Industry 4.0 (I4.0) is built upon the capabilities of Internet of Things technologies that facilitate the recollection and processing of data. Originally conceived to improve the performance of manufacturing facilities, the field of application for I4.0 has expanded to reach most industrial sectors. To make the best use of the capabilities of I4.0, machine architectures and design paradigms have had to evolve. This is particularly important as the development of certain advanced manufacturing technologies has been passed from large companies to their subsidiaries and suppliers from around the world. This work discusses how design methodologies, such as those based on functional analysis, can incorporate new functions to enhance the architecture of machines. In particular, the article discusses how connectivity facilitates the development of smart manufacturing capabilities through the incorporation of I4.0 principles and resources that in turn improve the computing capacity available to machine controls and edge devices. These concepts are applied to the development of an in-line metrology station for automotive components. The impact on the design of the machine, particularly on the conception of the control, is analyzed. The resulting machine architecture allows for measurement of critical features of all parts as they are processed at the manufacturing floor, a critical operation in smart factories. Finally, this article discusses how the I4.0 infrastructure can be used to collect and process data to obtain useful information about the process.

This study presents a numerical investigation of surface plasmon resonance (SPR) sensors based on multilayer configurations incorporating BK7, silver, silicon nitride (Si₃N₄), and black phosphorus (BP). Using the transfer matrix method, the optical performance of four architectures was evaluated under refractive index perturbations consistent with values reported in prior theoretical and experimental studies. The sensor response was characterized through metrics such as angular sensitivity, resonance shift, full width at half maximum, attenuation, and derived figures including detection accuracy and limit of detection. Parametric optimization was performed for the thickness of each functional layer to enhance sensing performance. Among all configurations, those incorporating both Si₃N₄ and BP demonstrated the highest angular sensitivity, reaching up to 394.46°/RIU. These enhancements were accompanied by increased attenuation and spectral broadening, revealing trade-offs in sensor design. The results, based entirely on numerical modeling, provide a comparative framework for guiding SPR sensor optimization under idealized optical conditions.

One of the most common products in the construction industry is brick due to its advantages over other building materials such as blocks. In developing countries like Ecuador, brick is handcrafted and is the source of income for thousands of families. These bricks lack a systematized process that optimizes resources to make a brick that meets local regulations. This study evaluates the use of the different raw materials used in production through experimental designs to determine the amount of optimal components locally available (“White Clay,” “Black Clay,” “Water”) and achieve the maximum compressive strength in dry-pressed ceramic bricks. This research also identifies the behavior of the compressive strength based on the compaction pressure in the molding stage, concluding that the model that best fits the behavior is a quadratic model, and from the trace plot, it was observed that compared with “White Clay,” “Black Clay” contributes further to the brick strength. The optimal amount of components to meet the 6 MPa pressure standard required in the brick was 5% water, 85% “Black Clay” and 10% “White Clay.” The pressure value required in the compaction stage was 4.9Mpa (712psi,) and greater strength can be achieved by only adding “Black Clay” and water.

The accurate dosing of polyaluminum chloride (PAC) is essential for achieving effective coagulation in drinking water treatment, yet conventional methods such as jar tests are limited in their responsiveness and operational efficiency. This study proposes a hybrid modeling framework that integrates artificial neural networks (ANN) with genetic algorithms (GA) to optimize PAC dosage under variable raw water conditions. Operational data from 400 jar test experiments, collected between 2022 and 2024 at the Yanahurco water treatment plant (Ecuador), were used to train an ANN model capable of predicting six post-treatment water quality indicators, including turbidity, color, and pH. The ANN achieved excellent predictive accuracy (R² > 0.95 for turbidity and color), supporting its use as a surrogate model within a GA-based optimization scheme. The genetic algorithm evaluated dosage strategies by minimizing treatment costs while enforcing compliance with national water quality standards. The results revealed a bimodal dosing pattern, favoring low PAC dosages (~4 ppm) during routine conditions and higher dosages (~12 ppm) when influent quality declined. Optimization yielded a 49% reduction in median chemical costs and improved color compliance from 52% to 63%, while maintaining pH compliance above 97%. Turbidity remained a challenge under some conditions, indicating the potential benefit of complementary coagulants. The proposed ANN–GA approach offers a scalable and adaptive solution for enhancing chemical dosing efficiency in water treatment operations.

Introduction: Scoliosis is a health problem that causes a side-to-side curvature in the spine. The curvature may have an “S” or “C” shape. To evaluate scoliosis, the Cobb angle has been commonly used. However, digital image processing allows the Cobb angle to be obtained easily and quickly, several researchers have determined that Cobb angle contains high variations (errors) in the measurements. Therefore, a more reproducible computer aided-method to evaluate scoliosis is presented.Material and Methods: Several polynomial curves were fitted to the spine curvature (4th to 8th order) of thirty plain films of scoliosis patients to obtain the Curvature-Length of the spine. Each plain film was evaluated by 3 physician observers. Curvature was measured twice using the Cobb method and the proposed Curvature-Length Technique (CLT). Data were analyzed by a paired-sample Student t-test and Pearson correlation method using SPSS Statistics 25.Results: The curve of 7th order polynomial had the best fit on the spine curvature and was also used for our proposed method (CLT) obtaining a significant positive correlation when compared to Cobb measurements (r=0.863, P<0.001). The Intraclass Correlation (ICC) was between 0.863 and 0.948 for Cobb method and0.974 to 0.984 for CLT method. In addition, mean measurement of the inter-observer COV (Coefficient of Variation) for Cobb method was of 0.185, that was significantly greater than the obtained with CLT method of 0.155, this means that CLT method is 16.2% more repeatable than Cobb Method.Conclusion: Based on results, it was concluded that CLT method is more reproducible than the Cobb method for measuring spinal curvature.