There is significant interest in accurately modeling the operational variables of the steel-making process in LD converters. Despite this, the task is challenging due to the complex interactions between process variables, which are not entirely comprehended. Often, decisions in the industry are grounded in experience. This study aims to introduce a robust model that can effectively guide engineers and technicians by forecasting the future behavior of steelmaking variables in the BOF furnace. We employed multivariate time series analysis to reach this goal, utilizing tools like Vector Autoregression models, ElasticNet, K-Nearest-Neighbors, Multiple Linear Regression, and Long Short-Term Memory Neural networks. These models were tested on data from three distinct steel production campaigns. A successful model was identified, predicting 35 out of the 42 chosen variables, demonstrating the potential to correlate a majority of the selected parameters.

Welding is a critical and energy-intensive process with significant importance in the manufacturing industry, enabling the creation of joints capable of withstanding diverse loads without failure. Accurate prediction of welding parameters' effects on the thermal cycle and strength of metals during and after welding is essential to ensure the reliability of welds. This study investigates the influence of welding parameters such as welding current, material thickness, number of weld passes, and electrode diameter on the impact strength of Cr-Mo steel bars. Pure tungsten with 2% thoriated Tungsten Inert Gas (TIG) electrodes was used to join the metal sheets autogenously. Artificial neural network (ANN) was used in creating the model that predicts the impact strength of the steel. Sample with welding parameters of 15 mm thickness, 90 A current, 3 weld passes, and Ø2.4 mm electrode size exhibited the highest impact strength. Furthermore, the analysis of variance (ANOVA) results show that the material thickness and number of weld passes contribute significantly to the impact strength of the steel. The ANN model trained by the Levenberg-Marquardt algorithm had an average training dataset root mean square error (RSME) of 4.12%. This study contributes to the reliability and performance of welded joints in various applications.

In order to remain competitive in the long term and to push the company's efficiency to its limits, entrepreneurs are more and more open to the idea of integrating into Industry 4.0 aiming mainly at filling the important downtimes and the associated productivity losses by implementing predictive maintenance. This concept, common in developed countries, is much less widespread in Morocco and even less in small and medium-sized Moroccan companies. The objective of this article is to study the maturity level of predictive maintenance in Moroccan small and medium-sized enterprises, through a questionnaire validated by experts and made available to several companies. Valid data from 115 companies throughout the kingdom operating in different sectors were collected and processed by descriptive and factorial analysis under SPSS software. The results obtained show that only 33% of our sample were able to implement predictive maintenance, and that the expected benefits of this approach are the minimization of downtime at 96.5% and the increase in productivity at 94.8%, The main challenges observed are the lack of team motivation and a corporate culture unsuited to digitalization, which represents 42.277% of the total variance, lack of financial resources at 12.916% of the total variance and lack of data protection at 11.644% of the total variance. This analysis indicates that the level of maturity regarding the application of predictive maintenance in Moroccan small and medium-sized companies is low, these rates can be used to improve the root causes.

This paper presents a comprehensive numerical simulation study focused on the cooling of integrated toroidal octagonal inductor using nanofluids within a microchannel heat sink. The investigation utilizes COMSOL Multiphysics 6.0 integrated with the Fluid Flow and Conjugate Heat Transfer Module. The primary objective is to explore and understand fluid flow and heat transfer characteristics within the integrated inductor. The study involves testing three distinct fluids, water, CuO-water nanofluid, and Al2O3-water nanofluid, under laminar flow conditions within microchannels. The choice of fluid plays a significant role in heat transfer, interacting with the microchannel geometry to optimize performance. Three-dimensional computational fluid dynamics (CFD) models are meticulously developed; focusing on toroidal inductors equipped with micro pin fins heat sinks. The study commences by detailing the geometry of the micro coil and the integrated heat sink. The simulation encompasses a mathematical model that captures the intricate interplay between the governing Navier-Stokes equations for fluid dynamics and the heat transfer equations within the integrated inductor. As φ increases, temperature, viscosity, and pressure decrease. CuO-water and Al2O3-water nanofluids play a significant role in influencing laminar flow and key thermal parameters in the toroidal inductor. These nanofluids, which consist of base fluids (water) with dispersed nanoparticles (CuO or Al2O3), are employed as cooling agents to enhance heat transfer. The presence of nanoparticles in the fluid alters its thermal properties, leading to changes in the flow dynamics and overall heat dissipation within the toroidal inductor.The laminar flow characteristics are affected by the nanofluid's viscosity, density, and thermal conductivity. Additionally, the Nusselt number, Reynolds number, and thermal resistance are key thermal parameters that reflect the performance of the cooling system. The nanofluid's influence on these parameters is crucial for understanding and optimizing the thermal management of the integrated toroidal inductor. The enhancement of heat dissipation in the toroidal inductor is achieved through improved thermal properties of the nanofluid. Higher nanoparticle concentrations result in better heat transfer rates, leading to lower temperatures in the toroidal inductor. This, in turn, improves the overall efficiency and performance of the cooling system. The viscosity of the nanofluid is influenced by the presence of nanoparticles. The pressure within the microchannels is also affected by the nanoparticle concentration. An increase in φ can lead to changes in pressure drop along the microchannels. Understanding these variations is crucial for designing an effective cooling system.

Many orthodontic clinics have problems with patients who have dental demineralization. This study aimed to evaluate “the shear bond strength (SBS)” of braces after being bonded to demineralized teeth treated with herbal materials. Our study samples were divided into five groups. The first group was left with no treatment. The surfaces of the second, third, and fourth groups were first treated with a demineralizing solution. The second group was left after being demineralized without any subsequent treatment; the third group was treated with rosemary oil; the fourth was treated with ginger–honey. Casein phosphopeptide–amorphous calcium phosphate with fluoride paste (CPP–ACPF) was applied to the fifth group. A universal testing machine evaluated the SBS. A stereomicroscope was used to determine the adhesive remnant index (ARI). The enamel surface changes were observed using surface microhardness (SMH) testing, scanning electron microscopy (SEM), and energy dispersive spectrometry (EDS) to determine the element percentages. Our data revealed that the values of both SBS and SMH were significantly (p < 0.05) increased after remineralization. Rosemary and ginger–honey significantly enhanced the SBS and SMH of the demineralized teeth.

The growing demands of magnesium (Mg) based materials had risen new challenges related to disposal of unused parts and a huge amount of waste made by such metals. Attempts to recycle the scrap of these materials through remelting had become one of the preferred choices. However, a series of preliminary steps should be carried out to reduce the impurities as well as to maintain the quality of the casted ingot, for instance, by applying de-coating for removing paints or coating substrates at the scrap surface prior to remelting. In this research, the effects of chemical reagent de-coating on the properties of ingot obtained from recycling Mg scrap were studied. A commercial paint removal liquid was preferred as the reagent for removing paint layers over the Mg scrap surface. The de-coated scrap was then remelted in a conventional furnace with NaCl powder as the fluxing layer. The results of this study noticed the importance of chemical de-coating process to reduce the impurity contents in the ingot which might be originated from the coating or paint substrates covering the Mg scrap. Meanwhile, the density and hardness of the Mg ingot processed without de-coating were obviously higher than that had been cleaned previously with paint removing agent.

The pandemic generated containment and mitigation policies, as well as distancing and confinement strategies that limited the supply of water resources to social sectors. Residential areas- The offer is maintained, but with an increase in rates. Marginalized areas were subsidized and exempted from paying for an increasingly intermittent supply. The anti-COVID-19 policies guided water policies in two ways: The first consisted of disseminating anti-COVID-19 policies in water management agencies. Another second consisted of the autonomy of the institutions and their disassociation or agreement with the anti-COVID-19 policies. In this way, the literature from 2020 to 2023 around anti-COVID-19 policies in their water dimensions, registers problems of scarcity, famine and unsanitary conditions. Scarcity had already been observed in marginalized sectors, famine in residential neighborhoods, but unsanitary conditions were appreciated in migrant communities. In fact, the type of exposure to occupational hazards determined the health status of the migrants. The water problems were recorded in the circulation press to highlight the asymmetries of the anti-COVID-19 policies in the public and private sectors, as well as in the political and social actors. The objective of the study was to reveal the network structure of relationships between nodes and edges related to press releases on water issues. A documentary, cross-sectional and retrospective study was carried out with national circulation newspapers: El País, El Reforma, La Jornada and El Universal, considering the water problems of scarcity, unsanitary conditions and famine. The results show a structure of nodes where the water problems were initiated by La Jornada and ended by El Reforma. Both findings are relevant considering the ideology of the newspaper. La Jornada, a newspaper identified with the political ideology of the left, began the diffusion of water problems in a city administered by a government of the same ideology. El Reforma, a newspaper designated by the executive as a spokesperson for the opposition ideology, culminates the network of notes on water problems. In other words, regardless of the type of political ideology attributed to newspapers, the problems of scarcity, unsanitary conditions and famine are spread. In relation to the state of the art where it is shown that ideology does not influence the establishment of the agenda, the present work corroborates and recommends expanding the study to other entities administered by the opposition such as the cities of Guadalajara and Monterrey.

In this paper the positive and negative aspects of diffusion metallization of steels were reviewed. It was shown that at high heating temperatures and prolonged exposure under these temperatures, steels show a tendency to enlarge austenitic grain. Overheating can occur at high exposure temperatures (T>1000ᵒC), which can be rectified by repeated heating however if burning of the steel microstructure occurs, it cannot be corrected. Given these circumstances, when assigning diffusion metallization modes, it is necessary to consider the factor of overheating or burning of steel in the process of exposure to high temperatures. To avoid this phenomenon, it is recommended to use alternative low-temperature processes of diffusion saturation of steels. Nitriding, nitro-cementation, gas-thermal spraying of the surface of steels are shown as such examples. It was suggested that these processes in comparison with diffusion metallization are more promising and acceptable for the restoration of worn surfaces of steels in the manufacture of parts of specialized equipment. Given that the parts of specialized equipment work in extreme conditions, repeated high-temperature heating of these steels is not recommended.To overcome the shortcomings of the diffusion metallization, the most frequently used coatings are applied by CVD, thermal spray, and cloth cladding techniques. As an alternative promising solution, the development of innovative methods of diffusion saturation, like an ion plantation of atoms on a relatively cold surface of the part could also be considered. It is shown that diffusion metallization is most acceptable for saturation of the surface of non-ferrous metals and alloys with the hardest and wear-resistant compounds.

Parametric and non-parametric are the main optimization methods that are used in various industrial fields. In non-parametric optimization, the process of manipulating the node locations (shape optimization) or removing mass without changing the node locations (topology optimization) is adopted to achieve a desired objective. This structural optimization is formulated as a non-parametric problem, and for analysis purposes, ABAQUS/CAE software is adopted for this approach. Manufacturing process like forming is always linked with stress concentration, especially in the sharp ends and variable cross sections like holes and fillets. The problems of representation and finding the optimal and better structural design of some known quantities such as reactions, loads and masses is not easy. A large deflection may be induced in a structure when experiencing severe mechanical loads. In this work, the numerical method has been presented to investigate a method for optimization of formed parts geometry. Numerical examination confirmed that high-stress concentrations are generated in many places. Material distribution is highly influenced by nonlinearity and the new layout will result in intermediate densities. In such cases, the nonlinear elasticity like nonlinear strain must be considered. As a result, the non-parametric optimization can offer good design flexibility to use the existing model with ease of setup and without the need for parameterization. It can provide a conceptual design that can reduce the structure's weight to the maximum extent in the early design stage. This work is going to optimize the design of the formed plates by reducing the volume while maximizing its stiffness. As a recommendation, in order to provide an attractive approach with suitable levels of structural performance, the combination of both optimization methods is the short way to achieve this aim.

Miniaturisation in avionics, electronics, and medical appliances has led to demands for rapid heat dissipation techniques. The spray cooling technique has gained importance recently due to its advantage over other cooling methods. Parameters affecting heat transfer mechanisms during spray cooling are contemplated. This review presents different heat transfer parameters and their effect on spray cooling by analysis from past studies. Heat transfer surface modifications and different coolant variations to enhance heat transfer effectiveness are also reviewed. Apart from high heat flux having more applications, low heat flux studies have also grabbed the researchers to find solutions with a temperature range lower than 250˚C. Therefore, the upcoming spray cooling technology will have broad applications that will contribute to the maximum efficiency of the heat removal rate.