As cyberattacks become increasingly sophisticated and challenging to counter, cybersecurity requires robust, broadly applicable, and easily understandable machine learning models. In the context of Intrusion Detection Systems (IDS), common learning theories often fail because captured data is frequently irregular due to hidden variables, overlearned models, or imbalanced data. Singular Learning Theory provides clear insights into these challenges through mathematical analysis of models exhibiting singularities where traditional statistical assumptions break down. SLT doesn’t depend only on Fisher information and regularity conditions; instead, it uses RLCT and the zeta function to assess the accuracy and complexity of models. By using SLT, the effectiveness of a model trained on known attack patterns or a model trained on cyberattack data is vital in generalizing to unfamiliar or emerging cyber threats, which plays a key role in stopping zero-day exploits and advanced persistent threats. As a result, using SLT can point to ways to strengthen machine learning systems used for detecting malware, catching intrusions, and safeguarding against phishing attacks, since these systems are designed to resist errors caused by network traffic logs. The structured approach of SLT in examining model uncertainty and its abilities enables cybersecurity researchers and users to develop systems that are both precise and backed by solid theory for the future.

Gender inequality is one of the biggest challenges leading to several other problems for girls and women, thus hampering women empowerment somewhere both in developed as well as developing countries. One of the key areas to be given importance in the developed countries like India is uplifting the issues related to the gender-wise educational system. It is required to understand the gender-wise differences with the aim of reducing gender-based violence or harmful practices in society. This understanding can be helpful in building and promoting fairness, completeness, and the quality of educational system in the country. According to current reports, states in India like Gujrat and Madhya Pradesh have started working on boosting sex ratio and applying strategies for women empowerment, which is real indication of the concern in this regard. Dealing with the gender- wise differences with reference to the educational system can ensure in the construction of not only a healthy and robust educational system but also in building an equitable society too. The current study shows that, though lot of provisions have been made in this respect, still we need to go a long way for improving the overall growth of the nation in terms of an elevated educational system.

Keywords: Gender-wise education; Educational systems; Gender bias; Educational programs; Education duality; Education inequality

This work presents the optical evaluation of the vibrational behavior of a concrete beam using an unconventional optoacoustic procedure, the LPD method. In this case, the beam is an integral part of our laboratory construction in Lima; it supports part of the laboratory roof. Using our procedure, we experimentally determined the first 10 Eigen frequencies (14.4 Hz, 40 Hz, 78 Hz, etc.) of the beam, which we can explain using simple cantilever theory.

Keywords: Physics; Engineering (Structural vibrations; Resonance; Eigen frequencies; Sound excitation; Optical measurements; Laser photo deflection)

This study presents a technical evaluation of an integrated automation framework combining ServiceNow, Splunk, and Robotic Process Automation (RPA) to support real-time compliance tracking, incident response, and governance in enterprise IT environments. As regulatory complexity and operational demands increase, automation is applied to reduce manual workload, accelerate incident resolution, and enhance audit traceability.

The framework enables a bidirectional integration: alerts detected in Splunk initiate incident tickets in ServiceNow, while ticket status updates and resolution data from ServiceNow are transmitted back to Splunk for continuous compliance monitoring. RPA bots, developed using UiPath and Automation Anywhere, execute rule-based tasks such as ticket routing, evidence collection, and audit documentation.

Methodologically, the study incorporates architectural analysis, API-level workflow mapping, and scenario-based testing. Performance metrics—including resolution time, documentation completeness, and system responsiveness—were compared across manual and automated workflows. Results indicate measurable improvements in operational efficiency and consistency in compliance reporting.

The framework addresses challenges in interoperability and orchestration, offering a scalable model for automation-driven IT service management. It contributes to the governance, risk, and compliance (GRC) literature by demonstrating how platform integration and automation can advance regulatory alignment and operational performance. Future research may investigate machine learning for intelligent ticket triage, integration with threat intelligence systems, and alignment with zero-trust security models.

Keywords: ServiceNow; Splunk; Robotic Process Automation (RPA); Compliance Automation; IT Operations; Governance Risk and Compliance (GRC); DevSecOps; Artificial Intelligence (AI); Emerging Technologies; Machine Learning (ML)

Predictive maintenance PdM has helped, in recent decades, manufacturing and industry to save costs and keep their operations safe. This study outlines how advanced machine learning systems, including LSTMs and Transformers, could enhance data-driven maintenance planning. Mainly using C-MAPSS datasets to test Deep Learning (DL) methods and estimate Remaining Useful Life (RUL) values, this research aims to compare LSTM networks and Transformer performance in prognostic via different evaluation criteria. The analysis manifests the peculiarities of both the proposed learning approaches with a marked difference in the performances in favor of the recurrent architecture (e.g., 40% in term R² and 43% in terms of MSE), thus not generally suggesting the usage of transformer-based architecture, especially in a data-scarcity situation common condition when working of critical and costly units, but opening to a new perspective in otherwise operational conditions where data is prosperous.

Cyber-Physical Systems (CPS) represent a transformative technological paradigm that tightly integrates computation, networking, and physical processes. By bridging the cyber (digital/computational) and physical (real-world) domains, CPS are revolutionizing how intelligent systems interact with their environments, offering enhanced autonomy, efficiency, and responsiveness across various sectors.

Definition and Conceptual Framework

Cyber-Physical Systems are engineered systems in which embedded computers and networks monitor and control physical processes, usually with feedback loops where physical processes affect computations and vice versa. These systems consist of sensors, actuators, controllers, and communication modules working in concert to collect data, process it in real-time, and respond with intelligent control actions.

CPS extend beyond traditional embedded systems by incorporating higher degrees of connectivity, interoperability, and autonomous decision-making. They form the backbone of next-generation infrastructures in domains such as smart manufacturing, autonomous vehicles, precision agriculture, smart healthcare, smart grids, and intelligent transportation systems.