Search Results (81)

The object of the study is a permanent joint of thin wires made of nitinol alloy. The problem of ensuring the formation of a joint of wires made of nitinol alloy was solved based on minimising changes in the structure of the welded joint material relative to the materials being joined. The properties of the welded joint material of the nitinol were studied using scanning electron microscopy and micro-X-ray spectral analysis. The studied permanent joint was obtained by TIG, microplasma (PAW) and capacitor discharge (CDW) welding. It was found that TIG welding can ensure the proximity of the microstructures of the wire and welded joint materials under conditions of sufficient protection in an argon atmosphere. Such TiNi welded joints have a welded joint material that retains its superelastic properties (within the limits of the shape memory effect). Capacitor discharge welding allows the joint to be brought closer to the required level of microstructure of the weld material. The results of mechanical tests demonstrated the limited capabilities of joints made of thin nitinol wires. At the same time, the appearance of only newly formed TiNi + TiNi₃ eutectics in the weld material and a sufficient level of restoration of the welded joint shape give reason to consider capacitor discharge welding promising for joining thin nitinol wires. PAW leads to the formation of a significant amount of oxides in the weld and an increase in the number of Ti₂Ni inclusions, which leads to brittle fracture of the welded joint even at low degrees of deformation. The results of the study can be used, in particular, for the manufacture of nitinol wire joints in medical devices. Full article

The permanent memory effect in polymer-dispersed liquid crystal systems imparts unique properties to these devices, making them well-suited for digital memory applications. By investigating the impact of homogeneous alignment layer types on this effect, we successfully developed and tested a proof-of-concept prototype capable of recording information in both opaque and transparent states within a digital process. Full article

Background: Permanent cancer resolution requires a complete immunological response with generation of memory against malignant cells. Immunogenic cell death (ICD) achieves this by coupling cell death with the emission of damage-associated molecular patterns (DAMPs). Current cancer treatments immunosuppress the host; thus, new alternatives are needed. Ganoderma species produce anticancer triterpenoids (GTs); however, their mechanism remains unclear. Objective: This systematic review aims to provide insights into GTs’ pharmacodynamics and assess hypothetical ICD potential. Methods: Web of Science and PubMed databases were consulted following PRISMA guidelines. Studies from inception until 2024, reporting molecular changes associated with GTs’ anticancer effects, were considered. Nonhuman models were excluded. GTs and GTs-ICD converging molecular targets were listed and submitted to Cytoscape’s stringApp to construct protein interaction networks. Topological and enrichment analysis were performed. Results: A total of 204 articles were found, and 69 remained after screening. Overall anticancer effects include loss of mitochondrial membrane potential, DNA and RNA damage, autophagy, cell cycle arrest, and leukocyte activation. 136 molecular targets of GTs were identified; upregulated proteins include CHOP, PERK, p-eIF2α, and HSP70, a key DAMP. GTs and ICD share 24 molecular targets. GO:BP and KEGG enrichment analysis suggest that GTs’ anticancer effects are related to stress response, cell death regulation, and PD-L1/PD-1 checkpoint inhibition. GT-ICD enrichment converges on endoplasmic reticulum stress, unfolded protein response, and organelle membrane perforation. Conclusions: GTs exhibit polypharmacological anticancer effects, including anti-immunosuppression, upregulation of ICD-adjacent machinery, and even an increase in HSP. However, further studies are required to confirm a proper causal link between GTs’ cancer cell treatment and DAMP emission. Full article

Background: Brain tumors can severely impair neurological function, leading to symptoms such as headaches, memory loss, motor coordination deficits, and visual disturbances. In severe cases, they may cause permanent cognitive damage or become life-threatening without early detection. Methods: To address this, we propose an interpretable deep ensemble model for tumor detection in Magnetic Resonance Imaging (MRI) by integrating pre-trained Convolutional Neural Networks—EfficientNetB7, InceptionV3, and Xception—using a soft voting ensemble to improve classification accuracy. The framework is further enhanced with a Light Gradient Boosting Machine as a meta-learner to increase prediction accuracy and robustness within a stacking architecture. Hyperparameter tuning is conducted using Optuna, and overfitting is mitigated through batch normalization, L2 weight decay, dropout, early stopping, and extensive data augmentation. Results: These regularization strategies significantly enhance the model’s generalization ability within the BR35H dataset. The framework achieves a classification accuracy of 99.83 on the MRI dataset of 3060 images. Conclusions: To improve interpretability and build clinical trust, Explainable Artificial Intelligence methods Grad-CAM++, LIME, and SHAP are employed to visualize the factors influencing model predictions, effectively highlighting tumor regions within MRI scans. This establishes a strong foundation for further advancements in radiology decision support systems. Full article

This study presents a novel state estimation approach integrating Deep Neural Networks (DNNs) into Moving Horizon Estimation (MHE). This is a shift from using traditional physics-based models within MHE towards data-driven techniques. Specifically, a Long Short-Term Memory (LSTM)-based DNN is trained using synthetic data derived from a high-fidelity thermal model of a Permanent Magnet Synchronous Machine (PMSM), applied within a thermal derating torque control strategy for battery electric vehicles. The trained DNN is directly embedded within an MHE formulation, forming a discrete-time nonlinear optimal control problem (OCP) solved via the acados optimization framework. Model-in-the-Loop simulations demonstrate accurate temperature estimation even under noisy sensor conditions and simulated sensor failures. Real-time implementation on embedded hardware confirms practical feasibility, achieving computational performance exceeding real-time requirements threefold. By integrating the learned LSTM-based dynamics directly into MHE, this work achieves state estimation accuracy, robustness, and adaptability while reducing modeling efforts and complexity. Overall, the results highlight the effectiveness of combining model-based and data-driven methods in safety-critical automotive control systems. Full article

Structural health monitoring (SHM) plays a pivotal role in ensuring the integrity and safety of critical infrastructure and mechanical components. While traditional non-destructive testing (NDT) methods offer high-resolution data, they typically require periodic access and disassembly of equipment to conduct inspections. In contrast, SHM employs permanently installed, cost-effective sensors to enable continuous monitoring, though often with reduced detail. This study presents an integrated hybrid SHM-NDT methodology enhanced by deep learning to enable the real-time monitoring and classification of mechanical stresses in structural components. As a case study, a 6-foot-long parallel flange I-beam, representing bridge truss elements, was subjected to variable bending loads to simulate operational conditions. The hybrid system utilized an ultrasonic transducer (NDT) for excitation and piezoelectric sensors (SHM) for signal acquisition. Signal data were analyzed using 1D and 2D convolutional neural networks (CNNs), long short-term memory (LSTM) models, and random forest classifiers to detect and classify load magnitudes. The AI-enhanced approach achieved 100% accuracy in 47 out of 48 tests and 94% in the remaining tests. These results demonstrate that the hybrid SHM-NDT framework, combined with machine learning, offers a powerful and adaptable solution for continuous monitoring and precise damage assessment of structural systems, significantly advancing maintenance practices and safety assurance. Full article

Fault detection is a critical research area, especially in the automotive sector, aiming to quickly assess component conditions. Machine Learning techniques, powered by Artificial Intelligence, now represent state-of-the-art methods for this purpose. This study focuses on durability testing of Permanent Magnet Synchronous Motors for automotive applications, using Autoencoders (AEs) to predict and prevent failures. This AI-based fault detection strategy employs acceleration signals coming from electric motors tested under challenging conditions with significant variations in torque and speed. This approach goes beyond typical fault detection in steady-state conditions. Based on a review of Neural Networks, including Variational Autoencoders (VAEs), Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the performance of six AI architectures is compared: AE, VAE, 1D CNN AE, 1D CNN VAE, LSTM AE and LSTM VAE. The 1D CNN AE outperformed the other networks in fault detection, showing high accuracy, stability and computational efficiency. The model is integrated into an algorithm for semi-real-time fault monitoring. The algorithm effectively detects potential motor failures in real-world scenarios, including bearing faults, mechanical misalignments, and progressive wear of components, thereby proactively preventing damage and halving test bench downtime. Full article

Modern computing systems increasingly rely on specialized hardware accelerators, such as Graphics Processing Units (GPUs), to meet growing computational demands. GPUs are essential for accelerating a wide range of applications, from machine learning and scientific computing to safety-critical domains like autonomous systems and aerospace. To enhance performance, modern GPUs integrate dedicated in-chip units, such as Tensor Cores(TCs), which are designed for efficient mixed-precision matrix operations. However, as semiconductor technologies scale down, reliability challenges emerge. Permanent hardware faults caused by aging, process variations, or environmental stress can lead to Silent Data Corruptions, which silently compromise computation results. In order to detect such faults, self-test libraries (STLs) are widely used, corresponding to suitably crafted pieces of code, able to activate faults and propagate their effects to visible points (e.g., the memory) and possibly signal their occurrence. This work introduces a structured method for generating STLs to detect permanent hardware faults that may arise in TCs. By leveraging the parallelism and regular structure of TCs, the method facilitates the creation of effective STLs for in-field fault detection without hardware modifications and with minimal requirements in terms of test time and memory. The proposed approach was validated on an NVIDIA GeForce RTX 3060 Ti GPU, installed in a Hewlett-Packard Z2 G5 workstation with an Intel Core i9-10800 CPU and 32 GB RAM, available at the Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Turin, Italy.This setup was used to address stuck-at faults in the arithmetic units of TCs. The results demonstrate that the methodology offers a practical, scalable, and non-intrusive solution for enhancing GPU reliability, applicable in both high-performance and safety-critical environments. Full article

Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), with millimeter-level accuracy and full-resolution capabilities, is essential for monitoring urban deformation. With the advancement of SAR sensors in spatial and temporal resolution and the expansion of wide-swath observation capabilities, the number of permanent scatterers (PSs) in high-density urban areas has surged exponentially. To address these computational and memory challenges in high-density urban PSInSAR processing, this paper proposes an efficient method for integrating regular grid partitioning and connected component constraints. First, adaptive dynamic regular grid partitioning was employed to divide monitoring areas into sub-blocks, balancing memory usage and computational efficiency. Second, a weighted least squares adjustment model using common PS points in overlapping regions eliminated systematic inter-sub-block biases, ensuring global consistency. A graph-based connected component constraint mechanism was introduced to resolve multi-component segmentation issues within sub-blocks to preserve discontinuous PS information. Experiments on TerraSAR-X data covering Fuzhou, China (590 km²), demonstrated that the method processed 1.4 × 10⁷ PS points under 32 GB memory constraints, where it achieved a 25-fold efficiency improvement over traditional global PSInSAR. The deformation rates and elevation residuals exhibited high consistency with conventional methods (correlation coefficient ≥ 0.98). This method effectively addresses the issues of memory overflow, connectivity loss between sub-blocks, and cumulative merging errors in large-scale PS networks. It provides an efficient solution for wide-area millimeter-scale deformation monitoring in high-density urban areas, supporting applications such as geohazard early warning and urban infrastructure safety assessment. Full article

The incidence of cutaneous squamous cell carcinoma (cSCC) is increasing, with UV radiation being the main cause. Other risk factors are age, sex, skin type and immunosuppression. Human papillomaviruses (HPVs) are associated with benign and malignant skin tumours. In contrast to anogenital and oropharyngeal carcinomas, which are caused by alpha papillomaviruses, the HPV types associated with cSCC belong to the beta-HPV genus. These viruses infect the skin epithelium and are widespread in skin samples from healthy people. It is assumed that HPV amplifies the DNA damage caused by UV radiation and disrupts the repair mechanisms of the cells, without remaining permanently detectable in the tumour tissue, the so-called hit-and-run theory. The HPV status of tumours appears to have a positive influence on prognosis and response to therapy due to increased immune infiltration, in particular by tissue-resident memory T cells and activation of immune effector cells. This favours responses to immunotherapies such as PD-1/PD-L1 inhibitors, whereas immunosuppression may promote a pro-carcinogenic effect. In conclusion, the role of beta HPV in the development of cSCC appears to be closely associated with the immune status of the host. Depending on the immune status, beta HPV can play either a protective or a tumour-promoting role, and in view of the increasing incidence of skin cancer worldwide, enhancing the immune response against virus-infected keratinocytes, e.g., through HPV vaccination, could represent a promising approach for the prevention and therapy of squamous cell carcinomas. Full article

A shape memory alloy with the chemical composition Fe-14Mn-6Si-9Cr-5Ni (mass %) was produced by powder metallurgy (PM) from as-blended powders mixed with mechanically alloyed (MA’ed) powder volumes in amounts of 0, 10 and 20. After powder blending, pressing and sintering, the specimens were hot-rolled, spark erosion cut with different configurations and solution-treated between 700 and 1100 °C. After metallographic preparation, structural analyses were performed by X-ray diffraction and microscopic observation performed by optical and scanning electron microscopy (SEM). The analyses revealed the presence of thermal- and stress-induced martensites caused by solution treatment and pre-straining. Due to the relatively low Mn amount, significant quantities of α′ body center cubic martensite were formed during post-solution treatment water cooling. Solution-treated lamellar specimens underwent a training thermomechanical treatment comprising repeated cycles of room temperature bending, heating and sputtered water cooling. By cinematographic analysis, the occurrence of the shape memory effect (SME) was revealed, in spite of the large amount of α′ bcc martensite. Tensile specimens were subjected to room temperature failure tests and pre-straining (up to 4% permanent strain, after loading–unloading). After tensile pre-straining, a diminution of α′ martensite amount was noticed on XRD patterns, which was associated with the formation of internal sub-bands in the substructure of martensite and were observed by high-resolution SEM. These results prove that SME can be obtained in trained PM_MA’ed Fe-14Mn-6Si-9Cr-5Ni specimens in spite of the large amount of thermally induced α′ bcc martensite, the stress-induced formation of which is impeded by the presence of internal sub-bands. Full article

The paper starts by describing the manufacturing process of cups thermoformed from extruded foils of 80% recycled PET (80r-PET), which comprises heating, hot deep drawing and cooling. The 80r-PET foils were heated up to 120 °C, at heating rates of the order of hundreds °C/min, and deep drawn with multiple punchers, having a depth-to-width ratio exceeding 1:1. After puncher-assisted deformation, the cups were air blown away from the punchers, thus being “frozen” in the deformed state. Due to the high cooling rate, most of the polymer’s structure reached a rigid, glassy state, the internal stresses that tended to recover the flat undeformed state were blocked and the polymer remained in a temporary cup form. When heating was applied, glass transition occurred, and the polymer reached a rubbery state and softened. This softening process released the blocked internal stresses and the polymer tended to recover its flat permanent shape. This relative volume contraction quantitatively describes the shape memory effect (SME) which can be obtained either with free recovery (FR-SME) or with work generation (WG-SME) when the cups lifted their bottoms with different loads placed inside them. The paper discusses the results obtained by differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), room-temperature tensile failure tests (TENS) and scanning electron microscopy (SEM). The DSC charts emphasized a glass transition, responsible for SME occurrence. The DMA thermograms and the TENS curves revealed that there are slight differences between the storage modulus and the tensile strains of the specimens cut on longitudinal, transversal, or 45° to the film rolling direction. The SEM micrographs enabled to observe structural differences between the specimens cut parallelly and transversally to the film’s rolling direction. The thermoformed cups were heated on a special experimental setup, which enabled the determination of FR-SME and WG-SME after applying different maximum temperatures and loads placed into the cups, respectively. Full article

The brain contains many interconnected and complex cellular and molecular mechanisms. Injury to the brain causes permanent dysfunctions in these mechanisms. So, it continues to be an area where surgical intervention cannot be performed except for the removal of tumors and the repair of some aneurysms. Some agents that can cross the blood–brain barrier and reach neurons show neuroprotective effects in the brain due to their anti-apoptotic, anti-inflammatory and antioxidant properties. In particular, some agents act by reducing or modulating the accumulation of protein aggregates in neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, Amyotrophic lateral sclerosis, and prion disease) caused by protein accumulation. Substrate accumulation causes increased oxidative stress and stimulates the brain’s immune cells, microglia, and astrocytes, to secrete proinflammatory cytokines. Long-term or chronic neuroinflammatory response triggers apoptosis. Brain damage is observed with neuronal apoptosis and brain functions are impaired. This situation negatively affects processes such as motor movements, memory, perception, and learning. Neuroprotective agents prevent apoptosis by modulating molecules that play a role in apoptosis. In addition, they can improve impaired brain functions by supporting neuroplasticity and neurogenesis. Due to the important roles that these agents play in central nervous system damage or neurodegenerative diseases, it is important to elucidate many mechanisms. This review provides an overview of the mechanisms of flavonoids, which constitute a large part of the agents with neuroprotective effects, as well as vitamins, neurotransmitters, hormones, amino acids, and their derivatives. It is thought that understanding these mechanisms will enable the development of new therapeutic agents and different treatment strategies. Full article

Permanent magnet synchronous motors (PMSMs) play a crucial role in industrial production, and in response to the problem of PMSM turn-to-turn short-circuit and demagnetization faults affecting production safety, this paper proposes a PMSM turn-to-turn short-circuit and demagnetization fault diagnostic method based on a convolutional neural network and bidirectional long and short-term memory neural network (CNN-BiLSTM). Firstly, analyzing the PMSM turn-to-turn short-circuit and demagnetization faults, one takes the PMSM stator current as the fault signal and optimizes the variational modal decomposition (VMD) by using the Gray Wolf Optimization (GWO) algorithm in order to achieve efficient noise reduction processing of the stator current signal and improve the fault feature content in the stator current signal. Finally, the fault diagnostics are classified by using the CNN-BiLSTM, which collects advanced optimization algorithms and deep learning networks. The effectiveness of the method is verified by simulation experiment results. This scheme has high practical value and broad application prospects in the field of PMSM turn-to-turn short circuit and demagnetization fault diagnosis. Full article

Backgrounds/Objectives: Despite the increasing importance of the condition of post-stroke cognitive impairment (PSCI), the current therapy efficacy is limited. Since oxidative stress and inflammation are targeted in anti-stroke therapy, we aimed to assess the protective effect against PSI of an orodispersible film loaded with silkworm pupae hydrolysate and a combined extract of holy basil and ginger (JP1), which show antioxidant, and anti-inflammation effects. Methods: Male Wistar rats (200–250 g) were administered JP1 at doses of 1, 10, and 100 mg/kg BW 45 min before a 6 h immobilization stress exposure for 14 days. Then, the right middle cerebral artery was permanently occluded (MCAO) and JP1 was continually administered for 21 days after MCAO. Spatial and non-spatial memory and the possible underlying mechanisms were also explored. Results: JP1 improved oxidative stress, inflammation, apoptosis, Erk signaling pathway, cholinergic function, and the growth of Lactobacillus and Bifidobacterium spp. in feces. These results suggest that JP1 improves PSCI, possibly involving the above mechanisms. Furthermore, serum corticosterone also decreased. Conclusions: Our results suggest that JP1 is a potential candidate for combating PSCI following exposure to stroke plus stress. However, a clear understanding of the precise active ingredient and the detailed mechanisms require further investigation. Full article