Discovering phase and causal dependencies on manufacturing processes

Discovering phase and causal dependencies on manufacturing processes. Keyword machine learning, causality, Industry 4.

CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods

Causal relationships are commonly examined in manufacturing processes to support faults investigations, perform interventions, and make strategic decisions. Industry 4.0 has made available an increasing amount of data that enable data-driven Causal Discovery (CD). Considering the growing number of recently proposed CD methods, it is necessary to introduce strict benchmarking procedures on publicly available datasets since they represent the foundation for a fair comparison and validation of different methods. This work introduces two novel public datasets for CD in continuous manufacturing processes. The first dataset employs the well-known Tennessee Eastman simulator for fault detection and process control. The second dataset is extracted from an ultra-processed food manufacturing plant, and it includes a description of the plant, as well as multiple ground truths. These datasets are used to propose a benchmarking procedure based on different metrics and evaluated on a wide selection of CD algorithms. This work allows testing CD methods in realistic conditions enabling the selection of the most suitable method for specific target applications. The datasets are available at the following link: https://github.com/giovanniMenComment: Supplementary Materials at: https://github.com/giovanniMen/CPCaD-Benc

automatic process modeling with time delay neural network based on low level data

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Global Gene Expression Profiling Of Human Pleural Mesotheliomas: Identification of Matrix Metalloproteinase 14 (MMP-14) as Potential Tumour Target

BACKGROUND:The goal of our study was to molecularly dissect mesothelioma tumour pathways by mean of microarray technologies in order to identify new tumour biomarkers that could be used as early diagnostic markers and possibly as specific molecular therapeutic targets. METHODOLOGY:We performed Affymetrix HGU133A plus 2.0 microarray analysis, containing probes for about 39,000 human transcripts, comparing 9 human pleural mesotheliomas with 4 normal pleural specimens. Stringent statistical feature selection detected a set of differentially expressed genes that have been further evaluated to identify potential biomarkers to be used in early diagnostics. Selected genes were confirmed by RT-PCR. As reported by other mesothelioma profiling studies, most of genes are involved in G2/M transition. Our list contains several genes previously described as prognostic classifier. Furthermore, we found novel genes, never associated before to mesotheliom that could be involved in tumour progression. Notable is the identification of MMP-14, a member of matrix metalloproteinase family. In a cohort of 70 mesothelioma patients, we found by a multivariate Cox regression analysis, that the only parameter influencing overall survival was expression of MMP14. The calculated relative risk of death in MM patients with low MMP14 expression was significantly lower than patients with high MMp14 expression (P = 0.002). CONCLUSIONS:Based on the results provided, this molecule could be viewed as a new and effective therapeutic target to test for the cure of mesothelioma

Global disparities in surgeons’ workloads, academic engagement and rest periods: the on-calL shIft fOr geNEral SurgeonS (LIONESS) study

: The workload of general surgeons is multifaceted, encompassing not only surgical procedures but also a myriad of other responsibilities. From April to May 2023, we conducted a CHERRIES-compliant internet-based survey analyzing clinical practice, academic engagement, and post-on-call rest. The questionnaire featured six sections with 35 questions. Statistical analysis used Chi-square tests, ANOVA, and logistic regression (SPSS® v. 28). The survey received a total of 1.046 responses (65.4%). Over 78.0% of responders came from Europe, 65.1% came from a general surgery unit; 92.8% of European and 87.5% of North American respondents were involved in research, compared to 71.7% in Africa. Europe led in publishing research studies (6.6 ± 8.6 yearly). Teaching involvement was high in North America (100%) and Africa (91.7%). Surgeons reported an average of 6.7 ± 4.9 on-call shifts per month, with European and North American surgeons experiencing 6.5 ± 4.9 and 7.8 ± 4.1 on-calls monthly, respectively. African surgeons had the highest on-call frequency (8.7 ± 6.1). Post-on-call, only 35.1% of respondents received a day off. Europeans were most likely (40%) to have a day off, while African surgeons were least likely (6.7%). On the adjusted multivariable analysis HDI (Human Development Index) (aOR 1.993) hospital capacity > 400 beds (aOR 2.423), working in a specialty surgery unit (aOR 2.087), and making the on-call in-house (aOR 5.446), significantly predicted the likelihood of having a day off after an on-call shift. Our study revealed critical insights into the disparities in workload, access to research, and professional opportunities for surgeons across different continents, underscored by the HDI

Surgical gesture recognition with time delay neural network based on kinematic data

Automatic gesture recognition during surgical procedures is an enabling technology for improving advanced assistance features in surgical robotic systems (SRSs). Examples of such advanced features are user-specific feedback during execution of complex actions, prompt detection of safety-critical situations and autonomous execution of procedure sub-steps. Video data are available for all minimally invasive surgical procedures, but SRS could also provide accurate movements measurements based on kinematic data. Kinematic data provide low dimensional features for gesture recognition that would enable on-line processing during data acquisition. Therefore, we propose a Time Delay Neural Network (TDNN) applied to kinematic data for introducing temporal modelling in gesture recognition. Wee valuate accuracy and precision of the proposed method on public benchmark dataset for surgical gesture recognition (JIGSAWS). To evaluate the generalization capability of the proposed method, we acquired a new dataset intruding a different training exercise executed in virtual environment.The dataset is publicly available to enable other methods to be tested on it. The obtained results are comparable with other methods available in literature keeping also computational performance compatible with on-line processing during surgical procedure. The proposed method and the novel dataset are key-components in the development of future autonomous SRSs with advanced situation awareness capabilities

Surgical Gesture and Error Recognition with Time Delay Neural Network on Kinematic Data

In this work we have introduced TDNN for surgical gestures and errors recognition based on kinematic data. We have evaluated the proposed method on novel VRASTED public dataset. The obtained results demonstrate state that TDNN applied to kinematic data can be very effective in modeling instrument movements, thus they are suitable for gesture recognition, but they need to be fused with other sensing modalities for obtaining error detection performance suitable for on-line situation awareness system

Angular metrics and an effort based metric used as features for an automatic classifying algorithm of surgical gestures

Automated surgical gestures classification and recognition are important precursors for achieving the goal of objective evaluation of surgical skills. Many works have been done to discover and validate metrics based on the motion of instruments that can be used as features for automatic classification of surgical gestures. In this work, we present a series of angular metrics that can be used together with Cartesian-based metrics to better describe different surgical gestures. These metrics can be calculated both in Cartesian and joint space, and they are used in this work as features for automatic classification of surgical gestures. To evaluate the proposed metrics, we introduce a novel surgical dataset that contains both Cartesian and joint spaces data acquired with da Vinci Research Kit (dVRK) while a single expert operator is performing 40 subsequent suturing exercises. The obtained results confirm that the application of metrics in the joint space improves the accuracy of automatic gesture classification

Joints-Space metrics for automatic robotic surgical gestures classification

Automated surgical gestures classification and recognition are important precursors for achieving the goal of objective evaluation of surgical skills. Many works have been done to discover and validate metrics based on the motion of instruments that can be used as features for automatic classification of surgical gestures. In this work, we present a series of angular metrics that can be used together with Cartesian-based metrics to better describe different surgical gestures. These metrics can be calculated both in Cartesian and joint space, and they are used in this work as features for automatic classification of surgical gestures. To evaluate the proposed metrics, we introduce a novel surgical dataset that contains both Cartesian and joint spaces data acquired with da Vinci Research Kit (dVRK) while a single expert operator is performing 40 subsequent suturing exercises. The obtained results confirm that the application of metrics in the joint space improves the accuracy of automatic gesture classification

ViTAS Gaming Suite: Virtual Therapy Against Stroke

Stroke is the leading cause of disability in Western Society, and rehabilitation is a fundamental support to ensure the best possible recovery of stroke patients after acute disease. To support and enhance conventional therapies we have developed a virtual rehabilitation gaming suite called ViTAS. ViTAS replicates some of the tests performed in clinical contexts, thus making the integration between virtual and traditional rehabilitation completely straightforward. We have collected the opinion of 35 clinicians with different degree of experience, following a customized self-report questionnaire. The results showed high acceptance of clinicians to use ViTAS system in rehabilitation. We conclude that ViTAS could become an essential supplement to standard post-stroke rehabilitatio