Magnesium alloys are a strong candidate for various applications in automobile and aerospace industries due to their low density and specific strength. Micro-alloying magnesium with zinc, yttrium, and cerium enhances mechanical properties of magnesium through grain refinement and precipitation hardening. In this work, a critical review of magnesium-based binary systems including Mg-Zn, Mg-Y, Mg-Ce, Zn-Y, and Zn-Ce is presented. Based on the CALPHAD approach and first-principles calculations, thermodynamic modeling of Mg-Zn-Y and Mg-Zn-Ce ternary phase diagrams have been summarized. The influence of micro-alloying (yttrium and cerium) on the mechanical properties of magnesium is discussed. A comparison between mechanical properties of magnesium commercial alloys and magnesium–zinc–{yttrium and cerium} have been summarized in tables.

With the continuous development in technology and changes in the logistic systems, organizations should review their logistic processes that evolve over time. To attain a good insight into the conformance of these processes with the designed process model, constant detection and monitoring is required. The main objective of this systematic review is to investigate the state of the art in process mining applications in logistics specifically related to manufacturing organizations. The review aims to analyze and assess the use of process mining techniques and models in the logistics domain, based on the selected studies. In this review, literature was searched between the years 2004 and 2022 using several inclusion and exclusion criteria. Fifteen published studies were selected and analyzed on the use of process mining in the logistics domain based on the process mining techniques and models used. All of the selected studies used models and thirteen of them used real case studies. All of the fifteen studies used one or more algorithms. Only three of these studies did not mention the modeling language used to represent the process. Moreover, seven studies focused on discussing the process discovery alone and five more addressed process discovery in addition to other types of process mining. Eight studies mentioned the process mining tools used that included DISCO and several versions of ProM.

The usage of Personal Protective Equipment (PPE) is crucial in a variety of sectors, including construction, manufacturing, healthcare, and hazardous material handling, to safeguard employees from possible dangers and risks. Employers may encourage a safer workplace and lower the risk of accidents and injuries by using an automated system that can determine whether people are wearing the required PPE. Several approaches have been proposed by scientists to monitor employees at workplaces, however, these techniques are limited to recognizing a small number of PPE objects. Further, historic approaches lack the generalization power to perform well in real-world cases and are not proficient in tackling several transformational alterations of the examined images like clutter, blurring, light, and brightness changes in the background settings of images. The work is focused on overcoming the limitations of existing works by proposing an effective DL strategy called the Faster-PPENet. Clearly, we have utilized the Faster-RCNN approach with a modified ResNet101 base to detect and classify various PPE. We have presented a robust feature extractor base by altering the conventional ResNet model. Specifically, we have replaced the activation approach of the ResNet approach by utilizing the Swish activation method as an alternative to the ReLU function to extract a more effective set of sample features which are later recognized by the 2-phase locator of the Faster-RCNN model. The model is evaluated on two complex databases named the Color Helmet and Vest (CHV), and Safety Helmet Detection (SHD) to show the effectiveness of our approach. Further, a real-world data sample is also utilized to show the generalization ability of our approach. For the CHV and SHD data samples, we have attained mAP scores of 0.8845, and 0.9321, along with accuracies of 90.25%, and 94.03%, while for the real-world sample, we have attained the mAP, and accuracy values of 0.901, 91% of which is clearly proving the robustness of our approach.

This study explores the integration of recycled polypropylene (PP) plastic (PL) pellets into concrete mixtures, to evaluate their impact on compressive strength, workability, and weight reduction. Concrete samples were prepared by replacing conventional aggregates with PL pellets at varying percentages (1%, 3%, and 5%) by weight. The primary objective was to determine the optimal PL content that enhanced the properties of concrete. The experimental results demonstrated that incorporating 3% PP-PL pellets led to an 11.3% increase in compressive strength compared with the reference mix (0% PL). Furthermore, the 3% PL mix maintained a slump value comparable to that of the reference mix, indicating that this level of PL inclusion did not negatively affect workability. However, increasing the PL content beyond 3% resulted in a significant reduction in workability, suggesting that excessive PL may limit practical applications. The inclusion of PL pellets also contributed to a decrease in the overall weight of the concrete, showcasing the potential for producing lightweight, high-performance concrete. These findings highlight the feasibility of utilizing recycled PL in concrete production as a sustainable strategy to enhance material properties while addressing the growing issue of PL waste. This study identified 3% PL as the optimal dosage for achieving the best balance between strength, workability, and weight reduction. This research contributes to the development of more sustainable construction materials while also offering insights into the role of recycled PL in improving concrete performance. Future research could focus on evaluating the long-term durability, microstructural behavior, and environmental impact of PL-modified concrete to better understand its potential for broader application in eco-friendly infrastructure, ensuring it meets the demands of sustainable and cost-effective construction practices. In addition, this study’s findings pave the way for future investigations into optimizing other types of recycled PL for use in concrete.

Process mining is an effective approach for organizations to better understand their end-to-end processes. There are two main types of the process modeling languages: procedural and declarative in process mining. However, a hybrid model can be created by combining both the procedural and declarative modeling languages. Procedural modeling languages can be ineffective to manage with a large number of different exceptions. Declarative modeling languages are easier to manage as they simply encode a set of constraints on the process behavior. Both modeling languages have strengths and weaknesses. The hybrid model was introduced by combining Petro nets and Declare to overcome some of the weaknesses of the procedural and declarative modeling languages. However, more real-life applications are required with different languages.