Performance anxiety is a common issue among musicians, and it could be fundamental to monitor their psychophysiological states during performances through non-invasive methods to support them in managing anxiety. Hence, infrared thermography (IRT) could be a valuable tool for this purpose. The study aims to assess whether IRT can effectively monitor musicians' psychophysiological states. The facial temperature of four musicians was recorded during two conditions: rehearsal and live performance. The temperature time course was extracted from 3 regions of interest (ROIS) (i.e., forehead, nose tip, and perioral) and the following metrics were computed: skewness, kurtosis, and sample entropy. Moreover, machine learning models were applied to evaluate the presence of stress and the balance between sympathetic and parasympathetic systems. The results showed notable changes in thermal metrics in all the ROIs. Moreover, the prevalence of the sympathetic system for 50% of the rehearsal and 92% of the live performance durations was assessed. Additionally, the presence of elevated stress indicators was assessed for 6% of the duration of the rehearsals and 9% for the live performances. These results demonstrated the capability of IRT to assess modifications of the psychophysiological state of the musicians secondary to the condition of the performance.

Solar Loading Thermography (SLT) has significant advantages in assessing the condition of large structures, particularly for heritage conservation. This technique uses the Solar Loading to detect thermal anomalies on the surface of materials, which may indicate hidden defects or degradation. This study presents a comprehensive methodology for evaluating the structural integrity of wooden columns by integrating infrared non-destructive testing (NDT) under solar loading with digital twin modeling and finite element simulation. The approach utilizes infrared thermography to detect the potential defects, such as soft rot. These anomalies are processed to extract defect contours, which are simplified into geometric shapes to represent the degradation of the material’s elastic modulus. Environmental factors, including wind speed, humidity, and precipitation, are incorporated into the simulation. Wind speed is applied as a loading on the entire ancient wooden structure, with adjustments made based on the column's specific location within the structure, while humidity and precipitation gradients are modeled to simulate their impact on the mechanical properties of the wood. Basic loading parameters, such as bending moments and axial pressure, are manually input into the model, which is then used in the finite element simulation to compute the internal stress distribution within the column. The material properties in the finite element model are dynamically adjusted according to moisture-induced degradation, and the resulting stress distribution is computed in real-time. The 3D stress and deformation distributions are visualized interactively, allowing for detailed inspection of the column’s condition and defect behavior. The system further evaluates the safety of the ancient wooden structure by comparing the computed stress values with established strength theories, providing a safety evaluation indicating whether the defect poses a significant structural risk. This method represents a significant advancement in the preservation and maintenance of heritage wooden structures, as it combines SLT, digital twin modeling, and real-time finite element simulation to enable more accurate and predictive evaluations of ancient wooden structure.

Acknowledgments: This work was supported by the Italian Ministry of University and Research (MUR) (Grant n. PGR02110), and the Ministry of Science and Technology of China (MOST) through the National Key Research and Development Program (2023YFE0197800).

An increase in the use of composite materials, owing to improved design and fabrication processes, has led to cost reductions in many industries. Resistance to corrosion, high specific strength, and stiffness are just a few of their many attractive properties. However, damage tolerance remains a major concern in the implementation of composites and uncertainty regarding component lifetimes can lead to over-design and under-use of such materials. A combination of non-destructive evaluation (NDE) and structural health monitoring (SHM) have shown promise in improving confidence by enabling data collection in-situ and in real time. In this work, infrared thermography (IRT) is employed for NDE of tubular composite specimens before and after impact. Four samples are impacted with energies of 5 J, 7.5 J, and 10 J by an un-instrumented falling weight set-up. Acoustic emissions (AE) are monitored using bonded piezoelectric sensors during one of the four impact tests. IRT data is used to generate diffusivity and thermal depth mappings of each sample using the thermographic signal reconstruction (TSR) red green blue (RGB) projection technique. Analysis of AE data alone for a 10 J impact suggest significant damage to the fibres and matrix; this is in good agreement with the generated thermal depth mappings for each sample, which indicate damage through multiple fibre layers. IRT and AE data are correlated and validated by optical micrographs taken along the cross section of damage.

This work concerns thermomechanical behavior of two Ni-free Ti-based shape memory alloys (SMAs) doped with nitrogen and oxygen subjected to tensile loading at strain rate of around
0.02 1/s. During the experiments, an infrared camara was used to investigate thermal effects accompanying deformation of the Ti–25Nb–0.5O and Ti–25Nb–0.5N SMAs. Simultaneously, digital image correlation (DIC) served to determine kinematic fields of these alloys in tension. The stress or temperature-induced martensitic transformation from the cubic β phase to the orthorhombic α″ phase is responsible for superelasticity or shape memory effect in the Ni-free Ti-based SMAs [1]. However, the addition of the oxygen and nitrogen interstitials changes tensile characteristics of these SMAs. For instance, the Ti–25Nb SMA exhibits shape memory effect and a Lüders-type deformation [2]. However, stress-strain plots of the Ti–25Nb–0.5O and Ti–25Nb–0.5N SMAs show hysteretic behaviors characterized by superelasticity combined with an increased transformation stress. Our results show that infrared thermography is a useful tool to locally track peculiar temperature changes of these Ni-free Ti-based SMAs. The temperature fields captured at selected stages of loading revealed heat sources associated with the dissipative processes. The thermal effects were discussed in view of the kinematic characteristics obtained using DIC as well as microstructural features and phase content analyses of the SMAs.

The properties of long-wave infrared (LWIR) interband cascade photodetectors (ICIPs) with type II superlattices (T2SLs) and gallium-free (Ga-free) InAs/InAsSb absorbers were determined using photoluminescence (PL) and spectral response (SR) measurements. The heterostructures were grown by molecular beam epitaxy (MBE) on a GaAs substrate. Three structures with different numbers of stages were compared. The structures were optimized for 10.7 μm at 300 K. Moreover, theoretical calculations were performed using APSYS to compare with the experimental results. The PL results provided information on transitions from minibands and intragap states in the studied structures. SR measurements helped isolate transitions involving minibands, which facilitated the analysis of visible transitions in the PL spectra, where point defect (NPD) transitions were also observed.

This study presents the development and characterization of a Long-Wave Infrared (LWIR) detector based on InAs/InAsSb superlattice technology for use in cooled Focal Plane Arrays (FPAs). The InAs/InAsSb superlattice structure offers improved performance over traditional LWIR detectors due to its ability to suppress Auger recombination and reduce dark current. The detector was designed to operate in the 8-12 μm spectral range and optimized for low-temperature operation. Experimental results demonstrate high quantum efficiency, and low dark current. The performance metrics, including responsivity, detectivity, and noise equivalent temperature difference (NETD), are reported and compared to state-of-the-art HgCdTe detectors. This novel InAs/InAsSb superlattice detector shows promise for advanced thermal imaging applications, particularly in scenarios requiring high sensitivity and low power consumption.

The “Heart of Voh”, immortalized by Yann Arthus Bertrand in his book "The Earth from the Air"depicts a sparse, heart-shaped clearing in the mangroves of New Caledonia. This highly poetic image is also the physical representation of thermal diffusion phenomena disrupted by fluid flow. This type of figure is a basic figure for analyzing source fields in a microfluidic channel surrounded by solid walls. Several analytical solutions will be presented and used for the estimation of crucial parameters related to the thermal diffusivity of the walls around the channel et the fluid flow inside the channel.

This study investigates the use of bio-based polyurethane foams (PUFs) containing phase change material (PCM) microparticles as a sustainable alternative for the automotive sector. These foams are synthesized using polyols derived from waste cooking oil (WCO), aligning with circular economy principles.

To evaluate the thermophysical properties of these materials and, more in general, their thermal behaviour, the use of non-destructive thermographic techniques has been proposed. This technique enables a rapid, full-field thermal analysis without physical contact, making it especially suitable for porous and heterogeneous structures like foams.

As a reference, both virgin and foams with PCM were characterized in terms of density and thermal conductivity using well-established methods. Then, Lock-in thermography has been used as the first attempt technique to investigate variations in thermal behavior due to different thermophysical material properties based on the thermal response in transmission configuration.

The thermographic approach proves to be an effective tool not only for assessing thermal behavior but also for supporting quality control and process optimization of sustainable polymeric materials.

Mode I Interlaminar Fracture Toughness test of carbon fiber reinforced polymer laminates requires a double cantilever beam (DCB) specimen with a pre-implanted non-adhesive insert at the mid-plane to initiate delamination. However, the insert's quality and placement within the DCB speci-men can be problematic, necessitating non-destructive testing methods. In this study, active ther-mography is employed to inspect potential defects around the Teflon insert in the DCB specimens. Both uniform and non-uniform heating methods have been applied, and thermal images was ana-lyzed to obtain quantitative information, such as the insert's location and non-contact area. TSR-enhanced images were obtained using two variations of the classical thermographic signal reconstruction. The analyzed results confirmed the presence of non-contact areas in the DCB structures composed of both 22-layer and 24-layer CFRP prepregs. These areas may be attributed to residual air gaps formed during the hot-press molding of the DCB structures.

Impact of the declining domestic workforce in Japan is becoming increasingly serious, making it difficult to secure workers for operation and maintenance of waste treatment facilities. To cope with this situation, there is an urgent need to develop technologies that can support and automate operation of the waste incineration facilities by utilizing such as IoT and AI techniques.

We KOBELCO Group have been developing combustion control techniques for rotary combustor (rotary stoker-type) to make the operation automatic instead of manual by the workers. As an effort to contribute to the automatic operation of the combustor, we have attempted to observe inside of the rotary combustor by using mid-infrared camera with passing-through-flame bandpass filter as shown in Figure 1, which is difficult to see inside the combustor by using visible light due to the influence of flame and gasses.

As a result of mid-infrared monitoring, it is confirmed that the refuse layer, which are fed from the hopper and in the process of combustion, can be clearly observed. For evaluating the condition of rotary combustor, image segmentation is applied to the mid-infrared images using deep learning model (Mask2Former1) by fine-tuning to our data set. As shown in Figure 2, we confirmed that the model can be identified the refuse layer and rotary tube (combustor structure) accurately in the mid-infrared image. In this presentation, a monitoring technique of the combustor condition by using image segmentation results of mid-infrared images is reported.