Enhancing ABP estimation through comprehensive PPG signal analysis and advanced loss function optimization

The estimation of arterial blood pressure (ABP) using photoplethysmography (PPG) signals has gained significant attention in recent years due to its non-invasive nature and potential for continuous monitoring. Deep learning (DL) techniques have emerged as promising tools for this task, by exploiting the complex relationship between PPG and ABP signals. This study investigates an inter-subject approach for ABP estimation from PPG signal using DL models. The focus of our approach is the design and evaluation of a novel loss function to enhance the accuracy and robustness of ABP estimation across different individuals. We propose the Peak Enhancing Loss Function (PELF) combining mean squared error (MSE) and physiological metrics, which effectively captures both the waveform similarity and clinical relevance of the predicted ABP signal by focusing on the systolic and diastolic points. Through extensive experimentation on different datasets, our results demonstrate that PELF improves the accuracy of ABP estimation compared to conventional ones. In conclusion, this work demonstrates the crucial role of loss function design in optimizing DL models for ABP estimation from PPG signals, advancing the state-of-the-art in non-invasive blood pressure (BP) monitoring. The insights gained contribute to enhancing the reliability and applicability of non-invasive BP monitoring systems in clinical practice

Modulation of Asymmetric Magnetic Domain-Wall Motion via Noncolinear Interlayer Exchange Coupling

The search of scalable approach to design field-free deterministic switching is currently a key challenge. Here, we investigate current and magnetic driven magnetization switching in a T-type magnetic heterojunction with a structure composed by a hybrid synthetic antiferromagnet (SAF) Co/Ta/CoTb/Pt, where the bottom Co layer has in-plane magnetic anisotropy (IMA) and the top CoTb layer has perpendicular magnetic anisotropy (PMA). The interlayer exchange coupling (IEC) interaction allows a tilted easy axis of the perpendicular CoTb layer. The main result achieved is the field-free magnetization switching driven by spin-orbit torque (SOT) with a switching direction (clockwise or counterclockwise), which can be controlled by the in-plane direction of the Co magnetization. Meanwhile, we demonstrate that the IEC also induces the asymmetric bubble expansion in the CoTb layer in field-driven experiments and favors the propagation of the domain walls (DWs) with internal magnetization antiparallel to the in-plane IEC field. Our results demonstrate versatile control of the DW motion by noncollinear IEC, which paves a potential way for designing energy-efficient spintronic memory and logic devices, as well as provides a promising and high-efficiency approach for detecting the IEC type by magneto-optical Kerr effect (MOKE) in T-type magnetic heterojunction

Multi-modal temporal action segmentation for manufacturing scenarios

Industrial robots have become prevalent in manufacturing due to their advantages of accuracy, speed, and reduced operator fatigue. Nevertheless, human operators play a crucial role in primary production lines. This study focuses on the temporal segmentation of human actions, aiming to identify the physical and cognitive behavior of operators working alongside collaborative robots. While existing literature explores temporal action segmentation datasets, there is a lack of evaluation for manufacturing tasks. This work assesses six state-of-the art action segmentation models using the Human Action Multi-Modal Monitoring in Manufacturing (HA4M) dataset, where subjects assemble an industrial object in realistic manufacturing scenarios. By employing Cross Subject and Cross-Location approaches, the study not only demonstrates the effectiveness of these models in industrial settings but also introduces a new benchmark for evaluating generalization across different subjects and locations. The evaluation further includes new videos in simulated industrial locations, assessed with both fully and semi-supervised learning approaches. The findings reveal that the Multi-Stage Temporal Convolutional Network ++ (MS-TCN++) and the Action Segmentation Transformer (ASFormer) architectures exhibit high performance in supervised and semi-supervised learning settings, also using new data, particularly when trained with Skeletal features, advancing the capabilities of temporal action segmentation in real-world manufacturing environments. This research lays the foundation for addressing video activity understanding challenges in manufacturing and presents opportunities for future investigations

Family CEO and radical innovation: A stewardship perspective

This article integrates the literature on radical innovation, the stewardship perspective, and family business research to develop and test a model examining the influence of a family CEO and the CEO's generational stage on radical innovation, considering different types of family CEOs as distinct manifestations of strategic leaders' stewardship behavior. Furthermore, building on the notion of “doing more with less”, we propose and empirically test the notion of “doing better with less”—specifically, whether the presence of a family CEO enhances the pursuit of radical innovation under resource constraints (i.e., with lower R&D intensity). Using longitudinal data over an 11-year period from 227 listed firms in the automotive and pharma/biotech industries from 29 countries, we find that firms led by a family CEO, especially those led by descendants, excel at radical innovation. Descendant-led firms are also better at radical innovation with lower R&D intensity, suggesting they do better with less. That is, our study shows that family CEOs at a later generational stage serve as catalysts for radical innovation, even under resource constraints. In addition to implications for theory and practice, our findings offer a more advanced understanding of the strategic leadership-innovation relationship in terms of distinct manifestations of stewardship behavior for radical innovation in firms with family leadership

On some inequalities for the two-parameter Mittag-Leffler function in the complex plane

For the two-parameter Mittag-Leffler function Eα,β with α>0 and β≥0, we consider the question whether |Eα,β(z)| and Eα,β(Rz) are comparable on the whole complex plane. We show that the inequality |Eα,β(z)|≤Eα,β(Rz) holds globally if and only if Eα,β(−x) is completely monotone on (0,∞). For α∈[1,2) we prove that the complete monotonicity of 1/Eα,β(x) on (0,∞) is necessary for the global inequality |Eα,β(z)|≥Eα,β(Rz), and also sufficient for α=1. For α≥2 we show that the absence of non-real zeros for Eα,β is sufficient for the global inequality |Eα,β(z)|≥Eα,β(Rz), and also necessary for α=2. All these results have an explicit description in terms of the values of the parameters α,β. Along the way, several inequalities for Eα,β on the half-plane {Rz≥0} are established, and a characterization of its log-convexity and log-concavity on the positive half-line is obtained

Advanced numerical modeling and validation of dynamic thermal behavior in layered composites for active thermography test

Non-Destructive Evaluation (NDE) techniques, such as thermography, have become essential for detecting and characterizing defects in composite materials, especially in aerospace and other high-performance applications. While traditional numerical approaches, including those implemented in software like ANSYS and COMSOL, have provided valuable insights into thermal behavior, they face limitations in computational efficiency and accuracy when applied to anisotropic, layered composites. This research introduces advanced numerical modeling methodologies based on the Carrera Unified Formulation (CUF) and the Sublaminate Generalized Unified Formulation (SGUF). For the first time, CUF and SGUF are implemented for transient thermal analysis using active thermography, specifically in Thermography. These formulations enable efficient and accurate simulations of dynamic thermal behavior, capturing interlaminar interactions and thermal gradients that are critical for defect detection. The study begins with a comprehensive review of traditional thermographic techniques and their numerical counterparts. Numerical approaches commonly implemented in finite element tools like Finite difference models (FDM), ANSYS and COMSOL are critically analyzed, revealing significant gaps in computational efficiency and their inability to fully capture interlaminar thermal interactions and anisotropic material behaviors. To address these challenges, CUF and SGUF-based models are developed, offering a more streamlined and accurate framework for transient thermal analysis. These models are further applied in various parametric studies, including lay-up sequence analysis, material anisotropy, and thermal gradient effects, to comprehensively evaluate their performance. Validation of the developed models is conducted through comparisons with experimental data and traditional numerical benchmarks, demonstrating their accuracy and robustness. Key findings demonstrate that CUF and SGUF enhance computational efficiency and accuracy in predicting thermal responses, such as temperature distribution and heat flux, making them effective for thermal analysis of composite materials. Their efficiency enables the application to real defect scenarios, such as simulating air-filled gaps in T-joint structures for debonding analysis. Moreover, the versatility of these advanced formulations enables their application to complex geometries and real-world scenarios, providing critical insights into defect detection and thermal behavior in layered composites

Frequency-dependent damping in the linear wave equation

We propose a model for frequency-dependent damping in the linear wave equation. After proving well-posedness of the problem, we study qualitative properties of the energy. In the one-dimensional case, we provide an explicit analysis for special choices of the damping operator. Finally, we show, in special cases, that solutions split into a dissipative and a conservative part

Risk management in wine supply chain: A state-of-the-art analysis

Although the wine market is experiencing significant growth, the Wine Supply Chain (WSC) remains remarkably fragile and susceptible to risky events that can impact vine health and wine quality. These factors, in turn, affect the performance of the entire chain. The distribution of vineyards is highly diverse, and in some cases, situated in extremely fragile territories. The evolution of climate change further exacerbates these vulnerabilities, exposing farms to several risks that, if not properly managed, can threaten production. To navigate the highly dynamic environment characterizing the Wine Supply Chain, there is an increasing need for proactive strategies through the introduction of tools that assist managers in avoiding and/or mitigating potential risks within the value chain. These strategies aim to preserve product quality and integrity, enhancing the resilience of the entire WSC. Despite the higher relevance of risk management in wine than in other agricultural industries and related supply chains, as the raw-material transformation constitutes high-level value-added processes, the scientific literature seems to lack an overall analysis related to the risk strategies and methods developed over the last years in the field of WSC. Therefore, the primary objective of this research is to provide a comprehensive analysis of the current state of Risk Management in the Wine Supply Chain. The study aims to: (i) identify the main processes and associated risks, (ii) establish the primary methods and tools developed by researchers to date, and (iii) highlight the key gaps that need to be addressed. The findings indicate a growing focus on agricultural risk events and reveal a lack of comprehensive methods that encompass the entire Wine Supply Chain - from farmers to end consumers - despite technological advancements in agriculture, logistics, and production. This gap underscores the need for integrated risk management approaches to prevent the degradation of business performance

Multiparametric Study ofWater-Gas Shift and Hydrogen Separation Performance in Membrane Reactors Fed with Biomass-Derived Syngas

A multiparametric study was conducted on a hydrogen (H2) production rig designed to process 0.25 nm3·h−1 of syngas. The rig consists of two Pd-Ag membrane permeator units and two Pd-Ag membrane reactor units for the water-gas shift (WGS) reaction, enabling a detailed and comprehensive analysis of its performance. The aim was to find the optimal conditions to maximise hydrogen production by WGS and its separation in a pure stream by varying temperature, pressure, and steam-to-CO ratio (S/CO). Two syngas mixtures obtained from an updraft gasifier using different gasification agents (air-steam and oxy-steam) were used to investigate the effect of gas composition. The performance of the rig were investigated at nine combinations of temperature, pressure, and S/CO in the respective ranges of 300 - 350 °C; 2 - 8 bar; 1.1- 2 mol·mol−1 as planned with the help of a design of experiment (DOE) software. The three parameters positively effected the performances, both as capacity of separating a pure stream of H2 reported as moles permeated per unit of surface area and time, and producing new H2 from WGS, reported as moles of H2 produced per volume of catalyst unit and time. The highest yields were obtained using syngas from oxy-steam gasification, which had the highest H2 concentration and was free of N2

Investigating aeroelastic and aeroacoustic responses of wind turbines using large-eddy simulations

This thesis investigates the aeroelastic and aeroacoustic behavior of large-scale wind turbines through high-fidelity numerical simulations, offering new insights into their aerodynamic performance, wake dynamics, and noise emissions. A detailed aeroelastic study is conducted on the NREL 5-MW and IEA 15-MW wind turbines, comparing their structural responses under different inflow conditions using a coupled Computational Fluid Dynamics (CFD) and Computational Structural Dynamics (CSD) approach and benchmarking the results against OpenFAST. The fluid model is based on the incompressible Navier-Stokes equations solved using Large-Eddy Simulation (LES), with the Actuator Line Model (ALM) employed to represent the aerodynamic forces exerted by the turbine blades. The structural model relies on a geometrically exact nonlinear beam formulation, ensuring an accurate representation of the blade deformations under aerodynamic and inertial loads. The study highlights significant discrepancies between high-fidelity and Blade Element Momentum (BEM)-based solvers, particularly in local incidence and blade deformation, with OpenFAST underestimating the impact of tower shadowing effects. Additionally, preliminary results assess the response of the IEA 15-MW turbine under a turbulent inflow, providing initial observations on the role of turbulence intensity in modifying aerodynamic loads and structural deflections. To further explore the influence of turbine size on wake dynamics, a Dynamic Mode Decomposition (DMD) analysis is performed under identical turbulent Atmospheric Boundary Layer (ABL) conditions. The results demonstrate that larger turbines exhibit lower-frequency wake structures, confirming that rotor size acts as a filter on coherent structures while also amplifying tip vortex-related high-frequency modes, thereby influencing wake evolution and recovery. In parallel, the aeroacoustic characteristics of the NREL 5-MW turbine are examined by coupling LES with the Ffowcs Williams-Hawkings (FWH-P) acoustic analogy, allowing for the identification of dominant aerodynamic modes contributing to tonal and broadband noise. The application of Sparsity-Promoting Dynamic Mode Decomposition (SPDMD) further enables a low-dimensional representation of the flow field, revealing key frequencies responsible for noise generation. The findings of this thesis contribute to the advancement of wind turbine modeling by improving the accuracy of predictive tools for aeroelastic response, wake development, and noise emissions, supporting the design of more efficient and quieter wind energy systems