In this article, we discus and test the Mayan approach to evaluate the solution of first order constant coefficients complex differential equations with applications in engineering and physics. This approach supplies an effective and efficient method for solving a wide range of linear differential equations.
This<strong> </strong>study presents a novel triple transform, dubbed the (Triple SEE Transform<strong>),</strong> the main properties of the transform are discussed and utilized to solve generic linear partial differential equations that have applications in engineering and physical systems. Several functions are applied to prove the importance and usefulness of this triple technique
<strong>The increasing complexity and proliferation of modern cyberattacks, such as modern Persistent Threats (APTs), ransomware, and encrypted threats, have rendered classic stateful firewalls obsolete. Next-Generation Firewalls (NGFWs) have become indispensable to protection, featuring deep packet inspection, intrusion prevention systems, application control, and threat intelligence. This survey provides an in-depth review of the evolution, capabilities, and effectiveness of NGFWs in combating modern cyber threats; which is needed for various applications, such as social protection system, social determinants of health, and others. our review involves support from a literature review spanning 2015 to 2025. We combine information from academic research and industry studies to evaluate how well technologies that support NGFWs work, focusing on their strengths in application-layer visibility and threat mitigation.&nbsp; The paper talks about big problems, like how advanced features like SSL/TLS inspection can slow down performance and how security protocols need to be adaptable and driven by AI.&nbsp; Finally, we end with suggestions for how to successfully deploy and ideas for future research, such as how to combine artificial intelligence and zero-trust architecture to make next-generation firewalls more effective against new threats.</strong>
<strong><em>This research evaluates the effectiveness of AI-generated test cases (using GPT-4) against test cases constructed using conventional manual testing approaches in scenario-driven software testing. Manual test cases developed by applying established black-box testing methods, while GPT-4 generated test cases through structured prompts. Three scenarios&mdash;easy, moderate, and complex&mdash;used to conduct the evaluation under equivalent conditions. The primary comparisons in the present study evaluated defect detection capability, test coverage, efficiency of execution, and scenario relevance. The results indicate that AI-generated test cases provide better coverage, are faster to generate, and more effectively detect edge case faults; notably when evaluating the complex scenario. Procedural/manual testing found to be stronger in contextual reasoning and for safety critical interpretation. Overall, this research concludes that AI-generated testing is a complement to procedural/manual testing methods not a replacement. The results support a &quot;hybrid&quot; testing approach for modern software testing and quality assurance. </em></strong>
Artificial intelligence (AI) importance is increasing in advancing personalized medicine, especially in oncology. This research introduces an AI-based therapeutic recommendation system especially designed for breast cancer patients, integrating not only biological and clinical data but also psychological and behavioral factors. The system is designed to enhance treatment adherence and improve patient results by fulfilling individual needs holistically. Recent studies, the system&#39;s algorithm is demonstrated in managing complex scenarios, such as co-morbid conditions and psychological challenges that affect therapy adherence. These examples highlight data-driven, patient-centric solutions that closed the gap between technology innovation and human-centered care. While the system offers significant advantages, challenges such as data integration, ethical considerations, and healthcare disparities are discussed. While the system offers spectacular advantages, challenges such as data integration, moral considerations, and healthcare discussed. This study highlights the importance of fostering multidisciplinary collaboration implementation strategies to make sure fair access to AI-driven healthcare solutions. The findings confirmed the transformation impact of integration AI into accurate medicine, paving the way for innovation, efficient, and empathetic healthcare practices especially designed to each patient.
<strong>Intracranial tumors are life-threatening and severe health hazards attributed to the unregulated and excessive growth of brain cells; thus, a diagnosis needs to be made at an early and accurate stage to achieve successful treatment. As much as medical practitioners tend to involve Brain MRI in diagnostic purposes, some cases of oversight or misdiagnosis may be caused by human interpretation. Machine learning-based detection can be effectively used to reduce this issue. In medical imaging experiments, especially in essential medical fields, such as brain tumor classification, the method of feature selection is also vital in enhancing the effectiveness and performance of models. The suggested study applies a pre-trained transfer learning model (MobileNet) to extract deep features, namely, textures, edges, lines, and shapes of objects. However, such high-dimensional features are effective; they can involve redundant and irrelevant factors that negatively impact on the model performance. The best tool in establishing the most pertinent subset of features is a powerful metaheuristic optimization plan. This is useful to reduce dimensionality efficiently as well as preserve the ability to discriminate. The proposed research offers a new hybrid system that brings to bear the TL with a nature-inspired Particle Swarm Optimization (PSO) algorithm used to select the best features based on the Brain MRI data. The proposed PSO-based feature selection algorithm proves good results when used in a Support Vector Classifier with a score of the R<sup>2</sup> as 93.7211%, a precision reached 95.2828%, an accuracy of 95.0995%, a recall as 95.0995%, and an F1-score as 95.0996%, which shows the effectiveness of the proposed framework for classifying the brain tumor.</strong>
Recently, Membrane Fusion (MF) based imperfect molecular transmitter (Tx) has been proposed in the literature to transmit the information molecule over diffusive channel which is based on the Fick&rsquo;s equation of diffusion. Also, it is noteworthy to mention that the MF process is adopted between the vesicles and the MF based transmitter. On the other hand, literatures shows that Fractional Diffusive Channel (FDC) is more general channel model for Diffusive Molecular Communication (DMC). Therefore, this paper presents a closed-form expression of molecule release probability over Fractional Diffusive Channel (FDC). The impact of several physical characteristics such as, forward reaction rate (k_f), radius of spherical T-BNM (x_T), the diffusion constant (Dv), fractional diffusion exponent 0 &le; &alpha; &le; 2 on molecule release probability is analyzed. Numerical analysis performed for molecule release probability over the fractional diffusion channel is in perfect alignment with the theoretical background.
In the domain of Railways, India is having second largest railway network in the world. This study focus on a data driven framework for the temporal and spatial analysis of operational disruptions in Indian Railways, mainly focus on Alarm Chain Pulling (ACP) incidents. We have categorized 20,000 ACP events into &ldquo;On Station&rdquo; and &ldquo;Out of Station&rdquo; occurrences. The analysis highlights key aspect of temporal patterns, identified high risk zones and train numbers then compared the frequency of ACP incidents between goods and passenger trains. The framework demonstrates how the data analytics in enhancing railway safety operational efficiency and cost effectiveness. By using modern and interactive analytical tool, Power BI dashboard offers a comprehensive view of ACP trends, optimizing targeted maintenance strategies and improved decision making for the huge railway network. Our proposed analysis framework offers a valuable insight as well as propose a way to achieve minimum operational disruptions and optimize resource allocation across the Indian Railways network resulting in improving transportation services significantly, which helps in achieving accessible transportation.
In this paper, a tunable absorber composed of graphene ring and quadrants, silicon dioxide layer and gold substrate is proposed. The absorption spectrum of the structure changes by variation of chemical potential of graphene. A perfect absorption peak is achieved for the chemical potential of 1eV, which is due to increasing the surface plasmons of graphene. Also, the effect of important geometric parameters on the absorption spectrum is investigated. It is shown that by changing the refractive index of the test medium, the proposed absorber has the high sensitivity of 2323 nm/RIU. According to the obtained results, the proposed absorber can be used in various applications.
Human Digital Twins (HDTs) are emerging as key components in personalized healthcare, requiring more sophisticated communication skills than conventional digital twins. The essential need for fast and reliable synchronization highlights the significance of Ultra-Reliable Low-Latency Communication (URLLC) in the 5G wireless standard, designed for machine and time-sensitive applications. This short survey seeks to systematically outline the key communication demands of HDT applications across clinical settings, examine the architectural and technological solutions offered by advanced 5G and 6G to address stringent URLLC needs, and identify the main challenges, opportunities, and future research directions from this vital convergence. Future 6G URLLC advancements, leveraging Terahertz (THz) communication, Reconfigurable Intelligent Surfaces (RIS), and Integrated Sensing and Communication (ISAC), aim to build strong, self-governing Digital Twin Networks (DTNs). These are crucial for creating secure, verified, and ethically managed HDTs, influencing the future of personalized healthcare.
Neurodegenerative conditions including dementia are a leading health challenge worldwide, and more often than not, progress implicitly, in a manner that they cannot be detected until very late in the disease. Risk prediction at an early stage thus becomes very essential to allow the provision of individualized care and even to curb cognitive impairment. The study proposes a multi-modal, federated AI and hierarchical attention&nbsp; framework for&nbsp; integrating clinical assessment, neuroimaging, genetic/omics biomarkers, digital behavior data and longitudinal electronic health records over 5-10 years across multi- center cohorts. The modalities are encoded by specialized deep learning decoders such as CNNs for imaging and clinical data, Transformers for behavioral data and graph neural networks for genetic/omic inputs. The modality-dependent embeddings get integrated by hierarchical attention operation to introduce trajectory-aware representations that realize temporal patient dynamics. Dynamic risk scores and time-to-event estimates are produced in a model-based optimization framework using recurrent neural networks and survival-transformers. The evaluation demonstrates robust performance with an AUC-ROC of 93.8 percent (95 percent CI) and a C-index of 0.87. The framework increases the predictive performance, interpretability, and privacy, and provides a clinically deployable tool of early dementia risk profiling and intervention planning.
