Scalable Deep Learning for Industry 4.0: Speedup with Distributed Deep Learning and Environmental Sustainability Considerations

peer reviewedDeep learning (DL) is increasingly used in industry, especially in industry 4.0. Thanks to DL, it possible to better prevent breakdowns and manufacturing defects. DL models are becoming more and more complex and efficient, requiring significant compute resources and compute time. The use of Graphic Processing Units (GPUs) makes it possible to speed up processing but at a higher cost. An alternative to them is the use of distributed DL (DDL) which differs from Federated Deep Learning in that it focuses on accelerating calculations and does not address data privacy. DLL requires having several computing nodes. This is where cloud computing comes in. Cloud computing allows resources or virtual machines to be allocated on demand, which reduces costs. However, the allocation of GPU resources has a higher cost than CPU resources, which can be problematic for small businesses. This article proposes to exploit the DDL on CPUs via the on-demand allocation of virtual machines in order to reduce costs. In addition, a solution for deploying the software stack necessary for proper operation is proposed. This is achieved using a containerization which is only composed of the software suites needed to run the DDL to minimize the container transfer size and consequently minimize the container deployment time

Drift‐Diffusion Modeling of Attentional Shifting During Frustration: Associations With State Frustration and Trait Irritability

peer reviewedIrritability, a prevalent and impairing symptom in many mood and anxiety disorders, is characterized by aberrant responses to frustrative nonreward. Past research investigating irritability have used a cued‐attention task with rigged feedback, the affective Posner task (AP), to induce frustrative nonreward. Previous studies have not been successful in linking differences in self‐reported irritability to traditional AP metrics (i.e., reaction time and accuracy). Computational modeling, via the estimation of parameters reflecting latent cognitive processes, may provide insight into the cognitive mechanisms of irritability and reveal potential targets for mechanism‐based interventions. This study applied the drift‐diffusion model (DDM) to the AP to determine if DDM parameters are associated with individual differences in irritability. Young adults (N = 152, Mage = 20.93 ± 1.98) completed the AP and self‐reported state frustration and trait irritability. Multiple linear regressions were used to evaluate whether DDM parameters better predict state frustration and trait irritability over traditional AP metrics. Higher state frustration was predicted by lower decision threshold during the frustration block and larger decrease in this parameter between nonfrustration and frustration blocks, over traditional AP metrics. These findings demonstrate the potential of applying the DDM to study frustrative nonreward in healthy adult populations. The utility of DDM awaits validation in populations with clinical levels of irritability

Exhaust gas recirculation cooler fouling morphology: Characterisation, spatiotemporal nature, and system variable-morphology-property correlation

peer reviewedFouling is one of the primary causes of failure in exhaust gas recirculation (EGR) coolers. Morphology may provide a powerful perspective for understanding the mechanisms, behaviours, and properties of fouling. However, a systematic review of fouling morphology is currently lacking. Considering the substantial progress made in morphology-related studies within the industry in recent years, this work reviews the findings on EGR cooler fouling morphology from four aspects: characterisation (scale, object, category, and technique), spatiotemporal nature, variable-morphology correlation, and morphology-property correlation. Furthermore, the current challenges and opportunities in this field are discussed. Based on this, we propose a framework for the morphological characterisation of EGR cooler fouling. It is demonstrated that morphology plays a crucial role in revealing the spatiotemporal characteristics of fouling, the formation and removal mechanisms, and the correlations among system variables, morphology, and properties. Morphology still holds significant potential in four areas: multi-scale and quantitative characterisation, nomenclature and taxonomy, and full lifecycle evolution. The findings provide a morphological perspective for fouling research within the industry and contribute to advancing the science of fouling morphology

Two-photon 3D printing optical Fabry-Perot microcavity for non-contact pressure detection

peer reviewedIn this work, a polymer Fabry-Perot interferometer-based microcavity via two-photon direct laser writing is proposed, designed, and experimentally demonstrated as an optical pressure sensor. The sensor comprises a film-based hollow cavity with a diameter of 350 μm and an annular flow channel with four drain holes, which is further sealed for pressure sensing. As the applied pressure varies, the cavity length changes accordingly. Here, a non-contact optical spectral demodulation system integrated with an optical microscopy was designed. An illuminated light was used for localizing the device and a broadband near-infrared light was coupled into an objective lens and the transmitted light was reflected by the device and detected by a spectral demodulation system. To demodulate the spectral signal, a Fourier demodulation algorithm was used to track the wavelength. The results showed that the sensor exhibited a high sensitivity of 398 pm/kPa and good stability. This work can be used for non-contact pressure detection in the critical field of biomedical and aerospace applications

Genomics vs. AI-enhanced electrocardiogram: predicting atrial fibrillation in the era of precision medicine

peer reviewedAtrial fibrillation (AF) is the most prevalent cardiac arrhythmia and can lead to severe complications such as stroke. Artificial intelligence (AI) has emerged as a vital tool in predicting and detecting AF, with machine learning (ML) models trained on electrocardiogram (ECG) data now capable of identifying high-risk patients or predicting the imminent onset of AF. Precision medicine aims to tailor medical interventions for specific sub-populations of patients who are most likely to benefit, utilizing large genomic datasets. Genetic studies have identified numerous loci associated with AF, yet translating this knowledge into clinical practice remains challenging. This paper explores the potential of AI in precision medicine for AF and examines its advantages, particularly when integrated with or compared to genomics. AI-driven ECG analysis provides a practical and cost-effective method for early detection and personalized treatment, complementing genomic approaches. AI-based diagnosis of AF allows for near-certain prediction, effectively relieving cardiologists of this task. In the context of preventive identification, AI enhances the accuracy of predictive models from 75% to 85% when ML is employed. In predicting the exact onset of AF—where human capability is virtually nonexistent—AI achieves a 74% accuracy rate, offering significant added value. The primary advantage of utilizing ECGs over genomic data lies in their ability to capture lifetime variations in a patient’s cardiac activity. AI-driven analysis of ECGs enables dynamic risk assessment and personalized adaptation of therapeutic strategies, optimizing patient outcomes. Genomics, on the other hand, enables the personalization of care for each patient. By integrating AI with ECG and genomic data, truly individualized care becomes achievable, surpassing the limitations of the “average patient” model.4855 - C2W - Come To Wallonia - Sources publiques européenne

A novel peptide-based therapeutic strategy for anaplastic thyroid carcinoma: Dual targeting of EGFR and PIP3

Anaplastic thyroid carcinoma (ATC) is one of the deadliest forms of cancer, notorious for its swift progression and resistance to treatment, although it accounts for only 2% of thyroid cancer cases. ATC has a dismal prognosis, with an average survival time of only 4 months post-diagnosis. Conventional treatments, including surgery, chemotherapy, and radiotherapy, show limited effectiveness due to ATC’s rapid progression and early metastasis. Therefore, novel treatment options are urgently needed. A key feature of ATC is the dysregulation of signaling pathways, particularly the PI3K/AKT/mTOR (PAM) pathway, which promotes cancer cell survival and proliferation. Our study introduces a targeted peptide-based therapy designed to inhibit the PAM pathway by targeting epidermal growth factor receptor (EGFR) and phosphatidylinositol (3,4,5)-trisphosphate (PIP3), thereby providing a potentially effective strategy to combat ATC. The therapy consists of a peptide complex (PC) comprising a vector peptide (VP) that targets EGFR and a therapeutic peptide (TP) targeting PIP3. Tests conducted on ATC cell lines demonstrated that the VP promotes PC endocytosis and induces apoptosis in tumor cells within one hour. Preliminary studies of tumor biodistribution were carried out by fluorescence lifetime imaging (FLI) using VP coupled to a fluorochrome (PV-IRDye800), which was injected at various doses (0.8, 1.2 and 2.4 µmol/kg) in a murine model of ATC developed in athymic nude mice. These findings suggest that the PC could represent a promising peptide-based therapeutic strategy for ATC by inhibiting the PAM pathway. Additional in vivo studies are necessary to validate this approach and explore its potential clinical applications.3. Good health and well-bein

Évaluation des performances du MIL-120 pour la capture du CO2 par procédé VPSA : étude à l’échelle pilote

4686 - MOF4AIR - Metal Organic Frameworks for carbon dioxide Adsorption processes in power production and energy Intensive - Sources publiques européenne

Heterovalent-doping-induced ultrasensitive and highly exclusive ethylene sensor: Application to crop quality inspection

peer reviewedA promising ethylene sensor based on Sb2MoO6 (SMO) with a permeable lamellar structure and tunable W dopants is proposed. The optimal 5 mol% W-doped SMO featuring atomically distributed heterovalent doping sites enables the ideal combination of high response (121.26/2.6 for 10/0.5 ppm), short response/recovery time (180 s/54 s for 10 ppm), low limit of detection (LoD) (23.18 ppb), excellent selectivity, good long-term stability (45 days), and robust performance in high humidity (LoD of 31.5 ppb at 80 % relative humidity). The rich W4+ doping-induced active sites are primarily responsible for the strengthened gas-sensing performances. Theoretical simulations reveal that W doping modulates the SMO lattice through substitutional and interstitial mechanisms, optimizing adsorption energy and charge transfer between ethylene and Mo sites, thereby resolving the trade-off between high response and recovery speed. Furthermore, the real-world application in detecting and differentiating moldy rice across storage periods underscores its potential for on-site quality monitoring in the grain industry. This work highlights the significant role of heteroatom doping in tailoring material properties, positioning W-doped SMO as a highly effective gas-sensing material for agricultural and environmental applications