCI), comprises 195 voice recordings collected from 31 patients. To optimize model performance, we employ various strategies including Synthetic Minority Over-sampling Technique (SMOTE) for addressing class imbalance, Feature Selection to identify the most relevant features, and hyperparameter tuning using RandomizedSearchCV. Our experimentation reveals that the FNN and KSVM models, trained on an 80–20 split of the dataset for training and testing respectively, yield the most promising results. The FNN model achieves an impressive overall accuracy of 99.11%, with 98.78% recall, 99.96% precision, and a 99.23% f1-score. Similarly, the KSVM model demonstrates strong performance with an overall accuracy of 95.89%, recall of 96.88%, precision of 98.71%, and an f1-score of 97.62%. Overall, our study showcases the efficacy of ML and DL techniques in accurately identifying PD from voice signals, underscoring the potential for these approaches to contribute significantly to early diagnosis and intervention strategies for Parkinson’s Disease.

Abstract The dimension and size of data is growing rapidly with the extensive applications of computer science and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy, irrelevancy, and noise, which imposes concerns in building effective learning models. Fuzzy rough set and its extensions have been applied to deal with these issues by various data reduction approaches. However, construction of a model that can cope with all these issues simultaneously is always a challenging task. None of the studies till date has addressed all these issues simultaneously. This paper investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish this task, firstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the established similarity relation and the lower approximation. Next, the mathematical theorems are used to validate the proposed notions. Then, the importance-degree of the IF granules is employed for redundant size elimination. Further, significance-degree-preserved dimensionality reduction is discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size, where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over the benchmark datasets to demonstrate the effectiveness of simultaneous feature and data point selection methods. Finally, our proposed methodology aided framework is discussed to enhance the regression performance for IC50 of Antiviral Peptides.

Abstract This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned to computer vision (CV) based automated strategies, incorporating object detection and image segmentation techniques. Recent efforts have integrated complex techniques such as deep convolutional neural networks (DCNNs) and transformers for this task. However, these techniques encounter challenges in localizing fine-grained cracks. This paper presents a self-supervised 'you only look once' (SS-YOLO) approach that utilizes a YOLOv8 model. The novel methodology amalgamates different attention approaches and pseudo-labeling techniques, effectively addressing challenges in fine-grained crack detection and segmentation in concrete structures. It utilizes convolution block attention (CBAM) and Gaussian adaptive weight distribution multi-head self-attention (GAWD-MHSA) modules to accurately identify and segment fine-grained cracks in concrete buildings. Additionally, the assimilation of curriculum learning-based self-supervised pseudo-labeling (CL-SSPL) enhances the model's ability when applied to limited-size data. The efficacy and viability of the proposed approach are demonstrated through experimentation, results, and ablation analysis. Experimental results indicate a mean average precision (mAP) of at least 90.01%, an F1 score of 87%, and an intersection over union threshold greater than 85%. It is evident from the results that the proposed method yielded at least 2.62% and 4.40% improvement in mAP and F1 values, respectively, when tested on three diverse datasets. Moreover, the inference time taken per image is 2 ms less than that of the compared methods.

Abstract This paper presents a new methodology for solving multiple-attribute decision-making problems (MADMs) using a complex Pythagorean normal interval-valued fuzzy set (CPNIVFS), which is an extended concept of a complex Pythagorean fuzzy set. Four types of different aggregating operations (AOs), including CPNIVF weighted averaging (CPNIVFWA), CPNIVF weighted geometric (CPNIVFWG), generalized CPNIVFWA (CGPNIVFWA), and generalized CPNIVFWG (CGPNIVFWG), are discussed. The scoring function, accuracy function, and operational laws of the CPNIVFS are defined. Algebraic structures, such as associative, distributive, idempotent, bounded, commutativity, and monotonicity properties, are also shown to be satisfied by complex Pythagorean normal interval-valued fuzzy numbers. Furthermore, an algorithm is proposed to solve the MADM problems based on the defined AOs. The proposed approach is then used for a medical diagnosis problem about brain tumors because computer science and machine tool technology are among the most important components of brain tumor research. The five types of brain tumors diagnosed in these patients are gliomas, meningiomas, metastases, embryonal tumors, and ependymomas. Several types of treatments are available, which are often combined as part of an overall treatment plan. Brain tumors can be treated in various ways, including surgery, radiation therapy, chemotherapy, immunotherapy, and clinical trials. Based on the comparisons and options gathered, the most suitable treatment can be chosen. In this regard, it is evident that the value of the integer $$\Game $$ ⅁ plays a significant role in determining the model. The candidate models under consideration can be validated by comparing them with the previously proposed ones. The proposed technique is compared with the existing method to demonstrate its superiority and validity, and the results conclude that the former is more reliable and effective than the latter. Finally, the criteria are evaluated by expert assessments to determine the most appropriate options.

Abstract Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance, achieving an impressive accuracy rate of 98.08%. It is noteworthy that the SIPaKMeD dataset, publicly accessible and utilized in this study, contributes to the transparency and reproducibility of our findings. The proposed hybrid methodology combines aspects of DL and ML for cervical cancer classification. Most intricate and complicated features from images can be extracted through DL. Further various ML algorithms can be implemented on extracted features. This innovative approach not only holds promise for significantly improving cervical cancer detection but also underscores the transformative potential of intelligent automation within the realm of medical diagnostics, paving the way for more accurate and timely interventions.

Abstract The article introduces a revolutionary Nanorouter structure, which is a crucial component in the Nano communication regime. To complete the connection, many key properties of Nanorouters are investigated and merged. QCA circuits with better speed and reduced power dissipation aid in meeting internet standards. Cryptography based on QCA design methodologies is a novel concept in digital circuit design. Data security in nano-communication is crucial in data transmission and reception; hence, cryptographic approaches are necessary. The data entering the input line is encrypted by an encoder, and then sent to the designated output line, where it is decoded and transferred. The Nanorouter is offered as a data path selector, and the proposed study analyses the cell count of QCA and the circuit delay. In this manuscript, novel designs of (4:1)) Mux and (1:4) Demux designs are utilized to implement the proposed nanorouter design. The proposed (4:1) Mux design requires 3–5% fewer cell counts and 20–25% fewer area, and the propsoed (1:4) Demux designs require 75–80% fewer cell counts and 90–95% fewer area compared to their latest counterparts. The QCAPro utility is used to analyse the power consumption of several components that make up the router. QCADesigner 2.0.3 is used to validate the simulation results and output validity.

Abstract Skin Cancer is caused due to the mutational differences in epidermis hormones and patch appearances. Many studies are focused on the design and development of effective approaches in diagnosis and categorization of skin cancer. The decisions are made on independent training dataset under limited editions and scenarios. In this research, the kaggle based datasets are optimized and categorized into a labeled data array towards indexing using Federated learning (FL). The technique is developed on grey wolf optimization algorithm to assure the dataset attribute dependencies are extracted and dimensional mapping is processed. The threshold value validation of the dimensional mapping datasets is effectively optimized and trained under the neural networking framework further expanded via federated learning standards. The technique has demonstrated 95.82% accuracy under GWO technique and 94.9% on inter-combination of Trained Neural Networking (TNN) framework and Recessive Learning (RL) in accuracy.

Abstract Brain tumor glioblastoma is a disease that is caused for a child who has abnormal cells in the brain, which is found using MRI “Magnetic Resonance Imaging” brain image using a powerful magnetic field, radio waves, and a computer to produce detailed images of the body's internal structures it is a standard diagnostic tool for a wide range of medical conditions, from detecting brain and spinal cord injuries to identifying tumors and also in evaluating joint problems. This is treatable, and by enabling the factor for happening, the factor for dissolving the dead tissues. If the brain tumor glioblastoma is untreated, the child will go to death; to avoid this, the child has to treat the brain problem using the scan of MRI images. Using the neural network, brain-related difficulties have to be resolved. It is identified to make the diagnosis of glioblastoma. This research deals with the techniques of max rationalizing and min rationalizing images, and the method of boosted division time attribute extraction has been involved in diagnosing glioblastoma. The process of maximum and min rationalization is used to recognize the Brain tumor glioblastoma in the brain images for treatment efficiency. The image segment is created for image recognition. The method of boosted division time attribute extraction is used in image recognition with the help of MRI for image extraction. The proposed boosted division time attribute extraction method helps to recognize the fetal images and find Brain tumor glioblastoma with feasible accuracy using image rationalization against the brain tumor glioblastoma diagnosis. In addition, 45% of adults are affected by the tumor, 40% of children and 5% are in death situations. To reduce this ratio, in this study, the Brain tumor glioblastoma is identified and segmented to recognize the fetal images and find the Brain tumor glioblastoma diagnosis. Then the tumor grades were analyzed using the efficient method for the imaging MRI with the diagnosis result of partially high. The accuracy of the proposed TAE-PIS system is 98.12% which is higher when compared to other methods like Genetic algorithm, Convolution neural network, fuzzy-based minimum and maximum neural network and kernel-based support vector machine respectively. Experimental results show that the proposed method archives rate of 98.12% accuracy with low response time and compared with the Genetic algorithm (GA), Convolutional Neural Network (CNN), fuzzy-based minimum and maximum neural network (Fuzzy min–max NN), and kernel-based support vector machine. Specifically, the proposed method achieves a substantial improvement of 80.82%, 82.13%, 85.61%, and 87.03% compared to GA, CNN, Fuzzy min–max NN, and kernel-based support vector machine, respectively.

Abstract Extensive research is now being conducted on the design and construction of logic circuits utilizing quantum-dot cellular automata (QCA) technology. This area of study is of great interest due to the inherent advantages it offers, such as its compact size, high speed, low power dissipation, and enhanced switching frequency in the nanoscale domain. This work presents a design of a highly efficient RAM cell in QCA, utilizing a combination of a 3-input and 5-input Majority Voter (MV) gate, together with a 2 × 1 Multiplexer (MUX). The proposed design is also investigated for various faults such as single cell deletion, single cell addition and single cell displacement or misalignment defects. The circuit under consideration has a high degree of fault tolerance. The functionality of the suggested design is showcased and verified through the utilization of the QCADesigner tool. Based on the observed performance correlation, it is evident that the proposed design demonstrates effectiveness in terms of cell count, area, and latency. Furthermore, it achieves a notable improvement of up to 76.72% compared to the present configuration in terms of quantum cost. The analysis of energy dissipation, conducted using the QCAPro tool, is also shown for various scenarios. It is seen that this design exhibits the lowest energy dispersion, hence enabling the development of ultra-low power designs for diverse microprocessors and microcontrollers.

The goal of the current study is to analyze several nonlinear two-dimensional time-fractional Rosenau–Hyman equations. The two-dimensional fractional Rosenau–Hyman equation has extensive use in engineering and applied sciences. The fractional view analysis of two-dimensional time-fractional Rosenau–Hyman equations is discussed using the homotopy perturbation approach, Adomian decomposition method, and Yang transformation. Some examples involving two-dimensional time-fractional Rosenau–Hyman equations are provided to better understand the suggested approaches. The solutions appear as infinite series. We offer a comparison between the accurate solutions and those that are generated employing the proposed approaches to demonstrate the effectiveness and applicability of the proposed techniques. The results are graphically illustrated using two-dimensional and three-dimensional graphs. It has been noted that the obtained results and the targeted problems real solutions are quite similar. Calculated solutions at various fractional levels describe some of the problems useful dynamics. A comparison between the numerical solutions of the models under study and the exact solutions in cases when a solution is known serves as a clear demonstration of the viability and dependability of the suggested approaches. Other fractional problems that arise in other fields of science and engineering can be solved using a modified version of the current techniques.

Small-scale Darrieus wind turbines have a wide scope in areas that are isolated from the power grid for such small-scale household applications. Applications of wind turbines on house roofs are one potential way to generate electricity from wind energy harvesting in low-wind urban locations. This work studies the aerodynamic behavior of a vertical axis wind turbine based on a MATLAB programming mathematical model. The NACA0021 airfoil profile blade was used in this present research investigation. The turbine was fabricated with dimensions such as chord length, c = 95 mm, blade height, h = 600 mm, and turbine diameter, D = 600 mm. The experimental results of the turbine for air velocity from 1 to 12 ms−1 were used in this paper and compared with analytical results. It has been observed that the fixed-pitch turbine does not start by itself at a low air velocity of 1 to 5 m/s due to a minimum and negative torque.

Abstract Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance of the dataset and improve the performance of the models, image enhancement techniques were applied to the data for the four categories: pituitary, normal, meningioma, and glioma. MobileNetv3 achieved the highest accuracy of 99.75%, significantly outperforming other existing methods. This demonstrates the potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis.

Abstract This review thoroughly explores numerous key areas contributing to sustainable health and well-being. It encompasses precision medicine, eco-friendly healthcare practices, digital health technologies, holistic well-being approaches, community health promotion, global health protection, and data-driven public health techniques, providing a roadmap for a greater resilient healthcare future. The study evaluates the effectiveness of mathematical modelling in predicting COVID-19 transmission patterns and variants. It starts by providing an overview of COVID-19 and its variants, which include their origins and modes of transmission, then delves into prediction techniques and mathematical modelling, focusing especially on the use of differential equations-based modelling to understand disease progression. The objective is to enhance scientific information of COVID-19 variants and their effect on public health by providing insights, situation analyses, and policy recommendations derived from mathematical modelling. This comprehensive review focuses on serving as a useful resource for researchers, policymakers, and healthcare experts in addressing the pandemic and its evolving variants.

Abstract Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.

Abstract A notable advancement in fuzzy set theory is the q-rung linear diophantine fuzzy set. The soft set theory was expanded into the hypersoft set theory. By combining both the q-rung linear diophantine fuzzy set and hypersoft set, this study describes the notion of q-rung linear diophantine fuzzy hypersoft set that can handle multi sub-attributed q-rung linear diophantine fuzzy situations in the real world. Furthermore, some of its algebraic operations such as union, intersection and complement are described in this study. In addtion, the entropy measure of the q-rung linear diophantine fuzzy hypersoft set is established as it is helpful in determining the degree of fuzziness of q-rung linear diophantine fuzzy hypersoft sets. A multi-attribute decision making algorithm based on suggested entropy is presented in this study along with a numerical example of selecting a suitable wastewater treatment technology to demonstrate the effectiveness of the proposed algorithm in real-life situations. A comparative study was undertaken that describes the validity, robustness and superiority of the proposed algorithm and notions by discussing the advantages and drawbacks of existing theories and algorithms. Overall, this study describes a novel fuzzy extension that prevails over the existing ones and contributes to the real world with a valid real-life multi-attribute decision making algorithm that can cover many real-world problems that are unable to be addressed by the existing methodology.

Abstract In this paper, NeuralProphet (NP), an explainable hybrid modular framework, enhances the forecasting performance of pandemics by adding two neural network modules; auto-regressor (AR) and lagged-regressor (LR). An advanced deep auto-regressor neural network (Deep-AR-Net) model is employed to implement these two modules. The enhanced NP is optimized via AdamW and Huber loss function to perform multivariate multi-step forecasting contrast to Prophet. The models are validated with COVID-19 time-series datasets. The NP’s efficiency is studied component-wise for a long-term forecast for India and an overall reduction of 60.36% and individually 34.7% by AR-module, 53.4% by LR-module in MASE compared to Prophet. The Deep-AR-Net model reduces the forecasting error of NP for all five countries, on average, by 49.21% and 46.07% for short-and-long-term, respectively. The visualizations confirm that forecasting curves are closer to the actual cases but significantly different from Prophet. Hence, it can develop a real-time decision-making system for highly infectious diseases.