Wave-particle duality is widely regarded as one of the conceptual pillars of modern quantum theory, providing the framework through which the behavior of microscopic matter can be consistently interpreted. According to this principle, microscopic entities such as electrons, photons, atoms and even complex molecules exhibit both wave-like and particle-like properties depending on the experimental configuration used to observe them. This duality is quantitatively formalized through the De Broglie relation, which associates a wavelength with any particle possessing momentum. The present work provides a comprehensive theoretical analysis of the physical and mathematical foundations of wave-particle duality, focusing particularly on the De Broglie wavelength and the kinetic energy of the electron in both non-relativistic and relativistic regimes. The connection between the De Broglie hypothesis and Schrödinger’s equation is discussed in detail, highlighting the role of the kinetic energy operator and the interpretation of matter waves in terms of quantum wave packets. Experimental evidence supporting the wave nature of matter, including electron diffraction experiments and interferometry techniques, is also reviewed. Beyond their conceptual importance, these principles underpin several modern technologies, including electron microscopy, nanoscale semiconductor devices and quantum metrology. The analysis demonstrates that the De Broglie relation provides a unifying framework connecting momentum, wavelength and kinetic energy, thereby forming a fundamental bridge between classical mechanics, quantum theory and modern technological applications.
High-energy physics (HEP) experiments generate extraordinarily large and complex datasets, posing significant challenges for real-time data analysis, event reconstruction, and reliable anomaly detection. Traditional analytical techniques often struggle to scale efficiently or fully exploit the rich structure of these data. In this context, machine learning (ML) has emerged as a transformative paradigm, offering powerful tools to enhance computational efficiency, precision, and adaptability in HEP data processing pipelines. This review provides a comprehensive overview of the integration of ML techniques in HEP, with a particular focus on their role in optimizing data analysis workflows and improving experimental performance. We examine a broad spectrum of ML approaches, including supervised and unsupervised learning methods, deep learning architectures, and ensemble models, highlighting their applications in tasks such as signal–background discrimination, feature extraction, noise reduction, and anomaly detection. Special attention is given to advanced algorithms designed for real-time data processing, which are critical for trigger systems and online event selection in modern collider experiments. The effectiveness of these methods is evaluated in the context of large-scale HEP datasets, demonstrating strong performance with metrics including an accuracy of 0.9421, sensitivity of 0.9314, specificity of 0.9507, precision of 0.9458, an F1-score of 0.9386, and an area under the ROC curve (AUC) of 0.9723. By critically analyzing current ML models and their integration into established HEP data analysis frameworks, this review identifies recent advancements, ongoing challenges related to model interpretability, scalability, and robustness, and promising directions for future research. The findings underscore the pivotal role of ML in advancing data-driven discoveries in HEP and support the development of more accurate, efficient, and scalable experimental analyses.
The current study is including the lithofacies analysis and petrophysical evaluation related to depositional development for Zubair Formation in X, XX and XXX oil fields (X-1, XX-5 and XXX-2). Zubair Formations was deposited in the Mesopotamian Zone during the Barremain age which belong to the Early Cretaceous epoch. Depositional settings and related facies that were interpreted using the supplied parameters and distribution of the observable lithofacies, there are three main associations of deltaic facies in studies succession: - Delta Front Association Facies, Back Shore Association Facies, Delta Plain Association Facie. The succession under study is separated into three main units utilizing well logging technologies (GR, SP, and DT). The upper part is primarily made up of sand layers with thin shale beds, the middle member is made up of thin layers of sandstone with a predominance of shale, and the lower member is primarily built of shale with less sandstone: - Lower unit shale dominated, Middle unit sandstone dominated and upper unit sand/shale dominated. The lower petrophysical zone of Zubair succession is represented by the shale dominated rock units, with low to non-pores rock unit (type III). Active porosity type (I) is present in low to moderate levels within the shale bands lithofacies across all examined wells. The middle zone in the studied succession is split into two subzones. The upper subzone exhibits high to moderate levels of active porosity (type I), whereas the lower subzone shows moderate levels of active porosity (type II) and a low to non-porous rock unit (type III) due to a significant amount of shale content. This zone represents the middle section of the Zubair succession within the shale-dominated layers. The upper zone consists of high to medium-sized active porosity rocks (Type I) due to well-graded coarse granitic sandstones. These features correspond to shale-dominated rocks and low to medium shale grades.
The increasing use of computed tomography (CT) in medical imaging has raised concerns regarding patient exposure to ionizing radiation, necessitating continuous evaluation of dose levels and optimization practices. This study presents a combined assessment of patient radiation dose and protection parameters during general and head CT examinations in selected diagnostic centers in Nigeria, utilizing Optically Stimulated Luminescence Dosimeters (OSLDs) for direct dose measurement. A retrospective analysis of 30 patients was conducted to evaluate skin dose during general CT examinations across three centers (A, B, and C), while a prospective study of 60 adult patients assessed head CT dose indices, including CTDI vol , CTDI w , dose length product (DLP), and effective dose. The mean skin doses for general CT were 9.92 mGy, 12.21 mGy, and 13.44 mGy for Centers A, B, and C respectively, corresponding to estimated effective doses of 0.15 mSv, 0.18 mSv, and 0.20 mSv. For head CT examinations, mean CTDIvol values ranged from 47.83 to 52.52 mGy, while mean DLP values varied from 1080.71 to 1485.48 mGy·cm, with effective doses between 2.49 and 3.17 mSv. Significant inter-center variations were observed, with general CT dose variability largely attributed to operator-dependent factors, whereas differences in head CT doses were primarily influenced by scan length and protocol implementation. Third-quartile values were used to establish local diagnostic reference levels (DRLs), which were higher than some international benchmarks but consistent with findings from similar healthcare settings. The study highlights the need for standardized CT protocols, routine dose monitoring, and strict adherence to the ALARA principle to enhance patient radiation protection while maintaining diagnostic image quality.
Fe-6.5wt%Si high silicon steel is recognized as an optimal magnetic material due to its low iron loss, near-zero magnetostriction, and high saturation magnetization, offering significant advantages in energy savings, weight reduction, and miniaturization of electrical equipment. However, its high brittleness presents substantial challenges for conventional manufacturing processes, and large-scale production remains a global challenge. This paper reports the successful preparation of Fe-6.5wt%Si high silicon steel using the Double Glow Plasma Surface Metallurgy Technology, also referred to as the Xu-Tec process. In this method, a dual-electrode glow discharge configuration is employed within a vacuum vessel, where silicon is sputtered from a pure source cathode and deposited onto a low-silicon steel workpiece cathode, followed by inward diffusion under argon ion bombardment at elevated temperatures. Through systematic optimization of process parameters—including source voltage, workpiece voltage, argon pressure, treatment temperature, and holding time—both homogeneous and gradient high silicon steels were successfully fabricated. Microstructural characterization and compositional analysis revealed that the homogeneous Xu-Tec high silicon steel achieved an average cross-sectional silicon content exceeding 6.5 wt%, while the gradient variant exhibited controlled silicon distribution from the surface to the core. Notably, the thickness of the Xu-Tec processed samples was twice that of the Japanese JNEX 900 and JNHF 600 products, indicating superior diffusion efficiency. The Xu-Tec process is simple, environmentally friendly, and free from corrosion and pollution, offering a promising new route for the large-scale production of high silicon steel. This study provides a foundational basis for future application research and industrialization efforts.
