Polypropylene (PP) cable insulation is commonly used in today’s electrical systems because of its desirable mechanical strength, chemical resistance, thermal stability, and electrical insulation properties. It is difficult to achieve high flame resistance and high dielectric properties at the same time as a better dielectric property may come at the cost of a poorer flame resistance. This study introduces a Fuzzy Reliability Assessment Framework that is proposed to systematically investigate the performance of PP cable insulation under uncertain and imprecise information. The framework incorporates the most important performance parameters such as UL-94, Limiting Oxygen Index (LOI), dielectric strength, permittivity and dissipation factor into a single composite reliability index which can be compared and ranked easily for insulation alternatives. The methodology involves several steps: identification of the criteria, fuzzification of quantitative and qualitative data, determination of the weights using expert judgement, fuzzy aggregation to fuse the criteria, defuzzification to get crisp reliability scores and sensitivity analysis to evaluate the sensitivity of the weight rankings under ± 10
A cyber-physical system (CPS) is a realtime integrated network of computation algorithms and real physical components utilized in different fields such as manufacturing, transportation, healthcare and energy system. As CPS continues to change the current manufacturing landscape, security has become more crucial than ever with the rising challenges of IoT in manufacturing including intelligent automation, real time communication and the burgeoning cyber threats. This review adopts a systematic approach to explore intrusion detection and intrusion mitigation technologies that have been designed for CPS within the industrial manufacturing space and provides an overview of detection and response technologies, summarizing recent advances and highlighting some of the current challenges. Unlike existing reviews that primarily focus on intrusion detection techniques, this study integrates intrusion detection and mitigation strategies within a unified analytical framework while explicitly considering industrial operational constraints, including latency, safety, interoperability and real-time deployment requirements. Current approaches to anomaly detection fall under three main categories like anomaly-based detection, signature-based systems and hybrid systems. All methodologies are based on research published between 2020 and 2025. The review examines these techniques with respect to their strengths, limitations, scalability and real time applicability and the applicability of the techniques in industrial CPS applications is specifically discussed. The results have shown that the machine learning (ML) and deep learning (DL) methods such as ensemble learning, federated learning, adversarial defense mechanisms and explainable artificial intelligence (XAI) have greatly enhanced the accuracy of intrusion detection while simultaneously enhancing the efficiency of response measures. Mitigation approaches like automated response, adaptive access control, resilience-based architecture and runtime assurance strategies help to reduce operations disruption in the event of cyberattacks. The integration of the cyber and physical worlds, however presents certain problems such as latency, data variability, interoperability, computational requirements and the requirement for explainable AI models. This review points to the necessity of providing context-aware, scalable and interpretable security solutions in industrial CPS environments, and identifies emerging trends and questions to be solved. This study provides helpful insights for researchers and practitioners to build stronger and intelligent defense mechanisms to maintain the operational continuity and allow evolution of secure smart manufacturing systems. The review considers intrusion detection, mitigation, and industrial deployment aspects, offering a complete reference for how secure and resilient cyber-physical manufacturing systems.
This study presents a theoretical investigation of the linear optical and photothermal properties of TiO2@Au core–shell nanocomposites for solar energy applications. The analysis is carried out within the quasi-static approximation, employing the Lorentz–Drude model to describe the dielectric response of the gold shell. The influence of key structural parameters, including core radius, shell thickness, and the surrounding dielectric environment, on the localized surface plasmon resonance (LSPR) is systematically examined over the spectral range of 300–1100 nm. The results demonstrate that increasing shell thickness enhances optical absorption through plasmonic resonance tuning, while larger core radii induce a pronounced red-shift toward the near-infrared region. The photothermal response reveals the existence of an optimal shell thickness, beyond which heat dissipation limits further temperature enhancement. Additionally, the dielectric environment plays a crucial role: a high-refractive-index TiO2 medium increases temperature rise but reduces photothermal conversion efficiency due to enhanced scattering, whereas a low-index SiO2 host maintains high efficiency (approximately 85
The increasing generation of industrial wastewater treatment sludge presents significant environmental challenges owing to its heavy metal content and limited disposal options. This study evaluates the feasibility of using detergent-industry wastewater treatment sludge as a partial cement replacement (5–30
This study addresses the limitation of our previous proof-of-concept work, which employed only six hybrid formulations and a small dataset for machine learning prediction. The experimental design was expanded to 15 distinct jute (JF)/coconut coir (CC)/banana fiber (BaF)/pineapple leaf fiber (PALF) hybrid epoxy formulations, generating 3000 specimen-level mechanical measurements (tensile, flexural, impact, and Shore D hardness). Nine regression algorithms were benchmarked using both conventional specimen-level random-split validation and formulation-level GroupKFold cross-validation. Although all models achieved high accuracy under random-split validation (R² = 0.87–0.97), formulation-level validation revealed that Gaussian Process Regression (R² = 0.73–0.85) and Linear Regression (R² = 0.61–0.80) generalized most reliably, whereas tree-based ensemble models and the Artificial Neural Network exhibited poor formulation-level generalization (interpolation within the sampled compositional region, not true extrapolation; see Sect. 4, Limitations). SHAP analysis identified banana fiber as the primary contributor to impact strength and hardness, while jute fiber dominated tensile and flexural strength. Multi-property optimization identified H-15 (30 wt
