Local Digital Twin-based control of a cobot-assisted assembly cell based on Dispatching Rules

In the context of an increasing digitalization of production processes, Digital Twins (DT) are emerging as new simulation paradigm for manufacturing, which leads to potential advances in the production planning and control of production systems. In particular, DT can support production control activities thanks to the bidirectional connection in near real-time with the modeled system. Research on DT for production planning and control of automated systems is already ongoing, but manual and semi-manual systems did not receive the same attention. In this paper, a novel framework focused on a local DT is proposed to control a cobot-assisted assembly cell. The DT replicates the behavior of the cell, providing accurate predictions of its performances in alternative scenarios. Then, building on these predicted estimates, the controller selects, among different dispatching rules, the most appropriate one to pursue different performance objectives. This has been proven beneficial through a simulation assessment of the whole assembly line considered as testbed

Group therapy with peer support provider participation in an acute psychiatric ward: 1-year analysis

Background: Group psychotherapy improves therapeutic process, fosters identification with others, and increases illness awareness; (2) Methods: In 40 weekly group sessions held in an acute psychiatric ward during one year, we retrospectively evaluated the inpatients’ participation and the demographic and clinical variables of the individuals hospitalized in the ward, the group type according to Bion’s assumptions, the main narrative themes expressed, and the mentalization processes by using the Mentalization-Based Therapy-Group Adherence and Quality Scale (MBT-GAQS); (3) Results: The “working” group was the prevailing one, and the most represented narrative theme was “treatment programs”; statistically significant correlations were found between the group types according to Bion’s assumptions and the main narrative themes (Fisher’s exact, p = 0.007); at our multivariate linear regression, the MBT-G-AQS overall occurrence score (dependent variable) was positively correlated with the number of group participants (coef. = 14.87; p = 0.011) and negatively with the number of participants speaking in groups (coef. = −16.87, p = 0.025); (4) Conclusion: our study suggests that the group shows consistent defense mechanisms, relationships, mentalization, and narrative themes, which can also maintain a therapeutic function in an acute ward

AB012. Transcriptional and chromatin profiling reveals the molecular architecture and druggable vulnerabilities of thymic epithelial tumors (TETs)

Thymic epithelial tumors (TETs) have been profiled to the present moment mainly through several analyses of FFPE samples. Despite the leap forward brought by the TCGA, several questions remain still unsolved. Among these, TETs are characterized by a strong component of immune infiltrate which makes the transcriptomic analyses conducted so far scarcely interpretable to profile stromal subpopulations constitutive of the tumor. Furthermore, rarely correspondent healthy tissue is available due to the lipomatous atrophy of aged thymi. Therefore, the recent report of (I) isolation, (II) propagation (III) and characterization of human thymic epithelial cells (TECs) and their capacity to reconstitute the functional organ ex vivo and in vivo, represents a novel approach to study the biology of both healthy and neoplastic thymi. Human thymic biopsies (both healthy and neoplastic) were digested and plated on a lethally irradiated murine feeder layer. Both RNA-Seq and CUTANDTAG were performed on cultivated TECs at different passages. Cultured TECs were injected with human thymic interstitial cells into rat decellularized scaffolds and cultivated for 10–12 days. sc-RNA Seq is currently being performed on both healthy and neoplastic thymic mini-organs and their correspondent primary tissues. Here show that we successfully cultivated a cohort of 21 clonogenic TECs in vitro including adult neoplastic TECs, their non-tumoral counterpart and pediatric TECs. We show that at the transcriptome level each class of TECs clusters independently and that neoplastic TECs belong to the same cloud independently from thymoma histotype. Around 1,400 differentially expressed genes (DEGs) can be found when comparing adult neoplastic and non-neoplastic counterpart, among which around 70 are transcription factors. Importantly, we prove for the first time that clonogenic TECs derived from TETs can repopulate a decellularized rat scaffold and recreate a 3D architecture mimicking the primary tumor. This work demonstrates that this culture system allows the expansion of clonogenic TECs from both tumor samples and their non-tumoral counterpart. Those cells, when transplanted into decellularized thymi, reproduce the architecture of the primary tissue, showing that TETs contain progenitor/stem epithelial cells. We are currently characterizing TECs at the transcriptomic and epigenomic level with aim of identifying new druggable targets prior to clinical trials

A Digital Twin-based Predictive Strategy for Workload Control

The paper aims at proposing a card controlling model to improve the standard CONWIP procedure, granting a similar system throughput while reducing Work In Progress (WIP) levels. To achieve this objective, the authors developed a Digital Twin-based production control system including a reinforcement learning algorithm (i.e. Q-Learning). The Digital Twin is responsible for short term predictions of the behavior of the system aimed at a what-if analysis with different numbers of cards. As there is lack of evidence of research related to Digital Twin applications for production control and for order release systems in particular, we aim at proposing this as an initial work to start the exploration of problems in this control area. The proposed model has been tested both in a Job Shop and in a Flow Shop systems with promising results

Toward Digital twin for sustainable manufacturing: A data-driven approach for energy consumption behavior model generation

International audienceTraditional manufacturing systems face a major transition challenge toward intelligent and sustainable manufacturing systems. Energy-efficient manufacturing has become one of the main focuses of this transition because of the high and critical consumption of energy for the production of many industrial sectors. To this aim, modeling Energy Consumption (EC) behavior is a primary step to monitor and reduce consumption. However, it requires a deep understanding of the system’s kinematic and dynamic behaviors. Hence, creating a model from scratch can be challenging, which may result in a model that does not correctly represent the real system. With the advancement of digital technologies, it is now possible to collect and analyze data from manufacturing systems in real-time. This opens the door to the possibility of modeling the energy consumption behavior of different machine states based on a data-driven approach and keeping the current energy consumption under control and monitored using real-time data from the equipment. Prior research has been conducted in the literature incorporating EC modeling into a Digital Twin (DT). However, the addressed issue remains an open challenge due to its complexity. The proposed methodology solves the lack of literature by proposing a methodology that makes the EC modeling within the reach of any researcher or practitioner in the field. The current paper proposes a data-driven methodology for integrating the EC model into Digital Twins. The methodology is based on measurements to identify different segments and sub-states of EC of production equipment, using techniques such as segmentation and regression. It relies on power absorption measurement of industrial equipment to generate EC related-parameters to be fed into the DT model to monitor the current operating condition of the physical system. This work contributes to the DT-based sustainable transition by allowing to monitor and quantitatively measure those parameters which could be controlled to reduce EC. A case study on an industrial robot is used to validate and assess the performance of the approach in a laboratory environment

Reduction of Cardiac Fibrosis by Interference With YAP-Dependent Transactivation

Conversion of cardiac stromal cells into myofibroblasts is typically associated with hypoxia conditions, metabolic insults, and/or inflammation, all of which are predisposing factors to cardiac fibrosis and heart failure. We hypothesized that this conversion could be also mediated by response of these cells to mechanical cues through activation of the Hippo transcriptional pathway. The objective of the present study was to assess the role of cellular/nuclear straining forces acting in myofibroblast differentiation of cardiac stromal cells under the control of YAP (yes-associated protein) transcription factor and to validate this finding using a pharmacological agent that interferes with the interactions of the YAP/TAZ (transcriptional coactivator with PDZ-binding motif) complex with their cognate transcription factors TEADs (TEA domain transcription factors), under high-strain and profibrotic stimulation

SARS-CoV-2 variants evolve convergent strategies to remodel the host response

SARS-CoV-2 variants of concern (VOCs) emerged during the COVID-19 pandemic. Here, we used unbiased systems approaches to study the host-selective forces driving VOC evolution. We discovered that VOCs evolved convergent strategies to remodel the host by modulating viral RNA and protein levels, altering viral and host protein phosphorylation, and rewiring virus-host protein-protein interactions. Integrative computational analyses revealed that although Alpha, Beta, Gamma, and Delta ultimately converged to suppress interferon-stimulated genes (ISGs), Omicron BA.1 did not. ISG suppression correlated with the expression of viral innate immune antagonist proteins, including Orf6, N, and Orf9b, which we mapped to specific mutations. Later Omicron subvariants BA.4 and BA.5 more potently suppressed innate immunity than early subvariant BA.1, which correlated with Orf6 levels, although muted in BA.4 by a mutation that disrupts the Orf6-nuclear pore interaction. Our findings suggest that SARS-CoV-2 convergent evolution overcame human adaptive and innate immune barriers, laying the groundwork to tackle future pandemics