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Abstract This work investigates the Kraenkel-Manna-Merle (KMM) system, which models the nonlinear propagation of short waves in saturated ferromagnetic materials subjected to an external magnetic field, despite the absence of electrical conductivity. The study aims to explore and derive new solitary wave solutions for this system using two distinct methodological approaches. In the first approach, the KMM system is transformed into a system of nonlinear ordinary differential equations (ODEs) via Lie group transformation. The resulting ODEs are then solved analytically using a similarity invariant approach, leading to the discovery of various types of solitary wave solutions, including bright, dark, and exponential solitons. The second approach involves applying wave and Galilean transformations to reduce the KMM system to a system of two ODEs, both with and without damping effects. This reduced system is further analyzed to investigate its bifurcation behavior, sensitivity to initial conditions, and chaotic dynamics. The analysis reveals the presence of strange multi-scroll chaotic dynamics in the presence of damping and off-boosting dynamics without damping. In addition to these approaches, the study also applies the planar dynamical theory to obtain further new soliton solutions of the KMM system. These solitons include bright, kink, dark, and periodic solutions, each of which has been visualized through 3D and 2D graphs. The results of this research provide new insights into the dynamics of the KMM system, with potential applications in magnetic data storage, magnonic devices, material science, and spintronics.

Abstract In this paper, we apply stochastic differential equations with the Wiener process to investigate the soliton solutions of the Chaffee–Infante (CI) equation. The CI equation, a fundamental model in mathematical physics, explains concepts such as wave propagation and diffusion processes. Exact soliton solutions are obtained through the application of the modified extended tanh (MET) method. The obtained wave figures in 3D, 2D, and contour are highly localized and determine an individual frequency shift under the behavior of sharp peak, periodic wave, and singular soliton. The MET method shows to be a valuable analytical tool for obtaining soliton solutions, essential for understanding the dynamics of nonlinear wave phenomena. Numerical simulations enable us to explore soliton solutions in two and three dimensions, shedding light on their properties over time. Our results have wide applications in various domains, including stochastic processes and nonlinear dynamics, impacting advancements in physics, engineering, finance, biology, and beyond.

Abstract Rice brown spot is an important disease of rice worldwide that inflicts substantial yield losses. The antimicrobial potential of methanol, acetone and dimethyl sulfoxide (DMSO) extracts of different medicinal plants, viz., Syzygium aromaticum, Saussurea costus, Acorus calamus, Bergenia ciliate, Geranium pratense, Mentha longifolia, Inula racemosa, Podophyllum hexandrum, Heracleum candicans and Picrorhiza kurroa, against the brown spot pathogen Bipolaris oryzae in vitro was evaluated via mycelial growth inhibition and spore germination inhibition assays. Among the plant extracts tested, 100% mycelial inhibition was observed for the methanol extract of Syzygium aromaticum at all three concentrations (2000 ppm, 3000 ppm and 4000 ppm), followed by the methanol extract of Inula racemosa (90.33%) at 4000 ppm. A maximum conidial germination inhibition of 83.54% was exhibited by the Heracleum candicans leaf extract. Phytochemical profiling of Syzygium aromaticum and Inula racemosa through liquid chromatography and mass spectrometry (HR-LCMS) revealed the presence of several compounds, such as eugenol, ursolic acid, quercetin, chlorogenic acid, and noscapine. A molecular docking approach was used to identify key inhibitory molecules against B. oryzae. Among the compounds detected in S. aromaticum and Inula racemosa, ursolic acid and noscapine were found to have the greatest binding affinity for the Big Mitogen Activated Protein Kinase (BMK-1) enzyme present in B. oryzae. In conclusion, S. aromaticum and Inula racemosa are potent compounds that could serve as lead compounds for drug discovery in the future.

The Ciprofloxacin (CIP) toxicity and salinity stress in agricultural soils cause risk to environmental and food safety. Consequently, it is essential to devise or use more effective techniques for mitigating salinity and ciprofloxacin-induced stress in soil. This study includes the nZVI-loaded biochar synthesis, integrating the unique characteristics of raw biochar with nZVI. The present study examined the impact of raw and nZVI-loaded biochar on soil quality and the mitigation of salinity stress and Ciprofloxacin toxicity in wheat plants. The results showed that the application of nZVI-loaded biochar treatments led to substantial enhancement in shoot biomass, root biomass, grain biomass, and spike biomass by 152.1, 54.3 %, 59.8 %, and 151 %, respectively compared to control treatment. The treatment with nZVI-loaded biochar significantly increased the rates of photosynthesis and transpiration, as well as the conductance of stomata. It also resulted in higher levels of intercellular CO2, photosynthetic pigments, and water use efficiency with increases of 49 %, 59 %, 57 %, 37 %, 40 %, and 95 %, respectively. The nZVI-loaded biochar significantly decreased electrolyte leakage, malondialdehyde (MDA), and hydrogen peroxide levels compared to the NaCl treatment alone. It also enhanced the activities of enzymatic antioxidants such as peroxidase (POD), superoxide dismutase (SOD), catalase (CAT), guaiacol peroxidase (GPX), and ascorbate peroxidase (APX). The non-enzymatic antioxidants including total soluble sugars (TSS), flavonoids, total soluble proteins (TSP), phenolics, ascorbic acid, anthocyanin, proline, and glycine betaine significantly influenced by the nZVI-loaded biochar. The nZVI-loaded biochar effectively alleviates the stress of soils that are contaminated with hazardous amounts of Ciprofloxacin while improving the soil and plant health.

Abstract The present study was carried out to investigate the palynoflora of Murree formation using microscopic techniques to understand the climatic changes and vegetation’s evolution. In this palynological study, 31 samples were collected, analyzed, and then identified as palynomorphs using different previous published literature. The results of this study will be described in terms of the evolutionary history of plants and the depositional environment of the reported taxa in the study area. The botanical affinities and systematic description of the taxa were examined using light and scanning electron microscopy. The floral record identified Asteraceae as the dominant family and Pinus as a genus. Most of angiosperms i.e Fabaceae and Poaceae, have a poor fossil record but have an abundant palynological record in the study area. The highest polar diameter (75.75 μm), colpus length (34.5 μm) and colpus width (31 μm) were examined for the Convolvulus. Most of the taxa explained here had a wide geographical occurrence in Southeast Asia and show the abundance of angiosperms in the Miocene epoch. The palynological record of Holocene samples is important to know about the vegetation’s origin and environmental fluctuations in the study area.

Vitiligo is the most common skin depigmentation disorder, characterized by skin or mucous membrane depigmentation. It has a prevalence rate of 0.5 % to 2.0 %. It has an effect on the patient’s appearance and is known to cause mental problems such as anxiety and depression. Vitiligo appears to be a multigenic condition based on the pattern of inheritance, which skips generations but obviously clusters within families. The TYR (Tyrosinase) as categorized as a protein coding gene diseases associated linked with encompass albinism, oculocutaneous, typeIA, Albinism, Oculocutaneous and Type IB. TYR gene is located on human chromosome 11q14.3. This gene is about 117844 bp long starting from the molecular location 89177875 to 89295759 base pairs on chromosome 11 according to Homo Sapiens Annotation Release 110, GRCh38.p14 (NCBI), which includes 6 exons. In this current study an effort was put into the identification of SNP which change the structure and function of coded protein TYR. We have checked the pathogenicity of mutation (c.575C > A resulting into p.Ser192TYR) with the help of different computational protein prediction tools e.g. POLYPHEN-2, phd-SNP, Panther, SNP&GO, PredictSNP, I-Mutant and Mupro identified the SNP as disease causing. We conclude that mutation p.Ser192TYR changing the structural confirmation of protein due which effect on the stability of protein resulting into disease. The stability analysis of the tyrosinase protein was conducted using the I-Mutant and Mupro server. Gene-gene interactions were explored through STRING. Energy minimization was performed on both wild and mutant protein revealing substantial differences. The biovia discovery studio was utilized to superimpose the model structure of wild and mutant protein, illustrating difference in protein helix fold. The study offer in silico evidence of vitiligo as a protein folding disorder. Molecular docking analysis revealed that the tyrosinase protein significantly altered stability in the active binding site and surrounding ligand interaction surface. Then a, 100 ns MD simulation was conducted to validate the behavior of the complex structure formed the vitiligo protein under in silico physiological condition and explore its temporal stability. The results demonstrated consistent conformation and binding pattern within simulated environment enriched drug derivatives. Furthermore, to check the identification of structural and functional annotated the TYR gene as both biological process and molecular function.

Antibacterial resistance is considered to be one of the major causes for mortality in coming years. In recent years green nanotechnology played a key role in addressing this problem. Biocompatible metal nanoparticles have gained popularity owing to their excellent therapeutic effects and minimal side effects. Method: We report the synthesis of AgNPs and their amoxicillin conjugates (Ag-amoxi) using Micromeria biflora crude flavonoid extracts. The physicochemical properties of the synthesized NPs and Ag-amoxi conjugates were systematically evaluated using scanning electron microscopy (SEM), energy dispersive X-ray (EDX) and X-ray diffraction (XRD) analysis, Fourier transform infrared (FTIR), and UV–visible (UV–Vis) spectroscopic techniques. Results: The average sizes of AgNPs and Ag-amoxi conjugates were 45 and 62 nm, respectively. We have also explored the antibacterial, antioxidant, anti-inflammatory, and analgesic properties of the AgNPs and Ag-amoxi conjugates through in vivo and in vitro analysis. The Ag-amoxi conjugates showed better antibacterial potential against Streptococcus Pneumoniae (S.P), Staphylococcus aureus (S.A), Pseudomonas aeruginosa (P.A), and Methicillin resistance Staphylococcus aureus (MRSA) strain both the drug and AgNPs. Similarly, in vivo anti-inflammatory studies revealed that both Ag-amoxi (68 %) and AgNPs (64 %) had strong anti-inflammatory effects, with (***p

This work offer a computational approach to the coupled nonlinear Shrödinger equation (CNLSE) by utilizing fractional operators and double Laplace Adomian decomposition method (DLTADM). Three various fractional operators i.e Caputo, Caputo–Fabrizio (CF) and Atangana–Baleanu Caputo (ABC) are used for studying the approximate solution of the CNLSE. Dynamics of solutions are provided via graphical examples that depict system behavior. Graphs of the deduced results portray dark soliton dynamics for various fractional values. The robustness of the applied approach is studied via comparative analysis of error estimations between exact and derived results via tables and 2D graphs.

Background: Polycystic Ovarian Syndrome, was previously thought to be an endocrine condition. Research has now demonstrated that polycystic ovarian syndrome affects a patient's quality of life and is a metabolic, hormonal and psychosocial condition. Women's joys are disrupted by PCOS because the stigma attached to hyperandrogenism is strongly felt and may harm women's psychological development. Methods: The aim of the study was to comprehend the psychosocial problems experienced by women in Kashmir and to obtain a thorough understanding of the coping mechanisms employed by women with PCOS. The research design was both qualitative and quantitative in nature. Thematic analysis was used to derive themes from the data. Sampling was purposeful in nature. Results: The study underscores that women with PCOS encounter numerous difficulties encompassing social and psychological aspects. Psychological issues like mood swings, stress, anxiety and sleeping disorders are prevalent among women with PCOS. Furthermore, the research has revealed that menstrual disorders significantly contribute to depression in women. The study also highlights a connection between infertility and psychological problems. Conclusion: In conclusion, the research underscores the multifaceted challenges faced by individuals with PCOS. Therefore, understanding these issues is crucial for developing effective coping mechanisms and improving the quality of life for women dealing with PCOS.

Pervasive Computing has become more personal with the widespread adoption of the Internet of Things (IoT) in our day-to-day lives. The emerging domain that encompasses devices, sensors, storage, and computing of personal use and surroundings leads to Personal IoT (PIoT). PIoT offers users high levels of personalization, automation, and convenience. This proliferation of PIoT technology has extended into society, social engagement, and the interconnectivity of PIoT objects, resulting in the emergence of the Social Internet of Things (SIoT). The combination of PIoT and SIoT has spurred the need for autonomous learning, comprehension, and understanding of both the physical and social worlds. Current research on PIoT is dedicated to enabling seamless communication among devices, striking a balance between observation, sensing, and perceiving the extended physical and social environment, and facilitating information exchange. Furthermore, the virtualization of independent learning from the social environment has given rise to Artificial Social Intelligence (ASI) in PIoT systems. However, autonomous data communication between different nodes within a social setup presents various resource management challenges that require careful consideration. This paper provides a comprehensive review of the evolving domains of PIoT, SIoT, and ASI. Moreover, the paper offers insightful modeling and a case study exploring the role of PIoT in post-COVID scenarios. This study contributes to a deeper understanding of the intricacies of PIoT and its various dimensions, paving the way for further advancements in this transformative field.

Delayed dynamical system plays a vital role in describing the dynamical phenomenon of neural networks. In this article, we proposed a class of new BAM neural networks involving time delay. The traits of solution and bifurcation behavior of the established BAM neural networks involving time delay were probed into. First, the existence and uniqueness is discussed using a fixed point theorem. Second, the boundedness of solution of the formulated BAM neural networks involving time delay was analyzed by applying an appropriate function and inequality techniques. Third, the stability peculiarity and bifurcation behavior of the addressed delayed BAM neural networks were investigated. Fourth, Hopf bifurcation control theme of the formulated delayed BAM neural networks was explored by virtue of a hybrid controller. By adjusting the parameters of the controller, we could control the stability domain and Hopf bifurcation onset, which was in favor of balancing the states of different neurons in engineering. To verify the correctness of gained major outcomes, computer simulations were performed. The acquired outcomes of this article were new and own enormous theoretical meaning in designing and dominating neural networks.

It is necessary to utilize certain stochastic methods while finding the soliton solutions since several physical systems are by their very nature stochastic. By adding randomness into the modeling process, researchers gain deeper insights into the impact of uncertainties on soliton evolution, stability, and interaction. In the realm of dynamics, deterministic models often encounter limitations, prompting the incorporation of stochastic techniques to provide a more comprehensive framework. Our attention was directed towards the short-wave intermediate dispersive variable (SIdV) equation with the Wiener process. By integrating advanced methodologies such as the modified Kudrayshov technique (KT), the generalized KT, and the sine-cosine method, we delved into the exploration of diverse solitary wave solutions. Through those sophisticated techniques, a spectrum of the traveling wave solutions was unveiled, encompassing both the bounded and singular manifestations. This approach not only expanded our understanding of wave dynamics but also shed light on the intricate interplay between deterministic and stochastic processes in physical systems. Solitons maintained stable periodicity but became vulnerable to increased noise, disrupting predictability. Dark solitons obtained in the results showed sensitivity to noise, amplifying variations in behavior. Furthermore, the localized wave patterns displayed sharp peaks and periodicity, with noise introducing heightened fluctuations, emphasizing stochastic influence on wave solutions.

Abstract Background Neurological disorders pose a significant health challenge, and their early detection is critical for effective treatment planning and prognosis. Traditional classification of neural disorders based on causes, symptoms, developmental stage, severity, and nervous system effects has limitations. Leveraging artificial intelligence (AI) and machine learning (ML) for pattern recognition provides a potent solution to address these challenges. Therefore, this study focuses on proposing an innovative approach—the Aggregated Pattern Classification Method (APCM)—for precise identification of neural disorder stages. Method The APCM was introduced to address prevalent issues in neural disorder detection, such as overfitting, robustness, and interoperability. This method utilizes aggregative patterns and classification learning functions to mitigate these challenges and enhance overall recognition accuracy, even in imbalanced data. The analysis involves neural images using observations from healthy individuals as a reference. Action response patterns from diverse inputs are mapped to identify similar features, establishing the disorder ratio. The stages are correlated based on available responses and associated neural data, with a preference for classification learning. This classification necessitates image and labeled data to prevent additional flaws in pattern recognition. Recognition and classification occur through multiple iterations, incorporating similar and diverse neural features. The learning process is finely tuned for minute classifications using labeled and unlabeled input data. Results The proposed APCM demonstrates notable achievements, with high pattern recognition (15.03%) and controlled classification errors (CEs) (10.61% less). The method effectively addresses overfitting, robustness, and interoperability issues, showcasing its potential as a powerful tool for detecting neural disorders at different stages. The ability to handle imbalanced data contributes to the overall success of the algorithm. Conclusion The APCM emerges as a promising and effective approach for identifying precise neural disorder stages. By leveraging AI and ML, the method successfully resolves key challenges in pattern recognition. The high pattern recognition and reduced CEs underscore the method's potential for clinical applications. However, it is essential to acknowledge the reliance on high‐quality neural image data, which may limit the generalizability of the approach. The proposed method allows future research to refine further and enhance its interpretability, providing valuable insights into neural disorder progression and underlying biological mechanisms.

Recent reforms in Saudi Arabia granted empowerment to women, opening new entrepreneurial avenues for them. This study aims to assess the influence of product innovation on the success of female entrepreneurs in Saudi Arabia. Employing a quantitative approach, data was collected through a survey questionnaire. The primary data set included responses from 256 Saudi female entrepreneurs. SmartPLS was utilized for analysis, employing the PLS-SEM statistical technique. The results revealed a positive overall effect of product innovation on female entrepreneurs and their intentions. The study offers practical insights for female entrepreneurs and the Saudi government to address challenges faced by Saudi women. Recommendations include the formulation of policies that provide sustained support for women entrepreneurs. Additionally, the study suggests potential avenues for future research in this domain.

The aim of this paper is to study the nonlinear couple Drinfel’d–Sokolov–Wilson (DSW) equation under conformable differential operator (CDO) that plays an important role in describing the shallow water in fluid mechanics, plasma physics, nonlinear optics and Bose–Einstein condensate (soliton formation). The Tanh–Coth and energy balance techniques are implemented in this work to explore some travelling wave solutions. These analytical methods yield various solutions for the considered DSW equation. The solutions obtained from both method are expressed in the form of hyperbolic and trigonometric function. Furthermore, the dynamic behaviour of the obtained solutions are illustrated, via 2D and 3D curves. Additionally, the effect of the conformable differential operator on dynamics of wave solutions is studied via 2D graphs.

The soft-max function, a well-known extension of the logistic function, has been extensively utilized in numerous stochastic classification methodologies, such as linear differential analysis, soft-max extrapolation, naive Bayes detectors, and neural networks. The focus of this study is the development of soft-max based fuzzy aggregation operators (AOs) for Pythagorean fuzzy sets (PyFS), capitalizing on the benefits provided by the soft-max function. In addition to introducing these novel AOs, we also present a comprehensive approach to multi-attribute decision-making (MADM) that employs the proposed operators. To demonstrate the efficacy and applicability of our MADM method, we applied it to a real-world problem involving Pythagorean fuzzy data. The analysis of supplier selection has been extensively examined in many academic works as a crucial component of supply chain management (SCM), recognised as a significant MADM challenge. The process of choosing healthcare suppliers is a pivotal element that has the potential to greatly influence the efficacy and calibre of healthcare provisions. In addition, we given a numerical example to rigorously evaluate the accuracy and dependability of the proposed procedures. This examination demonstrates the effectiveness and potential of our proposed soft-max based AOs and their applicability in Pythagorean fuzzy environments.

This study presents a novel approach to cryptographic algorithm design that harnesses the power of recurrent neural networks. Unlike traditional mathematical-based methods, neural networks offer nonlinear models that excel at capturing chaotic behavior within systems. We employ a recurrent neural network trained on Monte Carlo estimation to predict future states and generate confusion components. The resulting highly nonlinear substitution boxes exhibit exceptional characteristics, with a maximum nonlinearity of 114 and low linear and differential probabilities. To evaluate the efficacy of our methodology, we employ a comprehensive range of traditional and advanced metrics for assessing randomness and cryptanalytics. Comparative analysis against state-of-the-art methods demonstrates that our developed nonlinear confusion component offers remarkable efficiency for block-cipher applications.

Recently, delayed dynamical model has witnessed a great interest from many scholars in biological and mathematical areas due to its potential application in describing the interaction of different biological populations. In this article, relying the previous studies, we set up two new predator-prey systems incorporating delay. By virtue of fixed point theory, inequality tactics and an appropriate function, we explore well-posedness (includes existence and uniqueness, boundedness and non-negativeness) of the solution of the two formulated delayed predator-prey systems. With the aid of bifurcation theorem and stability theory of delayed differential equations, we gain the parameter conditions on the emergence of stability and bifurcation phenomenon of the two formulated delayed predator-prey systems. By applying two controllers (hybrid controller and extended delayed feedback controller) we can efficaciously regulate the region of stability and the time of occurrence of bifurcation phenomenon for the two delayed predator-prey systems. The effect of delay on stabilizing the system and adjusting bifurcation is investigated. Computer simulation plots are provided to sustain the acquired prime outcomes. The conclusions of this article are completely new and can provide some momentous instructions in dominating and balancing the densities of predator and prey.

Intelligent transportation systems (ITSs) have witnessed a rising interest from researchers because of their promising features. These features include lane change assistance, infotainment, and collision avoidance, among others. To effectively operate ITSs for these functions, there is a need for edge computing. One can install edge computing servers at the roadside units (RSUs). There must be seamless communication between the edge servers and the cars. Additionally, there will be some cars that experience higher delays and thus, are not preferable because they will highly degrade the performance. Therefore, in this work, we consider a vehicular network scenario and define a cost function that takes into account the latency that is determined by the car’s computing frequency, association, and resource allocation while considering fairness constraints. Our cost function is to minimize the total latency (i.e., both local computing latency and transmission latency). The cost of the optimization problem is minimized by optimizing the car’s local frequency allocation, resource allocation, and association. The problem is separable, therefore, we first compute the local frequencies of the cars using a convex optimizer. Next, we split the core problem into two separate problems: (a) the distribution of resources and (b) association, because the last defined problem (joint association and resource allocation) is NP-hard. We then suggest an iterative solution. In the end, we offer numerical findings to support the suggested solution.

Knee osteoporosis (KOP) is a skeletal disorder characterized by bone tissue degradation and low bone density, leading to a high risk of bone fractures in the knee area. The traditional method for identifying knee osteoporosis is knee radiography, which requires sufficient expertise from specialists. However, the sheer volume of X-rays and the subtle variations among them may lead to misinterpretation. In recent years, deep learning algorithms have revolutionized medical diagnosis and reduced misclassification. Specifically, convolutional neural network (CNN)-based algorithms have been utilized to automate the diagnostic process as they have the inherent ability to extract important features that are difficult to identify manually. However, relying on a single method may result in suboptimal performance, leading to ineffective deployment in the medical domain. To alleviate this issue, in this study, we propose a robust detection method, KONet, which utilizes a weighted ensemble approach to distinguish between normal and osteoporotic knee conditions, even when there are minor variations in the data. To validate the architectural choices in the ensemble approach, we conducted experiments on various state-of-the-art CNN-based models using transfer learning. Extensive experiments indicated that the proposed model achieves a higher accuracy than existing models, outperforming the state-of-the-art models by a significant margin.

This study investigates a mathematical model encompassing both HIV-1 and HIV-2 epidemics, with a focus on drug resistance and dual saturated incidence rates. We begin by analyzing the local stability of equilibrium points in the deterministic system using Routh-Hurwitz and Jacobian matrix approaches. Furthermore, we establish the existence of a global positive solution for our model. We identify specific parameter conditions that lead to disease extinction. Additionally, we explore the persistence of HIV-1 and HIV-2 infections by examining their mean values. To assess the practical implications of our theoretical analysis, we conduct graphical evaluations under various noise disturbances, employing Milstein's Higher Order Method. This research contributes to a deeper understanding of the dynamics of HIV-1 and HIV-2 co-infection, with potential implications for treatment strategies and disease management.

In this article, we study the complex coupled Kuralay system (CKS), which have many applications in different field such as ferromagnetic material and fiber optics. We implement an analytical method, namely, the tanh–coth method to considered system to formulate a system of algebraic equations. Solving the obtained system, we acquire different traveling wave solutions that are expressed in term of hyperbolic tangent functions and hyperbolic cotangent functions. Also, we employ the energy balance technique to the selected model to sort out the behavior of traveling wave solutions. By this method, we obtain periodic wave solution that are expressed in term of cosine function. In order to show the graphical presentation of the acquired solitary waves solutions, 3D and 2D curve are created by selecting well-suited parametric values via Mathematica.

Background: Blood transfusion has become a specialized modality of patient management and every year saves the millions of lives. However, it is not always safe and may lead to many life threatening complications among which transfusion-transmitted infections (TTIs) such as hepatitis and human immunodeficiency virus (HIV) are most significant and detrimental for the recipients. Hence, an integrated strategy for blood safety is required for the elimination of TTIs and or provision of safe and adequate blood transfusion services to the people. Aims and Objectives: To assess the seroprevalence of HIV infections among whole blood donors at Sheri Kashmir Institute of Medical Sciences (SKIMS), Soura, Srinagar. Materials and Methods: This was a hospital-based study carried in the postgraduate department of blood transfusion and immunohematology SKIMS, Soura, Srinagar over a period of 7 years from January 2015 to December 2021. All donor samples were screened for HIV by the ELISA method. Results: Overall, seroprevalence of HIV was found to be 0.013%. All the positive cases were male donors. Seroprevalence was the highest among replacement donors (0.011%) as compared to voluntary donors (0.001 %). Conclusion: It is very important to continue the screening of donated blood with highly sensitive and specific tests and to counsel donors who are reactive to any of the infectious diseases (like HIV, hepatitis B virus, HCV, syphilis, or malaria) and conduct extensive public awareness programs and measures to make transfusion of blood and blood components safe.

In this manuscript, we study new solutions of generalized version of geophysical KdV equation which is called generalized perturbed KdV (gpKdV) under time–space conformable operator. We implement two methods to get some novel waves solution of the gpKdV equation. First, we use extended Tanh-method to extract new solutions of considered equations in the form of trigonometric hyperbolic functions. To achieve Sine and Cosine hyperbolic solutions, we use generalized Kudryashov (GK) technique with Riccati equation. We show the behaviour of solutions via 2D and 3D figures. Also, we analyze the Corioles effect on the evolution of waves solutions of the considered equation.

In this article, the Korteweg-de Vries-Caudrey-Dodd-Gibbon (KdV-CDG) equation is explored via a fractional operator. A nonlocal differential operator with a nonsingular kernel is used to study the KdV-CDG equation. Some theoretical features concerned with the existence and uniqueness of the solution, convergence, and Picard-stability of the solution by using the concepts of fixed point theory are discussed. Analytical solutions of the KdV-CDG equation by using the Laplace transformation (LT) associated with the Adomian decomposition method (ADM) are retrieved. The solutions are presented using 3D and surface graphics.

This paper examines gender representation in Jammu and Kashmir Board of School Education (JKBOSE) language textbooks. These books were revised and developed as per the guidelines of National Curriculum Framework (NCF), 2005, which emphasizes on the elimination of gender inequality in education through the design and selection of learning tasks and pedagogic practices. The factors examined include the ratio of male-female appearances, titles of address for both sexes and the roles/activities assigned to males and females. To examine these factors, a method of qualitative content analysis was adopted. The study findings revealed that the revised textbooks lack relevance with the NCF (2005) guidelines and contribute towards the perpetuation of gender inequality. The bias in favor of males is still present in the core curriculum. Male's appearance dominates in written text and titles. Females are shown in their stereotypical gender roles and activities. This study shows concern towards the gender biased approach of curriculum designers and textbook writers; and recommends of producing gender fair educational material based on the guidelines of NCF (2005).

In recent years, religious tourism has gained popularity worldwide, with millions of people travelling to different destinations for religious purposes. The United Kingdom has a growing Muslim population, and Islamic religious tourism is no exception. However, little research has been conducted on the pilgrim’s perceptions of Islamic religious tour operators in the United Kingdom. Understanding these perceptions is essential for tour operators to provide better services and attract more customers. The present study aims to contribute to Islamic tourism research by assessing the perceptions of pilgrims toward religious tour operators in the United Kingdom. Employing a snowball sampling technique, this exploratory study surveyed 227 pilgrims who used the services of religious tour operators. The data were analysed in SPSS. The findings indicate that pilgrims in the United Kingdom generally hold a mixed perception towards religious tour operators offering religious journeys such as, Umrah, and Ziyara (pious visitation of sacred places). The pilgrims perceive these operators as profit-oriented enterprises that lack spiritual intentions which are not serving a noble cause. Most pilgrims expressed dissatisfaction with their services, although they are still preferred over government-sponsored schemes. The study provides insights for religious tour operators to improve their services and provide better experiences for pilgrims based on the identified factors in this study.

The present study investigates the suspected FMD outbreak in sheep flock in Jammu and Kashmir Union Territory of India during May 2021. Swab materials, saliva and serum samples from symptomatic and in-contact apparently healthy animals were collected. A total of 13, 41 and 15 serum samples from Beerwah Budgam, Poshnar sheep farm, and Gundipora/Shalkani Budgam and Dara Srinagar, respectively were collected. Also 360 serum samples were collected from the districts of Pulwama, Ananthnag, Shopian, Kupwara, Baramulla and Bandipora. The serum samples were subjected to the in-house indirect 3AB3 NSP ELISA, while the swab and saliva samples were processed and subjected to serotyping ELISA and multiplex RT-PCR. One sample from Gundipora/Shalkani Budgam and Dara Srinagar was found positive for FMDV serotype A in multiplex RT-PCR. FMD virus serotype A was last documented in Jammu and Kashmir pretty long years ago. In NSP serology, 53.8%, 26.8% and 33.3% serum samples from Beerwah Budgam, Poshnar farm and Gundipora/Shalkani Budgam and Dara Srinagar, respectively were found positive for 3AB NSP antibodies of FMDV. Further, 119 out of 360 (32.5%) serum samples collected from affected flock of different districts were also found positive for 3AB NSP antibodies suggesting a high level of virus circulation. Introduction of infected animals and intermixing of animals while sharing common pasture land followed by the unrestricted movement of diseased/ subclinically infected animals might have been the probable reason behind the origin and spread of the outbreak.

This manuscript addresses the rational solutions of a (3+1)-dimensional Boiti–Leon–Manna–Pempinelli (BLMP) equation, emphasizing key aspects in response to specific questions. The study begins by elucidating the problem’s significance, highlighting the pursuit of rational solutions for the mentioned equation. The methodology involves obtaining the Hirota bilinear (HB) form through a transformative process. Furthermore, the analysis extends to the dimensionally reduced BLMP equation, specifically exploring lump and rogue wave-like (Rwl) solutions, along with the interaction between kink and lump solutions. The HB forms play a crucial role in this examination. Importantly, the solutions presented in this study are characterized as non-singular and localized. Visualizations of these solutions are instrumental in understanding the equation’s behavior, particularly how the solutions evolve under varying physical conditions. The outcomes are visually conveyed through 3D and surface graphs, offering a comprehensive representation for specific parameter values. In conclusion, this work contributes novel insights beyond previous efforts in the literature, showcasing the importance and advancements in the study of the (3+1)-dimensional BLMP equation.

Recently, plant proteins have received notable attention as food and medicine. Further, plant proteins are used to produce and isolate peptides to treat and prevent diseases in humans and animals. Plant proteins can be extracted from various sources, including industrial waste materials. Conventional and nonconventional methods are used to carry out the protein extraction. This review aims to overview the advanced protein extraction methods and the therapeutic functionalities of plant proteins and peptides. It was observed that the advanced protein extraction methods, i.e., ultrasound-assisted and microwave-assisted, are more efficient, eco-friendly, and less energy-consuming. The advanced protein extraction methods will help to face the challenges of using protein in food processing. In addition to the food functionality, plant-based proteins and peptides show potential therapeutic activity. It includes antidiabetic activity, anticancer activity, and anti-inflammatory activity. The pharmaceutical and nutraceutical industries will likely rely heavily on plant-derived peptides because of their benefits as functional ingredients.

Abstract Background Clozapine is a second-generation antipsychotic which is known to have superior efficacy as compared to other antipsychotics. It is particularly used in treatment-resistant schizophrenia (10–30%). However, its use is limited due to the serious side effect of inducing seizures which can occur at all doses. The purpose of our study was to analyse patients taking clozapine for any electroencephalographic (EEG) abnormalities. We undertook a cross-sectional study on 107 patients with a different diagnosis taking clozapine who visited the outdoor treatment facility of a tertiary care psychiatric diseases hospital in Kashmir. A 32-channel digital EEG was obtained and evaluated visually for any abnormalities in these patients. The data was tabulated and analysed using SPSS 23.0. Results EEG abnormalities were reported in 60.75% of patients receiving clozapine out of which slow waves and epileptiform activities were present in 48.60% and 30.84% of patients, respectively. Background slowing was often in the delta and theta ranges. Our study showed that there was a statistically significant relationship between the dose of clozapine and EEG abnormalities. Clinical seizure occurred in only one patient receiving clozapine 450 mg/day and was a smoker who had recently stopped smoking. Conclusions EEG abnormalities occur frequently in clozapine-treated patients specifically those taking higher doses of clozapine. This calls for utilising EEG as a useful clinical investigation in these patients along with clinical monitoring of seizures especially in those taking higher doses of clozapine in order to hint towards early signs of clozapine toxicity. Caution needs to be taken in patients who are smokers and show a pattern of change in their smoking habits. Moreover, EEG abnormalities though frequently associated with clozapine are not reliable predictors of seizures. Therefore, anticonvulsants may not be routinely indicated in patients on clozapine even in the presence of epileptiform discharges.

Bartonella henselae is a Gram-negative bacterium causing a variety of clinical symptoms, ranging from cat-scratch disease to severe systemic infections, and it is primarily transmitted by infected fleas. Its status as an emerging zoonotic pathogen and its capacity to persist within host erythrocytes and endothelial cells emphasize its clinical significance. Despite progress in understanding its pathogenesis, limited knowledge exists about the virulence factors and regulatory mechanisms specific to the B. henselae strain Houston-1. Exploring these aspects is crucial for targeted therapeutic strategies against this versatile pathogen. Using reverse-vaccinology-based subtractive proteomics, this research aimed to identify the most antigenic proteins for formulating a multi-epitope vaccine against the B. henselae strain Houston-1. One crucial virulent and antigenic protein, the PAS domain-containing sensor histidine kinase protein, was identified. Subsequently, the identification of B-cell and T-cell epitopes for the specified protein was carried out and the evaluated epitopes were checked for their antigenicity, allergenicity, solubility, MHC binding capability, and toxicity. The filtered epitopes were merged using linkers and an adjuvant to create a multi-epitope vaccine construct. The structure was then refined, with 92.3% of amino acids falling within the allowed regions. Docking of the human receptor (TLR4) with the vaccine construct was performed and demonstrated a binding energy of −1047.2 Kcal/mol with more interactions. Molecular dynamic simulations confirmed the stability of this docked complex, emphasizing the conformation and interactions between the molecules. Further experimental validation is necessary to evaluate its effectiveness against B. henselae.

In recent decades, heavy metals (HMs) have emerged as a global health concern. Unfortunately, in Pakistan, there is a general lack of awareness regarding the potential health risks associated with HMs pollution among automobile workers. Herein, we investigated the concentration of heavy metals such as lead (Pb), cadmium (Cd), and chromium (Cr) among automobile workers who were occupationally exposed in Mingora City, Khyber Pakhtunkhwa, Pakistan. Three different automobile groups, i.e., battery recyclers, spray painters, and mechanics were studied in detail. A total of 40 blood samples were collected from automobile workers groups while 10 blood samples were collected as control individuals from different locations in the study area. We investigated heavy metals concentration with a standard method using an atomic absorption spectrometer AAS (PerkinElmer Analyst 700, United States). Based on our findings, the battery recycling group displayed the most elevated Pb levels (5.45 ± 2.11 μg/dL), exceeding those of both the spray painters’ group (5.12 ± 1.98 μg/dL) and the mechanics’ group (3.79 ± 2.21 μg/dL). This can be attributed to their higher exposure to Pb pollution resulting from the deterioration, dismantling, grinding, or crushing of old batteries. In the context of chromium (Cr) exposure, a similar trend was observed among the battery recycling group, as well as the spray painters and mechanics groups. However, in the case of cadmium (Cd), the mechanics’ group exhibited the highest level of exposure (4.45 ± 0.65 μg/dL), surpassing the battery recycling group (1.17 ± 0.45 μg/dL) and the spray painters’ group (1.35 ± 0.69 μg/dL), which was attributed to their greater exposure to welding fumes and other activities in their workplace. We believe that our findings will encourage regulatory measures to improve the health of automobile workers. However, further work is needed to determine various health-related issues associated with heavy metal exposure among automobile workers.

Spider monkey optimization (SMO) is a quite popular and recent swarm intelligence algorithm for numerical optimization. SMO is Fission-Fusion social structure based algorithm inspired by spider monkey’s behavior. The algorithm proves to be very efficient in solving various constrained and unconstrained optimization problems. This paper presents the application of SMO in fog computing. We propose a heuristic initialization based spider monkey optimization algorithm for resource allocation and scheduling in a fog computing network. The algorithm minimizes the total cost (service time and monetary cost) of tasks by choosing the optimal fog nodes. Longest job fastest processor (LJFP), shortest job fastest processor (SJFP), and minimum completion time (MCT) based initialization of SMO are proposed and compared with each other. The performance is compared based on the parameters of average cost, average service time, average monetary cost, and the average cost per schedule. The results demonstrate the efficacy of MCT-SMO as compared to other heuristic initialization based SMO algorithms and Particle Swarm Optimization (PSO).

We investigate a generalized scale-invariant analogue of the Korteweg–de Vries (KdV) equation, establishing a connection with the recently discovered short-wave intermediate dispersive variable (SIdV) equation. To conduct a comprehensive analysis, we employ the Generalized Kudryashov Technique (KT), Modified KT, and the sine–cosine method. Through the application of these advanced methods, a diverse range of traveling wave solutions is derived, encompassing both bounded and singular types. Among these solutions are dark and bell-shaped waves, as well as periodic waves. Significantly, our investigation reveals novel solutions that have not been previously documented in existing literature. These findings present novel contributions to the field and offer potential applications in various physical phenomena, enhancing our understanding of nonlinear wave equations.

This article uses a fractional approach to investigate the system of tsunami wave propagation along an oceanic coastline. The tsunami wave system is considered under singular and nonsingular fractional operators. The double Laplace transform (LT) with Adomian decomposition method (ADM) is implemented to analyse this model. Some theoretical features of the considered fractional tsunami systems are explored via fixed point notions. Based on the shallow-water hypothesis, the current model has been explored. It is shown that changes in sea depth and coast slope have an impact on the tsunami wave’s speed and amplification at various time scales. From the numerical simulations it is observed that decrease in the fractional order decreases the tsunami wave velocity as well as height.

Background: Sildenafil citrate (SC) is a vasoerectile drug used to treat impotence caused by erectile dysfunction by inhibiting phosphodiesterase type 5 enzyme. The drug is widely used and may have adverse effects on the liver as it is metabolized mainly in the liver. Therefore, the present research aimed to study the effect of graded doses of SC on the histological structure of the liver in albino rats. Methodology: The present prospective interventional experimental study was conducted on 30 albino rats. The rats were given SC in different doses and for different periods. Rats were sacrificed, and the liver was extracted and processed for histopathological examination. The slides were stained with hematoxylin and eosin and observed under a compound light microscope. Results: SC administrated in graded doses in male albino rats caused central venous congestion, sinusoidal dilatation, and fatty infiltration in the liver. These changes were more pronounced in rats that received the drug in higher doses and for a longer duration. The effects of SC on the rat liver microanatomy were dose and time dependent. Conclusion: SC is relatively safe in low doses, but at higher doses and longer duration, there can be adverse effects on the liver.

In this manuscript, the Korteweg-de Vries-Burgers (KdV-Burgers) partial differential equation (PDE) is investigated under nonlocal operators with the Mittag-Leffler kernel and the exponential decay kernel. For both fractional operators, the existence of the solution of the KdV-Burgers PDE is demonstrated through fixed point theorems of α-type ϝ contraction. The modified double Laplace transform is utilized to compute a series solution that leads to the exact values when fractional order equals unity. The effectiveness and reliability of the suggested approach are verified and confirmed by comparing the series outcomes to the exact values. Moreover, the series solution is demonstrated through graphs for a few fractional orders. Lastly, a comparison between the results of the two fractional operators is studied through numerical data and diagrams. The results show how consistently accurate the method is and how broadly applicable it is to fractional nonlinear evolution equations.

The Covid illness (COVID-19), which has emerged, is a highly infectious viral disease. This disease led to thousands of infected cases worldwide. Several mathematical compartmental models have been examined recently in order to better understand the Covid disease. The majority of these models rely on integer-order derivatives, which are incapable of capturing the fading memory and crossover behaviour observed in many biological phenomena. Similarly, the Covid disease is investigated in this paper by exploring the elements of COVID-19 pathogens using the non-integer Atangana-Baleanu-Caputo derivative. Using fixed point theory, we demonstrate the existence and uniqueness of the model's solution. All basic properties for the given model are investigated in addition to Ulam-Hyers stability analysis. The numerical scheme is based on Lagrange's interpolation polynomial developed to estimate the model's approximate solution. Using real-world data, we simulate the outcomes for different fractional orders in Matlab to illustrate the transmission patterns of the present Coronavirus-19 epidemic through graphs.

Microfinance has been evolving to be a decisive factor in poverty reduction, rural and entrepreneurial development. This research aims to find out the impact of microfinance on rural/socio-economic development and entrepreneurial skill development. Particularly, this study attempts to explore the impact of microfinance on social development, financial empowerment, education, healthcare, economic independence, and entrepreneurial development. Data was collected through questionnaires taken from the beneficiaries of the National Rural Livelihood Mission (NRLM) erstwhile Swaran Jayanti Shahri Rozgar Yojana (SJSRY) microfinance scheme in the Union Territory of Jammu and Kashmir, India. A purposive sampling method, exploratory factor analysis, and paired sample t-test were used in this study. The findings prove that microfinance facilitates social development, improved financial empowerment, and promotes decision-making relating to education and healthcare among beneficiaries. The finding also indicates that microfinance helps to increase entrepreneurial skill development among the beneficiaries. This study offers an insight how microfinance eliminates poverty and facilitates social development as well as entrepreneurial skills. These findings have essential implications for academics, policymakers, and microfinance institutions.

Abstract Deep learning for image retrieval has been used in this era, but image retrieval with the highest accuracy is the biggest challenge, which still lacks auto-correlation for feature extraction and description. In this paper, a novel deep learning technique for achieving highly accurate results for image retrieval is proposed, which implements a convolutional neural network with auto-correlation, gradient computation, scaling, filter, and localization coupled with state-of-the-art content-based image retrieval methods. For this purpose, novel image features are fused with signatures produced by the VGG-16. In the initial step, images from rectangular neighboring key points are auto-correlated. The image smoothing is achieved by computing intensities according to the local gradient. The result of Gaussian approximation with the lowest scale and suppression is adjusted by the by-box filter with the standard deviation adjusted to the lowest scale. The parameterized images are smoothed at different scales at various levels to achieve high accuracy. The principal component analysis has been used to reduce feature vectors and combine them with the VGG features. These features are integrated with the spatial color coordinates to represent color channels. This experimentation has been performed on Cifar-100, Cifar-10, Tropical fruits, 17 Flowers, Oxford, and Corel-1000 datasets. This study has achieved an extraordinary result for the Cifar-10 and Cifar-100 datasets. Similarly, the results of the study have shown efficient results for texture datasets of 17 Flowers and Tropical fruits. Moreover, when compared to state-of-the-art approaches, this research produced outstanding results for the Corel-1000 dataset.

Abstract Chronic lymphocytic leukemia (CLL) is an incurable malignancy of B-cells. In this study, bioinformatics analyses were conducted to identify possible pathogenic roles of CK2α, which is a protein encoded by CSNK2A1, in the progression and aggressiveness of CLL. Furthermore, various computational tools were used to search for a competent inhibitor of CK2α from fungal metabolites that could be proposed for CLL therapy. In CLL patients, high-expression of CSNK2A1 was associated with early need for therapy (n = 130, p

In spite of the insight commonality may have concerning agrarian practice, fact is that nowadays agricultural science diligence is accurate, precise, data-driven, and vigorous than ever. The emanation of the technologies based on Internet of Things (IoT) has reformed nearly each industry like smart city, smart health, smart grid, smart home, including “smart agriculture or precision agriculture”. Applying machine learning using the IoT data analytics in agricultural sector will rise new benefits to increase the quantity and quality of production from the crop fields to meet the increasing food demand. Such world-shattering advancements are rocking the current agrarian approaches and generating novel and best chances besides a number of limitations. This paper climaxes the power and capability of computing techniques including internet of things, wireless sensor networks, data analytics and machine learning in agriculture. The paper proposed the prediction model of Apple disease in the apple orchards of Kashmir valley using data analytics and Machine learning in IoT system. Furthermore, a local survey was conducted to know from the farmers about the trending technologies and their effect in precision agriculture. Finally, the paper discusses the challenges faced when incorporating these technologies in the traditional farming approaches.

Wearable Sensors (WSs) are widely used in healthcare applications to monitor patient health. During the data transmission, dissemination requires additional time to transmit the details with minimum computation difficulties. The existing techniques consume high overloaded while transmitting data in healthcare applications. The research problem is overcome by applying the non-delay-tolerant dissemination technique (NDTDT) to prevent overloaded dissemination and augment immediate, swift message delivery. The dissemination techniques utilize the intelligent decision-making process to provide the accumulated details to the healthcare center. The proposed approach is reliable in mitigating the errors due to inconsistent and discrete sensing intervals between the WSs. The constraints due to delay and interrupted transmission losses are reduced by selecting appropriate slots for WS information handling. This technique aims at maximizing the delivery of accumulated WS information through non-submissive or underlay dissemination. The method is designed to reduce dissemination delay and maximize successful message delivery. Two variations, sensors and data flows, validate the proposed NDTDT system's performance. The model increases the delivery rate by 0.91% and 0.932%, the dissemination probability by 0.964% and 0.98%, and the final metrics involved are an average delay of 12.78 ms and 11.67 ms.

This article investigates the significance of the unsteady nonlinear Landau–Ginzburg–Higgs equation in the context of superfluids and Bose–Einstein condensates. The problem of interest is the search for new exact solutions within this equation. To tackle this problem, the Sardar-subequation and energy balance approaches are employed. Through these methods, a variety of new exact solutions are obtained, expressed in terms of cosine functions, generalized hyperbolic functions, and generalized trigonometric functions. The obtained solutions encompass different types of solitons, including bright and dark solitons, singular periodic soliton, and hybrid solitons. The solutions are then visualized through 2D and 3D simulations. The findings of this study contribute to the understanding of the Landau–Ginzburg–Higgs equation and its application to superfluids and Bose–Einstein condensates. The novelty of this work lies in the utilization of the Sardar-subequation and energy balance approaches to obtain diverse traveling wave solutions, surpassing previous efforts in the literature.

The aim of this paper is to study new exact solutions Davey–Stewartson–Kadomtsev–Petviashvili (DSKP) equation. We use modified tanh method along associated with new Ricatti equation. The results are demonstrated for specific values of parameters, which show periodic singular as well as nonsingular solitons for specific values of parameters. Some solutions are plotted and presented in 3D and density graphs. The Mathematica are used for computation and simulations of the results, respectively.

Numerous scientific fields depend on precise solutions, which can be obtained for nonlinear partial differential equations (PDEs) through various techniques. It is important to note that solutions obtained through different approaches can vary. The main objective of this research is to identify novel traveling wave solutions for the (2+1)-dimensional Generalized Hirota–Satsuma–Ito equation (GHSIE). Initially, the reduced form of the GHSIE is derived, and then, using the G′G′+G+A-Expansion method, new exact solutions are obtained including kink and lumps. These solutions depict the dynamics of nonlinear phenomena and are represented graphically as periodically solitary waves.

There is an exponential increase in the number of smart devices, generating helpful information and posing a serious challenge while processing this huge data. The processing is either done at fog level or cloud level depending on the size and nature of the task. Offloading data to fog or cloud adds latency, which is less in fog and more in the cloud. The methods of processing data and tasks at fog level or cloud are mostly machine learning based. In this paper, we will discuss all three levels in terms of architecture, starting from the internet of things to fog and fog to cloud. Specifically, we will describe machine learning-based offloading from the internet of things to fog and fog to cloud. Finally, we will come up with current research directions, issues, and challenges in the IoT–fog–cloud environment.

The present study investigated the molecular changes induced by germination in cowpea cultivars by spectroscopic techniques. The ATR FT-IR, Intrinsic fluorescence, and UV spectroscopic techniques revealed that germination shows alteration in cowpea cultivars. The ATR-FTIR showed slight differences in band intensities corresponding to carbohydrates, lipids, and protein regions at 700 to 4000 cm-1 wavenumbers. Starch order structure understanding their functionality showed a reduction of short-range ordered structures in Black cowpea flour and long ordered starch fraction in White cowpea flour under germination. The study quantified the degree of ordered starch and their alteration in amorphous/crystallinity nature concerning germination and cowpea cultivars. The secondary structures revealed a reduction of β-sheets and α-helix content, representing decreased ordered protein structures and their shifting into random coils and turn structures. The germinated flours showed decreased fluorescence intensities with the shift from 347 to 348, reflecting the redshifts due to decreased concentrations of fluorescent compounds during germination in cowpea flour. UV spectrum revealed a similar spectrum with an increase in absorption of cowpea flour under germination. The electrophoresis showed variations in the intensities of high (25 to 225 kDa) and low (19 to 10 kDa) molecular weight proteins in germinated cowpea cultivars than the native one. Overall, this study demonstrated the potential of ATR-FTIR, fluorescence and UV-based spectroscopy as non-destructive techniques to assess the alteration of pulses at the molecular level and structural changes under the germination process.

In this work, we formulate a fractal fractional chaotic system with cubic and quadratic nonlinearities. A fractal fractional chaotic Lorenz type and financial systems are studied using the Caputo Fabrizo (CF) fractal fractional derivative. This study focuses on the characterization of the chaotic nature, and the effects of the fractal fractional-order derivative in the CF sense on the evolution and behavior of each proposed systems. The stability of the equilibrium points for the both systems are investigated using the Routh-Hurwitz criterion. The numerical scheme, which includes the discretization of the CF fractal-fractional derivative, is used to depict the phase portraits of the fractal fractional chaotic Lorenz system and the fractal fractional-order financial system. The simulation results presented in both cases include the two- and three-dimensional phase portraits to evaluate the applications of the proposed operators.