In the ever-evolving cybersecurity landscape, network forensics has emerged as a crucial field, dedicated to investigating and mitigating cyber threats. The rapid growth of digital communication and the increasing sophistication of cyber-attacks necessitate advanced methods for detecting, analyzing, and responding to security breaches. Machine learning (ML), a subset of artificial intelligence (AI), offers transformative potential in network forensics, promising to revolutionize how we protect and investigate our digital environments. Network forensics involves monitoring, capturing, and analyzing network traffic to identify security incidents and gather digital evidence. Traditionally, this process has relied heavily on manual analysis and signature-based detection methods, which can be time-consuming and inadequate against novel or complex attacks. The sheer volume of network data and the subtlety of modern cyber threats demand more sophisticated, scalable solutions. This is where machine learning comes into play.

There are different ways to train personnel in a manufacturing plant, ranging from in-person to virtual training. However, there is currently no standard model or methodology to measure the effectiveness of this training on the position, work cell, and indicators. This work proposes a method to measure the impact of training strategies through an indicator called a "Real Training Indicator." This method is based on requirement 7.2 of the ISO 9001:2015 Standard, which refers to the "Competence of Personnel." The Kirkpatrick, Wade, and Phillips models are used to measure the impact of training in a transnational organization in Mexico. This method estimates the level of transfer and effectiveness of the training to cover the requirement of Good Manufacturing Practices.

Keywords: Good Manufacturing Practices; Knowledge Transfer; Personal Hygiene; Real Training Indicator; Food Industry; Blue Collars; Mexico

Since its inception on December 10, 1993, when it was decreed by the Prime Minister, Vietnam National University, Hanoi (VNU) has had a substantial impact on higher education in Vietnam for the past three decades. At present, VNU has developed into a thriving center for education and research, with 37 members and connected units. These include 16 service units, 9 universities, 4 affiliated schools, 6 research institutes, 2 training and research centers, and 2 institutes of higher education. This paper presents an exhaustive examination of the research outputs of VNU spanning the years 1993 to 2023. It emphasizes the university's significant contributions to the Scopus Database, which contains more than 10,000 document records, and the Web of Science Database, which contains more than 9,100 records. The research study thoroughly examines the statistical data and analyses pertaining to these outputs, considering many factors including number, quality, subjects, authors, and collaborations. This report presents an exhaustive assessment of the research outputs of VNU spanning a period of thirty years. It emphasizes the efficacy of institutional measures in bolstering scientific production and fostering innovation.

Keywords: Bibliometric analysis; Scientific research; Creative Innovation; Research outputs; Policy impact

Polyurethane's versatility is limited by its weak flexural strength. This study explores methods to address this limitation. Recycled carbon fiber powder (RCFP) was investigated as a reinforcing agent. The research found that incorporating recycled carbon fiber particles (RCFP), particularly finer particles (100 mesh), significantly enhanced flexural strength and modulus. This improvement is attributed to RCFP effectively filling voids in the polyurethane, increasing its density and resistance to bending loads. While a higher content strengthens the material, it can also lead to excessive stiffness and a decrease in flexural strength. Polyester fabric reinforcement (FR) emerged as another effective method, with five layers increasing flexural strength to 2.1 MPa. FR also introduces flexibility, potentially reducing the risk of catastrophic failure under impact or bending. Additionally, FR improves the composite's compressive, shear, and tensile strength. The study highlights the importance of optimizing both RCFP size and content to achieve the desired mechanical properties. Future research could explore the combined effects of RCFP and FR for even greater improvements. This research paves the way for the development of stronger and more sustainable polyurethane composites using recycled materials.

Keywords: Recycled Carbon Fiber Powder; Fabric Reinforcement; Flexural Properties; Polyurethane

This paper discusses a case history of a highway tunnel located in lower Himalayas. The site information was collected primarily from geological mapping and limited site investigations carried out from tunnel portal due to inaccessible terrain conditions. The tunnel alignment, cross section, and elevations were assessed, evaluated, and designed based on the geological and geotechnical parameters ascertained from field and laboratory investigations. Numerical analysis was carried out to study stress-deformation behavior under varying ground conditions and multiple stages of excavation along with wedge stability for given joint conditions. Displacements obtained numerically were compared along with field monitoring values for crown and wall. Finally, a sensitivity analysis is carried out to ascertain impact of strength parameters on deformation and yielding of excavated tunnel face.

keywords: Numerical analysis; Geotechnical investigations; Geological mapping; Road tunnel; Field monitoring