Cancer’s staggering molecular heterogeneity demands innovative approaches beyond traditional single-omics methods. The integration of multi-omics data, spanning genomics, transcriptomics, proteomics, metabolomics and radiomics, can improve diagnostic and prognostic accuracy when accompanied by rigorous preprocessing and external validation; for example, recent integrated classifiers report AUCs around 0.81–0.87 for difficult early-detection tasks. This review synthesizes how artificial intelligence (AI), particularly deep learning and machine learning, bridges this gap by enabling scalable, non-linear integration of disparate omics layers into clinically actionable insights. We explore cutting-edge AI methodologies, including graph neural networks for biological network modeling, transformers for cross-modal fusion, and explainable AI (XAI) for transparent clinical decision support. Critical applications are highlighted, such as AI-driven therapy selection (e.g., predicting targeted therapy resistance), proteogenomic early detection, and radiogenomic non-invasive diagnostics. We further address translational challenges: data harmonization, batch correction, missing data imputation, and computational scalability. Emerging trends, federated learning for privacy-preserving collaboration, spatial/single-cell omics for microenvironment decoding, quantum computing, and patient-centric “N-of-1” models, signal a paradigm shift toward dynamic, personalized cancer management. Despite persistent hurdles in model generalizability, ethical equity, and regulatory alignment, AI-powered multi-omics integration promises to transform precision oncology from reactive population-based approaches to proactive, individualized care.

This paper reviews the advancements in wall-climbing robots, focusing on optimizing adsorption and introducing novel suction techniques. Wall-climbing robots have garnered significant attention due to their potential in hazardous operations and their ability to navigate various wall surfaces without compromising mobility. One such innovation includes a robot designed for smooth wall surfaces, integrating a vacuum adsorption system and adhesion belts. This design enhances the robot’s flexibility and steerability, with an in-depth analysis of its attachment mechanism for climbing tasks. Key parameters such as the required adsorption force and motor torque for stable climbing have been derived, with the robot’s prototype demonstrating high stability and adaptability across different wall surfaces. Another pivotal study delves into the modelling and experimental analysis of suction pressure in the suction chamber, emphasizing the role of different chamber contours. A novel addition of a bottom restrictor at the chamber’s base has been introduced, with its design and performance analysed using 3D modelling and computational fluid dynamics. The restrictor’s impact on the robot’s adhesion efficiency has been experimentally assessed, showcasing promising results on non-plastered brick walls. Through these studies, the paper underscores the continuous evolution and potential of wall-climbing robots in diverse applications.

Advances in renewable energy technologies are pivotal in addressing the challenges posed by the depletion of traditional energy sources and their associated environmental impacts. Among these, wind energy stands out as a promising avenue, with wind turbine farms proliferating globally. However, the unpredictable nature of wind and intricate interplay between turbines necessitate innovative solutions for efficient operation and maintenance. This paper reviews advancements in intelligent control systems, notably those proposed by Smart Wind technologies. These systems leverage a network of sensors and IoT devices to gather real-time data, such as wind speed, temperature, and humidity, to optimize turbine performance. A significant focus is on turbines employing doubly-fed induction generators, which offer benefits like adjustable speed and consistent frequency operation. Their integration into smart grids introduces challenges concerning power system dynamics’ security and reliability. This review delves into the dynamics, characteristics, and potential instabilities of such integrations, emphasizing the uncertainties in wind and nonlinear load predictions. A noteworthy finding is the rising prominence of artificial intelligence, particularly machine and deep learning, in predictive diagnostics. These methodologies offer costeffective, accurate, and efficient solutions, holding potential for enhancing power system stability and accuracy in the smart grid context.

Fines migration is a major cause of formation damage in clay‑rich sandstones, particularly under low‑salinity waterflooding where electrostatic double‑layer expansion destabilizes clay-mineral adhesion and causes severe permeability loss. This study investigates, at the laboratory scale, the potential of a Fe₃O₄@saponin/Cu nanocomposite to mitigate fines release and permeability loss during simulated low-salinity water flooding of clay-rich sandstone. The nanocomposite was synthesized by co‑precipitating magnetite nanoparticles, coating them with a saponin biopolymer via hydrogen bonding and hydrophobic interactions, and introducing Cu²⁺ through coordination with surface hydroxyl and glycosidic groups. Physicochemical characterization (FT‑IR, TGA, SEM, DLS) confirmed successful functionalization, enhanced thermal stability, controlled particle size, and colloidal stability up to 500 ppm loading. Zeta potential analysis revealed that the nanocomposite shifts mineral surface charge toward neutral or slightly positive values, reducing electrostatic repulsion between fines and the pore matrix. Core flooding of clay‑rich sandstone showed that untreated low‑salinity water caused catastrophic permeability loss (- 69.9%) and tripled injection pressure, whereas adding 500 ppm nanocomposite reduced impairment to - 4.1%, comparable to high‑salinity conditions without treatment. The mitigation mechanism involves synergistic electrostatic neutralization, steric stabilization from the saponin corona, and magnetic/coordination bridging that anchor fines to pore surfaces without plugging. These results demonstrate that Fe₃O₄@saponin/Cu nanofluid enables low‑salinity flooding while suppressing fines migration, providing a practical alternative to high‑salinity injection for enhanced oil recovery.