Fiber-optic Mach–Zehnder interferometers are widely used in research areas such as telecommunications, spectroscopy, and quantum information. These optical structures are known to be affected by phase fluctuations that are usually modeled as multiparametric noise. This multidimensional noise must be stabilized or compensated for to enable fiber-optic Mach–Zehnder architectures for practical applications. In this work, we study the effectiveness of a modified Perturb-and-Observe (P&O) algorithm to control multidimensional phase noise in fiber-based multi-arm Mach–Zehnder interferometers. We demonstrate the feasibility of stabilizing multidimensional phase noise by numerical simulations using a simple feedback control scheme and analyze the algorithm’s performance for systems up to dimension $8\times 8$. We achieved minimal steady-state errors that guarantee high optical visibility in complex optical systems with $N\times N$ matrices (with $N=[2,3,4,5,6,7,8]$).

Electrical impedance tomography (EIT) is a technology that estimates the electrical properties of a body or a cross section. Its main advantages are its non-invasiveness, low cost and operation free of radiation. The estimation of the conductivity field leads to low resolution images compared with other technologies, and high computational cost. However, in many applications the target information lies in a low intrinsic dimensionality of the conductivity field. The estimation of this low-dimensional information is addressed in this work. It proposes optimization-based and data-driven approaches for estimating this low-dimensional information. The accuracy of the results obtained with these approaches depends on modelling and experimental conditions. Optimization approaches are sensitive to model discretization, type of cost function and searching algorithms. Data-driven methods are sensitive to the assumed model structure and the data set used for parameter estimation. The system configuration and experimental conditions, such as number of electrodes and signal-to-noise ratio (SNR), also have an impact on the results. In order to illustrate the effects of all these factors, the position estimation of a circular anomaly is addressed. Optimization methods based on weighted error cost functions and derivate-free optimization algorithms provided the best results. Data-driven approaches based on linear models provided, in this case, good estimates, but the use of nonlinear models enhanced the estimation accuracy. The results obtained by optimization-based algorithms were less sensitive to experimental conditions, such as number of electrodes and SNR, than data-driven approaches. Position estimation mean squared errors for simulation and experimental conditions were more than twice for the optimization-based approaches compared with the data-driven ones. The experimental position estimation mean squared error of the data-driven models using a 16-electrode setup was less than 0.05% of the tomograph radius value. These results demonstrate that the proposed approaches can estimate an object’s position accurately based on EIT measurements if enough process information is available for training or modelling. Since they do not require complex calculations it is possible to use them in real-time applications without requiring high-performance computers.

Electric forklifts are increasingly adopted in industrial environments due to their energy efficiency, reduced emissions, and lower operating noise compared to combustion alternatives. This paper presents a novel methodology for estimating the transported load weight in electric forklifts based on the output power signal of the DC–DC converter driving the electro-hydraulic lifting system. The proposed method leverages non-intrusive measurements of voltage and current to compute the lifting power, lifting speed, and energy, also allowing the computation of the lifting efficiency. The analysis confirmed that lifting energy is not linearly correlated with transported weight but lifting efficiency can be reasonably approximated as a function of lifting power and lifting speed, subsequently allowing the estimation of the transported mass. Experimental validation using 53 lifting events demonstrated that the methodology can estimate load weight with a reasonable mean absolute percentage error of 10.6% and 6.4% when using linear or multivariable regression analysis, respectively. These results demonstrate that the approach is sufficiently accurate for practical applications such as triggering load warnings when the estimated mass exceeds predefined safety thresholds.

Heavy-duty freight transport remains one of the most difficult sectors to decarbonize due to its high energy demand, long-distance operation, and strong dependence on diesel fuel. In Chile, more than 90% of heavy trucks operate with diesel engines, contributing significantly to greenhouse gas emissions and local air pollutants. At the same time, Chile has favorable conditions for the development of green hydrogen due to its world-class solar and wind resources. This study presents a technical and economic assessment of green hydrogen fuel cell trucks recently introduced in Chile, comparing their operational performance with conventional diesel freight trucks. A techno-economic framework based on total cost of ownership, fuel consumption, operational range, fleet utilization, and hydrogen price scenarios was developed using information reported in public studies and official Chilean strategic documents. The results indicate that hydrogen trucks are technically suitable for long-haul and intensive-duty operations due to their rapid refueling capability and high operational autonomy. However, economic competitiveness remains strongly dependent on hydrogen price, fleet scale, infrastructure utilization, and vehicle capital cost. Under current market conditions, diesel trucks preserve cost advantages, while hydrogen trucks become increasingly competitive as hydrogen prices approach long-term target values and annual mileage increases. Chile’s renewable resource base positions the country as a strategic candidate for early adoption in mining, logistics corridors, and captive fleets. The study concludes that hydrogen freight transport can become a realistic decarbonization pathway if accompanied by targeted public policies, infrastructure deployment, and industrial scale-up.

Fiber-optic Mach-Zehnder interferometers are widely used in research areas such as telecommunications, spectroscopy, and quantum information. These optical structures are known to be affected by phase fluctuations that are usually modeled as multiparametric noise.This multidimensional noise must be stabilized or compensated for to enable fiber-optic Mach-Zehnder architectures for practical applications. In this work, we study the effectiveness of a modified Perturb-and-Observe (P&O) algorithm to control multidimensional phase noise in fiber-based multi-arm Mach-Zehnder interferometers. We demonstrate the feasibility of stabilizing multidimensional phase noise by numerical simulations using a simple feedback control scheme and analyze the algorithm’s performance for systems up to dimension 8x8. We achieved minimal steady-state errors that guarantee high optical visibility in complex optical systems with N×N matrices (with N=[2,3,4,5,6,7,8]).